Repository: GAIR-NLP/ToRL Branch: main Commit: 1db091d9cbd3 Files: 221 Total size: 1.6 MB Directory structure: gitextract_g7clz9ov/ ├── data/ │ └── torl_data/ │ ├── test.parquet │ ├── test_0524.parquet │ └── train.parquet ├── readme.md ├── requirements.txt ├── scripts/ │ └── torl_1.5b.sh └── verl/ ├── __init__.py ├── models/ │ ├── README.md │ ├── __init__.py │ ├── llama/ │ │ ├── __init__.py │ │ └── megatron/ │ │ ├── __init__.py │ │ ├── checkpoint_utils/ │ │ │ ├── __init__.py │ │ │ ├── llama_loader.py │ │ │ └── llama_saver.py │ │ ├── layers/ │ │ │ ├── __init__.py │ │ │ ├── parallel_attention.py │ │ │ ├── parallel_decoder.py │ │ │ ├── parallel_linear.py │ │ │ ├── parallel_mlp.py │ │ │ └── parallel_rmsnorm.py │ │ └── modeling_llama_megatron.py │ ├── qwen2/ │ │ ├── __init__.py │ │ └── megatron/ │ │ ├── __init__.py │ │ ├── checkpoint_utils/ │ │ │ ├── __init__.py │ │ │ ├── qwen2_loader.py │ │ │ └── qwen2_saver.py │ │ ├── layers/ │ │ │ ├── __init__.py │ │ │ ├── parallel_attention.py │ │ │ ├── parallel_decoder.py │ │ │ ├── parallel_linear.py │ │ │ ├── parallel_mlp.py │ │ │ └── parallel_rmsnorm.py │ │ └── modeling_qwen2_megatron.py │ ├── registry.py │ ├── transformers/ │ │ ├── __init__.py │ │ ├── llama.py │ │ ├── monkey_patch.py │ │ ├── qwen2.py │ │ └── qwen2_vl.py │ └── weight_loader_registry.py ├── protocol.py ├── single_controller/ │ ├── __init__.py │ ├── base/ │ │ ├── __init__.py │ │ ├── decorator.py │ │ ├── megatron/ │ │ │ ├── __init__.py │ │ │ ├── worker.py │ │ │ └── worker_group.py │ │ ├── register_center/ │ │ │ ├── __init__.py │ │ │ └── ray.py │ │ ├── worker.py │ │ └── worker_group.py │ └── ray/ │ ├── __init__.py │ ├── base.py │ └── megatron.py ├── third_party/ │ ├── __init__.py │ └── vllm/ │ ├── __init__.py │ ├── vllm_spmd/ │ │ ├── __init__.py │ │ └── dtensor_weight_loaders.py │ ├── vllm_v_0_3_1/ │ │ ├── __init__.py │ │ ├── arg_utils.py │ │ ├── config.py │ │ ├── llm.py │ │ ├── llm_engine_sp.py │ │ ├── model_loader.py │ │ ├── model_runner.py │ │ ├── parallel_state.py │ │ ├── tokenizer.py │ │ ├── weight_loaders.py │ │ └── worker.py │ ├── vllm_v_0_4_2/ │ │ ├── __init__.py │ │ ├── arg_utils.py │ │ ├── config.py │ │ ├── dtensor_weight_loaders.py │ │ ├── hf_weight_loader.py │ │ ├── llm.py │ │ ├── llm_engine_sp.py │ │ ├── megatron_weight_loaders.py │ │ ├── model_loader.py │ │ ├── model_runner.py │ │ ├── parallel_state.py │ │ ├── spmd_gpu_executor.py │ │ ├── tokenizer.py │ │ └── worker.py │ ├── vllm_v_0_5_4/ │ │ ├── __init__.py │ │ ├── arg_utils.py │ │ ├── config.py │ │ ├── dtensor_weight_loaders.py │ │ ├── hf_weight_loader.py │ │ ├── llm.py │ │ ├── llm_engine_sp.py │ │ ├── megatron_weight_loaders.py │ │ ├── model_loader.py │ │ ├── model_runner.py │ │ ├── parallel_state.py │ │ ├── spmd_gpu_executor.py │ │ ├── tokenizer.py │ │ └── worker.py │ └── vllm_v_0_6_3/ │ ├── __init__.py │ ├── arg_utils.py │ ├── config.py │ ├── dtensor_weight_loaders.py │ ├── hf_weight_loader.py │ ├── llm.py │ ├── llm_engine_sp.py │ ├── megatron_weight_loaders.py │ ├── model_loader.py │ ├── model_runner.py │ ├── parallel_state.py │ ├── spmd_gpu_executor.py │ ├── tokenizer.py │ └── worker.py ├── trainer/ │ ├── __init__.py │ ├── config/ │ │ ├── evaluation.yaml │ │ ├── generation.yaml │ │ ├── ppo_megatron_trainer.yaml │ │ ├── ppo_trainer.yaml │ │ └── sft_trainer.yaml │ ├── fsdp_sft_trainer.py │ ├── main_eval.py │ ├── main_generation.py │ ├── main_ppo.py │ ├── ppo/ │ │ ├── __init__.py │ │ ├── core_algos.py │ │ └── ray_trainer.py │ └── runtime_env.yaml ├── utils/ │ ├── __init__.py │ ├── checkpoint/ │ │ ├── __init__.py │ │ ├── checkpoint_manager.py │ │ └── fsdp_checkpoint_manager.py │ ├── config.py │ ├── dataset/ │ │ ├── README.md │ │ ├── __init__.py │ │ ├── rl_dataset.py │ │ ├── rm_dataset.py │ │ └── sft_dataset.py │ ├── debug/ │ │ ├── __init__.py │ │ ├── performance.py │ │ └── trajectory_tracker.py │ ├── distributed.py │ ├── flops_counter.py │ ├── fs.py │ ├── fsdp_utils.py │ ├── hdfs_io.py │ ├── import_utils.py │ ├── logger/ │ │ ├── __init__.py │ │ └── aggregate_logger.py │ ├── logging_utils.py │ ├── megatron/ │ │ ├── __init__.py │ │ ├── memory.py │ │ ├── optimizer.py │ │ ├── pipeline_parallel.py │ │ ├── sequence_parallel.py │ │ └── tensor_parallel.py │ ├── megatron_utils.py │ ├── memory_buffer.py │ ├── model.py │ ├── py_functional.py │ ├── ray_utils.py │ ├── rendezvous/ │ │ ├── __init__.py │ │ └── ray_backend.py │ ├── reward_score/ │ │ ├── __init__.py │ │ ├── eval.py │ │ ├── geo3k.py │ │ ├── gsm8k.py │ │ ├── math.py │ │ ├── math_verifier.py │ │ ├── prime_code/ │ │ │ ├── __init__.py │ │ │ ├── testing_util.py │ │ │ └── utils.py │ │ └── prime_math/ │ │ ├── __init__.py │ │ ├── grader.py │ │ └── math_normalize.py │ ├── seqlen_balancing.py │ ├── tokenizer.py │ ├── torch_dtypes.py │ ├── torch_functional.py │ ├── tracking.py │ └── ulysses.py ├── version/ │ └── version └── workers/ ├── __init__.py ├── actor/ │ ├── __init__.py │ ├── base.py │ ├── dp_actor.py │ └── megatron_actor.py ├── critic/ │ ├── __init__.py │ ├── base.py │ ├── dp_critic.py │ └── megatron_critic.py ├── fsdp_workers.py ├── megatron_workers.py ├── reward_manager/ │ ├── __init__.py │ ├── naive.py │ └── prime.py ├── reward_model/ │ ├── __init__.py │ ├── base.py │ └── megatron/ │ ├── __init__.py │ └── reward_model.py ├── rollout/ │ ├── __init__.py │ ├── base.py │ ├── hf_rollout.py │ ├── naive/ │ │ ├── __init__.py │ │ └── naive_rollout.py │ ├── tokenizer.py │ └── vllm_rollout/ │ ├── __init__.py │ ├── fire_vllm_rollout.py │ ├── qwen_agent/ │ │ ├── code/ │ │ │ ├── code_interpreter.py │ │ │ └── utils/ │ │ │ └── code_utils.py │ │ ├── llm/ │ │ │ └── schema.py │ │ ├── log.py │ │ ├── settings.py │ │ ├── tools/ │ │ │ ├── base.py │ │ │ ├── code_interpreter.py │ │ │ └── python_executor.py │ │ └── utils/ │ │ └── utils.py │ ├── vllm_rollout.py │ └── vllm_rollout_spmd.py └── sharding_manager/ ├── __init__.py ├── base.py ├── fsdp_ulysses.py ├── fsdp_vllm.py └── megatron_vllm.py ================================================ FILE CONTENTS ================================================ ================================================ FILE: readme.md ================================================ #
# ToRL: Scaling Tool-Integrated RL

📄 Paper   |   🌐 Dataset   |   📘 Model

torl-abstarct-1
> Performance comparison of ToRL versus baseline models(16-step moving). Both plots show AIME24 Accuracy (%) against training steps across 1.5B and 7B models. In both cases, **ToRL(Ours)** significantly outperforms the baseline without tool and Qwen-2.5-Math-Instruct-TIR, achieving up to **12\%(1.5B)** and **14\%(7B)** higher.
torl-abstarct-2
> **Emergent cognitive behavior** during training. ToRL first cross-validates the tool's output with reasoning results. Upon detecting inconsistencies, it engages in reflection and further verification through tool calls. ## Releases [2025/03/28] We're releasing the following components: - 🚀 **Training**: Complete implementation of our training pipeline - 🔥 **[ToRL Dataset](https://github.com/GAIR-NLP/ToRL/tree/main/data/torl_data)**: Our curated dataset of 28k mathematical questions - 🤖 **[ToRL Model](https://huggingface.co/GAIR/ToRL)**: Model training with ToRL. ## Overview This repository presents **ToRL (Tool-Integrated Reinforcement Learning)**, a framework that challenges traditional approaches to tool integration in language models by enabling LLMs to autonomously discover and refine tool usage strategies through reinforcement learning. Unlike prior methods constrained by supervised fine-tuning or predefined tool patterns, ToRL demonstrates that **exploration-driven learning** with computational tools can unlock emergent cognitive behaviors and achieve state-of-the-art performance on complex reasoning tasks. Notably, our approach operates directly from base models without imitation learning, achieving **43.3% accuracy on AIME2024** with a 7B model—matching the performance of larger 32B models trained with RL. ### Key Findings - **Autonomous Tool Integration**: Models learn **when and how** to invoke tools (e.g., code interpreters) through RL-driven exploration, eliminating dependency on human-curated tool usage patterns. - **Emergent Cognitive Abilities**: - Self-correction by cross-validating code execution results with reasoning steps - Adaptive strategy selection between tool-based and pure-reasoning approaches - Self-regulation of ineffective tool calls without explicit supervision ### ToRL Performance 1.5B Model Performance across challenging mathematical benchmarks: | Model | SFT/RL | Tool | AIME24 | AIME25 | MATH500 | Olympiad | AMC23 | Avg | |----------------------------------|--------|------|--------|--------|---------|----------|-------|-------| | Qwen2.5-Math-1.5B-Instruct | RL | ❌ | 10.0 | 10.0 | 66.0 | 31.0 | 62.5 | 35.9 | | Qwen2.5-Math-1.5B-Instruct-TIR | RL | ✅ | 13.3 | 13.3 | 73.8 | 41.3 | 55.0 | 41.3 | | **ToRL-1.5B(Ours)** | RL | ✅ | **26.7 (+13.3)** | **26.7 (+13.3)** | **77.8 (+3.0)** | **44.0 (+2.7)** | **67.5 (+5.0)** | **48.5 (+7.2)** | 7B Model Performance across challenging mathematical benchmarks: | Model | SFT/RL | Tool | AIME24 | AIME25 | MATH500 | Olympiad | AMC23 | Avg | |----------------------------------|--------|------|--------|--------|---------|----------|-------|-------| | Qwen2.5-Math-7B-Instruct | RL | ❌ | 10.0 | 16.7 | 74.8 | 32.4 | 65.0 | 39.8 | | Qwen2.5-Math-7B-Instruct-TIR | RL | ✅ | 26.7 | 16.7 | 78.8 | 45.0 | 70.0 | 47.4 | | SimpleRL-Zero | RL | ❌ | 33.3 | 6.7 | 77.2 | 37.6 | 62.5 | 43.5 | | rStar-Math-7B | SFT | ❌ | 26.7 | - | 78.4 | 47.1 | 47.5 | - | | Eurus-2-7B-PRIME | RL | ❌ | 26.7 | 13.3 | 79.2 | 42.1 | 57.4 | 43.1 | | **ToRL-7B(Ours)** | RL | ✅ | **43.3 (+10.0)** | **30.0 (+13.3)** | **82.2 (+3.0)** | **49.9 (+2.8)** | **75.0 (+5.0)** | **62.1 (+14.7)** | ### Cognitive Behavior via RL Scaling In the left figure, the code initially generate by the model encountered an execution error, then it is corrected by model and is successfully executed. In the right figure, the model first derives an incorrect result based on natural language reasoning, then discovers the error during code verification and makes corrections
cases cases2
## Quick Start ### Preparing the Sandbox Environment According to the instructions provided in [https://github.com/bytedance/SandboxFusion](https://github.com/bytedance/SandboxFusion), install SandboxFusion and launch it. ``` # The following command can be used to install the sandbox environment # to avoid dependency conflicts. # The sandbox environment must be named "sandbox-runtime". conda create -n sandbox-runtime python==3.11 pip install -r runtime/python/requirement.txt pip install poetry poetry install mkdir -p docs/build make run-online ``` Replace the `sandbox_url` on line `109` of `verl/workers/rollout/vllm_rollout/vllm_rollout_spmd.py` with the your sandbox. ### Environment setup ``` pip install -r requirements.txt pip install wandb jsonlines math-verify hydra-core==1.4.0.dev1 sortedcontainers qwen-agent[code_interpreter] qwen-agent[python_executor] ``` ### Training Execute `bash scripts/torl_1.5b` to run ToRL. ## Acknowledgements Our work builds upon the insightful technical reports from [DeepSeek R1](https://github.com/deepseek-ai/DeepSeek-R1) and [Kimi-k1.5](https://github.com/MoonshotAI/Kimi-k1.5) teams. We extend our appreciation to the [Qwen-Math](https://github.com/QwenLM/Qwen2.5-Math) team for their open-source model, to the creators of [VeRL](https://github.com/volcengine/verl) and [vLLM](https://github.com/vllm-project/vllm) for providing the essential reinforcement learning framework and inference infrastructure, respectively, that enabled this research. and to the [Qwen-Agent](https://github.com/QwenLM/Qwen-Agent) and [Sandbox Fusion](https://github.com/bytedance/SandboxFusion) team, which provided the necessary tools for our research. ## Citation If you find this work useful, please cite our paper: ```bibtex @misc{li2025torlscalingtoolintegratedrl, title={ToRL: Scaling Tool-Integrated RL}, author={Xuefeng Li and Haoyang Zou and Pengfei Liu}, year={2025}, eprint={2503.23383}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2503.23383}, } ``` ================================================ FILE: requirements.txt ================================================ accelerate==1.5.2 aiohappyeyeballs==2.6.1 aiohttp==3.11.14 aiohttp-cors==0.8.0 aiosignal==1.3.2 airportsdata==20250224 annotated-types==0.7.0 antlr4-python3-runtime==4.9.3 anyio==4.9.0 astor==0.8.1 async-timeout==5.0.1 attrs==25.3.0 blake3==1.0.4 cachetools==5.5.2 certifi==2025.1.31 charset-normalizer==3.4.1 click==8.1.8 cloudpickle==3.1.1 codetiming==1.4.0 colorful==0.5.6 compressed-tensors==0.9.2 cupy-cuda12x==13.4.1 datasets==3.4.1 depyf==0.18.0 dill==0.3.8 diskcache==5.6.3 distlib==0.3.9 distro==1.9.0 dnspython==2.7.0 docker-pycreds==0.4.0 einops==0.8.1 email_validator==2.2.0 exceptiongroup==1.2.2 fastapi==0.115.11 fastapi-cli==0.0.7 fastrlock==0.8.3 filelock==3.18.0 flash_attn==2.7.4.post1 frozenlist==1.5.0 fsspec==2024.12.0 gguf==0.10.0 gitdb==4.0.12 GitPython==3.1.44 google-api-core==2.24.2 google-auth==2.38.0 googleapis-common-protos==1.69.2 grpcio==1.71.0 h11==0.14.0 httpcore==1.0.7 httptools==0.6.4 httpx==0.28.1 huggingface-hub==0.29.3 hydra-core==1.3.2 idna==3.10 importlib_metadata==8.6.1 interegular==0.3.3 Jinja2==3.1.6 jiter==0.9.0 jsonschema==4.23.0 jsonschema-specifications==2024.10.1 lark==1.2.2 liger_kernel==0.5.5 llvmlite==0.43.0 lm-format-enforcer==0.10.11 markdown-it-py==3.0.0 MarkupSafe==3.0.2 mdurl==0.1.2 mistral_common==1.5.4 mpmath==1.3.0 msgpack==1.1.0 msgspec==0.19.0 multidict==6.2.0 multiprocess==0.70.16 nest-asyncio==1.6.0 networkx==3.4.2 ninja==1.11.1.4 numba==0.60.0 numpy==1.26.4 nvidia-cublas-cu12==12.4.5.8 nvidia-cuda-cupti-cu12==12.4.127 nvidia-cuda-nvrtc-cu12==12.4.127 nvidia-cuda-runtime-cu12==12.4.127 nvidia-cudnn-cu12==9.1.0.70 nvidia-cufft-cu12==11.2.1.3 nvidia-curand-cu12==10.3.5.147 nvidia-cusolver-cu12==11.6.1.9 nvidia-cusparse-cu12==12.3.1.170 nvidia-cusparselt-cu12==0.6.2 nvidia-nccl-cu12==2.21.5 nvidia-nvjitlink-cu12==12.4.127 nvidia-nvtx-cu12==12.4.127 omegaconf==2.3.0 openai==1.68.2 opencensus==0.11.4 opencensus-context==0.1.3 opencv-python-headless==4.11.0.86 orjson==3.10.15 outlines==0.1.11 outlines_core==0.1.26 packaging==24.2 pandas==2.2.3 partial-json-parser==0.2.1.1.post5 peft==0.15.0 pillow==11.1.0 platformdirs==4.3.7 prometheus-fastapi-instrumentator==7.1.0 prometheus_client==0.21.1 propcache==0.3.0 proto-plus==1.26.1 protobuf==5.29.4 psutil==7.0.0 py-cpuinfo==9.0.0 py-spy==0.4.0 pyarrow==19.0.1 pyasn1==0.6.1 pyasn1_modules==0.4.1 pybind11==2.13.6 pycountry==24.6.1 pydantic==2.10.6 pydantic_core==2.27.2 Pygments==2.19.1 pylatexenc==2.10 python-dateutil==2.9.0.post0 python-dotenv==1.0.1 python-json-logger==3.3.0 python-multipart==0.0.20 pytz==2025.1 PyYAML==6.0.2 pyzmq==26.3.0 ray==2.44.0 referencing==0.36.2 regex==2024.11.6 requests==2.32.3 rich==13.9.4 rich-toolkit==0.13.2 rpds-py==0.23.1 rsa==4.9 safetensors==0.5.3 scipy==1.15.2 sentencepiece==0.2.0 sentry-sdk==2.24.0 setproctitle==1.3.5 shellingham==1.5.4 six==1.17.0 smart-open==7.1.0 smmap==5.0.2 sniffio==1.3.1 starlette==0.46.1 sympy==1.13.1 tensordict==0.6.0 tiktoken==0.9.0 tokenizers==0.21.1 torch==2.6.0 torchaudio==2.6.0 torchdata==0.11.0 torchvision==0.21.0 tqdm==4.67.1 transformers==4.50.0 triton==3.2.0 typer==0.15.2 typing_extensions==4.12.2 tzdata==2025.1 urllib3==2.3.0 uvicorn==0.34.0 uvloop==0.21.0 virtualenv==20.29.3 vllm==0.8.1 wandb==0.19.8 watchfiles==1.0.4 websockets==15.0.1 wrapt==1.17.2 xformers==0.0.29.post2 xgrammar==0.1.16 xxhash==3.5.0 yarl==1.18.3 zipp==3.21.0 ================================================ FILE: scripts/torl_1.5b.sh ================================================ policy_path=Qwen/Qwen2.5-Math-1.5B rollout_batch_size=128 n_samples_per_prompts=16 episode=300 temperature=1.0 batch_size=1024 lr=1e-6 kl_loss_coef=0.0 kl_coef=0.0 entropy_coeff=0 max_gen_length=3072 numcall=1 dataset_name=torl_data run_name=rl.grpo_qwen.math.1.5b_${dataset_name}_numcall${numcall} data_path=../data/${dataset_name} samples_save_path=../data/samples/$run_name python3 -m verl.trainer.main_ppo \ algorithm.adv_estimator=grpo \ data.train_files=$data_path/train.parquet \ data.val_files=$data_path/test.parquet \ data.train_batch_size=$rollout_batch_size \ data.val_batch_size=2048 \ data.max_prompt_length=400 \ data.max_response_length=$max_gen_length \ data.template_type=tir_base_0309 \ actor_rollout_ref.model.path=$policy_path \ actor_rollout_ref.actor.optim.lr=$lr \ actor_rollout_ref.model.use_remove_padding=True \ actor_rollout_ref.actor.ppo_mini_batch_size=$batch_size \ actor_rollout_ref.actor.use_dynamic_bsz=True \ actor_rollout_ref.actor.use_kl_loss=True \ actor_rollout_ref.actor.kl_loss_coef=$kl_loss_coef \ actor_rollout_ref.actor.kl_loss_type=low_var_kl \ actor_rollout_ref.actor.entropy_coeff=$entropy_coeff \ actor_rollout_ref.model.enable_gradient_checkpointing=True \ actor_rollout_ref.actor.fsdp_config.param_offload=True \ actor_rollout_ref.actor.fsdp_config.optimizer_offload=True \ actor_rollout_ref.rollout.tensor_model_parallel_size=1 \ actor_rollout_ref.rollout.name=vllm \ actor_rollout_ref.rollout.num_llm_calls_available=$numcall \ actor_rollout_ref.rollout.temperature=$temperature \ actor_rollout_ref.rollout.top_p=1.0 \ actor_rollout_ref.rollout.gpu_memory_utilization=0.85 \ actor_rollout_ref.rollout.n=$n_samples_per_prompts \ actor_rollout_ref.rollout.log_prob_use_dynamic_bsz=True \ actor_rollout_ref.rollout.max_num_seqs=1024 \ actor_rollout_ref.ref.log_prob_use_dynamic_bsz=True \ actor_rollout_ref.ref.fsdp_config.param_offload=True \ algorithm.kl_ctrl.kl_coef=$kl_coef \ trainer.logger=['console','wandb'] \ trainer.project_name=torl \ trainer.experiment_name=${run_name} \ trainer.n_gpus_per_node=8 \ trainer.nnodes=1 \ trainer.save_freq=20 \ trainer.test_freq=4 \ trainer.default_local_dir=path_to_save/${run_name} \ trainer.resume_mode=auto \ trainer.samples_save_path=$samples_save_path \ trainer.total_epochs=$episode $@ ================================================ FILE: verl/__init__.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os version_folder = os.path.dirname(os.path.join(os.path.abspath(__file__))) with open(os.path.join(version_folder, 'version/version')) as f: __version__ = f.read().strip() from .protocol import DataProto from .utils.logging_utils import set_basic_config import logging set_basic_config(level=logging.WARNING) from . import single_controller __all__ = ['DataProto', "__version__"] if os.getenv('VERL_USE_MODELSCOPE', 'False').lower() == 'true': import importlib if importlib.util.find_spec("modelscope") is None: raise ImportError(f'You are using the modelscope hub, please install modelscope by `pip install modelscope -U`') # Patch hub to download models from modelscope to speed up. from modelscope.utils.hf_util import patch_hub patch_hub() ================================================ FILE: verl/models/README.md ================================================ # Models Common modelzoo such as huggingface/transformers stuggles when using Pytorch native model parallelism. Following the design principle of vLLM, we keep a simple, parallelizable, highly-optimized with packed inputs in verl. ## Adding a New Huggingface Model ### Step 1: Copy the model file from HF to verl - Add a new file under verl/models/hf - Copy ONLY the model file from huggingface/transformers/models to verl/models/hf ### Step 2: Modify the model file to use packed inputs - Remove all the code related to inference (kv cache) - Modify the inputs to include only - input_ids (total_nnz,) - cu_seqlens (total_nnz + 1,) - max_seqlen_in_batch: int - Note that this requires using flash attention with causal mask. ### Step 2.5: Add tests - Add a test to compare this version and the huggingface version - Following the infrastructure and add tests to tests/models/hf ### Step 3: Add a function to apply tensor parallelism - Please follow - https://pytorch.org/docs/stable/distributed.tensor.parallel.html - https://pytorch.org/tutorials/intermediate/TP_tutorial.html - General comments - Tensor Parallelism in native Pytorch is NOT auto-parallelism. The way it works is to specify how model parameters and input/output reshards using configs. These configs are then registered as hooks to perform input/output resharding before/after model forward. ### Step 4: Add a function to apply data parallelism - Please use FSDP2 APIs - See demo here https://github.com/pytorch/torchtitan/blob/main/torchtitan/parallelisms/parallelize_llama.py#L413 ### Step 5: Add a function to apply pipeline parallelism - Comes in Pytorch 2.4 - Currently only in alpha in nightly version - Check torchtitan for more details ================================================ FILE: verl/models/__init__.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. ================================================ FILE: verl/models/llama/__init__.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. ================================================ FILE: verl/models/llama/megatron/__init__.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from .modeling_llama_megatron import ( # original model with megatron ParallelLlamaModel, ParallelLlamaForCausalLM, # rmpad with megatron ParallelLlamaForCausalLMRmPad, ParallelLlamaForValueRmPad, # rmpad with megatron and pipeline parallelism ParallelLlamaForCausalLMRmPadPP, ParallelLlamaForValueRmPadPP) ================================================ FILE: verl/models/llama/megatron/checkpoint_utils/__init__.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. ================================================ FILE: verl/models/llama/megatron/checkpoint_utils/llama_loader.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import importlib from packaging.version import Version import torch import time from typing import Dict, Any, Callable, Optional import torch.distributed as dist def _megatron_calc_layer_map(config): """Calculate the mapping of global layer_idx to local layer_idx Returns: layer_map (Dict: int -> tuple(int, int, int)): mapping from the global layer index to a tuple of (pp_rank, virtual_pp_rank, layer_idx inside model) """ import megatron from megatron.core import mpu pp_size = mpu.get_pipeline_model_parallel_world_size() virtual_pp_size = mpu.get_virtual_pipeline_model_parallel_world_size() or 1 layer_map = dict() num_layers_per_model = config.num_hidden_layers // pp_size // virtual_pp_size assert num_layers_per_model * pp_size * virtual_pp_size == config.num_hidden_layers for pp_rank_idx in range(pp_size): for virtual_pp_rank_idx in range(virtual_pp_size): layer_offset = (virtual_pp_rank_idx * (config.num_hidden_layers // virtual_pp_size) + pp_rank_idx * num_layers_per_model) for layer_idx in range(num_layers_per_model): layer_map[layer_offset + layer_idx] = ( pp_rank_idx, virtual_pp_rank_idx, layer_idx, ) return layer_map def load_state_dict_to_megatron_llama(state_dict, wrapped_models, config, params_dtype, is_value_model=False): """Load merged state_dict to sharded Megatron module in training. """ import megatron from megatron.core import mpu from megatron.training.utils import print_rank_0, unwrap_model from megatron.core.transformer.module import Float16Module from megatron.core import DistributedDataParallel as LocalDDP from torch.nn.parallel import DistributedDataParallel as torchDDP start_time = time.time() def _get_gpt_model(model): return model def broadcast_params(module): for param in module.parameters(): torch.distributed.broadcast(param.data, src=mpu.get_data_parallel_src_rank(), group=mpu.get_data_parallel_group()) dp_rank = mpu.get_data_parallel_rank() pp_rank = mpu.get_pipeline_model_parallel_rank() pp_size = mpu.get_pipeline_model_parallel_world_size() virtual_pp_size = mpu.get_virtual_pipeline_model_parallel_world_size() or 1 mp_group = mpu.get_model_parallel_group() if torch.distributed.get_rank() == 0: assert mp_group.rank() == 0, f"mp_rank:[{mp_group.rank}] != 0 on rank #0" assert pp_rank == 0, f"pp_rank:[{pp_rank}] != 0 on rank #0" assert dp_rank == 0, f"dp_rank:[{dp_rank}] != 0 on rank #0" if not isinstance(wrapped_models, (list, tuple)): wrapped_models = list(wrapped_models) assert len(wrapped_models) == virtual_pp_size num_layers_per_model = config.num_hidden_layers // pp_size // virtual_pp_size assert num_layers_per_model * pp_size * virtual_pp_size == config.num_hidden_layers models = [None] * len(wrapped_models) for i, wrapped_model in enumerate(wrapped_models): models[i] = unwrap_model(wrapped_model, (torchDDP, LocalDDP, Float16Module)) gpt_model_module = _get_gpt_model(models[i]) assert len(gpt_model_module.model.layers) == num_layers_per_model def _broadcast_tensor(tensor, name) -> torch.Tensor: """broadcast tensor from rank0 across mp_group""" nonlocal state_dict nonlocal mp_group if torch.distributed.get_rank() == 0: if name in state_dict: weight = state_dict[name] tensor_shape = weight.shape else: tensor_shape = None else: weight = None tensor_shape = None obj_list = [tensor_shape] dist.broadcast_object_list(obj_list, src=0, group=mp_group) tensor_shape = obj_list[0] if tensor_shape is None: # all or none ranks in the mp_group should reach here print_rank_0(f"tensor:[{name}] not in state_dict, skip load") return if tensor is None: tensor = torch.empty( tensor_shape, dtype=params_dtype, device=torch.cuda.current_device(), requires_grad=False, ) if torch.distributed.get_rank() == 0: tensor.data.copy_(weight) dist.broadcast(tensor, src=0, group=mp_group) def _broadcast_tp_shard_tensor_vocab(tensor, name, chunk_dim=0, mutate_func=None) -> torch.Tensor: """broadcast tensor in tp shards across mp_group""" nonlocal state_dict nonlocal mp_group tp_rank = mpu.get_tensor_model_parallel_rank() tp_size = mpu.get_tensor_model_parallel_world_size() if torch.distributed.get_rank() == 0: if name in state_dict: full_weight = state_dict[name] if mutate_func is not None: full_weight = mutate_func(full_weight) tensor_chunk = torch.chunk(full_weight, tp_size, dim=chunk_dim) chunk_shape = tensor_chunk[0].shape else: chunk_shape = None else: chunk_shape = None obj_list = [chunk_shape] dist.broadcast_object_list(obj_list, src=0, group=mp_group) chunk_shape = obj_list[0] if chunk_shape is None: # all or none ranks in the mp_group should reach here print_rank_0(f"tp_shard tensor:[{name}] not in state_dict, skip loading") return if tensor is None: sync_tensor = torch.empty( chunk_shape, dtype=params_dtype, device=torch.cuda.current_device(), requires_grad=False, ) else: assert (tensor.shape == chunk_shape ), f"rank #{torch.distributed.get_rank()} tensor {name} shape {tensor.shape} != {chunk_shape}" sync_tensor = torch.empty_like(tensor, device=torch.cuda.current_device(), requires_grad=False) for i in range(tp_size): if torch.distributed.get_rank() == 0: sync_tensor.data.copy_(tensor_chunk[i]) dist.broadcast(sync_tensor, src=0, group=mp_group) if (i == tp_rank) and (tensor is not None): tensor.data.copy_(sync_tensor) def _broadcast_tp_shard_tensor(tensor, name, chunk_dim=0, mutate_func=None) -> torch.Tensor: """broadcast tensor in tp shards across mp_group""" nonlocal state_dict nonlocal mp_group tp_rank = mpu.get_tensor_model_parallel_rank() tp_size = mpu.get_tensor_model_parallel_world_size() if torch.distributed.get_rank() == 0: if name in state_dict: full_weight = state_dict[name] if mutate_func is not None: full_weight = mutate_func(full_weight) tensor_chunk = torch.chunk(full_weight, tp_size, dim=chunk_dim) chunk_shape = tensor_chunk[0].shape else: chunk_shape = None else: chunk_shape = None obj_list = [chunk_shape] dist.broadcast_object_list(obj_list, src=0, group=mp_group) chunk_shape = obj_list[0] if chunk_shape is None: # all or none ranks in the mp_group should reach here print_rank_0(f"tp_shard tensor:[{name}] not in state_dict, skip loading") return if tensor is None: sync_tensor = torch.empty( chunk_shape, dtype=params_dtype, device=torch.cuda.current_device(), requires_grad=False, ) else: assert (tensor.shape == chunk_shape ), f"rank #{torch.distributed.get_rank()} tensor {name} shape {tensor.shape} != {chunk_shape}" sync_tensor = torch.empty_like(tensor, device=torch.cuda.current_device(), requires_grad=False) for i in range(tp_size): if torch.distributed.get_rank() == 0: sync_tensor.data.copy_(tensor_chunk[i]) dist.broadcast(sync_tensor, src=0, group=mp_group) if (i == tp_rank) and (tensor is not None): tensor.data.copy_(sync_tensor) def _broadcast_tp_shard_tensor_gate_up(tensor, gate_name, up_name) -> torch.Tensor: """broadcast tensor in tp shards across mp_group""" nonlocal state_dict nonlocal mp_group tp_rank = mpu.get_tensor_model_parallel_rank() tp_size = mpu.get_tensor_model_parallel_world_size() if torch.distributed.get_rank() == 0: gate_weight = state_dict[gate_name] up_weight = state_dict[up_name] new_gate_up_weight = torch.empty(config.intermediate_size * 2, config.hidden_size, dtype=params_dtype, device=torch.cuda.current_device()) for i in range(tp_size): intermediate_size_tp = config.intermediate_size // tp_size gate_weight_tp = gate_weight[i * intermediate_size_tp:(i + 1) * intermediate_size_tp] up_weight_tp = up_weight[i * intermediate_size_tp:(i + 1) * intermediate_size_tp] new_gate_up_weight[intermediate_size_tp * 2 * i:intermediate_size_tp * 2 * (i + 1)].copy_( torch.cat([gate_weight_tp, up_weight_tp], dim=0)) tensor_chunk = torch.chunk(new_gate_up_weight, tp_size, dim=0) chunk_shape = tensor_chunk[0].shape else: chunk_shape = None obj_list = [chunk_shape] dist.broadcast_object_list(obj_list, src=0, group=mp_group) chunk_shape = obj_list[0] if chunk_shape is None: # all or none ranks in the mp_group should reach here print_rank_0(f"tp_shard tensor:[{gate_name, up_name}] not in state_dict, skip loading") return if tensor is None: sync_tensor = torch.empty( chunk_shape, dtype=params_dtype, device=torch.cuda.current_device(), requires_grad=False, ) else: assert ( tensor.shape == chunk_shape ), f"rank #{torch.distributed.get_rank() == 0:} tensor {gate_name, up_name} shape {tensor.shape} != {chunk_shape}" sync_tensor = torch.empty_like(tensor, device=torch.cuda.current_device(), requires_grad=False) for i in range(tp_size): if torch.distributed.get_rank() == 0: sync_tensor.data.copy_(tensor_chunk[i]) dist.broadcast(sync_tensor, src=0, group=mp_group) if (i == tp_rank) and (tensor is not None): tensor.data.copy_(sync_tensor) def _broadcast_tp_shard_tensor_qkv(tensor, q_name, k_name, v_name) -> torch.Tensor: """broadcast tensor in tp shards across mp_group""" nonlocal state_dict nonlocal mp_group tp_rank = mpu.get_tensor_model_parallel_rank() tp_size = mpu.get_tensor_model_parallel_world_size() if torch.distributed.get_rank() == 0: assert (q_name in state_dict and k_name in state_dict and v_name in state_dict) full_weight_q = state_dict[q_name] full_weight_k = state_dict[k_name] full_weight_v = state_dict[v_name] hidden_size_per_head = config.hidden_size // config.num_attention_heads if config.num_key_value_heads >= tp_size: q_size_tp = config.hidden_size // tp_size kv_size_tp = hidden_size_per_head * config.num_key_value_heads // tp_size total_size = q_size_tp + 2 * kv_size_tp new_weight_qkv = torch.empty(total_size * tp_size, config.hidden_size, dtype=params_dtype, device=torch.cuda.current_device()) for i in range(tp_size): q_part = full_weight_q[i * q_size_tp:(i + 1) * q_size_tp] k_part = full_weight_k[i * kv_size_tp:(i + 1) * kv_size_tp] v_part = full_weight_v[i * kv_size_tp:(i + 1) * kv_size_tp] new_weight_qkv[i * total_size:(i + 1) * total_size].copy_(torch.cat([q_part, k_part, v_part], dim=0)) else: q_size_tp = config.hidden_size // tp_size kv_size_tp = hidden_size_per_head total_size = q_size_tp + 2 * kv_size_tp new_weight_qkv = torch.empty(total_size * tp_size, config.hidden_size, dtype=params_dtype, device=torch.cuda.current_device()) for i in range(tp_size): q_part = full_weight_q[i * q_size_tp:(i + 1) * q_size_tp] start_idx = i * config.num_key_value_heads // tp_size * hidden_size_per_head end_idx = (i * config.num_key_value_heads // tp_size + 1) * hidden_size_per_head k_part = full_weight_k[start_idx:end_idx] v_part = full_weight_v[start_idx:end_idx] new_weight_qkv[i * total_size:(i + 1) * total_size].copy_(torch.cat([q_part, k_part, v_part], dim=0)) tensor_chunk = torch.chunk(new_weight_qkv, tp_size, dim=0) chunk_shape = tensor_chunk[0].shape else: chunk_shape = None obj_list = [chunk_shape] dist.broadcast_object_list(obj_list, src=0, group=mp_group) chunk_shape = obj_list[0] if chunk_shape is None: # all or none ranks in the mp_group should reach here print_rank_0(f"tp_shard tensor:[{name}] not in state_dict, skip loading") return if tensor is None: sync_tensor = torch.empty( chunk_shape, dtype=params_dtype, device=torch.cuda.current_device(), requires_grad=False, ) else: assert (tensor.shape == chunk_shape ), f"rank #{torch.distributed.get_rank()} tensor {q_name} shape {tensor.shape} != {chunk_shape}" sync_tensor = torch.empty_like(tensor, device=torch.cuda.current_device(), requires_grad=False) for i in range(tp_size): if torch.distributed.get_rank() == 0: sync_tensor.data.copy_(tensor_chunk[i]) dist.broadcast(sync_tensor, src=0, group=mp_group) if (i == tp_rank) and (tensor is not None): tensor.data.copy_(sync_tensor) if dp_rank == 0: # Embeddings # ------------------- print_rank_0("loading embeddings...") gpt_model_module = _get_gpt_model(models[0]) embed_tokens_weight = None if pp_rank == 0: embed_tokens_weight = gpt_model_module.model.embed_tokens.weight _broadcast_tp_shard_tensor_vocab(embed_tokens_weight, "model.embed_tokens.weight") # Transformer layers # ------------------- layer_map = _megatron_calc_layer_map(config) for layer in range(config.num_hidden_layers): print_rank_0(f"loading layer #{layer}...") layer_name = f"model.layers.{layer}" dst_pp_rank, dst_virtual_pp_rank, dst_layer_idx = layer_map[layer] gpt_model_module = _get_gpt_model(models[dst_virtual_pp_rank]) sync_layer = gpt_model_module.model.layers[dst_layer_idx] _broadcast_tensor( sync_layer.input_layernorm.weight if dst_pp_rank == pp_rank else None, f"{layer_name}.input_layernorm.weight", ) _broadcast_tp_shard_tensor_qkv( sync_layer.self_attn.qkv_proj.weight if dst_pp_rank == pp_rank else None, f"{layer_name}.self_attn.q_proj.weight", f"{layer_name}.self_attn.k_proj.weight", f"{layer_name}.self_attn.v_proj.weight", ) _broadcast_tp_shard_tensor( sync_layer.self_attn.o_proj.weight if dst_pp_rank == pp_rank else None, f"{layer_name}.self_attn.o_proj.weight", chunk_dim=1, ) _broadcast_tensor( sync_layer.post_attention_layernorm.weight if dst_pp_rank == pp_rank else None, f"{layer_name}.post_attention_layernorm.weight", ) _broadcast_tp_shard_tensor_gate_up(sync_layer.mlp.gate_up_proj.weight if dst_pp_rank == pp_rank else None, f"{layer_name}.mlp.gate_proj.weight", f"{layer_name}.mlp.up_proj.weight") _broadcast_tp_shard_tensor( sync_layer.mlp.down_proj.weight if dst_pp_rank == pp_rank else None, f"{layer_name}.mlp.down_proj.weight", chunk_dim=1, ) # Final Layernorm # ------------------- print_rank_0("loading final layernorm...") gpt_model_module = _get_gpt_model(models[-1]) _broadcast_tensor( getattr(gpt_model_module.model.norm, "weight", None), "model.norm.weight", ) print_rank_0("loading lm_head...") lm_head_weight = None if pp_rank + 1 == pp_size: lm_head_weight = gpt_model_module.lm_head.weight if is_value_model: # if torch.distributed.get_rank() == 0: if 'lm_head.weight' in state_dict and state_dict['lm_head.weight'].shape[0] == 1: _broadcast_tensor(lm_head_weight, "lm_head.weight") elif 'reward_head.weight' in state_dict and state_dict['reward_head.weight'].shape[0] == 1: _broadcast_tensor(lm_head_weight, "reward_head.weight") print_rank_0('load lm_head from value_head weight') else: _broadcast_tensor(None, "lm_head.weight") print_rank_0('fail to match lm_head in value_model') # else: # _broadcast_tensor(lm_head_weight, "lm_head.weight") else: _broadcast_tp_shard_tensor(lm_head_weight, "lm_head.weight") dist.barrier() # Broadcast weights inside data parallel groups for wrapped_model in wrapped_models: broadcast_params(wrapped_model) torch.cuda.empty_cache() print_rank_0(f"loading megatron ckpt done, time elapsed {time.time() - start_time}s") ================================================ FILE: verl/models/llama/megatron/checkpoint_utils/llama_saver.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import importlib from packaging.version import Version from torch.nn.parallel import DistributedDataParallel as torchDDP import torch import time from typing import Optional import torch.distributed as dist import megatron from megatron import get_args from megatron.core import mpu from megatron.core.transformer.module import Float16Module from megatron.core.distributed import DistributedDataParallel as LocalDDP from megatron.training.utils import print_rank_0, unwrap_model def _megatron_calc_global_rank(tp_rank: int = 0, dp_rank: int = 0, pp_rank: int = 0): """given TP,DP,PP rank to get the global rank.""" args = get_args() tp_size = mpu.get_tensor_model_parallel_world_size() dp_size = mpu.get_data_parallel_world_size() pp_size = mpu.get_pipeline_model_parallel_world_size() assert (tp_size * dp_size * pp_size == torch.distributed.get_world_size() ), f"{tp_size} x {dp_size} x {pp_size} != {torch.distributed.get_world_size()}" if args.switch_dp_and_pp_grouping: # TP-PP-DP grouping return (dp_rank * pp_size + pp_rank) * tp_size + tp_rank else: # TP-DP-PP grouping return (pp_rank * dp_size + dp_rank) * tp_size + tp_rank def _megatron_calc_layer_map(config): """Calculate the mapping of global layer_idx to local layer_idx Returns: layer_map (Dict: int -> tuple(int, int, int)): mapping from the global layer index to a tuple of (pp_rank, virtual_pp_rank, layer_idx inside model) """ import megatron from megatron.core import mpu pp_size = mpu.get_pipeline_model_parallel_world_size() virtual_pp_size = mpu.get_virtual_pipeline_model_parallel_world_size() or 1 args = megatron.get_args() layer_map = dict() num_layers_per_model = config.num_hidden_layers // pp_size // virtual_pp_size assert num_layers_per_model * pp_size * virtual_pp_size == config.num_hidden_layers for pp_rank_idx in range(pp_size): for virtual_pp_rank_idx in range(virtual_pp_size): layer_offset = (virtual_pp_rank_idx * (config.num_hidden_layers // virtual_pp_size) + pp_rank_idx * num_layers_per_model) for layer_idx in range(num_layers_per_model): layer_map[layer_offset + layer_idx] = ( pp_rank_idx, virtual_pp_rank_idx, layer_idx, ) return layer_map def merge_megatron_ckpt_llama(wrapped_models, config, is_value_model=False, dtype='bf16'): """Merge sharded parameters of a Megatron module into a merged checkpoint. Args: wrapped_models (list of megatron.core.distributed.DistributedDataParallel): The local DDP wrapped megatron modules. dtype (str or None): The data type of state_dict. if None, the data type of the original parameters is used. gpt_model_key: key to access model Returns: state_dict (dict): The merged state_dict in rank 0, and an empty dictionary in other ranks. """ start_time = time.time() args = megatron.get_args() def _get_gpt_model(model): return model dp_rank = mpu.get_data_parallel_rank() pp_size = mpu.get_pipeline_model_parallel_world_size() pp_rank = mpu.get_pipeline_model_parallel_rank() virtual_pp_size = mpu.get_virtual_pipeline_model_parallel_world_size() or 1 mp_group = mpu.get_model_parallel_group() if dist.get_rank() == 0: assert mp_group.rank() == 0, f"mp_rank:[{mp_group.rank}] != 0 on rank #0" assert pp_rank == 0, f"pp_rank:[{pp_rank}] != 0 on rank #0" assert dp_rank == 0, f"dp_rank:[{dp_rank}] != 0 on rank #0" if not isinstance(wrapped_models, (list, tuple)): wrapped_models = list(wrapped_models) assert len(wrapped_models) == virtual_pp_size num_layers_per_model = config.num_hidden_layers // pp_size // virtual_pp_size assert num_layers_per_model * pp_size * virtual_pp_size == config.num_hidden_layers models = [None] * len(wrapped_models) for i, wrapped_model in enumerate(wrapped_models): models[i] = unwrap_model(wrapped_model, (torchDDP, LocalDDP, Float16Module)) assert len(models[i].model.layers ) == num_layers_per_model, 'len model layers {} not equal to num_layers_per_model {}'.format( len(models[i].model.layers), num_layers_per_model) state_dict = dict() def _get_cpu_tensor(tensor: torch.Tensor): if tensor is None: return None if tensor.device == torch.device("cpu"): return tensor.detach().clone() return tensor.detach().cpu() def _broadcast_tensor(tensor, name, src_pp_rank) -> torch.Tensor: """broadcast tensor across mp_group""" nonlocal state_dict nonlocal mp_group src_rank = _megatron_calc_global_rank(tp_rank=0, dp_rank=0, pp_rank=src_pp_rank) if torch.distributed.get_rank() == src_rank: if tensor is None: weight = None tensor_shape = None else: weight = tensor tensor_shape = weight.shape else: weight = None tensor_shape = None obj_list = [tensor_shape] dist.broadcast_object_list(obj_list, src=src_rank, group=mp_group) tensor_shape = obj_list[0] if tensor_shape is None: # all or none ranks in the mp_group should reach here print_rank_0(f"tensor:[{name}] not exist, skip collect") return if weight is None: weight = torch.empty( tensor_shape, dtype=args.params_dtype, device=torch.cuda.current_device(), requires_grad=False, ) dist.broadcast(weight, src=src_rank, group=mp_group) if torch.distributed.get_rank() == 0: state_dict[name] = _get_cpu_tensor(weight) def _broadcast_tp_shard_tensor(tensor, name, src_pp_rank, concat_dim=0, mutate_func=None) -> torch.Tensor: """broadcast tensor in tp shards across mp_group""" nonlocal state_dict nonlocal mp_group tp_rank = mpu.get_tensor_model_parallel_rank() tp_size = mpu.get_tensor_model_parallel_world_size() src_rank = _megatron_calc_global_rank(tp_rank=0, dp_rank=0, pp_rank=src_pp_rank) if torch.distributed.get_rank() == src_rank: chunk_shape = tensor.shape else: chunk_shape = None obj_list = [chunk_shape] dist.broadcast_object_list(obj_list, src=src_rank, group=mp_group) chunk_shape = obj_list[0] if chunk_shape is None: # all or none ranks in the mp_group should reach here print_rank_0(f"tp_shard tensor:[{name}] not exist, skip collecting") return buffer_tensor = torch.empty( chunk_shape, dtype=args.params_dtype, device=torch.cuda.current_device(), requires_grad=False, ) chunk_tensors = [None] * tp_size for i in range(tp_size): cur_src_rank = _megatron_calc_global_rank(tp_rank=i, dp_rank=0, pp_rank=src_pp_rank) sync_tensor = tensor if torch.distributed.get_rank() == cur_src_rank else buffer_tensor dist.broadcast(sync_tensor, src=cur_src_rank, group=mp_group) if torch.distributed.get_rank() == 0: chunk_tensors[i] = _get_cpu_tensor(sync_tensor) if torch.distributed.get_rank() == 0: full_tensor = torch.concat(chunk_tensors, dim=concat_dim) if mutate_func is not None: full_tensor = mutate_func(full_tensor) state_dict[name] = full_tensor def _broadcast_tp_shard_tensor_gate_up(tensor, gate_name, up_name, src_pp_rank) -> torch.Tensor: """broadcast tensor in tp shards across mp_group""" nonlocal state_dict nonlocal mp_group tp_rank = mpu.get_tensor_model_parallel_rank() tp_size = mpu.get_tensor_model_parallel_world_size() src_rank = _megatron_calc_global_rank(tp_rank=0, dp_rank=0, pp_rank=src_pp_rank) if torch.distributed.get_rank() == src_rank: chunk_shape = tensor.shape else: chunk_shape = None obj_list = [chunk_shape] dist.broadcast_object_list(obj_list, src=src_rank, group=mp_group) chunk_shape = obj_list[0] if chunk_shape is None: # all or none ranks in the mp_group should reach here print_rank_0(f"tp_shard tensor:[{gate_name, up_name}] not exist, skip collecting") return buffer_tensor = torch.empty( chunk_shape, dtype=args.params_dtype, device=torch.cuda.current_device(), requires_grad=False, ) chunk_tensors = [None] * tp_size for i in range(tp_size): cur_src_rank = _megatron_calc_global_rank(tp_rank=i, dp_rank=0, pp_rank=src_pp_rank) sync_tensor = tensor if torch.distributed.get_rank() == cur_src_rank else buffer_tensor dist.broadcast(sync_tensor, src=cur_src_rank, group=mp_group) if torch.distributed.get_rank() == 0: chunk_tensors[i] = _get_cpu_tensor(sync_tensor) if torch.distributed.get_rank() == 0: full_tensor = torch.concat(chunk_tensors, dim=0) intermediate_size_tp = config.intermediate_size // tp_size gate_weight_list = [] up_weight_list = [] for i in range(tp_size): gate_up_weight_tp = full_tensor[intermediate_size_tp * 2 * i:intermediate_size_tp * 2 * (i + 1)] gate_weight_tp = gate_up_weight_tp[:intermediate_size_tp] up_weight_tp = gate_up_weight_tp[intermediate_size_tp:] gate_weight_list.append(gate_weight_tp) up_weight_list.append(up_weight_tp) state_dict[gate_name] = torch.cat(gate_weight_list, dim=0) state_dict[up_name] = torch.cat(up_weight_list, dim=0) def _broadcast_tp_shard_tensor_qkv(tensor, q_name, k_name, v_name, src_pp_rank): """broadcast tensor in tp shards across mp_group""" nonlocal state_dict nonlocal mp_group tp_rank = mpu.get_tensor_model_parallel_rank() tp_size = mpu.get_tensor_model_parallel_world_size() src_rank = _megatron_calc_global_rank(tp_rank=0, dp_rank=0, pp_rank=src_pp_rank) if torch.distributed.get_rank() == src_rank: chunk_shape = tensor.shape else: chunk_shape = None obj_list = [chunk_shape] dist.broadcast_object_list(obj_list, src=src_rank, group=mp_group) chunk_shape = obj_list[0] if chunk_shape is None: # all or none ranks in the mp_group should reach here print_rank_0(f"tp_shard tensor:[{q_name}] not exist, skip collecting") return buffer_tensor = torch.empty( chunk_shape, dtype=args.params_dtype, device=torch.cuda.current_device(), requires_grad=False, ) chunk_tensors = [None] * tp_size for i in range(tp_size): cur_src_rank = _megatron_calc_global_rank(tp_rank=i, dp_rank=0, pp_rank=src_pp_rank) sync_tensor = tensor if torch.distributed.get_rank() == cur_src_rank else buffer_tensor dist.broadcast(sync_tensor, src=cur_src_rank, group=mp_group) if torch.distributed.get_rank() == 0: chunk_tensors[i] = _get_cpu_tensor(sync_tensor) if torch.distributed.get_rank() == 0: full_tensor = torch.concat(chunk_tensors, dim=0) q_weight_list = [] k_weight_list = [] v_weight_list = [] hidden_size_per_head = config.hidden_size // config.num_attention_heads if config.num_key_value_heads >= tp_size: q_size_tp = config.hidden_size // tp_size kv_size_tp = hidden_size_per_head * config.num_key_value_heads // tp_size total_size = q_size_tp + 2 * kv_size_tp for i in range(tp_size): qkv_part = full_tensor[i * total_size:(i + 1) * total_size] q_part = qkv_part[:q_size_tp] k_part = qkv_part[q_size_tp:q_size_tp + kv_size_tp] v_part = qkv_part[q_size_tp + kv_size_tp:total_size] q_weight_list.append(q_part) k_weight_list.append(k_part) v_weight_list.append(v_part) else: q_size_tp = config.hidden_size // tp_size kv_size_tp = hidden_size_per_head total_size = q_size_tp + 2 * kv_size_tp for i in range(tp_size): qkv_part = full_tensor[i * total_size:(i + 1) * total_size] q_part = qkv_part[:q_size_tp] k_part = qkv_part[q_size_tp:q_size_tp + kv_size_tp] v_part = qkv_part[q_size_tp + kv_size_tp:total_size] q_weight_list.append(q_part) if i * config.num_key_value_heads % tp_size == 0: k_weight_list.append(k_part) v_weight_list.append(v_part) state_dict[q_name] = torch.cat(q_weight_list, dim=0) state_dict[k_name] = torch.cat(k_weight_list, dim=0) state_dict[v_name] = torch.cat(v_weight_list, dim=0) # empty cache before collecting weights torch.cuda.empty_cache() # Embeddings # ------------------- if dp_rank == 0: # Embeddings # ------------------- print_rank_0("collecting embeddings...") gpt_model_module = _get_gpt_model(models[0]) _broadcast_tp_shard_tensor( gpt_model_module.model.embed_tokens.weight if pp_rank == 0 else None, "model.embed_tokens.weight", src_pp_rank=0, ) # Transformer layers # ------------------- layer_map = _megatron_calc_layer_map(config) for layer in range(config.num_hidden_layers): print_rank_0(f"collecting layer #{layer}...") layer_name = f"model.layers.{layer}" src_pp_rank, src_virtual_pp_rank, src_layer_idx = layer_map[layer] gpt_model_module = _get_gpt_model(models[src_virtual_pp_rank]) sync_layer = gpt_model_module.model.layers[src_layer_idx] _broadcast_tensor( sync_layer.input_layernorm.weight, f"{layer_name}.input_layernorm.weight", src_pp_rank=src_pp_rank, ) _broadcast_tp_shard_tensor_qkv( sync_layer.self_attn.qkv_proj.weight, f"{layer_name}.self_attn.q_proj.weight", f"{layer_name}.self_attn.k_proj.weight", f"{layer_name}.self_attn.v_proj.weight", src_pp_rank=src_pp_rank, ) _broadcast_tp_shard_tensor( sync_layer.self_attn.o_proj.weight, f"{layer_name}.self_attn.o_proj.weight", concat_dim=1, src_pp_rank=src_pp_rank, ) _broadcast_tensor( sync_layer.post_attention_layernorm.weight, f"{layer_name}.post_attention_layernorm.weight", src_pp_rank=src_pp_rank, ) _broadcast_tp_shard_tensor_gate_up(sync_layer.mlp.gate_up_proj.weight, f"{layer_name}.mlp.gate_proj.weight", f"{layer_name}.mlp.up_proj.weight", src_pp_rank=src_pp_rank) _broadcast_tp_shard_tensor( sync_layer.mlp.down_proj.weight, f"{layer_name}.mlp.down_proj.weight", concat_dim=1, src_pp_rank=src_pp_rank, ) # Final Layernorm # ------------------- print_rank_0("collecting final layernorm...") gpt_model_module = _get_gpt_model(models[-1]) _broadcast_tensor( getattr(gpt_model_module.model.norm, "weight", None), "model.norm.weight", src_pp_rank=pp_size - 1, ) print_rank_0("collecting lm_head...") if is_value_model: _broadcast_tensor(getattr(gpt_model_module.lm_head, "weight", None) if pp_rank == pp_size - 1 else None, "reward_head.weight", src_pp_rank=pp_size - 1) else: _broadcast_tp_shard_tensor( getattr(gpt_model_module.lm_head, "weight", None) if pp_rank == pp_size - 1 else None, "lm_head.weight", src_pp_rank=pp_size - 1, ) dist.barrier() torch.cuda.empty_cache() if torch.distributed.get_rank() == 0: if dtype == "fp16": dtype = torch.float16 elif dtype == "bf16": dtype = torch.bfloat16 elif dtype is None or dtype == "fp32": dtype = torch.float32 else: print(f'Unknown/unsupported dtype to save: {dtype}"') exit(1) for k, v in state_dict.items(): if dtype != v.dtype: state_dict[k] = v.to(dtype) print_rank_0(f"merge megatron ckpt done, time elapsed {time.time() - start_time}s") return state_dict ================================================ FILE: verl/models/llama/megatron/layers/__init__.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from .parallel_attention import ParallelLlamaAttention from .parallel_decoder import ParallelLlamaDecoderLayer, ParallelLlamaDecoderLayerRmPad from .parallel_mlp import ParallelLlamaMLP from .parallel_rmsnorm import ParallelLlamaRMSNorm ================================================ FILE: verl/models/llama/megatron/layers/parallel_attention.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved. # # This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX # and OPT implementations in this library. It has been modified from its # original forms to accommodate minor architectural differences compared # to GPT-NeoX and OPT used by the Meta AI team that trained the model. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import math from typing import Optional, Tuple import torch from megatron.core import parallel_state as mpu from megatron.core import tensor_parallel from megatron.core import ModelParallelConfig from torch import nn from transformers import LlamaConfig from verl.models.llama.megatron.layers.parallel_linear import QKVParallelLinear from verl.utils.megatron import tensor_parallel as tp_utils class LlamaRotaryEmbedding(nn.Module): def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None): super().__init__() self.dim = dim self.max_position_embeddings = max_position_embeddings self.base = base inv_freq = 1.0 / (self.base**(torch.arange(0, self.dim, 2).float().to(device) / self.dim)) self.register_buffer("inv_freq", inv_freq, persistent=False) # Build here to make `torch.jit.trace` work. self._set_cos_sin_cache(seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype()) def _set_cos_sin_cache(self, seq_len, device, dtype): self.max_seq_len_cached = seq_len t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype) freqs = torch.einsum("i,j->ij", t, self.inv_freq) # Different from paper, but it uses a different permutation in order to obtain the same calculation emb = torch.cat((freqs, freqs), dim=-1) self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False) self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False) def forward(self, x, seq_len=None): # x: [bs, num_attention_heads, seq_len, head_size] if seq_len > self.max_seq_len_cached: self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype) return ( self.cos_cached[:seq_len].to(dtype=x.dtype), self.sin_cached[:seq_len].to(dtype=x.dtype), ) class LlamaLinearScalingRotaryEmbedding(LlamaRotaryEmbedding): """LlamaRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev""" def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0): self.scaling_factor = scaling_factor super().__init__(dim, max_position_embeddings, base, device) def _set_cos_sin_cache(self, seq_len, device, dtype): self.max_seq_len_cached = seq_len t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype) t = t / self.scaling_factor freqs = torch.einsum("i,j->ij", t, self.inv_freq) # Different from paper, but it uses a different permutation in order to obtain the same calculation emb = torch.cat((freqs, freqs), dim=-1) self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False) self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False) class LlamaDynamicNTKScalingRotaryEmbedding(LlamaRotaryEmbedding): """LlamaRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla""" def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0): self.scaling_factor = scaling_factor super().__init__(dim, max_position_embeddings, base, device) def _set_cos_sin_cache(self, seq_len, device, dtype): self.max_seq_len_cached = seq_len if seq_len > self.max_position_embeddings: base = self.base * ((self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1))**(self.dim / (self.dim - 2)) inv_freq = 1.0 / (base**(torch.arange(0, self.dim, 2).float().to(device) / self.dim)) self.register_buffer("inv_freq", inv_freq, persistent=False) t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype) freqs = torch.einsum("i,j->ij", t, self.inv_freq) # Different from paper, but it uses a different permutation in order to obtain the same calculation emb = torch.cat((freqs, freqs), dim=-1) self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False) self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False) def rotate_half(x): """Rotates half the hidden dims of the input.""" x1 = x[..., :x.shape[-1] // 2] x2 = x[..., x.shape[-1] // 2:] return torch.cat((-x2, x1), dim=-1) def apply_rotary_pos_emb(q, k, cos, sin, position_ids): cos = cos[position_ids].unsqueeze(1) # [bs, 1, seq_len, dim] sin = sin[position_ids].unsqueeze(1) # [bs, 1, seq_len, dim] q_embed = (q * cos) + (rotate_half(q) * sin) k_embed = (k * cos) + (rotate_half(k) * sin) return q_embed, k_embed def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor: """ This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch, num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim) """ batch, num_key_value_heads, slen, head_dim = hidden_states.shape if n_rep == 1: return hidden_states hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim) return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim) class ParallelLlamaAttention(nn.Module): """Multi-headed attention from 'Attention Is All You Need' paper""" def __init__(self, config: LlamaConfig, megatron_config: ModelParallelConfig): super().__init__() self.config = config self.megatron_config = megatron_config self.hidden_size = config.hidden_size self.num_heads = config.num_attention_heads self.head_dim = self.hidden_size // self.num_heads self.num_key_value_heads = config.num_key_value_heads self.num_key_value_groups = self.num_heads // self.num_key_value_heads self.max_position_embeddings = config.max_position_embeddings self.rope_theta = config.rope_theta # assign values after tp tp_size = mpu.get_tensor_model_parallel_world_size() assert self.num_heads % tp_size == 0, f'num_head must be divisible by tp_size. Got num_head={self.num_heads}, tp_size={tp_size}' assert self.num_key_value_heads % tp_size == 0, \ f'num_key_value_heads must be divisible by tp_size. Got num_key_value_heads={self.num_key_value_heads}, tp_size={tp_size}' self.num_heads_per_tp = self.num_heads // tp_size self.num_key_value_heads_per_tp = self.num_key_value_heads // tp_size self.hidden_size_per_tp = self.hidden_size // tp_size if (self.head_dim * self.num_heads) != self.hidden_size: raise ValueError(f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}" f" and `num_heads`: {self.num_heads}).") column_kwargs = tp_utils.get_default_kwargs_for_column_parallel_linear() row_kwargs = tp_utils.get_default_kwargs_for_row_parallel_linear() if megatron_config is not None: assert column_kwargs.get('config', False), 'must have ModelParallelConfig' assert row_kwargs.get('config', False), 'must have ModelParallelConfig' tp_utils.update_kwargs_with_config(column_kwargs, megatron_config) tp_utils.update_kwargs_with_config(row_kwargs, megatron_config) # [self.q_size, self.k_size, self.v_size] self.qkv_proj = QKVParallelLinear(input_size=self.hidden_size, num_heads=self.num_heads, num_key_value_heads=self.num_key_value_heads, head_dim=self.head_dim, bias=config.attention_bias, gather_output=False, skip_bias_add=False, **column_kwargs) self.q_size = self.num_heads_per_tp * self.head_dim self.k_size = self.num_key_value_heads_per_tp * self.head_dim self.v_size = self.num_key_value_heads_per_tp * self.head_dim self.o_proj = tensor_parallel.RowParallelLinear(input_size=self.num_heads * self.head_dim, output_size=self.hidden_size, bias=config.attention_bias, input_is_parallel=True, skip_bias_add=False, **row_kwargs) self._init_rope() def _init_rope(self): if self.config.rope_scaling is None: self.rotary_emb = LlamaRotaryEmbedding( self.head_dim, max_position_embeddings=self.max_position_embeddings, base=self.rope_theta, ) else: scaling_type = self.config.rope_scaling["type"] scaling_factor = self.config.rope_scaling["factor"] if scaling_type == "linear": self.rotary_emb = LlamaLinearScalingRotaryEmbedding( self.head_dim, max_position_embeddings=self.max_position_embeddings, scaling_factor=scaling_factor, base=self.rope_theta, ) elif scaling_type == "dynamic": self.rotary_emb = LlamaDynamicNTKScalingRotaryEmbedding( self.head_dim, max_position_embeddings=self.max_position_embeddings, scaling_factor=scaling_factor, base=self.rope_theta, ) else: raise ValueError(f"Unknown RoPE scaling type {scaling_type}") def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int): return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous() def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: bsz, q_len, _ = hidden_states.size() qkv = self.qkv_proj(hidden_states)[0] query_states, key_states, value_states = qkv.split([self.q_size, self.k_size, self.v_size], dim=-1) query_states = query_states.view(bsz, q_len, self.num_heads_per_tp, self.head_dim).transpose(1, 2) key_states = key_states.view(bsz, q_len, self.num_key_value_heads_per_tp, self.head_dim).transpose(1, 2) value_states = value_states.view(bsz, q_len, self.num_key_value_heads_per_tp, self.head_dim).transpose(1, 2) kv_seq_len = key_states.shape[-2] cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len) query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids) key_states = repeat_kv(key_states, self.num_key_value_groups) value_states = repeat_kv(value_states, self.num_key_value_groups) attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim) if attn_weights.size() != (bsz, self.num_heads_per_tp, q_len, kv_seq_len): raise ValueError( f"Attention weights should be of size {(bsz, self.num_heads_per_tp, q_len, kv_seq_len)}, but is" f" {attn_weights.size()}") if attention_mask is not None: if attention_mask.size() != (bsz, 1, q_len, kv_seq_len): raise ValueError( f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}") attn_weights = attn_weights + attention_mask # upcast attention to fp32 attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype) attn_output = torch.matmul(attn_weights, value_states) if attn_output.size() != (bsz, self.num_heads_per_tp, q_len, self.head_dim): raise ValueError( f"`attn_output` should be of size {(bsz, self.num_heads_per_tp, q_len, self.head_dim)}, but is" f" {attn_output.size()}") attn_output = attn_output.transpose(1, 2).contiguous() attn_output = attn_output.reshape(bsz, q_len, self.hidden_size_per_tp) attn_output = self.o_proj(attn_output)[0] return attn_output """ Remove padding Attention - Using Flash-attn 2 - Compatible with sequence parallel """ from transformers.utils import is_flash_attn_2_available import torch.nn.functional as F from einops import rearrange if is_flash_attn_2_available(): from flash_attn import flash_attn_varlen_func from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa def apply_rotary_pos_emb_rmpad(q, k, cos, sin, position_ids, indices, sequence_length): batch_size = position_ids.shape[0] q = pad_input(q, indices, batch_size, sequence_length) # (batch_size, seqlen, num_head, head_dim) k = pad_input(k, indices, batch_size, sequence_length) cos = cos[position_ids].unsqueeze(2) # [bs, seq_len, 1, dim] sin = sin[position_ids].unsqueeze(2) # [bs, seq_len, 1, dim] q_embed = (q * cos) + (rotate_half(q) * sin) k_embed = (k * cos) + (rotate_half(k) * sin) q_embed = index_first_axis(rearrange(q_embed, "b s ... -> (b s) ..."), indices) k_embed = index_first_axis(rearrange(k_embed, "b s ... -> (b s) ..."), indices) return q_embed, k_embed from flash_attn.layers.rotary import apply_rotary_emb # use flash-attn rotary embeddings with rmpad # cos/sin shoudl be: (seq_length, rotary_dim / 2) def apply_rotary_pos_emb_rmpad_flash(q, k, cos, sin, cu_seqlens, max_seqlen): q_embed = apply_rotary_emb(q, cos, sin, interleaved=False, inplace=False, cu_seqlens=cu_seqlens, max_seqlen=max_seqlen) k_embed = apply_rotary_emb(k, cos, sin, interleaved=False, inplace=False, cu_seqlens=cu_seqlens, max_seqlen=max_seqlen) return q_embed, k_embed class ParallelLlamaAttentionRmPad(ParallelLlamaAttention): def forward(self, hidden_states: torch.Tensor, position_ids: Optional[torch.LongTensor] = None, sequence_length: int = None, indices: torch.Tensor = None, cu_seqlens: torch.Tensor = None, max_seqlen_in_batch: int = None): total_nnz, _, _ = hidden_states.size() # This is the total_nnz padded after sequence parallel if self.megatron_config.sequence_parallel: total_nnz = total_nnz * mpu.get_tensor_model_parallel_world_size() qkv = self.qkv_proj(hidden_states)[0] query_states, key_states, value_states = qkv.split([self.q_size, self.k_size, self.v_size], dim=-1) # (total_nnz, 1, hidden_size) if self.megatron_config.sequence_parallel: sequence_parallel_pad = total_nnz - cu_seqlens[-1] total_nnz = cu_seqlens[-1] # total_nnz before sp padding query_states = query_states[:total_nnz] key_states = key_states[:total_nnz] value_states = value_states[:total_nnz] # Flash attention requires the input to have the shape # batch_size x seq_length x head_dime x hidden_dim # therefore we just need to keep the original shape query_states = query_states.view(total_nnz, self.num_heads_per_tp, self.head_dim) key_states = key_states.view(total_nnz, self.num_key_value_heads_per_tp, self.head_dim) value_states = value_states.view(total_nnz, self.num_key_value_heads_per_tp, self.head_dim) cos, sin = self.rotary_emb(value_states, seq_len=sequence_length) cos, sin = cos[:, :cos.shape[1] // 2], sin[:, :sin.shape[1] // 2] # flash attn only needs half query_states, key_states = apply_rotary_pos_emb_rmpad_flash(query_states, key_states, cos, sin, cu_seqlens=cu_seqlens, max_seqlen=max_seqlen_in_batch) # query_states, key_states = apply_rotary_pos_emb_rmpad(query_states, key_states, cos, sin, position_ids, indices, # TODO: llama does not have dropout in the config?? # It is recommended to use dropout with FA according to the docs # when training. dropout_rate = 0.0 # if not self.training else self.attn_dropout # In PEFT, usually we cast the layer norms in float32 for training stability reasons # therefore the input hidden states gets silently casted in float32. Hence, we need # cast them back in float16 just to be sure everything works as expected. # This might slowdown training & inference so it is recommended to not cast the LayerNorms # in fp32. (LlamaRMSNorm handles it correctly) input_dtype = query_states.dtype if input_dtype == torch.float32: query_states = query_states.to(torch.float16) key_states = key_states.to(torch.float16) value_states = value_states.to(torch.float16) attn_output_unpad = flash_attn_varlen_func( query_states, key_states, value_states, cu_seqlens_q=cu_seqlens, cu_seqlens_k=cu_seqlens, max_seqlen_q=max_seqlen_in_batch, max_seqlen_k=max_seqlen_in_batch, dropout_p=dropout_rate, softmax_scale=None, causal=True, ) attn_output_unpad = attn_output_unpad.to(input_dtype) attn_output_unpad = attn_output_unpad.reshape(total_nnz, 1, self.hidden_size_per_tp).contiguous() # sequence parallel reduce_scatter is performed inside RowColumnParallel if enabled # Here we need to repad if self.megatron_config.sequence_parallel: attn_output_unpad = F.pad(attn_output_unpad, pad=(0, 0, 0, 0, 0, sequence_parallel_pad)) attn_output_unpad = self.o_proj(attn_output_unpad)[0] return attn_output_unpad ================================================ FILE: verl/models/llama/megatron/layers/parallel_decoder.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved. # # This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX # and OPT implementations in this library. It has been modified from its # original forms to accommodate minor architectural differences compared # to GPT-NeoX and OPT used by the Meta AI team that trained the model. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import Optional, Tuple import torch from torch import nn from transformers import LlamaConfig from megatron.core import ModelParallelConfig from .parallel_attention import ParallelLlamaAttention, ParallelLlamaAttentionRmPad from .parallel_mlp import ParallelLlamaMLP from .parallel_rmsnorm import ParallelLlamaRMSNorm class ParallelLlamaDecoderLayer(nn.Module): def __init__(self, config: LlamaConfig, megatron_config: ModelParallelConfig): super().__init__() self.hidden_size = config.hidden_size self.self_attn = ParallelLlamaAttention(config=config, megatron_config=megatron_config) self.mlp = ParallelLlamaMLP(config, megatron_config=megatron_config) self.input_layernorm = ParallelLlamaRMSNorm(config, megatron_config) self.post_attention_layernorm = ParallelLlamaRMSNorm(config, megatron_config) def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]: """ Args: hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` attention_mask (`torch.FloatTensor`, *optional*): attention mask of size `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. use_cache (`bool`, *optional*): If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`). past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states """ residual = hidden_states hidden_states = self.input_layernorm(hidden_states) # Note: sequence parallel is hidden inside ColumnParallelLinear # reduce scatter is hidden inside RowParallelLinear # Self Attention hidden_states = self.self_attn( hidden_states=hidden_states, attention_mask=attention_mask, position_ids=position_ids, ) # TODO: add sequence parallel operator reduce_scatter here hidden_states = residual + hidden_states # Fully Connected residual = hidden_states hidden_states = self.post_attention_layernorm(hidden_states) # TODO: add sequence parallel operator all_gather here hidden_states = self.mlp(hidden_states) # TODO: add sequence parallel operator reduce_scatter here hidden_states = residual + hidden_states outputs = hidden_states return outputs class ParallelLlamaDecoderLayerRmPad(nn.Module): def __init__(self, config: LlamaConfig, megatron_config: ModelParallelConfig): super().__init__() self.config = config self.megatron_config = megatron_config self.hidden_size = config.hidden_size self.self_attn = ParallelLlamaAttentionRmPad(config=config, megatron_config=megatron_config) self.mlp = ParallelLlamaMLP(config, megatron_config=megatron_config) self.input_layernorm = ParallelLlamaRMSNorm(config, megatron_config) self.post_attention_layernorm = ParallelLlamaRMSNorm(config, megatron_config) def forward( self, hidden_states: torch.Tensor, position_ids: Optional[torch.LongTensor] = None, sequence_length: int = None, indices: torch.Tensor = None, cu_seqlens: int = None, max_seqlen_in_batch: int = None ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]: residual = hidden_states # (total_nnz // sp, 1, hidden_size) hidden_states = self.input_layernorm(hidden_states) # Self Attention # (total_nnz // sp, 1, hidden_size) -> all-gather (total_nnz, 1, hidden_size) # -> col + row -> reduce-scatter -> (total_nnz // sp, 1, hidden_size) hidden_states = self.self_attn(hidden_states=hidden_states, position_ids=position_ids, sequence_length=sequence_length, indices=indices, cu_seqlens=cu_seqlens, max_seqlen_in_batch=max_seqlen_in_batch) hidden_states = residual + hidden_states # Fully Connected # shape changes same as attn residual = hidden_states hidden_states = self.post_attention_layernorm(hidden_states) hidden_states = self.mlp(hidden_states) hidden_states = residual + hidden_states outputs = hidden_states return outputs ================================================ FILE: verl/models/llama/megatron/layers/parallel_linear.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Copyright 2023 The vLLM team. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # Adapted from https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/layers/linear.py from typing import Optional, Tuple from megatron.core import tensor_parallel class QKVParallelLinear(tensor_parallel.ColumnParallelLinear): def __init__(self, input_size, num_heads, num_key_value_heads, head_dim, *, bias=True, gather_output=True, skip_bias_add=False, **kwargs): # Keep input parameters, and already restrict the head numbers self.input_size = input_size self.q_output_size = num_heads * head_dim self.kv_output_size = num_key_value_heads * head_dim self.head_dim = head_dim self.gather_output = gather_output self.skip_bias_add = skip_bias_add input_size = self.input_size output_size = (num_heads + 2 * num_key_value_heads) * self.head_dim super().__init__(input_size=input_size, output_size=output_size, bias=bias, gather_output=gather_output, skip_bias_add=skip_bias_add, **kwargs) class MergedColumnParallelLinear(tensor_parallel.ColumnParallelLinear): def __init__(self, input_size, gate_ouput_size, up_output_size, *, bias=True, gather_output=True, skip_bias_add=False, **kwargs): # Keep input parameters, and already restrict the head numbers self.input_size = input_size self.output_size = gate_ouput_size + up_output_size self.gather_output = gather_output self.skip_bias_add = skip_bias_add super().__init__(input_size=self.input_size, output_size=self.output_size, bias=bias, gather_output=gather_output, skip_bias_add=skip_bias_add, **kwargs) ================================================ FILE: verl/models/llama/megatron/layers/parallel_mlp.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved. # # This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX # and OPT implementations in this library. It has been modified from its # original forms to accommodate minor architectural differences compared # to GPT-NeoX and OPT used by the Meta AI team that trained the model. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from megatron.core import parallel_state as mpu from megatron.core import tensor_parallel from megatron.core import ModelParallelConfig from torch import nn from transformers.activations import ACT2FN from verl.models.llama.megatron.layers.parallel_linear import MergedColumnParallelLinear from verl.utils.megatron import tensor_parallel as tp_utils class ParallelLlamaMLP(nn.Module): def __init__(self, config, megatron_config: ModelParallelConfig = None) -> None: super().__init__() self.config = config self.hidden_size = config.hidden_size self.intermediate_size = config.intermediate_size # The weight is only [hidden_size, intermediate_size // model_parallel_world_size] column_kwargs = tp_utils.get_default_kwargs_for_column_parallel_linear() row_kwargs = tp_utils.get_default_kwargs_for_row_parallel_linear() if megatron_config is not None: assert column_kwargs.get('config', False), 'must have ModelParallelConfig' assert row_kwargs.get('config', False), 'must have ModelParallelConfig' tp_utils.update_kwargs_with_config(row_kwargs, megatron_config) tp_utils.update_kwargs_with_config(column_kwargs, megatron_config) tp_size = mpu.get_tensor_model_parallel_world_size() self.gate_up_proj = MergedColumnParallelLinear( input_size=self.hidden_size, gate_ouput_size=self.intermediate_size, up_output_size=self.intermediate_size, bias=False, gather_output=False, skip_bias_add=False, **column_kwargs, ) self.gate_size = self.intermediate_size // tp_size self.down_proj = tensor_parallel.RowParallelLinear(input_size=self.intermediate_size, output_size=self.hidden_size, bias=False, input_is_parallel=True, skip_bias_add=False, **row_kwargs) self.act_fn = ACT2FN[config.hidden_act] def forward(self, x): gate_up = self.gate_up_proj(x)[0] gate, up = gate_up.split(self.gate_size, dim=-1) return self.down_proj(self.act_fn(gate) * up)[0] ================================================ FILE: verl/models/llama/megatron/layers/parallel_rmsnorm.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import numbers import torch from megatron.core import ModelParallelConfig from torch import nn from transformers import LlamaConfig from apex.normalization.fused_layer_norm import fused_rms_norm_affine from verl.utils.megatron import sequence_parallel as sp_utils class ParallelLlamaRMSNorm(nn.Module): def __init__(self, config: LlamaConfig, megatron_config: ModelParallelConfig): """ LlamaRMSNorm is equivalent to T5LayerNorm """ super().__init__() if isinstance(config.hidden_size, numbers.Integral): normalized_shape = (config.hidden_size,) self.normalized_shape = torch.Size(normalized_shape) self.weight = nn.Parameter(torch.ones(self.normalized_shape)) self.variance_epsilon = config.rms_norm_eps if megatron_config.sequence_parallel: sp_utils.mark_parameter_as_sequence_parallel(self.weight) def forward(self, hidden_states): return fused_rms_norm_affine(input=hidden_states, weight=self.weight, normalized_shape=self.normalized_shape, eps=self.variance_epsilon, memory_efficient=True) ================================================ FILE: verl/models/llama/megatron/modeling_llama_megatron.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved. # # This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX # and OPT implementations in this library. It has been modified from its # original forms to accommodate minor architectural differences compared # to GPT-NeoX and OPT used by the Meta AI team that trained the model. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """PyTorch LLaMA model with Megatron-style acceleration.""" from typing import Optional, Tuple, Union import torch import torch.utils.checkpoint from megatron.core import tensor_parallel from megatron.core import ModelParallelConfig from torch import nn from transformers.modeling_outputs import BaseModelOutputWithPast from transformers.models.llama.configuration_llama import LlamaConfig from transformers.models.llama.modeling_llama import CausalLMOutputWithPast from verl.utils.megatron import sequence_parallel as sp_utils from verl.utils.megatron import tensor_parallel as tp_utils from .layers import ParallelLlamaDecoderLayer, ParallelLlamaRMSNorm, ParallelLlamaDecoderLayerRmPad """ TODO: 1. Add weight initialization. Here we need to be careful on TP weight init. 2. Add sequence parallel 3. Load checkpoint from meta LLama pretrained checkpoint """ # Copied from transformers.models.bart.modeling_bart._make_causal_mask def _make_causal_mask(input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device): """ Make causal mask used for bi-directional self-attention. """ bsz, tgt_len = input_ids_shape mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device) mask_cond = torch.arange(mask.size(-1), device=device) mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0) mask = mask.to(dtype) return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len) # Copied from transformers.models.bart.modeling_bart._expand_mask def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None): """ Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`. """ bsz, src_len = mask.size() tgt_len = tgt_len if tgt_len is not None else src_len expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype) inverted_mask = 1.0 - expanded_mask return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min) class ParallelLlamaModel(nn.Module): """ Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`LlamaDecoderLayer`] Args: config: LlamaConfig """ def __init__(self, config: LlamaConfig, megatron_config: ModelParallelConfig): super().__init__() self.padding_idx = config.pad_token_id self.vocab_size = config.vocab_size embedding_kwargs = tp_utils.get_default_kwargs_for_parallel_embedding() if megatron_config is not None: assert embedding_kwargs.get('config', False), 'must have ModelParallelConfig' tp_utils.update_kwargs_with_config(embedding_kwargs, self.megatron_config) self.embed_tokens = tensor_parallel.VocabParallelEmbedding(num_embeddings=config.vocab_size, embedding_dim=config.hidden_size, **embedding_kwargs) self.layers = nn.ModuleList( [ParallelLlamaDecoderLayer(config, megatron_config) for _ in range(config.num_hidden_layers)]) self.norm = ParallelLlamaRMSNorm(config, megatron_config) # Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds): # create causal mask # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] combined_attention_mask = None if input_shape[-1] > 1: combined_attention_mask = _make_causal_mask( input_shape, inputs_embeds.dtype, device=inputs_embeds.device, ) if attention_mask is not None: # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to(inputs_embeds.device) combined_attention_mask = (expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask) return combined_attention_mask def forward( self, input_ids: torch.LongTensor = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, ) -> Union[Tuple, BaseModelOutputWithPast]: """ Args: input_ids: input ids. shape (batch_size, seq_length) attention_mask: attention_mask. shape (batch_size, seq_length) position_ids: position ids. shape (batch_size, seq_length) Returns: """ batch_size, seq_length = input_ids.shape inputs_embeds = self.embed_tokens(input_ids) # embed positions attention_mask = self._prepare_decoder_attention_mask(attention_mask, (batch_size, seq_length), inputs_embeds) hidden_states = inputs_embeds for idx, decoder_layer in enumerate(self.layers): layer_outputs = decoder_layer( hidden_states, attention_mask=attention_mask, position_ids=position_ids, ) hidden_states = layer_outputs hidden_states = self.norm(hidden_states) return hidden_states class ParallelLlamaForCausalLM(nn.Module): def __init__(self, config: LlamaConfig, megatron_config: ModelParallelConfig): super().__init__() self.model = ParallelLlamaModel(config, megatron_config=megatron_config) self.vocab_size = config.vocab_size column_kwargs = tp_utils.get_default_kwargs_for_column_parallel_linear() if megatron_config is not None: assert column_kwargs.get('config', False), 'must have ModelParallelConfig' tp_utils.update_kwargs_with_config(column_kwargs, self.megatron_config) self.lm_head = tensor_parallel.ColumnParallelLinear(input_size=config.hidden_size, output_size=config.vocab_size, bias=False, gather_output=False, skip_bias_add=False, **column_kwargs) def forward( self, input_ids: torch.LongTensor = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, ) -> Union[Tuple, CausalLMOutputWithPast]: r""" Args: labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. Returns: ```""" # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn) outputs = self.model( input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids, ) hidden_states = outputs logits = self.lm_head(hidden_states)[0] logits = tensor_parallel.gather_from_tensor_model_parallel_region(logits) logits = logits.float() return CausalLMOutputWithPast( loss=None, logits=logits, past_key_values=None, hidden_states=None, attentions=None, ) from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa class ParallelLlamaModelRmPad(nn.Module): """ Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`LlamaDecoderLayer`] Args: config: LlamaConfig """ def __init__(self, config: LlamaConfig, megatron_config: ModelParallelConfig): super().__init__() self.padding_idx = config.pad_token_id self.vocab_size = config.vocab_size embedding_kwargs = tp_utils.get_default_kwargs_for_parallel_embedding() self.megatron_config = megatron_config if megatron_config is not None: assert embedding_kwargs.get('config', False), 'must have ModelParallelConfig' tp_utils.update_kwargs_with_config(embedding_kwargs, self.megatron_config) self.embed_tokens = tensor_parallel.VocabParallelEmbedding(num_embeddings=config.vocab_size, embedding_dim=config.hidden_size, **embedding_kwargs) self.layers = nn.ModuleList( [ParallelLlamaDecoderLayerRmPad(config, megatron_config) for _ in range(config.num_hidden_layers)]) self.norm = ParallelLlamaRMSNorm(config, megatron_config) def forward(self, input_ids: torch.Tensor, position_ids: Optional[torch.LongTensor] = None, sequence_length: int = None, indices: torch.Tensor = None, cu_seqlens: int = None, max_seqlen_in_batch: int = None) -> Union[Tuple, BaseModelOutputWithPast]: """ Args: input_ids: input ids. shape (1, totol_nnz) position_ids: position ids. shape (batch_size, seq_length) Returns: """ inputs_embeds = self.embed_tokens(input_ids) # (1, total_nnz) -> (1, total_nnz, hidden_size) # (1, total_nnz, hidden_size) -> (total_nnz, 1, hidden_size) -> (total_nnz // sp, 1, hidden_size) inputs_embeds = inputs_embeds.transpose(0, 1) if self.megatron_config.sequence_parallel: inputs_embeds = tensor_parallel.scatter_to_sequence_parallel_region(inputs_embeds) hidden_states = inputs_embeds for idx, decoder_layer in enumerate(self.layers): layer_outputs = decoder_layer(hidden_states, position_ids=position_ids, sequence_length=sequence_length, indices=indices, cu_seqlens=cu_seqlens, max_seqlen_in_batch=max_seqlen_in_batch) hidden_states = layer_outputs hidden_states = self.norm(hidden_states) return hidden_states class ParallelLlamaForCausalLMRmPad(nn.Module): def __init__(self, config: LlamaConfig, megatron_config: ModelParallelConfig): super().__init__() self.config = config self.megatron_config = megatron_config self.model = ParallelLlamaModelRmPad(config, megatron_config=megatron_config) self.vocab_size = config.vocab_size self._init_head() def _init_head(self): column_kwargs = tp_utils.get_default_kwargs_for_column_parallel_linear() if self.megatron_config is not None: assert column_kwargs.get('config', False), 'must have ModelParallelConfig' tp_utils.update_kwargs_with_config(column_kwargs, self.megatron_config) self.lm_head = tensor_parallel.ColumnParallelLinear(input_size=self.config.hidden_size, output_size=self.config.vocab_size, bias=False, gather_output=False, skip_bias_add=False, **column_kwargs) def _forward_head(self, hidden_states): # all_gather from sequence parallel region is performed inside lm_head logits = self.lm_head(hidden_states)[0] logits = logits.float() # (total_nnz_padded, 1, vocab_size // tp) logits = tensor_parallel.gather_from_tensor_model_parallel_region(logits) # (total_nnz_padded, 1, vocab_size) return logits def forward( self, input_ids: torch.LongTensor = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, ) -> Union[Tuple, CausalLMOutputWithPast]: r""" Args: labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. Returns: ```""" batch_size, sequence_length = input_ids.shape # remove padding here input_ids, indices, cu_seqlens, max_seqlen_in_batch, *_ = unpad_input(input_ids.unsqueeze(dim=-1), attention_mask) # (total_nnz, 1) # pad input_ids to multiple of tp for all tp ranks # TODO: for better performance, the sp padding should be removed at each layer. Not sure the performance gap if self.megatron_config.sequence_parallel: input_ids = sp_utils.pad_to_sequence_parallel(input_ids) input_ids = input_ids.transpose(0, 1) # (1, total_nnz+pad) outputs = self.model(input_ids=input_ids, position_ids=position_ids, sequence_length=sequence_length, indices=indices, cu_seqlens=cu_seqlens, max_seqlen_in_batch=max_seqlen_in_batch) hidden_states = outputs logits = self._forward_head(hidden_states) # remove padding from sequence parallel if self.megatron_config.sequence_parallel: totol_nnz = cu_seqlens[-1] logits = logits[:totol_nnz] # (total_nnz_padded) logits = torch.squeeze(logits, dim=1) # remove the artificial batch dimension # add removed padding back logits = pad_input(logits, indices, batch_size, seqlen=sequence_length) # (batch_size, sequence_length, vocab_size) return CausalLMOutputWithPast( loss=None, logits=logits, past_key_values=None, hidden_states=None, attentions=None, ) class ParallelLlamaForValueRmPad(ParallelLlamaForCausalLMRmPad): def _init_head(self): column_kwargs = tp_utils.get_default_kwargs_for_column_parallel_linear() if self.megatron_config is not None: assert column_kwargs.get('config', False), 'must have ModelParallelConfig' tp_utils.update_kwargs_with_config(column_kwargs, self.megatron_config) self.lm_head = nn.Linear(in_features=self.config.hidden_size, out_features=1, bias=False) # lm_head is effectively the same as sequence parallel sp_utils.mark_parameter_as_sequence_parallel(self.lm_head.weight) def _forward_head(self, hidden_states): logits = self.lm_head(hidden_states) # (total_nnz_padded // tp, 1, 1) logits = logits.float() if self.megatron_config.sequence_parallel: logits = tensor_parallel.gather_from_sequence_parallel_region(logits, tensor_parallel_output_grad=False) return logits def forward( self, input_ids: torch.LongTensor = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, ) -> Union[Tuple, CausalLMOutputWithPast]: output = super().forward(input_ids, attention_mask, position_ids) output.logits = torch.squeeze(output.logits, dim=-1) return output """ Support pipeline parallelism """ class ParallelLlamaModelRmPadPP(nn.Module): """ Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`LlamaDecoderLayer`] This model definition supports pipeline parallelism. To support pp and vpp, - This model only contains layer in this pp stage and vpp chunk - When calling get_model in Megatron, this rank will instantiate all the vpp chunks in this pp. Args: config: LlamaConfig """ def __init__(self, config: LlamaConfig, megatron_config: ModelParallelConfig, pre_process, post_process): super().__init__() self.padding_idx = config.pad_token_id self.vocab_size = config.vocab_size self.pre_process = pre_process self.post_process = post_process self.megatron_config = megatron_config embedding_kwargs = tp_utils.get_default_kwargs_for_parallel_embedding() if megatron_config is not None: assert embedding_kwargs.get('config', False), 'must have ModelParallelConfig' tp_utils.update_kwargs_with_config(embedding_kwargs, self.megatron_config) if pre_process: self.embed_tokens = tensor_parallel.VocabParallelEmbedding(num_embeddings=config.vocab_size, embedding_dim=config.hidden_size, **embedding_kwargs) else: self.embed_tokens = None # pp_rank = megatron_config.pipeline_model_parallel_rank pp_size = megatron_config.pipeline_model_parallel_size self.num_layer_per_pp = config.num_hidden_layers // pp_size vpp_size = megatron_config.virtual_pipeline_model_parallel_size if vpp_size is not None: self.num_layer_vpp_chunk = self.num_layer_per_pp // vpp_size self.num_layer_this_model = self.num_layer_vpp_chunk # vpp_rank = megatron_config.virtual_pipeline_model_parallel_rank # self.offset = vpp_rank * ( # config.num_hidden_layers // megatron_config.virtual_pipeline_model_parallel_size) + \ # (megatron_config.pipeline_model_parallel_rank * self.num_layer_vpp_chunk) else: self.num_layer_this_model = self.num_layer_per_pp # self.offset = pp_rank * self.num_layer_per_pp layers = [] for i in range(self.num_layer_this_model): layer = ParallelLlamaDecoderLayerRmPad(config, megatron_config) # setattr(layer, 'hidden_layer_index', self.offset + i) layers.append(layer) self.layers = nn.ModuleList(layers) if post_process: self.norm = ParallelLlamaRMSNorm(config, megatron_config) else: self.norm = None def set_input_tensor(self, input_tensor): """Set input tensor to be used instead of forward()'s input. When doing pipeline parallelism the input from the previous stage comes from communication, not from the input, so the model's forward_step_func won't have it. This function is thus used by internal code to bypass the input provided by the forward_step_func""" self.input_tensor = input_tensor def forward(self, input_ids: torch.Tensor, position_ids: Optional[torch.LongTensor] = None, sequence_length: int = None, indices: torch.Tensor = None, cu_seqlens: int = None, max_seqlen_in_batch: int = None) -> Union[Tuple, BaseModelOutputWithPast]: """ Args: input_ids: input ids. shape (1, totol_nnz) position_ids: position ids. shape (batch_size, seq_length) Returns: """ if self.pre_process: inputs_embeds = self.embed_tokens(input_ids) # (1, total_nnz) -> (1, total_nnz, hidden_size) # vocab parallel embedding will not do sequence parallel reduce-scatter in open source megatron # so need to deal with it by handle here: # (1, total_nnz, hidden_size) -> (total_nnz, 1, hidden_size) -> (total_nnz // sp, 1, hidden_size) inputs_embeds = inputs_embeds.transpose(0, 1) if self.megatron_config.sequence_parallel: inputs_embeds = tensor_parallel.scatter_to_sequence_parallel_region(inputs_embeds) hidden_states = inputs_embeds else: # self.hidden_states should be passed by Megatron hidden_states = self.input_tensor for idx, decoder_layer in enumerate(self.layers): layer_outputs = decoder_layer(hidden_states, position_ids=position_ids, sequence_length=sequence_length, indices=indices, cu_seqlens=cu_seqlens, max_seqlen_in_batch=max_seqlen_in_batch) hidden_states = layer_outputs if self.post_process: hidden_states = self.norm(hidden_states) return hidden_states class ParallelLlamaForCausalLMRmPadPP(nn.Module): def __init__(self, config: LlamaConfig, megatron_config: ModelParallelConfig, pre_process, post_process, share_embeddings_and_output_weights): super().__init__() self.config = config self.megatron_config = megatron_config self.model = ParallelLlamaModelRmPadPP(config, megatron_config=megatron_config, pre_process=pre_process, post_process=post_process) self.share_embeddings_and_output_weights = share_embeddings_and_output_weights self.vocab_size = config.vocab_size self.pre_process = pre_process self.post_process = post_process if post_process: self._init_head() def set_input_tensor(self, input_tensor): """Set input tensor to be used instead of forward()'s input. When doing pipeline parallelism the input from the previous stage comes from communication, not from the input, so the model's forward_step_func won't have it. This function is thus used by internal code to bypass the input provided by the forward_step_func""" assert len(input_tensor) == 1 self.model.set_input_tensor(input_tensor[0]) def _init_head(self): column_kwargs = tp_utils.get_default_kwargs_for_column_parallel_linear() if self.megatron_config is not None: assert column_kwargs.get('config', False), 'must have ModelParallelConfig' tp_utils.update_kwargs_with_config(column_kwargs, self.megatron_config) self.lm_head = tensor_parallel.ColumnParallelLinear(input_size=self.config.hidden_size, output_size=self.config.vocab_size, bias=False, gather_output=False, skip_bias_add=False, **column_kwargs) def _forward_head(self, hidden_states): # all_gather from sequence parallel region is performed inside lm_head # logits shape before forward_head hidden_states.shape: [4, 32, 4096] logits = self.lm_head(hidden_states)[0] # logits shape after forward_head logits.shape: [8, 32, 8] logits = logits.float() # (total_nnz_padded, 1, vocab_size // tp) return logits def forward( self, # original input *, input_ids: torch.LongTensor = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, ) -> Union[Tuple, CausalLMOutputWithPast]: r""" Args: labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. Returns: ```""" # Note that input_ids, attention_mask and position_ids should be passed to every pp layer. # In the first pp, input_ids will be used, in other pp layers hidden_states will be used inside self.model batch_size, sequence_length = input_ids.shape # remove padding here input_ids_rmpad, indices, cu_seqlens, max_seqlen_in_batch, *_ = unpad_input(input_ids.unsqueeze(dim=-1), attention_mask) # (total_nnz, 1) # pad input_ids to multiple of tp for all tp ranks # TODO: for better performance, the sp padding should be removed at each layer. Not sure the performance gap if self.megatron_config.sequence_parallel: input_ids_rmpad = sp_utils.pad_to_sequence_parallel(input_ids_rmpad) input_ids_rmpad = input_ids_rmpad.transpose(0, 1) # (1, total_nnz+pad) outputs = self.model(input_ids=input_ids_rmpad, position_ids=position_ids, sequence_length=sequence_length, indices=indices, cu_seqlens=cu_seqlens, max_seqlen_in_batch=max_seqlen_in_batch) if self.post_process: hidden_states = outputs # print(f'hidden_states.shape = {hidden_states.shape}') # torch.Size([4, 32, 4096]) logits = self._forward_head(hidden_states) logits = torch.squeeze(logits, dim=1) # remove the artificial batch dimension # torch.Size([8, 32, 16]) # remove padding from sequence parallel if self.megatron_config.sequence_parallel: totol_nnz = cu_seqlens[-1] logits = logits[:totol_nnz] # (total_nnz_padded) # add removed padding back. If input is already rmpad, we let the caller pad_input logits = pad_input(logits, indices, batch_size, seqlen=sequence_length) # (batch_size, sequence_length, vocab_size) return CausalLMOutputWithPast( loss=None, logits=logits, past_key_values=None, hidden_states=None, attentions=None, ) else: return outputs class ParallelLlamaForValueRmPadPP(ParallelLlamaForCausalLMRmPadPP): def _init_head(self): column_kwargs = tp_utils.get_default_kwargs_for_column_parallel_linear() if self.megatron_config is not None: assert column_kwargs.get('config', False), 'must have ModelParallelConfig' tp_utils.update_kwargs_with_config(column_kwargs, self.megatron_config) self.lm_head = nn.Linear(in_features=self.config.hidden_size, out_features=1, bias=False) # lm_head is effectively the same as sequence parallel sp_utils.mark_parameter_as_sequence_parallel(self.lm_head.weight) def _forward_head(self, hidden_states): logits = self.lm_head(hidden_states) # (total_nnz_padded // tp, 1, 1) logits = logits.float() if self.megatron_config.sequence_parallel: logits = tensor_parallel.gather_from_sequence_parallel_region(logits, tensor_parallel_output_grad=False) return logits def forward( self, *, input_ids: torch.LongTensor = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, ) -> Union[Tuple, CausalLMOutputWithPast]: output = super().forward(input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids) if self.post_process: output.logits = torch.squeeze(output.logits, dim=-1) return output else: return output ================================================ FILE: verl/models/qwen2/__init__.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. ================================================ FILE: verl/models/qwen2/megatron/__init__.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from .modeling_qwen2_megatron import ( # original model with megatron ParallelQwen2Model, ParallelQwen2ForCausalLM, # rmpad with megatron ParallelQwen2ForCausalLMRmPad, ParallelQwen2ForValueRmPad, # rmpad with megatron and pipeline parallelism ParallelQwen2ForCausalLMRmPadPP, ParallelQwen2ForValueRmPadPP) ================================================ FILE: verl/models/qwen2/megatron/checkpoint_utils/__init__.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. ================================================ FILE: verl/models/qwen2/megatron/checkpoint_utils/qwen2_loader.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import torch import time from typing import Dict, Any, Callable, Optional import torch.distributed as dist def _megatron_calc_layer_map(config): """Calculate the mapping of global layer_idx to local layer_idx Returns: layer_map (Dict: int -> tuple(int, int, int)): mapping from the global layer index to a tuple of (pp_rank, virtual_pp_rank, layer_idx inside model) """ import megatron from megatron.core import mpu pp_size = mpu.get_pipeline_model_parallel_world_size() virtual_pp_size = mpu.get_virtual_pipeline_model_parallel_world_size() or 1 layer_map = dict() num_layers_per_model = config.num_hidden_layers // pp_size // virtual_pp_size assert num_layers_per_model * pp_size * virtual_pp_size == config.num_hidden_layers for pp_rank_idx in range(pp_size): for virtual_pp_rank_idx in range(virtual_pp_size): layer_offset = (virtual_pp_rank_idx * (config.num_hidden_layers // virtual_pp_size) + pp_rank_idx * num_layers_per_model) for layer_idx in range(num_layers_per_model): layer_map[layer_offset + layer_idx] = ( pp_rank_idx, virtual_pp_rank_idx, layer_idx, ) return layer_map def load_state_dict_to_megatron_qwen2(state_dict, wrapped_models, config, params_dtype, is_value_model=False): """Load merged state_dict to sharded Megatron module in training. """ import megatron from megatron.core import mpu from megatron.training.utils import print_rank_0, unwrap_model from megatron.core.transformer.module import Float16Module from megatron.core import DistributedDataParallel as LocalDDP from torch.nn.parallel import DistributedDataParallel as torchDDP start_time = time.time() def _get_gpt_model(model): return model def broadcast_params(module): for param in module.parameters(): torch.distributed.broadcast(param.data, src=mpu.get_data_parallel_src_rank(), group=mpu.get_data_parallel_group()) dp_rank = mpu.get_data_parallel_rank() pp_rank = mpu.get_pipeline_model_parallel_rank() pp_size = mpu.get_pipeline_model_parallel_world_size() virtual_pp_size = mpu.get_virtual_pipeline_model_parallel_world_size() or 1 mp_group = mpu.get_model_parallel_group() if torch.distributed.get_rank() == 0: assert mp_group.rank() == 0, f"mp_rank:[{mp_group.rank}] != 0 on rank #0" assert pp_rank == 0, f"pp_rank:[{pp_rank}] != 0 on rank #0" assert dp_rank == 0, f"dp_rank:[{dp_rank}] != 0 on rank #0" if not isinstance(wrapped_models, (list, tuple)): wrapped_models = list(wrapped_models) assert len(wrapped_models) == virtual_pp_size num_layers_per_model = config.num_hidden_layers // pp_size // virtual_pp_size assert num_layers_per_model * pp_size * virtual_pp_size == config.num_hidden_layers models = [None] * len(wrapped_models) for i, wrapped_model in enumerate(wrapped_models): models[i] = unwrap_model(wrapped_model, (torchDDP, LocalDDP, Float16Module)) gpt_model_module = _get_gpt_model(models[i]) assert len(gpt_model_module.model.layers) == num_layers_per_model def _broadcast_tensor(tensor, name) -> torch.Tensor: """broadcast tensor from rank0 across mp_group""" nonlocal state_dict nonlocal mp_group if torch.distributed.get_rank() == 0: if name in state_dict: weight = state_dict[name] tensor_shape = weight.shape else: tensor_shape = None else: weight = None tensor_shape = None obj_list = [tensor_shape] dist.broadcast_object_list(obj_list, src=0, group=mp_group) tensor_shape = obj_list[0] if tensor_shape is None: # all or none ranks in the mp_group should reach here print_rank_0(f"tensor:[{name}] not in state_dict, skip load") return if tensor is None: tensor = torch.empty( tensor_shape, dtype=params_dtype, device=torch.cuda.current_device(), requires_grad=False, ) if torch.distributed.get_rank() == 0: tensor.data.copy_(weight) dist.broadcast(tensor, src=0, group=mp_group) def _broadcast_tp_shard_tensor_vocab(tensor, name, chunk_dim=0, mutate_func=None) -> torch.Tensor: """broadcast tensor in tp shards across mp_group""" nonlocal state_dict nonlocal mp_group tp_rank = mpu.get_tensor_model_parallel_rank() tp_size = mpu.get_tensor_model_parallel_world_size() if torch.distributed.get_rank() == 0: if name in state_dict: full_weight = state_dict[name] if mutate_func is not None: full_weight = mutate_func(full_weight) tensor_chunk = torch.chunk(full_weight, tp_size, dim=chunk_dim) chunk_shape = tensor_chunk[0].shape else: chunk_shape = None else: chunk_shape = None obj_list = [chunk_shape] dist.broadcast_object_list(obj_list, src=0, group=mp_group) chunk_shape = obj_list[0] if chunk_shape is None: # all or none ranks in the mp_group should reach here print_rank_0(f"tp_shard tensor:[{name}] not in state_dict, skip loading") return if tensor is None: sync_tensor = torch.empty( chunk_shape, dtype=params_dtype, device=torch.cuda.current_device(), requires_grad=False, ) else: assert (tensor.shape == chunk_shape ), f"rank #{torch.distributed.get_rank()} tensor {name} shape {tensor.shape} != {chunk_shape}" sync_tensor = torch.empty_like(tensor, device=torch.cuda.current_device(), requires_grad=False) for i in range(tp_size): if torch.distributed.get_rank() == 0: sync_tensor.data.copy_(tensor_chunk[i]) dist.broadcast(sync_tensor, src=0, group=mp_group) if (i == tp_rank) and (tensor is not None): tensor.data.copy_(sync_tensor) def _broadcast_tp_shard_tensor(tensor, name, chunk_dim=0, mutate_func=None) -> torch.Tensor: """broadcast tensor in tp shards across mp_group""" nonlocal state_dict nonlocal mp_group tp_rank = mpu.get_tensor_model_parallel_rank() tp_size = mpu.get_tensor_model_parallel_world_size() if torch.distributed.get_rank() == 0: if name in state_dict: full_weight = state_dict[name] if mutate_func is not None: full_weight = mutate_func(full_weight) tensor_chunk = torch.chunk(full_weight, tp_size, dim=chunk_dim) chunk_shape = tensor_chunk[0].shape else: chunk_shape = None else: chunk_shape = None obj_list = [chunk_shape] dist.broadcast_object_list(obj_list, src=0, group=mp_group) chunk_shape = obj_list[0] if chunk_shape is None: # all or none ranks in the mp_group should reach here print_rank_0(f"tp_shard tensor:[{name}] not in state_dict, skip loading") return if tensor is None: sync_tensor = torch.empty( chunk_shape, dtype=params_dtype, device=torch.cuda.current_device(), requires_grad=False, ) else: assert (tensor.shape == chunk_shape ), f"rank #{torch.distributed.get_rank()} tensor {name} shape {tensor.shape} != {chunk_shape}" sync_tensor = torch.empty_like(tensor, device=torch.cuda.current_device(), requires_grad=False) for i in range(tp_size): if torch.distributed.get_rank() == 0: sync_tensor.data.copy_(tensor_chunk[i]) dist.broadcast(sync_tensor, src=0, group=mp_group) if (i == tp_rank) and (tensor is not None): tensor.data.copy_(sync_tensor) def _broadcast_tp_shard_tensor_gate_up(tensor, gate_name, up_name) -> torch.Tensor: """broadcast tensor in tp shards across mp_group""" nonlocal state_dict nonlocal mp_group tp_rank = mpu.get_tensor_model_parallel_rank() tp_size = mpu.get_tensor_model_parallel_world_size() if torch.distributed.get_rank() == 0: gate_weight = state_dict[gate_name] up_weight = state_dict[up_name] new_gate_up_weight = torch.empty(config.intermediate_size * 2, config.hidden_size, dtype=params_dtype, device=torch.cuda.current_device()) for i in range(tp_size): intermediate_size_tp = config.intermediate_size // tp_size gate_weight_tp = gate_weight[i * intermediate_size_tp:(i + 1) * intermediate_size_tp] up_weight_tp = up_weight[i * intermediate_size_tp:(i + 1) * intermediate_size_tp] new_gate_up_weight[intermediate_size_tp * 2 * i:intermediate_size_tp * 2 * (i + 1)].copy_( torch.cat([gate_weight_tp, up_weight_tp], dim=0)) tensor_chunk = torch.chunk(new_gate_up_weight, tp_size, dim=0) chunk_shape = tensor_chunk[0].shape else: chunk_shape = None obj_list = [chunk_shape] dist.broadcast_object_list(obj_list, src=0, group=mp_group) chunk_shape = obj_list[0] if chunk_shape is None: # all or none ranks in the mp_group should reach here print_rank_0(f"tp_shard tensor:[{gate_name, up_name}] not in state_dict, skip loading") return if tensor is None: sync_tensor = torch.empty( chunk_shape, dtype=params_dtype, device=torch.cuda.current_device(), requires_grad=False, ) else: assert ( tensor.shape == chunk_shape ), f"rank #{torch.distributed.get_rank() == 0:} tensor {gate_name, up_name} shape {tensor.shape} != {chunk_shape}" sync_tensor = torch.empty_like(tensor, device=torch.cuda.current_device(), requires_grad=False) for i in range(tp_size): if torch.distributed.get_rank() == 0: sync_tensor.data.copy_(tensor_chunk[i]) dist.broadcast(sync_tensor, src=0, group=mp_group) if (i == tp_rank) and (tensor is not None): tensor.data.copy_(sync_tensor) def _broadcast_tp_shard_tensor_qkv(tensor, q_name, k_name, v_name, bias=False) -> torch.Tensor: """broadcast tensor in tp shards across mp_group""" nonlocal state_dict nonlocal mp_group tp_rank = mpu.get_tensor_model_parallel_rank() tp_size = mpu.get_tensor_model_parallel_world_size() if torch.distributed.get_rank() == 0: assert (q_name in state_dict and k_name in state_dict and v_name in state_dict) full_weight_q = state_dict[q_name] full_weight_k = state_dict[k_name] full_weight_v = state_dict[v_name] hidden_size_per_head = config.hidden_size // config.num_attention_heads if config.num_key_value_heads >= tp_size: q_size_tp = config.hidden_size // tp_size kv_size_tp = hidden_size_per_head * config.num_key_value_heads // tp_size total_size = q_size_tp + 2 * kv_size_tp if not bias: new_weight_qkv = torch.empty(total_size * tp_size, config.hidden_size, dtype=params_dtype, device=torch.cuda.current_device()) else: new_weight_qkv = torch.empty(total_size * tp_size, dtype=params_dtype, device=torch.cuda.current_device()) for i in range(tp_size): q_part = full_weight_q[i * q_size_tp:(i + 1) * q_size_tp] k_part = full_weight_k[i * kv_size_tp:(i + 1) * kv_size_tp] v_part = full_weight_v[i * kv_size_tp:(i + 1) * kv_size_tp] new_weight_qkv[i * total_size:(i + 1) * total_size].copy_(torch.cat([q_part, k_part, v_part], dim=0)) else: q_size_tp = config.hidden_size // tp_size kv_size_tp = hidden_size_per_head total_size = q_size_tp + 2 * kv_size_tp if not bias: new_weight_qkv = torch.empty(total_size * tp_size, config.hidden_size, dtype=params_dtype, device=torch.cuda.current_device()) else: new_weight_qkv = torch.empty(total_size * tp_size, dtype=params_dtype, device=torch.cuda.current_device()) for i in range(tp_size): q_part = full_weight_q[i * q_size_tp:(i + 1) * q_size_tp] start_idx = i * config.num_key_value_heads // tp_size * hidden_size_per_head end_idx = (i * config.num_key_value_heads // tp_size + 1) * hidden_size_per_head k_part = full_weight_k[start_idx:end_idx] v_part = full_weight_v[start_idx:end_idx] new_weight_qkv[i * total_size:(i + 1) * total_size].copy_(torch.cat([q_part, k_part, v_part], dim=0)) tensor_chunk = torch.chunk(new_weight_qkv, tp_size, dim=0) chunk_shape = tensor_chunk[0].shape else: chunk_shape = None obj_list = [chunk_shape] dist.broadcast_object_list(obj_list, src=0, group=mp_group) chunk_shape = obj_list[0] if chunk_shape is None: # all or none ranks in the mp_group should reach here print_rank_0(f"tp_shard tensor:[{name}] not in state_dict, skip loading") return if tensor is None: sync_tensor = torch.empty( chunk_shape, dtype=params_dtype, device=torch.cuda.current_device(), requires_grad=False, ) else: assert (tensor.shape == chunk_shape ), f"rank #{torch.distributed.get_rank()} tensor {q_name} shape {tensor.shape} != {chunk_shape}" sync_tensor = torch.empty_like(tensor, device=torch.cuda.current_device(), requires_grad=False) for i in range(tp_size): if torch.distributed.get_rank() == 0: sync_tensor.data.copy_(tensor_chunk[i]) dist.broadcast(sync_tensor, src=0, group=mp_group) if (i == tp_rank) and (tensor is not None): tensor.data.copy_(sync_tensor) if dp_rank == 0: # Embeddings # ------------------- print_rank_0("loading embeddings...") gpt_model_module = _get_gpt_model(models[0]) embed_tokens_weight = None if pp_rank == 0: embed_tokens_weight = gpt_model_module.model.embed_tokens.weight _broadcast_tp_shard_tensor_vocab(embed_tokens_weight, "model.embed_tokens.weight") # Transformer layers # ------------------- layer_map = _megatron_calc_layer_map(config) for layer in range(config.num_hidden_layers): print_rank_0(f"loading layer #{layer}...") layer_name = f"model.layers.{layer}" dst_pp_rank, dst_virtual_pp_rank, dst_layer_idx = layer_map[layer] gpt_model_module = _get_gpt_model(models[dst_virtual_pp_rank]) sync_layer = gpt_model_module.model.layers[dst_layer_idx] _broadcast_tensor( sync_layer.input_layernorm.weight if dst_pp_rank == pp_rank else None, f"{layer_name}.input_layernorm.weight", ) _broadcast_tp_shard_tensor_qkv( sync_layer.self_attn.qkv_proj.weight if dst_pp_rank == pp_rank else None, f"{layer_name}.self_attn.q_proj.weight", f"{layer_name}.self_attn.k_proj.weight", f"{layer_name}.self_attn.v_proj.weight", ) _broadcast_tp_shard_tensor_qkv(sync_layer.self_attn.qkv_proj.bias if dst_pp_rank == pp_rank else None, f"{layer_name}.self_attn.q_proj.bias", f"{layer_name}.self_attn.k_proj.bias", f"{layer_name}.self_attn.v_proj.bias", bias=True) _broadcast_tp_shard_tensor( sync_layer.self_attn.o_proj.weight if dst_pp_rank == pp_rank else None, f"{layer_name}.self_attn.o_proj.weight", chunk_dim=1, ) _broadcast_tensor( sync_layer.post_attention_layernorm.weight if dst_pp_rank == pp_rank else None, f"{layer_name}.post_attention_layernorm.weight", ) _broadcast_tp_shard_tensor_gate_up(sync_layer.mlp.gate_up_proj.weight if dst_pp_rank == pp_rank else None, f"{layer_name}.mlp.gate_proj.weight", f"{layer_name}.mlp.up_proj.weight") _broadcast_tp_shard_tensor( sync_layer.mlp.down_proj.weight if dst_pp_rank == pp_rank else None, f"{layer_name}.mlp.down_proj.weight", chunk_dim=1, ) # Final Layernorm # ------------------- print_rank_0("loading final layernorm...") gpt_model_module = _get_gpt_model(models[-1]) _broadcast_tensor( getattr(gpt_model_module.model.norm, "weight", None), "model.norm.weight", ) print_rank_0("loading lm_head...") lm_head_weight = None if pp_rank + 1 == pp_size: lm_head_weight = gpt_model_module.lm_head.weight if is_value_model: # if torch.distributed.get_rank() == 0: if 'lm_head.weight' in state_dict and state_dict['lm_head.weight'].shape[0] == 1: _broadcast_tensor(lm_head_weight, "lm_head.weight") elif 'reward_head.weight' in state_dict and state_dict['reward_head.weight'].shape[0] == 1: _broadcast_tensor(lm_head_weight, "reward_head.weight") print_rank_0('load lm_head from value_head weight') else: _broadcast_tensor(None, "lm_head.weight") print_rank_0('fail to match lm_head in value_model') # else: # _broadcast_tensor(lm_head_weight, "lm_head.weight") else: _broadcast_tp_shard_tensor(lm_head_weight, "lm_head.weight") dist.barrier() # Broadcast weights inside data parallel groups for wrapped_model in wrapped_models: broadcast_params(wrapped_model) torch.cuda.empty_cache() print_rank_0(f"loading megatron ckpt done, time elapsed {time.time() - start_time}s") ================================================ FILE: verl/models/qwen2/megatron/checkpoint_utils/qwen2_saver.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import megatron from megatron.core import mpu from megatron.training.utils import print_rank_0, unwrap_model from megatron.core.transformer.module import Float16Module from megatron.core.distributed import DistributedDataParallel as LocalDDP from torch.nn.parallel import DistributedDataParallel as torchDDP import torch import time from typing import Optional import torch.distributed as dist from megatron import get_args def _megatron_calc_global_rank(tp_rank: int = 0, dp_rank: int = 0, pp_rank: int = 0): """given TP,DP,PP rank to get the global rank.""" args = get_args() tp_size = mpu.get_tensor_model_parallel_world_size() dp_size = mpu.get_data_parallel_world_size() pp_size = mpu.get_pipeline_model_parallel_world_size() assert (tp_size * dp_size * pp_size == torch.distributed.get_world_size() ), f"{tp_size} x {dp_size} x {pp_size} != {torch.distributed.get_world_size()}" if args.switch_dp_and_pp_grouping: # TP-PP-DP grouping return (dp_rank * pp_size + pp_rank) * tp_size + tp_rank else: # TP-DP-PP grouping return (pp_rank * dp_size + dp_rank) * tp_size + tp_rank def _megatron_calc_layer_map(config): """Calculate the mapping of global layer_idx to local layer_idx Returns: layer_map (Dict: int -> tuple(int, int, int)): mapping from the global layer index to a tuple of (pp_rank, virtual_pp_rank, layer_idx inside model) """ import megatron from megatron.core import mpu pp_size = mpu.get_pipeline_model_parallel_world_size() virtual_pp_size = mpu.get_virtual_pipeline_model_parallel_world_size() or 1 args = megatron.get_args() layer_map = dict() num_layers_per_model = config.num_hidden_layers // pp_size // virtual_pp_size assert num_layers_per_model * pp_size * virtual_pp_size == config.num_hidden_layers for pp_rank_idx in range(pp_size): for virtual_pp_rank_idx in range(virtual_pp_size): layer_offset = (virtual_pp_rank_idx * (config.num_hidden_layers // virtual_pp_size) + pp_rank_idx * num_layers_per_model) for layer_idx in range(num_layers_per_model): layer_map[layer_offset + layer_idx] = ( pp_rank_idx, virtual_pp_rank_idx, layer_idx, ) return layer_map def merge_megatron_ckpt_llama(wrapped_models, config, is_value_model=False, dtype='bf16'): """Merge sharded parameters of a Megatron module into a merged checkpoint. Args: wrapped_modelss (list of megatron.core.distributed.DistributedDataParallel): The local DDP wrapped megatron modules. dtype (str or None): The data type of state_dict. if None, the data type of the original parameters is used. gpt_model_key: key to access model Returns: state_dict (dict): The merged state_dict in rank 0, and an empty dictionary in other ranks. """ start_time = time.time() args = megatron.get_args() def _get_gpt_model(model): return model dp_rank = mpu.get_data_parallel_rank() pp_size = mpu.get_pipeline_model_parallel_world_size() pp_rank = mpu.get_pipeline_model_parallel_rank() virtual_pp_size = mpu.get_virtual_pipeline_model_parallel_world_size() or 1 mp_group = mpu.get_model_parallel_group() if dist.get_rank() == 0: assert mp_group.rank() == 0, f"mp_rank:[{mp_group.rank}] != 0 on rank #0" assert pp_rank == 0, f"pp_rank:[{pp_rank}] != 0 on rank #0" assert dp_rank == 0, f"dp_rank:[{dp_rank}] != 0 on rank #0" if not isinstance(wrapped_models, (list, tuple)): wrapped_models = list(wrapped_models) assert len(wrapped_models) == virtual_pp_size num_layers_per_model = config.num_hidden_layers // pp_size // virtual_pp_size assert num_layers_per_model * pp_size * virtual_pp_size == config.num_hidden_layers models = [None] * len(wrapped_models) for i, wrapped_model in enumerate(wrapped_models): models[i] = unwrap_model(wrapped_model, (torchDDP, LocalDDP, Float16Module)) assert len(models[i].model.layers ) == num_layers_per_model, 'len model layers {} not equal to num_layers_per_model {}'.format( len(models[i].model.layers), num_layers_per_model) state_dict = dict() def _get_cpu_tensor(tensor: torch.Tensor): if tensor is None: return None if tensor.device == torch.device("cpu"): return tensor.detach().clone() return tensor.detach().cpu() def _broadcast_tensor(tensor, name, src_pp_rank) -> torch.Tensor: """broadcast tensor across mp_group""" nonlocal state_dict nonlocal mp_group src_rank = _megatron_calc_global_rank(tp_rank=0, dp_rank=0, pp_rank=src_pp_rank) if torch.distributed.get_rank() == src_rank: if tensor is None: weight = None tensor_shape = None else: weight = tensor tensor_shape = weight.shape else: weight = None tensor_shape = None obj_list = [tensor_shape] dist.broadcast_object_list(obj_list, src=src_rank, group=mp_group) tensor_shape = obj_list[0] if tensor_shape is None: # all or none ranks in the mp_group should reach here print_rank_0(f"tensor:[{name}] not exist, skip collect") return if weight is None: weight = torch.empty( tensor_shape, dtype=args.params_dtype, device=torch.cuda.current_device(), requires_grad=False, ) dist.broadcast(weight, src=src_rank, group=mp_group) if torch.distributed.get_rank() == 0: state_dict[name] = _get_cpu_tensor(weight) def _broadcast_tp_shard_tensor(tensor, name, src_pp_rank, concat_dim=0, mutate_func=None) -> torch.Tensor: """broadcast tensor in tp shards across mp_group""" nonlocal state_dict nonlocal mp_group tp_rank = mpu.get_tensor_model_parallel_rank() tp_size = mpu.get_tensor_model_parallel_world_size() src_rank = _megatron_calc_global_rank(tp_rank=0, dp_rank=0, pp_rank=src_pp_rank) if torch.distributed.get_rank() == src_rank: chunk_shape = tensor.shape else: chunk_shape = None obj_list = [chunk_shape] dist.broadcast_object_list(obj_list, src=src_rank, group=mp_group) chunk_shape = obj_list[0] if chunk_shape is None: # all or none ranks in the mp_group should reach here print_rank_0(f"tp_shard tensor:[{name}] not exist, skip collecting") return buffer_tensor = torch.empty( chunk_shape, dtype=args.params_dtype, device=torch.cuda.current_device(), requires_grad=False, ) chunk_tensors = [None] * tp_size for i in range(tp_size): cur_src_rank = _megatron_calc_global_rank(tp_rank=i, dp_rank=0, pp_rank=src_pp_rank) sync_tensor = tensor if torch.distributed.get_rank() == cur_src_rank else buffer_tensor dist.broadcast(sync_tensor, src=cur_src_rank, group=mp_group) if torch.distributed.get_rank() == 0: chunk_tensors[i] = _get_cpu_tensor(sync_tensor) if torch.distributed.get_rank() == 0: full_tensor = torch.concat(chunk_tensors, dim=concat_dim) if mutate_func is not None: full_tensor = mutate_func(full_tensor) state_dict[name] = full_tensor def _broadcast_tp_shard_tensor_gate_up(tensor, gate_name, up_name, src_pp_rank) -> torch.Tensor: """broadcast tensor in tp shards across mp_group""" nonlocal state_dict nonlocal mp_group tp_rank = mpu.get_tensor_model_parallel_rank() tp_size = mpu.get_tensor_model_parallel_world_size() src_rank = _megatron_calc_global_rank(tp_rank=0, dp_rank=0, pp_rank=src_pp_rank) if torch.distributed.get_rank() == src_rank: chunk_shape = tensor.shape else: chunk_shape = None obj_list = [chunk_shape] dist.broadcast_object_list(obj_list, src=src_rank, group=mp_group) chunk_shape = obj_list[0] if chunk_shape is None: # all or none ranks in the mp_group should reach here print_rank_0(f"tp_shard tensor:[{gate_name, up_name}] not exist, skip collecting") return buffer_tensor = torch.empty( chunk_shape, dtype=args.params_dtype, device=torch.cuda.current_device(), requires_grad=False, ) chunk_tensors = [None] * tp_size for i in range(tp_size): cur_src_rank = _megatron_calc_global_rank(tp_rank=i, dp_rank=0, pp_rank=src_pp_rank) sync_tensor = tensor if torch.distributed.get_rank() == cur_src_rank else buffer_tensor dist.broadcast(sync_tensor, src=cur_src_rank, group=mp_group) if torch.distributed.get_rank() == 0: chunk_tensors[i] = _get_cpu_tensor(sync_tensor) if torch.distributed.get_rank() == 0: full_tensor = torch.concat(chunk_tensors, dim=0) intermediate_size_tp = config.intermediate_size // tp_size gate_weight_list = [] up_weight_list = [] for i in range(tp_size): gate_up_weight_tp = full_tensor[intermediate_size_tp * 2 * i:intermediate_size_tp * 2 * (i + 1)] gate_weight_tp = gate_up_weight_tp[:intermediate_size_tp] up_weight_tp = gate_up_weight_tp[intermediate_size_tp:] gate_weight_list.append(gate_weight_tp) up_weight_list.append(up_weight_tp) state_dict[gate_name] = torch.cat(gate_weight_list, dim=0) state_dict[up_name] = torch.cat(up_weight_list, dim=0) def _broadcast_tp_shard_tensor_qkv(tensor, q_name, k_name, v_name, src_pp_rank): """broadcast tensor in tp shards across mp_group""" nonlocal state_dict nonlocal mp_group tp_rank = mpu.get_tensor_model_parallel_rank() tp_size = mpu.get_tensor_model_parallel_world_size() src_rank = _megatron_calc_global_rank(tp_rank=0, dp_rank=0, pp_rank=src_pp_rank) if torch.distributed.get_rank() == src_rank: chunk_shape = tensor.shape else: chunk_shape = None obj_list = [chunk_shape] dist.broadcast_object_list(obj_list, src=src_rank, group=mp_group) chunk_shape = obj_list[0] if chunk_shape is None: # all or none ranks in the mp_group should reach here print_rank_0(f"tp_shard tensor:[{q_name}] not exist, skip collecting") return buffer_tensor = torch.empty( chunk_shape, dtype=args.params_dtype, device=torch.cuda.current_device(), requires_grad=False, ) chunk_tensors = [None] * tp_size for i in range(tp_size): cur_src_rank = _megatron_calc_global_rank(tp_rank=i, dp_rank=0, pp_rank=src_pp_rank) sync_tensor = tensor if torch.distributed.get_rank() == cur_src_rank else buffer_tensor dist.broadcast(sync_tensor, src=cur_src_rank, group=mp_group) if torch.distributed.get_rank() == 0: chunk_tensors[i] = _get_cpu_tensor(sync_tensor) if torch.distributed.get_rank() == 0: full_tensor = torch.concat(chunk_tensors, dim=0) q_weight_list = [] k_weight_list = [] v_weight_list = [] hidden_size_per_head = config.hidden_size // config.num_attention_heads if config.num_key_value_heads >= tp_size: q_size_tp = config.hidden_size // tp_size kv_size_tp = hidden_size_per_head * config.num_key_value_heads // tp_size total_size = q_size_tp + 2 * kv_size_tp for i in range(tp_size): qkv_part = full_tensor[i * total_size:(i + 1) * total_size] q_part = qkv_part[:q_size_tp] k_part = qkv_part[q_size_tp:q_size_tp + kv_size_tp] v_part = qkv_part[q_size_tp + kv_size_tp:total_size] q_weight_list.append(q_part) k_weight_list.append(k_part) v_weight_list.append(v_part) else: q_size_tp = config.hidden_size // tp_size kv_size_tp = hidden_size_per_head total_size = q_size_tp + 2 * kv_size_tp for i in range(tp_size): qkv_part = full_tensor[i * total_size:(i + 1) * total_size] q_part = qkv_part[:q_size_tp] k_part = qkv_part[q_size_tp:q_size_tp + kv_size_tp] v_part = qkv_part[q_size_tp + kv_size_tp:total_size] q_weight_list.append(q_part) if i * config.num_key_value_heads % tp_size == 0: k_weight_list.append(k_part) v_weight_list.append(v_part) state_dict[q_name] = torch.cat(q_weight_list, dim=0) state_dict[k_name] = torch.cat(k_weight_list, dim=0) state_dict[v_name] = torch.cat(v_weight_list, dim=0) # empty cache before collecting weights torch.cuda.empty_cache() # Embeddings # ------------------- if dp_rank == 0: # Embeddings # ------------------- print_rank_0("collecting embeddings...") gpt_model_module = _get_gpt_model(models[0]) _broadcast_tp_shard_tensor( gpt_model_module.model.embed_tokens.weight if pp_rank == 0 else None, "model.embed_tokens.weight", src_pp_rank=0, ) # Transformer layers # ------------------- layer_map = _megatron_calc_layer_map(config) for layer in range(config.num_hidden_layers): print_rank_0(f"collecting layer #{layer}...") layer_name = f"model.layers.{layer}" src_pp_rank, src_virtual_pp_rank, src_layer_idx = layer_map[layer] gpt_model_module = _get_gpt_model(models[src_virtual_pp_rank]) sync_layer = gpt_model_module.model.layers[src_layer_idx] _broadcast_tensor( sync_layer.input_layernorm.weight, f"{layer_name}.input_layernorm.weight", src_pp_rank=src_pp_rank, ) _broadcast_tp_shard_tensor_qkv( sync_layer.self_attn.qkv_proj.weight, f"{layer_name}.self_attn.q_proj.weight", f"{layer_name}.self_attn.k_proj.weight", f"{layer_name}.self_attn.v_proj.weight", src_pp_rank=src_pp_rank, ) _broadcast_tp_shard_tensor( sync_layer.self_attn.o_proj.weight, f"{layer_name}.self_attn.o_proj.weight", concat_dim=1, src_pp_rank=src_pp_rank, ) _broadcast_tensor( sync_layer.post_attention_layernorm.weight, f"{layer_name}.post_attention_layernorm.weight", src_pp_rank=src_pp_rank, ) _broadcast_tp_shard_tensor_gate_up(sync_layer.mlp.gate_up_proj.weight, f"{layer_name}.mlp.gate_proj.weight", f"{layer_name}.mlp.up_proj.weight", src_pp_rank=src_pp_rank) _broadcast_tp_shard_tensor( sync_layer.mlp.down_proj.weight, f"{layer_name}.mlp.down_proj.weight", concat_dim=1, src_pp_rank=src_pp_rank, ) # Final Layernorm # ------------------- print_rank_0("collecting final layernorm...") gpt_model_module = _get_gpt_model(models[-1]) _broadcast_tensor( getattr(gpt_model_module.model.norm, "weight", None), "model.norm.weight", src_pp_rank=pp_size - 1, ) print_rank_0("collecting lm_head...") if is_value_model: _broadcast_tensor(getattr(gpt_model_module.lm_head, "weight", None) if pp_rank == pp_size - 1 else None, "reward_head.weight", src_pp_rank=pp_size - 1) else: _broadcast_tp_shard_tensor( getattr(gpt_model_module.lm_head, "weight", None) if pp_rank == pp_size - 1 else None, "lm_head.weight", src_pp_rank=pp_size - 1, ) dist.barrier() torch.cuda.empty_cache() if torch.distributed.get_rank() == 0: if dtype == "fp16": dtype = torch.float16 elif dtype == "bf16": dtype = torch.bfloat16 elif dtype is None or dtype == "fp32": dtype = torch.float32 else: print(f'Unknown/unsupported dtype to save: {dtype}"') exit(1) for k, v in state_dict.items(): if dtype != v.dtype: state_dict[k] = v.to(dtype) print_rank_0(f"merge megatron ckpt done, time elapsed {time.time() - start_time}s") return state_dict ================================================ FILE: verl/models/qwen2/megatron/layers/__init__.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from .parallel_attention import ParallelQwen2Attention from .parallel_decoder import ParallelQwen2DecoderLayer, ParallelQwen2DecoderLayerRmPad from .parallel_mlp import ParallelQwen2MLP from .parallel_rmsnorm import ParallelQwen2RMSNorm ================================================ FILE: verl/models/qwen2/megatron/layers/parallel_attention.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved. # # This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX # and OPT implementations in this library. It has been modified from its # original forms to accommodate minor architectural differences compared # to GPT-NeoX and OPT used by the Meta AI team that trained the model. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import math from typing import Optional, Tuple import torch from megatron.core import parallel_state as mpu from megatron.core import tensor_parallel from megatron.core import ModelParallelConfig from torch import nn from transformers import Qwen2Config from verl.models.qwen2.megatron.layers.parallel_linear import QKVParallelLinear from verl.utils.megatron import tensor_parallel as tp_utils class Qwen2RotaryEmbedding(nn.Module): def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None): super().__init__() self.dim = dim self.max_position_embeddings = max_position_embeddings self.base = base inv_freq = 1.0 / (self.base**(torch.arange(0, self.dim, 2).float().to(device) / self.dim)) self.register_buffer("inv_freq", inv_freq, persistent=False) # Build here to make `torch.jit.trace` work. self._set_cos_sin_cache(seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype()) def _set_cos_sin_cache(self, seq_len, device, dtype): self.max_seq_len_cached = seq_len t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype) freqs = torch.einsum("i,j->ij", t, self.inv_freq) # Different from paper, but it uses a different permutation in order to obtain the same calculation emb = torch.cat((freqs, freqs), dim=-1) self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False) self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False) def forward(self, x, seq_len=None): # x: [bs, num_attention_heads, seq_len, head_size] if seq_len > self.max_seq_len_cached: self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype) return ( self.cos_cached[:seq_len].to(dtype=x.dtype), self.sin_cached[:seq_len].to(dtype=x.dtype), ) class Qwen2LinearScalingRotaryEmbedding(Qwen2RotaryEmbedding): """Qwen2RotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev""" def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0): self.scaling_factor = scaling_factor super().__init__(dim, max_position_embeddings, base, device) def _set_cos_sin_cache(self, seq_len, device, dtype): self.max_seq_len_cached = seq_len t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype) t = t / self.scaling_factor freqs = torch.einsum("i,j->ij", t, self.inv_freq) # Different from paper, but it uses a different permutation in order to obtain the same calculation emb = torch.cat((freqs, freqs), dim=-1) self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False) self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False) class Qwen2DynamicNTKScalingRotaryEmbedding(Qwen2RotaryEmbedding): """Qwen2RotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla""" def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0): self.scaling_factor = scaling_factor super().__init__(dim, max_position_embeddings, base, device) def _set_cos_sin_cache(self, seq_len, device, dtype): self.max_seq_len_cached = seq_len if seq_len > self.max_position_embeddings: base = self.base * ((self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1))**(self.dim / (self.dim - 2)) inv_freq = 1.0 / (base**(torch.arange(0, self.dim, 2).float().to(device) / self.dim)) self.register_buffer("inv_freq", inv_freq, persistent=False) t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype) freqs = torch.einsum("i,j->ij", t, self.inv_freq) # Different from paper, but it uses a different permutation in order to obtain the same calculation emb = torch.cat((freqs, freqs), dim=-1) self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False) self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False) def rotate_half(x): """Rotates half the hidden dims of the input.""" x1 = x[..., :x.shape[-1] // 2] x2 = x[..., x.shape[-1] // 2:] return torch.cat((-x2, x1), dim=-1) def apply_rotary_pos_emb(q, k, cos, sin, position_ids): cos = cos[position_ids].unsqueeze(1) # [bs, 1, seq_len, dim] sin = sin[position_ids].unsqueeze(1) # [bs, 1, seq_len, dim] q_embed = (q * cos) + (rotate_half(q) * sin) k_embed = (k * cos) + (rotate_half(k) * sin) return q_embed, k_embed def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor: """ This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch, num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim) """ batch, num_key_value_heads, slen, head_dim = hidden_states.shape if n_rep == 1: return hidden_states hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim) return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim) class ParallelQwen2Attention(nn.Module): """Multi-headed attention from 'Attention Is All You Need' paper""" def __init__(self, config: Qwen2Config, megatron_config: ModelParallelConfig): super().__init__() self.config = config self.megatron_config = megatron_config self.hidden_size = config.hidden_size self.num_heads = config.num_attention_heads self.head_dim = self.hidden_size // self.num_heads self.num_key_value_heads = config.num_key_value_heads self.num_key_value_groups = self.num_heads // self.num_key_value_heads self.max_position_embeddings = config.max_position_embeddings self.rope_theta = config.rope_theta # assign values after tp tp_size = mpu.get_tensor_model_parallel_world_size() assert self.num_heads % tp_size == 0, f'num_head must be divisible by tp_size. Got num_head={self.num_heads}, tp_size={tp_size}' assert self.num_key_value_heads % tp_size == 0, \ f'num_key_value_heads must be divisible by tp_size. Got num_key_value_heads={self.num_key_value_heads}, tp_size={tp_size}' self.num_heads_per_tp = self.num_heads // tp_size self.num_key_value_heads_per_tp = self.num_key_value_heads // tp_size self.hidden_size_per_tp = self.hidden_size // tp_size if (self.head_dim * self.num_heads) != self.hidden_size: raise ValueError(f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}" f" and `num_heads`: {self.num_heads}).") column_kwargs = tp_utils.get_default_kwargs_for_column_parallel_linear() row_kwargs = tp_utils.get_default_kwargs_for_row_parallel_linear() if megatron_config is not None: assert column_kwargs.get('config', False), 'must have ModelParallelConfig' assert row_kwargs.get('config', False), 'must have ModelParallelConfig' tp_utils.update_kwargs_with_config(column_kwargs, megatron_config) tp_utils.update_kwargs_with_config(row_kwargs, megatron_config) # [self.q_size, self.k_size, self.v_size] self.qkv_proj = QKVParallelLinear( input_size=self.hidden_size, num_heads=self.num_heads, num_key_value_heads=self.num_key_value_heads, head_dim=self.head_dim, # bias=config.attention_bias, bias=True, gather_output=False, skip_bias_add=False, **column_kwargs) self.q_size = self.num_heads_per_tp * self.head_dim self.k_size = self.num_key_value_heads_per_tp * self.head_dim self.v_size = self.num_key_value_heads_per_tp * self.head_dim self.o_proj = tensor_parallel.RowParallelLinear( input_size=self.num_heads * self.head_dim, output_size=self.hidden_size, # bias=config.attention_bias, bias=False, input_is_parallel=True, skip_bias_add=False, **row_kwargs) self._init_rope() def _init_rope(self): self.rotary_emb = Qwen2RotaryEmbedding( self.head_dim, max_position_embeddings=self.max_position_embeddings, base=self.rope_theta, ) def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int): return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous() def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: bsz, q_len, _ = hidden_states.size() qkv = self.qkv_proj(hidden_states)[0] query_states, key_states, value_states = qkv.split([self.q_size, self.k_size, self.v_size], dim=-1) query_states = query_states.view(bsz, q_len, self.num_heads_per_tp, self.head_dim).transpose(1, 2) key_states = key_states.view(bsz, q_len, self.num_key_value_heads_per_tp, self.head_dim).transpose(1, 2) value_states = value_states.view(bsz, q_len, self.num_key_value_heads_per_tp, self.head_dim).transpose(1, 2) kv_seq_len = key_states.shape[-2] cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len) query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids) key_states = repeat_kv(key_states, self.num_key_value_groups) value_states = repeat_kv(value_states, self.num_key_value_groups) attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim) if attn_weights.size() != (bsz, self.num_heads_per_tp, q_len, kv_seq_len): raise ValueError( f"Attention weights should be of size {(bsz, self.num_heads_per_tp, q_len, kv_seq_len)}, but is" f" {attn_weights.size()}") if attention_mask is not None: if attention_mask.size() != (bsz, 1, q_len, kv_seq_len): raise ValueError( f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}") attn_weights = attn_weights + attention_mask # upcast attention to fp32 attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype) attn_output = torch.matmul(attn_weights, value_states) if attn_output.size() != (bsz, self.num_heads_per_tp, q_len, self.head_dim): raise ValueError( f"`attn_output` should be of size {(bsz, self.num_heads_per_tp, q_len, self.head_dim)}, but is" f" {attn_output.size()}") attn_output = attn_output.transpose(1, 2).contiguous() attn_output = attn_output.reshape(bsz, q_len, self.hidden_size_per_tp) attn_output = self.o_proj(attn_output)[0] return attn_output """ Remove padding Attention - Using Flash-attn 2 - Compatible with sequence parallel """ from transformers.utils import is_flash_attn_2_available import torch.nn.functional as F from einops import rearrange if is_flash_attn_2_available(): from flash_attn import flash_attn_varlen_func from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa def apply_rotary_pos_emb_rmpad(q, k, cos, sin, position_ids, indices, sequence_length): batch_size = position_ids.shape[0] q = pad_input(q, indices, batch_size, sequence_length) # (batch_size, seqlen, num_head, head_dim) k = pad_input(k, indices, batch_size, sequence_length) cos = cos[position_ids].unsqueeze(2) # [bs, seq_len, 1, dim] sin = sin[position_ids].unsqueeze(2) # [bs, seq_len, 1, dim] q_embed = (q * cos) + (rotate_half(q) * sin) k_embed = (k * cos) + (rotate_half(k) * sin) q_embed = index_first_axis(rearrange(q_embed, "b s ... -> (b s) ..."), indices) k_embed = index_first_axis(rearrange(k_embed, "b s ... -> (b s) ..."), indices) return q_embed, k_embed from flash_attn.layers.rotary import apply_rotary_emb # use flash-attn rotary embeddings with rmpad # cos/sin shoudl be: (seq_length, rotary_dim / 2) def apply_rotary_pos_emb_rmpad_flash(q, k, cos, sin, cu_seqlens, max_seqlen): q_embed = apply_rotary_emb(q, cos, sin, interleaved=False, inplace=False, cu_seqlens=cu_seqlens, max_seqlen=max_seqlen) k_embed = apply_rotary_emb(k, cos, sin, interleaved=False, inplace=False, cu_seqlens=cu_seqlens, max_seqlen=max_seqlen) return q_embed, k_embed class ParallelQwen2AttentionRmPad(ParallelQwen2Attention): def forward(self, hidden_states: torch.Tensor, position_ids: Optional[torch.LongTensor] = None, sequence_length: int = None, indices: torch.Tensor = None, cu_seqlens: torch.Tensor = None, max_seqlen_in_batch: int = None): total_nnz, _, _ = hidden_states.size() # This is the total_nnz padded after sequence parallel if self.megatron_config.sequence_parallel: total_nnz = total_nnz * mpu.get_tensor_model_parallel_world_size() qkv = self.qkv_proj(hidden_states)[0] query_states, key_states, value_states = qkv.split([self.q_size, self.k_size, self.v_size], dim=-1) # (total_nnz, 1, hidden_size) if self.megatron_config.sequence_parallel: sequence_parallel_pad = total_nnz - cu_seqlens[-1] total_nnz = cu_seqlens[-1] # total_nnz before sp padding query_states = query_states[:total_nnz] key_states = key_states[:total_nnz] value_states = value_states[:total_nnz] # Flash attention requires the input to have the shape # batch_size x seq_length x head_dime x hidden_dim # therefore we just need to keep the original shape query_states = query_states.view(total_nnz, self.num_heads_per_tp, self.head_dim) key_states = key_states.view(total_nnz, self.num_key_value_heads_per_tp, self.head_dim) value_states = value_states.view(total_nnz, self.num_key_value_heads_per_tp, self.head_dim) cos, sin = self.rotary_emb(value_states, seq_len=sequence_length) cos, sin = cos[:, :cos.shape[1] // 2], sin[:, :sin.shape[1] // 2] # flash attn only needs half query_states, key_states = apply_rotary_pos_emb_rmpad_flash(query_states, key_states, cos, sin, cu_seqlens=cu_seqlens, max_seqlen=max_seqlen_in_batch) # query_states, key_states = apply_rotary_pos_emb_rmpad(query_states, key_states, cos, sin, position_ids, indices, # It is recommended to use dropout with FA according to the docs # when training. dropout_rate = 0.0 # if not self.training else self.attn_dropout # In PEFT, usually we cast the layer norms in float32 for training stability reasons # therefore the input hidden states gets silently casted in float32. Hence, we need # cast them back in float16 just to be sure everything works as expected. # This might slowdown training & inference so it is recommended to not cast the LayerNorms # in fp32. (Qwen2RMSNorm handles it correctly) input_dtype = query_states.dtype if input_dtype == torch.float32: query_states = query_states.to(torch.float16) key_states = key_states.to(torch.float16) value_states = value_states.to(torch.float16) attn_output_unpad = flash_attn_varlen_func( query_states, key_states, value_states, cu_seqlens_q=cu_seqlens, cu_seqlens_k=cu_seqlens, max_seqlen_q=max_seqlen_in_batch, max_seqlen_k=max_seqlen_in_batch, dropout_p=dropout_rate, softmax_scale=None, causal=True, ) attn_output_unpad = attn_output_unpad.to(input_dtype) attn_output_unpad = attn_output_unpad.reshape(total_nnz, 1, self.hidden_size_per_tp).contiguous() # sequence parallel reduce_scatter is performed inside RowColumnParallel if enabled # Here we need to repad if self.megatron_config.sequence_parallel: attn_output_unpad = F.pad(attn_output_unpad, pad=(0, 0, 0, 0, 0, sequence_parallel_pad)) attn_output_unpad = self.o_proj(attn_output_unpad)[0] return attn_output_unpad ================================================ FILE: verl/models/qwen2/megatron/layers/parallel_decoder.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved. # # This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX # and OPT implementations in this library. It has been modified from its # original forms to accommodate minor architectural differences compared # to GPT-NeoX and OPT used by the Meta AI team that trained the model. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import Optional, Tuple import torch from torch import nn from transformers import Qwen2Config from megatron.core import ModelParallelConfig from .parallel_attention import ParallelQwen2Attention, ParallelQwen2AttentionRmPad from .parallel_mlp import ParallelQwen2MLP from .parallel_rmsnorm import ParallelQwen2RMSNorm class ParallelQwen2DecoderLayer(nn.Module): def __init__(self, config: Qwen2Config, megatron_config: ModelParallelConfig): super().__init__() self.hidden_size = config.hidden_size self.self_attn = ParallelQwen2Attention(config=config, megatron_config=megatron_config) self.mlp = ParallelQwen2MLP(config, megatron_config=megatron_config) self.input_layernorm = ParallelQwen2RMSNorm(config, megatron_config) self.post_attention_layernorm = ParallelQwen2RMSNorm(config, megatron_config) def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]: """ Args: hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` attention_mask (`torch.FloatTensor`, *optional*): attention mask of size `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. use_cache (`bool`, *optional*): If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`). past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states """ residual = hidden_states hidden_states = self.input_layernorm(hidden_states) # Note: sequence parallel is hidden inside ColumnParallelLinear # reduce scatter is hidden inside RowParallelLinear # Self Attention hidden_states = self.self_attn( hidden_states=hidden_states, attention_mask=attention_mask, position_ids=position_ids, ) # TODO: add sequence parallel operator reduce_scatter here hidden_states = residual + hidden_states # Fully Connected residual = hidden_states hidden_states = self.post_attention_layernorm(hidden_states) # TODO: add sequence parallel operator all_gather here hidden_states = self.mlp(hidden_states) # TODO: add sequence parallel operator reduce_scatter here hidden_states = residual + hidden_states outputs = hidden_states return outputs class ParallelQwen2DecoderLayerRmPad(nn.Module): def __init__(self, config: Qwen2Config, megatron_config: ModelParallelConfig): super().__init__() self.config = config self.megatron_config = megatron_config self.hidden_size = config.hidden_size self.self_attn = ParallelQwen2AttentionRmPad(config=config, megatron_config=megatron_config) self.mlp = ParallelQwen2MLP(config, megatron_config=megatron_config) self.input_layernorm = ParallelQwen2RMSNorm(config, megatron_config) self.post_attention_layernorm = ParallelQwen2RMSNorm(config, megatron_config) def forward( self, hidden_states: torch.Tensor, position_ids: Optional[torch.LongTensor] = None, sequence_length: int = None, indices: torch.Tensor = None, cu_seqlens: int = None, max_seqlen_in_batch: int = None ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]: residual = hidden_states # (total_nnz // sp, 1, hidden_size) hidden_states = self.input_layernorm(hidden_states) # Self Attention # (total_nnz // sp, 1, hidden_size) -> all-gather (total_nnz, 1, hidden_size) # -> col + row -> reduce-scatter -> (total_nnz // sp, 1, hidden_size) hidden_states = self.self_attn(hidden_states=hidden_states, position_ids=position_ids, sequence_length=sequence_length, indices=indices, cu_seqlens=cu_seqlens, max_seqlen_in_batch=max_seqlen_in_batch) hidden_states = residual + hidden_states # Fully Connected # shape changes same as attn residual = hidden_states hidden_states = self.post_attention_layernorm(hidden_states) hidden_states = self.mlp(hidden_states) hidden_states = residual + hidden_states outputs = hidden_states return outputs ================================================ FILE: verl/models/qwen2/megatron/layers/parallel_linear.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Copyright 2023 The vLLM team. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # Adapted from https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/layers/linear.py from typing import Optional, Tuple from megatron.core import tensor_parallel class QKVParallelLinear(tensor_parallel.ColumnParallelLinear): def __init__(self, input_size, num_heads, num_key_value_heads, head_dim, *, bias=True, gather_output=True, skip_bias_add=False, **kwargs): # Keep input parameters, and already restrict the head numbers self.input_size = input_size self.q_output_size = num_heads * head_dim self.kv_output_size = num_key_value_heads * head_dim self.head_dim = head_dim self.gather_output = gather_output self.skip_bias_add = skip_bias_add input_size = self.input_size output_size = (num_heads + 2 * num_key_value_heads) * self.head_dim super().__init__(input_size=input_size, output_size=output_size, bias=bias, gather_output=gather_output, skip_bias_add=skip_bias_add, **kwargs) class MergedColumnParallelLinear(tensor_parallel.ColumnParallelLinear): def __init__(self, input_size, gate_ouput_size, up_output_size, *, bias=True, gather_output=True, skip_bias_add=False, **kwargs): # Keep input parameters, and already restrict the head numbers self.input_size = input_size self.output_size = gate_ouput_size + up_output_size self.gather_output = gather_output self.skip_bias_add = skip_bias_add super().__init__(input_size=self.input_size, output_size=self.output_size, bias=bias, gather_output=gather_output, skip_bias_add=skip_bias_add, **kwargs) ================================================ FILE: verl/models/qwen2/megatron/layers/parallel_mlp.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved. # # This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX # and OPT implementations in this library. It has been modified from its # original forms to accommodate minor architectural differences compared # to GPT-NeoX and OPT used by the Meta AI team that trained the model. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from megatron.core import parallel_state as mpu from megatron.core import tensor_parallel from megatron.core import ModelParallelConfig from torch import nn from transformers.activations import ACT2FN from verl.models.qwen2.megatron.layers.parallel_linear import MergedColumnParallelLinear from verl.utils.megatron import tensor_parallel as tp_utils class ParallelQwen2MLP(nn.Module): def __init__(self, config, megatron_config: ModelParallelConfig = None) -> None: super().__init__() self.config = config self.hidden_size = config.hidden_size self.intermediate_size = config.intermediate_size # The weight is only [hidden_size, intermediate_size // model_parallel_world_size] column_kwargs = tp_utils.get_default_kwargs_for_column_parallel_linear() row_kwargs = tp_utils.get_default_kwargs_for_row_parallel_linear() if megatron_config is not None: assert column_kwargs.get('config', False), 'must have ModelParallelConfig' assert row_kwargs.get('config', False), 'must have ModelParallelConfig' tp_utils.update_kwargs_with_config(row_kwargs, megatron_config) tp_utils.update_kwargs_with_config(column_kwargs, megatron_config) tp_size = mpu.get_tensor_model_parallel_world_size() self.gate_up_proj = MergedColumnParallelLinear( input_size=self.hidden_size, gate_ouput_size=self.intermediate_size, up_output_size=self.intermediate_size, bias=False, gather_output=False, skip_bias_add=False, **column_kwargs, ) self.gate_size = self.intermediate_size // tp_size self.down_proj = tensor_parallel.RowParallelLinear(input_size=self.intermediate_size, output_size=self.hidden_size, bias=False, input_is_parallel=True, skip_bias_add=False, **row_kwargs) self.act_fn = ACT2FN[config.hidden_act] def forward(self, x): gate_up = self.gate_up_proj(x)[0] gate, up = gate_up.split(self.gate_size, dim=-1) return self.down_proj(self.act_fn(gate) * up)[0] ================================================ FILE: verl/models/qwen2/megatron/layers/parallel_rmsnorm.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import numbers import torch from megatron.core import ModelParallelConfig from torch import nn from transformers import Qwen2Config from apex.normalization.fused_layer_norm import fused_rms_norm_affine from verl.utils.megatron import sequence_parallel as sp_utils class ParallelQwen2RMSNorm(nn.Module): def __init__(self, config: Qwen2Config, megatron_config: ModelParallelConfig): """ Qwen2RMSNorm is equivalent to T5LayerNorm """ super().__init__() if isinstance(config.hidden_size, numbers.Integral): normalized_shape = (config.hidden_size,) self.normalized_shape = torch.Size(normalized_shape) self.weight = nn.Parameter(torch.ones(self.normalized_shape)) self.variance_epsilon = config.rms_norm_eps if megatron_config.sequence_parallel: sp_utils.mark_parameter_as_sequence_parallel(self.weight) def forward(self, hidden_states): return fused_rms_norm_affine(input=hidden_states, weight=self.weight, normalized_shape=self.normalized_shape, eps=self.variance_epsilon, memory_efficient=True) ================================================ FILE: verl/models/qwen2/megatron/modeling_qwen2_megatron.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved. # # This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX # and OPT implementations in this library. It has been modified from its # original forms to accommodate minor architectural differences compared # to GPT-NeoX and OPT used by the Meta AI team that trained the model. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ PyTorch Qwen2 model.""" from typing import Optional, Tuple, Union import torch import torch.utils.checkpoint from megatron.core import tensor_parallel, parallel_state from megatron.core import ModelParallelConfig from torch import nn from transformers.modeling_outputs import BaseModelOutputWithPast from transformers.models.qwen2.configuration_qwen2 import Qwen2Config from transformers.models.qwen2.modeling_qwen2 import CausalLMOutputWithPast from verl.utils.megatron import sequence_parallel as sp_utils from verl.utils.megatron import tensor_parallel as tp_utils from .layers import ParallelQwen2DecoderLayer, ParallelQwen2RMSNorm, ParallelQwen2DecoderLayerRmPad """ TODO: 1. Add weight initialization. Here we need to be careful on TP weight init. 2. Add sequence parallel 3. Load checkpoint from Qwen2 pretrained checkpoint """ # Copied from transformers.models.bart.modeling_bart._make_causal_mask def _make_causal_mask(input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device): """ Make causal mask used for bi-directional self-attention. """ bsz, tgt_len = input_ids_shape mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device) mask_cond = torch.arange(mask.size(-1), device=device) mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0) mask = mask.to(dtype) return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len) # Copied from transformers.models.bart.modeling_bart._expand_mask def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None): """ Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`. """ bsz, src_len = mask.size() tgt_len = tgt_len if tgt_len is not None else src_len expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype) inverted_mask = 1.0 - expanded_mask return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min) class ParallelQwen2Model(nn.Module): """ Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`Qwen2DecoderLayer`] Args: config: Qwen2Config """ def __init__(self, config: Qwen2Config, megatron_config: ModelParallelConfig): super().__init__() self.padding_idx = config.pad_token_id self.vocab_size = config.vocab_size embedding_kwargs = tp_utils.get_default_kwargs_for_parallel_embedding() if megatron_config is not None: assert embedding_kwargs.get('config', False), 'must have ModelParallelConfig' tp_utils.update_kwargs_with_config(embedding_kwargs, self.megatron_config) self.embed_tokens = tensor_parallel.VocabParallelEmbedding(num_embeddings=config.vocab_size, embedding_dim=config.hidden_size, **embedding_kwargs) self.layers = nn.ModuleList( [ParallelQwen2DecoderLayer(config, megatron_config) for _ in range(config.num_hidden_layers)]) self.norm = ParallelQwen2RMSNorm(config, megatron_config) # Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds): # create causal mask # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] combined_attention_mask = None if input_shape[-1] > 1: combined_attention_mask = _make_causal_mask( input_shape, inputs_embeds.dtype, device=inputs_embeds.device, ) if attention_mask is not None: # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to(inputs_embeds.device) combined_attention_mask = (expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask) return combined_attention_mask def forward( self, input_ids: torch.LongTensor = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, ) -> Union[Tuple, BaseModelOutputWithPast]: """ Args: input_ids: input ids. shape (batch_size, seq_length) attention_mask: attention_mask. shape (batch_size, seq_length) position_ids: position ids. shape (batch_size, seq_length) Returns: """ batch_size, seq_length = input_ids.shape inputs_embeds = self.embed_tokens(input_ids) # embed positions attention_mask = self._prepare_decoder_attention_mask(attention_mask, (batch_size, seq_length), inputs_embeds) hidden_states = inputs_embeds for idx, decoder_layer in enumerate(self.layers): layer_outputs = decoder_layer( hidden_states, attention_mask=attention_mask, position_ids=position_ids, ) hidden_states = layer_outputs hidden_states = self.norm(hidden_states) return hidden_states class ParallelQwen2ForCausalLM(nn.Module): def __init__(self, config: Qwen2Config, megatron_config: ModelParallelConfig): super().__init__() self.model = ParallelQwen2Model(config, megatron_config=megatron_config) self.vocab_size = config.vocab_size column_kwargs = tp_utils.get_default_kwargs_for_column_parallel_linear() if megatron_config is not None: assert column_kwargs.get('config', False), 'must have ModelParallelConfig' tp_utils.update_kwargs_with_config(column_kwargs, self.megatron_config) self.lm_head = tensor_parallel.ColumnParallelLinear(input_size=config.hidden_size, output_size=config.vocab_size, bias=False, gather_output=False, skip_bias_add=False, **column_kwargs) def forward( self, input_ids: torch.LongTensor = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, ) -> Union[Tuple, CausalLMOutputWithPast]: r""" Args: labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. Returns: ```""" # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn) outputs = self.model( input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids, ) hidden_states = outputs logits = self.lm_head(hidden_states)[0] logits = tensor_parallel.gather_from_tensor_model_parallel_region(logits) logits = logits.float() return CausalLMOutputWithPast( loss=None, logits=logits, past_key_values=None, hidden_states=None, attentions=None, ) from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa class ParallelQwen2ModelRmPad(nn.Module): """ Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`Qwen2DecoderLayer`] Args: config: Qwen2Config """ def __init__(self, config: Qwen2Config, megatron_config: ModelParallelConfig): super().__init__() self.padding_idx = config.pad_token_id self.vocab_size = config.vocab_size embedding_kwargs = tp_utils.get_default_kwargs_for_parallel_embedding() self.megatron_config = megatron_config if megatron_config is not None: assert embedding_kwargs.get('config', False), 'must have ModelParallelConfig' tp_utils.update_kwargs_with_config(embedding_kwargs, self.megatron_config) self.embed_tokens = tensor_parallel.VocabParallelEmbedding(num_embeddings=config.vocab_size, embedding_dim=config.hidden_size, **embedding_kwargs) self.layers = nn.ModuleList( [ParallelQwen2DecoderLayerRmPad(config, megatron_config) for _ in range(config.num_hidden_layers)]) self.norm = ParallelQwen2RMSNorm(config, megatron_config) def forward(self, input_ids: torch.Tensor, position_ids: Optional[torch.LongTensor] = None, sequence_length: int = None, indices: torch.Tensor = None, cu_seqlens: int = None, max_seqlen_in_batch: int = None) -> Union[Tuple, BaseModelOutputWithPast]: """ Args: input_ids: input ids. shape (1, totol_nnz) position_ids: position ids. shape (batch_size, seq_length) Returns: """ inputs_embeds = self.embed_tokens(input_ids) # (1, total_nnz) -> (1, total_nnz, hidden_size) # (1, total_nnz, hidden_size) -> (total_nnz, 1, hidden_size) -> (total_nnz // sp, 1, hidden_size) inputs_embeds = inputs_embeds.transpose(0, 1) if self.megatron_config.sequence_parallel: inputs_embeds = tensor_parallel.scatter_to_sequence_parallel_region(inputs_embeds) hidden_states = inputs_embeds for idx, decoder_layer in enumerate(self.layers): layer_outputs = decoder_layer(hidden_states, position_ids=position_ids, sequence_length=sequence_length, indices=indices, cu_seqlens=cu_seqlens, max_seqlen_in_batch=max_seqlen_in_batch) hidden_states = layer_outputs hidden_states = self.norm(hidden_states) return hidden_states class ParallelQwen2ForCausalLMRmPad(nn.Module): def __init__(self, config: Qwen2Config, megatron_config: ModelParallelConfig): super().__init__() self.config = config self.megatron_config = megatron_config self.model = ParallelQwen2ModelRmPad(config, megatron_config=megatron_config) self.vocab_size = config.vocab_size self._init_head() def _init_head(self): column_kwargs = tp_utils.get_default_kwargs_for_column_parallel_linear() if self.megatron_config is not None: assert column_kwargs.get('config', False), 'must have ModelParallelConfig' tp_utils.update_kwargs_with_config(column_kwargs, self.megatron_config) self.lm_head = tensor_parallel.ColumnParallelLinear(input_size=self.config.hidden_size, output_size=self.config.vocab_size, bias=False, gather_output=False, skip_bias_add=False, **column_kwargs) def _forward_head(self, hidden_states): # all_gather from sequence parallel region is performed inside lm_head logits = self.lm_head(hidden_states)[0] logits = logits.float() # (total_nnz_padded, 1, vocab_size // tp) logits = tensor_parallel.gather_from_tensor_model_parallel_region(logits) # (total_nnz_padded, 1, vocab_size) return logits def forward( self, input_ids: torch.LongTensor = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, ) -> Union[Tuple, CausalLMOutputWithPast]: r""" Args: labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. Returns: ```""" batch_size, sequence_length = input_ids.shape # remove padding here input_ids, indices, cu_seqlens, max_seqlen_in_batch, *_ = unpad_input(input_ids.unsqueeze(dim=-1), attention_mask) # (total_nnz, 1) # pad input_ids to multiple of tp for all tp ranks # TODO: for better performance, the sp padding should be removed at each layer. Not sure the performance gap if self.megatron_config.sequence_parallel: input_ids = sp_utils.pad_to_sequence_parallel(input_ids) input_ids = input_ids.transpose(0, 1) # (1, total_nnz+pad) outputs = self.model(input_ids=input_ids, position_ids=position_ids, sequence_length=sequence_length, indices=indices, cu_seqlens=cu_seqlens, max_seqlen_in_batch=max_seqlen_in_batch) hidden_states = outputs logits = self._forward_head(hidden_states) # remove padding from sequence parallel if self.megatron_config.sequence_parallel: totol_nnz = cu_seqlens[-1] logits = logits[:totol_nnz] # (total_nnz_padded) logits = torch.squeeze(logits, dim=1) # remove the artificial batch dimension # add removed padding back logits = pad_input(logits, indices, batch_size, seqlen=sequence_length) # (batch_size, sequence_length, vocab_size) return CausalLMOutputWithPast( loss=None, logits=logits, past_key_values=None, hidden_states=None, attentions=None, ) class ParallelQwen2ForValueRmPad(ParallelQwen2ForCausalLMRmPad): def _init_head(self): column_kwargs = tp_utils.get_default_kwargs_for_column_parallel_linear() if self.megatron_config is not None: assert column_kwargs.get('config', False), 'must have ModelParallelConfig' tp_utils.update_kwargs_with_config(column_kwargs, self.megatron_config) self.lm_head = nn.Linear(in_features=self.config.hidden_size, out_features=1, bias=False) # lm_head is effectively the same as sequence parallel sp_utils.mark_parameter_as_sequence_parallel(self.lm_head.weight) def _forward_head(self, hidden_states): logits = self.lm_head(hidden_states) # (total_nnz_padded // tp, 1, 1) logits = logits.float() if self.megatron_config.sequence_parallel: logits = tensor_parallel.gather_from_sequence_parallel_region(logits, tensor_parallel_output_grad=False) return logits def forward( self, input_ids: torch.LongTensor = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, ) -> Union[Tuple, CausalLMOutputWithPast]: output = super().forward(input_ids, attention_mask, position_ids) output.logits = torch.squeeze(output.logits, dim=-1) return output """ Support pipeline parallelism """ class ParallelQwen2ModelRmPadPP(nn.Module): """ Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`Qwen2DecoderLayer`] This model definition supports pipeline parallelism. To support pp and vpp, - This model only contains layer in this pp stage and vpp chunk - When calling get_model in Megatron, this rank will instantiate all the vpp chunks in this pp. Args: config: Qwen2Config """ def __init__(self, config: Qwen2Config, megatron_config: ModelParallelConfig, pre_process, post_process): super().__init__() self.padding_idx = config.pad_token_id self.vocab_size = config.vocab_size self.pre_process = pre_process self.post_process = post_process self.megatron_config = megatron_config embedding_kwargs = tp_utils.get_default_kwargs_for_parallel_embedding() if megatron_config is not None: assert embedding_kwargs.get('config', False), 'must have ModelParallelConfig' tp_utils.update_kwargs_with_config(embedding_kwargs, self.megatron_config) if pre_process: self.embed_tokens = tensor_parallel.VocabParallelEmbedding(num_embeddings=config.vocab_size, embedding_dim=config.hidden_size, **embedding_kwargs) else: self.embed_tokens = None # pp_rank = megatron_config.pipeline_model_parallel_rank pp_size = megatron_config.pipeline_model_parallel_size self.num_layer_per_pp = config.num_hidden_layers // pp_size vpp_size = megatron_config.virtual_pipeline_model_parallel_size if vpp_size is not None: self.num_layer_vpp_chunk = self.num_layer_per_pp // vpp_size self.num_layer_this_model = self.num_layer_vpp_chunk # vpp_rank = megatron_config.virtual_pipeline_model_parallel_rank # self.offset = vpp_rank * ( # config.num_hidden_layers // megatron_config.virtual_pipeline_model_parallel_size) + \ # (megatron_config.pipeline_model_parallel_rank * self.num_layer_vpp_chunk) else: self.num_layer_this_model = self.num_layer_per_pp # self.offset = pp_rank * self.num_layer_per_pp layers = [] for i in range(self.num_layer_this_model): layer = ParallelQwen2DecoderLayerRmPad(config, megatron_config) # setattr(layer, 'hidden_layer_index', self.offset + i) layers.append(layer) self.layers = nn.ModuleList(layers) if post_process: self.norm = ParallelQwen2RMSNorm(config, megatron_config) else: self.norm = None def set_input_tensor(self, input_tensor): """Set input tensor to be used instead of forward()'s input. When doing pipeline parallelism the input from the previous stage comes from communication, not from the input, so the model's forward_step_func won't have it. This function is thus used by internal code to bypass the input provided by the forward_step_func""" self.input_tensor = input_tensor def forward(self, input_ids: torch.Tensor, position_ids: Optional[torch.LongTensor] = None, sequence_length: int = None, indices: torch.Tensor = None, cu_seqlens: int = None, max_seqlen_in_batch: int = None) -> Union[Tuple, BaseModelOutputWithPast]: """ Args: input_ids: input ids. shape (1, totol_nnz) position_ids: position ids. shape (batch_size, seq_length) Returns: """ if self.pre_process: inputs_embeds = self.embed_tokens(input_ids) # (1, total_nnz) -> (1, total_nnz, hidden_size) # vocab parallel embedding will not do sequence parallel reduce-scatter in open source megatron # so need to deal with it by handle here: # (1, total_nnz, hidden_size) -> (total_nnz, 1, hidden_size) -> (total_nnz // sp, 1, hidden_size) inputs_embeds = inputs_embeds.transpose(0, 1) if self.megatron_config.sequence_parallel: inputs_embeds = tensor_parallel.scatter_to_sequence_parallel_region(inputs_embeds) hidden_states = inputs_embeds else: # self.hidden_states should be passed by Megatron hidden_states = self.input_tensor for idx, decoder_layer in enumerate(self.layers): layer_outputs = decoder_layer(hidden_states, position_ids=position_ids, sequence_length=sequence_length, indices=indices, cu_seqlens=cu_seqlens, max_seqlen_in_batch=max_seqlen_in_batch) hidden_states = layer_outputs if self.post_process: hidden_states = self.norm(hidden_states) return hidden_states class ParallelQwen2ForCausalLMRmPadPP(nn.Module): def __init__(self, config: Qwen2Config, megatron_config: ModelParallelConfig, pre_process, post_process, share_embeddings_and_output_weights): super().__init__() self.config = config self.megatron_config = megatron_config self.model = ParallelQwen2ModelRmPadPP(config, megatron_config=megatron_config, pre_process=pre_process, post_process=post_process) self.share_embeddings_and_output_weights = share_embeddings_and_output_weights self.vocab_size = config.vocab_size self.pre_process = pre_process self.post_process = post_process if post_process: self._init_head() if pre_process or post_process: self.setup_embeddings_and_output_layer() def set_input_tensor(self, input_tensor): """Set input tensor to be used instead of forward()'s input. When doing pipeline parallelism the input from the previous stage comes from communication, not from the input, so the model's forward_step_func won't have it. This function is thus used by internal code to bypass the input provided by the forward_step_func""" assert len(input_tensor) == 1 self.model.set_input_tensor(input_tensor[0]) def _init_head(self): column_kwargs = tp_utils.get_default_kwargs_for_column_parallel_linear() if self.megatron_config is not None: assert column_kwargs.get('config', False), 'must have ModelParallelConfig' tp_utils.update_kwargs_with_config(column_kwargs, self.megatron_config) self.lm_head = tensor_parallel.ColumnParallelLinear(input_size=self.config.hidden_size, output_size=self.config.vocab_size, bias=False, gather_output=False, skip_bias_add=False, skip_weight_param_allocation=self.pre_process and self.share_embeddings_and_output_weights, **column_kwargs) def setup_embeddings_and_output_layer(self) -> None: """Sets up embedding layer in first stage and output layer in last stage. This function initalizes word embeddings in the final stage when we are using pipeline parallelism and sharing word embeddings, and sets up param attributes on the embedding and output layers. """ # Set `is_embedding_or_output_parameter` attribute. if self.pre_process: self.model.embed_tokens.weight.is_embedding_or_output_parameter = True if self.post_process and self.lm_head.weight is not None: self.lm_head.weight.is_embedding_or_output_parameter = True if not self.share_embeddings_and_output_weights: return if parallel_state.get_pipeline_model_parallel_world_size() == 1: # Zero out wgrad if sharing embeddings between two layers on same # pipeline stage to make sure grad accumulation into main_grad is # correct and does not include garbage values (e.g., from torch.empty). self.shared_embedding_or_output_weight().zero_out_wgrad = True return if parallel_state.is_pipeline_first_stage() and self.pre_process and not self.post_process: self.shared_embedding_or_output_weight().shared_embedding = True if self.post_process and not self.pre_process: assert not parallel_state.is_pipeline_first_stage() # set word_embeddings weights to 0 here, then copy first # stage's weights using all_reduce below. self.lm_head.weight.data.fill_(0) self.lm_head.weight.shared = True self.lm_head.weight.shared_embedding = True if torch.distributed.is_initialized(): if parallel_state.is_rank_in_embedding_group(): weight = self.shared_embedding_or_output_weight() weight.data = weight.data.cuda() torch.distributed.all_reduce(weight.data, group=parallel_state.get_embedding_group()) def shared_embedding_or_output_weight(self) -> torch.Tensor: if self.pre_process: return self.model.embed_tokens.weight elif self.post_process: return self.lm_head.weight return None def _forward_head(self, hidden_states): # all_gather from sequence parallel region is performed inside lm_head # print(f'logits shape before forward_head: {hidden_states.shape}, vocab_size = {self.config.vocab_size}') # [4, 32, 4096] output_weight = None if self.share_embeddings_and_output_weights: output_weight = self.shared_embedding_or_output_weight() logits = self.lm_head(hidden_states, weight=output_weight)[0] # print(f'logits shape after forward_head: {logits.shape}') # [8, 32, 8] logits = logits.float() # (total_nnz_padded, 1, vocab_size // tp) return logits def forward( self, # original input *, input_ids: torch.LongTensor = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, ) -> Union[Tuple, CausalLMOutputWithPast]: r""" Args: labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. Returns: ```""" # Note that input_ids, attention_mask and position_ids should be passed to every pp layer. # In the first pp, input_ids will be used, in other pp layers hidden_states will be used inside self.model batch_size, sequence_length = input_ids.shape # remove padding here input_ids_rmpad, indices, cu_seqlens, max_seqlen_in_batch, *_ = unpad_input(input_ids.unsqueeze(dim=-1), attention_mask) # (total_nnz, 1) # pad input_ids to multiple of tp for all tp ranks # TODO: for better performance, the sp padding should be removed at each layer. Not sure the performance gap if self.megatron_config.sequence_parallel: input_ids_rmpad = sp_utils.pad_to_sequence_parallel(input_ids_rmpad) input_ids_rmpad = input_ids_rmpad.transpose(0, 1) # (1, total_nnz+pad) outputs = self.model(input_ids=input_ids_rmpad, position_ids=position_ids, sequence_length=sequence_length, indices=indices, cu_seqlens=cu_seqlens, max_seqlen_in_batch=max_seqlen_in_batch) if self.post_process: hidden_states = outputs logits = self._forward_head(hidden_states) logits = torch.squeeze(logits, dim=1) # remove the artificial batch dimension # torch.Size([8, 32, 16]) # remove padding from sequence parallel if self.megatron_config.sequence_parallel: totol_nnz = cu_seqlens[-1] logits = logits[:totol_nnz] # (total_nnz_padded) # add removed padding back. If input is already rmpad, we let the caller pad_input logits = pad_input(logits, indices, batch_size, seqlen=sequence_length) # (batch_size, sequence_length, vocab_size) return CausalLMOutputWithPast( loss=None, logits=logits, past_key_values=None, hidden_states=None, attentions=None, ) else: return outputs class ParallelQwen2ForValueRmPadPP(ParallelQwen2ForCausalLMRmPadPP): def _init_head(self): column_kwargs = tp_utils.get_default_kwargs_for_column_parallel_linear() if self.megatron_config is not None: assert column_kwargs.get('config', False), 'must have ModelParallelConfig' tp_utils.update_kwargs_with_config(column_kwargs, self.megatron_config) self.lm_head = nn.Linear(in_features=self.config.hidden_size, out_features=1, bias=False) # lm_head is effectively the same as sequence parallel sp_utils.mark_parameter_as_sequence_parallel(self.lm_head.weight) def _forward_head(self, hidden_states): logits = self.lm_head(hidden_states) # (total_nnz_padded // tp, 1, 1) logits = logits.float() if self.megatron_config.sequence_parallel: logits = tensor_parallel.gather_from_sequence_parallel_region(logits, tensor_parallel_output_grad=False) return logits def forward( self, *, input_ids: torch.LongTensor = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, ) -> Union[Tuple, CausalLMOutputWithPast]: output = super().forward(input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids) if self.post_process: output.logits = torch.squeeze(output.logits, dim=-1) return output else: return output ================================================ FILE: verl/models/registry.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import importlib from typing import List, Optional, Type import torch.nn as nn # Supported models using HF Rmpad # TODO(sgm): HF may supported more than listed here, we should add more after testing _MODELS_SUPPORT_RMPAD = {'llama', 'mistral', 'gemma', 'qwen2', 'qwen2_vl', 'qwen2_5_vl'} def check_model_support_rmpad(model_type: str): assert isinstance(model_type, str) if not model_type in _MODELS_SUPPORT_RMPAD: raise ValueError(f"Model architecture {model_type} is not supported for now. " f"RMPad supported architectures: {_MODELS_SUPPORT_RMPAD}." f"Please set `use_remove_padding=False` in the model config.") if model_type in ("qwen2_vl", "qwen2_5_vl"): # patch remove padding for qwen2vl mrope from verl.models.transformers.qwen2_vl import ulysses_flash_attn_forward from transformers.models.qwen2_vl.modeling_qwen2_vl import Qwen2VLFlashAttention2 from transformers.models.qwen2_5_vl.modeling_qwen2_5_vl import Qwen2_5_VLFlashAttention2 Qwen2VLFlashAttention2.forward = ulysses_flash_attn_forward Qwen2_5_VLFlashAttention2.forward = ulysses_flash_attn_forward print("Qwen2vl patch applied!") # Supported models in Megatron-LM # Architecture -> (module, class). _MODELS = { "LlamaForCausalLM": ("llama", ("ParallelLlamaForCausalLMRmPadPP", "ParallelLlamaForValueRmPadPP", "ParallelLlamaForCausalLMRmPad")), "Qwen2ForCausalLM": ("qwen2", ("ParallelQwen2ForCausalLMRmPadPP", "ParallelQwen2ForValueRmPadPP", "ParallelQwen2ForCausalLMRmPad")), "MistralForCausalLM": ("mistral", ("ParallelMistralForCausalLMRmPadPP", "ParallelMistralForValueRmPadPP", "ParallelMistralForCausalLMRmPad")) } # return model class class ModelRegistry: @staticmethod def load_model_cls(model_arch: str, value=False) -> Optional[Type[nn.Module]]: if model_arch not in _MODELS: return None megatron = "megatron" module_name, model_cls_name = _MODELS[model_arch] if not value: # actor/ref model_cls_name = model_cls_name[0] elif value: # critic/rm model_cls_name = model_cls_name[1] module = importlib.import_module(f"verl.models.{module_name}.{megatron}.modeling_{module_name}_megatron") return getattr(module, model_cls_name, None) @staticmethod def get_supported_archs() -> List[str]: return list(_MODELS.keys()) ================================================ FILE: verl/models/transformers/__init__.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. ================================================ FILE: verl/models/transformers/llama.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import torch from typing import Optional, Tuple, Callable import sys if sys.version_info >= (3, 11): from typing import Unpack else: from typing_extensions import Unpack from transformers.models.llama.modeling_llama import apply_rotary_pos_emb from transformers.cache_utils import Cache from transformers.utils import logging from transformers.modeling_flash_attention_utils import _flash_attention_forward from verl.utils.ulysses import gather_heads_scatter_seq, gather_seq_scatter_heads, get_ulysses_sequence_parallel_world_size logger = logging.get_logger(__name__) def llama_flash_attn_forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_value: Optional[Cache] = None, output_attentions: bool = False, use_cache: bool = False, cache_position: Optional[torch.LongTensor] = None, position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, # will become mandatory in v4.46 **kwargs, ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: """ Adapted from transformers 4.47.1 to support Ulysses sequence parallelism. NOTE: This function is used for transformers versions in the range [4.45.0, 4.47.1]. """ output_attentions = False bsz, q_len, _ = hidden_states.size() query_states = self.q_proj(hidden_states) key_states = self.k_proj(hidden_states) value_states = self.v_proj(hidden_states) # Flash attention requires the input to have the shape # batch_size x seq_length x head_dim x hidden_dim # therefore we just need to keep the original shape query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) # trade off: repeat first and then all to all # key_states = repeat_kv(key_states, self.num_key_value_groups) # value_states = repeat_kv(value_states, self.num_key_value_groups) ########## AlltoAll for Ulysses ########## ulysses_sp_size = get_ulysses_sequence_parallel_world_size() if ulysses_sp_size > 1: # (bsz, n_head, seq_len/n, head_dim) -> (bsz, n_head/n, seq_len, head_dim) query_states = gather_seq_scatter_heads(query_states, seq_dim=2, head_dim=1) key_states = gather_seq_scatter_heads(key_states, seq_dim=2, head_dim=1) value_states = gather_seq_scatter_heads(value_states, seq_dim=2, head_dim=1) full_q_len = query_states.size(2) # full seq length if position_embeddings is None: logger.warning_once( "The attention layers in this model are transitioning from computing the RoPE embeddings internally " "through `position_ids` (2D tensor with the indexes of the tokens), to using externally computed " "`position_embeddings` (Tuple of tensors, containing cos and sin). In v4.46 `position_ids` will be " "removed and `position_embeddings` will be mandatory.") cos, sin = self.rotary_emb(value_states, position_ids) else: cos, sin = position_embeddings query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin) if past_key_value is not None: # sin and cos are specific to RoPE models; cache_position needed for the static cache cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position} key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs) # TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache # to be able to avoid many of these transpose/reshape/view. query_states = query_states.transpose(1, 2) key_states = key_states.transpose(1, 2) value_states = value_states.transpose(1, 2) dropout_rate = self.attention_dropout if self.training else 0.0 # In PEFT, usually we cast the layer norms in float32 for training stability reasons # therefore the input hidden states gets silently casted in float32. Hence, we need # cast them back in the correct dtype just to be sure everything works as expected. # This might slowdown training & inference so it is recommended to not cast the LayerNorms # in fp32. (LlamaRMSNorm handles it correctly) input_dtype = query_states.dtype if input_dtype == torch.float32: if torch.is_autocast_enabled(): target_dtype = torch.get_autocast_gpu_dtype() # Handle the case where the model is quantized elif hasattr(self.config, "_pre_quantization_dtype"): target_dtype = self.config._pre_quantization_dtype else: target_dtype = self.q_proj.weight.dtype logger.warning_once( f"The input hidden states seems to be silently casted in float32, this might be related to" f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in" f" {target_dtype}.") query_states = query_states.to(target_dtype) key_states = key_states.to(target_dtype) value_states = value_states.to(target_dtype) attn_output = _flash_attention_forward( query_states, key_states, value_states, attention_mask, full_q_len, position_ids=position_ids, dropout=dropout_rate, sliding_window=getattr(self, "sliding_window", None), use_top_left_mask=self._flash_attn_uses_top_left_mask, is_causal=self.is_causal, **kwargs, ) attn_output = attn_output.reshape(bsz, full_q_len, -1, self.head_dim).contiguous() ########## AlltoAll for Ulysses ########## if ulysses_sp_size > 1: attn_output = gather_heads_scatter_seq(attn_output, seq_dim=1, head_dim=2) attn_output = attn_output.reshape(bsz, q_len, -1).contiguous() attn_output = self.o_proj(attn_output) if not output_attentions: attn_weights = None return attn_output, attn_weights, past_key_value def llama_attn_forward( self, hidden_states: torch.Tensor, position_embeddings: Tuple[torch.Tensor, torch.Tensor], attention_mask: Optional[torch.Tensor], past_key_value: Optional[Cache] = None, cache_position: Optional[torch.LongTensor] = None, **kwargs, ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: """ Adapted from transformers 4.49.0 to support Ulysses sequence parallelism for transformers >= 4.48.0. NOTE: This function has been tested only on transformers versions between 4.48.0 and 4.49.0. """ from transformers.models.llama.modeling_llama import eager_attention_forward from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS bsz, q_len, _ = hidden_states.shape query_states = self.q_proj(hidden_states).view(bsz, q_len, -1, self.head_dim).transpose(1, 2) key_states = self.k_proj(hidden_states).view(bsz, q_len, -1, self.head_dim).transpose(1, 2) value_states = self.v_proj(hidden_states).view(bsz, q_len, -1, self.head_dim).transpose(1, 2) ########## AlltoAll for Ulysses ########## ulysses_sp_size = get_ulysses_sequence_parallel_world_size() if ulysses_sp_size > 1: query_states = gather_seq_scatter_heads(query_states, seq_dim=2, head_dim=1) key_states = gather_seq_scatter_heads(key_states, seq_dim=2, head_dim=1) value_states = gather_seq_scatter_heads(value_states, seq_dim=2, head_dim=1) full_q_len = query_states.size(2) cos, sin = position_embeddings query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin) if past_key_value is not None: # sin and cos are specific to RoPE models; cache_position needed for the static cache cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position} key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs) attention_interface: Callable = eager_attention_forward if self.config._attn_implementation != "eager": if self.config._attn_implementation == "sdpa" and kwargs.get("output_attentions", False): logger.warning_once( "`torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to " 'eager attention. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.' ) else: attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation] attn_output, attn_weights = attention_interface( self, query_states, key_states, value_states, attention_mask, dropout=0.0 if not self.training else self.attention_dropout, scaling=self.scaling, **kwargs, ) attn_output = attn_output.reshape(bsz, full_q_len, -1, self.head_dim).contiguous() ########## AlltoAll for Ulysses ########## if ulysses_sp_size > 1: attn_output = gather_heads_scatter_seq(attn_output, seq_dim=1, head_dim=2) attn_output = attn_output.reshape(bsz, q_len, -1).contiguous() attn_output = self.o_proj(attn_output) return attn_output, attn_weights ================================================ FILE: verl/models/transformers/monkey_patch.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Apply monkey-patch function to models """ def apply_monkey_patch_to_llama(): if is_transformers_version_in_range("4.45.0", "4.47.1"): from transformers.models.llama.modeling_llama import LlamaFlashAttention2 from verl.models.transformers.llama import llama_flash_attn_forward LlamaFlashAttention2.forward = llama_flash_attn_forward elif is_transformers_version_in_range("4.48.0", "4.49.0"): from transformers.models.llama.modeling_llama import LlamaAttention from verl.models.transformers.llama import llama_attn_forward LlamaAttention.forward = llama_attn_forward def apply_monkey_patch_to_qwen2(): if is_transformers_version_in_range("4.45.0", "4.47.1"): from transformers.models.qwen2.modeling_qwen2 import Qwen2FlashAttention2 from verl.models.transformers.qwen2 import qwen2_flash_attn_forward Qwen2FlashAttention2.forward = qwen2_flash_attn_forward elif is_transformers_version_in_range("4.48.0", "4.49.0"): from transformers.models.qwen2.modeling_qwen2 import Qwen2Attention from verl.models.transformers.qwen2 import qwen2_attn_forward Qwen2Attention.forward = qwen2_attn_forward _PATCH_NAME_TO_FUNC = { 'llama': apply_monkey_patch_to_llama, 'qwen2': apply_monkey_patch_to_qwen2, } from transformers import PretrainedConfig def apply_monkey_patch(config: PretrainedConfig, verbose=True): if not is_transformers_version_in_range("4.45.0", "4.49.0"): raise AssertionError("The installed `transformers` version doesn't support ulysses patch. " "Please install a version between 4.45.0 and 4.49.0 to use this ulysses feature.") success_apply_monkey_patch = False if config.model_type in _PATCH_NAME_TO_FUNC: _PATCH_NAME_TO_FUNC[config.model_type]() success_apply_monkey_patch = True if success_apply_monkey_patch and verbose: print(f'Applying monkey patch to model {config.model_type}') elif not success_apply_monkey_patch: raise NotImplementedError(f'Ulysses for model {config.model_type} is not implemented, \ please set `ulysses_sequence_parallel_size=1`') return success_apply_monkey_patch from functools import lru_cache from packaging import version import importlib.metadata @lru_cache() def is_transformers_version_in_range(min_version: str, max_version: str) -> bool: try: # Get the installed version of the transformers library transformers_version = importlib.metadata.version("transformers") except importlib.metadata.PackageNotFoundError: raise ModuleNotFoundError("The `transformers` package is not installed.") # Check if the version is within the specified range return version.parse(min_version) <= version.parse(transformers_version) <= version.parse(max_version) ================================================ FILE: verl/models/transformers/qwen2.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import torch from typing import Optional, Tuple, Callable from transformers.models.llama.modeling_llama import apply_rotary_pos_emb, repeat_kv from transformers.cache_utils import Cache from transformers.utils import logging from transformers.modeling_flash_attention_utils import _flash_attention_forward from transformers.processing_utils import Unpack from verl.utils.ulysses import gather_heads_scatter_seq, gather_seq_scatter_heads, get_ulysses_sequence_parallel_world_size logger = logging.get_logger(__name__) def qwen2_flash_attn_forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_value: Optional[Cache] = None, output_attentions: bool = False, use_cache: bool = False, cache_position: Optional[torch.LongTensor] = None, position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, # will become mandatory in v4.46 ): """ Adapted from transformers 4.47.1 to support Ulysses sequence parallelism. NOTE: This function is only tested on transformers versions between 4.45.0 and 4.47.1. """ bsz, q_len, _ = hidden_states.size() query_states = self.q_proj(hidden_states) key_states = self.k_proj(hidden_states) value_states = self.v_proj(hidden_states) query_states = query_states.view(bsz, q_len, -1, self.head_dim).transpose(1, 2) key_states = key_states.view(bsz, q_len, -1, self.head_dim).transpose(1, 2) value_states = value_states.view(bsz, q_len, -1, self.head_dim).transpose(1, 2) ########## AlltoAll for Ulysses ########## ulysses_sp_size = get_ulysses_sequence_parallel_world_size() if ulysses_sp_size > 1: # (bsz, n_head, seq_len/n, head_dim) -> (bsz, n_head/n, seq_len, head_dim) query_states = gather_seq_scatter_heads(query_states, seq_dim=2, head_dim=1) key_states = gather_seq_scatter_heads(key_states, seq_dim=2, head_dim=1) value_states = gather_seq_scatter_heads(value_states, seq_dim=2, head_dim=1) full_q_len = query_states.size(2) # full seq length if position_embeddings is None: logger.warning_once( "The attention layers in this model are transitioning from computing the RoPE embeddings internally " "through `position_ids` (2D tensor with the indexes of the tokens), to using externally computed " "`position_embeddings` (Tuple of tensors, containing cos and sin). In v4.46 `position_ids` will be " "removed and `position_embeddings` will be mandatory.") cos, sin = self.rotary_emb(value_states, position_ids) else: cos, sin = position_embeddings query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin) if past_key_value is not None: cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position} # Specific to RoPE models key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs) # repeat k/v heads if n_kv_heads < n_heads key_states = repeat_kv(key_states, self.num_key_value_groups) value_states = repeat_kv(value_states, self.num_key_value_groups) dropout_rate = 0.0 if not self.training else self.attention_dropout # In PEFT, usually we cast the layer norms in float32 for training stability reasons # therefore the input hidden states gets silently casted in float32. Hence, we need # cast them back in float16 just to be sure everything works as expected. input_dtype = query_states.dtype if input_dtype == torch.float32: if torch.is_autocast_enabled(): target_dtype = torch.get_autocast_gpu_dtype() # Handle the case where the model is quantized elif hasattr(self.config, "_pre_quantization_dtype"): target_dtype = self.config._pre_quantization_dtype else: target_dtype = self.q_proj.weight.dtype logger.warning_once( f"The input hidden states seems to be silently casted in float32, this might be related to" f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in" f" {target_dtype}.") query_states = query_states.to(target_dtype) key_states = key_states.to(target_dtype) value_states = value_states.to(target_dtype) # Reashape to the expected shape for Flash Attention query_states = query_states.transpose(1, 2) key_states = key_states.transpose(1, 2) value_states = value_states.transpose(1, 2) if (self.config.use_sliding_window and getattr(self.config, "sliding_window", None) is not None and self.layer_idx >= self.config.max_window_layers): sliding_window = self.config.sliding_window else: sliding_window = None attn_output = _flash_attention_forward( query_states, key_states, value_states, attention_mask, full_q_len, position_ids=position_ids, dropout=dropout_rate, sliding_window=sliding_window, is_causal=self.is_causal, use_top_left_mask=self._flash_attn_uses_top_left_mask, ) # use full_q_len to reshape attn_output = attn_output.reshape(bsz, full_q_len, -1, self.head_dim).contiguous() ########## AlltoAll for Ulysses ########## if ulysses_sp_size > 1: attn_output = gather_heads_scatter_seq(attn_output, seq_dim=1, head_dim=2) attn_output = attn_output.reshape(bsz, q_len, -1).contiguous() attn_output = self.o_proj(attn_output) if not output_attentions: attn_weights = None return attn_output, attn_weights, past_key_value def qwen2_attn_forward( self, hidden_states: torch.Tensor, position_embeddings: Tuple[torch.Tensor, torch.Tensor], attention_mask: Optional[torch.Tensor], past_key_value: Optional[Cache] = None, cache_position: Optional[torch.LongTensor] = None, **kwargs, ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: """ Adapted from transformers 4.49.0 to support Ulysses sequence parallelism for transformers >= 4.48.0. NOTE: This function has been tested only on transformers versions between 4.48.0 and 4.49.0. """ from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS bsz, q_len, _ = hidden_states.shape hidden_shape = (bsz, q_len, -1, self.head_dim) query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2) key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2) value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2) ########## AlltoAll for Ulysses ########## ulysses_sp_size = get_ulysses_sequence_parallel_world_size() if ulysses_sp_size > 1: # (bsz, n_head, seq_len/n, head_dim) -> (bsz, n_head/n, seq_len, head_dim) query_states = gather_seq_scatter_heads(query_states, seq_dim=2, head_dim=1) key_states = gather_seq_scatter_heads(key_states, seq_dim=2, head_dim=1) value_states = gather_seq_scatter_heads(value_states, seq_dim=2, head_dim=1) full_q_len = query_states.size(2) cos, sin = position_embeddings query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin) if past_key_value is not None: # sin and cos are specific to RoPE models; cache_position needed for the static cache cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position} key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs) sliding_window = None if (self.config.use_sliding_window and getattr(self.config, "sliding_window", None) is not None and self.layer_idx >= self.config.max_window_layers): sliding_window = self.config.sliding_window from transformers.models.qwen2.modeling_qwen2 import eager_attention_forward attention_interface: Callable = eager_attention_forward if self.config._attn_implementation != "eager": if self.config._attn_implementation == "sdpa" and kwargs.get("output_attentions", False): logger.warning_once( "`torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to " 'eager attention. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.' ) else: attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation] attn_output, attn_weights = attention_interface( self, query_states, key_states, value_states, attention_mask, dropout=0.0 if not self.training else self.attention_dropout, scaling=self.scaling, sliding_window=sliding_window, # main diff with Llama **kwargs, ) attn_output = attn_output.reshape(bsz, full_q_len, -1, self.head_dim).contiguous() ########## AlltoAll for Ulysses ########## if ulysses_sp_size > 1: # (bsz, seq_len, n_head/n, head_dim) -> (bsz, seq_len/n, n_head, head_dim) attn_output = gather_heads_scatter_seq(attn_output, seq_dim=1, head_dim=2) attn_output = attn_output.reshape(bsz, q_len, -1).contiguous() attn_output = self.o_proj(attn_output) return attn_output, attn_weights ================================================ FILE: verl/models/transformers/qwen2_vl.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import Optional, Tuple import inspect import torch import os from transformers.utils import is_flash_attn_greater_or_equal from transformers.modeling_flash_attention_utils import _flash_attention_forward from verl.utils.ulysses import gather_heads_scatter_seq, gather_seq_scatter_heads, get_ulysses_sequence_parallel_world_size try: from flash_attn import flash_attn_func, flash_attn_varlen_func _flash_supports_window_size = "window_size" in list(inspect.signature(flash_attn_func).parameters) except ImportError: flash_attn_varlen_func = None def get_rope_index( processor, input_ids: torch.Tensor, image_grid_thw: Optional[torch.Tensor] = None, video_grid_thw: Optional[torch.Tensor] = None, second_per_grid_ts: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, ) -> torch.Tensor: """ Gets the position ids for Qwen2-VL, it should be generated before sharding the sequence. The batch dim has been removed and the input_ids should be a 1D tensor representing a single example. https://github.com/huggingface/transformers/blob/v4.49.0/src/transformers/models/qwen2_5_vl/modeling_qwen2_5_vl.py#L1546 """ spatial_merge_size = processor.image_processor.merge_size tokens_per_second = 2 image_token_id = processor.tokenizer.convert_tokens_to_ids("<|image_pad|>") video_token_id = processor.tokenizer.convert_tokens_to_ids("<|video_pad|>") vision_start_token_id = processor.tokenizer.convert_tokens_to_ids("<|vision_start|>") if input_ids is not None and (image_grid_thw is not None or video_grid_thw is not None): if attention_mask is None: attention_mask = torch.ones_like(input_ids) position_ids = torch.ones(3, input_ids.size(0), dtype=input_ids.dtype, device=input_ids.device) # (3, seqlen) image_index, video_index = 0, 0 input_ids = input_ids[attention_mask == 1] image_nums, video_nums = 0, 0 vision_start_indices = torch.argwhere(input_ids == vision_start_token_id) vision_tokens = input_ids[vision_start_indices + 1] image_nums = (vision_tokens == image_token_id).sum() video_nums = (vision_tokens == video_token_id).sum() input_tokens = input_ids.tolist() llm_pos_ids_list: list = [] st = 0 remain_images, remain_videos = image_nums, video_nums for _ in range(image_nums + video_nums): if image_token_id in input_tokens and remain_images > 0: ed_image = input_tokens.index(image_token_id, st) else: ed_image = len(input_tokens) + 1 if video_token_id in input_tokens and remain_videos > 0: ed_video = input_tokens.index(video_token_id, st) else: ed_video = len(input_tokens) + 1 if ed_image < ed_video: t, h, w = ( image_grid_thw[image_index][0], image_grid_thw[image_index][1], image_grid_thw[image_index][2], ) second_per_grid_t = 0 image_index += 1 remain_images -= 1 ed = ed_image else: t, h, w = ( video_grid_thw[video_index][0], video_grid_thw[video_index][1], video_grid_thw[video_index][2], ) if second_per_grid_ts is not None: second_per_grid_t = second_per_grid_ts[video_index] else: second_per_grid_t = 1.0 video_index += 1 remain_videos -= 1 ed = ed_video llm_grid_t, llm_grid_h, llm_grid_w = ( t.item(), h.item() // spatial_merge_size, w.item() // spatial_merge_size, ) text_len = ed - st st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0 llm_pos_ids_list.append(torch.arange(text_len).view(1, -1).expand(3, -1) + st_idx) t_index = torch.arange(llm_grid_t).view(-1, 1).expand(-1, llm_grid_h * llm_grid_w) t_index = (t_index * second_per_grid_t * tokens_per_second).long().flatten() h_index = torch.arange(llm_grid_h).view(1, -1, 1).expand(llm_grid_t, -1, llm_grid_w).flatten() w_index = torch.arange(llm_grid_w).view(1, 1, -1).expand(llm_grid_t, llm_grid_h, -1).flatten() llm_pos_ids_list.append(torch.stack([t_index, h_index, w_index]) + text_len + st_idx) st = ed + llm_grid_t * llm_grid_h * llm_grid_w if st < len(input_tokens): st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0 text_len = len(input_tokens) - st llm_pos_ids_list.append(torch.arange(text_len).view(1, -1).expand(3, -1) + st_idx) llm_positions = torch.cat(llm_pos_ids_list, dim=1).reshape(3, -1) position_ids[..., attention_mask == 1] = llm_positions.to(position_ids.device) else: if attention_mask is not None: position_ids = attention_mask.long().cumsum(-1) - 1 position_ids.masked_fill_(attention_mask == 0, 1) position_ids = position_ids.unsqueeze(0).expand(3, -1).to(input_ids.device) else: position_ids = torch.arange(input_ids.shape[1], device=input_ids.device).view(1, -1).expand(3, -1) return position_ids def prepare_fa2_from_position_ids(query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, position_ids: torch.Tensor): query = query.view(-1, query.size(-2), query.size(-1)) key = key.view(-1, key.size(-2), key.size(-1)) value = value.view(-1, value.size(-2), value.size(-1)) position_ids = position_ids.flatten() indices_q = torch.arange(position_ids.size(0), device=position_ids.device, dtype=torch.int32) cu_seqlens = torch.cat(( indices_q[position_ids == 0], torch.tensor(position_ids.size(), device=position_ids.device, dtype=torch.int32), )) max_length = cu_seqlens.diff().max() # use cu_seqlens to infer max_length for qwen2vl mrope return (query, key, value, indices_q, (cu_seqlens, cu_seqlens), (max_length, max_length)) def flash_attention_forward( query_states: torch.Tensor, key_states: torch.Tensor, value_states: torch.Tensor, attention_mask: torch.Tensor, query_length: int, is_causal: bool = True, position_ids: Optional[torch.Tensor] = None, sliding_window: Optional[int] = None, use_top_left_mask: bool = False, deterministic: Optional[bool] = None, **kwargs, ): """ Patches flash attention forward to handle 3D position ids in mrope. (3, batch_size, seq_length) """ if not use_top_left_mask: causal = is_causal else: causal = is_causal and query_length != 1 # Assuming 4D tensors, key_states.shape[1] is the key/value sequence length (source length). use_sliding_windows = (_flash_supports_window_size and sliding_window is not None and key_states.shape[1] > sliding_window) flash_kwargs = {"window_size": (sliding_window, sliding_window)} if use_sliding_windows else {} if is_flash_attn_greater_or_equal("2.4.1"): if deterministic is None: deterministic = os.environ.get("FLASH_ATTENTION_DETERMINISTIC", "0") == "1" flash_kwargs["deterministic"] = deterministic if position_ids is not None and query_length != 1 and not (torch.diff(position_ids[0], dim=-1) >= 0).all(): batch_size = query_states.size(0) query_states, key_states, value_states, _, cu_seq_lens, max_seq_lens = prepare_fa2_from_position_ids( query_states, key_states, value_states, position_ids[0]) # remove channel dimension cu_seqlens_q, cu_seqlens_k = cu_seq_lens max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens attn_output = flash_attn_varlen_func( query_states, key_states, value_states, cu_seqlens_q=cu_seqlens_q, cu_seqlens_k=cu_seqlens_k, max_seqlen_q=max_seqlen_in_batch_q, max_seqlen_k=max_seqlen_in_batch_k, dropout_p=kwargs.pop("dropout", 0.0), softmax_scale=kwargs.pop("softmax_scale", None), causal=causal, **flash_kwargs, ) attn_output = attn_output.view(batch_size, -1, attn_output.size(-2), attn_output.size(-1)) else: attn_output = _flash_attention_forward( query_states, key_states, value_states, attention_mask, query_length, is_causal=is_causal, sliding_window=sliding_window, use_top_left_mask=use_top_left_mask, deterministic=deterministic, **kwargs, ) # do not pass position_ids to old flash_attention_forward return attn_output def ulysses_flash_attn_forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, # will become mandatory in v4.46 **kwargs, ) -> Tuple[torch.Tensor, None, None]: from transformers.models.qwen2_vl.modeling_qwen2_vl import repeat_kv, apply_multimodal_rotary_pos_emb bsz, q_len, _ = hidden_states.size() # q_len = seq_length / sp_size query_states = self.q_proj(hidden_states) # (batch_size, seq_length / sp_size, num_heads * head_size) key_states = self.k_proj(hidden_states) value_states = self.v_proj(hidden_states) query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) ulysses_sp_size = get_ulysses_sequence_parallel_world_size() if ulysses_sp_size > 1: key_states = repeat_kv(key_states, self.num_key_value_groups) value_states = repeat_kv(value_states, self.num_key_value_groups) query_states = gather_seq_scatter_heads(query_states, seq_dim=2, head_dim=1) key_states = gather_seq_scatter_heads(key_states, seq_dim=2, head_dim=1) value_states = gather_seq_scatter_heads(value_states, seq_dim=2, head_dim=1) # (batch_size, num_head / sp_size, seq_length, head_size) full_q_len = query_states.size(2) # full_q_len = seq_length else: full_q_len = q_len # Because the input can be padded, the absolute sequence length depends on the max position id. if position_embeddings is None: cos, sin = self.rotary_emb(value_states, position_ids) else: cos, sin = position_embeddings query_states, key_states = apply_multimodal_rotary_pos_emb(query_states, key_states, cos, sin, self.rope_scaling["mrope_section"]) dropout_rate = 0.0 if not self.training else self.attention_dropout # Reashape to the expected shape for Flash Attention query_states = query_states.transpose(1, 2) key_states = key_states.transpose(1, 2) value_states = value_states.transpose(1, 2) if (self.config.use_sliding_window and getattr(self.config, "sliding_window", None) is not None and self.layer_idx >= self.config.max_window_layers): sliding_window = self.config.sliding_window else: sliding_window = None attn_output = flash_attention_forward( query_states, key_states, value_states, attention_mask, full_q_len, dropout=dropout_rate, sliding_window=sliding_window, is_causal=self.is_causal, use_top_left_mask=self._flash_attn_uses_top_left_mask, position_ids=position_ids, # important: pass position ids ) # (batch_size, seq_length, num_head / sp_size, head_size) if ulysses_sp_size > 1: attn_output = gather_heads_scatter_seq(attn_output, head_dim=2, seq_dim=1) attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous() attn_output = self.o_proj(attn_output) return attn_output, None, None ================================================ FILE: verl/models/weight_loader_registry.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. def get_weight_loader(arch: str): from verl.models.llama.megatron.checkpoint_utils.llama_loader import load_state_dict_to_megatron_llama from verl.models.qwen2.megatron.checkpoint_utils.qwen2_loader import load_state_dict_to_megatron_qwen2 _MODEL_WEIGHT_MEGATRON_LOADER_REGISTRY = { 'LlamaForCausalLM': load_state_dict_to_megatron_llama, 'Qwen2ForCausalLM': load_state_dict_to_megatron_qwen2, } if arch in _MODEL_WEIGHT_MEGATRON_LOADER_REGISTRY: return _MODEL_WEIGHT_MEGATRON_LOADER_REGISTRY[arch] raise ValueError(f"Model architectures {arch} are not supported for now. " f"Supported architectures: {_MODEL_WEIGHT_MEGATRON_LOADER_REGISTRY.keys()}") ================================================ FILE: verl/protocol.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Implement base data transfer protocol between any two functions, modules. We can subclass Protocol to define more detailed batch info with specific keys """ import pickle import numpy as np import pandas as pd import copy from dataclasses import dataclass, field from typing import Callable, Dict, List, Union import torch import tensordict from tensordict import TensorDict from torch.utils.data import DataLoader, Dataset from verl.utils.py_functional import union_two_dict __all__ = ['DataProto', 'union_tensor_dict'] try: tensordict.set_lazy_legacy(False).set() except: pass def pad_dataproto_to_divisor(data: 'DataProto', size_divisor: int): """Pad a DataProto to size divisible by size_divisor Args: size_divisor (int): size divisor Returns: data: (DataProto): the padded DataProto pad_size (int) """ assert isinstance(data, DataProto), 'data must be a DataProto' if len(data) % size_divisor != 0: pad_size = size_divisor - len(data) % size_divisor padding_protos = [] remaining_pad = pad_size while remaining_pad > 0: take_size = min(remaining_pad, len(data)) padding_protos.append(data[:take_size]) remaining_pad -= take_size data_padded = DataProto.concat([data] + padding_protos) else: pad_size = 0 data_padded = data return data_padded, pad_size def unpad_dataproto(data: 'DataProto', pad_size): if pad_size != 0: data = data[:-pad_size] return data def union_tensor_dict(tensor_dict1: TensorDict, tensor_dict2: TensorDict) -> TensorDict: """Union two tensordicts.""" assert tensor_dict1.batch_size == tensor_dict2.batch_size, \ f'Two tensor dict must have identical batch size. Got {tensor_dict1.batch_size} and {tensor_dict2.batch_size}' for key in tensor_dict2.keys(): if key not in tensor_dict1.keys(): tensor_dict1[key] = tensor_dict2[key] else: assert tensor_dict1[key].equal(tensor_dict2[key]), \ f'{key} in tensor_dict1 and tensor_dict2 are not the same object' return tensor_dict1 def union_numpy_dict(tensor_dict1: dict[str, np.ndarray], tensor_dict2: dict[str, np.ndarray]) -> dict[str, np.ndarray]: for key, val in tensor_dict2.items(): if key in tensor_dict1: assert isinstance(tensor_dict2[key], np.ndarray) assert isinstance(tensor_dict1[key], np.ndarray) # to properly deal with nan and object type assert pd.DataFrame(tensor_dict2[key]).equals(pd.DataFrame(tensor_dict1[key])), \ f'{key} in tensor_dict1 and tensor_dict2 are not the same object' tensor_dict1[key] = val return tensor_dict1 def list_of_dict_to_dict_of_list(list_of_dict: list[dict]): if len(list_of_dict) == 0: return {} keys = list_of_dict[0].keys() output = {key: [] for key in keys} for data in list_of_dict: for key, item in data.items(): assert key in output output[key].append(item) return output def fold_batch_dim(data: 'DataProto', new_batch_size): """ Fold a batch dim from [bsz, xxx] into [new_bsz, bsz // new_bsz, xxx] """ batch_size = data.batch.batch_size[0] assert batch_size % new_batch_size == 0 tensor: TensorDict = data.batch non_tensor = data.non_tensor_batch tensor = tensor.view(new_batch_size, -1) tensor.auto_batch_size_(batch_dims=1) for key, val in non_tensor.items(): non_tensor[key] = np.reshape(val, newshape=(new_batch_size, -1, *val.shape[1:])) return DataProto(batch=tensor, non_tensor_batch=non_tensor, meta_info=data.meta_info) def unfold_batch_dim(data: 'DataProto', batch_dims=2): """ Unfold the first n dims as new batch dim """ tensor: TensorDict = data.batch non_tensor = data.non_tensor_batch tensor.auto_batch_size_(batch_dims=batch_dims) tensor = tensor.view(-1) batch_size = tensor.batch_size[0] non_tensor_new = {} for key, val in non_tensor.items(): non_tensor_new[key] = np.reshape(val, newshape=(batch_size, *val.shape[batch_dims:])) return DataProto(batch=tensor, non_tensor_batch=non_tensor_new, meta_info=data.meta_info) def collate_fn(x: list['DataProtoItem']): batch = [] non_tensor_batch = [] for data in x: batch.append(data.batch) non_tensor_batch.append(data.non_tensor_batch) batch = torch.stack(batch).contiguous() non_tensor_batch = list_of_dict_to_dict_of_list(non_tensor_batch) for key, val in non_tensor_batch.items(): non_tensor_batch[key] = np.array(val, dtype=object) return DataProto(batch=batch, non_tensor_batch=non_tensor_batch) @dataclass class DataProtoItem: # TODO(zhangchi.usc1992) add consistency check batch: TensorDict = None non_tensor_batch: Dict = field(default_factory=dict) meta_info: Dict = field(default_factory=dict) @dataclass class DataProto: """ A DataProto is a data structure that aims to provide a standard protocol for data exchange between functions. It contains a batch (TensorDict) and a meta_info (Dict). The batch is a TensorDict https://pytorch.org/tensordict/. TensorDict allows you to manipulate a dictionary of Tensors like a single Tensor. Ideally, the tensors with the same batch size should be put inside batch. """ batch: TensorDict = None non_tensor_batch: Dict = field(default_factory=dict) meta_info: Dict = field(default_factory=dict) def __post_init__(self): # perform necessary checking self.check_consistency() def __len__(self): if self.batch is not None: return self.batch.batch_size[0] elif self.non_tensor_batch is not None and len(self.non_tensor_batch) > 0: random_key = list(self.non_tensor_batch.keys())[0] return self.non_tensor_batch[random_key].shape[0] else: return 0 def __getitem__(self, item): tensor_data = self.batch[item] non_tensor_data = {key: val[item] for key, val in self.non_tensor_batch.items()} return DataProtoItem(batch=tensor_data, non_tensor_batch=non_tensor_data, meta_info=self.meta_info) def __getstate__(self): import io buffer = io.BytesIO() if tensordict.__version__ >= '0.5.0' and self.batch is not None: self.batch = self.batch.contiguous() self.batch = self.batch.consolidate() torch.save(self.batch, buffer) buffer_bytes = buffer.getvalue() return buffer_bytes, self.non_tensor_batch, self.meta_info def __setstate__(self, data): import io batch_deserialized_bytes, non_tensor_batch, meta_info = data batch_deserialized = io.BytesIO(initial_bytes=batch_deserialized_bytes) batch = torch.load(batch_deserialized, weights_only=False, map_location='cpu' if not torch.cuda.is_available() else None) self.batch = batch self.non_tensor_batch = non_tensor_batch self.meta_info = meta_info def save_to_disk(self, filepath): with open(filepath, 'wb') as f: pickle.dump(self, f) @staticmethod def load_from_disk(filepath) -> 'DataProto': with open(filepath, 'rb') as f: data = pickle.load(f) return data def print_size(self, prefix=""): size_of_tensordict = 0 for key, tensor in self.batch.items(): size_of_tensordict += tensor.element_size() * tensor.numel() size_of_numpy_array = 0 for key, numpy_array in self.non_tensor_batch.items(): size_of_numpy_array += numpy_array.nbytes size_of_numpy_array /= 1024**3 size_of_tensordict /= 1024**3 message = f'Size of tensordict: {size_of_tensordict} GB, size of non_tensor_batch: {size_of_numpy_array} GB' if prefix: message = f'{prefix}, ' + message print(message) def check_consistency(self): """Check the consistency of the DataProto. Mainly for batch and non_tensor_batch We expose this function as a public one so that user can call themselves directly """ if self.batch is not None: assert len(self.batch.batch_size) == 1, 'only support num_batch_dims=1' if self.non_tensor_batch is not None: for key, val in self.non_tensor_batch.items(): assert isinstance(val, np.ndarray) if self.batch is not None and len(self.non_tensor_batch) != 0: # TODO: we can actually lift this restriction if needed assert len(self.batch.batch_size) == 1, 'only support num_batch_dims=1 when non_tensor_batch is not empty.' batch_size = self.batch.batch_size[0] for key, val in self.non_tensor_batch.items(): assert isinstance( val, np.ndarray ) and val.dtype == object, 'data in the non_tensor_batch must be a numpy.array with dtype=object' assert val.shape[ 0] == batch_size, f'key {key} length {len(val)} is not equal to batch size {batch_size}' @classmethod def from_single_dict(cls, data: Dict[str, Union[torch.Tensor, np.ndarray]], meta_info=None): tensors = {} non_tensors = {} for key, val in data.items(): if isinstance(val, torch.Tensor): tensors[key] = val elif isinstance(val, np.ndarray): non_tensors[key] = val else: raise ValueError(f'Unsupported type in data {type(val)}') return DataProto.from_dict(tensors=tensors, non_tensors=non_tensors, meta_info=meta_info) @classmethod def from_dict(cls, tensors: Dict[str, torch.Tensor], non_tensors=None, meta_info=None, num_batch_dims=1): """Create a DataProto from a dict of tensors. This assumes that 1. All the tensor in tensors have the same dim0 2. Only dim0 is the batch dim """ assert len(tensors) > 0, 'tensors must not be empty' assert num_batch_dims > 0, 'num_batch_dims must be greater than zero' if non_tensors is not None: assert num_batch_dims == 1, 'only support num_batch_dims=1 when non_tensors is not None.' if meta_info is None: meta_info = {} if non_tensors is None: non_tensors = {} assert isinstance(non_tensors, dict) # get and check batch size batch_size = None pivot_key = None for key, tensor in tensors.items(): if batch_size is None: batch_size = tensor.shape[:num_batch_dims] pivot_key = key else: current_batch = tensor.shape[:num_batch_dims] assert batch_size == current_batch, \ f'Not all the tensor in tensors have the same batch size with batch_dims={num_batch_dims}. Got {pivot_key} has {batch_size}, {key} has {current_batch}' for key, val in non_tensors.items(): non_tensors[key] = np.array(val, dtype=object) tensor_dict = TensorDict(source=tensors, batch_size=batch_size) return cls(batch=tensor_dict, non_tensor_batch=non_tensors, meta_info=meta_info) def to(self, device) -> 'DataProto': """move the batch to device Args: device (torch.device, str): torch device Returns: DataProto: the current DataProto """ if self.batch is not None: self.batch = self.batch.to(device) return self def select(self, batch_keys=None, non_tensor_batch_keys=None, meta_info_keys=None, deepcopy=False) -> 'DataProto': """Select a subset of the DataProto via batch_keys and meta_info_keys Args: batch_keys (list, optional): a list of strings indicating the keys in batch to select meta_info_keys (list, optional): a list of keys indicating the meta info to select Returns: DataProto: the DataProto with the selected batch_keys and meta_info_keys """ # TODO (zhangchi.usc1992) whether to copy if batch_keys is not None: batch_keys = tuple(batch_keys) sub_batch = self.batch.select(*batch_keys) else: sub_batch = self.batch if non_tensor_batch_keys is not None: non_tensor_batch = {key: val for key, val in self.non_tensor_batch.items() if key in non_tensor_batch_keys} else: non_tensor_batch = self.non_tensor_batch if deepcopy: non_tensor_batch = copy.deepcopy(non_tensor_batch) if meta_info_keys is not None: sub_meta_info = {key: val for key, val in self.meta_info.items() if key in meta_info_keys} else: sub_meta_info = self.meta_info if deepcopy: sub_meta_info = copy.deepcopy(sub_meta_info) return DataProto(batch=sub_batch, non_tensor_batch=non_tensor_batch, meta_info=sub_meta_info) def pop(self, batch_keys=None, non_tensor_batch_keys=None, meta_info_keys=None) -> 'DataProto': """Pop a subset of the DataProto via `batch_keys` and `meta_info_keys` Args: batch_keys (list, optional): a list of strings indicating the keys in batch to pop meta_info_keys (list, optional): a list of keys indicating the meta info to pop Returns: DataProto: the DataProto with the poped batch_keys and meta_info_keys """ assert batch_keys is not None if meta_info_keys is None: meta_info_keys = [] if non_tensor_batch_keys is None: non_tensor_batch_keys = [] tensors = {} # tensor batch for key in batch_keys: assert key in self.batch.keys() tensors[key] = self.batch.pop(key) non_tensors = {} # non tensor batch for key in non_tensor_batch_keys: assert key in self.non_tensor_batch.keys() non_tensors[key] = self.non_tensor_batch.pop(key) meta_info = {} for key in meta_info_keys: assert key in self.meta_info.keys() meta_info[key] = self.meta_info.pop(key) return DataProto.from_dict(tensors=tensors, non_tensors=non_tensors, meta_info=meta_info) def rename(self, old_keys=None, new_keys=None) -> 'DataProto': """ Note that this function only rename the key in the batch """ def validate_input(keys): if keys is not None: if isinstance(keys, str): keys = [keys] elif isinstance(keys, list): pass else: raise TypeError(f'keys must be a list or a string, but got {type(keys)}') return keys old_keys = validate_input(old_keys) new_keys = validate_input(new_keys) if len(new_keys) != len(old_keys): raise ValueError( f'new_keys and old_keys must have the same length, but got {len(new_keys)} and {len(old_keys)}') self.batch.rename_key_(tuple(old_keys), tuple(new_keys)) return self def union(self, other: 'DataProto') -> 'DataProto': """Union with another DataProto. Union batch and meta_info separately. Throw an error if - there are conflict keys in batch and they are not equal - the batch size of two data batch is not the same - there are conflict keys in meta_info and they are not the same. Args: other (DataProto): another DataProto to union Returns: DataProto: the DataProto after union """ self.batch = union_tensor_dict(self.batch, other.batch) self.non_tensor_batch = union_numpy_dict(self.non_tensor_batch, other.non_tensor_batch) self.meta_info = union_two_dict(self.meta_info, other.meta_info) return self def make_iterator(self, mini_batch_size, epochs, seed=None, dataloader_kwargs=None): r"""Make an iterator from the DataProto. This is built upon that TensorDict can be used as a normal Pytorch dataset. See https://pytorch.org/tensordict/tutorials/data_fashion for more details. Args: mini_batch_size (int): mini-batch size when iterating the dataset. We require that ``batch.batch_size[0] % mini_batch_size == 0``. epochs (int): number of epochs when iterating the dataset. dataloader_kwargs (Any): internally, it returns a DataLoader over the batch. The dataloader_kwargs is the kwargs passed to the DataLoader. Returns: Iterator: an iterator that yields a mini-batch data at a time. The total number of iteration steps is ``self.batch.batch_size * epochs // mini_batch_size`` """ assert self.batch.batch_size[0] % mini_batch_size == 0, f"{self.batch.batch_size[0]} % {mini_batch_size} != 0" # we can directly create a dataloader from TensorDict if dataloader_kwargs is None: dataloader_kwargs = {} if seed is not None: generator = torch.Generator() generator.manual_seed(seed) else: generator = None assert isinstance(dataloader_kwargs, Dict) train_dataloader = DataLoader(dataset=self, batch_size=mini_batch_size, collate_fn=collate_fn, generator=generator, **dataloader_kwargs) def get_data(): for _ in range(epochs): for d in train_dataloader: d.meta_info = self.meta_info yield d return iter(get_data()) def chunk(self, chunks: int) -> List['DataProto']: """Split the batch among dim=0 into chunks. The meta_info is passed to each DataProto after split. Args: chunks (int): the number of chunks to split on dim=0 Returns: List[DataProto]: a list of DataProto after splitting """ assert len( self) % chunks == 0, f'only support equal chunk. Got size of DataProto {len(self)} and chunk {chunks}.' if self.batch is not None: batch_lst = self.batch.chunk(chunks=chunks, dim=0) else: batch_lst = [None for _ in range(chunks)] non_tensor_batch_lst = [{} for _ in range(chunks)] for key, val in self.non_tensor_batch.items(): assert isinstance(val, np.ndarray) non_tensor_lst = np.array_split(val, chunks) assert len(non_tensor_lst) == chunks for i in range(chunks): non_tensor_batch_lst[i][key] = non_tensor_lst[i] output = [] for i in range(chunks): output.append( DataProto(batch=batch_lst[i], non_tensor_batch=non_tensor_batch_lst[i], meta_info=self.meta_info)) return output @staticmethod def concat(data: List['DataProto']) -> 'DataProto': """Concat a list of DataProto. The batch is concatenated among dim=0. The meta_info is assumed to be identical and will use the first one. Args: data (List[DataProto]): list of DataProto Returns: DataProto: concatenated DataProto """ batch_lst = [] for batch in data: batch_lst.append(batch.batch) if batch_lst[0] is not None: new_batch = torch.cat(batch_lst, dim=0) else: new_batch = None non_tensor_batch = list_of_dict_to_dict_of_list(list_of_dict=[d.non_tensor_batch for d in data]) for key, val in non_tensor_batch.items(): non_tensor_batch[key] = np.concatenate(val, axis=0) return DataProto(batch=new_batch, non_tensor_batch=non_tensor_batch, meta_info=data[0].meta_info) def reorder(self, indices): """ Note that this operation is in-place """ indices_np = indices.detach().numpy() self.batch = self.batch[indices] self.non_tensor_batch = {key: val[indices_np] for key, val in self.non_tensor_batch.items()} def repeat(self, repeat_times=2, interleave=True): """ Repeat the batch data a specified number of times. Args: repeat_times (int): Number of times to repeat the data. interleave (bool): Whether to interleave the repeated data. Returns: DataProto: A new DataProto with repeated data. """ if self.batch is not None: if interleave: # Interleave the data repeated_tensors = { key: tensor.repeat_interleave(repeat_times, dim=0) for key, tensor in self.batch.items() } else: # Stack the data repeated_tensors = { key: tensor.unsqueeze(0).expand(repeat_times, *tensor.shape).reshape(-1, *tensor.shape[1:]) for key, tensor in self.batch.items() } repeated_batch = TensorDict( source=repeated_tensors, batch_size=(self.batch.batch_size[0] * repeat_times,), ) else: repeated_batch = None repeated_non_tensor_batch = {} for key, val in self.non_tensor_batch.items(): if interleave: repeated_non_tensor_batch[key] = np.repeat(val, repeat_times, axis=0) else: repeated_non_tensor_batch[key] = np.tile(val, (repeat_times,) + (1,) * (val.ndim - 1)) return DataProto( batch=repeated_batch, non_tensor_batch=repeated_non_tensor_batch, meta_info=self.meta_info, ) import ray @dataclass class DataProtoFuture: """ DataProtoFuture aims to eliminate actual data fetching on driver. By doing so, the driver doesn't have to wait for data so that asynchronous execution becomes possible. DataProtoFuture contains a list of futures from another WorkerGroup of size world_size. - collect_fn is a Callable that reduces the list of futures to a DataProto - dispatch_fn is a Callable that partitions the DataProto into a list of DataProto of size world_size and then select Potential issue: we can optimize dispatch_fn(collect_fn) such that only needed data is fetched on destination - DataProtoFuture only supports directly passing from the output of a method to another input. You can't perform any operation on the DataProtoFuture in driver. """ collect_fn: Callable futures: List[ray.ObjectRef] dispatch_fn: Callable = None @staticmethod def concat(data: List[ray.ObjectRef]) -> 'DataProtoFuture': output = DataProtoFuture(collect_fn=DataProto.concat, futures=data) return output def chunk(self, chunks: int) -> List['DataProtoFuture']: from functools import partial arg_future_lst = [] for i in range(chunks): # note that we can't directly pass i and chunks def dispatch_fn(x, i, chunks): return x.chunk(chunks=chunks)[i] arg_future = DataProtoFuture(collect_fn=self.collect_fn, dispatch_fn=partial(dispatch_fn, i=i, chunks=chunks), futures=self.futures) arg_future_lst.append(arg_future) return arg_future_lst def get(self): output = ray.get(self.futures) # dp_size. for o in output: assert isinstance(o, DataProto) output = self.collect_fn(output) # select dp, concat if self.dispatch_fn is not None: output = self.dispatch_fn(output) # split in batch dim, select using dp return output ================================================ FILE: verl/single_controller/__init__.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os version_folder = os.path.dirname(os.path.join(os.path.abspath(__file__))) # Note(haibin.lin): single_controller.__version__ is deprecated with open(os.path.join(os.path.join(version_folder, os.pardir), 'version/version')) as f: __version__ = f.read().strip() from . import base from .base import * __all__ = base.__all__ ================================================ FILE: verl/single_controller/base/__init__.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from .worker import Worker from .worker_group import WorkerGroup, ClassWithInitArgs, ResourcePool __all__ = ['Worker', 'WorkerGroup', 'ClassWithInitArgs', 'ResourcePool'] ================================================ FILE: verl/single_controller/base/decorator.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from enum import Enum from functools import wraps from typing import Dict, List, Tuple from types import FunctionType from verl.protocol import DataProtoFuture # here we add a magic number of avoid user-defined function already have this attribute MAGIC_ATTR = 'attrs_3141562937' class Dispatch(Enum): RANK_ZERO = 0 ONE_TO_ALL = 1 ALL_TO_ALL = 2 MEGATRON_COMPUTE = 3 MEGATRON_PP_AS_DP = 4 MEGATRON_PP_ONLY = 5 MEGATRON_COMPUTE_PROTO = 6 MEGATRON_PP_AS_DP_PROTO = 7 DP_COMPUTE = 8 DP_COMPUTE_PROTO = 9 DP_COMPUTE_PROTO_WITH_FUNC = 10 DP_COMPUTE_METRIC = 11 class Execute(Enum): ALL = 0 RANK_ZERO = 1 def _split_args_kwargs_data_proto(chunks, *args, **kwargs): from verl.protocol import DataProto, DataProtoFuture splitted_args = [] for arg in args: assert isinstance(arg, (DataProto, DataProtoFuture)) splitted_args.append(arg.chunk(chunks=chunks)) splitted_kwargs = {} for key, val in kwargs.items(): assert isinstance(val, (DataProto, DataProtoFuture)) splitted_kwargs[key] = val.chunk(chunks=chunks) return splitted_args, splitted_kwargs def dispatch_one_to_all(worker_group, *args, **kwargs): args = tuple([arg] * worker_group.world_size for arg in args) kwargs = {k: [v] * worker_group.world_size for k, v in kwargs.items()} return args, kwargs def dispatch_all_to_all(worker_group, *args, **kwargs): return args, kwargs def collect_all_to_all(worker_group, output): return output def dispatch_megatron_compute(worker_group, *args, **kwargs): """ User passes in dp data. The data is dispatched to all tp/pp ranks with the same dp """ from verl.single_controller.base.megatron.worker_group import MegatronWorkerGroup assert isinstance(worker_group, MegatronWorkerGroup), f'worker_group must be MegatronWorkerGroup, Got {type(worker_group)}' all_args = [] for arg in args: assert isinstance(arg, (Tuple, List)) and len(arg) == worker_group.dp_size transformed_args = [] for i in range(worker_group.world_size): local_dp_rank = worker_group.get_megatron_rank_info(rank=i).dp_rank transformed_args.append(arg[local_dp_rank]) all_args.append(transformed_args) all_args = tuple(all_args) all_kwargs = {} for k, v in kwargs.items(): assert isinstance(v, (Tuple, List)) and len(v) == worker_group.dp_size transformed_v = [] for i in range(worker_group.world_size): local_dp_rank = worker_group.get_megatron_rank_info(rank=i).dp_rank transformed_v.append(v[local_dp_rank]) all_kwargs[k] = transformed_v return all_args, all_kwargs def collect_megatron_compute(worker_group, output): """ Only collect the data from the tp=0 and pp=last and every dp ranks """ from verl.single_controller.base.megatron.worker_group import MegatronWorkerGroup assert isinstance(worker_group, MegatronWorkerGroup) output_in_dp = [] pp_size = worker_group.get_megatron_global_info().pp_size for global_rank in range(worker_group.world_size): local_rank_info = worker_group.get_megatron_rank_info(rank=global_rank) if local_rank_info.tp_rank == 0 and local_rank_info.pp_rank == pp_size - 1: output_in_dp.append(output[global_rank]) return output_in_dp def dispatch_megatron_compute_data_proto(worker_group, *args, **kwargs): """ All the args and kwargs must be DataProto. The batch will be chunked by dp_size and passed to each rank """ from verl.single_controller.base.megatron.worker_group import MegatronWorkerGroup assert isinstance(worker_group, MegatronWorkerGroup) splitted_args, splitted_kwargs = _split_args_kwargs_data_proto(worker_group.dp_size, *args, **kwargs) return dispatch_megatron_compute(worker_group, *splitted_args, **splitted_kwargs) def _concat_data_proto_or_future(output: List): from verl.protocol import DataProto, DataProtoFuture import ray # make sure all the elements in output has the same type for o in output: assert type(o) == type(output[0]) o = output[0] if isinstance(o, DataProto): return DataProto.concat(output) elif isinstance(o, ray.ObjectRef): return DataProtoFuture.concat(output) else: raise NotImplementedError def collect_megatron_compute_data_proto(worker_group, output): """ Each output must be a DataProto. We concat the dim=0 of output """ from verl.protocol import DataProto import ray output = collect_megatron_compute(worker_group, output) for o in output: assert isinstance(o, (DataProto, ray.ObjectRef)), f"expecting {o} to be DataProto, but got {type(o)}" return _concat_data_proto_or_future(output) def dispatch_megatron_pp_as_dp(worker_group, *args, **kwargs): """ treat pp as dp. """ from verl.single_controller.base.megatron.worker_group import MegatronWorkerGroup assert isinstance(worker_group, MegatronWorkerGroup) pp_size = worker_group.pp_size dp_size = worker_group.dp_size pp_dp_size = pp_size * dp_size all_args = [] for arg in args: assert isinstance(arg, (List, Tuple)) and len(arg) == pp_dp_size transformed_args = [] for i in range(worker_group.world_size): local_dp_rank = worker_group.get_megatron_rank_info(rank=i).dp_rank local_pp_rank = worker_group.get_megatron_rank_info(rank=i).pp_rank # compute the rank in arg. Note that the order is dp then pp # Also note that the outputs within a pp group will be firstly allgathered, then only the output of pp0 will be collected. # For pp=2 dp=4, a batch of data "ABCDEFGH" should be dispatched and collected in below order: # dispatch: pp_allgther: collect: # dp 0 1 2 3 dp 0 1 2 3 # pp +---------+ pp +-------------+ # 0 | A C E G | 0 | AB CD EF GH | ABCDEFGH # 1 | B D F H | 1 | AB CD EF GH | # +---------+ +-------------+ arg_rank = local_dp_rank * worker_group.pp_size + local_pp_rank transformed_args.append(arg[arg_rank]) all_args.append(transformed_args) all_args = tuple(all_args) all_kwargs = {} for k, v in kwargs.items(): assert isinstance(v, (List, Tuple)) and len(v) == pp_dp_size, f'expect len(v)=={pp_dp_size}, got {len(v)}' transformed_v = [] for i in range(worker_group.world_size): local_dp_rank = worker_group.get_megatron_rank_info(rank=i).dp_rank local_pp_rank = worker_group.get_megatron_rank_info(rank=i).pp_rank # compute the rank in arg. Note that the order is dp then pp arg_rank = local_dp_rank * worker_group.pp_size + local_pp_rank transformed_v.append(v[arg_rank]) all_kwargs[k] = transformed_v return all_args, all_kwargs def collect_megatron_pp_as_dp(worker_group, output): """ treat pp as dp. Only collect data on tp=0 """ from verl.single_controller.base.megatron.worker_group import MegatronWorkerGroup assert isinstance(worker_group, MegatronWorkerGroup) output_in_dp = [] for global_rank in range(worker_group.world_size): local_rank_info = worker_group.get_megatron_rank_info(rank=global_rank) if local_rank_info.tp_rank == 0 and local_rank_info.pp_rank == 0: output_in_dp.append(output[global_rank]) return output_in_dp def collect_megatron_pp_only(worker_group, output): """ Only collect output of megatron pp. This is useful when examine weight names as they are identical in tp/dp """ from verl.single_controller.base.megatron.worker_group import MegatronWorkerGroup assert isinstance(worker_group, MegatronWorkerGroup) output_in_pp = [] for global_rank in range(worker_group.world_size): local_rank_info = worker_group.get_megatron_rank_info(rank=global_rank) if local_rank_info.tp_rank == 0 and local_rank_info.dp_rank == 0: output_in_pp.append(output[global_rank]) return output_in_pp def dispatch_megatron_pp_as_dp_data_proto(worker_group, *args, **kwargs): from verl.single_controller.base.megatron.worker_group import MegatronWorkerGroup assert isinstance(worker_group, MegatronWorkerGroup) pp_dp_size = worker_group.dp_size * worker_group.pp_size splitted_args, splitted_kwargs = _split_args_kwargs_data_proto(pp_dp_size, *args, **kwargs) return dispatch_megatron_pp_as_dp(worker_group, *splitted_args, **splitted_kwargs) def collect_megatron_pp_as_dp_data_proto(worker_group, output): from verl.protocol import DataProto from verl.single_controller.base.megatron.worker_group import MegatronWorkerGroup assert isinstance(worker_group, MegatronWorkerGroup) output = collect_megatron_pp_as_dp(worker_group, output) return _concat_data_proto_or_future(output) def dispatch_dp_compute(worker_group, *args, **kwargs): from verl.single_controller.base.worker_group import WorkerGroup assert isinstance(worker_group, WorkerGroup) for arg in args: assert isinstance(arg, (Tuple, List)) and len(arg) == worker_group.world_size for k, v in kwargs.items(): assert isinstance(v, (Tuple, List)) and len(v) == worker_group.world_size return args, kwargs def collect_dp_compute(worker_group, output): from verl.single_controller.base.worker_group import WorkerGroup assert isinstance(worker_group, WorkerGroup) assert len(output) == worker_group.world_size return output def dispatch_dp_compute_data_proto(worker_group, *args, **kwargs): from verl.single_controller.base.worker_group import WorkerGroup assert isinstance(worker_group, WorkerGroup) splitted_args, splitted_kwargs = _split_args_kwargs_data_proto(worker_group.world_size, *args, **kwargs) return splitted_args, splitted_kwargs def dispatch_dp_compute_data_proto_with_func(worker_group, *args, **kwargs): from verl.single_controller.base.worker_group import WorkerGroup assert isinstance(worker_group, WorkerGroup) assert type(args[0]) == FunctionType # NOTE: The first one args is a function! splitted_args, splitted_kwargs = _split_args_kwargs_data_proto(worker_group.world_size, *args[1:], **kwargs) splitted_args_with_func = [[args[0]] * worker_group.world_size] + splitted_args return splitted_args_with_func, splitted_kwargs def collect_dp_compute_data_proto(worker_group, output): from verl.protocol import DataProto import ray for o in output: assert isinstance(o, (DataProto, ray.ObjectRef)), f"expecting {o} to be DataProto, but got {type(o)}" output = collect_dp_compute(worker_group, output) return _concat_data_proto_or_future(output) def get_predefined_dispatch_fn(dispatch_mode): predefined_dispatch_mode_fn = { Dispatch.ONE_TO_ALL: { 'dispatch_fn': dispatch_one_to_all, 'collect_fn': collect_all_to_all, }, Dispatch.ALL_TO_ALL: { 'dispatch_fn': dispatch_all_to_all, 'collect_fn': collect_all_to_all, }, Dispatch.MEGATRON_COMPUTE: { 'dispatch_fn': dispatch_megatron_compute, 'collect_fn': collect_megatron_compute, }, Dispatch.MEGATRON_PP_AS_DP: { 'dispatch_fn': dispatch_megatron_pp_as_dp, 'collect_fn': collect_megatron_pp_as_dp, }, Dispatch.MEGATRON_PP_ONLY: { 'dispatch_fn': dispatch_one_to_all, 'collect_fn': collect_megatron_pp_only }, Dispatch.MEGATRON_COMPUTE_PROTO: { 'dispatch_fn': dispatch_megatron_compute_data_proto, 'collect_fn': collect_megatron_compute_data_proto }, Dispatch.MEGATRON_PP_AS_DP_PROTO: { 'dispatch_fn': dispatch_megatron_pp_as_dp_data_proto, 'collect_fn': collect_megatron_pp_as_dp_data_proto }, Dispatch.DP_COMPUTE: { 'dispatch_fn': dispatch_dp_compute, 'collect_fn': collect_dp_compute }, Dispatch.DP_COMPUTE_PROTO: { 'dispatch_fn': dispatch_dp_compute_data_proto, 'collect_fn': collect_dp_compute_data_proto }, Dispatch.DP_COMPUTE_PROTO_WITH_FUNC: { 'dispatch_fn': dispatch_dp_compute_data_proto_with_func, 'collect_fn': collect_dp_compute_data_proto }, Dispatch.DP_COMPUTE_METRIC: { 'dispatch_fn': dispatch_dp_compute_data_proto, 'collect_fn': collect_dp_compute } } return predefined_dispatch_mode_fn[dispatch_mode] def get_predefined_execute_fn(execute_mode): """ Note that here we only asks execute_all and execute_rank_zero to be implemented Leave the choice of how these two functions handle argument 'blocking' to users """ predefined_execute_mode_fn = { Execute.ALL: { 'execute_fn_name': 'execute_all' }, Execute.RANK_ZERO: { 'execute_fn_name': 'execute_rank_zero' } } return predefined_execute_mode_fn[execute_mode] def _check_dispatch_mode(dispatch_mode): assert isinstance(dispatch_mode, (Dispatch, Dict)), f'dispatch_mode must be a Dispatch or a Dict. Got {dispatch_mode}' if isinstance(dispatch_mode, Dict): necessary_keys = ['dispatch_fn', 'collect_fn'] for key in necessary_keys: assert key in dispatch_mode, f'key {key} should be in dispatch_mode if it is a dictionary' def _check_execute_mode(execute_mode): assert isinstance(execute_mode, Execute), f'execute_mode must be a Execute. Got {execute_mode}' def _materialize_futures(*args, **kwargs): new_args = [] for arg in args: if isinstance(arg, DataProtoFuture): arg = arg.get() # add more type to materialize new_args.append(arg) for k, v in kwargs.items(): if isinstance(v, DataProtoFuture): kwargs[k] = v.get() new_args = tuple(new_args) return new_args, kwargs def register(dispatch_mode=Dispatch.ALL_TO_ALL, execute_mode=Execute.ALL, blocking=True, materialize_futures=True): _check_dispatch_mode(dispatch_mode=dispatch_mode) _check_execute_mode(execute_mode=execute_mode) def decorator(func): @wraps(func) def inner(*args, **kwargs): if materialize_futures: args, kwargs = _materialize_futures(*args, **kwargs) return func(*args, **kwargs) attrs = {'dispatch_mode': dispatch_mode, 'execute_mode': execute_mode, 'blocking': blocking} setattr(inner, MAGIC_ATTR, attrs) return inner return decorator ================================================ FILE: verl/single_controller/base/megatron/__init__.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. ================================================ FILE: verl/single_controller/base/megatron/worker.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from verl.single_controller.base.worker import Worker, DistRankInfo, DistGlobalInfo class MegatronWorker(Worker): def __init__(self, cuda_visible_devices=None) -> None: super().__init__(cuda_visible_devices) def get_megatron_global_info(self): from megatron.core import parallel_state as mpu tp_size = mpu.get_tensor_model_parallel_world_size() dp_size = mpu.get_data_parallel_world_size() pp_size = mpu.get_pipeline_model_parallel_world_size() info = DistGlobalInfo(tp_size=tp_size, dp_size=dp_size, pp_size=pp_size) return info def get_megatron_rank_info(self): from megatron.core import parallel_state as mpu tp_rank = mpu.get_tensor_model_parallel_rank() dp_rank = mpu.get_data_parallel_rank() pp_rank = mpu.get_pipeline_model_parallel_rank() info = DistRankInfo(tp_rank=tp_rank, dp_rank=dp_rank, pp_rank=pp_rank) return info ================================================ FILE: verl/single_controller/base/megatron/worker_group.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import Dict from .worker import DistRankInfo, DistGlobalInfo from verl.single_controller.base import ResourcePool, WorkerGroup class MegatronWorkerGroup(WorkerGroup): def __init__(self, resource_pool: ResourcePool, **kwargs): super().__init__(resource_pool=resource_pool, **kwargs) self._megatron_rank_info = None self._megatron_global_info: DistGlobalInfo = None def init_megatron(self, default_megatron_kwargs: Dict = None): raise NotImplementedError(f"MegatronWorkerGroup.init_megatron should be overwritten") def get_megatron_rank_info(self, rank: int) -> DistRankInfo: assert 0 <= rank < self.world_size, f'rank must be from [0, world_size), Got {rank}' return self._megatron_rank_info[rank] @property def tp_size(self): assert self._megatron_global_info is not None, "MegatronWorkerGroup._megatron_global_info must be initialized" return self._megatron_global_info.tp_size @property def dp_size(self): assert self._megatron_global_info is not None, "MegatronWorkerGroup._megatron_global_info must be initialized" return self._megatron_global_info.dp_size @property def pp_size(self): assert self._megatron_global_info is not None, "MegatronWorkerGroup._megatron_global_info must be initialized" return self._megatron_global_info.pp_size def get_megatron_global_info(self): return self._megatron_global_info ================================================ FILE: verl/single_controller/base/register_center/__init__.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. ================================================ FILE: verl/single_controller/base/register_center/ray.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import ray @ray.remote class WorkerGroupRegisterCenter: def __init__(self, rank_zero_info): self.rank_zero_info = rank_zero_info def get_rank_zero_info(self): return self.rank_zero_info def create_worker_group_register_center(name, info): return WorkerGroupRegisterCenter.options(name=name).remote(info) ================================================ FILE: verl/single_controller/base/worker.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ the class for Worker """ import os import socket from dataclasses import dataclass from .decorator import register, Dispatch, Execute @dataclass class DistRankInfo: tp_rank: int dp_rank: int pp_rank: int @dataclass class DistGlobalInfo: tp_size: int dp_size: int pp_size: int class WorkerHelper: def _get_node_ip(self): def get_node_ip_by_sdk(): if os.getenv("WG_BACKEND", None) == "ray": import ray return ray._private.services.get_node_ip_address() else: raise NotImplementedError("WG_BACKEND now just support ray mode.") host_ipv4 = os.getenv("MY_HOST_IP", None) host_ipv6 = os.getenv("MY_HOST_IPV6", None) host_ip_by_env = host_ipv4 or host_ipv6 host_ip_by_sdk = get_node_ip_by_sdk() host_ip = host_ip_by_env or host_ip_by_sdk return host_ip def _get_free_port(self): with socket.socket() as sock: sock.bind(('', 0)) return sock.getsockname()[1] def get_availale_master_addr_port(self): return self._get_node_ip(), str(self._get_free_port()) def _get_pid(self): return class WorkerMeta: keys = [ "WORLD_SIZE", "RANK", "LOCAL_WORLD_SIZE", "LOCAL_RANK", "MASTER_ADDR", "MASTER_PORT", "CUDA_VISIBLE_DEVICES" ] def __init__(self, store) -> None: self._store = store def to_dict(self): return {f"_{key.lower()}": self._store.get(f"_{key.lower()}", None) for key in WorkerMeta.keys} # we assume that in each WorkerGroup, there is a Master Worker class Worker(WorkerHelper): """A (distributed) worker.""" def __new__(cls, *args, **kwargs): instance = super().__new__(cls) # note that here we use int to distinguish disable_worker_init = int(os.environ.get('DISABLE_WORKER_INIT', 0)) if disable_worker_init: return instance rank = os.environ.get("RANK", None) worker_group_prefix = os.environ.get("WG_PREFIX", None) # when decorator @ray.remote applies, __new__ will be called while we don't want to apply _configure_before_init if None not in [rank, worker_group_prefix] and 'ActorClass(' not in cls.__name__: instance._configure_before_init(f"{worker_group_prefix}_register_center", int(rank)) return instance def _configure_before_init(self, register_center_name: str, rank: int): assert isinstance(rank, int), f"rank must be int, instead of {type(rank)}" if rank == 0: master_addr, master_port = self.get_availale_master_addr_port() rank_zero_info = { "MASTER_ADDR": master_addr, "MASTER_PORT": master_port, } if os.getenv("WG_BACKEND", None) == "ray": from verl.single_controller.base.register_center.ray import create_worker_group_register_center self.register_center = create_worker_group_register_center(name=register_center_name, info=rank_zero_info) os.environ.update(rank_zero_info) def __init__(self, cuda_visible_devices=None) -> None: # construct a meta from envrionment variable. Note that the import must be inside the class because it is executed remotely import os world_size = int(os.environ['WORLD_SIZE']) rank = int(os.environ['RANK']) self._rank = rank self._world_size = world_size master_addr = os.environ["MASTER_ADDR"] master_port = os.environ["MASTER_PORT"] local_world_size = int(os.getenv("LOCAL_WORLD_SIZE", "1")) local_rank = int(os.getenv("LOCAL_RANK", "0")) store = { '_world_size': world_size, '_rank': rank, '_local_world_size': local_world_size, '_local_rank': local_rank, '_master_addr': master_addr, '_master_port': master_port } if cuda_visible_devices is not None: store['_cuda_visible_devices'] = cuda_visible_devices meta = WorkerMeta(store=store) self._configure_with_meta(meta=meta) def _configure_with_meta(self, meta: WorkerMeta): """ This function should only be called inside by WorkerGroup """ assert isinstance(meta, WorkerMeta) self.__dict__.update(meta.to_dict()) # this is hacky # print(f"__dict__: {self.__dict__}") for key in WorkerMeta.keys: val = self.__dict__.get(f"_{key.lower()}", None) if val is not None: # print(f"set {key} to {val}") os.environ[key] = str(val) os.environ["REDIS_STORE_SERVER_HOST"] = str(self._master_addr).replace("[", "").replace( "]", "") if self._master_addr else "" def get_master_addr_port(self): return self._master_addr, self._master_port def get_cuda_visible_devices(self): import os cuda_visible_devices = os.environ.get("CUDA_VISIBLE_DEVICES", "not set") return cuda_visible_devices @property def world_size(self): return self._world_size @property def rank(self): return self._rank @register(dispatch_mode=Dispatch.DP_COMPUTE_PROTO_WITH_FUNC) def execute_with_func_generator(self, func, *args, **kwargs): ret_proto = func(self, *args, **kwargs) return ret_proto @register(dispatch_mode=Dispatch.ALL_TO_ALL, execute_mode=Execute.RANK_ZERO) def execute_func_rank_zero(self, func, *args, **kwargs): result = func(*args, **kwargs) return result ================================================ FILE: verl/single_controller/base/worker_group.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ the class of WorkerGroup """ import logging import threading import signal import time from typing import List, Any, Callable, Dict from .decorator import MAGIC_ATTR, Dispatch, get_predefined_dispatch_fn, get_predefined_execute_fn class ResourcePool: """The resource pool with meta info such as world_size.""" def __init__(self, process_on_nodes=None, max_collocate_count: int = 10, n_gpus_per_node=8) -> None: if process_on_nodes is None: process_on_nodes = [] self._store = process_on_nodes self.max_collocate_count = max_collocate_count self.n_gpus_per_node = n_gpus_per_node # this is left for future huawei GPU that contains 16 GPUs per node def add_node(self, process_count): self._store.append(process_count) @property def world_size(self): return sum(self._store) def __call__(self) -> Any: return self._store @property def store(self): return self._store def local_world_size_list(self) -> List[int]: nested_local_world_size_list = [ [local_world_size for _ in range(local_world_size)] for local_world_size in self._store ] return [item for row in nested_local_world_size_list for item in row] def local_rank_list(self) -> List[int]: nested_local_rank_list = [[i for i in range(local_world_size)] for local_world_size in self._store] return [item for row in nested_local_rank_list for item in row] class ClassWithInitArgs: """ This class stores a class constructor and the args/kwargs to construct the class. It is used to instantiate the remote class. """ def __init__(self, cls, *args, **kwargs) -> None: self.cls = cls self.args = args self.kwargs = kwargs # def add_arg(self, arg): # self.args += (arg,) # def add_kwarg(self, key, value): # self.kwargs[key] = value def __call__(self) -> Any: return self.cls(*self.args, **self.kwargs) def check_workers_alive(workers: List, is_alive: Callable, gap_time: float = 1) -> None: import time while True: for worker in workers: if not is_alive(worker): logging.warning(f"worker {worker} is not alive" + " sending signal to main thread") signal.raise_signal(signal.SIGABRT) time.sleep(gap_time) class WorkerGroup: """A group of workers""" def __init__(self, resource_pool: ResourcePool, **kwargs) -> None: self._is_init_with_detached_workers = True if resource_pool is None else False if resource_pool is not None: # handle the case when WorkGroup is attached to an existing one self._procecss_dispatch_config = resource_pool() else: self._procecss_dispatch_config = None self._workers = [] self._worker_names = [] self._master_addr = None self._master_port = None self._checker_thread: threading.Thread = None def _is_worker_alive(self, worker): raise NotImplementedError(f"WorkerGroup._is_worker_alive called, should be implemented in derived class.") def _block_until_all_workers_alive(self) -> None: while True: all_state = [self._is_worker_alive(worker) for worker in self._workers] if False in all_state: time.sleep(1) else: break def start_worker_aliveness_check(self, every_n_seconds=1) -> None: # before starting checking worker aliveness, make sure all workers are already alive self._block_until_all_workers_alive() self._checker_thread = threading.Thread(target=check_workers_alive, args=(self._workers, self._is_worker_alive, every_n_seconds)) self._checker_thread.start() @property def world_size(self): return len(self._workers) # execute_all_async and execute_rank_zero_async should be implemented by RayWorkerGroup, TorchRPCWorkerGroup, # MegatronWorkerGroup, XperfWorkerGroup should skip def _bind_worker_method(self, user_defined_cls, func_generator): """ Bind the worker method to the WorkerGroup """ for method_name in dir(user_defined_cls): try: method = getattr(user_defined_cls, method_name) assert callable(method), f"{method_name} in {user_defined_cls} is not callable" except Exception as e: # if it is a property, it will fail because Class doesn't have instance property continue if hasattr(method, MAGIC_ATTR): # this method is decorated by register attribute = getattr(method, MAGIC_ATTR) assert isinstance(attribute, Dict), f'attribute must be a dictionary. Got {type(attribute)}' assert 'dispatch_mode' in attribute, f'attribute must contain dispatch_mode in its key' dispatch_mode = attribute['dispatch_mode'] execute_mode = attribute['execute_mode'] blocking = attribute['blocking'] # get dispatch fn if isinstance(dispatch_mode, Dispatch): # get default dispatch fn fn = get_predefined_dispatch_fn(dispatch_mode=dispatch_mode) dispatch_fn = fn['dispatch_fn'] collect_fn = fn['collect_fn'] else: assert isinstance(dispatch_mode, dict) assert 'dispatch_fn' in dispatch_mode assert 'collect_fn' in dispatch_mode dispatch_fn = dispatch_mode['dispatch_fn'] collect_fn = dispatch_mode['collect_fn'] # get execute_fn_name execute_mode = get_predefined_execute_fn(execute_mode=execute_mode) wg_execute_fn_name = execute_mode['execute_fn_name'] # get execute_fn from string try: execute_fn = getattr(self, wg_execute_fn_name) assert callable(execute_fn), 'execute_fn must be callable' except Exception as e: print(f'execute_fn {wg_execute_fn_name} is invalid') raise # bind a new method to the RayWorkerGroup func = func_generator(self, method_name, dispatch_fn=dispatch_fn, collect_fn=collect_fn, execute_fn=execute_fn, blocking=blocking) try: setattr(self, method_name, func) except Exception as e: raise ValueError(f'Fail to set method_name {method_name}') ================================================ FILE: verl/single_controller/ray/__init__.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from .base import RayResourcePool, RayClassWithInitArgs, RayWorkerGroup, create_colocated_worker_cls ================================================ FILE: verl/single_controller/ray/base.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import time from typing import Dict, List, Any, Tuple import ray from ray.util import list_named_actors from ray.util.placement_group import placement_group, PlacementGroup from ray.util.scheduling_strategies import PlacementGroupSchedulingStrategy, NodeAffinitySchedulingStrategy from ray.experimental.state.api import get_actor from verl.single_controller.base import WorkerGroup, ResourcePool, ClassWithInitArgs, Worker __all__ = ['Worker'] def get_random_string(length: int) -> str: import random import string letters_digits = string.ascii_letters + string.digits return ''.join(random.choice(letters_digits) for _ in range(length)) def func_generator(self, method_name, dispatch_fn, collect_fn, execute_fn, blocking): def func(*args, **kwargs): args, kwargs = dispatch_fn(self, *args, **kwargs) output = execute_fn(method_name, *args, **kwargs) if blocking: output = ray.get(output) output = collect_fn(self, output) return output return func class RayResourcePool(ResourcePool): def __init__(self, process_on_nodes: List[int] = None, use_gpu: bool = True, name_prefix: str = "", max_colocate_count: int = 5, detached=False) -> None: super().__init__(process_on_nodes, max_colocate_count) self.use_gpu = use_gpu # print(f"in RayProcessDispatchConfiguration: name_prefix = {name_prefix}") self.name_prefix = name_prefix self.pgs = None self.detached = detached def get_placement_groups(self, strategy="STRICT_PACK", name=None): if self.pgs is not None: return self.pgs pg_name_prefix = name if name else \ f"{self.name_prefix}verl_group_{'_'.join([str(count) for count in self._store])}:" # print(f"pg_name_prefix = {pg_name_prefix}") pg_scheme = [[{ "CPU": self.max_collocate_count, "GPU": 1 } if self.use_gpu else { "CPU": self.max_collocate_count } for _ in range(process_count)] for process_count in self._store] lifetime = 'detached' if self.detached else None pgs = [ placement_group(bundles=bundles, strategy=strategy, name=pg_name_prefix + str(idx), lifetime=lifetime) for idx, bundles in enumerate(pg_scheme) ] ray.get([pg.ready() for pg in pgs]) self.pgs = pgs return pgs def extract_pg_from_exist(resource_pools: Dict[str, RayResourcePool], src_role_names: List[str], resource_pool: RayResourcePool) -> List: src_pgs = [ pg for role_name, resource_pool in resource_pools.items() for pg in resource_pool.get_placement_groups() if role_name in src_role_names ] sorted_src_pgs = sorted(src_pgs, key=lambda pg: pg.bundle_count, reverse=True) sorted_process_on_nodes = sorted([(val, idx) for idx, val in enumerate(resource_pool.store)], reverse=True) unsorted_pgs: List[Tuple[int, PlacementGroup]] = [] searching_idx = 0 for request_process, original_idx in sorted_process_on_nodes: assert searching_idx < len(sorted_src_pgs), f"no enough nodes for request: searching {searching_idx} th node" assert request_process <= sorted_src_pgs[searching_idx].bundle_count, \ f"requesting {request_process} processes, bundle count cannot satisfy" unsorted_pgs.append((original_idx, sorted_src_pgs[searching_idx])) searching_idx += 1 return [pg for _, pg in sorted(unsorted_pgs)] def merge_resource_pool(rp1: RayResourcePool, rp2: RayResourcePool) -> RayResourcePool: assert rp1.use_gpu == rp2.use_gpu, 'Both RayResourcePool must either use_gpu or not' assert rp1.max_collocate_count == rp2.max_collocate_count, 'Both RayResourcePool must has the same max_collocate_count' assert rp1.n_gpus_per_node == rp2.n_gpus_per_node, 'Both RayResourcePool must has the same n_gpus_per_node' assert rp1.detached == rp2.detached, 'Detached ResourcePool cannot be merged with non-detached ResourcePool' new_store = rp1.store + rp2.store merged = RayResourcePool(new_store, rp1.use_gpu, f"{rp1.name_prefix}_{rp2.name_prefix}") merged.pgs = rp1.get_placement_groups() + rp2.get_placement_groups() return merged class RayClassWithInitArgs(ClassWithInitArgs): def __init__(self, cls, *args, **kwargs) -> None: # self._options = kwargs.pop('options', dict()) super().__init__(cls, *args, **kwargs) self._options = {} self._additional_resource = {} def set_additional_resource(self, additional_resource): self._additional_resource = additional_resource def update_options(self, options: Dict): self._options.update(options) def __call__(self, placement_group, placement_group_bundle_idx, use_gpu: bool = True, num_gpus=1, sharing_with=None) -> Any: if sharing_with is not None: target_node_id = ray.get(sharing_with.get_node_id.remote()) cuda_visible_devices = ray.get(sharing_with.get_cuda_visible_devices.remote()) options = {"scheduling_strategy": NodeAffinitySchedulingStrategy(node_id=target_node_id, soft=False)} return self.cls.options(**options).remote(*self.args, cuda_visible_devices=cuda_visible_devices, **self.kwargs) options = { "scheduling_strategy": PlacementGroupSchedulingStrategy(placement_group=placement_group, placement_group_bundle_index=placement_group_bundle_idx) } options.update(self._options) if use_gpu: options["num_gpus"] = num_gpus if len(self._additional_resource) > 1: for k, v in self._additional_resource.items(): options[k] = v # print("cls:", self.cls) # print("args: ", self.args) # print("kwargs: ", self.kwargs) return self.cls.options(**options).remote(*self.args, **self.kwargs) class RayWorkerGroup(WorkerGroup): def __init__(self, resource_pool: RayResourcePool = None, ray_cls_with_init: RayClassWithInitArgs = None, bin_pack: bool = True, name_prefix: str = None, detached=False, worker_names=None, **kwargs) -> None: super().__init__(resource_pool=resource_pool, **kwargs) self.ray_cls_with_init = ray_cls_with_init self.name_prefix = get_random_string(length=6) if name_prefix is None else name_prefix if worker_names is not None: assert self._is_init_with_detached_workers self._worker_names = worker_names if self._is_init_with_detached_workers: self._init_with_detached_workers(worker_names=worker_names) else: self._init_with_resource_pool(resource_pool=resource_pool, ray_cls_with_init=ray_cls_with_init, bin_pack=bin_pack, detached=detached) if ray_cls_with_init is not None: self._bind_worker_method(self.ray_cls_with_init.cls, func_generator) def _is_worker_alive(self, worker: ray.actor.ActorHandle): worker_state_dict = get_actor(worker._actor_id.hex()) return worker_state_dict.get("state", "undefined") == "ALIVE" if worker_state_dict is not None else False def _init_with_detached_workers(self, worker_names): workers = [ray.get_actor(name=name) for name in worker_names] self._workers = workers self._world_size = len(worker_names) def _init_with_resource_pool(self, resource_pool, ray_cls_with_init, bin_pack, detached): use_gpu = resource_pool.use_gpu strategy = "PACK" if bin_pack: strategy = "STRICT_PACK" pgs = resource_pool.get_placement_groups(strategy=strategy) world_size = resource_pool.world_size self._world_size = world_size # cia.add_kwarg("_world_size", world_size) num_gpus = 1 / resource_pool.max_collocate_count rank = -1 for pg_idx, local_world_size in enumerate(resource_pool.store): pg = pgs[pg_idx] assert local_world_size <= pg.bundle_count, \ f"when generating for {self.name_prefix}, for the " for local_rank in range(local_world_size): rank += 1 # we pass in environment variable at option so that Worker can use environment variable to set env_vars = { 'WORLD_SIZE': str(world_size), 'RANK': str(rank), 'WG_PREFIX': self.name_prefix, 'WG_BACKEND': 'ray', 'RAY_LOCAL_WORLD_SIZE': str(local_world_size), 'RAY_LOCAL_RANK': str(local_rank), } if rank != 0: env_vars['MASTER_ADDR'] = self._master_addr env_vars['MASTER_PORT'] = self._master_port import re cia_name = type(ray_cls_with_init.cls).__name__ match = re.search(r"ActorClass\(([^)]+)\)", cia_name) # ray.remote(Obj) -> "ActorClass(Obj)" cia_name = match.group(1) if match else cia_name # "ActorClass(Obj)" -> "Obj" name = f"{self.name_prefix}{cia_name}_{pg_idx}:{local_rank}" # e.g. Worker_2:5 ray_cls_with_init.update_options({'runtime_env': {'env_vars': env_vars}, 'name': name}) if detached: ray_cls_with_init.update_options({'lifetime': 'detached'}) # create a worker worker = ray_cls_with_init(placement_group=pg, placement_group_bundle_idx=local_rank, use_gpu=use_gpu, num_gpus=num_gpus) self._workers.append(worker) self._worker_names.append(name) if rank == 0: register_center_actor = None for _ in range(120): if f"{self.name_prefix}_register_center" not in list_named_actors(): time.sleep(1) else: register_center_actor = ray.get_actor(f"{self.name_prefix}_register_center") break assert register_center_actor is not None, f"failed to get register_center_actor: {self.name_prefix}_register_center in {list_named_actors(all_namespaces=True)}" rank_zero_info = ray.get(register_center_actor.get_rank_zero_info.remote()) self._master_addr, self._master_port = rank_zero_info['MASTER_ADDR'], rank_zero_info['MASTER_PORT'] # print(f"rank_zero_info: {rank_zero_info}") # print(f"master_addr: {self._master_addr}, master_port: {self._master_port}") @property def worker_names(self): return self._worker_names @classmethod def from_detached(cls, worker_names=None, ray_cls_with_init=None): worker_group = cls(resource_pool=None, ray_cls_with_init=ray_cls_with_init, name_prefix=None, worker_names=worker_names) return worker_group def spawn(self, prefix_set): """ spawn to a dictionary of worker groups, each with a subset of method with prefix. """ def _rebind_actor_methods(worker_group, actor_name): """ bind the method with actor_prefix to its original name """ prefix: str = actor_name + '_' for method_name in dir(worker_group): if method_name.startswith(prefix): # only valid when Python >= 3.9 original_method_name = method_name.removeprefix(prefix) method = getattr(worker_group, method_name) setattr(worker_group, original_method_name, method) new_worker_group_dict = {} for prefix in prefix_set: new_worker_group = self.from_detached(worker_names=self._worker_names, ray_cls_with_init=self.ray_cls_with_init) _rebind_actor_methods(new_worker_group, prefix) new_worker_group_dict[prefix] = new_worker_group return new_worker_group_dict def execute_rank_zero_sync(self, method_name: str, *args, **kwargs): return ray.get(self.execute_rank_zero_async(method_name, *args, **kwargs)) def execute_rank_zero_async(self, method_name: str, *args, **kwargs): remote_call = getattr(self._workers[0], method_name) return remote_call.remote(*args, **kwargs) def execute_rank_zero(self, method_name: str, *args, **kwargs): return self.execute_rank_zero_async(method_name, *args, **kwargs) def execute_all(self, method_name: str, *args, **kwargs): return self.execute_all_async(method_name, *args, **kwargs) def execute_all_sync(self, method_name: str, *args, **kwargs): return ray.get(self.execute_all_async(method_name, *args, **kwargs)) def execute_all_async(self, method_name: str, *args, **kwargs): # Here, we assume that if all arguments in args and kwargs are lists, and their lengths match len(self._workers), # we'll distribute each element in these lists to the corresponding worker # print(f"execute_all_async: method {method_name}({args}, {kwargs})") length = len(self._workers) if all(isinstance(arg, list) for arg in args) and all(isinstance(kwarg, list) for kwarg in kwargs.values()): if all(len(arg) == length for arg in args) and all(len(kwarg) == length for kwarg in kwargs.values()): # print(f"splitting args and kwargs into {length} shards") result = [] for i in range(length): sliced_args = tuple(arg[i] for arg in args) sliced_kwargs = {k: v[i] for k, v in kwargs.items()} remote_call = getattr(self._workers[i], method_name) result.append(remote_call.remote(*sliced_args, **sliced_kwargs)) return result return [getattr(worker, method_name).remote(*args, **kwargs) for worker in self._workers] @property def master_address(self): return self._master_addr @property def master_port(self): return self._master_port @property def workers(self): return self._workers @property def world_size(self): return self._world_size """ Utilities that enables creating workers inside the same ray.Actor, with code written in separate ray.Actors. """ from unittest.mock import patch from verl.single_controller.base.decorator import MAGIC_ATTR import os def _bind_workers_method_to_parent(cls, key, user_defined_cls): """ Binds the methods of each worker to the WorkerDict. Note that we only bind public methods that are decorated by register """ for method_name in dir(user_defined_cls): try: method = getattr(user_defined_cls, method_name) assert callable(method), f"{method_name} in {user_defined_cls} is not callable" except Exception as e: # if it is a property, it will fail because Class doesn't have instance property continue if hasattr(method, MAGIC_ATTR): def generate_function(name): def func(self, *args, **kwargs): # dispatch to the actual worker return getattr(self.worker_dict[key], name)(*args, **kwargs) return func func = generate_function(method_name) # pass MAGIC_ATTR for outer worker group setattr(func, MAGIC_ATTR, getattr(method, MAGIC_ATTR)) try: method_name_with_prefix = key + '_' + method_name setattr(cls, method_name_with_prefix, func) # print(f'Binding {method_name_with_prefix}') except Exception as e: raise ValueError(f'Fail to set method_name {method_name}') def _unwrap_ray_remote(cls): if hasattr(cls, '__ray_actor_class__'): cls = cls.__ray_actor_class__ return cls def create_colocated_worker_cls(class_dict: dict[str, RayClassWithInitArgs]): """ This function should return a class instance that delegates the calls to every cls in cls_dict """ cls_dict = {} init_args_dict = {} worker_cls = None for key, cls in class_dict.items(): if worker_cls == None: worker_cls = cls.cls.__ray_actor_class__.__base__ else: assert worker_cls == cls.cls.__ray_actor_class__.__base__, \ 'the worker class should be the same when share the same process' cls_dict[key] = cls.cls init_args_dict[key] = {'args': cls.args, 'kwargs': cls.kwargs} assert cls_dict.keys() == init_args_dict.keys() # TODO: create a class with customizable name class WorkerDict(worker_cls): def __init__(self): super().__init__() self.worker_dict = {} for key, user_defined_cls in cls_dict.items(): user_defined_cls = _unwrap_ray_remote(user_defined_cls) # directly instantiate the class without remote with patch.dict(os.environ, {'DISABLE_WORKER_INIT': '1'}): self.worker_dict[key] = user_defined_cls(*init_args_dict[key].get('args', ()), **init_args_dict[key].get('kwargs', {})) # now monkey-patch the methods from inner class to WorkerDict for key, user_defined_cls in cls_dict.items(): user_defined_cls = _unwrap_ray_remote(user_defined_cls) _bind_workers_method_to_parent(WorkerDict, key, user_defined_cls) remote_cls = ray.remote(WorkerDict) remote_cls = RayClassWithInitArgs(cls=remote_cls) return remote_cls ================================================ FILE: verl/single_controller/ray/megatron.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import Dict, Optional import ray from .base import RayWorkerGroup, RayResourcePool, RayClassWithInitArgs from verl.single_controller.base.megatron.worker import DistRankInfo, DistGlobalInfo from verl.single_controller.base.megatron.worker_group import MegatronWorkerGroup # NOTE(sgm): for open-source megatron-core class NVMegatronRayWorkerGroup(RayWorkerGroup, MegatronWorkerGroup): """ MegatronWorkerGroup will query each worker of its megatron rank info and store it inside the WorkerGroup so that the dispatcher can use it to dispatch data. """ def __init__(self, resource_pool: RayResourcePool, ray_cls_with_init: RayClassWithInitArgs, **kwargs): super().__init__(resource_pool=resource_pool, ray_cls_with_init=ray_cls_with_init, **kwargs) self._megatron_rank_info: DistRankInfo = self.execute_all_sync(method_name='get_megatron_rank_info') self._megatron_global_info: DistGlobalInfo = ray.get( self.execute_rank_zero_async(method_name='get_megatron_global_info')) class MegatronRayWorkerGroup(RayWorkerGroup, MegatronWorkerGroup): """ MegatronWorkerGroup will query each worker of its megatron rank info and store it inside the WorkerGroup so that the dispatcher can use it to dispatch data. """ def __init__(self, resource_pool: RayResourcePool, ray_cls_with_init: RayClassWithInitArgs, default_megatron_kwargs: Dict = None, **kwargs): super().__init__(resource_pool=resource_pool, ray_cls_with_init=ray_cls_with_init, default_megatron_kwargs=default_megatron_kwargs, **kwargs) self.init_megatron(default_megatron_kwargs=default_megatron_kwargs) self._megatron_rank_info: DistRankInfo = self.execute_all_sync(method_name='get_megatron_rank_info') self._megatron_global_info: DistGlobalInfo = ray.get( self.execute_rank_zero_async(method_name='get_megatron_global_info')) def init_megatron(self, default_megatron_kwargs: Optional[Dict] = None): # after super, we will call init of each worker if not self._is_init_with_detached_workers: # only init_megatron if the WorkerGroup is created from scratch self.execute_all_sync(method_name='init_megatron', default_megatron_kwargs=default_megatron_kwargs) ================================================ FILE: verl/third_party/__init__.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. ================================================ FILE: verl/third_party/vllm/__init__.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from importlib.metadata import version, PackageNotFoundError from packaging import version as vs def get_version(pkg): try: return version(pkg) except PackageNotFoundError: return None package_name = 'vllm' package_version = get_version(package_name) vllm_version = None if package_version == '0.3.1': vllm_version = '0.3.1' from .vllm_v_0_3_1.llm import LLM from .vllm_v_0_3_1.llm import LLMEngine from .vllm_v_0_3_1 import parallel_state elif package_version == '0.4.2': vllm_version = '0.4.2' from .vllm_v_0_4_2.llm import LLM from .vllm_v_0_4_2.llm import LLMEngine from .vllm_v_0_4_2 import parallel_state elif package_version == '0.5.4': vllm_version = '0.5.4' from .vllm_v_0_5_4.llm import LLM from .vllm_v_0_5_4.llm import LLMEngine from .vllm_v_0_5_4 import parallel_state elif package_version == '0.6.3': vllm_version = '0.6.3' from .vllm_v_0_6_3.llm import LLM from .vllm_v_0_6_3.llm import LLMEngine from .vllm_v_0_6_3 import parallel_state elif vs.parse(package_version) >= vs.parse('0.6.6.post2.dev252+g8027a724'): # From 0.6.6.post2 on, vllm supports SPMD inference # See https://github.com/vllm-project/vllm/pull/12071 from vllm import LLM from vllm.distributed import parallel_state from .vllm_spmd.dtensor_weight_loaders import load_dtensor_weights else: raise ValueError( f'vllm version {package_version} not supported. Currently supported versions are 0.3.1, 0.4.2, 0.5.4, 0.6.3 and 0.7.0+' ) ================================================ FILE: verl/third_party/vllm/vllm_spmd/__init__.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. ================================================ FILE: verl/third_party/vllm/vllm_spmd/dtensor_weight_loaders.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Copyright 2023 The vLLM team. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # Adapted from https://github.com/vllm-project/vllm/tree/main/vllm/model_executor/model_loader from typing import Dict import torch.nn as nn from torch.distributed._tensor import DTensor from vllm.model_executor.model_loader.weight_utils import default_weight_loader from vllm.model_executor.models.utils import is_pp_missing_parameter def gemma_dtensor_weight_loader(actor_weights: Dict, vllm_model: nn.Module) -> nn.Module: stacked_params_mapping = [ # (param_name, shard_name, shard_id) ("qkv_proj", "q_proj", "q"), ("qkv_proj", "k_proj", "k"), ("qkv_proj", "v_proj", "v"), ("gate_up_proj", "gate_proj", 0), ("gate_up_proj", "up_proj", 1), ] params_dict = dict(vllm_model.named_parameters()) for name, loaded_weight in actor_weights.items(): for param_name, shard_name, shard_id in stacked_params_mapping: if shard_name not in name: continue stacked_name = name.replace(shard_name, param_name) # Skip loading extra bias for GPTQ models. if stacked_name.endswith(".bias") and stacked_name not in params_dict: continue local_loaded_weight = redistribute_dtensor(param_name=name, loaded_weights=loaded_weight) param = params_dict[stacked_name] weight_loader = getattr(param, "weight_loader", default_weight_loader) weight_loader(param, local_loaded_weight.to(dtype=param.dtype), shard_id) break else: # lm_head is not used in vllm as it is tied with embed_token. # To prevent errors, skip loading lm_head.weight. if "lm_head.weight" in name: continue # Skip loading extra bias for GPTQ models. if name.endswith(".bias") and name not in params_dict: continue local_loaded_weight = redistribute_dtensor(param_name=name, loaded_weights=loaded_weight) param = params_dict[name] weight_loader = getattr(param, "weight_loader", default_weight_loader) weight_loader(param, local_loaded_weight.to(dtype=param.dtype)) def gptbigcode_dtensor_load_weights(actor_weights: Dict, vllm_model: nn.Module): params_dict = dict(vllm_model.named_parameters(remove_duplicate=False)) for name, loaded_weight in actor_weights.items(): if "lm_head.weight" in name: continue if ".attn.bias" in name: # Skip attention mask. # NOTE: "c_attn.bias" should not be skipped. continue local_loaded_weight = redistribute_dtensor(param_name=name, loaded_weights=loaded_weight) param = params_dict[name] weight_loader = getattr(param, "weight_loader", default_weight_loader) weight_loader(param, local_loaded_weight.to(dtype=param.dtype)) def starcoder2_dtensor_load_weights(actor_weights: Dict, vllm_model: nn.Module): stacked_params_mapping = [ # (param_name, shard_name, shard_id) ("qkv_proj", "q_proj", "q"), ("qkv_proj", "k_proj", "k"), ("qkv_proj", "v_proj", "v"), ] params_dict = dict(vllm_model.named_parameters(remove_duplicate=False)) for name, loaded_weight in actor_weights.items(): if "rotary_emb.inv_freq" in name: continue for param_name, weight_name, shard_id in stacked_params_mapping: if weight_name not in name: continue name = name.replace(weight_name, param_name) local_loaded_weight = redistribute_dtensor(param_name=name, loaded_weights=loaded_weight) param = params_dict[name] weight_loader = param.weight_loader weight_loader(param, local_loaded_weight.to(dtype=param.dtype), shard_id) break else: if vllm_model.config.tie_word_embeddings and "lm_head.weight" in name: continue param = params_dict[name] local_loaded_weight = redistribute_dtensor(param_name=name, loaded_weights=loaded_weight) weight_loader = getattr(param, "weight_loader", default_weight_loader) weight_loader(param, local_loaded_weight.to(dtype=param.dtype)) def llama_dtensor_weight_loader(actor_weights: Dict, vllm_model: nn.Module) -> nn.Module: stacked_params_mapping = [ # (param_name, shard_name, shard_id) (".qkv_proj", ".q_proj", "q"), (".qkv_proj", ".k_proj", "k"), (".qkv_proj", ".v_proj", "v"), (".gate_up_proj", ".gate_proj", 0), (".gate_up_proj", ".up_proj", 1), ] params_dict = dict(vllm_model.named_parameters()) for name, loaded_weight in actor_weights.items(): if "rotary_emb.inv_freq" in name: continue if "rotary_emb.cos_cached" in name or "rotary_emb.sin_cached" in name: # Models trained using ColossalAI may include these tensors in # the checkpoint. Skip them. continue # With tie_word_embeddings, we can skip lm_head.weight # The weight might appear unnecessarily in the files if the model is # processed with quantization, LoRA, fine-tuning, etc. if vllm_model.config.tie_word_embeddings and "lm_head.weight" in name: continue for param_name, weight_name, shard_id in stacked_params_mapping: if weight_name not in name: continue name = name.replace(weight_name, param_name) # Skip loading extra bias for GPTQ models. if name.endswith(".bias") and name not in params_dict: continue local_loaded_weight = redistribute_dtensor(param_name=name, loaded_weights=loaded_weight) param = params_dict[name] weight_loader = param.weight_loader weight_loader(param, local_loaded_weight.to(dtype=param.dtype), shard_id) break else: # Skip loading extra bias for GPTQ models. if name.endswith(".bias") and name not in params_dict: continue local_loaded_weight = redistribute_dtensor(param_name=name, loaded_weights=loaded_weight) param = params_dict[name] weight_loader = getattr(param, "weight_loader", default_weight_loader) weight_loader(param, local_loaded_weight) def qwen2_dtensor_weight_loader(actor_weights: Dict, vllm_model: nn.Module) -> nn.Module: stacked_params_mapping = [ # (param_name, shard_name, shard_id) ("qkv_proj", "q_proj", "q"), ("qkv_proj", "k_proj", "k"), ("qkv_proj", "v_proj", "v"), ("gate_up_proj", "gate_proj", 0), ("gate_up_proj", "up_proj", 1), ] params_dict = dict(vllm_model.named_parameters(remove_duplicate=False)) for name, loaded_weight in actor_weights.items(): if "rotary_emb.inv_freq" in name: continue if vllm_model.config.tie_word_embeddings and "lm_head.weight" in name: continue for param_name, weight_name, shard_id in stacked_params_mapping: if weight_name not in name: continue name = name.replace(weight_name, param_name) # Skip loading extra bias for GPTQ models. if name.endswith(".bias") and name not in params_dict: continue local_loaded_weight = redistribute_dtensor(param_name=name, loaded_weights=loaded_weight) param = params_dict[name] weight_loader = param.weight_loader weight_loader(param, local_loaded_weight.to(dtype=param.dtype), shard_id) break else: # Skip loading extra bias for GPTQ models. if name.endswith(".bias") and name not in params_dict: continue param = params_dict[name] local_loaded_weight = redistribute_dtensor(param_name=name, loaded_weights=loaded_weight) weight_loader = getattr(param, "weight_loader", default_weight_loader) weight_loader(param, local_loaded_weight.to(dtype=param.dtype)) def qwen2vl_dtensor_weight_loader(actor_weights: Dict, vllm_model: nn.Module) -> nn.Module: stacked_params_mapping = [ # (param_name, shard_name, shard_id) ("qkv_proj", "q_proj", "q"), ("qkv_proj", "k_proj", "k"), ("qkv_proj", "v_proj", "v"), ("gate_up_proj", "gate_proj", 0), ("gate_up_proj", "up_proj", 1), ] params_dict = dict(vllm_model.named_parameters(remove_duplicate=False)) for name, loaded_weight in actor_weights.items(): if "rotary_emb.inv_freq" in name: continue if vllm_model.config.tie_word_embeddings and "lm_head.weight" in name: continue for param_name, weight_name, shard_id in stacked_params_mapping: if weight_name not in name: continue if "visual" in name: continue name = "language_model." + name.replace(weight_name, param_name) # Skip loading extra bias for GPTQ models. if name.endswith(".bias") and name not in params_dict: continue local_loaded_weight = redistribute_dtensor(param_name=name, loaded_weights=loaded_weight) param = params_dict[name] weight_loader = param.weight_loader weight_loader(param, local_loaded_weight.to(dtype=param.dtype), shard_id) break else: # Skip loading extra bias for GPTQ models. if name.endswith(".bias") and name not in params_dict: continue if "visual" in name: name = name else: name = "language_model." + name param = params_dict[name] local_loaded_weight = redistribute_dtensor(param_name=name, loaded_weights=loaded_weight) weight_loader = getattr(param, "weight_loader", default_weight_loader) weight_loader(param, local_loaded_weight.to(dtype=param.dtype)) from vllm.model_executor.layers.fused_moe import FusedMoE def deepseekv2_dtensor_weight_loader(actor_weights: Dict, vllm_model: nn.Module) -> nn.Module: stacked_params_mapping = [ # (param_name, shard_name, shard_id) ("gate_up_proj", "gate_proj", 0), ("gate_up_proj", "up_proj", 1), ] # Params for weights, fp8 weight scales, fp8 activation scales # (param_name, weight_name, expert_id, shard_id) expert_params_mapping = FusedMoE.make_expert_params_mapping( ckpt_gate_proj_name="gate_proj", ckpt_down_proj_name="down_proj", ckpt_up_proj_name="up_proj", num_experts=vllm_model.config.n_routed_experts, ) params_dict = dict(vllm_model.named_parameters(remove_duplicate=False)) for name, loaded_weight in actor_weights.items(): if "rotary_emb.inv_freq" in name: continue for param_name, weight_name, shard_id in stacked_params_mapping: # Skip non-stacked layers and experts (experts handled below). if weight_name not in name: continue # We have mlp.experts[0].gate_proj in the checkpoint. # Since we handle the experts below in expert_params_mapping, # we need to skip here BEFORE we update the name, otherwise # name will be updated to mlp.experts[0].gate_up_proj, which # will then be updated below in expert_params_mapping # for mlp.experts[0].gate_gate_up_proj, which breaks load. if ("mlp.experts." in name) and name not in params_dict: continue name = name.replace(weight_name, param_name) # Skip loading extra bias for GPTQ models. if name.endswith(".bias") and name not in params_dict: continue if is_pp_missing_parameter(name, vllm_model): continue param = params_dict[name] local_loaded_weight = redistribute_dtensor(param_name=name, loaded_weights=loaded_weight) weight_loader = getattr(param, "weight_loader", default_weight_loader) weight_loader(param, local_loaded_weight.to(dtype=param.dtype), shard_id) break else: for mapping in expert_params_mapping: param_name, weight_name, expert_id, shard_id = mapping if weight_name not in name: continue name = name.replace(weight_name, param_name) if is_pp_missing_parameter(name, vllm_model): continue param = params_dict[name] local_loaded_weight = redistribute_dtensor(param_name=name, loaded_weights=loaded_weight) weight_loader = getattr(param, "weight_loader", default_weight_loader) weight_loader( param, local_loaded_weight.to(dtype=param.dtype), weight_name, shard_id=shard_id, expert_id=expert_id, ) break else: # Skip loading extra bias for GPTQ models. if name.endswith(".bias") and name not in params_dict: continue if is_pp_missing_parameter(name, vllm_model): continue param = params_dict[name] local_loaded_weight = redistribute_dtensor(param_name=name, loaded_weights=loaded_weight) weight_loader = getattr(param, "weight_loader", default_weight_loader) weight_loader(param, local_loaded_weight.to(dtype=param.dtype)) def gpt2_dtensor_weight_loader(actor_weights: Dict, vllm_model: nn.Module) -> nn.Module: pass def redistribute_dtensor(param_name: str, loaded_weights: DTensor, parallelize_plan: Dict = None): param_name = _process_parameter_names(name=param_name) if parallelize_plan is not None: assert ( param_name in parallelize_plan.keys()), f"param name: {param_name} not in parallelize_plan :{parallelize_plan.keys()}" placement = parallelize_plan[param_name] local_loaded_weights = loaded_weights.redistribute(device_mesh=loaded_weights.device_mesh, placements=placement).to_local() else: local_loaded_weights = loaded_weights.full_tensor() return local_loaded_weights def _process_parameter_names(name): # Remove '.weight' if it exists at the end of the string if name.endswith(".weight"): name = name[:-7] # Remove 'model.layers.x.' or 'model.' prefix if "model.layers" in name: parts = name.split(".") # Reconstruct the string without 'model.layers.x.' name = ".".join(parts[3:]) # parts[0] is 'model', parts[1] is 'layers', parts[2] is 'x' elif name.startswith("model."): name = name[6:] # Remove 'model.' return name __MODEL_DTENSOR_WEIGHT_LOADER_REGISTRY__ = { "GPT2LMHeadModel": gpt2_dtensor_weight_loader, "LlamaForCausalLM": llama_dtensor_weight_loader, "LLaMAForCausalLM": llama_dtensor_weight_loader, "MistralForCausalLM": llama_dtensor_weight_loader, # mistral is the same as llama in vLLM "InternLMForCausalLM": llama_dtensor_weight_loader, "AquilaModel": llama_dtensor_weight_loader, "AquilaForCausalLM": llama_dtensor_weight_loader, "Phi3ForCausalLM": llama_dtensor_weight_loader, "GemmaForCausalLM": gemma_dtensor_weight_loader, "Gemma2ForCausalLM": gemma_dtensor_weight_loader, "GPTBigCodeForCausalLM": gptbigcode_dtensor_load_weights, "Starcoder2ForCausalLM": starcoder2_dtensor_load_weights, "Qwen2ForCausalLM": qwen2_dtensor_weight_loader, "DeepseekV2ForCausalLM": deepseekv2_dtensor_weight_loader, "Qwen2VLForConditionalGeneration": qwen2vl_dtensor_weight_loader, "Qwen2_5_VLForConditionalGeneration": qwen2vl_dtensor_weight_loader, } # the actor model is .state_dict() # Load dtensor weights def load_dtensor_weights(actor_weights: Dict, vllm_model: nn.Module): weight_loader = _get_model_weight_loader(vllm_model.__class__.__name__) weight_loader(actor_weights, vllm_model) # NOTE(sgm) to reduce peak memory usage, we offload vllm model to cpu # after init, and we need this after sync model weights for in first iter. vllm_model = vllm_model.cuda() def _get_model_weight_loader(arch: str): if arch in __MODEL_DTENSOR_WEIGHT_LOADER_REGISTRY__: return __MODEL_DTENSOR_WEIGHT_LOADER_REGISTRY__[arch] raise ValueError(f"Model architectures {arch} are not supported for now. " f"Supported architectures: {__MODEL_DTENSOR_WEIGHT_LOADER_REGISTRY__.keys()}") # NOTE(sgm): we use per-parameter weight loader in each vllm sub def update_dtensor_weight_loader(): pass ================================================ FILE: verl/third_party/vllm/vllm_v_0_3_1/__init__.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. ================================================ FILE: verl/third_party/vllm/vllm_v_0_3_1/arg_utils.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Copyright 2023 The vLLM team. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # Adapted from https://github.com/vllm-project/vllm/blob/main/vllm/engine/arg_utils.py import argparse import dataclasses from dataclasses import dataclass from typing import Dict, Optional, Tuple import torch.nn as nn from vllm.config import (CacheConfig, DeviceConfig, ModelConfig, ParallelConfig, SchedulerConfig, LoRAConfig) from transformers import PretrainedConfig from .config import ModelConfig @dataclass class EngineArgs: """Arguments for vLLM engine.""" model_hf_config: PretrainedConfig = None dtype: str = 'auto' kv_cache_dtype: str = 'auto' seed: int = 0 max_model_len: Optional[int] = None worker_use_ray: bool = False pipeline_parallel_size: int = 1 tensor_parallel_size: int = 1 max_parallel_loading_workers: Optional[int] = None block_size: int = 16 swap_space: int = 4 # GiB gpu_memory_utilization: float = 0.90 max_num_batched_tokens: Optional[int] = None max_num_seqs: int = 256 max_paddings: int = 256 disable_log_stats: bool = False revision: Optional[str] = None tokenizer_revision: Optional[str] = None quantization: Optional[str] = None load_format: str = 'model' enforce_eager: bool = False max_context_len_to_capture: int = 8192 disable_custom_all_reduce: bool = False enable_lora: bool = False max_loras: int = 1 max_lora_rank: int = 16 lora_extra_vocab_size: int = 256 lora_dtype = 'auto' max_cpu_loras: Optional[int] = None device: str = 'cuda' @staticmethod def add_cli_args(parser: argparse.ArgumentParser) -> argparse.ArgumentParser: """Shared CLI arguments for vLLM engine.""" # Model arguments # TODO(shengguangming): delete the unused args parser.add_argument('--model', type=str, default='facebook/opt-125m', help='name or path of the huggingface model to use') parser.add_argument('--tokenizer', type=str, default=EngineArgs.tokenizer, help='name or path of the huggingface tokenizer to use') parser.add_argument('--revision', type=str, default=None, help='the specific model version to use. It can be a branch ' 'name, a tag name, or a commit id. If unspecified, will use ' 'the default version.') parser.add_argument('--tokenizer-revision', type=str, default=None, help='the specific tokenizer version to use. It can be a branch ' 'name, a tag name, or a commit id. If unspecified, will use ' 'the default version.') parser.add_argument('--tokenizer-mode', type=str, default=EngineArgs.tokenizer_mode, choices=['auto', 'slow'], help='tokenizer mode. "auto" will use the fast ' 'tokenizer if available, and "slow" will ' 'always use the slow tokenizer.') parser.add_argument('--trust-remote-code', action='store_true', help='trust remote code from huggingface') parser.add_argument('--download-dir', type=str, default=EngineArgs.download_dir, help='directory to download and load the weights, ' 'default to the default cache dir of ' 'huggingface') parser.add_argument('--load-format', type=str, default=EngineArgs.load_format, choices=['auto', 'pt', 'safetensors', 'npcache', 'dummy'], help='The format of the model weights to load. ' '"auto" will try to load the weights in the safetensors format ' 'and fall back to the pytorch bin format if safetensors format ' 'is not available. ' '"pt" will load the weights in the pytorch bin format. ' '"safetensors" will load the weights in the safetensors format. ' '"npcache" will load the weights in pytorch format and store ' 'a numpy cache to speed up the loading. ' '"dummy" will initialize the weights with random values, ' 'which is mainly for profiling.') parser.add_argument('--dtype', type=str, default=EngineArgs.dtype, choices=['auto', 'half', 'float16', 'bfloat16', 'float', 'float32'], help='data type for model weights and activations. ' 'The "auto" option will use FP16 precision ' 'for FP32 and FP16 models, and BF16 precision ' 'for BF16 models.') parser.add_argument('--max-model-len', type=int, default=None, help='model context length. If unspecified, ' 'will be automatically derived from the model.') # Parallel arguments parser.add_argument('--worker-use-ray', action='store_true', help='use Ray for distributed serving, will be ' 'automatically set when using more than 1 GPU') parser.add_argument('--pipeline-parallel-size', '-pp', type=int, default=EngineArgs.pipeline_parallel_size, help='number of pipeline stages') parser.add_argument('--tensor-parallel-size', '-tp', type=int, default=EngineArgs.tensor_parallel_size, help='number of tensor parallel replicas') # KV cache arguments parser.add_argument('--block-size', type=int, default=EngineArgs.block_size, choices=[8, 16, 32], help='token block size') # TODO(woosuk): Support fine-grained seeds (e.g., seed per request). parser.add_argument('--seed', type=int, default=EngineArgs.seed, help='random seed') parser.add_argument('--swap-space', type=int, default=EngineArgs.swap_space, help='CPU swap space size (GiB) per GPU') parser.add_argument('--gpu-memory-utilization', type=float, default=EngineArgs.gpu_memory_utilization, help='the percentage of GPU memory to be used for' 'the model executor') parser.add_argument('--max-num-batched-tokens', type=int, default=EngineArgs.max_num_batched_tokens, help='maximum number of batched tokens per ' 'iteration') parser.add_argument('--max-num-seqs', type=int, default=EngineArgs.max_num_seqs, help='maximum number of sequences per iteration') parser.add_argument('--disable-log-stats', action='store_true', help='disable logging statistics') # Quantization settings. parser.add_argument('--quantization', '-q', type=str, choices=['awq', None], default=None, help='Method used to quantize the weights') return parser @classmethod def from_cli_args(cls, args: argparse.Namespace) -> 'EngineArgs': # Get the list of attributes of this dataclass. attrs = [attr.name for attr in dataclasses.fields(cls)] # Set the attributes from the parsed arguments. engine_args = cls(**{attr: getattr(args, attr) for attr in attrs}) return engine_args def create_engine_configs( self, ) -> Tuple[ModelConfig, CacheConfig, ParallelConfig, SchedulerConfig]: device_config = DeviceConfig(self.device) model_config = ModelConfig(self.model_hf_config, self.dtype, self.seed, self.load_format, self.revision, self.tokenizer_revision, self.max_model_len, self.quantization, self.enforce_eager, self.max_context_len_to_capture) cache_config = CacheConfig(self.block_size, self.gpu_memory_utilization, self.swap_space, self.kv_cache_dtype, model_config.get_sliding_window()) parallel_config = ParallelConfig(self.pipeline_parallel_size, self.tensor_parallel_size, self.worker_use_ray, self.max_parallel_loading_workers, self.disable_custom_all_reduce) scheduler_config = SchedulerConfig(self.max_num_batched_tokens, self.max_num_seqs, model_config.max_model_len, self.max_paddings) lora_config = LoRAConfig(max_lora_rank=self.max_lora_rank, max_loras=self.max_loras, lora_extra_vocab_size=self.lora_extra_vocab_size, lora_dtype=self.lora_dtype, max_cpu_loras=self.max_cpu_loras if self.max_cpu_loras and self.max_cpu_loras > 0 else None) if self.enable_lora else None return (model_config, cache_config, parallel_config, scheduler_config, device_config, lora_config) @dataclass class AsyncEngineArgs(EngineArgs): """Arguments for asynchronous vLLM engine.""" engine_use_ray: bool = False disable_log_requests: bool = False max_log_len: Optional[int] = None @staticmethod def add_cli_args(parser: argparse.ArgumentParser) -> argparse.ArgumentParser: parser = EngineArgs.add_cli_args(parser) parser.add_argument('--engine-use-ray', action='store_true', help='use Ray to start the LLM engine in a ' 'separate process as the server process.') parser.add_argument('--disable-log-requests', action='store_true', help='disable logging requests') parser.add_argument('--max-log-len', type=int, default=None, help='max number of prompt characters or prompt ' 'ID numbers being printed in log. ' 'Default: unlimited.') return parser ================================================ FILE: verl/third_party/vllm/vllm_v_0_3_1/config.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Copyright 2023 The vLLM team. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # Adapted from https://github.com/vllm-project/vllm/blob/main/vllm/config.py from typing import Optional, Union, ClassVar from dataclasses import dataclass import torch from transformers import PretrainedConfig from packaging.version import Version from vllm.logger import init_logger from vllm.transformers_utils.config import get_config from vllm.utils import get_cpu_memory, is_hip, get_nvcc_cuda_version logger = init_logger(__name__) _GB = 1 << 30 class ModelConfig: """Configuration for the model. Args: model: Name or path of the huggingface model to use. tokenizer: Name or path of the huggingface tokenizer to use. tokenizer_mode: Tokenizer mode. "auto" will use the fast tokenizer if available, and "slow" will always use the slow tokenizer. trust_remote_code: Trust remote code (e.g., from HuggingFace) when downloading the model and tokenizer. download_dir: Directory to download and load the weights, default to the default cache directory of huggingface. load_format: The format of the model weights to load: "auto" will try to load the weights in the safetensors format and fall back to the pytorch bin format if safetensors format is not available. "pt" will load the weights in the pytorch bin format. "safetensors" will load the weights in the safetensors format. "npcache" will load the weights in pytorch format and store a numpy cache to speed up the loading. "dummy" will initialize the weights with random values, which is mainly for profiling. dtype: Data type for model weights and activations. The "auto" option will use FP16 precision for FP32 and FP16 models, and BF16 precision for BF16 models. seed: Random seed for reproducibility. revision: The specific model version to use. It can be a branch name, a tag name, or a commit id. If unspecified, will use the default version. tokenizer_revision: The specific tokenizer version to use. It can be a branch name, a tag name, or a commit id. If unspecified, will use the default version. max_model_len: Maximum length of a sequence (including prompt and output). If None, will be derived from the model. quantization: Quantization method that was used to quantize the model weights. If None, we assume the model weights are not quantized. enforce_eager: Whether to enforce eager execution. If True, we will disable CUDA graph and always execute the model in eager mode. If False, we will use CUDA graph and eager execution in hybrid. max_context_len_to_capture: Maximum context len covered by CUDA graphs. When a sequence has context length larger than this, we fall back to eager mode. """ def __init__( self, hf_config: PretrainedConfig, dtype: str, seed: int, load_format: str = 'model', revision: Optional[str] = None, tokenizer_revision: Optional[str] = None, max_model_len: Optional[int] = None, quantization: Optional[str] = None, trust_remote_code: Optional[bool] = True, enforce_eager: bool = False, max_context_len_to_capture: Optional[int] = None, ) -> None: self.model = hf_config._name_or_path self.tokenizer = hf_config._name_or_path self.load_format = load_format self.seed = seed self.revision = revision self.tokenizer_revision = tokenizer_revision self.quantization = quantization self.trust_remote_code = trust_remote_code self.enforce_eager = enforce_eager self.max_context_len_to_capture = max_context_len_to_capture # self.hf_config = get_config(model, trust_remote_code, revision) self.hf_config = hf_config self.dtype = _get_and_verify_dtype(self.hf_config, dtype) self.max_model_len = _get_and_verify_max_len(self.hf_config, max_model_len) # self._verify_load_format() # self._verify_tokenizer_mode() self._verify_quantization() self._verify_cuda_graph() def _verify_load_format(self) -> None: load_format = self.load_format.lower() if load_format not in ["auto", "pt", "safetensors", "npcache", "dummy", "model"]: raise ValueError(f"Unknown load format: {self.load_format}. Must be one of " "'auto', 'pt', 'safetensors', 'npcache', 'dummy' or 'model'.") self.load_format = load_format # def _verify_tokenizer_mode(self) -> None: # tokenizer_mode = self.tokenizer_mode.lower() # if tokenizer_mode not in ["auto", "slow"]: # raise ValueError( # f"Unknown tokenizer mode: {self.tokenizer_mode}. Must be " # "either 'auto' or 'slow'.") # self.tokenizer_mode = tokenizer_mode def _verify_quantization(self) -> None: supported_quantization = ["awq", "gptq", "squeezellm"] rocm_not_supported_quantization = ["awq", "gptq"] if self.quantization is not None: self.quantization = self.quantization.lower() # Parse quantization method from the HF model config, if available. hf_quant_config = getattr(self.hf_config, "quantization_config", None) if hf_quant_config is not None: hf_quant_method = str(hf_quant_config["quant_method"]).lower() if self.quantization is None: self.quantization = hf_quant_method elif self.quantization != hf_quant_method: raise ValueError("Quantization method specified in the model config " f"({hf_quant_method}) does not match the quantization " f"method specified in the `quantization` argument " f"({self.quantization}).") if self.quantization is not None: if self.quantization not in supported_quantization: raise ValueError(f"Unknown quantization method: {self.quantization}. Must " f"be one of {supported_quantization}.") if is_hip() and self.quantization in rocm_not_supported_quantization: raise ValueError(f"{self.quantization} quantization is currently not supported " f"in ROCm.") logger.warning(f"{self.quantization} quantization is not fully " "optimized yet. The speed can be slower than " "non-quantized models.") def _verify_cuda_graph(self) -> None: if self.max_context_len_to_capture is None: self.max_context_len_to_capture = self.max_model_len self.max_context_len_to_capture = min(self.max_context_len_to_capture, self.max_model_len) if (self.quantization in ["gptq", "squeezellm"] and not self.enforce_eager): # Related issue: https://github.com/vllm-project/vllm/issues/2147 logger.warning(f"{self.quantization} does not support CUDA graph " "yet. Disabling CUDA graph.") self.enforce_eager = True def verify_with_parallel_config( self, parallel_config: "ParallelConfig", ) -> None: total_num_attention_heads = self.hf_config.num_attention_heads tensor_parallel_size = parallel_config.tensor_parallel_size if total_num_attention_heads % tensor_parallel_size != 0: raise ValueError(f"Total number of attention heads ({total_num_attention_heads})" " must be divisible by tensor parallel size " f"({tensor_parallel_size}).") total_num_hidden_layers = self.hf_config.num_hidden_layers pipeline_parallel_size = parallel_config.pipeline_parallel_size if total_num_hidden_layers % pipeline_parallel_size != 0: raise ValueError(f"Total number of hidden layers ({total_num_hidden_layers}) " "must be divisible by pipeline parallel size " f"({pipeline_parallel_size}).") def get_sliding_window(self) -> Optional[int]: return getattr(self.hf_config, "sliding_window", None) def get_vocab_size(self) -> int: return self.hf_config.vocab_size def get_hidden_size(self) -> int: return self.hf_config.hidden_size def get_head_size(self) -> int: # FIXME(woosuk): This may not be true for all models. return self.hf_config.hidden_size // self.hf_config.num_attention_heads def get_total_num_kv_heads(self) -> int: """Returns the total number of KV heads.""" # For GPTBigCode & Falcon: # NOTE: for falcon, when new_decoder_architecture is True, the # multi_query flag is ignored and we use n_head_kv for the number of # KV heads. falcon_model_types = ["falcon", "RefinedWeb", "RefinedWebModel"] new_decoder_arch_falcon = (self.hf_config.model_type in falcon_model_types and getattr(self.hf_config, "new_decoder_architecture", False)) if not new_decoder_arch_falcon and getattr(self.hf_config, "multi_query", False): # Multi-query attention, only one KV head. # Currently, tensor parallelism is not supported in this case. return 1 attributes = [ # For Falcon: "n_head_kv", "num_kv_heads", # For LLaMA-2: "num_key_value_heads", # For ChatGLM: "multi_query_group_num", ] for attr in attributes: num_kv_heads = getattr(self.hf_config, attr, None) if num_kv_heads is not None: return num_kv_heads # For non-grouped-query attention models, the number of KV heads is # equal to the number of attention heads. return self.hf_config.num_attention_heads def get_num_kv_heads(self, parallel_config: "ParallelConfig") -> int: """Returns the number of KV heads per GPU.""" total_num_kv_heads = self.get_total_num_kv_heads() # If tensor parallelism is used, we divide the number of KV heads by # the tensor parallel size. We will replicate the KV heads in the # case where the number of KV heads is smaller than the tensor # parallel size so each GPU has at least one KV head. return max(1, total_num_kv_heads // parallel_config.tensor_parallel_size) def get_num_layers(self, parallel_config: "ParallelConfig") -> int: total_num_hidden_layers = self.hf_config.num_hidden_layers return total_num_hidden_layers // parallel_config.pipeline_parallel_size class CacheConfig: """Configuration for the KV cache. Args: block_size: Size of a cache block in number of tokens. gpu_memory_utilization: Fraction of GPU memory to use for the vLLM execution. swap_space: Size of the CPU swap space per GPU (in GiB). cache_dtype: Data type for kv cache storage. """ def __init__( self, block_size: int, gpu_memory_utilization: float, swap_space: int, cache_dtype: str, sliding_window: Optional[int] = None, ) -> None: self.block_size = block_size self.gpu_memory_utilization = gpu_memory_utilization self.swap_space_bytes = swap_space * _GB self.cache_dtype = cache_dtype self.sliding_window = sliding_window self._verify_args() self._verify_cache_dtype() # Will be set after profiling. self.num_gpu_blocks = None self.num_cpu_blocks = None def _verify_args(self) -> None: if self.gpu_memory_utilization > 1.0: raise ValueError("GPU memory utilization must be less than 1.0. Got " f"{self.gpu_memory_utilization}.") def _verify_cache_dtype(self) -> None: if self.cache_dtype == "auto": pass elif self.cache_dtype == "fp8_e5m2": nvcc_cuda_version = get_nvcc_cuda_version() if nvcc_cuda_version < Version("11.8"): raise ValueError("FP8 is not supported when cuda version is lower than 11.8.") device_name = torch.cuda.get_device_name() if "AMD" in device_name: raise NotImplementedError("FP8_E5M2 KV Cache on AMD GPU has not been supported yet.") logger.info("Using fp8_e5m2 data type to store kv cache. It reduces " "the GPU memory footprint and boosts the performance. " "But it may cause slight accuracy drop. " "Currently we only support fp8 without scaling factors and " "make e5m2 as a default format.") else: raise ValueError(f"Unknown kv cache dtype: {self.cache_dtype}") def verify_with_parallel_config( self, parallel_config: "ParallelConfig", ) -> None: total_cpu_memory = get_cpu_memory() # FIXME(woosuk): Here, it is assumed that the GPUs in a tensor parallel # group are in the same node. However, the GPUs may span multiple nodes. num_gpus_per_node = parallel_config.tensor_parallel_size cpu_memory_usage = self.swap_space_bytes * num_gpus_per_node msg = (f"{cpu_memory_usage / _GB:.2f} GiB out of " f"the {total_cpu_memory / _GB:.2f} GiB total CPU memory is " "allocated for the swap space.") if cpu_memory_usage > 0.7 * total_cpu_memory: raise ValueError("Too large swap space. " + msg) elif cpu_memory_usage > 0.4 * total_cpu_memory: logger.warning("Possibly too large swap space. " + msg) class ParallelConfig: """Configuration for the distributed execution. Args: pipeline_parallel_size: Number of pipeline parallel groups. tensor_parallel_size: Number of tensor parallel groups. worker_use_ray: Whether to use Ray for model workers. Will be set to True if either pipeline_parallel_size or tensor_parallel_size is greater than 1. max_parallel_loading_workers: Maximum number of multiple batches when load model sequentially. To avoid RAM OOM when using tensor parallel and large models. disable_custom_all_reduce: Disable the custom all-reduce kernel and fall back to NCCL. """ def __init__( self, pipeline_parallel_size: int, tensor_parallel_size: int, worker_use_ray: bool, max_parallel_loading_workers: Optional[int] = None, disable_custom_all_reduce: bool = False, ) -> None: self.pipeline_parallel_size = pipeline_parallel_size self.tensor_parallel_size = tensor_parallel_size self.worker_use_ray = worker_use_ray self.max_parallel_loading_workers = max_parallel_loading_workers self.disable_custom_all_reduce = disable_custom_all_reduce self.world_size = pipeline_parallel_size * tensor_parallel_size if self.world_size > 1: self.worker_use_ray = True self._verify_args() def _verify_args(self) -> None: if self.pipeline_parallel_size > 1: raise NotImplementedError("Pipeline parallelism is not supported yet.") if not self.disable_custom_all_reduce and self.world_size > 1: if is_hip(): self.disable_custom_all_reduce = True logger.info("Disabled the custom all-reduce kernel because it is not " "supported on AMD GPUs.") elif self.pipeline_parallel_size > 1: self.disable_custom_all_reduce = True logger.info("Disabled the custom all-reduce kernel because it is not " "supported with pipeline parallelism.") # FIXME(woosuk): Fix the stability issues and re-enable the custom # all-reduce kernel. if not self.disable_custom_all_reduce and self.world_size > 1: self.disable_custom_all_reduce = True logger.info("Custom all-reduce kernels are temporarily disabled due to " "stability issues. We will re-enable them once the issues are " "resolved.") class SchedulerConfig: """Scheduler configuration. Args: max_num_batched_tokens: Maximum number of tokens to be processed in a single iteration. max_num_seqs: Maximum number of sequences to be processed in a single iteration. max_model_len: Maximum length of a sequence (including prompt and generated text). max_paddings: Maximum number of paddings to be added to a batch. """ def __init__( self, max_num_batched_tokens: Optional[int], max_num_seqs: int, max_model_len: int, max_paddings: int, ) -> None: if max_num_batched_tokens is not None: self.max_num_batched_tokens = max_num_batched_tokens else: # If max_model_len is too short, use 2048 as the default value for # higher throughput. self.max_num_batched_tokens = max(max_model_len, 2048) self.max_num_seqs = max_num_seqs self.max_model_len = max_model_len self.max_paddings = max_paddings self._verify_args() def _verify_args(self) -> None: if self.max_num_batched_tokens < self.max_model_len: raise ValueError(f"max_num_batched_tokens ({self.max_num_batched_tokens}) is " f"smaller than max_model_len ({self.max_model_len}). " "This effectively limits the maximum sequence length to " "max_num_batched_tokens and makes vLLM reject longer " "sequences. Please increase max_num_batched_tokens or " "decrease max_model_len.") if self.max_num_batched_tokens < self.max_num_seqs: raise ValueError(f"max_num_batched_tokens ({self.max_num_batched_tokens}) must " "be greater than or equal to max_num_seqs " f"({self.max_num_seqs}).") class DeviceConfig: def __init__(self, device: str = "cuda") -> None: self.device = torch.device(device) @dataclass class LoRAConfig: max_lora_rank: int max_loras: int max_cpu_loras: Optional[int] = None lora_dtype: Optional[torch.dtype] = None lora_extra_vocab_size: int = 256 # This is a constant. lora_vocab_padding_size: ClassVar[int] = 256 def __post_init__(self): # Keep this in sync with csrc/punica/bgmv/bgmv_config.h possible_max_ranks = (8, 16, 32, 64) possible_lora_extra_vocab_size = (0, 256, 512) if self.max_lora_rank not in possible_max_ranks: raise ValueError(f"max_lora_rank ({self.max_lora_rank}) must be one of " f"{possible_max_ranks}.") if self.lora_extra_vocab_size not in possible_lora_extra_vocab_size: raise ValueError(f"lora_extra_vocab_size ({self.lora_extra_vocab_size}) " f"must be one of {possible_lora_extra_vocab_size}.") if self.max_loras < 1: raise ValueError(f"max_loras ({self.max_loras}) must be >= 1.") if self.max_cpu_loras is None: self.max_cpu_loras = self.max_loras elif self.max_cpu_loras < self.max_loras: raise ValueError(f"max_cpu_loras ({self.max_cpu_loras}) must be >= " f"max_loras ({self.max_loras})") def verify_with_model_config(self, model_config: ModelConfig): if self.lora_dtype in (None, "auto"): self.lora_dtype = model_config.dtype elif isinstance(self.lora_dtype, str): self.lora_dtype = getattr(torch, self.lora_dtype) if model_config.quantization is not None: raise ValueError("LoRA is not supported with quantized models yet.") def verify_with_scheduler_config(self, scheduler_config: SchedulerConfig): if scheduler_config.max_num_batched_tokens > 65528: raise ValueError("Due to limitations of the custom LoRA CUDA kernel, " "max_num_batched_tokens must be <= 65528 when " "LoRA is enabled.") _STR_DTYPE_TO_TORCH_DTYPE = { "half": torch.float16, "float16": torch.float16, "float": torch.float32, "float32": torch.float32, "bfloat16": torch.bfloat16, } _ROCM_NOT_SUPPORTED_DTYPE = ["float", "float32"] def _get_and_verify_dtype( config: PretrainedConfig, dtype: Union[str, torch.dtype], ) -> torch.dtype: # NOTE: getattr(config, "torch_dtype", torch.float32) is not correct # because config.torch_dtype can be None. config_dtype = getattr(config, "torch_dtype", None) if config_dtype is None: config_dtype = torch.float32 if isinstance(dtype, str): dtype = dtype.lower() if dtype == "auto": if config_dtype == torch.float32: # Following the common practice, we use float16 for float32 # models. torch_dtype = torch.float16 else: torch_dtype = config_dtype else: if dtype not in _STR_DTYPE_TO_TORCH_DTYPE: raise ValueError(f"Unknown dtype: {dtype}") torch_dtype = _STR_DTYPE_TO_TORCH_DTYPE[dtype] elif isinstance(dtype, torch.dtype): torch_dtype = dtype else: raise ValueError(f"Unknown dtype: {dtype}") if is_hip() and torch_dtype == torch.float32: rocm_supported_dtypes = [ k for k, v in _STR_DTYPE_TO_TORCH_DTYPE.items() if (k not in _ROCM_NOT_SUPPORTED_DTYPE) ] raise ValueError(f"dtype \'{dtype}\' is not supported in ROCm. " f"Supported dtypes are {rocm_supported_dtypes}") # Verify the dtype. if torch_dtype != config_dtype: if torch_dtype == torch.float32: # Upcasting to float32 is allowed. pass elif config_dtype == torch.float32: # Downcasting from float32 to float16 or bfloat16 is allowed. pass else: # Casting between float16 and bfloat16 is allowed with a warning. logger.warning(f"Casting {config_dtype} to {torch_dtype}.") return torch_dtype def _get_and_verify_max_len( hf_config: PretrainedConfig, max_model_len: Optional[int], ) -> int: """Get and verify the model's maximum length.""" derived_max_model_len = float("inf") possible_keys = [ # OPT "max_position_embeddings", # GPT-2 "n_positions", # MPT "max_seq_len", # ChatGLM2 "seq_length", # Others "max_sequence_length", "max_seq_length", "seq_len", ] for key in possible_keys: max_len_key = getattr(hf_config, key, None) if max_len_key is not None: derived_max_model_len = min(derived_max_model_len, max_len_key) if derived_max_model_len == float("inf"): if max_model_len is not None: # If max_model_len is specified, we use it. return max_model_len default_max_len = 2048 logger.warning("The model's config.json does not contain any of the following " "keys to determine the original maximum length of the model: " f"{possible_keys}. Assuming the model's maximum length is " f"{default_max_len}.") derived_max_model_len = default_max_len rope_scaling = getattr(hf_config, "rope_scaling", None) if rope_scaling is not None: assert "factor" in rope_scaling scaling_factor = rope_scaling["factor"] if rope_scaling["type"] == "yarn": derived_max_model_len = rope_scaling["original_max_position_embeddings"] derived_max_model_len *= scaling_factor if max_model_len is None: max_model_len = derived_max_model_len elif max_model_len > derived_max_model_len: raise ValueError(f"User-specified max_model_len ({max_model_len}) is greater than " f"the derived max_model_len ({max_len_key}={derived_max_model_len}" " in model's config.json). This may lead to incorrect model " "outputs or CUDA errors. Make sure the value is correct and " "within the model context size.") return int(max_model_len) ================================================ FILE: verl/third_party/vllm/vllm_v_0_3_1/llm.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Copyright 2023 The vLLM team. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # Adapted from https://github.com/vllm-project/vllm/blob/main/vllm/entrypoints/llm.py from typing import Dict, List, Optional, Tuple, Union from tqdm import tqdm from transformers import PreTrainedTokenizer, PreTrainedTokenizerFast from transformers import PretrainedConfig import torch.nn as nn from .arg_utils import EngineArgs from .llm_engine_sp import LLMEngine from vllm.lora.request import LoRARequest from vllm.outputs import RequestOutput from vllm.sampling_params import SamplingParams from vllm.utils import Counter import torch from torch.nn.utils.rnn import pad_sequence from verl.workers.rollout.tokenizer import HybridEngineBaseTokenizer class LLM: """An LLM for generating texts from given prompts and sampling parameters. This class includes a tokenizer, a language model (possibly distributed across multiple GPUs), and GPU memory space allocated for intermediate states (aka KV cache). Given a batch of prompts and sampling parameters, this class generates texts from the model, using an intelligent batching mechanism and efficient memory management. NOTE: This class is intended to be used for offline inference. For online serving, use the `AsyncLLMEngine` class instead. NOTE: For the comprehensive list of arguments, see `EngineArgs`. Args: model: A HuggingFace Transformers model instance. tokenizer: A HuggingFace Transformers tokenizer instance. tokenizer_mode: The tokenizer mode. "auto" will use the fast tokenizer if available, and "slow" will always use the slow tokenizer. trust_remote_code: Trust remote code (e.g., from HuggingFace) when downloading the model and tokenizer. tensor_parallel_size: The number of GPUs to use for distributed execution with tensor parallelism. dtype: The data type for the model weights and activations. Currently, we support `float32`, `float16`, and `bfloat16`. If `auto`, we use the `torch_dtype` attribute specified in the model config file. However, if the `torch_dtype` in the config is `float32`, we will use `float16` instead. quantization: The method used to quantize the model weights. Currently, we support "awq". If None, we assume the model weights are not quantized and use `dtype` to determine the data type of the weights. revision: The specific model version to use. It can be a branch name, a tag name, or a commit id. tokenizer_revision: The specific tokenizer version to use. It can be a branch name, a tag name, or a commit id. seed: The seed to initialize the random number generator for sampling. gpu_memory_utilization: The ratio (between 0 and 1) of GPU memory to reserve for the model weights, activations, and KV cache. Higher values will increase the KV cache size and thus improve the model's throughput. However, if the value is too high, it may cause out-of- memory (OOM) errors. swap_space: The size (GiB) of CPU memory per GPU to use as swap space. This can be used for temporarily storing the states of the requests when their `best_of` sampling parameters are larger than 1. If all requests will have `best_of=1`, you can safely set this to 0. Otherwise, too small values may cause out-of-memory (OOM) errors. enforce_eager: Whether to enforce eager execution. If True, we will disable CUDA graph and always execute the model in eager mode. If False, we will use CUDA graph and eager execution in hybrid. max_context_len_to_capture: Maximum context len covered by CUDA graphs. When a sequence has context length larger than this, we fall back to eager mode. disable_custom_all_reduce: See ParallelConfig """ def __init__( self, model: Union[nn.Module, Dict], # model itself or its parameter dict tokenizer: Union[PreTrainedTokenizer, PreTrainedTokenizerFast, HybridEngineBaseTokenizer], model_hf_config: PretrainedConfig, tokenizer_mode: str = "auto", trust_remote_code: bool = False, tensor_parallel_size: int = 1, dtype: str = "auto", quantization: Optional[str] = None, revision: Optional[str] = None, tokenizer_revision: Optional[str] = None, seed: int = 0, gpu_memory_utilization: float = 0.9, swap_space: int = 4, enforce_eager: bool = False, max_context_len_to_capture: int = 8192, disable_custom_all_reduce: bool = False, **kwargs, ) -> None: if "disable_log_stats" not in kwargs: kwargs["disable_log_stats"] = True engine_args = EngineArgs( model_hf_config=model_hf_config, tensor_parallel_size=tensor_parallel_size, dtype=dtype, quantization=quantization, revision=revision, tokenizer_revision=tokenizer_revision, seed=seed, gpu_memory_utilization=gpu_memory_utilization, swap_space=swap_space, enforce_eager=enforce_eager, max_context_len_to_capture=max_context_len_to_capture, disable_custom_all_reduce=disable_custom_all_reduce, **kwargs, ) tokenizer_cls = (PreTrainedTokenizer, PreTrainedTokenizerFast, HybridEngineBaseTokenizer) if not isinstance(tokenizer, tokenizer_cls): raise ValueError( f"Unexpected tokenizer type: {type(tokenizer)}. Must be" "one of the following: PreTrainedTokenizer, PreTrainedTokenizerFast, verl.workers.rollout.HybridEngineBaseTokenizer" ) self.llm_engine = LLMEngine.from_engine_args(model, tokenizer, engine_args) self.request_counter = Counter() def init_cache_engine(self): self.llm_engine.init_cache_engine() def free_cache_engine(self): self.llm_engine.free_cache_engine() def get_tokenizer(self) -> Union[PreTrainedTokenizer, PreTrainedTokenizerFast]: return self.llm_engine.tokenizer def set_tokenizer( self, tokenizer: Union[PreTrainedTokenizer, PreTrainedTokenizerFast], ) -> None: self.llm_engine.tokenizer = tokenizer def generate( self, prompts: Optional[Union[str, List[str]]] = None, sampling_params: Optional[SamplingParams] = None, prompt_token_ids: Optional[List[List[int]]] = None, prefix_pos: Optional[Union[int, List[int]]] = None, use_tqdm: bool = True, lora_request: Optional[LoRARequest] = None, ) -> List[RequestOutput]: """Generates the completions for the input prompts. NOTE: This class automatically batches the given prompts, considering the memory constraint. For the best performance, put all of your prompts into a single list and pass it to this method. Args: prompts: A list of prompts to generate completions for. sampling_params: The sampling parameters for text generation. If None, we use the default sampling parameters. prompt_token_ids: A list of token IDs for the prompts. If None, we use the tokenizer to convert the prompts to token IDs. use_tqdm: Whether to use tqdm to display the progress bar. Returns: A list of `RequestOutput` objects containing the generated completions in the same order as the input prompts. """ if prompts is None and prompt_token_ids is None: raise ValueError("Either prompts or prompt_token_ids must be " "provided.") if isinstance(prompts, str): # Convert a single prompt to a list. prompts = [prompts] if prompts is not None and prompt_token_ids is not None: if len(prompts) != len(prompt_token_ids): raise ValueError("The lengths of prompts and prompt_token_ids " "must be the same.") if sampling_params is None: # Use default sampling params. sampling_params = SamplingParams() # Add requests to the engine. num_requests = len(prompts) if prompts is not None else len(prompt_token_ids) for i in range(num_requests): prompt = prompts[i] if prompts is not None else None prefix_pos_i = prefix_pos[i] if prefix_pos is not None else None token_ids = None if prompt_token_ids is None else prompt_token_ids[i] if not isinstance(token_ids, list): # NOTE(shengguangming): convert the rollout input into List[str] token_ids = self._pre_process_inputs(token_ids) self._add_request(prompt, sampling_params, token_ids, lora_request=lora_request, prefix_pos=prefix_pos_i) return self._run_engine(use_tqdm) def _add_request( self, prompt: Optional[str], sampling_params: SamplingParams, prompt_token_ids: Optional[List[int]], lora_request: Optional[LoRARequest] = None, prefix_pos: Optional[int] = None, ) -> None: request_id = str(next(self.request_counter)) self.llm_engine.add_request(request_id, prompt, sampling_params, prompt_token_ids, lora_request=lora_request, prefix_pos=prefix_pos) def _run_engine(self, use_tqdm: bool) -> List[RequestOutput]: # Initialize tqdm. if use_tqdm: num_requests = self.llm_engine.get_num_unfinished_requests() pbar = tqdm(total=num_requests, desc="Processed prompts") # Run the engine. outputs: List[RequestOutput] = [] while self.llm_engine.has_unfinished_requests(): step_outputs = self.llm_engine.step() for output in step_outputs: if output.finished: outputs.append(output) if use_tqdm: pbar.update(1) if use_tqdm: pbar.close() # Sort the outputs by request ID. # This is necessary because some requests may be finished earlier than # its previous requests. outputs = sorted(outputs, key=lambda x: int(x.request_id)) # TODO(shengguangming): maybe we can hack the autoregressive logics without only apply post process for better performance return self._post_process_outputs(outputs) # NOTE(shengguangming): add for verl # TODO(sgm): we can optimize it by making the dataloader yield List[int] without padding. def _pre_process_inputs(self, prompt_token_ids: torch.Tensor) -> List[int]: # remove the left padding in the prompt token_id pad_token_id = self.llm_engine.tokenizer.pad_token_id if self.llm_engine.tokenizer.pad_token_id is not None else self.llm_engine.tokenizer.eos_token_id non_pad_index = torch.nonzero(prompt_token_ids != pad_token_id, as_tuple=False)[0][0] token_ids = prompt_token_ids[non_pad_index:].tolist() return token_ids # NOTE(shengguangming): add for verl def _post_process_outputs(self, outputs: List[RequestOutput]) -> Tuple[torch.Tensor, torch.Tensor]: output_token_ids = [] logprobs = [] for output in outputs: # List[RequestOutput] output = output.outputs for output in output: # List[CompletionOutput], usually len == 1 output_token_ids.append(torch.tensor(output.token_ids)) # TODO(shengguangming): can be optimzied by rewrite the Sampler._get_logprobs() logits logprobs_dicts = output.logprobs if logprobs_dicts is not None: logprob = [] for logprobs_dict, id in zip(logprobs_dicts, output.token_ids): logprob.append(logprobs_dict[id]) logprobs.append(torch.tensor(logprob)) pad_token_id = self.llm_engine.tokenizer.pad_token_id if self.llm_engine.tokenizer.pad_token_id is not None else self.llm_engine.tokenizer.eos_token_id output_token_ids = pad_sequence(output_token_ids, batch_first=True, padding_value=pad_token_id) if len(logprobs) > 0: logprobs = pad_sequence(logprobs, batch_first=True, padding_value=pad_token_id) return output_token_ids, logprobs def sync_model_weights(self, actor_weights: Dict[str, torch.Tensor]) -> None: self.llm_engine.sync_model_weights(actor_weights=actor_weights) def offload_model_weights(self) -> None: self.llm_engine.offload_model_weights() ================================================ FILE: verl/third_party/vllm/vllm_v_0_3_1/llm_engine_sp.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Copyright 2023 The vLLM team. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # Adapted from https://github.com/vllm-project/vllm/blob/main/vllm/engine/llm_engine.py import os import socket import time import torch from typing import TYPE_CHECKING, Any, Dict, Iterable, List, Optional, Tuple, Union from vllm.lora.request import LoRARequest from vllm.config import (CacheConfig, DeviceConfig, ModelConfig, ParallelConfig, SchedulerConfig, LoRAConfig) from vllm.core.scheduler import Scheduler, SchedulerOutputs from vllm.logger import init_logger from vllm.outputs import RequestOutput from vllm.sampling_params import SamplingParams from vllm.sequence import (SamplerOutput, Sequence, SequenceGroup, SequenceGroupMetadata, SequenceGroupOutput, SequenceOutput, SequenceStatus) from vllm.transformers_utils.tokenizer import detokenize_incrementally from vllm.engine.metrics import StatLogger, Stats from vllm.utils import Counter import torch.nn as nn from .arg_utils import EngineArgs from .tokenizer import TokenizerGroup logger = init_logger(__name__) _LOCAL_LOGGING_INTERVAL_SEC = 5 class LLMEngine: """An LLM engine that receives requests and generates texts. This is the main class for the vLLM engine. It receives requests from clients and generates texts from the LLM. It includes a tokenizer, a language model (possibly distributed across multiple GPUs), and GPU memory space allocated for intermediate states (aka KV cache). This class utilizes iteration-level scheduling and efficient memory management to maximize the serving throughput. The `LLM` class wraps this class for offline batched inference and the `AsyncLLMEngine` class wraps this class for online serving. NOTE: The config arguments are derived from the `EngineArgs` class. For the comprehensive list of arguments, see `EngineArgs`. Args: model_config: The configuration related to the LLM model. cache_config: The configuration related to the KV cache memory management. parallel_config: The configuration related to distributed execution. scheduler_config: The configuration related to the request scheduler. distributed_init_method: The initialization method for distributed execution. See `torch.distributed.init_process_group` for details. placement_group: Ray placement group for distributed execution. Required for distributed execution. log_stats: Whether to log statistics. """ def __init__( self, model: Union[nn.Module, Dict], # model itself or its parameter dict tokenizer: nn.Module, model_config: ModelConfig, cache_config: CacheConfig, parallel_config: ParallelConfig, scheduler_config: SchedulerConfig, device_config: DeviceConfig, lora_config: Optional[LoRAConfig], distributed_init_method: str, placement_group: Optional[None], log_stats: bool, ) -> None: logger.info("Initializing an LLM engine with config: " f"model={model_config.model!r}, " f"tokenizer={model_config.tokenizer!r}, " # f"tokenizer_mode={model_config.tokenizer_mode}, " f"revision={model_config.revision}, " f"tokenizer_revision={model_config.tokenizer_revision}, " # f"trust_remote_code={model_config.trust_remote_code}, " f"dtype={model_config.dtype}, " f"max_seq_len={model_config.max_model_len}, " # f"download_dir={model_config.download_dir!r}, " # f"load_format={model_config.load_format}, " f"disable_custom_all_reduce={parallel_config.disable_custom_all_reduce}, " f"tensor_parallel_size={parallel_config.tensor_parallel_size}, " f"quantization={model_config.quantization}, " f"seed={model_config.seed})") # TODO(woosuk): Print more configs in debug mode. self.model_config = model_config # TODO: currently is hfconfig self.cache_config = cache_config self.lora_config = lora_config assert self.cache_config.sliding_window == getattr(self.model_config.hf_config, "sliding_window", None) self.parallel_config = parallel_config self.scheduler_config = scheduler_config self.device_config = device_config self.log_stats = log_stats self._verify_args() # self.model = model # should not store the model, it should be deleted # TODO(shengguangming): maybe we can choose init here or from arguments self._init_tokenizer(tokenizer) self.seq_counter = Counter() # Create the parallel GPU workers. self._init_workers_sp(model, distributed_init_method) # Profile the memory usage and initialize the cache. self._init_cache_sp() # Create the scheduler. # NOTE(shengguangming): each process will have independent scheduler self.scheduler = Scheduler(scheduler_config, cache_config, lora_config) # Metric Logging. if self.log_stats: self.stat_logger = StatLogger(local_interval=_LOCAL_LOGGING_INTERVAL_SEC) # Logging. self.last_logging_time = 0.0 # List of (timestamp, num_tokens) self.num_prompt_tokens: List[Tuple[float, int]] = [] # List of (timestamp, num_tokens) self.num_generation_tokens: List[Tuple[float, int]] = [] def _init_tokenizer(self, tokenizer, **tokenizer_init_kwargs): init_kwargs = dict(enable_lora=bool(self.lora_config), max_num_seqs=self.scheduler_config.max_num_seqs, max_input_length=None) init_kwargs.update(tokenizer_init_kwargs) self.tokenizer: TokenizerGroup = TokenizerGroup(tokenizer, **init_kwargs) # TODO: check get_lora_tokenizer func def get_tokenizer_for_seq(self, sequence: Sequence): return self.tokenizer.get_lora_tokenizer(sequence.lora_request) def _init_workers_sp(self, model, distributed_init_method: str): # Lazy import the Worker to avoid importing torch.cuda/xformers # before CUDA_VISIBLE_DEVICES is set in the Worker from .worker import Worker # pylint: disable=import-outside-toplevel rank = int(os.getenv("RANK")) self.worker = Worker( model, self.model_config, self.parallel_config, self.scheduler_config, self.device_config, rank, distributed_init_method, lora_config=self.lora_config, kv_cache_dtype=self.cache_config.cache_dtype, ) # NOTE(shengguangming): torch.distributed.init_process_group will be called inside the init_model() self.worker.init_model() self.worker.load_model() def _verify_args(self) -> None: self.model_config.verify_with_parallel_config(self.parallel_config) self.cache_config.verify_with_parallel_config(self.parallel_config) def _init_cache_sp(self) -> None: """Profiles the memory usage and initializes the KV cache.""" # Get the maximum number of blocks that can be allocated on GPU and CPU. num_blocks = self.worker.profile_num_available_blocks( block_size=self.cache_config.block_size, gpu_memory_utilization=self.cache_config.gpu_memory_utilization, cpu_swap_space=self.cache_config.swap_space_bytes, cache_dtype=self.cache_config.cache_dtype, ) # NOTE(shengguangming): Now we don't use a shared centralized controler but each process will # have its own scheduler num_gpu_blocks = num_blocks[0] num_cpu_blocks = num_blocks[1] # FIXME(woosuk): Change to debug log. logger.info(f"# GPU blocks: {num_gpu_blocks}, " f"# CPU blocks: {num_cpu_blocks}") if num_gpu_blocks <= 0: raise ValueError("No available memory for the cache blocks. " "Try increasing `gpu_memory_utilization` when " "initializing the engine.") max_seq_len = self.cache_config.block_size * num_gpu_blocks if self.model_config.max_model_len > max_seq_len: raise ValueError(f"The model's max seq len ({self.model_config.max_model_len}) " "is larger than the maximum number of tokens that can be " f"stored in KV cache ({max_seq_len}). Try increasing " "`gpu_memory_utilization` or decreasing `max_model_len` when " "initializing the engine.") self.cache_config.num_gpu_blocks = num_gpu_blocks self.cache_config.num_cpu_blocks = num_cpu_blocks # Initialize the cache. self.worker.init_cache_engine(cache_config=self.cache_config) self.worker.warm_up_model() def init_cache_engine(self): self.worker.init_cache_engine(cache_config=self.cache_config) def free_cache_engine(self): self.worker.free_cache_engine() @classmethod def from_engine_args(cls, model, tokenizer, engine_args: EngineArgs) -> "LLMEngine": """Creates an LLM engine from the engine arguments.""" # Create the engine configs. engine_configs = engine_args.create_engine_configs() parallel_config = engine_configs[2] # Initialize the cluster. distributed_init_method, placement_group = initialize_cluster(parallel_config) # Create the LLM engine. engine = cls(model, tokenizer, *engine_configs, distributed_init_method, placement_group, log_stats=not engine_args.disable_log_stats) return engine def add_request( self, request_id: str, prompt: Optional[str], sampling_params: SamplingParams, prompt_token_ids: Optional[List[int]] = None, arrival_time: Optional[float] = None, lora_request: Optional[LoRARequest] = None, prefix_pos: Optional[int] = None, ) -> None: """Add a request to the engine's request pool. The request is added to the request pool and will be processed by the scheduler as `engine.step()` is called. The exact scheduling policy is determined by the scheduler. Args: request_id: The unique ID of the request. prompt: The prompt string. Can be None if prompt_token_ids is provided. sampling_params: The sampling parameters for text generation. prompt_token_ids: The token IDs of the prompt. If None, we use the tokenizer to convert the prompts to token IDs. arrival_time: The arrival time of the request. If None, we use the current monotonic time. prefix_pos: If not None, we use the given position as the prefix position for each prompt. We will cache the prefix's KV cache and reuse it for the next request with the same prefix. This is an experimental feature, and may be replaced with automatic prefix caching in the future. Details: - Set arrival_time to the current time if it is None. - Set prompt_token_ids to the encoded prompt if it is None. - Create `best_of` number of :class:`~vllm.Sequence` objects. - Create a :class:`~vllm.SequenceGroup` object from the list of :class:`~vllm.Sequence`. - Add the :class:`~vllm.SequenceGroup` object to the scheduler. Example: >>> # initialize engine >>> engine = LLMEngine.from_engine_args(engine_args) >>> # set request arguments >>> example_prompt = "Who is the president of the United States?" >>> sampling_params = SamplingParams(temperature=0.0) >>> request_id = 0 >>> >>> # add the request to the engine >>> engine.add_request( >>> str(request_id), >>> example_prompt, >>> SamplingParams(temperature=0.0)) >>> # continue the request processing >>> ... """ if lora_request is not None and not self.lora_config: raise ValueError(f"Got lora_request {lora_request} but LoRA is " "not enabled!") if arrival_time is None: arrival_time = time.monotonic() if prompt_token_ids is None: assert prompt is not None prompt_token_ids = self.tokenizer.encode(prompt) # Create the sequences. block_size = self.cache_config.block_size seq_id = next(self.seq_counter) seq = Sequence(seq_id, prompt, prompt_token_ids, block_size, lora_request) # Check whether the input specifies prefix prefix = self.scheduler.prefix_pool.add_or_get_prefix(prompt_token_ids[:prefix_pos], lora_request.lora_int_id if lora_request else 0) if prefix_pos is not None else None # Create the sequence group. seq_group = SequenceGroup(request_id, [seq], sampling_params, arrival_time, lora_request, prefix) # Add the sequence group to the scheduler. self.scheduler.add_seq_group(seq_group) def abort_request(self, request_id: Union[str, Iterable[str]]) -> None: """Aborts a request(s) with the given ID. Args: request_id: The ID(s) of the request to abort. Details: - Refer to the :meth:`~vllm.core.scheduler.Scheduler.abort_seq_group` from class :class:`~vllm.core.scheduler.Scheduler`. Example: >>> # initialize engine and add a request with request_id >>> request_id = str(0) >>> # abort the request >>> engine.abort_request(request_id) """ self.scheduler.abort_seq_group(request_id) def get_model_config(self) -> ModelConfig: """Gets the model configuration.""" return self.model_config def get_num_unfinished_requests(self) -> int: """Gets the number of unfinished requests.""" return self.scheduler.get_num_unfinished_seq_groups() def has_unfinished_requests(self) -> bool: """Returns True if there are unfinished requests.""" return self.scheduler.has_unfinished_seqs() def _check_beam_search_early_stopping( self, early_stopping: Union[bool, str], sampling_params: SamplingParams, best_running_seq: Sequence, current_worst_seq: Sequence, ) -> bool: assert sampling_params.use_beam_search length_penalty = sampling_params.length_penalty if early_stopping is True: return True current_worst_score = (current_worst_seq.get_beam_search_score( length_penalty=length_penalty, eos_token_id=self.get_tokenizer_for_seq(current_worst_seq).eos_token_id)) if early_stopping is False: highest_attainable_score = (best_running_seq.get_beam_search_score( length_penalty=length_penalty, eos_token_id=self.get_tokenizer_for_seq(best_running_seq).eos_token_id)) else: assert early_stopping == "never" if length_penalty > 0.0: # If length_penalty > 0.0, beam search will prefer longer # sequences. The highest attainable score calculation is # based on the longest possible sequence length in this case. max_possible_length = max(best_running_seq.get_prompt_len() + sampling_params.max_tokens, self.scheduler_config.max_model_len) highest_attainable_score = (best_running_seq.get_beam_search_score( length_penalty=length_penalty, eos_token_id=self.get_tokenizer_for_seq(best_running_seq).eos_token_id, seq_len=max_possible_length)) else: # Otherwise, beam search will prefer shorter sequences. The # highest attainable score calculation is based on the current # sequence length. highest_attainable_score = (best_running_seq.get_beam_search_score( length_penalty=length_penalty, eos_token_id=self.get_tokenizer_for_seq(best_running_seq).eos_token_id)) def _process_sequence_group_outputs(self, seq_group: SequenceGroup, outputs: SequenceGroupOutput) -> None: # Process prompt logprobs prompt_logprobs = outputs.prompt_logprobs if prompt_logprobs is not None: seq_group.prompt_logprobs = prompt_logprobs # Process samples samples = outputs.samples parent_seqs = seq_group.get_seqs(status=SequenceStatus.RUNNING) existing_finished_seqs = seq_group.get_finished_seqs() parent_child_dict = {parent_seq.seq_id: [] for parent_seq in parent_seqs} for sample in samples: parent_child_dict[sample.parent_seq_id].append(sample) # List of (child, parent) child_seqs: List[Tuple[Sequence, Sequence]] = [] # Process the child samples for each parent sequence for parent in parent_seqs: child_samples: List[SequenceOutput] = parent_child_dict[parent.seq_id] if len(child_samples) == 0: # This parent sequence has no children samples. Remove # the parent sequence from the sequence group since it will # not be used in the future iterations. parent.status = SequenceStatus.FINISHED_ABORTED seq_group.remove(parent.seq_id) self.scheduler.free_seq(parent) continue # Fork the parent sequence if there are multiple child samples. for child_sample in child_samples[:-1]: new_child_seq_id = next(self.seq_counter) child = parent.fork(new_child_seq_id) child.append_token_id(child_sample.output_token, child_sample.logprobs) child_seqs.append((child, parent)) # Continue the parent sequence for the last child sample. # We reuse the parent sequence here to reduce redundant memory # copies, especially when using non-beam search sampling methods. last_child_sample = child_samples[-1] parent.append_token_id(last_child_sample.output_token, last_child_sample.logprobs) child_seqs.append((parent, parent)) for seq, _ in child_seqs: # self._decode_sequence(seq, seq_group.sampling_params) self._check_stop(seq, seq_group.sampling_params) # Non-beam search case if not seq_group.sampling_params.use_beam_search: # For newly created child sequences, add them to the sequence group # and fork them in block manager if they are not finished. for seq, parent in child_seqs: if seq is not parent: seq_group.add(seq) if not seq.is_finished(): self.scheduler.fork_seq(parent, seq) # Free the finished and selected parent sequences' memory in block # manager. Keep them in the sequence group as candidate output. # NOTE: we need to fork the new sequences before freeing the # old sequences. for seq, parent in child_seqs: if seq is parent and seq.is_finished(): self.scheduler.free_seq(seq) return # Beam search case # Select the child sequences to keep in the sequence group. selected_child_seqs = [] unselected_child_seqs = [] beam_width = seq_group.sampling_params.best_of length_penalty = seq_group.sampling_params.length_penalty # Select the newly finished sequences with the highest scores # to replace existing finished sequences. # Tuple of (seq, parent, is_new) existing_finished_seqs = [(seq, None, False) for seq in existing_finished_seqs] new_finished_seqs = [(seq, parent, True) for seq, parent in child_seqs if seq.is_finished()] all_finished_seqs = existing_finished_seqs + new_finished_seqs # Sort the finished sequences by their scores. all_finished_seqs.sort(key=lambda x: x[0].get_beam_search_score( length_penalty=length_penalty, eos_token_id=self.get_tokenizer_for_seq(x[0]).eos_token_id), reverse=True) for seq, parent, is_new in all_finished_seqs[:beam_width]: if is_new: # A newly generated child sequence finishes and has a high # score, so we will add it into the sequence group. selected_child_seqs.append((seq, parent)) for seq, parent, is_new in all_finished_seqs[beam_width:]: if is_new: # A newly generated child sequence finishes but has a low # score, so we will not add it into the sequence group. # Additionally, if this sequence is a continuation of a # parent sequence, we will need remove the parent sequence # from the sequence group. unselected_child_seqs.append((seq, parent)) else: # An existing finished sequence has a low score, so we will # remove it from the sequence group. seq_group.remove(seq.seq_id) # select the top beam_width sequences from the running # sequences for the next iteration to continue the beam # search. running_child_seqs = [(seq, parent) for seq, parent in child_seqs if not seq.is_finished()] # Sort the running sequences by their scores. running_child_seqs.sort(key=lambda x: x[0].get_beam_search_score( length_penalty=length_penalty, eos_token_id=self.get_tokenizer_for_seq(x[0]).eos_token_id), reverse=True) # Check if we can stop the beam search. if len(running_child_seqs) == 0: # No running sequences, stop the beam search. stop_beam_search = True elif len(all_finished_seqs) < beam_width: # Not enough finished sequences, continue the beam search. stop_beam_search = False else: # Check the early stopping criteria best_running_seq = running_child_seqs[0][0] current_worst_seq = all_finished_seqs[beam_width - 1][0] stop_beam_search = self._check_beam_search_early_stopping(seq_group.sampling_params.early_stopping, seq_group.sampling_params, best_running_seq, current_worst_seq) if stop_beam_search: # Stop the beam search and remove all the running sequences from # the sequence group. unselected_child_seqs.extend(running_child_seqs) else: # Continue the beam search and select the top beam_width sequences # to continue the beam search. selected_child_seqs.extend(running_child_seqs[:beam_width]) # The remaining running sequences will not be used in the next # iteration. Again, if these sequences are continuations of # parent sequences, we will need to remove the parent sequences # from the sequence group. unselected_child_seqs.extend(running_child_seqs[beam_width:]) # For newly created child sequences, add them to the sequence group # and fork them in block manager if they are not finished. for seq, parent in selected_child_seqs: if seq is not parent: seq_group.add(seq) if not seq.is_finished(): self.scheduler.fork_seq(parent, seq) # Free the finished and selected parent sequences' memory in block # manager. Keep them in the sequence group as candidate output. for seq, parent in selected_child_seqs: if seq is parent and seq.is_finished(): self.scheduler.free_seq(seq) # Remove the unselected parent sequences from the sequence group and # free their memory in block manager. for seq, parent in unselected_child_seqs: if seq is parent: # Remove the parent sequence if it is not selected for next # iteration seq_group.remove(seq.seq_id) self.scheduler.free_seq(seq) def _process_model_outputs(self, output: SamplerOutput, scheduler_outputs: SchedulerOutputs) -> List[RequestOutput]: # Update the scheduled sequence groups with the model outputs. scheduled_seq_groups = scheduler_outputs.scheduled_seq_groups for seq_group, outputs in zip(scheduled_seq_groups, output): self._process_sequence_group_outputs(seq_group, outputs) # Free the finished sequence groups. self.scheduler.free_finished_seq_groups() # Create the outputs. request_outputs: List[RequestOutput] = [] for seq_group in scheduled_seq_groups: request_output = RequestOutput.from_seq_group(seq_group) request_outputs.append(request_output) for seq_group in scheduler_outputs.ignored_seq_groups: request_output = RequestOutput.from_seq_group(seq_group) request_outputs.append(request_output) # Update prefix state, now all the uncomputed prefixes are computed. for seq_group in scheduled_seq_groups: if (seq_group.prefix is not None and seq_group.prefix.allocated and not seq_group.prefix.computed): seq_group.prefix.computed = True # Log stats. if self.log_stats: self.stat_logger.log(self._get_stats(scheduler_outputs)) return request_outputs def step(self) -> List[RequestOutput]: """Performs one decoding iteration and returns newly generated results. This function performs one decoding iteration of the engine. It first schedules the sequences to be executed in the next iteration and the token blocks to be swapped in/out/copy. Then, it executes the model and updates the scheduler with the model outputs. Finally, it decodes the sequences and returns the newly generated results. """ seq_group_metadata_list, scheduler_outputs = self.scheduler.schedule() if not scheduler_outputs.is_empty(): output = self.worker.execute_model( seq_group_metadata_list=seq_group_metadata_list, # TODO: check this input blocks_to_swap_in=scheduler_outputs.blocks_to_swap_in, blocks_to_swap_out=scheduler_outputs.blocks_to_swap_out, blocks_to_copy=scheduler_outputs.blocks_to_copy,) else: return [RequestOutput.from_seq_group(seq_group) for seq_group in scheduler_outputs.ignored_seq_groups] return self._process_model_outputs(output, scheduler_outputs) def do_log_stats(self) -> None: """Forced log when no requests active.""" if self.log_stats: self.stat_logger.log(self._get_stats(scheduler_outputs=None)) def _get_stats(self, scheduler_outputs: Optional[SchedulerOutputs]) -> Stats: """Get Stats to be Logged to Prometheus.""" now = time.monotonic() # KV Cache Usage in %. num_total_gpu = self.cache_config.num_gpu_blocks num_free_gpu = self.scheduler.block_manager.get_num_free_gpu_blocks() gpu_cache_usage = 1.0 - (num_free_gpu / num_total_gpu) num_total_cpu = self.cache_config.num_cpu_blocks cpu_cache_usage = 0. if num_total_cpu > 0: num_free_cpu = self.scheduler.block_manager.get_num_free_cpu_blocks() cpu_cache_usage = 1.0 - (num_free_cpu / num_total_cpu) # Scheduler State num_running = len(self.scheduler.running) num_swapped = len(self.scheduler.swapped) num_waiting = len(self.scheduler.waiting) # Iteration stats if we have scheduler output. num_prompt_tokens = 0 num_generation_tokens = 0 time_to_first_tokens = [] time_per_output_tokens = [] time_e2e_requests = [] if scheduler_outputs is not None: prompt_run = scheduler_outputs.prompt_run # Number of Tokens. if prompt_run: num_prompt_tokens = scheduler_outputs.num_batched_tokens else: num_generation_tokens = scheduler_outputs.num_batched_tokens # Latency Timings. time_last_iters = [] for seq_group in scheduler_outputs.scheduled_seq_groups: # Time since last token. (n.b. updates seq_group.last_token_time) time_last_iters.append(seq_group.get_last_latency(now)) # Time since arrival for all finished requests. if seq_group.is_finished(): time_e2e_requests.append(now - seq_group.arrival_time) time_to_first_tokens = time_last_iters if prompt_run else [] time_per_output_tokens = [] if prompt_run else time_last_iters return Stats( now=now, num_running=num_running, num_swapped=num_swapped, num_waiting=num_waiting, gpu_cache_usage=gpu_cache_usage, cpu_cache_usage=cpu_cache_usage, num_prompt_tokens=num_prompt_tokens, num_generation_tokens=num_generation_tokens, time_to_first_tokens=time_to_first_tokens, time_per_output_tokens=time_per_output_tokens, time_e2e_requests=time_e2e_requests, ) # TODO: we may not need to decode def _decode_sequence(self, seq: Sequence, prms: SamplingParams) -> None: """Decodes the new token for a sequence.""" (new_tokens, new_output_text, prefix_offset, read_offset) = detokenize_incrementally( self.get_tokenizer_for_seq(seq), all_input_ids=seq.get_token_ids(), prev_tokens=seq.tokens, prefix_offset=seq.prefix_offset, read_offset=seq.read_offset, skip_special_tokens=prms.skip_special_tokens, spaces_between_special_tokens=prms.spaces_between_special_tokens, ) if seq.tokens is None: seq.tokens = new_tokens else: seq.tokens.extend(new_tokens) seq.prefix_offset = prefix_offset seq.read_offset = read_offset seq.output_text += new_output_text def _check_stop(self, seq: Sequence, sampling_params: SamplingParams) -> None: """Stop the finished sequences.""" # for stop_str in sampling_params.stop: # if seq.output_text.endswith(stop_str): # self._finalize_sequence(seq, sampling_params, stop_str) # seq.status = SequenceStatus.FINISHED_STOPPED # return # if seq.get_last_token_id() in sampling_params.stop_token_ids: # stop_str = self.get_tokenizer_for_seq(seq).convert_ids_to_tokens(seq.get_last_token_id()) # self._finalize_sequence(seq, sampling_params, stop_str) # seq.status = SequenceStatus.FINISHED_STOPPED # return # Check if the sequence has reached max_model_len. if seq.get_len() > self.scheduler_config.max_model_len: seq.status = SequenceStatus.FINISHED_LENGTH_CAPPED return # Check if the sequence has reached max_tokens. if seq.get_output_len() == sampling_params.max_tokens: seq.status = SequenceStatus.FINISHED_LENGTH_CAPPED return # Check if the sequence has generated the EOS token. if ((not sampling_params.ignore_eos) and seq.get_last_token_id() == self.get_tokenizer_for_seq(seq).eos_token_id): seq.status = SequenceStatus.FINISHED_STOPPED return def _finalize_sequence(self, seq: Sequence, sampling_params: SamplingParams, stop_string: str) -> None: if not sampling_params.include_stop_str_in_output and stop_string: # Truncate the output text so that the stop string is # not included in the output. seq.output_text = seq.output_text[:-len(stop_string)] def add_lora(self, lora_request: LoRARequest) -> bool: assert lora_request.lora_int_id > 0, "lora_id must be greater than 0." return self.worker.add_lora(lora_request) def remove_lora(self, lora_id: int) -> bool: assert lora_id > 0, "lora_id must be greater than 0." return self.worker.remove_lora(lora_id) def list_loras(self) -> List[int]: return self.worker.list_loras() def sync_model_weights(self, actor_weights: Dict[str, torch.Tensor]) -> None: self.worker.sync_model_weights(actor_weights=actor_weights) def offload_model_weights(self) -> None: self.worker.offload_model_weights() def initialize_cluster( parallel_config: ParallelConfig, engine_use_ray: bool = False, ray_address: Optional[str] = None, ) -> Tuple[str, Optional[None]]: """Initialize the distributed cluster probably with Ray. Args: parallel_config: The configurations for parallel execution. engine_use_ray: Whether to use Ray for async engine. ray_address: The address of the Ray cluster. If None, uses the default Ray cluster address. Returns: A tuple of (`distributed_init_method`, `placement_group`). The `distributed_init_method` is the address for initializing the distributed backend. `placement_group` includes the specification of the resources for each distributed worker. """ # Initialize cluster locally. port = get_open_port() # We need to setup the distributed init method to make sure # the distributed megatron code (e.g., get world size) works correctly. distributed_init_method = f"tcp://localhost:{port}" return distributed_init_method, None def get_open_port(): with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: s.bind(("", 0)) return s.getsockname()[1] ================================================ FILE: verl/third_party/vllm/vllm_v_0_3_1/model_loader.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Copyright 2023 The vLLM team. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # Adapted from https://github.com/vllm-project/vllm/tree/main/vllm/model_executor/model_loader """Utilities for selecting and loading models.""" import contextlib from typing import Dict, Type, Union import torch import torch.nn as nn from transformers import PretrainedConfig, PreTrainedModel from megatron.core.tensor_parallel.utils import VocabUtility from vllm.model_executor.models import ModelRegistry from vllm.model_executor.weight_utils import (get_quant_config, initialize_dummy_weights) from .config import ModelConfig from vllm.config import DeviceConfig, LoRAConfig from .weight_loaders import * from vllm.model_executor.sampling_metadata import SamplingMetadata, SamplingTensors from vllm.sequence import SamplerOutput from typing import Optional from vllm.model_executor.layers.sampler import Sampler from vllm.model_executor.layers.sampler import _prune_hidden_states, _apply_logits_processors, _apply_penalties, _apply_top_k_top_p, _apply_min_p, _apply_penalties, _sample, _get_logprobs, _build_sampler_output @contextlib.contextmanager def _set_default_torch_dtype(dtype: torch.dtype): """Sets the default torch dtype to the given dtype.""" old_dtype = torch.get_default_dtype() torch.set_default_dtype(dtype) yield torch.set_default_dtype(old_dtype) def _get_model_architecture(config: PretrainedConfig) -> Type[nn.Module]: architectures = getattr(config, "architectures", []) for arch in architectures: model_cls = ModelRegistry.load_model_cls(arch) if model_cls is not None: return model_cls raise ValueError(f"Model architectures {architectures} are not supported for now. " f"Supported architectures: {ModelRegistry.get_supported_archs()}") from vllm.model_executor.layers.linear import * from vllm.model_executor.layers.vocab_parallel_embedding import VocabParallelEmbedding, ParallelLMHead from vllm.model_executor.layers.activation import ScaledActivation __LAYER_WEIGHT_LOADER_REGISTRY__ = { ColumnParallelLinear: parallel_weight_loader, MergedColumnParallelLinear: parallel_weight_loader, QKVParallelLinear: parallel_weight_loader, RowParallelLinear: parallel_weight_loader, VocabParallelEmbedding: parallel_weight_loader, ParallelLMHead: parallel_weight_loader # "ScaledActivation.weight_loader": ScaledActivation, # TODO(shengguangming): latest commit in vllm fix awq for this function and add load_weights # "default_weight_loader": default_weight_loader } # NOTE(gmsheng): change the weight_loader function in runtime for layer_class, weight_loader in __LAYER_WEIGHT_LOADER_REGISTRY__.items(): layer_class.weight_loader = weight_loader __MODEL_WEIGHT_LOADER_REGISTRY__ = { 'GPT2LMHeadModel': gpt2_weight_loader, 'LlamaForCausalLM': llama_weight_loader, 'LLaMAForCausalLM': llama_weight_loader, 'MistralForCausalLM': mistral_weight_loader, } # FIXME(shengguangming): the vLLM vocab will pad to 64, which may incur out of bounds # so we need to rewrite the init function of vocab DEFAULT_VOCAB_PADDING_SIZE = 64 def vocab_init(self, num_embeddings: int, embedding_dim: int, params_dtype: Optional[torch.dtype] = None, org_num_embeddings: Optional[int] = None, padding_size: int = DEFAULT_VOCAB_PADDING_SIZE): super(VocabParallelEmbedding, self).__init__() # Keep the input dimensions. # TODO (pad to be divided by 4) self.num_embeddings = num_embeddings self.org_vocab_size = org_num_embeddings or num_embeddings # self.num_embeddings_padded = pad_vocab_size(num_embeddings, # padding_size) self.embedding_dim = embedding_dim if params_dtype is None: params_dtype = torch.get_default_dtype() self.tp_size = get_tensor_model_parallel_world_size() # Divide the weight matrix along the vocaburaly dimension. self.vocab_start_index, self.vocab_end_index = (VocabUtility.vocab_range_from_global_vocab_size( self.num_embeddings, get_tensor_model_parallel_rank(), self.tp_size)) self.num_embeddings_per_partition = (self.vocab_end_index - self.vocab_start_index) self.weight = Parameter( torch.empty( self.num_embeddings_per_partition, self.embedding_dim, # device=torch.cuda.current_device(), dtype=params_dtype)) set_weight_attrs(self.weight, {"parallel_dim": 0, "weight_loader": self.weight_loader}) VocabParallelEmbedding.__init__ = vocab_init def _get_model_weight_loader(arch: str): if arch in __MODEL_WEIGHT_LOADER_REGISTRY__: return __MODEL_WEIGHT_LOADER_REGISTRY__[arch] raise ValueError(f"Model architectures {arch} are not supported for now. " f"Supported architectures: {ModelRegistry.get_supported_archs()}") def get_model(actor_model: Union[PreTrainedModel, Dict], model_config: ModelConfig, device_config: DeviceConfig, lora_config: Optional[LoRAConfig] = None) -> nn.Module: model_class = _get_model_architecture(model_config.hf_config) # Get the quantization config. linear_method = None quant_config = None if model_config.quantization is not None: quant_config = get_quant_config(model_config.quantization, model_config.model, model_config.hf_config, model_config.download_dir) capability = torch.cuda.get_device_capability() capability = capability[0] * 10 + capability[1] if capability < quant_config.get_min_capability(): raise ValueError(f"The quantization method {model_config.quantization} is not " "supported for the current GPU. " f"Minimum capability: {quant_config.get_min_capability()}. " f"Current capability: {capability}.") supported_dtypes = quant_config.get_supported_act_dtypes() if model_config.dtype not in supported_dtypes: raise ValueError(f"{model_config.dtype} is not supported for quantization " f"method {model_config.quantization}. Supported dtypes: " f"{supported_dtypes}") linear_method = quant_config.get_linear_method() with _set_default_torch_dtype(model_config.dtype): # Create a model instance. # The weights will be initialized as empty tensors. # with torch.device(device_config.device): # NOTE(sgm): init the model in cpu model = model_class(model_config.hf_config, linear_method) if model_config.load_format == "dummy": model = model.cuda() # NOTE(woosuk): For accurate performance evaluation, we assign # random values to the weights. initialize_dummy_weights(model) elif model_config.load_format == 'model' or model_config.load_format == 'auto': # NOTE(shengguangming) Load the weights from the actor model if isinstance(actor_model, nn.Module): load_weights(actor_weights=dict(actor_model.named_parameters(remove_duplicate=False)), vllm_model=model) else: load_weights(actor_weights=actor_model, vllm_model=model) # NOTE(sgm) Some weights are point to gpu, but still need this. model = model.cuda() # NOTE (zhangchi.usc1992) We need this for vllm to profile memory usage return model.eval() # the actor model is .state_dict() def load_weights(actor_weights: Dict, vllm_model: nn.Module): weight_loader = _get_model_weight_loader(vllm_model.__class__.__name__) weight_loader(actor_weights, vllm_model) # NOTE(sgm) to reduce peak memory usage, we offload vllm model to cpu # after init, and we need this after sync model weights for in first iter. vllm_model = vllm_model.cuda() # FIXME(sgm): hack the Sampler function in vllm v0.3.1 # as they use ray, the sampler result will only need to return to the driver node, # therefore gather is enough. However, we use SPMD instead of a central scheduler, # all_gather is required (aligned with v0.2.6) def _get_logits(self, hidden_states: torch.Tensor, embedding: torch.Tensor, embedding_bias: Optional[torch.Tensor]) -> torch.Tensor: # Get the logits for the next tokens. logits = torch.matmul(hidden_states, embedding.t()) if embedding_bias is not None: logits += embedding_bias logits = tensor_model_parallel_all_gather(logits) # Remove paddings in vocab (if any). if logits is not None: logits = logits[:, :self.org_vocab_size] return logits def forward( self, embedding: torch.Tensor, hidden_states: torch.Tensor, sampling_metadata: SamplingMetadata, embedding_bias: Optional[torch.Tensor] = None, ) -> Optional[SamplerOutput]: # Get the hidden states that we use for sampling. hidden_states = _prune_hidden_states(hidden_states, sampling_metadata) # Get the logits for the next tokens. logits = self._get_logits(hidden_states, embedding, embedding_bias) # save origin logprobs for sampler_output origin_logprobs = torch.log_softmax(logits, dim=-1, dtype=torch.float) # Only perform sampling in the driver worker. # Note: `_get_logits` is still distributed across TP workers because # the `embedding` weight is distributed across TP workers. # TODO(zhuohan): Change the get_logits part to a separate stage. if not sampling_metadata.perform_sampling: return None assert logits is not None _, vocab_size = logits.shape # Apply logits processors (if any). logits = _apply_logits_processors(logits, sampling_metadata) # Prepare sampling tensors with pinned memory to avoid blocking. (sampling_tensors, do_penalties, do_top_p_top_k, do_min_p) = SamplingTensors.from_sampling_metadata(sampling_metadata, vocab_size, logits.device, logits.dtype) # Apply presence and frequency penalties. if do_penalties: logits = _apply_penalties(logits, sampling_tensors.prompt_tokens, sampling_tensors.output_tokens, sampling_tensors.presence_penalties, sampling_tensors.frequency_penalties, sampling_tensors.repetition_penalties) # Apply temperature scaling. # Use in-place division to avoid creating a new tensor. logits.div_(sampling_tensors.temperatures.unsqueeze_(dim=1)) if do_top_p_top_k: logits = _apply_top_k_top_p(logits, sampling_tensors.top_ps, sampling_tensors.top_ks) if do_min_p: logits = _apply_min_p(logits, sampling_tensors.min_ps) # We use float32 for probabilities and log probabilities. # Compute the probabilities. probs = torch.softmax(logits, dim=-1, dtype=torch.float) # Compute the log probabilities. # Use log_softmax to ensure numerical stability. logprobs = torch.log_softmax(logits, dim=-1, dtype=torch.float) # Sample the next tokens. sample_results = _sample(probs, logprobs, sampling_metadata) # Get the logprobs query results. # prompt_logprobs, sample_logprobs = _get_logprobs( # logprobs, sampling_metadata, sample_results) prompt_logprobs, sample_logprobs = _get_logprobs(origin_logprobs, sampling_metadata, sample_results) return _build_sampler_output(sample_results, sampling_metadata, prompt_logprobs, sample_logprobs) from vllm.model_executor.layers.sampler import Sampler Sampler._get_logits = _get_logits Sampler.forward = forward ================================================ FILE: verl/third_party/vllm/vllm_v_0_3_1/model_runner.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Copyright 2023 The vLLM team. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # Adapted from https://github.com/vllm-project/vllm/blob/main/vllm/worker/model_runner.py from typing import Dict, List, Optional, Tuple, Set, Union import contextlib import time import numpy as np import torch import torch.nn as nn from vllm.config import (DeviceConfig, ModelConfig, LoRAConfig, ParallelConfig, SchedulerConfig) from vllm.logger import init_logger from vllm.model_executor import InputMetadata, SamplingMetadata from vllm.sampling_params import SamplingParams, SamplingType from vllm.sequence import SamplerOutput, SequenceData, SequenceGroupMetadata from vllm.lora.worker_manager import LRUCacheWorkerLoRAManager from vllm.lora.layers import LoRAMapping from vllm.lora.request import LoRARequest from vllm.utils import in_wsl from vllm.worker.model_runner import ModelRunner, CUDAGraphRunner, _async_h2d from .model_loader import get_model logger = init_logger(__name__) KVCache = Tuple[torch.Tensor, torch.Tensor] _PAD_SLOT_ID = -1 LORA_WARMUP_RANK = 8 # Capture graphs for batch size 1, 2, 4, 8, 16, 24, 32, 40, ..., 256. # NOTE: _get_graph_batch_size needs to be updated if this list is changed. _BATCH_SIZES_TO_CAPTURE = [1, 2, 4] + [8 * i for i in range(1, 33)] class ModelRunner(ModelRunner): def __init__( self, model: Union[nn.Module, Dict], # model itself or its parameter dict model_config: ModelConfig, parallel_config: ParallelConfig, scheduler_config: SchedulerConfig, device_config: DeviceConfig, lora_config: Optional[LoRAConfig], kv_cache_dtype: Optional[str] = "auto", ): self.model_config = model_config self.parallel_config = parallel_config self.scheduler_config = scheduler_config self.lora_config = lora_config # model_config can be None in tests/samplers/test_sampler.py. # FIXME(woosuk): This is a hack to make the tests work. Refactor this. self.sliding_window = (model_config.get_sliding_window() if model_config is not None else None) self.device_config = (device_config if device_config is not None else DeviceConfig()) self.device = self.device_config.device self.model = model # this will be replaced by get_model() self.block_size = None # Set after initial profiling. self.lora_manager = None self.graph_runners: Dict[int, CUDAGraphRunner] = {} self.graph_memory_pool = None # Set during graph capture. self.max_context_len_to_capture = (self.model_config.max_context_len_to_capture if self.model_config is not None else 0) # When using CUDA graph, the input block tables must be padded to # max_context_len_to_capture. However, creating the block table in # Python can be expensive. To optimize this, we cache the block table # in numpy and only copy the actual input content at every iteration. # The shape of the cached block table will be # (max batch size to capture, max context len to capture / block size). self.graph_block_tables = None # Set after initial profiling. # cache in_wsl result self.in_wsl = in_wsl() self.kv_cache_dtype = kv_cache_dtype def load_model(self) -> None: self.model = get_model(actor_model=self.model, model_config=self.model_config, device_config=self.device_config, lora_config=self.lora_config) vocab_size = self.model.config.vocab_size if self.lora_config: assert hasattr( self.model, "supported_lora_modules") and self.model.supported_lora_modules, "Model does not support LoRA" assert hasattr(self.model, "embedding_modules"), "Model does not have embedding_modules" assert hasattr(self.model, "embedding_padding_modules"), "Model does not have embedding_padding_modules" self.lora_manager = LRUCacheWorkerLoRAManager( self.scheduler_config.max_num_seqs, self.scheduler_config.max_num_batched_tokens + self.scheduler_config.max_paddings, vocab_size, self.lora_config, self.device, self.model.embedding_modules, self.model.embedding_padding_modules) self.model = self.lora_manager.create_lora_manager(self.model) def _prepare_sample( self, seq_group_metadata_list: List[SequenceGroupMetadata], prompt_lens: List[int], subquery_lens: Optional[List[int]], ) -> SamplingMetadata: seq_groups: List[Tuple[List[int], SamplingParams]] = [] selected_token_indices: List[int] = [] selected_token_start_idx = 0 categorized_sample_indices = {t: [] for t in SamplingType} categorized_sample_indices_start_idx = 0 max_subquery_len = max(subquery_lens) if subquery_lens else 1 for i, seq_group_metadata in enumerate(seq_group_metadata_list): seq_ids = list(seq_group_metadata.seq_data.keys()) sampling_params = seq_group_metadata.sampling_params seq_groups.append((seq_ids, sampling_params)) if seq_group_metadata.is_prompt: assert len(seq_ids) == 1 assert subquery_lens is not None subquery_len = subquery_lens[i] if sampling_params.prompt_logprobs is not None: # NOTE: prompt token positions do not need sample, skip categorized_sample_indices_start_idx += subquery_len - 1 categorized_sample_indices[sampling_params.sampling_type].append(categorized_sample_indices_start_idx) categorized_sample_indices_start_idx += 1 if sampling_params.prompt_logprobs is not None: selected_token_indices.extend( range(selected_token_start_idx, selected_token_start_idx + subquery_len - 1)) selected_token_indices.append(selected_token_start_idx + subquery_len - 1) selected_token_start_idx += max_subquery_len else: num_seqs = len(seq_ids) selected_token_indices.extend(range(selected_token_start_idx, selected_token_start_idx + num_seqs)) selected_token_start_idx += num_seqs categorized_sample_indices[sampling_params.sampling_type].extend( range(categorized_sample_indices_start_idx, categorized_sample_indices_start_idx + num_seqs)) categorized_sample_indices_start_idx += num_seqs selected_token_indices = _async_h2d(selected_token_indices, dtype=torch.long, target_device=self.device, pin_memory=not self.in_wsl) categorized_sample_indices = { t: _async_h2d(seq_ids, dtype=torch.int, target_device=self.device, pin_memory=not self.in_wsl) for t, seq_ids in categorized_sample_indices.items() } seq_data: Dict[int, SequenceData] = {} for seq_group_metadata in seq_group_metadata_list: seq_data.update(seq_group_metadata.seq_data) sampling_metadata = SamplingMetadata( seq_groups=seq_groups, seq_data=seq_data, prompt_lens=prompt_lens, selected_token_indices=selected_token_indices, categorized_sample_indices=categorized_sample_indices, ) return sampling_metadata def prepare_input_tensors( self, seq_group_metadata_list: Optional[List[SequenceGroupMetadata]], ) -> Tuple[torch.Tensor, torch.Tensor, InputMetadata, SamplingMetadata, Set[int], LoRAMapping]: # NOTE: We assume that all sequences in the group are all prompts or # all decodes. is_prompt = seq_group_metadata_list[0].is_prompt # Prepare input tensors. if is_prompt: (input_tokens, input_positions, input_metadata, prompt_lens, subquery_lens, lora_index_mapping, lora_prompt_mapping, lora_requests) = self._prepare_prompt(seq_group_metadata_list) else: (input_tokens, input_positions, input_metadata, lora_index_mapping, lora_prompt_mapping, lora_requests) = self._prepare_decode(seq_group_metadata_list) prompt_lens = [] subquery_lens = None sampling_metadata = self._prepare_sample(seq_group_metadata_list, prompt_lens, subquery_lens) if self.lora_config: flat_lora_index_mapping = [item for sublist in lora_index_mapping for item in sublist] lora_mapping = LoRAMapping( flat_lora_index_mapping, lora_prompt_mapping, ) else: lora_mapping = None return (input_tokens, input_positions, input_metadata, sampling_metadata, lora_requests, lora_mapping) @torch.inference_mode() def execute_model( self, seq_group_metadata_list: Optional[List[SequenceGroupMetadata]], kv_caches: List[Tuple[torch.Tensor, torch.Tensor]], ) -> Optional[SamplerOutput]: (input_tokens, input_positions, input_metadata, sampling_metadata, lora_requests, lora_mapping) = self.prepare_input_tensors(seq_group_metadata_list) if self.lora_config: self.set_active_loras(lora_requests, lora_mapping) # Execute the model. if input_metadata.use_cuda_graph: graph_batch_size = input_tokens.shape[0] model_executable = self.graph_runners[graph_batch_size] else: model_executable = self.model hidden_states = model_executable( input_ids=input_tokens, positions=input_positions, kv_caches=kv_caches, input_metadata=input_metadata, ) # Sample the next token. output = self.model.sample( hidden_states=hidden_states, sampling_metadata=sampling_metadata, ) return output @torch.inference_mode() def profile_run(self) -> None: # Enable top-k sampling to reflect the accurate memory usage. vocab_size = self.model_config.get_vocab_size() # FIXME(sgm): this sampling params will call cumsum(), causing the # deterministic cumsum throw error sampling_params = SamplingParams(top_p=0.99, top_k=vocab_size - 1) max_num_batched_tokens = self.scheduler_config.max_num_batched_tokens max_num_seqs = self.scheduler_config.max_num_seqs # This represents the maximum number of different requests # that will have unique loras, an therefore the max amount of memory # consumption create dummy lora request copies from the lora request # passed in, which contains a lora from the lora warmup path. dummy_lora_requests = [] dummy_lora_requests_per_seq = [] if self.lora_config: for idx in range(self.lora_config.max_loras): lora_id = idx + 1 dummy_lora_request = LoRARequest( lora_name=f"warmup_{lora_id}", lora_int_id=lora_id, lora_local_path="/not/a/real/path", ) self.lora_manager.add_dummy_lora(dummy_lora_request, rank=LORA_WARMUP_RANK) dummy_lora_requests.append(dummy_lora_request) dummy_lora_requests_per_seq = [ dummy_lora_requests[idx % len(dummy_lora_requests)] for idx in range(max_num_seqs) ] # Profile memory usage with max_num_sequences sequences and the total # number of tokens equal to max_num_batched_tokens. seqs: List[SequenceGroupMetadata] = [] for group_id in range(max_num_seqs): seq_len = (max_num_batched_tokens // max_num_seqs + (group_id < max_num_batched_tokens % max_num_seqs)) seq_data = SequenceData([0] * seq_len) seq = SequenceGroupMetadata( request_id=str(group_id), is_prompt=True, seq_data={group_id: seq_data}, sampling_params=sampling_params, block_tables=None, lora_request=dummy_lora_requests_per_seq[group_id] if dummy_lora_requests_per_seq else None, ) seqs.append(seq) # Run the model with the dummy inputs. num_layers = self.model_config.get_num_layers(self.parallel_config) kv_caches = [(None, None)] * num_layers self.execute_model(seqs, kv_caches) torch.cuda.synchronize() return ================================================ FILE: verl/third_party/vllm/vllm_v_0_3_1/parallel_state.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Copyright 2023 The vLLM team. # Adapted from # https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/core/parallel_state.py # Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. """Model and data parallel groups.""" import torch import torch.distributed import vllm.model_executor.parallel_utils.parallel_state as ps """ This version is strongly tied with Megatron to implement HybridEngine and weight sharing between vllm and Megatron. - We assume the Megatron tp+dp+pp world is already established before calling this function. """ # Tensor model parallel group that the current rank belongs to. _TENSOR_MODEL_PARALLEL_GROUP = None # Micro Data parallel group. Micro data parallel group is additional dp group that origins from splitting training tp # into infer_tp and micro_tp. By default, we use order micro_dp - tp _MICRO_DATA_PARALLEL_GROUP = None def initialize_model_parallel_from_megatron( tensor_model_parallel_size=None # we set None for backward compatibility to set infer_tp = train_tp ) -> None: from megatron.core import parallel_state as mpu from megatron.distributed import new_group # Get world size and rank. Ensure some consistencies. assert torch.distributed.is_initialized() if tensor_model_parallel_size is None: tensor_model_parallel_size = mpu.get_tensor_model_parallel_world_size() else: assert isinstance(tensor_model_parallel_size, int) # Build the tensor model-parallel groups. assert ps._TENSOR_MODEL_PARALLEL_GROUP is None, ("tensor model parallel group is already initialized") assert tensor_model_parallel_size <= mpu.get_tensor_model_parallel_world_size( ), 'Not implemented for infer_tp > train_tp' global _TENSOR_MODEL_PARALLEL_GROUP global _MICRO_DATA_PARALLEL_GROUP assert mpu.get_tensor_model_parallel_world_size() % tensor_model_parallel_size == 0 micro_dp_size = mpu.get_tensor_model_parallel_world_size() // tensor_model_parallel_size world_size: int = torch.distributed.get_world_size() num_micro_dp_groups = world_size // micro_dp_size rank = torch.distributed.get_rank() # Build the micro dp groups. assert _MICRO_DATA_PARALLEL_GROUP is None, ("micro data parallel group is already initialized") for i in range(num_micro_dp_groups): ranks = range(i * micro_dp_size, (i + 1) * micro_dp_size) group = new_group(rank=rank, ranks=ranks, group_type='micro_dp') if rank in ranks: _MICRO_DATA_PARALLEL_GROUP = group if tensor_model_parallel_size == mpu.get_tensor_model_parallel_world_size(): # using the same tp group as Megatron ps._TENSOR_MODEL_PARALLEL_GROUP = mpu.get_tensor_model_parallel_group() _TENSOR_MODEL_PARALLEL_GROUP = mpu.get_tensor_model_parallel_group() # no _MICRO_DATA_PARALLEL_GROUP else: # initialize a micro_dp group and a tp group # assume training tp=4, infer tp=2, then, weight is partitioned as # [1], [2], [3], [4] for training and [1,2], [1,2], [3,4], [3,4] for inference # Build the inference tp groups train_tp = mpu.get_tensor_model_parallel_world_size() num_tensor_model_parallel_groups_per_train_tp = train_tp // tensor_model_parallel_size num_tensor_model_parallel_groups = world_size // tensor_model_parallel_size assert _TENSOR_MODEL_PARALLEL_GROUP is None, ("tensor model parallel group is already initialized") for i in range(num_tensor_model_parallel_groups // num_tensor_model_parallel_groups_per_train_tp): start = train_tp * i end = train_tp * (i + 1) for j in range(num_tensor_model_parallel_groups_per_train_tp): ranks = list(range(start, end, num_tensor_model_parallel_groups_per_train_tp)) for i in range(len(ranks)): ranks[i] += j # group = torch.distributed.new_group(ranks) group = new_group(rank=rank, ranks=ranks, group_type='infer_tp') if rank in ranks: _TENSOR_MODEL_PARALLEL_GROUP = group ps._TENSOR_MODEL_PARALLEL_GROUP = _TENSOR_MODEL_PARALLEL_GROUP # Build the pipeline model-parallel groups. # global _PIPELINE_MODEL_PARALLEL_GROUP # global _PIPELINE_GLOBAL_RANKS # assert ps._PIPELINE_MODEL_PARALLEL_GROUP is None, ("pipeline model parallel group is already initialized") # ps._PIPELINE_MODEL_PARALLEL_GROUP = mpu.get_pipeline_model_parallel_group() # ps._PIPELINE_GLOBAL_RANKS = mpu.get_pipeline_model_parallel_ranks() """ Tensor model parallel utilities """ def get_tensor_model_parallel_group(): """Get the tensor model parallel group the caller rank belongs to.""" assert _TENSOR_MODEL_PARALLEL_GROUP is not None, ("tensor model parallel group is not initialized") return _TENSOR_MODEL_PARALLEL_GROUP def get_tensor_model_parallel_world_size(): """Return world size for the tensor model parallel group.""" return torch.distributed.get_world_size(group=get_tensor_model_parallel_group()) def get_tensor_model_parallel_rank(): """Return my rank for the tensor model parallel group.""" return torch.distributed.get_rank(group=get_tensor_model_parallel_group()) def get_tensor_model_parallel_src_rank(): """Calculate the global rank corresponding to the first local rank in the tensor model parallel group.""" global_rank = torch.distributed.get_rank() local_world_size = get_tensor_model_parallel_world_size() return (global_rank // local_world_size) * local_world_size """ Micro Data parallel group """ def get_micro_data_parallel_group(): assert _MICRO_DATA_PARALLEL_GROUP is not None return _MICRO_DATA_PARALLEL_GROUP def get_micro_data_parallel_world_size(): return torch.distributed.get_world_size(group=get_micro_data_parallel_group()) def get_micro_data_parallel_rank(): return torch.distributed.get_rank(group=get_micro_data_parallel_group()) ================================================ FILE: verl/third_party/vllm/vllm_v_0_3_1/tokenizer.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Copyright 2023 The vLLM team. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # Adapted from https://github.com/vllm-project/vllm/blob/main/vllm/transformers_utils/tokenizer_group/tokenizer_group.py from typing import List, Optional, Tuple, Union from transformers import (AutoTokenizer, PreTrainedTokenizer, PreTrainedTokenizerFast) from vllm.lora.request import LoRARequest from vllm.utils import make_async, LRUCache from vllm.transformers_utils.tokenizers import * class TokenizerGroup: """A group of tokenizers that can be used for LoRA adapters.""" def __init__(self, tokenizer: PreTrainedTokenizer, enable_lora: bool, max_num_seqs: int, max_input_length: Optional[int]): self.enable_lora = enable_lora self.max_input_length = max_input_length self.tokenizer = tokenizer if enable_lora: self.lora_tokenizers = LRUCache(capacity=max_num_seqs) else: self.lora_tokenizers = None def encode(self, prompt: str, request_id: Optional[str] = None, lora_request: Optional[LoRARequest] = None) -> List[int]: tokenizer = self.get_lora_tokenizer(lora_request) return tokenizer.encode(prompt) async def encode_async(self, prompt: str, request_id: Optional[str] = None, lora_request: Optional[LoRARequest] = None) -> List[int]: tokenizer = await self.get_lora_tokenizer_async(lora_request) return tokenizer.encode(prompt) def get_lora_tokenizer(self, lora_request: Optional[LoRARequest]) -> "PreTrainedTokenizer": if not lora_request or not self.enable_lora: return self.tokenizer if lora_request.lora_int_id not in self.lora_tokenizers: # TODO(sgm): the lora tokenizer is also passed, but may be different tokenizer = self.tokenizer # tokenizer = (get_lora_tokenizer( # lora_request, **self.tokenizer_config) or self.tokenizer) self.lora_tokenizers.put(lora_request.lora_int_id, tokenizer) return tokenizer else: return self.lora_tokenizers.get(lora_request.lora_int_id) # FIXME(sgm): for simplicity, we assign the special token here @property def pad_token_id(self): return self.tokenizer.pad_token_id @property def eos_token_id(self): return self.tokenizer.eos_token_id ================================================ FILE: verl/third_party/vllm/vllm_v_0_3_1/weight_loaders.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Copyright 2023 The vLLM team. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # Adapted from https://github.com/vllm-project/vllm/tree/main/vllm/model_executor/models from typing import Dict import torch import torch.nn as nn # NOTE(shengguangming): replace the origin weight loader function in the class def parallel_weight_loader(self, param: torch.Tensor, loaded_weight: torch.Tensor) -> None: """Parallel Linear weight loader.""" assert param.size() == loaded_weight.size( ), 'the parameter size is not align with the loaded weight size, param size: {}, loaded_weight size: {}'.format( param.size(), loaded_weight.size()) assert param.data.dtype == loaded_weight.data.dtype, "if we want to shared weights, the data type should also be the same" param.data = loaded_weight.data def default_weight_loader(param: torch.Tensor, loaded_weight: torch.Tensor) -> None: """Default weight loader.""" assert param.size() == loaded_weight.size() assert param.data.dtype == loaded_weight.data.dtype, "if we want to shared weights, the data type should also be the same" param.data = loaded_weight.data def gpt2_weight_loader(actor_weights: Dict, vllm_model: nn.Module) -> nn.Module: params_dict = dict(vllm_model.named_parameters(remove_duplicate=False)) for name, loaded_weight in actor_weights.items(): if "lm_head.weight" in name: # GPT-2 ties the weights of the embedding layer and the final # linear layer. continue if ".attn.bias" in name or ".attn.masked_bias" in name: # Skip attention mask. # NOTE: "c_attn.bias" should not be skipped. continue if not name.startswith("transformer."): name = "transformer." + name param = params_dict[name] # The HF's GPT-2 implementation uses Conv1D instead of Linear. # Because of this, we need to transpose the weights. # Note(zhuohan): the logic below might break quantized models. for conv1d_weight_name in ["c_attn", "c_proj", "c_fc"]: if conv1d_weight_name not in name: continue if not name.endswith(".weight"): continue # TODO: check megatron loaded_weight = loaded_weight.t() weight_loader = getattr(param, "weight_loader", default_weight_loader) weight_loader(param, loaded_weight) def llama_weight_loader(actor_weights: Dict, vllm_model: nn.Module) -> nn.Module: # NOTE(shengguangming): the megatron llama may have this prefix prefix = '0.module.module.' params_dict = dict(vllm_model.named_parameters()) for name, loaded_weight in actor_weights.items(): if name[:len(prefix)] == prefix: name = name[len(prefix):] if "rotary_emb.inv_freq" in name: continue else: param = params_dict[name] weight_loader = getattr(param, "weight_loader", default_weight_loader) weight_loader(param, loaded_weight) def mistral_weight_loader(actor_weights: Dict, vllm_model: nn.Module) -> nn.Module: # TODO: need to implement a general way to deal with prefix prefix = '0.module.module.' params_dict = dict(vllm_model.named_parameters()) for name, loaded_weight in actor_weights.items(): if name[:len(prefix)] == prefix: name = name[len(prefix):] if "rotary_emb.inv_freq" in name: continue else: param = params_dict[name] weight_loader = getattr(param, "weight_loader", default_weight_loader) weight_loader(param, loaded_weight) ================================================ FILE: verl/third_party/vllm/vllm_v_0_3_1/worker.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Copyright 2023 The vLLM team. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # Adapted from https://github.com/vllm-project/vllm/blob/main/vllm/worker/worker.py """A GPU worker class.""" import os import gc from typing import Dict, List, Tuple, Optional, Union, Set import torch import torch.distributed import torch.nn as nn from vllm.config import (CacheConfig, DeviceConfig, ModelConfig, ParallelConfig, SchedulerConfig, LoRAConfig) from vllm.model_executor import InputMetadata, set_random_seed from vllm.model_executor.parallel_utils.parallel_state import (initialize_model_parallel) from vllm.sampling_params import SamplingParams, SamplingType from vllm.sequence import SamplerOutput, SequenceData, SequenceGroupMetadata from vllm.worker.cache_engine import CacheEngine from vllm.model_executor.parallel_utils.custom_all_reduce import init_custom_ar from vllm.model_executor.parallel_utils.parallel_state import get_tensor_model_parallel_group from .model_runner import ModelRunner from .model_loader import load_weights from .parallel_state import initialize_model_parallel_from_megatron from vllm.lora.request import LoRARequest class Worker: """A worker class that executes (a partition of) the model on a GPU. Each worker is associated with a single GPU. The worker is responsible for maintaining the KV cache and executing the model on the GPU. In case of distributed inference, each worker is assigned a partition of the model. """ def __init__( self, model: Union[nn.Module, Dict], # model itself or its parameter dict model_config: ModelConfig, parallel_config: ParallelConfig, scheduler_config: SchedulerConfig, device_config: DeviceConfig, rank: Optional[int] = None, distributed_init_method: Optional[str] = None, lora_config: Optional[LoRAConfig] = None, kv_cache_dtype: Optional[str] = "auto", ) -> None: # self.model = model # will be replaced in the init_model self.model_config = model_config self.parallel_config = parallel_config self.scheduler_config = scheduler_config self.rank = rank self.distributed_init_method = distributed_init_method self.lora_config = lora_config self.model_runner = ModelRunner( model, model_config, parallel_config, scheduler_config, device_config, lora_config=self.lora_config, kv_cache_dtype=kv_cache_dtype, ) # Uninitialized cache engine. Will be initialized by # self.init_cache_engine(). self.cache_config = None self.block_size = None self.sliding_window = None self.cache_engine = None self.cache_events = None self.gpu_cache = None # For offloading inference engine params self.cpu_model = None def init_model(self, cupy_port: Optional[int] = None): # torch.distributed.all_reduce does not free the input tensor until # the synchronization point. This causes the memory usage to grow # as the number of all_reduce calls increases. This env var disables # this behavior. # Related issue: # https://discuss.pytorch.org/t/cuda-allocation-lifetime-for-inputs-to-distributed-all-reduce/191573 os.environ["TORCH_NCCL_AVOID_RECORD_STREAMS"] = "1" # Env vars will be set by TORCHRUN. self.rank = self.rank if self.rank is not None else int(os.getenv("RANK", "-1")) local_rank = int(os.getenv("LOCAL_RANK", "0")) self.device = torch.device(f"cuda:{local_rank}") if self.rank < 0: raise ValueError("Invalid or unspecified rank.") torch.cuda.set_device(self.device) _check_if_gpu_supports_dtype(self.model_config.dtype) # Initialize the distributed environment. # TODO: do not use cupy _init_distributed_environment(self.parallel_config, self.rank, self.distributed_init_method) if not self.parallel_config.disable_custom_all_reduce: init_custom_ar() # Initialize the model. set_random_seed(self.model_config.seed) # self.model = get_model(actor_model=self.model, model_config=self.model_config) def load_model(self): self.model_runner.load_model() @torch.inference_mode() def profile_num_available_blocks( self, block_size: int, gpu_memory_utilization: float, cpu_swap_space: int, cache_dtype: str, ) -> Tuple[int, int]: # Profile the memory usage of the model and get the maximum number of # cache blocks that can be allocated with the remaining free memory. torch.cuda.empty_cache() # torch.cuda.reset_peak_memory_stats() # Execute a forward pass with dummy inputs to profile the memory usage # of the model. self.model_runner.profile_run() # Calculate the number of blocks that can be allocated with the # profiled peak memory. torch.cuda.synchronize() free_gpu_memory, total_gpu_memory = torch.cuda.mem_get_info() peak_memory = total_gpu_memory - free_gpu_memory cache_block_size = CacheEngine.get_cache_block_size(block_size, cache_dtype, self.model_config, self.parallel_config) # NOTE(sgm) use the remaining memory num_gpu_blocks = int((free_gpu_memory * gpu_memory_utilization) // cache_block_size) # num_gpu_blocks = int((total_gpu_memory * gpu_memory_utilization - peak_memory) // cache_block_size) num_cpu_blocks = int(cpu_swap_space // cache_block_size) num_gpu_blocks = max(num_gpu_blocks, 0) num_cpu_blocks = max(num_cpu_blocks, 0) if self.model_runner.lora_manager: self.model_runner.remove_all_loras() gc.collect() torch.cuda.empty_cache() # Synchronize number of blocks with all the rank num_gpu_blocks = torch.tensor([num_gpu_blocks], device='cuda') num_cpu_blocks = torch.tensor([num_cpu_blocks], device='cuda') torch.distributed.all_reduce(num_gpu_blocks, op=torch.distributed.ReduceOp.MIN, group=get_tensor_model_parallel_group()) torch.distributed.all_reduce(num_cpu_blocks, op=torch.distributed.ReduceOp.MIN, group=get_tensor_model_parallel_group()) num_gpu_blocks = num_gpu_blocks.item() num_cpu_blocks = num_cpu_blocks.item() return num_gpu_blocks, num_cpu_blocks def init_cache_engine(self, cache_config: CacheConfig) -> None: if self.cache_engine is None and self.gpu_cache is None: self.cache_config = cache_config self.cache_engine = CacheEngine(self.cache_config, self.model_config, self.parallel_config) self.cache_events = self.cache_engine.events self.gpu_cache = self.cache_engine.gpu_cache self.model_runner.set_block_size(self.cache_engine.block_size) def free_cache_engine(self): # ensure `enforce_eager=True` self.cache_engine = None self.gpu_cache = None def warm_up_model(self) -> None: if not self.model_config.enforce_eager: self.model_runner.capture_model(self.gpu_cache) # Reset the seed to ensure that the random state is not affected by # the model initialization and profiling. set_random_seed(self.model_config.seed) def cache_swap( self, blocks_to_swap_in: Dict[int, int], blocks_to_swap_out: Dict[int, int], blocks_to_copy: Dict[int, List[int]], ) -> None: # Issue cache operations. issued_cache_op = False if blocks_to_swap_in: self.cache_engine.swap_in(blocks_to_swap_in) issued_cache_op = True if blocks_to_swap_out: self.cache_engine.swap_out(blocks_to_swap_out) issued_cache_op = True if blocks_to_copy: self.cache_engine.copy(blocks_to_copy) issued_cache_op = True cache_events = self.cache_events if issued_cache_op else None # Wait for cache operations to finish. # TODO(woosuk): Profile swapping overhead and optimize if needed. if cache_events is not None: for event in cache_events: event.wait() @torch.inference_mode() def execute_model( self, seq_group_metadata_list: List[SequenceGroupMetadata], blocks_to_swap_in: Dict[int, int], blocks_to_swap_out: Dict[int, int], blocks_to_copy: Dict[int, List[int]], ) -> SamplerOutput: num_seq_groups = len(seq_group_metadata_list) self.cache_swap(blocks_to_swap_in, blocks_to_swap_out, blocks_to_copy) # If there is no input, we don't need to execute the model. if num_seq_groups == 0: return {} output = self.model_runner.execute_model(seq_group_metadata_list, self.gpu_cache) return output # # Prepare input tensors. # # NOTE(shengguangming): currently we pad in our dataloader and unpad it in pre_process_input, j # # we can just input un-padded sequence for better performance # input_tokens, input_positions, input_metadata = self._prepare_inputs(seq_group_metadata_list) # # Execute the model. # output = self.model( # input_ids=input_tokens, # positions=input_positions, # kv_caches=self.gpu_cache, # input_metadata=input_metadata, # cache_events=cache_events, # ) # return output # assume the input is .state_dict() def sync_model_weights(self, actor_weights: Dict): load_weights(actor_weights, self.model_runner.model) def offload_model_weights(self) -> None: if self.cpu_model == None: self.cpu_model = {} for name, params in self.model_runner.model.named_parameters(): self.cpu_model[name] = torch.empty_like(params, device='cpu') params.data = self.cpu_model[name] else: for name, params in self.model_runner.model.named_parameters(): params.data = self.cpu_model[name] def add_lora(self, lora_request: LoRARequest) -> bool: return self.model_runner.add_lora(lora_request) def remove_lora(self, lora_id: int) -> bool: return self.model_runner.remove_lora(lora_id) def list_loras(self) -> Set[int]: return self.model_runner.list_loras() def _init_distributed_environment( parallel_config: ParallelConfig, rank: int, distributed_init_method: Optional[str] = None, ) -> None: """Initialize the distributed environment.""" if torch.distributed.is_initialized(): print('The distributed environment has been initialized before vLLM') elif not distributed_init_method: raise ValueError("distributed_init_method must be set if torch.distributed " "is not already initialized") else: torch.distributed.init_process_group( backend="nccl", world_size=parallel_config.world_size, rank=rank, # init_method=distributed_init_method, ) # A small all_reduce for warmup. torch.distributed.all_reduce(torch.zeros(1).cuda()) # TODO (shengguangming): maybe we should also flag the megatron is initialized if torch.distributed.get_world_size() > 1: initialize_model_parallel_from_megatron(tensor_model_parallel_size=parallel_config.tensor_parallel_size) else: initialize_model_parallel() def _pad_to_alignment(x: List[int], multiple_of: int, pad: int) -> List[int]: return x + [pad] * ((-len(x)) % multiple_of) def _pad_to_max(x: List[int], max_len: int, pad: int) -> List[int]: return x + [pad] * (max_len - len(x)) def _check_if_gpu_supports_dtype(torch_dtype: torch.dtype): # Check if the GPU supports the dtype. if torch_dtype == torch.bfloat16: compute_capability = torch.cuda.get_device_capability() if compute_capability[0] < 8: gpu_name = torch.cuda.get_device_name() raise ValueError("Bfloat16 is only supported on GPUs with compute capability " f"of at least 8.0. Your {gpu_name} GPU has compute capability " f"{compute_capability[0]}.{compute_capability[1]}.") ================================================ FILE: verl/third_party/vllm/vllm_v_0_4_2/__init__.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. ================================================ FILE: verl/third_party/vllm/vllm_v_0_4_2/arg_utils.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Copyright 2023 The vLLM team. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # Adapted from https://github.com/vllm-project/vllm/blob/main/vllm/engine/arg_utils.py import os import argparse import dataclasses from dataclasses import dataclass from typing import List, Optional, Union import torch.nn as nn from transformers import PretrainedConfig from .config import ModelConfig, LoadConfig from vllm.config import (CacheConfig, DecodingConfig, DeviceConfig, EngineConfig, LoRAConfig, ParallelConfig, SchedulerConfig, SpeculativeConfig, TokenizerPoolConfig, VisionLanguageConfig) from vllm.model_executor.layers.quantization import QUANTIZATION_METHODS from vllm.utils import str_to_int_tuple def nullable_str(val: str): if not val or val == "None": return None return val @dataclass class EngineArgs: """Arguments for vLLM engine.""" model_hf_config: PretrainedConfig = None skip_tokenizer_init: bool = False served_model_name: Optional[Union[str, List[str]]] = None # TODO download_dir: Optional[str] = None load_format: str = 'auto' dtype: str = 'auto' kv_cache_dtype: str = 'auto' quantization_param_path: Optional[str] = None seed: int = 0 max_model_len: Optional[int] = None worker_use_ray: bool = False pipeline_parallel_size: int = 1 tensor_parallel_size: int = 1 max_parallel_loading_workers: Optional[int] = None block_size: int = 16 enable_prefix_caching: bool = False use_v2_block_manager: bool = False swap_space: int = 4 # GiB gpu_memory_utilization: float = 0.90 max_num_batched_tokens: Optional[int] = None max_num_seqs: int = 256 max_logprobs: int = 5 # OpenAI default value disable_log_stats: bool = False revision: Optional[str] = None code_revision: Optional[str] = None tokenizer_revision: Optional[str] = None quantization: Optional[str] = None enforce_eager: bool = False max_context_len_to_capture: Optional[int] = None max_seq_len_to_capture: int = 8192 disable_custom_all_reduce: bool = False tokenizer_pool_size: int = 0 tokenizer_pool_type: str = "ray" tokenizer_pool_extra_config: Optional[dict] = None enable_lora: bool = False max_loras: int = 1 max_lora_rank: int = 16 fully_sharded_loras: bool = False lora_extra_vocab_size: int = 256 lora_dtype = 'auto' max_cpu_loras: Optional[int] = None device: str = 'auto' ray_workers_use_nsight: bool = False num_gpu_blocks_override: Optional[int] = None num_lookahead_slots: int = 0 model_loader_extra_config: Optional[dict] = None # Related to Vision-language models such as llava image_input_type: Optional[str] = None image_token_id: Optional[int] = None image_input_shape: Optional[str] = None image_feature_size: Optional[int] = None scheduler_delay_factor: float = 0.0 enable_chunked_prefill: bool = False guided_decoding_backend: str = 'outlines' # Speculative decoding configuration. speculative_model: Optional[str] = None num_speculative_tokens: Optional[int] = None speculative_max_model_len: Optional[int] = None ngram_prompt_lookup_max: Optional[int] = None ngram_prompt_lookup_min: Optional[int] = None @staticmethod def add_cli_args(parser: argparse.ArgumentParser) -> argparse.ArgumentParser: """Shared CLI arguments for vLLM engine.""" # Model arguments # TODO(shengguangming): delete the unused args parser.add_argument('--model', type=str, default='facebook/opt-125m', help='name or path of the huggingface model to use') parser.add_argument('--tokenizer', type=str, default=EngineArgs.tokenizer, help='name or path of the huggingface tokenizer to use') parser.add_argument('--revision', type=str, default=None, help='the specific model version to use. It can be a branch ' 'name, a tag name, or a commit id. If unspecified, will use ' 'the default version.') parser.add_argument('--tokenizer-revision', type=str, default=None, help='the specific tokenizer version to use. It can be a branch ' 'name, a tag name, or a commit id. If unspecified, will use ' 'the default version.') parser.add_argument('--tokenizer-mode', type=str, default=EngineArgs.tokenizer_mode, choices=['auto', 'slow'], help='tokenizer mode. "auto" will use the fast ' 'tokenizer if available, and "slow" will ' 'always use the slow tokenizer.') parser.add_argument('--trust-remote-code', action='store_true', help='trust remote code from huggingface') parser.add_argument('--download-dir', type=str, default=EngineArgs.download_dir, help='directory to download and load the weights, ' 'default to the default cache dir of ' 'huggingface') parser.add_argument('--load-format', type=str, default=EngineArgs.load_format, choices=['auto', 'pt', 'safetensors', 'npcache', 'dummy'], help='The format of the model weights to load. ' '"auto" will try to load the weights in the safetensors format ' 'and fall back to the pytorch bin format if safetensors format ' 'is not available. ' '"pt" will load the weights in the pytorch bin format. ' '"safetensors" will load the weights in the safetensors format. ' '"npcache" will load the weights in pytorch format and store ' 'a numpy cache to speed up the loading. ' '"dummy" will initialize the weights with random values, ' 'which is mainly for profiling.') parser.add_argument('--dtype', type=str, default=EngineArgs.dtype, choices=['auto', 'half', 'float16', 'bfloat16', 'float', 'float32'], help='data type for model weights and activations. ' 'The "auto" option will use FP16 precision ' 'for FP32 and FP16 models, and BF16 precision ' 'for BF16 models.') parser.add_argument('--max-model-len', type=int, default=None, help='model context length. If unspecified, ' 'will be automatically derived from the model.') # Parallel arguments parser.add_argument('--worker-use-ray', action='store_true', help='use Ray for distributed serving, will be ' 'automatically set when using more than 1 GPU') parser.add_argument('--pipeline-parallel-size', '-pp', type=int, default=EngineArgs.pipeline_parallel_size, help='number of pipeline stages') parser.add_argument('--tensor-parallel-size', '-tp', type=int, default=EngineArgs.tensor_parallel_size, help='number of tensor parallel replicas') # KV cache arguments parser.add_argument('--block-size', type=int, default=EngineArgs.block_size, choices=[8, 16, 32], help='token block size') # TODO(woosuk): Support fine-grained seeds (e.g., seed per request). parser.add_argument('--seed', type=int, default=EngineArgs.seed, help='random seed') parser.add_argument('--swap-space', type=int, default=EngineArgs.swap_space, help='CPU swap space size (GiB) per GPU') parser.add_argument('--gpu-memory-utilization', type=float, default=EngineArgs.gpu_memory_utilization, help='the percentage of GPU memory to be used for' 'the model executor') parser.add_argument('--max-num-batched-tokens', type=int, default=EngineArgs.max_num_batched_tokens, help='maximum number of batched tokens per ' 'iteration') parser.add_argument('--max-num-seqs', type=int, default=EngineArgs.max_num_seqs, help='maximum number of sequences per iteration') parser.add_argument('--disable-log-stats', action='store_true', help='disable logging statistics') # Quantization settings. parser.add_argument('--quantization', '-q', type=str, choices=['awq', None], default=None, help='Method used to quantize the weights') return parser @classmethod def from_cli_args(cls, args: argparse.Namespace) -> 'EngineArgs': # Get the list of attributes of this dataclass. attrs = [attr.name for attr in dataclasses.fields(cls)] # Set the attributes from the parsed arguments. engine_args = cls(**{attr: getattr(args, attr) for attr in attrs}) return engine_args def create_engine_config( self, ) -> EngineConfig: device_config = DeviceConfig(self.device) # NOTE(sgm): we only modify ModelConfig, other configs are import from vllm model_config = ModelConfig(self.model_hf_config, self.dtype, self.seed, self.revision, self.code_revision, self.tokenizer_revision, self.max_model_len, self.quantization, self.quantization_param_path, self.enforce_eager, self.max_context_len_to_capture, self.max_seq_len_to_capture, self.max_logprobs, self.skip_tokenizer_init, self.served_model_name) cache_config = CacheConfig(self.block_size, self.gpu_memory_utilization, self.swap_space, self.kv_cache_dtype, self.num_gpu_blocks_override, model_config.get_sliding_window(), self.enable_prefix_caching) parallel_config = ParallelConfig( self.pipeline_parallel_size, self.tensor_parallel_size, self.worker_use_ray, self.max_parallel_loading_workers, self.disable_custom_all_reduce, TokenizerPoolConfig.create_config( self.tokenizer_pool_size, self.tokenizer_pool_type, self.tokenizer_pool_extra_config, ), self.ray_workers_use_nsight) # Use the world_size set by TORCHRUN world_size = int(os.getenv("WORLD_SIZE", "-1")) assert world_size != -1, "The world_size is set to -1, not initialized by TORCHRUN" parallel_config.world_size = world_size # TODO: spec config speculative_config = SpeculativeConfig.maybe_create_spec_config( target_model_config=model_config, target_parallel_config=parallel_config, target_dtype=self.dtype, speculative_model=self.speculative_model, num_speculative_tokens=self.num_speculative_tokens, speculative_max_model_len=self.speculative_max_model_len, enable_chunked_prefill=self.enable_chunked_prefill, use_v2_block_manager=self.use_v2_block_manager, ngram_prompt_lookup_max=self.ngram_prompt_lookup_max, ngram_prompt_lookup_min=self.ngram_prompt_lookup_min, ) scheduler_config = SchedulerConfig( self.max_num_batched_tokens, self.max_num_seqs, model_config.max_model_len, self.use_v2_block_manager, num_lookahead_slots=(self.num_lookahead_slots if speculative_config is None else speculative_config.num_lookahead_slots), delay_factor=self.scheduler_delay_factor, enable_chunked_prefill=self.enable_chunked_prefill, ) lora_config = LoRAConfig(max_lora_rank=self.max_lora_rank, max_loras=self.max_loras, fully_sharded_loras=self.fully_sharded_loras, lora_extra_vocab_size=self.lora_extra_vocab_size, lora_dtype=self.lora_dtype, max_cpu_loras=self.max_cpu_loras if self.max_cpu_loras and self.max_cpu_loras > 0 else None) if self.enable_lora else None load_config = LoadConfig( load_format=self.load_format, download_dir=self.download_dir, model_loader_extra_config=self.model_loader_extra_config, ) if self.image_input_type: if (not self.image_token_id or not self.image_input_shape or not self.image_feature_size): raise ValueError('Specify `image_token_id`, `image_input_shape` and ' '`image_feature_size` together with `image_input_type`.') vision_language_config = VisionLanguageConfig( image_input_type=VisionLanguageConfig.get_image_input_enum_type(self.image_input_type), image_token_id=self.image_token_id, image_input_shape=str_to_int_tuple(self.image_input_shape), image_feature_size=self.image_feature_size, ) else: vision_language_config = None decoding_config = DecodingConfig(guided_decoding_backend=self.guided_decoding_backend) return EngineConfig(model_config=model_config, cache_config=cache_config, parallel_config=parallel_config, scheduler_config=scheduler_config, device_config=device_config, lora_config=lora_config, vision_language_config=vision_language_config, speculative_config=speculative_config, load_config=load_config, decoding_config=decoding_config) ================================================ FILE: verl/third_party/vllm/vllm_v_0_4_2/config.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Copyright 2023 The vLLM team. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # Adapted from https://github.com/vllm-project/vllm/blob/main/vllm/config.py import enum import json from typing import List, Optional, Union from dataclasses import dataclass, field, fields from transformers import PretrainedConfig from vllm.logger import init_logger from vllm.model_executor.layers.quantization import get_quantization_config from vllm.transformers_utils.config import get_hf_text_config from vllm.utils import is_hip # Add for verl from vllm.config import ModelConfig, _get_and_verify_dtype, _get_and_verify_max_len GPTQMarlinConfig = get_quantization_config("gptq_marlin") logger = init_logger(__name__) _GB = 1 << 30 class ModelConfig(ModelConfig): """Configuration for the model. Args: model: Name or path of the huggingface model to use. tokenizer: Name or path of the huggingface tokenizer to use. tokenizer_mode: Tokenizer mode. "auto" will use the fast tokenizer if available, and "slow" will always use the slow tokenizer. trust_remote_code: Trust remote code (e.g., from HuggingFace) when downloading the model and tokenizer. download_dir: Directory to download and load the weights, default to the default cache directory of huggingface. load_format: The format of the model weights to load: "auto" will try to load the weights in the safetensors format and fall back to the pytorch bin format if safetensors format is not available. "pt" will load the weights in the pytorch bin format. "safetensors" will load the weights in the safetensors format. "npcache" will load the weights in pytorch format and store a numpy cache to speed up the loading. "dummy" will initialize the weights with random values, which is mainly for profiling. dtype: Data type for model weights and activations. The "auto" option will use FP16 precision for FP32 and FP16 models, and BF16 precision for BF16 models. seed: Random seed for reproducibility. revision: The specific model version to use. It can be a branch name, a tag name, or a commit id. If unspecified, will use the default version. code_revision: The specific revision to use for the model code on Hugging Face Hub. It can be a branch name, a tag name, or a commit id. If unspecified, will use the default version. tokenizer_revision: The specific tokenizer version to use. It can be a branch name, a tag name, or a commit id. If unspecified, will use the default version. max_model_len: Maximum length of a sequence (including prompt and output). If None, will be derived from the model. quantization: Quantization method that was used to quantize the model weights. If None, we assume the model weights are not quantized. quantization_param_path: Path to JSON file containing scaling factors. Used to load KV cache scaling factors into the model when KV cache type is FP8_E4M3 on ROCm (AMD GPU). In the future these will also be used to load activation and weight scaling factors when the model dtype is FP8_E4M3 on ROCm. enforce_eager: Whether to enforce eager execution. If True, we will disable CUDA graph and always execute the model in eager mode. If False, we will use CUDA graph and eager execution in hybrid. max_context_len_to_capture: Maximum context len covered by CUDA graphs. When a sequence has context length larger than this, we fall back to eager mode (DEPRECATED. Use max_seq_len_to_capture instead). max_seq_len_to_capture: Maximum sequence len covered by CUDA graphs. When a sequence has context length larger than this, we fall back to eager mode skip_tokenizer_init: If true, skip initialization of tokenizer and detokenizer. served_model_name: The model name used in metrics tag `model_name`, matches the model name exposed via the APIs. If multiple model names provided, the first name will be used. If not specified, the model name will be the same as `model`. """ def __init__( self, hf_config: PretrainedConfig, dtype: str, seed: int, revision: Optional[str] = None, code_revision: Optional[str] = None, tokenizer_revision: Optional[str] = None, max_model_len: Optional[int] = None, quantization: Optional[str] = None, quantization_param_path: Optional[str] = None, enforce_eager: bool = False, max_context_len_to_capture: Optional[int] = None, max_seq_len_to_capture: Optional[int] = None, max_logprobs: int = 5, skip_tokenizer_init: bool = False, served_model_name: Optional[Union[str, List[str]]] = None, ) -> None: self.model = hf_config._name_or_path self.tokenizer = hf_config._name_or_path self.seed = seed self.revision = revision self.code_revision = code_revision self.tokenizer_revision = tokenizer_revision self.quantization = quantization self.quantization_param_path = quantization_param_path self.enforce_eager = enforce_eager self.max_context_len_to_capture = max_context_len_to_capture if self.max_context_len_to_capture is not None: raise ValueError("`max_context_len_to_capture` is deprecated. " "Use `max_seq_len_to_capture` instead.") self.max_seq_len_to_capture = (max_seq_len_to_capture or max_context_len_to_capture) self.max_logprobs = max_logprobs self.skip_tokenizer_init = skip_tokenizer_init # self.hf_config = get_config(model, trust_remote_code, revision) self.hf_config = hf_config self.hf_text_config = get_hf_text_config(hf_config) # TODO: for multimodal model self.dtype = _get_and_verify_dtype(self.hf_config, dtype) self.max_model_len = _get_and_verify_max_len(self.hf_config, max_model_len) # self.served_model_name = get_served_model_name(model, # served_model_name) # self._verify_load_format() # self._verify_tokenizer_mode() self._verify_quantization() self._verify_cuda_graph() class LoadFormat(str, enum.Enum): AUTO = 'auto' MEGATRON = "megatron" HF = "hf" DTENSOR = 'dtensor' DUMMY_HF = 'dummy_hf' DUMMY_MEGATRON = 'dummy_megatron' DUMMY_DTENSOR = 'dummy_dtensor' @dataclass class LoadConfig: """ download_dir: Directory to download and load the weights, default to the default cache directory of huggingface. load_format: The format of the model weights to load: "auto" will try to load the weights in the safetensors format and fall back to the pytorch bin format if safetensors format is not available. "pt" will load the weights in the pytorch bin format. "safetensors" will load the weights in the safetensors format. "npcache" will load the weights in pytorch format and store a numpy cache to speed up the loading. "dummy" will initialize the weights with random values, which is mainly for profiling. "tensorizer" will use CoreWeave's tensorizer library for fast weight loading. """ load_format: Union[str, LoadFormat, "BaseModelLoader"] = LoadFormat.AUTO download_dir: Optional[str] = None model_loader_extra_config: Optional[Union[str, dict]] = field(default_factory=dict) def __post_init__(self): model_loader_extra_config = self.model_loader_extra_config or {} if isinstance(model_loader_extra_config, str): self.model_loader_extra_config = json.loads(model_loader_extra_config) self._verify_load_format() def _verify_load_format(self) -> None: if not isinstance(self.load_format, str): return load_format = self.load_format.lower() self.load_format = LoadFormat(load_format) rocm_not_supported_load_format: List[str] = [] if is_hip() and load_format in rocm_not_supported_load_format: rocm_supported_load_format = [ f for f in LoadFormat.__members__ if (f not in rocm_not_supported_load_format) ] raise ValueError(f"load format '{load_format}' is not supported in ROCm. " f"Supported load formats are " f"{rocm_supported_load_format}") ================================================ FILE: verl/third_party/vllm/vllm_v_0_4_2/dtensor_weight_loaders.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Copyright 2023 The vLLM team. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # Adapted from https://github.com/vllm-project/vllm/tree/main/vllm/model_executor/models from typing import Dict, Iterable, Tuple import torch import torch.nn as nn from torch.distributed._tensor import DTensor, Shard, Replicate from vllm.model_executor.layers.linear import * from vllm.model_executor.models import ModelRegistry from vllm.model_executor.model_loader.weight_utils import default_weight_loader def gemma_dtensor_weight_loader(actor_weights: Dict, vllm_model: nn.Module) -> nn.Module: stacked_params_mapping = [ # (param_name, shard_name, shard_id) ("qkv_proj", "q_proj", "q"), ("qkv_proj", "k_proj", "k"), ("qkv_proj", "v_proj", "v"), ("gate_up_proj", "gate_proj", 0), ("gate_up_proj", "up_proj", 1), ] params_dict = dict(vllm_model.named_parameters()) for name, loaded_weight in actor_weights.items(): for (param_name, shard_name, shard_id) in stacked_params_mapping: if shard_name not in name: continue stacked_name = name.replace(shard_name, param_name) # Skip loading extra bias for GPTQ models. if stacked_name.endswith(".bias") and stacked_name not in params_dict: continue local_loaded_weight = redistribute_dtensor(param_name=name, loaded_weights=loaded_weight) param = params_dict[stacked_name] weight_loader = getattr(param, "weight_loader", default_weight_loader) weight_loader(param, local_loaded_weight.to(dtype=param.dtype), shard_id) break else: # lm_head is not used in vllm as it is tied with embed_token. # To prevent errors, skip loading lm_head.weight. if "lm_head.weight" in name: continue # Skip loading extra bias for GPTQ models. if name.endswith(".bias") and name not in params_dict: continue # GemmaRMSNorm is different from Llama's in that it multiplies # (1 + weight) to the output, instead of just weight. if "norm.weight" in name: local_loaded_weight = redistribute_dtensor(param_name=name, loaded_weights=loaded_weight) norm_weight = local_loaded_weight + 1.0 param = params_dict[name] weight_loader = getattr(param, "weight_loader", default_weight_loader) weight_loader(param, norm_weight.to(dtype=param.dtype)) else: local_loaded_weight = redistribute_dtensor(param_name=name, loaded_weights=loaded_weight) param = params_dict[name] weight_loader = getattr(param, "weight_loader", default_weight_loader) weight_loader(param, local_loaded_weight.to(dtype=param.dtype)) def gptbigcode_dtensor_load_weights(actor_weights: Dict, vllm_model: nn.Module): params_dict = dict(vllm_model.named_parameters(remove_duplicate=False)) for name, loaded_weight in actor_weights.items(): if "lm_head.weight" in name: continue if ".attn.bias" in name: # Skip attention mask. # NOTE: "c_attn.bias" should not be skipped. continue local_loaded_weight = redistribute_dtensor(param_name=name, loaded_weights=loaded_weight) param = params_dict[name] weight_loader = getattr(param, "weight_loader", default_weight_loader) weight_loader(param, local_loaded_weight.to(dtype=param.dtype)) def starcoder2_dtensor_load_weights(actor_weights: Dict, vllm_model: nn.Module): stacked_params_mapping = [ # (param_name, shard_name, shard_id) ("qkv_proj", "q_proj", "q"), ("qkv_proj", "k_proj", "k"), ("qkv_proj", "v_proj", "v"), ] params_dict = dict(vllm_model.named_parameters(remove_duplicate=False)) for name, loaded_weight in actor_weights.items(): if "rotary_emb.inv_freq" in name: continue for (param_name, weight_name, shard_id) in stacked_params_mapping: if weight_name not in name: continue name = name.replace(weight_name, param_name) local_loaded_weight = redistribute_dtensor(param_name=name, loaded_weights=loaded_weight) param = params_dict[name] weight_loader = param.weight_loader weight_loader(param, local_loaded_weight.to(dtype=param.dtype), shard_id) break else: if vllm_model.config.tie_word_embeddings and "lm_head.weight" in name: continue param = params_dict[name] local_loaded_weight = redistribute_dtensor(param_name=name, loaded_weights=loaded_weight) weight_loader = getattr(param, "weight_loader", default_weight_loader) weight_loader(param, local_loaded_weight.to(dtype=param.dtype)) def llama_dtensor_weight_loader(actor_weights: Dict, vllm_model: nn.Module) -> nn.Module: stacked_params_mapping = [ # (param_name, shard_name, shard_id) (".qkv_proj", ".q_proj", "q"), (".qkv_proj", ".k_proj", "k"), (".qkv_proj", ".v_proj", "v"), (".gate_up_proj", ".gate_proj", 0), (".gate_up_proj", ".up_proj", 1), ] params_dict = dict(vllm_model.named_parameters()) for name, loaded_weight in actor_weights.items(): if "rotary_emb.inv_freq" in name: continue if ("rotary_emb.cos_cached" in name or "rotary_emb.sin_cached" in name): # Models trained using ColossalAI may include these tensors in # the checkpoint. Skip them. continue # With tie_word_embeddings, we can skip lm_head.weight # The weight might appear unnecessarily in the files if the model is # processed with quantization, LoRA, fine-tuning, etc. if vllm_model.config.tie_word_embeddings and "lm_head.weight" in name: continue for (param_name, weight_name, shard_id) in stacked_params_mapping: if weight_name not in name: continue name = name.replace(weight_name, param_name) # Skip loading extra bias for GPTQ models. if name.endswith(".bias") and name not in params_dict: continue local_loaded_weight = redistribute_dtensor(param_name=name, loaded_weights=loaded_weight) param = params_dict[name] weight_loader = param.weight_loader weight_loader(param, local_loaded_weight.to(dtype=param.dtype), shard_id) break else: # Skip loading extra bias for GPTQ models. if name.endswith(".bias") and name not in params_dict: continue local_loaded_weight = redistribute_dtensor(param_name=name, loaded_weights=loaded_weight) param = params_dict[name] weight_loader = getattr(param, "weight_loader", default_weight_loader) weight_loader(param, local_loaded_weight) def qwen2_dtensor_weight_loader(actor_weights: Dict, vllm_model: nn.Module) -> nn.Module: stacked_params_mapping = [ # (param_name, shard_name, shard_id) ("qkv_proj", "q_proj", "q"), ("qkv_proj", "k_proj", "k"), ("qkv_proj", "v_proj", "v"), ("gate_up_proj", "gate_proj", 0), ("gate_up_proj", "up_proj", 1), ] params_dict = dict(vllm_model.named_parameters(remove_duplicate=False)) for name, loaded_weight in actor_weights.items(): if "rotary_emb.inv_freq" in name: continue if vllm_model.config.tie_word_embeddings and "lm_head.weight" in name: continue for (param_name, weight_name, shard_id) in stacked_params_mapping: if weight_name not in name: continue name = name.replace(weight_name, param_name) # Skip loading extra bias for GPTQ models. if name.endswith(".bias") and name not in params_dict: continue local_loaded_weight = redistribute_dtensor(param_name=name, loaded_weights=loaded_weight) param = params_dict[name] weight_loader = param.weight_loader weight_loader(param, local_loaded_weight.to(dtype=param.dtype), shard_id) break else: # Skip loading extra bias for GPTQ models. if name.endswith(".bias") and name not in params_dict: continue param = params_dict[name] local_loaded_weight = redistribute_dtensor(param_name=name, loaded_weights=loaded_weight) weight_loader = getattr(param, "weight_loader", default_weight_loader) weight_loader(param, local_loaded_weight.to(dtype=param.dtype)) def gpt2_dtensor_weight_loader(actor_weights: Dict, vllm_model: nn.Module) -> nn.Module: pass def redistribute_dtensor(param_name: str, loaded_weights: DTensor, parallelize_plan: Dict = None): param_name = _process_parameter_names(name=param_name) if parallelize_plan is not None: assert param_name in parallelize_plan.keys(), \ f"param name: {param_name} not in parallelize_plan :{parallelize_plan.keys()}" placement = parallelize_plan[param_name] local_loaded_weights = loaded_weights.redistribute(device_mesh=loaded_weights.device_mesh, placements=placement).to_local() else: local_loaded_weights = loaded_weights.full_tensor() return local_loaded_weights def _process_parameter_names(name): # Remove '.weight' if it exists at the end of the string if name.endswith(".weight"): name = name[:-7] # Remove 'model.layers.x.' or 'model.' prefix if "model.layers" in name: parts = name.split('.') # Reconstruct the string without 'model.layers.x.' name = '.'.join(parts[3:]) # parts[0] is 'model', parts[1] is 'layers', parts[2] is 'x' elif name.startswith("model."): name = name[6:] # Remove 'model.' return name __MODEL_DTENSOR_WEIGHT_LOADER_REGISTRY__ = { 'GPT2LMHeadModel': gpt2_dtensor_weight_loader, 'LlamaForCausalLM': llama_dtensor_weight_loader, 'LLaMAForCausalLM': llama_dtensor_weight_loader, 'MistralForCausalLM': llama_dtensor_weight_loader, # mistral is the same as llama in vLLM 'InternLMForCausalLM': llama_dtensor_weight_loader, 'AquilaModel': llama_dtensor_weight_loader, 'AquilaForCausalLM': llama_dtensor_weight_loader, 'Phi3ForCausalLM': llama_dtensor_weight_loader, 'GemmaForCausalLM': gemma_dtensor_weight_loader, 'GPTBigCodeForCausalLM': gptbigcode_dtensor_load_weights, 'Starcoder2ForCausalLM': starcoder2_dtensor_load_weights, 'Qwen2ForCausalLM': qwen2_dtensor_weight_loader } # the actor model is .state_dict() # Load dtensor weights def load_dtensor_weights(actor_weights: Dict, vllm_model: nn.Module): weight_loader = _get_model_weight_loader(vllm_model.__class__.__name__) weight_loader(actor_weights, vllm_model) # NOTE(sgm) to reduce peak memory usage, we offload vllm model to cpu # after init, and we need this after sync model weights for in first iter. vllm_model = vllm_model.cuda() def _get_model_weight_loader(arch: str): if arch in __MODEL_DTENSOR_WEIGHT_LOADER_REGISTRY__: return __MODEL_DTENSOR_WEIGHT_LOADER_REGISTRY__[arch] raise ValueError(f"Model architectures {arch} are not supported for now. " f"Supported architectures: {__MODEL_DTENSOR_WEIGHT_LOADER_REGISTRY__.keys()}") # NOTE(sgm): we use per-parameter weight loader in each vllm sub def update_dtensor_weight_loader(): pass ================================================ FILE: verl/third_party/vllm/vllm_v_0_4_2/hf_weight_loader.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Copyright 2023 The vLLM team. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # Adapted from https://github.com/vllm-project/vllm/tree/main/vllm/model_executor/models from typing import Dict, Union, Optional, Iterable, Tuple import torch import torch.nn as nn from vllm.model_executor.model_loader.utils import set_default_torch_dtype from vllm.model_executor.model_loader.weight_utils import default_weight_loader def update_hf_weight_loader(): from vllm.model_executor.models.gemma import GemmaForCausalLM GemmaForCausalLM.load_weights = gemma_load_weights def gemma_load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]): stacked_params_mapping = [ # (param_name, shard_name, shard_id) ("qkv_proj", "q_proj", "q"), ("qkv_proj", "k_proj", "k"), ("qkv_proj", "v_proj", "v"), ("gate_up_proj", "gate_proj", 0), ("gate_up_proj", "up_proj", 1), ] params_dict = dict(self.named_parameters()) loaded_params = set() for name, loaded_weight in weights: for (param_name, shard_name, shard_id) in stacked_params_mapping: if shard_name not in name: continue name = name.replace(shard_name, param_name) # Skip loading extra bias for GPTQ models. if name.endswith(".bias") and name not in params_dict: continue param = params_dict[name] weight_loader = param.weight_loader weight_loader(param, loaded_weight, shard_id) break else: # lm_head is not used in vllm as it is tied with embed_token. # To prevent errors, skip loading lm_head.weight. if "lm_head.weight" in name: continue # Skip loading extra bias for GPTQ models. if name.endswith(".bias") and name not in params_dict: continue # GemmaRMSNorm is different from Llama's in that it multiplies # (1 + weight) to the output, instead of just weight. if "norm.weight" in name: norm_weight = loaded_weight + 1.0 # prevent inplace modify actor weights param = params_dict[name] weight_loader = getattr(param, "weight_loader", default_weight_loader) weight_loader(param, norm_weight) else: param = params_dict[name] weight_loader = getattr(param, "weight_loader", default_weight_loader) weight_loader(param, loaded_weight) loaded_params.add(name) unloaded_params = params_dict.keys() - loaded_params if unloaded_params: raise RuntimeError("Some weights are not initialized from checkpoints: " f"{unloaded_params}") def load_hf_weights(actor_weights: Dict, vllm_model: nn.Module): assert isinstance(actor_weights, Dict) with set_default_torch_dtype(next(vllm_model.parameters()).dtype): # TODO vllm_model.load_weights(actor_weights.items()) for _, module in vllm_model.named_modules(): quant_method = getattr(module, "quant_method", None) if quant_method is not None: quant_method.process_weights_after_loading(module) # FIXME: Remove this after Mixtral is updated # to use quant_method. if hasattr(module, "process_weights_after_loading"): module.process_weights_after_loading() vllm_model = vllm_model.cuda() ================================================ FILE: verl/third_party/vllm/vllm_v_0_4_2/llm.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Copyright 2023 The vLLM team. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # Adapted from https://github.com/vllm-project/vllm/blob/main/vllm/entrypoints/llm.py from typing import Dict, List, Optional, Tuple, Union from tqdm import tqdm from transformers import PreTrainedTokenizer, PreTrainedTokenizerFast from transformers import PretrainedConfig import torch.nn as nn from .arg_utils import EngineArgs from .llm_engine_sp import LLMEngine from vllm.lora.request import LoRARequest from vllm.outputs import RequestOutput from vllm.sampling_params import SamplingParams from vllm.sequence import MultiModalData from vllm.usage.usage_lib import UsageContext from vllm.utils import Counter import torch from torch.nn.utils.rnn import pad_sequence from verl.workers.rollout.tokenizer import HybridEngineBaseTokenizer class LLM: """An LLM for generating texts from given prompts and sampling parameters. This class includes a tokenizer, a language model (possibly distributed across multiple GPUs), and GPU memory space allocated for intermediate states (aka KV cache). Given a batch of prompts and sampling parameters, this class generates texts from the model, using an intelligent batching mechanism and efficient memory management. NOTE: This class is intended to be used for offline inference. For online serving, use the `AsyncLLMEngine` class instead. NOTE: For the comprehensive list of arguments, see `EngineArgs`. Args: model: A HuggingFace Transformers model instance. tokenizer: A HuggingFace Transformers tokenizer instance. tokenizer_mode: The tokenizer mode. "auto" will use the fast tokenizer if available, and "slow" will always use the slow tokenizer. trust_remote_code: Trust remote code (e.g., from HuggingFace) when downloading the model and tokenizer. tensor_parallel_size: The number of GPUs to use for distributed execution with tensor parallelism. dtype: The data type for the model weights and activations. Currently, we support `float32`, `float16`, and `bfloat16`. If `auto`, we use the `torch_dtype` attribute specified in the model config file. However, if the `torch_dtype` in the config is `float32`, we will use `float16` instead. quantization: The method used to quantize the model weights. Currently, we support "awq". If None, we assume the model weights are not quantized and use `dtype` to determine the data type of the weights. revision: The specific model version to use. It can be a branch name, a tag name, or a commit id. tokenizer_revision: The specific tokenizer version to use. It can be a branch name, a tag name, or a commit id. seed: The seed to initialize the random number generator for sampling. gpu_memory_utilization: The ratio (between 0 and 1) of GPU memory to reserve for the model weights, activations, and KV cache. Higher values will increase the KV cache size and thus improve the model's throughput. However, if the value is too high, it may cause out-of- memory (OOM) errors. swap_space: The size (GiB) of CPU memory per GPU to use as swap space. This can be used for temporarily storing the states of the requests when their `best_of` sampling parameters are larger than 1. If all requests will have `best_of=1`, you can safely set this to 0. Otherwise, too small values may cause out-of-memory (OOM) errors. enforce_eager: Whether to enforce eager execution. If True, we will disable CUDA graph and always execute the model in eager mode. If False, we will use CUDA graph and eager execution in hybrid. max_context_len_to_capture: Maximum context len covered by CUDA graphs. When a sequence has context length larger than this, we fall back to eager mode. disable_custom_all_reduce: See ParallelConfig """ def __init__( self, model: Union[nn.Module, Dict], # model itself or its parameter dict tokenizer: Union[PreTrainedTokenizer, PreTrainedTokenizerFast, HybridEngineBaseTokenizer], model_hf_config: PretrainedConfig, tokenizer_mode: str = "auto", trust_remote_code: bool = False, tensor_parallel_size: int = 1, dtype: str = "auto", quantization: Optional[str] = None, revision: Optional[str] = None, tokenizer_revision: Optional[str] = None, seed: int = 0, gpu_memory_utilization: float = 0.9, swap_space: int = 4, enforce_eager: bool = False, max_context_len_to_capture: int = None, disable_custom_all_reduce: bool = False, load_format = 'auto', **kwargs, ) -> None: if "disable_log_stats" not in kwargs: kwargs["disable_log_stats"] = True engine_args = EngineArgs( model_hf_config=model_hf_config, tensor_parallel_size=tensor_parallel_size, dtype=dtype, quantization=quantization, revision=revision, tokenizer_revision=tokenizer_revision, seed=seed, gpu_memory_utilization=gpu_memory_utilization, swap_space=swap_space, enforce_eager=enforce_eager, max_context_len_to_capture=max_context_len_to_capture, disable_custom_all_reduce=disable_custom_all_reduce, load_format=load_format, **kwargs, ) tokenizer_cls = (PreTrainedTokenizer, PreTrainedTokenizerFast, HybridEngineBaseTokenizer) if not isinstance(tokenizer, tokenizer_cls): raise ValueError( f"Unexpected tokenizer type: {type(tokenizer)}. Must be" "one of the following: PreTrainedTokenizer, PreTrainedTokenizerFast, verl.workers.rollout.HybridEngineBaseTokenizer" ) self.llm_engine = LLMEngine.from_engine_args(model, tokenizer, engine_args) self.request_counter = Counter() def init_cache_engine(self): self.llm_engine.init_cache_engine() def free_cache_engine(self): self.llm_engine.free_cache_engine() def get_tokenizer(self) -> Union[PreTrainedTokenizer, PreTrainedTokenizerFast]: return self.llm_engine.tokenizer def set_tokenizer( self, tokenizer: Union[PreTrainedTokenizer, PreTrainedTokenizerFast], ) -> None: self.llm_engine.tokenizer = tokenizer def generate( self, prompts: Optional[Union[str, List[str]]] = None, sampling_params: Optional[Union[SamplingParams, List[SamplingParams]]] = None, prompt_token_ids: Optional[List[List[int]]] = None, use_tqdm: bool = True, lora_request: Optional[LoRARequest] = None, multi_modal_data: Optional[MultiModalData] = None, ) -> List[RequestOutput]: """Generates the completions for the input prompts. NOTE: This class automatically batches the given prompts, considering the memory constraint. For the best performance, put all of your prompts into a single list and pass it to this method. Args: prompts: A list of prompts to generate completions for. sampling_params: The sampling parameters for text generation. If None, we use the default sampling parameters. When it is a single value, it is applied to every prompt. When it is a list, the list must have the same length as the prompts and it is paired one by one with the prompt. prompt_token_ids: A list of token IDs for the prompts. If None, we use the tokenizer to convert the prompts to token IDs. use_tqdm: Whether to use tqdm to display the progress bar. lora_request: LoRA request to use for generation, if any. multi_modal_data: Multi modal data. Returns: A list of `RequestOutput` objects containing the generated completions in the same order as the input prompts. """ if prompts is None and prompt_token_ids is None: raise ValueError("Either prompts or prompt_token_ids must be " "provided.") if self.llm_engine.model_config.skip_tokenizer_init \ and prompts is not None: raise ValueError("prompts must be None if skip_tokenizer_init " "is True") if isinstance(prompts, str): # Convert a single prompt to a list. prompts = [prompts] if (prompts is not None and prompt_token_ids is not None and len(prompts) != len(prompt_token_ids)): raise ValueError("The lengths of prompts and prompt_token_ids " "must be the same.") if prompts is not None: num_requests = len(prompts) else: assert prompt_token_ids is not None num_requests = len(prompt_token_ids) if sampling_params is None: # Use default sampling params. sampling_params = SamplingParams() elif isinstance(sampling_params, list) and len(sampling_params) != num_requests: raise ValueError("The lengths of prompts and sampling_params " "must be the same.") if multi_modal_data: multi_modal_data.data = multi_modal_data.data.to(torch.float16) # Add requests to the engine. for i in range(num_requests): prompt = prompts[i] if prompts is not None else None token_ids = None if prompt_token_ids is None else prompt_token_ids[i] if not isinstance(token_ids, list): # NOTE(shengguangming): convert the rollout input into List[str] token_ids = self._pre_process_inputs(token_ids) self._add_request( prompt, sampling_params[i] if isinstance(sampling_params, list) else sampling_params, token_ids, lora_request=lora_request, # Get ith image while maintaining the batch dim. multi_modal_data=MultiModalData(type=multi_modal_data.type, data=multi_modal_data.data[i].unsqueeze(0)) if multi_modal_data else None, ) return self._run_engine(use_tqdm) def _add_request( self, prompt: Optional[str], sampling_params: SamplingParams, prompt_token_ids: Optional[List[int]], lora_request: Optional[LoRARequest] = None, multi_modal_data: Optional[MultiModalData] = None, ) -> None: request_id = str(next(self.request_counter)) self.llm_engine.add_request(request_id, prompt, sampling_params, prompt_token_ids, lora_request=lora_request, multi_modal_data=multi_modal_data) def _run_engine(self, use_tqdm: bool) -> List[RequestOutput]: # Initialize tqdm. if use_tqdm: num_requests = self.llm_engine.get_num_unfinished_requests() pbar = tqdm(total=num_requests, desc="Processed prompts", dynamic_ncols=True) # Run the engine. outputs: List[RequestOutput] = [] while self.llm_engine.has_unfinished_requests(): step_outputs = self.llm_engine.step() for output in step_outputs: if output.finished: outputs.append(output) if use_tqdm: pbar.update(1) if use_tqdm: pbar.close() # Sort the outputs by request ID. # This is necessary because some requests may be finished earlier than # its previous requests. outputs = sorted(outputs, key=lambda x: int(x.request_id)) # TODO(shengguangming): maybe we can hack the autoregressive logics without only apply post process for better performance return self._post_process_outputs(outputs) # NOTE(shengguangming): add for verl # TODO(sgm): we can optimize it by making the dataloader yield List[int] without padding. def _pre_process_inputs(self, prompt_token_ids: torch.Tensor) -> List[int]: # remove the left padding in the prompt token_id pad_token_id = self.llm_engine.tokenizer.pad_token_id if self.llm_engine.tokenizer.pad_token_id is not None else self.llm_engine.tokenizer.eos_token_id non_pad_index = torch.nonzero(prompt_token_ids != pad_token_id, as_tuple=False)[0][0] token_ids = prompt_token_ids[non_pad_index:].tolist() return token_ids # NOTE(shengguangming): add for verl def _post_process_outputs(self, request_outputs: List[RequestOutput]) -> Tuple[torch.Tensor, torch.Tensor]: output_token_ids = [] logprobs = [] for request_output in request_outputs: # List[RequestOutput] outputs = request_output.outputs for output in outputs: # List[CompletionOutput], usually len == 1 output_token_ids.append(torch.tensor(output.token_ids)) # TODO(shengguangming): can be optimzied by rewrite the Sampler._get_logprobs() logits logprobs_dicts = output.logprobs if logprobs_dicts is not None: logprob = [] for logprobs_dict, id in zip(logprobs_dicts, output.token_ids): logprob.append(logprobs_dict[id].logprob) logprobs.append(torch.tensor(logprob)) pad_token_id = self.llm_engine.tokenizer.pad_token_id if self.llm_engine.tokenizer.pad_token_id is not None else self.llm_engine.tokenizer.eos_token_id output_token_ids = pad_sequence(output_token_ids, batch_first=True, padding_value=pad_token_id) if len(logprobs) > 0: logprobs = pad_sequence(logprobs, batch_first=True, padding_value=pad_token_id) return output_token_ids, logprobs def sync_model_weights(self, actor_weights: Dict[str, torch.Tensor], load_format: str) -> None: self.llm_engine.sync_model_weights(actor_weights=actor_weights, load_format=load_format) def offload_model_weights(self) -> None: self.llm_engine.offload_model_weights() ================================================ FILE: verl/third_party/vllm/vllm_v_0_4_2/llm_engine_sp.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Copyright 2023 The vLLM team. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # Adapted from https://github.com/vllm-project/vllm/blob/main/vllm/engine/llm_engine.py import torch from typing import Dict, Optional, Union, Type import vllm from vllm.config import (CacheConfig, DecodingConfig, DeviceConfig, LoRAConfig, ParallelConfig, SchedulerConfig, SpeculativeConfig, VisionLanguageConfig) from vllm.core.scheduler import Scheduler from vllm.engine.output_processor.interfaces import (SequenceGroupOutputProcessor) from vllm.engine.output_processor.stop_checker import StopChecker from vllm.executor.executor_base import ExecutorBase from vllm.logger import init_logger from vllm.transformers_utils.detokenizer import Detokenizer from vllm.engine.metrics import StatLogger from vllm.usage.usage_lib import (UsageContext, is_usage_stats_enabled, usage_message) from vllm.utils import Counter from vllm.engine.llm_engine import _load_generation_config_dict from vllm.engine.llm_engine import LLMEngine import torch.nn as nn from .arg_utils import EngineArgs from .tokenizer import TokenizerGroup from .config import ModelConfig, LoadConfig logger = init_logger(__name__) _LOCAL_LOGGING_INTERVAL_SEC = 5 class LLMEngine(LLMEngine): """An LLM engine that receives requests and generates texts. This is the main class for the vLLM engine. It receives requests from clients and generates texts from the LLM. It includes a tokenizer, a language model (possibly distributed across multiple GPUs), and GPU memory space allocated for intermediate states (aka KV cache). This class utilizes iteration-level scheduling and efficient memory management to maximize the serving throughput. The `LLM` class wraps this class for offline batched inference and the `AsyncLLMEngine` class wraps this class for online serving. NOTE: The config arguments are derived from the `EngineArgs` class. For the comprehensive list of arguments, see `EngineArgs`. Args: model: the actor model initialize outside vllm (add for verl) tokenizer: the initialized tokenizer (add for verl) model_config: The configuration related to the LLM model. cache_config: The configuration related to the KV cache memory management. parallel_config: The configuration related to distributed execution. scheduler_config: The configuration related to the request scheduler. distributed_init_method: The initialization method for distributed execution. See `torch.distributed.init_process_group` for details. placement_group: Ray placement group for distributed execution. Required for distributed execution. log_stats: Whether to log statistics. """ def __init__( self, # NOTE(sgm): first two arguments are added for verl model: Union[nn.Module, Dict], # model itself or its parameter dict tokenizer: nn.Module, # NOTE(sgm): vllm original arguments model_config: ModelConfig, cache_config: CacheConfig, parallel_config: ParallelConfig, scheduler_config: SchedulerConfig, device_config: DeviceConfig, load_config: LoadConfig, lora_config: Optional[LoRAConfig], vision_language_config: Optional[VisionLanguageConfig], speculative_config: Optional[SpeculativeConfig], decoding_config: Optional[DecodingConfig], executor_class: Type[ExecutorBase], log_stats: bool, usage_context: UsageContext = UsageContext.ENGINE_CONTEXT, ) -> None: logger.info( "Initializing an LLM engine (v%s) with config: " "model=%r, speculative_config=%r, tokenizer=%r, " "skip_tokenizer_init=%s, tokenizer_mode=%s, revision=%s, " "tokenizer_revision=%s, trust_remote_code=%s, dtype=%s, " "max_seq_len=%d, download_dir=%r, load_format=%s, " "tensor_parallel_size=%d, disable_custom_all_reduce=%s, " "quantization=%s, enforce_eager=%s, kv_cache_dtype=%s, " "quantization_param_path=%s, device_config=%s, " "decoding_config=%r, seed=%d, served_model_name=%s)", vllm.__version__, model_config.model, speculative_config, model_config.tokenizer, model_config.skip_tokenizer_init, # model_config.tokenizer_mode, model_config.revision, model_config.tokenizer_revision, # model_config.trust_remote_code, model_config.dtype, model_config.max_model_len, load_config.download_dir, load_config.load_format, parallel_config.tensor_parallel_size, parallel_config.disable_custom_all_reduce, model_config.quantization, model_config.enforce_eager, cache_config.cache_dtype, model_config.quantization_param_path, device_config.device, decoding_config, model_config.seed, # model_config.served_model_name, ) # TODO(woosuk): Print more configs in debug mode. self.model_config = model_config # TODO: currently is hfconfig self.cache_config = cache_config self.lora_config = lora_config self.vision_language_config = vision_language_config self.parallel_config = parallel_config self.scheduler_config = scheduler_config self.device_config = device_config self.speculative_config = speculative_config self.load_config = load_config self.decoding_config = decoding_config or DecodingConfig() self.log_stats = log_stats # self.model = model # should not store the model, it should be deleted # TODO(shengguangming): maybe we can choose init here or from arguments if not self.model_config.skip_tokenizer_init: # TODO: check tokenizer class self._init_tokenizer(tokenizer) self.detokenizer = Detokenizer(self.tokenizer) else: self.detokenizer = None self.tokenizer = None self.seq_counter = Counter() # TODO: don't know what's the usage self.generation_config_fields = _load_generation_config_dict(model_config) self.model_executor = executor_class( model=model, # add for spmd_gpu_executor model_config=model_config, cache_config=cache_config, parallel_config=parallel_config, scheduler_config=scheduler_config, device_config=device_config, lora_config=lora_config, vision_language_config=vision_language_config, speculative_config=speculative_config, load_config=load_config, ) # Profile the memory usage and initialize the cache. self._initialize_kv_caches() # If usage stat is enabled, collect relevant info. if is_usage_stats_enabled(): from vllm.model_executor.model_loader import (get_architecture_class_name) usage_message.report_usage( get_architecture_class_name(model_config), usage_context, extra_kvs={ # Common configuration "dtype": str(model_config.dtype), "tensor_parallel_size": parallel_config.tensor_parallel_size, "block_size": cache_config.block_size, "gpu_memory_utilization": cache_config.gpu_memory_utilization, # Quantization "quantization": model_config.quantization, "kv_cache_dtype": cache_config.cache_dtype, # Feature flags "enable_lora": bool(lora_config), "enable_prefix_caching": cache_config.enable_prefix_caching, "enforce_eager": model_config.enforce_eager, "disable_custom_all_reduce": parallel_config.disable_custom_all_reduce, }) if self.tokenizer: # Ping the tokenizer to ensure liveness if it runs in a # different process. self.tokenizer.ping() # Create the scheduler. # NOTE: the cache_config here have been updated with the numbers of # GPU and CPU blocks, which are profiled in the distributed executor. # NOTE(shengguangming): each process will have independent scheduler self.scheduler = Scheduler(scheduler_config, cache_config, lora_config) # Metric Logging. if self.log_stats: self.stat_logger = StatLogger(local_interval=_LOCAL_LOGGING_INTERVAL_SEC, labels=dict(model_name=model_config.served_model_name), max_model_len=self.model_config.max_model_len) self.stat_logger.info("cache_config", self.cache_config) # Create sequence output processor, e.g. for beam search or # speculative decoding. self.output_processor = (SequenceGroupOutputProcessor.create_output_processor( self.scheduler_config, self.detokenizer, self.scheduler, self.seq_counter, self.get_tokenizer_for_seq, stop_checker=StopChecker( self.scheduler_config.max_model_len, self.get_tokenizer_for_seq, ), )) # TODO(sgm): add for verl but we may not tokenizer in Rollout def _init_tokenizer(self, tokenizer, **tokenizer_init_kwargs): init_kwargs = dict(enable_lora=bool(self.lora_config), max_num_seqs=self.scheduler_config.max_num_seqs, max_input_length=None) init_kwargs.update(tokenizer_init_kwargs) self.tokenizer: TokenizerGroup = TokenizerGroup(tokenizer, **init_kwargs) def init_cache_engine(self): # TODO: check whether we should rebuild the CUDAGraph every iter when offload/load KVCache # Re-capture CUDAGraph would be time-consuming self.model_executor.init_cache_engine() def free_cache_engine(self): self.model_executor.free_cache_engine() # NOTE(sgm): currently, we only support GPU executor # The GPUExecutor remove the Ray dependency @classmethod def from_engine_args( cls, model, tokenizer, engine_args: EngineArgs, usage_context: UsageContext = UsageContext.ENGINE_CONTEXT, ) -> "LLMEngine": """Creates an LLM engine from the engine arguments.""" # Create the engine configs. engine_config = engine_args.create_engine_config() # Initialize the cluster and specify the executor class. assert engine_config.device_config.device_type == "cuda", \ "Currently, the vllm in verl only support running on GPU" if engine_config.parallel_config.world_size == 1: engine_config.load_config.load_format = "dummy_hf" from .spmd_gpu_executor import SPMDGPUExecutor executor_class = SPMDGPUExecutor # Create the LLM engine. engine = cls( model, tokenizer, **engine_config.to_dict(), executor_class=executor_class, log_stats=not engine_args.disable_log_stats, usage_context=usage_context, ) return engine def sync_model_weights(self, actor_weights: Dict[str, torch.Tensor], load_format: str) -> None: self.model_executor.sync_model_weights(actor_weights=actor_weights, load_format=load_format) def offload_model_weights(self) -> None: self.model_executor.offload_model_weights() ================================================ FILE: verl/third_party/vllm/vllm_v_0_4_2/megatron_weight_loaders.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Copyright 2023 The vLLM team. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # Adapted from https://github.com/vllm-project/vllm/tree/main/vllm/model_executor/models from typing import Dict import torch import torch.nn as nn from vllm.model_executor.layers.linear import * from vllm.model_executor.layers.vocab_parallel_embedding import VocabParallelEmbedding, ParallelLMHead from vllm.model_executor.layers.activation import ScaledActivation from vllm.model_executor.models import ModelRegistry # NOTE(shengguangming): replace the origin weight loader function in the class def parallel_weight_loader(self, param: torch.Tensor, loaded_weight: torch.Tensor) -> None: """Parallel Linear weight loader.""" assert param.size() == loaded_weight.size( ), 'the parameter size is not align with the loaded weight size, param size: {}, loaded_weight size: {}'.format( param.size(), loaded_weight.size()) assert param.data.dtype == loaded_weight.data.dtype, "if we want to shared weights, the data type should also be the same" param.data = loaded_weight.data def default_weight_loader(param: torch.Tensor, loaded_weight: torch.Tensor) -> None: """Default weight loader.""" assert param.size() == loaded_weight.size() assert param.data.dtype == loaded_weight.data.dtype, "if we want to shared weights, the data type should also be the same" param.data = loaded_weight.data def gpt2_weight_loader(actor_weights: Dict, vllm_model: nn.Module) -> nn.Module: params_dict = dict(vllm_model.named_parameters(remove_duplicate=False)) for name, loaded_weight in actor_weights.items(): if "lm_head.weight" in name: # GPT-2 ties the weights of the embedding layer and the final # linear layer. continue if ".attn.bias" in name or ".attn.masked_bias" in name: # Skip attention mask. # NOTE: "c_attn.bias" should not be skipped. continue if not name.startswith("transformer."): name = "transformer." + name param = params_dict[name] # The HF's GPT-2 implementation uses Conv1D instead of Linear. # Because of this, we need to transpose the weights. # Note(zhuohan): the logic below might break quantized models. for conv1d_weight_name in ["c_attn", "c_proj", "c_fc"]: if conv1d_weight_name not in name: continue if not name.endswith(".weight"): continue # TODO: check megatron loaded_weight = loaded_weight.t() weight_loader = getattr(param, "weight_loader", default_weight_loader) weight_loader(param, loaded_weight) def llama_megatron_weight_loader(actor_weights: Dict, vllm_model: nn.Module) -> nn.Module: # NOTE(shengguangming): the megatron llama may have this prefix params_dict = dict(vllm_model.named_parameters()) for name, loaded_weight in actor_weights.items(): if "rotary_emb.inv_freq" in name: continue else: param = params_dict[name] weight_loader = getattr(param, "weight_loader", default_weight_loader) weight_loader(param, loaded_weight) def llama_megatron_core_te_weight_loader(actor_weights: Dict, vllm_model: nn.Module) -> nn.Module: params_mapping = [ # (megatron core gpt model name, vllm model name) ("embedding.word_embeddings", "model.embed_tokens"), ("self_attention.linear_qkv.layer_norm_weight", "input_layernorm.weight"), ("self_attention.linear_qkv.layer_norm_bias", "input_layernorm.bias"), ("self_attention.linear_qkv", "self_attn.qkv_proj"), ("self_attention.linear_qkv", "self_attn.qkv_proj"), ("self_attention.linear_proj", 'self_attn.o_proj'), ('pre_mlp_layernorm', 'post_attention_layernorm'), ('mlp.linear_fc1.layer_norm_weight', 'post_attention_layernorm.weight'), ('mlp.linear_fc1.layer_norm_bias', 'post_attention_layernorm.bias'), ('mlp.linear_fc1', 'mlp.gate_up_proj'), ('mlp.linear_fc2', 'mlp.down_proj'), ('decoder.final_layernorm', 'model.norm'), ('output_layer', 'lm_head'), ] # NOTE(shengguangming): the megatron llama may have this prefix params_dict = dict(vllm_model.named_parameters()) for name, loaded_weight in actor_weights.items(): name = _replace_name(name, params_mapping) if name.endswith('.bias') and name not in params_dict: continue if "rotary_emb.inv_freq" in name: continue else: param = params_dict[name] weight_loader = getattr(param, "weight_loader", default_weight_loader) weight_loader(param, loaded_weight) def llama_megatron_core_weight_loader(actor_weights: Dict, vllm_model: nn.Module) -> nn.Module: params_mapping = [ # (megatron core gpt model name, vllm model name) ("embedding.word_embeddings", "model.embed_tokens"), ("self_attention.linear_qkv", "self_attn.qkv_proj"), ("self_attention.linear_proj", 'self_attn.o_proj'), ( 'input_layernorm', 'input_layernorm', ), ('pre_mlp_layernorm', 'post_attention_layernorm'), ('mlp.linear_fc1', 'mlp.gate_up_proj'), ('mlp.linear_fc2', 'mlp.down_proj'), ('decoder.final_layernorm', 'model.norm'), ('output_layer', 'lm_head'), ] # NOTE(shengguangming): the megatron llama may have this prefix params_dict = dict(vllm_model.named_parameters()) for name, loaded_weight in actor_weights.items(): name = _replace_name(name, params_mapping) if name.endswith('.bias') and name not in params_dict: continue if "rotary_emb.inv_freq" in name: continue else: param = params_dict[name] weight_loader = getattr(param, "weight_loader", default_weight_loader) weight_loader(param, loaded_weight) def _replace_name(megatron_name, name_mapping): for m_name, v_name in name_mapping: if m_name not in megatron_name: continue if 'layers' in megatron_name: # deal with decoder layers megatron_name = megatron_name.replace('decoder', 'model') megatron_name_list = megatron_name.split('.') if 'layer_norm_weight' in megatron_name_list or 'layer_norm_bias' in megatron_name_list: param_name_list = megatron_name_list[:3] param_name_list.append(v_name) param_name = '.'.join(param_name_list) else: param_name_list = megatron_name_list[:3] weight_or_bias = megatron_name_list[-1] param_name_list.append(v_name) param_name_list.append(weight_or_bias) param_name = '.'.join(param_name_list) return param_name else: param_name = megatron_name.replace(m_name, v_name) return param_name def llama_megatron_core_te_weight_loader(actor_weights: Dict, vllm_model: nn.Module) -> nn.Module: params_mapping = [ # (megatron core gpt model name, vllm model name) ("embedding.word_embeddings", "model.embed_tokens"), ("self_attention.linear_qkv.layer_norm_weight", "input_layernorm.weight"), ("self_attention.linear_qkv.layer_norm_bias", "input_layernorm.bias"), ("self_attention.linear_qkv", "self_attn.qkv_proj"), ("self_attention.linear_qkv", "self_attn.qkv_proj"), ("self_attention.linear_proj", 'self_attn.o_proj'), ('pre_mlp_layernorm', 'post_attention_layernorm'), ('mlp.linear_fc1.layer_norm_weight', 'post_attention_layernorm.weight'), ('mlp.linear_fc1.layer_norm_bias', 'post_attention_layernorm.bias'), ('mlp.linear_fc1', 'mlp.gate_up_proj'), ('mlp.linear_fc2', 'mlp.down_proj'), ('decoder.final_layernorm', 'model.norm'), ('output_layer', 'lm_head'), ] # NOTE(shengguangming): the megatron llama may have this prefix params_dict = dict(vllm_model.named_parameters()) for name, loaded_weight in actor_weights.items(): name = _replace_name(name, params_mapping) if name.endswith('.bias') and name not in params_dict: continue if "rotary_emb.inv_freq" in name: continue else: param = params_dict[name] weight_loader = getattr(param, "weight_loader", default_weight_loader) weight_loader(param, loaded_weight) def llama_megatron_core_weight_loader(actor_weights: Dict, vllm_model: nn.Module) -> nn.Module: params_mapping = [ # (megatron core gpt model name, vllm model name) ("embedding.word_embeddings", "model.embed_tokens"), ("self_attention.linear_qkv", "self_attn.qkv_proj"), ("self_attention.linear_proj", 'self_attn.o_proj'), ( 'input_layernorm', 'input_layernorm', ), ('pre_mlp_layernorm', 'post_attention_layernorm'), ('mlp.linear_fc1', 'mlp.gate_up_proj'), ('mlp.linear_fc2', 'mlp.down_proj'), ('decoder.final_layernorm', 'model.norm'), ('output_layer', 'lm_head'), ] # NOTE(shengguangming): the megatron llama may have this prefix params_dict = dict(vllm_model.named_parameters()) for name, loaded_weight in actor_weights.items(): name = _replace_name(name, params_mapping) if name.endswith('.bias') and name not in params_dict: continue if "rotary_emb.inv_freq" in name: continue else: param = params_dict[name] weight_loader = getattr(param, "weight_loader", default_weight_loader) weight_loader(param, loaded_weight) def _replace_name(megatron_name, name_mapping): for m_name, v_name in name_mapping: if m_name not in megatron_name: continue if 'layers' in megatron_name: # deal with decoder layers megatron_name = megatron_name.replace('decoder', 'model') megatron_name_list = megatron_name.split('.') if 'layer_norm_weight' in megatron_name_list or 'layer_norm_bias' in megatron_name_list: param_name_list = megatron_name_list[:3] param_name_list.append(v_name) param_name = '.'.join(param_name_list) else: param_name_list = megatron_name_list[:3] weight_or_bias = megatron_name_list[-1] param_name_list.append(v_name) param_name_list.append(weight_or_bias) param_name = '.'.join(param_name_list) return param_name else: param_name = megatron_name.replace(m_name, v_name) return param_name def mistral_megatron_weight_loader(actor_weights: Dict, vllm_model: nn.Module) -> nn.Module: # TODO: need to implement a general way to deal with prefix params_dict = dict(vllm_model.named_parameters()) for name, loaded_weight in actor_weights.items(): if "rotary_emb.inv_freq" in name: continue else: param = params_dict[name] weight_loader = getattr(param, "weight_loader", default_weight_loader) weight_loader(param, loaded_weight) __LAYER_WEIGHT_MEGATRON_LOADER_REGISTRY__ = { ColumnParallelLinear: parallel_weight_loader, MergedColumnParallelLinear: parallel_weight_loader, QKVParallelLinear: parallel_weight_loader, RowParallelLinear: parallel_weight_loader, VocabParallelEmbedding: parallel_weight_loader, ParallelLMHead: parallel_weight_loader # "ScaledActivation.weight_loader": ScaledActivation, # TODO(shengguangming): latest commit in vllm fix awq for this function and add load_weights # "default_weight_loader": default_weight_loader } # for layer_class, weight_loader in __LAYER_WEIGHT_MEGATRON_LOADER_REGISTRY__.items(): # # setattr(layer_class, 'megatron_weight_loader', weight_loader) # layer_class.weight_loader = weight_loader __MODEL_MEGATRON_WEIGHT_LOADER_REGISTRY__ = { 'GPT2LMHeadModel': gpt2_weight_loader, 'LlamaForCausalLM': llama_megatron_core_te_weight_loader, # use te backend for open-source megatron 'LLaMAForCausalLM': llama_megatron_core_te_weight_loader, 'MistralForCausalLM': mistral_megatron_weight_loader, } # the actor model is .state_dict() # Load megatron weights def load_megatron_weights(actor_weights: Dict, vllm_model: nn.Module): weight_loader = _get_model_weight_loader(vllm_model.__class__.__name__) weight_loader(actor_weights, vllm_model) # NOTE(sgm) to reduce peak memory usage, we offload vllm model to cpu # after init, and we need this after sync model weights for in first iter. vllm_model = vllm_model.cuda() def _get_model_weight_loader(arch: str): if arch in __MODEL_MEGATRON_WEIGHT_LOADER_REGISTRY__: return __MODEL_MEGATRON_WEIGHT_LOADER_REGISTRY__[arch] raise ValueError(f"Model architectures {arch} are not supported for now. " f"Supported architectures: {ModelRegistry.get_supported_archs()}") def update_megatron_weight_loader(): for layer_class, weight_loader in __LAYER_WEIGHT_MEGATRON_LOADER_REGISTRY__.items(): layer_class.weight_loader = weight_loader VocabParallelEmbedding.__init__ = vocab_init # FIXME(shengguangming): the vLLM vocab will pad to 64, which may incur out of bounds # so we need to rewrite the init function of vocab DEFAULT_VOCAB_PADDING_SIZE = 64 def vocab_init(self, num_embeddings: int, embedding_dim: int, params_dtype: Optional[torch.dtype] = None, org_num_embeddings: Optional[int] = None, padding_size: int = DEFAULT_VOCAB_PADDING_SIZE): super(VocabParallelEmbedding, self).__init__() # Keep the input dimensions. # TODO (pad to be divided by 4) self.num_embeddings = num_embeddings self.org_vocab_size = org_num_embeddings or num_embeddings # self.num_embeddings_padded = pad_vocab_size(num_embeddings, # padding_size) self.embedding_dim = embedding_dim if params_dtype is None: params_dtype = torch.get_default_dtype() self.tp_size = get_tensor_model_parallel_world_size() # Divide the weight matrix along the vocaburaly dimension. # TODO: remove dependencies from megatron from megatron.core.tensor_parallel.utils import VocabUtility self.vocab_start_index, self.vocab_end_index = (VocabUtility.vocab_range_from_global_vocab_size( self.num_embeddings, get_tensor_model_parallel_rank(), self.tp_size)) self.num_embeddings_per_partition = (self.vocab_end_index - self.vocab_start_index) self.weight = Parameter( torch.empty( self.num_embeddings_per_partition, self.embedding_dim, # device=torch.cuda.current_device(), dtype=params_dtype)) set_weight_attrs(self.weight, {"parallel_dim": 0, "weight_loader": self.weight_loader}) ================================================ FILE: verl/third_party/vllm/vllm_v_0_4_2/model_loader.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Copyright 2023 The vLLM team. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # Adapted from https://github.com/vllm-project/vllm/tree/main/vllm/model_executor/model_loader """Utilities for selecting and loading models.""" from typing import Dict, Union, Optional, Iterable, Tuple import torch import torch.nn as nn from transformers import PreTrainedModel from vllm.config import (DeviceConfig, LoRAConfig, ParallelConfig, SchedulerConfig, VisionLanguageConfig) from vllm.model_executor.model_loader import BaseModelLoader from vllm.model_executor.model_loader.loader import _initialize_model from vllm.model_executor.model_loader.utils import set_default_torch_dtype from vllm.distributed.communication_op import tensor_model_parallel_all_gather from .config import ModelConfig, LoadFormat, LoadConfig from .megatron_weight_loaders import load_megatron_weights, update_megatron_weight_loader from .dtensor_weight_loaders import load_dtensor_weights, update_dtensor_weight_loader from .hf_weight_loader import update_hf_weight_loader def get_model(actor_model: Union[PreTrainedModel, Dict], model_config: ModelConfig, load_config: LoadConfig, device_config: DeviceConfig, parallel_config: ParallelConfig, scheduler_config: SchedulerConfig, lora_config: Optional[LoRAConfig], vision_language_config: Optional[VisionLanguageConfig]) -> nn.Module: loader = get_model_loader(load_config) if load_config.load_format.startswith('dummy'): return loader.load_model(model_config=model_config, device_config=device_config, lora_config=lora_config, vision_language_config=vision_language_config, parallel_config=parallel_config, scheduler_config=scheduler_config) else: return loader.load_model(actor_model=actor_model, model_config=model_config, device_config=device_config, lora_config=lora_config, vision_language_config=vision_language_config, parallel_config=parallel_config, scheduler_config=scheduler_config) def get_model_loader(load_config: LoadConfig) -> BaseModelLoader: """Get a model loader based on the load format.""" if isinstance(load_config.load_format, type): return load_config.load_format(load_config) if load_config.load_format == LoadFormat.AUTO: update_megatron_weight_loader() return MegatronLoader(load_config) # NOTE(sgm): change the weight_loader function in runtime if load_config.load_format == LoadFormat.MEGATRON: update_megatron_weight_loader() return MegatronLoader(load_config) if load_config.load_format == LoadFormat.HF: update_hf_weight_loader() return HFLoader(load_config) if load_config.load_format == LoadFormat.DTENSOR: update_dtensor_weight_loader() return DTensorLoader(load_config) if load_config.load_format == LoadFormat.DUMMY_HF: update_hf_weight_loader() return DummyModelLoader(load_config) if load_config.load_format == LoadFormat.DUMMY_MEGATRON: update_megatron_weight_loader() return DummyModelLoader(load_config) if load_config.load_format == LoadFormat.DUMMY_DTENSOR: update_dtensor_weight_loader() return DummyModelLoader(load_config) raise ValueError('load format not supported in verl: {}, only support {} and {}'.format( load_config.load_format, LoadFormat.MEGATRON, LoadFormat.HF)) class DummyModelLoader(BaseModelLoader): """Model loader that will set model weights to random values.""" def __init__(self, load_config: LoadConfig): super().__init__(load_config) if load_config.model_loader_extra_config: raise ValueError(f"Model loader extra config is not supported for " f"load format {load_config.load_format}") def load_model(self, *, model_config: ModelConfig, device_config: DeviceConfig, lora_config: Optional[LoRAConfig], vision_language_config: Optional[VisionLanguageConfig], parallel_config: ParallelConfig, scheduler_config: SchedulerConfig) -> nn.Module: with set_default_torch_dtype(model_config.dtype): with torch.device(device_config.device): model = _initialize_model(model_config, self.load_config, lora_config, vision_language_config) # NOTE(woosuk): For accurate performance evaluation, we assign # random values to the weights. # initialize_dummy_weights(model) return model.eval() class MegatronLoader(BaseModelLoader): """Model loader that can load the model weights from partitioned megatron model.""" def __init__(self, load_config: LoadConfig): super().__init__(load_config) if load_config.model_loader_extra_config: raise ValueError(f"Model loader extra config is not supported for " f"load format {load_config.load_format}") def _get_weights_iterator(actor_model: Union[PreTrainedModel, Dict]): # NOTE(shengguangming) Load the weights from the actor model pass # if isinstance(actor_model, nn.Module): # load_weights(actor_weights=dict(actor_model.named_parameters(remove_duplicate=False)), vllm_model=model) # else: # load_weights(actor_weights=actor_model, vllm_model=model) # return actor_model def load_model(self, actor_model: Union[PreTrainedModel, Dict], model_config: ModelConfig, device_config: DeviceConfig, lora_config: Optional[LoRAConfig], vision_language_config: Optional[VisionLanguageConfig], parallel_config: ParallelConfig, scheduler_config: SchedulerConfig) -> nn.Module: with set_default_torch_dtype(model_config.dtype): with torch.device(device_config.device): model = _initialize_model(model_config, self.load_config, lora_config, vision_language_config) # TODO(sgm): This is a hack, we need to register the load_weight() func for each model in vllm if isinstance(actor_model, nn.Module): load_megatron_weights(actor_weights=dict(actor_model.named_parameters(remove_duplicate=False)), vllm_model=model) else: load_megatron_weights(actor_weights=actor_model, vllm_model=model) for _, module in model.named_modules(): quant_method = getattr(module, "quant_method", None) if quant_method is not None: quant_method.process_weights_after_loading(module) # FIXME: Remove this after Mixtral is updated # to use quant_method. if hasattr(module, "process_weights_after_loading"): module.process_weights_after_loading() # NOTE(sgm) Some weights are point to gpu, but still need this. model = model.cuda() # NOTE (zhangchi.usc1992) We need this for vllm to profile memory usage return model.eval() class HFLoader(BaseModelLoader): """Model loader that can load the model weights from model's full params.""" def __init__(self, load_config: LoadConfig): super().__init__(load_config) if load_config.model_loader_extra_config: raise ValueError(f"Model loader extra config is not supported for " f"load format {load_config.load_format}") def _get_weights_iterator(self, actor_model: Union[PreTrainedModel, Dict]): if isinstance(actor_model, Dict): return actor_model.items() elif isinstance(actor_model, nn.Module): return dict(actor_model.named_parameters()).items() else: raise ValueError(f'actor model should be Dict or nn.Module, but get {type(actor_model)}') def load_model(self, actor_model: Union[PreTrainedModel, Dict], model_config: ModelConfig, device_config: DeviceConfig, lora_config: Optional[LoRAConfig], vision_language_config: Optional[VisionLanguageConfig], parallel_config: ParallelConfig, scheduler_config: SchedulerConfig) -> nn.Module: with set_default_torch_dtype(model_config.dtype): # with torch.device(device_config.device): # NOTE(sgm): init the model in cpu model = _initialize_model(model_config, self.load_config, lora_config, vision_language_config) model.load_weights(self._get_weights_iterator(actor_model)) for _, module in model.named_modules(): quant_method = getattr(module, "quant_method", None) if quant_method is not None: quant_method.process_weights_after_loading(module) # FIXME: Remove this after Mixtral is updated # to use quant_method. if hasattr(module, "process_weights_after_loading"): module.process_weights_after_loading() # NOTE(sgm) Some weights are point to gpu, but still need this. model = model.cuda() # NOTE (zhangchi.usc1992) We need this for vllm to profile memory usage return model.eval() class DTensorLoader(BaseModelLoader): """Model loader that can load the model weights from partitioned megatron model.""" def __init__(self, load_config: LoadConfig): super().__init__(load_config) if load_config.model_loader_extra_config: raise ValueError(f"Model loader extra config is not supported for " f"load format {load_config.load_format}") def _get_weights_iterator(actor_model: Union[PreTrainedModel, Dict]): # NOTE(shengguangming) Load the weights from the actor model pass # if isinstance(actor_model, nn.Module): # load_weights(actor_weights=dict(actor_model.named_parameters(remove_duplicate=False)), vllm_model=model) # else: # load_weights(actor_weights=actor_model, vllm_model=model) # return actor_model def load_model(self, actor_model: Union[PreTrainedModel, Dict], model_config: ModelConfig, device_config: DeviceConfig, lora_config: Optional[LoRAConfig], vision_language_config: Optional[VisionLanguageConfig], parallel_config: ParallelConfig, scheduler_config: SchedulerConfig) -> nn.Module: with set_default_torch_dtype(model_config.dtype): with torch.device(device_config.device): model = _initialize_model(model_config, self.load_config, lora_config, vision_language_config) # TODO(sgm): This is a hack, we need to register the load_weight() func for each model in vllm if isinstance(actor_model, nn.Module): load_dtensor_weights(actor_weights=dict(actor_model.named_parameters(remove_duplicate=False)), vllm_model=model) else: load_dtensor_weights(actor_weights=actor_model, vllm_model=model) for _, module in model.named_modules(): quant_method = getattr(module, "quant_method", None) if quant_method is not None: quant_method.process_weights_after_loading(module) # FIXME: Remove this after Mixtral is updated # to use quant_method. if hasattr(module, "process_weights_after_loading"): module.process_weights_after_loading() # NOTE(sgm) Some weights are point to gpu, but still need this. model = model.cuda() # NOTE (zhangchi.usc1992) We need this for vllm to profile memory usage return model.eval() # FIXME(sgm): hack the _get_logits function in vllm v0.4.2 # as they use ray, the _get_logits result will only need to return to the driver node, # therefore gather is enough. However, we use SPMD instead of a central scheduler, # all_gather is required (aligned with v0.2.6) def _get_logits(self, hidden_states: torch.Tensor, embedding: torch.Tensor, embedding_bias: Optional[torch.Tensor]) -> torch.Tensor: # Get the logits for the next tokens. logits = torch.matmul(hidden_states, embedding.t()) if embedding_bias is not None: logits += embedding_bias logits = tensor_model_parallel_all_gather(logits) # Remove paddings in vocab (if any). if logits is not None: logits = logits[:, :self.org_vocab_size] return logits from vllm.model_executor.layers.logits_processor import LogitsProcessor LogitsProcessor._get_logits = _get_logits ================================================ FILE: verl/third_party/vllm/vllm_v_0_4_2/model_runner.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Copyright 2023 The vLLM team. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # Adapted from https://github.com/vllm-project/vllm/blob/main/vllm/worker/model_runner.py import torch import torch.nn as nn from enum import IntEnum from typing import Dict, List, Optional, Set, Tuple, Union from vllm.attention import (AttentionMetadata, get_attn_backend) from vllm.config import (DeviceConfig, LoRAConfig, ParallelConfig, SchedulerConfig, VisionLanguageConfig) from vllm.logger import init_logger from vllm.lora.layers import LoRAMapping from vllm.lora.request import LoRARequest from vllm.lora.worker_manager import LRUCacheWorkerLoRAManager from vllm.model_executor import SamplingMetadata from vllm.sequence import (MultiModalData, SamplerOutput, SequenceData, SequenceGroupMetadata) from vllm.utils import (CudaMemoryProfiler, is_hip, is_pin_memory_available) from vllm.worker.model_runner import ModelRunner, CUDAGraphRunner from .model_loader import get_model from .config import ModelConfig, LoadConfig logger = init_logger(__name__) # How batches are constructed. class BatchType(IntEnum): # Every batch is prefill. PREFILL = 0 # Every batch is decode. DECODE = 1 # Batch is a mixture of prefill and decode. MIXED = 2 class ModelRunner(ModelRunner): def __init__( self, model: Union[nn.Module, Dict], # model itself or its parameter dict model_config: ModelConfig, parallel_config: ParallelConfig, scheduler_config: SchedulerConfig, device_config: DeviceConfig, load_config: LoadConfig, lora_config: Optional[LoRAConfig], kv_cache_dtype: Optional[str] = "auto", vision_language_config: Optional[VisionLanguageConfig] = None, ): self.model_config = model_config self.parallel_config = parallel_config self.scheduler_config = scheduler_config self.lora_config = lora_config self.load_config = load_config # model_config can be None in tests/samplers/test_sampler.py. # FIXME(woosuk): This is a hack to make the tests work. Refactor this. self.sliding_window = (model_config.get_sliding_window() if model_config is not None else None) self.device_config = (device_config if device_config is not None else DeviceConfig()) self.device = self.device_config.device # NOTE(sgm): add for verl self.model = model # this will be replaced by get_model() # Set after load_model. self.lora_manager: LRUCacheWorkerLoRAManager = None self.graph_runners: Dict[int, CUDAGraphRunner] = {} self.graph_memory_pool: Optional[Tuple[int, int]] = None # Set during graph capture. self.max_seq_len_to_capture = (self.model_config.max_seq_len_to_capture if self.model_config is not None else 0) self.pin_memory = is_pin_memory_available() self.kv_cache_dtype = kv_cache_dtype self.vision_language_config = vision_language_config self.attn_backend = get_attn_backend(self.model_config.dtype if model_config is not None else None) # Lazy initialization self.block_size: int # Set after initial profiling. # When using CUDA graph, the input block tables must be padded to # max_seq_len_to_capture. However, creating the block table in # Python can be expensive. To optimize this, we cache the block table # in numpy and only copy the actual input content at every iteration. # The shape of the cached block table will be # (max batch size to capture, max context len to capture / block size). self.graph_block_tables: torch.Tensor # Set after initial profiling. # Set if the backend is flashinfer. self.flashinfer_workspace_buffer: torch.Tensor # NOTE(sgm): initialize model using the actor model def load_model(self) -> None: with CudaMemoryProfiler() as m: self.model = get_model(actor_model=self.model, model_config=self.model_config, device_config=self.device_config, lora_config=self.lora_config, load_config=self.load_config, parallel_config=self.parallel_config, scheduler_config=self.scheduler_config, vision_language_config=self.vision_language_config) self.model_memory_usage = m.consumed_memory logger.info("Loading model weights took %.4f GB", self.model_memory_usage / float(2**30)) if self.lora_config: assert hasattr(self.model, "supported_lora_modules") and self.model.supported_lora_modules, ( "Model does not support LoRA") assert hasattr(self.model, "embedding_modules"), "Model does not have embedding_modules" assert hasattr(self.model, "embedding_padding_modules"), "Model does not have embedding_padding_modules" self.lora_manager = LRUCacheWorkerLoRAManager(self.scheduler_config.max_num_seqs, self.scheduler_config.max_num_batched_tokens, self.vocab_size, self.lora_config, self.device, self.model.embedding_modules, self.model.embedding_padding_modules) self.model = self.lora_manager.create_lora_manager(self.model) if self.kv_cache_dtype == "fp8" and is_hip(): # Currently scaled KV cache is only enabled on ROCm if self.model_config.quantization_param_path is not None: if callable(getattr(self.model, "load_kv_cache_scales", None)): self.model.load_kv_cache_scales(self.model_config.quantization_param_path) else: raise RuntimeError( "Using FP8 KV cache and scaling factors provided but " "model %s does not support loading scaling factors.", self.model.__class__) else: logger.warning("Using FP8 KV cache but no scaling factors " "provided. Defaulting to scaling factors of 1.0. " "This may lead to less accurate results!") elif self.model_config.quantization_param_path is not None: logger.warning("KV cache scaling factors provided, " "but the KV cache data type is not FP8. " "KV cache scaling factors will not be used.") def prepare_input_tensors( self, seq_group_metadata_list: List[SequenceGroupMetadata], ) -> Tuple[torch.Tensor, torch.Tensor, AttentionMetadata, SamplingMetadata, Set[LoRARequest], LoRAMapping, torch.Tensor]: # NOTE(sgm): all workers prepare the input in the same way prefill_reqs = [] decode_reqs = [] for seq_group_meta in seq_group_metadata_list: if seq_group_meta.is_prompt: prefill_reqs.append(seq_group_meta) else: decode_reqs.append(seq_group_meta) # Prepare input tensors. ( input_tokens, input_positions, prefill_attn_metadata, seq_lens, query_lens, lora_index_mapping, lora_prompt_mapping, lora_requests, multi_modal_input, slot_mapping, ) = self._prepare_prompt(prefill_reqs) ( decode_input_tokens, decode_input_positions, decode_attn_metadata, decode_lora_index_mapping, decode_lora_prompt_mapping, decode_lora_requests, decode_slot_mapping, ) = self._prepare_decode(decode_reqs) sampling_metadata = SamplingMetadata.prepare(seq_group_metadata_list, seq_lens, query_lens, self.device, self.pin_memory) if not self.scheduler_config.chunked_prefill_enabled: assert (len(prefill_reqs) and len(decode_reqs)) == 0 num_prefills = len(seq_lens) num_prefill_tokens = len(input_tokens) num_decode_tokens = len(decode_input_tokens) # Coalesce tensors. Note that attn_metadata is currently not # coalesced for simplicity. input_tokens.extend(decode_input_tokens) input_positions.extend(decode_input_positions) slot_mapping.extend(decode_slot_mapping) lora_index_mapping.extend(decode_lora_index_mapping) lora_prompt_mapping.extend(decode_lora_prompt_mapping) lora_requests.update(decode_lora_requests) input_tokens = torch.tensor(input_tokens, dtype=torch.long, device=self.device) input_positions = torch.tensor(input_positions, dtype=torch.long, device=self.device) slot_mapping = torch.tensor(slot_mapping, dtype=torch.long, device=self.device) if self.lora_config: lora_mapping = LoRAMapping( lora_index_mapping, lora_prompt_mapping, ) else: lora_mapping = None # Broadcast the metadata. # If batch contains both prefill and decode, it sends 2 broadcasts. # If it only contains 1 type, it triggers a single broadcast. if (prefill_attn_metadata is not None and decode_attn_metadata is not None): batch_type = BatchType.MIXED elif prefill_attn_metadata is not None: batch_type = BatchType.PREFILL else: batch_type = BatchType.DECODE attn_metadata = AttentionMetadata( num_prefills=num_prefills, slot_mapping=slot_mapping, num_prefill_tokens=num_prefill_tokens, num_decode_tokens=num_decode_tokens, prefill_metadata=prefill_attn_metadata, decode_metadata=decode_attn_metadata, kv_cache_dtype=self.kv_cache_dtype, ) return (input_tokens, input_positions, attn_metadata, sampling_metadata, lora_requests, lora_mapping, multi_modal_input) @torch.inference_mode() def execute_model( self, seq_group_metadata_list: List[SequenceGroupMetadata], kv_caches: List[torch.Tensor], ) -> Optional[SamplerOutput]: (input_tokens, input_positions, attn_metadata, sampling_metadata, lora_requests, lora_mapping, multi_modal_input) = self.prepare_input_tensors(seq_group_metadata_list) if self.lora_config: self.set_active_loras(lora_requests, lora_mapping) # Currently cuda graph is only supported by the decode phase. prefill_meta = attn_metadata.prefill_metadata decode_meta = attn_metadata.decode_metadata if prefill_meta is None and decode_meta.use_cuda_graph: graph_batch_size = input_tokens.shape[0] model_executable = self.graph_runners[graph_batch_size] else: model_executable = self.model execute_model_kwargs = { "input_ids": input_tokens, "positions": input_positions, "kv_caches": kv_caches, "attn_metadata": attn_metadata, } if self.vision_language_config: execute_model_kwargs.update({"image_input": multi_modal_input}) hidden_states = model_executable(**execute_model_kwargs) # Compute the logits. logits = self.model.compute_logits(hidden_states, sampling_metadata) # Only perform sampling in the driver worker. # if not self.is_driver_worker: # return None # TODO(sgm): perform sampling on rank 0 # Sample the next token. output = self.model.sample( logits=logits, sampling_metadata=sampling_metadata, ) return output ================================================ FILE: verl/third_party/vllm/vllm_v_0_4_2/parallel_state.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Copyright 2023 The vLLM team. # Adapted from # https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/core/parallel_state.py # Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. """Model and data parallel groups.""" import os import torch import torch.distributed from typing import Optional import vllm.distributed.parallel_state as ps import vllm.envs as envs from vllm.logger import init_logger from torch.distributed.device_mesh import init_device_mesh logger = init_logger(__name__) """ This version is strongly tied with Megatron to implement HybridEngine and weight sharing between vllm and Megatron. - We assume the Megatron tp+dp+pp world is already established before calling this function. """ # Device mesh for using DTensor _DEVICE_MESH = None # Tensor model parallel group that the current rank belongs to. _TP_DEVICE_GROUP = None _TP_CPU_GROUP = None # This method is for initializing the ParallelGroup when using HybridEngine def initialize_parallel_state( distributed_init_method: str = "env://", backend: str = "nccl", tensor_model_parallel_size: int = 1, num_tp_per_train_tp: int = 1, pipeline_model_parallel_size: int = 1, ): # torch.distributed.all_reduce does not free the input tensor until # the synchronization point. This causes the memory usage to grow # as the number of all_reduce calls increases. This env var disables # this behavior. # Related issue: # https://discuss.pytorch.org/t/cuda-allocation-lifetime-for-inputs-to-distributed-all-reduce/191573 os.environ["TORCH_NCCL_AVOID_RECORD_STREAMS"] = "1" # NOTE(sgm): Modify for verl, Env vars will be set by TORCHRUN. rank = int(os.getenv("RANK", "-1")) local_rank = int(os.getenv("LOCAL_RANK", "0")) # Use the world_size set by TORCHRUN world_size = int(os.getenv("WORLD_SIZE", "-1")) assert world_size != -1, "The world_size is set to -1, not initialized by TORCHRUN" ps.init_distributed_environment(world_size, rank, distributed_init_method, local_rank, backend) if torch.distributed.get_world_size() > 1: # NOTE: build a sepearate inference group with infer tp & micro dp initialize_model_parallel_for_vllm(tensor_model_parallel_size=tensor_model_parallel_size, num_tensor_model_parallel_groups_per_train_tp=num_tp_per_train_tp) else: initialize_model_parallel(tensor_model_parallel_size, pipeline_model_parallel_size, backend) def ensure_model_parallel_initialized( tensor_model_parallel_size: int, pipeline_model_parallel_size: int = 1, backend: Optional[str] = None, ) -> None: """Helper to initialize model parallel groups if they are not initialized, or ensure tensor-parallel and pipeline-parallel sizes are equal to expected values if the model parallel groups are initialized. """ # get the backend of _DEVICE_WORLD_GROUP backend = backend or torch.distributed.get_backend() if not model_parallel_is_initialized(): initialize_model_parallel(tensor_model_parallel_size, pipeline_model_parallel_size, backend) return assert (get_tensor_model_parallel_world_size() == tensor_model_parallel_size), ( "tensor parallel group already initialized, but of unexpected size: " f"{get_tensor_model_parallel_world_size()=} vs. " f"{tensor_model_parallel_size=}") # assert (get_pipeline_model_parallel_world_size( # ) == pipeline_model_parallel_size), ( # "pipeline parallel group already initialized, but of unexpected size: " # f"{get_pipeline_model_parallel_world_size()=} vs. " # f"{pipeline_model_parallel_size=}") def model_parallel_is_initialized(): """Check if tensor and pipeline parallel groups are initialized.""" return (ps._TP_DEVICE_GROUP is not None) # and _PIPELINE_MODEL_PARALLEL_GROUP is not None) def initialize_model_parallel_for_vllm(tensor_model_parallel_size: int, num_tensor_model_parallel_groups_per_train_tp: int = 1) -> None: from torch.distributed import new_group # Get world size and rank. Ensure some consistencies. assert torch.distributed.is_initialized() assert isinstance(tensor_model_parallel_size, int) # assert num_tensor_model_parallel_groups_per_train_tp == 1 and not different_tp_group # assert num_tensor_model_parallel_groups_per_train_tp > 1 and different_tp_group # Build the tensor model-parallel groups. assert ps._TP_DEVICE_GROUP is None, ("tensor model parallel group is already initialized") global _TP_DEVICE_GROUP global _TP_CPU_GROUP global _DEVICE_MESH world_size: int = torch.distributed.get_world_size() rank = torch.distributed.get_rank() backend = torch.distributed.get_backend() num_tensor_model_parallel_groups = world_size // tensor_model_parallel_size if num_tensor_model_parallel_groups_per_train_tp == 1: # if tensor_model_parallel_size == train_tensor_parallel_size: # using the same tp group as Megatron/vllm for i in range(num_tensor_model_parallel_groups): ranks = range(i * tensor_model_parallel_size, (i + 1) * tensor_model_parallel_size) group = torch.distributed.new_group(ranks, backend=backend) cpu_group = torch.distributed.new_group(ranks, backend="gloo") if rank in ranks: _TP_DEVICE_GROUP = group _TP_CPU_GROUP = cpu_group ps._TP_DEVICE_GROUP = group ps._TP_CPU_GROUP = cpu_group # no _MICRO_DATA_PARALLEL_GROUP else: # initialize a micro_dp group and a tp group # assume training tp=4, infer tp=2, then, weight is partitioned as # [1], [2], [3], [4] for training and [1,2], [1,2], [3,4], [3,4] for inference # Build the inference tp groups # train_tp = train_tensor_parallel_size train_tp = num_tensor_model_parallel_groups_per_train_tp * tensor_model_parallel_size # num_tensor_model_parallel_groups_per_train_tp = train_tp // tensor_model_parallel_size assert _TP_DEVICE_GROUP is None, ("tensor model parallel group is already initialized") for i in range(num_tensor_model_parallel_groups // num_tensor_model_parallel_groups_per_train_tp): start = train_tp * i end = train_tp * (i + 1) for j in range(num_tensor_model_parallel_groups_per_train_tp): ranks = list(range(start, end, num_tensor_model_parallel_groups_per_train_tp)) for i in range(len(ranks)): ranks[i] += j group = torch.distributed.new_group(ranks) cpu_group = torch.distributed.new_group(ranks, backend='gloo') if rank in ranks: _TP_DEVICE_GROUP = group _TP_CPU_GROUP = cpu_group ps._TP_DEVICE_GROUP = _TP_DEVICE_GROUP ps._TP_CPU_GROUP = cpu_group # Build the pipeline model-parallel groups. # global _PIPELINE_MODEL_PARALLEL_GROUP # global _PIPELINE_GLOBAL_RANKS # assert ps._PIPELINE_MODEL_PARALLEL_GROUP is None, ("pipeline model parallel group is already initialized") # ps._PIPELINE_MODEL_PARALLEL_GROUP = mpu.get_pipeline_model_parallel_group() # ps._PIPELINE_GLOBAL_RANKS = mpu.get_pipeline_model_parallel_ranks() def initialize_model_parallel( tensor_model_parallel_size: int = 1, pipeline_model_parallel_size: int = 1, backend: Optional[str] = None, ) -> None: """ NOTE: This method is a hack from the open-sourced version without asertion of world_size = tp * pp Initialize model parallel groups. Arguments: tensor_model_parallel_size: number of GPUs used for tensor model parallelism. pipeline_model_parallel_size: number of GPUs used for pipeline model parallelism. Let's say we have a total of 8 GPUs denoted by g0 ... g7 and we use 2 GPUs to parallelize the model tensor, and 4 GPUs to parallelize the model pipeline. The present function will create 4 tensor model-parallel groups and 2 pipeline model-parallel groups: 4 tensor model-parallel groups: [g0, g1], [g2, g3], [g4, g5], [g6, g7] 2 pipeline model-parallel groups: [g0, g2, g4, g6], [g1, g3, g5, g7] Note that for efficiency, the caller should make sure adjacent ranks are on the same DGX box. For example if we are using 2 DGX-1 boxes with a total of 16 GPUs, rank 0 to 7 belong to the first box and ranks 8 to 15 belong to the second box. """ # Get world size and rank. Ensure some consistencies. assert torch.distributed.is_initialized() world_size: int = torch.distributed.get_world_size() # get the backend of _DEVICE_WORLD_GROUP backend = backend or torch.distributed.get_backend() # NOTE(sgm) we don't assert world_size == tp * pp # DP is not managed by vllm but by the verl WorkerGroup num_tensor_model_parallel_groups: int = (world_size // tensor_model_parallel_size) num_pipeline_model_parallel_groups: int = (world_size // pipeline_model_parallel_size) rank = torch.distributed.get_rank() # Build device mesh for TP if num_tensor_model_parallel_groups > 1: device_mesh = init_device_mesh("cuda", (num_tensor_model_parallel_groups, tensor_model_parallel_size), mesh_dim_names=("replicate", "tp_shard")) else: device_mesh = init_device_mesh("cuda", (tensor_model_parallel_size,), mesh_dim_names=["tp_shard"]) shard_group = device_mesh.get_group(mesh_dim="tp_shard") # Build the tensor model-parallel groups. global _TP_DEVICE_GROUP, _TP_CPU_GROUP global _DEVICE_MESH assert _TP_DEVICE_GROUP is None, ("tensor model parallel group is already initialized") assert _DEVICE_MESH is None, ("device mesh in vllm is already initialized") _DEVICE_MESH = device_mesh # for i in range(num_tensor_model_parallel_groups): # ranks = range(i * tensor_model_parallel_size, (i + 1) * tensor_model_parallel_size) # group = torch.distributed.new_group(ranks, backend=backend) # cpu_group = torch.distributed.new_group(ranks, backend="gloo") # assert torch.distributed.get_process_group_ranks(shard_group) == torch.distributed.get_process_group_ranks(cpu_group) # ranks = torch.distributed.get_process_group_ranks(shard_group) # cpu_group = torch.distributed.new_group(ranks, backend="gloo") # TODO: this will hang # cpu_group = torch.distributed.new_group(, backend="gloo") # if rank == 0: # print(f'rank: {rank}') # print(f'ranks: {ranks}') # print(f'torch.distributed.get_process_group_ranks(shard_group): {torch.distributed.get_process_group_ranks(shard_group)}') # if rank in ranks: _TP_DEVICE_GROUP = shard_group ps._TP_DEVICE_GROUP = _TP_DEVICE_GROUP # ps._TP_CPU_GROUP = cpu_group # TODO: will hang when used with device mesh # TODO: init using device mesh # Build the pipeline model-parallel groups. assert ps._PIPELINE_MODEL_PARALLEL_GROUP is None, ("pipeline model parallel group is already initialized") for i in range(num_pipeline_model_parallel_groups): ranks = range(i, world_size, num_pipeline_model_parallel_groups) group = torch.distributed.new_group(ranks, backend=backend) if rank in ranks: ps._PIPELINE_MODEL_PARALLEL_GROUP = group ps._PIPELINE_GLOBAL_RANKS = ranks """ Device mesh utilities """ def get_device_mesh(): assert _DEVICE_MESH is not None, ("device mesh is not initialized") return _DEVICE_MESH """ Tensor model parallel utilities """ def get_tensor_model_parallel_group(): """Get the tensor model parallel group the caller rank belongs to.""" assert _TP_DEVICE_GROUP is not None, ("tensor model parallel group is not initialized") return _TP_DEVICE_GROUP def get_tensor_model_parallel_world_size(): """Return world size for the tensor model parallel group.""" return torch.distributed.get_world_size(group=get_tensor_model_parallel_group()) def get_tensor_model_parallel_rank(): """Return my rank for the tensor model parallel group.""" return torch.distributed.get_rank(group=get_tensor_model_parallel_group()) def get_tensor_model_parallel_src_rank(): """Calculate the global rank corresponding to the first local rank in the tensor model parallel group.""" global_rank = torch.distributed.get_rank() local_world_size = get_tensor_model_parallel_world_size() return (global_rank // local_world_size) * local_world_size ================================================ FILE: verl/third_party/vllm/vllm_v_0_4_2/spmd_gpu_executor.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Copyright 2023 The vLLM team. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # Adapted from https://github.com/vllm-project/vllm/blob/main/vllm/executor/gpu_executor.py import os import socket from typing import Any, Dict, List, Optional, Set, Tuple import torch import vllm.envs as envs from vllm.executor.executor_base import ExecutorBase, ExecutorAsyncBase from vllm.logger import init_logger from vllm.lora.request import LoRARequest from vllm.sequence import SamplerOutput, ExecuteModelRequest from vllm.config import (CacheConfig, DeviceConfig, LoRAConfig, ParallelConfig, SchedulerConfig, SpeculativeConfig, VisionLanguageConfig) from .config import ModelConfig, LoadConfig logger = init_logger(__name__) class SPMDGPUExecutor(ExecutorBase): """SPMD-based multi-GPU executor implementations.""" def __init__( self, model, # pytorch model itself or its parameter dict model_config: ModelConfig, cache_config: CacheConfig, parallel_config: ParallelConfig, scheduler_config: SchedulerConfig, device_config: DeviceConfig, load_config: LoadConfig, lora_config: Optional[LoRAConfig], vision_language_config: Optional[VisionLanguageConfig], speculative_config: Optional[SpeculativeConfig], ) -> None: self.model_config = model_config self.cache_config = cache_config self.lora_config = lora_config self.load_config = load_config self.parallel_config = parallel_config self.scheduler_config = scheduler_config self.device_config = device_config self.vision_language_config = vision_language_config self.speculative_config = speculative_config distributed_init_method = initialize_cluster(parallel_config) self._init_executor(model, distributed_init_method) # TODO(sgm): verl not support speculative decode now def _init_executor(self, model, distributed_init_method) -> None: assert (not self.speculative_config), "Speculative decoding not yet supported for multi-GPU backend." # Create the parallel worker for each GPU. self._init_workers_sp(model, distributed_init_method) def _init_workers_sp(self, model, distributed_init_method: str): # Lazy import the Worker to avoid importing torch.cuda/xformers # before CUDA_VISIBLE_DEVICES is set in the Worker from .worker import Worker # pylint: disable=import-outside-toplevel rank = int(os.getenv("RANK")) local_rank = int(os.getenv("LOCAL_RANK")) print(f'local rank {local_rank}') self.worker = Worker( model, self.model_config, self.parallel_config, self.scheduler_config, self.device_config, self.cache_config, self.load_config, local_rank, rank, distributed_init_method, lora_config=self.lora_config, vision_language_config=self.vision_language_config, ) # NOTE(shengguangming): torch.distributed.init_process_group will be called inside the init_model() self.worker.init_device() self.worker.load_model() def determine_num_available_blocks(self) -> Tuple[int, int]: """Determine the number of available KV blocks. This invokes `determine_num_available_blocks` on each worker and takes the min of the results, guaranteeing that the selected cache sizes are compatible with all workers. Returns: - tuple[num_gpu_blocks, num_cpu_blocks] """ # Get the maximum number of blocks that can be allocated on GPU and CPU. num_blocks = self.worker.determine_num_available_blocks() # NOTE(shengguangming): Now we don't use a shared centralized controler but each process will # have its own scheduler num_gpu_blocks = num_blocks[0] num_cpu_blocks = num_blocks[1] return num_gpu_blocks, num_cpu_blocks def initialize_cache(self, num_gpu_blocks: int, num_cpu_blocks: int) -> None: """Initialize the KV cache in all workers. """ # NOTE: We log here to avoid multiple logs when number of workers is # greater than one. We could log in the engine, but not all executors # have GPUs. logger.info("# GPU blocks: %d, # CPU blocks: %d", num_gpu_blocks, num_cpu_blocks) self.cache_config.num_gpu_blocks = num_gpu_blocks self.cache_config.num_cpu_blocks = num_cpu_blocks if torch.distributed.get_rank() == 0: print( f'before init cache memory allocated: {torch.cuda.memory_allocated() / 1e9}GB, reserved: {torch.cuda.memory_reserved() / 1e9}GB' ) self.worker.initialize_cache(num_gpu_blocks=num_gpu_blocks, num_cpu_blocks=num_cpu_blocks) if torch.distributed.get_rank() == 0: print( f'after init cache memory allocated: {torch.cuda.memory_allocated() / 1e9}GB, reserved: {torch.cuda.memory_reserved() / 1e9}GB' ) # NOTE(sgm): This will not profile & capture the model(CUDAGraph) when rebuilding KVCache def init_cache_engine(self) -> None: self.worker._init_cache_engine() def free_cache_engine(self) -> None: self.worker.free_cache_engine() def execute_model(self, execute_model_req) -> List[SamplerOutput]: all_outputs = self.worker.execute_model(execute_model_req=execute_model_req) # NOTE(sgm): # Each GPU in vllm under verl has its own spmd_gpu_executor, therefore all GPUs should return the outputs # In vllm with ray, only the driver worker returns the sampling results. return all_outputs def add_lora(self, lora_request: LoRARequest) -> bool: assert lora_request.lora_int_id > 0, "lora_id must be greater than 0." return self.worker.add_lora(lora_request=lora_request) def remove_lora(self, lora_id: int) -> bool: assert lora_id > 0, "lora_id must be greater than 0." return self.worker.remove_lora(lora_id=lora_id) def list_loras(self) -> Set[int]: return self.worker.list_loras() def check_health(self) -> None: # SPMDExecutor will always be healthy as long as # it's running. return # NOTE(sgm): add for verl def offload_model_weights(self) -> None: self.worker.offload_model_weights() def sync_model_weights(self, actor_weights: Dict[str, torch.Tensor], load_format: str) -> None: self.worker.sync_model_weights(actor_weights=actor_weights, load_format=load_format) def initialize_cluster( parallel_config: ParallelConfig, engine_use_ray: bool = False, ray_address: Optional[str] = None, ) -> Tuple[str, Optional[None]]: """Initialize the distributed cluster probably with Ray. Args: parallel_config: The configurations for parallel execution. Returns: The `distributed_init_method` is the address for initializing the distributed backend. """ # Initialize cluster locally. port = get_open_port() # We need to setup the distributed init method to make sure # the distributed megatron code (e.g., get world size) works correctly. # distributed_init_method = f"tcp://localhost:{port}" distributed_init_method = 'env://' return distributed_init_method def get_open_port(): with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: s.bind(("", 0)) return s.getsockname()[1] # TODO(sgm): not implemented async executor yet class SPMDGPUExecutorAsync(SPMDGPUExecutor, ExecutorAsyncBase): async def execute_model_async(self, execute_model_req: ExecuteModelRequest) -> List[SamplerOutput]: """Executes one model step on the given sequences.""" raise NotImplementedError async def check_health_async(self) -> None: """Checks if the executor is healthy. If not, it should raise an exception.""" self.check_health() ================================================ FILE: verl/third_party/vllm/vllm_v_0_4_2/tokenizer.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Copyright 2023 The vLLM team. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # Adapted from https://github.com/vllm-project/vllm/blob/main/vllm/transformers_utils/tokenizer_group/tokenizer_group.py from typing import List, Optional, Tuple, Union from transformers import (AutoTokenizer, PreTrainedTokenizer, PreTrainedTokenizerFast) from vllm.lora.request import LoRARequest from vllm.utils import make_async, LRUCache from vllm.transformers_utils.tokenizers import * class TokenizerGroup: """A group of tokenizers that can be used for LoRA adapters.""" def __init__(self, tokenizer: PreTrainedTokenizer, enable_lora: bool, max_num_seqs: int, max_input_length: Optional[int]): self.enable_lora = enable_lora self.max_input_length = max_input_length self.tokenizer = tokenizer self.lora_tokenizers = LRUCache[PreTrainedTokenizer](capacity=max_num_seqs) if enable_lora else None def ping(self) -> bool: """Check if the tokenizer group is alive.""" return True def get_max_input_len(self, lora_request: Optional[LoRARequest] = None) -> Optional[int]: """Get the maximum input length for the LoRA request.""" return self.max_input_length def encode(self, prompt: str, request_id: Optional[str] = None, lora_request: Optional[LoRARequest] = None) -> List[int]: tokenizer = self.get_lora_tokenizer(lora_request) return tokenizer.encode(prompt) async def encode_async(self, prompt: str, request_id: Optional[str] = None, lora_request: Optional[LoRARequest] = None) -> List[int]: tokenizer = await self.get_lora_tokenizer_async(lora_request) return tokenizer.encode(prompt) def get_lora_tokenizer(self, lora_request: Optional[LoRARequest]) -> "PreTrainedTokenizer": if not lora_request or not self.enable_lora: return self.tokenizer if lora_request.lora_int_id not in self.lora_tokenizers: # TODO(sgm): the lora tokenizer is also passed, but may be different tokenizer = self.tokenizer # tokenizer = (get_lora_tokenizer( # lora_request, **self.tokenizer_config) or self.tokenizer) self.lora_tokenizers.put(lora_request.lora_int_id, tokenizer) return tokenizer else: return self.lora_tokenizers.get(lora_request.lora_int_id) # FIXME(sgm): for simplicity, we assign the special token here @property def pad_token_id(self): return self.tokenizer.pad_token_id @property def eos_token_id(self): return self.tokenizer.eos_token_id ================================================ FILE: verl/third_party/vllm/vllm_v_0_4_2/worker.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Copyright 2023 The vLLM team. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # Adapted from https://github.com/vllm-project/vllm/blob/main/vllm/worker/worker.py """A GPU worker class.""" import os import gc from typing import Dict, List, Tuple, Optional, Union import torch import torch.distributed import torch.nn as nn from vllm.config import (CacheConfig, DeviceConfig, LoRAConfig, ParallelConfig, SchedulerConfig, VisionLanguageConfig) from vllm.model_executor import set_random_seed from vllm.sequence import SamplerOutput, ExecuteModelRequest from vllm.worker.cache_engine import CacheEngine from vllm.distributed.device_communicators import pynccl_utils from vllm.distributed.device_communicators.custom_all_reduce import (init_custom_ar) # TODO(sgm): check why vllm has similar file in vllm.model_executor.parallel_utils.parallel_state from vllm.distributed import get_tensor_model_parallel_cpu_group, init_distributed_environment, get_tensor_model_parallel_group from vllm.worker.worker import Worker, _check_if_gpu_supports_dtype from .model_runner import ModelRunner from .megatron_weight_loaders import load_megatron_weights from .hf_weight_loader import load_hf_weights from .dtensor_weight_loaders import load_dtensor_weights from .parallel_state import (ensure_model_parallel_initialized) from .config import ModelConfig, LoadConfig, LoadFormat class Worker(Worker): """A worker class that executes (a partition of) the model on a GPU. Each worker is associated with a single GPU. The worker is responsible for maintaining the KV cache and executing the model on the GPU. In case of distributed inference, each worker is assigned a partition of the model. """ def __init__( self, model: Union[nn.Module, Dict], # model itself or its parameter dict model_config: ModelConfig, parallel_config: ParallelConfig, scheduler_config: SchedulerConfig, device_config: DeviceConfig, cache_config: CacheConfig, load_config: LoadConfig, local_rank: int, rank: int, distributed_init_method: str, lora_config: Optional[LoRAConfig] = None, vision_language_config: Optional[VisionLanguageConfig] = None, is_driver_worker: bool = False, ) -> None: # self.model = model # will be replaced in the init_model self.model_config = model_config self.parallel_config = parallel_config self.scheduler_config = scheduler_config self.device_config = device_config self.cache_config = cache_config self.local_rank = local_rank self.rank = rank self.distributed_init_method = distributed_init_method self.lora_config = lora_config self.load_config = load_config self.is_driver_worker = is_driver_worker if self.is_driver_worker: assert self.rank == 0, "The driver worker must have rank 0." self.vision_language_config = vision_language_config if self.vision_language_config: assert not self.lora_config, ("To be tested: vision language model with LoRA settings.") self.model_runner = ModelRunner( model, model_config, parallel_config, scheduler_config, device_config, load_config=load_config, lora_config=self.lora_config, kv_cache_dtype=self.cache_config.cache_dtype, vision_language_config=vision_language_config, ) # Uninitialized cache engine. Will be initialized by # init_cache_engine. self.cache_engine: CacheEngine = None self.gpu_cache: List[torch.Tensor] = None # NOTE(sgm): For offloading inference engine params self.cpu_model = None def init_device(self) -> None: if self.device_config.device.type == "cuda": # torch.distributed.all_reduce does not free the input tensor until # the synchronization point. This causes the memory usage to grow # as the number of all_reduce calls increases. This env var disables # this behavior. # Related issue: # https://discuss.pytorch.org/t/cuda-allocation-lifetime-for-inputs-to-distributed-all-reduce/191573 os.environ["TORCH_NCCL_AVOID_RECORD_STREAMS"] = "1" # NOTE(sgm): Modify for verl, Env vars will be set by TORCHRUN. self.rank = self.rank if self.rank is not None else int(os.getenv("RANK", "-1")) local_rank = int(os.getenv("LOCAL_RANK", "0")) self.device = torch.device(f"cuda:{local_rank}") if self.rank < 0: raise ValueError("Invalid or unspecified rank.") torch.cuda.set_device(self.device) # Use the world_size set by TORCHRUN world_size = int(os.getenv("WORLD_SIZE", "-1")) assert world_size != -1, "The world_size is set to -1, not initialized by TORCHRUN" self.parallel_config.world_size = world_size _check_if_gpu_supports_dtype(self.model_config.dtype) torch.cuda.empty_cache() self.init_gpu_memory = torch.cuda.mem_get_info()[0] else: raise RuntimeError(f"Not support device type: {self.device_config.device}") # Initialize the distributed environment. init_worker_distributed_environment(self.parallel_config, self.rank, self.distributed_init_method, self.local_rank) # Set random seed. set_random_seed(self.model_config.seed) # self.model = get_model(actor_model=self.model, model_config=self.model_config) @torch.inference_mode() def determine_num_available_blocks(self) -> Tuple[int, int]: """Profiles the peak memory usage of the model to determine how many KV blocks may be allocated without OOMs. The engine will first conduct a profiling of the existing memory usage. Then, it calculate the maximum possible number of GPU and CPU blocks that can be allocated with the remaining free memory. .. tip:: You may limit the usage of GPU memory by adjusting the `gpu_memory_utilization` parameter. """ # Profile the memory usage of the model and get the maximum number of # cache blocks that can be allocated with the remaining free memory. torch.cuda.empty_cache() # torch.cuda.reset_peak_memory_stats() # Execute a forward pass with dummy inputs to profile the memory usage # of the model. self.model_runner.profile_run() # Calculate the number of blocks that can be allocated with the # profiled peak memory. torch.cuda.synchronize() free_gpu_memory, total_gpu_memory = torch.cuda.mem_get_info() peak_memory = total_gpu_memory - free_gpu_memory assert peak_memory > 0, ("Error in memory profiling. This happens when the GPU memory was " "not properly cleaned up before initializing the vLLM instance.") cache_block_size = self.get_cache_block_size_bytes() # NOTE(sgm) use the remaining memory num_gpu_blocks = int((free_gpu_memory * self.cache_config.gpu_memory_utilization) // cache_block_size) # num_gpu_blocks = int((total_gpu_memory * self.cache_config.gpu_memory_utilization - peak_memory) // cache_block_size) num_cpu_blocks = int(self.cache_config.swap_space_bytes // cache_block_size) num_gpu_blocks = max(num_gpu_blocks, 0) num_cpu_blocks = max(num_cpu_blocks, 0) if self.model_runner.lora_manager: self.model_runner.remove_all_loras() # NOTE(sgm): Add for verl, synchronize number of blocks with all the rank num_gpu_blocks = torch.tensor([num_gpu_blocks], device='cuda') num_cpu_blocks = torch.tensor([num_cpu_blocks], device='cuda') torch.distributed.all_reduce(num_gpu_blocks, op=torch.distributed.ReduceOp.MIN, group=get_tensor_model_parallel_group()) torch.distributed.all_reduce(num_cpu_blocks, op=torch.distributed.ReduceOp.MIN, group=get_tensor_model_parallel_group()) num_gpu_blocks = num_gpu_blocks.item() num_cpu_blocks = num_cpu_blocks.item() gc.collect() torch.cuda.empty_cache() return num_gpu_blocks, num_cpu_blocks def _init_cache_engine(self): if self.cache_engine is None and self.gpu_cache is None: super()._init_cache_engine() def free_cache_engine(self): # ensure `enforce_eager=True` self.cache_engine = None self.gpu_cache = None @torch.inference_mode() def execute_model(self, execute_model_req: Optional[ExecuteModelRequest] = None) -> List[SamplerOutput]: if execute_model_req is None: seq_group_metadata_list = None else: seq_group_metadata_list = execute_model_req.seq_group_metadata_list # NOTE(sgm): each SPMD rank will have identical input assert seq_group_metadata_list is not None assert execute_model_req is not None num_seq_groups = len(seq_group_metadata_list) blocks_to_swap_in = execute_model_req.blocks_to_swap_in blocks_to_swap_out = execute_model_req.blocks_to_swap_out blocks_to_copy = execute_model_req.blocks_to_copy self.cache_swap(blocks_to_swap_in, blocks_to_swap_out, blocks_to_copy) # If there is no input, we don't need to execute the model. if num_seq_groups == 0: return [] output = self.model_runner.execute_model(seq_group_metadata_list, self.gpu_cache) # Worker only supports single-step execution. Wrap the output in a list # to conform to interface. return [output] # assume the input is .state_dict() def sync_model_weights(self, actor_weights: Dict, load_format: str): if load_format in [LoadFormat.MEGATRON, LoadFormat.AUTO]: load_megatron_weights(actor_weights, self.model_runner.model) elif load_format == LoadFormat.HF: # full model state dict without no sharding load_hf_weights(actor_weights, self.model_runner.model) elif load_format == LoadFormat.DTENSOR: load_dtensor_weights(actor_weights, self.model_runner.model) def offload_model_weights(self) -> None: if self.cpu_model == None: self.cpu_model = {} for name, params in self.model_runner.model.named_parameters(): self.cpu_model[name] = torch.empty_like(params, device='cpu') params.data = self.cpu_model[name] else: for name, params in self.model_runner.model.named_parameters(): params.data = self.cpu_model[name] def init_worker_distributed_environment( parallel_config: ParallelConfig, rank: int, distributed_init_method: Optional[str] = "env://", local_rank: int = -1, ) -> None: """Initialize the distributed environment.""" # NOTE(sgm) use tcp://localhost:xxxx will hang in HF setting without megatron init_distributed_environment(parallel_config.world_size, rank, distributed_init_method, local_rank) ensure_model_parallel_initialized(tensor_model_parallel_size=parallel_config.tensor_parallel_size, pipeline_model_parallel_size=parallel_config.pipeline_parallel_size) # TODO(sgm): check whether need this # if pynccl_utils.is_initialized(): # pynccl_world_size = pynccl_utils.get_world_size() # if pynccl_world_size != parallel_config.world_size: # raise RuntimeError( # "pynccl is already initialized but the pynccl world " # "size does not match parallel_config.world_size " # f"({pynccl_world_size} vs. {parallel_config.world_size}).") # elif parallel_config.world_size > 1: # # NOTE(woosuk): We don't initialize pynccl process group when world size # # is 1. # # NOTE(kaichao): By default, pynccl is initialized for tp group. # pynccl_utils.init_process_group( # group=get_tensor_model_parallel_cpu_group()) # # Initialize a custom fast all-reduce implementation. # if not parallel_config.disable_custom_all_reduce: # init_custom_ar() # A small all_reduce for warmup. torch.distributed.all_reduce(torch.zeros(1).cuda()) # if pynccl_utils.is_initialized(): # pynccl_utils.all_reduce(torch.zeros(1).cuda()) ================================================ FILE: verl/third_party/vllm/vllm_v_0_5_4/__init__.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. ================================================ FILE: verl/third_party/vllm/vllm_v_0_5_4/arg_utils.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Copyright 2023 The vLLM team. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # Adapted from https://github.com/vllm-project/vllm/blob/main/vllm/engine/arg_utils.py import os import argparse import dataclasses import json from dataclasses import dataclass from typing import TYPE_CHECKING, List, Optional, Tuple, Type, Union import torch.nn as nn from transformers import PretrainedConfig from .config import ModelConfig, LoadConfig from vllm.config import (CacheConfig, DecodingConfig, DeviceConfig, EngineConfig, LoRAConfig, MultiModalConfig, ObservabilityConfig, ParallelConfig, PromptAdapterConfig, SchedulerConfig, SpeculativeConfig, TokenizerPoolConfig) from vllm.executor.executor_base import ExecutorBase from vllm.logger import init_logger from vllm.utils import FlexibleArgumentParser from vllm.model_executor.layers.quantization import QUANTIZATION_METHODS from vllm.utils import str_to_int_tuple if TYPE_CHECKING: from vllm.transformers_utils.tokenizer_group.base_tokenizer_group import (BaseTokenizerGroup) logger = init_logger(__name__) def nullable_str(val: str): if not val or val == "None": return None return val @dataclass class EngineArgs: """Arguments for vLLM engine.""" model_hf_config: PretrainedConfig = None # for verl served_model_name = None # TODO(sgm): check this # tokenizer: Optional[str] = None # TODO(sgm): check this skip_tokenizer_init: bool = False tokenizer_mode: str = 'auto' trust_remote_code: bool = False download_dir: Optional[str] = None load_format: str = 'auto' dtype: str = 'auto' kv_cache_dtype: str = 'auto' quantization_param_path: Optional[str] = None seed: int = 0 max_model_len: Optional[int] = None worker_use_ray: bool = False # Note: Specifying a custom executor backend by passing a class # is intended for expert use only. The API may change without # notice. distributed_executor_backend: Optional[Union[str, Type[ExecutorBase]]] = None pipeline_parallel_size: int = 1 tensor_parallel_size: int = 1 max_parallel_loading_workers: Optional[int] = None block_size: int = 16 enable_prefix_caching: bool = False disable_sliding_window: bool = False use_v2_block_manager: bool = False swap_space: int = 4 # GiB cpu_offload_gb: int = 0 # GiB gpu_memory_utilization: float = 0.90 max_num_batched_tokens: Optional[int] = None max_num_seqs: int = 256 max_logprobs: int = 20 # Default value for OpenAI Chat Completions API disable_log_stats: bool = False revision: Optional[str] = None code_revision: Optional[str] = None rope_scaling: Optional[dict] = None rope_theta: Optional[float] = None tokenizer_revision: Optional[str] = None quantization: Optional[str] = None enforce_eager: bool = False max_context_len_to_capture: Optional[int] = None max_seq_len_to_capture: int = 8192 disable_custom_all_reduce: bool = False tokenizer_pool_size: int = 0 # Note: Specifying a tokenizer pool by passing a class # is intended for expert use only. The API may change without # notice. tokenizer_pool_type: Union[str, Type["BaseTokenizerGroup"]] = "ray" tokenizer_pool_extra_config: Optional[dict] = None enable_lora: bool = False max_loras: int = 1 max_lora_rank: int = 16 enable_prompt_adapter: bool = False max_prompt_adapters: int = 1 max_prompt_adapter_token: int = 0 fully_sharded_loras: bool = False lora_extra_vocab_size: int = 256 long_lora_scaling_factors: Optional[Tuple[float]] = None lora_dtype: str = 'auto' max_cpu_loras: Optional[int] = None device: str = 'auto' ray_workers_use_nsight: bool = False num_gpu_blocks_override: Optional[int] = None num_lookahead_slots: int = 0 model_loader_extra_config: Optional[dict] = None ignore_patterns: Optional[Union[str, List[str]]] = None preemption_mode: Optional[str] = None scheduler_delay_factor: float = 0.0 enable_chunked_prefill: Optional[bool] = None guided_decoding_backend: str = 'outlines' # Speculative decoding configuration. speculative_model: Optional[str] = None speculative_draft_tensor_parallel_size: Optional[int] = None num_speculative_tokens: Optional[int] = None speculative_max_model_len: Optional[int] = None speculative_disable_by_batch_size: Optional[int] = None ngram_prompt_lookup_max: Optional[int] = None ngram_prompt_lookup_min: Optional[int] = None spec_decoding_acceptance_method: str = 'rejection_sampler' typical_acceptance_sampler_posterior_threshold: Optional[float] = None typical_acceptance_sampler_posterior_alpha: Optional[float] = None qlora_adapter_name_or_path: Optional[str] = None disable_logprobs_during_spec_decoding: Optional[bool] = None otlp_traces_endpoint: Optional[str] = None @staticmethod def add_cli_args(parser: argparse.ArgumentParser) -> argparse.ArgumentParser: """Shared CLI arguments for vLLM engine.""" # Model arguments # TODO(shengguangming): delete the unused args parser.add_argument('--model', type=str, default='facebook/opt-125m', help='name or path of the huggingface model to use') parser.add_argument('--tokenizer', type=str, default=EngineArgs.tokenizer, help='name or path of the huggingface tokenizer to use') parser.add_argument('--revision', type=str, default=None, help='the specific model version to use. It can be a branch ' 'name, a tag name, or a commit id. If unspecified, will use ' 'the default version.') parser.add_argument('--tokenizer-revision', type=str, default=None, help='the specific tokenizer version to use. It can be a branch ' 'name, a tag name, or a commit id. If unspecified, will use ' 'the default version.') parser.add_argument('--tokenizer-mode', type=str, default=EngineArgs.tokenizer_mode, choices=['auto', 'slow'], help='tokenizer mode. "auto" will use the fast ' 'tokenizer if available, and "slow" will ' 'always use the slow tokenizer.') parser.add_argument('--trust-remote-code', action='store_true', help='trust remote code from huggingface') parser.add_argument('--download-dir', type=str, default=EngineArgs.download_dir, help='directory to download and load the weights, ' 'default to the default cache dir of ' 'huggingface') parser.add_argument('--load-format', type=str, default=EngineArgs.load_format, choices=['auto', 'pt', 'safetensors', 'npcache', 'dummy'], help='The format of the model weights to load. ' '"auto" will try to load the weights in the safetensors format ' 'and fall back to the pytorch bin format if safetensors format ' 'is not available. ' '"pt" will load the weights in the pytorch bin format. ' '"safetensors" will load the weights in the safetensors format. ' '"npcache" will load the weights in pytorch format and store ' 'a numpy cache to speed up the loading. ' '"dummy" will initialize the weights with random values, ' 'which is mainly for profiling.') parser.add_argument('--dtype', type=str, default=EngineArgs.dtype, choices=['auto', 'half', 'float16', 'bfloat16', 'float', 'float32'], help='data type for model weights and activations. ' 'The "auto" option will use FP16 precision ' 'for FP32 and FP16 models, and BF16 precision ' 'for BF16 models.') parser.add_argument('--max-model-len', type=int, default=None, help='model context length. If unspecified, ' 'will be automatically derived from the model.') # Parallel arguments parser.add_argument('--worker-use-ray', action='store_true', help='use Ray for distributed serving, will be ' 'automatically set when using more than 1 GPU') parser.add_argument('--pipeline-parallel-size', '-pp', type=int, default=EngineArgs.pipeline_parallel_size, help='number of pipeline stages') parser.add_argument('--tensor-parallel-size', '-tp', type=int, default=EngineArgs.tensor_parallel_size, help='number of tensor parallel replicas') # KV cache arguments parser.add_argument('--block-size', type=int, default=EngineArgs.block_size, choices=[8, 16, 32], help='token block size') # TODO(woosuk): Support fine-grained seeds (e.g., seed per request). parser.add_argument('--seed', type=int, default=EngineArgs.seed, help='random seed') parser.add_argument('--swap-space', type=int, default=EngineArgs.swap_space, help='CPU swap space size (GiB) per GPU') parser.add_argument('--gpu-memory-utilization', type=float, default=EngineArgs.gpu_memory_utilization, help='the percentage of GPU memory to be used for' 'the model executor') parser.add_argument('--max-num-batched-tokens', type=int, default=EngineArgs.max_num_batched_tokens, help='maximum number of batched tokens per ' 'iteration') parser.add_argument('--max-num-seqs', type=int, default=EngineArgs.max_num_seqs, help='maximum number of sequences per iteration') parser.add_argument('--disable-log-stats', action='store_true', help='disable logging statistics') # Quantization settings. parser.add_argument('--quantization', '-q', type=str, choices=['awq', None], default=None, help='Method used to quantize the weights') return parser @classmethod def from_cli_args(cls, args: argparse.Namespace) -> 'EngineArgs': # Get the list of attributes of this dataclass. attrs = [attr.name for attr in dataclasses.fields(cls)] # Set the attributes from the parsed arguments. engine_args = cls(**{attr: getattr(args, attr) for attr in attrs}) return engine_args def create_engine_config( self, ) -> EngineConfig: # bitsandbytes quantization needs a specific model loader # so we make sure the quant method and the load format are consistent if (self.quantization == "bitsandbytes" or self.qlora_adapter_name_or_path is not None) and \ self.load_format != "bitsandbytes": raise ValueError("BitsAndBytes quantization and QLoRA adapter only support " f"'bitsandbytes' load format, but got {self.load_format}") if (self.load_format == "bitsandbytes" or self.qlora_adapter_name_or_path is not None) and \ self.quantization != "bitsandbytes": raise ValueError("BitsAndBytes load format and QLoRA adapter only support " f"'bitsandbytes' quantization, but got {self.quantization}") assert self.cpu_offload_gb >= 0, ("CPU offload space must be non-negative" f", but got {self.cpu_offload_gb}") multimodal_config = MultiModalConfig() device_config = DeviceConfig(self.device) # NOTE(sgm): we only modify ModelConfig, other configs are import from vllm model_config = ModelConfig(hf_config=self.model_hf_config, tokenizer_mode=self.tokenizer_mode, trust_remote_code=self.trust_remote_code, dtype=self.dtype, seed=self.seed, revision=self.revision, code_revision=self.code_revision, rope_scaling=self.rope_scaling, rope_theta=self.rope_theta, tokenizer_revision=self.tokenizer_revision, max_model_len=self.max_model_len, quantization=self.quantization, quantization_param_path=self.quantization_param_path, enforce_eager=self.enforce_eager, max_context_len_to_capture=self.max_context_len_to_capture, max_seq_len_to_capture=self.max_seq_len_to_capture, max_logprobs=self.max_logprobs, disable_sliding_window=self.disable_sliding_window, skip_tokenizer_init=self.skip_tokenizer_init, served_model_name=self.served_model_name, multimodal_config=multimodal_config) cache_config = CacheConfig( block_size=self.block_size, gpu_memory_utilization=self.gpu_memory_utilization, swap_space=self.swap_space, cache_dtype=self.kv_cache_dtype, num_gpu_blocks_override=self.num_gpu_blocks_override, sliding_window=model_config.get_sliding_window(), enable_prefix_caching=self.enable_prefix_caching, cpu_offload_gb=self.cpu_offload_gb, ) parallel_config = ParallelConfig(pipeline_parallel_size=self.pipeline_parallel_size, tensor_parallel_size=self.tensor_parallel_size, worker_use_ray=self.worker_use_ray, max_parallel_loading_workers=self.max_parallel_loading_workers, disable_custom_all_reduce=self.disable_custom_all_reduce, tokenizer_pool_config=TokenizerPoolConfig.create_config( self.tokenizer_pool_size, self.tokenizer_pool_type, self.tokenizer_pool_extra_config, ), ray_workers_use_nsight=self.ray_workers_use_nsight, distributed_executor_backend=self.distributed_executor_backend) # NOTE[VERL]: Use the world_size set by TORCHRUN world_size = int(os.getenv("WORLD_SIZE", "-1")) assert world_size != -1, "The world_size is set to -1, not initialized by TORCHRUN" parallel_config.world_size = world_size max_model_len = model_config.max_model_len use_long_context = max_model_len > 32768 if self.enable_chunked_prefill is None: # If not explicitly set, enable chunked prefill by default for # long context (> 32K) models. This is to avoid OOM errors in the # initial memory profiling phase. if use_long_context: is_gpu = device_config.device_type == "cuda" use_sliding_window = (model_config.get_sliding_window() is not None) use_spec_decode = self.speculative_model is not None has_seqlen_agnostic_layers = (model_config.contains_seqlen_agnostic_layers(parallel_config)) if (is_gpu and not use_sliding_window and not use_spec_decode and not self.enable_lora and not self.enable_prompt_adapter and not self.enable_prefix_caching and not has_seqlen_agnostic_layers): self.enable_chunked_prefill = True logger.warning("Chunked prefill is enabled by default for models with " "max_model_len > 32K. Currently, chunked prefill might " "not work with some features or models. If you " "encounter any issues, please disable chunked prefill " "by setting --enable-chunked-prefill=False.") if self.enable_chunked_prefill is None: self.enable_chunked_prefill = False if not self.enable_chunked_prefill and use_long_context: logger.warning( "The model has a long context length (%s). This may cause OOM " "errors during the initial memory profiling phase, or result " "in low performance due to small KV cache space. Consider " "setting --max-model-len to a smaller value.", max_model_len) # TODO: spec config speculative_config = SpeculativeConfig.maybe_create_spec_config( target_model_config=model_config, target_parallel_config=parallel_config, target_dtype=self.dtype, speculative_model=self.speculative_model, speculative_draft_tensor_parallel_size = \ self.speculative_draft_tensor_parallel_size, num_speculative_tokens=self.num_speculative_tokens, speculative_disable_by_batch_size=self. speculative_disable_by_batch_size, speculative_max_model_len=self.speculative_max_model_len, enable_chunked_prefill=self.enable_chunked_prefill, use_v2_block_manager=self.use_v2_block_manager, disable_log_stats=self.disable_log_stats, ngram_prompt_lookup_max=self.ngram_prompt_lookup_max, ngram_prompt_lookup_min=self.ngram_prompt_lookup_min, draft_token_acceptance_method=\ self.spec_decoding_acceptance_method, typical_acceptance_sampler_posterior_threshold=self. typical_acceptance_sampler_posterior_threshold, typical_acceptance_sampler_posterior_alpha=self. typical_acceptance_sampler_posterior_alpha, disable_logprobs=self.disable_logprobs_during_spec_decoding, ) scheduler_config = SchedulerConfig( max_num_batched_tokens=self.max_num_batched_tokens, max_num_seqs=self.max_num_seqs, max_model_len=model_config.max_model_len, use_v2_block_manager=self.use_v2_block_manager, num_lookahead_slots=(self.num_lookahead_slots if speculative_config is None else speculative_config.num_lookahead_slots), delay_factor=self.scheduler_delay_factor, enable_chunked_prefill=self.enable_chunked_prefill, embedding_mode=model_config.embedding_mode, preemption_mode=self.preemption_mode, ) lora_config = LoRAConfig(max_lora_rank=self.max_lora_rank, max_loras=self.max_loras, fully_sharded_loras=self.fully_sharded_loras, lora_extra_vocab_size=self.lora_extra_vocab_size, long_lora_scaling_factors=self.long_lora_scaling_factors, lora_dtype=self.lora_dtype, max_cpu_loras=self.max_cpu_loras if self.max_cpu_loras and self.max_cpu_loras > 0 else None) if self.enable_lora else None if self.qlora_adapter_name_or_path is not None and \ self.qlora_adapter_name_or_path != "": if self.model_loader_extra_config is None: self.model_loader_extra_config = {} self.model_loader_extra_config["qlora_adapter_name_or_path"] = self.qlora_adapter_name_or_path load_config = LoadConfig( load_format=self.load_format, download_dir=self.download_dir, model_loader_extra_config=self.model_loader_extra_config, ignore_patterns=self.ignore_patterns, ) prompt_adapter_config = PromptAdapterConfig( max_prompt_adapters=self.max_prompt_adapters, max_prompt_adapter_token=self.max_prompt_adapter_token) \ if self.enable_prompt_adapter else None decoding_config = DecodingConfig(guided_decoding_backend=self.guided_decoding_backend) observability_config = ObservabilityConfig(otlp_traces_endpoint=self.otlp_traces_endpoint) if (model_config.get_sliding_window() is not None and scheduler_config.chunked_prefill_enabled and not scheduler_config.use_v2_block_manager): raise ValueError("Chunked prefill is not supported with sliding window. " "Set --disable-sliding-window to disable sliding window.") return EngineConfig( model_config=model_config, cache_config=cache_config, parallel_config=parallel_config, scheduler_config=scheduler_config, device_config=device_config, lora_config=lora_config, multimodal_config=multimodal_config, speculative_config=speculative_config, load_config=load_config, decoding_config=decoding_config, observability_config=observability_config, prompt_adapter_config=prompt_adapter_config, ) ================================================ FILE: verl/third_party/vllm/vllm_v_0_5_4/config.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Copyright 2023 The vLLM team. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # Adapted from https://github.com/vllm-project/vllm/blob/main/vllm/config.py import enum import json from typing import List, Optional, Union from dataclasses import dataclass, field, fields import torch from transformers import PretrainedConfig from vllm.logger import init_logger from vllm.model_executor.layers.quantization import get_quantization_config from vllm.transformers_utils.config import get_hf_text_config from vllm.utils import is_hip, print_warning_once # Add for verl from vllm.config import ModelConfig, _get_and_verify_dtype, _get_and_verify_max_len, get_served_model_name GPTQMarlinConfig = get_quantization_config("gptq_marlin") logger = init_logger(__name__) _GB = 1 << 30 class ModelConfig(ModelConfig): """Configuration for the model. Args: model: Name or path of the huggingface model to use. tokenizer: Name or path of the huggingface tokenizer to use. tokenizer_mode: Tokenizer mode. "auto" will use the fast tokenizer if available, and "slow" will always use the slow tokenizer. trust_remote_code: Trust remote code (e.g., from HuggingFace) when downloading the model and tokenizer. download_dir: Directory to download and load the weights, default to the default cache directory of huggingface. load_format: The format of the model weights to load: "auto" will try to load the weights in the safetensors format and fall back to the pytorch bin format if safetensors format is not available. "pt" will load the weights in the pytorch bin format. "safetensors" will load the weights in the safetensors format. "npcache" will load the weights in pytorch format and store a numpy cache to speed up the loading. "dummy" will initialize the weights with random values, which is mainly for profiling. dtype: Data type for model weights and activations. The "auto" option will use FP16 precision for FP32 and FP16 models, and BF16 precision for BF16 models. seed: Random seed for reproducibility. revision: The specific model version to use. It can be a branch name, a tag name, or a commit id. If unspecified, will use the default version. code_revision: The specific revision to use for the model code on Hugging Face Hub. It can be a branch name, a tag name, or a commit id. If unspecified, will use the default version. tokenizer_revision: The specific tokenizer version to use. It can be a branch name, a tag name, or a commit id. If unspecified, will use the default version. max_model_len: Maximum length of a sequence (including prompt and output). If None, will be derived from the model. quantization: Quantization method that was used to quantize the model weights. If None, we assume the model weights are not quantized. quantization_param_path: Path to JSON file containing scaling factors. Used to load KV cache scaling factors into the model when KV cache type is FP8_E4M3 on ROCm (AMD GPU). In the future these will also be used to load activation and weight scaling factors when the model dtype is FP8_E4M3 on ROCm. enforce_eager: Whether to enforce eager execution. If True, we will disable CUDA graph and always execute the model in eager mode. If False, we will use CUDA graph and eager execution in hybrid. max_context_len_to_capture: Maximum context len covered by CUDA graphs. When a sequence has context length larger than this, we fall back to eager mode (DEPRECATED. Use max_seq_len_to_capture instead). max_seq_len_to_capture: Maximum sequence len covered by CUDA graphs. When a sequence has context length larger than this, we fall back to eager mode skip_tokenizer_init: If true, skip initialization of tokenizer and detokenizer. served_model_name: The model name used in metrics tag `model_name`, matches the model name exposed via the APIs. If multiple model names provided, the first name will be used. If not specified, the model name will be the same as `model`. """ def __init__( self, hf_config: PretrainedConfig, tokenizer_mode: str, trust_remote_code: bool, dtype: Union[str, torch.dtype], seed: int, revision: Optional[str] = None, code_revision: Optional[str] = None, rope_scaling: Optional[dict] = None, rope_theta: Optional[float] = None, tokenizer_revision: Optional[str] = None, max_model_len: Optional[int] = None, quantization: Optional[str] = None, quantization_param_path: Optional[str] = None, enforce_eager: bool = False, max_context_len_to_capture: Optional[int] = None, max_seq_len_to_capture: Optional[int] = None, max_logprobs: int = 20, disable_sliding_window: bool = False, skip_tokenizer_init: bool = False, served_model_name: Optional[Union[str, List[str]]] = None, multimodal_config: Optional["MultiModalConfig"] = None, ) -> None: self.model = hf_config._name_or_path self.tokenizer = hf_config._name_or_path # NOTE(sgm): same as open-sourced self.tokenizer_mode = tokenizer_mode self.trust_remote_code = trust_remote_code self.seed = seed self.revision = revision self.code_revision = code_revision self.rope_scaling = rope_scaling self.rope_theta = rope_theta # The tokenizer version is consistent with the model version by default. if tokenizer_revision is None: self.tokenizer_revision = revision else: self.tokenizer_revision = tokenizer_revision self.quantization = quantization self.quantization_param_path = quantization_param_path self.enforce_eager = enforce_eager if max_context_len_to_capture is not None: raise ValueError("`max_context_len_to_capture` is deprecated. " "Use `max_seq_len_to_capture` instead.") self.max_seq_len_to_capture = max_seq_len_to_capture self.max_logprobs = max_logprobs self.disable_sliding_window = disable_sliding_window self.skip_tokenizer_init = skip_tokenizer_init # self.hf_config = get_config(model, trust_remote_code, revision) self.hf_config = hf_config self.hf_text_config = get_hf_text_config(hf_config) self.dtype = _get_and_verify_dtype(self.hf_text_config, dtype) # self.served_model_name = get_served_model_name(model, # served_model_name) # self._verify_load_format() # self._verify_tokenizer_mode() if (not self.disable_sliding_window and self.hf_text_config.model_type == "gemma2" and self.hf_text_config.sliding_window is not None): print_warning_once("Gemma 2 uses sliding window attention for every odd layer, " "which is currently not supported by vLLM. Disabling sliding " "window and capping the max length to the sliding window size " f"({self.hf_text_config.sliding_window}).") self.disable_sliding_window = True self.max_model_len = _get_and_verify_max_len(hf_config=self.hf_text_config, max_model_len=max_model_len, disable_sliding_window=self.disable_sliding_window, sliding_window_len=self.get_hf_config_sliding_window()) self.served_model_name = get_served_model_name( self.model, # str served_model_name) self.multimodal_config = multimodal_config if not self.skip_tokenizer_init: self._verify_tokenizer_mode() self._verify_embedding_mode() self._verify_quantization() self._verify_cuda_graph() class LoadFormat(str, enum.Enum): AUTO = 'auto' MEGATRON = "megatron" HF = "hf" DTENSOR = 'dtensor' DUMMY_HF = 'dummy_hf' DUMMY_MEGATRON = 'dummy_megatron' DUMMY_DTENSOR = 'dummy_dtensor' # TODO: check whether this is necessary @dataclass class LoadConfig: """ download_dir: Directory to download and load the weights, default to the default cache directory of huggingface. load_format: The format of the model weights to load: "auto" will try to load the weights in the safetensors format and fall back to the pytorch bin format if safetensors format is not available. "pt" will load the weights in the pytorch bin format. "safetensors" will load the weights in the safetensors format. "npcache" will load the weights in pytorch format and store a numpy cache to speed up the loading. "dummy" will initialize the weights with random values, which is mainly for profiling. "tensorizer" will use CoreWeave's tensorizer library for fast weight loading. "bitsandbytes" will load nf4 type weights. ignore_patterns: The list of patterns to ignore when loading the model. Default to "original/**/*" to avoid repeated loading of llama's checkpoints. """ load_format: Union[str, LoadFormat, "BaseModelLoader"] = LoadFormat.AUTO download_dir: Optional[str] = None model_loader_extra_config: Optional[Union[str, dict]] = field(default_factory=dict) ignore_patterns: Optional[Union[List[str], str]] = None def __post_init__(self): model_loader_extra_config = self.model_loader_extra_config or {} if isinstance(model_loader_extra_config, str): self.model_loader_extra_config = json.loads(model_loader_extra_config) self._verify_load_format() if self.ignore_patterns is not None and len(self.ignore_patterns) > 0: logger.info("Ignoring the following patterns when downloading weights: %s", self.ignore_patterns) else: self.ignore_patterns = ["original/**/*"] def _verify_load_format(self) -> None: if not isinstance(self.load_format, str): return load_format = self.load_format.lower() self.load_format = LoadFormat(load_format) rocm_not_supported_load_format: List[str] = [] if is_hip() and load_format in rocm_not_supported_load_format: rocm_supported_load_format = [ f for f in LoadFormat.__members__ if (f not in rocm_not_supported_load_format) ] raise ValueError(f"load format '{load_format}' is not supported in ROCm. " f"Supported load formats are " f"{rocm_supported_load_format}") ================================================ FILE: verl/third_party/vllm/vllm_v_0_5_4/dtensor_weight_loaders.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Copyright 2023 The vLLM team. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # Adapted from https://github.com/vllm-project/vllm/tree/main/vllm/model_executor/models from typing import Dict, Iterable, Tuple import torch import torch.nn as nn from torch.distributed._tensor import DTensor, Shard, Replicate from vllm.model_executor.layers.linear import * from vllm.model_executor.models import ModelRegistry from vllm.model_executor.model_loader.weight_utils import default_weight_loader from vllm.model_executor.models.utils import is_pp_missing_parameter def gemma_dtensor_weight_loader(actor_weights: Dict, vllm_model: nn.Module) -> nn.Module: stacked_params_mapping = [ # (param_name, shard_name, shard_id) ("qkv_proj", "q_proj", "q"), ("qkv_proj", "k_proj", "k"), ("qkv_proj", "v_proj", "v"), ("gate_up_proj", "gate_proj", 0), ("gate_up_proj", "up_proj", 1), ] params_dict = dict(vllm_model.named_parameters()) for name, loaded_weight in actor_weights.items(): for (param_name, shard_name, shard_id) in stacked_params_mapping: if shard_name not in name: continue stacked_name = name.replace(shard_name, param_name) # Skip loading extra bias for GPTQ models. if stacked_name.endswith(".bias") and stacked_name not in params_dict: continue local_loaded_weight = redistribute_dtensor(param_name=name, loaded_weights=loaded_weight) param = params_dict[stacked_name] weight_loader = getattr(param, "weight_loader", default_weight_loader) weight_loader(param, local_loaded_weight.to(dtype=param.dtype), shard_id) break else: # lm_head is not used in vllm as it is tied with embed_token. # To prevent errors, skip loading lm_head.weight. if "lm_head.weight" in name: continue # Skip loading extra bias for GPTQ models. if name.endswith(".bias") and name not in params_dict: continue local_loaded_weight = redistribute_dtensor(param_name=name, loaded_weights=loaded_weight) param = params_dict[name] weight_loader = getattr(param, "weight_loader", default_weight_loader) weight_loader(param, local_loaded_weight.to(dtype=param.dtype)) def gptbigcode_dtensor_load_weights(actor_weights: Dict, vllm_model: nn.Module): params_dict = dict(vllm_model.named_parameters(remove_duplicate=False)) for name, loaded_weight in actor_weights.items(): if "lm_head.weight" in name: continue if ".attn.bias" in name: # Skip attention mask. # NOTE: "c_attn.bias" should not be skipped. continue local_loaded_weight = redistribute_dtensor(param_name=name, loaded_weights=loaded_weight) param = params_dict[name] weight_loader = getattr(param, "weight_loader", default_weight_loader) weight_loader(param, local_loaded_weight.to(dtype=param.dtype)) def starcoder2_dtensor_load_weights(actor_weights: Dict, vllm_model: nn.Module): stacked_params_mapping = [ # (param_name, shard_name, shard_id) ("qkv_proj", "q_proj", "q"), ("qkv_proj", "k_proj", "k"), ("qkv_proj", "v_proj", "v"), ] params_dict = dict(vllm_model.named_parameters(remove_duplicate=False)) for name, loaded_weight in actor_weights.items(): if "rotary_emb.inv_freq" in name: continue for (param_name, weight_name, shard_id) in stacked_params_mapping: if weight_name not in name: continue name = name.replace(weight_name, param_name) local_loaded_weight = redistribute_dtensor(param_name=name, loaded_weights=loaded_weight) param = params_dict[name] weight_loader = param.weight_loader weight_loader(param, local_loaded_weight.to(dtype=param.dtype), shard_id) break else: if vllm_model.config.tie_word_embeddings and "lm_head.weight" in name: continue param = params_dict[name] local_loaded_weight = redistribute_dtensor(param_name=name, loaded_weights=loaded_weight) weight_loader = getattr(param, "weight_loader", default_weight_loader) weight_loader(param, local_loaded_weight.to(dtype=param.dtype)) def llama_dtensor_weight_loader(actor_weights: Dict, vllm_model: nn.Module) -> nn.Module: stacked_params_mapping = [ # (param_name, shard_name, shard_id) (".qkv_proj", ".q_proj", "q"), (".qkv_proj", ".k_proj", "k"), (".qkv_proj", ".v_proj", "v"), (".gate_up_proj", ".gate_proj", 0), (".gate_up_proj", ".up_proj", 1), ] params_dict = dict(vllm_model.named_parameters()) for name, loaded_weight in actor_weights.items(): if "rotary_emb.inv_freq" in name: continue if ("rotary_emb.cos_cached" in name or "rotary_emb.sin_cached" in name): # Models trained using ColossalAI may include these tensors in # the checkpoint. Skip them. continue # With tie_word_embeddings, we can skip lm_head.weight # The weight might appear unnecessarily in the files if the model is # processed with quantization, LoRA, fine-tuning, etc. if vllm_model.config.tie_word_embeddings and "lm_head.weight" in name: continue for (param_name, weight_name, shard_id) in stacked_params_mapping: if weight_name not in name: continue name = name.replace(weight_name, param_name) # Skip loading extra bias for GPTQ models. if name.endswith(".bias") and name not in params_dict: continue local_loaded_weight = redistribute_dtensor(param_name=name, loaded_weights=loaded_weight) param = params_dict[name] weight_loader = param.weight_loader weight_loader(param, local_loaded_weight.to(dtype=param.dtype), shard_id) break else: # Skip loading extra bias for GPTQ models. if name.endswith(".bias") and name not in params_dict: continue local_loaded_weight = redistribute_dtensor(param_name=name, loaded_weights=loaded_weight) param = params_dict[name] weight_loader = getattr(param, "weight_loader", default_weight_loader) weight_loader(param, local_loaded_weight) def qwen2_dtensor_weight_loader(actor_weights: Dict, vllm_model: nn.Module) -> nn.Module: stacked_params_mapping = [ # (param_name, shard_name, shard_id) ("qkv_proj", "q_proj", "q"), ("qkv_proj", "k_proj", "k"), ("qkv_proj", "v_proj", "v"), ("gate_up_proj", "gate_proj", 0), ("gate_up_proj", "up_proj", 1), ] params_dict = dict(vllm_model.named_parameters(remove_duplicate=False)) for name, loaded_weight in actor_weights.items(): if "rotary_emb.inv_freq" in name: continue if vllm_model.config.tie_word_embeddings and "lm_head.weight" in name: continue for (param_name, weight_name, shard_id) in stacked_params_mapping: if weight_name not in name: continue name = name.replace(weight_name, param_name) # Skip loading extra bias for GPTQ models. if name.endswith(".bias") and name not in params_dict: continue local_loaded_weight = redistribute_dtensor(param_name=name, loaded_weights=loaded_weight) param = params_dict[name] weight_loader = param.weight_loader weight_loader(param, local_loaded_weight.to(dtype=param.dtype), shard_id) break else: # Skip loading extra bias for GPTQ models. if name.endswith(".bias") and name not in params_dict: continue param = params_dict[name] local_loaded_weight = redistribute_dtensor(param_name=name, loaded_weights=loaded_weight) weight_loader = getattr(param, "weight_loader", default_weight_loader) weight_loader(param, local_loaded_weight.to(dtype=param.dtype)) from vllm.model_executor.layers.fused_moe import FusedMoE def deepseekv2_dtensor_weight_loader(actor_weights: Dict, vllm_model: nn.Module) -> nn.Module: stacked_params_mapping = [ # (param_name, shard_name, shard_id) ("gate_up_proj", "gate_proj", 0), ("gate_up_proj", "up_proj", 1), ] # Params for weights, fp8 weight scales, fp8 activation scales # (param_name, weight_name, expert_id, shard_id) expert_params_mapping = FusedMoE.make_expert_params_mapping(ckpt_gate_proj_name="gate_proj", ckpt_down_proj_name="down_proj", ckpt_up_proj_name="up_proj", num_experts=vllm_model.config.n_routed_experts) params_dict = dict(vllm_model.named_parameters(remove_duplicate=False)) for name, loaded_weight in actor_weights.items(): if "rotary_emb.inv_freq" in name: continue for (param_name, weight_name, shard_id) in stacked_params_mapping: # Skip non-stacked layers and experts (experts handled below). if weight_name not in name: continue # We have mlp.experts[0].gate_proj in the checkpoint. # Since we handle the experts below in expert_params_mapping, # we need to skip here BEFORE we update the name, otherwise # name will be updated to mlp.experts[0].gate_up_proj, which # will then be updated below in expert_params_mapping # for mlp.experts[0].gate_gate_up_proj, which breaks load. if (("mlp.experts." in name) and name not in params_dict): continue name = name.replace(weight_name, param_name) # Skip loading extra bias for GPTQ models. if name.endswith(".bias") and name not in params_dict: continue if is_pp_missing_parameter(name, vllm_model): continue param = params_dict[name] local_loaded_weight = redistribute_dtensor(param_name=name, loaded_weights=loaded_weight) weight_loader = getattr(param, "weight_loader", default_weight_loader) weight_loader(param, local_loaded_weight.to(dtype=param.dtype), shard_id) break else: for mapping in expert_params_mapping: param_name, weight_name, expert_id, shard_id = mapping if weight_name not in name: continue name = name.replace(weight_name, param_name) if is_pp_missing_parameter(name, vllm_model): continue param = params_dict[name] local_loaded_weight = redistribute_dtensor(param_name=name, loaded_weights=loaded_weight) weight_loader = getattr(param, "weight_loader", default_weight_loader) weight_loader(param, local_loaded_weight.to(dtype=param.dtype), weight_name, shard_id=shard_id, expert_id=expert_id) break else: # Skip loading extra bias for GPTQ models. if name.endswith(".bias") and name not in params_dict: continue if is_pp_missing_parameter(name, vllm_model): continue param = params_dict[name] local_loaded_weight = redistribute_dtensor(param_name=name, loaded_weights=loaded_weight) weight_loader = getattr(param, "weight_loader", default_weight_loader) weight_loader(param, local_loaded_weight.to(dtype=param.dtype)) def gpt2_dtensor_weight_loader(actor_weights: Dict, vllm_model: nn.Module) -> nn.Module: pass def redistribute_dtensor(param_name: str, loaded_weights: DTensor, parallelize_plan: Dict = None): param_name = _process_parameter_names(name=param_name) if parallelize_plan is not None: assert param_name in parallelize_plan.keys(), \ f"param name: {param_name} not in parallelize_plan :{parallelize_plan.keys()}" placement = parallelize_plan[param_name] local_loaded_weights = loaded_weights.redistribute(device_mesh=loaded_weights.device_mesh, placements=placement).to_local() else: local_loaded_weights = loaded_weights.full_tensor() return local_loaded_weights def _process_parameter_names(name): # Remove '.weight' if it exists at the end of the string if name.endswith(".weight"): name = name[:-7] # Remove 'model.layers.x.' or 'model.' prefix if "model.layers" in name: parts = name.split('.') # Reconstruct the string without 'model.layers.x.' name = '.'.join(parts[3:]) # parts[0] is 'model', parts[1] is 'layers', parts[2] is 'x' elif name.startswith("model."): name = name[6:] # Remove 'model.' return name __MODEL_DTENSOR_WEIGHT_LOADER_REGISTRY__ = { 'GPT2LMHeadModel': gpt2_dtensor_weight_loader, 'LlamaForCausalLM': llama_dtensor_weight_loader, 'LLaMAForCausalLM': llama_dtensor_weight_loader, 'MistralForCausalLM': llama_dtensor_weight_loader, # mistral is the same as llama in vLLM 'InternLMForCausalLM': llama_dtensor_weight_loader, 'AquilaModel': llama_dtensor_weight_loader, 'AquilaForCausalLM': llama_dtensor_weight_loader, 'Phi3ForCausalLM': llama_dtensor_weight_loader, 'GemmaForCausalLM': gemma_dtensor_weight_loader, 'Gemma2ForCausalLM': gemma_dtensor_weight_loader, 'GPTBigCodeForCausalLM': gptbigcode_dtensor_load_weights, 'Starcoder2ForCausalLM': starcoder2_dtensor_load_weights, 'Qwen2ForCausalLM': qwen2_dtensor_weight_loader, 'DeepseekV2ForCausalLM': deepseekv2_dtensor_weight_loader } # the actor model is .state_dict() # Load dtensor weights def load_dtensor_weights(actor_weights: Dict, vllm_model: nn.Module): weight_loader = _get_model_weight_loader(vllm_model.__class__.__name__) weight_loader(actor_weights, vllm_model) # NOTE(sgm) to reduce peak memory usage, we offload vllm model to cpu # after init, and we need this after sync model weights for in first iter. vllm_model = vllm_model.cuda() def _get_model_weight_loader(arch: str): if arch in __MODEL_DTENSOR_WEIGHT_LOADER_REGISTRY__: return __MODEL_DTENSOR_WEIGHT_LOADER_REGISTRY__[arch] raise ValueError(f"Model architectures {arch} are not supported for now. " f"Supported architectures: {__MODEL_DTENSOR_WEIGHT_LOADER_REGISTRY__.keys()}") # NOTE(sgm): we use per-parameter weight loader in each vllm sub def update_dtensor_weight_loader(): pass ================================================ FILE: verl/third_party/vllm/vllm_v_0_5_4/hf_weight_loader.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Copyright 2023 The vLLM team. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # Adapted from https://github.com/vllm-project/vllm/tree/main/vllm/model_executor/models from typing import Dict, Union, Optional, Iterable, Tuple import torch import torch.nn as nn from vllm.model_executor.model_loader.utils import set_default_torch_dtype from vllm.model_executor.model_loader.weight_utils import default_weight_loader def update_hf_weight_loader(): print('no hf weight loader need to be updated') return def load_hf_weights(actor_weights: Dict, vllm_model: nn.Module): assert isinstance(actor_weights, Dict) with set_default_torch_dtype(next(vllm_model.parameters()).dtype): # TODO if vllm_model.config.tie_word_embeddings and "lm_head.weight" in actor_weights.keys(): del actor_weights["lm_head.weight"] vllm_model.load_weights(actor_weights.items()) for _, module in vllm_model.named_modules(): quant_method = getattr(module, "quant_method", None) if quant_method is not None: quant_method.process_weights_after_loading(module) # FIXME: Remove this after Mixtral is updated # to use quant_method. if hasattr(module, "process_weights_after_loading"): module.process_weights_after_loading() vllm_model = vllm_model.cuda() ================================================ FILE: verl/third_party/vllm/vllm_v_0_5_4/llm.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Copyright 2023 The vLLM team. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # Adapted from https://github.com/vllm-project/vllm/blob/main/vllm/entrypoints/llm.py from contextlib import contextmanager from typing import ClassVar, List, Optional, Sequence, Union, cast, overload, Dict, Tuple from tqdm import tqdm from transformers import PreTrainedTokenizer, PreTrainedTokenizerFast from transformers import PretrainedConfig import torch.nn as nn from .arg_utils import EngineArgs from .llm_engine_sp import LLMEngine from vllm import LLM from vllm.inputs import (PromptInputs, TextPrompt, TokensPrompt, parse_and_batch_prompt) from vllm.logger import init_logger from vllm.lora.request import LoRARequest from vllm.model_executor.guided_decoding import (GuidedDecodingRequest, get_local_guided_decoding_logits_processor) from vllm.model_executor.guided_decoding.guided_fields import LLMGuidedOptions from vllm.outputs import EmbeddingRequestOutput, RequestOutput from vllm.pooling_params import PoolingParams from vllm.prompt_adapter.request import PromptAdapterRequest from vllm.sampling_params import SamplingParams from vllm.transformers_utils.tokenizer import get_cached_tokenizer from vllm.usage.usage_lib import UsageContext from vllm.utils import Counter, deprecate_kwargs import torch from torch.nn.utils.rnn import pad_sequence from verl.workers.rollout.tokenizer import HybridEngineBaseTokenizer class LLM(LLM): """An LLM for generating texts from given prompts and sampling parameters. This class includes a tokenizer, a language model (possibly distributed across multiple GPUs), and GPU memory space allocated for intermediate states (aka KV cache). Given a batch of prompts and sampling parameters, this class generates texts from the model, using an intelligent batching mechanism and efficient memory management. NOTE: This class is intended to be used for offline inference. For online serving, use the `AsyncLLMEngine` class instead. NOTE: For the comprehensive list of arguments, see `EngineArgs`. Args: model: A HuggingFace Transformers model instance. tokenizer: A HuggingFace Transformers tokenizer instance. tokenizer_mode: The tokenizer mode. "auto" will use the fast tokenizer if available, and "slow" will always use the slow tokenizer. trust_remote_code: Trust remote code (e.g., from HuggingFace) when downloading the model and tokenizer. tensor_parallel_size: The number of GPUs to use for distributed execution with tensor parallelism. dtype: The data type for the model weights and activations. Currently, we support `float32`, `float16`, and `bfloat16`. If `auto`, we use the `torch_dtype` attribute specified in the model config file. However, if the `torch_dtype` in the config is `float32`, we will use `float16` instead. quantization: The method used to quantize the model weights. Currently, we support "awq". If None, we assume the model weights are not quantized and use `dtype` to determine the data type of the weights. revision: The specific model version to use. It can be a branch name, a tag name, or a commit id. tokenizer_revision: The specific tokenizer version to use. It can be a branch name, a tag name, or a commit id. seed: The seed to initialize the random number generator for sampling. gpu_memory_utilization: The ratio (between 0 and 1) of GPU memory to reserve for the model weights, activations, and KV cache. Higher values will increase the KV cache size and thus improve the model's throughput. However, if the value is too high, it may cause out-of- memory (OOM) errors. swap_space: The size (GiB) of CPU memory per GPU to use as swap space. This can be used for temporarily storing the states of the requests when their `best_of` sampling parameters are larger than 1. If all requests will have `best_of=1`, you can safely set this to 0. Otherwise, too small values may cause out-of-memory (OOM) errors. enforce_eager: Whether to enforce eager execution. If True, we will disable CUDA graph and always execute the model in eager mode. If False, we will use CUDA graph and eager execution in hybrid. max_context_len_to_capture: Maximum context len covered by CUDA graphs. When a sequence has context length larger than this, we fall back to eager mode. disable_custom_all_reduce: See ParallelConfig """ def __init__( self, model: Union[nn.Module, Dict], # model itself or its parameter dict tokenizer: Union[PreTrainedTokenizer, PreTrainedTokenizerFast, HybridEngineBaseTokenizer], model_hf_config: PretrainedConfig, tokenizer_mode: str = "auto", trust_remote_code: bool = False, skip_tokenizer_init: bool = False, tensor_parallel_size: int = 1, dtype: str = "auto", quantization: Optional[str] = None, revision: Optional[str] = None, tokenizer_revision: Optional[str] = None, seed: int = 0, gpu_memory_utilization: float = 0.9, swap_space: int = 4, cpu_offload_gb: float = 0, enforce_eager: bool = False, max_context_len_to_capture: Optional[int] = None, max_seq_len_to_capture: int = 8192, disable_custom_all_reduce: bool = False, load_format = 'auto', **kwargs, ) -> None: if "disable_log_stats" not in kwargs: kwargs["disable_log_stats"] = True engine_args = EngineArgs( model_hf_config=model_hf_config, tensor_parallel_size=tensor_parallel_size, dtype=dtype, quantization=quantization, revision=revision, tokenizer_revision=tokenizer_revision, seed=seed, gpu_memory_utilization=gpu_memory_utilization, swap_space=swap_space, cpu_offload_gb=cpu_offload_gb, enforce_eager=enforce_eager, max_context_len_to_capture=max_context_len_to_capture, max_seq_len_to_capture=max_seq_len_to_capture, disable_custom_all_reduce=disable_custom_all_reduce, load_format=load_format, skip_tokenizer_init=skip_tokenizer_init, **kwargs, ) tokenizer_cls = (PreTrainedTokenizer, PreTrainedTokenizerFast, HybridEngineBaseTokenizer) if not isinstance(tokenizer, tokenizer_cls): raise ValueError( f"Unexpected tokenizer type: {type(tokenizer)}. Must be" "one of the following: PreTrainedTokenizer, PreTrainedTokenizerFast, verl.workers.rollout.HybridEngineBaseTokenizer" ) self.llm_engine = LLMEngine.from_engine_args(model, tokenizer, engine_args) # TODO: check usagecontext self.request_counter = Counter() def init_cache_engine(self): self.llm_engine.init_cache_engine() def free_cache_engine(self): self.llm_engine.free_cache_engine() def get_tokenizer(self) -> Union[PreTrainedTokenizer, PreTrainedTokenizerFast]: return self.llm_engine.tokenizer def set_tokenizer( self, tokenizer: Union[PreTrainedTokenizer, PreTrainedTokenizerFast], ) -> None: self.llm_engine.tokenizer = tokenizer def _run_engine(self, *, use_tqdm: bool) -> List[Union[RequestOutput, EmbeddingRequestOutput]]: # Initialize tqdm. if use_tqdm: num_requests = self.llm_engine.get_num_unfinished_requests() pbar = tqdm( total=num_requests, desc="Processed prompts", dynamic_ncols=True, postfix=(f"est. speed input: {0:.2f} toks/s, " f"output: {0:.2f} toks/s"), ) # Run the engine. outputs: List[Union[RequestOutput, EmbeddingRequestOutput]] = [] total_in_toks = 0 total_out_toks = 0 while self.llm_engine.has_unfinished_requests(): step_outputs = self.llm_engine.step() for output in step_outputs: if output.finished: outputs.append(output) if use_tqdm: if isinstance(output, RequestOutput): # Calculate tokens only for RequestOutput total_in_toks += len(output.prompt_token_ids) in_spd = total_in_toks / pbar.format_dict["elapsed"] total_out_toks += sum(len(stp.token_ids) for stp in output.outputs) out_spd = total_out_toks / pbar.format_dict["elapsed"] pbar.postfix = (f"est. speed input: {in_spd:.2f} toks/s, " f"output: {out_spd:.2f} toks/s") pbar.update(1) if use_tqdm: pbar.close() # Sort the outputs by request ID. # This is necessary because some requests may be finished earlier than # its previous requests. outputs = sorted(outputs, key=lambda x: int(x.request_id)) return self._post_process_outputs(outputs) # # NOTE(shengguangming): add for verl # # TODO(sgm): we can optimize it by making the dataloader yield List[int] without padding. # def _pre_process_inputs(self, prompt_token_ids: torch.Tensor) -> List[int]: # # remove the left padding in the prompt token_id # pad_token_id = self.llm_engine.tokenizer.pad_token_id if self.llm_engine.tokenizer.pad_token_id is not None else self.llm_engine.tokenizer.eos_token_id # non_pad_index = torch.nonzero(prompt_token_ids != pad_token_id, as_tuple=False)[0][0] # token_ids = prompt_token_ids[non_pad_index:].tolist() # return token_ids # NOTE(shengguangming): add for verl def _post_process_outputs(self, request_outputs: List[RequestOutput]) -> Tuple[torch.Tensor, torch.Tensor]: output_token_ids = [] logprobs = [] for request_output in request_outputs: # List[RequestOutput] outputs = request_output.outputs for output in outputs: # List[CompletionOutput], usually len == 1 output_token_ids.append(torch.tensor(output.token_ids)) # TODO(shengguangming): can be optimzied by rewrite the Sampler._get_logprobs() logits logprobs_dicts = output.logprobs if logprobs_dicts is not None: logprob = [] for logprobs_dict, id in zip(logprobs_dicts, output.token_ids): logprob.append(logprobs_dict[id].logprob) logprobs.append(torch.tensor(logprob)) pad_token_id = self.llm_engine.tokenizer.pad_token_id if self.llm_engine.tokenizer.pad_token_id is not None else self.llm_engine.tokenizer.eos_token_id output_token_ids = pad_sequence(output_token_ids, batch_first=True, padding_value=pad_token_id) if len(logprobs) > 0: logprobs = pad_sequence(logprobs, batch_first=True, padding_value=pad_token_id) return output_token_ids, logprobs def sync_model_weights(self, actor_weights: Dict[str, torch.Tensor], load_format: str) -> None: self.llm_engine.sync_model_weights(actor_weights=actor_weights, load_format=load_format) def offload_model_weights(self) -> None: self.llm_engine.offload_model_weights() ================================================ FILE: verl/third_party/vllm/vllm_v_0_5_4/llm_engine_sp.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Copyright 2023 The vLLM team. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # Adapted from https://github.com/vllm-project/vllm/blob/main/vllm/engine/llm_engine.py import torch from typing import Dict, Optional, Union, Type import vllm.envs as envs from vllm.config import (CacheConfig, DecodingConfig, DeviceConfig, EngineConfig, LoRAConfig, MultiModalConfig, ObservabilityConfig, ParallelConfig, PromptAdapterConfig, SchedulerConfig, SpeculativeConfig) from vllm.core.scheduler import Scheduler from vllm.engine.output_processor.interfaces import (SequenceGroupOutputProcessor) from vllm.engine.output_processor.stop_checker import StopChecker from vllm.executor.executor_base import ExecutorBase from vllm.inputs import INPUT_REGISTRY, LLMInputs, PromptInputs from vllm.logger import init_logger from vllm.transformers_utils.detokenizer import Detokenizer from vllm.engine.metrics import (LoggingStatLogger, PrometheusStatLogger, StatLoggerBase, Stats) from vllm.tracing import (SpanAttributes, SpanKind, extract_trace_context, init_tracer) from vllm.usage.usage_lib import (UsageContext, is_usage_stats_enabled, usage_message) from vllm.utils import Counter from vllm.engine.llm_engine import _load_generation_config_dict from vllm.engine.llm_engine import LLMEngine from vllm.version import __version__ as VLLM_VERSION import torch.nn as nn from .arg_utils import EngineArgs from .tokenizer import TokenizerGroup from .config import ModelConfig, LoadConfig logger = init_logger(__name__) _LOCAL_LOGGING_INTERVAL_SEC = 5 class LLMEngine(LLMEngine): """An LLM engine that receives requests and generates texts. This is the main class for the vLLM engine. It receives requests from clients and generates texts from the LLM. It includes a tokenizer, a language model (possibly distributed across multiple GPUs), and GPU memory space allocated for intermediate states (aka KV cache). This class utilizes iteration-level scheduling and efficient memory management to maximize the serving throughput. The `LLM` class wraps this class for offline batched inference and the `AsyncLLMEngine` class wraps this class for online serving. NOTE: The config arguments are derived from the `EngineArgs` class. For the comprehensive list of arguments, see `EngineArgs`. Args: model: the actor model initialize outside vllm (add for verl) tokenizer: the initialized tokenizer (add for verl) model_config: The configuration related to the LLM model. cache_config: The configuration related to the KV cache memory management. parallel_config: The configuration related to distributed execution. scheduler_config: The configuration related to the request scheduler. distributed_init_method: The initialization method for distributed execution. See `torch.distributed.init_process_group` for details. placement_group: Ray placement group for distributed execution. Required for distributed execution. log_stats: Whether to log statistics. """ def __init__( self, # NOTE(sgm): first two arguments are added for verl model: Union[nn.Module, Dict], # model itself or its parameter dict tokenizer: nn.Module, # NOTE(sgm): vllm original arguments model_config: ModelConfig, cache_config: CacheConfig, parallel_config: ParallelConfig, scheduler_config: SchedulerConfig, device_config: DeviceConfig, load_config: LoadConfig, lora_config: Optional[LoRAConfig], multimodal_config: Optional[MultiModalConfig], speculative_config: Optional[SpeculativeConfig], decoding_config: Optional[DecodingConfig], observability_config: Optional[ObservabilityConfig], prompt_adapter_config: Optional[PromptAdapterConfig], executor_class: Type[ExecutorBase], log_stats: bool, usage_context: UsageContext = UsageContext.ENGINE_CONTEXT, stat_loggers: Optional[Dict[str, StatLoggerBase]] = None, ) -> None: logger.info( "Initializing an LLM engine (v%s) with config: " "model=%r, speculative_config=%r, tokenizer=%r, " "skip_tokenizer_init=%s, revision=%s, " "rope_scaling=%r, rope_theta=%r, tokenizer_revision=%s, " "trust_remote_code=%s, dtype=%s, max_seq_len=%d, " "download_dir=%r, load_format=%s, tensor_parallel_size=%d, " "pipeline_parallel_size=%d, " "disable_custom_all_reduce=%s, quantization=%s, " "enforce_eager=%s, kv_cache_dtype=%s, " "quantization_param_path=%s, device_config=%s, " "decoding_config=%r, observability_config=%r, " "seed=%d, served_model_name=%s, use_v2_block_manager=%s, " "enable_prefix_caching=%s)", VLLM_VERSION, model_config.model, speculative_config, model_config.tokenizer, model_config.skip_tokenizer_init, model_config.revision, model_config.rope_scaling, model_config.rope_theta, model_config.tokenizer_revision, model_config.trust_remote_code, model_config.dtype, model_config.max_model_len, load_config.download_dir, load_config.load_format, parallel_config.tensor_parallel_size, parallel_config.pipeline_parallel_size, parallel_config.disable_custom_all_reduce, model_config.quantization, model_config.enforce_eager, cache_config.cache_dtype, model_config.quantization_param_path, device_config.device, decoding_config, observability_config, model_config.seed, model_config.served_model_name, scheduler_config.use_v2_block_manager, cache_config.enable_prefix_caching, ) # TODO(woosuk): Print more configs in debug mode. self.model_config = model_config self.cache_config = cache_config self.lora_config = lora_config self.multimodal_config = multimodal_config self.parallel_config = parallel_config self.scheduler_config = scheduler_config self.device_config = device_config self.speculative_config = speculative_config self.load_config = load_config self.decoding_config = decoding_config or DecodingConfig() self.prompt_adapter_config = prompt_adapter_config self.observability_config = observability_config or ObservabilityConfig() self.log_stats = log_stats # self.model = model # should not store the model, it should be deleted # TODO(shengguangming): maybe we can choose init here or from arguments if not self.model_config.skip_tokenizer_init: self.tokenizer = self._init_tokenizer(tokenizer) self.detokenizer = Detokenizer(self.tokenizer) else: self.tokenizer = None self.detokenizer = None self.seq_counter = Counter() self.generation_config_fields = _load_generation_config_dict(model_config) self.input_processor = INPUT_REGISTRY.create_input_processor(self.model_config) self.model_executor = executor_class( model=model, # add for spmd_gpu_executor model_config=model_config, cache_config=cache_config, parallel_config=parallel_config, scheduler_config=scheduler_config, device_config=device_config, lora_config=lora_config, multimodal_config=multimodal_config, speculative_config=speculative_config, load_config=load_config, prompt_adapter_config=prompt_adapter_config, ) # Profile the memory usage and initialize the cache. if not self.model_config.embedding_mode: self._initialize_kv_caches() # If usage stat is enabled, collect relevant info. if is_usage_stats_enabled(): from vllm.model_executor.model_loader import (get_architecture_class_name) usage_message.report_usage( get_architecture_class_name(model_config), usage_context, extra_kvs={ # Common configuration "dtype": str(model_config.dtype), "tensor_parallel_size": parallel_config.tensor_parallel_size, "block_size": cache_config.block_size, "gpu_memory_utilization": cache_config.gpu_memory_utilization, # Quantization "quantization": model_config.quantization, "kv_cache_dtype": str(cache_config.cache_dtype), # Feature flags "enable_lora": bool(lora_config), "enable_prompt_adapter": bool(prompt_adapter_config), "enable_prefix_caching": cache_config.enable_prefix_caching, "enforce_eager": model_config.enforce_eager, "disable_custom_all_reduce": parallel_config.disable_custom_all_reduce, }) if self.tokenizer: # Ping the tokenizer to ensure liveness if it runs in a # different process. self.tokenizer.ping() # Create the scheduler. # NOTE: the cache_config here have been updated with the numbers of # GPU and CPU blocks, which are profiled in the distributed executor. self.scheduler = [ Scheduler(scheduler_config, cache_config, lora_config, parallel_config.pipeline_parallel_size) for _ in range(parallel_config.pipeline_parallel_size) ] # Metric Logging. if self.log_stats: if stat_loggers is not None: self.stat_loggers = stat_loggers else: self.stat_loggers = { "logging": LoggingStatLogger(local_interval=_LOCAL_LOGGING_INTERVAL_SEC), "prometheus": PrometheusStatLogger(local_interval=_LOCAL_LOGGING_INTERVAL_SEC, labels=dict(model_name=model_config.served_model_name), max_model_len=self.model_config.max_model_len), } self.stat_loggers["prometheus"].info("cache_config", self.cache_config) self.tracer = None if self.observability_config.otlp_traces_endpoint: self.tracer = init_tracer("vllm.llm_engine", self.observability_config.otlp_traces_endpoint) # Create sequence output processor, e.g. for beam search or # speculative decoding. self.output_processor = (SequenceGroupOutputProcessor.create_output_processor( self.scheduler_config, self.detokenizer, self.scheduler, self.seq_counter, self.get_tokenizer_for_seq, stop_checker=StopChecker( self.scheduler_config.max_model_len, self.get_tokenizer_for_seq, ), )) # TODO(sgm): add for verl but we may not tokenizer in Rollout def _init_tokenizer(self, tokenizer, **tokenizer_init_kwargs): init_kwargs = dict(enable_lora=bool(self.lora_config), max_num_seqs=self.scheduler_config.max_num_seqs, max_input_length=None) init_kwargs.update(tokenizer_init_kwargs) return TokenizerGroup(tokenizer, **init_kwargs) def init_cache_engine(self): # TODO: check whether we should rebuild the CUDAGraph every iter when offload/load KVCache # Re-capture CUDAGraph would be time-consuming self.model_executor.init_cache_engine() def free_cache_engine(self): self.model_executor.free_cache_engine() # NOTE(sgm): currently, we only support GPU executor # The GPUExecutor remove the Ray dependency @classmethod def _get_executor_cls(cls, engine_config: EngineConfig) -> Type[ExecutorBase]: assert engine_config.device_config.device_type == "cuda", \ "Currently, the vllm in verl only support running on GPU" if engine_config.parallel_config.world_size == 1: engine_config.load_config.load_format = "dummy_hf" from .spmd_gpu_executor import SPMDGPUExecutor executor_class = SPMDGPUExecutor return executor_class @classmethod def from_engine_args( cls, model, tokenizer, engine_args: EngineArgs, usage_context: UsageContext = UsageContext.ENGINE_CONTEXT, stat_loggers: Optional[Dict[str, StatLoggerBase]] = None, ) -> "LLMEngine": """Creates an LLM engine from the engine arguments.""" # Create the engine configs. engine_config = engine_args.create_engine_config() executor_class = cls._get_executor_cls(engine_config) # Initialize the cluster and specify the executor class. assert engine_config.device_config.device_type == "cuda", \ "Currently, the vllm in verl only support running on GPU" from .spmd_gpu_executor import SPMDGPUExecutor executor_class = SPMDGPUExecutor # Create the LLM engine. engine = cls( model, tokenizer, **engine_config.to_dict(), executor_class=executor_class, log_stats=not engine_args.disable_log_stats, usage_context=usage_context, stat_loggers=stat_loggers, ) return engine def sync_model_weights(self, actor_weights: Dict[str, torch.Tensor], load_format: str) -> None: self.model_executor.sync_model_weights(actor_weights=actor_weights, load_format=load_format) def offload_model_weights(self) -> None: self.model_executor.offload_model_weights() ================================================ FILE: verl/third_party/vllm/vllm_v_0_5_4/megatron_weight_loaders.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Copyright 2023 The vLLM team. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # Adapted from https://github.com/vllm-project/vllm/tree/main/vllm/model_executor/models from typing import Dict import torch import torch.nn as nn from vllm.model_executor.layers.linear import * from vllm.model_executor.layers.vocab_parallel_embedding import VocabParallelEmbedding, ParallelLMHead from vllm.model_executor.layers.activation import ScaledActivation from vllm.model_executor.models import ModelRegistry # NOTE(shengguangming): replace the origin weight loader function in the class def parallel_weight_loader(self, param: torch.Tensor, loaded_weight: torch.Tensor) -> None: """Parallel Linear weight loader.""" assert param.size() == loaded_weight.size( ), 'the parameter size is not align with the loaded weight size, param size: {}, loaded_weight size: {}'.format( param.size(), loaded_weight.size()) assert param.data.dtype == loaded_weight.data.dtype, "if we want to shared weights, the data type should also be the same" param.data = loaded_weight.data def default_weight_loader(param: torch.Tensor, loaded_weight: torch.Tensor) -> None: """Default weight loader.""" assert param.size() == loaded_weight.size() assert param.data.dtype == loaded_weight.data.dtype, "if we want to shared weights, the data type should also be the same" param.data = loaded_weight.data def gpt2_weight_loader(actor_weights: Dict, vllm_model: nn.Module) -> nn.Module: params_dict = dict(vllm_model.named_parameters(remove_duplicate=False)) for name, loaded_weight in actor_weights.items(): if "lm_head.weight" in name: # GPT-2 ties the weights of the embedding layer and the final # linear layer. continue if ".attn.bias" in name or ".attn.masked_bias" in name: # Skip attention mask. # NOTE: "c_attn.bias" should not be skipped. continue if not name.startswith("transformer."): name = "transformer." + name param = params_dict[name] # The HF's GPT-2 implementation uses Conv1D instead of Linear. # Because of this, we need to transpose the weights. # Note(zhuohan): the logic below might break quantized models. for conv1d_weight_name in ["c_attn", "c_proj", "c_fc"]: if conv1d_weight_name not in name: continue if not name.endswith(".weight"): continue # TODO: check megatron loaded_weight = loaded_weight.t() weight_loader = getattr(param, "weight_loader", default_weight_loader) weight_loader(param, loaded_weight) def llama_megatron_weight_loader(actor_weights: Dict, vllm_model: nn.Module) -> nn.Module: # NOTE(shengguangming): the megatron llama may have this prefix params_dict = dict(vllm_model.named_parameters()) for name, loaded_weight in actor_weights.items(): if "rotary_emb.inv_freq" in name: continue else: param = params_dict[name] weight_loader = getattr(param, "weight_loader", default_weight_loader) weight_loader(param, loaded_weight) def llama_megatron_core_te_weight_loader(actor_weights: Dict, vllm_model: nn.Module) -> nn.Module: params_mapping = [ # (megatron core gpt model name, vllm model name) ("embedding.word_embeddings", "model.embed_tokens"), ("self_attention.linear_qkv.layer_norm_weight", "input_layernorm.weight"), ("self_attention.linear_qkv.layer_norm_bias", "input_layernorm.bias"), ("self_attention.linear_qkv", "self_attn.qkv_proj"), ("self_attention.linear_qkv", "self_attn.qkv_proj"), ("self_attention.linear_proj", 'self_attn.o_proj'), ('pre_mlp_layernorm', 'post_attention_layernorm'), ('mlp.linear_fc1.layer_norm_weight', 'post_attention_layernorm.weight'), ('mlp.linear_fc1.layer_norm_bias', 'post_attention_layernorm.bias'), ('mlp.linear_fc1', 'mlp.gate_up_proj'), ('mlp.linear_fc2', 'mlp.down_proj'), ('decoder.final_layernorm', 'model.norm'), ('output_layer', 'lm_head'), ] # NOTE(shengguangming): the megatron llama may have this prefix params_dict = dict(vllm_model.named_parameters()) for name, loaded_weight in actor_weights.items(): name = _replace_name(name, params_mapping) if name.endswith('.bias') and name not in params_dict: continue if "rotary_emb.inv_freq" in name: continue else: param = params_dict[name] weight_loader = getattr(param, "weight_loader", default_weight_loader) weight_loader(param, loaded_weight) def llama_megatron_core_weight_loader(actor_weights: Dict, vllm_model: nn.Module) -> nn.Module: params_mapping = [ # (megatron core gpt model name, vllm model name) ("embedding.word_embeddings", "model.embed_tokens"), ("self_attention.linear_qkv", "self_attn.qkv_proj"), ("self_attention.linear_proj", 'self_attn.o_proj'), ( 'input_layernorm', 'input_layernorm', ), ('pre_mlp_layernorm', 'post_attention_layernorm'), ('mlp.linear_fc1', 'mlp.gate_up_proj'), ('mlp.linear_fc2', 'mlp.down_proj'), ('decoder.final_layernorm', 'model.norm'), ('output_layer', 'lm_head'), ] # NOTE(shengguangming): the megatron llama may have this prefix params_dict = dict(vllm_model.named_parameters()) for name, loaded_weight in actor_weights.items(): name = _replace_name(name, params_mapping) if name.endswith('.bias') and name not in params_dict: continue if "rotary_emb.inv_freq" in name: continue else: param = params_dict[name] weight_loader = getattr(param, "weight_loader", default_weight_loader) weight_loader(param, loaded_weight) def _replace_name(megatron_name, name_mapping): for m_name, v_name in name_mapping: if m_name not in megatron_name: continue if 'layers' in megatron_name: # deal with decoder layers megatron_name = megatron_name.replace('decoder', 'model') megatron_name_list = megatron_name.split('.') if 'layer_norm_weight' in megatron_name_list or 'layer_norm_bias' in megatron_name_list: param_name_list = megatron_name_list[:3] param_name_list.append(v_name) param_name = '.'.join(param_name_list) else: param_name_list = megatron_name_list[:3] weight_or_bias = megatron_name_list[-1] param_name_list.append(v_name) param_name_list.append(weight_or_bias) param_name = '.'.join(param_name_list) return param_name else: param_name = megatron_name.replace(m_name, v_name) return param_name def llama_megatron_core_te_weight_loader(actor_weights: Dict, vllm_model: nn.Module) -> nn.Module: params_mapping = [ # (megatron core gpt model name, vllm model name) ("embedding.word_embeddings", "model.embed_tokens"), ("self_attention.linear_qkv.layer_norm_weight", "input_layernorm.weight"), ("self_attention.linear_qkv.layer_norm_bias", "input_layernorm.bias"), ("self_attention.linear_qkv", "self_attn.qkv_proj"), ("self_attention.linear_qkv", "self_attn.qkv_proj"), ("self_attention.linear_proj", 'self_attn.o_proj'), ('pre_mlp_layernorm', 'post_attention_layernorm'), ('mlp.linear_fc1.layer_norm_weight', 'post_attention_layernorm.weight'), ('mlp.linear_fc1.layer_norm_bias', 'post_attention_layernorm.bias'), ('mlp.linear_fc1', 'mlp.gate_up_proj'), ('mlp.linear_fc2', 'mlp.down_proj'), ('decoder.final_layernorm', 'model.norm'), ('output_layer', 'lm_head'), ] # NOTE(shengguangming): the megatron llama may have this prefix params_dict = dict(vllm_model.named_parameters()) for name, loaded_weight in actor_weights.items(): name = _replace_name(name, params_mapping) if name.endswith('.bias') and name not in params_dict: continue if "rotary_emb.inv_freq" in name: continue else: param = params_dict[name] weight_loader = getattr(param, "weight_loader", default_weight_loader) weight_loader(param, loaded_weight) def llama_megatron_core_weight_loader(actor_weights: Dict, vllm_model: nn.Module) -> nn.Module: params_mapping = [ # (megatron core gpt model name, vllm model name) ("embedding.word_embeddings", "model.embed_tokens"), ("self_attention.linear_qkv", "self_attn.qkv_proj"), ("self_attention.linear_proj", 'self_attn.o_proj'), ( 'input_layernorm', 'input_layernorm', ), ('pre_mlp_layernorm', 'post_attention_layernorm'), ('mlp.linear_fc1', 'mlp.gate_up_proj'), ('mlp.linear_fc2', 'mlp.down_proj'), ('decoder.final_layernorm', 'model.norm'), ('output_layer', 'lm_head'), ] # NOTE(shengguangming): the megatron llama may have this prefix params_dict = dict(vllm_model.named_parameters()) for name, loaded_weight in actor_weights.items(): name = _replace_name(name, params_mapping) if name.endswith('.bias') and name not in params_dict: continue if "rotary_emb.inv_freq" in name: continue else: param = params_dict[name] weight_loader = getattr(param, "weight_loader", default_weight_loader) weight_loader(param, loaded_weight) def _replace_name(megatron_name, name_mapping): for m_name, v_name in name_mapping: if m_name not in megatron_name: continue if 'layers' in megatron_name: # deal with decoder layers megatron_name = megatron_name.replace('decoder', 'model') megatron_name_list = megatron_name.split('.') if 'layer_norm_weight' in megatron_name_list or 'layer_norm_bias' in megatron_name_list: param_name_list = megatron_name_list[:3] param_name_list.append(v_name) param_name = '.'.join(param_name_list) else: param_name_list = megatron_name_list[:3] weight_or_bias = megatron_name_list[-1] param_name_list.append(v_name) param_name_list.append(weight_or_bias) param_name = '.'.join(param_name_list) return param_name else: param_name = megatron_name.replace(m_name, v_name) return param_name def mistral_megatron_weight_loader(actor_weights: Dict, vllm_model: nn.Module) -> nn.Module: # TODO: need to implement a general way to deal with prefix params_dict = dict(vllm_model.named_parameters()) for name, loaded_weight in actor_weights.items(): if "rotary_emb.inv_freq" in name: continue else: param = params_dict[name] weight_loader = getattr(param, "weight_loader", default_weight_loader) weight_loader(param, loaded_weight) __LAYER_WEIGHT_MEGATRON_LOADER_REGISTRY__ = { ColumnParallelLinear: parallel_weight_loader, MergedColumnParallelLinear: parallel_weight_loader, QKVParallelLinear: parallel_weight_loader, RowParallelLinear: parallel_weight_loader, VocabParallelEmbedding: parallel_weight_loader, ParallelLMHead: parallel_weight_loader # "ScaledActivation.weight_loader": ScaledActivation, # TODO(shengguangming): latest commit in vllm fix awq for this function and add load_weights # "default_weight_loader": default_weight_loader } # for layer_class, weight_loader in __LAYER_WEIGHT_MEGATRON_LOADER_REGISTRY__.items(): # # setattr(layer_class, 'megatron_weight_loader', weight_loader) # layer_class.weight_loader = weight_loader __MODEL_MEGATRON_WEIGHT_LOADER_REGISTRY__ = { 'GPT2LMHeadModel': gpt2_weight_loader, 'LlamaForCausalLM': llama_megatron_weight_loader, # use te backend for open-source megatron 'LLaMAForCausalLM': llama_megatron_weight_loader, 'MistralForCausalLM': mistral_megatron_weight_loader, } # the actor model is .state_dict() # Load megatron weights def load_megatron_weights(actor_weights: Dict, vllm_model: nn.Module): weight_loader = _get_model_weight_loader(vllm_model.__class__.__name__) weight_loader(actor_weights, vllm_model) # NOTE(sgm) to reduce peak memory usage, we offload vllm model to cpu # after init, and we need this after sync model weights for in first iter. vllm_model = vllm_model.cuda() def _get_model_weight_loader(arch: str): if arch in __MODEL_MEGATRON_WEIGHT_LOADER_REGISTRY__: return __MODEL_MEGATRON_WEIGHT_LOADER_REGISTRY__[arch] raise ValueError(f"Model architectures {arch} are not supported for now. " f"Supported architectures: {ModelRegistry.get_supported_archs()}") def update_megatron_weight_loader(): for layer_class, weight_loader in __LAYER_WEIGHT_MEGATRON_LOADER_REGISTRY__.items(): layer_class.weight_loader = weight_loader ================================================ FILE: verl/third_party/vllm/vllm_v_0_5_4/model_loader.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Copyright 2023 The vLLM team. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # Adapted from https://github.com/vllm-project/vllm/tree/main/vllm/model_executor/model_loader from typing import Dict, Union, Optional, Iterable, Tuple import torch import torch.nn as nn from transformers import PreTrainedModel from vllm.config import (CacheConfig, DeviceConfig, LoadConfig, LoRAConfig, ModelConfig, MultiModalConfig, ParallelConfig, SchedulerConfig) from vllm.model_executor.model_loader import BaseModelLoader from vllm.model_executor.model_loader.loader import _initialize_model from vllm.model_executor.model_loader.utils import set_default_torch_dtype from vllm.distributed.communication_op import tensor_model_parallel_all_gather from .config import ModelConfig, LoadFormat, LoadConfig from .megatron_weight_loaders import load_megatron_weights, update_megatron_weight_loader from .dtensor_weight_loaders import load_dtensor_weights, update_dtensor_weight_loader from .hf_weight_loader import update_hf_weight_loader def get_model(actor_model: Union[PreTrainedModel, Dict], model_config: ModelConfig, load_config: LoadConfig, device_config: DeviceConfig, parallel_config: ParallelConfig, scheduler_config: SchedulerConfig, lora_config: Optional[LoRAConfig], multimodal_config: Optional[MultiModalConfig], cache_config: CacheConfig = None) -> nn.Module: loader = get_model_loader(load_config) if load_config.load_format.startswith('dummy'): return loader.load_model(model_config=model_config, device_config=device_config, lora_config=lora_config, multimodal_config=multimodal_config, parallel_config=parallel_config, scheduler_config=scheduler_config, cache_config=cache_config) else: return loader.load_model(actor_model=actor_model, model_config=model_config, device_config=device_config, lora_config=lora_config, multimodal_config=multimodal_config, parallel_config=parallel_config, scheduler_config=scheduler_config, cache_config=cache_config) def get_model_loader(load_config: LoadConfig) -> BaseModelLoader: """Get a model loader based on the load format.""" if isinstance(load_config.load_format, type): return load_config.load_format(load_config) if load_config.load_format == LoadFormat.AUTO: update_megatron_weight_loader() return MegatronLoader(load_config) # NOTE(sgm): change the weight_loader function in runtime if load_config.load_format == LoadFormat.MEGATRON: update_megatron_weight_loader() return MegatronLoader(load_config) if load_config.load_format == LoadFormat.HF: update_hf_weight_loader() return HFLoader(load_config) if load_config.load_format == LoadFormat.DTENSOR: update_dtensor_weight_loader() return DTensorLoader(load_config) if load_config.load_format == LoadFormat.DUMMY_HF: update_hf_weight_loader() return DummyModelLoader(load_config) if load_config.load_format == LoadFormat.DUMMY_MEGATRON: update_megatron_weight_loader() return DummyModelLoader(load_config) if load_config.load_format == LoadFormat.DUMMY_DTENSOR: update_dtensor_weight_loader() return DummyModelLoader(load_config) raise ValueError('load format not supported in verl: {}, only support {} and {}'.format( load_config.load_format, LoadFormat.MEGATRON, LoadFormat.HF)) class DummyModelLoader(BaseModelLoader): """Model loader that will set model weights to random values.""" def __init__(self, load_config: LoadConfig): super().__init__(load_config) if load_config.model_loader_extra_config: raise ValueError(f"Model loader extra config is not supported for " f"load format {load_config.load_format}") def load_model(self, *, model_config: ModelConfig, device_config: DeviceConfig, lora_config: Optional[LoRAConfig], multimodal_config: Optional[MultiModalConfig], parallel_config: ParallelConfig, scheduler_config: SchedulerConfig, cache_config: CacheConfig) -> nn.Module: with set_default_torch_dtype(model_config.dtype): with torch.device(device_config.device): model = _initialize_model(model_config, self.load_config, lora_config, multimodal_config, cache_config, scheduler_config) # NOTE(woosuk): For accurate performance evaluation, we assign # random values to the weights. # initialize_dummy_weights(model) return model.eval() class MegatronLoader(BaseModelLoader): """Model loader that can load the model weights from partitioned megatron model.""" def __init__(self, load_config: LoadConfig): super().__init__(load_config) if load_config.model_loader_extra_config: raise ValueError(f"Model loader extra config is not supported for " f"load format {load_config.load_format}") def _get_weights_iterator(actor_model: Union[PreTrainedModel, Dict]): # NOTE(shengguangming) Load the weights from the actor model pass # if isinstance(actor_model, nn.Module): # load_weights(actor_weights=dict(actor_model.named_parameters(remove_duplicate=False)), vllm_model=model) # else: # load_weights(actor_weights=actor_model, vllm_model=model) # return actor_model def load_model(self, actor_model: Union[PreTrainedModel, Dict], model_config: ModelConfig, device_config: DeviceConfig, lora_config: Optional[LoRAConfig], multimodal_config: Optional[MultiModalConfig], parallel_config: ParallelConfig, scheduler_config: SchedulerConfig, cache_config: CacheConfig) -> nn.Module: with set_default_torch_dtype(model_config.dtype): with torch.device(device_config.device): model = _initialize_model(model_config, self.load_config, lora_config, multimodal_config, cache_config, scheduler_config) # TODO(sgm): This is a hack, we need to register the load_weight() func for each model in vllm if isinstance(actor_model, nn.Module): load_megatron_weights(actor_weights=dict(actor_model.named_parameters(remove_duplicate=False)), vllm_model=model) else: load_megatron_weights(actor_weights=actor_model, vllm_model=model) for _, module in model.named_modules(): quant_method = getattr(module, "quant_method", None) if quant_method is not None: quant_method.process_weights_after_loading(module) # FIXME: Remove this after Mixtral is updated # to use quant_method. if hasattr(module, "process_weights_after_loading"): module.process_weights_after_loading() # NOTE(sgm) Some weights are point to gpu, but still need this. model = model.cuda() # NOTE (zhangchi.usc1992) We need this for vllm to profile memory usage return model.eval() class HFLoader(BaseModelLoader): """Model loader that can load the model weights from model's full params.""" def __init__(self, load_config: LoadConfig): super().__init__(load_config) if load_config.model_loader_extra_config: raise ValueError(f"Model loader extra config is not supported for " f"load format {load_config.load_format}") def _get_weights_iterator(self, actor_model: Union[PreTrainedModel, Dict]): if isinstance(actor_model, Dict): return actor_model.items() elif isinstance(actor_model, nn.Module): return dict(actor_model.named_parameters()).items() else: raise ValueError(f'actor model should be Dict or nn.Module, but get {type(actor_model)}') def load_model(self, actor_model: Union[PreTrainedModel, Dict], model_config: ModelConfig, device_config: DeviceConfig, lora_config: Optional[LoRAConfig], multimodal_config: Optional[MultiModalConfig], parallel_config: ParallelConfig, scheduler_config: SchedulerConfig, cache_config: CacheConfig) -> nn.Module: with set_default_torch_dtype(model_config.dtype): # with torch.device(device_config.device): # NOTE(sgm): init the model in cpu model = _initialize_model(model_config, self.load_config, lora_config, multimodal_config, cache_config, scheduler_config) model.load_weights(self._get_weights_iterator(actor_model)) for _, module in model.named_modules(): quant_method = getattr(module, "quant_method", None) if quant_method is not None: quant_method.process_weights_after_loading(module) # FIXME: Remove this after Mixtral is updated # to use quant_method. if hasattr(module, "process_weights_after_loading"): module.process_weights_after_loading() # NOTE(sgm) Some weights are point to gpu, but still need this. model = model.cuda() # NOTE (zhangchi.usc1992) We need this for vllm to profile memory usage return model.eval() class DTensorLoader(BaseModelLoader): """Model loader that can load the model weights from partitioned megatron model.""" def __init__(self, load_config: LoadConfig): super().__init__(load_config) if load_config.model_loader_extra_config: raise ValueError(f"Model loader extra config is not supported for " f"load format {load_config.load_format}") def _get_weights_iterator(actor_model: Union[PreTrainedModel, Dict]): # NOTE(shengguangming) Load the weights from the actor model pass # if isinstance(actor_model, nn.Module): # load_weights(actor_weights=dict(actor_model.named_parameters(remove_duplicate=False)), vllm_model=model) # else: # load_weights(actor_weights=actor_model, vllm_model=model) # return actor_model def load_model(self, actor_model: Union[PreTrainedModel, Dict], model_config: ModelConfig, device_config: DeviceConfig, lora_config: Optional[LoRAConfig], multimodal_config: Optional[MultiModalConfig], parallel_config: ParallelConfig, scheduler_config: SchedulerConfig, cache_config: CacheConfig) -> nn.Module: with set_default_torch_dtype(model_config.dtype): with torch.device(device_config.device): model = _initialize_model(model_config, self.load_config, lora_config, multimodal_config, cache_config, scheduler_config) # TODO(sgm): This is a hack, we need to register the load_weight() func for each model in vllm if isinstance(actor_model, nn.Module): load_dtensor_weights(actor_weights=dict(actor_model.named_parameters(remove_duplicate=False)), vllm_model=model) else: load_dtensor_weights(actor_weights=actor_model, vllm_model=model) for _, module in model.named_modules(): quant_method = getattr(module, "quant_method", None) if quant_method is not None: quant_method.process_weights_after_loading(module) # FIXME: Remove this after Mixtral is updated # to use quant_method. if hasattr(module, "process_weights_after_loading"): module.process_weights_after_loading() # NOTE(sgm) Some weights are point to gpu, but still need this. model = model.cuda() # NOTE (zhangchi.usc1992) We need this for vllm to profile memory usage return model.eval() # FIXME(sgm): hack the _get_logits function in vllm v0.4.2 # as they use ray, the _get_logits result will only need to return to the driver node, # therefore gather is enough. However, we use SPMD instead of a central scheduler, # all_gather is required (aligned with v0.2.6) def _get_logits(self, hidden_states: torch.Tensor, embedding: torch.Tensor, embedding_bias: Optional[torch.Tensor]) -> torch.Tensor: # Get the logits for the next tokens. logits = torch.matmul(hidden_states, embedding.t()) if embedding_bias is not None: logits += embedding_bias logits = tensor_model_parallel_all_gather(logits) # Remove paddings in vocab (if any). if logits is not None: logits = logits[:, :self.org_vocab_size] return logits from vllm.model_executor.layers.logits_processor import LogitsProcessor def logitsprocessor_init(self, vocab_size: int, org_vocab_size: Optional[int] = None, scale: float = 1.0, logits_as_input: bool = False, soft_cap: Optional[float] = None) -> None: """ Args: scale: A scaling factor to apply to the logits. """ super(LogitsProcessor, self).__init__() self.scale = scale self.vocab_size = vocab_size # Whether the input is logits (default is hidden states). self.logits_as_input = logits_as_input # original vocabulary size (without LoRA). self.org_vocab_size = org_vocab_size or vocab_size # Soft cap the logits. Used in Gemma 2. self.soft_cap = soft_cap # Whether to use gather or all-gather to gather the logits. self.use_gather = False LogitsProcessor.__init__ = logitsprocessor_init # use all_gather ================================================ FILE: verl/third_party/vllm/vllm_v_0_5_4/model_runner.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Copyright 2023 The vLLM team. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # Adapted from https://github.com/vllm-project/vllm/blob/main/vllm/worker/model_runner.py import torch import torch.nn as nn from enum import IntEnum from typing import Dict, List, Optional, Set, Tuple, Union import warnings import vllm.envs as envs from vllm.attention import (AttentionMetadata, get_attn_backend) from vllm.config import (CacheConfig, DeviceConfig, LoRAConfig, MultiModalConfig, ParallelConfig, PromptAdapterConfig, SchedulerConfig) from vllm.logger import init_logger from vllm.lora.layers import LoRAMapping from vllm.lora.request import LoRARequest from vllm.lora.worker_manager import LRUCacheWorkerLoRAManager from vllm.model_executor import SamplingMetadata from vllm.model_executor.models.interfaces import (supports_lora, supports_vision) from vllm.utils import (CudaMemoryProfiler, is_hip, is_pin_memory_available) from vllm.worker.model_runner import ModelRunner, CUDAGraphRunner from vllm.prompt_adapter.worker_manager import (LRUCacheWorkerPromptAdapterManager) from .model_loader import get_model from .config import ModelConfig, LoadConfig logger = init_logger(__name__) # How batches are constructed. class BatchType(IntEnum): # Every batch is prefill. PREFILL = 0 # Every batch is decode. DECODE = 1 # Batch is a mixture of prefill and decode. MIXED = 2 class ModelRunner(ModelRunner): def __init__( self, model: Union[nn.Module, Dict], # [verl] model itself or its parameter dict model_config: ModelConfig, parallel_config: ParallelConfig, scheduler_config: SchedulerConfig, device_config: DeviceConfig, cache_config: CacheConfig, load_config: LoadConfig, lora_config: Optional[LoRAConfig], kv_cache_dtype: Optional[str] = "auto", is_driver_worker: bool = False, prompt_adapter_config: Optional[PromptAdapterConfig] = None, multimodal_config: Optional[MultiModalConfig] = None, return_hidden_states: bool = False, ): super().__init__( model_config, parallel_config, scheduler_config, device_config, cache_config, load_config, lora_config, kv_cache_dtype, is_driver_worker=True, # a hack prompt_adapter_config=prompt_adapter_config, multimodal_config=multimodal_config, return_hidden_states=return_hidden_states) # NOTE(sgm): add for verl self.model = model # this will be replaced by get_model() # NOTE(sgm): initialize model using the actor model def load_model(self) -> None: logger.info("Starting to load model %s...", self.model_config.model) with CudaMemoryProfiler() as m: self.model = get_model(actor_model=self.model, model_config=self.model_config, device_config=self.device_config, lora_config=self.lora_config, load_config=self.load_config, parallel_config=self.parallel_config, scheduler_config=self.scheduler_config, multimodal_config=self.multimodal_config, cache_config=self.cache_config) self.model_memory_usage = m.consumed_memory logger.info("Loading model weights took %.4f GB", self.model_memory_usage / float(2**30)) if self.lora_config: assert supports_lora(self.model), "Model does not support LoRA" assert not supports_vision(self.model), "To be tested: vision language model with LoRA settings." self.lora_manager = LRUCacheWorkerLoRAManager( self.scheduler_config.max_num_seqs, self.scheduler_config.max_num_batched_tokens, self.vocab_size, self.lora_config, self.device, self.model.embedding_modules, self.model.embedding_padding_modules, max_position_embeddings=self.model.config.max_position_embeddings, ) self.model = self.lora_manager.create_lora_manager(self.model) if self.prompt_adapter_config: self.prompt_adapter_manager = LRUCacheWorkerPromptAdapterManager( self.scheduler_config.max_num_seqs, self.scheduler_config.max_num_batched_tokens, self.device, self.prompt_adapter_config) self.model = (self.prompt_adapter_manager.create_prompt_adapter_manager(self.model)) if self.kv_cache_dtype == "fp8" and is_hip(): # Currently only ROCm accepts kv-cache scaling factors # via quantization_param_path and this will be deprecated # in the future. if self.model_config.quantization_param_path is not None: if callable(getattr(self.model, "load_kv_cache_scales", None)): warnings.warn( "Loading kv cache scaling factor from JSON is " "deprecated and will be removed. Please include " "kv cache scaling factors in the model checkpoint.", FutureWarning, stacklevel=2) self.model.load_kv_cache_scales(self.model_config.quantization_param_path) logger.info("Loaded KV cache scaling factors from %s", self.model_config.quantization_param_path) else: raise RuntimeError( "Using FP8 KV cache and scaling factors provided but " "model %s does not support loading scaling factors.", self.model.__class__) else: logger.warning("Using FP8 KV cache but no scaling factors " "provided. Defaulting to scaling factors of 1.0. " "This may lead to less accurate results!") if envs.VLLM_TEST_DYNAMO_GRAPH_CAPTURE: self.model = torch.compile(self.model, fullgraph=True, backend="eager") ================================================ FILE: verl/third_party/vllm/vllm_v_0_5_4/parallel_state.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Copyright 2023 The vLLM team. # Adapted from # https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/core/parallel_state.py # Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. """Model and data parallel groups.""" import os import torch import torch.distributed from typing import Optional import vllm.distributed.parallel_state as ps from vllm.distributed.parallel_state import get_pp_group, get_world_group, init_distributed_environment, init_model_parallel_group import vllm.envs as envs from vllm.logger import init_logger from torch.distributed.device_mesh import init_device_mesh logger = init_logger(__name__) """ This version is strongly tied with Megatron to implement HybridEngine and weight sharing between vllm and Megatron. - We assume the Megatron tp+dp+pp world is already established before calling this function. """ # Device mesh for using DTensor _DEVICE_MESH = None # Tensor model parallel group that the current rank belongs to. _TP = None # Pipeline model parallel group that the current rank belongs to. _PP = None # This method is for initializing the ParallelGroup when using HybridEngine def initialize_parallel_state( distributed_init_method: str = "env://", backend: str = "nccl", tensor_model_parallel_size: int = 1, num_tp_per_train_tp: int = 1, pipeline_model_parallel_size: int = 1, ): # torch.distributed.all_reduce does not free the input tensor until # the synchronization point. This causes the memory usage to grow # as the number of all_reduce calls increases. This env var disables # this behavior. # Related issue: # https://discuss.pytorch.org/t/cuda-allocation-lifetime-for-inputs-to-distributed-all-reduce/191573 os.environ["TORCH_NCCL_AVOID_RECORD_STREAMS"] = "1" # NOTE(sgm): Modify for verl, Env vars will be set by TORCHRUN. rank = int(os.getenv("RANK", "-1")) local_rank = int(os.getenv("LOCAL_RANK", "0")) # Use the world_size set by TORCHRUN world_size = int(os.getenv("WORLD_SIZE", "-1")) assert world_size != -1, "The world_size is set to -1, not initialized by TORCHRUN" init_distributed_environment(world_size, rank, distributed_init_method, local_rank, backend) if torch.distributed.get_world_size() > 1: # NOTE: build a sepearate inference group with infer tp & micro dp initialize_model_parallel_for_vllm(tensor_model_parallel_size=tensor_model_parallel_size, num_tensor_model_parallel_groups_per_train_tp=num_tp_per_train_tp) else: initialize_model_parallel(tensor_model_parallel_size, pipeline_model_parallel_size, backend) def ensure_model_parallel_initialized( tensor_model_parallel_size: int, pipeline_model_parallel_size: int = 1, backend: Optional[str] = None, ) -> None: """Helper to initialize model parallel groups if they are not initialized, or ensure tensor-parallel and pipeline-parallel sizes are equal to expected values if the model parallel groups are initialized. """ # get the backend of _DEVICE_WORLD_GROUP backend = backend or torch.distributed.get_backend(get_world_group().device_group) if not model_parallel_is_initialized(): initialize_model_parallel(tensor_model_parallel_size, pipeline_model_parallel_size, backend) return assert (get_tensor_model_parallel_world_size() == tensor_model_parallel_size), ( "tensor parallel group already initialized, but of unexpected size: " f"{get_tensor_model_parallel_world_size()=} vs. " f"{tensor_model_parallel_size=}") pp_world_size = get_pp_group().world_size assert (pp_world_size == pipeline_model_parallel_size), ( "pipeline parallel group already initialized, but of unexpected size: " f"{pp_world_size=} vs. " f"{pipeline_model_parallel_size=}") # TODO(sgm): deviate from the v0.5.4, not pp now def model_parallel_is_initialized(): """Check if tensor and pipeline parallel groups are initialized.""" return (ps._TP is not None) # and _PIPELINE_MODEL_PARALLEL_GROUP is not None) def initialize_model_parallel_for_vllm(tensor_model_parallel_size: int, num_tensor_model_parallel_groups_per_train_tp: int = 1, pipeline_model_parallel_size: int = 1) -> None: from torch.distributed import new_group # Get world size and rank. Ensure some consistencies. assert torch.distributed.is_initialized() assert isinstance(tensor_model_parallel_size, int) # assert num_tensor_model_parallel_groups_per_train_tp == 1 and not different_tp_group # assert num_tensor_model_parallel_groups_per_train_tp > 1 and different_tp_group # Build the tensor model-parallel groups. assert ps._TP is None, ("tensor model parallel group is already initialized") global _TP world_size: int = torch.distributed.get_world_size() rank = torch.distributed.get_rank() backend = torch.distributed.get_backend() num_tensor_model_parallel_groups = world_size // tensor_model_parallel_size if num_tensor_model_parallel_groups_per_train_tp == 1: # if tensor_model_parallel_size == train_tensor_parallel_size: # using the same tp group as Megatron/vllm assert _TP is None, ("tensor model parallel group is already initialized") group_ranks = [] for i in range(num_tensor_model_parallel_groups): ranks = range(i * tensor_model_parallel_size, (i + 1) * tensor_model_parallel_size) group_ranks.append(ranks) _TP = init_model_parallel_group( group_ranks=group_ranks, local_rank=get_world_group().local_rank, backend=backend, use_custom_allreduce=False, # TODO: check why True is not work in Ray trainer use_message_queue_broadcaster=True) ps._TP = _TP # _MICRO_DATA_PARALLEL_GROUP is move to hybrid engine else: # initialize a micro_dp group and a tp group # assume training tp=4, infer tp=2, then, weight is partitioned as # [1], [2], [3], [4] for training and [1,2], [1,2], [3,4], [3,4] for inference # Build the inference tp groups # train_tp = train_tensor_parallel_size train_tp = num_tensor_model_parallel_groups_per_train_tp * tensor_model_parallel_size # num_tensor_model_parallel_groups_per_train_tp = train_tp // tensor_model_parallel_size assert _TP is None, ("tensor model parallel group is already initialized") group_ranks = [] for i in range(num_tensor_model_parallel_groups // num_tensor_model_parallel_groups_per_train_tp): start = train_tp * i end = train_tp * (i + 1) for j in range(num_tensor_model_parallel_groups_per_train_tp): ranks = list(range(start, end, num_tensor_model_parallel_groups_per_train_tp)) for i in range(len(ranks)): ranks[i] += j group_ranks.append(ranks) _TP = init_model_parallel_group( group_ranks=group_ranks, local_rank=get_world_group().local_rank, backend=backend, use_custom_allreduce=False, # TODO: check why True is not work in Ray trainer use_message_queue_broadcaster=True) ps._TP = _TP # Build the pipeline model-parallel groups. # global _PIPELINE_MODEL_PARALLEL_GROUP # global _PIPELINE_GLOBAL_RANKS # assert ps._PIPELINE_MODEL_PARALLEL_GROUP is None, ("pipeline model parallel group is already initialized") # ps._PIPELINE_MODEL_PARALLEL_GROUP = mpu.get_pipeline_model_parallel_group() # ps._PIPELINE_GLOBAL_RANKS = mpu.get_pipeline_model_parallel_ranks() # TODO: init using device mesh (not support hybrid engine now) # Build the pipeline model-parallel groups. num_pipeline_model_parallel_groups: int = (world_size // pipeline_model_parallel_size) global _PP assert _PP is None, ("pipeline model parallel group is already initialized") group_ranks = [] for i in range(num_pipeline_model_parallel_groups): ranks = list(range(i, world_size, num_pipeline_model_parallel_groups)) group_ranks.append(ranks) # pipeline parallel does not need custom allreduce _PP = init_model_parallel_group(group_ranks, get_world_group().local_rank, backend, use_custom_allreduce=False) ps._PP = _PP # for verl def initialize_model_parallel( tensor_model_parallel_size: int = 1, pipeline_model_parallel_size: int = 1, backend: Optional[str] = None, ) -> None: """ NOTE: This method is a hack from the open-sourced version without asertion of world_size = tp * pp Initialize model parallel groups. Arguments: tensor_model_parallel_size: number of GPUs used for tensor model parallelism. pipeline_model_parallel_size: number of GPUs used for pipeline model parallelism. Let's say we have a total of 8 GPUs denoted by g0 ... g7 and we use 2 GPUs to parallelize the model tensor, and 4 GPUs to parallelize the model pipeline. The present function will create 4 tensor model-parallel groups and 2 pipeline model-parallel groups: 4 tensor model-parallel groups: [g0, g1], [g2, g3], [g4, g5], [g6, g7] 2 pipeline model-parallel groups: [g0, g2, g4, g6], [g1, g3, g5, g7] Note that for efficiency, the caller should make sure adjacent ranks are on the same DGX box. For example if we are using 2 DGX-1 boxes with a total of 16 GPUs, rank 0 to 7 belong to the first box and ranks 8 to 15 belong to the second box. """ # Get world size and rank. Ensure some consistencies. assert torch.distributed.is_initialized() world_size: int = torch.distributed.get_world_size() backend = backend or torch.distributed.get_backend(ps.get_world_group().device_group) # NOTE(sgm) we don't assert world_size == tp * pp # DP is not managed by vllm but by the verl WorkerGroup # if (world_size != # tensor_model_parallel_size * pipeline_model_parallel_size): # raise RuntimeError( # f"world_size ({world_size}) is not equal to " # f"tensor_model_parallel_size ({tensor_model_parallel_size}) x " # f"pipeline_model_parallel_size ({pipeline_model_parallel_size})") num_tensor_model_parallel_groups: int = (world_size // tensor_model_parallel_size) rank = torch.distributed.get_rank() global _TP assert _TP is None, ("tensor model parallel group is already initialized") group_ranks = [] for i in range(num_tensor_model_parallel_groups): ranks = list(range(i * tensor_model_parallel_size, (i + 1) * tensor_model_parallel_size)) group_ranks.append(ranks) # message queue broadcaster is only used in tensor model parallel group _TP = init_model_parallel_group( group_ranks, get_world_group().local_rank, backend, use_custom_allreduce=False, # TODO: check why True is not work in Ray trainer use_message_queue_broadcaster=True) ps._TP = _TP # TODO: init using device mesh (not support hybrid engine now) # Build the pipeline model-parallel groups. num_pipeline_model_parallel_groups: int = (world_size // pipeline_model_parallel_size) global _PP assert _PP is None, ("pipeline model parallel group is already initialized") group_ranks = [] for i in range(num_pipeline_model_parallel_groups): ranks = list(range(i, world_size, num_pipeline_model_parallel_groups)) group_ranks.append(ranks) # pipeline parallel does not need custom allreduce _PP = init_model_parallel_group(group_ranks, get_world_group().local_rank, backend, use_custom_allreduce=False) ps._PP = _PP # for verl """ Device mesh utilities """ def get_device_mesh(): assert _DEVICE_MESH is not None, ("device mesh is not initialized") return _DEVICE_MESH """ Tensor model parallel utilities """ def get_tensor_model_parallel_group(): """Get the tensor model parallel group the caller rank belongs to.""" assert _TP is not None, ("tensor model parallel group is not initialized") return _TP.device_group def get_tensor_model_parallel_world_size(): """Return world size for the tensor model parallel group.""" return torch.distributed.get_world_size(group=get_tensor_model_parallel_group()) def get_tensor_model_parallel_rank(): """Return my rank for the tensor model parallel group.""" return torch.distributed.get_rank(group=get_tensor_model_parallel_group()) def get_tensor_model_parallel_src_rank(): """Calculate the global rank corresponding to the first local rank in the tensor model parallel group.""" global_rank = torch.distributed.get_rank() local_world_size = get_tensor_model_parallel_world_size() return (global_rank // local_world_size) * local_world_size ================================================ FILE: verl/third_party/vllm/vllm_v_0_5_4/spmd_gpu_executor.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Copyright 2023 The vLLM team. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # Adapted from https://github.com/vllm-project/vllm/blob/main/vllm/executor/gpu_executor.py import os import socket from typing import Any, Dict, List, Optional, Set, Tuple import torch import vllm.envs as envs from vllm.executor.executor_base import ExecutorBase, ExecutorAsyncBase from vllm.logger import init_logger from vllm.lora.request import LoRARequest from vllm.sequence import SamplerOutput, ExecuteModelRequest from vllm.config import (CacheConfig, DeviceConfig, LoRAConfig, MultiModalConfig, ParallelConfig, PromptAdapterConfig, SchedulerConfig, SpeculativeConfig) from .config import ModelConfig, LoadConfig logger = init_logger(__name__) class SPMDGPUExecutor(ExecutorBase): """SPMD-based multi-GPU executor implementations.""" def __init__( self, model, # pytorch model itself or its parameter dict model_config: ModelConfig, cache_config: CacheConfig, parallel_config: ParallelConfig, scheduler_config: SchedulerConfig, device_config: DeviceConfig, load_config: LoadConfig, lora_config: Optional[LoRAConfig], multimodal_config: Optional[MultiModalConfig], speculative_config: Optional[SpeculativeConfig], prompt_adapter_config: Optional[PromptAdapterConfig], ) -> None: self.model_config = model_config self.cache_config = cache_config self.lora_config = lora_config self.load_config = load_config self.parallel_config = parallel_config self.scheduler_config = scheduler_config self.device_config = device_config self.multimodal_config = multimodal_config self.speculative_config = speculative_config self.prompt_adapter_config = prompt_adapter_config distributed_init_method = initialize_cluster(parallel_config) self._init_executor(model, distributed_init_method) # TODO(sgm): verl not support speculative decode now def _init_executor(self, model, distributed_init_method) -> None: assert (not self.speculative_config), "Speculative decoding not yet supported for multi-GPU backend." # Create the parallel worker for each GPU. self._init_workers_sp(model, distributed_init_method) def _init_workers_sp(self, model, distributed_init_method: str): # Lazy import the Worker to avoid importing torch.cuda/xformers # before CUDA_VISIBLE_DEVICES is set in the Worker from .worker import Worker # pylint: disable=import-outside-toplevel rank = int(os.getenv("RANK")) local_rank = int(os.getenv("LOCAL_RANK")) print(f'local rank {local_rank}') # see https://github.com/NVIDIA/nccl/issues/1234 os.environ['NCCL_CUMEM_ENABLE'] = '0' self.worker = Worker( model, self.model_config, self.parallel_config, self.scheduler_config, self.device_config, self.cache_config, self.load_config, local_rank, rank, distributed_init_method, lora_config=self.lora_config, multimodal_config=self.multimodal_config, speculative_config=None, prompt_adapter_config=self.speculative_config, is_driver_worker=True, model_runner_cls=None, # use the default one ) # NOTE(shengguangming): torch.distributed.init_process_group will be called inside the init_model() self.worker.init_device() self.worker.load_model() def determine_num_available_blocks(self) -> Tuple[int, int]: """Determine the number of available KV blocks. This invokes `determine_num_available_blocks` on each worker and takes the min of the results, guaranteeing that the selected cache sizes are compatible with all workers. Returns: - tuple[num_gpu_blocks, num_cpu_blocks] """ # Get the maximum number of blocks that can be allocated on GPU and CPU. num_blocks = self.worker.determine_num_available_blocks() # NOTE(shengguangming): Now we don't use a shared centralized controler but each process will # have its own scheduler num_gpu_blocks = num_blocks[0] num_cpu_blocks = num_blocks[1] return num_gpu_blocks, num_cpu_blocks def initialize_cache(self, num_gpu_blocks: int, num_cpu_blocks: int) -> None: """Initialize the KV cache in all workers. """ # NOTE: We log here to avoid multiple logs when number of workers is # greater than one. We could log in the engine, but not all executors # have GPUs. logger.info("# GPU blocks: %d, # CPU blocks: %d", num_gpu_blocks, num_cpu_blocks) self.cache_config.num_gpu_blocks = num_gpu_blocks self.cache_config.num_cpu_blocks = num_cpu_blocks if torch.distributed.get_rank() == 0: print( f'before init cache memory allocated: {torch.cuda.memory_allocated() / 1e9}GB, reserved: {torch.cuda.memory_reserved() / 1e9}GB' ) self.worker.initialize_cache(num_gpu_blocks=num_gpu_blocks, num_cpu_blocks=num_cpu_blocks) if torch.distributed.get_rank() == 0: print( f'after init cache memory allocated: {torch.cuda.memory_allocated() / 1e9}GB, reserved: {torch.cuda.memory_reserved() / 1e9}GB' ) # NOTE(sgm): This will not profile & capture the model(CUDAGraph) when rebuilding KVCache def init_cache_engine(self) -> None: self.worker._init_cache_engine() def free_cache_engine(self) -> None: self.worker.free_cache_engine() def execute_model(self, execute_model_req) -> List[SamplerOutput]: all_outputs = self.worker.execute_model(execute_model_req=execute_model_req) # NOTE(sgm): # Each GPU in vllm under verl has its own spmd_gpu_executor, therefore all GPUs should return the outputs # In vllm with ray, only the driver worker returns the sampling results. return all_outputs def add_lora(self, lora_request: LoRARequest) -> bool: assert lora_request.lora_int_id > 0, "lora_id must be greater than 0." return self.worker.add_lora(lora_request=lora_request) def remove_lora(self, lora_id: int) -> bool: assert lora_id > 0, "lora_id must be greater than 0." return self.worker.remove_lora(lora_id=lora_id) def list_loras(self) -> Set[int]: return self.worker.list_loras() def check_health(self) -> None: # SPMDExecutor will always be healthy as long as # it's running. return # NOTE(sgm) add for verl to pass the abstract class test, not used from vllm.prompt_adapter.request import PromptAdapterRequest def add_prompt_adapter(self, prompt_adapter_request: PromptAdapterRequest) -> bool: assert prompt_adapter_request.prompt_adapter_id > 0, \ "prompt_adapter_id must be greater than 0." return self.worker.add_prompt_adapter(prompt_adapter_request) def list_prompt_adapters(self) -> Set[int]: return self.worker.list_prompt_adapters() def pin_lora(self, lora_id: int) -> bool: assert lora_id > 0, "lora_id must be greater than 0." return self.worker.pin_lora(lora_id) def pin_prompt_adapter(self, prompt_adapter_id: int) -> bool: assert prompt_adapter_id > 0, \ "prompt_adapter_id must be greater than 0." return self.worker.pin_prompt_adapter(prompt_adapter_id) def remove_prompt_adapter(self, prompt_adapter_id: int) -> bool: assert prompt_adapter_id > 0, \ "prompt_adapter_id must be greater than 0." return self.worker.remove_prompt_adapter(prompt_adapter_id) # NOTE(sgm): add for verl def offload_model_weights(self) -> None: self.worker.offload_model_weights() def sync_model_weights(self, actor_weights: Dict[str, torch.Tensor], load_format: str) -> None: self.worker.sync_model_weights(actor_weights=actor_weights, load_format=load_format) def initialize_cluster( parallel_config: ParallelConfig, engine_use_ray: bool = False, ray_address: Optional[str] = None, ) -> Tuple[str, Optional[None]]: """Initialize the distributed cluster probably with Ray. Args: parallel_config: The configurations for parallel execution. Returns: The `distributed_init_method` is the address for initializing the distributed backend. """ # Initialize cluster locally. port = get_open_port() # We need to setup the distributed init method to make sure # the distributed megatron code (e.g., get world size) works correctly. # distributed_init_method = f"tcp://localhost:{port}" distributed_init_method = 'env://' return distributed_init_method def get_open_port(): with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: s.bind(("", 0)) return s.getsockname()[1] # TODO(sgm): not implemented async executor yet class SPMDGPUExecutorAsync(SPMDGPUExecutor, ExecutorAsyncBase): async def execute_model_async(self, execute_model_req: ExecuteModelRequest) -> List[SamplerOutput]: """Executes one model step on the given sequences.""" raise NotImplementedError async def check_health_async(self) -> None: """Checks if the executor is healthy. If not, it should raise an exception.""" self.check_health() ================================================ FILE: verl/third_party/vllm/vllm_v_0_5_4/tokenizer.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Copyright 2023 The vLLM team. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # Adapted from https://github.com/vllm-project/vllm/blob/main/vllm/transformers_utils/tokenizer_group/tokenizer_group.py from typing import List, Optional, Tuple, Union from transformers import (AutoTokenizer, PreTrainedTokenizer, PreTrainedTokenizerFast) from vllm.lora.request import LoRARequest from vllm.utils import make_async, LRUCache from vllm.transformers_utils.tokenizers import * class TokenizerGroup: """A group of tokenizers that can be used for LoRA adapters.""" def __init__(self, tokenizer: PreTrainedTokenizer, enable_lora: bool, max_num_seqs: int, max_input_length: Optional[int]): self.enable_lora = enable_lora self.max_input_length = max_input_length self.tokenizer = tokenizer self.lora_tokenizers = LRUCache[PreTrainedTokenizer](capacity=max_num_seqs) if enable_lora else None def ping(self) -> bool: """Check if the tokenizer group is alive.""" return True def get_max_input_len(self, lora_request: Optional[LoRARequest] = None) -> Optional[int]: """Get the maximum input length for the LoRA request.""" return self.max_input_length def encode(self, prompt: str, request_id: Optional[str] = None, lora_request: Optional[LoRARequest] = None) -> List[int]: tokenizer = self.get_lora_tokenizer(lora_request) return tokenizer.encode(prompt) async def encode_async(self, prompt: str, request_id: Optional[str] = None, lora_request: Optional[LoRARequest] = None) -> List[int]: tokenizer = await self.get_lora_tokenizer_async(lora_request) return tokenizer.encode(prompt) def get_lora_tokenizer(self, lora_request: Optional[LoRARequest]) -> "PreTrainedTokenizer": if not lora_request or not self.enable_lora: return self.tokenizer if lora_request.lora_int_id not in self.lora_tokenizers: # TODO(sgm): the lora tokenizer is also passed, but may be different tokenizer = self.tokenizer # tokenizer = (get_lora_tokenizer( # lora_request, **self.tokenizer_config) or self.tokenizer) self.lora_tokenizers.put(lora_request.lora_int_id, tokenizer) return tokenizer else: return self.lora_tokenizers.get(lora_request.lora_int_id) # FIXME(sgm): for simplicity, we assign the special token here @property def pad_token_id(self): return self.tokenizer.pad_token_id @property def eos_token_id(self): return self.tokenizer.eos_token_id ================================================ FILE: verl/third_party/vllm/vllm_v_0_5_4/worker.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Copyright 2023 The vLLM team. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # Adapted from https://github.com/vllm-project/vllm/blob/main/vllm/worker/worker.py """A GPU worker class.""" import os import gc from typing import Dict, List, Tuple, Optional, Union, Type import torch import torch.distributed import torch.nn as nn from vllm.config import (CacheConfig, DeviceConfig, LoRAConfig, MultiModalConfig, ParallelConfig, PromptAdapterConfig, SchedulerConfig, SpeculativeConfig) from vllm.model_executor import set_random_seed from vllm.sequence import (ExecuteModelRequest, IntermediateTensors, SamplerOutput) from vllm.worker.cache_engine import CacheEngine # TODO(sgm): check why vllm has similar file in vllm.model_executor.parallel_utils.parallel_state from vllm.distributed import (init_distributed_environment, set_custom_all_reduce, get_tensor_model_parallel_group) from vllm.worker.worker_base import WorkerInput from vllm.worker.worker import Worker, _check_if_gpu_supports_dtype from vllm.worker.model_runner_base import ModelRunnerBase, ModelRunnerInputBase from vllm.worker.embedding_model_runner import EmbeddingModelRunner from vllm.worker.model_runner import GPUModelRunnerBase from .model_runner import ModelRunner from .megatron_weight_loaders import load_megatron_weights from .hf_weight_loader import load_hf_weights from .dtensor_weight_loaders import load_dtensor_weights from .parallel_state import (ensure_model_parallel_initialized) from .config import ModelConfig, LoadConfig, LoadFormat class Worker(Worker): """A worker class that executes (a partition of) the model on a GPU. Each worker is associated with a single GPU. The worker is responsible for maintaining the KV cache and executing the model on the GPU. In case of distributed inference, each worker is assigned a partition of the model. """ def __init__( self, model: Union[nn.Module, Dict], # model itself or its parameter dict model_config: ModelConfig, parallel_config: ParallelConfig, scheduler_config: SchedulerConfig, device_config: DeviceConfig, cache_config: CacheConfig, load_config: LoadConfig, local_rank: int, rank: int, distributed_init_method: str, lora_config: Optional[LoRAConfig] = None, multimodal_config: Optional[MultiModalConfig] = None, speculative_config: Optional[SpeculativeConfig] = None, prompt_adapter_config: Optional[PromptAdapterConfig] = None, is_driver_worker: bool = False, model_runner_cls: Optional[Type[GPUModelRunnerBase]] = None, ) -> None: # self.model = model # will be replaced in the init_model self.model_config = model_config self.parallel_config = parallel_config self.parallel_config.rank = rank self.scheduler_config = scheduler_config self.device_config = device_config self.cache_config = cache_config self.local_rank = local_rank self.rank = rank self.distributed_init_method = distributed_init_method self.lora_config = lora_config self.load_config = load_config self.prompt_adapter_config = prompt_adapter_config self.is_driver_worker = is_driver_worker # TODO: we don't need driver # if parallel_config and is_driver_worker: # assert rank % parallel_config.tensor_parallel_size == 0, \ # "Driver worker should be rank 0 of tensor parallel group." if self.model_config.trust_remote_code: # note: lazy import to avoid importing torch before initializing from vllm.utils import init_cached_hf_modules init_cached_hf_modules() self.multimodal_config = multimodal_config # Return hidden states from target model if the draft model is an # mlp_speculator speculative_args = {} if speculative_config is None \ or (speculative_config.draft_model_config.model == model_config.model) \ or (speculative_config.draft_model_config.hf_config.model_type not in ["medusa", "mlp_speculator"]) \ else {"return_hidden_states": True} # TODO(sgm): set correct model runner class ModelRunnerClass: Type[GPUModelRunnerBase] = ModelRunner if model_runner_cls is not None: ModelRunnerClass = model_runner_cls elif self.model_config.embedding_mode: ModelRunnerClass = EmbeddingModelRunner self.model_runner: GPUModelRunnerBase = ModelRunnerClass( model, # [VERL]: add for verl model_config, parallel_config, scheduler_config, device_config, cache_config, load_config=load_config, lora_config=self.lora_config, kv_cache_dtype=self.cache_config.cache_dtype, is_driver_worker=is_driver_worker, prompt_adapter_config=prompt_adapter_config, multimodal_config=multimodal_config, **speculative_args, ) # Uninitialized cache engine. Will be initialized by # initialize_cache. self.cache_engine: List[CacheEngine] = None # Initialize gpu_cache as embedding models don't initialize kv_caches self.gpu_cache: Optional[List[List[torch.Tensor]]] = None # NOTE(sgm): [VERL] For offloading inference engine params self.cpu_model = None def init_device(self) -> None: if self.device_config.device.type == "cuda": # torch.distributed.all_reduce does not free the input tensor until # the synchronization point. This causes the memory usage to grow # as the number of all_reduce calls increases. This env var disables # this behavior. # Related issue: # https://discuss.pytorch.org/t/cuda-allocation-lifetime-for-inputs-to-distributed-all-reduce/191573 os.environ["TORCH_NCCL_AVOID_RECORD_STREAMS"] = "1" # NOTE(sgm): Modify for verl, Env vars will be set by TORCHRUN. self.rank = self.rank if self.rank is not None else int(os.getenv("RANK", "-1")) local_rank = int(os.getenv("LOCAL_RANK", "0")) self.device = torch.device(f"cuda:{local_rank}") if self.rank < 0: raise ValueError("Invalid or unspecified rank.") torch.cuda.set_device(self.device) # Use the world_size set by TORCHRUN world_size = int(os.getenv("WORLD_SIZE", "-1")) assert world_size != -1, "The world_size is set to -1, not initialized by TORCHRUN" self.parallel_config.world_size = world_size _check_if_gpu_supports_dtype(self.model_config.dtype) torch.cuda.empty_cache() self.init_gpu_memory = torch.cuda.mem_get_info()[0] else: raise RuntimeError(f"Not support device type: {self.device_config.device}") # Initialize the distributed environment. init_worker_distributed_environment(self.parallel_config, self.rank, self.distributed_init_method, self.local_rank) # Set random seed. set_random_seed(self.model_config.seed) # self.model = get_model(actor_model=self.model, model_config=self.model_config) @torch.inference_mode() def determine_num_available_blocks(self) -> Tuple[int, int]: """Profiles the peak memory usage of the model to determine how many KV blocks may be allocated without OOMs. The engine will first conduct a profiling of the existing memory usage. Then, it calculate the maximum possible number of GPU and CPU blocks that can be allocated with the remaining free memory. .. tip:: You may limit the usage of GPU memory by adjusting the `gpu_memory_utilization` parameter. """ # Profile the memory usage of the model and get the maximum number of # cache blocks that can be allocated with the remaining free memory. torch.cuda.empty_cache() # torch.cuda.reset_peak_memory_stats() # Execute a forward pass with dummy inputs to profile the memory usage # of the model. self.model_runner.profile_run() # Calculate the number of blocks that can be allocated with the # profiled peak memory. torch.cuda.synchronize() free_gpu_memory, total_gpu_memory = torch.cuda.mem_get_info() peak_memory = total_gpu_memory - free_gpu_memory assert peak_memory > 0, ("Error in memory profiling. This happens when the GPU memory was " "not properly cleaned up before initializing the vLLM instance.") cache_block_size = self.get_cache_block_size_bytes() # NOTE(sgm) [VERL] use the remaining memory num_gpu_blocks = int((free_gpu_memory * self.cache_config.gpu_memory_utilization) // cache_block_size) # num_gpu_blocks = int((total_gpu_memory * self.cache_config.gpu_memory_utilization - peak_memory) // cache_block_size) num_cpu_blocks = int(self.cache_config.swap_space_bytes // cache_block_size) num_gpu_blocks = max(num_gpu_blocks, 0) num_cpu_blocks = max(num_cpu_blocks, 0) if self.model_runner.lora_manager: self.model_runner.remove_all_loras() # NOTE(sgm): Add for [VERL], synchronize number of blocks with all the rank num_gpu_blocks = torch.tensor([num_gpu_blocks], device='cuda') num_cpu_blocks = torch.tensor([num_cpu_blocks], device='cuda') torch.distributed.all_reduce(num_gpu_blocks, op=torch.distributed.ReduceOp.MIN, group=get_tensor_model_parallel_group().device_group) torch.distributed.all_reduce(num_cpu_blocks, op=torch.distributed.ReduceOp.MIN, group=get_tensor_model_parallel_group().device_group) num_gpu_blocks = num_gpu_blocks.item() num_cpu_blocks = num_cpu_blocks.item() gc.collect() torch.cuda.empty_cache() return num_gpu_blocks, num_cpu_blocks def _init_cache_engine(self): if self.cache_engine is None and self.gpu_cache is None: super()._init_cache_engine() def free_cache_engine(self): # ensure `enforce_eager=True` self.cache_engine = None self.gpu_cache = None # NOTE(sgm): [VERL]: adapt from _execute_model_spmd() def execute_model(self, execute_model_req: ExecuteModelRequest, intermediate_tensors: Optional[IntermediateTensors] = None) -> Optional[List[SamplerOutput]]: """ Execute model in Single Program Multiple Data (SPMD) fashion. All workers take the same request, prepare the input and execute the model. """ assert execute_model_req is not None, ("_execute_model_spmd() requires each worker to take in an " "ExecuteModelRequest") worker_input: WorkerInput = self.prepare_worker_input(execute_model_req=execute_model_req) model_input: ModelRunnerInputBase = (self.model_runner.prepare_model_input( execute_model_req.seq_group_metadata_list)) # verl.worker.workerbase.WorkerBase # swap cache super().execute_worker(worker_input) # If there is no input, we don't need to execute the model. if worker_input.num_seq_groups == 0: return [] return self.model_runner.execute_model( model_input, self.kv_cache[worker_input.virtual_engine] if self.kv_cache is not None else None, intermediate_tensors) # assume the input is .state_dict() def sync_model_weights(self, actor_weights: Dict, load_format: str): if load_format in [LoadFormat.MEGATRON, LoadFormat.AUTO]: load_megatron_weights(actor_weights, self.model_runner.model) elif load_format == LoadFormat.HF: # full model state dict without no sharding load_hf_weights(actor_weights, self.model_runner.model) elif load_format == LoadFormat.DTENSOR: load_dtensor_weights(actor_weights, self.model_runner.model) def offload_model_weights(self) -> None: if self.cpu_model == None: self.cpu_model = {} for name, params in self.model_runner.model.named_parameters(): self.cpu_model[name] = torch.empty_like(params, device='cpu') params.data = self.cpu_model[name] else: for name, params in self.model_runner.model.named_parameters(): params.data = self.cpu_model[name] def init_worker_distributed_environment( parallel_config: ParallelConfig, rank: int, distributed_init_method: Optional[str] = "env://", local_rank: int = -1, ) -> None: """Initialize the distributed environment.""" set_custom_all_reduce(not parallel_config.disable_custom_all_reduce) # NOTE(sgm) use tcp://localhost:xxxx will hang in HF setting without megatron init_distributed_environment(parallel_config.world_size, rank, distributed_init_method, local_rank) ensure_model_parallel_initialized(tensor_model_parallel_size=parallel_config.tensor_parallel_size, pipeline_model_parallel_size=parallel_config.pipeline_parallel_size) # TODO(sgm): check whether need this # if pynccl_utils.is_initialized(): # pynccl_world_size = pynccl_utils.get_world_size() # if pynccl_world_size != parallel_config.world_size: # raise RuntimeError( # "pynccl is already initialized but the pynccl world " # "size does not match parallel_config.world_size " # f"({pynccl_world_size} vs. {parallel_config.world_size}).") # elif parallel_config.world_size > 1: # # NOTE(woosuk): We don't initialize pynccl process group when world size # # is 1. # # NOTE(kaichao): By default, pynccl is initialized for tp group. # pynccl_utils.init_process_group( # group=get_tensor_model_parallel_cpu_group()) # # Initialize a custom fast all-reduce implementation. # if not parallel_config.disable_custom_all_reduce: # init_custom_ar() # A small all_reduce for warmup. torch.distributed.all_reduce(torch.zeros(1).cuda()) # if pynccl_utils.is_initialized(): # pynccl_utils.all_reduce(torch.zeros(1).cuda()) ================================================ FILE: verl/third_party/vllm/vllm_v_0_6_3/__init__.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. ================================================ FILE: verl/third_party/vllm/vllm_v_0_6_3/arg_utils.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Copyright 2023 The vLLM team. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # Adapted from https://github.com/vllm-project/vllm/blob/main/vllm/engine/arg_utils.py import os from dataclasses import dataclass from transformers import PretrainedConfig from vllm.config import EngineConfig from vllm.engine.arg_utils import EngineArgs from .config import LoadConfig, ModelConfig @dataclass class EngineArgs(EngineArgs): model_hf_config: PretrainedConfig = None # for verl def __post_init__(self): pass def create_model_config(self) -> ModelConfig: return ModelConfig( hf_config=self.model_hf_config, tokenizer_mode=self.tokenizer_mode, trust_remote_code=self.trust_remote_code, dtype=self.dtype, seed=self.seed, revision=self.revision, code_revision=self.code_revision, rope_scaling=self.rope_scaling, rope_theta=self.rope_theta, tokenizer_revision=self.tokenizer_revision, max_model_len=self.max_model_len, quantization=self.quantization, quantization_param_path=self.quantization_param_path, enforce_eager=self.enforce_eager, max_context_len_to_capture=self.max_context_len_to_capture, max_seq_len_to_capture=self.max_seq_len_to_capture, max_logprobs=self.max_logprobs, disable_sliding_window=self.disable_sliding_window, skip_tokenizer_init=self.skip_tokenizer_init, served_model_name=self.served_model_name, limit_mm_per_prompt=self.limit_mm_per_prompt, use_async_output_proc=not self.disable_async_output_proc, override_neuron_config=self.override_neuron_config, config_format=self.config_format, mm_processor_kwargs=self.mm_processor_kwargs, ) def create_load_config(self) -> LoadConfig: return LoadConfig( load_format=self.load_format, download_dir=self.download_dir, model_loader_extra_config=self.model_loader_extra_config, ignore_patterns=self.ignore_patterns, ) def create_engine_config(self) -> EngineConfig: engine_config = super().create_engine_config() # NOTE[VERL]: Use the world_size set by torchrun world_size = int(os.getenv("WORLD_SIZE", "-1")) assert world_size != -1, "The world_size is set to -1, not initialized by TORCHRUN" engine_config.parallel_config.world_size = world_size return engine_config ================================================ FILE: verl/third_party/vllm/vllm_v_0_6_3/config.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Copyright 2023 The vLLM team. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # Adapted from https://github.com/vllm-project/vllm/blob/main/vllm/config.py import enum import json from dataclasses import dataclass, field from typing import TYPE_CHECKING, List, Optional, Union from transformers import PretrainedConfig # Add for verl from vllm.config import ModelConfig from vllm.logger import init_logger from vllm.utils import is_hip if TYPE_CHECKING: from vllm.model_executor.model_loader.loader import BaseModelLoader logger = init_logger(__name__) class LoadFormat(str, enum.Enum): AUTO = "auto" MEGATRON = "megatron" HF = "hf" DTENSOR = "dtensor" DUMMY_HF = "dummy_hf" DUMMY_MEGATRON = "dummy_megatron" DUMMY_DTENSOR = "dummy_dtensor" class ModelConfig(ModelConfig): def __init__(self, hf_config: PretrainedConfig, *args, **kwargs) -> None: super().__init__(model=hf_config._name_or_path, tokenizer=hf_config._name_or_path, *args, **kwargs) self.hf_config = hf_config @dataclass class LoadConfig: """ download_dir: Directory to download and load the weights, default to the default cache directory of huggingface. load_format: The format of the model weights to load: "auto" will try to load the weights in the safetensors format and fall back to the pytorch bin format if safetensors format is not available. "pt" will load the weights in the pytorch bin format. "safetensors" will load the weights in the safetensors format. "npcache" will load the weights in pytorch format and store a numpy cache to speed up the loading. "dummy" will initialize the weights with random values, which is mainly for profiling. "tensorizer" will use CoreWeave's tensorizer library for fast weight loading. "bitsandbytes" will load nf4 type weights. ignore_patterns: The list of patterns to ignore when loading the model. Default to "original/**/*" to avoid repeated loading of llama's checkpoints. """ load_format: Union[str, LoadFormat, "BaseModelLoader"] = LoadFormat.AUTO download_dir: Optional[str] = None model_loader_extra_config: Optional[Union[str, dict]] = field(default_factory=dict) ignore_patterns: Optional[Union[List[str], str]] = None def __post_init__(self): model_loader_extra_config = self.model_loader_extra_config or {} if isinstance(model_loader_extra_config, str): self.model_loader_extra_config = json.loads(model_loader_extra_config) self._verify_load_format() if self.ignore_patterns is not None and len(self.ignore_patterns) > 0: logger.info("Ignoring the following patterns when downloading weights: %s", self.ignore_patterns) else: self.ignore_patterns = ["original/**/*"] def _verify_load_format(self) -> None: if not isinstance(self.load_format, str): return load_format = self.load_format.lower() self.load_format = LoadFormat(load_format) rocm_not_supported_load_format: List[str] = [] if is_hip() and load_format in rocm_not_supported_load_format: rocm_supported_load_format = [ f for f in LoadFormat.__members__ if (f not in rocm_not_supported_load_format) ] raise ValueError(f"load format '{load_format}' is not supported in ROCm. " f"Supported load formats are " f"{rocm_supported_load_format}") ================================================ FILE: verl/third_party/vllm/vllm_v_0_6_3/dtensor_weight_loaders.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Copyright 2023 The vLLM team. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # Adapted from https://github.com/vllm-project/vllm/tree/main/vllm/model_executor/model_loader from typing import Dict import torch.nn as nn from torch.distributed._tensor import DTensor from vllm.model_executor.model_loader.weight_utils import default_weight_loader from vllm.model_executor.models.utils import is_pp_missing_parameter def gemma_dtensor_weight_loader(actor_weights: Dict, vllm_model: nn.Module) -> nn.Module: stacked_params_mapping = [ # (param_name, shard_name, shard_id) ("qkv_proj", "q_proj", "q"), ("qkv_proj", "k_proj", "k"), ("qkv_proj", "v_proj", "v"), ("gate_up_proj", "gate_proj", 0), ("gate_up_proj", "up_proj", 1), ] params_dict = dict(vllm_model.named_parameters()) for name, loaded_weight in actor_weights.items(): for param_name, shard_name, shard_id in stacked_params_mapping: if shard_name not in name: continue stacked_name = name.replace(shard_name, param_name) # Skip loading extra bias for GPTQ models. if stacked_name.endswith(".bias") and stacked_name not in params_dict: continue local_loaded_weight = redistribute_dtensor(param_name=name, loaded_weights=loaded_weight) param = params_dict[stacked_name] weight_loader = getattr(param, "weight_loader", default_weight_loader) weight_loader(param, local_loaded_weight.to(dtype=param.dtype), shard_id) break else: # lm_head is not used in vllm as it is tied with embed_token. # To prevent errors, skip loading lm_head.weight. if "lm_head.weight" in name: continue # Skip loading extra bias for GPTQ models. if name.endswith(".bias") and name not in params_dict: continue local_loaded_weight = redistribute_dtensor(param_name=name, loaded_weights=loaded_weight) param = params_dict[name] weight_loader = getattr(param, "weight_loader", default_weight_loader) weight_loader(param, local_loaded_weight.to(dtype=param.dtype)) def gptbigcode_dtensor_load_weights(actor_weights: Dict, vllm_model: nn.Module): params_dict = dict(vllm_model.named_parameters(remove_duplicate=False)) for name, loaded_weight in actor_weights.items(): if "lm_head.weight" in name: continue if ".attn.bias" in name: # Skip attention mask. # NOTE: "c_attn.bias" should not be skipped. continue local_loaded_weight = redistribute_dtensor(param_name=name, loaded_weights=loaded_weight) param = params_dict[name] weight_loader = getattr(param, "weight_loader", default_weight_loader) weight_loader(param, local_loaded_weight.to(dtype=param.dtype)) def starcoder2_dtensor_load_weights(actor_weights: Dict, vllm_model: nn.Module): stacked_params_mapping = [ # (param_name, shard_name, shard_id) ("qkv_proj", "q_proj", "q"), ("qkv_proj", "k_proj", "k"), ("qkv_proj", "v_proj", "v"), ] params_dict = dict(vllm_model.named_parameters(remove_duplicate=False)) for name, loaded_weight in actor_weights.items(): if "rotary_emb.inv_freq" in name: continue for param_name, weight_name, shard_id in stacked_params_mapping: if weight_name not in name: continue name = name.replace(weight_name, param_name) local_loaded_weight = redistribute_dtensor(param_name=name, loaded_weights=loaded_weight) param = params_dict[name] weight_loader = param.weight_loader weight_loader(param, local_loaded_weight.to(dtype=param.dtype), shard_id) break else: if vllm_model.config.tie_word_embeddings and "lm_head.weight" in name: continue param = params_dict[name] local_loaded_weight = redistribute_dtensor(param_name=name, loaded_weights=loaded_weight) weight_loader = getattr(param, "weight_loader", default_weight_loader) weight_loader(param, local_loaded_weight.to(dtype=param.dtype)) def llama_dtensor_weight_loader(actor_weights: Dict, vllm_model: nn.Module) -> nn.Module: stacked_params_mapping = [ # (param_name, shard_name, shard_id) (".qkv_proj", ".q_proj", "q"), (".qkv_proj", ".k_proj", "k"), (".qkv_proj", ".v_proj", "v"), (".gate_up_proj", ".gate_proj", 0), (".gate_up_proj", ".up_proj", 1), ] params_dict = dict(vllm_model.named_parameters()) for name, loaded_weight in actor_weights.items(): if "rotary_emb.inv_freq" in name: continue if "rotary_emb.cos_cached" in name or "rotary_emb.sin_cached" in name: # Models trained using ColossalAI may include these tensors in # the checkpoint. Skip them. continue # With tie_word_embeddings, we can skip lm_head.weight # The weight might appear unnecessarily in the files if the model is # processed with quantization, LoRA, fine-tuning, etc. if vllm_model.config.tie_word_embeddings and "lm_head.weight" in name: continue for param_name, weight_name, shard_id in stacked_params_mapping: if weight_name not in name: continue name = name.replace(weight_name, param_name) # Skip loading extra bias for GPTQ models. if name.endswith(".bias") and name not in params_dict: continue local_loaded_weight = redistribute_dtensor(param_name=name, loaded_weights=loaded_weight) param = params_dict[name] weight_loader = param.weight_loader weight_loader(param, local_loaded_weight.to(dtype=param.dtype), shard_id) break else: # Skip loading extra bias for GPTQ models. if name.endswith(".bias") and name not in params_dict: continue local_loaded_weight = redistribute_dtensor(param_name=name, loaded_weights=loaded_weight) param = params_dict[name] weight_loader = getattr(param, "weight_loader", default_weight_loader) weight_loader(param, local_loaded_weight) def qwen2_dtensor_weight_loader(actor_weights: Dict, vllm_model: nn.Module) -> nn.Module: stacked_params_mapping = [ # (param_name, shard_name, shard_id) ("qkv_proj", "q_proj", "q"), ("qkv_proj", "k_proj", "k"), ("qkv_proj", "v_proj", "v"), ("gate_up_proj", "gate_proj", 0), ("gate_up_proj", "up_proj", 1), ] params_dict = dict(vllm_model.named_parameters(remove_duplicate=False)) for name, loaded_weight in actor_weights.items(): if "rotary_emb.inv_freq" in name: continue if vllm_model.config.tie_word_embeddings and "lm_head.weight" in name: continue for param_name, weight_name, shard_id in stacked_params_mapping: if weight_name not in name: continue name = name.replace(weight_name, param_name) # Skip loading extra bias for GPTQ models. if name.endswith(".bias") and name not in params_dict: continue local_loaded_weight = redistribute_dtensor(param_name=name, loaded_weights=loaded_weight) param = params_dict[name] weight_loader = param.weight_loader weight_loader(param, local_loaded_weight.to(dtype=param.dtype), shard_id) break else: # Skip loading extra bias for GPTQ models. if name.endswith(".bias") and name not in params_dict: continue param = params_dict[name] local_loaded_weight = redistribute_dtensor(param_name=name, loaded_weights=loaded_weight) weight_loader = getattr(param, "weight_loader", default_weight_loader) weight_loader(param, local_loaded_weight.to(dtype=param.dtype)) def qwen2vl_dtensor_weight_loader(actor_weights: Dict, vllm_model: nn.Module) -> nn.Module: stacked_params_mapping = [ # (param_name, shard_name, shard_id) ("qkv_proj", "q_proj", "q"), ("qkv_proj", "k_proj", "k"), ("qkv_proj", "v_proj", "v"), ("gate_up_proj", "gate_proj", 0), ("gate_up_proj", "up_proj", 1), ] params_dict = dict(vllm_model.named_parameters(remove_duplicate=False)) for name, loaded_weight in actor_weights.items(): if "rotary_emb.inv_freq" in name: continue if vllm_model.config.tie_word_embeddings and "lm_head.weight" in name: continue for param_name, weight_name, shard_id in stacked_params_mapping: if weight_name not in name: continue name = name.replace(weight_name, param_name) # Skip loading extra bias for GPTQ models. if name.endswith(".bias") and name not in params_dict: continue local_loaded_weight = redistribute_dtensor(param_name=name, loaded_weights=loaded_weight) param = params_dict[name] weight_loader = param.weight_loader weight_loader(param, local_loaded_weight.to(dtype=param.dtype), shard_id) break else: # Skip loading extra bias for GPTQ models. if name.endswith(".bias") and name not in params_dict: continue param = params_dict[name] local_loaded_weight = redistribute_dtensor(param_name=name, loaded_weights=loaded_weight) weight_loader = getattr(param, "weight_loader", default_weight_loader) weight_loader(param, local_loaded_weight.to(dtype=param.dtype)) from vllm.model_executor.layers.fused_moe import FusedMoE def deepseekv2_dtensor_weight_loader(actor_weights: Dict, vllm_model: nn.Module) -> nn.Module: stacked_params_mapping = [ # (param_name, shard_name, shard_id) ("gate_up_proj", "gate_proj", 0), ("gate_up_proj", "up_proj", 1), ] # Params for weights, fp8 weight scales, fp8 activation scales # (param_name, weight_name, expert_id, shard_id) expert_params_mapping = FusedMoE.make_expert_params_mapping( ckpt_gate_proj_name="gate_proj", ckpt_down_proj_name="down_proj", ckpt_up_proj_name="up_proj", num_experts=vllm_model.config.n_routed_experts, ) params_dict = dict(vllm_model.named_parameters(remove_duplicate=False)) for name, loaded_weight in actor_weights.items(): if "rotary_emb.inv_freq" in name: continue for param_name, weight_name, shard_id in stacked_params_mapping: # Skip non-stacked layers and experts (experts handled below). if weight_name not in name: continue # We have mlp.experts[0].gate_proj in the checkpoint. # Since we handle the experts below in expert_params_mapping, # we need to skip here BEFORE we update the name, otherwise # name will be updated to mlp.experts[0].gate_up_proj, which # will then be updated below in expert_params_mapping # for mlp.experts[0].gate_gate_up_proj, which breaks load. if ("mlp.experts." in name) and name not in params_dict: continue name = name.replace(weight_name, param_name) # Skip loading extra bias for GPTQ models. if name.endswith(".bias") and name not in params_dict: continue if is_pp_missing_parameter(name, vllm_model): continue param = params_dict[name] local_loaded_weight = redistribute_dtensor(param_name=name, loaded_weights=loaded_weight) weight_loader = getattr(param, "weight_loader", default_weight_loader) weight_loader(param, local_loaded_weight.to(dtype=param.dtype), shard_id) break else: for mapping in expert_params_mapping: param_name, weight_name, expert_id, shard_id = mapping if weight_name not in name: continue name = name.replace(weight_name, param_name) if is_pp_missing_parameter(name, vllm_model): continue param = params_dict[name] local_loaded_weight = redistribute_dtensor(param_name=name, loaded_weights=loaded_weight) weight_loader = getattr(param, "weight_loader", default_weight_loader) weight_loader( param, local_loaded_weight.to(dtype=param.dtype), weight_name, shard_id=shard_id, expert_id=expert_id, ) break else: # Skip loading extra bias for GPTQ models. if name.endswith(".bias") and name not in params_dict: continue if is_pp_missing_parameter(name, vllm_model): continue param = params_dict[name] local_loaded_weight = redistribute_dtensor(param_name=name, loaded_weights=loaded_weight) weight_loader = getattr(param, "weight_loader", default_weight_loader) weight_loader(param, local_loaded_weight.to(dtype=param.dtype)) def gpt2_dtensor_weight_loader(actor_weights: Dict, vllm_model: nn.Module) -> nn.Module: pass def redistribute_dtensor(param_name: str, loaded_weights: DTensor, parallelize_plan: Dict = None): param_name = _process_parameter_names(name=param_name) if parallelize_plan is not None: assert ( param_name in parallelize_plan.keys()), f"param name: {param_name} not in parallelize_plan :{parallelize_plan.keys()}" placement = parallelize_plan[param_name] local_loaded_weights = loaded_weights.redistribute(device_mesh=loaded_weights.device_mesh, placements=placement).to_local() else: local_loaded_weights = loaded_weights.full_tensor() return local_loaded_weights def _process_parameter_names(name): # Remove '.weight' if it exists at the end of the string if name.endswith(".weight"): name = name[:-7] # Remove 'model.layers.x.' or 'model.' prefix if "model.layers" in name: parts = name.split(".") # Reconstruct the string without 'model.layers.x.' name = ".".join(parts[3:]) # parts[0] is 'model', parts[1] is 'layers', parts[2] is 'x' elif name.startswith("model."): name = name[6:] # Remove 'model.' return name __MODEL_DTENSOR_WEIGHT_LOADER_REGISTRY__ = { "GPT2LMHeadModel": gpt2_dtensor_weight_loader, "LlamaForCausalLM": llama_dtensor_weight_loader, "LLaMAForCausalLM": llama_dtensor_weight_loader, "MistralForCausalLM": llama_dtensor_weight_loader, # mistral is the same as llama in vLLM "InternLMForCausalLM": llama_dtensor_weight_loader, "AquilaModel": llama_dtensor_weight_loader, "AquilaForCausalLM": llama_dtensor_weight_loader, "Phi3ForCausalLM": llama_dtensor_weight_loader, "GemmaForCausalLM": gemma_dtensor_weight_loader, "Gemma2ForCausalLM": gemma_dtensor_weight_loader, "GPTBigCodeForCausalLM": gptbigcode_dtensor_load_weights, "Starcoder2ForCausalLM": starcoder2_dtensor_load_weights, "Qwen2ForCausalLM": qwen2_dtensor_weight_loader, "DeepseekV2ForCausalLM": deepseekv2_dtensor_weight_loader, "Qwen2VLForConditionalGeneration": qwen2vl_dtensor_weight_loader, } # the actor model is .state_dict() # Load dtensor weights def load_dtensor_weights(actor_weights: Dict, vllm_model: nn.Module): weight_loader = _get_model_weight_loader(vllm_model.__class__.__name__) weight_loader(actor_weights, vllm_model) # NOTE(sgm) to reduce peak memory usage, we offload vllm model to cpu # after init, and we need this after sync model weights for in first iter. vllm_model = vllm_model.cuda() def _get_model_weight_loader(arch: str): if arch in __MODEL_DTENSOR_WEIGHT_LOADER_REGISTRY__: return __MODEL_DTENSOR_WEIGHT_LOADER_REGISTRY__[arch] raise ValueError(f"Model architectures {arch} are not supported for now. " f"Supported architectures: {__MODEL_DTENSOR_WEIGHT_LOADER_REGISTRY__.keys()}") # NOTE(sgm): we use per-parameter weight loader in each vllm sub def update_dtensor_weight_loader(): pass ================================================ FILE: verl/third_party/vllm/vllm_v_0_6_3/hf_weight_loader.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Copyright 2023 The vLLM team. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # Adapted from https://github.com/vllm-project/vllm/tree/main/vllm/model_executor/model_loader from typing import Dict import torch.nn as nn from vllm.model_executor.model_loader.utils import set_default_torch_dtype def update_hf_weight_loader(): print("no hf weight loader need to be updated") return def load_hf_weights(actor_weights: Dict, vllm_model: nn.Module): assert isinstance(actor_weights, Dict) with set_default_torch_dtype(next(vllm_model.parameters()).dtype): # TODO if vllm_model.config.tie_word_embeddings and "lm_head.weight" in actor_weights.keys(): del actor_weights["lm_head.weight"] vllm_model.load_weights(actor_weights.items()) for _, module in vllm_model.named_modules(): quant_method = getattr(module, "quant_method", None) if quant_method is not None: quant_method.process_weights_after_loading(module) # FIXME: Remove this after Mixtral is updated # to use quant_method. if hasattr(module, "process_weights_after_loading"): module.process_weights_after_loading() vllm_model = vllm_model.cuda() ================================================ FILE: verl/third_party/vllm/vllm_v_0_6_3/llm.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Copyright 2023 The vLLM team. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # Adapted from https://github.com/vllm-project/vllm/blob/main/vllm/entrypoints/llm.py from typing import Dict, List, Optional, Tuple, Union import torch import torch.nn as nn from torch.nn.utils.rnn import pad_sequence from transformers import PretrainedConfig, PreTrainedTokenizer, PreTrainedTokenizerFast from verl.workers.rollout.tokenizer import HybridEngineBaseTokenizer from vllm import LLM from vllm.outputs import EmbeddingRequestOutput, RequestOutput from vllm.utils import Counter from .arg_utils import EngineArgs from .llm_engine_sp import LLMEngine class LLM(LLM): """An LLM for generating texts from given prompts and sampling parameters. This class includes a tokenizer, a language model (possibly distributed across multiple GPUs), and GPU memory space allocated for intermediate states (aka KV cache). Given a batch of prompts and sampling parameters, this class generates texts from the model, using an intelligent batching mechanism and efficient memory management. NOTE: This class is intended to be used for offline inference. For online serving, use the `AsyncLLMEngine` class instead. NOTE: For the comprehensive list of arguments, see `EngineArgs`. Args: model: A HuggingFace Transformers model instance. tokenizer: A HuggingFace Transformers tokenizer instance. tokenizer_mode: The tokenizer mode. "auto" will use the fast tokenizer if available, and "slow" will always use the slow tokenizer. trust_remote_code: Trust remote code (e.g., from HuggingFace) when downloading the model and tokenizer. tensor_parallel_size: The number of GPUs to use for distributed execution with tensor parallelism. dtype: The data type for the model weights and activations. Currently, we support `float32`, `float16`, and `bfloat16`. If `auto`, we use the `torch_dtype` attribute specified in the model config file. However, if the `torch_dtype` in the config is `float32`, we will use `float16` instead. quantization: The method used to quantize the model weights. Currently, we support "awq". If None, we assume the model weights are not quantized and use `dtype` to determine the data type of the weights. revision: The specific model version to use. It can be a branch name, a tag name, or a commit id. tokenizer_revision: The specific tokenizer version to use. It can be a branch name, a tag name, or a commit id. seed: The seed to initialize the random number generator for sampling. gpu_memory_utilization: The ratio (between 0 and 1) of GPU memory to reserve for the model weights, activations, and KV cache. Higher values will increase the KV cache size and thus improve the model's throughput. However, if the value is too high, it may cause out-of- memory (OOM) errors. swap_space: The size (GiB) of CPU memory per GPU to use as swap space. This can be used for temporarily storing the states of the requests when their `best_of` sampling parameters are larger than 1. If all requests will have `best_of=1`, you can safely set this to 0. Otherwise, too small values may cause out-of-memory (OOM) errors. enforce_eager: Whether to enforce eager execution. If True, we will disable CUDA graph and always execute the model in eager mode. If False, we will use CUDA graph and eager execution in hybrid. max_context_len_to_capture: Maximum context len covered by CUDA graphs. When a sequence has context length larger than this, we fall back to eager mode. disable_custom_all_reduce: See ParallelConfig """ def __init__( self, model: Union[nn.Module, Dict], # model itself or its parameter dict tokenizer: Union[PreTrainedTokenizer, PreTrainedTokenizerFast, HybridEngineBaseTokenizer], model_hf_config: PretrainedConfig, tokenizer_mode: str = "auto", trust_remote_code: bool = False, skip_tokenizer_init: bool = False, tensor_parallel_size: int = 1, dtype: str = "auto", quantization: Optional[str] = None, revision: Optional[str] = None, tokenizer_revision: Optional[str] = None, seed: int = 0, gpu_memory_utilization: float = 0.9, swap_space: int = 4, cpu_offload_gb: float = 0, enforce_eager: bool = False, max_context_len_to_capture: Optional[int] = None, max_seq_len_to_capture: int = 8192, disable_custom_all_reduce: bool = False, load_format="auto", **kwargs, ) -> None: if "disable_log_stats" not in kwargs: kwargs["disable_log_stats"] = True removed_vision_keys = ("image_token_id", "image_feature_size", "image_input_shape", "image_input_type") if any(k in kwargs for k in removed_vision_keys): raise TypeError("There is no need to pass vision-related arguments anymore.") engine_args = EngineArgs( model_hf_config=model_hf_config, # tokenizer=tokenizer, tokenizer_mode=tokenizer_mode, skip_tokenizer_init=skip_tokenizer_init, trust_remote_code=trust_remote_code, tensor_parallel_size=tensor_parallel_size, dtype=dtype, quantization=quantization, revision=revision, tokenizer_revision=tokenizer_revision, seed=seed, gpu_memory_utilization=gpu_memory_utilization, swap_space=swap_space, cpu_offload_gb=cpu_offload_gb, enforce_eager=enforce_eager, max_context_len_to_capture=max_context_len_to_capture, max_seq_len_to_capture=max_seq_len_to_capture, disable_custom_all_reduce=disable_custom_all_reduce, load_format=load_format, **kwargs, ) tokenizer_cls = (PreTrainedTokenizer, PreTrainedTokenizerFast, HybridEngineBaseTokenizer) if not isinstance(tokenizer, tokenizer_cls): raise ValueError( f"Unexpected tokenizer type: {type(tokenizer)}. Must be" "one of the following: PreTrainedTokenizer, PreTrainedTokenizerFast, verl.workers.rollout.HybridEngineBaseTokenizer" ) self.llm_engine = LLMEngine.from_engine_args(model, tokenizer, engine_args) # TODO: check usagecontext self.request_counter = Counter() def init_cache_engine(self): self.llm_engine.init_cache_engine() def free_cache_engine(self): self.llm_engine.free_cache_engine() def get_tokenizer(self) -> Union[PreTrainedTokenizer, PreTrainedTokenizerFast]: return self.llm_engine.tokenizer def set_tokenizer( self, tokenizer: Union[PreTrainedTokenizer, PreTrainedTokenizerFast], ) -> None: self.llm_engine.tokenizer = tokenizer def _run_engine(self, *, use_tqdm: bool) -> List[Union[RequestOutput, EmbeddingRequestOutput]]: outputs = super()._run_engine(use_tqdm=use_tqdm) return self._post_process_outputs(outputs) # # NOTE(shengguangming): add for verl # # TODO(sgm): we can optimize it by making the dataloader yield List[int] without padding. # def _pre_process_inputs(self, prompt_token_ids: torch.Tensor) -> List[int]: # # remove the left padding in the prompt token_id # pad_token_id = self.llm_engine.tokenizer.pad_token_id if self.llm_engine.tokenizer.pad_token_id is not None else self.llm_engine.tokenizer.eos_token_id # non_pad_index = torch.nonzero(prompt_token_ids != pad_token_id, as_tuple=False)[0][0] # token_ids = prompt_token_ids[non_pad_index:].tolist() # return token_ids # NOTE(shengguangming): add for verl def _post_process_outputs(self, request_outputs: List[RequestOutput]) -> Tuple[torch.Tensor, torch.Tensor]: output_token_ids = [] logprobs = [] for request_output in request_outputs: # List[RequestOutput] outputs = request_output.outputs for output in outputs: # List[CompletionOutput], usually len == 1 output_token_ids.append(torch.tensor(output.token_ids)) # TODO(shengguangming): can be optimzied by rewrite the Sampler._get_logprobs() logits logprobs_dicts = output.logprobs if logprobs_dicts is not None: logprob = [] for logprobs_dict, id in zip(logprobs_dicts, output.token_ids): logprob.append(logprobs_dict[id].logprob) logprobs.append(torch.tensor(logprob)) pad_token_id = (self.llm_engine.tokenizer.pad_token_id if self.llm_engine.tokenizer.pad_token_id is not None else self.llm_engine.tokenizer.eos_token_id) output_token_ids = pad_sequence(output_token_ids, batch_first=True, padding_value=pad_token_id) if len(logprobs) > 0: logprobs = pad_sequence(logprobs, batch_first=True, padding_value=pad_token_id) return output_token_ids, logprobs def sync_model_weights(self, actor_weights: Dict[str, torch.Tensor], load_format: str) -> None: self.llm_engine.sync_model_weights(actor_weights=actor_weights, load_format=load_format) def offload_model_weights(self) -> None: self.llm_engine.offload_model_weights() ================================================ FILE: verl/third_party/vllm/vllm_v_0_6_3/llm_engine_sp.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Copyright 2023 The vLLM team. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # Adapted from https://github.com/vllm-project/vllm/blob/main/vllm/engine/llm_engine.py from functools import partial from typing import Callable, Dict, Optional, Type, Union import torch import torch.nn as nn from vllm.config import ( CacheConfig, DecodingConfig, DeviceConfig, EngineConfig, LoadConfig, LoRAConfig, ModelConfig, ObservabilityConfig, ParallelConfig, PromptAdapterConfig, SchedulerConfig, SpeculativeConfig, ) from vllm.core.scheduler import Scheduler from vllm.engine.arg_utils import EngineArgs from vllm.engine.llm_engine import LLMEngine, SchedulerContext, SchedulerOutputState, _load_generation_config_dict from vllm.engine.metrics_types import StatLoggerBase from vllm.engine.output_processor.interfaces import SequenceGroupOutputProcessor from vllm.engine.output_processor.stop_checker import StopChecker from vllm.executor.executor_base import ExecutorBase from vllm.inputs import INPUT_REGISTRY, InputRegistry from vllm.inputs.preprocess import InputPreprocessor from vllm.logger import init_logger from vllm.sequence import Sequence from vllm.tracing import init_tracer from vllm.transformers_utils.detokenizer import Detokenizer from vllm.transformers_utils.tokenizer import AnyTokenizer from vllm.usage.usage_lib import UsageContext, is_usage_stats_enabled, usage_message from vllm.utils import Counter, weak_bind from vllm.version import __version__ as VLLM_VERSION from .arg_utils import EngineArgs from .config import LoadConfig, ModelConfig from .tokenizer import TokenizerGroup logger = init_logger(__name__) _LOCAL_LOGGING_INTERVAL_SEC = 5 class LLMEngine(LLMEngine): """An LLM engine that receives requests and generates texts. This is the main class for the vLLM engine. It receives requests from clients and generates texts from the LLM. It includes a tokenizer, a language model (possibly distributed across multiple GPUs), and GPU memory space allocated for intermediate states (aka KV cache). This class utilizes iteration-level scheduling and efficient memory management to maximize the serving throughput. The :class:`~vllm.LLM` class wraps this class for offline batched inference and the :class:`AsyncLLMEngine` class wraps this class for online serving. The config arguments are derived from :class:`~vllm.EngineArgs`. (See :ref:`engine_args`) Args: model_config: The configuration related to the LLM model. cache_config: The configuration related to the KV cache memory management. parallel_config: The configuration related to distributed execution. scheduler_config: The configuration related to the request scheduler. device_config: The configuration related to the device. lora_config (Optional): The configuration related to serving multi-LoRA. speculative_config (Optional): The configuration related to speculative decoding. executor_class: The model executor class for managing distributed execution. prompt_adapter_config (Optional): The configuration related to serving prompt adapters. log_stats: Whether to log statistics. usage_context: Specified entry point, used for usage info collection. """ def __init__( self, # NOTE(sgm): first two arguments are added for verl model: Union[nn.Module, Dict], # model itself or its parameter dict tokenizer: nn.Module, # NOTE(sgm): vllm original arguments model_config: ModelConfig, cache_config: CacheConfig, parallel_config: ParallelConfig, scheduler_config: SchedulerConfig, device_config: DeviceConfig, load_config: LoadConfig, lora_config: Optional[LoRAConfig], speculative_config: Optional[SpeculativeConfig], decoding_config: Optional[DecodingConfig], observability_config: Optional[ObservabilityConfig], prompt_adapter_config: Optional[PromptAdapterConfig], executor_class: Type[ExecutorBase], log_stats: bool, usage_context: UsageContext = UsageContext.ENGINE_CONTEXT, stat_loggers: Optional[Dict[str, StatLoggerBase]] = None, input_registry: InputRegistry = INPUT_REGISTRY, use_cached_outputs: bool = False, ) -> None: logger.info( "Initializing an LLM engine (v%s) with config: " "model=%r, speculative_config=%r, tokenizer=%r, " "skip_tokenizer_init=%s, tokenizer_mode=%s, revision=%s, " "override_neuron_config=%s, " "rope_scaling=%r, rope_theta=%r, tokenizer_revision=%s, " "trust_remote_code=%s, dtype=%s, max_seq_len=%d, " "download_dir=%r, load_format=%s, tensor_parallel_size=%d, " "pipeline_parallel_size=%d, " "disable_custom_all_reduce=%s, quantization=%s, " "enforce_eager=%s, kv_cache_dtype=%s, " "quantization_param_path=%s, device_config=%s, " "decoding_config=%r, observability_config=%r, " "seed=%d, served_model_name=%s, use_v2_block_manager=%s, " "num_scheduler_steps=%d, chunked_prefill_enabled=%s " "multi_step_stream_outputs=%s, enable_prefix_caching=%s, " "use_async_output_proc=%s, use_cached_outputs=%s, " "mm_processor_kwargs=%s)", VLLM_VERSION, model_config.model, speculative_config, model_config.tokenizer, model_config.skip_tokenizer_init, model_config.tokenizer_mode, model_config.revision, model_config.override_neuron_config, model_config.rope_scaling, model_config.rope_theta, model_config.tokenizer_revision, model_config.trust_remote_code, model_config.dtype, model_config.max_model_len, load_config.download_dir, load_config.load_format, parallel_config.tensor_parallel_size, parallel_config.pipeline_parallel_size, parallel_config.disable_custom_all_reduce, model_config.quantization, model_config.enforce_eager, cache_config.cache_dtype, model_config.quantization_param_path, device_config.device, decoding_config, observability_config, model_config.seed, model_config.served_model_name, scheduler_config.use_v2_block_manager, scheduler_config.num_scheduler_steps, scheduler_config.chunked_prefill_enabled, scheduler_config.multi_step_stream_outputs, cache_config.enable_prefix_caching, model_config.use_async_output_proc, use_cached_outputs, model_config.mm_processor_kwargs, ) # TODO(woosuk): Print more configs in debug mode. self.model_config = model_config self.cache_config = cache_config self.lora_config = lora_config self.parallel_config = parallel_config self.scheduler_config = scheduler_config self.device_config = device_config self.speculative_config = speculative_config self.load_config = load_config self.decoding_config = decoding_config or DecodingConfig() self.prompt_adapter_config = prompt_adapter_config self.observability_config = observability_config or ObservabilityConfig() self.log_stats = log_stats self.use_cached_outputs = use_cached_outputs if not self.model_config.skip_tokenizer_init: self.tokenizer = self._init_tokenizer(tokenizer) self.detokenizer = Detokenizer(self.tokenizer) tokenizer_group = self.get_tokenizer_group() else: self.tokenizer = None self.detokenizer = None tokenizer_group = None # Ensure that the function doesn't contain a reference to self, # to avoid engine GC issues def get_tokenizer_for_seq(sequence: Sequence) -> AnyTokenizer: assert tokenizer_group, "tokenizer_group cannot be None, " "make sure skip_tokenizer_init is False" return tokenizer_group.get_lora_tokenizer(sequence.lora_request) self.seq_counter = Counter() self.generation_config_fields = _load_generation_config_dict(model_config) self.input_preprocessor = InputPreprocessor(model_config, self.tokenizer) self.input_registry = input_registry self.input_processor = input_registry.create_input_processor(model_config) self.model_executor = executor_class( model=model, # add for spmd_gpu_executor model_config=model_config, cache_config=cache_config, parallel_config=parallel_config, scheduler_config=scheduler_config, device_config=device_config, lora_config=lora_config, speculative_config=speculative_config, load_config=load_config, prompt_adapter_config=prompt_adapter_config, observability_config=self.observability_config, ) if not self.model_config.embedding_mode: self._initialize_kv_caches() # If usage stat is enabled, collect relevant info. if is_usage_stats_enabled(): from vllm.model_executor.model_loader import get_architecture_class_name usage_message.report_usage( get_architecture_class_name(model_config), usage_context, extra_kvs={ # Common configuration "dtype": str(model_config.dtype), "tensor_parallel_size": parallel_config.tensor_parallel_size, "block_size": cache_config.block_size, "gpu_memory_utilization": cache_config.gpu_memory_utilization, # Quantization "quantization": model_config.quantization, "kv_cache_dtype": str(cache_config.cache_dtype), # Feature flags "enable_lora": bool(lora_config), "enable_prompt_adapter": bool(prompt_adapter_config), "enable_prefix_caching": cache_config.enable_prefix_caching, "enforce_eager": model_config.enforce_eager, "disable_custom_all_reduce": parallel_config.disable_custom_all_reduce, }, ) if self.tokenizer: # Ping the tokenizer to ensure liveness if it runs in a # different process. self.tokenizer.ping() self.cached_scheduler_outputs = [ SchedulerOutputState() for _ in range(self.parallel_config.pipeline_parallel_size) ] self.scheduler_contexts = [ SchedulerContext(multi_step_stream_outputs=self.scheduler_config.multi_step_stream_outputs) for _ in range(self.parallel_config.pipeline_parallel_size) ] if model_config.use_async_output_proc: process_model_outputs = weak_bind(self._process_model_outputs) self.async_callbacks = [ partial(process_model_outputs, ctx=self.scheduler_contexts[v_id]) for v_id in range(self.parallel_config.pipeline_parallel_size) ] else: self.async_callbacks = [] # Currently used by AsyncLLMEngine to ensure quick append # of request outputs to asyncio queues self.process_request_outputs_callback: Optional[Callable] = None # Create the scheduler. # NOTE: the cache_config here have been updated with the numbers of # GPU and CPU blocks, which are profiled in the distributed executor. self.scheduler = [ Scheduler( scheduler_config, cache_config, lora_config, parallel_config.pipeline_parallel_size, self.async_callbacks[v_id] if model_config.use_async_output_proc else None, ) for v_id in range(parallel_config.pipeline_parallel_size) ] # Metric Logging. if self.log_stats: if stat_loggers is not None: self.stat_loggers = stat_loggers else: # Lazy import for prometheus multiprocessing. # We need to set PROMETHEUS_MULTIPROC_DIR environment variable # before prometheus_client is imported. # See https://prometheus.github.io/client_python/multiprocess/ from vllm.engine.metrics import LoggingStatLogger, PrometheusStatLogger self.stat_loggers = { "logging": LoggingStatLogger(local_interval=_LOCAL_LOGGING_INTERVAL_SEC), "prometheus": PrometheusStatLogger( local_interval=_LOCAL_LOGGING_INTERVAL_SEC, labels=dict(model_name=model_config.served_model_name), max_model_len=self.model_config.max_model_len, ), } self.stat_loggers["prometheus"].info("cache_config", self.cache_config) self.tracer = None if self.observability_config.otlp_traces_endpoint: self.tracer = init_tracer("vllm.llm_engine", self.observability_config.otlp_traces_endpoint) # Create sequence output processor, e.g. for beam search or # speculative decoding. self.output_processor = SequenceGroupOutputProcessor.create_output_processor( self.scheduler_config, self.detokenizer, self.scheduler, self.seq_counter, get_tokenizer_for_seq, stop_checker=StopChecker( self.scheduler_config.max_model_len, get_tokenizer_for_seq, ), ) # TODO(sgm): add for verl but we may not tokenizer in Rollout def _init_tokenizer(self, tokenizer, **tokenizer_init_kwargs): init_kwargs = dict(enable_lora=bool(self.lora_config), max_num_seqs=self.scheduler_config.max_num_seqs, max_input_length=None) init_kwargs.update(tokenizer_init_kwargs) return TokenizerGroup(tokenizer, **init_kwargs) def init_cache_engine(self): # TODO: check whether we should rebuild the CUDAGraph every iter when offload/load KVCache # Re-capture CUDAGraph would be time-consuming self.model_executor.init_cache_engine() def free_cache_engine(self): self.model_executor.free_cache_engine() # NOTE(sgm): currently, we only support GPU executor # The GPUExecutor remove the Ray dependency @classmethod def _get_executor_cls(cls, engine_config: EngineConfig) -> Type[ExecutorBase]: distributed_executor_backend = engine_config.parallel_config.distributed_executor_backend # Initialize the cluster and specify the executor class.] assert (engine_config.device_config.device_type == "cuda" ), "Currently, the vllm in verl only support running on GPU" # print('Waiting for debugger'); import os,debugpy; debugpy.listen(('localhost', 5678 + int(os.getenv('RANK', '0')))); debugpy.wait_for_client() if engine_config.parallel_config.world_size == 1: engine_config.load_config.load_format = "dummy_hf" from .spmd_gpu_executor import SPMDGPUExecutor executor_class = SPMDGPUExecutor return executor_class @classmethod def from_engine_args( cls, model, tokenizer, engine_args: EngineArgs, usage_context: UsageContext = UsageContext.ENGINE_CONTEXT, stat_loggers: Optional[Dict[str, StatLoggerBase]] = None, ) -> "LLMEngine": """Creates an LLM engine from the engine arguments.""" # Create the engine configs. engine_config = engine_args.create_engine_config() executor_class = cls._get_executor_cls(engine_config) # Initialize the cluster and specify the executor class. assert (engine_config.device_config.device_type == "cuda" ), "Currently, the vllm in verl only support running on GPU" from .spmd_gpu_executor import SPMDGPUExecutor executor_class = SPMDGPUExecutor # Create the LLM engine. engine = cls( model, tokenizer, **engine_config.to_dict(), executor_class=executor_class, log_stats=not engine_args.disable_log_stats, usage_context=usage_context, stat_loggers=stat_loggers, ) return engine def sync_model_weights(self, actor_weights: Dict[str, torch.Tensor], load_format: str) -> None: self.model_executor.sync_model_weights(actor_weights=actor_weights, load_format=load_format) def offload_model_weights(self) -> None: self.model_executor.offload_model_weights() ================================================ FILE: verl/third_party/vllm/vllm_v_0_6_3/megatron_weight_loaders.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Copyright 2023 The vLLM team. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # Adapted from https://github.com/vllm-project/vllm/tree/main/vllm/model_executor/model_loader from typing import Dict import torch import torch.nn as nn from vllm.model_executor.layers.linear import * from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead, VocabParallelEmbedding from vllm.model_executor.models import ModelRegistry # NOTE(shengguangming): replace the origin weight loader function in the class def parallel_weight_loader(self, param: torch.Tensor, loaded_weight: torch.Tensor) -> None: """Parallel Linear weight loader.""" assert (param.size() == loaded_weight.size( )), "the parameter size is not align with the loaded weight size, param size: {}, loaded_weight size: {}".format( param.size(), loaded_weight.size()) assert (param.data.dtype == loaded_weight.data.dtype ), "if we want to shared weights, the data type should also be the same" param.data = loaded_weight.data def default_weight_loader(param: torch.Tensor, loaded_weight: torch.Tensor) -> None: """Default weight loader.""" assert param.size() == loaded_weight.size() assert (param.data.dtype == loaded_weight.data.dtype ), "if we want to shared weights, the data type should also be the same" param.data = loaded_weight.data def gpt2_weight_loader(actor_weights: Dict, vllm_model: nn.Module) -> nn.Module: params_dict = dict(vllm_model.named_parameters(remove_duplicate=False)) for name, loaded_weight in actor_weights.items(): if "lm_head.weight" in name: # GPT-2 ties the weights of the embedding layer and the final # linear layer. continue if ".attn.bias" in name or ".attn.masked_bias" in name: # Skip attention mask. # NOTE: "c_attn.bias" should not be skipped. continue if not name.startswith("transformer."): name = "transformer." + name param = params_dict[name] # The HF's GPT-2 implementation uses Conv1D instead of Linear. # Because of this, we need to transpose the weights. # Note(zhuohan): the logic below might break quantized models. for conv1d_weight_name in ["c_attn", "c_proj", "c_fc"]: if conv1d_weight_name not in name: continue if not name.endswith(".weight"): continue # TODO: check megatron loaded_weight = loaded_weight.t() weight_loader = getattr(param, "weight_loader", default_weight_loader) weight_loader(param, loaded_weight) def llama_megatron_weight_loader(actor_weights: Dict, vllm_model: nn.Module) -> nn.Module: # NOTE(shengguangming): the megatron llama may have this prefix params_dict = dict(vllm_model.named_parameters()) for name, loaded_weight in actor_weights.items(): if "rotary_emb.inv_freq" in name: continue else: param = params_dict[name] weight_loader = getattr(param, "weight_loader", default_weight_loader) weight_loader(param, loaded_weight) def qwen2_megatron_weight_loader(actor_weights: Dict, vllm_model: nn.Module) -> nn.Module: params_dict = dict(vllm_model.named_parameters()) for name, loaded_weight in actor_weights.items(): if "rotary_emb.inv_freq" in name: continue if vllm_model.config.tie_word_embeddings and "lm_head.weight" in name: continue param = params_dict[name] weight_loader = getattr(param, "weight_loader", default_weight_loader) weight_loader(param, loaded_weight) def llama_megatron_core_te_weight_loader(actor_weights: Dict, vllm_model: nn.Module) -> nn.Module: params_mapping = [ # (megatron core gpt model name, vllm model name) ("embedding.word_embeddings", "model.embed_tokens"), ("self_attention.linear_qkv.layer_norm_weight", "input_layernorm.weight"), ("self_attention.linear_qkv.layer_norm_bias", "input_layernorm.bias"), ("self_attention.linear_qkv", "self_attn.qkv_proj"), ("self_attention.linear_qkv", "self_attn.qkv_proj"), ("self_attention.linear_proj", "self_attn.o_proj"), ("pre_mlp_layernorm", "post_attention_layernorm"), ("mlp.linear_fc1.layer_norm_weight", "post_attention_layernorm.weight"), ("mlp.linear_fc1.layer_norm_bias", "post_attention_layernorm.bias"), ("mlp.linear_fc1", "mlp.gate_up_proj"), ("mlp.linear_fc2", "mlp.down_proj"), ("decoder.final_layernorm", "model.norm"), ("output_layer", "lm_head"), ] # NOTE(shengguangming): the megatron llama may have this prefix params_dict = dict(vllm_model.named_parameters()) for name, loaded_weight in actor_weights.items(): name = _replace_name(name, params_mapping) if name.endswith(".bias") and name not in params_dict: continue if "rotary_emb.inv_freq" in name: continue else: param = params_dict[name] weight_loader = getattr(param, "weight_loader", default_weight_loader) weight_loader(param, loaded_weight) def llama_megatron_core_weight_loader(actor_weights: Dict, vllm_model: nn.Module) -> nn.Module: params_mapping = [ # (megatron core gpt model name, vllm model name) ("embedding.word_embeddings", "model.embed_tokens"), ("self_attention.linear_qkv", "self_attn.qkv_proj"), ("self_attention.linear_proj", "self_attn.o_proj"), ( "input_layernorm", "input_layernorm", ), ("pre_mlp_layernorm", "post_attention_layernorm"), ("mlp.linear_fc1", "mlp.gate_up_proj"), ("mlp.linear_fc2", "mlp.down_proj"), ("decoder.final_layernorm", "model.norm"), ("output_layer", "lm_head"), ] # NOTE(shengguangming): the megatron llama may have this prefix params_dict = dict(vllm_model.named_parameters()) for name, loaded_weight in actor_weights.items(): name = _replace_name(name, params_mapping) if name.endswith(".bias") and name not in params_dict: continue if "rotary_emb.inv_freq" in name: continue else: param = params_dict[name] weight_loader = getattr(param, "weight_loader", default_weight_loader) weight_loader(param, loaded_weight) def _replace_name(megatron_name, name_mapping): for m_name, v_name in name_mapping: if m_name not in megatron_name: continue if "layers" in megatron_name: # deal with decoder layers megatron_name = megatron_name.replace("decoder", "model") megatron_name_list = megatron_name.split(".") if "layer_norm_weight" in megatron_name_list or "layer_norm_bias" in megatron_name_list: param_name_list = megatron_name_list[:3] param_name_list.append(v_name) param_name = ".".join(param_name_list) else: param_name_list = megatron_name_list[:3] weight_or_bias = megatron_name_list[-1] param_name_list.append(v_name) param_name_list.append(weight_or_bias) param_name = ".".join(param_name_list) return param_name else: param_name = megatron_name.replace(m_name, v_name) return param_name def llama_megatron_core_te_weight_loader(actor_weights: Dict, vllm_model: nn.Module) -> nn.Module: params_mapping = [ # (megatron core gpt model name, vllm model name) ("embedding.word_embeddings", "model.embed_tokens"), ("self_attention.linear_qkv.layer_norm_weight", "input_layernorm.weight"), ("self_attention.linear_qkv.layer_norm_bias", "input_layernorm.bias"), ("self_attention.linear_qkv", "self_attn.qkv_proj"), ("self_attention.linear_qkv", "self_attn.qkv_proj"), ("self_attention.linear_proj", "self_attn.o_proj"), ("pre_mlp_layernorm", "post_attention_layernorm"), ("mlp.linear_fc1.layer_norm_weight", "post_attention_layernorm.weight"), ("mlp.linear_fc1.layer_norm_bias", "post_attention_layernorm.bias"), ("mlp.linear_fc1", "mlp.gate_up_proj"), ("mlp.linear_fc2", "mlp.down_proj"), ("decoder.final_layernorm", "model.norm"), ("output_layer", "lm_head"), ] # NOTE(shengguangming): the megatron llama may have this prefix params_dict = dict(vllm_model.named_parameters()) for name, loaded_weight in actor_weights.items(): name = _replace_name(name, params_mapping) if name.endswith(".bias") and name not in params_dict: continue if "rotary_emb.inv_freq" in name: continue else: param = params_dict[name] weight_loader = getattr(param, "weight_loader", default_weight_loader) weight_loader(param, loaded_weight) def llama_megatron_core_weight_loader(actor_weights: Dict, vllm_model: nn.Module) -> nn.Module: params_mapping = [ # (megatron core gpt model name, vllm model name) ("embedding.word_embeddings", "model.embed_tokens"), ("self_attention.linear_qkv", "self_attn.qkv_proj"), ("self_attention.linear_proj", "self_attn.o_proj"), ( "input_layernorm", "input_layernorm", ), ("pre_mlp_layernorm", "post_attention_layernorm"), ("mlp.linear_fc1", "mlp.gate_up_proj"), ("mlp.linear_fc2", "mlp.down_proj"), ("decoder.final_layernorm", "model.norm"), ("output_layer", "lm_head"), ] # NOTE(shengguangming): the megatron llama may have this prefix params_dict = dict(vllm_model.named_parameters()) for name, loaded_weight in actor_weights.items(): name = _replace_name(name, params_mapping) if name.endswith(".bias") and name not in params_dict: continue if "rotary_emb.inv_freq" in name: continue else: param = params_dict[name] weight_loader = getattr(param, "weight_loader", default_weight_loader) weight_loader(param, loaded_weight) def _replace_name(megatron_name, name_mapping): for m_name, v_name in name_mapping: if m_name not in megatron_name: continue if "layers" in megatron_name: # deal with decoder layers megatron_name = megatron_name.replace("decoder", "model") megatron_name_list = megatron_name.split(".") if "layer_norm_weight" in megatron_name_list or "layer_norm_bias" in megatron_name_list: param_name_list = megatron_name_list[:3] param_name_list.append(v_name) param_name = ".".join(param_name_list) else: param_name_list = megatron_name_list[:3] weight_or_bias = megatron_name_list[-1] param_name_list.append(v_name) param_name_list.append(weight_or_bias) param_name = ".".join(param_name_list) return param_name else: param_name = megatron_name.replace(m_name, v_name) return param_name def mistral_megatron_weight_loader(actor_weights: Dict, vllm_model: nn.Module) -> nn.Module: # TODO: need to implement a general way to deal with prefix params_dict = dict(vllm_model.named_parameters()) for name, loaded_weight in actor_weights.items(): if "rotary_emb.inv_freq" in name: continue else: param = params_dict[name] weight_loader = getattr(param, "weight_loader", default_weight_loader) weight_loader(param, loaded_weight) __LAYER_WEIGHT_MEGATRON_LOADER_REGISTRY__ = { ColumnParallelLinear: parallel_weight_loader, MergedColumnParallelLinear: parallel_weight_loader, QKVParallelLinear: parallel_weight_loader, RowParallelLinear: parallel_weight_loader, VocabParallelEmbedding: parallel_weight_loader, ParallelLMHead: parallel_weight_loader, # "ScaledActivation.weight_loader": ScaledActivation, # TODO(shengguangming): latest commit in vllm fix awq for this function and add load_weights # "default_weight_loader": default_weight_loader } # for layer_class, weight_loader in __LAYER_WEIGHT_MEGATRON_LOADER_REGISTRY__.items(): # # setattr(layer_class, 'megatron_weight_loader', weight_loader) # layer_class.weight_loader = weight_loader __MODEL_MEGATRON_WEIGHT_LOADER_REGISTRY__ = { "GPT2LMHeadModel": gpt2_weight_loader, "LlamaForCausalLM": llama_megatron_weight_loader, # use te backend for open-source megatron "LLaMAForCausalLM": llama_megatron_weight_loader, "MistralForCausalLM": mistral_megatron_weight_loader, 'Qwen2ForCausalLM': qwen2_megatron_weight_loader, } # the actor model is .state_dict() # Load megatron weights def load_megatron_weights(actor_weights: Dict, vllm_model: nn.Module): weight_loader = _get_model_weight_loader(vllm_model.__class__.__name__) weight_loader(actor_weights, vllm_model) # NOTE(sgm) to reduce peak memory usage, we offload vllm model to cpu # after init, and we need this after sync model weights for in first iter. vllm_model = vllm_model.cuda() def _get_model_weight_loader(arch: str): if arch in __MODEL_MEGATRON_WEIGHT_LOADER_REGISTRY__: return __MODEL_MEGATRON_WEIGHT_LOADER_REGISTRY__[arch] raise ValueError(f"Model architectures {arch} are not supported for now. " f"Supported architectures: {ModelRegistry.get_supported_archs()}") def update_megatron_weight_loader(): for layer_class, weight_loader in __LAYER_WEIGHT_MEGATRON_LOADER_REGISTRY__.items(): layer_class.weight_loader = weight_loader ================================================ FILE: verl/third_party/vllm/vllm_v_0_6_3/model_loader.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Copyright 2023 The vLLM team. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # Adapted from https://github.com/vllm-project/vllm/tree/main/vllm/model_executor/models """Utilities for selecting and loading models.""" from typing import Dict, Optional, Union import torch import torch.nn as nn from transformers import PreTrainedModel from vllm.config import CacheConfig, DeviceConfig, LoadConfig, LoRAConfig, ModelConfig, ParallelConfig, SchedulerConfig from vllm.distributed.communication_op import tensor_model_parallel_all_gather from vllm.model_executor.model_loader import BaseModelLoader from vllm.model_executor.model_loader.loader import _initialize_model from vllm.model_executor.model_loader.utils import set_default_torch_dtype from .config import LoadConfig, LoadFormat, ModelConfig from .dtensor_weight_loaders import load_dtensor_weights, update_dtensor_weight_loader from .hf_weight_loader import update_hf_weight_loader from .megatron_weight_loaders import load_megatron_weights, update_megatron_weight_loader def get_model( actor_model: Union[PreTrainedModel, Dict], model_config: ModelConfig, load_config: LoadConfig, device_config: DeviceConfig, parallel_config: ParallelConfig, scheduler_config: SchedulerConfig, lora_config: Optional[LoRAConfig], cache_config: CacheConfig = None, ) -> nn.Module: loader = get_model_loader(load_config) if load_config.load_format.startswith("dummy"): return loader.load_model( model_config=model_config, device_config=device_config, lora_config=lora_config, parallel_config=parallel_config, scheduler_config=scheduler_config, cache_config=cache_config, ) else: return loader.load_model( actor_model=actor_model, model_config=model_config, device_config=device_config, lora_config=lora_config, parallel_config=parallel_config, scheduler_config=scheduler_config, cache_config=cache_config, ) def get_model_loader(load_config: LoadConfig) -> BaseModelLoader: """Get a model loader based on the load format.""" if isinstance(load_config.load_format, type): return load_config.load_format(load_config) if load_config.load_format == LoadFormat.AUTO: update_megatron_weight_loader() return MegatronLoader(load_config) # NOTE(sgm): change the weight_loader function in runtime if load_config.load_format == LoadFormat.MEGATRON: update_megatron_weight_loader() return MegatronLoader(load_config) if load_config.load_format == LoadFormat.HF: update_hf_weight_loader() return HFLoader(load_config) if load_config.load_format == LoadFormat.DTENSOR: update_dtensor_weight_loader() return DTensorLoader(load_config) if load_config.load_format == LoadFormat.DUMMY_HF: update_hf_weight_loader() return DummyModelLoader(load_config) if load_config.load_format == LoadFormat.DUMMY_MEGATRON: update_megatron_weight_loader() return DummyModelLoader(load_config) if load_config.load_format == LoadFormat.DUMMY_DTENSOR: update_dtensor_weight_loader() return DummyModelLoader(load_config) raise ValueError("load format not supported in verl: {}, only support {} and {}".format( load_config.load_format, LoadFormat.MEGATRON, LoadFormat.HF)) class DummyModelLoader(BaseModelLoader): """Model loader that will set model weights to random values.""" def __init__(self, load_config: LoadConfig): super().__init__(load_config) if load_config.model_loader_extra_config: raise ValueError(f"Model loader extra config is not supported for " f"load format {load_config.load_format}") def download_model(self, model_config: ModelConfig) -> None: pass def load_model( self, *, model_config: ModelConfig, device_config: DeviceConfig, lora_config: Optional[LoRAConfig], parallel_config: ParallelConfig, scheduler_config: SchedulerConfig, cache_config: CacheConfig, ) -> nn.Module: with set_default_torch_dtype(model_config.dtype): with torch.device(device_config.device): model = _initialize_model(model_config, self.load_config, lora_config, cache_config, scheduler_config) # NOTE(woosuk): For accurate performance evaluation, we assign # random values to the weights. # initialize_dummy_weights(model) return model.eval() class MegatronLoader(BaseModelLoader): """Model loader that can load the model weights from partitioned megatron model.""" def __init__(self, load_config: LoadConfig): super().__init__(load_config) if load_config.model_loader_extra_config: raise ValueError(f"Model loader extra config is not supported for " f"load format {load_config.load_format}") def download_model(self, model_config: ModelConfig) -> None: pass # Nothing to download def _get_weights_iterator(actor_model: Union[PreTrainedModel, Dict]): # NOTE(shengguangming) Load the weights from the actor model pass # if isinstance(actor_model, nn.Module): # load_weights(actor_weights=dict(actor_model.named_parameters(remove_duplicate=False)), vllm_model=model) # else: # load_weights(actor_weights=actor_model, vllm_model=model) # return actor_model def load_model( self, actor_model: Union[PreTrainedModel, Dict], model_config: ModelConfig, device_config: DeviceConfig, lora_config: Optional[LoRAConfig], parallel_config: ParallelConfig, scheduler_config: SchedulerConfig, cache_config: CacheConfig, ) -> nn.Module: with set_default_torch_dtype(model_config.dtype): with torch.device(device_config.device): model = _initialize_model(model_config, self.load_config, lora_config, cache_config, scheduler_config) # TODO(sgm): This is a hack, we need to register the load_weight() func for each model in vllm if isinstance(actor_model, nn.Module): load_megatron_weights(actor_weights=dict(actor_model.named_parameters(remove_duplicate=False)), vllm_model=model) else: load_megatron_weights(actor_weights=actor_model, vllm_model=model) for _, module in model.named_modules(): quant_method = getattr(module, "quant_method", None) if quant_method is not None: quant_method.process_weights_after_loading(module) # FIXME: Remove this after Mixtral is updated # to use quant_method. if hasattr(module, "process_weights_after_loading"): module.process_weights_after_loading() # NOTE(sgm) Some weights are point to gpu, but still need this. model = model.cuda() # NOTE (zhangchi.usc1992) We need this for vllm to profile memory usage return model.eval() class HFLoader(BaseModelLoader): """Model loader that can load the model weights from model's full params.""" def __init__(self, load_config: LoadConfig): super().__init__(load_config) if load_config.model_loader_extra_config: raise ValueError(f"Model loader extra config is not supported for " f"load format {load_config.load_format}") def download_model(self, model_config: ModelConfig) -> None: pass # Nothing to download def _get_weights_iterator(self, actor_model: Union[PreTrainedModel, Dict]): if isinstance(actor_model, Dict): return actor_model.items() elif isinstance(actor_model, nn.Module): return dict(actor_model.named_parameters()).items() else: raise ValueError(f"actor model should be Dict or nn.Module, but get {type(actor_model)}") def load_model( self, actor_model: Union[PreTrainedModel, Dict], model_config: ModelConfig, device_config: DeviceConfig, lora_config: Optional[LoRAConfig], parallel_config: ParallelConfig, scheduler_config: SchedulerConfig, cache_config: CacheConfig, ) -> nn.Module: with set_default_torch_dtype(model_config.dtype): # with torch.device(device_config.device): # NOTE(sgm): init the model in cpu model = _initialize_model(model_config, self.load_config, lora_config, cache_config, scheduler_config) model.load_weights(self._get_weights_iterator(actor_model)) for _, module in model.named_modules(): quant_method = getattr(module, "quant_method", None) if quant_method is not None: quant_method.process_weights_after_loading(module) # FIXME: Remove this after Mixtral is updated # to use quant_method. if hasattr(module, "process_weights_after_loading"): module.process_weights_after_loading() # NOTE(sgm) Some weights are point to gpu, but still need this. model = model.cuda() # NOTE (zhangchi.usc1992) We need this for vllm to profile memory usage return model.eval() class DTensorLoader(BaseModelLoader): """Model loader that can load the model weights from partitioned megatron model.""" def __init__(self, load_config: LoadConfig): super().__init__(load_config) if load_config.model_loader_extra_config: raise ValueError(f"Model loader extra config is not supported for " f"load format {load_config.load_format}") def download_model(self, model_config: ModelConfig) -> None: pass # Nothing to download def _get_weights_iterator(actor_model: Union[PreTrainedModel, Dict]): # NOTE(shengguangming) Load the weights from the actor model pass # if isinstance(actor_model, nn.Module): # load_weights(actor_weights=dict(actor_model.named_parameters(remove_duplicate=False)), vllm_model=model) # else: # load_weights(actor_weights=actor_model, vllm_model=model) # return actor_model def load_model( self, actor_model: Union[PreTrainedModel, Dict], model_config: ModelConfig, device_config: DeviceConfig, lora_config: Optional[LoRAConfig], parallel_config: ParallelConfig, scheduler_config: SchedulerConfig, cache_config: CacheConfig, ) -> nn.Module: with set_default_torch_dtype(model_config.dtype): with torch.device(device_config.device): model = _initialize_model(model_config, self.load_config, lora_config, cache_config, scheduler_config) # TODO(sgm): This is a hack, we need to register the load_weight() func for each model in vllm if isinstance(actor_model, nn.Module): load_dtensor_weights(actor_weights=dict(actor_model.named_parameters(remove_duplicate=False)), vllm_model=model) else: load_dtensor_weights(actor_weights=actor_model, vllm_model=model) for _, module in model.named_modules(): quant_method = getattr(module, "quant_method", None) if quant_method is not None: quant_method.process_weights_after_loading(module) # FIXME: Remove this after Mixtral is updated # to use quant_method. if hasattr(module, "process_weights_after_loading"): module.process_weights_after_loading() # NOTE(sgm) Some weights are point to gpu, but still need this. model = model.cuda() # NOTE (zhangchi.usc1992) We need this for vllm to profile memory usage return model.eval() # FIXME(sgm): hack the _get_logits function in vllm v0.4.2 # as they use ray, the _get_logits result will only need to return to the driver node, # therefore gather is enough. However, we use SPMD instead of a central scheduler, # all_gather is required (aligned with v0.2.6) def _get_logits(self, hidden_states: torch.Tensor, embedding: torch.Tensor, embedding_bias: Optional[torch.Tensor]) -> torch.Tensor: # Get the logits for the next tokens. logits = torch.matmul(hidden_states, embedding.t()) if embedding_bias is not None: logits += embedding_bias logits = tensor_model_parallel_all_gather(logits) # Remove paddings in vocab (if any). if logits is not None: logits = logits[:, :self.org_vocab_size] return logits from vllm.model_executor.layers.logits_processor import LogitsProcessor def logitsprocessor_init( self, vocab_size: int, org_vocab_size: Optional[int] = None, scale: float = 1.0, logits_as_input: bool = False, soft_cap: Optional[float] = None, ) -> None: """ Args: scale: A scaling factor to apply to the logits. """ super(LogitsProcessor, self).__init__() self.scale = scale self.vocab_size = vocab_size # Whether the input is logits (default is hidden states). self.logits_as_input = logits_as_input # original vocabulary size (without LoRA). self.org_vocab_size = org_vocab_size or vocab_size # Soft cap the logits. Used in Gemma 2. self.soft_cap = soft_cap # Whether to use gather or all-gather to gather the logits. self.use_gather = False LogitsProcessor.__init__ = logitsprocessor_init # use all_gather ================================================ FILE: verl/third_party/vllm/vllm_v_0_6_3/model_runner.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Copyright 2023 The vLLM team. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # Adapted from https://github.com/vllm-project/vllm/blob/main/vllm/worker/model_runner.py import warnings from enum import IntEnum from typing import Dict, Optional, Union import torch import torch.nn as nn import vllm.envs as envs from vllm.compilation.levels import CompilationLevel from vllm.config import ( CacheConfig, DeviceConfig, LoadConfig, LoRAConfig, ModelConfig, ObservabilityConfig, ParallelConfig, PromptAdapterConfig, SchedulerConfig, ) from vllm.inputs import INPUT_REGISTRY, InputRegistry from vllm.logger import init_logger from vllm.lora.worker_manager import LRUCacheWorkerLoRAManager from vllm.model_executor.models.interfaces import supports_lora from vllm.multimodal import MULTIMODAL_REGISTRY, MultiModalRegistry from vllm.prompt_adapter.worker_manager import LRUCacheWorkerPromptAdapterManager from vllm.utils import DeviceMemoryProfiler, is_hip, supports_dynamo from vllm.worker.model_runner import ModelRunner from .config import LoadConfig, ModelConfig from .model_loader import get_model logger = init_logger(__name__) # How batches are constructed. class BatchType(IntEnum): # Every batch is prefill. PREFILL = 0 # Every batch is decode. DECODE = 1 # Batch is a mixture of prefill and decode. MIXED = 2 class ModelRunner(ModelRunner): def __init__( self, model: Union[nn.Module, Dict], # [verl] model itself or its parameter dict model_config: ModelConfig, parallel_config: ParallelConfig, scheduler_config: SchedulerConfig, device_config: DeviceConfig, cache_config: CacheConfig, load_config: LoadConfig, lora_config: Optional[LoRAConfig], kv_cache_dtype: Optional[str] = "auto", is_driver_worker: bool = False, prompt_adapter_config: Optional[PromptAdapterConfig] = None, return_hidden_states: bool = False, observability_config: Optional[ObservabilityConfig] = None, input_registry: InputRegistry = INPUT_REGISTRY, mm_registry: MultiModalRegistry = MULTIMODAL_REGISTRY, ): super().__init__( model_config, parallel_config, scheduler_config, device_config, cache_config, load_config, lora_config, kv_cache_dtype, is_driver_worker=True, # a hack prompt_adapter_config=prompt_adapter_config, return_hidden_states=return_hidden_states, observability_config=observability_config, input_registry=input_registry, mm_registry=mm_registry, ) # NOTE(sgm): add for verl self.model = model # this will be replaced by get_model() def load_model(self) -> None: logger.info("Starting to load model %s...", self.model_config.model) with DeviceMemoryProfiler() as m: self.model = get_model( self.model, model_config=self.model_config, device_config=self.device_config, load_config=self.load_config, lora_config=self.lora_config, parallel_config=self.parallel_config, scheduler_config=self.scheduler_config, cache_config=self.cache_config, ) self.model_memory_usage = m.consumed_memory logger.info("Loading model weights took %.4f GB", self.model_memory_usage / float(2**30)) if self.lora_config: assert supports_lora(self.model), f"{self.model.__class__.__name__} does not support LoRA yet." if supports_multimodal(self.model): logger.warning("Regarding multimodal models, vLLM currently " "only supports adding LoRA to language model.") # It's necessary to distinguish between the max_position_embeddings # of VLMs and LLMs. if hasattr(self.model.config, "max_position_embeddings"): max_pos_embeddings = self.model.config.max_position_embeddings else: max_pos_embeddings = self.model.config.text_config.max_position_embeddings self.lora_manager = LRUCacheWorkerLoRAManager( self.scheduler_config.max_num_seqs, self.scheduler_config.max_num_batched_tokens, self.vocab_size, self.lora_config, self.device, self.model.embedding_modules, self.model.embedding_padding_modules, max_position_embeddings=max_pos_embeddings, ) self.model = self.lora_manager.create_lora_manager(self.model) if self.prompt_adapter_config: self.prompt_adapter_manager = LRUCacheWorkerPromptAdapterManager( self.scheduler_config.max_num_seqs, self.scheduler_config.max_num_batched_tokens, self.device, self.prompt_adapter_config, ) self.model = self.prompt_adapter_manager.create_prompt_adapter_manager(self.model) if self.kv_cache_dtype == "fp8" and is_hip(): # Currently only ROCm accepts kv-cache scaling factors # via quantization_param_path and this will be deprecated # in the future. if self.model_config.quantization_param_path is not None: if callable(getattr(self.model, "load_kv_cache_scales", None)): warnings.warn( "Loading kv cache scaling factor from JSON is " "deprecated and will be removed. Please include " "kv cache scaling factors in the model checkpoint.", FutureWarning, stacklevel=2, ) self.model.load_kv_cache_scales(self.model_config.quantization_param_path) logger.info("Loaded KV cache scaling factors from %s", self.model_config.quantization_param_path) else: raise RuntimeError( "Using FP8 KV cache and scaling factors provided but " "model %s does not support loading scaling factors.", self.model.__class__, ) else: logger.warning("Using FP8 KV cache but no scaling factors " "provided. Defaulting to scaling factors of 1.0. " "This may lead to less accurate results!") if envs.VLLM_TORCH_COMPILE_LEVEL == CompilationLevel.DYNAMO_AS_IS and supports_dynamo(): from vllm.plugins import get_torch_compile_backend backend = get_torch_compile_backend() or "eager" self.model = torch.compile(self.model, fullgraph=envs.VLLM_TEST_DYNAMO_FULLGRAPH_CAPTURE, backend=backend) ================================================ FILE: verl/third_party/vllm/vllm_v_0_6_3/parallel_state.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Copyright 2023 The vLLM team. # Adapted from # https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/core/parallel_state.py # Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. """Model and data parallel groups.""" import os from typing import Optional import torch import torch.distributed import vllm.distributed.parallel_state as ps from vllm.distributed.parallel_state import ( get_pp_group, get_world_group, init_distributed_environment, init_model_parallel_group, ) from vllm.logger import init_logger logger = init_logger(__name__) """ This version is strongly tied with Megatron to implement HybridEngine and weight sharing between vllm and Megatron. - We assume the Megatron tp+dp+pp world is already established before calling this function. """ # Device mesh for using DTensor _DEVICE_MESH = None # Tensor model parallel group that the current rank belongs to. _TP = None # Pipeline model parallel group that the current rank belongs to. _PP = None # This method is for initializing the ParallelGroup when using HybridEngine def initialize_parallel_state( distributed_init_method: str = "env://", backend: str = "nccl", tensor_model_parallel_size: int = 1, num_tp_per_train_tp: int = 1, pipeline_model_parallel_size: int = 1, ): # torch.distributed.all_reduce does not free the input tensor until # the synchronization point. This causes the memory usage to grow # as the number of all_reduce calls increases. This env var disables # this behavior. # Related issue: # https://discuss.pytorch.org/t/cuda-allocation-lifetime-for-inputs-to-distributed-all-reduce/191573 os.environ["TORCH_NCCL_AVOID_RECORD_STREAMS"] = "1" # NOTE(sgm): Modify for verl, Env vars will be set by TORCHRUN. rank = int(os.getenv("RANK", "-1")) local_rank = int(os.getenv("LOCAL_RANK", "0")) # Use the world_size set by TORCHRUN world_size = int(os.getenv("WORLD_SIZE", "-1")) assert world_size != -1, "The world_size is set to -1, not initialized by TORCHRUN" init_distributed_environment(world_size, rank, distributed_init_method, local_rank, backend) if torch.distributed.get_world_size() > 1: # NOTE: build a sepearate inference group with infer tp & micro dp initialize_model_parallel_for_vllm( tensor_model_parallel_size=tensor_model_parallel_size, num_tensor_model_parallel_groups_per_train_tp=num_tp_per_train_tp, ) else: initialize_model_parallel(tensor_model_parallel_size, pipeline_model_parallel_size, backend) def ensure_model_parallel_initialized( tensor_model_parallel_size: int, pipeline_model_parallel_size: int = 1, backend: Optional[str] = None, ) -> None: """Helper to initialize model parallel groups if they are not initialized, or ensure tensor-parallel and pipeline-parallel sizes are equal to expected values if the model parallel groups are initialized. """ # get the backend of _DEVICE_WORLD_GROUP backend = backend or torch.distributed.get_backend(get_world_group().device_group) if not model_parallel_is_initialized(): initialize_model_parallel(tensor_model_parallel_size, pipeline_model_parallel_size, backend) return assert get_tensor_model_parallel_world_size() == tensor_model_parallel_size, ( "tensor parallel group already initialized, but of unexpected size: " f"{get_tensor_model_parallel_world_size()=} vs. " f"{tensor_model_parallel_size=}") pp_world_size = get_pp_group().world_size assert pp_world_size == pipeline_model_parallel_size, ( "pipeline parallel group already initialized, but of unexpected size: " f"{pp_world_size=} vs. " f"{pipeline_model_parallel_size=}") # TODO(sgm): deviate from the v0.5.4, not pp now def model_parallel_is_initialized(): """Check if tensor and pipeline parallel groups are initialized.""" return ps._TP is not None # and _PIPELINE_MODEL_PARALLEL_GROUP is not None) def initialize_model_parallel_for_vllm( tensor_model_parallel_size: int, num_tensor_model_parallel_groups_per_train_tp: int = 1, pipeline_model_parallel_size: int = 1, ) -> None: pass # Get world size and rank. Ensure some consistencies. assert torch.distributed.is_initialized() assert isinstance(tensor_model_parallel_size, int) # assert num_tensor_model_parallel_groups_per_train_tp == 1 and not different_tp_group # assert num_tensor_model_parallel_groups_per_train_tp > 1 and different_tp_group # Build the tensor model-parallel groups. assert ps._TP is None, "tensor model parallel group is already initialized" global _TP world_size: int = torch.distributed.get_world_size() rank = torch.distributed.get_rank() backend = torch.distributed.get_backend() num_tensor_model_parallel_groups = world_size // tensor_model_parallel_size if num_tensor_model_parallel_groups_per_train_tp == 1: # if tensor_model_parallel_size == train_tensor_parallel_size: # using the same tp group as Megatron/vllm assert _TP is None, "tensor model parallel group is already initialized" group_ranks = [] for i in range(num_tensor_model_parallel_groups): ranks = range(i * tensor_model_parallel_size, (i + 1) * tensor_model_parallel_size) group_ranks.append(ranks) _TP = init_model_parallel_group( group_ranks=group_ranks, local_rank=get_world_group().local_rank, backend=backend, use_custom_allreduce=False, # TODO: check why True is not work in Ray trainer use_message_queue_broadcaster=True, ) ps._TP = _TP # _MICRO_DATA_PARALLEL_GROUP is move to hybrid engine else: # initialize a micro_dp group and a tp group # assume training tp=4, infer tp=2, then, weight is partitioned as # [1], [2], [3], [4] for training and [1,2], [1,2], [3,4], [3,4] for inference # Build the inference tp groups # train_tp = train_tensor_parallel_size train_tp = num_tensor_model_parallel_groups_per_train_tp * tensor_model_parallel_size # num_tensor_model_parallel_groups_per_train_tp = train_tp // tensor_model_parallel_size assert _TP is None, "tensor model parallel group is already initialized" group_ranks = [] for i in range(num_tensor_model_parallel_groups // num_tensor_model_parallel_groups_per_train_tp): start = train_tp * i end = train_tp * (i + 1) for j in range(num_tensor_model_parallel_groups_per_train_tp): ranks = list(range(start, end, num_tensor_model_parallel_groups_per_train_tp)) for i in range(len(ranks)): ranks[i] += j group_ranks.append(ranks) _TP = init_model_parallel_group( group_ranks=group_ranks, local_rank=get_world_group().local_rank, backend=backend, use_custom_allreduce=False, # TODO: check why True is not work in Ray trainer use_message_queue_broadcaster=True, ) ps._TP = _TP # Build the pipeline model-parallel groups. # global _PIPELINE_MODEL_PARALLEL_GROUP # global _PIPELINE_GLOBAL_RANKS # assert ps._PIPELINE_MODEL_PARALLEL_GROUP is None, ("pipeline model parallel group is already initialized") # ps._PIPELINE_MODEL_PARALLEL_GROUP = mpu.get_pipeline_model_parallel_group() # ps._PIPELINE_GLOBAL_RANKS = mpu.get_pipeline_model_parallel_ranks() # TODO: init using device mesh (not support hybrid engine now) # Build the pipeline model-parallel groups. num_pipeline_model_parallel_groups: int = world_size // pipeline_model_parallel_size global _PP assert _PP is None, "pipeline model parallel group is already initialized" group_ranks = [] for i in range(num_pipeline_model_parallel_groups): ranks = list(range(i, world_size, num_pipeline_model_parallel_groups)) group_ranks.append(ranks) # pipeline parallel does not need custom allreduce _PP = init_model_parallel_group(group_ranks, get_world_group().local_rank, backend, use_custom_allreduce=False) ps._PP = _PP # for verl def initialize_model_parallel( tensor_model_parallel_size: int = 1, pipeline_model_parallel_size: int = 1, backend: Optional[str] = None, ) -> None: """ NOTE: This method is a hack from the open-sourced version without asertion of world_size = tp * pp Initialize model parallel groups. Arguments: tensor_model_parallel_size: number of GPUs used for tensor model parallelism. pipeline_model_parallel_size: number of GPUs used for pipeline model parallelism. Let's say we have a total of 8 GPUs denoted by g0 ... g7 and we use 2 GPUs to parallelize the model tensor, and 4 GPUs to parallelize the model pipeline. The present function will create 4 tensor model-parallel groups and 2 pipeline model-parallel groups: 4 tensor model-parallel groups: [g0, g1], [g2, g3], [g4, g5], [g6, g7] 2 pipeline model-parallel groups: [g0, g2, g4, g6], [g1, g3, g5, g7] Note that for efficiency, the caller should make sure adjacent ranks are on the same DGX box. For example if we are using 2 DGX-1 boxes with a total of 16 GPUs, rank 0 to 7 belong to the first box and ranks 8 to 15 belong to the second box. """ # Get world size and rank. Ensure some consistencies. assert torch.distributed.is_initialized() world_size: int = torch.distributed.get_world_size() backend = backend or torch.distributed.get_backend(ps.get_world_group().device_group) # NOTE(sgm) we don't assert world_size == tp * pp # DP is not managed by vllm but by the VeRL WorkerGroup # if (world_size != # tensor_model_parallel_size * pipeline_model_parallel_size): # raise RuntimeError( # f"world_size ({world_size}) is not equal to " # f"tensor_model_parallel_size ({tensor_model_parallel_size}) x " # f"pipeline_model_parallel_size ({pipeline_model_parallel_size})") num_tensor_model_parallel_groups: int = world_size // tensor_model_parallel_size rank = torch.distributed.get_rank() global _TP assert _TP is None, "tensor model parallel group is already initialized" group_ranks = [] for i in range(num_tensor_model_parallel_groups): ranks = list(range(i * tensor_model_parallel_size, (i + 1) * tensor_model_parallel_size)) group_ranks.append(ranks) # message queue broadcaster is only used in tensor model parallel group _TP = init_model_parallel_group( group_ranks, get_world_group().local_rank, backend, use_custom_allreduce=False, # TODO: check why True is not work in Ray trainer use_message_queue_broadcaster=True, ) ps._TP = _TP # TODO: init using device mesh (not support hybrid engine now) # Build the pipeline model-parallel groups. num_pipeline_model_parallel_groups: int = world_size // pipeline_model_parallel_size global _PP assert _PP is None, "pipeline model parallel group is already initialized" group_ranks = [] for i in range(num_pipeline_model_parallel_groups): ranks = list(range(i, world_size, num_pipeline_model_parallel_groups)) group_ranks.append(ranks) # pipeline parallel does not need custom allreduce _PP = init_model_parallel_group(group_ranks, get_world_group().local_rank, backend, use_custom_allreduce=False) ps._PP = _PP # for verl """ Device mesh utilities """ def get_device_mesh(): assert _DEVICE_MESH is not None, "device mesh is not initialized" return _DEVICE_MESH """ Tensor model parallel utilities """ def get_tensor_model_parallel_group(): """Get the tensor model parallel group the caller rank belongs to.""" assert _TP is not None, "tensor model parallel group is not initialized" return _TP.device_group def get_tensor_model_parallel_world_size(): """Return world size for the tensor model parallel group.""" return torch.distributed.get_world_size(group=get_tensor_model_parallel_group()) def get_tensor_model_parallel_rank(): """Return my rank for the tensor model parallel group.""" return torch.distributed.get_rank(group=get_tensor_model_parallel_group()) def get_tensor_model_parallel_src_rank(): """Calculate the global rank corresponding to the first local rank in the tensor model parallel group.""" global_rank = torch.distributed.get_rank() local_world_size = get_tensor_model_parallel_world_size() return (global_rank // local_world_size) * local_world_size ================================================ FILE: verl/third_party/vllm/vllm_v_0_6_3/spmd_gpu_executor.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Copyright 2023 The vLLM team. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # Adapted from https://github.com/vllm-project/vllm/blob/main/vllm/executor/gpu_executor.py import os import socket from typing import Dict, List, Optional, Set, Tuple import torch from vllm.config import ( CacheConfig, DeviceConfig, LoRAConfig, ObservabilityConfig, ParallelConfig, PromptAdapterConfig, SchedulerConfig, SpeculativeConfig, ) from vllm.executor.executor_base import ExecutorAsyncBase, ExecutorBase from vllm.logger import init_logger from vllm.lora.request import LoRARequest from vllm.model_executor.layers.sampler import SamplerOutput from vllm.sequence import ExecuteModelRequest from .config import LoadConfig, ModelConfig logger = init_logger(__name__) class SPMDGPUExecutor(ExecutorBase): """SPMD-based multi-GPU executor implementations.""" def __init__( self, model, # pytorch model itself or its parameter dict model_config: ModelConfig, cache_config: CacheConfig, parallel_config: ParallelConfig, scheduler_config: SchedulerConfig, device_config: DeviceConfig, load_config: LoadConfig, lora_config: Optional[LoRAConfig], speculative_config: Optional[SpeculativeConfig], prompt_adapter_config: Optional[PromptAdapterConfig], observability_config: Optional[ObservabilityConfig], ) -> None: self.model_config = model_config self.cache_config = cache_config self.lora_config = lora_config self.load_config = load_config self.parallel_config = parallel_config self.scheduler_config = scheduler_config self.device_config = device_config self.speculative_config = speculative_config self.prompt_adapter_config = prompt_adapter_config self.observability_config = observability_config distributed_init_method = initialize_cluster(parallel_config) self._init_executor(model, distributed_init_method) # TODO(sgm): verl not support speculative decode now def _init_executor(self, model, distributed_init_method) -> None: assert not self.speculative_config, "Speculative decoding not yet supported for multi-GPU backend." # Create the parallel worker for each GPU. self._init_workers_sp(model, distributed_init_method) def _init_workers_sp(self, model, distributed_init_method: str): # Lazy import the Worker to avoid importing torch.cuda/xformers # before CUDA_VISIBLE_DEVICES is set in the Worker from .worker import Worker # pylint: disable=import-outside-toplevel rank = int(os.getenv("RANK")) local_rank = int(os.getenv("LOCAL_RANK")) print(f"local rank {local_rank}") # see https://github.com/NVIDIA/nccl/issues/1234 os.environ["NCCL_CUMEM_ENABLE"] = "0" self.worker = Worker( model, self.model_config, self.parallel_config, self.scheduler_config, self.device_config, self.cache_config, self.load_config, local_rank, rank, distributed_init_method, lora_config=self.lora_config, speculative_config=None, prompt_adapter_config=self.speculative_config, is_driver_worker=True, model_runner_cls=None, # use the default one ) # NOTE(shengguangming): torch.distributed.init_process_group will be called inside the init_model() self.worker.init_device() self.worker.load_model() def determine_num_available_blocks(self) -> Tuple[int, int]: """Determine the number of available KV blocks. This invokes `determine_num_available_blocks` on each worker and takes the min of the results, guaranteeing that the selected cache sizes are compatible with all workers. Returns: - tuple[num_gpu_blocks, num_cpu_blocks] """ # Get the maximum number of blocks that can be allocated on GPU and CPU. num_blocks = self.worker.determine_num_available_blocks() # NOTE(shengguangming): Now we don't use a shared centralized controler but each process will # have its own scheduler num_gpu_blocks = num_blocks[0] num_cpu_blocks = num_blocks[1] return num_gpu_blocks, num_cpu_blocks def initialize_cache(self, num_gpu_blocks: int, num_cpu_blocks: int) -> None: """Initialize the KV cache in all workers.""" # NOTE: We log here to avoid multiple logs when number of workers is # greater than one. We could log in the engine, but not all executors # have GPUs. logger.info("# GPU blocks: %d, # CPU blocks: %d", num_gpu_blocks, num_cpu_blocks) self.cache_config.num_gpu_blocks = num_gpu_blocks self.cache_config.num_cpu_blocks = num_cpu_blocks if torch.distributed.get_rank() == 0: print( f"before init cache memory allocated: {torch.cuda.memory_allocated() / 1e9}GB, reserved: {torch.cuda.memory_reserved() / 1e9}GB" ) self.worker.initialize_cache(num_gpu_blocks=num_gpu_blocks, num_cpu_blocks=num_cpu_blocks) if torch.distributed.get_rank() == 0: print( f"after init cache memory allocated: {torch.cuda.memory_allocated() / 1e9}GB, reserved: {torch.cuda.memory_reserved() / 1e9}GB" ) # NOTE(sgm): This will not profile & capture the model(CUDAGraph) when rebuilding KVCache def init_cache_engine(self) -> None: self.worker._init_cache_engine() def free_cache_engine(self) -> None: self.worker.free_cache_engine() def execute_model(self, execute_model_req) -> List[SamplerOutput]: all_outputs = self.worker.execute_model(execute_model_req=execute_model_req) # NOTE(sgm): # Each GPU in vllm under verl has its own spmd_gpu_executor, therefore all GPUs should return the outputs # In vllm with ray, only the driver worker returns the sampling results. return all_outputs def add_lora(self, lora_request: LoRARequest) -> bool: assert lora_request.lora_int_id > 0, "lora_id must be greater than 0." return self.worker.add_lora(lora_request=lora_request) def remove_lora(self, lora_id: int) -> bool: assert lora_id > 0, "lora_id must be greater than 0." return self.worker.remove_lora(lora_id=lora_id) def list_loras(self) -> Set[int]: return self.worker.list_loras() def check_health(self) -> None: # SPMDExecutor will always be healthy as long as # it's running. return # NOTE(sgm) add for verl to pass the abstract class test, not used from vllm.prompt_adapter.request import PromptAdapterRequest def add_prompt_adapter(self, prompt_adapter_request: PromptAdapterRequest) -> bool: assert prompt_adapter_request.prompt_adapter_id > 0, "prompt_adapter_id must be greater than 0." return self.worker.add_prompt_adapter(prompt_adapter_request) def list_prompt_adapters(self) -> Set[int]: return self.worker.list_prompt_adapters() def pin_lora(self, lora_id: int) -> bool: assert lora_id > 0, "lora_id must be greater than 0." return self.worker.pin_lora(lora_id) def pin_prompt_adapter(self, prompt_adapter_id: int) -> bool: assert prompt_adapter_id > 0, "prompt_adapter_id must be greater than 0." return self.worker.pin_prompt_adapter(prompt_adapter_id) def remove_prompt_adapter(self, prompt_adapter_id: int) -> bool: assert prompt_adapter_id > 0, "prompt_adapter_id must be greater than 0." return self.worker.remove_prompt_adapter(prompt_adapter_id) # NOTE(sgm): add for verl def offload_model_weights(self) -> None: self.worker.offload_model_weights() def sync_model_weights(self, actor_weights: Dict[str, torch.Tensor], load_format: str) -> None: self.worker.sync_model_weights(actor_weights=actor_weights, load_format=load_format) def initialize_cluster( parallel_config: ParallelConfig, engine_use_ray: bool = False, ray_address: Optional[str] = None, ) -> Tuple[str, Optional[None]]: """Initialize the distributed cluster probably with Ray. Args: parallel_config: The configurations for parallel execution. Returns: The `distributed_init_method` is the address for initializing the distributed backend. """ # Initialize cluster locally. port = get_open_port() # We need to setup the distributed init method to make sure # the distributed megatron code (e.g., get world size) works correctly. # distributed_init_method = f"tcp://localhost:{port}" distributed_init_method = "env://" return distributed_init_method def get_open_port(): with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: s.bind(("", 0)) return s.getsockname()[1] # TODO(sgm): not implemented async executor yet class SPMDGPUExecutorAsync(SPMDGPUExecutor, ExecutorAsyncBase): async def execute_model_async(self, execute_model_req: ExecuteModelRequest) -> List[SamplerOutput]: """Executes one model step on the given sequences.""" raise NotImplementedError async def check_health_async(self) -> None: """Checks if the executor is healthy. If not, it should raise an exception.""" self.check_health() ================================================ FILE: verl/third_party/vllm/vllm_v_0_6_3/tokenizer.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Copyright 2023 The vLLM team. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # Adapted from https://github.com/vllm-project/vllm/blob/main/vllm/transformers_utils/tokenizer_group/tokenizer_group.py from typing import Optional from transformers import PreTrainedTokenizer from vllm.transformers_utils.tokenizer_group import TokenizerGroup from vllm.utils import LRUCache class TokenizerGroup(TokenizerGroup): """A group of tokenizers that can be used for LoRA adapters.""" def __init__(self, tokenizer: PreTrainedTokenizer, enable_lora: bool, max_num_seqs: int, max_input_length: Optional[int]): self.enable_lora = enable_lora self.max_input_length = max_input_length self.tokenizer = tokenizer self.lora_tokenizers = LRUCache[PreTrainedTokenizer](capacity=max_num_seqs) if enable_lora else None # FIXME(sgm): for simplicity, we assign the special token here @property def pad_token_id(self): return self.tokenizer.pad_token_id @property def eos_token_id(self): return self.tokenizer.eos_token_id ================================================ FILE: verl/third_party/vllm/vllm_v_0_6_3/worker.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Copyright 2023 The vLLM team. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # Adapted from https://github.com/vllm-project/vllm/blob/main/vllm/worker/worker.py """A GPU worker class.""" import gc import os from typing import Dict, List, Optional, Tuple, Type, Union import torch import torch.distributed import torch.nn as nn from vllm.config import ( CacheConfig, DeviceConfig, LoRAConfig, ParallelConfig, PromptAdapterConfig, SchedulerConfig, SpeculativeConfig, ) # TODO(sgm): check why vllm has similar file in vllm.model_executor.parallel_utils.parallel_state from vllm.distributed import get_tensor_model_parallel_group, init_distributed_environment, set_custom_all_reduce from vllm.model_executor import set_random_seed from vllm.model_executor.layers.sampler import SamplerOutput from vllm.sequence import ExecuteModelRequest, IntermediateTensors from vllm.worker.cache_engine import CacheEngine from vllm.worker.embedding_model_runner import EmbeddingModelRunner from vllm.worker.model_runner import GPUModelRunnerBase from vllm.worker.model_runner_base import ModelRunnerInputBase from vllm.worker.worker import Worker, _check_if_gpu_supports_dtype from vllm.worker.worker_base import WorkerInput from .config import LoadConfig, LoadFormat, ModelConfig from .dtensor_weight_loaders import load_dtensor_weights from .hf_weight_loader import load_hf_weights from .megatron_weight_loaders import load_megatron_weights from .model_runner import ModelRunner from .parallel_state import ensure_model_parallel_initialized class Worker(Worker): """A worker class that executes (a partition of) the model on a GPU. Each worker is associated with a single GPU. The worker is responsible for maintaining the KV cache and executing the model on the GPU. In case of distributed inference, each worker is assigned a partition of the model. """ def __init__( self, model: Union[nn.Module, Dict], # model itself or its parameter dict model_config: ModelConfig, parallel_config: ParallelConfig, scheduler_config: SchedulerConfig, device_config: DeviceConfig, cache_config: CacheConfig, load_config: LoadConfig, local_rank: int, rank: int, distributed_init_method: str, lora_config: Optional[LoRAConfig] = None, speculative_config: Optional[SpeculativeConfig] = None, prompt_adapter_config: Optional[PromptAdapterConfig] = None, is_driver_worker: bool = False, model_runner_cls: Optional[Type[GPUModelRunnerBase]] = None, ) -> None: # self.model = model # will be replaced in the init_model self.model_config = model_config self.parallel_config = parallel_config self.parallel_config.rank = rank self.scheduler_config = scheduler_config self.device_config = device_config self.cache_config = cache_config self.local_rank = local_rank self.rank = rank self.distributed_init_method = distributed_init_method self.lora_config = lora_config self.load_config = load_config self.prompt_adapter_config = prompt_adapter_config self.is_driver_worker = is_driver_worker # TODO: we don't need driver # if parallel_config and is_driver_worker: # assert rank % parallel_config.tensor_parallel_size == 0, \ # "Driver worker should be rank 0 of tensor parallel group." if self.model_config.trust_remote_code: # note: lazy import to avoid importing torch before initializing from vllm.utils import init_cached_hf_modules init_cached_hf_modules() # Return hidden states from target model if the draft model is an # mlp_speculator speculative_args = ( {} if speculative_config is None or (speculative_config.draft_model_config.model == model_config.model) or (speculative_config.draft_model_config.hf_config.model_type not in ["medusa", "mlp_speculator"]) else { "return_hidden_states": True }) # TODO(sgm): set correct model runner class ModelRunnerClass: Type[GPUModelRunnerBase] = ModelRunner if model_runner_cls is not None: ModelRunnerClass = model_runner_cls elif self.model_config.embedding_mode: ModelRunnerClass = EmbeddingModelRunner self.model_runner: GPUModelRunnerBase = ModelRunnerClass( model, # [VERL]: add for verl model_config, parallel_config, scheduler_config, device_config, cache_config, load_config=load_config, lora_config=self.lora_config, kv_cache_dtype=self.cache_config.cache_dtype, is_driver_worker=is_driver_worker, prompt_adapter_config=prompt_adapter_config, **speculative_args, ) # Uninitialized cache engine. Will be initialized by # initialize_cache. self.cache_engine: List[CacheEngine] = None # Initialize gpu_cache as embedding models don't initialize kv_caches self.gpu_cache: Optional[List[List[torch.Tensor]]] = None # NOTE(sgm): [VERL] For offloading inference engine params self.cpu_model = None def init_device(self) -> None: if self.device_config.device.type == "cuda": # torch.distributed.all_reduce does not free the input tensor until # the synchronization point. This causes the memory usage to grow # as the number of all_reduce calls increases. This env var disables # this behavior. # Related issue: # https://discuss.pytorch.org/t/cuda-allocation-lifetime-for-inputs-to-distributed-all-reduce/191573 os.environ["TORCH_NCCL_AVOID_RECORD_STREAMS"] = "1" # NOTE(sgm): Modify for verl, Env vars will be set by TORCHRUN. self.rank = self.rank if self.rank is not None else int(os.getenv("RANK", "-1")) local_rank = int(os.getenv("LOCAL_RANK", "0")) self.device = torch.device(f"cuda:{local_rank}") if self.rank < 0: raise ValueError("Invalid or unspecified rank.") torch.cuda.set_device(self.device) # Use the world_size set by TORCHRUN world_size = int(os.getenv("WORLD_SIZE", "-1")) assert world_size != -1, "The world_size is set to -1, not initialized by TORCHRUN" self.parallel_config.world_size = world_size _check_if_gpu_supports_dtype(self.model_config.dtype) torch.cuda.empty_cache() self.init_gpu_memory = torch.cuda.mem_get_info()[0] else: raise RuntimeError(f"Not support device type: {self.device_config.device}") # Initialize the distributed environment. init_worker_distributed_environment(self.parallel_config, self.rank, self.distributed_init_method, self.local_rank) # Set random seed. set_random_seed(self.model_config.seed) # self.model = get_model(actor_model=self.model, model_config=self.model_config) @torch.inference_mode() def determine_num_available_blocks(self) -> Tuple[int, int]: """Profiles the peak memory usage of the model to determine how many KV blocks may be allocated without OOMs. The engine will first conduct a profiling of the existing memory usage. Then, it calculate the maximum possible number of GPU and CPU blocks that can be allocated with the remaining free memory. .. tip:: You may limit the usage of GPU memory by adjusting the `gpu_memory_utilization` parameter. """ # Profile the memory usage of the model and get the maximum number of # cache blocks that can be allocated with the remaining free memory. torch.cuda.empty_cache() # torch.cuda.reset_peak_memory_stats() # Execute a forward pass with dummy inputs to profile the memory usage # of the model. self.model_runner.profile_run() # Calculate the number of blocks that can be allocated with the # profiled peak memory. torch.cuda.synchronize() free_gpu_memory, total_gpu_memory = torch.cuda.mem_get_info() peak_memory = total_gpu_memory - free_gpu_memory assert peak_memory > 0, ("Error in memory profiling. This happens when the GPU memory was " "not properly cleaned up before initializing the vLLM instance.") cache_block_size = self.get_cache_block_size_bytes() # NOTE(sgm) [VERL] use the remaining memory num_gpu_blocks = int((free_gpu_memory * self.cache_config.gpu_memory_utilization) // cache_block_size) # num_gpu_blocks = int((total_gpu_memory * self.cache_config.gpu_memory_utilization - peak_memory) // cache_block_size) num_cpu_blocks = int(self.cache_config.swap_space_bytes // cache_block_size) num_gpu_blocks = max(num_gpu_blocks, 0) num_cpu_blocks = max(num_cpu_blocks, 0) if self.model_runner.lora_manager: self.model_runner.remove_all_loras() # NOTE(sgm): Add for [VERL], synchronize number of blocks with all the rank num_gpu_blocks = torch.tensor([num_gpu_blocks], device="cuda") num_cpu_blocks = torch.tensor([num_cpu_blocks], device="cuda") torch.distributed.all_reduce(num_gpu_blocks, op=torch.distributed.ReduceOp.MIN, group=get_tensor_model_parallel_group().device_group) torch.distributed.all_reduce(num_cpu_blocks, op=torch.distributed.ReduceOp.MIN, group=get_tensor_model_parallel_group().device_group) num_gpu_blocks = num_gpu_blocks.item() num_cpu_blocks = num_cpu_blocks.item() gc.collect() torch.cuda.empty_cache() return num_gpu_blocks, num_cpu_blocks def _init_cache_engine(self): if self.cache_engine is None and self.gpu_cache is None: super()._init_cache_engine() def free_cache_engine(self): # ensure `enforce_eager=True` self.cache_engine = None self.gpu_cache = None # NOTE(sgm): [VERL]: adapt from _execute_model_spmd() def execute_model(self, execute_model_req: ExecuteModelRequest, intermediate_tensors: Optional[IntermediateTensors] = None) -> Optional[List[SamplerOutput]]: """ Execute model in Single Program Multiple Data (SPMD) fashion. All workers take the same request, prepare the input and execute the model. """ assert execute_model_req is not None, ("_execute_model_spmd() requires each worker to take in an " "ExecuteModelRequest") worker_input: WorkerInput = self.prepare_worker_input(execute_model_req=execute_model_req) model_input: ModelRunnerInputBase = self.model_runner.prepare_model_input( execute_model_req.seq_group_metadata_list) # verl.worker.workerbase.WorkerBase # swap cache super().execute_worker(worker_input) # If there is no input, we don't need to execute the model. if worker_input.num_seq_groups == 0: return [] return self.model_runner.execute_model( model_input, self.kv_cache[worker_input.virtual_engine] if self.kv_cache is not None else None, intermediate_tensors, ) # assume the input is .state_dict() def sync_model_weights(self, actor_weights: Dict, load_format: str): if load_format in [LoadFormat.MEGATRON, LoadFormat.AUTO]: load_megatron_weights(actor_weights, self.model_runner.model) elif load_format == LoadFormat.HF: # full model state dict without no sharding load_hf_weights(actor_weights, self.model_runner.model) elif load_format == LoadFormat.DTENSOR: load_dtensor_weights(actor_weights, self.model_runner.model) def offload_model_weights(self) -> None: if self.cpu_model == None: self.cpu_model = {} for name, params in self.model_runner.model.named_parameters(): self.cpu_model[name] = torch.empty_like(params, device="cpu") params.data = self.cpu_model[name] else: for name, params in self.model_runner.model.named_parameters(): params.data = self.cpu_model[name] def init_worker_distributed_environment( parallel_config: ParallelConfig, rank: int, distributed_init_method: Optional[str] = "env://", local_rank: int = -1, ) -> None: """Initialize the distributed environment.""" set_custom_all_reduce(not parallel_config.disable_custom_all_reduce) # NOTE(sgm) use tcp://localhost:xxxx will hang in HF setting without megatron init_distributed_environment(parallel_config.world_size, rank, distributed_init_method, local_rank) ensure_model_parallel_initialized( tensor_model_parallel_size=parallel_config.tensor_parallel_size, pipeline_model_parallel_size=parallel_config.pipeline_parallel_size, ) # TODO(sgm): check whether need this # if pynccl_utils.is_initialized(): # pynccl_world_size = pynccl_utils.get_world_size() # if pynccl_world_size != parallel_config.world_size: # raise RuntimeError( # "pynccl is already initialized but the pynccl world " # "size does not match parallel_config.world_size " # f"({pynccl_world_size} vs. {parallel_config.world_size}).") # elif parallel_config.world_size > 1: # # NOTE(woosuk): We don't initialize pynccl process group when world size # # is 1. # # NOTE(kaichao): By default, pynccl is initialized for tp group. # pynccl_utils.init_process_group( # group=get_tensor_model_parallel_cpu_group()) # # Initialize a custom fast all-reduce implementation. # if not parallel_config.disable_custom_all_reduce: # init_custom_ar() # A small all_reduce for warmup. torch.distributed.all_reduce(torch.zeros(1).cuda()) # if pynccl_utils.is_initialized(): # pynccl_utils.all_reduce(torch.zeros(1).cuda()) ================================================ FILE: verl/trainer/__init__.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. ================================================ FILE: verl/trainer/config/evaluation.yaml ================================================ data: path: /tmp/math_Qwen2-7B-Instruct.parquet prompt_key: prompt response_key: responses data_source_key: data_source reward_model_key: reward_model ================================================ FILE: verl/trainer/config/generation.yaml ================================================ trainer: nnodes: 1 n_gpus_per_node: 8 data: path: ~/data/rlhf/math/test.parquet prompt_key: prompt n_samples: 5 output_path: /opt/tiger/math_Qwen2-7B-Instruct.parquet batch_size: 128 model: path: ~/models/Qwen2-7B-Instruct external_lib: null rollout: name: vllm temperature: 1.0 top_k: 50 # 0 for hf rollout, -1 for vllm rollout top_p: 0.7 prompt_length: 1536 response_length: 512 # for vllm rollout dtype: bfloat16 # should align with FSDP gpu_memory_utilization: 0.5 ignore_eos: False enforce_eager: True free_cache_engine: True load_format: dummy_dtensor tensor_model_parallel_size: 1 max_num_batched_tokens: 8192 max_model_len: null max_num_seqs: 1024 log_prob_micro_batch_size: null # will be deprecated, use log_prob_micro_batch_size_per_gpu log_prob_micro_batch_size_per_gpu: 8 # for fire vllm rollout use_fire_sampling: False # enable FIRE https://arxiv.org/abs/2410.21236 # for hf rollout do_sample: True disable_log_stats: True enable_chunked_prefill: True n: 1 actor: strategy: fsdp # This is for backward-compatibility ppo_mini_batch_size: 256 ppo_micro_batch_size: null # will be deprecated, use ppo_micro_batch_size_per_gpu ppo_micro_batch_size_per_gpu: null use_dynamic_bsz: False ppo_max_token_len_per_gpu: 16384 # n * ${data.max_prompt_length} + ${data.max_response_length} grad_clip: 1.0 clip_ratio: 0.2 entropy_coeff: 0.001 use_kl_loss: False # True for GRPO kl_loss_coef: 0.001 # for grpo kl_loss_type: low_var_kl # for grpo ppo_epochs: 1 shuffle: False ulysses_sequence_parallel_size: 1 # sp size optim: lr: 1e-6 lr_warmup_steps_ratio: 0. # the total steps will be injected during runtime min_lr_ratio: null # only useful for warmup with cosine warmup_style: constant # select from constant/cosine total_training_steps: -1 # must be override by program fsdp_config: wrap_policy: min_num_params: 0 param_offload: False optimizer_offload: False fsdp_size: -1 ================================================ FILE: verl/trainer/config/ppo_megatron_trainer.yaml ================================================ data: tokenizer: null train_files: ~/data/rlhf/gsm8k/train.parquet val_files: ~/data/rlhf/gsm8k/test.parquet prompt_key: prompt max_prompt_length: 512 max_response_length: 512 train_batch_size: 1024 val_batch_size: null # DEPRECATED: Validation datasets are sent to inference engines as a whole batch, which will schedule the memory themselves return_raw_input_ids: False # This should be set to true when the tokenizer between policy and rm differs return_raw_chat: False shuffle: True actor_rollout_ref: hybrid_engine: True model: path: ~/models/deepseek-llm-7b-chat external_lib: null override_config: {} enable_gradient_checkpointing: False actor: strategy: megatron # This is for backward-compatibility ppo_mini_batch_size: 256 ppo_micro_batch_size: null # will be deprecated, use ppo_micro_batch_size_per_gpu ppo_micro_batch_size_per_gpu: null use_dynamic_bsz: False clip_ratio: 0.2 entropy_coeff: 0.001 ppo_epochs: 1 shuffle: True optim: lr: 1e-6 clip_grad: 1.0 lr_warmup_steps_ratio: 0. # the total steps will be injected during runtime min_lr_ratio: null # only useful for warmup with cosine warmup_style: constant # select from constant/cosine total_training_steps: -1 # must be override by program megatron: tensor_model_parallel_size: 4 pipeline_model_parallel_size: 1 num_layers_per_virtual_pipeline_stage: null # vpp will hang. need debug. sequence_parallel: True seed: 1 load_weight: True ref: megatron: tensor_model_parallel_size: 4 pipeline_model_parallel_size: 1 num_layers_per_virtual_pipeline_stage: null # vpp will hang. need debug. sequence_parallel: True seed: 1 load_weight: True param_offload: False log_prob_micro_batch_size: null # will be deprecated, use log_prob_micro_batch_size_per_gpu log_prob_micro_batch_size_per_gpu: null rollout: name: vllm temperature: 1.0 top_k: -1 # 0 for hf rollout, -1 for vllm rollout top_p: 1 prompt_length: ${data.max_prompt_length} # for xperf_gpt response_length: ${data.max_response_length} # for vllm rollout dtype: bfloat16 # should align with FSDP gpu_memory_utilization: 0.5 ignore_eos: False enforce_eager: True free_cache_engine: True load_format: dummy_megatron tensor_model_parallel_size: 2 max_num_batched_tokens: 8192 max_model_len: null max_num_seqs: 1024 log_prob_micro_batch_size: null # will be deprecated, use log_prob_micro_batch_size_per_gpu log_prob_micro_batch_size_per_gpu: null disable_log_stats: True enable_chunked_prefill: False # could get higher throughput # for hf rollout do_sample: True layer_name_map: qkv_layer_name: qkv gate_proj_layer_name: gate_up # number of responses (i.e. num sample times) n: 1 critic: strategy: megatron optim: lr: 1e-5 clip_grad: 1.0 lr_warmup_steps_ratio: 0. # the total steps will be injected during runtime min_lr_ratio: null # only useful for warmup with cosine warmup_style: constant # select from constant/cosine total_training_steps: -1 # must be override by program model: path: ~/models/deepseek-llm-7b-chat tokenizer_path: ${actor_rollout_ref.model.path} override_config: {} external_lib: ${actor_rollout_ref.model.external_lib} enable_gradient_checkpointing: False megatron: tensor_model_parallel_size: 4 pipeline_model_parallel_size: 1 num_layers_per_virtual_pipeline_stage: null # vpp will hang. need debug. sequence_parallel: True seed: 1 load_weight: True ppo_mini_batch_size: ${actor_rollout_ref.actor.ppo_mini_batch_size} ppo_micro_batch_size: null # will be deprecated, use ppo_micro_batch_size_per_gpu ppo_micro_batch_size_per_gpu: null use_dynamic_bsz: ${actor_rollout_ref.actor.use_dynamic_bsz} ppo_epochs: ${actor_rollout_ref.actor.ppo_epochs} shuffle: ${actor_rollout_ref.actor.shuffle} cliprange_value: 0.5 kl_ctrl: type: fixed kl_coef: 0.001 reward_model: enable: False strategy: megatron megatron: tensor_model_parallel_size: 4 pipeline_model_parallel_size: 1 num_layers_per_virtual_pipeline_stage: null # vpp will hang. need debug. sequence_parallel: True seed: 1 model: input_tokenizer: ${actor_rollout_ref.model.path} # set this to null if the chat template is identical path: ~/models/FsfairX-LLaMA3-RM-v0.1 external_lib: ${actor_rollout_ref.model.external_lib} load_weight: True param_offload: False micro_batch_size: null # will be deprecated, use micro_batch_size_per_gpu micro_batch_size_per_gpu: null use_dynamic_bsz: ${critic.use_dynamic_bsz} max_length: null algorithm: gamma: 1.0 lam: 1.0 adv_estimator: gae kl_penalty: kl # how to estimate kl divergence kl_ctrl: type: fixed kl_coef: 0.001 trainer: total_epochs: 30 total_training_steps: null project_name: verl_examples experiment_name: gsm8k logger: ['console', 'wandb'] val_generations_to_log_to_wandb: 0 nnodes: 1 n_gpus_per_node: 8 save_freq: -1 # auto: find the last ckpt to resume. If can't find, start from scratch resume_mode: disable # or auto or resume_path if resume_from_path: False test_freq: 2 critic_warmup: 0 default_hdfs_dir: null default_local_dir: checkpoints/${trainer.project_name}/${trainer.experiment_name} ================================================ FILE: verl/trainer/config/ppo_trainer.yaml ================================================ data: tokenizer: null train_files: ~/data/rlhf/gsm8k/train.parquet val_files: ~/data/rlhf/gsm8k/test.parquet prompt_key: prompt template_type: default max_prompt_length: 512 val_max_prompt_length: 1024 max_response_length: 512 train_batch_size: 1024 val_batch_size: null # DEPRECATED: Validation datasets are sent to inference engines as a whole batch, which will schedule the memory themselves return_raw_input_ids: False # This should be set to true when the tokenizer between policy and rm differs return_raw_chat: False shuffle: True image_key: images reward_type: default execution_error_penalty: False actor_rollout_ref: hybrid_engine: True model: path: ~/models/deepseek-llm-7b-chat external_lib: null override_config: { } enable_gradient_checkpointing: True use_remove_padding: False actor: strategy: fsdp # This is for backward-compatibility ppo_mini_batch_size: 256 ppo_micro_batch_size: null # will be deprecated, use ppo_micro_batch_size_per_gpu ppo_micro_batch_size_per_gpu: null use_dynamic_bsz: False ppo_max_token_len_per_gpu: 16384 # n * ${data.max_prompt_length} + ${data.max_response_length} grad_clip: 1.0 clip_ratio: 0.2 entropy_coeff: 0.001 use_kl_loss: False # True for GRPO kl_loss_coef: 0.001 # for grpo kl_loss_type: low_var_kl # for grpo ppo_epochs: 1 shuffle: False ulysses_sequence_parallel_size: 1 # sp size optim: lr: 1e-6 lr_warmup_steps_ratio: 0. # the total steps will be injected during runtime min_lr_ratio: null # only useful for warmup with cosine warmup_style: constant # select from constant/cosine total_training_steps: -1 # must be override by program fsdp_config: wrap_policy: # transformer_layer_cls_to_wrap: None min_num_params: 0 param_offload: False optimizer_offload: False fsdp_size: -1 ref: fsdp_config: param_offload: False wrap_policy: # transformer_layer_cls_to_wrap: None min_num_params: 0 log_prob_micro_batch_size: null # will be deprecated, use log_prob_micro_batch_size_per_gpu log_prob_micro_batch_size_per_gpu: null log_prob_use_dynamic_bsz: ${actor_rollout_ref.actor.use_dynamic_bsz} log_prob_max_token_len_per_gpu: ${actor_rollout_ref.actor.ppo_max_token_len_per_gpu} ulysses_sequence_parallel_size: ${actor_rollout_ref.actor.ulysses_sequence_parallel_size} # sp size rollout: num_llm_calls_available: 3 name: vllm temperature: 1.0 top_k: -1 # 0 for hf rollout, -1 for vllm rollout top_p: 1 use_fire_sampling: False # https://arxiv.org/abs/2410.21236 prompt_length: ${data.max_prompt_length} # not use for opensource response_length: ${data.max_response_length} # for vllm rollout dtype: bfloat16 # should align with FSDP gpu_memory_utilization: 0.5 ignore_eos: False enforce_eager: True free_cache_engine: True load_format: dummy_dtensor tensor_model_parallel_size: 2 max_num_batched_tokens: 8192 max_model_len: null max_num_seqs: 1024 log_prob_micro_batch_size: null # will be deprecated, use log_prob_micro_batch_size_per_gpu log_prob_micro_batch_size_per_gpu: null log_prob_use_dynamic_bsz: ${actor_rollout_ref.actor.use_dynamic_bsz} log_prob_max_token_len_per_gpu: ${actor_rollout_ref.actor.ppo_max_token_len_per_gpu} disable_log_stats: True enable_chunked_prefill: True # may get higher throughput when set to True. When activated, Please increase max_num_batched_tokens or decrease max_model_len. # for hf rollout do_sample: True # number of responses (i.e. num sample times) n: 1 # > 1 for grpo critic: strategy: fsdp optim: lr: 1e-5 lr_warmup_steps_ratio: 0. # the total steps will be injected during runtime min_lr_ratio: null # only useful for warmup with cosine warmup_style: constant # select from constant/cosine total_training_steps: -1 # must be override by program model: path: ~/models/deepseek-llm-7b-chat tokenizer_path: ${actor_rollout_ref.model.path} override_config: { } external_lib: ${actor_rollout_ref.model.external_lib} enable_gradient_checkpointing: True use_remove_padding: False fsdp_config: param_offload: False optimizer_offload: False wrap_policy: # transformer_layer_cls_to_wrap: None min_num_params: 0 fsdp_size: -1 ppo_mini_batch_size: ${actor_rollout_ref.actor.ppo_mini_batch_size} ppo_micro_batch_size: null # will be deprecated, use ppo_micro_batch_size_per_gpu ppo_micro_batch_size_per_gpu: null forward_micro_batch_size: ${critic.ppo_micro_batch_size} forward_micro_batch_size_per_gpu: ${critic.ppo_micro_batch_size_per_gpu} use_dynamic_bsz: ${actor_rollout_ref.actor.use_dynamic_bsz} ppo_max_token_len_per_gpu: 32768 # (${actor_rollout_ref.actor.ppo_max_token_len_per_gpu}) * 2 forward_max_token_len_per_gpu: ${critic.ppo_max_token_len_per_gpu} ulysses_sequence_parallel_size: 1 # sp size ppo_epochs: ${actor_rollout_ref.actor.ppo_epochs} shuffle: ${actor_rollout_ref.actor.shuffle} grad_clip: 1.0 cliprange_value: 0.5 reward_model: enable: False strategy: fsdp model: input_tokenizer: ${actor_rollout_ref.model.path} # set this to null if the chat template is identical path: ~/models/FsfairX-LLaMA3-RM-v0.1 external_lib: ${actor_rollout_ref.model.external_lib} use_remove_padding: False fsdp_config: min_num_params: 0 param_offload: False fsdp_size: -1 micro_batch_size: null # will be deprecated, use micro_batch_size_per_gpu micro_batch_size_per_gpu: null # set a number max_length: null ulysses_sequence_parallel_size: 1 # sp size use_dynamic_bsz: ${critic.use_dynamic_bsz} forward_max_token_len_per_gpu: ${critic.forward_max_token_len_per_gpu} reward_manager: naive algorithm: gamma: 1.0 lam: 1.0 adv_estimator: gae kl_penalty: kl # how to estimate kl divergence kl_ctrl: type: fixed kl_coef: 0.001 trainer: total_epochs: 30 total_training_steps: null project_name: verl_examples experiment_name: gsm8k logger: [ 'console', 'wandb' ] val_generations_to_log_to_wandb: 0 nnodes: 1 n_gpus_per_node: 8 save_freq: -1 samples_save_path: null # auto: find the last ckpt to resume. If can't find, start from scratch resume_mode: auto # or auto or resume_path if resume_from_path: False test_freq: -1 critic_warmup: 0 default_hdfs_dir: null remove_previous_ckpt_in_save: False del_local_ckpt_after_load: False default_local_dir: checkpoints/${trainer.project_name}/${trainer.experiment_name} ================================================ FILE: verl/trainer/config/sft_trainer.yaml ================================================ data: train_batch_size: 256 micro_batch_size: null # will be deprecated, use micro_batch_size_per_gpu micro_batch_size_per_gpu: 4 # this is also val batch size train_files: ~/data/gsm8k/train.parquet val_files: ~/data/gsm8k/test.parquet prompt_key: question response_key: answer max_length: 1024 truncation: error balance_dp_token: False chat_template: null model: partial_pretrain: ~/models/gemma-1.1-7b-it fsdp_config: wrap_policy: min_num_params: 0 cpu_offload: False offload_params: False external_lib: null enable_gradient_checkpointing: False trust_remote_code: False lora_rank: 0 # Set to positive value to enable LoRA (e.g., 32) lora_alpha: 16 # LoRA scaling factor target_modules: all-linear # Target modules for LoRA adaptation use_liger: False optim: lr: 1e-5 betas: [0.9, 0.95] weight_decay: 0.01 warmup_steps_ratio: 0.1 clip_grad: 1.0 ulysses_sequence_parallel_size: 1 use_remove_padding: False trainer: default_local_dir: /tmp/sft_model default_hdfs_dir: hdfs://tmp/experiments/gsm8k/gemma-1.1-7b-it/ # change the hdfs path here resume_path: null project_name: gsm8k-sft experiment_name: test total_epochs: 4 total_training_steps: null logger: ['console'] seed: 1 ================================================ FILE: verl/trainer/fsdp_sft_trainer.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ A lightweight one-file FSDP SFT Trainer TODO(zhangchi.usc1992) - Add calculation of mfu - Add validation """ import os os.environ['NCCL_DEBUG'] = 'WARN' os.environ['TOKENIZERS_PARALLELISM'] = 'true' import logging import re from contextlib import nullcontext import torch import torch.distributed from torch import nn, optim from torch.distributed.fsdp import FullyShardedDataParallel as FSDP, MixedPrecision, ShardingStrategy, CPUOffload from tqdm import tqdm from transformers import AutoTokenizer, AutoModelForCausalLM, PreTrainedModel, AutoConfig from verl.utils.torch_functional import get_cosine_schedule_with_warmup from tensordict import TensorDict from torch.utils.data import DataLoader, DistributedSampler from flash_attn.bert_padding import pad_input, unpad_input, rearrange, index_first_axis from verl.utils.fsdp_utils import get_fsdp_wrap_policy, init_fn, get_init_weight_context_manager from verl.utils.dataset import SFTDataset from verl.utils.fs import copy_to_local from verl.utils.tracking import Tracking from verl.utils.ulysses import get_ulysses_sequence_parallel_world_size, set_ulysses_sequence_parallel_group from torch.distributed.device_mesh import DeviceMesh import verl.utils.hdfs_io as hdfs_io from verl.utils.debug import log_gpu_memory_usage from peft import LoraConfig, TaskType, get_peft_model from verl.workers.sharding_manager import FSDPUlyssesShardingManager from verl.utils.ulysses import ulysses_pad_and_slice_inputs, gather_outpus_and_unpad from verl import DataProto logger = logging.getLogger(__file__) logger.setLevel(os.getenv('VERL_SFT_LOGGING_LEVEL', 'WARN')) def extract_step(path): match = re.search(r'global_step_(\d+)', path) if match: return int(match.group(1)) return None def convert_to_regular_types(obj): """Convert Hydra configs and other special types to regular Python types.""" from omegaconf import ListConfig, DictConfig if isinstance(obj, (ListConfig, DictConfig)): return {k: convert_to_regular_types(v) for k, v in obj.items()} if isinstance(obj, DictConfig) else list(obj) elif isinstance(obj, (list, tuple)): return [convert_to_regular_types(x) for x in obj] elif isinstance(obj, dict): return {k: convert_to_regular_types(v) for k, v in obj.items()} return obj class FSDPSFTTrainer(object): def __init__(self, config, device_mesh: DeviceMesh, ulysses_device_mesh: DeviceMesh): self.config = config self.device_mesh = device_mesh self.ulysses_device_mesh = ulysses_device_mesh self.sharding_manager = FSDPUlyssesShardingManager(self.ulysses_device_mesh) # build tokenizer first local_model_path = copy_to_local(src=self.config.model.partial_pretrain, verbose=True) from verl.utils import hf_tokenizer self.tokenizer = hf_tokenizer(local_model_path, trust_remote_code=self.config.model.trust_remote_code) if self.config.data.chat_template is not None: raise ValueError('Apply Chat template from config is not supported yet.') # normalize dp size self._normalize_config_bsz() # Set sequence parallel size self.config.ulysses_sequence_parallel_size = getattr(self.config, 'ulysses_sequence_parallel_size', 1) self.use_remove_padding = getattr(self.config, 'use_remove_padding', False) if self.device_mesh.get_rank() == 0: print(f'Using sequence parallel size: {self.config.ulysses_sequence_parallel_size}') print(f'Using remove padding: {self.use_remove_padding}') self._build_dataloader() # build model self._build_model_optimizer() # TODO: add checkpoint manager if self.device_mesh.get_rank() == 0: print(self.config) def _normalize_config_bsz(self): dp_size = self.device_mesh.size(0) if not self.ulysses_device_mesh else self.ulysses_device_mesh.size(0) if self.device_mesh.get_rank() == 0: print(f'Normalize batch size by dp {dp_size}') assert self.config.data.train_batch_size % dp_size == 0, f"Global batch size {self.config.data.train_batch_size} is not divisible by dp size {dp_size}" self.config.data.train_batch_size //= dp_size assert self.config.data.train_batch_size % self.config.data.micro_batch_size_per_gpu == 0 def _build_dataloader(self): config = self.config # build dataset self.train_dataset = SFTDataset(parquet_files=config.data.train_files, tokenizer=self.tokenizer, prompt_key=config.data.prompt_key, prompt_dict_keys=config.data.get('prompt_dict_keys', None), response_key=config.data.response_key, response_dict_keys=config.data.get('response_dict_keys', None), max_length=config.data.max_length, truncation=config.data.truncation) self.val_dataset = SFTDataset(parquet_files=config.data.val_files, tokenizer=self.tokenizer, prompt_key=config.data.prompt_key, prompt_dict_keys=config.data.get('prompt_dict_keys', None), response_key=config.data.response_key, response_dict_keys=config.data.get('response_dict_keys', None), max_length=config.data.max_length, truncation=config.data.truncation) # build dataloader # Use data parallel rank and size instead of global rank and world size # If doing SP, we need to use the local rank and size if self.config.ulysses_sequence_parallel_size > 1: rank = self.ulysses_device_mesh.get_local_rank('dp') world_size = self.ulysses_device_mesh.size(0) if self.ulysses_device_mesh.get_rank() == 0: print(f'Using SP rank {rank} and size {world_size} for data distribution') print(f'Each SP rank gets different data, but the same data WITHIN the same rank') else: rank = self.device_mesh.get_rank() world_size = self.device_mesh.size() if self.device_mesh.get_rank() == 0: print(f'Using FSDP rank {rank} and size {world_size} for data distribution') self.train_sampler = DistributedSampler(self.train_dataset, shuffle=True, num_replicas=world_size, rank=rank, drop_last=True) self.train_dataloader = DataLoader(dataset=self.train_dataset, batch_size=config.data.train_batch_size, sampler=self.train_sampler, num_workers=8, pin_memory=True, drop_last=True) self.val_sampler = DistributedSampler(self.val_dataset, shuffle=False, num_replicas=world_size, rank=rank, drop_last=True) self.val_dataloader = DataLoader(dataset=self.val_dataset, batch_size=config.data.micro_batch_size_per_gpu, sampler=self.val_sampler, num_workers=8, pin_memory=True, drop_last=True) def _build_model_optimizer(self): # TODO (zhangchi.usc1992): # 1. support pretrain from random weights # 2. support init directly from sharded weights local_model_path = copy_to_local(src=self.config.model.partial_pretrain, verbose=True) if self.config.model.get('external_lib', None) is not None: # This is used to import external_lib into the huggingface systems import importlib importlib.import_module(self.config.model.external_lib) log_gpu_memory_usage('Before model allocation', logger=logger) trust_remote_code = self.config.model.trust_remote_code # load config first config = AutoConfig.from_pretrained(local_model_path, trust_remote_code=trust_remote_code) if self.config.ulysses_sequence_parallel_size > 1: assert self.use_remove_padding, "Sequence parallel is only supported when remove_padding is enabled" from verl.models.registry import check_model_support_rmpad check_model_support_rmpad(config.model_type) if self.use_remove_padding and self.config.ulysses_sequence_parallel_size > 1: from verl.models.transformers.monkey_patch import apply_monkey_patch apply_monkey_patch(config, verbose=True) # This may be very large init_context = get_init_weight_context_manager(use_meta_tensor=not config.tie_word_embeddings) with init_context(): self.model: PreTrainedModel = AutoModelForCausalLM.from_pretrained(local_model_path, config=config, torch_dtype=torch.float32, attn_implementation='flash_attention_2', trust_remote_code=trust_remote_code) # Apply Liger kernel if use_liger is enabled if self.config.model.get('use_liger', False): from liger_kernel.transformers.monkey_patch import _apply_liger_kernel_to_instance _apply_liger_kernel_to_instance(model=self.model) if self.config.model.get('lora_rank', 0) > 0: self.model.enable_input_require_grads() # Convert config to regular Python types before creating PEFT model lora_config = { 'task_type': TaskType.CAUSAL_LM, 'r': self.config.model.lora_rank, 'lora_alpha': self.config.model.lora_alpha, 'target_modules': convert_to_regular_types(self.config.model.target_modules), 'bias': "none" } self.model = get_peft_model(self.model, LoraConfig(**lora_config)) if self.config.model.enable_gradient_checkpointing: self.model.gradient_checkpointing_enable(gradient_checkpointing_kwargs={'use_reentrant': False}) log_gpu_memory_usage('After model allocation', logger=logger) mixed_precision = MixedPrecision(param_dtype=torch.bfloat16, reduce_dtype=torch.float32, buffer_dtype=torch.float32) auto_wrap_policy = get_fsdp_wrap_policy(self.model, config=self.config.model.fsdp_config.wrap_policy, is_lora=self.config.model.get('lora_rank', 0) > 0) if self.device_mesh.get_rank() == 0: print(auto_wrap_policy) if not self.config.model.fsdp_config.cpu_offload: cpu_offload = None else: cpu_offload = CPUOffload(offload_params=self.config.model.fsdp_config.offload_params) self.fsdp_model = FSDP(module=self.model, auto_wrap_policy=auto_wrap_policy, param_init_fn=init_fn, sharding_strategy=ShardingStrategy.FULL_SHARD, mixed_precision=mixed_precision, device_mesh=self.device_mesh, sync_module_states=True, device_id=torch.cuda.current_device(), cpu_offload=cpu_offload, use_orig_params=False) log_gpu_memory_usage('After FSDP wrapping', logger=logger) self.optimizer = optim.AdamW(self.fsdp_model.parameters(), lr=self.config.optim.lr, betas=self.config.optim.betas, weight_decay=self.config.optim.weight_decay) log_gpu_memory_usage('After initialize optimizer', logger=logger) self.steps_per_epoch = len(self.train_dataloader) self.total_steps = self.steps_per_epoch * self.config.trainer.total_epochs if self.device_mesh.get_rank() == 0: print( f'Number of steps/epoch {self.steps_per_epoch}, number of epochs {self.config.trainer.total_epochs}, total number of steps {self.total_steps}' ) num_warmup_steps = int(self.total_steps * self.config.optim.warmup_steps_ratio) self.lr_scheduler = get_cosine_schedule_with_warmup(optimizer=self.optimizer, num_warmup_steps=num_warmup_steps, num_training_steps=self.total_steps) def _compute_loss_and_backward(self, batch, do_backward=True): """Compute loss with optional sequence parallelism and remove padding features""" use_sp = self.use_remove_padding and self.config.ulysses_sequence_parallel_size > 1 # Move inputs to GPU and prepare loss mask input_ids = batch['input_ids'].cuda() attention_mask = batch['attention_mask'].cuda() position_ids = batch['position_ids'].cuda() loss_mask = batch.pop('loss_mask')[:, :-1].reshape(-1).cuda() loss_fct = nn.CrossEntropyLoss(reduction='none') # Context manager for sequence parallel if needed context = self.sharding_manager if use_sp else nullcontext() with context: with torch.autocast(device_type='cuda', dtype=torch.bfloat16): if not use_sp: # Standard forward pass without sequence parallel labels = input_ids[:, 1:].contiguous() output = self.fsdp_model(input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids, use_cache=False) logits = output.logits shift_logits = logits[..., :-1, :].contiguous() shift_labels = labels.contiguous() # Flatten the tokens shift_logits = shift_logits.view(-1, self.model.config.vocab_size) shift_labels = shift_labels.view(-1) # Enable model parallelism shift_labels = shift_labels.to(shift_logits.device) loss = loss_fct(shift_logits, shift_labels) loss = loss * loss_mask.to(loss.device) else: # IMPORTANT: We have a big assumption here, so we can shard the SAME sequence across SP ranks # i.e., each GPU has <1 sequence, and each SP group has 1 sequence # 1. All SP ranks will receive the *SAME* batch # 2. Different SP groups will receive *DIFFERENT* batches # This is implemented by the DistributedSampler batch_size, seqlen = input_ids.shape # Remove padding input_ids_rmpad, indices, *_ = unpad_input(input_ids.unsqueeze(-1), attention_mask) # input_ids_rmpad (total_nnz, ...) input_ids_rmpad = input_ids_rmpad.transpose(0, 1) # (1, total_nnz) # Unpad position_ids to align rotary position_ids_rmpad = index_first_axis(rearrange(position_ids.unsqueeze(-1), "b s ... -> (b s) ..."), indices).transpose(0, 1) # Pad and slice inputs for sequence parallelism input_ids_rmpad_sliced, position_ids_rmpad_padded, pad_size = ulysses_pad_and_slice_inputs( input_ids_rmpad, position_ids_rmpad, sp_size=get_ulysses_sequence_parallel_world_size()) # For computing loss input_ids_rmpad_rolled = torch.roll(input_ids_rmpad, shifts=-1, dims=1) # (1, total_nnz) input_ids_rmpad_rolled, _, _ = ulysses_pad_and_slice_inputs( input_ids_rmpad_rolled, None, get_ulysses_sequence_parallel_world_size()) input_ids_rmpad_rolled = input_ids_rmpad_rolled.squeeze(0) # ((total_nnz / sp) + pad) # Forward pass output = self.fsdp_model( input_ids=input_ids_rmpad_sliced, attention_mask=None, # Not needed with flash attention varlen position_ids=position_ids_rmpad_padded, use_cache=False) # Compute loss locally then aggregate logits_rmpad = output.logits.squeeze(0) input_ids_rmpad_rolled = input_ids_rmpad_rolled.to(logits_rmpad.device) loss = loss_fct(logits_rmpad, input_ids_rmpad_rolled) # Gather and unpad for sequence parallelism loss = gather_outpus_and_unpad(loss, gather_dim=0, unpad_dim=0, padding_size=pad_size) # This is the loss collected from all ulysses ranks full_loss = pad_input(hidden_states=loss.unsqueeze(-1), indices=indices, batch=batch_size, seqlen=seqlen) full_loss = full_loss.squeeze(-1)[:, :-1] # Remove last token's loss full_loss = full_loss.reshape(-1) loss_mask = loss_mask.to(full_loss.device) loss = full_loss * loss_mask valid_token_this_rank = torch.sum(loss_mask) if self.config.data.balance_dp_token: torch.distributed.all_reduce(valid_token_this_rank) dp_size = self.ulysses_device_mesh.size('dp') if use_sp else torch.distributed.get_world_size() else: dp_size = 1 loss = torch.sum(loss) / valid_token_this_rank * dp_size if do_backward: loss.backward() return loss def training_step(self, batch: TensorDict): self.fsdp_model.train() log_gpu_memory_usage('Before optimizer zero_grad', logger=logger) self.optimizer.zero_grad() log_gpu_memory_usage('After optimizer zero_grad', logger=logger) micro_batches = batch.split(self.config.data.micro_batch_size_per_gpu) n_micro_batches = len(micro_batches) step_loss = 0 for micro_batch in micro_batches: loss = self._compute_loss_and_backward(batch=micro_batch) / n_micro_batches step_loss += loss.item() self.fsdp_model.clip_grad_norm_(max_norm=self.config.optim.clip_grad) log_gpu_memory_usage('Before optimizer step', logger=logger) self.optimizer.step() log_gpu_memory_usage('After optimizer step', logger=logger) self.lr_scheduler.step() # reduce loss across dp ranks lr = self.lr_scheduler.get_last_lr()[0] log_gpu_memory_usage('After offload weights', logger=logger) step_loss = torch.tensor(step_loss).cuda() torch.distributed.all_reduce(step_loss, op=torch.distributed.ReduceOp.AVG) return {'train/loss': step_loss.detach().item(), 'train/lr(1e-3)': lr * 1e3} def validation_step(self, batch: TensorDict): self.fsdp_model.eval() with torch.no_grad(): loss = self._compute_loss_and_backward(batch, do_backward=False) torch.distributed.all_reduce(loss, op=torch.distributed.ReduceOp.AVG) return loss def save_checkpoint(self, step): # save checkpoint from torch.distributed.fsdp import FullStateDictConfig, StateDictType cfg = FullStateDictConfig(offload_to_cpu=True, rank0_only=True) with FSDP.state_dict_type(self.fsdp_model, StateDictType.FULL_STATE_DICT, cfg): state_dict = self.fsdp_model.state_dict() path = os.path.join(self.config.trainer.default_local_dir, f'global_step_{step}') # save huggingface model if self.device_mesh.get_rank() == 0: os.makedirs(path, exist_ok=True) self.model.save_pretrained(path, state_dict=state_dict) self.tokenizer.save_pretrained(path) if self.config.trainer.default_hdfs_dir: hdfs_io.makedirs(self.config.trainer.default_hdfs_dir, exist_ok=True) hdfs_io.copy(src=path, dst=self.config.trainer.default_hdfs_dir, dirs_exist_ok=True) torch.distributed.barrier() def fit(self): rank = self.device_mesh.get_rank() # TODO: add a unified tracking if rank == 0: tracking = Tracking(project_name=self.config.trainer.project_name, experiment_name=self.config.trainer.experiment_name, default_backend=self.config.trainer.logger) global_step = 0 # compute the total training steps. # the total training steps in SFT is mainly for early exit total_training_steps = len(self.train_dataloader) * self.config.trainer.total_epochs if self.config.trainer.total_training_steps is not None: total_training_steps = self.config.trainer.total_training_steps self.total_training_steps = total_training_steps print(f'Total training steps: {self.total_training_steps}') # TODO (zhangchi.usc1992) add back checkpoint manager. Currently, it blocks when uploading to hdfs. So very slow. for epoch in range(self.config.trainer.total_epochs): self.train_sampler.set_epoch(epoch=epoch) for data in tqdm(self.train_dataloader, total=self.steps_per_epoch, desc=f"Epoch {epoch+1}/{self.config.trainer.total_epochs}"): data = TensorDict(data, batch_size=self.config.data.train_batch_size).cuda() metric = self.training_step(data) if rank == 0: tracking.log(data=metric, step=global_step) global_step += 1 # for early exit validation if global_step >= self.total_training_steps: # Perform final validation val_losses = [] for val_data in self.val_dataloader: val_data = TensorDict(val_data, batch_size=self.config.data.micro_batch_size_per_gpu).cuda() val_loss = self.validation_step(val_data) val_losses.append(val_loss) if rank == 0: avg_val_loss = torch.mean(torch.stack(val_losses)) metric = {'val/loss': avg_val_loss.detach().item()} tracking.log(data=metric, step=global_step) torch.distributed.barrier() # Save final checkpoint self.save_checkpoint(step=global_step) return # validation val_losses = [] for data in self.val_dataloader: data = TensorDict(data, batch_size=self.config.data.micro_batch_size_per_gpu).cuda() val_loss = self.validation_step(data) val_losses.append(val_loss) if rank == 0: val_loss = torch.mean(torch.stack(val_losses)) metric = {'val/loss': val_loss.detach().item()} tracking.log(data=metric, step=global_step) torch.distributed.barrier() # save checkpoint self.save_checkpoint(step=global_step) from verl.trainer.fsdp_sft_trainer import FSDPSFTTrainer import hydra from torch.distributed.device_mesh import init_device_mesh from verl.utils.distributed import initialize_global_process_group @hydra.main(config_path='config', config_name='sft_trainer', version_base=None) def main(config): local_rank, rank, world_size = initialize_global_process_group() device_mesh = init_device_mesh(device_type='cuda', mesh_shape=(world_size,), mesh_dim_names=('fsdp',)) dp_size = world_size // config.ulysses_sequence_parallel_size ulysses_device_mesh = init_device_mesh(device_type='cuda', mesh_shape=(dp_size, config.ulysses_sequence_parallel_size), mesh_dim_names=('dp', 'sp')) trainer = FSDPSFTTrainer(config=config, device_mesh=device_mesh, ulysses_device_mesh=ulysses_device_mesh) trainer.fit() if __name__ == '__main__': main() ================================================ FILE: verl/trainer/main_eval.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Offline evaluate the performance of a generated file using reward model and ground truth verifier. The input is a parquet file that contains N generated sequences and (optional) the ground truth. """ import hydra from verl.utils.fs import copy_to_local from verl.utils.reward_score import math, gsm8k import pandas as pd import numpy as np def select_reward_fn(data_source): if data_source == 'lighteval/MATH': return math.compute_score else: raise NotImplementedError @hydra.main(config_path='config', config_name='evaluation', version_base=None) def main(config): local_path = copy_to_local(config.data.path) dataset = pd.read_parquet(local_path) prompts = dataset[config.data.prompt_key] responses = dataset[config.data.response_key] data_sources = dataset[config.data.data_source_key] reward_model_data = dataset[config.data.reward_model_key] passes = 0 total = len(dataset) for i in range(total): response_lst = responses[i] data_source = data_sources[i] # select reward score based on data_source prompt = prompts[i] reward_data = reward_model_data[i] reward_fn = select_reward_fn(data_source) ground_truth = reward_data['ground_truth'] score_lst = [] for r in response_lst: score = reward_fn(r, ground_truth) score_lst.append(score) max_score = np.max(score_lst) if max_score == 1: passes += 1 print(f'pass@5: {passes / total}') if __name__ == '__main__': main() ================================================ FILE: verl/trainer/main_generation.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Generate responses given a dataset of prompts """ import ray import numpy as np import hydra import os os.environ['NCCL_DEBUG'] = 'WARN' os.environ['TOKENIZERS_PARALLELISM'] = 'true' # os.environ['TORCH_COMPILE_DISABLE'] = '1' from verl.utils.model import compute_position_id_with_mask import pandas as pd from transformers import AutoTokenizer from verl import DataProto from verl.utils.fs import copy_to_local from verl.workers.fsdp_workers import ActorRolloutRefWorker from verl.utils.hdfs_io import makedirs from verl.single_controller.ray import RayClassWithInitArgs, RayResourcePool, RayWorkerGroup @hydra.main(config_path='config', config_name='generation', version_base=None) def main(config): from pprint import pprint from omegaconf import OmegaConf pprint(OmegaConf.to_container(config, resolve=True)) # resolve=True will eval symbol values OmegaConf.resolve(config) local_path = copy_to_local(config.model.path) from verl.utils import hf_tokenizer tokenizer = hf_tokenizer(local_path) if config.rollout.temperature == 0.: assert config.data.n_samples == 1, 'When temperature=0, n_samples must be 1.' # read dataset. Note that the dataset should directly contain chat template format (e.g., a list of dictionary) dataset = pd.read_parquet(config.data.path) chat_lst = dataset[config.data.prompt_key].tolist() chat_lst = [chat.tolist() for chat in chat_lst] tokenizer.padding_side = 'left' if tokenizer.pad_token is None: tokenizer.pad_token = tokenizer.eos_token ray_cls_with_init = RayClassWithInitArgs(cls=ray.remote(ActorRolloutRefWorker), config=config, role='actor_rollout') resource_pool = RayResourcePool(process_on_nodes=[config.trainer.n_gpus_per_node] * config.trainer.nnodes) wg = RayWorkerGroup(resource_pool=resource_pool, ray_cls_with_init=ray_cls_with_init) wg.init_model() total_samples = len(dataset) # real_batch_size = data.batch['input_ids'].shape[0] config_batch_size = config.data.batch_size dp_size = wg.world_size // config.rollout.tensor_model_parallel_size num_batch = (total_samples // config_batch_size) + 1 output_lst = [[] for _ in range(config.data.n_samples)] for batch_idx in range(num_batch): print(f'[{batch_idx+1}/{num_batch}] Start to process.') batch_chat_lst = chat_lst[batch_idx * config_batch_size:(batch_idx + 1) * config_batch_size] inputs = tokenizer.apply_chat_template(batch_chat_lst, add_generation_prompt=True, padding=True, truncation=True, max_length=config.rollout.prompt_length, return_tensors='pt', return_dict=True, tokenize=True) input_ids = inputs['input_ids'] attention_mask = inputs['attention_mask'] position_ids = compute_position_id_with_mask(attention_mask) batch_dict = {'input_ids': input_ids, 'attention_mask': attention_mask, 'position_ids': position_ids} data = DataProto.from_dict(batch_dict) real_batch_size = data.batch['input_ids'].shape[0] if real_batch_size % dp_size != 0: dummy_data_size = dp_size - real_batch_size % dp_size dummy_data = data[:dummy_data_size] data = DataProto.concat([data, dummy_data]) print( f'dp_size {dp_size} is not divisible by real_batch_size {real_batch_size}, add {dummy_data_size} dummy data' ) batch_size = data.batch['input_ids'].shape[0] assert batch_size % dp_size == 0, f'batch_size {batch_size} is not divisible by dp_size {dp_size}' print(f'[{batch_idx+1}/{num_batch}] Start to generate.') # START TO GENERATE FOR n_samples TIMES for i in range(config.data.n_samples): output = wg.generate_sequences(data) # remove dummy data output = output[:real_batch_size] output_text = tokenizer.batch_decode(output.batch['input_ids'][:, -config.rollout.response_length:], skip_special_tokens=False) # remove the padding pad_token = tokenizer.pad_token output_text_unpad = [] for text in output_text: output_text_unpad.append(text.replace(pad_token, '')) output_lst[i].extend(output_text_unpad) # convert output_lst from (n_samples, n_data) to (n_data, n_sampels) output_lst = np.array(output_lst, dtype=object) output_lst = np.transpose(output_lst, axes=(1, 0)).tolist() # add to the data frame dataset[f'responses'] = output_lst # write to a new parquet output_dir = os.path.dirname(config.data.output_path) makedirs(output_dir, exist_ok=True) dataset.to_parquet(config.data.output_path) return output_text if __name__ == '__main__': main() ================================================ FILE: verl/trainer/main_ppo.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Note that we don't combine the main with ray_trainer as ray_trainer is used by other main. """ from verl.trainer.ppo.ray_trainer import RayPPOTrainer import ray import hydra @hydra.main(config_path='config', config_name='ppo_trainer', version_base=None) def main(config): run_ppo(config) def run_ppo(config, compute_score=None): if not ray.is_initialized(): # this is for local ray cluster ray.init(runtime_env={'env_vars': {'TOKENIZERS_PARALLELISM': 'true', 'NCCL_DEBUG': 'WARN'}}) ray.get(main_task.remote(config, compute_score)) @ray.remote(num_cpus=1) # please make sure main_task is not scheduled on head def main_task(config, compute_score=None): from verl.utils.fs import copy_to_local # print initial config from pprint import pprint from omegaconf import OmegaConf pprint(OmegaConf.to_container(config, resolve=True)) # resolve=True will eval symbol values OmegaConf.resolve(config) # download the checkpoint from hdfs local_path = copy_to_local(config.actor_rollout_ref.model.path) # instantiate tokenizer from verl.utils import hf_tokenizer, hf_processor tokenizer = hf_tokenizer(local_path) processor = hf_processor(local_path, use_fast=True) # used for multimodal LLM, could be none # define worker classes if config.actor_rollout_ref.actor.strategy == 'fsdp': assert config.actor_rollout_ref.actor.strategy == config.critic.strategy from verl.workers.fsdp_workers import ActorRolloutRefWorker, CriticWorker from verl.single_controller.ray import RayWorkerGroup ray_worker_group_cls = RayWorkerGroup elif config.actor_rollout_ref.actor.strategy == 'megatron': assert config.actor_rollout_ref.actor.strategy == config.critic.strategy from verl.workers.megatron_workers import ActorRolloutRefWorker, CriticWorker from verl.single_controller.ray.megatron import NVMegatronRayWorkerGroup ray_worker_group_cls = NVMegatronRayWorkerGroup else: raise NotImplementedError from verl.trainer.ppo.ray_trainer import ResourcePoolManager, Role role_worker_mapping = { Role.ActorRollout: ray.remote(ActorRolloutRefWorker), Role.Critic: ray.remote(CriticWorker), } if not (config.actor_rollout_ref.actor.kl_loss_coef==0 and config.algorithm.kl_ctrl.kl_coef==0): role_worker_mapping[Role.RefPolicy] = ray.remote(ActorRolloutRefWorker) else: config.actor_rollout_ref.actor.use_kl_loss=False global_pool_id = 'global_pool' resource_pool_spec = { global_pool_id: [config.trainer.n_gpus_per_node] * config.trainer.nnodes, } mapping = { Role.ActorRollout: global_pool_id, Role.Critic: global_pool_id, Role.RefPolicy: global_pool_id, } # we should adopt a multi-source reward function here # - for rule-based rm, we directly call a reward score # - for model-based rm, we call a model # - for code related prompt, we send to a sandbox if there are test cases # - finally, we combine all the rewards together # - The reward type depends on the tag of the data if config.reward_model.enable: if config.reward_model.strategy == 'fsdp': from verl.workers.fsdp_workers import RewardModelWorker elif config.reward_model.strategy == 'megatron': from verl.workers.megatron_workers import RewardModelWorker else: raise NotImplementedError role_worker_mapping[Role.RewardModel] = ray.remote(RewardModelWorker) mapping[Role.RewardModel] = global_pool_id reward_manager_name = config.reward_model.get("reward_manager", "naive") if reward_manager_name == 'naive': from verl.workers.reward_manager import NaiveRewardManager reward_manager_cls = NaiveRewardManager elif reward_manager_name == 'prime': from verl.workers.reward_manager import PrimeRewardManager reward_manager_cls = PrimeRewardManager else: raise NotImplementedError reward_fn = reward_manager_cls(config=config, tokenizer=tokenizer, num_examine=0, compute_score=compute_score) # Note that we always use function-based RM for validation val_reward_fn = reward_manager_cls(config=config, tokenizer=tokenizer, num_examine=1, compute_score=compute_score) resource_pool_manager = ResourcePoolManager(resource_pool_spec=resource_pool_spec, mapping=mapping) trainer = RayPPOTrainer(config=config, tokenizer=tokenizer, processor=processor, role_worker_mapping=role_worker_mapping, resource_pool_manager=resource_pool_manager, ray_worker_group_cls=ray_worker_group_cls, reward_fn=reward_fn, val_reward_fn=val_reward_fn) trainer.init_workers() trainer.fit() if __name__ == '__main__': main() ================================================ FILE: verl/trainer/ppo/__init__.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. ================================================ FILE: verl/trainer/ppo/core_algos.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Copyright 2022 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Core functions to implement PPO algorithms. The function implemented in this file should be used by trainer with different distributed strategies to implement PPO """ import numpy as np import torch from collections import defaultdict import verl.utils.torch_functional as verl_F class AdaptiveKLController: """ Adaptive KL controller described in the paper: https://arxiv.org/pdf/1909.08593.pdf """ def __init__(self, init_kl_coef, target_kl, horizon): self.value = init_kl_coef self.target = target_kl self.horizon = horizon def update(self, current_kl, n_steps): target = self.target proportional_error = np.clip(current_kl / target - 1, -0.2, 0.2) mult = 1 + proportional_error * n_steps / self.horizon self.value *= mult class FixedKLController: """Fixed KL controller.""" def __init__(self, kl_coef): self.value = kl_coef def update(self, current_kl, n_steps): pass def get_kl_controller(config): if config.critic.kl_ctrl.type == 'fixed': kl_ctrl = FixedKLController(kl_coef=config.critic.kl_ctrl.kl_coef) elif config.critic.kl_ctrl.type == 'adaptive': assert config.kl_ctrl.horizon > 0, f'horizon must be larger than 0. Got {config.critic.kl_ctrl.horizon}' kl_ctrl = AdaptiveKLController(init_kl_coef=config.critic.kl_ctrl.kl_coef, target_kl=config.critic.kl_ctrl.target_kl, horizon=config.critic.kl_ctrl.horizon) else: raise ValueError('Unknown kl_ctrl type') return kl_ctrl def compute_gae_advantage_return(token_level_rewards: torch.Tensor, values: torch.Tensor, eos_mask: torch.Tensor, gamma: torch.Tensor, lam: torch.Tensor): """Adapted from https://github.com/huggingface/trl/blob/main/trl/trainer/ppo_trainer.py Args: token_level_rewards: `(torch.Tensor)` shape: (bs, response_length) values: `(torch.Tensor)` shape: (bs, response_length) eos_mask: `(torch.Tensor)` shape: (bs, response_length). [EOS] mask. The token after [EOS] have mask zero. gamma: `(float)` discounted factor used in RL lam: `(float)` lambda value when computing Generalized Advantage Estimation (https://arxiv.org/abs/1506.02438) Returns: advantages: `(torch.Tensor)` shape: (bs, response_length) Returns: `(torch.Tensor)` shape: (bs, response_length) """ with torch.no_grad(): lastgaelam = 0 advantages_reversed = [] gen_len = token_level_rewards.shape[-1] for t in reversed(range(gen_len)): nextvalues = values[:, t + 1] if t < gen_len - 1 else 0.0 delta = token_level_rewards[:, t] + gamma * nextvalues - values[:, t] lastgaelam = delta + gamma * lam * lastgaelam advantages_reversed.append(lastgaelam) advantages = torch.stack(advantages_reversed[::-1], dim=1) returns = advantages + values advantages = verl_F.masked_whiten(advantages, eos_mask) return advantages, returns # NOTE(sgm): this implementation only consider outcome supervision, where the reward is a scalar. def compute_grpo_outcome_advantage(token_level_rewards: torch.Tensor, eos_mask: torch.Tensor, index: torch.Tensor, epsilon: float = 1e-6): """ Compute advantage for GRPO, operating only on Outcome reward (with only one scalar reward for each response). Args: token_level_rewards: `(torch.Tensor)` shape: (bs, response_length) eos_mask: `(torch.Tensor)` shape: (bs, response_length) Returns: advantages: `(torch.Tensor)` shape: (bs, response_length) Returns: `(torch.Tensor)` shape: (bs, response_length) """ response_length = token_level_rewards.shape[-1] scores = token_level_rewards.sum(dim=-1) id2score = defaultdict(list) id2mean = {} id2std = {} with torch.no_grad(): bsz = scores.shape[0] for i in range(bsz): id2score[index[i]].append(scores[i]) for idx in id2score: if len(id2score[idx]) == 1: id2mean[idx] = torch.tensor(0.0) id2std[idx] = torch.tensor(1.0) elif len(id2score[idx]) > 1: id2mean[idx] = torch.mean(torch.tensor(id2score[idx])) id2std[idx] = torch.std(torch.tensor([id2score[idx]])) else: raise ValueError(f"no score in prompt index: {idx}") for i in range(bsz): scores[i] = (scores[i] - id2mean[index[i]]) / (id2std[index[i]] + epsilon) scores = scores.unsqueeze(-1).tile([1, response_length]) * eos_mask return scores, scores def compute_rloo_outcome_advantage(token_level_rewards: torch.Tensor, eos_mask: torch.Tensor, index: torch.Tensor, epsilon: float = 1e-6): """ Compute advantage for RLOO based on https://arxiv.org/abs/2402.14740 Args: token_level_rewards: `(torch.Tensor)` shape: (bs, response_length) eos_mask: `(torch.Tensor)` shape: (bs, response_length) Returns: advantages: `(torch.Tensor)` shape: (bs, response_length) Returns: `(torch.Tensor)` shape: (bs, response_length) """ response_length = token_level_rewards.shape[-1] scores = token_level_rewards.sum(dim=-1) id2score = defaultdict(list) id2mean = {} with torch.no_grad(): bsz = scores.shape[0] for i in range(bsz): id2score[index[i]].append(scores[i]) for idx in id2score: if len(id2score[idx]) == 1: id2mean[idx] = torch.tensor(0.0) elif len(id2score[idx]) > 1: id2mean[idx] = torch.mean(torch.tensor(id2score[idx])) else: raise ValueError(f"no score in prompt index: {idx}") for i in range(bsz): response_num = len(id2score[index[i]]) if response_num > 1: scores[i] = scores[i] * response_num / (response_num - 1) - id2mean[index[i]] * response_num / (response_num - 1) scores = scores.unsqueeze(-1).tile([1, response_length]) * eos_mask return scores, scores def compute_reinforce_plus_plus_outcome_advantage(token_level_rewards: torch.Tensor, eos_mask: torch.Tensor, gamma: torch.Tensor): """ Compute advantage for REINFORCE++. This implementation is based on the paper: https://arxiv.org/abs/2501.03262 Args: token_level_rewards: `(torch.Tensor)` shape: (bs, response_length) eos_mask: `(torch.Tensor)` shape: (bs, response_length) Returns: advantages: `(torch.Tensor)` shape: (bs, response_length) Returns: `(torch.Tensor)` shape: (bs, response_length) """ with torch.no_grad(): returns = torch.zeros_like(token_level_rewards) running_return = 0 for t in reversed(range(token_level_rewards.shape[1])): running_return = token_level_rewards[:, t] + gamma * running_return returns[:, t] = running_return # Reset after EOS running_return = running_return * eos_mask[:, t] advantages = verl_F.masked_whiten(returns, eos_mask) advantages = advantages * eos_mask return advantages, returns def compute_remax_outcome_advantage(token_level_rewards: torch.Tensor, reward_baselines: torch.Tensor, eos_mask: torch.Tensor): """ Compute advantage for ReMax, operating only on Outcome reward This implementation is based on the paper: https://arxiv.org/abs/2310.10505 (with only one scalar reward for each response). Args: token_level_rewards: `(torch.Tensor)` shape: (bs, response_length) reward_baselines: `(torch.Tensor)` shape: (bs,) eos_mask: `(torch.Tensor)` shape: (bs, response_length) Returns: advantages: `(torch.Tensor)` shape: (bs, response_length) Returns: `(torch.Tensor)` shape: (bs, response_length) """ response_length = token_level_rewards.shape[-1] scores = token_level_rewards.sum(dim=-1) with torch.no_grad(): returns = (token_level_rewards * eos_mask).flip(dims=[-1]).cumsum(dim=-1).flip(dims=[-1]) advantages = returns - reward_baselines.unsqueeze(-1).tile([1, response_length]) * eos_mask return advantages, returns def compute_rewards(token_level_scores, old_log_prob, ref_log_prob, kl_ratio): kl = old_log_prob - ref_log_prob return token_level_scores - kl * kl_ratio def compute_policy_loss(old_log_prob, log_prob, advantages, eos_mask, cliprange): """Adapted from https://github.com/huggingface/trl/blob/main/trl/trainer/ppo_trainer.py#L1122 Args: old_log_prob: `(torch.Tensor)` shape: (bs, response_length) log_prob: `(torch.Tensor)` shape: (bs, response_length) advantages: `(torch.Tensor)` shape: (bs, response_length) eos_mask: `(torch.Tensor)` shape: (bs, response_length) cliprange: (float) The clip range used in PPO. See https://arxiv.org/abs/1707.06347 Returns: pg_loss: `a scalar torch.Tensor` policy gradient loss computed via PPO pg_clipfrac: (float) a float number indicating the fraction of policy gradient loss being clipped """ negative_approx_kl = log_prob - old_log_prob ratio = torch.exp(negative_approx_kl) ppo_kl = verl_F.masked_mean(-negative_approx_kl, eos_mask) pg_losses = -advantages * ratio pg_losses2 = -advantages * torch.clamp(ratio, 1.0 - cliprange, 1.0 + cliprange) pg_loss = verl_F.masked_mean(torch.max(pg_losses, pg_losses2), eos_mask) pg_clipfrac = verl_F.masked_mean(torch.gt(pg_losses2, pg_losses).float(), eos_mask) return pg_loss, pg_clipfrac, ppo_kl def compute_entropy_loss(logits, eos_mask): """Compute Categorical entropy loss Args: logits: `(torch.Tensor)` shape: (bs, response_length, vocab_size) eos_mask: `(torch.Tensor)` shape: (bs, response_length) Returns: entropy: a scalar torch.Tensor """ # compute entropy entropy = verl_F.entropy_from_logits(logits) # (bs, response_len) entropy_loss = verl_F.masked_mean(entropy, mask=eos_mask) return entropy_loss def compute_value_loss(vpreds, returns, values, eos_mask, cliprange_value): """Compute the value loss. Copied from https://github.com/huggingface/trl/blob/main/trl/trainer/ppo_trainer.py#L1151 Args: vpreds (`torch.FloatTensor`): Predicted values of the value head, shape (`batch_size`, `response_length`) values (`torch.FloatTensor`): Old values of value head, shape (`batch_size`, `response_length`) returns: (`torch.FloatTensor`): Ground truth returns, shape (`batch_size`, `response_length`) Returns: vf_loss: a scalar (`torch.FloatTensor`): value function loss vf_clipfrac: a float The ratio of vf being clipped """ vpredclipped = verl_F.clip_by_value(vpreds, values - cliprange_value, values + cliprange_value) vf_losses1 = (vpreds - returns)**2 vf_losses2 = (vpredclipped - returns)**2 vf_loss = 0.5 * verl_F.masked_mean(torch.max(vf_losses1, vf_losses2), eos_mask) vf_clipfrac = verl_F.masked_mean(torch.gt(vf_losses2, vf_losses1).float(), eos_mask) return vf_loss, vf_clipfrac def kl_penalty(logprob: torch.FloatTensor, ref_logprob: torch.FloatTensor, kl_penalty) -> torch.FloatTensor: """Compute KL divergence given logprob and ref_logprob. Copied from https://github.com/huggingface/trl/blob/main/trl/trainer/ppo_trainer.py#L1104 Args: logprob: ref_logprob: Returns: """ if kl_penalty == "kl": return logprob - ref_logprob if kl_penalty == "abs": return (logprob - ref_logprob).abs() if kl_penalty == "mse": return 0.5 * (logprob - ref_logprob).square() # J. Schulman. Approximating kl divergence, 2020. # # URL http://joschu.net/blog/kl-approx.html. if kl_penalty == 'low_var_kl': kl = ref_logprob - logprob ratio = torch.exp(kl) kld = (ratio - kl - 1).contiguous() return torch.clamp(kld, min=-10, max=10) if kl_penalty == "full": # so, here logprob and ref_logprob should contain the logits for every token in vocabulary raise NotImplementedError raise NotImplementedError ================================================ FILE: verl/trainer/ppo/ray_trainer.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ FSDP PPO Trainer with Ray-based single controller. This trainer supports model-agonistic model initialization with huggingface """ import os import uuid from contextlib import contextmanager from dataclasses import dataclass, field from enum import Enum from pprint import pprint from typing import Type, Dict from copy import deepcopy import json import numpy as np from codetiming import Timer from omegaconf import OmegaConf, open_dict from verl import DataProto from verl.protocol import pad_dataproto_to_divisor, unpad_dataproto from verl.single_controller.base import Worker from verl.single_controller.ray import RayResourcePool, RayWorkerGroup, RayClassWithInitArgs from verl.single_controller.ray.base import create_colocated_worker_cls from verl.trainer.ppo import core_algos from verl.utils.seqlen_balancing import get_seqlen_balanced_partitions, log_seqlen_unbalance from verl.utils.checkpoint.checkpoint_manager import find_latest_ckpt_path from verl.utils.dataset.rl_dataset import RLHFDataset, collate_fn from torch.utils.data import RandomSampler, SequentialSampler from torchdata.stateful_dataloader import StatefulDataLoader WorkerType = Type[Worker] class Role(Enum): """ To create more roles dynamically, you can subclass Role and add new members """ Actor = 0 Rollout = 1 ActorRollout = 2 Critic = 3 RefPolicy = 4 RewardModel = 5 ActorRolloutRef = 6 class AdvantageEstimator(str, Enum): """ Using an enumeration class to avoid spelling errors in adv_estimator """ GAE = 'gae' GRPO = 'grpo' REINFORCE_PLUS_PLUS = 'reinforce_plus_plus' REMAX = 'remax' RLOO = 'rloo' @dataclass class ResourcePoolManager: """ Define a resource pool specification. Resource pool will be initialized first. Mapping """ resource_pool_spec: dict[str, list[int]] mapping: dict[Role, str] resource_pool_dict: dict[str, RayResourcePool] = field(default_factory=dict) def create_resource_pool(self): for resource_pool_name, process_on_nodes in self.resource_pool_spec.items(): # max_colocate_count means the number of WorkerGroups (i.e. processes) in each RayResourcePool # For FSDP backend, we recommend using max_colocate_count=1 that merge all WorkerGroups into one. # For Megatron backend, we recommend using max_colocate_count>1 that can utilize different WorkerGroup for differnt models resource_pool = RayResourcePool(process_on_nodes=process_on_nodes, use_gpu=True, max_colocate_count=1, name_prefix=resource_pool_name) self.resource_pool_dict[resource_pool_name] = resource_pool def get_resource_pool(self, role: Role) -> RayResourcePool: """Get the resource pool of the worker_cls""" return self.resource_pool_dict[self.mapping[role]] import torch from verl.utils.torch_functional import masked_mean def apply_kl_penalty(data: DataProto, kl_ctrl: core_algos.AdaptiveKLController, kl_penalty='kl'): responses = data.batch['responses'] response_length = responses.size(1) token_level_scores = data.batch['token_level_scores'] batch_size = data.batch.batch_size[0] attention_mask = data.batch['attention_mask'] response_mask = attention_mask[:, -response_length:] # compute kl between ref_policy and current policy if 'ref_log_prob' in data.batch.keys(): kld = core_algos.kl_penalty(data.batch['old_log_probs'], data.batch['ref_log_prob'], kl_penalty=kl_penalty) # (batch_size, response_length) kld = kld * response_mask beta = kl_ctrl.value else: beta = 0 kld = torch.zeros_like(response_mask, dtype=torch.float32) token_level_rewards = token_level_scores - beta * kld current_kl = masked_mean(kld, mask=response_mask, axis=-1) # average over sequence current_kl = torch.mean(current_kl, dim=0).item() # according to https://github.com/huggingface/trl/blob/951ca1841f29114b969b57b26c7d3e80a39f75a0/trl/trainer/ppo_trainer.py#L837 kl_ctrl.update(current_kl=current_kl, n_steps=batch_size) data.batch['token_level_rewards'] = token_level_rewards metrics = {'critic/kl': current_kl, 'critic/kl_coeff': beta} return data, metrics def compute_advantage(data: DataProto, adv_estimator, gamma=1.0, lam=1.0, num_repeat=1): # prepare response group # TODO: add other ways to estimate advantages if adv_estimator == AdvantageEstimator.GAE: values = data.batch['values'] responses = data.batch['responses'] response_length = responses.size(-1) attention_mask = data.batch['attention_mask'] response_mask = attention_mask[:, -response_length:] token_level_rewards = data.batch['token_level_rewards'] advantages, returns = core_algos.compute_gae_advantage_return(token_level_rewards=token_level_rewards, values=values, eos_mask=response_mask, gamma=gamma, lam=lam) data.batch['advantages'] = advantages data.batch['returns'] = returns elif adv_estimator == AdvantageEstimator.GRPO: token_level_rewards = data.batch['token_level_rewards'] index = data.non_tensor_batch['uid'] responses = data.batch['responses'] response_length = responses.size(-1) attention_mask = data.batch['attention_mask'] response_mask = attention_mask[:, -response_length:] advantages, returns = core_algos.compute_grpo_outcome_advantage(token_level_rewards=token_level_rewards, eos_mask=response_mask, index=index) data.batch['advantages'] = advantages data.batch['returns'] = returns elif adv_estimator == AdvantageEstimator.REINFORCE_PLUS_PLUS: token_level_rewards = data.batch['token_level_rewards'] responses = data.batch['responses'] response_length = responses.size(-1) attention_mask = data.batch['attention_mask'] response_mask = attention_mask[:, -response_length:] advantages, returns = core_algos.compute_reinforce_plus_plus_outcome_advantage( token_level_rewards=token_level_rewards, eos_mask=response_mask, gamma=gamma) data.batch['advantages'] = advantages data.batch['returns'] = returns elif adv_estimator == AdvantageEstimator.REMAX: token_level_rewards = data.batch['token_level_rewards'] index = data.non_tensor_batch['uid'] responses = data.batch['responses'] response_length = responses.size(-1) attention_mask = data.batch['attention_mask'] response_mask = attention_mask[:, -response_length:] reward_baselines = data.batch['reward_baselines'] advantages, returns = core_algos.compute_remax_outcome_advantage(token_level_rewards=token_level_rewards, reward_baselines=reward_baselines, eos_mask=response_mask) data.batch['advantages'] = advantages data.batch['returns'] = returns elif adv_estimator == AdvantageEstimator.RLOO: token_level_rewards = data.batch['token_level_rewards'] index = data.non_tensor_batch['uid'] responses = data.batch['responses'] response_length = responses.size(-1) attention_mask = data.batch['attention_mask'] response_mask = attention_mask[:, -response_length:] advantages, returns = core_algos.compute_rloo_outcome_advantage(token_level_rewards=token_level_rewards, eos_mask=response_mask, index=index) data.batch['advantages'] = advantages data.batch['returns'] = returns else: raise NotImplementedError return data def reduce_metrics(metrics: dict): for key, val in metrics.items(): metrics[key] = np.mean(val) return metrics def _compute_response_info(batch): response_length = batch.batch['responses'].shape[-1] prompt_mask = batch.batch['attention_mask'][:, :-response_length] response_mask = batch.batch['attention_mask'][:, -response_length:] prompt_length = prompt_mask.sum(-1).float() response_length = response_mask.sum(-1).float() # (batch_size,) return dict( response_mask=response_mask, prompt_length=prompt_length, response_length=response_length, ) def compute_data_metrics(batch, use_critic=True): # TODO: add response length sequence_score = batch.batch['token_level_scores'].sum(-1) sequence_reward = batch.batch['token_level_rewards'].sum(-1) advantages = batch.batch['advantages'] returns = batch.batch['returns'] max_response_length = batch.batch['responses'].shape[-1] prompt_mask = batch.batch['attention_mask'][:, :-max_response_length].bool() response_mask = batch.batch['attention_mask'][:, -max_response_length:].bool() max_prompt_length = prompt_mask.size(-1) response_info = _compute_response_info(batch) prompt_length = response_info['prompt_length'] response_length = response_info['response_length'] valid_adv = torch.masked_select(advantages, response_mask) valid_returns = torch.masked_select(returns, response_mask) if use_critic: values = batch.batch['values'] valid_values = torch.masked_select(values, response_mask) return_diff_var = torch.var(valid_returns - valid_values) return_var = torch.var(valid_returns) metrics = { # score 'critic/score/mean': torch.mean(sequence_score).detach().item(), 'critic/score/max': torch.max(sequence_score).detach().item(), 'critic/score/min': torch.min(sequence_score).detach().item(), # reward 'critic/rewards/mean': torch.mean(sequence_reward).detach().item(), 'critic/rewards/max': torch.max(sequence_reward).detach().item(), 'critic/rewards/min': torch.min(sequence_reward).detach().item(), # adv 'critic/advantages/mean': torch.mean(valid_adv).detach().item(), 'critic/advantages/max': torch.max(valid_adv).detach().item(), 'critic/advantages/min': torch.min(valid_adv).detach().item(), # returns 'critic/returns/mean': torch.mean(valid_returns).detach().item(), 'critic/returns/max': torch.max(valid_returns).detach().item(), 'critic/returns/min': torch.min(valid_returns).detach().item(), **({ # values 'critic/values/mean': torch.mean(valid_values).detach().item(), 'critic/values/max': torch.max(valid_values).detach().item(), 'critic/values/min': torch.min(valid_values).detach().item(), # vf explained var 'critic/vf_explained_var': (1.0 - return_diff_var / (return_var + 1e-5)).detach().item(), } if use_critic else {}), # response length 'response_length/mean': torch.mean(response_length).detach().item(), 'response_length/max': torch.max(response_length).detach().item(), 'response_length/min': torch.min(response_length).detach().item(), 'response_length/clip_ratio': torch.mean(torch.eq(response_length, max_response_length).float()).detach().item(), # prompt length 'prompt_length/mean': torch.mean(prompt_length).detach().item(), 'prompt_length/max': torch.max(prompt_length).detach().item(), 'prompt_length/min': torch.min(prompt_length).detach().item(), 'prompt_length/clip_ratio': torch.mean(torch.eq(prompt_length, max_prompt_length).float()).detach().item(), } return metrics def compute_timing_metrics(batch, timing_raw): response_info = _compute_response_info(batch) num_prompt_tokens = torch.sum(response_info['prompt_length']).item() num_response_tokens = torch.sum(response_info['response_length']).item() num_overall_tokens = num_prompt_tokens + num_response_tokens num_tokens_of_section = { 'gen': num_response_tokens, **{ name: num_overall_tokens for name in ['ref', 'values', 'adv', 'update_critic', 'update_actor'] }, } return { **{ f'timing_s/{name}': value for name, value in timing_raw.items() }, **{ f'timing_per_token_ms/{name}': timing_raw[name] * 1000 / num_tokens_of_section[name] for name in set(num_tokens_of_section.keys( )) & set(timing_raw.keys()) }, } @contextmanager def _timer(name: str, timing_raw: Dict[str, float]): with Timer(name=name, logger=None) as timer: yield timing_raw[name] = timer.last class RayPPOTrainer(object): """ Note that this trainer runs on the driver process on a single CPU/GPU node. """ # TODO: support each role have individual ray_worker_group_cls, # i.e., support different backend of different role def __init__(self, config, tokenizer, role_worker_mapping: dict[Role, WorkerType], resource_pool_manager: ResourcePoolManager, ray_worker_group_cls: RayWorkerGroup = RayWorkerGroup, processor=None, reward_fn=None, val_reward_fn=None): # assert torch.cuda.is_available(), 'cuda must be available on driver' self.tokenizer = tokenizer self.processor = processor self.config = config self.reward_fn = reward_fn self.val_reward_fn = val_reward_fn self.hybrid_engine = config.actor_rollout_ref.hybrid_engine assert self.hybrid_engine, 'Currently, only support hybrid engine' if self.hybrid_engine: assert Role.ActorRollout in role_worker_mapping, f'{role_worker_mapping.keys()=}' self.role_worker_mapping = role_worker_mapping self.resource_pool_manager = resource_pool_manager self.use_reference_policy = Role.RefPolicy in role_worker_mapping self.use_rm = Role.RewardModel in role_worker_mapping self.ray_worker_group_cls = ray_worker_group_cls # define KL control if self.use_reference_policy: if config.algorithm.kl_ctrl.type == 'fixed': self.kl_ctrl = core_algos.FixedKLController(kl_coef=config.algorithm.kl_ctrl.kl_coef) elif config.algorithm.kl_ctrl.type == 'adaptive': assert config.algorithm.kl_ctrl.horizon > 0, f'horizon must be larger than 0. Got {config.critic.kl_ctrl.horizon}' self.kl_ctrl = core_algos.AdaptiveKLController(init_kl_coef=config.algorithm.kl_ctrl.kl_coef, target_kl=config.algorithm.kl_ctrl.target_kl, horizon=config.algorithm.kl_ctrl.horizon) else: raise NotImplementedError else: self.kl_ctrl = core_algos.FixedKLController(kl_coef=0.) if self.config.algorithm.adv_estimator == AdvantageEstimator.GAE: self.use_critic = True elif self.config.algorithm.adv_estimator in [ AdvantageEstimator.GRPO, AdvantageEstimator.REINFORCE_PLUS_PLUS, AdvantageEstimator.REMAX, AdvantageEstimator.RLOO ]: self.use_critic = False else: raise NotImplementedError self._validate_config() self._create_dataloader() def _validate_config(self): config = self.config # number of GPUs total n_gpus = config.trainer.n_gpus_per_node * config.trainer.nnodes # 1. Check total batch size for data correctness real_train_batch_size = config.data.train_batch_size * config.actor_rollout_ref.rollout.n assert real_train_batch_size % n_gpus == 0, \ f"real_train_batch_size ({real_train_batch_size}) must be divisible by total n_gpus ({n_gpus})." # A helper function to check "micro_batch_size" vs "micro_batch_size_per_gpu" # We throw an error if the user sets both. The new convention is "..._micro_batch_size_per_gpu". def check_mutually_exclusive(mbs, mbs_per_gpu, name: str): if mbs is None and mbs_per_gpu is None: raise ValueError(f"[{name}] Please set at least one of '{name}.micro_batch_size' or " f"'{name}.micro_batch_size_per_gpu'.") if mbs is not None and mbs_per_gpu is not None: raise ValueError(f"[{name}] You have set both '{name}.micro_batch_size' AND " f"'{name}.micro_batch_size_per_gpu'. Please remove '{name}.micro_batch_size' " f"because only '*_micro_batch_size_per_gpu' is supported (the former is deprecated).") if not config.actor_rollout_ref.actor.use_dynamic_bsz: # actor: ppo_micro_batch_size vs. ppo_micro_batch_size_per_gpu check_mutually_exclusive(config.actor_rollout_ref.actor.ppo_micro_batch_size, config.actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu, "actor_rollout_ref.actor") # reference: log_prob_micro_batch_size vs. log_prob_micro_batch_size_per_gpu check_mutually_exclusive(config.actor_rollout_ref.ref.log_prob_micro_batch_size, config.actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu, "actor_rollout_ref.ref") # The rollout section also has log_prob_micro_batch_size vs. log_prob_micro_batch_size_per_gpu check_mutually_exclusive(config.actor_rollout_ref.rollout.log_prob_micro_batch_size, config.actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu, "actor_rollout_ref.rollout") if self.use_critic and not config.critic.use_dynamic_bsz: # Check for critic micro-batch size conflicts check_mutually_exclusive(config.critic.ppo_micro_batch_size, config.critic.ppo_micro_batch_size_per_gpu, "critic") # Check for reward model micro-batch size conflicts if config.reward_model.enable and not config.reward_model.use_dynamic_bsz: check_mutually_exclusive(config.reward_model.micro_batch_size, config.reward_model.micro_batch_size_per_gpu, "reward_model") # Actor # if NOT dynamic_bsz, we must ensure: # ppo_mini_batch_size is divisible by ppo_micro_batch_size # ppo_micro_batch_size * sequence_parallel_size >= n_gpus if not config.actor_rollout_ref.actor.use_dynamic_bsz: sp_size = config.actor_rollout_ref.actor.get('ulysses_sequence_parallel_size', 1) if config.actor_rollout_ref.actor.ppo_micro_batch_size is not None: assert config.actor_rollout_ref.actor.ppo_mini_batch_size % config.actor_rollout_ref.actor.ppo_micro_batch_size == 0 assert config.actor_rollout_ref.actor.ppo_micro_batch_size * sp_size >= n_gpus # critic if self.use_critic and not config.critic.use_dynamic_bsz: sp_size = config.critic.get('ulysses_sequence_parallel_size', 1) if config.critic.ppo_micro_batch_size is not None: assert config.critic.ppo_mini_batch_size % config.critic.ppo_micro_batch_size == 0 assert config.critic.ppo_micro_batch_size * sp_size >= n_gpus # Check if use_remove_padding is enabled when using sequence parallelism for fsdp if config.actor_rollout_ref.actor.strategy == 'fsdp': if config.actor_rollout_ref.actor.get('ulysses_sequence_parallel_size', 1) > 1 or \ config.actor_rollout_ref.ref.get('ulysses_sequence_parallel_size', 1) > 1: assert config.actor_rollout_ref.model.use_remove_padding, \ "When using sequence parallelism for actor/ref policy, you must enable `use_remove_padding`." if self.use_critic and config.critic.strategy == 'fsdp': if config.critic.get('ulysses_sequence_parallel_size', 1) > 1: assert config.critic.model.use_remove_padding, \ "When using sequence parallelism for critic, you must enable `use_remove_padding`." if config.data.get('val_batch_size', None) is not None: print( f"WARNING: val_batch_size is deprecated. Validation datasets are sent to inference engines as a whole batch, which will schedule the memory themselves." ) print("[validate_config] All configuration checks passed successfully!") def _create_dataloader(self): # TODO: we have to make sure the batch size is divisible by the dp size self.train_dataset = RLHFDataset(parquet_files=self.config.data.train_files, tokenizer=self.tokenizer, processor=self.processor, prompt_key=self.config.data.prompt_key, image_key=self.config.data.get('image_key', 'images'), max_prompt_length=self.config.data.max_prompt_length, filter_prompts=True, return_raw_chat=self.config.data.get('return_raw_chat', False), truncation='error', template_type=self.config.data.template_type) # use sampler for better ckpt resume if self.config.data.shuffle: train_dataloader_generator = torch.Generator() train_dataloader_generator.manual_seed(self.config.data.get('seed', 1)) sampler = RandomSampler(data_source=self.train_dataset, generator=train_dataloader_generator) else: sampler = SequentialSampler(data_source=self.train_dataset) self.train_dataloader = StatefulDataLoader(dataset=self.train_dataset, batch_size=self.config.data.train_batch_size, num_workers=8, drop_last=True, collate_fn=collate_fn, sampler=sampler) self.val_dataset = RLHFDataset(parquet_files=self.config.data.val_files, tokenizer=self.tokenizer, processor=self.processor, prompt_key=self.config.data.prompt_key, image_key=self.config.data.get('image_key', 'images'), max_prompt_length=self.config.data.val_max_prompt_length, filter_prompts=True, return_raw_chat=self.config.data.get('return_raw_chat', False), truncation='error', template_type=self.config.data.template_type) self.val_dataloader = StatefulDataLoader( dataset=self.val_dataset, # Validation datasets are sent to inference engines as a whole batch, # which will schedule the memory themselves. batch_size=len(self.val_dataset), num_workers=8, shuffle=False, drop_last=False, collate_fn=collate_fn) assert len(self.train_dataloader) >= 1 assert len( self.val_dataloader ) == 1, "Validation dataloader must have a single batch, which inference engines will schedule the memory themselves." print(f'Size of train dataloader: {len(self.train_dataloader)}') # inject total_training_steps to actor/critic optim_config. This is hacky. total_training_steps = len(self.train_dataloader) * self.config.trainer.total_epochs if self.config.trainer.total_training_steps is not None: total_training_steps = self.config.trainer.total_training_steps self.total_training_steps = total_training_steps print(f'Total training steps: {self.total_training_steps}') OmegaConf.set_struct(self.config, True) with open_dict(self.config): self.config.actor_rollout_ref.actor.optim.total_training_steps = total_training_steps self.config.critic.optim.total_training_steps = total_training_steps def _maybe_log_val_generations_to_wandb(self, inputs, outputs, scores): """Log a table of validation samples to wandb""" generations_to_log = self.config.trainer.val_generations_to_log_to_wandb if generations_to_log == 0: return if generations_to_log > 0 and 'wandb' not in self.config.trainer.logger: print( 'WARNING: `val_generations_to_log_to_wandb` is set to a positive value, but no wandb logger is found. ') return import wandb import numpy as np # Create tuples of (input, output, score) and sort by input text samples = list(zip(inputs, outputs, scores)) samples.sort(key=lambda x: x[0]) # Sort by input text # Use fixed random seed for deterministic shuffling rng = np.random.RandomState(42) rng.shuffle(samples) # Take first N samples after shuffling samples = samples[:generations_to_log] # Create column names for all samples columns = ["step"] + sum([[f"input_{i+1}", f"output_{i+1}", f"score_{i+1}"] for i in range(len(samples))], []) if not hasattr(self, 'validation_table'): # Initialize the table on first call self.validation_table = wandb.Table(columns=columns) # Create a new table with same columns and existing data # Workaround for https://github.com/wandb/wandb/issues/2981#issuecomment-1997445737 new_table = wandb.Table(columns=columns, data=self.validation_table.data) # Add new row with all data row_data = [] row_data.append(self.global_steps) for sample in samples: row_data.extend(sample) new_table.add_data(*row_data) # Update reference and log wandb.log({"val/generations": new_table}, step=self.global_steps) self.validation_table = new_table def _validate(self): reward_tensor_lst = [] data_source_lst = [] # Lists to collect samples for the table sample_inputs = [] sample_outputs = [] sample_scores = [] sample_lengths = [] for test_data in self.val_dataloader: test_batch = DataProto.from_single_dict(test_data) # we only do validation on rule-based rm if self.config.reward_model.enable and test_batch[0].non_tensor_batch['reward_model']['style'] == 'model': return {} # Store original inputs input_ids = test_batch.batch['input_ids'] input_texts = [self.tokenizer.decode(ids, skip_special_tokens=True) for ids in input_ids] sample_inputs.extend(input_texts) if 'multi_modal_inputs' in test_batch.non_tensor_batch.keys(): test_gen_batch = test_batch.pop( batch_keys=['input_ids', 'attention_mask', 'position_ids'], non_tensor_batch_keys=['raw_prompt_ids', 'multi_modal_data', 'multi_modal_inputs'], ) else: test_gen_batch = test_batch.pop( batch_keys=['input_ids', 'attention_mask', 'position_ids'], non_tensor_batch_keys=['raw_prompt_ids'], ) test_gen_batch.meta_info = { 'eos_token_id': self.tokenizer.eos_token_id, 'pad_token_id': self.tokenizer.pad_token_id, 'recompute_log_prob': False, 'do_sample': False, 'validate': True, } # pad to be divisible by dp_size test_gen_batch_padded, pad_size = pad_dataproto_to_divisor(test_gen_batch, self.actor_rollout_wg.world_size) test_output_gen_batch_padded = self.actor_rollout_wg.generate_sequences(test_gen_batch_padded) # unpad test_output_gen_batch = unpad_dataproto(test_output_gen_batch_padded, pad_size=pad_size) print('validation generation end') # Store generated outputs output_ids = test_output_gen_batch.batch['responses'] output_texts = [self.tokenizer.decode(ids, skip_special_tokens=True) for ids in output_ids] sample_outputs.extend(output_texts) sample_lengths.extend((~torch.isin(output_ids, torch.tensor(self.tokenizer.pad_token_id, device=output_ids.device))).sum(-1).tolist()) test_batch = test_batch.union(test_output_gen_batch) # evaluate using reward_function reward_tensor = self.val_reward_fn(test_batch) reward_tensor = torch.where((reward_tensor == -1.0) | (reward_tensor == -0.5), torch.tensor(0.0), reward_tensor) test_batch.batch['token_level_scores']=reward_tensor self._save_samples(test_batch, "test") # Store scores scores = reward_tensor.sum(-1).cpu().tolist() sample_scores.extend(scores) reward_tensor_lst.append(reward_tensor) data_source_lst.append(test_batch.non_tensor_batch.get('data_source', ['unknown'] * reward_tensor.shape[0])) self._maybe_log_val_generations_to_wandb(inputs=sample_inputs, outputs=sample_outputs, scores=sample_scores) reward_tensor = torch.cat(reward_tensor_lst, dim=0).sum(-1).cpu() # (batch_size,) data_sources = np.concatenate(data_source_lst, axis=0) # evaluate test_score based on data source data_source_reward = {} data_source_length = {} for i in range(reward_tensor.shape[0]): data_source = data_sources[i] if data_source not in data_source_reward: data_source_reward[data_source] = [] data_source_length[data_source] = [] data_source_reward[data_source].append(reward_tensor[i].item()) data_source_length[data_source].append(sample_lengths[i]) metric_dict = {} average_acc=[] for data_source, rewards in data_source_reward.items(): metric_dict[f'val/test_score/{data_source}'] = np.mean(rewards) average_acc.append(np.mean(rewards)) metric_dict[f'val/test_score/avg_acc'] = np.mean(average_acc) for data_source, lengths in data_source_length.items(): metric_dict[f'val_response_length/test_length/{data_source}'] = np.mean(lengths) rewards=data_source_reward[data_source] if np.mean(rewards)==0: continue metric_dict[f'val_response_length/test_length_correct/{data_source}'] = sum([lengths[i] for i in range(len(lengths)) if rewards[i]==1.0])/sum(rewards) return metric_dict def _save_samples(self, batch: DataProto, split: str): prompts, response, token_level_scores=batch.batch['prompts'], batch.batch['responses'], batch.batch['token_level_scores'] sources=batch.non_tensor_batch['data_source'] prompts=self.tokenizer.batch_decode(prompts, skip_special_tokens=True) response=self.tokenizer.batch_decode(response, skip_special_tokens=True) rewards=[] for row in token_level_scores: non_zero_indices = torch.nonzero(row, as_tuple=True)[0] if len(non_zero_indices) > 0: rewards.append( row[non_zero_indices[-1]].item() ) else: rewards.append(0.0) data=[] for idx in range(len(prompts)): data.append({ 'prompt': prompts[idx], 'response': response[idx], 'reward': rewards[idx], 'source': sources[idx], }) os.makedirs(os.path.join(self.config.trainer.samples_save_path, split), exist_ok=True) with open(os.path.join(self.config.trainer.samples_save_path, split, f"step_{self.global_steps}.json"), 'a+', encoding='utf-8') as f: json.dump(data, f, ensure_ascii=False, indent=4) def init_workers(self): """Init resource pool and worker group""" self.resource_pool_manager.create_resource_pool() self.resource_pool_to_cls = {pool: {} for pool in self.resource_pool_manager.resource_pool_dict.values()} # create actor and rollout if self.hybrid_engine: resource_pool = self.resource_pool_manager.get_resource_pool(Role.ActorRollout) actor_rollout_cls = RayClassWithInitArgs(cls=self.role_worker_mapping[Role.ActorRollout], config=self.config.actor_rollout_ref, role='actor_rollout') self.resource_pool_to_cls[resource_pool]['actor_rollout'] = actor_rollout_cls else: raise NotImplementedError # create critic if self.use_critic: resource_pool = self.resource_pool_manager.get_resource_pool(Role.Critic) critic_cls = RayClassWithInitArgs(cls=self.role_worker_mapping[Role.Critic], config=self.config.critic) self.resource_pool_to_cls[resource_pool]['critic'] = critic_cls # create reference policy if needed if self.use_reference_policy: resource_pool = self.resource_pool_manager.get_resource_pool(Role.RefPolicy) ref_policy_cls = RayClassWithInitArgs(self.role_worker_mapping[Role.RefPolicy], config=self.config.actor_rollout_ref, role='ref') self.resource_pool_to_cls[resource_pool]['ref'] = ref_policy_cls # create a reward model if reward_fn is None if self.use_rm: # we create a RM here resource_pool = self.resource_pool_manager.get_resource_pool(Role.RewardModel) rm_cls = RayClassWithInitArgs(self.role_worker_mapping[Role.RewardModel], config=self.config.reward_model) self.resource_pool_to_cls[resource_pool]['rm'] = rm_cls # initialize WorkerGroup # NOTE: if you want to use a different resource pool for each role, which can support different parallel size, # you should not use `create_colocated_worker_cls`. Instead, directly pass different resource pool to different worker groups. # See https://github.com/volcengine/verl/blob/master/examples/ray/tutorial.ipynb for more information. all_wg = {} self.wg_dicts = [] for resource_pool, class_dict in self.resource_pool_to_cls.items(): worker_dict_cls = create_colocated_worker_cls(class_dict=class_dict) wg_dict = self.ray_worker_group_cls(resource_pool=resource_pool, ray_cls_with_init=worker_dict_cls) spawn_wg = wg_dict.spawn(prefix_set=class_dict.keys()) all_wg.update(spawn_wg) # keep the referece of WorkerDict to support ray >= 2.31. Ref: https://github.com/ray-project/ray/pull/45699 self.wg_dicts.append(wg_dict) if self.use_critic: self.critic_wg = all_wg['critic'] self.critic_wg.init_model() if self.use_reference_policy: self.ref_policy_wg = all_wg['ref'] self.ref_policy_wg.init_model() if self.use_rm: self.rm_wg = all_wg['rm'] self.rm_wg.init_model() # we should create rollout at the end so that vllm can have a better estimation of kv cache memory self.actor_rollout_wg = all_wg['actor_rollout'] self.actor_rollout_wg.init_model() def _save_checkpoint(self): # path: given_path + `/global_step_{global_steps}` + `/actor` local_global_step_folder = os.path.join(self.config.trainer.default_local_dir, f'global_step_{self.global_steps}') actor_local_path = os.path.join(local_global_step_folder, 'actor') actor_remote_path = None if self.config.trainer.default_hdfs_dir is None else os.path.join( self.config.trainer.default_hdfs_dir, f'global_step_{self.global_steps}', 'actor') self.actor_rollout_wg.save_checkpoint(actor_local_path, actor_remote_path, self.global_steps, remove_previous_ckpt=self.config.trainer.remove_previous_ckpt_in_save) if self.use_critic: critic_local_path = os.path.join(local_global_step_folder, 'critic') critic_remote_path = None if self.config.trainer.default_hdfs_dir is None else os.path.join( self.config.trainer.default_hdfs_dir, f'global_step_{self.global_steps}', 'critic') self.critic_wg.save_checkpoint(critic_local_path, critic_remote_path, self.global_steps, remove_previous_ckpt=self.config.trainer.remove_previous_ckpt_in_save) # save dataloader dataloader_local_path = os.path.join(local_global_step_folder, 'data.pt') dataloader_state_dict = self.train_dataloader.state_dict() torch.save(dataloader_state_dict, dataloader_local_path) # latest checkpointed iteration tracker (for atomic usage) local_latest_checkpointed_iteration = os.path.join(self.config.trainer.default_local_dir, 'latest_checkpointed_iteration.txt') with open(local_latest_checkpointed_iteration, 'w') as f: f.write(str(self.global_steps)) def _load_checkpoint(self): if self.config.trainer.resume_mode == 'disable': return 0 # load from hdfs if self.config.trainer.default_hdfs_dir is not None: NotImplementedError('load from hdfs is not implemented yet') else: checkpoint_folder = self.config.trainer.default_local_dir # TODO: check path if not os.path.isabs(checkpoint_folder): working_dir = os.getcwd() checkpoint_folder = os.path.join(working_dir, checkpoint_folder) global_step_folder = find_latest_ckpt_path(checkpoint_folder) # None if no latest # find global_step_folder if self.config.trainer.resume_mode == 'auto': if global_step_folder is None: print('Training from scratch') return 0 else: if not (self.config.trainer.resume_from_path and global_step_folder is not None): assert isinstance(self.config.trainer.resume_mode, str), "resume ckpt must be str type" assert 'global_step_' in self.config.trainer.resume_mode, "resume ckpt must specify the global_steps" global_step_folder = self.config.trainer.resume_mode if not os.path.isabs(global_step_folder): working_dir = os.getcwd() global_step_folder = os.path.join(working_dir, global_step_folder) print(f'Load from checkpoint folder: {global_step_folder}') # set global step self.global_steps = int(global_step_folder.split('global_step_')[-1]) print(f'Setting global step to {self.global_steps}') print(f'Resuming from {global_step_folder}') actor_path = os.path.join(global_step_folder, 'actor') critic_path = os.path.join(global_step_folder, 'critic') # load actor self.actor_rollout_wg.load_checkpoint(actor_path, del_local_after_load=self.config.trainer.del_local_ckpt_after_load) # load critic if self.use_critic: self.critic_wg.load_checkpoint(critic_path, del_local_after_load=self.config.trainer.del_local_ckpt_after_load) # load dataloader, # TODO: from remote not implemented yet dataloader_local_path = os.path.join(global_step_folder, 'data.pt') if os.path.exists(dataloader_local_path): dataloader_state_dict = torch.load(dataloader_local_path) self.train_dataloader.load_state_dict(dataloader_state_dict) else: print(f"Warning: No dataloader state found at {dataloader_local_path}, will start from scratch") def _balance_batch(self, batch: DataProto, metrics, logging_prefix='global_seqlen'): """Reorder the data on single controller such that each dp rank gets similar total tokens""" attention_mask = batch.batch['attention_mask'] batch_size = attention_mask.shape[0] global_seqlen_lst = batch.batch['attention_mask'].view(batch_size, -1).sum(-1).tolist() # (train_batch_size,) world_size = self.actor_rollout_wg.world_size global_partition_lst = get_seqlen_balanced_partitions(global_seqlen_lst, k_partitions=world_size, equal_size=True) # reorder based on index. The data will be automatically equally partitioned by dispatch function global_idx = torch.tensor([j for partition in global_partition_lst for j in partition]) batch.reorder(global_idx) global_balance_stats = log_seqlen_unbalance(seqlen_list=global_seqlen_lst, partitions=global_partition_lst, prefix=logging_prefix) metrics.update(global_balance_stats) def fit(self): """ The training loop of PPO. The driver process only need to call the compute functions of the worker group through RPC to construct the PPO dataflow. The light-weight advantage computation is done on the driver process. """ from verl.utils.tracking import Tracking from omegaconf import OmegaConf logger = Tracking(project_name=self.config.trainer.project_name, experiment_name=self.config.trainer.experiment_name, default_backend=self.config.trainer.logger, config=OmegaConf.to_container(self.config, resolve=True)) self.global_steps = 0 # load checkpoint before doing anything self._load_checkpoint() # perform validation before training # currently, we only support validation using the reward_function. if self.val_reward_fn is not None and self.config.trainer.get('val_before_train', True): val_metrics = self._validate() pprint(f'Initial validation metrics: {val_metrics}') logger.log(data=val_metrics, step=self.global_steps) if self.config.trainer.get('val_only', False): return # we start from step 1 self.global_steps += 1 for epoch in range(self.config.trainer.total_epochs): for batch_dict in self.train_dataloader: metrics = {} timing_raw = {} batch: DataProto = DataProto.from_single_dict(batch_dict) # pop those keys for generation if 'multi_modal_inputs' in batch.non_tensor_batch.keys(): gen_batch = batch.pop( batch_keys=['input_ids', 'attention_mask', 'position_ids'], non_tensor_batch_keys=['raw_prompt_ids', 'multi_modal_data', 'multi_modal_inputs'], ) else: gen_batch = batch.pop( batch_keys=['input_ids', 'attention_mask', 'position_ids'], non_tensor_batch_keys=['raw_prompt_ids'], ) with _timer('step', timing_raw): # generate a batch with _timer('gen', timing_raw): gen_batch_output = self.actor_rollout_wg.generate_sequences(gen_batch) if self.config.algorithm.adv_estimator == AdvantageEstimator.REMAX: with _timer('gen_max', timing_raw): gen_baseline_batch = deepcopy(gen_batch) gen_baseline_batch.meta_info['do_sample'] = False gen_baseline_output = self.actor_rollout_wg.generate_sequences(gen_baseline_batch) batch = batch.union(gen_baseline_output) reward_baseline_tensor = self.reward_fn(batch) reward_baseline_tensor = reward_baseline_tensor.sum(dim=-1) batch.pop(batch_keys=list(gen_baseline_output.batch.keys())) batch.batch['reward_baselines'] = reward_baseline_tensor del gen_baseline_batch, gen_baseline_output batch.non_tensor_batch['uid'] = np.array([str(uuid.uuid4()) for _ in range(len(batch.batch))], dtype=object) # repeat to align with repeated responses in rollout batch = batch.repeat(repeat_times=self.config.actor_rollout_ref.rollout.n, interleave=True) batch = batch.union(gen_batch_output) # balance the number of valid tokens on each dp rank. # Note that this breaks the order of data inside the batch. # Please take care when you implement group based adv computation such as GRPO and rloo self._balance_batch(batch, metrics=metrics) # compute global_valid tokens batch.meta_info['global_token_num'] = torch.sum(batch.batch['attention_mask'], dim=-1).tolist() # recompute old_log_probs with _timer('old_log_prob', timing_raw): old_log_prob = self.actor_rollout_wg.compute_log_prob(batch) batch = batch.union(old_log_prob) if self.use_reference_policy: # compute reference log_prob with _timer('ref', timing_raw): ref_log_prob = self.ref_policy_wg.compute_ref_log_prob(batch) batch = batch.union(ref_log_prob) # compute values if self.use_critic: with _timer('values', timing_raw): values = self.critic_wg.compute_values(batch) batch = batch.union(values) with _timer('adv', timing_raw): # compute scores. Support both model and function-based. # We first compute the scores using reward model. Then, we call reward_fn to combine # the results from reward model and rule-based results. if self.use_rm: # we first compute reward model score reward_tensor = self.rm_wg.compute_rm_score(batch) batch = batch.union(reward_tensor) # we combine with rule-based rm reward_tensor = self.reward_fn(batch) batch.batch['token_level_scores'] = reward_tensor # compute rewards. apply_kl_penalty if available if not self.config.actor_rollout_ref.actor.get('use_kl_loss', False): batch, kl_metrics = apply_kl_penalty(batch, kl_ctrl=self.kl_ctrl, kl_penalty=self.config.algorithm.kl_penalty) metrics.update(kl_metrics) else: batch.batch['token_level_rewards'] = batch.batch['token_level_scores'] # compute advantages, executed on the driver process batch = compute_advantage(batch, adv_estimator=self.config.algorithm.adv_estimator, gamma=self.config.algorithm.gamma, lam=self.config.algorithm.lam, num_repeat=self.config.actor_rollout_ref.rollout.n) # update critic if self.use_critic: with _timer('update_critic', timing_raw): critic_output = self.critic_wg.update_critic(batch) critic_output_metrics = reduce_metrics(critic_output.meta_info['metrics']) metrics.update(critic_output_metrics) # implement critic warmup if self.config.trainer.critic_warmup <= self.global_steps: # update actor with _timer('update_actor', timing_raw): actor_output = self.actor_rollout_wg.update_actor(batch) actor_output_metrics = reduce_metrics(actor_output.meta_info['metrics']) metrics.update(actor_output_metrics) # validate if self.val_reward_fn is not None and self.config.trainer.test_freq > 0 and \ self.global_steps % self.config.trainer.test_freq == 0: with _timer('testing', timing_raw): val_metrics: dict = self._validate() metrics.update(val_metrics) if self.config.trainer.save_freq > 0 and \ self.global_steps % self.config.trainer.save_freq == 0: with _timer('save_checkpoint', timing_raw): self._save_checkpoint() # collect metrics self._save_samples(batch, split="train") metrics.update(compute_data_metrics(batch=batch, use_critic=self.use_critic)) metrics.update(compute_timing_metrics(batch=batch, timing_raw=timing_raw)) # TODO: make a canonical logger that supports various backend logger.log(data=metrics, step=self.global_steps) self.global_steps += 1 if self.global_steps >= self.total_training_steps: # perform validation after training if self.val_reward_fn is not None: val_metrics = self._validate() pprint(f'Final validation metrics: {val_metrics}') logger.log(data=val_metrics, step=self.global_steps) if self.config.trainer.save_freq > 0 and \ (self.global_steps - 1) % self.config.trainer.save_freq != 0: with _timer('save_checkpoint', timing_raw): self._save_checkpoint() return ================================================ FILE: verl/trainer/runtime_env.yaml ================================================ working_dir: ./ excludes: ["/.git/"] env_vars: TORCH_NCCL_AVOID_RECORD_STREAMS: "1" VLLM_ATTENTION_BACKEND: "XFORMERS" ================================================ FILE: verl/utils/__init__.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from . import tokenizer from .tokenizer import hf_tokenizer, hf_processor __all__ = tokenizer.__all__ ================================================ FILE: verl/utils/checkpoint/__init__.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. ================================================ FILE: verl/utils/checkpoint/checkpoint_manager.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import shutil from filelock import FileLock import tempfile from typing import Union import torch import torch.distributed from torch.distributed.fsdp import FullyShardedDataParallel as FSDP, StateDictType from transformers import PreTrainedTokenizer, ProcessorMixin import numpy as np import random class BaseCheckpointManager: """ A checkpoint manager that saves and loads - model - optimizer - lr_scheduler - extra_states in a SPMD way. We save - sharded model states and optimizer states - full lr_scheduler states - huggingface tokenizer and config for ckpt merge """ def __init__(self, model: FSDP, optimizer: torch.optim.Optimizer, lr_scheduler: torch.optim.lr_scheduler.LRScheduler, processing_class: Union[PreTrainedTokenizer, ProcessorMixin]): self.previous_global_step = None self.previous_save_local_path = None self.model = model self.optimizer = optimizer self.lr_scheduler = lr_scheduler self.processing_class = processing_class assert isinstance(self.model, FSDP) self.rank = torch.distributed.get_rank() self.world_size = torch.distributed.get_world_size() def load_checkpoint(self, *args, **kwargs): raise NotImplementedError def save_checkpoint(self, *args, **kwargs): raise NotImplementedError def remove_previous_save_local_path(self): if not self.previous_save_local_path: return abs_path = os.path.abspath(self.previous_save_local_path) print(f'Checkpoint manager remove previous save local path: {abs_path}') if not os.path.exists(abs_path): return # remove previous local_path shutil.rmtree(abs_path, ignore_errors=True) @staticmethod def local_mkdir(path): if not os.path.isabs(path): working_dir = os.getcwd() path = os.path.join(working_dir, path) # Using hash value of path as lock file name to avoid long file name lock_filename = f"ckpt_{hash(path) & 0xFFFFFFFF:08x}.lock" lock_path = os.path.join(tempfile.gettempdir(), lock_filename) try: with FileLock(lock_path, timeout=60): # Add timeout # make a new dir os.makedirs(path, exist_ok=True) except Exception as e: print(f"Warning: Failed to acquire lock for {path}: {e}") # Even if the lock is not acquired, try to create the directory os.makedirs(path, exist_ok=True) return path @staticmethod def get_rng_state(): rng_state = { 'cpu': torch.get_rng_state(), 'cuda': torch.cuda.get_rng_state(), 'numpy': np.random.get_state(), 'random': random.getstate(), } return rng_state @staticmethod def load_rng_state(rng_state): torch.set_rng_state(rng_state['cpu']) torch.cuda.set_rng_state(rng_state['cuda']) np.random.set_state(rng_state['numpy']) random.setstate(rng_state['random']) def find_latest_ckpt_path(path, directory_format="global_step_{}"): if path is None: return None tracker_file = get_checkpoint_tracker_filename(path) if not os.path.exists(tracker_file): print("Checkpoint tracker file does not exist: %s", tracker_file) return None with open(tracker_file, "rb") as f: iteration = int(f.read().decode()) ckpt_path = os.path.join(path, directory_format.format(iteration)) if not os.path.exists(ckpt_path): print("Checkpoint does not exist: %s", ckpt_path) return None print("Found checkpoint: %s", ckpt_path) return ckpt_path def get_checkpoint_tracker_filename(root_path: str): """ Tracker file rescords the latest chckpoint during training to restart from. """ return os.path.join(root_path, "latest_checkpointed_iteration.txt") ================================================ FILE: verl/utils/checkpoint/fsdp_checkpoint_manager.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import ray import os import warnings from typing import Union import torch import torch.distributed from torch.distributed.fsdp import FullyShardedDataParallel as FSDP, StateDictType from torch.distributed.fsdp import ShardedStateDictConfig, ShardedOptimStateDictConfig from verl.utils.fs import copy_to_local, is_non_local from transformers import PreTrainedTokenizer, ProcessorMixin from .checkpoint_manager import BaseCheckpointManager class FSDPCheckpointManager(BaseCheckpointManager): """ A checkpoint manager that saves and loads - model - optimizer - lr_scheduler - extra_states in a SPMD way. We save - sharded model states and optimizer states - full lr_scheduler states - huggingface tokenizer/processor and config for ckpt merge """ def __init__(self, model: FSDP, optimizer: torch.optim.Optimizer, lr_scheduler: torch.optim.lr_scheduler.LRScheduler, processing_class: Union[PreTrainedTokenizer, ProcessorMixin] = None, **kwargs): if processing_class is None: assert "tokenizer" in kwargs, "tokenizer or processor must be provided" warnings.warn("`tokenizer` is deprecated. use `processing_class` instead.", DeprecationWarning) processing_class = kwargs.pop("tokenizer") super().__init__(model, optimizer, lr_scheduler, processing_class) def load_checkpoint(self, path=None, del_local_after_load=False, *args, **kwargs): if path is None: return # every rank download its own checkpoint remote_model_path = os.path.join(path, f'model_world_size_{self.world_size}_rank_{self.rank}.pt') remote_optim_path = os.path.join(path, f'optim_world_size_{self.world_size}_rank_{self.rank}.pt') remote_extra_state_path = os.path.join(path, f'extra_state_world_size_{self.world_size}_rank_{self.rank}.pt') print( f'[rank-{self.rank}]: Loading from {remote_model_path} and {remote_optim_path} and {remote_extra_state_path}' ) local_model_path = copy_to_local(remote_model_path) local_optim_path = copy_to_local(remote_optim_path) local_extra_state_path = copy_to_local(remote_extra_state_path) model_state_dict = torch.load(local_model_path) optimizer_state_dict = torch.load(local_optim_path) extra_state_dict = torch.load(local_extra_state_path) if del_local_after_load: try: os.remove(local_model_path) if is_non_local(local_model_path) else None os.remove(local_optim_path) if is_non_local(local_optim_path) else None os.remove(local_extra_state_path) if is_non_local(local_extra_state_path) else None except Exception as e: print( f'[rank-{self.rank}]: remove local resume ckpt file after loading failed, exception {e} will be ignored' ) lr_scheduler_state_dict = extra_state_dict['lr_scheduler'] state_dict_cfg = ShardedStateDictConfig(offload_to_cpu=True) optim_cfg = ShardedOptimStateDictConfig(offload_to_cpu=True) with FSDP.state_dict_type(self.model, StateDictType.SHARDED_STATE_DICT, state_dict_cfg, optim_cfg): self.model.load_state_dict(model_state_dict) if self.optimizer is not None: self.optimizer.load_state_dict(optimizer_state_dict) # recover random state if 'rng' in extra_state_dict: # 'rng' may not exist for backward compatibility self.load_rng_state(extra_state_dict['rng']) if self.lr_scheduler is not None: self.lr_scheduler.load_state_dict(lr_scheduler_state_dict) def save_checkpoint(self, local_path: str, global_step: int, remove_previous_ckpt=False, *args, **kwargs): # record the previous global step self.previous_global_step = global_step # remove previous local_path # TODO: shall we remove previous ckpt every save? if remove_previous_ckpt: self.remove_previous_save_local_path() local_path = self.local_mkdir(local_path) torch.distributed.barrier() # every rank will save its own model and optim shard state_dict_cfg = ShardedStateDictConfig(offload_to_cpu=True) optim_cfg = ShardedOptimStateDictConfig(offload_to_cpu=True) with warnings.catch_warnings(): warnings.simplefilter("ignore") with FSDP.state_dict_type(self.model, StateDictType.SHARDED_STATE_DICT, state_dict_cfg, optim_cfg): model_state_dict = self.model.state_dict() if self.optimizer is not None: optimizer_state_dict = self.optimizer.state_dict() else: optimizer_state_dict = None if self.lr_scheduler is not None: lr_scheduler_state_dict = self.lr_scheduler.state_dict() else: lr_scheduler_state_dict = None extra_state_dict = { 'lr_scheduler': lr_scheduler_state_dict, 'rng': self.get_rng_state(), } model_path = os.path.join(local_path, f'model_world_size_{self.world_size}_rank_{self.rank}.pt') optim_path = os.path.join(local_path, f'optim_world_size_{self.world_size}_rank_{self.rank}.pt') extra_path = os.path.join(local_path, f'extra_state_world_size_{self.world_size}_rank_{self.rank}.pt') print(f'[rank-{self.rank}]: Saving model to {os.path.abspath(model_path)}') print(f'[rank-{self.rank}]: Saving checkpoint to {os.path.abspath(model_path)}') print(f'[rank-{self.rank}]: Saving extra_state to {os.path.abspath(extra_path)}') torch.save(model_state_dict, model_path) torch.save(optimizer_state_dict, optim_path) # TODO: address optimizer is None torch.save(extra_state_dict, extra_path) # wait for everyone to dump to local torch.distributed.barrier() if self.rank == 0: hf_local_path = os.path.join(local_path, 'huggingface') os.makedirs(hf_local_path, exist_ok=True) self.model._fsdp_wrapped_module.config.save_pretrained(hf_local_path) self.processing_class.save_pretrained(hf_local_path) torch.distributed.barrier() self.previous_save_local_path = local_path ================================================ FILE: verl/utils/config.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import Dict from omegaconf import DictConfig def update_dict_with_config(dictionary: Dict, config: DictConfig): for key in dictionary: if hasattr(config, key): dictionary[key] = getattr(config, key) ================================================ FILE: verl/utils/dataset/README.md ================================================ # Dataset Format ## RLHF dataset We combine all the data sources into a single parquet files. We directly organize the prompt into the chat format so that multi-turn chats can be easily incorporated. In the prompt, we may add instruction following texts to guide the model output the answers in a particular format so that we can extract the answers. Math problems ```json { "data_source": "openai/gsm8k", "prompt": [{"role": "user", "content": "Natalia sold clips to 48 of her friends in April, and then she sold half as many clips in May. How many clips did Natalia sell altogether in April and May? Let's think step by step and output the final answer after \"####\""}], "ability": "math", "reward_model": { "style": "rule", "ground_truth": ["72"] }, } ``` ================================================ FILE: verl/utils/dataset/__init__.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from .rl_dataset import RLHFDataset from .rm_dataset import RMDataset from .sft_dataset import SFTDataset ================================================ FILE: verl/utils/dataset/rl_dataset.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from omegaconf import ListConfig import os from typing import List, Union, Optional import copy import pandas as pd from collections import defaultdict import torch import numpy as np from torch.utils.data import Dataset from transformers import PreTrainedTokenizer, ProcessorMixin from verl.utils.model import compute_position_id_with_mask import verl.utils.torch_functional as verl_F tir_base_0307='''A conversation between User and Assistant. The user asks a question, and the Assistant solves it. The assistant first thinks about the reasoning process in the mind and then provides the user with the answer. User: Please integrate natural language reasoning with programs to solve the problem above, and put your final answer within \\boxed{}.\n[PROMPT]\nAssistant: ''' tir_base_0309='''A conversation between User and Assistant. The user asks a question, and the Assistant solves it.\nUser: Please integrate natural language reasoning with programs to solve the problem above, and put your final answer within \\boxed{}.\n[PROMPT]\nAssistant: ''' def collate_fn(data_list: list[dict]) -> dict: tensors = defaultdict(list) non_tensors = defaultdict(list) for data in data_list: for key, val in data.items(): if isinstance(val, torch.Tensor): tensors[key].append(val) else: non_tensors[key].append(val) for key, val in tensors.items(): tensors[key] = torch.stack(val, dim=0) for key, val in non_tensors.items(): non_tensors[key] = np.array(val, dtype=object) return {**tensors, **non_tensors} def process_image(image: dict, max_pixels: int = 2048 * 2048, min_pixels: int = 512 * 512): import math from io import BytesIO from PIL import Image if isinstance(image, dict): image = Image.open(BytesIO(image['bytes'])) if (image.width * image.height) > max_pixels: resize_factor = math.sqrt(max_pixels / (image.width * image.height)) width, height = int(image.width * resize_factor), int(image.height * resize_factor) image = image.resize((width, height)) if (image.width * image.height) < min_pixels: resize_factor = math.sqrt(min_pixels / (image.width * image.height)) width, height = int(image.width * resize_factor), int(image.height * resize_factor) image = image.resize((width, height)) if image.mode != 'RGB': image = image.convert('RGB') return image class RLHFDataset(Dataset): """ We assume the dataset contains a column that contains prompts and other information """ def __init__(self, parquet_files: Union[str, List[str]], tokenizer: PreTrainedTokenizer, processor: Optional[ProcessorMixin] = None, prompt_key='prompt', image_key='images', max_prompt_length=1024, filter_prompts=True, cache_dir='~/.cache/verl/rlhf', chat_template_func=None, return_raw_chat=False, truncation='error', template_type='default'): if not isinstance(parquet_files, (List, ListConfig)): parquet_files = [parquet_files] self.parquet_files = copy.deepcopy(parquet_files) self.original_parquet_files = copy.deepcopy(parquet_files) # use for resume self.cache_dir = os.path.expanduser(cache_dir) self.tokenizer = tokenizer self.processor = processor self.prompt_key = prompt_key self.image_key = image_key self.max_prompt_length = max_prompt_length self.filter_prompts = filter_prompts self.return_raw_chat = return_raw_chat self.chat_template_func = chat_template_func self.truncation = truncation self.template_type = template_type # whether to store the dataset in state_dict() # default not store self.serialize_dataset = False self._download() self._read_files_and_tokenize() def _download(self, use_origin_parquet=False): from verl.utils.fs import copy_to_local parquet_files = self.parquet_files if not use_origin_parquet else self.original_parquet_files for i, parquet_file in enumerate(parquet_files): self.parquet_files[i] = copy_to_local(src=parquet_file, cache_dir=self.cache_dir) def _read_files_and_tokenize(self): dataframes = [] for parquet_file in self.parquet_files: # read parquet files and cache dataframe = pd.read_parquet(parquet_file) dataframes.append(dataframe) self.dataframe = pd.concat(dataframes) print(f'original dataset len: {len(self.dataframe)}') # filter out too long prompts tokenizer = self.tokenizer prompt_key = self.prompt_key if self.template_type=="default": self.dataframe = self.dataframe[self.dataframe.apply(lambda doc: len( tokenizer.apply_chat_template(doc[prompt_key], add_generation_prompt=True)) <= self.max_prompt_length, axis=1)] elif self.template_type=="tir_base_0307": self.dataframe = self.dataframe[self.dataframe.apply(lambda doc: len(tokenizer.encode(tir_base_0307.replace("[PROMPT]", doc[prompt_key][1]['content']))) <= self.max_prompt_length-10, axis=1)] elif self.template_type=="tir_base_0309": self.dataframe = self.dataframe[self.dataframe.apply(lambda doc: len(tokenizer.encode(tir_base_0309.replace("[PROMPT]", doc[prompt_key][1]['content']))) <= self.max_prompt_length-10, axis=1)] print(f'filter dataset len: {len(self.dataframe)}') def resume_dataset_state(self): self.serialize_dataset = False if hasattr(self, 'original_parquet_files') else True # resume dataframe if not it's serialized in data.pt if not self.serialize_dataset: self._download(use_origin_parquet=True) # download and resume from original parquet files self._read_files_and_tokenize() else: print(r'old dataloader ckpt file is used, please train from scratch for better ckpt performance') def __len__(self): return len(self.dataframe) def __getitem__(self, item): """ Note that we also return the raw_input_ids so that it can be combined with other chat template """ row_dict: dict = self.dataframe.iloc[item].to_dict() chat = row_dict.pop(self.prompt_key) if self.template_type=="default": prompt_with_chat_template = self.tokenizer.apply_chat_template(chat, add_generation_prompt=True, tokenize=False) elif self.template_type=="tir_base_0307": prompt_with_chat_template = tir_base_0307.replace("[PROMPT]", chat[1]['content']) elif self.template_type=="tir_base_0309": prompt_with_chat_template = tir_base_0309.replace("[PROMPT]", chat[1]['content']) if self.image_key in row_dict: # expand image token raw_prompt = prompt_with_chat_template.replace('', '<|vision_start|><|image_pad|><|vision_end|>') row_dict['multi_modal_data'] = {'image': [process_image(image) for image in row_dict.pop(self.image_key)]} image_inputs = self.processor.image_processor(row_dict['multi_modal_data']['image'], return_tensors='pt') image_grid_thw = image_inputs['image_grid_thw'] row_dict['multi_modal_inputs'] = {key: val for key, val in image_inputs.items()} if image_grid_thw is not None: merge_length = self.processor.image_processor.merge_size**2 index = 0 while '' in prompt_with_chat_template: prompt_with_chat_template = prompt_with_chat_template.replace( '', '<|vision_start|>' + '<|placeholder|>' * (image_grid_thw[index].prod() // merge_length) + '<|vision_end|>', 1, ) index += 1 prompt_with_chat_template = prompt_with_chat_template.replace('<|placeholder|>', self.processor.image_token) else: raw_prompt = prompt_with_chat_template input_ids, attention_mask = verl_F.tokenize_and_postprocess_data(prompt=prompt_with_chat_template, tokenizer=self.tokenizer, max_length=self.max_prompt_length, pad_token_id=self.tokenizer.pad_token_id, left_pad=True, truncation=self.truncation) if self.image_key in row_dict: from verl.models.transformers.qwen2_vl import get_rope_index position_ids = get_rope_index( self.processor, input_ids=input_ids[0], image_grid_thw=image_grid_thw, attention_mask=attention_mask[0], ) # (3, seq_len) else: position_ids = compute_position_id_with_mask(attention_mask) row_dict['input_ids'] = input_ids[0] row_dict['attention_mask'] = attention_mask[0] row_dict['position_ids'] = position_ids[0] row_dict['raw_prompt_ids'] = self.tokenizer.encode(raw_prompt, add_special_tokens=False) # encode prompts without chat template if self.return_raw_chat: row_dict['raw_prompt'] = chat.tolist() # add index for each prompt index = row_dict.get("extra_info", {}).get("index", 0) row_dict["index"] = index return row_dict def __getstate__(self): if not self.serialize_dataset: state = self.__dict__.copy() if 'dataframe' in state: del state['dataframe'] return state return self.__dict__.copy() ================================================ FILE: verl/utils/dataset/rm_dataset.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os from typing import List, Union import pandas as pd import torch from torch.utils.data import Dataset from transformers import AutoTokenizer from verl.utils import hf_tokenizer def download_files_distributed(download_fn): import torch.distributed if torch.distributed.is_initialized(): if torch.distributed.get_rank() == 0: # download files download_fn() torch.distributed.barrier() else: # download anyway download_fn() class RMDataset(Dataset): def __init__(self, parquet_files: Union[str, List[str]], tokenizer, prompt_key='prompt', chosen_key='chosen', rejected_key='rejected', max_length=1024, add_eos=True, cache_dir='~/.cache/verl/rm'): if not isinstance(parquet_files, List): parquet_files = [parquet_files] self.parquet_files = parquet_files self.cache_dir = os.path.expanduser(cache_dir) if isinstance(tokenizer, str): tokenizer = hf_tokenizer(tokenizer) self.tokenizer = tokenizer self.prompt_key = prompt_key self.chosen_key = chosen_key self.rejected_key = rejected_key self.add_eos = add_eos self.max_length = max_length self._download() self._read_files_and_tokenize() def _download(self): def _download_files(): from verl.utils.fs import copy, is_non_local os.makedirs(self.cache_dir, exist_ok=True) assert os.path.exists(self.cache_dir) for i, parquet_file in enumerate(self.parquet_files): if is_non_local(parquet_file): dst = os.path.join(self.cache_dir, os.path.basename(parquet_file)) if not os.path.exists(dst): copy(src=parquet_file, dst=dst) self.parquet_files[i] = dst download_files_distributed(_download_files) def _read_files_and_tokenize(self): dataframes = [] for parquet_file in self.parquet_files: # read parquet files and cache dataframe = pd.read_parquet(parquet_file) dataframes.append(dataframe) self.dataframe = pd.concat(dataframes) self.prompts = self.dataframe[self.prompt_key].tolist() self.chosen_responses = self.dataframe[self.chosen_key].tolist() self.rejected_responses = self.dataframe[self.rejected_key].tolist() def __len__(self): return len(self.prompts) def _pad_to_length(self, input_ids, attention_mask): curr_length = input_ids.shape[-1] if curr_length < self.max_length: input_ids = torch.cat( (input_ids, torch.zeros(size=(self.max_length - curr_length,), dtype=input_ids.dtype)), dim=-1) attention_mask = torch.cat( (attention_mask, torch.zeros(size=(self.max_length - curr_length,), dtype=attention_mask.dtype)), dim=-1) elif curr_length > self.max_length: input_ids = input_ids[:self.max_length] attention_mask = attention_mask[:self.max_length] return input_ids, attention_mask def __getitem__(self, item): prompt = self.prompts[item] chosen_response = self.chosen_responses[item] rejected_response = self.rejected_responses[item] prompt_ids = self.tokenizer(prompt, return_tensors='pt')['input_ids'][0] chosen_response_ids = self.tokenizer(chosen_response, return_tensors='pt')['input_ids'][0] rejected_response_ids = self.tokenizer(rejected_response, return_tensors='pt')['input_ids'][0] if self.add_eos: chosen_response_ids = torch.cat((chosen_response_ids, torch.tensor([self.tokenizer.eos_token_id])), dim=-1) rejected_response_ids = torch.cat((rejected_response_ids, torch.tensor([self.tokenizer.eos_token_id])), dim=-1) chosen_input_ids = torch.cat((prompt_ids, chosen_response_ids), dim=-1) chosen_attention_mask = torch.ones_like(chosen_input_ids) rejected_input_ids = torch.cat((prompt_ids, rejected_response_ids), dim=-1) rejected_attention_mask = torch.ones_like(rejected_input_ids) chosen_input_ids, chosen_attention_mask = self._pad_to_length(chosen_input_ids, chosen_attention_mask) rejected_input_ids, rejected_attention_mask = self._pad_to_length(rejected_input_ids, rejected_attention_mask) input_ids = torch.stack((chosen_input_ids, rejected_input_ids), dim=0) attention_mask = torch.stack((rejected_input_ids, rejected_attention_mask), dim=0) return { 'input_ids': input_ids, 'attention_mask': attention_mask, } ================================================ FILE: verl/utils/dataset/sft_dataset.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ SFT dataset - We assume user pass a single parquet file. - We load all the data into the memory. Each parquet file contains """ from typing import List, Union import pandas as pd import torch from torch.utils.data import Dataset from transformers import AutoTokenizer, PreTrainedTokenizer from verl.utils.fs import copy_to_local from verl.utils.model import compute_position_id_with_mask from verl.utils import hf_tokenizer class SFTDataset(Dataset): """ This is an in-memory SFTDataset """ def __init__(self, parquet_files: Union[str, List[str]], tokenizer, prompt_key='prompt', prompt_dict_keys=None, response_key='response', response_dict_keys=None, max_length=1024, truncation='error'): assert truncation in ['error', 'left', 'right'] self.truncation = truncation if not isinstance(parquet_files, List): parquet_files = [parquet_files] self.parquet_files = parquet_files if isinstance(tokenizer, str): tokenizer = hf_tokenizer(tokenizer) self.tokenizer: PreTrainedTokenizer = tokenizer self.prompt_key = prompt_key if isinstance(prompt_key, (tuple, list)) else [prompt_key] self.response_key = response_key if isinstance(response_key, (tuple, list)) else [response_key] self.prompt_dict_keys = [] if not prompt_dict_keys else prompt_dict_keys self.response_dict_keys = [] if not response_dict_keys else response_dict_keys self.max_length = max_length self._download() self._read_files_and_tokenize() def _download(self): for i, parquet_file in enumerate(self.parquet_files): self.parquet_files[i] = copy_to_local(parquet_file, verbose=True) def _read_files_and_tokenize(self): def series_to_item(ls): import pandas, numpy while isinstance(ls, (pandas.core.series.Series, numpy.ndarray)) and len(ls) == 1: ls = ls[0] return ls dataframes = [] for parquet_file in self.parquet_files: # read parquet files and cache dataframe = pd.read_parquet(parquet_file) dataframes.append(dataframe) self.dataframe = pd.concat(dataframes) self.prompts = self.dataframe[self.prompt_key] for key in self.prompt_dict_keys: # type(x): pandas.core.series.Series # type(x[0]): numpy.ndarray # type(x[0][0]): dict try: self.prompts = self.prompts.apply(lambda x: series_to_item(x)[key], axis=1) except Exception: print(f'self.prompts={self.prompts}') raise self.prompts = self.prompts.tolist() self.responses = self.dataframe[self.response_key] for key in self.response_dict_keys: try: self.responses = self.responses.apply(lambda x: series_to_item(x)[key], axis=1) except Exception: print(f'self.responses={self.responses}') raise self.responses = self.responses.tolist() def __len__(self): return len(self.prompts) def __getitem__(self, item): tokenizer = self.tokenizer prompt = self.prompts[item] response = self.responses[item] # apply chat template prompt_chat = [{'role': 'user', 'content': prompt}] # string prompt_chat_str = tokenizer.apply_chat_template(prompt_chat, add_generation_prompt=True, tokenize=False) response_chat_str = response + tokenizer.eos_token # tokenize prompt_ids_output = tokenizer(prompt_chat_str, return_tensors='pt', add_special_tokens=False) prompt_ids = prompt_ids_output['input_ids'][0] prompt_attention_mask = prompt_ids_output['attention_mask'][0] response_ids_output = tokenizer(response_chat_str, return_tensors='pt', add_special_tokens=False) response_ids = response_ids_output['input_ids'][0] response_attention_mask = response_ids_output['attention_mask'][0] prompt_length = prompt_ids.shape[0] response_length = response_ids.shape[0] input_ids = torch.cat((prompt_ids, response_ids), dim=-1) attention_mask = torch.cat((prompt_attention_mask, response_attention_mask), dim=-1) # padding to max length sequence_length = input_ids.shape[0] if sequence_length < self.max_length: padded_input_ids = torch.ones(size=(self.max_length - sequence_length,), dtype=input_ids.dtype) * self.tokenizer.pad_token_id padded_attention_mask = torch.zeros(size=(self.max_length - sequence_length,), dtype=attention_mask.dtype) input_ids = torch.cat((input_ids, padded_input_ids)) attention_mask = torch.cat((attention_mask, padded_attention_mask)) elif sequence_length > self.max_length: if self.truncation == 'left': # actually, left truncation may not be reasonable input_ids = input_ids[-self.max_length:] attention_mask = attention_mask[-self.max_length:] elif self.truncation == 'right': input_ids = input_ids[:self.max_length] attention_mask = attention_mask[:self.max_length] elif self.truncation == 'error': raise NotImplementedError(f'{sequence_length=} is larger than {self.max_length=}') else: raise NotImplementedError(f'Unknown truncation method {self.truncation}') position_ids = compute_position_id_with_mask(attention_mask) loss_mask = attention_mask.clone() if prompt_length > 1: # mask out prompt for SFT. loss_mask[:min(prompt_length, loss_mask.size(0)) - 1] = 0 # mask out the last token in response loss_mask[min(prompt_length + response_length, loss_mask.size(0)) - 1] = 0 return { 'input_ids': input_ids, 'attention_mask': attention_mask, 'position_ids': position_ids, 'loss_mask': loss_mask } ================================================ FILE: verl/utils/debug/__init__.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from .performance import log_gpu_memory_usage ================================================ FILE: verl/utils/debug/performance.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import torch import torch.distributed as dist import logging def log_gpu_memory_usage(head: str, logger: logging.Logger = None, level=logging.DEBUG, rank: int = 0): if (not dist.is_initialized()) or (rank is None) or (dist.get_rank() == rank): memory_allocated = torch.cuda.memory_allocated() / 1024**3 memory_reserved = torch.cuda.memory_reserved() / 1024**3 message = f'{head}, memory allocated (GB): {memory_allocated}, memory reserved (GB): {memory_reserved}' if logger is None: print(message) else: logger.log(msg=message, level=level) ================================================ FILE: verl/utils/debug/trajectory_tracker.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Trajectory tracker can be inserted into code to save the intermediate results. The results will be dump to hdfs for offline comparison. Each process will have a client that first move all the tensors to CPU """ from verl.utils.hdfs_io import makedirs, copy import torch import os import ray import io import tempfile from collections import deque remote_copy = ray.remote(copy) @ray.remote def save_to_hdfs(data: io.BytesIO, name, hdfs_dir, verbose): filename = name + '.pth' with tempfile.TemporaryDirectory() as tmpdirname: local_filepath = os.path.join(tmpdirname, filename) with open(local_filepath, 'wb') as f: f.write(data.getbuffer()) # upload to hdfs if verbose: print(f'Saving {local_filepath} to {hdfs_dir}') try: copy(local_filepath, hdfs_dir) except Exception as e: print(e) @ray.remote class TrajectoryTracker(): def __init__(self, hdfs_dir, verbose) -> None: self.hdfs_dir = hdfs_dir makedirs(hdfs_dir) self.verbose = verbose self.handle = deque() def dump(self, data: io.BytesIO, name): # get a temp file and write to it self.handle.append(save_to_hdfs.remote(data, name, self.hdfs_dir, self.verbose)) def wait_for_hdfs(self): while len(self.handle) != 0: future = self.handle.popleft() ray.get(future) def dump_data(data, name): enable = os.getenv('VERL_ENABLE_TRACKER', '0') == '1' if not enable: return buffer = io.BytesIO() torch.save(data, buffer) tracker = get_trajectory_tracker() ray.get(tracker.dump.remote(buffer, name)) def get_trajectory_tracker(): hdfs_dir = os.getenv('VERL_TRACKER_HDFS_DIR', default=None) verbose = os.getenv('VERL_TRACKER_VERBOSE', default='0') == '1' assert hdfs_dir is not None tracker = TrajectoryTracker.options(name="global_tracker", get_if_exists=True, lifetime="detached").remote(hdfs_dir, verbose) return tracker if __name__ == '__main__': # testing os.environ['VERL_ENABLE_TRACKER'] = '1' os.environ['VERL_TRACKER_HDFS_DIR'] = '~/debug/test' @ray.remote def process(iter): data = {'obs': torch.randn(10, 20)} dump_data(data, f'process_{iter}_obs') ray.init() output_lst = [] for i in range(10): output_lst.append(process.remote(i)) out = ray.get(output_lst) tracker = get_trajectory_tracker() ray.get(tracker.wait_for_hdfs.remote()) ================================================ FILE: verl/utils/distributed.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Utilities for distributed training.""" import os def initialize_global_process_group(timeout_second=36000): import torch.distributed from datetime import timedelta torch.distributed.init_process_group('nccl', timeout=timedelta(seconds=timeout_second)) local_rank = int(os.environ["LOCAL_RANK"]) rank = int(os.environ["RANK"]) world_size = int(os.environ["WORLD_SIZE"]) if torch.distributed.is_initialized(): torch.cuda.set_device(local_rank) return local_rank, rank, world_size ================================================ FILE: verl/utils/flops_counter.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import torch from transformers import PretrainedConfig VALID_CONFIG_TYPE = {"llama", "qwen2", "qwen2_vl", "qwen2_5_vl"} def get_device_flops(unit="T"): def unit_convert(number, level): units = ["B", "K", "M", "G", "T", "P"] if number <= 0: return number ptr = 0 while ptr < len(units) and units[ptr] != level: number /= 1000 ptr += 1 return number device_name = torch.cuda.get_device_name() flops = float("inf") # INF flops for unkown gpu type if "H100" in device_name or "H800" in device_name: flops = 989e12 elif "A100" in device_name or "A800" in device_name: flops = 312e12 elif "L40" in device_name: flops = 181.05e12 elif "L20" in device_name: flops = 119.5e12 elif "H20" in device_name: flops = 148e12 elif "910B" in device_name: flops = 354e12 flops_unit = unit_convert(flops, unit) return flops_unit class FlopsCounter: """ Used to count mfu during training loop Example: flops_counter = FlopsCounter(config) flops_achieved, flops_promised = flops_counter.estimate_flops(tokens_list, delta_time) """ def __init__(self, config: PretrainedConfig): if not config.model_type in VALID_CONFIG_TYPE: print(f"Only support config type of {VALID_CONFIG_TYPE}, but got {self.config.model_type}. " f"MFU will always be zero.") self.estimate_func = { 'qwen2': self._estimate_qwen2_flops, 'llama': self._estimate_qwen2_flops, 'qwen2_vl': self._estimate_qwen2_flops, 'qwen2_5_vl': self._estimate_qwen2_flops } self.config = config def _estimate_unknown_flops(self, tokens_sum, batch_seqlens, delta_time): return 0 def _estimate_qwen2_flops(self, tokens_sum, batch_seqlens, delta_time): hidden_size = self.config.hidden_size vocab_size = self.config.vocab_size num_hidden_layers = self.config.num_hidden_layers num_key_value_heads = self.config.num_key_value_heads num_attention_heads = self.config.num_attention_heads intermediate_size = self.config.intermediate_size head_dim = hidden_size // num_attention_heads q_size = num_attention_heads * head_dim k_size = num_key_value_heads * head_dim v_size = num_key_value_heads * head_dim # non-attn per layer parm # Qwen2/LLama use SwiGelu, gate, having up and down linear layer in mlp mlp_N = hidden_size * intermediate_size * 3 attn_linear_N = hidden_size * (q_size + k_size + v_size + num_attention_heads * head_dim) emd_and_lm_head_N = vocab_size * hidden_size * 2 # non-attn all_layer parm dense_N = (mlp_N + attn_linear_N) * num_hidden_layers + emd_and_lm_head_N # non-attn all_layer & all_token fwd & bwd flops dense_N_flops = 6 * dense_N * tokens_sum # attn all_layer & all_token fwd & bwd flops seqlen_square_sum = 0 for seqlen in batch_seqlens: seqlen_square_sum += seqlen * seqlen attn_qkv_flops = 12 * seqlen_square_sum * head_dim * num_attention_heads * num_hidden_layers # all_layer & all_token fwd & bwd flops flops_all_token = dense_N_flops + attn_qkv_flops flops_achieved = flops_all_token * (1.0 / delta_time) / 1e12 return flops_achieved def estimate_flops(self, batch_seqlens, delta_time): """ Estimate the FLOPS based on the number of valid tokens in the current batch and the time taken. Args: batch_seqlens (List[int]): A list where each element represents the number of valid tokens in the current batch. delta_time (float): The time taken to process the batch, in seconds. Returns: estimated_flops (float): The estimated FLOPS based on the input tokens and time. promised_flops (float): The expected FLOPS of the current device. """ tokens_sum = sum(batch_seqlens) func = self.estimate_func.get(self.config.model_type, self._estimate_unknown_flops) estimated_flops = func(tokens_sum, batch_seqlens, delta_time) promised_flops = get_device_flops() return estimated_flops, promised_flops ================================================ FILE: verl/utils/fs.py ================================================ #!/usr/bin/env python # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # -*- coding: utf-8 -*- """File-system agnostic IO APIs""" import os import tempfile import hashlib try: from hdfs_io import copy, makedirs, exists # for internal use only except ImportError: from .hdfs_io import copy, makedirs, exists __all__ = ["copy", "exists", "makedirs"] _HDFS_PREFIX = "hdfs://" def is_non_local(path): return path.startswith(_HDFS_PREFIX) def md5_encode(path: str) -> str: return hashlib.md5(path.encode()).hexdigest() def get_local_temp_path(hdfs_path: str, cache_dir: str) -> str: """Return a local temp path that joins cache_dir and basename of hdfs_path Args: hdfs_path: cache_dir: Returns: """ # make a base64 encoding of hdfs_path to avoid directory conflict encoded_hdfs_path = md5_encode(hdfs_path) temp_dir = os.path.join(cache_dir, encoded_hdfs_path) os.makedirs(temp_dir, exist_ok=True) dst = os.path.join(temp_dir, os.path.basename(hdfs_path)) return dst def copy_to_local(src: str, cache_dir=None, filelock='.file.lock', verbose=False) -> str: """Copy src from hdfs to local if src is on hdfs or directly return src. If cache_dir is None, we will use the default cache dir of the system. Note that this may cause conflicts if the src name is the same between calls Args: src (str): a HDFS path of a local path Returns: a local path of the copied file """ return copy_local_path_from_hdfs(src, cache_dir, filelock, verbose) def copy_local_path_from_hdfs(src: str, cache_dir=None, filelock='.file.lock', verbose=False) -> str: """Deprecated. Please use copy_to_local instead.""" from filelock import FileLock assert src[-1] != '/', f'Make sure the last char in src is not / because it will cause error. Got {src}' if is_non_local(src): # download from hdfs to local if cache_dir is None: # get a temp folder cache_dir = tempfile.gettempdir() os.makedirs(cache_dir, exist_ok=True) assert os.path.exists(cache_dir) local_path = get_local_temp_path(src, cache_dir) # get a specific lock filelock = md5_encode(src) + '.lock' lock_file = os.path.join(cache_dir, filelock) with FileLock(lock_file=lock_file): if not os.path.exists(local_path): if verbose: print(f'Copy from {src} to {local_path}') copy(src, local_path) return local_path else: return src ================================================ FILE: verl/utils/fsdp_utils.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import Dict import functools import json import math import itertools import os from contextlib import contextmanager from torch.distributed.fsdp.wrap import size_based_auto_wrap_policy, transformer_auto_wrap_policy from torch.distributed.fsdp import FullyShardedDataParallel as FSDP from torch.distributed.fsdp._runtime_utils import _lazy_init from transformers.trainer_pt_utils import get_module_class_from_name import torch import torch.nn as nn import torch.distributed as dist def init_fn(x: torch.nn.Module): if not torch.distributed.get_rank() == 0: x = x.to_empty(device=torch.cuda.current_device(), recurse=False) torch.cuda.empty_cache() return x def get_init_weight_context_manager(use_meta_tensor=True): from accelerate import init_empty_weights cpu_init_weights = lambda: torch.device('cpu') if use_meta_tensor: init_context = init_empty_weights if torch.distributed.get_rank() != 0 else cpu_init_weights else: init_context = cpu_init_weights return init_context # Copyright 2020-present the HuggingFace Inc. team. # Adapted from https://github.com/huggingface/transformers/src/transformers/trainer.py def get_fsdp_wrap_policy(module, config=None, is_lora=False): """Get FSDP wrap policy for the module. Args: module: The module to get wrap policy for config: Configuration for wrap policy is_lora: Whether to enable lambda policy for LoRA modules """ if config is None: config = {} if config.get('disable', False): return None default_transformer_cls_names_to_wrap = getattr(module, "_no_split_modules", None) fsdp_transformer_layer_cls_to_wrap = config.get("transformer_layer_cls_to_wrap", default_transformer_cls_names_to_wrap) min_num_params = config.get('min_num_params', 0) auto_wrap_policy = None policies = [] from torch.distributed.fsdp.wrap import _or_policy, lambda_auto_wrap_policy, transformer_auto_wrap_policy # Add lambda policy for LoRA modules if is_lora is True if is_lora: def lambda_policy_fn(module): if (len(list(module.named_children())) == 0 and getattr(module, "weight", None) is not None and module.weight.requires_grad): return True return False lambda_policy = functools.partial(lambda_auto_wrap_policy, lambda_fn=lambda_policy_fn) policies.append(lambda_policy) if min_num_params > 0: size_policy = functools.partial(size_based_auto_wrap_policy, min_num_params=min_num_params) policies.append(size_policy) elif fsdp_transformer_layer_cls_to_wrap is not None: transformer_cls_to_wrap = set() for layer_class in fsdp_transformer_layer_cls_to_wrap: transformer_cls = get_module_class_from_name(module, layer_class) if transformer_cls is None: raise Exception("Could not find the transformer layer class to wrap in the model.") else: transformer_cls_to_wrap.add(transformer_cls) transformer_policy = functools.partial( transformer_auto_wrap_policy, transformer_layer_cls=transformer_cls_to_wrap, ) policies.append(transformer_policy) if len(policies) > 0: auto_wrap_policy = functools.partial(_or_policy, policies=policies) return auto_wrap_policy @torch.no_grad() def offload_fsdp_model_to_cpu(model: FSDP, empty_cache: bool = True): assert isinstance(model, FSDP) # lazy init FSDP model _lazy_init(model, model) assert model._is_root, f"Only support root model offloading to CPU" for handle in model._all_handles: if handle._offload_params: continue flat_param = handle.flat_param assert flat_param.data.data_ptr() == flat_param._local_shard.data_ptr() and \ id(flat_param.data) != id(flat_param._local_shard) and \ flat_param.data.size() == flat_param._local_shard.size() handle.flat_param_to(torch.device("cpu"), non_blocking=True) # the following still keeps id(._local_shard) != id(.data) flat_param._local_shard = flat_param.data assert id(flat_param._local_shard) != id(flat_param.data) if empty_cache: torch.cuda.empty_cache() @torch.no_grad() def load_fsdp_model_to_gpu(model: FSDP): assert isinstance(model, FSDP) # lazy init FSDP model _lazy_init(model, model) assert model._is_root, f"Only support root model loading to GPU" device_id = torch.cuda.current_device() for handle in model._all_handles: if handle._offload_params: continue flat_param = handle.flat_param handle.flat_param_to(torch.device(f"cuda:{device_id}"), non_blocking=True) # the following still keeps id(._local_shard) != id(.data) flat_param._local_shard = flat_param.data @torch.no_grad() def offload_fsdp_optimizer(optimizer): if not optimizer.state: return for param_group in optimizer.param_groups: for param in param_group['params']: state = optimizer.state[param] for key, value in state.items(): if isinstance(value, torch.Tensor): state[key] = value.to("cpu", non_blocking=True) @torch.no_grad() def load_fsdp_optimizer(optimizer, device_id): if not optimizer.state: return for param_group in optimizer.param_groups: for param in param_group['params']: state = optimizer.state[param] for key, value in state.items(): if isinstance(value, torch.Tensor): state[key] = value.to(device_id, non_blocking=True) @contextmanager def meta_device_init(): """ Create model parameters with meta device. Note buffers in model will still be initialized in default device (e.g., CPU), since the buffers can be non-persistent and filled with expected values that can NOT be captured in meta device. """ device = torch.device("meta") old_register_parameter = nn.Module.register_parameter registered = set() def register_empty_parameter(module, name, param): old_register_parameter(module, name, param) # we will skip register shared parameters as it # is already registered previously if param is not None and param not in registered: param_cls = type(module._parameters[name]) kwargs = module._parameters[name].__dict__ kwargs["requires_grad"] = param.requires_grad module._parameters[name] = param_cls(module._parameters[name].to(device), **kwargs) registered.add(module._parameters[name]) try: nn.Module.register_parameter = register_empty_parameter yield finally: registered.clear() nn.Module.register_parameter = old_register_parameter def parallel_load_safetensors(filepath): """ Parallel load safetensors from huggingface checkpoint Huggingface checkpoint contains: - config.json: a json file for model configuration - model.safetensor.index.json: a json file for safetensors (parameters & buffers) index - model-000x-of-ooxx.safetensors: a binary file for safetensors (parameters & buffers) chunks Or (when model is small), - model.safetensors: a binary file for all parameters and buffers Each rank will own a part of model chunks and load them directly into GPU memory. """ from safetensors.torch import load_file safetensors2param = {} index_file = os.path.join(filepath, "model.safetensors.index.json") if os.path.exists(index_file): index = json.load(open(index_file, "rb")) for param_name, filename in index["weight_map"].items(): safetensors2param.setdefault(filename, []).append(param_name) else: # in this case, the model is small and we can load it all at once param_file = os.path.join(filepath, "model.safetensors") assert os.path.exists(param_file), f"Cannot find {param_file}" states = load_file(param_file) for param_name in states: safetensors2param.setdefault("model.safetensors", []).append(param_name) del states total_files = len(safetensors2param) ckpt_chunks = sorted(safetensors2param.keys()) world_size = dist.get_world_size() size = int(math.ceil(total_files / world_size)) ckpt_chunks = [ckpt_chunks[rank * size:rank * size + size] for rank in range(world_size)] shard_states = {} device = torch.cuda.current_device() for rank, files in enumerate(ckpt_chunks): if rank == dist.get_rank(): for file in files: file = os.path.join(filepath, file) states = load_file(file, device=device) # print(f"rank {rank} loading {file}...") shard_states.update(states) else: for file in files: for param_name in safetensors2param[file]: shard_states[param_name] = rank return shard_states def parallel_init_module_fn(module: torch.nn.Module, shard_states: Dict[str, torch.nn.Parameter]): """ Generate a function to initialize sub-modules in the `module` with `shard_states` from huggingface checkpoint. Args: module (torch.nn.Module): the global module to be initialized shard_states (Dict[str, torch.nn.Parameter]): the shard states from huggingface checkpoint Returns: init_fn (Callable): a function to initialize sub-modules in the `module` with `shard_states` """ state2fqn = {} for name, state in itertools.chain(module.named_parameters(remove_duplicate=False), module.named_buffers(remove_duplicate=False)): state2fqn.setdefault(state, []).append(name) # remove standalone parameters and buffers shared = {s for s, names in state2fqn.items() if len(names) > 1} materialized_states = {} @torch.no_grad() def create_and_sync_state(param_name, state, is_param): assert param_name in shard_states, f"{param_name} not loaded" device = torch.cuda.current_device() if is_param: param = torch.nn.Parameter(torch.empty_like(state.data, device=device), requires_grad=state.requires_grad) else: # buffer param = torch.empty_like(state.data, device=device) loaded = shard_states[param_name] if isinstance(loaded, (torch.nn.Parameter, torch.Tensor)): # NOTE: loaded.dtype can be different with param.dtype param.data.copy_(loaded.data) dist.broadcast(param.data, src=dist.get_rank()) else: assert isinstance(loaded, int) # the rank that holds the state dist.broadcast(param.data, src=loaded) shard_states.pop(param_name) del loaded return param def init_fn(sub_mod: torch.nn.Module, recurse: bool = True): param_and_buffers = tuple(sub_mod.named_parameters(recurse=False)) + tuple(sub_mod.named_buffers(recurse=False)) # param_and_buffers = sorted(sub_mod.named_parameters(recurse=False), key=lambda x: x[0]) for name, state in param_and_buffers: if not state.is_meta: continue is_param = name in sub_mod._parameters fqn = state2fqn[state].pop(0) # non-persistent buffers will not be saved in state dict, we can safely skip it if (not is_param) and fqn not in shard_states: if state.is_meta: raise RuntimeError( f"find a non-persistent buffer ({fqn}) initiated with device meta. " "Such buffer is not saved in checkpoint and user should guarantee to init in CPU / GPU device.") continue # for shared parameter, we get it from the first time it is created if state in shared: if state not in materialized_states: materialized_states[state] = create_and_sync_state(fqn, state, is_param) else: if fqn in shard_states: shard_states.pop(fqn) materialize_state = materialized_states[state] # for not shared parameter, we create it directly else: materialize_state = create_and_sync_state(fqn, state, is_param) if is_param: sub_mod._parameters[name] = materialize_state else: sub_mod._buffers[name] = materialize_state if recurse: for module in sub_mod.children(): init_fn(module, recurse=True) # for debug # if len(shard_states) == 0: print("clear") return sub_mod return init_fn ================================================ FILE: verl/utils/hdfs_io.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import shutil import logging logger = logging.getLogger(__file__) logger.setLevel(os.getenv('VERL_SFT_LOGGING_LEVEL', 'WARN')) _HDFS_PREFIX = "hdfs://" _HDFS_BIN_PATH = shutil.which('hdfs') def exists(path: str, **kwargs) -> bool: r"""Works like os.path.exists() but supports hdfs. Test whether a path exists. Returns False for broken symbolic links. Args: path (str): path to test Returns: bool: True if the path exists, False otherwise """ if _is_non_local(path): return _exists(path, **kwargs) return os.path.exists(path) def _exists(file_path: str): """ hdfs capable to check whether a file_path is exists """ if file_path.startswith("hdfs"): return _run_cmd(_hdfs_cmd(f"-test -e {file_path}")) == 0 return os.path.exists(file_path) def makedirs(name, mode=0o777, exist_ok=False, **kwargs) -> None: r"""Works like os.makedirs() but supports hdfs. Super-mkdir; create a leaf directory and all intermediate ones. Works like mkdir, except that any intermediate path segment (not just the rightmost) will be created if it does not exist. If the target directory already exists, raise an OSError if exist_ok is False. Otherwise no exception is raised. This is recursive. Args: name (str): directory to create mode (int): file mode bits exist_ok (bool): if True, do not raise an exception if the directory already exists kwargs: keyword arguments for hdfs """ if _is_non_local(name): # TODO(haibin.lin): # - handle OSError for hdfs(?) # - support exist_ok for hdfs(?) _mkdir(name, **kwargs) else: os.makedirs(name, mode=mode, exist_ok=exist_ok) def _mkdir(file_path: str) -> bool: """hdfs mkdir""" if file_path.startswith("hdfs"): _run_cmd(_hdfs_cmd(f"-mkdir -p {file_path}")) else: os.makedirs(file_path, exist_ok=True) return True def copy(src: str, dst: str, **kwargs) -> bool: r"""Works like shutil.copy() for file, and shutil.copytree for dir, and supports hdfs. Copy data and mode bits ("cp src dst"). Return the file's destination. The destination may be a directory. If source and destination are the same file, a SameFileError will be raised. Arg: src (str): source file path dst (str): destination file path kwargs: keyword arguments for hdfs copy Returns: str: destination file path """ if _is_non_local(src) or _is_non_local(dst): # TODO(haibin.lin): # - handle SameFileError for hdfs files(?) # - return file destination for hdfs files return _copy(src, dst) else: if os.path.isdir(src): return shutil.copytree(src, dst, **kwargs) else: return shutil.copy(src, dst, **kwargs) def _copy(from_path: str, to_path: str, timeout: int = None) -> bool: if to_path.startswith("hdfs"): if from_path.startswith("hdfs"): returncode = _run_cmd(_hdfs_cmd(f"-cp -f {from_path} {to_path}"), timeout=timeout) else: returncode = _run_cmd(_hdfs_cmd(f"-put -f {from_path} {to_path}"), timeout=timeout) else: if from_path.startswith("hdfs"): returncode = _run_cmd(_hdfs_cmd(f"-get \ {from_path} {to_path}"), timeout=timeout) else: try: shutil.copy(from_path, to_path) returncode = 0 except shutil.SameFileError: returncode = 0 except Exception as e: logger.warning(f"copy {from_path} {to_path} failed: {e}") returncode = -1 return returncode == 0 def _run_cmd(cmd: str, timeout=None): return os.system(cmd) def _hdfs_cmd(cmd: str) -> str: return f"{_HDFS_BIN_PATH} dfs {cmd}" def _is_non_local(path: str): return path.startswith(_HDFS_PREFIX) ================================================ FILE: verl/utils/import_utils.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Utilities to check if packages are available. We assume package availability won't change during runtime. """ from functools import cache from typing import List @cache def is_megatron_core_available(): try: from megatron.core import parallel_state as mpu return True except ImportError: return False @cache def is_vllm_available(): try: import vllm return True except ImportError: return False def import_external_libs(external_libs=None): if external_libs is None: return if not isinstance(external_libs, List): external_libs = [external_libs] import importlib for external_lib in external_libs: importlib.import_module(external_lib) ================================================ FILE: verl/utils/logger/__init__.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. ================================================ FILE: verl/utils/logger/aggregate_logger.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ A Ray logger will receive logging info from different processes. """ import numbers from typing import Dict def concat_dict_to_str(dict: Dict, step): output = [f'step:{step}'] for k, v in dict.items(): if isinstance(v, numbers.Number): output.append(f'{k}:{v:.3f}') output_str = ' - '.join(output) return output_str class LocalLogger: def __init__(self, remote_logger=None, enable_wandb=False, print_to_console=False): self.print_to_console = print_to_console if print_to_console: print('Using LocalLogger is deprecated. The constructor API will change ') def flush(self): pass def log(self, data, step): if self.print_to_console: print(concat_dict_to_str(data, step=step), flush=True) ================================================ FILE: verl/utils/logging_utils.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import logging def set_basic_config(level): """ This function sets the global logging format and level. It will be called when import verl """ logging.basicConfig(format='%(levelname)s:%(asctime)s:%(message)s', level=level) ================================================ FILE: verl/utils/megatron/__init__.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. ================================================ FILE: verl/utils/megatron/memory.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import torch class MemoryBuffer: def __init__(self, numel, numel_padded, dtype): self.numel = numel self.numel_padded = numel_padded self.dtype = dtype self.data = torch.zeros(self.numel_padded, dtype=self.dtype, device=torch.cuda.current_device(), requires_grad=False) def zero(self): """Reset the buffer to zero.""" self.data.zero_() def get(self, shape, start_index): """Return a tensor with the input `shape` as a view into the 1-D data starting at `start_index`.""" end_index = start_index + shape.numel() assert end_index <= self.numel, \ 'requested tensor is out of the buffer range.' buffer_tensor = self.data[start_index:end_index] buffer_tensor = buffer_tensor.view(shape) return buffer_tensor ================================================ FILE: verl/utils/megatron/optimizer.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import importlib from packaging.version import Version from apex.optimizers import FusedAdam as Adam from apex.optimizers import FusedSGD as SGD from megatron.core.optimizer import OptimizerConfig from megatron.core.optimizer import get_megatron_optimizer as get_megatron_optimizer_native def get_megatron_optimizer( model, config: OptimizerConfig, no_weight_decay_cond=None, scale_lr_cond=None, lr_mult=1.0, check_for_nan_in_loss_and_grad=False, overlap_param_gather=False # add for verl ): # Base optimizer. return get_megatron_optimizer_native(config=config, model_chunks=model, no_weight_decay_cond=no_weight_decay_cond, scale_lr_cond=scale_lr_cond, lr_mult=lr_mult) ================================================ FILE: verl/utils/megatron/pipeline_parallel.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import torch from megatron.core import parallel_state as mpu from .sequence_parallel import pad_to_sequence_parallel def compute_transformers_input_shapes(batches, meta_info): from flash_attn.bert_padding import unpad_input # flash 2 is a must for Megatron # pre-compute input shapes for each micro-batch at each pp stage input_shapes = [] for model_inputs in batches: input_ids = model_inputs['input_ids'] attention_mask = model_inputs['attention_mask'] input_ids_rmpad = unpad_input(input_ids.unsqueeze(dim=-1), attention_mask)[0] # (total_nnz, 1) if meta_info['sequence_parallel']: input_ids_rmpad = pad_to_sequence_parallel(input_ids_rmpad) # compute shapes for model_inputs input_shapes.append( torch.Size([ input_ids_rmpad.shape[0] // mpu.get_tensor_model_parallel_world_size(), 1, meta_info['hidden_size'] ])) else: # compute shapes for model_inputs input_shapes.append(torch.Size([input_ids_rmpad.shape[0], 1, meta_info['hidden_size']])) return input_shapes def make_batch_generator(batches, vpp_size): if vpp_size > 1: # has vpp batch_generator = [batches] * vpp_size # number of vpp chunks batch_generator = [iter(b) for b in batch_generator] else: # no vpp batch_generator = iter(batches) return batch_generator ================================================ FILE: verl/utils/megatron/sequence_parallel.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import torch import torch.nn.functional as F from megatron.core import parallel_state as mpu def mark_parameter_as_sequence_parallel(parameter): setattr(parameter, 'sequence_parallel', True) def is_sequence_parallel_param(param): return hasattr(param, 'sequence_parallel') and param.sequence_parallel def pad_to_sequence_parallel(unpad_tokens: torch.Tensor): """pad the tokens such that the total length is a multiple of sp world size Args: unpad_tokens: (total_nnz, ...). Tokens after removing padding Returns: """ total_nnz = unpad_tokens.shape[0] sp_world_size = mpu.get_tensor_model_parallel_world_size() if total_nnz % sp_world_size == 0: pad_size = 0 else: pad_size = sp_world_size - total_nnz % sp_world_size if pad_size > 0: if unpad_tokens.ndim == 1: unpad_tokens = F.pad(unpad_tokens, (0, pad_size)) elif unpad_tokens.ndim == 2: unpad_tokens = F.pad(unpad_tokens, (0, 0, 0, pad_size)) else: raise NotImplementedError(f'Padding dim {unpad_tokens.ndim()} is not supported') return unpad_tokens ================================================ FILE: verl/utils/megatron/tensor_parallel.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Utilities for using tensor_parallel in megatron """ from typing import Dict import torch from torch.nn import init import torch.distributed as dist from megatron.core import ModelParallelConfig from megatron.core import parallel_state as mpu, tensor_parallel import verl.utils.torch_functional as verl_F def update_kwargs_with_config(dictionary: Dict, config: ModelParallelConfig): dictionary['config'] = config return dictionary def get_default_kwargs_for_model_parallel_config(): model_parallel_config_kwargs = { 'params_dtype': torch.float32, 'use_cpu_initialization': False, 'perform_initialization': True, 'gradient_accumulation_fusion': False, 'sequence_parallel': False, } return model_parallel_config_kwargs def get_default_model_parallel_config(): return ModelParallelConfig(**get_default_kwargs_for_model_parallel_config()) def get_common_default_kwargs_for_parallel_linear(): default_model_parallel_config = get_default_model_parallel_config() common_default_kwargs = { 'init_method': init.xavier_normal_, 'stride': 1, 'keep_master_weight_for_test': False, 'config': default_model_parallel_config, } return common_default_kwargs def get_default_kwargs_for_column_parallel_linear(): model_parallel_config_kwargs = get_default_kwargs_for_model_parallel_config() column_parallel_config_kwargs = { 'async_tensor_model_parallel_allreduce': False, } model_parallel_config_kwargs.update(column_parallel_config_kwargs) column_default_kwargs = { 'config': ModelParallelConfig(**model_parallel_config_kwargs), } common_default_kwargs = get_common_default_kwargs_for_parallel_linear() common_default_kwargs.update(column_default_kwargs) return common_default_kwargs def get_default_kwargs_for_row_parallel_linear(): common_default_kwargs = get_common_default_kwargs_for_parallel_linear() return common_default_kwargs def get_default_kwargs_for_parallel_embedding(): model_parallel_config_kwargs = get_default_kwargs_for_model_parallel_config() embedding_default_kwargs = { 'init_method': init.xavier_normal_, 'config': ModelParallelConfig(**model_parallel_config_kwargs), } return embedding_default_kwargs def is_tensor_parallel_param(param): return (hasattr(param, 'tensor_model_parallel') and param.tensor_model_parallel) def get_tensor_parallel_partition_dim(param): assert is_tensor_parallel_param(param) return param.partition_dim def get_tensor_parallel_partition_stride(param): assert is_tensor_parallel_param(param) return param.partition_stride class _VocabParallelEntropy(torch.autograd.Function): @staticmethod def forward(ctx, vocab_parallel_logits: torch.Tensor) -> torch.Tensor: logits_max = vocab_parallel_logits.max(dim=-1, keepdim=True).values dist.all_reduce(logits_max, op=dist.ReduceOp.MAX, group=mpu.get_tensor_model_parallel_group()) normalized_vocab_parallel_logits = vocab_parallel_logits - logits_max normalized_exp_logits = normalized_vocab_parallel_logits.exp() normalized_sum_exp_logits = normalized_exp_logits.sum(dim=-1, keepdim=True) dist.all_reduce(normalized_sum_exp_logits, group=mpu.get_tensor_model_parallel_group()) softmax_logits = normalized_exp_logits / normalized_sum_exp_logits sum_softmax_times_logits = (softmax_logits * vocab_parallel_logits).sum(dim=-1, keepdim=True) dist.all_reduce(sum_softmax_times_logits, group=mpu.get_tensor_model_parallel_group()) entropy = logits_max + normalized_sum_exp_logits.log() - sum_softmax_times_logits ctx.save_for_backward(vocab_parallel_logits, softmax_logits, sum_softmax_times_logits) return entropy.squeeze(dim=-1) @staticmethod def backward(ctx, grad_output: torch.Tensor) -> torch.Tensor: vocab_parallel_logits, softmax_logits, sum_softmax_times_logits = ctx.saved_tensors grad_input = grad_output.unsqueeze(dim=-1) * softmax_logits * (sum_softmax_times_logits - vocab_parallel_logits) return grad_input def vocab_parallel_entropy(vocab_parallel_logits: torch.Tensor) -> torch.Tensor: """Compute entropy when the logits are sharded in tp ranks Args: vocab_parallel_logits: (total_nnz, vocab_size // tp_size) Returns: (total_nnz,) """ return _VocabParallelEntropy.apply(vocab_parallel_logits) def vocab_parallel_log_probs_from_logits(logits, labels): """TODO(zhangchi.usc1992): We may change the implementation later""" return -tensor_parallel.vocab_parallel_cross_entropy(vocab_parallel_logits=logits, target=labels) def vocab_parallel_log_probs_from_logits_response_rmpad(input_ids, attention_mask, logits_rmpad, response_length): """Similar to log_probs_from_logits_response_rmpad, but the logits_rmpad is now spliited across tensor parallel region. This will further reduce the peak memory usage during training Args: input_ids: [batch_size, seqlen] attention_mask: [batch_size, seqlen] logits_rmpad: [total_nnz, vocab_size // tp_size] response_length: int """ from flash_attn.bert_padding import pad_input, unpad_input batch_size, seqlen = input_ids.shape input_ids_rmpad, indices, *_ = unpad_input(input_ids.unsqueeze(-1), attention_mask=attention_mask) input_ids_rmpad = input_ids_rmpad.squeeze(-1) input_ids_rmpad_rolled = torch.roll(input_ids_rmpad, shifts=-1, dims=0) full_log_probs_rmpad = vocab_parallel_log_probs_from_logits(logits=logits_rmpad, labels=input_ids_rmpad_rolled) # (total_nnz,) full_output = pad_input(hidden_states=full_log_probs_rmpad.unsqueeze(-1), indices=indices, batch=batch_size, seqlen=seqlen) output = full_output.squeeze(-1)[:, -response_length - 1:-1] # [batch_size, response_length] return output def vocab_parallel_compute_entropy_loss(logits, eos_mask): """Compute Categorical entropy loss Args: logits: `(torch.Tensor)` shape: (bs, response_length, vocab_size) eos_mask: `(torch.Tensor)` shape: (bs, response_length) Returns: entropy: a scalar torch.Tensor """ # compute entropy entropy = vocab_parallel_entropy(logits) entropy_loss = verl_F.masked_mean(entropy, mask=eos_mask) return entropy_loss ================================================ FILE: verl/utils/megatron_utils.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Pretrain utilities.""" import importlib from packaging.version import Version from typing import Any, Dict import time from omegaconf import DictConfig from verl.utils.torch_dtypes import PrecisionType from verl.utils.memory_buffer import build_memory_reference_from_module import torch import torch.nn as nn import torch.nn.functional as F from megatron.core import mpu, tensor_parallel from megatron.core.utils import get_attr_wrapped_model from megatron.core.transformer import TransformerConfig from megatron.core.transformer.module import Float16Module from megatron.core.distributed import DistributedDataParallelConfig from megatron.core.distributed import DistributedDataParallel as DDP from megatron.core.enums import ModelType from megatron.core import ModelParallelConfig from megatron.core.optimizer import OptimizerConfig def get_model_config(model): return get_attr_wrapped_model(model, 'megatron_config', allow_none=False) def get_model(model_provider_func, model_type=ModelType.encoder_or_decoder, wrap_with_ddp=True): """Build the model.""" # Build model. if mpu.get_pipeline_model_parallel_world_size() > 1 and \ mpu.get_virtual_pipeline_model_parallel_world_size() is not None: assert model_type != ModelType.encoder_and_decoder, \ "Interleaved schedule not supported for model with both encoder and decoder" model = [] for i in range(mpu.get_virtual_pipeline_model_parallel_world_size()): mpu.set_virtual_pipeline_model_parallel_rank(i) # Set pre_process and post_process only after virtual rank is set. pre_process = mpu.is_pipeline_first_stage() post_process = mpu.is_pipeline_last_stage() this_model = model_provider_func(pre_process=pre_process, post_process=post_process) this_model.model_type = model_type model.append(this_model) else: pre_process = mpu.is_pipeline_first_stage() post_process = mpu.is_pipeline_last_stage() add_encoder = True add_decoder = True if model_type == ModelType.encoder_and_decoder: if mpu.get_pipeline_model_parallel_world_size() > 1: assert mpu.get_pipeline_model_parallel_split_rank() is not None, \ "Split rank needs to be specified for model with both encoder and decoder" rank = mpu.get_pipeline_model_parallel_rank() split_rank = mpu.get_pipeline_model_parallel_split_rank() world_size = mpu.get_pipeline_model_parallel_world_size() pre_process = rank == 0 or rank == split_rank post_process = (rank == (split_rank - 1)) or (rank == (world_size - 1)) add_encoder = mpu.is_pipeline_stage_before_split() add_decoder = mpu.is_pipeline_stage_after_split() model = model_provider_func(pre_process=pre_process, post_process=post_process, add_encoder=add_encoder, add_decoder=add_decoder) else: model = model_provider_func(pre_process=pre_process, post_process=post_process) model.model_type = model_type if not isinstance(model, list): model = [model] # Set tensor model parallel attributes if not set. # Only parameters that are already tensor model parallel have these # attributes set for them. We should make sure the default attributes # are set for all params so the optimizer can use them. for model_module in model: for param in model_module.parameters(): tensor_parallel.set_defaults_if_not_set_tensor_model_parallel_attributes(param) # Print number of parameters. if mpu.get_data_parallel_rank() == 0: print(' > number of parameters on (tensor, pipeline) ' 'model parallel rank ({}, {}): {}'.format( mpu.get_tensor_model_parallel_rank(), mpu.get_pipeline_model_parallel_rank(), sum([sum([p.nelement() for p in model_module.parameters()]) for model_module in model])), flush=True) # GPU allocation. for model_module in model: model_module.cuda(torch.cuda.current_device()) # Fp16 conversion. config: ModelParallelConfig = get_model_config(model[0]) config.fp8 = None tfconfig: TransformerConfig = convert_config(model[0].config, config) if config.fp16 or config.bf16: # the ModelParallelConfig in GPTModel model = [Float16Module(config, model_module) for model_module in model] if wrap_with_ddp: ddp_models = [] for model_chunk_idx, model_chunk in enumerate(model): ddp_model = DDP( config=tfconfig, module=model_chunk, disable_bucketing=(model_chunk_idx > 0), ddp_config=DistributedDataParallelConfig( overlap_grad_reduce=False, use_distributed_optimizer=True, grad_reduce_in_fp32=True, # [old] accumulate_allreduce_grads_in_fp32=True, )) ddp_models.append(ddp_model) model = ddp_models # # Broadcast params from data parallel src rank to other data parallel ranks. # # if args.data_parallel_random_init: for model_module in model: model_module.broadcast_params() return model ALL_MODULE_WRAPPER_CLASSNAMES = (DDP, Float16Module) def unwrap_model(model, module_instances=ALL_MODULE_WRAPPER_CLASSNAMES): return_list = True if not isinstance(model, list): model = [model] return_list = False unwrapped_model = [] for model_module in model: while isinstance(model_module, module_instances): model_module = model_module.module unwrapped_model.append(model_module) if not return_list: return unwrapped_model[0] return unwrapped_model from transformers import PretrainedConfig def convert_config(hf_config: PretrainedConfig, megatron_config) -> TransformerConfig: print(f'megatron config {megatron_config}') dt = PrecisionType.to_dtype(megatron_config.params_dtype) print(f'pipeline_dtype=megatron_config {dt}') transformer_config = TransformerConfig( num_layers=hf_config.num_hidden_layers, hidden_size=hf_config.hidden_size, num_attention_heads=hf_config.num_attention_heads, num_query_groups=hf_config.num_key_value_heads, ffn_hidden_size=hf_config.intermediate_size, # max_position_embeddings=hf_config.max_position_embeddings, activation_func=F.silu, normalization='RMSNorm', # rotary_percent=False, # default, gated_linear_unit=True, # for llama use_cpu_initialization=True, apply_residual_connection_post_layernorm=False, # check what's this mean add_bias_linear=False, tensor_model_parallel_size=mpu.get_tensor_model_parallel_world_size(), pipeline_model_parallel_size=mpu.get_pipeline_model_parallel_world_size(), virtual_pipeline_model_parallel_size=mpu.get_virtual_pipeline_model_parallel_world_size(), pipeline_dtype=dt, params_dtype=dt, sequence_parallel=True, variable_seq_lengths=True, masked_softmax_fusion=True, moe_token_dispatcher_type="alltoall", bf16=dt is torch.bfloat16) if torch.distributed.get_rank() == 0: print(f'tensor_parallel_size={transformer_config.tensor_model_parallel_size} \n \ pipeline_model_parallel_size={transformer_config.pipeline_model_parallel_size} \n \ virtual_pipeline_model_parallel_size={transformer_config.virtual_pipeline_model_parallel_size} \n \ pipeline_dtype={transformer_config.pipeline_dtype} \n \ params_dtype={transformer_config.params_dtype} \n \ sequence_parallel={transformer_config.sequence_parallel} \n \ variable_seq_lengths={transformer_config.variable_seq_lengths} \n \ masked_softmax_fusion={transformer_config.masked_softmax_fusion} \n ') return transformer_config def init_megatron_optim_config(optim_config: Dict) -> OptimizerConfig: config = OptimizerConfig( optimizer='adam', lr=optim_config.get('lr'), clip_grad=optim_config.get('clip_grad'), weight_decay=1e-2, bf16=True, params_dtype=torch.bfloat16, use_distributed_optimizer=True, ) return config def init_model_parallel_config(config: DictConfig) -> ModelParallelConfig: # TODO(sgm): check how to disable megatron timers timers = None return ModelParallelConfig(tensor_model_parallel_size=config.get('tensor_model_parallel_size'), pipeline_model_parallel_size=config.get('pipeline_model_parallel_size'), virtual_pipeline_model_parallel_size=config.get('virtual_pipeline_model_parallel_size'), sequence_parallel=config.get('sequence_parallel'), params_dtype=PrecisionType.to_dtype(config.get('param_dtype')), pipeline_dtype=PrecisionType.to_dtype(config.get('param_dtype')), bf16=True, fp16=False, timers=timers) def offload_megatron_param_and_grad(module_list: nn.ModuleList, offload_grad=False, hybrid_engine=None): if hybrid_engine is not None: pp_rank = mpu.get_pipeline_model_parallel_rank() for buffer in hybrid_engine.memory_buffers[pp_rank].values(): buffer.data = buffer.data.to('cpu', non_blocking=True) build_memory_reference_from_module(module_list, hybrid_engine.memory_buffers[pp_rank], maintain_weight=True) else: for module in module_list: for _, param in module.named_parameters(): param.data = param.data.to('cpu', non_blocking=True) if offload_grad and param.grad is not None: param.grad = param.grad.to("cpu", non_blocking=True) torch.cuda.empty_cache() def load_megatron_param_and_grad(module_list: nn.ModuleList, device_id, load_grad=False, hybrid_engine=None): if hybrid_engine is not None: pp_rank = mpu.get_pipeline_model_parallel_rank() for buffer in hybrid_engine.memory_buffers[pp_rank].values(): buffer.data = buffer.data.to(device_id, non_blocking=True) build_memory_reference_from_module(module_list, hybrid_engine.memory_buffers[pp_rank], maintain_weight=True) else: for module in module_list: for _, param in module.named_parameters(): param.data = param.data.to(device_id, non_blocking=True) if load_grad and param.grad is not None: param.grad = param.grad.to(device_id, non_blocking=True) torch.cuda.empty_cache() ================================================ FILE: verl/utils/memory_buffer.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ This file contains utilities to manipulate torch memory buffers """ from typing import Dict, List, Optional import torch from torch import nn class MemoryBuffer: """ A memory buffer is a contiguous torch tensor that may combine multiple tensors sharing with the underlying memory. It must have a unique type to support this behavior. """ def __init__(self, numel: int, numel_padded: int, dtype: torch.dtype, source: Optional[torch.Tensor] = None): self.numel = numel self.numel_padded = numel_padded self.dtype = dtype if source is not None: self.data = source else: self.data = torch.zeros(self.numel_padded, dtype=self.dtype, device='cuda', requires_grad=False) def zero(self): """Reset the buffer to zero.""" self.data.zero_() def get(self, shape, start_index): """Return a tensor with the input `shape` as a view into the 1-D data starting at `start_index`.""" end_index = start_index + shape.numel() assert end_index <= self.numel, \ 'requested tensor is out of the buffer range.' buffer_tensor = self.data[start_index:end_index] buffer_tensor = buffer_tensor.view(shape) return buffer_tensor def calc_padded_numel(shape: torch.Size, dtype: torch.dtype): """for cuda memory alignment, make sure alignment by 128-bits""" align_numel = 128 // torch.finfo(dtype).bits numel = shape.numel() return (numel + align_numel - 1) // align_numel * align_numel def get_weight_buffer_meta_from_module(module: nn.Module) -> Dict[str, Dict]: """ Return a dictionary containing name to a shape and dtype. """ weight_buffer_meta = {} for name, param in sorted(module.named_parameters()): weight_buffer_meta[name] = {'shape': param.shape, 'dtype': param.dtype} return weight_buffer_meta def build_memory_buffer(weight_buffer_meta: Dict[str, Dict]) -> Dict[torch.dtype, MemoryBuffer]: """Build the memory buffer given weight_buffer_meta Args: weight_buffer_meta: contains mapping from name to a dictionary containing shape and dtype of the tensors Returns: a large memory buffer for each dtype that can hold all the tensors """ memory_buffers = {} total_numel_map = {} # map from dtype to the total numel for name, meta_info in sorted(weight_buffer_meta.items()): shape = meta_info['shape'] dtype = meta_info['dtype'] assert isinstance(shape, torch.Size) assert isinstance(dtype, torch.dtype) if dtype not in total_numel_map: total_numel_map[dtype] = 0 total_numel_map[dtype] += calc_padded_numel(shape, dtype) for dtype, total_numel in total_numel_map.items(): memory_buffers[dtype] = MemoryBuffer(total_numel, total_numel, dtype) return memory_buffers def build_memory_reference_from_module(module: torch.nn.Module, memory_buffers: Dict[torch.dtype, MemoryBuffer], maintain_weight=True): start_index = {} for dtype in memory_buffers.keys(): start_index[dtype] = 0 for name, param in sorted(module.named_parameters()): memory_buffer = memory_buffers[param.dtype] buffer = memory_buffer.get(shape=param.shape, start_index=start_index[param.dtype]) # need to increment start_index start_index[param.dtype] += calc_padded_numel(param.shape, dtype) if maintain_weight: buffer.copy_(param.data) param.data = buffer def build_memory_reference(weight_buffer_meta: Dict[str, Dict], memory_buffers: Dict[torch.dtype, MemoryBuffer]): """Build the memory references. The memory buffers are built using the build_memory_buffer API. This API will allocate a weight buffer pointer to the memory buffer according to the weight_buffer_meta. Args: weight_buffer_meta: memory_buffers: Returns: """ start_idx = {} weight_buffers = {} for dtype in memory_buffers.keys(): start_idx[dtype] = 0 for name, meta_info in sorted(weight_buffer_meta.items()): shape = meta_info['shape'] dtype = meta_info['dtype'] buffer = memory_buffers[dtype].get(shape, start_index=start_idx[dtype]) start_idx[dtype] += calc_padded_numel(shape, dtype) weight_buffers[name] = buffer return weight_buffers class MemoryBufferModuleWrapper: """ Note that we do not design MemoryBufferModuleWrapper as an nn.Module due to - It will change the checkpoint name """ def __init__(self, module: nn.Module): super().__init__() self.module = module self.weight_buffer_meta = get_weight_buffer_meta_from_module(self.module) self.memory_buffers = build_memory_buffer(self.weight_buffer_meta) build_memory_reference_from_module(self.module, self.memory_buffers) def get_memory_buffers(self): return self.memory_buffers def get_weight_buffer_meta(self): return self.weight_buffer_meta class MegatronMemoryBufferForRollout(object): """ We assume that - inference engine has tp + dp - actor has tp + pp + dp - the tp between inference engine and actor should be the same - memory_buffers: contains a list of memory_buffers, each is a dict from dtype to MemoryBuffer - weight_buffers: contains a list of weight_buffers, each is a dict from name to param - named_parameters: a dict from name to parameter that normalizes the names from pp and vpp. Note that the named_parameters may not be directly compatible with inference engine. User has to take care of this part such as the layout mismatches. (e.g. qkv transpose) - Note that weight_buffer, named_parameters and memory_buffers share the same underlying GPU memory. - When doing weight sync, the data is transfer via memory buffers """ def __init__(self, transform_memory_param_fn): self._memory_buffers = [] self._weight_buffers = [] self._named_parameters = {} self.transform_memory_param_fn = transform_memory_param_fn def initialize_weight_buffer(self, weight_buffer_meta_pp: List[Dict[str, Dict]]): """ Initialize the weight buffer. The weight buffer is obtained according to the actor. We will construct a large buffer for each dtype in the weight_buffer. Args: weight_buffer_meta: contains pp models, each pp models contains a dictionary of mapping from Returns: None """ self.weight_buffer_meta_pp = weight_buffer_meta_pp for weight_buffer_meta in self.weight_buffer_meta_pp: memory_buffer = build_memory_buffer(weight_buffer_meta) self._memory_buffers.append(memory_buffer) self._weight_buffers.append(None) def build_memory_reference(self): for i, weight_buffer_meta in enumerate(self.weight_buffer_meta_pp): self._weight_buffers[i] = build_memory_reference(weight_buffer_meta, self._memory_buffers[i]) self._named_parameters = self.transform_memory_param_fn(self._weight_buffers) @property def named_parameters(self): return self._named_parameters @property def weight_buffers(self): return self._weight_buffers @property def memory_buffers(self): return self._memory_buffers ================================================ FILE: verl/utils/model.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Utilities to create common models from huggingface """ import os import warnings from typing import Dict, Type, Optional import numpy as np import torch from torch import nn from transformers import AutoConfig, AutoModelForCausalLM, PretrainedConfig, MistralForSequenceClassification, GenerationConfig from verl.models.registry import ModelRegistry class LambdaLayer(nn.Module): def __init__(self, fn): super().__init__() self.fn = fn def forward(self, *args, **kwargs): return self.fn(*args, **kwargs) def squeeze(x): return torch.squeeze(x, dim=-1) def update_model_config(module_config, override_config_kwargs): for key, val in override_config_kwargs.items(): setattr(module_config, key, val) def get_huggingface_actor_config(model_name: str, override_config_kwargs=None, trust_remote_code=False) -> Dict: if override_config_kwargs is None: override_config_kwargs = {} assert isinstance(override_config_kwargs, Dict), \ f'override_config_kwargs must be a dict, got {type(override_config_kwargs)}' module_config = AutoConfig.from_pretrained(model_name, trust_remote_code=trust_remote_code) update_model_config(module_config, override_config_kwargs) return module_config def get_generation_config( model: str, trust_remote_code: bool = False, ) -> Optional[GenerationConfig]: try: return GenerationConfig.from_pretrained(model) except OSError: # Not found try: config = get_huggingface_actor_config( model, trust_remote_code=trust_remote_code, ) return GenerationConfig.from_model_config(config) except OSError: # Not found return None def create_huggingface_actor(model_name: str, override_config_kwargs=None, automodel_kwargs=None) -> nn.Module: """ Args: model_name: override_config_kwargs: Returns: """ if override_config_kwargs is None: override_config_kwargs = {} if automodel_kwargs is None: automodel_kwargs = {} assert isinstance(override_config_kwargs, Dict), \ f'override_config_kwargs must be a dict, got {type(override_config_kwargs)}' module_config = get_huggingface_actor_config(model_name, override_config_kwargs, trust_remote_code=automodel_kwargs.get('trust_remote_code', False)) module: nn.Module = AutoModelForCausalLM.from_config(module_config, **automodel_kwargs) return module def create_huggingface_critic(model_name: str, override_config_kwargs=None, automodel_kwargs=None) -> nn.Module: """ Args: model_name: override_config_kwargs: Returns: """ critic_module: nn.Module = create_huggingface_actor(model_name, override_config_kwargs=override_config_kwargs, automodel_kwargs=automodel_kwargs) if automodel_kwargs is None: automodel_kwargs = {} torch_dtype = automodel_kwargs.get('torch_dtype', torch.float32) critic_module.lm_head = nn.Sequential(nn.Linear(critic_module.config.hidden_size, 1, dtype=torch_dtype), LambdaLayer(fn=squeeze)) return critic_module def get_model_size(model: nn.Module, scale='auto'): n_params = sum(p.numel() for p in model.parameters()) if scale == 'auto': if n_params > 1e9: scale = 'B' elif n_params > 1e6: scale = 'M' elif n_params > 1e3: scale = 'K' else: scale = '' if scale == 'B': n_params = n_params / 1e9 elif scale == 'M': n_params = n_params / 1e6 elif scale == 'K': n_params = n_params / 1e3 elif scale == '': pass else: raise NotImplemented(f'Unknown scale {scale}') return n_params, scale def print_model_size(model: nn.Module, name: str = None): n_params, scale = get_model_size(model, scale='auto') if name is None: name = model.__class__.__name__ print(f'{name} contains {n_params:.2f}{scale} parameters') def create_random_mask(input_ids: torch.Tensor, max_ratio_of_valid_token: float, max_ratio_of_left_padding: float, min_ratio_of_valid_token: float = 0): """Create a random mask given input_ids. Support left padding and right padding. Process: - Sample valid token length - Sample left_padding length - Generate padding Args: input_ids: shape (batch_size, seq_len) Returns: """ assert max_ratio_of_valid_token > 0 and max_ratio_of_valid_token <= 1. assert max_ratio_of_left_padding >= 0 and max_ratio_of_left_padding < 1. assert min_ratio_of_valid_token <= max_ratio_of_valid_token batch_size, sequence_length = input_ids.shape max_num_valid_tokens = int(sequence_length * max_ratio_of_valid_token) min_num_valid_tokens = max(1, int(sequence_length * min_ratio_of_valid_token)) max_left_padding = int(sequence_length * max_ratio_of_left_padding) assert max_num_valid_tokens + max_left_padding <= sequence_length assert max_num_valid_tokens > 0 and max_ratio_of_valid_token <= sequence_length masks = torch.ones_like(input_ids, dtype=torch.int64) # TODO: we can make this faster for i in range(batch_size): num_left_padding = np.random.randint(low=0, high=max_left_padding + 1, dtype=np.int64) num_valid = np.random.randint(low=min_num_valid_tokens, high=max_num_valid_tokens + 1, dtype=np.int64) for index in range(num_left_padding): masks[i, index] = 0 for index in range(num_left_padding + num_valid, sequence_length): masks[i, index] = 0 return masks def compute_position_id_with_mask(mask): return torch.clip(torch.cumsum(mask, dim=-1) - 1, min=0, max=None) def normalize_pp_vpp_params(params, num_hidden_layers, layer_name='layers'): """ Normalize the pp vpp params into a complete named parameters. This is useful when gather parameters from pp ranks and passed to a model without pp params: List[List[Dict[str, param]]] params contains a list of pp, with a list of vpp named_parameters in each vpp chunk. output: Dict[str, param] """ def normalize_model_name(name, pp_rank, vpp_rank, pp_size, vpp_size, num_layers): """ Transform the model name in each model_chunk in each pp stage into the name in inference engine """ if vpp_size > 1: # print(f'try to bind vpp params to inference engine...') layers_per_pp = num_layers // pp_size layers_per_vpp = layers_per_pp // vpp_size pp_offset = layers_per_vpp * pp_rank vpp_offset = (layers_per_vpp * pp_size) * vpp_rank layer_offset = pp_offset + vpp_offset else: layers_per_pp = num_layers // pp_size layer_offset = layers_per_pp * pp_rank if layer_name in name: # belong to an intermediate layer split_name = name.split('.') # find the num next to split_name for i, name in enumerate(split_name): if name == layer_name: break layer_num_idx = i + 1 # check the name assert len(split_name) >= layer_num_idx + 1, f'split_name = {split_name}' assert split_name[layer_num_idx].isdigit(), f'split_name = {split_name}' # increment layer_num_idx by layer_offset split_name[layer_num_idx] = str(int(split_name[layer_num_idx]) + layer_offset) name = '.'.join(split_name) # weight name in inference_tp_model return name pp_size = len(params) normalized_name_to_param = {} for pp_rank in range(len(params)): vpp_size = len(params[pp_rank]) for vpp_rank in range(vpp_size): for name, param in params[pp_rank][vpp_rank].items(): normalized_name = normalize_model_name(name, pp_rank, vpp_rank, pp_size, vpp_size, num_hidden_layers) normalized_name_to_param[normalized_name] = param return normalized_name_to_param def get_parallel_model_from_config(config, megatron_config, pre_process=None, post_process=None, share_embeddings_and_output_weights=False, value=False): from megatron.core import ModelParallelConfig assert isinstance(megatron_config, ModelParallelConfig) model_class = _get_parallel_model_architecture_from_config(config, value) model = model_class(config, megatron_config, pre_process=pre_process, post_process=post_process, share_embeddings_and_output_weights=share_embeddings_and_output_weights) return model def _get_parallel_model_architecture_from_config(config: PretrainedConfig, value=False) -> Type[nn.Module]: architectures = getattr(config, "architectures", []) for arch in architectures: model_cls = ModelRegistry.load_model_cls(arch, value) if model_cls is not None: return model_cls raise ValueError(f"Model architectures {architectures} are not supported for now. " f"Supported architectures: {ModelRegistry.get_supported_archs()}") def load_megatron_model_weights(config, model_config, parallel_model, params_dtype, is_value_model=False, local_cache_path='~/.cache/verl/rlhf'): assert hasattr(model_config, "architectures"), "architectures cannot be empty when load weight!" architectures = getattr(model_config, "architectures", []) local_cache_path = os.path.expanduser(local_cache_path) if config.model.path.startswith("hdfs:"): from verl.utils.fs import copy_to_local print(f'start download from {config.model.path}') local_model_path = copy_to_local(src=config.model.path, cache_dir=local_cache_path) print('finish download') else: print(f"load from local dir {config.model.path}") local_model_path = config.model.path # TODO: to find a better way to load mistral7b-rm lm_head if 'mistral7b-rm' in config.model.path: model = MistralForSequenceClassification.from_pretrained(local_model_path) # use score head instead of lm_head state_dict = model.state_dict() state_dict['lm_head.weight'] = state_dict['score.weight'] state_dict['model.embed_tokens.weight'] = state_dict[ 'model.embed_tokens.weight'][:32000] # workaround, 32001 -> 32000 is_value_model = True else: with warnings.catch_warnings(): warnings.simplefilter("ignore") model = AutoModelForCausalLM.from_pretrained(local_model_path) state_dict = model.state_dict() from verl.models.weight_loader_registry import get_weight_loader print(f'before weight loader: architectures = {architectures}...') for arch in architectures: print(f'call weight loader arch = {arch}, model config = {model.config}') weight_loader = get_weight_loader(arch) weight_loader(state_dict=state_dict, wrapped_models=parallel_model, config=model.config, params_dtype=params_dtype, is_value_model=is_value_model) # pad input_ids_rmpad, cu_seqlens and max_seqlen_in_batch to be divisible by tp def pad_packed_inputs(unpad_tokens: torch.Tensor, cu_seqlens, max_seqlen_in_batch, size): """pad the tokens such that the total length is a multiple of size. This function is useful when applying sequence parallel and context parallel Args: unpad_tokens: (total_nnz, ...). Tokens after removing padding cu_seqlens: (total_nnz + 1,) max_seqlen_in_batch: int Returns: """ F = nn.functional total_nnz = unpad_tokens.shape[0] if total_nnz % size == 0: pad_size = 0 else: pad_size = size - total_nnz % size # we assume adding a new data in the batch with seqlen pad_size if pad_size > 0: if unpad_tokens.ndim == 1: unpad_tokens = F.pad(unpad_tokens, (0, pad_size)) elif unpad_tokens.ndim == 2: unpad_tokens = F.pad(unpad_tokens, (0, 0, 0, pad_size)) else: raise NotImplementedError(f'Padding dim {unpad_tokens.ndim()} is not supported') cu_seqlens = F.pad(cu_seqlens, (0, 1), value=pad_size + cu_seqlens[-1]) max_seqlen_in_batch = max(max_seqlen_in_batch, pad_size) return unpad_tokens, cu_seqlens, max_seqlen_in_batch ================================================ FILE: verl/utils/py_functional.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Contain small python utility functions """ from typing import Dict from types import SimpleNamespace def union_two_dict(dict1: Dict, dict2: Dict): """Union two dict. Will throw an error if there is an item not the same object with the same key. Args: dict1: dict2: Returns: """ for key, val in dict2.items(): if key in dict1: assert dict2[key] == dict1[key], \ f'{key} in meta_dict1 and meta_dict2 are not the same object' dict1[key] = val return dict1 def append_to_dict(data: Dict, new_data: Dict): for key, val in new_data.items(): if key not in data: data[key] = [] data[key].append(val) class NestedNamespace(SimpleNamespace): def __init__(self, dictionary, **kwargs): super().__init__(**kwargs) for key, value in dictionary.items(): if isinstance(value, dict): self.__setattr__(key, NestedNamespace(value)) else: self.__setattr__(key, value) ================================================ FILE: verl/utils/ray_utils.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Contains commonly used utilities for ray """ import ray import concurrent.futures def parallel_put(data_list, max_workers=None): def put_data(index, data): return index, ray.put(data) if max_workers is None: max_workers = min(len(data_list), 16) with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as executor: data_list_f = [executor.submit(put_data, i, data) for i, data in enumerate(data_list)] res_lst = [] for future in concurrent.futures.as_completed(data_list_f): res_lst.append(future.result()) # reorder based on index output = [None for _ in range(len(data_list))] for res in res_lst: index, data_ref = res output[index] = data_ref return output ================================================ FILE: verl/utils/rendezvous/__init__.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. ================================================ FILE: verl/utils/rendezvous/ray_backend.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import logging import time from cupy.cuda.nccl import NcclCommunicator, get_unique_id import ray from ray.util import list_named_actors @ray.remote class NCCLIDStore: def __init__(self, nccl_id): self._nccl_id = nccl_id def get(self): return self._nccl_id def get_nccl_id_store_by_name(name): all_actors = list_named_actors(all_namespaces=True) matched_actors = [actor for actor in all_actors if actor.get("name", None) == name] if len(matched_actors) == 1: actor = matched_actors[0] return ray.get_actor(**actor) elif len(matched_actors) > 1: logging.warning(f"multiple actors with same name found: {matched_actors}") elif len(matched_actors) == 0: logging.info(f"failed to get any actor named {name}") return None def create_nccl_communicator_in_ray(rank: int, world_size: int, group_name: str, max_retries: int = 100, interval_s: int = 5): if rank == 0: nccl_id = get_unique_id() nccl_id_store = NCCLIDStore.options(name=group_name).remote(nccl_id) assert ray.get(nccl_id_store.get.remote()) == nccl_id communicator = NcclCommunicator( ndev=world_size, commId=nccl_id, rank=0, ) return communicator else: for i in range(max_retries): nccl_id_store = get_nccl_id_store_by_name(group_name) if nccl_id_store is not None: logging.info(f"nccl_id_store {group_name} got") nccl_id = ray.get(nccl_id_store.get.remote()) logging.info(f"nccl id for {group_name} got: {nccl_id}") communicator = NcclCommunicator( ndev=world_size, commId=nccl_id, rank=rank, ) return communicator logging.info(f"failed to get nccl_id for {i+1} time, sleep for {interval_s} seconds") time.sleep(interval_s) ================================================ FILE: verl/utils/reward_score/__init__.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # from . import gsm8k, math, prime_math, prime_code def _default_compute_score(data_source, solution_str, ground_truth, extra_info=None, reward_type=None): if data_source == 'openai/gsm8k': from . import gsm8k res = gsm8k.compute_score(solution_str, ground_truth) elif data_source in ['lighteval/MATH', 'DigitalLearningGmbH/MATH-lighteval']: from . import math res = math.compute_score(solution_str, ground_truth) elif data_source in [ 'numina_aops_forum', 'numina_synthetic_math', 'numina_amc_aime', 'numina_synthetic_amc', 'numina_cn_k12', 'numina_olympiads' ]: from . import prime_math res = prime_math.compute_score(solution_str, ground_truth) elif data_source in ['codecontests', 'apps', 'codeforces', 'taco']: from . import prime_code res = prime_code.compute_score(solution_str, ground_truth, continuous=True) elif data_source in ['hiyouga/geometry3k']: from . import geo3k res = geo3k.compute_score(solution_str, ground_truth) else: from . import math_verifier res = math_verifier.compute_score(solution_str, ground_truth, reward_type) if isinstance(res, (int, float, bool)): return float(res) else: return float(res[0]) ================================================ FILE: verl/utils/reward_score/eval.py ================================================ import os import json import jsonlines import re import copy PATTERNS=[ r"(?i)Answer\s*:\s*([^\n]+)", r"\\boxed\{((?:[^{}]|\\{|\\}|(?:\{(?:[^{}]|\\{|\\}|(?:\{(?:[^{}]|\\{|\\}|(?:\{[^{}]*\}))*\}))*\}))*\})", ] def extract_pattern(pred: str, pattern: str): match = re.findall(pattern, pred) # 从pred中extract出一个answerlist，代表所有可能的answer if match: extracted_answer = match[-1] if pattern==r"\\boxed\{((?:[^{}]|\\{|\\}|(?:\{(?:[^{}]|\\{|\\}|(?:\{(?:[^{}]|\\{|\\}|(?:\{[^{}]*\}))*\}))*\}))*\})": extracted_answer=extracted_answer[:-1] return extracted_answer.strip("*").strip().strip("*") else: return "" SPLIT=[ "####" "\n", "Answer:", ] def extract_split(pred: str, split: str): ''' 最后一个换行符之后的部分 ''' pred=pred.split(split)[-1] return pred.strip("*").strip().strip("*") def expansion(answer_list: str): org_answer_list=copy.deepcopy(answer_list) for answer in org_answer_list: if "=" in answer: answer_list.append(answer.split("=")[-1]) for choice in ["A", "B", "C", "D", "E", "F"]: if f"({choice.upper()})" in answer.upper() or f"{choice.upper()}:" in answer.upper() or f"{choice.upper()}. " in answer.upper(): answer_list.append(f"{choice.upper()}") break for answer in org_answer_list: pattern = r'^(\d+(\.\d+)?)\s+[a-zA-Z]+(?:\s+[a-zA-Z]+)*$' if bool(re.match(pattern, answer)): answer_list.append(answer.split(" ")[0]) for answer in org_answer_list: if "\\in" in answer: answer_list.append(answer.split("\\in")[-1].strip()) if "\u2208" in answer: answer_list.append(answer.split("\u2208")[-1].strip()) return answer_list def extract(pred: str): answer_list=[] answer_list.append(pred.split("####")[-1].strip()) for split in SPLIT: answer_list.append(extract_split(copy.deepcopy(pred), split=split)) for pattern in PATTERNS: answer_list.append(extract_pattern(copy.deepcopy(pred), pattern=pattern)) answer_list=expansion(answer_list) return answer_list import re SUBSTITUTIONS = [ ("an ", ""), # ("a ", ""), (".$", "$"), ("\\$", ""), (r"\ ", ""), (" ", ""), ("mbox", "text"), (",\\text{and}", ","), ("\\text{and}", ","), ("\\text{m}", "\\text{}"), ("\\left", ""), ("\\right", ""), ("∶", ":"), ("，", ","), ("$", ""), ("\\approx", "="), ("\\simeq", "="), ("\\sim", "="), ("^\\prime", "'"), ("^{\\prime}", "'"), ("\\dfrac", "\\frac"), ("\\tfrac", "\\frac"), ("^\\circ", ""), ("%", ""), ("\u221a", "\\sqrt"), ("\u221e", "\\infty"), ("\u222a", "\\cup"), ] REMOVED_EXPRESSIONS = [ "square", "ways", "integers", "dollars", "mph", "inches", # "ft", #this is dangerous, infty, left will be damaged! "hours", "km", "units", "\\ldots", "sue", "points", "feet", "minutes", "digits", "cents", "degrees", "cm", "gm", "pounds", "meters", "meals", "edges", "students", "childrentickets", "multiples", "\\text{s}", "\\text{.}", "\\text{\ns}", "\\text{}^2", "\\text{}^3", "\\text{\n}", "\\text{}", r"\mathrm{th}", r"^\circ", r"^{\circ}", r"\;", r",\!", "{,}", '"', "\\dots", ] def normalize_final_answer(final_answer: str) -> str: """ Normalize a final answer to a quantitative reasoning question. Copied character for character from appendix D of Lewkowycz et al. (2022) """ # final_answer = final_answer.split("=")[-1] final_answer=final_answer.strip() if final_answer[:2]=="\\(" or final_answer[:2]=='\\[': final_answer=final_answer[2:] if final_answer[-2:]=='\\)' or final_answer[-2:]=='\\]': final_answer=final_answer[:-2] for before, after in SUBSTITUTIONS: final_answer = final_answer.replace(before, after) for expr in REMOVED_EXPRESSIONS: final_answer = final_answer.replace(expr, "") # Extract answer that is in LaTeX math, is bold, # is surrounded by a box, etc. final_answer = re.sub(r"(.*?)(\$)(.*?)(\$)(.*)", "$\\3$", final_answer) final_answer = re.sub(r"(\\text\{)(.*?)(\})", "\\2", final_answer) final_answer = re.sub(r"(\\textbf\{)(.*?)(\})", "\\2", final_answer) final_answer = re.sub(r"(\\overline\{)(.*?)(\})", "\\2", final_answer) final_answer = re.sub(r"(\\boxed\{)(.*)(\})", "\\2", final_answer) # Normalize shorthand TeX: # \fracab -> \frac{a}{b} # \frac{abc}{bef} -> \frac{abc}{bef} # \fracabc -> \frac{a}{b}c # \sqrta -> \sqrt{a} # \sqrtab -> sqrt{a}b final_answer = re.sub(r"(frac)([^{])(.)", "frac{\\2}{\\3}", final_answer) final_answer = re.sub(r"(sqrt)([^{])", "sqrt{\\2}", final_answer) final_answer = final_answer.replace("$", "") # Normalize 100,000 -> 100000 if final_answer.replace(",", "").isdigit(): final_answer = final_answer.replace(",", "") if final_answer[:2]=="\\(" or final_answer[:2]=='\\[': final_answer=final_answer[2:] if final_answer[-2:]=='\\)' or final_answer[-2:]=='\\]': final_answer=final_answer[:-2] return final_answer.strip() """ This logic is largely copied from the Hendrycks' MATH release (math_equivalence), and borrowed from: - https://github.com/microsoft/ProphetNet/tree/master/CRITIC - https://github.com/openai/prm800k - https://github.com/microsoft/ToRA/blob/main/src/eval/grader.py - https://github.com/deepseek-ai/DeepSeek-Math/blob/main/evaluation/eval/eval_utils.py """ import re import regex import multiprocessing from math import isclose from typing import Union from collections import defaultdict from sympy import simplify, N from sympy.parsing.sympy_parser import parse_expr from sympy.parsing.latex import parse_latex # from latex2sympy2 import latex2sympy # from .parser import choice_answer_clean, strip_string # from parser import choice_answer_clean def choice_answer_clean(pred: str): pred = pred.strip("\n").rstrip(".").rstrip("/").strip(" ").lstrip(":") # Clean the answer based on the dataset tmp = re.findall(r"\b(A|B|C|D|E)\b", pred.upper()) if tmp: pred = tmp else: pred = [pred.strip().strip(".")] pred = pred[-1] # Remove the period at the end, again! pred = pred.rstrip(".").rstrip("/") return pred def parse_digits(num): num = regex.sub(",", "", str(num)) try: return float(num) except: if num.endswith("%"): num = num[:-1] if num.endswith("\\"): num = num[:-1] try: return float(num) / 100 except: pass return None def is_digit(num): # paired with parse_digits return parse_digits(num) is not None def str_to_pmatrix(input_str): input_str = input_str.strip() matrix_str = re.findall(r"\{.*,.*\}", input_str) pmatrix_list = [] for m in matrix_str: m = m.strip("{}") pmatrix = r"\begin{pmatrix}" + m.replace(",", "\\") + r"\end{pmatrix}" pmatrix_list.append(pmatrix) return ", ".join(pmatrix_list) def math_equal( prediction: Union[bool, float, str], reference: Union[float, str], include_percentage: bool = True, is_close: bool = True, timeout: bool = False, ) -> bool: """ Exact match of math if and only if: 1. numerical equal: both can convert to float and are equal 2. symbolic equal: both can convert to sympy expression and are equal """ # print("Judge:", prediction, reference) if prediction is None or reference is None: return False if str(prediction.strip().lower()) == str(reference.strip().lower()): return True if ( reference in ["A", "B", "C", "D", "E"] and choice_answer_clean(prediction) == reference ): return True try: # 1. numerical equal if is_digit(prediction) and is_digit(reference): prediction = parse_digits(prediction) reference = parse_digits(reference) # number questions if include_percentage: gt_result = [reference / 100, reference, reference * 100] else: gt_result = [reference] for item in gt_result: try: if is_close: if numeric_equal(prediction, item): return True else: if item == prediction: return True except Exception: continue return False except: pass if not prediction and prediction not in [0, False]: return False # 2. symbolic equal reference = str(reference).strip() prediction = str(prediction).strip() ## pmatrix (amps) if "pmatrix" in prediction and not "pmatrix" in reference: reference = str_to_pmatrix(reference) ## deal with [], (), {} pred_str, ref_str = prediction, reference if ( prediction.startswith("[") and prediction.endswith("]") and not reference.startswith("(") ) or ( prediction.startswith("(") and prediction.endswith(")") and not reference.startswith("[") ): pred_str = pred_str.strip("[]()") ref_str = ref_str.strip("[]()") for s in ["{", "}", "(", ")"]: ref_str = ref_str.replace(s, "") pred_str = pred_str.replace(s, "") if pred_str.lower() == ref_str.lower(): return True ## [a, b] vs. [c, d], return a==c and b==d if ( regex.match(r"(\(|\[).+(\)|\])", prediction) is not None and regex.match(r"(\(|\[).+(\)|\])", reference) is not None ): pred_parts = prediction[1:-1].split(",") ref_parts = reference[1:-1].split(",") if len(pred_parts) == len(ref_parts): if all( [ math_equal( pred_parts[i], ref_parts[i], include_percentage, is_close ) for i in range(len(pred_parts)) ] ): return True if ( ( prediction.startswith("\\begin{pmatrix}") or prediction.startswith("\\begin{bmatrix}") ) and ( prediction.endswith("\\end{pmatrix}") or prediction.endswith("\\end{bmatrix}") ) and ( reference.startswith("\\begin{pmatrix}") or reference.startswith("\\begin{bmatrix}") ) and ( reference.endswith("\\end{pmatrix}") or reference.endswith("\\end{bmatrix}") ) ): pred_lines = [ line.strip() for line in prediction[ len("\\begin{pmatrix}") : -len("\\end{pmatrix}") ].split("\\\\") if line.strip() ] ref_lines = [ line.strip() for line in reference[ len("\\begin{pmatrix}") : -len("\\end{pmatrix}") ].split("\\\\") if line.strip() ] matched = True if len(pred_lines) == len(ref_lines): for pred_line, ref_line in zip(pred_lines, ref_lines): pred_parts = pred_line.split("&") ref_parts = ref_line.split("&") if len(pred_parts) == len(ref_parts): if not all( [ math_equal( pred_parts[i], ref_parts[i], include_percentage, is_close, ) for i in range(len(pred_parts)) ] ): matched = False break else: matched = False if not matched: break else: matched = False if matched: return True if prediction.count("=") == 1 and reference.count("=") == 1: pred = prediction.split("=") pred = f"{pred[0].strip()} - ({pred[1].strip()})" ref = reference.split("=") ref = f"{ref[0].strip()} - ({ref[1].strip()})" if symbolic_equal(pred, ref) or symbolic_equal(f"-({pred})", ref): return True elif ( prediction.count("=") == 1 and len(prediction.split("=")[0].strip()) <= 2 and "=" not in reference ): if math_equal( prediction.split("=")[1], reference, include_percentage, is_close ): return True elif ( reference.count("=") == 1 and len(reference.split("=")[0].strip()) <= 2 and "=" not in prediction ): if math_equal( prediction, reference.split("=")[1], include_percentage, is_close ): return True # symbolic equal with sympy if timeout: if call_with_timeout(symbolic_equal_process, prediction, reference): return True else: if symbolic_equal(prediction, reference): return True # symbolic == numeric try: prediction=float(N(parse_latex(prediction))) if abs(prediction-float(reference))<=1e-8: True except: pass try: reference=float(N(parse_latex(reference))) if abs(prediction-reference)<=1e-8: return True except: pass return False def math_equal_process(param): return math_equal(param[-2], param[-1]) def numeric_equal(prediction: float, reference: float): # prediction = round(prediction, len(str(reference).split(".")[-1])) return isclose(reference, prediction, rel_tol=1e-4) def symbolic_equal(a, b): def _parse(s): for f in [parse_latex, parse_expr]: try: return f(s.replace("\\\\", "\\")) except: try: return f(s) except: pass return s a = _parse(a) b = _parse(b) # direct equal try: if str(a) == str(b) or a == b: return True except: pass # simplify equal try: if a.equals(b) or simplify(a - b) == 0: return True except: pass # equation equal try: if (abs(a.lhs - a.rhs)).equals(abs(b.lhs - b.rhs)): return True except: pass try: if numeric_equal(float(N(a)), float(N(b))): return True except: pass # matrix try: # if a and b are matrix if a.shape == b.shape: _a = a.applyfunc(lambda x: round(x, 3)) _b = b.applyfunc(lambda x: round(x, 3)) if _a.equals(_b): return True except: pass return False def symbolic_equal_process(a, b, output_queue): result = symbolic_equal(a, b) output_queue.put(result) def call_with_timeout(func, *args, timeout=1, **kwargs): output_queue = multiprocessing.Queue() process_args = args + (output_queue,) process = multiprocessing.Process(target=func, args=process_args, kwargs=kwargs) process.start() process.join(timeout) if process.is_alive(): process.terminate() process.join() return False return output_queue.get() def process_answer_list(answer_list): answer_list = list(set(answer_list)) if "" in answer_list: answer_list.remove("") return answer_list import os import json import jsonlines import copy from tqdm import tqdm import pandas as pd from multiprocessing import Pool from functools import partial from datetime import datetime # api def is_equal(pred, gt): pred=normalize_final_answer(pred) gt=normalize_final_answer(gt) return math_equal(pred, gt) def exact_match_eval(pred, gt): gt=normalize_final_answer(gt) answer_list=extract(pred) normalized_answer_list=[] for answer in copy.deepcopy(answer_list): normalized_answer_list.append(normalize_final_answer(answer)) normalized_answer_list=process_answer_list(normalized_answer_list) for answer in normalized_answer_list: if math_equal(gt, answer): return normalized_answer_list, True return normalized_answer_list, False ================================================ FILE: verl/utils/reward_score/geo3k.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import re from mathruler.grader import extract_boxed_content, grade_answer def format_reward(predict_str: str) -> float: pattern = re.compile(r'.*.*\\boxed\{.*\}.*', re.DOTALL) match_result = re.fullmatch(pattern, predict_str) return 1.0 if match_result else 0.0 def acc_reward(predict_str: str, ground_truth: str) -> float: answer = extract_boxed_content(predict_str) return 1.0 if grade_answer(answer, ground_truth) else 0.0 def compute_score(predict_str: str, ground_truth: str) -> float: return 0.9 * acc_reward(predict_str, ground_truth) + 0.1 * format_reward(predict_str) ================================================ FILE: verl/utils/reward_score/gsm8k.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import re def extract_solution(solution_str, method='strict'): assert method in ['strict', 'flexible'] if method == 'strict': # this also tests the formatting of the model solution = re.search("#### (\\-?[0-9\\.\\,]+)", solution_str) if solution is None: final_answer = None else: final_answer = solution.group(0) final_answer = final_answer.split('#### ')[1].replace(',', '').replace('$', '') elif method == 'flexible': answer = re.findall("(\\-?[0-9\\.\\,]+)", solution_str) final_answer = None if len(answer) == 0: # no reward is there is no answer pass else: invalid_str = ['', '.'] # find the last number that is not '.' for final_answer in reversed(answer): if final_answer not in invalid_str: break return final_answer def compute_score(solution_str, ground_truth, method='strict', format_score=0., score=1.): """The scoring function for GSM8k. Reference: Trung, Luong, et al. "Reft: Reasoning with reinforced fine-tuning." Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2024. Args: solution_str: the solution text ground_truth: the ground truth method: the method to extract the solution, choices are 'strict' and 'flexible' format_score: the score for the format score: the score for the correct answer """ answer = extract_solution(solution_str=solution_str, method=method) if answer is None: return 0 else: if answer == ground_truth: return score else: return format_score ================================================ FILE: verl/utils/reward_score/math.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # Adapted from https://github.com/EleutherAI/lm-evaluation-harness/blob/main/lm_eval/tasks/hendrycks_math/utils.py def compute_score(solution_str, ground_truth) -> float: retval = 0. try: string_in_last_boxed = last_boxed_only_string(solution_str) if string_in_last_boxed is not None: answer = remove_boxed(string_in_last_boxed) if is_equiv(answer, ground_truth): retval = 1. except Exception as e: print(e) return retval # string normalization from https://github.com/EleutherAI/lm-evaluation-harness/blob/master/lm_eval/tasks/hendrycks_math.py def is_equiv(str1, str2, verbose=False): if str1 is None and str2 is None: print("WARNING: Both None") return True if str1 is None or str2 is None: return False try: ss1 = strip_string(str1) ss2 = strip_string(str2) if verbose: print(ss1, ss2) return ss1 == ss2 except Exception: return str1 == str2 def remove_boxed(s): if "\\boxed " in s: left = "\\boxed " assert s[:len(left)] == left return s[len(left):] left = "\\boxed{" assert s[:len(left)] == left assert s[-1] == "}" return s[len(left):-1] def last_boxed_only_string(string): idx = string.rfind("\\boxed") if "\\boxed " in string: return "\\boxed " + string.split("\\boxed ")[-1].split("$")[0] if idx < 0: idx = string.rfind("\\fbox") if idx < 0: return None i = idx right_brace_idx = None num_left_braces_open = 0 while i < len(string): if string[i] == "{": num_left_braces_open += 1 if string[i] == "}": num_left_braces_open -= 1 if num_left_braces_open == 0: right_brace_idx = i break i += 1 if right_brace_idx is None: retval = None else: retval = string[idx:right_brace_idx + 1] return retval def fix_fracs(string): substrs = string.split("\\frac") new_str = substrs[0] if len(substrs) > 1: substrs = substrs[1:] for substr in substrs: new_str += "\\frac" if substr[0] == "{": new_str += substr else: try: assert len(substr) >= 2 except AssertionError: return string a = substr[0] b = substr[1] if b != "{": if len(substr) > 2: post_substr = substr[2:] new_str += "{" + a + "}{" + b + "}" + post_substr else: new_str += "{" + a + "}{" + b + "}" else: if len(substr) > 2: post_substr = substr[2:] new_str += "{" + a + "}" + b + post_substr else: new_str += "{" + a + "}" + b string = new_str return string def fix_a_slash_b(string): if len(string.split("/")) != 2: return string a = string.split("/")[0] b = string.split("/")[1] try: a = int(a) b = int(b) assert string == "{}/{}".format(a, b) new_string = "\\frac{" + str(a) + "}{" + str(b) + "}" return new_string except AssertionError: return string def remove_right_units(string): # "\\text{ " only ever occurs (at least in the val set) when describing units if "\\text{ " in string: splits = string.split("\\text{ ") assert len(splits) == 2 return splits[0] else: return string def fix_sqrt(string): if "\\sqrt" not in string: return string splits = string.split("\\sqrt") new_string = splits[0] for split in splits[1:]: if split[0] != "{": a = split[0] new_substr = "\\sqrt{" + a + "}" + split[1:] else: new_substr = "\\sqrt" + split new_string += new_substr return new_string def strip_string(string): # linebreaks string = string.replace("\n", "") # remove inverse spaces string = string.replace("\\!", "") # replace \\ with \ string = string.replace("\\\\", "\\") # replace tfrac and dfrac with frac string = string.replace("tfrac", "frac") string = string.replace("dfrac", "frac") # remove \left and \right string = string.replace("\\left", "") string = string.replace("\\right", "") # Remove circ (degrees) string = string.replace("^{\\circ}", "") string = string.replace("^\\circ", "") # remove dollar signs string = string.replace("\\$", "") # remove units (on the right) string = remove_right_units(string) # remove percentage string = string.replace("\\%", "") string = string.replace("\%", "") # noqa: W605 # " 0." equivalent to " ." and "{0." equivalent to "{." Alternatively, add "0" if "." is the start of the string string = string.replace(" .", " 0.") string = string.replace("{.", "{0.") # if empty, return empty string if len(string) == 0: return string if string[0] == ".": string = "0" + string # to consider: get rid of e.g. "k = " or "q = " at beginning if len(string.split("=")) == 2: if len(string.split("=")[0]) <= 2: string = string.split("=")[1] # fix sqrt3 --> sqrt{3} string = fix_sqrt(string) # remove spaces string = string.replace(" ", "") # \frac1b or \frac12 --> \frac{1}{b} and \frac{1}{2}, etc. Even works with \frac1{72} (but not \frac{72}1). Also does a/b --> \\frac{a}{b} string = fix_fracs(string) # manually change 0.5 --> \frac{1}{2} if string == "0.5": string = "\\frac{1}{2}" # NOTE: X/Y changed to \frac{X}{Y} in dataset, but in simple cases fix in case the model output is X/Y string = fix_a_slash_b(string) return string ================================================ FILE: verl/utils/reward_score/math_verifier.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # Adapted from https://github.com/EleutherAI/lm-evaluation-harness/blob/main/lm_eval/tasks/hendrycks_math/utils.py import signal import re from contextlib import contextmanager import importlib.util from verl.utils.reward_score.eval import normalize_final_answer from math_verify import parse, verify class TimeoutException(Exception): pass @contextmanager def timeout(seconds): def signal_handler(signum, frame): raise TimeoutException("Timed out!") signal.signal(signal.SIGALRM, signal_handler) signal.alarm(seconds) try: yield finally: signal.alarm(0) timeout_seconds=2 chinese_pattern = re.compile(r'[\u4e00-\u9fff]') english_pattern = re.compile(r'[a-zA-Z]') boxed_pattern = re.compile(r"\\boxed\{((?:[^{}]|\\{|\\}|(?:\{(?:[^{}]|\\{|\\}|(?:\{(?:[^{}]|\\{|\\}|(?:\{[^{}]*\}))*\}))*\}))*\})") valid_char_pattern = re.compile(r'[a-zA-Z0-9\s\.,!?"\'\(\)\{\}\[\]_\-+=<>/@#$%^&*\\|:;~`\u2200-\u22FF]') repeat_pattern = re.compile(r'(.{5,}?)\1{4,}') def check_mixed_languages(text): chinese_chars = len(chinese_pattern.findall(text)) english_chars = len(english_pattern.findall(text)) return chinese_chars >= 20 and english_chars >= 20 def undesired_format(text): if "<|endoftext|>" not in text: return True else: return False def check_garbled_characters(text): valid_chars = valid_char_pattern.sub('', text) if not text: return False invalid_ratio = len(valid_chars) / len(text) return invalid_ratio > 0.3 def has_repeated_patterns(text): return bool(repeat_pattern.search(text)) def correctness_score_default(response, gt): matches = boxed_pattern.findall(response) if not matches: return -1.0 pred = matches[-1][:-1] return 1.0 if is_equiv(pred, gt) else -1.0 def correctness_score_v2(response, gt): matches = boxed_pattern.findall(response) if not matches: return -1.0 pred = matches[-1][:-1] return 1.0 if is_equiv(pred, gt) else -0.5 def compute_score(solution_str, ground_truth, reward_type) -> float: if reward_type=='default': try: # if undesired_format(solution_str): return -1.0 return correctness_score_default(solution_str, ground_truth) except TimeoutException: return -1.0 except Exception as e: return -1.0 elif reward_type=="v2.wformat": try: return correctness_score_v2(solution_str, ground_truth) except TimeoutException: return -1.0 except Exception as e: return -1.0 else: try: # if undesired_format(solution_str): return -1.0 return correctness_score_default(solution_str, ground_truth) except TimeoutException: return -1.0 except Exception as e: return -1.0 # string normalization from https://github.com/EleutherAI/lm-evaluation-harness/blob/master/lm_eval/tasks/hendrycks_math.py def is_equiv(str1, str2, verbose=False): if str1 is None and str2 is None: print("WARNING: Both None") return True if str1 is None or str2 is None: return False if str1.strip().lower() == str2.strip().lower(): return True try: str1=normalize_final_answer(str1) str2=normalize_final_answer(str2) str1=parse(str1) str2=parse(str2) return verify(str1, str2) except: pass try: ss1 = strip_string(str1) ss2 = strip_string(str2) if verbose: print(ss1, ss2) return ss1==ss2 except Exception: return str1 == str2 def remove_boxed(s): if "\\boxed " in s: left = "\\boxed " assert s[:len(left)] == left return s[len(left):] left = "\\boxed{" assert s[:len(left)] == left assert s[-1] == "}" return s[len(left):-1] def last_boxed_only_string(string): idx = string.rfind("\\boxed") if "\\boxed " in string: return "\\boxed " + string.split("\\boxed ")[-1].split("$")[0] if idx < 0: idx = string.rfind("\\fbox") if idx < 0: return None i = idx right_brace_idx = None num_left_braces_open = 0 while i < len(string): if string[i] == "{": num_left_braces_open += 1 if string[i] == "}": num_left_braces_open -= 1 if num_left_braces_open == 0: right_brace_idx = i break i += 1 if right_brace_idx is None: retval = None else: retval = string[idx:right_brace_idx + 1] return retval def fix_fracs(string): substrs = string.split("\\frac") new_str = substrs[0] if len(substrs) > 1: substrs = substrs[1:] for substr in substrs: new_str += "\\frac" if substr[0] == "{": new_str += substr else: try: assert len(substr) >= 2 except AssertionError: return string a = substr[0] b = substr[1] if b != "{": if len(substr) > 2: post_substr = substr[2:] new_str += "{" + a + "}{" + b + "}" + post_substr else: new_str += "{" + a + "}{" + b + "}" else: if len(substr) > 2: post_substr = substr[2:] new_str += "{" + a + "}" + b + post_substr else: new_str += "{" + a + "}" + b string = new_str return string def fix_a_slash_b(string): if len(string.split("/")) != 2: return string a = string.split("/")[0] b = string.split("/")[1] try: a = int(a) b = int(b) assert string == "{}/{}".format(a, b) new_string = "\\frac{" + str(a) + "}{" + str(b) + "}" return new_string except AssertionError: return string def remove_right_units(string): # "\\text{ " only ever occurs (at least in the val set) when describing units if "\\text{ " in string: splits = string.split("\\text{ ") assert len(splits) == 2 return splits[0] else: return string def fix_sqrt(string): if "\\sqrt" not in string: return string splits = string.split("\\sqrt") new_string = splits[0] for split in splits[1:]: if split[0] != "{": a = split[0] new_substr = "\\sqrt{" + a + "}" + split[1:] else: new_substr = "\\sqrt" + split new_string += new_substr return new_string def strip_string(string): # linebreaks string = string.replace("\n", "") # remove inverse spaces string = string.replace("\\!", "") # replace \\ with \ string = string.replace("\\\\", "\\") # replace tfrac and dfrac with frac string = string.replace("tfrac", "frac") string = string.replace("dfrac", "frac") # remove \left and \right string = string.replace("\\left", "") string = string.replace("\\right", "") # Remove circ (degrees) string = string.replace("^{\\circ}", "") string = string.replace("^\\circ", "") # remove dollar signs string = string.replace("\\$", "") # remove units (on the right) string = remove_right_units(string) # remove percentage string = string.replace("\\%", "") string = string.replace("\%", "") # noqa: W605 # " 0." equivalent to " ." and "{0." equivalent to "{." Alternatively, add "0" if "." is the start of the string string = string.replace(" .", " 0.") string = string.replace("{.", "{0.") # if empty, return empty string if len(string) == 0: return string if string[0] == ".": string = "0" + string # to consider: get rid of e.g. "k = " or "q = " at beginning if len(string.split("=")) == 2: if len(string.split("=")[0]) <= 2: string = string.split("=")[1] # fix sqrt3 --> sqrt{3} string = fix_sqrt(string) # remove spaces string = string.replace(" ", "") # \frac1b or \frac12 --> \frac{1}{b} and \frac{1}{2}, etc. Even works with \frac1{72} (but not \frac{72}1). Also does a/b --> \\frac{a}{b} string = fix_fracs(string) # manually change 0.5 --> \frac{1}{2} if string == "0.5": string = "\\frac{1}{2}" # NOTE: X/Y changed to \frac{X}{Y} in dataset, but in simple cases fix in case the model output is X/Y string = fix_a_slash_b(string) return string if __name__ == "__main__": response="To determine which digit appears in the 534th place after the decimal point in the decimal representation of $\\frac{5}{13}$, we need to first find the repeating decimal sequence of $\\frac{5}{13}$. \n\nLet's start by calculating the decimal representation of $\\frac{5}{13}$.\n```python\nfrom decimal import Decimal, getcontext\r\n\r\n# Set the precision high enough to see the repeating pattern clearly\r\ngetcontext().prec = 1000\r\n\r\n# Calculate the decimal representation of 5/13\r\ndecimal_rep = Decimal(5) / Decimal(13)\r\nprint(str(decimal_rep))\n```\n```output\n0.3846153846153846153846153846153846153846153846153846153846153846153846153846153846153846153846153846153846153846153846153846153846153846153846153846153846153846153846153846153846153846153846153846153846153846153846153846153846\n```\nThe decimal representation of $\\frac{5}{13}$ is $0.\\overline{384615}$. This means the repeating sequence is \"384615\" and it has a length of 6 digits.\n\nTo find the digit in the 534th place after the decimal point, we need to determine the position within the repeating sequence. Since the sequence repeats every 6 digits, we can find the position by calculating the remainder when 534 is divided by 6.\n\nLet's calculate this.\n```python\n# Length of the repeating sequence\r\nrepeating_sequence = \"384615\"\r\nsequence_length = len(repeating_sequence)\r\n\r\n# Find the position within the repeating sequence\r\nposition = (534 - 1) % sequence_length # -1 because indexing starts from 0\r\n\r\n# Get the digit at that position\r\ndigit_in_534th_place = repeating_sequence[position]\r\nprint(digit_in_534th_place)\n```\n```output\n6\n```\nThe digit in the 534th place after the decimal point in the decimal representation of $\\frac{5}{13}$ is $\\boxed{6}$. <|endoftext|>" answer="6" res=compute_score(response, answer) print(res) ================================================ FILE: verl/utils/reward_score/prime_code/__init__.py ================================================ # Copyright 2024 PRIME team and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from .utils import check_correctness as apps_check_correctness import json import re import traceback def compute_score(completion, test_cases, continuous=False): # try to get code solution from completion. if the completion is pure code, this will not take effect. solution = completion.split('```python')[-1].split('```')[0] try: try: if not isinstance(test_cases, dict): test_cases = json.loads(test_cases) except Exception as e: print(f"Error:{e}") # Complete check on all in-out pairs first. If there is no failure, per-sample test can be skipped. try: res, metadata = apps_check_correctness(in_outs=test_cases, generation=solution, timeout=5, debug=False) metadata = dict(enumerate(metadata))[0] success = all(map(lambda x: x == True, res)) if success: return success, metadata except Exception as e: pass test_cases_list = [] inputs = test_cases["inputs"] outputs = test_cases["outputs"] for i in range(len(inputs)): test_cases_list.append({"inputs": [inputs[i]], "outputs": [outputs[i]]}) if continuous: # per sample test: if continuous score is needed, test first 10 samples regardless of failures # do not test all samples cuz some problems have enormous test cases metadata_list = [] res_list = [] for test_case_id, test_case in enumerate(test_cases_list): res, metadata = apps_check_correctness(in_outs=test_case, generation=solution, timeout=5, debug=False) try: metadata = dict(enumerate(metadata))[0] # metadata can be empty occasionally except Exception as e: metadata = {} metadata["test_case"] = {} metadata["test_case"]["input"] = str(test_case["inputs"][0]) metadata["test_case"]["output"] = str(test_case["outputs"][0]) metadata["test_case"]["res"] = str(res) metadata_list.append(metadata) res_list.extend(res) if test_case_id >= 9: break res_count = len(res_list) if len(res_list) > 0 else 1 success = sum(map(lambda x: x == True, res_list)) / res_count except Exception as e: traceback.print_exc(10) success = False metadata_list = None return success, metadata_list ================================================ FILE: verl/utils/reward_score/prime_code/testing_util.py ================================================ # Copyright 2024 PRIME team and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import ast import json import sys import faulthandler import platform # used for debugging to time steps from datetime import datetime # to run the solution files we're using a timing based approach import signal import numpy as np # for capturing the stdout from io import StringIO # used for testing the code that reads from input from unittest.mock import patch, mock_open from pyext import RuntimeModule from enum import Enum import traceback def truncatefn(s, length=300): assert isinstance(s, str) if len(s) <= length: return s return s[:length // 2] + "...(truncated) ..." + s[-length // 2:] class CODE_TYPE(Enum): call_based = 0 standard_input = 1 # stuff for setting up signal timer class TimeoutException(Exception): pass def timeout_handler(signum, frame): print("alarm went off") return # raise TimeoutException # this is an unhandled exception. just return None is OK signal.signal(signal.SIGALRM, timeout_handler) # timeout = 6 # seconds # used to capture stdout as a list # from https://stackoverflow.com/a/16571630/6416660 # alternative use redirect_stdout() from contextlib class Capturing(list): def __enter__(self): self._stdout = sys.stdout sys.stdout = self._stringio = StringIO() # Make closing the StringIO a no-op self._stringio.close = lambda x: 1 return self def __exit__(self, *args): self.append(self._stringio.getvalue()) del self._stringio # free up some memory sys.stdout = self._stdout def only_int_check(val): return isinstance(val, int) def string_int_check(val): return isinstance(val, str) and val.isdigit() def combined_int_check(val): return only_int_check(val) or string_int_check(val) def clean_traceback(error_traceback): file_start = error_traceback.find('File \"\"') # print(file_start) error_traceback = "Traceback (most recent call last):\n " + error_traceback[file_start:] return error_traceback def run_test(in_outs, test=None, debug=False, timeout=15): """ if test(generated_code) is not None it'll try to run the code. otherwise it'll just return an input and output pair. """ # Disable functionalities that can make destructive changes to the test. reliability_guard() if debug: print(f"start = {datetime.now().time()}") if in_outs: if in_outs.get("fn_name") is None: which_type = CODE_TYPE.standard_input # Standard input method_name = None else: which_type = CODE_TYPE.call_based # Call-based method_name = in_outs["fn_name"] if debug: print(f"loaded input_output = {datetime.now().time()}") if test is None: assert False, "should not happen: test code is none" return in_outs, {"error": "no test code provided"} elif test is not None: results = [] sol = "from string import *\nfrom re import *\nfrom datetime import *\nfrom collections import *\nfrom heapq import *\nfrom bisect import *\nfrom copy import *\nfrom math import *\nfrom random import *\nfrom statistics import *\nfrom itertools import *\nfrom functools import *\nfrom operator import *\nfrom io import *\nfrom sys import *\nfrom json import *\nfrom builtins import *\nfrom typing import *\nimport string\nimport re\nimport datetime\nimport collections\nimport heapq\nimport bisect\nimport copy\nimport math\nimport random\nimport statistics\nimport itertools\nimport functools\nimport operator\nimport io\nimport sys\nimport json\nsys.setrecursionlimit(6*10**5)\n" if debug: print(f"loading test code = {datetime.now().time()}") if which_type == CODE_TYPE.call_based: sol += test if debug: print(f"sol = {sol}") signal.alarm(timeout) try: tmp_sol = RuntimeModule.from_string("tmp_sol", "", sol) if "class Solution" not in test: tmp = tmp_sol else: tmp = tmp_sol.Solution() signal.alarm(0) except Exception as e: signal.alarm(0) error_traceback = traceback.format_exc() if debug: print(f"type 0 compilation error = {e}") results.append(-2) return results, { "error": repr(e), # "error_code": -1, # "error_message": "Compilation Error", "traceback": clean_traceback(error_traceback), } signal.alarm(0) elif which_type == CODE_TYPE.standard_input: # sol # if code has if __name__ == "__main__": then remove it try: astree = ast.parse(test) last_block = astree.body[-1] if isinstance(last_block, ast.If): condition = last_block.test if ast.unparse(condition).strip() == "__name__ == '__main__'": test = (ast.unparse(astree.body[:-1]) + "\n" + ast.unparse(last_block.body)) except: pass tmp_test = test.split("\n") new_test = [] for x in tmp_test: if (not x.startswith("from ")) and (not x.startswith("import ")): new_test.append("\t" + x + "\n") else: new_test.append(x + "\n") tmp_test = new_test new_test = "" started = False for i in tmp_test: if i.startswith("\t") and not started: new_test += "stdin = sys.stdin\nstdout = sys.stdout\n" new_test += "def code():\n" new_test += i started = True elif started and ((i.startswith("from ")) or (i.startswith("import "))): new_test += "\t" + i else: new_test += i tmp_test = new_test sol += tmp_test if debug: print(f"sol = {sol}") method_name = "code" signal.alarm(timeout) try: tmp_sol = RuntimeModule.from_string("tmp_sol", "", sol) tmp = tmp_sol signal.alarm(0) except Exception as e: signal.alarm(0) error_traceback = traceback.format_exc() if debug: print(f"type 1 compilation error = {e}") results.append(-2) return results, { "error": repr(e), # "error_code": -1, # "error_message": "Compilation Error", "traceback": clean_traceback(error_traceback), } signal.alarm(0) if debug: print(f"get method = {datetime.now().time()}") try: method = getattr(tmp, method_name) # get_attr second arg must be str except: signal.alarm(0) error_traceback = traceback.format_exc() e = sys.exc_info() print(f"unable to get function error = {e}") results.append(-2) return results, { "error": repr(e), # "error_code": -1, # "error_message": "Unable to extract code", "traceback": clean_traceback(error_traceback), } for index, inputs in enumerate(in_outs["inputs"]): raw_inputs = inputs raw_outputs = in_outs["outputs"][index] if which_type == CODE_TYPE.call_based: inputs = [json.loads(line) for line in inputs.split("\n")] in_outs["outputs"][index] = json.loads(in_outs["outputs"][index]) truncate_line_size = 300 // (raw_inputs.count("\n") + 1) raw_inputs = "\n".join( [truncatefn(line, truncate_line_size) for line in raw_inputs.strip().split("\n")]) raw_outputs = truncatefn(raw_outputs, 200) else: raw_inputs = truncatefn(raw_inputs) raw_outputs = truncatefn(raw_outputs, 200) # JSON forces dictionaries to have string keys; this undoes this (assuming a singleton list) try: if isinstance(inputs[0], dict): inputs = [{int(k): v for k, v in inputs[0].items()}] except: True try: if isinstance(in_outs["outputs"][index], dict): in_outs["outputs"][index] = [{int(k): v for k, v in in_outs["outputs"][index].items()}] except: True try: if isinstance(in_outs["outputs"][index][0], dict): in_outs["outputs"][index] = [{int(k): v for k, v in in_outs["outputs"][index][0].items()}] except: True if debug: print( f"time: {datetime.now().time()} testing index = {index} inputs = {inputs}, {type(inputs)}. type = {which_type}" ) if which_type == CODE_TYPE.call_based: # Call-based signal.alarm(timeout) faulthandler.enable() try: output = method(*inputs) raw_true_output = output raw_true_output_copy = json.dumps(output) raw_true_output_copy = truncatefn(raw_true_output_copy, 200) # ground truth sequences are not tuples if isinstance(output, tuple): output = list(output) tmp_result = output == in_outs["outputs"][index] if (isinstance(in_outs["outputs"][index], list) and in_outs["outputs"][index]): tmp_result = tmp_result or (output == in_outs["outputs"][index][0]) # ground truth sequences are not tuples try: if isinstance(output[0], tuple): tmp_result = tmp_result or ([list(x) for x in output] == in_outs["outputs"][index][0]) except: True results.append(tmp_result) if tmp_result != True: return results, { "output": raw_true_output_copy, "expected": raw_outputs, "inputs": raw_inputs, # "error_code": -2, "error_message": "Wrong Answer", } # reset the alarm signal.alarm(0) except Exception as e: signal.alarm(0) error_traceback = traceback.format_exc() faulthandler.disable() if debug: print(f"Standard input runtime error or time limit exceeded error = {e}") results.append(-1) if "timeoutexception" in repr(e).lower(): return results, { "error": repr(e), # "error_code": -3, # "error_message": "Time Limit Exceeded", # "inputs": raw_inputs, # "expected": raw_outputs, "traceback": clean_traceback(error_traceback), } else: return results, { "error": repr(e), # "error_code": -4, # "error_message": "Runtime Error", # "inputs": raw_inputs, # "expected": raw_outputs, "traceback": clean_traceback(error_traceback), } faulthandler.disable() signal.alarm(0) if debug: print( f"outputs = {output}, test outputs = {in_outs['outputs'][index]}, inputs = {inputs}, {type(inputs)}, {output == [in_outs['outputs'][index]]}" ) elif which_type == CODE_TYPE.standard_input: # Standard input faulthandler.enable() passed = False if isinstance(inputs, list): inputs = "\n".join(inputs) if isinstance(in_outs["outputs"][index], list): in_outs["outputs"][index] = "\n".join(in_outs["outputs"][index]) signal.alarm(timeout) with Capturing() as output: try: call_method(method, inputs) # reset the alarm signal.alarm(0) passed = True except Exception as e: # runtime error or took too long signal.alarm(0) error_traceback = traceback.format_exc() print(f"Call-based runtime error or time limit exceeded error = {repr(e)}{e}") results.append(-1) if "timeoutexception" in repr(e).lower(): return results, { "error": repr(e), # "error_code": -3, # "error_message": "Time Limit Exceeded", # "inputs": raw_inputs, # "expected": raw_outputs, "traceback": clean_traceback(error_traceback), } else: return results, { "error": repr(e), # "error_code": -4, # "error_message": "Runtime Error", # "inputs": raw_inputs, # "expected": raw_outputs, "traceback": clean_traceback(error_traceback), } signal.alarm(0) raw_true_output = output[0] raw_true_output_copy = truncatefn(raw_true_output, 200) output = raw_true_output.splitlines() if not passed: if debug: nl = "\n" if not isinstance(inputs, list): print( f"not passed output = {output}, test outputs = {in_outs['outputs'][index]}, inputs = {inputs.replace(nl,' new-line ')}, {type(inputs)}, {output == [in_outs['outputs'][index]]}" ) else: print( f"not passed output = {output}, test outputs = {in_outs['outputs'][index]}, inputs = {inputs}, {type(inputs)}, {output == [in_outs['outputs'][index]]}" ) continue if passed and debug: print(f"==> output = {output}, test outputs = {in_outs['outputs'][index]}") if custom_compare_(output, in_outs["outputs"][index]): tmp_result = True results.append(tmp_result) continue # ground truth sequences are expressed as lists not tuples if isinstance(output, tuple): output = list(output) tmp_result = False try: tmp_result = output == [in_outs["outputs"][index]] if isinstance(in_outs["outputs"][index], list): tmp_result = tmp_result or (output == in_outs["outputs"][index]) if isinstance(output[0], str): tmp_result = tmp_result or ([e.strip() for e in output] == in_outs["outputs"][index]) except Exception as e: if debug: print(f"Failed check1 exception = {e}") pass if tmp_result == True: results.append(tmp_result) continue # try one more time without \n if isinstance(in_outs["outputs"][index], list): for tmp_index, i in enumerate(in_outs["outputs"][index]): in_outs["outputs"][index][tmp_index] = i.split("\n") in_outs["outputs"][index][tmp_index] = [ x.strip() for x in in_outs["outputs"][index][tmp_index] if x ] else: in_outs["outputs"][index] = in_outs["outputs"][index].split("\n") in_outs["outputs"][index] = list(filter(len, in_outs["outputs"][index])) in_outs["outputs"][index] = list(map(lambda x: x.strip(), in_outs["outputs"][index])) try: tmp_result = output == [in_outs["outputs"][index]] if isinstance(in_outs["outputs"][index], list): tmp_result = tmp_result or (output == in_outs["outputs"][index]) except Exception as e: if debug: print(f"Failed check2 exception = {e}") pass if tmp_result == True: results.append(tmp_result) continue # try by converting the output into a split up list too if isinstance(output, list): output = list(filter(len, output)) if debug: nl = "\n" if not isinstance(inputs, list): print( f"@1 output = {output}, test outputs = {in_outs['outputs'][index]}, inputs = {inputs.replace(nl,' new-line ')}, {type(inputs)}, {output == [in_outs['outputs'][index]]} {tmp_result=}" ) else: print( f"@1 output = {output}, test outputs = {in_outs['outputs'][index]}, inputs = {inputs}, {type(inputs)}, {output == [in_outs['outputs'][index]]} {tmp_result=}" ) if tmp_result == True: results.append(tmp_result) continue if debug: print(f"{tmp_result=} @a") try: tmp_result = output == [in_outs["outputs"][index]] if isinstance(in_outs["outputs"][index], list): tmp_result = tmp_result or (output == in_outs["outputs"][index]) except Exception as e: if debug: print(f"Failed check3 exception = {e}") pass if debug: print(f"{tmp_result=} @b") try: all_ints = all( combined_int_check(e1) and combined_int_check(e2) for e1, e2 in zip(output, in_outs["outputs"][index])) if not all_ints: if debug: print([ combined_int_check(e1) and combined_int_check(e2) for e1, e2 in zip(output, in_outs["outputs"][index]) ]) output_float = [float(e) for e in output] gt_float = [float(e) for e in in_outs["outputs"][index]] tmp_result = tmp_result or ((len(output_float) == len(gt_float)) and np.allclose(output_float, gt_float)) except Exception as e: pass if debug: print(f"{tmp_result=} @c") try: if isinstance(output[0], list): all_ints = all( combined_int_check(e1) and combined_int_check(e2) for e1, e2 in zip(output[0], in_outs["outputs"][index])) if not all_ints: output_float = [float(e) for e in output[0]] gt_float = [float(e) for e in in_outs["outputs"][index][0]] tmp_result = tmp_result or ((len(output_float) == len(gt_float)) and np.allclose(output_float, gt_float)) except Exception as e: pass if tmp_result == True: results.append(tmp_result) continue if debug: print(f"{tmp_result=} @d") # try by converting the stuff into split up list if isinstance(in_outs["outputs"][index], list): for tmp_index, i in enumerate(in_outs["outputs"][index]): in_outs["outputs"][index][tmp_index] = set(i.split()) else: in_outs["outputs"][index] = set(in_outs["outputs"][index].split()) if debug: print(f"{tmp_result=} @e") try: tmp_result = output == in_outs["outputs"][index] except Exception as e: if debug: print(f"Failed check4 exception = {e}") continue if tmp_result == True: results.append(tmp_result) continue if debug: print(f"{tmp_result=} @f") # try by converting the output into a split up list too if isinstance(output, list): for tmp_index, i in enumerate(output): output[tmp_index] = i.split() output = list(filter(len, output)) for tmp_index, i in enumerate(output): output[tmp_index] = set(i) else: output = output.split() output = list(filter(len, output)) output = set(output) if debug: print(f"{tmp_result=} @g") if tmp_result == True and debug: print("PASSED") results.append(tmp_result) if tmp_result != True: return results, { "output": raw_true_output_copy, "expected": raw_outputs, "inputs": raw_inputs, # "error_code": -2, "error_message": "Wrong Answer", } if debug: nl = "\n" if not isinstance(inputs, list): print( f"@2 output = {output}, test outputs = {in_outs['outputs'][index]}, inputs = {inputs.replace(nl,' new-line ')}, {type(inputs)}, {output == [in_outs['outputs'][index]]}" ) else: print( f"@2 output = {output}, test outputs = {in_outs['outputs'][index]}, inputs = {inputs}, {type(inputs)}, {output == [in_outs['outputs'][index]]}" ) print(f"results = {results}") return results, {} def custom_compare_(output, ground_truth): if isinstance(output, list): output_1 = "\n".join(output) if stripped_string_compare(output_1, ground_truth): return True if isinstance(output, list): output_2 = [o.lstrip().rstrip() for o in output] output_2 = "\n".join(output_2) if stripped_string_compare(output_2, ground_truth): return True return False def stripped_string_compare(s1, s2): s1 = s1.lstrip().rstrip() s2 = s2.lstrip().rstrip() return s1 == s2 def call_method(method, inputs): if isinstance(inputs, list): inputs = "\n".join(inputs) inputs_line_iterator = iter(inputs.split("\n")) # sys.setrecursionlimit(10000) # @patch('builtins.input', side_effect=inputs.split("\n")) @patch("builtins.open", mock_open(read_data=inputs)) @patch("sys.stdin", StringIO(inputs)) @patch("sys.stdin.readline", lambda *args: next(inputs_line_iterator)) @patch("sys.stdin.readlines", lambda *args: inputs.split("\n")) @patch("sys.stdin.read", lambda *args: inputs) # @patch('sys.stdout.write', print) def _inner_call_method(_method): try: return _method() except SystemExit as e: pass finally: pass return _inner_call_method(method) def reliability_guard(maximum_memory_bytes=None): """ This disables various destructive functions and prevents the generated code from interfering with the test (e.g. fork bomb, killing other processes, removing filesystem files, etc.) WARNING This function is NOT a security sandbox. Untrusted code, including, model- generated code, should not be blindly executed outside of one. See the Codex paper for more information about OpenAI's code sandbox, and proceed with caution. """ if maximum_memory_bytes is not None: import resource resource.setrlimit(resource.RLIMIT_AS, (maximum_memory_bytes, maximum_memory_bytes)) resource.setrlimit(resource.RLIMIT_DATA, (maximum_memory_bytes, maximum_memory_bytes)) if not platform.uname().system == "Darwin": resource.setrlimit(resource.RLIMIT_STACK, (maximum_memory_bytes, maximum_memory_bytes)) faulthandler.disable() import builtins builtins.exit = None builtins.quit = None import os os.environ["OMP_NUM_THREADS"] = "1" os.kill = None os.system = None # 防止干扰repl评测 os.putenv = None os.remove = None os.removedirs = None os.rmdir = None os.fchdir = None os.setuid = None os.fork = None os.forkpty = None os.killpg = None os.rename = None os.renames = None os.truncate = None os.replace = None os.unlink = None os.fchmod = None os.fchown = None os.chmod = None os.chown = None os.chroot = None os.fchdir = None os.lchflags = None os.lchmod = None os.lchown = None os.getcwd = None os.chdir = None import shutil shutil.rmtree = None shutil.move = None shutil.chown = None import subprocess subprocess.Popen = None # type: ignore __builtins__["help"] = None import sys sys.modules["ipdb"] = None sys.modules["joblib"] = None sys.modules["resource"] = None sys.modules["psutil"] = None sys.modules["tkinter"] = None ================================================ FILE: verl/utils/reward_score/prime_code/utils.py ================================================ # Copyright 2024 PRIME team and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # Borrowed from: https://huggingface.co/spaces/codeparrot/apps_metric/blob/main/utils.py import multiprocessing from typing import Dict, Optional from datasets import load_dataset from .testing_util import run_test import traceback import os, sys def _temp_run(sample, generation, debug, result, metadata_list, timeout): with open(os.devnull, 'w') as devnull: sys.stdout = devnull sys.stderr = devnull try: res, metadata = run_test(in_outs=sample, test=generation, debug=debug, timeout=timeout) result.append(res) metadata_list.append(metadata) except Exception as e: # print(e) # some tracebacks are extremely long. traceback.print_exc(10) result.append([-1 for i in range(len(sample['inputs']))]) metadata_list.append({}) def check_correctness(in_outs: Optional[dict], generation, timeout=10, debug=True): """Check correctness of code generation with a global timeout. The global timeout is to catch some extreme/rare cases not handled by the timeouts inside `run_test`""" manager = multiprocessing.Manager() result = manager.list() metadata_list = manager.list() p = multiprocessing.Process(target=_temp_run, args=(in_outs, generation, debug, result, metadata_list, timeout)) p.start() p.join(timeout=timeout + 1) if p.is_alive(): p.kill() # p.terminate() if not result: # consider that all tests failed result = [[-1 for i in range(len(in_outs["inputs"]))]] if debug: print(f"global timeout") return result[0], metadata_list ================================================ FILE: verl/utils/reward_score/prime_math/__init__.py ================================================ # Copyright 2024 PRIME team and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Answer checker API that uses sympy to simplify expressions and check for equality. Call grade_answer(given_answer: str, ground_truth: str). FROM: https://github.com/openai/prm800k/blob/main/prm800k/grading/grader.py """ import re import sympy from pylatexenc import latex2text from sympy.parsing import sympy_parser from . import math_normalize from .grader import math_equal # import math_normalize # from grader import math_equal # sympy might hang -- we don't care about trying to be lenient in these cases BAD_SUBSTRINGS = ["^{", "^("] BAD_REGEXES = ["\^[0-9]+\^", "\^[0-9][0-9]+"] TUPLE_CHARS = "()[]" def _sympy_parse(expr: str): """Parses an expression with sympy.""" py_expr = expr.replace("^", "**") return sympy_parser.parse_expr( py_expr, transformations=(sympy_parser.standard_transformations + (sympy_parser.implicit_multiplication_application,)), ) def _parse_latex(expr: str) -> str: """Attempts to parse latex to an expression sympy can read.""" expr = expr.replace("\\tfrac", "\\frac") expr = expr.replace("\\dfrac", "\\frac") expr = expr.replace("\\frac", " \\frac") # Play nice with mixed numbers. expr = latex2text.LatexNodes2Text().latex_to_text(expr) # Replace the specific characters that this parser uses. expr = expr.replace("√", "sqrt") expr = expr.replace("π", "pi") expr = expr.replace("∞", "inf") expr = expr.replace("∪", "U") expr = expr.replace("·", "*") expr = expr.replace("×", "*") return expr.strip() def _is_float(num: str) -> bool: try: float(num) return True except ValueError: return False def _is_int(x: float) -> bool: try: return abs(x - int(round(x))) <= 1e-7 except: return False def _is_frac(expr: str) -> bool: return bool(re.search(r"^-?[0-9]+.?/0*[1-9][0-9]*.?$", expr)) def _str_is_int(x: str) -> bool: try: x = _strip_properly_formatted_commas(x) x = float(x) return abs(x - int(round(x))) <= 1e-7 except: return False def _str_to_int(x: str) -> bool: x = x.replace(",", "") x = float(x) return int(x) def _inject_implicit_mixed_number(step: str): """ Automatically make a mixed number evalable e.g. 7 3/4 => 7+3/4 """ p1 = re.compile("([0-9]) +([0-9])") step = p1.sub("\\1+\\2", step) ## implicit mults return step def _strip_properly_formatted_commas(expr: str): # We want to be careful because we don't want to strip tuple commas p1 = re.compile("(\d)(,)(\d\d\d)($|\D)") while True: next_expr = p1.sub("\\1\\3\\4", expr) if next_expr == expr: break expr = next_expr return next_expr def _normalize(expr: str) -> str: """Normalize answer expressions.""" if expr is None: return None # Remove enclosing `\text{}`. m = re.search("^\\\\text\{(?P.+?)\}$", expr) if m is not None: expr = m.group("text") expr = expr.replace("\\%", "%") expr = expr.replace("\\$", "$") expr = expr.replace("$", "") expr = expr.replace("%", "") expr = expr.replace(" or ", " , ") expr = expr.replace(" and ", " , ") expr = expr.replace("million", "*10^6") expr = expr.replace("billion", "*10^9") expr = expr.replace("trillion", "*10^12") for unit in [ "degree", "cm", "centimeter", "meter", "mile", "second", "minute", "hour", "day", "week", "month", "year", "foot", "feet", "inch", "yard", "liter", ]: expr = re.sub(f"{unit}(es)?(s)? *(\^[0-9]+)?", "", expr) expr = re.sub(f"\^ *\\\\circ", "", expr) if len(expr) > 0 and expr[0] == "{" and expr[-1] == "}": expr = expr[1:-1] expr = re.sub(",\\\\! *", "", expr) if _is_float(expr) and _is_int(float(expr)): expr = str(int(round(float(expr)))) if "\\" in expr: try: expr = _parse_latex(expr) except: pass # edge case with mixed numbers and negative signs expr = re.sub("- *", "-", expr) expr = _inject_implicit_mixed_number(expr) # don't be case sensitive for text answers expr = expr.lower() if _str_is_int(expr): expr = str(_str_to_int(expr)) return expr def count_unknown_letters_in_expr(expr: str): expr = expr.replace("sqrt", "") expr = expr.replace("frac", "") letters_in_expr = set([x for x in expr if x.isalpha()]) return len(letters_in_expr) def should_allow_eval(expr: str): # we don't want to try parsing unknown text or functions of more than two variables if count_unknown_letters_in_expr(expr) > 2: return False for bad_string in BAD_SUBSTRINGS: if bad_string in expr: return False for bad_regex in BAD_REGEXES: if re.search(bad_regex, expr) is not None: return False return True def are_equal_under_sympy(ground_truth_normalized: str, given_normalized: str): are_equal = False try: expr = f"({ground_truth_normalized})-({given_normalized})" if should_allow_eval(expr): sympy_diff = _sympy_parse(expr) simplified = sympy.simplify(sympy_diff) if simplified == 0: are_equal = True except: pass return are_equal def split_tuple(expr: str): """ Split the elements in a tuple/interval, while handling well-formatted commas in large numbers """ expr = _strip_properly_formatted_commas(expr) if len(expr) == 0: return [] if (len(expr) > 2 and expr[0] in TUPLE_CHARS and expr[-1] in TUPLE_CHARS and all([ch not in expr[1:-1] for ch in TUPLE_CHARS])): elems = [elem.strip() for elem in expr[1:-1].split(",")] else: elems = [expr] return elems def grade_answer(given_answer: str, ground_truth: str) -> bool: """ The answer will be considered correct if: (a) it normalizes to the same string as the ground truth answer OR (b) sympy can simplify the difference between the expressions to 0 """ if given_answer is None: return False ground_truth_normalized_mathd = math_normalize.normalize_answer(ground_truth) given_answer_normalized_mathd = math_normalize.normalize_answer(given_answer) # be at least as lenient as mathd if ground_truth_normalized_mathd == given_answer_normalized_mathd: return True ground_truth_normalized = _normalize(ground_truth) given_normalized = _normalize(given_answer) if ground_truth_normalized is None: return False if ground_truth_normalized == given_normalized: return True if len(given_normalized) == 0: return False ground_truth_elems = split_tuple(ground_truth_normalized) given_elems = split_tuple(given_normalized) if len(ground_truth_elems) > 1 and (ground_truth_normalized[0] != given_normalized[0] or ground_truth_normalized[-1] != given_normalized[-1]): is_correct = False elif len(ground_truth_elems) != len(given_elems): is_correct = False else: for ground_truth_elem, given_elem in zip(ground_truth_elems, given_elems): if _is_frac(ground_truth_elem) and _is_frac(given_elem): # if fractions aren't reduced, then shouldn't be marked as correct # so, we don't want to allow sympy.simplify in this case is_correct = ground_truth_elem == given_elem elif _str_is_int(ground_truth_elem) != _str_is_int(given_elem): # if the ground truth answer is an integer, we require the given answer to be a strict match (no sympy.simplify) is_correct = False else: is_correct = are_equal_under_sympy(ground_truth_elem, given_elem) if not is_correct: break return is_correct def remove_boxed(s): left = "\\boxed{" try: assert s[:len(left)] == left assert s[-1] == "}" return s[len(left):-1] except: return None def _last_boxed_only_string(string): idx = string.rfind("\\boxed") if idx < 0: idx = string.rfind("\\fbox") if idx < 0: return None i = idx left_brace_idx = None right_brace_idx = None num_left_braces_open = 0 while i < len(string): if string[i] == "{": num_left_braces_open += 1 if left_brace_idx is None: left_brace_idx = i elif string[i] == "}": num_left_braces_open -= 1 if num_left_braces_open == 0: right_brace_idx = i break i += 1 if left_brace_idx is None or right_brace_idx is None: return None return string[left_brace_idx + 1:right_brace_idx].strip() def match_answer(response): is_matched = False for ans_marker in ['answer:', "answer is", "answers are"]: ans_idx = response.lower().rfind(ans_marker) if ans_idx != -1: is_matched = True response = response[ans_idx + len(ans_marker):].strip() if response.endswith("\n"): response = response[:-2] for ans_marker in ["is answer", "is the answer", "are answers", "are the answers"]: ans_idx = response.lower().rfind(ans_marker) if ans_idx != -1: is_matched = True response = response[:ans_idx].strip() if response.endswith("\n"): response = response[:-2] # Find boxed ans_boxed = _last_boxed_only_string(response) if ans_boxed: is_matched = True response = ans_boxed if ". " in response: dot_idx = response.lower().rfind(". ") if dot_idx != -1: response = response[:dot_idx].strip() for ans_marker in ['be ', "is ", "are ", "=", ": ", "get ", 'be\n', "is\n", "are\n", ":\n", "get\n"]: ans_idx = response.lower().rfind(ans_marker) if ans_idx != -1: is_matched = True response = response[ans_idx + len(ans_marker):].strip() if response.endswith("\n"): response = response[:-2] is_matched = is_matched if any([c.isdigit() for c in response]) else False # answer must have a digit # Grade return is_matched, response import math def compute_score(model_output: str, ground_truth: str) -> bool: model_output = str(model_output) ground_truth = str(ground_truth) is_matched, extracted_model_output = match_answer(model_output) format_correctness = "Step 2:" in model_output and "\\box" in model_output # grade simple algebra questions. if succeeded, return; otherwise, proceed to more complex grading if grade_answer(extracted_model_output, ground_truth): return True, True, extracted_model_output try: if "\pi" in extracted_model_output or "\pi" in ground_truth: equivs = [] for pi in [math.pi, 3.14]: equivs.append(math_equal(extracted_model_output, ground_truth, timeout=True, pi=pi)) is_correct = any(equivs) else: is_correct = math_equal(extracted_model_output, ground_truth, timeout=True) except: is_correct = False return is_correct, format_correctness, extracted_model_output ================================================ FILE: verl/utils/reward_score/prime_math/grader.py ================================================ # Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # Copyright (c) Microsoft Corporation. # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in all # copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE # Copyright (c) 2023 OpenAI # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # The above copyright notice and this permission notice shall be included in all # copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. # Copyright (c) 2021 Dan Hendrycks # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in all # copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. # Copyright 2024 PRIME team and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ This logic is largely copied from the Hendrycks' MATH release (math_equivalence), and borrowed from: - https://github.com/microsoft/ToRA/blob/main/src/eval/grader.py - https://github.com/microsoft/ProphetNet/tree/master/CRITIC - https://github.com/openai/prm800k """ import contextlib import re import signal import math from math import isclose from typing import Union from sympy import N, simplify from sympy.parsing.latex import parse_latex from sympy.parsing.sympy_parser import parse_expr def is_digit(s): try: if "{,}" in str(s): num = float(str(s).replace("{,}", "")) return True, num num = float(str(s).replace(",", "")) return True, num except ValueError: return False, None def normalize(answer, pi) -> str: # checking if answer is $ and removing $ in that case to compare if isinstance(answer, str) and bool(re.match(r'\$\d+(\.\d+)?', answer)): return answer[1:] # checking if answer is % or \\% and removing % if isinstance(answer, str) and (bool(re.match(r'^\d+(\.\d+)?%$', answer)) or bool(re.match(r'^\d+(\.\d+)?\\%$', answer))): return answer.replace("\\%", "").replace("%", "") # handle base answer = handle_base(answer) # handle pi answer = handle_pi(answer, pi) return answer def handle_base(x) -> str: if isinstance(x, str) and "_" in x: # Due to base x = x.split("_")[0] x = float(x) return int(x) return x def handle_pi(string, pi): if isinstance(string, str) and "\pi" in string: # Find the first occurrence of "\pi" idx = string.find("\pi") # Iterate over the string and find all occurrences of "\pi" with a valid previous character while idx != -1: if idx > 0 and string[idx - 1].isdigit(): # Replace "\pi" with "*math.pi" if the previous character is a digit string = string[:idx] + f"*{pi}" + string[idx + 3:] else: # Replace "\pi" with "1*math.pi" if the previous character is not a digit string = string[:idx] + f"1*{pi}" + string[idx + 3:] # Find the next occurrence of "\pi" idx = string.find("\pi", idx + 1) # Evaluate the expression using eval() function try: string = eval(string) except: pass return string def math_equal(prediction: Union[bool, float, str], reference: Union[float, str], include_percentage: bool = True, tolerance: float = 1e-4, timeout: float = 10.0, pi: float = math.pi) -> bool: """ Exact match of math if and only if: 1. numerical equal: both can convert to float and are equal 2. symbolic equal: both can convert to sympy expression and are equal """ prediction = normalize(prediction, pi) reference = normalize(reference, pi) if isinstance(prediction, str) and len(prediction) > 1000: # handling weird corner-cases prediction = prediction[:1000] # 0. string comparison if isinstance(prediction, str) and isinstance(reference, str): if prediction.strip().lower() == reference.strip().lower(): return True if prediction.replace(" ", "") == reference.replace(" ", ""): return True try: # 1. numerical equal if is_digit(prediction)[0] and is_digit(reference)[0]: prediction = is_digit(prediction)[1] reference = is_digit(reference)[1] # number questions if include_percentage: gt_result = [reference / 100, reference, reference * 100] else: gt_result = [reference] for item in gt_result: try: if isclose(item, prediction, rel_tol=tolerance): return True except Exception: continue return False except Exception: pass if not prediction and prediction not in [0, False]: return False # 2. symbolic equal reference = str(reference).strip() prediction = str(prediction).strip() ## deal with [], (), {} prediction = format_intervals(prediction) pred_str, ref_str = prediction, reference if (prediction.startswith("[") and prediction.endswith("]") and not reference.startswith("(")) or (prediction.startswith("(") and prediction.endswith(")") and not reference.startswith("[")): pred_str = pred_str.strip("[]()") ref_str = ref_str.strip("[]()") for s in ["{", "}", "(", ")"]: ref_str = ref_str.replace(s, "") pred_str = pred_str.replace(s, "") if pred_str == ref_str: return True ## [a, b] vs. [c, d], return a==c and b==d if (prediction and reference and prediction[0] in "([" and prediction[-1] in ")]" and prediction[0] == reference[0] and prediction[-1] == reference[-1]): pred_parts = prediction[1:-1].split(",") ref_parts = reference[1:-1].split(",") if len(pred_parts) == len(ref_parts): if all([ math_equal(pred_pt, ref_pt, include_percentage, tolerance) for pred_pt, ref_pt in zip(pred_parts, ref_parts) ]): return True if "," in prediction and "," in reference: pred_parts = [item.strip() for item in prediction.split(",")] ref_parts = [item.strip() for item in reference.split(",")] if len(pred_parts) == len(ref_parts): if all([ math_equal(pred_parts[i], ref_parts[i], include_percentage, tolerance) for i in range(len(pred_parts)) ]): return True else: return False # if we have point == tuple of values if prediction.startswith("Point") and reference[0] == "(" and reference[-1] == ")": pred_parts = prediction[prediction.find("(") + 1:-1].split(",") ref_parts = reference[1:-1].split(",") if len(pred_parts) == len(ref_parts): if all([ math_equal(pred_pt, ref_pt, include_percentage, tolerance) for pred_pt, ref_pt in zip(pred_parts, ref_parts) ]): return True # if reference is a matrix if "\begin{pmatrix}" in reference and prediction.startswith("Matrix"): try: pred_matrix = parse_expr(prediction) ref_matrix_items = reference.split()[1:-1:2] if len(pred_matrix) == len(ref_matrix_items): if all([ math_equal(pred, ref, include_percentage, tolerance) for ref, pred in zip(ref_matrix_items, pred_matrix) ]): return True except Exception: pass elif "\begin{pmatrix}" in reference and prediction.startswith("[") and prediction.endswith("]"): if isinstance(eval(prediction), list): try: pred_matrix = eval(prediction) # ref_matrix_items = reference.split()[1:-1:2] ref_matrix_items = reference.lstrip("\\begin{pmatrix}").lstrip("\begin{pmatrix}").rstrip( "\\end{pmatrix}").rstrip("\end{pmatrix}") ref_matrix_items = ref_matrix_items.split("\\") ref_matrix_items = [row.split("&") if "&" in row else row for row in ref_matrix_items] if len(pred_matrix) == len(ref_matrix_items): if all([ math_equal(pred, ref, include_percentage, tolerance) for ref, pred in zip(ref_matrix_items, pred_matrix) ]): return True except Exception: pass return symbolic_equal(prediction, reference, tolerance, timeout) def symbolic_equal(a, b, tolerance, timeout=10.0): def _parse(s): for f in [parse_expr, parse_latex]: try: with time_limit(timeout): return f(s) except Exception: pass return s a = _parse(a) b = _parse(b) try: with time_limit(timeout): if simplify(a - b) == 0: return True except Exception: pass try: with time_limit(timeout): if isclose(N(a), N(b), rel_tol=tolerance): return True except Exception: pass return False class TimeoutException(Exception): pass @contextlib.contextmanager def time_limit(seconds: float): def signal_handler(signum, frame): raise TimeoutException("Timed out!") signal.setitimer(signal.ITIMER_REAL, seconds) signal.signal(signal.SIGALRM, signal_handler) try: yield finally: signal.setitimer(signal.ITIMER_REAL, 0) def format_intervals(prediction): patterns = { "Interval(": r"^Interval\((.*)\)$", "Interval.Ropen(": r"^Interval\.Ropen\((.*)\)$", "Interval.Lopen(": r"^Interval\.Lopen\((.*)\)$", "Interval.open(": r"^Interval\.open\((.*)\)$", } for key, pattern in patterns.items(): match = re.match(pattern, prediction) if match: inner_content = match.group(1) if key == "Interval(": # Intarval(a, b) == [a, b] return f"[{inner_content}]" elif key == "Interval.Ropen(": # Intarval.Ropen(a, b) == [a, b) return f"[{inner_content})" elif key == "Interval.Lopen(": # Intarval.Lopen(a, b) == (a, b] return f"({inner_content}]" elif key == "Interval.open(": # Intarval.open(a, b) == (a, b) return f"({inner_content})" return prediction ================================================ FILE: verl/utils/reward_score/prime_math/math_normalize.py ================================================ # Copyright 2024 PRIME team and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # Copyright (c) 2021 Dan Hendrycks # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in all # copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. """ This logic is largely copied from the Hendrycks' MATH release (math_equivalence). From: https://github.com/openai/prm800k/blob/main/prm800k/grading/math_normalize.py """ import re from typing import Optional def normalize_answer(answer: Optional[str]) -> Optional[str]: if answer is None: return None answer = answer.strip() try: # Remove enclosing `\text{}`. m = re.search("^\\\\text\{(?P.+?)\}$", answer) if m is not None: answer = m.group("text").strip() return _strip_string(answer) except: return answer def _fix_fracs(string): substrs = string.split("\\frac") new_str = substrs[0] if len(substrs) > 1: substrs = substrs[1:] for substr in substrs: new_str += "\\frac" if substr[0] == "{": new_str += substr else: try: assert len(substr) >= 2 except: return string a = substr[0] b = substr[1] if b != "{": if len(substr) > 2: post_substr = substr[2:] new_str += "{" + a + "}{" + b + "}" + post_substr else: new_str += "{" + a + "}{" + b + "}" else: if len(substr) > 2: post_substr = substr[2:] new_str += "{" + a + "}" + b + post_substr else: new_str += "{" + a + "}" + b string = new_str return string def _fix_a_slash_b(string): if len(string.split("/")) != 2: return string a = string.split("/")[0] b = string.split("/")[1] try: a = int(a) b = int(b) assert string == "{}/{}".format(a, b) new_string = "\\frac{" + str(a) + "}{" + str(b) + "}" return new_string except: return string def _remove_right_units(string): # "\\text{ " only ever occurs (at least in the val set) when describing units if "\\text{ " in string: splits = string.split("\\text{ ") assert len(splits) == 2 return splits[0] else: return string def _fix_sqrt(string): if "\\sqrt" not in string: return string splits = string.split("\\sqrt") new_string = splits[0] for split in splits[1:]: if split[0] != "{": a = split[0] new_substr = "\\sqrt{" + a + "}" + split[1:] else: new_substr = "\\sqrt" + split new_string += new_substr return new_string def _strip_string(string): # linebreaks string = string.replace("\n", "") # remove inverse spaces string = string.replace("\\!", "") # replace \\ with \ string = string.replace("\\\\", "\\") # replace tfrac and dfrac with frac string = string.replace("tfrac", "frac") string = string.replace("dfrac", "frac") # remove \left and \right string = string.replace("\\left", "") string = string.replace("\\right", "") # Remove circ (degrees) string = string.replace("^{\\circ}", "") string = string.replace("^\\circ", "") # remove dollar signs string = string.replace("\\$", "") # remove units (on the right) string = _remove_right_units(string) # remove percentage string = string.replace("\\%", "") string = string.replace("\%", "") # " 0." equivalent to " ." and "{0." equivalent to "{." Alternatively, add "0" if "." is the start of the string string = string.replace(" .", " 0.") string = string.replace("{.", "{0.") # if empty, return empty string if len(string) == 0: return string if string[0] == ".": string = "0" + string # to consider: get rid of e.g. "k = " or "q = " at beginning if len(string.split("=")) == 2: if len(string.split("=")[0]) <= 2: string = string.split("=")[1] # fix sqrt3 --> sqrt{3} string = _fix_sqrt(string) # remove spaces string = string.replace(" ", "") # \frac1b or \frac12 --> \frac{1}{b} and \frac{1}{2}, etc. Even works with \frac1{72} (but not \frac{72}1). Also does a/b --> \\frac{a}{b} string = _fix_fracs(string) # manually change 0.5 --> \frac{1}{2} if string == "0.5": string = "\\frac{1}{2}" # NOTE: X/Y changed to \frac{X}{Y} in dataset, but in simple cases fix in case the model output is X/Y string = _fix_a_slash_b(string) return string ================================================ FILE: verl/utils/seqlen_balancing.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import List, Tuple, Callable import heapq import torch from torch import distributed as dist from tensordict import TensorDict import copy def karmarkar_karp(seqlen_list: List[int], k_partitions: int, equal_size: bool): # see: https://en.wikipedia.org/wiki/Largest_differencing_method class Set: def __init__(self) -> None: self.sum = 0 self.items = [] def add(self, idx: int, val: int): self.items.append((idx, val)) self.sum += val def merge(self, other): for idx, val in other.items: self.items.append((idx, val)) self.sum += val def __lt__(self, other): if self.sum != other.sum: return self.sum < other.sum if len(self.items) != len(other.items): return len(self.items) < len(other.items) return self.items < other.items class State: def __init__(self, items: List[Tuple[int, int]], k: int) -> None: self.k = k # sets should always be decreasing order self.sets = [Set() for _ in range(k)] assert len(items) in [1, k], f"{len(items)} not in [1, {k}]" for i, (idx, seqlen) in enumerate(items): self.sets[i].add(idx=idx, val=seqlen) self.sets = sorted(self.sets, reverse=True) def spread(self): return self.sets[0].sum - self.sets[-1].sum def get_partitions(self): partitions = [] for i in range(len(self.sets)): cur_partition = [] for idx, _ in self.sets[i].items: cur_partition.append(idx) partitions.append(cur_partition) return partitions def merge(self, other): for i in range(self.k): self.sets[i].merge(other.sets[self.k - 1 - i]) self.sets = sorted(self.sets, reverse=True) @property def spread(self) -> int: return self.sets[0].sum - self.sets[-1].sum def __lt__(self, other): # least heap, let the state with largest spread to be popped first, # if the spread is the same, let the state who has the largest set # to be popped first. if self.spread != other.spread: return self.spread > other.spread return self.sets[0] > other.sets[0] def __repr__(self) -> str: repr_str = "[" for i in range(self.k): if i > 0: repr_str += "," repr_str += "{" for j, (_, seqlen) in enumerate(self.sets[i].items): if j > 0: repr_str += "," repr_str += str(seqlen) repr_str += "}" repr_str += "]" return repr_str sorted_seqlen_list = sorted([(seqlen, i) for i, seqlen in enumerate(seqlen_list)]) states_pq = [] if equal_size: assert len(seqlen_list) % k_partitions == 0, f"{len(seqlen_list)} % {k_partitions} != 0" for offset in range(0, len(sorted_seqlen_list), k_partitions): items = [] for i in range(k_partitions): seqlen, idx = sorted_seqlen_list[offset + i] items.append((idx, seqlen)) heapq.heappush(states_pq, State(items=items, k=k_partitions)) else: for seqlen, idx in sorted_seqlen_list: heapq.heappush(states_pq, State(items=[(idx, seqlen)], k=k_partitions)) while len(states_pq) > 1: state0 = heapq.heappop(states_pq) state1 = heapq.heappop(states_pq) # merge states state0.merge(state1) heapq.heappush(states_pq, state0) final_state = states_pq[0] partitions = final_state.get_partitions() if equal_size: for i, partition in enumerate(partitions): assert len(partition) * \ k_partitions == len(seqlen_list), f"{len(partition)} * {k_partitions} != {len(seqlen_list)}" return partitions def greedy_partition(seqlen_list: List[int], k_partitions: int, equal_size: bool): bias = sum(seqlen_list) + 1 if equal_size else 0 sorted_seqlen = [(seqlen + bias, i) for i, seqlen in enumerate(seqlen_list)] partitions = [[] for _ in range(k_partitions)] partition_sums = [0 for _ in range(k_partitions)] for seqlen, i in sorted_seqlen: min_idx = None for j in range(k_partitions): if min_idx is None or partition_sums[j] < partition_sums[min_idx]: min_idx = j partitions[min_idx].append(i) partition_sums[min_idx] += seqlen if equal_size: for i, partition in enumerate(partitions): assert len(partition) * \ k_partitions == len(seqlen_list), f"{len(partition)} * {k_partitions} != {len(seqlen_list)}" return partitions def get_seqlen_balanced_partitions(seqlen_list: List[int], k_partitions: int, equal_size: bool): """ get order of seq lengths to make partitions balanced, this is used in balacing sum of seqlength across dp ranks and microbatches Parameters: seqlen_list (List[int]): seq lengths of each items k_partitions (int): resulting number of partitions equal_size (bool): if True, number of items in each partitions must be equal. if False, only consider balancing the sum, each partition can have variable number of items Returns: partitions (List[List[int]]): return k_partitions list containing the index of items. """ assert len(seqlen_list) >= k_partitions, f"number of items:[{len(seqlen_list)}] < k_partitions:[{k_partitions}]" def _check_and_sort_partitions(partitions): assert len(partitions) == k_partitions, f"{len(partitions)} != {k_partitions}" seen_idx = set() sorted_partitions = [None] * k_partitions for i, partition in enumerate(partitions): assert len(partition) > 0, f"the {i}-th partition is empty" for idx in partition: seen_idx.add(idx) sorted_partitions[i] = sorted(partition) assert seen_idx == set(range(len(seqlen_list))) return sorted_partitions partitions = karmarkar_karp(seqlen_list=seqlen_list, k_partitions=k_partitions, equal_size=equal_size) return _check_and_sort_partitions(partitions) def log_seqlen_unbalance(seqlen_list: List[int], partitions: List[List[int]], prefix): # add some metrics of seqlen sum on dp ranks k_partition = len(partitions) # assert len(seqlen_list) % k_partition == 0 batch_size = len(seqlen_list) // k_partition min_sum_seqlen = None max_sum_seqlen = None total_sum_seqlen = 0 for offset in range(0, len(seqlen_list), batch_size): cur_sum_seqlen = sum(seqlen_list[offset:offset + batch_size]) if min_sum_seqlen is None or cur_sum_seqlen < min_sum_seqlen: min_sum_seqlen = cur_sum_seqlen if max_sum_seqlen is None or cur_sum_seqlen > max_sum_seqlen: max_sum_seqlen = cur_sum_seqlen total_sum_seqlen += cur_sum_seqlen balanced_sum_seqlen_list = [] for partition in partitions: cur_sum_seqlen_balanced = sum([seqlen_list[i] for i in partition]) balanced_sum_seqlen_list.append(cur_sum_seqlen_balanced) # print("balanced_sum_seqlen_list: ", balanced_sum_seqlen_list) min_sum_seqlen_balanced = min(balanced_sum_seqlen_list) max_sum_seqlen_balanced = max(balanced_sum_seqlen_list) return { f'{prefix}/min': min_sum_seqlen, f'{prefix}/max': max_sum_seqlen, f'{prefix}/minmax_diff': max_sum_seqlen - min_sum_seqlen, f'{prefix}/balanced_min': min_sum_seqlen_balanced, f'{prefix}/balanced_max': max_sum_seqlen_balanced, f'{prefix}/mean': total_sum_seqlen / len(partitions) } def ceildiv(a, b): return -(a // -b) def rearrange_micro_batches(batch: TensorDict, max_token_len, dp_group=None): """Split the batch into a list of micro_batches, where the max_token_len is smaller than max_token_len and the number of valid tokens in each micro batch is well balanced. """ # this is per local micro_bsz max_seq_len = batch['attention_mask'].shape[-1] assert max_token_len >= max_seq_len, \ f'max_token_len must be greater than the sequence length. Got {max_token_len=} and {max_seq_len=}' seq_len_effective: torch.Tensor = batch['attention_mask'].sum(dim=1) total_seqlen = seq_len_effective.sum().item() num_micro_batches = ceildiv(total_seqlen, max_token_len) if dist.is_initialized(): num_micro_batches = torch.tensor([num_micro_batches], device='cuda') dist.all_reduce(num_micro_batches, op=dist.ReduceOp.MAX, group=dp_group) num_micro_batches = num_micro_batches.cpu().item() seq_len_effective = seq_len_effective.tolist() assert num_micro_batches <= len(seq_len_effective) micro_bsz_idx = get_seqlen_balanced_partitions(seq_len_effective, num_micro_batches, equal_size=False) micro_batches = [] for partition in micro_bsz_idx: curr_micro_batch = [] for idx in partition: curr_micro_batch.append(batch[idx:idx + 1]) curr_micro_batch = torch.cat(curr_micro_batch) micro_batches.append(curr_micro_batch) return micro_batches, micro_bsz_idx def get_reverse_idx(idx_map): reverse_idx_map = copy.deepcopy(idx_map) for i, idx in enumerate(idx_map): reverse_idx_map[idx] = i return reverse_idx_map ================================================ FILE: verl/utils/tokenizer.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Utils for tokenization.""" import warnings __all__ = ['hf_tokenizer', 'hf_processor'] def set_pad_token_id(tokenizer): """Set pad_token_id to eos_token_id if it is None. Args: tokenizer (transformers.PreTrainedTokenizer): The tokenizer to be set. """ if tokenizer.pad_token_id is None: tokenizer.pad_token_id = tokenizer.eos_token_id warnings.warn(f'tokenizer.pad_token_id is None. Now set to {tokenizer.eos_token_id}') if tokenizer.pad_token is None: tokenizer.pad_token = tokenizer.eos_token warnings.warn(f'tokenizer.pad_token is None. Now set to {tokenizer.eos_token}') def hf_tokenizer(name_or_path, correct_pad_token=True, correct_gemma2=True, **kwargs): """Create a huggingface pretrained tokenizer which correctness handles eos and pad tokens. Args: name (str): The name of the tokenizer. correct_pad_token (bool): Whether to correct the pad token id. correct_gemma2 (bool): Whether to correct the gemma2 tokenizer. Returns: transformers.PreTrainedTokenizer: The pretrained tokenizer. """ from transformers import AutoTokenizer if correct_gemma2 and isinstance(name_or_path, str) and 'gemma-2-2b-it' in name_or_path: # the EOS token in gemma2 is ambiguious, which may worsen RL performance. # https://huggingface.co/google/gemma-2-2b-it/commit/17a01657f5c87135bcdd0ec7abb4b2dece04408a warnings.warn('Found gemma-2-2b-it tokenizer. Set eos_token and eos_token_id to and 107.') kwargs['eos_token'] = '' kwargs['eos_token_id'] = 107 tokenizer = AutoTokenizer.from_pretrained(name_or_path, **kwargs) if correct_pad_token: set_pad_token_id(tokenizer) return tokenizer def hf_processor(name_or_path, **kwargs): """Create a huggingface processor to process multimodal data. Args: name_or_path (str): The name of the processor. Returns: transformers.ProcessorMixin: The pretrained processor. """ from transformers import AutoProcessor try: processor = AutoProcessor.from_pretrained(name_or_path, **kwargs) except Exception: processor = None # Avoid load tokenizer, see: # https://github.com/huggingface/transformers/blob/v4.49.0/src/transformers/models/auto/processing_auto.py#L344 if processor is not None and "Processor" not in processor.__class__.__name__: processor = None return processor ================================================ FILE: verl/utils/torch_dtypes.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Adapted from Cruise. """ import torch from typing import Union HALF_LIST = [16, "16", "fp16", "float16", torch.float16] FLOAT_LIST = [32, "32", "fp32", "float32", torch.float32] BFLOAT_LIST = ["bf16", "bfloat16", torch.bfloat16] class PrecisionType(object): """Type of precision used. >>> PrecisionType.HALF == 16 True >>> PrecisionType.HALF in (16, "16") True """ HALF = "16" FLOAT = "32" FULL = "64" BFLOAT = "bf16" MIXED = "mixed" @staticmethod def supported_type(precision: Union[str, int]) -> bool: return any(x == precision for x in PrecisionType) @staticmethod def supported_types() -> list[str]: return [x.value for x in PrecisionType] @staticmethod def is_fp16(precision): return precision in HALF_LIST @staticmethod def is_fp32(precision): return precision in FLOAT_LIST @staticmethod def is_bf16(precision): return precision in BFLOAT_LIST @staticmethod def to_dtype(precision): if precision in HALF_LIST: return torch.float16 elif precision in FLOAT_LIST: return torch.float32 elif precision in BFLOAT_LIST: return torch.bfloat16 else: raise RuntimeError(f"unexpected precision: {precision}") @staticmethod def to_str(precision): if precision == torch.float16: return 'fp16' elif precision == torch.float32: return 'fp32' elif precision == torch.bfloat16: return 'bf16' else: raise RuntimeError(f"unexpected precision: {precision}") ================================================ FILE: verl/utils/torch_functional.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Contain small torch utilities """ from typing import Dict, Union, List, Optional import torch import torch.distributed import torch.nn.functional as F from tensordict import TensorDict from torch import nn try: from flash_attn.ops.triton.cross_entropy import cross_entropy_loss FLAH_ATTN_CROSS_ENTROPY_LOSS_AVAILABLE = True except ImportError: FLAH_ATTN_CROSS_ENTROPY_LOSS_AVAILABLE = False def gather_from_labels(data, label): """Gather the label from data. The value in label should be [0, vocab_size) Args: data: (..., vocab_size) label (torch.IntTensor) : (...,) Returns: """ output = torch.gather(data, -1, label.unsqueeze(-1)).squeeze(-1) return output def logprobs_from_logits(logits, labels): """ See: https://github.com/pytorch/pytorch/issues/563#issuecomment-330103591 """ if FLAH_ATTN_CROSS_ENTROPY_LOSS_AVAILABLE: batch_dim = logits.shape[:-1] last_dim = logits.shape[-1] logits = logits.reshape(-1, last_dim) labels = labels.reshape(-1) output = logprobs_from_logits_flash_attn(logits, labels) output = output.view(*batch_dim) else: output = logprobs_from_logits_v2(logits, labels) return output def logprobs_from_logits_flash_attn(logits, labels): output = cross_entropy_loss(logits, labels) assert isinstance( output, tuple), "please make sure flash-attn>=2.4.3 where cross_entropy_loss returns Tuple[losses, z_losses]." return -output[0] def logprobs_from_logits_naive(logits, labels): logp = F.log_softmax(logits, dim=-1) logpy = gather_from_labels(logp, labels) return logpy def logprobs_from_logits_v2(logits: torch.FloatTensor, labels): """ A memory efficient implementation of logprobs_from_logits """ if logits.dtype in [torch.float32, torch.float64]: logits_labels = torch.gather(logits, dim=-1, index=labels.unsqueeze(-1)).squeeze(-1) # loop to reduce peak mem consumption logsumexp_values = torch.stack([torch.logsumexp(l, dim=-1) for l in logits]) logprobs_labels = logits_labels - logsumexp_values # log_softmax(x_i) = x_i - logsumexp(x) else: # logsumexp approach is unstable with bfloat16, fall back to slightly less efficent approach logprobs_labels = [] for row_logits, row_labels in zip(logits, labels): # loop to reduce peak mem consumption row_logprobs = F.log_softmax(row_logits, dim=-1) row_logprobs_labels = row_logprobs.gather(dim=-1, index=row_labels.unsqueeze(-1)).squeeze(-1) logprobs_labels.append(row_logprobs_labels) logprobs_labels = torch.stack(logprobs_labels) return logprobs_labels def clip_by_value(x, tensor_min, tensor_max): """ Tensor extenstion to torch.clamp https://github.com/pytorch/pytorch/issues/2793#issuecomment-428784713 """ clipped = torch.max(torch.min(x, tensor_max), tensor_min) return clipped def entropy_from_logits(logits: torch.Tensor): """Calculate entropy from logits.""" pd = torch.nn.functional.softmax(logits, dim=-1) entropy = torch.logsumexp(logits, dim=-1) - torch.sum(pd * logits, dim=-1) return entropy def masked_sum(values, mask, axis=None): """Compute mean of tensor with a masked values.""" return (values * mask).sum(axis=axis) def masked_mean(values, mask, axis=None): """Compute mean of tensor with a masked values.""" return (values * mask).sum(axis=axis) / mask.sum(axis=axis) def masked_var(values, mask, unbiased=True): """Compute variance of tensor with masked values.""" mean = masked_mean(values, mask) centered_values = values - mean variance = masked_mean(centered_values**2, mask) if unbiased: mask_sum = mask.sum() if mask_sum == 0: raise ValueError("At least one element in the mask has to be 1.") # note that if mask_sum == 1, then there is a division by zero issue # to avoid it you just need to use a larger minibatch_size if mask_sum == 1: raise ValueError("The sum of the mask is one, which can cause a division by zero.") bessel_correction = mask_sum / (mask_sum - 1) variance = variance * bessel_correction return variance def masked_whiten(values, mask, shift_mean=True): """Whiten values with masked values.""" mean, var = masked_mean(values, mask), masked_var(values, mask) whitened = (values - mean) * torch.rsqrt(var + 1e-8) if not shift_mean: whitened += mean return whitened def get_eos_mask(response_id: torch.Tensor, eos_token: Union[int, List[int]] = 2, dtype=torch.int64): ''' end of sentence token can be int or list: 1 or [1, 2] e.g. eos_token=1 response_id: [0, 0, 2, 42, 3, 5, 1, 0, 0] eos_mask: [1, 1, 1, 1, 1, 1, 1, 0, 0] ''' if isinstance(eos_token, int): eos_token = [eos_token] eos_mask = torch.zeros_like(response_id, dtype=torch.bool) for token in eos_token: eos_mask |= response_id.eq(token) eos_mask = eos_mask.long() eos_mask = (torch.cumsum(eos_mask, dim=1) - eos_mask).bool() eos_mask = torch.logical_not(eos_mask).to(dtype) return eos_mask def compute_grad_norm(model: nn.Module): total_grad_square = 0 total_params = 0 for param in model.parameters(): if param.grad is not None: total_grad_square += torch.sum(torch.square(param.grad.detach())).item() return total_grad_square def broadcast_dict_tensor(tensors: Union[Dict[str, torch.Tensor], TensorDict], src, group): """ TODO: optimize this. Technically, we only need one broadcast """ for key in tensors.sorted_keys: torch.distributed.broadcast(tensors[key], src=src, group=group, async_op=False) def allgather_dict_tensors(tensors: Union[Dict[str, torch.Tensor], TensorDict], size, group, dim=0): """ TODO: optimize this. - We can use async ops - We can use only one allgather Args: tensors: size: group: Returns: """ if isinstance(tensors, TensorDict): is_tensor_dict = True tensors_as_dict = tensors.to_dict() else: tensors_as_dict = tensors is_tensor_dict = False output = {} sorted_keys = sorted(tensors_as_dict.keys()) for key in sorted_keys: val = tensors_as_dict[key] output[key] = [torch.empty_like(val) for _ in range(size)] torch.distributed.all_gather(output[key], val, group=group, async_op=False) output[key] = torch.cat(output[key], dim=dim) if is_tensor_dict: output = TensorDict(source=output, batch_size=tensors.batch_size[0] * size) return output def split_dict_tensor_into_batches(tensors: TensorDict, batch_size) -> List[TensorDict]: assert tensors.batch_size[0] % batch_size == 0, \ f'input data batch size: {tensors.batch_size[0]}, split batch size: {batch_size}' return tensors.split(batch_size) def pad_2d_list_to_length(response, pad_token_id, max_length=None): """ pad a 2D list (e.g. responses, logprobs) to a 2D tensor. """ response_length = max(len(sub_list) for sub_list in response) if max_length is not None and max_length > response_length: target_length = max_length else: target_length = response_length padded_response = [tuple(sub_list) + (pad_token_id,) * (target_length - len(sub_list)) for sub_list in response] tensor = torch.tensor(padded_response) return tensor def pad_sequence_to_length(tensors, max_seq_len, pad_token_id, left_pad=False): """ pad a 2D tensors (e.g. responses, logprobs) in the last dim to max_seq_length. input shape: [bs, seq_length] output shape: [bs, max_seq_length] (0, max_seq_len - tensors.shape[-1]) means right pad to max_seq_length and no left pad """ if tensors.shape[-1] >= max_seq_len: return tensors pad_tuple = (max_seq_len - tensors.shape[-1], 0) if left_pad else (0, max_seq_len - tensors.shape[-1]) return F.pad(tensors, pad_tuple, 'constant', pad_token_id) from transformers import PreTrainedTokenizer def tokenize_and_postprocess_data(prompt: str, tokenizer: PreTrainedTokenizer, max_length: int, pad_token_id: int, left_pad=True, truncation='error'): """ input_data is the output from tokenizer. """ assert truncation in ['left', 'right', 'error'] input_data = tokenizer(prompt, return_tensors='pt', add_special_tokens=False) input_ids = input_data['input_ids'] attention_mask = input_data['attention_mask'] assert input_ids.ndim == 2 sequence_length = input_ids.shape[-1] if sequence_length < max_length: input_ids = pad_sequence_to_length(input_ids, max_seq_len=max_length, pad_token_id=pad_token_id, left_pad=left_pad) attention_mask = pad_sequence_to_length(attention_mask, max_seq_len=max_length, pad_token_id=0, left_pad=left_pad) elif sequence_length > max_length: if truncation == 'left': # actually, left truncation may not be reasonable input_ids = input_ids[:, -max_length:] attention_mask = attention_mask[:, -max_length:] elif truncation == 'right': input_ids = input_ids[:, :max_length] attention_mask = attention_mask[:, :max_length] elif truncation == 'error': raise NotImplementedError(f'{sequence_length=} is larger than {max_length=}') else: raise NotImplementedError(f'Unknown truncation method {truncation}') return input_ids, attention_mask def remove_pad_token(input_ids: torch.Tensor, attention_mask: torch.Tensor): """ Remove the pad token. Args: input_ids shape: [bs, seq_length] attention_mask shape: [bs, seq_length] Returns: no_padding_batch(List[List[int]]): contains the rmpad token ids per query. """ no_padding_batch = [] for ids, mask in zip(input_ids, attention_mask): no_padding_batch.append((ids[len(ids) - mask.sum():]).cpu().numpy().tolist()) return no_padding_batch def log_probs_from_logits_response(input_ids, logits, response_length): """Compute the response log_probs from full logits. Note that logits = model(input_ids) Args: input_ids: [batch_size, seqlen] logits: [batch_size, seqlen, vocab_size] Returns: response_log_prob: """ response_logits = logits[:, -response_length - 1:-1] response = input_ids[:, -response_length:] response_log_prob = logprobs_from_logits(logits=response_logits, labels=response) return response_log_prob def log_probs_from_logits_response_rmpad(input_ids, attention_mask, logits_rmpad, response_length): """Compute the log_probs from logits with rmpad logits and pad input. Note that logits_rmpad = model(input_ids_rmpad). For each sentences, there is a shift between logits and input_ids. The reason for this function to is to compute logprobs_from_logits in rmpad mode because it is memory-intensive for large vocab_size Args: input_ids: [batch_size, seqlen] attention_mask: [batch_size, seqlen] logits_rmpad: [total_nnz, vocab_size] response_length: int """ from flash_attn.bert_padding import pad_input, unpad_input batch_size, seqlen = input_ids.shape input_ids_rmpad, indices, *_ = unpad_input(input_ids.unsqueeze(-1), attention_mask=attention_mask) input_ids_rmpad = input_ids_rmpad.squeeze(-1) input_ids_rmpad_rolled = torch.roll(input_ids_rmpad, shifts=-1, dims=0) full_log_probs_rmpad = logprobs_from_logits(logits=logits_rmpad, labels=input_ids_rmpad_rolled) # (total_nnz,) full_output = pad_input(hidden_states=full_log_probs_rmpad.unsqueeze(-1), indices=indices, batch=batch_size, seqlen=seqlen) output = full_output.squeeze(-1)[:, -response_length - 1:-1] # [batch_size, response_length] return output def log_probs_from_logits_all_rmpad(input_ids_rmpad, logits_rmpad, indices, batch_size, seqlen, response_length): """Compute the log_probs from logits with rmpad input_ids and logits. Note that logits_rmpad = model(input_ids_rmpad). For each sentences, there is a shift between logits and input_ids. The reason for this function to is to compute logprobs_from_logits in rmpad mode because it is memory-intensive for large vocab_size Args: input_ids_rmpad: [1, total_nnz] logits_rmpad: [total_nnz, vocab_size] indices: [total_nnz] batch_size: int seqlen: int response_length: int """ from flash_attn.bert_padding import pad_input input_ids_rmpad = input_ids_rmpad.transpose(0, 1) # transpose back to [total_nnz, 1] input_ids_rmpad = input_ids_rmpad.squeeze(-1) input_ids_rmpad_rolled = torch.roll(input_ids_rmpad, shifts=-1, dims=0) full_log_probs_rmpad = logprobs_from_logits(logits=logits_rmpad, labels=input_ids_rmpad_rolled) # (total_nnz,) full_output = pad_input(hidden_states=full_log_probs_rmpad.unsqueeze(-1), indices=indices, batch=batch_size, seqlen=seqlen) output = full_output.squeeze(-1)[:, -response_length - 1:-1] # [batch_size, response_length] return output from transformers.generation.logits_process import (TemperatureLogitsWarper, TopKLogitsWarper, TopPLogitsWarper) def post_process_logits(input_ids, logits, temperature, top_k, top_p): if temperature != 1.: logits = logits.div_(temperature) # inplace operation to avoid OOM # TODO: add them back # if top_k is not None and top_k > 0: # logits = TopKLogitsWarper(top_k=top_k)(input_ids, logits) # if top_p is not None and top_p < 1.0 and top_p > 0.0: # logits = TopPLogitsWarper(top_p=top_p)(input_ids, logits) return logits """ Optimizer related """ from torch.optim import Optimizer from torch.optim.lr_scheduler import LambdaLR import math def get_cosine_schedule_with_warmup( optimizer: Optimizer, num_warmup_steps: int, num_training_steps: int, min_lr_ratio: float = 0.0, num_cycles: float = 0.5, last_epoch: int = -1, ): """ Create a schedule with a learning rate that decreases following the values of the cosine function between the initial lr set in the optimizer to 0, after a warmup period during which it increases linearly between 0 and the initial lr set in the optimizer. Args: optimizer (:class:`~torch.optim.Optimizer`): The optimizer for which to schedule the learning rate. num_warmup_steps (:obj:`int`): The number of steps for the warmup phase. num_training_steps (:obj:`int`): The total number of training steps. min_lr_ratio (:obj:`float`, `optional`, defaults to 0.0): The minimum lr ratio w.r.t the maximum. num_cycles (:obj:`float`, `optional`, defaults to 0.5): The number of waves in the cosine schedule (the defaults is to just decrease from the max value to 0 following a half-cosine). last_epoch (:obj:`int`, `optional`, defaults to -1): The index of the last epoch when resuming training. Return: :obj:`torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule. """ assert min_lr_ratio >= 0 and min_lr_ratio <= 1. coef = (1 - min_lr_ratio) * 0.5 intercept = (1 + min_lr_ratio) * 0.5 def lr_lambda(current_step): if current_step < num_warmup_steps: return float(current_step) / float(max(1, num_warmup_steps)) progress = float(current_step - num_warmup_steps) / float(max(1, num_training_steps - num_warmup_steps)) x = math.cos(math.pi * float(num_cycles) * 2.0 * progress) return max(0.0, x * coef + intercept) return LambdaLR(optimizer, lr_lambda, last_epoch) def get_constant_schedule_with_warmup( optimizer: Optimizer, num_warmup_steps: int, last_epoch: int = -1, ): def lr_lambda(current_step): return min(1, float(current_step) / float(max(1, num_warmup_steps))) return LambdaLR(optimizer, lr_lambda, last_epoch) def prepare_decoder_attention_mask(attention_mask, input_shape, inputs_embeds): # create causal mask # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] combined_attention_mask = None if input_shape[-1] > 1: combined_attention_mask = _make_causal_mask( input_shape, inputs_embeds.dtype, device=inputs_embeds.device, ) if attention_mask is not None: # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to(inputs_embeds.device) combined_attention_mask = (expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask) return combined_attention_mask # Copied from transformers.models.bart.modeling_bart._make_causal_mask def _make_causal_mask(input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device): """ Make causal mask used for bi-directional self-attention. """ bsz, tgt_len = input_ids_shape mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device) mask_cond = torch.arange(mask.size(-1), device=device) mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0) mask = mask.to(dtype) return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len) # Copied from transformers.models.bart.modeling_bart._expand_mask def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None): """ Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`. """ bsz, src_len = mask.size() tgt_len = tgt_len if tgt_len is not None else src_len expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype) inverted_mask = 1.0 - expanded_mask return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min) def get_unpad_data(attention_mask): seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32) indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten() max_seqlen_in_batch = seqlens_in_batch.max().item() cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0)) return ( indices, cu_seqlens, max_seqlen_in_batch, ) ================================================ FILE: verl/utils/tracking.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ A unified tracking interface that supports logging data to different backend """ import dataclasses from enum import Enum from functools import partial from pathlib import Path from typing import List, Union, Dict, Any class Tracking(object): supported_backend = ["wandb", "mlflow", "swanlab", "vemlp_wandb", "tensorboard", "console"] def __init__(self, project_name, experiment_name, default_backend: Union[str, List[str]] = 'console', config=None): if isinstance(default_backend, str): default_backend = [default_backend] for backend in default_backend: if backend == 'tracking': import warnings warnings.warn("`tracking` logger is deprecated. use `wandb` instead.", DeprecationWarning) else: assert backend in self.supported_backend, f'{backend} is not supported' self.logger = {} if 'tracking' in default_backend or 'wandb' in default_backend: import wandb wandb.init(project=project_name, name=experiment_name, config=config) self.logger['wandb'] = wandb if 'mlflow' in default_backend: import mlflow mlflow.start_run(run_name=experiment_name) mlflow.log_params(_compute_mlflow_params_from_objects(config)) self.logger['mlflow'] = _MlflowLoggingAdapter() if "swanlab" in default_backend: import swanlab import os SWANLAB_API_KEY = os.environ.get("SWANLAB_API_KEY", None) SWANLAB_LOG_DIR = os.environ.get("SWANLAB_LOG_DIR", "swanlog") SWANLAB_MODE = os.environ.get("SWANLAB_MODE", "cloud") if SWANLAB_API_KEY: swanlab.login(SWANLAB_API_KEY) # NOTE: previous login information will be overwritten swanlab.init(project=project_name, experiment_name=experiment_name, config=config, logdir=SWANLAB_LOG_DIR, mode=SWANLAB_MODE) self.logger["swanlab"] = swanlab if 'vemlp_wandb' in default_backend: import os import volcengine_ml_platform from volcengine_ml_platform import wandb as vemlp_wandb volcengine_ml_platform.init( ak=os.environ["VOLC_ACCESS_KEY_ID"], sk=os.environ["VOLC_SECRET_ACCESS_KEY"], region=os.environ["MLP_TRACKING_REGION"], ) vemlp_wandb.init( project=project_name, name=experiment_name, config=config, sync_tensorboard=True, ) self.logger['vemlp_wandb'] = vemlp_wandb if 'tensorboard' in default_backend: self.logger['tensorboard'] = _TensorboardAdapter() if 'console' in default_backend: from verl.utils.logger.aggregate_logger import LocalLogger self.console_logger = LocalLogger(print_to_console=True) self.logger['console'] = self.console_logger def log(self, data, step, backend=None): for default_backend, logger_instance in self.logger.items(): if backend is None or default_backend in backend: logger_instance.log(data=data, step=step) def __del__(self): if 'wandb' in self.logger: self.logger['wandb'].finish(exit_code=0) if 'swanlab' in self.logger: self.logger['swanlab'].finish() if 'vemlp_wandb' in self.logger: self.logger['vemlp_wandb'].finish(exit_code=0) if 'tensorboard' in self.logger: self.logger['tensorboard'].finish() class _TensorboardAdapter: def __init__(self): from torch.utils.tensorboard import SummaryWriter import os tensorboard_dir = os.environ.get("TENSORBOARD_DIR", "tensorboard_log") os.makedirs(tensorboard_dir, exist_ok=True) print(f"Saving tensorboard log to {tensorboard_dir}.") self.writer = SummaryWriter(tensorboard_dir) def log(self, data, step): for key in data: self.writer.add_scalar(key, data[key], step) def finish(self): self.writer.close() class _MlflowLoggingAdapter: def log(self, data, step): import mlflow mlflow.log_metrics(metrics=data, step=step) def _compute_mlflow_params_from_objects(params) -> Dict[str, Any]: if params is None: return {} return _flatten_dict(_transform_params_to_json_serializable(params, convert_list_to_dict=True), sep='/') def _transform_params_to_json_serializable(x, convert_list_to_dict: bool): _transform = partial(_transform_params_to_json_serializable, convert_list_to_dict=convert_list_to_dict) if dataclasses.is_dataclass(x): return _transform(dataclasses.asdict(x)) if isinstance(x, dict): return {k: _transform(v) for k, v in x.items()} if isinstance(x, list): if convert_list_to_dict: return {'list_len': len(x)} | {f'{i}': _transform(v) for i, v in enumerate(x)} else: return [_transform(v) for v in x] if isinstance(x, Path): return str(x) if isinstance(x, Enum): return x.value return x def _flatten_dict(raw: Dict[str, Any], *, sep: str) -> Dict[str, Any]: import pandas as pd ans = pd.json_normalize(raw, sep=sep).to_dict(orient='records')[0] assert isinstance(ans, dict) return ans ================================================ FILE: verl/utils/ulysses.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Utilities for DeepSpeed Ulysses Sequence Parallelism. DeepSpeed Ulysses Paper: https://arxiv.org/abs/2309.14509 Inspired from: https://github.com/microsoft/DeepSpeed/blob/master/deepspeed/sequence/layer.py """ from typing import Any, Optional, List, Tuple import torch from torch import Tensor import torch.distributed as dist from torch.distributed import ProcessGroup _ULYSSES_SEQUENCE_PARALLEL_GROUP = None def set_ulysses_sequence_parallel_group(group: dist.ProcessGroup): """ Set ulysses sequence parallel process group. """ global _ULYSSES_SEQUENCE_PARALLEL_GROUP _ULYSSES_SEQUENCE_PARALLEL_GROUP = group def get_ulysses_sequence_parallel_group() -> Optional[dist.ProcessGroup]: """ Get ulysses sequence parallel process group. """ global _ULYSSES_SEQUENCE_PARALLEL_GROUP return _ULYSSES_SEQUENCE_PARALLEL_GROUP def get_ulysses_sequence_parallel_world_size(group: ProcessGroup = None) -> int: """ Get ulysses sequence parallel world size. """ group = get_ulysses_sequence_parallel_group() if group is None else group return dist.get_world_size(group) if group else 1 def get_ulysses_sequence_parallel_rank(group: ProcessGroup = None) -> int: """ Get ulysses sequence parallel rank. """ group = get_ulysses_sequence_parallel_group() if group is None else group return dist.get_rank(group) if group else 0 def gather_seq_scatter_heads( x: Tensor, seq_dim: int, head_dim: int, unpadded_dim_size: int = 0, group: ProcessGroup = None, ) -> Tensor: """ A func to sync embedding input with alltoall in sequence parallel gather sequence dimension and scatter head dim: e.g. seq_dim: 1, head_dim: 2 [bsz, seq/n, h, ...] -> [bsz, seq, h/n, ...] """ group = get_ulysses_sequence_parallel_group() if group is None else group if not group: return x sp_world = get_ulysses_sequence_parallel_world_size(group) x = SeqAllToAll.apply(group, x, head_dim, seq_dim) if unpadded_dim_size and unpadded_dim_size % sp_world != 0: padding_size = x.size(seq_dim) - unpadded_dim_size x = _unpad_tensor(x, seq_dim, padding_size) return x def gather_heads_scatter_seq(x: Tensor, head_dim: int, seq_dim: int, group: ProcessGroup = None) -> Tensor: """ A func to sync attention result with alltoall in sequence parallel gather head dimension and scatter seq dim: e.g. seq_dim: 1, head_dim: 2 [bsz, seq, h/n, ...] -> [bsz, seq/n, h, ...] """ group = get_ulysses_sequence_parallel_group() if group is None else group if not group: return x dim_size = x.size(seq_dim) sp_world = get_ulysses_sequence_parallel_world_size(group) if dim_size % sp_world != 0: padding_size = sp_world - (dim_size % sp_world) x = _pad_tensor(x, seq_dim, padding_size) return SeqAllToAll.apply(group, x, seq_dim, head_dim, False) def _pad_tensor(x: Tensor, dim: int, padding_size: int) -> Tensor: shape = list(x.shape) shape[dim] = padding_size pad = torch.zeros(shape, dtype=x.dtype, device=x.device) return torch.cat([x, pad], dim=dim) def _unpad_tensor(x: Tensor, dim: int, padding_size: int) -> Tensor: slc = [slice(None)] * len(x.shape) slc[dim] = slice(0, -padding_size) return x[slc] def slice_input_tensor(x: Tensor, dim: int, padding: bool = True, group: ProcessGroup = None) -> Tensor: group = get_ulysses_sequence_parallel_group() if group is None else group sp_world_size = dist.get_world_size(group) sp_rank = get_ulysses_sequence_parallel_rank() dim_size = x.size(dim) # pad before slice if padding and dim_size % sp_world_size: padding_size = sp_world_size - (dim_size % sp_world_size) x = _pad_tensor(x, dim, padding_size) # slice the input tensor parts = x.size(dim) // sp_world_size slc = [slice(None)] * len(x.shape) slc[dim] = slice(sp_rank * parts, (sp_rank + 1) * parts) return x[slc].contiguous() def all_to_all_tensor( local_input: Tensor, scatter_dim: int, gather_dim: int, group: Optional[dist.ProcessGroup] = None, async_op: bool = False, ): group = get_ulysses_sequence_parallel_group() if group is None else group seq_world_size = dist.get_world_size(group) input_list = [t.contiguous() for t in torch.tensor_split(local_input, seq_world_size, scatter_dim)] output_list = [torch.empty_like(input_list[0]) for _ in range(seq_world_size)] comm = dist.all_to_all(output_list, input_list, group=group, async_op=async_op) if async_op: def wait(): comm.wait() return torch.cat(output_list, dim=gather_dim).contiguous() return wait return torch.cat(output_list, dim=gather_dim).contiguous() def all_gather_tensor(local_tensor: Tensor, group: Optional[dist.ProcessGroup] = None, async_op: bool = False): group = get_ulysses_sequence_parallel_group() if group is None else group sp_world_size = dist.get_world_size(group=group) output_shape = list(local_tensor.shape) output_shape[0] = output_shape[0] * sp_world_size output = torch.empty(output_shape, dtype=local_tensor.dtype, device=local_tensor.device) dist.all_gather_into_tensor(output, local_tensor, group=group, async_op=async_op) return output class SeqAllToAll(torch.autograd.Function): @staticmethod def forward( ctx: Any, group: dist.ProcessGroup, local_input: Tensor, scatter_dim: int, gather_dim: int, async_op: bool = False, ) -> Tensor: ctx.group = group ctx.scatter_dim = scatter_dim ctx.gather_dim = gather_dim ctx.async_op = async_op return all_to_all_tensor(local_input, scatter_dim, gather_dim, group, async_op) @staticmethod def backward(ctx: Any, *grad_output: Tensor) -> Tuple[None, Tensor, None, None]: if ctx.async_op: input_t = torch.cat(grad_output[1:], dim=ctx.gather_dim).contiguous() else: input_t = grad_output[0] return ( None, all_to_all_tensor(input_t, ctx.gather_dim, ctx.scatter_dim, ctx.group, False), None, None, None, None, ) class Gather(torch.autograd.Function): @staticmethod def forward(ctx: Any, group: dist.ProcessGroup, local_tensor: Tensor, gather_dim: int, grad_scaler: bool = True, async_op=False) -> Tensor: ctx.group = group ctx.gather_dim = gather_dim ctx.grad_scaler = grad_scaler ctx.async_op = async_op sp_world_size = dist.get_world_size(group=group) ctx.sp_world_size = sp_world_size sp_rank = dist.get_rank(group=group) ctx.sp_rank = sp_rank local_shape = list(local_tensor.size()) split_size = local_shape[0] part_size = local_shape[gather_dim] # store original size ctx.part_size = part_size output = all_gather_tensor(local_tensor, group, async_op) return torch.cat(output.split(split_size, dim=0), dim=gather_dim) @staticmethod def backward(ctx: Any, grad_output: Tensor) -> Any: if ctx.grad_scaler: grad_output = grad_output * ctx.sp_world_size return (None, grad_output.split(ctx.part_size, dim=ctx.gather_dim)[ctx.sp_rank].contiguous(), None, None, None, None) def gather_outpus_and_unpad(x: Tensor, gather_dim: int, unpad_dim: int = None, padding_size: int = 0, grad_scaler: bool = True, group: Optional[dist.ProcessGroup] = None): group = get_ulysses_sequence_parallel_group() if group is None else group sp_size = get_ulysses_sequence_parallel_world_size() if group == None: return x x = Gather.apply(group, x, gather_dim, grad_scaler) if unpad_dim is not None: assert isinstance(padding_size, int), 'padding size is not given or is not an integer' if padding_size == 0: return x x = _unpad_tensor(x, unpad_dim, padding_size) return x def ulysses_pad_and_slice_inputs(input_ids_rmpad: torch.Tensor, position_ids_rmpad: Optional[torch.Tensor] = None, sp_size: int = 1): """ Pad and slice input_ids to be divisible by sp_size Pad position_ids to be divisible by sp_size. Note both input_ids_rmpad and position_ids_rmpad will be padded, but only input_ids will be sliced. The is the utility of pre-forward for ulysses sequence parallelism Args: input_ids_rmpad: shape of [bsz, seqlen] position_ids_rmpad: shape of [bsz, seqlen], where bsz must be 1 sp_size (int): ulysses sequence parallelism size Returns: torch.Tensor: padded and sliced input_ids torch.Tensor: padded and sliced position_ids int: pad size """ if position_ids_rmpad is not None: assert position_ids_rmpad.size(0) == 1 assert input_ids_rmpad.size(1) == position_ids_rmpad.size(1) if sp_size <= 1: return input_ids_rmpad, position_ids_rmpad, 0 _, total_seq_len = input_ids_rmpad.shape pad_size = (sp_size - total_seq_len % sp_size) % sp_size if pad_size > 0: input_ids_rmpad = torch.nn.functional.pad(input_ids_rmpad, (0, pad_size), value=0) if position_ids_rmpad is not None: pad_pos_ids = torch.arange(pad_size, device=position_ids_rmpad.device).unsqueeze(0) position_ids_rmpad = torch.cat((position_ids_rmpad, pad_pos_ids), dim=-1) # we don't need to slice position ids input_ids_rmpad = slice_input_tensor(input_ids_rmpad, dim=1, padding=False) return input_ids_rmpad, position_ids_rmpad, pad_size ================================================ FILE: verl/version/version ================================================ 0.2.0.dev ================================================ FILE: verl/workers/__init__.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. ================================================ FILE: verl/workers/actor/__init__.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from .base import BasePPOActor from .dp_actor import DataParallelPPOActor __all__ = ["BasePPOActor", "DataParallelPPOActor"] ================================================ FILE: verl/workers/actor/base.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ The base class for Actor """ from abc import ABC, abstractmethod from typing import Iterable, Dict from verl import DataProto import torch __all__ = ['BasePPOActor'] class BasePPOActor(ABC): def __init__(self, config): """The base class for PPO actor Args: config (DictConfig): a config passed to the PPOActor. We expect the type to be DictConfig (https://omegaconf.readthedocs.io/), but it can be any namedtuple in general. """ super().__init__() self.config = config @abstractmethod def compute_log_prob(self, data: DataProto) -> torch.Tensor: """Compute logits given a batch of data. Args: data (DataProto): a batch of data represented by DataProto. It must contain key ```input_ids```, ```attention_mask``` and ```position_ids```. Returns: DataProto: a DataProto containing the key ```log_probs``` """ pass @abstractmethod def update_policy(self, data: DataProto) -> Dict: """Update the policy with an iterator of DataProto Args: data (DataProto): an iterator over the DataProto that returns by ```make_minibatch_iterator``` Returns: Dict: a dictionary contains anything. Typically, it contains the statistics during updating the model such as ```loss```, ```grad_norm```, etc,. """ pass ================================================ FILE: verl/workers/actor/dp_actor.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Single Process Actor """ import itertools from typing import Iterable, Tuple import torch from torch import nn from torch.distributed.fsdp import FullyShardedDataParallel as FSDP from verl import DataProto from verl.trainer.ppo import core_algos from verl.workers.actor import BasePPOActor from verl.utils.py_functional import append_to_dict from verl.utils.torch_functional import logprobs_from_logits, masked_mean from verl.utils.ulysses import ulysses_pad_and_slice_inputs, gather_outpus_and_unpad from verl.utils.seqlen_balancing import rearrange_micro_batches, get_reverse_idx import verl.utils.torch_functional as verl_F from flash_attn.bert_padding import pad_input, unpad_input, rearrange, index_first_axis __all__ = ['DataParallelPPOActor'] class DataParallelPPOActor(BasePPOActor): def __init__( self, config, actor_module: nn.Module, actor_optimizer: torch.optim.Optimizer = None, ): """When optimizer is None, it is Reference Policy""" super().__init__(config) self.actor_module = actor_module self.actor_optimizer = actor_optimizer self.use_remove_padding = self.config.get('use_remove_padding', False) print(f'Actor use_remove_padding={self.use_remove_padding}') self.ulysses_sequence_parallel_size = self.config.ulysses_sequence_parallel_size self.use_ulysses_sp = self.ulysses_sequence_parallel_size > 1 self.compute_entropy_from_logits = torch.compile(verl_F.entropy_from_logits, dynamic=True) def _forward_micro_batch(self, micro_batch, temperature) -> Tuple[torch.Tensor, torch.Tensor]: """ Returns: entropy: # (bs, response_len) log_probs: # (bs, response_len) """ response_length = micro_batch['responses'].size(-1) multi_modal_inputs = {} if 'multi_modal_inputs' in micro_batch: for key in micro_batch['multi_modal_inputs'][0].keys(): multi_modal_inputs[key] = torch.cat([inputs[key] for inputs in micro_batch['multi_modal_inputs']], dim=0) with torch.autocast(device_type='cuda', dtype=torch.bfloat16): input_ids = micro_batch['input_ids'] batch_size, seqlen = input_ids.shape attention_mask = micro_batch['attention_mask'] position_ids = micro_batch['position_ids'] if position_ids.dim() == 3: # qwen2vl mrope position_ids = position_ids.transpose(0, 1) # (bsz, 3, seqlen) -> (3, bsz, seqlen) if self.use_remove_padding: input_ids_rmpad, indices, *_ = unpad_input(input_ids.unsqueeze(-1), attention_mask) # input_ids_rmpad (total_nnz, ...) input_ids_rmpad = input_ids_rmpad.transpose(0, 1) # (1, total_nnz) # unpad the position_ids to align the rotary if position_ids.dim() == 3: position_ids_rmpad = index_first_axis(rearrange(position_ids, "c b s ... -> (b s) c ..."), indices).transpose(0, 1).unsqueeze( 1) # (3, bsz, seqlen) -> (3, 1, bsz * seqlen) else: position_ids_rmpad = index_first_axis(rearrange(position_ids.unsqueeze(-1), "b s ... -> (b s) ..."), indices).transpose(0, 1) # for compute the log_prob input_ids_rmpad_rolled = torch.roll(input_ids_rmpad, shifts=-1, dims=1) # (1, total_nnz) # pad and slice the inputs if sp > 1 if self.use_ulysses_sp: input_ids_rmpad, position_ids_rmpad, pad_size = ulysses_pad_and_slice_inputs(input_ids_rmpad, \ position_ids_rmpad, \ sp_size=self.ulysses_sequence_parallel_size) input_ids_rmpad_rolled, _, _ = ulysses_pad_and_slice_inputs(input_ids_rmpad_rolled, None, self.ulysses_sequence_parallel_size) input_ids_rmpad_rolled = input_ids_rmpad_rolled.squeeze(0) # ((total_nnz / sp) + pad) # only pass input_ids and position_ids to enable flash_attn_varlen output = self.actor_module(input_ids=input_ids_rmpad, attention_mask=None, position_ids=position_ids_rmpad, **multi_modal_inputs, use_cache=False) # prevent model thinks we are generating logits_rmpad = output.logits.squeeze(0) # (total_nnz, vocab_size) logits_rmpad.div_(temperature) # compute entropy entropy_rmpad = self.compute_entropy_from_logits(logits_rmpad) # ((total_nnz / sp) + pad) # if use_sp: ((total_nnz / sp) + pad) ; if not use_sp: (batch, seqlen) log_probs = logprobs_from_logits(logits=logits_rmpad, labels=input_ids_rmpad_rolled) # gather log_prob if sp > 1 if self.use_ulysses_sp: # gather and unpad for the ulysses sp log_probs = gather_outpus_and_unpad(log_probs, gather_dim=0, unpad_dim=0, padding_size=pad_size) entropy_rmpad = gather_outpus_and_unpad(entropy_rmpad, gather_dim=0, unpad_dim=0, padding_size=pad_size) # pad back to (bsz, seqlen) full_entropy = pad_input(hidden_states=entropy_rmpad.unsqueeze(-1), indices=indices, batch=batch_size, seqlen=seqlen) full_log_probs = pad_input(hidden_states=log_probs.unsqueeze(-1), indices=indices, batch=batch_size, seqlen=seqlen) # only return response part: entropy = full_entropy.squeeze(-1)[:, -response_length - 1:-1] # (bsz, response_length) log_probs = full_log_probs.squeeze(-1)[:, -response_length - 1:-1] # (bsz, response_length) else: # not using rmpad and no ulysses sp output = self.actor_module(input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids, **multi_modal_inputs, use_cache=False) # prevent model thinks we are generating logits = output.logits logits.div_(temperature) logits = logits[:, -response_length - 1:-1, :] # (bsz, response_length, vocab_size) log_probs = logprobs_from_logits(logits, micro_batch['responses']) entropy = verl_F.entropy_from_logits(logits) # (bsz, response_length) return entropy, log_probs def _optimizer_step(self): assert self.config.grad_clip is not None if isinstance(self.actor_module, FSDP): grad_norm = self.actor_module.clip_grad_norm_(max_norm=self.config.grad_clip) else: grad_norm = torch.nn.utils.clip_grad_norm_(self.actor_module.parameters(), max_norm=self.config.grad_clip) self.actor_optimizer.step() return grad_norm def compute_log_prob(self, data: DataProto) -> torch.Tensor: """Compute the log probability of the responses given input_ids, attention_mask and position_ids Args: data (DataProto): a DataProto containing keys ``input_ids``: tensor of shape [batch_size, sequence_length]. torch.int64. Note that input_ids is the concatenation of prompt and response. Note that ``sequence_length = prompt_length + response_length``. ``attention_mask``: tensor of shape [batch_size, sequence_length]. torch.int64. ``position_ids``: tensor of shape [batch_size, sequence_length]. torch.int64. ``responses``: tensor of shape [batch_size, response_length]. torch.int64. Returns: torch.Tensor: the log_prob tensor """ # set to eval self.actor_module.eval() micro_batch_size = data.meta_info['micro_batch_size'] temperature = data.meta_info['temperature'] # temperature must be in the data.meta_info to avoid slient error use_dynamic_bsz = data.meta_info['use_dynamic_bsz'] select_keys = ['responses', 'input_ids', 'attention_mask', 'position_ids'] batch = data.select(batch_keys=select_keys).batch has_multi_modal_inputs = 'multi_modal_inputs' in data.non_tensor_batch.keys() if has_multi_modal_inputs: num_micro_batches = data.batch.batch_size[0] // micro_batch_size non_tensor_select_keys = ['multi_modal_inputs'] micro_batches = data.select(select_keys, non_tensor_select_keys).chunk(num_micro_batches) elif use_dynamic_bsz: # split using dynamic bsz max_token_len = data.meta_info['max_token_len'] * self.ulysses_sequence_parallel_size micro_batches, indices = rearrange_micro_batches(batch=batch, max_token_len=max_token_len) else: micro_batches = batch.split(micro_batch_size) log_probs_lst = [] for micro_batch in micro_batches: if isinstance(micro_batch, DataProto): micro_batch = {**micro_batch.batch, **micro_batch.non_tensor_batch} with torch.no_grad(): _, log_probs = self._forward_micro_batch(micro_batch, temperature=temperature) log_probs_lst.append(log_probs) log_probs = torch.concat(log_probs_lst, dim=0) if use_dynamic_bsz: indices = list(itertools.chain.from_iterable(indices)) assert len(indices) == log_probs.size(0), f"{len(indices)} vs. {log_probs.size()}" revert_indices = torch.tensor(get_reverse_idx(indices), dtype=torch.long) log_probs = log_probs[revert_indices] return log_probs def update_policy(self, data: DataProto): # make sure we are in training mode self.actor_module.train() temperature = data.meta_info['temperature'] # temperature must be in the data.meta_info to avoid slient error select_keys = ['responses', 'input_ids', 'attention_mask', 'tool_output_masks', 'position_ids', 'old_log_probs', 'advantages'] if self.config.use_kl_loss: select_keys.append('ref_log_prob') batch = data.select(batch_keys=select_keys).batch has_multi_modal_inputs = 'multi_modal_inputs' in data.non_tensor_batch.keys() # Split to make minibatch iterator for updating the actor # See PPO paper for details. https://arxiv.org/abs/1707.06347 if has_multi_modal_inputs: num_mini_batches = data.batch.batch_size[0] // self.config.ppo_mini_batch_size non_tensor_select_keys = ['multi_modal_inputs'] dataloader = data.select(select_keys, non_tensor_select_keys).chunk(num_mini_batches) else: dataloader = batch.split(self.config.ppo_mini_batch_size) metrics = {} for epoch in range(self.config.ppo_epochs): for batch_idx, data in enumerate(dataloader): # split batch into micro_batches mini_batch = data if has_multi_modal_inputs: self.gradient_accumulation = self.config.ppo_mini_batch_size // self.config.ppo_micro_batch_size_per_gpu num_micro_batches = mini_batch.batch.batch_size[0] // self.config.ppo_micro_batch_size_per_gpu micro_batches = data.select(select_keys, non_tensor_select_keys).chunk(num_micro_batches) elif self.config.use_dynamic_bsz: max_token_len = self.config.ppo_max_token_len_per_gpu * self.ulysses_sequence_parallel_size micro_batches, _ = rearrange_micro_batches(batch=mini_batch, max_token_len=max_token_len) else: self.gradient_accumulation = self.config.ppo_mini_batch_size // self.config.ppo_micro_batch_size_per_gpu # split batch into micro_batches micro_batches = mini_batch.split(self.config.ppo_micro_batch_size_per_gpu) self.actor_optimizer.zero_grad() for data in micro_batches: if isinstance(data, DataProto): data = {**data.batch.cuda(), **data.non_tensor_batch} else: data = data.cuda() # actor device is cpu when using offload responses = data['responses'] response_length = responses.size(1) attention_mask = data['attention_mask'] tool_output_masks = data['tool_output_masks'] if 'tool_output_masks' in data else None response_mask = attention_mask[:, -response_length:] response_mask = response_mask & tool_output_masks old_log_prob = data['old_log_probs'] advantages = data['advantages'] clip_ratio = self.config.clip_ratio entropy_coeff = self.config.entropy_coeff # all return: (bsz, response_length) entropy, log_prob = self._forward_micro_batch(micro_batch=data, temperature=temperature) pg_loss, pg_clipfrac, ppo_kl = core_algos.compute_policy_loss(old_log_prob=old_log_prob, log_prob=log_prob, advantages=advantages, eos_mask=response_mask, cliprange=clip_ratio) # compute entropy loss from entropy entropy_loss = verl_F.masked_mean(entropy, response_mask) # compute policy loss policy_loss = pg_loss - entropy_loss * entropy_coeff if self.config.use_kl_loss: ref_log_prob = data['ref_log_prob'] # compute kl loss kld = core_algos.kl_penalty(logprob=log_prob, ref_logprob=ref_log_prob, kl_penalty=self.config.kl_loss_type) kl_loss = masked_mean(kld, response_mask) policy_loss = policy_loss + kl_loss * self.config.kl_loss_coef metrics['actor/kl_loss'] = kl_loss.detach().item() metrics['actor/kl_coef'] = self.config.kl_loss_coef if self.config.use_dynamic_bsz: # relative to the dynamic bsz loss = policy_loss * (len(data) / self.config.ppo_mini_batch_size) else: loss = policy_loss / self.gradient_accumulation loss.backward() data = { 'actor/entropy_loss': entropy_loss.detach().item(), 'actor/pg_loss': pg_loss.detach().item(), 'actor/pg_clipfrac': pg_clipfrac.detach().item(), 'actor/ppo_kl': ppo_kl.detach().item(), } append_to_dict(metrics, data) grad_norm = self._optimizer_step() data = {'actor/grad_norm': grad_norm.detach().item()} append_to_dict(metrics, data) self.actor_optimizer.zero_grad() return metrics ================================================ FILE: verl/workers/actor/megatron_actor.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Megatron Actor. In megatron actor, the differences are: 1. We only make minibatch Note that our model doesn't have to be `MegatronModule` because we don't share embedding in the last layer """ import importlib from functools import partial from packaging.version import Version from typing import Iterable, Dict import torch from torch import nn import torch.distributed # from megatron import get_args from megatron.core.optimizer import OptimizerConfig from megatron.core import parallel_state as mpu from megatron.core import ModelParallelConfig from verl.utils.megatron_utils import get_model_config from megatron.core.pipeline_parallel import get_forward_backward_func from megatron.core.distributed import finalize_model_grads # from megatron.core.optimizer import DistributedOptimizer from megatron.core.optimizer import DistributedOptimizer from omegaconf import OmegaConf from verl.utils.megatron.tensor_parallel import vocab_parallel_compute_entropy_loss, vocab_parallel_log_probs_from_logits from verl.utils.megatron.pipeline_parallel import (compute_transformers_input_shapes, make_batch_generator) from verl import DataProto from verl.trainer.ppo import core_algos from verl.workers.actor import BasePPOActor from verl.utils.py_functional import append_to_dict from verl.utils.torch_functional import logprobs_from_logits, broadcast_dict_tensor, split_dict_tensor_into_batches __all__ = ['MegatronPPOActor'] class MegatronPPOActor(BasePPOActor): def __init__(self, config, model_config, megatron_config: ModelParallelConfig, actor_module: nn.ModuleList, actor_optimizer: DistributedOptimizer, actor_optimizer_config: OptimizerConfig): """MeagtronPPOActor class. This class implements the simple PPO logics when the model is built with Megatron. Args: config (OmegaConf): the basic config that contains the hyper-parameters of PPO Actor. It must contain ``ppo_micro_batch_size_per_gpu``: micro batch size when updating ppo. ``ppo_mini_batch_size``: minibatch size when updating ppo using the batch data. ``ppo_epochs``: number of epochs to update the actor using the batch data. ``shuffle``: whether to shuffle the data after each ppo epoch. ``clip_ratio``: clip ratio of the ppo algorithm. See https://arxiv.org/abs/1707.06347. ``entropy_coeff``: entropy coefficient of the PPO loss. See https://arxiv.org/abs/1707.06347. model_config (OmegaConf): model configuration. It must contains ``model_config.vocab_size`` and ``model_config.hidden_size`` megatron_config (OmegaConf): megatron configuration. It must contains ``sequence_parallel_enabled``: whether the sequence parallel is enabled. ``param_dtype``: the dtype of the parameters. ``virtual_pipeline_model_parallel_size``: virtual pipeline model parallel size. a.k.a number of chunks in each pp stage. actor_module (nn.ModuleList): actor module is a ModuleList that contains a list of nn.Module in this pp stage. each nn.Module in this rank holds a vpp module chunk. See https://arxiv.org/pdf/2104.04473.pdf for more details. The actor module has some constraints to follow in order to use the updating logics implemented here 1. It must implement unpad_input before any computation and pad_input after all the computation. Remove padding is an optimization that removes the padding tokens. See unpad_input and pad_input function in flash-attn (https://github.com/Dao-AILab/flash-attention/blob/main/flash_attn/bert_padding.py). 2. Each pp stage must return the hidden state with the same shape [total_nnz, 1, hidden_size], where total_nnz is the number of valid tokens in this batch. If sequence parallel is enabled, the size of the hidden state is [total_nnz // tp, 1, hidden_size]. actor_optimizer (DistributedOptimizer): currently, we only support DistributedOptimizer in Megatron. It implements zero1 optimizer that shards the optimizer state across dp ranks. >>> def megatron_actor_model_provider(pre_process, post_process): >>> vpp_rank = mpu.get_virtual_pipeline_model_parallel_rank() >>> parallel_model = ParallelMistralForCausalLMRmPadPP(config=actor_model_config, >>> megatron_config=megatron_config, >>> pre_process=pre_process, >>> post_process=post_process).cuda() >>> return parallel_model >>> from megatron.training import get_model >>> from megatron.optimizer import get_megatron_optimizer >>> actor_module = get_model(megatron_actor_model_provider, wrap_with_ddp=True) >>> actor_module = nn.ModuleList(actor_module) >>> actor_optimizer = get_megatron_optimizer(actor_module) >>> actor = MegatronPPOActor(config=config, >>> model_config=actor_model_config, >>> megatron_config=megatron_config, >>> actor_module=actor_module, >>> actor_optimizer=actor_optimizer) """ super().__init__(config) self._validate_config(config) self.model_config = model_config self.megatron_config = megatron_config # self.megatron_args = get_args() self.actor_module = actor_module self.actor_optimizer: DistributedOptimizer = actor_optimizer self.actor_optimizer_config = actor_optimizer_config self.optimizer_step_args = OmegaConf.create({ 'skip_grad': None, 'overlap_dp_param_comm': False, 'overlap_dp_grad_comm': False, 'gradient_accumulation_steps': 1, 'sequence_parallel': self.megatron_config.sequence_parallel, 'DDP_impl': 'local', 'layernorm_allreduce_bucket_threshold': 0, 'pipeline_model_parallel_split_rank': None, 'reduce_grads_use_alltoall': False }) config = get_model_config(self.actor_module[0]) config.finalize_model_grads_func = finalize_model_grads def _validate_config(self, config) -> None: """Validate config options not implemented for Megatron backend""" assert config.get('ulysses_sequence_parallel_size', 1) == 1 def compute_log_prob(self, data: DataProto) -> torch.Tensor: """Compute the log probability of the responses given input_ids, attention_mask and position_ids Args: data (DataProto): a DataProto containing keys ``input_ids``: tensor of shape [batch_size, sequence_length]. torch.int64. Note that input_ids is the concatenation of prompt and response. Note that ``sequence_length = prompt_length + response_length``. ``attention_mask``: tensor of shape [batch_size, sequence_length]. torch.int64. ``position_ids``: tensor of shape [batch_size, sequence_length]. torch.int64. ``responses``: tensor of shape [batch_size, response_length]. torch.int64. Returns: DataProto: torch.Tensor: the log_prob tensor """ data.batch = data.batch.contiguous() def compute_logprobs_fn(output, data): response = data['responses'] response_length = response.size(1) logits = output['logits'] logits = logits[:, -response_length - 1:-1].contiguous() log_probs = vocab_parallel_log_probs_from_logits(logits, response) return {'log_probs': log_probs} # We make recompute_old_log_prob by default here. # TODO (zhangchi.usc1992): actually, this function should only return log_prob and this logic should be handled by user outside recompute_old_log_prob = self.config.get('recompute_old_log_prob', True) if recompute_old_log_prob or 'old_log_probs' not in data.batch.keys(): select_keys = ['responses', 'input_ids', 'attention_mask', 'position_ids'] batch = data.select(batch_keys=select_keys).batch input_ids = batch['input_ids'] batch_size = input_ids.size(0) response = batch['responses'] response_length = response.size(1) with torch.no_grad(): output = self.forward_backward_batch(data, forward_only=True, post_process_fn=compute_logprobs_fn) if mpu.is_pipeline_last_stage(ignore_virtual=True): # only on last rank. It should be on every tp rank log_probs = torch.cat([o['log_probs'] for o in output], dim=0) # (bs, seq_size) log_probs = log_probs.to(torch.float32) else: log_probs = torch.empty(size=(batch_size, response_length), dtype=torch.float32, device=input_ids.device) # broadcast across pp ranks torch.distributed.broadcast(tensor=log_probs, src=mpu.get_pipeline_model_parallel_last_rank(), group=mpu.get_pipeline_model_parallel_group(), async_op=False) # add empty cache after each compute torch.cuda.empty_cache() return log_probs def make_minibatch_iterator(self, data: DataProto) -> Iterable[DataProto]: """Make minibatch iterator for updating the actor Args: data (DataProto): a DataProto containing keys ``input_ids``: tensor of shape [batch_size, sequence_length]. torch.int64, where ``sequence_length = prompt_length + response_length`` ``attention_mask``: tensor of shape [batch_size, sequence_length]. torch.int64 ``position_ids``: tensor of shape [batch_size, sequence_length]. torch.int64 ``responses``: tensor of shape [batch_size, response_length]. torch.int64. Note that responses = input_ids[:, -response_length:] ``old_log_probs``: tensor of shape [batch_size, response_length]. torch.float32. The log probability of responses. ``advantages``: tensor of shape [batch_size, response_length]. torch.float32. The advantages of responses. See PPO paper for details. https://arxiv.org/abs/1707.06347 Returns: """ select_keys = ['responses', 'input_ids', 'attention_mask', 'position_ids', 'old_log_probs', 'advantages'] data = data.select(batch_keys=select_keys) return data.make_iterator(mini_batch_size=self.config.ppo_mini_batch_size, epochs=self.config.ppo_epochs, dataloader_kwargs={'shuffle': self.config.shuffle}) def forward_backward_batch(self, data: DataProto, forward_only=False, post_process_fn=None): """ We assume: - The model takes input: (input_ids, attention_mask, position_ids). No rmpad for the input - The communication shape is (total_nnz_pad_to_sp // tp_size, 1, hidden_size) if sequence parallel is enabled """ # broadcast from last pp rank to all other pp ranks # TODO: actually, we just need to control the sampling order. broadcast_dict_tensor(data.batch, src=mpu.get_pipeline_model_parallel_last_rank(), group=mpu.get_pipeline_model_parallel_group()) # split into micro-batches data.batch['attention_mask'] = data.batch['attention_mask'].to(bool) if data.meta_info.get('micro_batch_size', None) is not None: batch_size = data.meta_info['micro_batch_size'] else: batch_size = self.config.ppo_micro_batch_size_per_gpu batches = split_dict_tensor_into_batches(data.batch, batch_size=batch_size) # compute input shapes for pp stages input_shapes = compute_transformers_input_shapes( batches, meta_info={ 'sequence_parallel': self.megatron_config.sequence_parallel, 'hidden_size': self.model_config.hidden_size }) n_micro_batch = len(batches) seq_len = batches[0]['input_ids'].shape[1] forward_backward_func = get_forward_backward_func() def loss_func(output, data, meta_info): if forward_only: if post_process_fn is None: return 1.0, {'logits': output.logits} else: return 1.0, post_process_fn(output, data) responses = data['responses'] response_length = responses.size(1) attention_mask = data['attention_mask'] response_mask = attention_mask[:, -response_length:] old_log_prob = data['old_log_probs'] advantages = data['advantages'] clip_ratio = meta_info['clip_ratio'] entropy_coeff = meta_info['entropy_coeff'] # compute policy loss logits = output.logits logits = logits[:, -response_length - 1:-1].contiguous() logits_back = logits.clone() log_prob = vocab_parallel_log_probs_from_logits(logits, responses) logits = logits_back pg_loss, pg_clipfrac, ppo_kl = core_algos.compute_policy_loss(old_log_prob=old_log_prob, log_prob=log_prob, advantages=advantages, eos_mask=response_mask, cliprange=clip_ratio) entropy_loss = vocab_parallel_compute_entropy_loss(logits, eos_mask=response_mask) policy_loss = pg_loss - entropy_loss * entropy_coeff # return loss and stats stats = { 'actor/entropy_loss': entropy_loss.detach().item(), 'actor/pg_loss': pg_loss.detach().item(), 'actor/pg_clipfrac': pg_clipfrac.detach().item(), 'actor/ppo_kl': ppo_kl.detach().item() } return policy_loss, stats def forward_step(batch_iter, model): batch = next(batch_iter) input_ids = batch['input_ids'] attention_mask = batch['attention_mask'] position_ids = batch['position_ids'] output = model(input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids) if forward_only: meta_info = None else: meta_info = {'clip_ratio': self.config.clip_ratio, 'entropy_coeff': self.config.entropy_coeff} return output, partial(loss_func, data=batch, meta_info=meta_info) # batch should be a list of batches inside micro-batches batch_generator = make_batch_generator(batches, vpp_size=len(self.actor_module)) # TODO: we may use the new schedule instead # for flash-attn: (seq_len, batch_size, hidden_size) = (mbs*seq_len, 1, hidden_size) if mpu.get_pipeline_model_parallel_world_size() > 1: losses_reduced = forward_backward_func( forward_step_func=forward_step, data_iterator=batch_generator, model=self.actor_module, num_microbatches=n_micro_batch, seq_length=batch_size * seq_len, # no use when input_shapes was set micro_batch_size=1, # no use when input_shapes was set forward_only=forward_only, ) else: losses_reduced = forward_backward_func( forward_step_func=forward_step, data_iterator=batch_generator, model=self.actor_module, num_microbatches=n_micro_batch, seq_length=batch_size * seq_len, # in use for pp = 1 micro_batch_size=1, # in use for pp = 1 forward_only=forward_only, ) # loss_reduces contains the stats returned from loss_func return losses_reduced def update_policy(self, dataloader: Iterable[DataProto]) -> Dict: """Update the policy with an iterator of DataProto Args: dataloader (Iterable[DataProto]): an iterator over the DataProto that returns by ``make_minibatch_iterator`` The keys of each data batch is described in the make_minibatch_iterator. Returns: Dict: a dictionary containing the statistics. Note that the statistics are only valid in the last pp stage and users have to combine the output in each dp rank manually. """ metrics = {} for data in dataloader: # data = data.batch.to(self.actor_module.device) self.actor_optimizer.zero_grad() # use use_contiguous_buffers_in_local_ddp and no overlap_dp_param_comm for chunk in self.actor_module: # if use distributed optimizer, zero grad buffer will be handled by optimizer chunk.zero_grad_buffer() metric_micro_batch = self.forward_backward_batch(data) for metric in metric_micro_batch: append_to_dict(metrics, metric) # append the metric from this micro-batch to global metrics. update_successful, grad_norm, num_zeros_in_grad = self.actor_optimizer.step() if update_successful: # allgather already execute in optimizer.step in new megatron pass else: raise NotImplementedError # add empty cache after each compute torch.cuda.empty_cache() return metrics ================================================ FILE: verl/workers/critic/__init__.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from .base import BasePPOCritic from .dp_critic import DataParallelPPOCritic __all__ = ["BasePPOCritic", "DataParallelPPOCritic"] ================================================ FILE: verl/workers/critic/base.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Base class for a critic """ from abc import ABC, abstractmethod import torch from verl import DataProto __all__ = ['BasePPOCritic'] class BasePPOCritic(ABC): def __init__(self, config): super().__init__() self.config = config @abstractmethod def compute_values(self, data: DataProto) -> torch.Tensor: """Compute values""" pass @abstractmethod def update_critic(self, data: DataProto): """Update the critic""" pass ================================================ FILE: verl/workers/critic/dp_critic.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Implement a multiprocess PPOCritic """ import itertools from typing import Iterable import torch import torch.distributed from torch import nn, optim from torch.distributed.fsdp import FullyShardedDataParallel as FSDP from verl import DataProto from verl.trainer.ppo import core_algos from verl.workers.critic import BasePPOCritic from verl.utils.py_functional import append_to_dict from verl.utils.torch_functional import masked_mean from verl.utils.ulysses import ulysses_pad_and_slice_inputs, gather_outpus_and_unpad from verl.utils.seqlen_balancing import rearrange_micro_batches, get_reverse_idx from flash_attn.bert_padding import pad_input, unpad_input, rearrange, index_first_axis __all__ = ['DataParallelPPOCritic'] class DataParallelPPOCritic(BasePPOCritic): def __init__(self, config, critic_module: nn.Module, critic_optimizer: optim.Optimizer): super().__init__(config=config) self.critic_module = critic_module self.critic_optimizer = critic_optimizer self.use_remove_padding = self.config.model.get('use_remove_padding', False) print(f'Critic use_remove_padding={self.use_remove_padding}') self.ulysses_sequence_parallel_size = self.config.get('ulysses_sequence_parallel_size', 1) def _forward_micro_batch(self, micro_batch): response_length = micro_batch['responses'].size(-1) multi_modal_inputs = {} if 'multi_modal_inputs' in micro_batch: for key in micro_batch['multi_modal_inputs'][0].keys(): multi_modal_inputs[key] = torch.cat([inputs[key] for inputs in micro_batch['multi_modal_inputs']], dim=0) with torch.autocast(device_type='cuda', dtype=torch.bfloat16): input_ids = micro_batch['input_ids'] batch, seqlen = input_ids.shape attention_mask = micro_batch['attention_mask'] position_ids = micro_batch['position_ids'] if position_ids.dim() == 3: # qwen2vl mrope position_ids = position_ids.transpose(0, 1) if self.use_remove_padding: input_ids_rmpad, indices, *_ = unpad_input(input_ids.unsqueeze(-1), attention_mask) # input_ids_rmpad (total_nnz, ...) input_ids_rmpad = input_ids_rmpad.transpose(0, 1) # (1, total_nnz) # unpad the position_ids to align the rotary if position_ids.dim() == 3: position_ids_rmpad = index_first_axis(rearrange(position_ids, "c b s ... -> (b s) c ..."), indices).transpose(0, 1).unsqueeze( 1) # (3, bsz, seqlen) -> (3, 1, bsz * seqlen) else: position_ids_rmpad = index_first_axis(rearrange(position_ids.unsqueeze(-1), "b s ... -> (b s) ..."), indices).transpose(0, 1) # pad and slice the inputs if sp > 1 if self.ulysses_sequence_parallel_size > 1: input_ids_rmpad, position_ids_rmpad, pad_size = ulysses_pad_and_slice_inputs(input_ids_rmpad, \ position_ids_rmpad, \ sp_size=self.ulysses_sequence_parallel_size) # only pass input_ids and position_ids to enable flash_attn_varlen output = self.critic_module(input_ids=input_ids_rmpad, attention_mask=None, position_ids=position_ids_rmpad, **multi_modal_inputs, use_cache=False) # prevent model thinks we are generating values_rmpad = output.logits values_rmpad = values_rmpad.squeeze(0) # (total_nnz) # gather output if sp > 1 if self.ulysses_sequence_parallel_size > 1: values_rmpad = gather_outpus_and_unpad(values_rmpad, gather_dim=0, unpad_dim=0, padding_size=pad_size) # pad it back values = pad_input(values_rmpad, indices=indices, batch=batch, seqlen=seqlen).squeeze(-1) values = values[:, -response_length - 1:-1] else: output = self.critic_module(input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids, **multi_modal_inputs, use_cache=False) # prevent model thinks we are generating values = output.logits values = values[:, -response_length - 1:-1].squeeze(-1) return values def _optimizer_step(self): assert self.config.grad_clip is not None if isinstance(self.critic_module, FSDP): grad_norm = self.critic_module.clip_grad_norm_(self.config.grad_clip) else: grad_norm = torch.nn.utils.clip_grad_norm_(self.critic_module.parameters(), max_norm=self.config.grad_clip) self.critic_optimizer.step() return grad_norm def compute_values(self, data: DataProto) -> torch.Tensor: self.critic_module.eval() micro_batch_size = data.meta_info['micro_batch_size'] select_keys = ['responses', 'input_ids', 'attention_mask', 'position_ids'] batch = data.select(batch_keys=select_keys).batch use_dynamic_bsz = data.meta_info['use_dynamic_bsz'] has_multi_modal_inputs = 'multi_modal_inputs' in data.non_tensor_batch.keys() if has_multi_modal_inputs: num_micro_batches = data.batch.batch_size[0] // micro_batch_size non_tensor_select_keys = ['multi_modal_inputs'] micro_batches = data.select(select_keys, non_tensor_select_keys).chunk(num_micro_batches) elif use_dynamic_bsz: # split using dynamic bsz max_token_len = data.meta_info['max_token_len'] * self.ulysses_sequence_parallel_size micro_batches, indices = rearrange_micro_batches(batch=batch, max_token_len=max_token_len) else: micro_batches = batch.split(micro_batch_size) values_lst = [] for micro_batch in micro_batches: if isinstance(micro_batch, DataProto): micro_batch = {**micro_batch.batch, **micro_batch.non_tensor_batch} with torch.no_grad(): values = self._forward_micro_batch(micro_batch) values_lst.append(values) values = torch.concat(values_lst, dim=0) responses = data.batch['responses'] attention_mask = data.batch['attention_mask'] response_length = responses.size(1) values = values * attention_mask[:, -response_length - 1:-1] if use_dynamic_bsz: indices = list(itertools.chain.from_iterable(indices)) assert len(indices) == values.size(0), f"{len(indices)} vs. {values.size()}" revert_indices = torch.tensor(get_reverse_idx(indices), dtype=torch.long) values = values[revert_indices] return values def update_critic(self, data: DataProto): # make sure we are in training mode self.critic_module.train() metrics = {} select_keys = ['input_ids', 'responses', 'attention_mask', 'position_ids', 'values', 'returns'] batch = data.select(batch_keys=select_keys).batch has_multi_modal_inputs = 'multi_modal_inputs' in data.non_tensor_batch.keys() # Split to make minibatch iterator for updating the actor # See PPO paper for details. https://arxiv.org/abs/1707.06347 if has_multi_modal_inputs: num_mini_batches = data.batch.batch_size[0] // self.config.ppo_mini_batch_size non_tensor_select_keys = ['multi_modal_inputs'] dataloader = data.select(select_keys, non_tensor_select_keys).chunk(num_mini_batches) else: dataloader = batch.split(self.config.ppo_mini_batch_size) for epoch in range(self.config.ppo_epochs): for batch_idx, data in enumerate(dataloader): # split batch into micro_batches mini_batch = data if has_multi_modal_inputs: num_micro_batches = mini_batch.batch.batch_size[0] // self.config.ppo_micro_batch_size_per_gpu micro_batches = data.select(select_keys, non_tensor_select_keys).chunk(num_micro_batches) elif self.config.use_dynamic_bsz: max_token_len = self.config.ppo_max_token_len_per_gpu * self.ulysses_sequence_parallel_size micro_batches, _ = rearrange_micro_batches(batch=mini_batch, max_token_len=max_token_len) else: micro_batches = mini_batch.split(self.config.ppo_micro_batch_size_per_gpu) self.gradient_accumulation = self.config.ppo_mini_batch_size // self.config.ppo_micro_batch_size_per_gpu self.critic_optimizer.zero_grad() for data in micro_batches: if isinstance(data, DataProto): data = {**data.batch.cuda(), **data.non_tensor_batch} else: data = data.cuda() # critic device is cpu when using offload input_ids = data['input_ids'] responses = data['responses'] attention_mask = data['attention_mask'] position_ids = data['position_ids'] values = data['values'] returns = data['returns'] response_length = responses.size(1) eos_mask = attention_mask[:, -response_length - 1:-1] vpreds = self._forward_micro_batch(data) # assert not torch.any(torch.isnan(vpreds)).item() vf_loss, vf_clipfrac = core_algos.compute_value_loss(vpreds=vpreds, values=values, returns=returns, eos_mask=eos_mask, cliprange_value=self.config.cliprange_value) if self.config.use_dynamic_bsz: # relative to the dynamic bsz loss = vf_loss * (len(data) / self.config.ppo_mini_batch_size) else: loss = vf_loss / self.gradient_accumulation loss.backward() data = { 'critic/vf_loss': vf_loss.detach().item(), 'critic/vf_clipfrac': vf_clipfrac.detach().item(), 'critic/vpred_mean': masked_mean(vpreds, eos_mask).detach().item(), } append_to_dict(metrics, data) grad_norm = self._optimizer_step() data = {'critic/grad_norm': grad_norm.detach().item()} append_to_dict(metrics, data) self.critic_optimizer.zero_grad() return metrics ================================================ FILE: verl/workers/critic/megatron_critic.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Implement a multiprocess PPOCritic """ import importlib from functools import partial from packaging.version import Version from typing import Iterable import torch import torch.distributed from omegaconf import OmegaConf from torch import nn from verl import DataProto from verl.trainer.ppo import core_algos from verl.workers.critic import BasePPOCritic from verl.utils.megatron.pipeline_parallel import (compute_transformers_input_shapes, make_batch_generator) from verl.utils.py_functional import append_to_dict from verl.utils.torch_dtypes import PrecisionType from verl.utils.torch_functional import masked_mean, broadcast_dict_tensor, split_dict_tensor_into_batches from verl.utils.megatron import sequence_parallel as sp_utils from megatron.core.optimizer import OptimizerConfig from megatron.core import parallel_state as mpu from megatron.core.pipeline_parallel import get_forward_backward_func from megatron.core.optimizer import DistributedOptimizer class MegatronPPOCritic(BasePPOCritic): def __init__(self, config, model_config, megatron_config, critic_module: nn.ModuleList, critic_optimizer: DistributedOptimizer, critic_optimizer_config: OptimizerConfig): super().__init__(config=config) self._validate_config(config) self.model_config = model_config self.megatron_config = megatron_config self.critic_module = critic_module self.critic_optimizer = critic_optimizer self.critic_optimizer_config = critic_optimizer_config # we create a separate nametuple for optimizer step so that global args won't affect it. self.optimizer_step_args = OmegaConf.create({ 'skip_grad': None, 'overlap_dp_param_comm': False, 'overlap_dp_grad_comm': False, 'gradient_accumulation_steps': 1, 'sequence_parallel': self.megatron_config.sequence_parallel, 'DDP_impl': 'local', 'layernorm_allreduce_bucket_threshold': 0, 'pipeline_model_parallel_split_rank': None, 'reduce_grads_use_alltoall': False }) if self.config.kl_ctrl.type == 'fixed': self.kl_ctrl = core_algos.FixedKLController(kl_coef=self.config.kl_ctrl.kl_coef) elif self.config.kl_ctrl.type == 'adaptive': assert self.config.kl_ctrl.horizon > 0, f'horizon must be larger than 0. Got {self.config.kl_ctrl.horizon}' self.kl_ctrl = core_algos.AdaptiveKLController(init_kl_coef=self.config.kl_ctrl.kl_coef, target_kl=self.config.kl_ctrl.target_kl, horizon=self.config.kl_ctrl.horizon) else: raise NotImplementedError def _validate_config(self, config) -> None: """Validate config options not implemented for Megatron backend""" assert config.get('ulysses_sequence_parallel_size', 1) == 1 def compute_values(self, data: DataProto) -> DataProto: # data.batch = data.batch.to(self.critic_module.module.device) responses = data.batch['responses'] attention_mask = data.batch['attention_mask'] response_length = responses.size(1) with torch.no_grad(): output = self.forward_backward_batch(data=data, forward_only=True) if mpu.is_pipeline_last_stage(ignore_virtual=True): # only on last rank. It should be on every tp rank values = torch.cat([o['vpreds'] for o in output], dim=0) # (bs, seq_size, vocal_size) values = values.to(torch.float32) else: values = torch.empty_like(attention_mask, dtype=torch.float32) # each tp ranks should contain the same value values = values * attention_mask values = values[:, -response_length - 1:-1] values = values.contiguous() # sync among pp ranks torch.distributed.broadcast(tensor=values, src=mpu.get_pipeline_model_parallel_last_rank(), group=mpu.get_pipeline_model_parallel_group()) # add empty cache after each compute torch.cuda.empty_cache() return values def make_minibatch_iterator(self, data: DataProto) -> Iterable[DataProto]: select_keys = ['input_ids', 'responses', 'attention_mask', 'position_ids', 'values', 'returns'] data = data.select(batch_keys=select_keys) return data.make_iterator(mini_batch_size=self.config.ppo_mini_batch_size, epochs=self.config.ppo_epochs, dataloader_kwargs={'shuffle': self.config.shuffle}) def forward_backward_batch(self, data: DataProto, forward_only=False): # broadcast from last pp rank to all other pp ranks data.batch = data.batch.contiguous() broadcast_dict_tensor(data.batch, src=mpu.get_pipeline_model_parallel_last_rank(), group=mpu.get_pipeline_model_parallel_group()) # split into micro-batches data.batch['attention_mask'] = data.batch['attention_mask'].to(bool) batches = split_dict_tensor_into_batches(data.batch, batch_size=self.config.ppo_micro_batch_size_per_gpu) n_micro_batch = len(batches) seq_len = batches[0]['input_ids'].shape[1] # compute input shapes for pp stages input_shapes = compute_transformers_input_shapes( batches, meta_info={ 'sequence_parallel': self.megatron_config.sequence_parallel, 'hidden_size': self.model_config.hidden_size }) forward_backward_func = get_forward_backward_func() def loss_func(output, data, meta_info): if forward_only: return 1.0, {'vpreds': output.logits} responses = data['responses'] attention_mask = data['attention_mask'] values = data['values'] returns = data['returns'] response_length = responses.size(1) eos_mask = attention_mask[:, -response_length:] cliprange_value = self.config.cliprange_value vpreds = output.logits # (bs, sequence_length) vpreds = vpreds[:, -response_length - 1:-1] vf_loss, vf_clipfrac = core_algos.compute_value_loss(vpreds=vpreds, values=values, returns=returns, eos_mask=eos_mask, cliprange_value=cliprange_value) stats = { 'critic/vf_loss': vf_loss.detach().item(), 'critic/vf_clipfrac': vf_clipfrac.detach().item(), 'critic/vpred_mean': masked_mean(vpreds, eos_mask).detach().item(), } return vf_loss, stats def forward_step(batch_iter, model): batch = next(batch_iter) input_ids = batch['input_ids'] attention_mask = batch['attention_mask'] position_ids = batch['position_ids'] output = model(input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids) return output, partial(loss_func, data=batch, meta_info={}) # batch should be a list of batches inside micro-batches batch_generator = make_batch_generator(batches, vpp_size=len(self.critic_module)) # TODO: we may use the new schedule instead # for flash-attn: (seq_len, batch_size, hidden_size) = (mbs*seq_len, 1, hidden_size) if mpu.get_pipeline_model_parallel_world_size() > 1: losses_reduced = forward_backward_func( forward_step_func=forward_step, data_iterator=batch_generator, model=self.critic_module, num_microbatches=n_micro_batch, seq_length=self.config.ppo_micro_batch_size_per_gpu * seq_len, # no use when input_shapes was set micro_batch_size=1, # no use when input_shapes was set forward_only=forward_only, ) else: losses_reduced = forward_backward_func( forward_step_func=forward_step, data_iterator=batch_generator, model=self.critic_module, num_microbatches=n_micro_batch, seq_length=self.config.ppo_micro_batch_size_per_gpu * seq_len, # in use for pp = 1 micro_batch_size=1, # in use for pp = 1 forward_only=forward_only, ) # loss_reduces contains the stats returned from loss_func return losses_reduced def update_critic(self, dataloader: Iterable[DataProto]): metrics = {} for data in dataloader: # data = data.batch.to(self.critic_module.device) self.critic_optimizer.zero_grad() # use use_contiguous_buffers_in_local_ddp and no overlap_dp_param_comm for chunk in self.critic_module: chunk.zero_grad_buffer() metric_micro_batch = self.forward_backward_batch(data) update_successful, grad_norm, num_zeros_in_grad = self.critic_optimizer.step() if update_successful: # allgather already execute in optimizer.step in new megatron pass else: raise NotImplementedError for metric in metric_micro_batch: append_to_dict(metrics, metric) # append the metric from this micro-batch to global metrics. # add empty cache after each compute torch.cuda.empty_cache() return metrics ================================================ FILE: verl/workers/fsdp_workers.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ The main entry point to run the PPO algorithm """ import logging import os import warnings import torch import torch.distributed from torch.distributed.device_mesh import init_device_mesh import verl.utils.torch_functional as verl_F from omegaconf import DictConfig, open_dict from verl import DataProto from verl.single_controller.base import Worker from verl.single_controller.base.decorator import register, Dispatch from verl.utils import hf_tokenizer, hf_processor from verl.utils.debug import log_gpu_memory_usage from verl.utils.fs import copy_to_local from verl.utils.fsdp_utils import get_fsdp_wrap_policy, init_fn, get_init_weight_context_manager from verl.utils.fsdp_utils import offload_fsdp_optimizer, offload_fsdp_model_to_cpu, load_fsdp_optimizer, \ load_fsdp_model_to_gpu from verl.utils.import_utils import import_external_libs from verl.utils.model import compute_position_id_with_mask from verl.utils.flops_counter import FlopsCounter from verl.utils.checkpoint.fsdp_checkpoint_manager import FSDPCheckpointManager from verl.workers.sharding_manager.fsdp_ulysses import FSDPUlyssesShardingManager from codetiming import Timer logger = logging.getLogger(__file__) logger.setLevel(os.getenv('VERL_PPO_LOGGING_LEVEL', 'WARN')) def create_device_mesh(world_size, fsdp_size): if fsdp_size < 0 or fsdp_size >= world_size: device_mesh = init_device_mesh('cuda', mesh_shape=(world_size,), mesh_dim_names=['fsdp']) else: raise ValueError( 'HSDP is not supported yet because it produces incorrect results for now. Please set fsdp_size=-1') assert world_size % fsdp_size == 0 device_mesh = init_device_mesh('cuda', mesh_shape=(world_size // fsdp_size, fsdp_size), mesh_dim_names=['ddp', 'fsdp']) return device_mesh def get_sharding_strategy(device_mesh): from torch.distributed.fsdp import ShardingStrategy if device_mesh.ndim == 1: sharding_strategy = ShardingStrategy.FULL_SHARD elif device_mesh.ndim == 2: sharding_strategy = ShardingStrategy.HYBRID_SHARD else: raise NotImplementedError(f"Get device mesh ndim={device_mesh.ndim}, but only support 1 or 2") return sharding_strategy class ActorRolloutRefWorker(Worker): """ This worker can be instantiated as a standalone actor or a standalone rollout or a standalone reference policy or a hybrid engine based on the config.rollout """ def __init__(self, config: DictConfig, role: str): super().__init__() self.config = config import torch.distributed if not torch.distributed.is_initialized(): torch.distributed.init_process_group(backend="nccl") # build device mesh for FSDP world_size = torch.distributed.get_world_size() # TODO(sgm): support FSDP hybrid shard for larger model self.device_mesh = create_device_mesh(world_size=world_size, fsdp_size=self.config.actor.fsdp_config.fsdp_size) # build device mesh for Ulysses Sequence Parallel self.ulysses_device_mesh = None self.ulysses_sequence_parallel_size = self.config.actor.get('ulysses_sequence_parallel_size', 1) dp = world_size // self.ulysses_sequence_parallel_size if self.ulysses_sequence_parallel_size > 1: self.ulysses_device_mesh = init_device_mesh('cuda', mesh_shape=(dp, self.ulysses_sequence_parallel_size), mesh_dim_names=['dp', 'sp']) self.ulysses_sharding_manager = FSDPUlyssesShardingManager(self.ulysses_device_mesh) self.role = role assert self.role in ['actor', 'rollout', 'ref', 'actor_rollout', 'actor_rollout_ref'] self._is_actor = self.role in ['actor', 'actor_rollout', 'actor_rollout_ref'] self._is_rollout = self.role in ['rollout', 'actor_rollout', 'actor_rollout_ref'] self._is_ref = self.role in ['ref', 'actor_rollout_ref'] self._is_offload_param = False self._is_offload_optimizer = False if self._is_actor: self._is_offload_param = self.config.actor.fsdp_config.get('param_offload', False) self._is_offload_optimizer = self.config.actor.fsdp_config.get('optimizer_offload', False) elif self._is_ref: # TODO: it seems that manual offload is slowly than FSDP offload self._is_offload_param = self.config.ref.fsdp_config.get('param_offload', False) # normalize config if self._is_actor: self.config.actor.ppo_mini_batch_size *= self.config.rollout.n self.config.actor.ppo_mini_batch_size //= (self.device_mesh.shape[0] // self.ulysses_sequence_parallel_size) # micro bsz if self.config.actor.ppo_micro_batch_size is not None: self.config.actor.ppo_micro_batch_size //= (self.device_mesh.shape[0] // self.ulysses_sequence_parallel_size) self.config.actor.ppo_micro_batch_size_per_gpu = self.config.actor.ppo_micro_batch_size assert self.config.actor.ppo_mini_batch_size % self.config.actor.ppo_micro_batch_size_per_gpu == 0, \ f'normalized ppo_mini_batch_size {self.config.actor.ppo_mini_batch_size} should be divisible by ppo_micro_batch_size_per_gpu {self.config.actor.ppo_micro_batch_size_per_gpu}' assert self.config.actor.ppo_mini_batch_size // self.config.actor.ppo_micro_batch_size_per_gpu > 0, \ f'normalized ppo_mini_batch_size {self.config.actor.ppo_mini_batch_size} should be larger than ppo_micro_batch_size_per_gpu {self.config.actor.ppo_micro_batch_size_per_gpu}' # normalize rollout config if self._is_rollout and self.config.rollout.log_prob_micro_batch_size is not None: self.config.rollout.log_prob_micro_batch_size //= (self.device_mesh.shape[0] // self.ulysses_sequence_parallel_size) self.config.rollout.log_prob_micro_batch_size_per_gpu = self.config.rollout.log_prob_micro_batch_size # normalize ref config if self._is_ref and self.config.ref.log_prob_micro_batch_size is not None: self.config.ref.log_prob_micro_batch_size //= (self.device_mesh.shape[0] // self.ulysses_sequence_parallel_size) self.config.ref.log_prob_micro_batch_size_per_gpu = self.config.ref.log_prob_micro_batch_size def _build_model_optimizer(self, model_path, fsdp_config, optim_config, override_model_config, use_remove_padding=False, enable_gradient_checkpointing=False, trust_remote_code=False, use_liger=False, role='actor'): from verl.utils.model import print_model_size, update_model_config, get_generation_config from verl.utils.torch_dtypes import PrecisionType from transformers import AutoModelForCausalLM, AutoConfig, AutoModelForVision2Seq from torch.distributed.fsdp import FullyShardedDataParallel as FSDP, ShardingStrategy, MixedPrecision, CPUOffload from torch import optim assert role in ['actor', 'ref'] log_gpu_memory_usage('Before init from HF AutoModel', logger=logger) local_path = copy_to_local(model_path) # note that we have to create model in fp32. Otherwise, the optimizer is in bf16, which is incorrect # TODO(zhangchi.usc1992): 1. support create from random initialized model. 2. Support init with FSDP directly self.tokenizer = hf_tokenizer(local_path, trust_remote_code=trust_remote_code) self.processor = hf_processor(local_path, trust_remote_code=trust_remote_code) torch_dtype = fsdp_config.get('model_dtype', None) if torch_dtype is None: torch_dtype = torch.float32 if self._is_actor else torch.bfloat16 else: torch_dtype = PrecisionType.to_dtype(torch_dtype) # override model kwargs actor_model_config = AutoConfig.from_pretrained(local_path, trust_remote_code=trust_remote_code) self.generation_config = get_generation_config(local_path, trust_remote_code=trust_remote_code) if use_remove_padding: from verl.models.registry import check_model_support_rmpad check_model_support_rmpad(actor_model_config.model_type) if use_remove_padding and self.ulysses_sequence_parallel_size > 1: from verl.models.transformers.monkey_patch import apply_monkey_patch apply_monkey_patch(actor_model_config, verbose=True) override_config_kwargs = { 'bos_token_id': self.tokenizer.bos_token_id, 'eos_token_id': self.tokenizer.eos_token_id, 'pad_token_id': self.tokenizer.pad_token_id, } override_config_kwargs.update(override_model_config) update_model_config(actor_model_config, override_config_kwargs=override_config_kwargs) if self.rank == 0: print(f'Model config after override: {actor_model_config}') # NOTE(fix me): tie_word_embedding causes meta_tensor init to hang init_context = get_init_weight_context_manager(use_meta_tensor=not actor_model_config.tie_word_embeddings) with init_context(), warnings.catch_warnings(): warnings.simplefilter("ignore") if type(actor_model_config) in AutoModelForVision2Seq._model_mapping.keys(): actor_module_class = AutoModelForVision2Seq else: actor_module_class = AutoModelForCausalLM actor_module = actor_module_class.from_pretrained(pretrained_model_name_or_path=local_path, torch_dtype=torch_dtype, config=actor_model_config, attn_implementation='flash_attention_2', trust_remote_code=trust_remote_code) # Apply Liger kernel to the model if use_liger is set to True if use_liger: from liger_kernel.transformers.monkey_patch import _apply_liger_kernel_to_instance _apply_liger_kernel_to_instance(model=actor_module) # some parameters may not in torch_dtype. TODO(zhangchi.usc1992) remove this after we switch to fsdp2 actor_module.to(torch_dtype) if enable_gradient_checkpointing: actor_module.gradient_checkpointing_enable(gradient_checkpointing_kwargs={'use_reentrant': False}) torch.distributed.barrier() if self.rank == 0: print_model_size(actor_module) log_gpu_memory_usage('After init from HF AutoModel', logger=logger) # We wrap FSDP for rollout as well mixed_precision_config = fsdp_config.get('mixed_precision', None) if mixed_precision_config is not None: param_dtype = PrecisionType.to_dtype(mixed_precision_config.get('param_dtype', 'bf16')) reduce_dtype = PrecisionType.to_dtype(mixed_precision_config.get('reduce_dtype', 'fp32')) buffer_dtype = PrecisionType.to_dtype(mixed_precision_config.get('buffer_dtype', 'fp32')) else: param_dtype = torch.bfloat16 reduce_dtype = torch.float32 buffer_dtype = torch.float32 mixed_precision = MixedPrecision(param_dtype=param_dtype, reduce_dtype=reduce_dtype, buffer_dtype=buffer_dtype) auto_wrap_policy = get_fsdp_wrap_policy(module=actor_module, config=fsdp_config.get('wrap_policy', None)) if self._is_rollout and self.config.rollout.name == 'hf': # TODO(zhangchi.usc1992, shengguangming) fix me. Current, auto_wrap_policy causes HFRollout to hang in Gemma auto_wrap_policy = None print(f'wrap_policy: {auto_wrap_policy}') fsdp_mesh = self.device_mesh sharding_strategy = get_sharding_strategy(fsdp_mesh) # TODO: add transformer policy # We force reference policy to use CPUOffload to save memory. # We force turn off CPUOffload for actor because it causes incorrect results when using grad accumulation cpu_offload = None if role == 'actor' else CPUOffload(offload_params=True) actor_module_fsdp = FSDP( actor_module, cpu_offload=cpu_offload, param_init_fn=init_fn, use_orig_params=False, auto_wrap_policy=auto_wrap_policy, device_id=torch.cuda.current_device(), sharding_strategy=sharding_strategy, # zero3 mixed_precision=mixed_precision, sync_module_states=True, device_mesh=self.device_mesh, forward_prefetch=False) log_gpu_memory_usage('After Actor FSDP init', logger=logger) # TODO: add more optimizer args into config if role == 'actor': from verl.utils.torch_functional import get_constant_schedule_with_warmup actor_optimizer = optim.AdamW(actor_module_fsdp.parameters(), lr=optim_config.lr, betas=optim_config.get('betas', (0.9, 0.999)), weight_decay=optim_config.get('weight_decay', 1e-2)) total_steps = optim_config.get('total_training_steps', 0) num_warmup_steps_ratio = optim_config.get('lr_warmup_steps_ratio', 0.) num_warmup_steps = int(num_warmup_steps_ratio * total_steps) print(f'Total steps: {total_steps}, num_warmup_steps: {num_warmup_steps}') actor_lr_scheduler = get_constant_schedule_with_warmup(optimizer=actor_optimizer, num_warmup_steps=num_warmup_steps) else: actor_optimizer = None actor_lr_scheduler = None log_gpu_memory_usage('After actor optimizer init', logger=logger) return actor_module_fsdp, actor_optimizer, actor_lr_scheduler, actor_model_config def _build_rollout(self): from torch.distributed.device_mesh import init_device_mesh # TODO(sgm): support FSDP hybrid shard for larger model infer_tp = self.config.rollout.tensor_model_parallel_size dp = self.world_size // infer_tp assert self.world_size % infer_tp == 0, f'rollout world_size: {self.world_size} is not divisible by infer_tp: {infer_tp}' rollout_device_mesh = init_device_mesh('cuda', mesh_shape=(dp, infer_tp), mesh_dim_names=['dp', 'infer_tp']) if self.config.rollout.name == 'hf': from verl.workers.rollout import HFRollout from verl.workers.sharding_manager import BaseShardingManager rollout = HFRollout(module=self.actor_module_fsdp, config=self.config.rollout) rollout_sharding_manager = BaseShardingManager() # TODO: a sharding manager that do nothing? elif self.config.rollout.name == 'vllm': if self.config.rollout.use_fire_sampling: from verl.workers.rollout.vllm_rollout import FIREvLLMRollout as vLLMRollout from verl.workers.rollout.vllm_rollout import vllm_mode else: from verl.workers.rollout.vllm_rollout import vLLMRollout, vllm_mode from verl.workers.sharding_manager import FSDPVLLMShardingManager log_gpu_memory_usage('Before building vllm rollout', logger=None) local_path = copy_to_local(self.config.model.path) if vllm_mode == 'customized': rollout = vLLMRollout(actor_module=self.actor_module_fsdp, config=self.config.rollout, tokenizer=self.tokenizer, model_hf_config=self.actor_model_config) elif vllm_mode == 'spmd': rollout = vLLMRollout(model_path=local_path, config=self.config.rollout, tokenizer=self.tokenizer, model_hf_config=self.actor_model_config, device_mesh=rollout_device_mesh) else: raise NotImplementedError("vllm_mode must be 'customized' or 'spmd'") log_gpu_memory_usage('After building vllm rollout', logger=None) if torch.distributed.get_world_size() == 1: self.config.rollout.load_format = 'dummy_hf' rollout_sharding_manager = FSDPVLLMShardingManager(module=self.actor_module_fsdp, inference_engine=rollout.inference_engine, model_config=self.actor_model_config, full_params='hf' in self.config.rollout.load_format, device_mesh=rollout_device_mesh) log_gpu_memory_usage('After building sharding manager', logger=None) return rollout, rollout_sharding_manager @register(dispatch_mode=Dispatch.ONE_TO_ALL) def init_model(self): from verl.workers.actor import DataParallelPPOActor # This is used to import external_lib into the huggingface systems import_external_libs(self.config.model.get('external_lib', None)) from omegaconf import OmegaConf override_model_config = OmegaConf.to_container(self.config.model.get('override_config', OmegaConf.create())) use_remove_padding = self.config.model.get('use_remove_padding', False) if self._is_actor or self._is_rollout: # we need the model for actor and rollout if self._is_actor: optim_config = self.config.actor.optim fsdp_config = self.config.actor.fsdp_config else: optim_config = None fsdp_config = OmegaConf.create() self.actor_module_fsdp, self.actor_optimizer, self.actor_lr_scheduler, self.actor_model_config = self._build_model_optimizer( model_path=self.config.model.path, fsdp_config=fsdp_config, optim_config=optim_config, override_model_config=override_model_config, use_remove_padding=use_remove_padding, enable_gradient_checkpointing=self.config.model.get('enable_gradient_checkpointing', False), trust_remote_code=self.config.model.get('trust_remote_code', False), use_liger=self.config.model.get('use_liger', False), role='actor') # get the original unwrapped module self.actor_module = self.actor_module_fsdp._fsdp_wrapped_module if self._is_offload_optimizer: offload_fsdp_optimizer(optimizer=self.actor_optimizer) log_gpu_memory_usage('After offload actor optimizer during init', logger=logger) # load from checkpoint if self._is_actor: OmegaConf.set_struct(self.config.actor, True) with open_dict(self.config.actor): self.config.actor.use_remove_padding = use_remove_padding self.actor = DataParallelPPOActor(config=self.config.actor, actor_module=self.actor_module_fsdp, actor_optimizer=self.actor_optimizer) if self._is_rollout: self.rollout, self.rollout_sharding_manager = self._build_rollout() if self._is_ref: self.ref_module_fsdp = self._build_model_optimizer(model_path=self.config.model.path, fsdp_config=self.config.ref.fsdp_config, optim_config=None, override_model_config=override_model_config, use_remove_padding=use_remove_padding, trust_remote_code=self.config.model.get( 'trust_remote_code', False), use_liger=self.config.model.get('use_liger', False), role='ref')[0] OmegaConf.set_struct(self.config.ref, True) with open_dict(self.config.ref): self.config.ref.use_remove_padding = use_remove_padding self.ref_policy = DataParallelPPOActor(config=self.config.ref, actor_module=self.ref_module_fsdp) if self._is_actor: self.flops_counter = FlopsCounter(self.actor_model_config) self.checkpoint_manager = FSDPCheckpointManager( model=self.actor_module_fsdp, optimizer=self.actor.actor_optimizer, lr_scheduler=self.actor_lr_scheduler, processing_class=self.processor if self.processor is not None else self.tokenizer) torch.cuda.empty_cache() @register(dispatch_mode=Dispatch.DP_COMPUTE_PROTO) def update_actor(self, data: DataProto): data = data.to('cuda') assert self._is_actor if self._is_offload_param: load_fsdp_model_to_gpu(self.actor_module_fsdp) if self._is_offload_optimizer: load_fsdp_optimizer(optimizer=self.actor_optimizer, device_id=torch.cuda.current_device()) data.batch = data.batch.cuda() log_gpu_memory_usage('Before update policy', logger=logger) with self.ulysses_sharding_manager: data = self.ulysses_sharding_manager.preprocess_data(data=data) # perform training with Timer(name='update_policy', logger=None) as timer: metrics = self.actor.update_policy(data=data) delta_time = timer.last global_num_tokens = data.meta_info['global_token_num'] estimated_flops, promised_flops = self.flops_counter.estimate_flops(global_num_tokens, delta_time) metrics['mfu/actor'] = estimated_flops * self.config.actor.ppo_epochs / promised_flops / self.world_size self.actor_lr_scheduler.step() lr = self.actor_lr_scheduler.get_last_lr()[0] metrics['actor/lr'] = lr log_gpu_memory_usage('After update policy', logger=logger) # TODO: here, we should return all metrics output = DataProto(meta_info={'metrics': metrics}) output = self.ulysses_sharding_manager.postprocess_data(data=output) output = output.to('cpu') if self._is_offload_param: offload_fsdp_model_to_cpu(self.actor_module_fsdp) if self._is_offload_optimizer: offload_fsdp_optimizer(optimizer=self.actor_optimizer) torch.cuda.empty_cache() return output @register(dispatch_mode=Dispatch.DP_COMPUTE_PROTO) def generate_sequences(self, prompts: DataProto): prompts = prompts.to('cuda') assert self._is_rollout if self._is_offload_param: load_fsdp_model_to_gpu(self.actor_module_fsdp) prompts.batch = prompts.batch.cuda() meta_info = { 'eos_token_id': self.generation_config.eos_token_id if self.generation_config is not None else self.tokenizer.eos_token_id, 'pad_token_id': self.generation_config.pad_token_id if self.generation_config is not None else self.tokenizer.pad_token_id, } prompts.meta_info.update(meta_info) with self.rollout_sharding_manager: # after parameters sync with rollout, offload actor model to CPU if self._is_offload_param: offload_fsdp_model_to_cpu(self.actor_module_fsdp) if self._is_offload_optimizer: offload_fsdp_optimizer(optimizer=self.actor_optimizer) log_gpu_memory_usage('After entering rollout sharding manager', logger=logger) prompts = self.rollout_sharding_manager.preprocess_data(prompts) output = self.rollout.generate_sequences(prompts=prompts) log_gpu_memory_usage('After rollout generation', logger=logger) output = self.rollout_sharding_manager.postprocess_data(output) output = output.to('cpu') # clear kv cache torch.cuda.empty_cache() log_gpu_memory_usage('After recompute log prob', logger=logger) return output @register(dispatch_mode=Dispatch.DP_COMPUTE_PROTO) def compute_log_prob(self, data: DataProto): assert self._is_actor if self._is_offload_param: load_fsdp_model_to_gpu(self.actor_module_fsdp) data = data.to('cuda') # we should always recompute old_log_probs when it is HybridEngine data.meta_info['micro_batch_size'] = self.config.rollout.log_prob_micro_batch_size_per_gpu data.meta_info['max_token_len'] = self.config.rollout.log_prob_max_token_len_per_gpu data.meta_info['use_dynamic_bsz'] = self.config.rollout.log_prob_use_dynamic_bsz data.meta_info['temperature'] = self.config.rollout.temperature # perform recompute log_prob with self.ulysses_sharding_manager: data = self.ulysses_sharding_manager.preprocess_data(data) output = self.actor.compute_log_prob(data=data) output = DataProto.from_dict(tensors={'old_log_probs': output}, meta_info={'temperature': self.config.rollout.temperature}) output = self.ulysses_sharding_manager.postprocess_data(output) output = output.to('cpu') # https://pytorch.org/docs/stable/notes/fsdp.html#fsdp-notes # unshard the root FSDP module if self.world_size > 1: self.actor.actor_module._handle.reshard(True) if self._is_offload_param: offload_fsdp_model_to_cpu(self.actor_module_fsdp) # clear kv cache torch.cuda.empty_cache() log_gpu_memory_usage('After compute_log_prob', logger=logger) return output @register(dispatch_mode=Dispatch.DP_COMPUTE_PROTO) def compute_ref_log_prob(self, data: DataProto): assert self._is_ref data = data.to('cuda') micro_batch_size = self.config.ref.log_prob_micro_batch_size_per_gpu data.meta_info['micro_batch_size'] = micro_batch_size data.meta_info['temperature'] = self.config.rollout.temperature data.meta_info['max_token_len'] = self.config.ref.log_prob_max_token_len_per_gpu data.meta_info['use_dynamic_bsz'] = self.config.ref.log_prob_use_dynamic_bsz with self.ulysses_sharding_manager: data = self.ulysses_sharding_manager.preprocess_data(data) output = self.ref_policy.compute_log_prob(data=data) output = DataProto.from_dict(tensors={'ref_log_prob': output}) output = self.ulysses_sharding_manager.postprocess_data(output) output = output.to('cpu') # https://pytorch.org/docs/stable/notes/fsdp.html#fsdp-notes # unshard the root FSDP module if self.world_size > 1: self.ref_policy.actor_module._handle.reshard(True) torch.cuda.empty_cache() return output @register(dispatch_mode=Dispatch.ONE_TO_ALL) def save_checkpoint(self, local_path, hdfs_path=None, global_step=0, remove_previous_ckpt=False): # only support save and load ckpt for actor assert self._is_actor import torch if self._is_offload_param: load_fsdp_model_to_gpu(self.actor_module_fsdp) self.checkpoint_manager.save_checkpoint(local_path=local_path, hdfs_path=hdfs_path, global_step=global_step, remove_previous_ckpt=remove_previous_ckpt) torch.distributed.barrier() if self._is_offload_param: offload_fsdp_model_to_cpu(self.actor_module_fsdp) @register(dispatch_mode=Dispatch.ONE_TO_ALL) def load_checkpoint(self, path, del_local_after_load=False): if self._is_offload_param: load_fsdp_model_to_gpu(self.actor_module_fsdp) self.checkpoint_manager.load_checkpoint(path=path, del_local_after_load=del_local_after_load) if self._is_offload_param: offload_fsdp_model_to_cpu(self.actor_module_fsdp) if self._is_offload_optimizer: offload_fsdp_optimizer(self.actor_optimizer) class CriticWorker(Worker): def __init__(self, config): super().__init__() import torch.distributed if not torch.distributed.is_initialized(): torch.distributed.init_process_group(backend="nccl") self.config = config # build device mesh for Ulysses Sequence Parallel world_size = torch.distributed.get_world_size() from torch.distributed.device_mesh import init_device_mesh fsdp_size = self.config.model.fsdp_config.fsdp_size self.device_mesh = create_device_mesh(world_size=world_size, fsdp_size=fsdp_size) self.ulysses_device_mesh = None self.ulysses_sequence_parallel_size = self.config.get('ulysses_sequence_parallel_size', 1) dp = world_size // self.ulysses_sequence_parallel_size if self.ulysses_sequence_parallel_size > 1: self.ulysses_device_mesh = init_device_mesh('cuda', mesh_shape=(dp, self.ulysses_sequence_parallel_size), mesh_dim_names=['dp', 'sp']) self.ulysses_sharding_manager = FSDPUlyssesShardingManager(self.ulysses_device_mesh) # set FSDP offload params self._is_offload_param = self.config.model.fsdp_config.param_offload self._is_offload_optimizer = self.config.model.fsdp_config.optimizer_offload # normalize config self.config.ppo_mini_batch_size //= (torch.distributed.get_world_size() // self.ulysses_sequence_parallel_size) if self.config.ppo_micro_batch_size is not None: self.config.ppo_micro_batch_size //= (torch.distributed.get_world_size() // self.ulysses_sequence_parallel_size) self.config.forward_micro_batch_size //= (torch.distributed.get_world_size() // self.ulysses_sequence_parallel_size) self.config.ppo_micro_batch_size_per_gpu = self.config.ppo_micro_batch_size self.config.forward_micro_batch_size_per_gpu = self.config.forward_micro_batch_size assert self.config.ppo_mini_batch_size % self.config.ppo_micro_batch_size_per_gpu == 0, \ f'normalized ppo_mini_batch_size {self.config.ppo_mini_batch_size} should be divisible by ppo_micro_batch_size_per_gpu {self.config.ppo_micro_batch_size_per_gpu}' assert self.config.ppo_mini_batch_size // self.config.ppo_micro_batch_size_per_gpu > 0, \ f'normalized ppo_mini_batch_size {self.config.ppo_mini_batch_size} should be larger than ppo_micro_batch_size_per_gpu {self.config.ppo_micro_batch_size_per_gpu}' def _build_critic_model_optimizer(self, config): # the following line is necessary from verl.utils.model import LambdaLayer, print_model_size, squeeze from verl.utils.torch_dtypes import PrecisionType from torch.distributed.fsdp import FullyShardedDataParallel as FSDP, ShardingStrategy, MixedPrecision from torch import optim local_path = copy_to_local(config.model.path) # note that the tokenizer between actor and critic may be different. So override tokenizer info with actor info # using random initialized model from any architecture. May not be the same as Actor. tokenizer_path = copy_to_local(config.model.tokenizer_path) self.tokenizer = hf_tokenizer(tokenizer_path, trust_remote_code=config.model.get('trust_remote_code', False)) self.processor = hf_processor(tokenizer_path, trust_remote_code=config.model.get('trust_remote_code', False)) from omegaconf import OmegaConf override_config = OmegaConf.to_container(self.config.model.get('override_config', OmegaConf.create())) override_config_kwargs = { 'bos_token_id': self.tokenizer.bos_token_id, 'eos_token_id': self.tokenizer.eos_token_id, 'pad_token_id': self.tokenizer.pad_token_id, } override_config_kwargs.update(override_config) if self.rank == 0: print(f'Critic overriding config {override_config_kwargs}') torch_dtype = self.config.model.fsdp_config.get('model_dtype', 'fp32') torch_dtype = PrecisionType.to_dtype(torch_dtype) from transformers import AutoConfig, AutoModelForTokenClassification from torch import nn trust_remote_code = False critic_model_config = AutoConfig.from_pretrained(local_path, trust_remote_code=trust_remote_code) critic_model_config.num_labels = 1 use_remove_padding = config.model.get('use_remove_padding', False) if use_remove_padding: from verl.models.registry import check_model_support_rmpad check_model_support_rmpad(critic_model_config.model_type) if use_remove_padding and self.ulysses_sequence_parallel_size > 1: from verl.models.transformers.monkey_patch import apply_monkey_patch apply_monkey_patch(critic_model_config, verbose=True) init_context = get_init_weight_context_manager() with init_context(), warnings.catch_warnings(): warnings.simplefilter("ignore") setattr(critic_model_config, 'classifier_dropout', 0.) setattr(critic_model_config, 'hidden_dropout', '0') critic_module = AutoModelForTokenClassification.from_pretrained(pretrained_model_name_or_path=local_path, torch_dtype=torch_dtype, config=critic_model_config, attn_implementation='flash_attention_2', trust_remote_code=trust_remote_code) # some parameters may not in torch_dtype critic_module.to(torch_dtype) if config.model.get('enable_gradient_checkpointing', False): critic_module.gradient_checkpointing_enable(gradient_checkpointing_kwargs={'use_reentrant': False}) if self.rank == 0: print_model_size(critic_module) self.critic_model_config = critic_model_config fsdp_config = self.config.model.fsdp_config mixed_precision_config = fsdp_config.get('mixed_precision', None) if mixed_precision_config is not None: param_dtype = PrecisionType.to_dtype(mixed_precision_config.get('param_dtype', 'bf16')) reduce_dtype = PrecisionType.to_dtype(mixed_precision_config.get('reduce_dtype', 'fp32')) buffer_dtype = PrecisionType.to_dtype(mixed_precision_config.get('buffer_dtype', 'fp32')) else: param_dtype = torch.bfloat16 reduce_dtype = torch.float32 buffer_dtype = torch.float32 mixed_precision = MixedPrecision(param_dtype=param_dtype, reduce_dtype=reduce_dtype, buffer_dtype=buffer_dtype) auto_wrap_policy = get_fsdp_wrap_policy(module=critic_module, config=self.config.model.fsdp_config.wrap_policy) log_gpu_memory_usage('Before critic FSDP', logger=None) fsdp_mesh = self.device_mesh sharding_strategy = get_sharding_strategy(fsdp_mesh) # Note: We force turn off CPUOffload for critic because it causes incorrect results when using grad accumulation critic_module = FSDP(critic_module, param_init_fn=init_fn, use_orig_params=False, auto_wrap_policy=auto_wrap_policy, device_id=torch.cuda.current_device(), sharding_strategy=sharding_strategy, mixed_precision=mixed_precision, sync_module_states=True, forward_prefetch=False, device_mesh=self.device_mesh, cpu_offload=None) log_gpu_memory_usage('After critic FSDP', logger=None) critic_optimizer = optim.AdamW(critic_module.parameters(), lr=config.optim.lr, betas=config.optim.get('betas', (0.9, 0.999)), weight_decay=config.optim.get('weight_decay', 1e-2)) total_steps = config.optim.get('total_training_steps', 0) num_warmup_steps_ratio = config.optim.get('lr_warmup_steps_ratio', 0.) num_warmup_steps = int(num_warmup_steps_ratio * total_steps) print(f'Total steps: {total_steps}, num_warmup_steps: {num_warmup_steps}') from verl.utils.torch_functional import get_constant_schedule_with_warmup critic_lr_scheduler = get_constant_schedule_with_warmup(optimizer=critic_optimizer, num_warmup_steps=num_warmup_steps) return critic_module, critic_optimizer, critic_lr_scheduler @register(dispatch_mode=Dispatch.ONE_TO_ALL) def init_model(self): # This is used to import external_lib into the huggingface systems import_external_libs(self.config.model.get('external_lib', None)) from verl.workers.critic import DataParallelPPOCritic self.critic_module, self.critic_optimizer, self.critic_lr_scheduler = self._build_critic_model_optimizer( self.config) if self._is_offload_param: offload_fsdp_model_to_cpu(self.critic_module) if self._is_offload_optimizer: offload_fsdp_optimizer(optimizer=self.critic_optimizer) self.critic = DataParallelPPOCritic(config=self.config, critic_module=self.critic_module, critic_optimizer=self.critic_optimizer) self.flops_counter = FlopsCounter(self.critic_model_config) self.checkpoint_manager = FSDPCheckpointManager( model=self.critic_module, optimizer=self.critic_optimizer, lr_scheduler=self.critic_lr_scheduler, processing_class=self.processor if self.processor is not None else self.tokenizer) torch.cuda.empty_cache() @register(dispatch_mode=Dispatch.DP_COMPUTE_PROTO) def compute_values(self, data: DataProto): data = data.to('cuda') if self._is_offload_param: load_fsdp_model_to_gpu(self.critic_module) micro_batch_size = self.config.forward_micro_batch_size_per_gpu data.meta_info['micro_batch_size'] = micro_batch_size data.meta_info['max_token_len'] = self.config.forward_max_token_len_per_gpu data.meta_info['use_dynamic_bsz'] = self.config.use_dynamic_bsz # perform forward computation with self.ulysses_sharding_manager: data = self.ulysses_sharding_manager.preprocess_data(data=data) values = self.critic.compute_values(data=data) output = DataProto.from_dict(tensors={'values': values}) output = self.ulysses_sharding_manager.postprocess_data(data=output) output = output.to('cpu') if self._is_offload_param: offload_fsdp_model_to_cpu(self.critic_module) return output @register(dispatch_mode=Dispatch.DP_COMPUTE_PROTO) def update_critic(self, data: DataProto): data = data.to('cuda') if self._is_offload_param: load_fsdp_model_to_gpu(self.critic_module) if self._is_offload_optimizer: load_fsdp_optimizer(optimizer=self.critic_optimizer, device_id=torch.cuda.current_device()) # perform forward computation with self.ulysses_sharding_manager: data = self.ulysses_sharding_manager.preprocess_data(data=data) with Timer(name='update_critic', logger=None) as timer: metrics = self.critic.update_critic(data=data) delta_time = timer.last global_num_tokens = data.meta_info['global_token_num'] estimated_flops, promised_flops = self.flops_counter.estimate_flops(global_num_tokens, delta_time) metrics['mfu/critic'] = estimated_flops * self.config.ppo_epochs / promised_flops / self.world_size self.critic_lr_scheduler.step() lr = self.critic_lr_scheduler.get_last_lr()[0] metrics['critic/lr'] = lr output = DataProto(batch=None, meta_info={'metrics': metrics}) output = self.ulysses_sharding_manager.postprocess_data(data=output) if self._is_offload_param: offload_fsdp_model_to_cpu(self.critic_module) if self._is_offload_optimizer: offload_fsdp_optimizer(optimizer=self.critic_optimizer) torch.cuda.empty_cache() output = output.to('cpu') return output @register(dispatch_mode=Dispatch.ONE_TO_ALL) def save_checkpoint(self, local_path, hdfs_path=None, global_step=0, remove_previous_ckpt=False): import torch if self._is_offload_param: load_fsdp_model_to_gpu(self.critic_module) self.checkpoint_manager.save_checkpoint(local_path=local_path, hdfs_path=hdfs_path, global_step=global_step, remove_previous_ckpt=remove_previous_ckpt) torch.distributed.barrier() if self._is_offload_param: offload_fsdp_model_to_cpu(self.critic_module) @register(dispatch_mode=Dispatch.ONE_TO_ALL) def load_checkpoint(self, path, del_local_after_load=True): import torch if self._is_offload_param: load_fsdp_model_to_gpu(self.critic_module) self.checkpoint_manager.load_checkpoint(path=path, del_local_after_load=del_local_after_load) torch.distributed.barrier() if self._is_offload_param: offload_fsdp_model_to_cpu(self.critic_module) if self._is_offload_optimizer: offload_fsdp_optimizer(self.critic_optimizer) # TODO(sgm): we may need to extract it to dp_reward_model.py class RewardModelWorker(Worker): """ Note that we only implement the reward model that is subclass of AutoModelForTokenClassification. """ def __init__(self, config): super().__init__() import torch.distributed if not torch.distributed.is_initialized(): torch.distributed.init_process_group(backend="nccl") self.config = config # build device mesh for Ulysses Sequence Parallel world_size = torch.distributed.get_world_size() from torch.distributed.device_mesh import init_device_mesh fsdp_size = self.config.model.fsdp_config.fsdp_size self.device_mesh = create_device_mesh(world_size=world_size, fsdp_size=fsdp_size) self.ulysses_device_mesh = None self.ulysses_sequence_parallel_size = self.config.get('ulysses_sequence_parallel_size', 1) dp = world_size // self.ulysses_sequence_parallel_size if self.ulysses_sequence_parallel_size > 1: self.ulysses_device_mesh = init_device_mesh('cuda', mesh_shape=(dp, self.ulysses_sequence_parallel_size), mesh_dim_names=['dp', 'sp']) self.ulysses_sharding_manager = FSDPUlyssesShardingManager(self.ulysses_device_mesh) self.use_remove_padding = self.config.model.get('use_remove_padding', False) # normalize config if self.config.micro_batch_size is not None: self.config.micro_batch_size //= torch.distributed.get_world_size() self.config.micro_batch_size_per_gpu = self.config.micro_batch_size def _build_model(self, config): # the following line is necessary from transformers import AutoModelForTokenClassification, AutoConfig from torch.distributed.fsdp import FullyShardedDataParallel as FSDP, ShardingStrategy, CPUOffload # download the checkpoint from hdfs local_path = copy_to_local(config.model.path) if self.config.model.input_tokenizer is None: self._do_switch_chat_template = False else: self._do_switch_chat_template = True input_tokenizer_local_path = copy_to_local(config.model.input_tokenizer) self.input_tokenizer = hf_tokenizer(input_tokenizer_local_path, trust_remote_code=config.model.get('trust_remote_code', False)) self.tokenizer = hf_tokenizer(local_path, trust_remote_code=config.model.get('trust_remote_code', False)) trust_remote_code = config.model.get('trust_remote_code', False) model_config = AutoConfig.from_pretrained(local_path, trust_remote_code=trust_remote_code) model_config.num_labels = 1 use_remove_padding = config.model.get('use_remove_padding', False) if use_remove_padding: from verl.models.registry import check_model_support_rmpad check_model_support_rmpad(model_config.model_type) if use_remove_padding and self.ulysses_sequence_parallel_size > 1: from verl.models.transformers.monkey_patch import apply_monkey_patch apply_monkey_patch(model_config, verbose=True) # note that we have to create model in fp32. Otherwise, the optimizer is in bf16, which is incorrect init_context = get_init_weight_context_manager(use_meta_tensor=not model_config.tie_word_embeddings) with init_context(), warnings.catch_warnings(): warnings.simplefilter("ignore") setattr(model_config, 'classifier_dropout', 0.) reward_module = AutoModelForTokenClassification.from_pretrained(pretrained_model_name_or_path=local_path, config=model_config, torch_dtype=torch.bfloat16, attn_implementation='flash_attention_2', trust_remote_code=trust_remote_code) reward_module.to(torch.bfloat16) auto_wrap_policy = get_fsdp_wrap_policy(module=reward_module, config=self.config.model.fsdp_config) fsdp_mesh = self.device_mesh sharding_strategy = get_sharding_strategy(fsdp_mesh) reward_module = FSDP( reward_module, param_init_fn=init_fn, use_orig_params=False, auto_wrap_policy=auto_wrap_policy, device_id=torch.cuda.current_device(), sharding_strategy=sharding_strategy, # zero3 sync_module_states=True, cpu_offload=CPUOffload(offload_params=True), forward_prefetch=False, device_mesh=self.device_mesh) return reward_module @register(dispatch_mode=Dispatch.ONE_TO_ALL) def init_model(self): # This is used to import external_lib into the huggingface systems import_external_libs(self.config.model.get('external_lib', None)) self.reward_module = self._build_model(config=self.config) torch.cuda.empty_cache() def _forward_micro_batch(self, micro_batch): from flash_attn.bert_padding import pad_input, unpad_input, index_first_axis, rearrange from verl.utils.ulysses import ulysses_pad_and_slice_inputs, gather_outpus_and_unpad with torch.no_grad(), torch.autocast(device_type='cuda', dtype=torch.bfloat16): input_ids = micro_batch['input_ids'] batch_size, seqlen = input_ids.shape attention_mask = micro_batch['attention_mask'] position_ids = micro_batch['position_ids'] if self.use_remove_padding: input_ids_rmpad, indices, *_ = unpad_input(input_ids.unsqueeze(-1), attention_mask) # input_ids_rmpad (total_nnz, ...) input_ids_rmpad = input_ids_rmpad.transpose(0, 1) # (1, total_nnz) # unpad the position_ids to align the rotary position_ids_rmpad = index_first_axis(rearrange(position_ids.unsqueeze(-1), "b s ... -> (b s) ..."), indices).transpose(0, 1) # pad and slice the inputs if sp > 1 if self.ulysses_sequence_parallel_size > 1: input_ids_rmpad, position_ids_rmpad, pad_size = ulysses_pad_and_slice_inputs(input_ids_rmpad, \ position_ids_rmpad, \ sp_size=self.ulysses_sequence_parallel_size) # only pass input_ids and position_ids to enable flash_attn_varlen output = self.reward_module(input_ids=input_ids_rmpad, attention_mask=None, position_ids=position_ids_rmpad, use_cache=False) # prevent model thinks we are generating reward_rmpad = output.logits reward_rmpad = reward_rmpad.squeeze(0) # (total_nnz) # gather output if sp > 1 if self.ulysses_sequence_parallel_size > 1: reward_rmpad = gather_outpus_and_unpad(reward_rmpad, gather_dim=0, unpad_dim=0, padding_size=pad_size) # pad it back rm_score = pad_input(reward_rmpad, indices=indices, batch=batch_size, seqlen=seqlen).squeeze(-1) else: output = self.reward_module(input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids) rm_score = output.logits # (batch_size, seq_len, 1) rm_score = rm_score.squeeze(-1) # extract the result of the last valid token eos_mask_idx = torch.argmax(position_ids * attention_mask, dim=-1) # (bsz,) rm_score = rm_score[torch.arange(batch_size), eos_mask_idx] return rm_score def _expand_to_token_level(self, data: DataProto, scores: torch.Tensor): batch_size = data.batch.batch_size[0] # expand as token_level_reward attention_mask = data.batch['attention_mask'] position_ids = data.batch['position_ids'] response_length = data.batch['responses'].shape[-1] eos_mask_idx = torch.argmax(position_ids * attention_mask, dim=-1) # (bsz,) token_level_scores = torch.zeros_like(attention_mask, dtype=scores.dtype) # (bsz, seqlen) token_level_scores[torch.arange(batch_size), eos_mask_idx] = scores # select the response part token_level_scores = token_level_scores[:, -response_length:] return token_level_scores def _switch_chat_template(self, data: DataProto): src_max_length = data.batch['attention_mask'].shape[-1] src_tokenizer = self.input_tokenizer target_tokenizer = self.tokenizer rm_input_ids = [] rm_attention_mask = [] for i in range(data.batch.batch_size[0]): # extract raw prompt chat: list = data.non_tensor_batch['raw_prompt'][i].tolist() # extract response response_ids = data.batch['responses'][i] response_length = response_ids.shape[-1] valid_response_length = data.batch['attention_mask'][i][-response_length:].sum() valid_response_ids = response_ids[:valid_response_length] # decode response = src_tokenizer.decode(valid_response_ids) # remove bos and eos response = response.replace(src_tokenizer.eos_token, '') chat.append({'role': 'assistant', 'content': response}) prompt_with_chat_template = target_tokenizer.apply_chat_template(chat, add_generation_prompt=False, tokenize=False) if self.rank == 0 and i == 0: # for debugging purpose print(f'Switch template. chat: {prompt_with_chat_template}') # the maximum length is actually determined by the reward model itself max_length = self.config.get('max_length', src_max_length) if max_length is None: max_length = src_max_length input_ids, attention_mask = verl_F.tokenize_and_postprocess_data( prompt=prompt_with_chat_template, tokenizer=target_tokenizer, max_length=max_length, pad_token_id=target_tokenizer.pad_token_id, left_pad=False, # right padding truncation=self.config.get('truncation', 'right')) # truncate from the right rm_input_ids.append(input_ids) rm_attention_mask.append(attention_mask) rm_input_ids = torch.cat(rm_input_ids, dim=0) rm_attention_mask = torch.cat(rm_attention_mask, dim=0) rm_position_ids = compute_position_id_with_mask(rm_attention_mask) rm_inputs = {'input_ids': rm_input_ids, 'attention_mask': rm_attention_mask, 'position_ids': rm_position_ids} return DataProto.from_dict(rm_inputs) @register(dispatch_mode=Dispatch.DP_COMPUTE_PROTO) def compute_rm_score(self, data: DataProto): import itertools from verl.utils.seqlen_balancing import rearrange_micro_batches, get_reverse_idx data = data.to('cuda') if self._do_switch_chat_template: rm_data = self._switch_chat_template(data) rm_data.batch = rm_data.batch.cuda() # perform forward computation with self.ulysses_sharding_manager: rm_data = self.ulysses_sharding_manager.preprocess_data(data=rm_data) data = self.ulysses_sharding_manager.preprocess_data(data=data) use_dynamic_bsz = self.config.use_dynamic_bsz if use_dynamic_bsz: max_token_len = self.config.forward_max_token_len_per_gpu * self.ulysses_sequence_parallel_size micro_batches, indices = rearrange_micro_batches(batch=rm_data.batch, max_token_len=max_token_len) else: micro_batches = rm_data.batch.split(self.config.micro_batch_size_per_gpu) output = [] for micro_batch in micro_batches: rm_score = self._forward_micro_batch(micro_batch) output.append(rm_score) scores = torch.cat(output, dim=0) # (batch_size) if use_dynamic_bsz: indices = list(itertools.chain.from_iterable(indices)) assert len(indices) == scores.size(0), f"{len(indices)} vs. {scores.size()}" revert_indices = torch.tensor(get_reverse_idx(indices), dtype=torch.long) scores = scores[revert_indices] token_level_scores = self._expand_to_token_level(data, scores) # Note that this is only the scores, may not be the final rewards used to train RL output = DataProto.from_dict(tensors={'rm_scores': token_level_scores}) output = self.ulysses_sharding_manager.postprocess_data(data=output) # https://pytorch.org/docs/stable/notes/fsdp.html#fsdp-notes # unshard the root FSDP module self.reward_module._handle.reshard(True) output = output.to('cpu') torch.cuda.empty_cache() return output ================================================ FILE: verl/workers/megatron_workers.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ The main entry point to run the PPO algorithm """ import os import logging import ray import torch import torch.distributed import torch.nn as nn from omegaconf import DictConfig from verl.single_controller.base.megatron.worker import MegatronWorker from verl.workers.actor.megatron_actor import MegatronPPOActor from verl.workers.critic.megatron_critic import MegatronPPOCritic from verl.workers.sharding_manager import AllGatherPPModel from verl.workers.reward_model.megatron.reward_model import MegatronRewardModel from verl.single_controller.base.decorator import register, Dispatch from verl import DataProto from verl.utils.fs import copy_to_local from verl.utils.debug import log_gpu_memory_usage from verl.utils.model import load_megatron_model_weights from verl.utils.flops_counter import FlopsCounter from verl.utils.megatron_utils import init_model_parallel_config from verl.utils.megatron_utils import offload_megatron_param_and_grad, load_megatron_param_and_grad from verl.utils import hf_tokenizer from codetiming import Timer from megatron.core import parallel_state as mpu from megatron.core import ModelParallelConfig logger = logging.getLogger(__file__) logger.setLevel(os.getenv('VERL_PPO_LOGGING_LEVEL', 'WARN')) def set_random_seed(seed): import torch import numpy as np import random torch.manual_seed(seed) np.random.seed(seed) random.seed(seed) if torch.cuda.device_count() > 0: from megatron.core import tensor_parallel tensor_parallel.model_parallel_cuda_manual_seed(seed) # FIXME: torch cumsum not support deterministic (used in vllm sampler), # https://github.com/pytorch/pytorch/issues/89492 # torch.use_deterministic_algorithms(True, warn_only=True) # os.environ['CUBLAS_WORKSPACE_CONFIG'] = ':4096:8' class ActorRolloutRefWorker(MegatronWorker): """ This worker can be instantiated as a standalone actor or a standalone rollout or a standalone reference policy or a hybrid engine based on the config.rollout """ def __init__(self, config: DictConfig, role: str): super().__init__() self.config = config # NOTE(sgm): We utilize colocate WorkerGroup by default. # As a result, Workers for different model share the same process. # Therefore, we only require one distribute initialization. # To utilize different parallel startegy in different models: # 1, users should disable WorkerDict; 2.assign different ResourcePool to different models, # 3. and apply the following patch in ray==2.10, https://github.com/ray-project/ray/pull/44385 if not torch.distributed.is_initialized(): rank = int(os.environ['LOCAL_RANK']) torch.distributed.init_process_group(backend="nccl") torch.cuda.set_device(rank) if self.config.actor.megatron.sequence_parallel: os.environ['CUDA_DEVICE_MAX_CONNECTIONS'] = '1' mpu.initialize_model_parallel( tensor_model_parallel_size=self.config.actor.megatron.tensor_model_parallel_size, pipeline_model_parallel_size=self.config.actor.megatron.pipeline_model_parallel_size, virtual_pipeline_model_parallel_size=None, pipeline_model_parallel_split_rank=None, use_sharp=False, context_parallel_size=1, expert_model_parallel_size=1, nccl_communicator_config_path=None, ) set_random_seed(seed=self.config.actor.megatron.seed) self.role = role assert self.role in ['actor', 'rollout', 'ref', 'actor_rollout', 'actor_rollout_ref'] self._is_actor = self.role in ['actor', 'actor_rollout', 'actor_rollout_ref'] self._is_rollout = self.role in ['rollout', 'actor_rollout', 'actor_rollout_ref'] self._is_ref = self.role in ['ref', 'actor_rollout_ref'] # TODO(sgm): Currently, we only support reference model param offload # will support other offload later self._is_offload_param = False self._is_offload_grad = False self._is_offload_optimizer = False # normalize config if self._is_actor and self._is_rollout: self.config.actor.ppo_mini_batch_size *= self.config.rollout.n self.config.actor.ppo_mini_batch_size //= mpu.get_data_parallel_world_size() if self.config.actor.get('ppo_micro_batch_size', None): self.config.actor.ppo_micro_batch_size //= mpu.get_data_parallel_world_size() self.config.rollout.log_prob_micro_batch_size //= mpu.get_data_parallel_world_size() self.config.actor.ppo_micro_batch_size_per_gpu = self.config.actor.ppo_micro_batch_size self.config.rollout.log_prob_micro_batch_size_per_gpu = self.config.rollout.log_prob_micro_batch_size self._is_offload_param = self.config.actor.get('param_offload', False) self._is_offload_grad = self.config.actor.get('grad_offload', False) self._is_offload_optimizer = self.config.actor.get('optimizer_offload', False) elif self._is_ref: if self.config.ref.get('ppo_micro_batch_size', None): self.config.ref.log_prob_micro_batch_size //= mpu.get_data_parallel_world_size() self.config.ref.ppo_micro_batch_size_per_gpu = self.config.ref.ppo_micro_batch_size self._is_offload_param = self.config.ref.get('param_offload', False) def _build_model_optimizer(self, model_path, megatron_config: ModelParallelConfig, optim_config, override_model_config, enable_gradient_checkpointing=False): from verl.utils.megatron.optimizer import get_megatron_optimizer from megatron.core.models.gpt.gpt_model import ModelType from verl.utils.model import print_model_size, update_model_config, get_generation_config from verl.utils.megatron_utils import get_model, init_megatron_optim_config from transformers import AutoModelForCausalLM, AutoTokenizer, AutoConfig, GenerationConfig # Step 1: initialize the tokenizer local_path = copy_to_local(model_path) self.tokenizer = hf_tokenizer(local_path) # Step 2: get the actor_model_config actor_model_config = AutoConfig.from_pretrained(local_path) self.generation_config = get_generation_config(local_path) override_config_kwargs = { 'bos_token_id': self.tokenizer.bos_token_id, 'eos_token_id': self.tokenizer.eos_token_id, 'pad_token_id': self.tokenizer.pad_token_id, } override_config_kwargs.update(override_model_config) update_model_config(actor_model_config, override_config_kwargs=override_config_kwargs) if self.rank == 0: print(f'Model config after override: {actor_model_config}') def megatron_actor_model_provider(pre_process, post_process): from verl.utils.model import get_parallel_model_from_config # vpp is not supported yet because it will hang for some reason. Need debugging vpp_rank = mpu.get_virtual_pipeline_model_parallel_rank() # this will be set inside get_model # this_megatron_config = copy.deepcopy(megatron_config) # this_megatron_config.virtual_pipeline_model_parallel_rank = vpp_rank share_embeddings_and_output_weights = getattr(actor_model_config, "tie_word_embeddings", False) parallel_model = get_parallel_model_from_config( config=actor_model_config, megatron_config=megatron_config, pre_process=pre_process, post_process=post_process, share_embeddings_and_output_weights=share_embeddings_and_output_weights, value=False) parallel_model.cuda() return parallel_model # Step 3: initialize the megatron model if self._is_actor and self._is_rollout: # Initialize the 3D HybridEngine hybrid_engine = AllGatherPPModel(model_provider=megatron_actor_model_provider) # Fetch the model at current rank actor_module = hybrid_engine.this_rank_models if isinstance(actor_module, nn.ModuleList): actor_module = [actor_module[0]] if self.config.actor.load_weight: load_megatron_model_weights(self.config, actor_model_config, actor_module, params_dtype=megatron_config.params_dtype, is_value_model=False) if self.rank == 0: print_model_size(actor_module[0]) log_gpu_memory_usage('After AllGatherPPModel init', logger=logger) elif self._is_ref: print(f'self.config.ref.load_weight: {self.config.ref.load_weight}') ref_module = get_model(model_provider_func=megatron_actor_model_provider, model_type=ModelType.encoder_or_decoder, wrap_with_ddp=False) # ref_module = nn.ModuleList(ref_module) if self.config.ref.load_weight: # should align with the actor: assert self.config.actor.load_weight == self.config.ref.load_weight print(f'load ref weight start') load_megatron_model_weights(self.config, actor_model_config, ref_module, params_dtype=megatron_config.params_dtype, is_value_model=False) log_gpu_memory_usage('After ref module init', logger=logger) return ref_module, actor_model_config # TODO: add more optimizer args into config if self._is_actor: optim_config = init_megatron_optim_config(optim_config) actor_optimizer = get_megatron_optimizer(model=actor_module, config=optim_config) else: optim_config = None actor_optimizer = None log_gpu_memory_usage('After actor optimizer init', logger=logger) return actor_module, hybrid_engine, actor_optimizer, actor_model_config, optim_config def _build_rollout(self): if self.config.rollout.name == 'vllm': from verl.workers.rollout.vllm_rollout import vLLMRollout, vllm_mode from verl.workers.sharding_manager import MegatronVLLMShardingManager from verl.utils.model import normalize_pp_vpp_params # NOTE(sgm): If the QKV and gate_up projection layer are concate together in actor, # we will reorganize their weight format when resharding from actor to rollout. layer_name_mapping = { "qkv_layer_name": self.config.rollout.layer_name_map.get("qkv_layer_name", "qkv"), "gate_proj_layer_name": self.config.rollout.layer_name_map.get("gate_proj_layer_name", "linear_fc1.weight"), } # reshard the weight partition from actor to rollout to initialize the rollout class # create a new cuda space for parameters not in this pp rank self.hybrid_engine.load_params_to_cuda() # broadcast the parameters from pp rank to other ranks self.hybrid_engine.allgather_params() # obtain name to parameters in pp/vpp params = self.hybrid_engine.get_all_params() # update the param name for the params = normalize_pp_vpp_params(params=params, num_hidden_layers=self.actor_model_config.num_hidden_layers, layer_name='layers') assert vllm_mode == 'customized', "Support for vllm>=0.7 for Megatron-LM backend has not been implemented yet." rollout = vLLMRollout(actor_module=params, config=self.config.rollout, tokenizer=self.tokenizer, model_hf_config=self.actor_model_config, train_tp=mpu.get_tensor_model_parallel_world_size()) log_gpu_memory_usage('After building vllm rollout', logger=logger) # perform weight resharding between actor and rollout sharding_manager = MegatronVLLMShardingManager(module=self.hybrid_engine, inference_engine=rollout.inference_engine, model_config=self.actor_model_config, layer_name_mapping=layer_name_mapping) log_gpu_memory_usage('After building sharding manager', logger=logger) else: NotImplementedError('Only vllmRollout is supported with Megatron now') return rollout, sharding_manager @register(dispatch_mode=Dispatch.ONE_TO_ALL) def init_model(self): if self.config.model.get('external_lib', None) is not None: # This is used to import external_lib into the huggingface systems import importlib importlib.import_module(self.config.model.external_lib) from omegaconf import OmegaConf from verl.utils.torch_dtypes import PrecisionType override_model_config = OmegaConf.to_container(self.config.model.get('override_config', OmegaConf.create())) torch_dtype = torch.bfloat16 megatron_config = OmegaConf.create({ 'sequence_parallel': self.config.actor.megatron.get('sequence_parallel', True), 'param_dtype': PrecisionType.to_str(torch_dtype), 'tensor_model_parallel_size': mpu.get_tensor_model_parallel_world_size(), 'pipeline_model_parallel_rank': mpu.get_pipeline_model_parallel_rank(), 'pipeline_model_parallel_size': mpu.get_pipeline_model_parallel_world_size(), 'virtual_pipeline_model_parallel_rank': mpu.get_virtual_pipeline_model_parallel_rank(), 'virtual_pipeline_model_parallel_size': mpu.get_virtual_pipeline_model_parallel_world_size() }) megatron_config = init_model_parallel_config(megatron_config) if self._is_actor or self._is_rollout: # we need the model for actor and rollout if self._is_actor: optim_config = self.config.actor.optim else: optim_config = None self.actor_module, self.hybrid_engine, self.actor_optimizer, \ self.actor_model_config, self.actor_optim_config = self._build_model_optimizer( model_path=self.config.model.path, megatron_config=megatron_config, optim_config=optim_config, override_model_config=override_model_config, ) if self._is_actor: self.actor = MegatronPPOActor(config=self.config.actor, model_config=self.actor_model_config, megatron_config=megatron_config, actor_module=self.actor_module, actor_optimizer=self.actor_optimizer, actor_optimizer_config=self.actor_optim_config) if self._is_rollout: self.rollout, self.sharding_manager = self._build_rollout() if self._is_ref: self.ref_module, self.ref_model_config = self._build_model_optimizer( model_path=self.config.model.path, megatron_config=megatron_config, optim_config=None, override_model_config=override_model_config, ) self.ref_policy = MegatronPPOActor(config=self.config.ref, model_config=self.ref_model_config, megatron_config=megatron_config, actor_module=self.ref_module, actor_optimizer=None, actor_optimizer_config=None) if self._is_actor: self.flops_counter = FlopsCounter(self.actor_model_config) torch.cuda.empty_cache() @register(dispatch_mode=Dispatch.MEGATRON_COMPUTE_PROTO) def update_actor(self, data: DataProto): assert self._is_actor data.batch = data.batch.cuda() log_gpu_memory_usage('Before update policy', logger=logger) dataloader = self.actor.make_minibatch_iterator(data=data) with Timer(name='update_policy', logger=None) as timer: metrics = self.actor.update_policy(dataloader=dataloader) delta_time = timer.last global_num_tokens = data.meta_info['global_token_num'] estimated_flops, promised_flops = self.flops_counter.estimate_flops(global_num_tokens, delta_time) metrics['mfu/actor'] = estimated_flops * self.config.actor.ppo_epochs / promised_flops / self.world_size log_gpu_memory_usage('After update policy', logger=logger) # TODO: here, we should return all metrics output = DataProto(meta_info={'metrics': metrics}) output = output.to('cpu') torch.cuda.empty_cache() return output @register(dispatch_mode=Dispatch.MEGATRON_PP_AS_DP_PROTO) def generate_sequences(self, prompts: DataProto): assert self._is_rollout prompts.batch = prompts.batch.cuda() meta_info = { 'eos_token_id': self.generation_config.eos_token_id if self.generation_config is not None else self.tokenizer.eos_token_id, 'pad_token_id': self.generation_config.pad_token_id if self.generation_config is not None else self.tokenizer.pad_token_id, } prompts.meta_info.update(meta_info) with self.sharding_manager: log_gpu_memory_usage('After entering sharding manager', logger=logger) prompts = self.sharding_manager.preprocess_data(prompts) output = self.rollout.generate_sequences(prompts=prompts) log_gpu_memory_usage('After rollout generation', logger=logger) output = self.sharding_manager.postprocess_data(output) output = output.to('cpu') # clear kv cache torch.cuda.empty_cache() log_gpu_memory_usage('After recompute log prob', logger=logger) return output @register(dispatch_mode=Dispatch.MEGATRON_COMPUTE_PROTO) def compute_ref_log_prob(self, data: DataProto): data = data.to('cuda') assert self._is_ref if self._is_offload_param: load_megatron_param_and_grad(self.ref_module, torch.cuda.current_device(), self._is_offload_grad) micro_batch_size = self.config.rollout.log_prob_micro_batch_size_per_gpu data.meta_info['micro_batch_size'] = micro_batch_size data.meta_info['temperature'] = self.config.rollout.temperature output = self.ref_policy.compute_log_prob(data=data) output = DataProto.from_dict(tensors={'ref_log_prob': output}) output = output.to('cpu') if self._is_offload_param: offload_megatron_param_and_grad(self.ref_module, self._is_offload_grad) torch.cuda.empty_cache() return output @register(dispatch_mode=Dispatch.MEGATRON_COMPUTE_PROTO) def compute_log_prob(self, data: DataProto): assert self._is_actor data = data.to('cuda') output = data # we should always recompute old_log_probs when it is HybridEngine output.meta_info['micro_batch_size'] = self.config.rollout.log_prob_micro_batch_size_per_gpu output.meta_info['temperature'] = self.config.rollout.temperature old_log_probs = self.actor.compute_log_prob(data=output) output.batch['old_log_probs'] = old_log_probs output = output.to('cpu') # clear kv cache torch.cuda.empty_cache() log_gpu_memory_usage('After recompute log prob', logger=logger) return output @register(dispatch_mode=Dispatch.ONE_TO_ALL) def load_checkpoint(self, checkpoint_path, **kwargs): pass @register(dispatch_mode=Dispatch.ONE_TO_ALL) def load_pretrained_model(self, checkpoint_path, **kwargs): pass @register(dispatch_mode=Dispatch.ONE_TO_ALL) def save_checkpoint(self, checkpoint_path, **kwargs): assert self._is_actor pass class CriticWorker(MegatronWorker): def __init__(self, config): super().__init__() self.config = config # NOTE(sgm): We utilize colocate WorkerGroup by default. # As a result, Workers for different model share the same process. # Therefore, we only require one distribute initialization. # To utilize different parallel startegy in different models: # 1, users should disable WorkerDict; 2.assign different ResourcePool to different models, # 3. and apply the following patch in ray==2.10, https://github.com/ray-project/ray/pull/44385 if not torch.distributed.is_initialized(): rank = int(os.environ['LOCAL_RANK']) torch.distributed.init_process_group(backend="nccl") torch.cuda.set_device(rank) if self.config.megatron.sequence_parallel: os.environ['CUDA_DEVICE_MAX_CONNECTIONS'] = '1' mpu.initialize_model_parallel( tensor_model_parallel_size=self.config.megatron.tensor_model_parallel_size, pipeline_model_parallel_size=self.config.megatron.pipeline_model_parallel_size, virtual_pipeline_model_parallel_size=None, pipeline_model_parallel_split_rank=None, use_sharp=False, context_parallel_size=1, expert_model_parallel_size=1, nccl_communicator_config_path=None, ) set_random_seed(seed=self.config.megatron.seed) # normalize config self.config.ppo_mini_batch_size //= mpu.get_data_parallel_world_size() if self.config.get('ppo_micro_batch_size', None): self.config.ppo_micro_batch_size //= mpu.get_data_parallel_world_size() self.config.ppo_micro_batch_size_per_gpu = self.config.ppo_micro_batch_size # TODO(sgm): support critic model offload def _build_critic_model_optimizer(self, model_path, megatron_config: ModelParallelConfig, optim_config, override_model_config, enable_gradient_checkpointing=False): from megatron.core.models.gpt.gpt_model import ModelType from verl.utils.model import print_model_size, update_model_config from verl.utils.megatron.optimizer import get_megatron_optimizer from verl.utils.megatron_utils import get_model, init_megatron_optim_config, init_model_parallel_config from transformers import AutoModelForCausalLM, AutoTokenizer, AutoConfig # Step 1: initialize the tokenizer local_path = copy_to_local(model_path) self.tokenizer = hf_tokenizer(local_path) # Step 2: get the actor_model_config critic_model_config = AutoConfig.from_pretrained(local_path) override_config_kwargs = { 'bos_token_id': self.tokenizer.bos_token_id, 'eos_token_id': self.tokenizer.eos_token_id, 'pad_token_id': self.tokenizer.pad_token_id, } override_config_kwargs.update(override_model_config) update_model_config(critic_model_config, override_config_kwargs=override_config_kwargs) if self.rank == 0: print(f'Model config after override: {critic_model_config}') def megatron_critic_model_provider(pre_process, post_process): from verl.utils.model import get_parallel_model_from_config # TODO: support vpp here # vpp_rank = mpu.get_virtual_pipeline_model_parallel_rank() # this will be set inside get_model # this_megatron_config = copy.deepcopy(megatron_config) # this_megatron_config.virtual_pipeline_model_parallel_rank = vpp_rank parallel_model = get_parallel_model_from_config(config=critic_model_config, megatron_config=megatron_config, pre_process=pre_process, post_process=post_process, share_embeddings_and_output_weights=False, value=True) parallel_model.cuda() return parallel_model # Step 3: initialize the megatron model critic_module = get_model(model_provider_func=megatron_critic_model_provider, model_type=ModelType.encoder_or_decoder, wrap_with_ddp=True) # note that here critic_module will be a list to be compatible with the construction of interleaved pp (vpp). # but here, we do not use pp (vpp) yet. For simplicity, we remove the list # critic_module = nn.ModuleList(critic_module) if self.config.load_weight: load_megatron_model_weights(self.config, critic_model_config, critic_module, params_dtype=megatron_config.params_dtype, is_value_model=True) if self.rank == 0: print_model_size(critic_module[0]) # TODO: add more optimizer args into config optim_config = init_megatron_optim_config(optim_config) critic_optimizer = get_megatron_optimizer(model=critic_module, config=optim_config) torch.cuda.empty_cache() return critic_module, critic_optimizer, critic_model_config, optim_config @register(dispatch_mode=Dispatch.ONE_TO_ALL) def init_model(self): # create critic from omegaconf import OmegaConf from verl.utils.torch_dtypes import PrecisionType if self.config.model.get('external_lib', None) is not None: # This is used to import external_lib into the huggingface systems import importlib importlib.import_module(self.config.model.external_lib) override_model_config = OmegaConf.to_container(self.config.model.get('override_config', OmegaConf.create())) torch_dtype = torch.bfloat16 megatron_config = OmegaConf.create({ 'sequence_parallel': self.config.megatron.get('sequence_parallel', True), 'param_dtype': PrecisionType.to_str(torch_dtype), 'tensor_model_parallel_size': mpu.get_tensor_model_parallel_world_size(), 'pipeline_model_parallel_rank': mpu.get_pipeline_model_parallel_rank(), 'pipeline_model_parallel_size': mpu.get_pipeline_model_parallel_world_size(), 'virtual_pipeline_model_parallel_rank': mpu.get_virtual_pipeline_model_parallel_rank(), 'virtual_pipeline_model_parallel_size': mpu.get_virtual_pipeline_model_parallel_world_size() }) megatron_config = init_model_parallel_config(megatron_config) critic_module, critic_optimizer, critic_model_config, critic_optimizer_config = self._build_critic_model_optimizer( model_path=self.config.model.path, megatron_config=megatron_config, optim_config=self.config.optim, override_model_config=override_model_config) self.critic = MegatronPPOCritic(config=self.config, model_config=critic_model_config, megatron_config=megatron_config, critic_module=critic_module, critic_optimizer=critic_optimizer, critic_optimizer_config=critic_optimizer_config) self.flops_counter = FlopsCounter(critic_model_config) @register(dispatch_mode=Dispatch.MEGATRON_COMPUTE_PROTO) def compute_values(self, data: DataProto): data = data.to('cuda') values = self.critic.compute_values(data=data) output = DataProto.from_dict(tensors={'values': values}) output = output.to('cpu') return output @register(dispatch_mode=Dispatch.MEGATRON_COMPUTE_PROTO) def update_critic(self, data: DataProto): data = data.to('cuda') dataloader = self.critic.make_minibatch_iterator(data) with Timer(name='update_critic', logger=None) as timer: metrics = self.critic.update_critic(dataloader=dataloader) delta_time = timer.last global_num_tokens = data.meta_info['global_token_num'] estimated_flops, promised_flops = self.flops_counter.estimate_flops(global_num_tokens, delta_time) metrics['mfu/critic'] = estimated_flops * self.config.ppo_epochs / promised_flops / self.world_size output = DataProto(batch=None, meta_info={'metrics': metrics}) output = output.to('cpu') return output @register(dispatch_mode=Dispatch.ONE_TO_ALL) def load_checkpoint(self, checkpoint_path, **kwargs): pass @register(dispatch_mode=Dispatch.ONE_TO_ALL) def save_checkpoint(self, checkpoint_path, **kwargs): pass class RewardModelWorker(MegatronWorker): """ Note that we only implement the reward model that is subclass of AutoModelForSequenceClassification. """ def __init__(self, config): super().__init__() self.config = config # NOTE(sgm): We utilize colocate WorkerGroup by default. # As a result, Workers for different model share the same process. # Therefore, we only require one distribute initialization. # To utilize different parallel startegy in different models: # 1, users should disable WorkerDict; 2.assign different ResourcePool to different models, # 3. and apply the following patch in ray==2.10, https://github.com/ray-project/ray/pull/44385 if not torch.distributed.is_initialized(): rank = int(os.environ['LOCAL_RANK']) torch.distributed.init_process_group(backend="nccl") torch.cuda.set_device(rank) if self.config.megatron.sequence_parallel: os.environ['CUDA_DEVICE_MAX_CONNECTIONS'] = '1' mpu.initialize_model_parallel( tensor_model_parallel_size=self.config.megatron.tensor_model_parallel_size, pipeline_model_parallel_size=self.config.megatron.pipeline_model_parallel_size, virtual_pipeline_model_parallel_size=None, pipeline_model_parallel_split_rank=None, use_sharp=False, context_parallel_size=1, expert_model_parallel_size=1, nccl_communicator_config_path=None, ) set_random_seed(seed=self.config.megatron.seed) # normalize config if self.config.micro_batch_size is not None: self.config.micro_batch_size //= mpu.get_data_parallel_world_size() self.config.micro_batch_size_per_gpu = self.config.micro_batch_size def _build_rm_model(self, model_path, megatron_config: ModelParallelConfig, override_model_config): from megatron.core.models.gpt.gpt_model import ModelType from verl.utils.model import print_model_size, update_model_config from verl.utils.megatron_utils import get_model from transformers import AutoModelForCausalLM, AutoTokenizer, AutoConfig # Step 1: initialize the tokenizer local_path = copy_to_local(model_path) self.tokenizer = hf_tokenizer(local_path) # Step 2: get the actor_model_config rm_model_config = AutoConfig.from_pretrained(local_path) override_config_kwargs = { 'bos_token_id': self.tokenizer.bos_token_id, 'eos_token_id': self.tokenizer.eos_token_id, 'pad_token_id': self.tokenizer.pad_token_id, } override_config_kwargs.update(override_model_config) update_model_config(rm_model_config, override_config_kwargs=override_config_kwargs) if self.rank == 0: print(f'Model config after override: {rm_model_config}') def megatron_rm_model_provider(pre_process, post_process): from verl.utils.model import get_parallel_model_from_config # vpp is not supported yet because it will hang for some reason. Need debugging vpp_rank = mpu.get_virtual_pipeline_model_parallel_rank() # this will be set inside get_model # this_megatron_config = copy.deepcopy(megatron_config) # this_megatron_config.virtual_pipeline_model_parallel_rank = vpp_rank parallel_model = get_parallel_model_from_config(config=rm_model_config, megatron_config=megatron_config, pre_process=pre_process, post_process=post_process, share_embeddings_and_output_weights=False, value=True) parallel_model.cuda() return parallel_model # Step 3: initialize the megatron model reward_model = get_model(model_provider_func=megatron_rm_model_provider, model_type=ModelType.encoder_or_decoder, wrap_with_ddp=False) # note that here critic_module will be a list to be compatible with the construction of interleaved pp (vpp). # but here, we do not use pp (vpp) yet. For simplicity, we remove the list # reward_model = nn.ModuleList(reward_model) if self.config.load_weight: load_megatron_model_weights(self.config, rm_model_config, reward_model, params_dtype=megatron_config.params_dtype, is_value_model=True) # TODO: add more optimizer args into config torch.cuda.empty_cache() return reward_model, rm_model_config @register(dispatch_mode=Dispatch.ONE_TO_ALL) def init_model(self): # create critic from omegaconf import OmegaConf from verl.utils.torch_dtypes import PrecisionType from transformers import AutoTokenizer if self.config.model.get('external_lib', None) is not None: # This is used to import external_lib into the huggingface systems import importlib importlib.import_module(self.config.model.external_lib) override_model_config = OmegaConf.to_container(self.config.model.get('override_config', OmegaConf.create())) sft_tokenizer_local_path = copy_to_local(self.config.model.input_tokenizer) sft_tokenizer = hf_tokenizer(sft_tokenizer_local_path) rm_tokenizer_path = self.config.model.get('rm_tokenizer', None) rm_tokenizer = None if rm_tokenizer_path is not None: rm_tokenizer_local_path = copy_to_local(rm_tokenizer_path) rm_tokenizer = hf_tokenizer(rm_tokenizer_local_path) torch_dtype = torch.bfloat16 megatron_config = OmegaConf.create({ 'sequence_parallel': self.config.megatron.get('sequence_parallel', True), 'param_dtype': PrecisionType.to_str(torch_dtype), 'tensor_model_parallel_size': mpu.get_tensor_model_parallel_world_size(), 'pipeline_model_parallel_rank': mpu.get_pipeline_model_parallel_rank(), 'pipeline_model_parallel_size': mpu.get_pipeline_model_parallel_world_size(), 'virtual_pipeline_model_parallel_rank': mpu.get_virtual_pipeline_model_parallel_rank(), 'virtual_pipeline_model_parallel_size': mpu.get_virtual_pipeline_model_parallel_world_size() }) megatron_config = init_model_parallel_config(megatron_config) reward_model_module, reward_model_config = self._build_rm_model( model_path=self.config.model.path, megatron_config=megatron_config, override_model_config=override_model_config, ) # FIXME(sgm): reward model param offload is implemented in MegatronRewardModel # should be implemented in workers self.rm = MegatronRewardModel(config=self.config, reward_model_module=reward_model_module, model_config=reward_model_config, megatron_config=megatron_config, sft_tokenizer=sft_tokenizer, rm_tokenizer=rm_tokenizer) # TODO: reward model use itself tokenizer instead of sft tokenizer # the input_ids, responses, attention_mask and position_ids may be different! @register(dispatch_mode=Dispatch.MEGATRON_COMPUTE_PROTO) def compute_rm_score(self, data: DataProto): data.batch = data.batch.cuda() output = self.rm.compute_reward(data) output = output.to('cpu') return output ================================================ FILE: verl/workers/reward_manager/__init__.py ================================================ # Copyright 2024 PRIME team and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from .naive import NaiveRewardManager from .prime import PrimeRewardManager ================================================ FILE: verl/workers/reward_manager/naive.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from verl import DataProto from verl.utils.reward_score import _default_compute_score import torch class NaiveRewardManager: """The reward manager. """ def __init__(self, config, tokenizer, num_examine, compute_score=None) -> None: self.tokenizer = tokenizer self.num_examine = num_examine # the number of batches of decoded responses to print to the console self.compute_score = compute_score or _default_compute_score self.config = config def __call__(self, data: DataProto): """We will expand this function gradually based on the available datasets""" # If there is rm score, we directly return rm score. Otherwise, we compute via rm_score_fn if 'rm_scores' in data.batch.keys(): return data.batch['rm_scores'] reward_tensor = torch.zeros_like(data.batch['responses'], dtype=torch.float32) already_print_data_sources = {} for i in range(len(data)): data_item = data[i] # DataProtoItem prompt_ids = data_item.batch['prompts'] prompt_length = prompt_ids.shape[-1] valid_prompt_length = data_item.batch['attention_mask'][:prompt_length].sum() valid_prompt_ids = prompt_ids[-valid_prompt_length:] response_ids = data_item.batch['responses'] valid_response_length = data_item.batch['attention_mask'][prompt_length:].sum() valid_response_ids = response_ids[:valid_response_length] # decode prompt_str = self.tokenizer.decode(valid_prompt_ids) response_str = self.tokenizer.decode(valid_response_ids) ground_truth = data_item.non_tensor_batch['reward_model']['ground_truth'] data_source = data_item.non_tensor_batch['data_source'] extra_info = data_item.non_tensor_batch.get('extra_info', None) score = self.compute_score( data_source=data_source, solution_str=response_str, ground_truth=ground_truth, extra_info=extra_info, reward_type=self.config.data.reward_type ) if self.config.data.execution_error_penalty and data_item.batch['execution_passes'].item()==0: score-=0.5 reward_tensor[i, valid_response_length - 1] = score if data_source not in already_print_data_sources: already_print_data_sources[data_source] = 0 if already_print_data_sources[data_source] < self.num_examine: already_print_data_sources[data_source] += 1 # print("[prompt]", prompt_str) # print("[response]", response_str) # print("[ground_truth]", ground_truth) # print("[score]", score) return reward_tensor ================================================ FILE: verl/workers/reward_manager/prime.py ================================================ # Copyright 2024 PRIME team and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import asyncio from concurrent.futures import ProcessPoolExecutor from functools import partial import torch from verl import DataProto from verl.utils.reward_score import _default_compute_score async def single_compute_score(evaluation_func, completion, reference, task, task_extra_info, executor, timeout=300.): loop = asyncio.get_running_loop() try: # Ensure process_completion is called properly tasks = [ asyncio.wait_for( loop.run_in_executor( executor, partial(evaluation_func, task, completion, reference, task_extra_info) # Ensure synchronous ), timeout=timeout) ] return await asyncio.gather(*tasks) except asyncio.TimeoutError: print(f"Timeout occurred for completion: {completion}") return None # Default value for timed-out rows except Exception as e: print(f"Error processing completion: {completion[:10]}, Error: {e}") return None # Default value for failed rows async def parallel_compute_score_async(evaluation_func, completions, references, tasks, extra_info=None, num_processes=64): scores = [] with ProcessPoolExecutor(max_workers=num_processes) as executor: if extra_info is None: extra_info = [None] * len(tasks) # Create tasks for all rows tasks_async = [ single_compute_score(evaluation_func, completion, reference, task, task_extra_info, executor, timeout=300.) for completion, reference, task, task_extra_info in zip(completions, references, tasks, extra_info) ] # to prevent very occasional starvation caused by some anomalous programs ( like infinite loop ), the exceptions in async programs will instantly halt the evaluation, and all summoned processes will be killed. try: results = await asyncio.gather(*tasks_async, return_exceptions=False) except: for pid, proc in executor._processes.items(): try: proc.kill() except Exception as kill_err: print('shut down failed: ' + str(kill_err)) raise # Process results for result, completion, reference, task in zip(results, completions, references, tasks): if isinstance(result, Exception) or result is None: # Handle failed or timed-out tasks scores.append(0.0) elif isinstance(result[0], (int, float, bool)): scores.append(float(result[0])) else: scores.append(float(result[0][0])) return scores class PrimeRewardManager: """ The Reward Manager used in https://github.com/PRIME-RL/PRIME """ def __init__(self, tokenizer, num_examine, compute_score=None) -> None: self.tokenizer = tokenizer self.num_examine = num_examine # the number of batches of decoded responses to print to the console self.compute_score = compute_score or _default_compute_score def __call__(self, data: DataProto): """We will expand this function gradually based on the available datasets""" # If there is rm score, we directly return rm score. Otherwise, we compute via rm_score_fn if 'rm_scores' in data.batch.keys(): return data.batch['rm_scores'] reward_tensor = torch.zeros_like(data.batch['responses'], dtype=torch.float32) already_print_data_sources = {} # batched scoring prompt_ids = data.batch['prompts'] prompt_length = prompt_ids.shape[-1] response_ids = data.batch['responses'] valid_response_length = data.batch['attention_mask'][:, prompt_length:].sum(dim=-1) response_str = self.tokenizer.batch_decode(response_ids, skip_special_tokens=True) ground_truth = [data_item.non_tensor_batch['reward_model']['ground_truth'] for data_item in data] data_sources = data.non_tensor_batch['data_source'] extra_info = data.non_tensor_batch.get('extra_info', [None] * len(data_sources)) assert len(response_str) == len(ground_truth) == len(data_sources) == len(extra_info) try: scores = asyncio.run( parallel_compute_score_async(self.compute_score, response_str, ground_truth, data_sources, extra_info, num_processes=64)) except asyncio.TimeoutError as e: print('Global timeout in reward computing! Setting all as 0.') scores = [0. for _ in range(len(response_str))] except Exception as e: print(f"Unexpected error in batched reward computing. Setting all as 0.: {e}") scores = [0. for _ in range(len(response_str))] for i in range(len(data)): data_source = data_sources[i] reward_tensor[i, valid_response_length[i].item() - 1] = scores[i] if data_source not in already_print_data_sources: already_print_data_sources[data_source] = 0 if already_print_data_sources[data_source] < self.num_examine: already_print_data_sources[data_source] += 1 print("[response]", response_str[i]) return reward_tensor ================================================ FILE: verl/workers/reward_model/__init__.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from .base import BasePPORewardModel ================================================ FILE: verl/workers/reward_model/base.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ The base class for reward model """ from abc import ABC, abstractmethod from verl import DataProto class BasePPORewardModel(ABC): def __init__(self, config): self.config = config @abstractmethod def compute_reward(self, data: DataProto) -> DataProto: """Computing reward given input_ids. The transformers should output a tensor with shape [batch_size, sequence_length], and the value at [EOS] mask should be gathered. Args: data: must contain keys "input_ids", "attention_mask" and "position_ids". - input_ids: [batch_size, sequence_length] - attention_mask: [batch_size, sequence_length] - position_ids: [batch_size, sequence_length] Returns: a data pass protocol containing "reward". Only the [EOS] position contains the reward. Other position should have zero reward. Note that this may change in the future if we use dense reward. So, we leave the interface for general case. - reward: [batch_size, sequence_length]. """ pass ================================================ FILE: verl/workers/reward_model/megatron/__init__.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from .reward_model import MegatronRewardModel ================================================ FILE: verl/workers/reward_model/megatron/reward_model.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Megatron Reward Model. """ from tensordict import TensorDict from verl import DataProto import torch import torch.distributed from verl.utils.torch_functional import pad_sequence_to_length from verl.utils.megatron.pipeline_parallel import (compute_transformers_input_shapes, make_batch_generator) from verl import DataProto from verl.utils.torch_functional import broadcast_dict_tensor, split_dict_tensor_into_batches from verl.workers.reward_model.base import BasePPORewardModel from megatron.core import parallel_state as mpu from megatron.core.pipeline_parallel import get_forward_backward_func class MegatronRewardModel(BasePPORewardModel): def __init__(self, config, model_config, reward_model_module: torch.nn.ModuleList, megatron_config, sft_tokenizer=None, rm_tokenizer=None): self.config = config self.reward_model_module = reward_model_module self.megatron_config = megatron_config self.model_config = model_config self.device = 'cuda' self.sft_tokenizer = sft_tokenizer self.rm_tokenizer = rm_tokenizer self.use_different_tokenizer = rm_tokenizer is not None if self.config.param_offload: self.offload_params_to_cpu() def re_encode_by_rm_tokenizer(self, data: DataProto) -> DataProto: assert self.use_different_tokenizer, 're-encode need rm tokenizer not be None!' # need to use rm tokenizer to re-generate input_ids, attention_mask and position_ids # 1. remove pad for each sequence # 2. decode by sft_tokenizer, remove sft system prompts # 3. encode by rm_tokenizer with rm system prompts, get rm_input_ids # 4. generate attention_mask and position_ids input_ids = data.batch['input_ids'] # (bs, seq_len) attention_mask = data.batch['attention_mask'] position_ids = data.batch['position_ids'] ori_values = {'input_ids': input_ids, 'attention_mask': attention_mask, 'position_ids': position_ids} ori_bs, ori_seqlen = input_ids.size(0), input_ids.size(1) input_ids_for_rm = [] attention_mask_for_rm = [] position_ids_for_rm = [] print_decode = True ori_seqlen = ori_seqlen + 128 for id, mask in zip(input_ids, attention_mask): # 1. remove pad for each sequence non_zero_indices = torch.nonzero(mask).view(-1) begin_pos, end_pos = non_zero_indices[0].item(), non_zero_indices[-1].item() valid_id = id[begin_pos:end_pos + 1] # 2. decode by sft_tokenizer, remove sft system prompts decode_result = self.sft_tokenizer.decode(valid_id) # workaround decode_with_rm_chat = decode_result.replace("<|user|>\n", "[INST] ").replace( "\n<|assistant|>\n", " [/INST]").replace(" \n<|assistant|>\n", " [/INST]") + "" if print_decode and torch.distributed.get_rank() == 0: # only print first decode result print(f'device {torch.cuda.current_device()}: sft decode result:\n{decode_result}\n \ \ndevice {torch.cuda.current_device()}: sft decode result with rm chat template:\n{decode_with_rm_chat}\n\n' ) print_decode = False # 3. encode by rm_tokenizer rm_input_ids = self.rm_tokenizer(decode_with_rm_chat, return_tensors='pt')['input_ids'][0].to(input_ids.device) # 4. generate attention_mask and position_ids rm_attention_mask = torch.ones_like(rm_input_ids, device=input_ids.device) cur_seqlen = rm_input_ids.shape[-1] # NOTE(gh): the later reward compute will process the shape (bs, seqlen_pad_128) if cur_seqlen > ori_seqlen: print(f'warninig: rm encode seqlen {cur_seqlen} > sft encode seqlen {ori_seqlen}') rm_input_ids = rm_input_ids[:ori_seqlen] rm_attention_mask = rm_attention_mask[:ori_seqlen] else: # right padding rm_input_ids = pad_sequence_to_length(rm_input_ids, ori_seqlen, self.rm_tokenizer.pad_token_id) rm_attention_mask = pad_sequence_to_length(rm_attention_mask, ori_seqlen, 0) rm_position_ids = torch.arange(0, ori_seqlen, device=input_ids.device) input_ids_for_rm.append(torch.unsqueeze(rm_input_ids, dim=0)) attention_mask_for_rm.append(torch.unsqueeze(rm_attention_mask, dim=0)) position_ids_for_rm.append(torch.unsqueeze(rm_position_ids, dim=0)) input_ids_for_rm = torch.cat(input_ids_for_rm, dim=0) attention_mask_for_rm = torch.cat(attention_mask_for_rm, dim=0) position_ids_for_rm = torch.cat(position_ids_for_rm, dim=0) # (bs, seqlen) will not change, but input_ids, attention_mask and position_ids will change # NOTE(gh): need to replace into origin values after compute reward! data.batch['input_ids'] = input_ids_for_rm data.batch['attention_mask'] = attention_mask_for_rm data.batch['position_ids'] = position_ids_for_rm return data, ori_values @torch.no_grad() def compute_reward(self, data: DataProto) -> DataProto: if self.config.param_offload: self.load_params_to_cuda() if self.use_different_tokenizer: data, ori_values = self.re_encode_by_rm_tokenizer(data) input_ids = data.batch['input_ids'] # (bs, seq_len') attention_mask = data.batch['attention_mask'] position_ids = data.batch['position_ids'] responses = data.batch['responses'] batch_size = responses.size(0) response_length = responses.size(1) with torch.no_grad(): output = self.forward_batch(data) if mpu.is_pipeline_last_stage(ignore_virtual=True): logits = torch.cat([o['logits'] for o in output], dim=0) else: logits = torch.empty( (input_ids.shape[0], input_ids.shape[1]), dtype=torch.bfloat16, # TODO(sgm): check why is bfloat16 device=input_ids.device) # broadcast across pp ranks torch.distributed.broadcast(tensor=logits, src=mpu.get_pipeline_model_parallel_last_rank(), group=mpu.get_pipeline_model_parallel_group(), async_op=False) # (bs, seqlen', hidden_size) -> (bs, seqlen', 1) -> (bs, seqlen') token_level_rewards = logits # find the last token reward ends = attention_mask.cumsum(dim=-1).argmax(dim=-1).view(-1, 1) # (bs, 1) rewards = torch.gather(token_level_rewards, dim=1, index=ends) # (bs, 1) if self.use_different_tokenizer: data.batch.update(ori_values) input_ids = ori_values['input_ids'] attention_mask = ori_values['attention_mask'] position_ids = ori_values['position_ids'] token_level_rewards = rewards.expand(attention_mask.shape[0], attention_mask.shape[1]) # (bs, ori_seqlen) # assign last valid token reward to ori position eos_mask_idx = torch.argmax(position_ids * attention_mask, dim=-1) # (bs,) eos_mask = torch.zeros_like(attention_mask) eos_mask[torch.arange(batch_size), eos_mask_idx] = 1. token_level_rewards = token_level_rewards * eos_mask token_level_rewards = token_level_rewards[:, -response_length:] if self.config.param_offload: self.offload_params_to_cpu() else: # add empty cache after each compute torch.cuda.empty_cache() batch = TensorDict({'rm_scores': token_level_rewards}, batch_size=input_ids.shape[0]) return DataProto(batch=batch) def forward_batch(self, data: DataProto): """ We assume: - The model takes input: (input_ids, attention_mask, position_ids). No rmpad for the input - The communication shape is (total_nnz_pad_to_sp // tp_size, 1, hidden_size) if sequence parallel is enabled """ # broadcast from last pp rank to all other pp ranks # TODO: actually, we just need to control the sampling order. data.batch = data.batch.contiguous() broadcast_dict_tensor(data.batch, src=mpu.get_pipeline_model_parallel_last_rank(), group=mpu.get_pipeline_model_parallel_group()) # split into micro-batches if self.config is not None and 'ppo_micro_batch_size_per_gpu' in self.config: infer_batch_size = self.config.ppo_micro_batch_size_per_gpu else: infer_batch_size = data.batch.batch_size[0] data.batch['attention_mask'] = data.batch['attention_mask'].to(bool) batches = split_dict_tensor_into_batches(data.batch, batch_size=infer_batch_size) n_micro_batch = len(batches) seq_len = batches[0]['input_ids'].shape[1] # compute input shapes for pp stages input_shapes = compute_transformers_input_shapes( batches, meta_info={ 'sequence_parallel': self.megatron_config.sequence_parallel, 'hidden_size': self.model_config.hidden_size }) # compute input shapes for pp stages forward_backward_func = get_forward_backward_func() def loss_func(output): return 1., {'logits': output.logits} def forward_step(batch_iter, model): batch = next(batch_iter) input_ids = batch['input_ids'] attention_mask = batch['attention_mask'] position_ids = batch['position_ids'] output = model(input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids) return output, loss_func # batch should be a list of batches inside micro-batches batch_generator = make_batch_generator(batches, vpp_size=len(self.reward_model_module)) # TODO: we may use the new schedule instead # for flash-attn: (seq_len, batch_size, hidden_size) = (mbs*seq_len, 1, hidden_size) if mpu.get_pipeline_model_parallel_world_size() > 1: losses_reduced = forward_backward_func( forward_step_func=forward_step, data_iterator=batch_generator, model=self.reward_model_module, num_microbatches=n_micro_batch, seq_length=infer_batch_size * seq_len, # no use when input_shapes was set micro_batch_size=1, # no use when input_shapes was set forward_only=True, ) else: losses_reduced = forward_backward_func( forward_step_func=forward_step, data_iterator=batch_generator, model=self.reward_model_module, num_microbatches=n_micro_batch, seq_length=infer_batch_size * seq_len, # in use for pp = 1 micro_batch_size=1, # in use for pp = 1 forward_only=True, ) # loss_reduces contains the stats returned from loss_func return losses_reduced def offload_params_to_cpu(self): if self.device == 'cuda': for reward_model_module in self.reward_model_module: for name, param in reward_model_module.named_parameters(): param.data = param.data.to('cpu', non_blocking=True) self.device = 'cpu' torch.cuda.empty_cache() def load_params_to_cuda(self): if self.device == 'cpu': for reward_model_module in self.reward_model_module: for name, param in reward_model_module.named_parameters(): param.data = param.data.to(torch.cuda.current_device(), non_blocking=True) self.device = 'cuda' ================================================ FILE: verl/workers/rollout/__init__.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from .base import BaseRollout from .naive import NaiveRollout from .hf_rollout import HFRollout __all__ = ["BaseRollout", "NaiveRollout", "HFRollout"] ================================================ FILE: verl/workers/rollout/base.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from abc import ABC, abstractmethod from typing import Iterable, Union from verl import DataProto __all__ = ['BaseRollout'] class BaseRollout(ABC): def __init__(self): """ Args: dataloader: an Iterable of TensorDict that consistently generates prompts. Note that the dataloader should handle when the training stops. """ super().__init__() @abstractmethod def generate_sequences(self, prompts: DataProto) -> DataProto: """Generate sequences""" pass ================================================ FILE: verl/workers/rollout/hf_rollout.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Rollout with huggingface models. TODO: refactor this class. Currently, it will hang when using FSDP HybridShard. We should actually create a single GPU model. Then, get full state_dict and bind the state_dict to the single GPU model. Then, use the single GPU model to perform generation. """ import contextlib import torch import torch.distributed from tensordict import TensorDict from torch import nn from torch.distributed.fsdp import FullyShardedDataParallel as FSDP from verl import DataProto from verl.utils.torch_functional import get_eos_mask from .base import BaseRollout from transformers import GenerationConfig __all__ = ['HFRollout'] class HFRollout(BaseRollout): def __init__(self, module: nn.Module, config): super().__init__() self.config = config self.module = module def generate_sequences(self, prompts: DataProto) -> DataProto: batch_size = prompts.batch.batch_size[0] num_chunks = max(batch_size // self.config.get('micro_batch_size', batch_size), 1) batch_prompts = prompts.chunk(chunks=num_chunks) output = [self._generate_minibatch(p) for p in batch_prompts] output = DataProto.concat(output) return output @torch.no_grad() def _generate_minibatch(self, prompts: DataProto) -> DataProto: idx = prompts.batch['input_ids'] # (bs, prompt_length) attention_mask = prompts.batch['attention_mask'] # left-padded attention_mask position_ids = prompts.batch['position_ids'] # used to construct attention_mask eos_token_id = prompts.meta_info['eos_token_id'] pad_token_id = prompts.meta_info['pad_token_id'] batch_size = idx.size(0) prompt_length = idx.size(1) self.module.eval() param_ctx = contextlib.nullcontext() # make sampling args can be overriden by inputs do_sample = prompts.meta_info.get('do_sample', self.config.do_sample) response_length = prompts.meta_info.get('response_length', self.config.response_length) top_p = prompts.meta_info.get('top_p', self.config.get('top_p', 1.0)) top_k = prompts.meta_info.get('top_k', self.config.get('top_k', 0)) if top_k is None: top_k = 0 top_k = max(0, top_k) # to be compatible with vllm temperature = prompts.meta_info.get('temperature', self.config.temperature) generation_config = GenerationConfig(temperature=temperature, top_p=top_p, top_k=top_k) if isinstance(self.module, FSDP): # recurse need to set to False according to https://github.com/pytorch/pytorch/issues/100069 param_ctx = FSDP.summon_full_params(self.module, writeback=False, recurse=False) with param_ctx: with torch.autocast(device_type='cuda', dtype=torch.bfloat16): output = self.module.generate( input_ids=idx, attention_mask=attention_mask, do_sample=do_sample, max_new_tokens=response_length, # max_length=max_length, eos_token_id=eos_token_id, pad_token_id=pad_token_id, generation_config=generation_config, # renormalize_logits=True, output_scores=False, # this is potentially very large return_dict_in_generate=True, use_cache=True) # TODO: filter out the seq with no answers like ds-chat seq = output.sequences # huggingface generate will stop generating when all the batch reaches [EOS]. # We have to pad to response_length sequence_length = prompt_length + self.config.response_length delta_length = sequence_length - seq.shape[1] if delta_length > 0: delta_tokens = torch.ones(size=(batch_size, delta_length), device=seq.device, dtype=seq.dtype) delta_tokens = pad_token_id * delta_tokens seq = torch.cat((seq, delta_tokens), dim=1) assert seq.shape[1] == sequence_length prompt = seq[:, :prompt_length] # (bs, prompt_length) response = seq[:, prompt_length:] # (bs, response_length) response_length = response.size(1) delta_position_id = torch.arange(1, response_length + 1, device=position_ids.device) delta_position_id = delta_position_id.unsqueeze(0).repeat(batch_size, 1) response_position_ids = position_ids[:, -1:] + delta_position_id position_ids = torch.cat([position_ids, response_position_ids], dim=-1) response_attention_mask = get_eos_mask(response_id=response, eos_token=eos_token_id, dtype=attention_mask.dtype) attention_mask = torch.cat((attention_mask, response_attention_mask), dim=-1) batch = TensorDict( { 'prompts': prompt, 'responses': response, 'input_ids': seq, 'attention_mask': attention_mask, 'position_ids': position_ids }, batch_size=batch_size) # empty cache before compute old_log_prob torch.cuda.empty_cache() self.module.train() return DataProto(batch=batch) ================================================ FILE: verl/workers/rollout/naive/__init__.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from .naive_rollout import NaiveRollout ================================================ FILE: verl/workers/rollout/naive/naive_rollout.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ In single GPU rollout, the sequences are generated directly by sampling from the model. The output will contain 1. output_ids 2. attention_masks (left padding) 3. eos_masks 4. log_probs """ from typing import Iterable, Union import torch import torch.nn.functional as F from tensordict import TensorDict from torch import nn from verl import DataProto from verl.utils.torch_functional import logprobs_from_logits from ..base import BaseRollout __all__ = ['NaiveRollout'] class NaiveRollout(BaseRollout): def __init__(self, module: nn.Module, config): """A naive rollout. It requires the module to be compatible with huggingface APIs. That is: The module should define __call__ to receive input_ids, attention_mask and position_ids. It outputs a structure that contains logits field. Args: module: module here follows huggingface APIs config: DictConfig """ super().__init__() self.config = config self.module = module @torch.no_grad() def generate_sequences(self, prompts: DataProto) -> DataProto: """Generate sequences""" idx = prompts.batch['input_ids'] # (bs, prompt_length) attention_mask = prompts.batch['attention_mask'] # left-padded attention_mask position_ids = prompts.batch['position_ids'] # used to construct attention_mask eos_token_id = prompts.meta_info['eos_token_id'] if isinstance(eos_token, int): eos_token = [eos_token] batch_size = idx.size(0) prompt_length = idx.size(1) self.module.eval() prev_attention_mask = torch.ones(size=(batch_size, 1), dtype=attention_mask.dtype, device=attention_mask.device) logits_lst = [] for _ in range(self.config.response_length): # if the sequence context is growing too long we must crop it at block_size # idx_cond = idx if idx.size(1) <= self.config.block_size else idx[:, -self.config.block_size:] idx_cond = idx # forward the model to get the logits for the index in the sequence # we use huggingface APIs here output = self.module(input_ids=idx_cond, attention_mask=attention_mask, position_ids=position_ids) logits = output.logits # pluck the logits at the final step and scale by desired temperature logits = logits[:, -1, :] / self.config.temperature # (bs, vocab_size) # optionally crop the logits to only the top k options if self.config.top_k is not None: v, _ = torch.topk(logits, min(self.config.top_k, logits.size(-1))) logits[logits < v[:, [-1]]] = -float('Inf') # apply softmax to convert logits to (normalized) probabilities probs = F.softmax(logits, dim=-1) # sample from the distribution if self.config.do_sample: idx_next = torch.multinomial(probs, num_samples=1) else: idx_next = torch.argmax(probs, dim=-1, keepdim=True) attention_mask = torch.cat((attention_mask, prev_attention_mask), dim=-1) for token_id in eos_token_id: prev_attention_mask = torch.logical_and(idx_next != token_id, prev_attention_mask.bool()) prev_attention_mask.to(attention_mask.dtype) position_ids = torch.cat((position_ids, position_ids[:, -1:] + 1), dim=-1) # append sampled index to the running sequence and continue idx = torch.cat((idx, idx_next), dim=1) logits_lst.append(logits) logits = torch.stack(logits_lst, dim=1) # (bs, response_length, vocab_size) prompts = idx[:, :prompt_length] # (bs, prompt_length) response = idx[:, prompt_length:] # (bs, response_length) log_probs = logprobs_from_logits(logits=logits, labels=response) batch = TensorDict( { 'input_ids': prompts, 'responses': response, 'sequences': idx, 'old_log_probs': log_probs, 'attention_mask': attention_mask, 'position_ids': position_ids, }, batch_size=batch_size) self.module.train() return DataProto(batch=batch) ================================================ FILE: verl/workers/rollout/tokenizer.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ The base tokenizer class, required for any hybrid engine based rollout or inference with vLLM. """ from abc import ABC, abstractmethod from typing import Dict, List, Union __all__ = ['HybridEngineBaseTokenizer'] class HybridEngineBaseTokenizer(ABC): """the tokenizer property and function name should align with HF's to meet vllm requirement""" @property @abstractmethod def vocab_size(self): """ `int`: Size of the base vocabulary (without the added tokens). """ pass @property @abstractmethod def pad_token_id(self): """ `Optional[int]`: Id of the padding token in the vocabulary. Returns `None` if the token has not been set. """ pass @property @abstractmethod def eos_token_id(self): """ `Optional[int]`: Id of the end of sentence token in the vocabulary. Returns `None` if the token has not been set. """ pass @property @abstractmethod def all_special_ids(self) -> List[int]: """ `List[int]`: List the ids of the special tokens(`''`, `''`, etc.) mapped to class attributes. """ pass @property @abstractmethod def all_special_tokens(self) -> List[str]: """ `List[str]`: A list of the unique special tokens (`''`, `''`, ..., etc.). Convert tokens of `tokenizers.AddedToken` type to string. """ pass @abstractmethod def encode(self, text): """ Converts a string to a sequence of ids (integer), using the tokenizer and vocabulary. Args: text (`str`, `List[str]` or `List[int]`): The first sequence to be encoded. This can be a string, a list of strings (tokenized string using the `tokenize` method) or a list of integers. text_pair (`str`, `List[str]` or `List[int]`, *optional*): Optional second sequence to be encoded. This can be a string, a list of strings (tokenized string using the `tokenize` method) or a list of integers. """ pass @abstractmethod def decode( self, token_ids: Union[int, List[int], "np.ndarray", "torch.Tensor", "tf.Tensor"], skip_special_tokens: bool = False, clean_up_tokenization_spaces: bool = None, **kwargs, ) -> str: """ Converts a sequence of ids in a string, using the tokenizer and vocabulary with options to remove special tokens and clean up tokenization spaces. Similar to doing `self.convert_tokens_to_string(self.convert_ids_to_tokens(token_ids))`. Args: token_ids (`Union[int, List[int], np.ndarray, torch.Tensor, tf.Tensor]`): List of tokenized input ids. Can be obtained using the `__call__` method. skip_special_tokens (`bool`, *optional*, defaults to `False`): Whether or not to remove special tokens in the decoding. clean_up_tokenization_spaces (`bool`, *optional*): Whether or not to clean up the tokenization spaces. If `None`, will default to `self.clean_up_tokenization_spaces`. kwargs (additional keyword arguments, *optional*): Will be passed to the underlying model specific decode method. Returns: `str`: The decoded sentence. """ pass @abstractmethod def convert_ids_to_tokens(self, ids: Union[int, List[int]], skip_special_tokens: bool = False) -> Union[str, List[str]]: """ Converts a single index or a sequence of indices in a token or a sequence of tokens, using the vocabulary and added tokens. Args: ids (`int` or `List[int]`): The token id (or token ids) to convert to tokens. skip_special_tokens (`bool`, *optional*, defaults to `False`): Whether or not to remove special tokens in the decoding. Returns: `str` or `List[str]`: The decoded token(s). """ pass @abstractmethod def get_added_vocab(self) -> Dict[str, int]: """ Returns the added tokens in the vocabulary as a dictionary of token to index. Results might be different from the fast call because for now we always add the tokens even if they are already in the vocabulary. This is something we should change. Returns: `Dict[str, int]`: The added tokens. """ pass @abstractmethod def convert_tokens_to_string(self, tokens: List[str]) -> str: """ Converts a sequence of tokens in a single string. The most simple way to do it is `" ".join(tokens)` but we often want to remove sub-word tokenization artifacts at the same time. Args: tokens (`List[str]`): The token to join in a string. Returns: `str`: The joined tokens. """ pass @property def is_fast(self): return False ================================================ FILE: verl/workers/rollout/vllm_rollout/__init__.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from importlib.metadata import version, PackageNotFoundError def get_version(pkg): try: return version(pkg) except PackageNotFoundError: return None package_name = 'vllm' package_version = get_version(package_name) if package_version <= '0.6.3': vllm_mode = 'customized' from .vllm_rollout import vLLMRollout from .fire_vllm_rollout import FIREvLLMRollout else: vllm_mode = 'spmd' from .vllm_rollout_spmd import vLLMRollout ================================================ FILE: verl/workers/rollout/vllm_rollout/fire_vllm_rollout.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ The vllm_rollout that can be applied in different backend When working with FSDP: - Use DTensor weight loader (recommended) or HF weight loader - Utilize state_dict from the FSDP to synchronize the weights among tp ranks in vLLM When working with Megatron: - Use Megatron weight loader - During training, only the current pp stage holds the parameters - Before inference, broadcast the parameters of the current pp rank to all other pp ranks (all pp ranks holds all the parameters) - Bind the parameters to the inference engine - Do inference in tp. pp is treated as additional dp - After inference, all the parameters that doesn't belong to this pp rank is freed. """ from typing import List from contextlib import contextmanager from omegaconf import DictConfig import torch import torch.distributed from tensordict import TensorDict from torch import nn from verl import DataProto from verl.utils.torch_functional import get_eos_mask, pad_sequence_to_length from verl.workers.rollout.base import BaseRollout from verl.workers.rollout.vllm_rollout.vllm_rollout import vLLMRollout from verl.third_party.vllm import LLM, vllm_version from verl.third_party.vllm import parallel_state as vllm_ps from vllm import SamplingParams # TODO # 1. support pp in vllm # 2. passing tokenizer is not necessary? no encoding/decoding is happending here # 3. simplify init logics # NOTE(sgm): add for verl. We can optimize it by making the dataloader yield List[int] without padding. def _pre_process_inputs(pad_token_id, prompt_token_ids: torch.Tensor) -> List[int]: # remove the left padding in the prompt token_id # pad_token_id = self.llm_engine.tokenizer.pad_token_id if self.llm_engine.tokenizer.pad_token_id is not None else self.llm_engine.tokenizer.eos_token_id non_pad_index = torch.nonzero(prompt_token_ids != pad_token_id, as_tuple=False)[0][0] token_ids = prompt_token_ids[non_pad_index:].tolist() return token_ids class FIREvLLMRollout(vLLMRollout): def __init__(self, actor_module: nn.Module, config: DictConfig, tokenizer, model_hf_config, **kwargs): """A vLLM rollout. It requires the module is supported by the vllm. Args: module: module here follows huggingface APIs config: DictConfig tokenizer: the task/model tokenizer model_hf_config: the huggingface config to initiallize the generating model in vllm **kwargs: train_tp, for Megatron Backend to initialize hybrid engine (zero redundancy) process group """ super().__init__(actor_module, config, tokenizer, model_hf_config, **kwargs) self.use_fire_sampling = config.get('use_fire_sampling', False) if self.use_fire_sampling: kwargs_0 = kwargs.copy() kwargs_0['temperature'] = 30 kwargs_0['max_tokens'] = 1 if 'top_k' not in kwargs_0 or kwargs_0['top_k'] <= 0: kwargs_0['top_k'] = 16 kwargs['max_tokens'] -= 1 self.sampling_params_0 = SamplingParams(**kwargs_0) @contextmanager def update_sampling_params(self, **kwargs): # update sampling params old_sampling_params_args = {} if kwargs: for key, value in kwargs.items(): if hasattr(self.sampling_params, key): old_value = getattr(self.sampling_params, key) old_sampling_params_args[key] = old_value setattr(self.sampling_params, key, value) if self.use_fire_sampling: old_sampling_params_args_0 = {} if kwargs: for key, value in kwargs.items(): if hasattr(self.sampling_params_0, key): old_value = getattr(self.sampling_params_0, key) old_sampling_params_args_0[key] = old_value setattr(self.sampling_params_0, key, value) yield # roll back to previous sampling params # if len(old_sampling_params_args): for key, value in old_sampling_params_args.items(): setattr(self.sampling_params, key, value) if self.use_fire_sampling: for key, value in old_sampling_params_args_0.items(): setattr(self.sampling_params_0, key, value) @torch.no_grad() def generate_sequences(self, prompts: DataProto, **kwargs) -> DataProto: # rebuild vllm cache engine if self.config.free_cache_engine: self.inference_engine.init_cache_engine() idx = prompts.batch['input_ids'] # (bs, prompt_length) # left-padded attention_mask attention_mask = prompts.batch['attention_mask'] position_ids = prompts.batch['position_ids'] # used to construct attention_mask eos_token_id = prompts.meta_info['eos_token_id'] batch_size = idx.size(0) idx_list = [] # parse idx from torch.Tensor to List[List[str]] for i in range(batch_size): idx_list.append(_pre_process_inputs(self.pad_token_id, idx[i])) do_sample = prompts.meta_info.get('do_sample', True) if not do_sample: kwargs = { 'best_of': 1, 'top_p': 1.0, 'top_k': -1, 'min_p': 0.0, 'temperature': 0, 'n': 1 # if greedy, only 1 response } if not self.use_fire_sampling: # users can customize different sampling_params at different run with self.update_sampling_params(**kwargs): output = self.inference_engine.generate( prompts=None, # because we have already convert it to prompt token id sampling_params=self.sampling_params, prompt_token_ids=idx_list, use_tqdm=False) response = output[0].to(idx.device) # (bs, response_length) log_probs = output[1].to(idx.device) # (bs, response_length) else: with self.update_sampling_params(**kwargs): output_0 = self.inference_engine.generate( prompts=None, # because we have already convert it to prompt token id sampling_params=self.sampling_params_0, prompt_token_ids=idx_list, use_tqdm=False) new_idx_list = [] for i in range(batch_size): new_idx_list.append(idx_list[i] + output_0[0][i].tolist()) output = self.inference_engine.generate( prompts=None, # because we have already convert it to prompt token id sampling_params=self.sampling_params, prompt_token_ids=new_idx_list, use_tqdm=False) response = torch.cat([output_0[0], output[0]], dim=1).to(idx.device) # (bs, response_length) log_probs = torch.cat([output_0[1], output[1]], dim=1).to(idx.device) # (bs, response_length) if response.shape[1] < self.config.response_length: response = pad_sequence_to_length(response, self.config.response_length, self.pad_token_id) log_probs = pad_sequence_to_length(log_probs, self.config.response_length, self.pad_token_id) if self.config.n > 1 and do_sample: idx = idx.repeat_interleave(self.config.n, dim=0) attention_mask = attention_mask.repeat_interleave(self.config.n, dim=0) position_ids = position_ids.repeat_interleave(self.config.n, dim=0) batch_size = batch_size * self.config.n seq = torch.cat([idx, response], dim=-1) response_length = response.size(1) delta_position_id = torch.arange(1, response_length + 1, device=position_ids.device) delta_position_id = delta_position_id.unsqueeze(0).repeat(batch_size, 1) # TODO(sgm): fix position_ids on right_pad # prompt: left pad + response: right pad # attention_mask: [0,0,0,0,1,1,1,1, | 1,1,1,0,0,0,0,0] # position_ids: [0,0,0,0,0,1,2,3, | 4,5,6,7,8,9,10,11] response_position_ids = position_ids[:, -1:] + delta_position_id position_ids = torch.cat([position_ids, response_position_ids], dim=-1) response_attention_mask = get_eos_mask(response_id=response, eos_token=eos_token_id, dtype=attention_mask.dtype) attention_mask = torch.cat((attention_mask, response_attention_mask), dim=-1) # all the tp ranks should contain the same data here. data in all ranks are valid batch = TensorDict( { 'prompts': idx, 'responses': response, 'input_ids': seq, # here input_ids become the whole sentences # 'old_log_probs': log_probs, # we will recompute old log prob with actor 'attention_mask': attention_mask, 'position_ids': position_ids }, batch_size=batch_size) # free vllm cache engine if self.config.free_cache_engine: self.inference_engine.free_cache_engine() return DataProto(batch=batch) ================================================ FILE: verl/workers/rollout/vllm_rollout/qwen_agent/code/code_interpreter.py ================================================ import base64 import io import json import logging import os import queue import re import subprocess import sys import time import traceback import uuid import matplotlib import PIL.Image from jupyter_client import BlockingKernelClient from utils.code_utils import extract_code WORK_DIR = os.getenv('CODE_INTERPRETER_WORK_DIR', '/tmp/workspace') LAUNCH_KERNEL_PY = """ from ipykernel import kernelapp as app app.launch_new_instance() """ _KERNEL_CLIENTS = {} # Run this fix before jupyter starts if matplotlib cannot render CJK fonts. # And we need to additionally run the following lines in the jupyter notebook. # ```python # import matplotlib.pyplot as plt # plt.rcParams['font.sans-serif'] = ['SimHei'] # plt.rcParams['axes.unicode_minus'] = False # ```` def fix_matplotlib_cjk_font_issue(): local_ttf = os.path.join(os.path.abspath(os.path.join(matplotlib.matplotlib_fname(), os.path.pardir)), 'fonts', 'ttf', 'simhei.ttf') if not os.path.exists(local_ttf): logging.warning( f'Missing font file `{local_ttf}` for matplotlib. It may cause some error when using matplotlib.') def start_kernel(pid): fix_matplotlib_cjk_font_issue() connection_file = os.path.join(WORK_DIR, f'kernel_connection_file_{pid}.json') launch_kernel_script = os.path.join(WORK_DIR, f'launch_kernel_{pid}.py') for f in [connection_file, launch_kernel_script]: if os.path.exists(f): logging.warning(f'{f} already exists') os.remove(f) os.makedirs(WORK_DIR, exist_ok=True) with open(launch_kernel_script, 'w') as fout: fout.write(LAUNCH_KERNEL_PY) kernel_process = subprocess.Popen([ sys.executable, launch_kernel_script, '--IPKernelApp.connection_file', connection_file, '--matplotlib=inline', '--quiet', ], cwd=WORK_DIR) logging.info(f"INFO: kernel process's PID = {kernel_process.pid}") # Wait for kernel connection file to be written while True: if not os.path.isfile(connection_file): time.sleep(0.1) else: # Keep looping if JSON parsing fails, file may be partially written try: with open(connection_file, 'r') as fp: json.load(fp) break except json.JSONDecodeError: pass # Client kc = BlockingKernelClient(connection_file=connection_file) kc.load_connection_file() kc.start_channels() kc.wait_for_ready() return kc def escape_ansi(line): ansi_escape = re.compile(r'(?:\x1B[@-_]|[\x80-\x9F])[0-?]*[ -/]*[@-~]') return ansi_escape.sub('', line) def publish_image_to_local(image_base64: str): image_file = str(uuid.uuid4()) + '.png' local_image_file = os.path.join(WORK_DIR, image_file) png_bytes = base64.b64decode(image_base64) assert isinstance(png_bytes, bytes) bytes_io = io.BytesIO(png_bytes) PIL.Image.open(bytes_io).save(local_image_file, 'png') return local_image_file START_CODE = """ import signal def _m6_code_interpreter_timeout_handler(signum, frame): raise TimeoutError("M6_CODE_INTERPRETER_TIMEOUT") signal.signal(signal.SIGALRM, _m6_code_interpreter_timeout_handler) def input(*args, **kwargs): raise NotImplementedError('Python input() function is disabled.') import os if 'upload_file' not in os.getcwd(): os.chdir("./upload_file/") import math import re import json import seaborn as sns sns.set_theme() import matplotlib import matplotlib.pyplot as plt plt.rcParams['font.sans-serif'] = ['SimHei'] plt.rcParams['axes.unicode_minus'] = False import numpy as np import pandas as pd from sympy import Eq, symbols, solve """ def code_interpreter(action_input_list: list, timeout=30, clear=False): code = '' for action_input in action_input_list: code += (extract_code(action_input) + '\n') fixed_code = [] for line in code.split('\n'): fixed_code.append(line) if line.startswith('sns.set_theme('): fixed_code.append('plt.rcParams["font.sans-serif"] = ["SimHei"]') fixed_code.append('plt.rcParams["axes.unicode_minus"] = False') fixed_code = '\n'.join(fixed_code) if 'def solution()' in fixed_code: fixed_code += '\nsolution()' return _code_interpreter(fixed_code, timeout, clear) def _code_interpreter(code: str, timeout, clear=False): if not code.strip(): return '' if timeout: code = f'signal.alarm({timeout})\n{code}' if clear: code = "get_ipython().run_line_magic('reset', '-f')\n" + START_CODE + code pid = os.getpid() if pid not in _KERNEL_CLIENTS: _KERNEL_CLIENTS[pid] = start_kernel(pid) _code_interpreter(START_CODE, timeout=None) kc = _KERNEL_CLIENTS[pid] kc.wait_for_ready() kc.execute(code) result = '' image_idx = 0 while True: text = '' image = '' finished = False msg_type = 'error' try: msg = kc.get_iopub_msg() msg_type = msg['msg_type'] if msg_type == 'status': if msg['content'].get('execution_state') == 'idle': finished = True elif msg_type == 'execute_result': text = msg['content']['data'].get('text/plain', '') if 'image/png' in msg['content']['data']: image_b64 = msg['content']['data']['image/png'] image_url = publish_image_to_local(image_b64) image_idx += 1 image = '![fig-%03d](%s)' % (image_idx, image_url) elif msg_type == 'display_data': if 'image/png' in msg['content']['data']: image_b64 = msg['content']['data']['image/png'] image_url = publish_image_to_local(image_b64) image_idx += 1 image = '![fig-%03d](%s)' % (image_idx, image_url) else: text = msg['content']['data'].get('text/plain', '') elif msg_type == 'stream': msg_type = msg['content']['name'] # stdout, stderr text = msg['content']['text'] elif msg_type == 'error': text = escape_ansi('\n'.join(msg['content']['traceback'])) if 'M6_CODE_INTERPRETER_TIMEOUT' in text: text = f'Timeout. No response after {timeout} seconds.' except queue.Empty: text = f'Timeout. No response after {timeout} seconds.' finished = True except Exception: text = 'The code interpreter encountered an unexpected error.' logging.warning(''.join(traceback.format_exception(*sys.exc_info()))) finished = True if text: result += f'\n\n{msg_type}:\n\n```\n{text}\n```' if image: result += f'\n\n{image}' if finished: break result = result.lstrip('\n') if timeout: _code_interpreter('signal.alarm(0)', timeout=None) return result def get_multiline_input(hint): print(hint) print('// Press ENTER to make a new line. Press CTRL-D to end input.') lines = [] while True: try: line = input() except EOFError: # CTRL-D break lines.append(line) print('// Input received.') if lines: return '\n'.join(lines) else: return '' if __name__ == '__main__': while True: print(code_interpreter([get_multiline_input('Enter python code:')])) ================================================ FILE: verl/workers/rollout/vllm_rollout/qwen_agent/code/utils/code_utils.py ================================================ import os import re import json5 def replace_upload_fname(text, upload_fname_list): for full_input_fname in upload_fname_list: if full_input_fname not in text and os.path.basename(full_input_fname) in text: text = text.replace(os.path.basename(full_input_fname), full_input_fname) return text def extract_code(text): # Match triple backtick blocks first triple_match = re.search(r'```[^\n]*\n(.+?)```', text, re.DOTALL) # Match single backtick blocks second single_match = re.search(r'`([^`]*)`', text, re.DOTALL) if triple_match: text = triple_match.group(1) elif single_match: text = single_match.group(1) else: try: text = json5.loads(text)['code'] except Exception: pass # If no code blocks found, return original text return text ================================================ FILE: verl/workers/rollout/vllm_rollout/qwen_agent/llm/schema.py ================================================ from typing import List, Literal, Optional, Tuple, Union from pydantic import BaseModel, field_validator, model_validator DEFAULT_SYSTEM_MESSAGE = 'You are a helpful assistant.' ROLE = 'role' CONTENT = 'content' NAME = 'name' SYSTEM = 'system' USER = 'user' ASSISTANT = 'assistant' FUNCTION = 'function' FILE = 'file' IMAGE = 'image' AUDIO = 'audio' VIDEO = 'video' class BaseModelCompatibleDict(BaseModel): def __getitem__(self, item): return getattr(self, item) def __setitem__(self, key, value): setattr(self, key, value) def model_dump(self, **kwargs): if 'exclude_none' not in kwargs: kwargs['exclude_none'] = True return super().model_dump(**kwargs) def model_dump_json(self, **kwargs): if 'exclude_none' not in kwargs: kwargs['exclude_none'] = True return super().model_dump_json(**kwargs) def get(self, key, default=None): try: value = getattr(self, key) if value: return value else: return default except AttributeError: return default def __str__(self): return f'{self.model_dump()}' class FunctionCall(BaseModelCompatibleDict): name: str arguments: str def __init__(self, name: str, arguments: str): super().__init__(name=name, arguments=arguments) def __repr__(self): return f'FunctionCall({self.model_dump()})' class ContentItem(BaseModelCompatibleDict): text: Optional[str] = None image: Optional[str] = None file: Optional[str] = None audio: Optional[str] = None video: Optional[Union[str, list]] = None def __init__(self, text: Optional[str] = None, image: Optional[str] = None, file: Optional[str] = None, audio: Optional[str] = None, video: Optional[Union[str, list]] = None): super().__init__(text=text, image=image, file=file, audio=audio, video=video) @model_validator(mode='after') def check_exclusivity(self): provided_fields = 0 if self.text is not None: provided_fields += 1 if self.image: provided_fields += 1 if self.file: provided_fields += 1 if self.audio: provided_fields += 1 if self.video: provided_fields += 1 if provided_fields != 1: raise ValueError("Exactly one of 'text', 'image', 'file', 'audio', or 'video' must be provided.") return self def __repr__(self): return f'ContentItem({self.model_dump()})' def get_type_and_value(self) -> Tuple[Literal['text', 'image', 'file', 'audio', 'video'], str]: (t, v), = self.model_dump().items() assert t in ('text', 'image', 'file', 'audio', 'video') return t, v @property def type(self) -> Literal['text', 'image', 'file', 'audio', 'video']: t, v = self.get_type_and_value() return t @property def value(self) -> str: t, v = self.get_type_and_value() return v class Message(BaseModelCompatibleDict): role: str content: Union[str, List[ContentItem]] name: Optional[str] = None function_call: Optional[FunctionCall] = None extra: Optional[dict] = None def __init__(self, role: str, content: Optional[Union[str, List[ContentItem]]], name: Optional[str] = None, function_call: Optional[FunctionCall] = None, extra: Optional[dict] = None, **kwargs): if content is None: content = '' super().__init__(role=role, content=content, name=name, function_call=function_call, extra=extra) def __repr__(self): return f'Message({self.model_dump()})' @field_validator('role') def role_checker(cls, value: str) -> str: if value not in [USER, ASSISTANT, SYSTEM, FUNCTION]: raise ValueError(f'{value} must be one of {",".join([USER, ASSISTANT, SYSTEM, FUNCTION])}') return value ================================================ FILE: verl/workers/rollout/vllm_rollout/qwen_agent/log.py ================================================ import logging import os def setup_logger(level=None): if level is None: if os.getenv('QWEN_AGENT_DEBUG', '0').strip().lower() in ('1', 'true'): level = logging.DEBUG else: level = logging.INFO handler = logging.StreamHandler() # Do not run handler.setLevel(level) so that users can change the level via logger.setLevel later formatter = logging.Formatter('%(asctime)s - %(filename)s - %(lineno)d - %(levelname)s - %(message)s') handler.setFormatter(formatter) _logger = logging.getLogger('qwen_agent_logger') _logger.setLevel(level) _logger.addHandler(handler) return _logger logger = setup_logger() ================================================ FILE: verl/workers/rollout/vllm_rollout/qwen_agent/settings.py ================================================ import ast import os from typing import List, Literal # Settings for LLMs DEFAULT_MAX_INPUT_TOKENS: int = int(os.getenv( 'QWEN_AGENT_DEFAULT_MAX_INPUT_TOKENS', 30000)) # The LLM will truncate the input messages if they exceed this limit # Settings for agents MAX_LLM_CALL_PER_RUN: int = int(os.getenv('QWEN_AGENT_MAX_LLM_CALL_PER_RUN', 8)) # Settings for tools DEFAULT_WORKSPACE: str = os.getenv('QWEN_AGENT_DEFAULT_WORKSPACE', 'workspace') # Settings for RAG DEFAULT_MAX_REF_TOKEN: int = int(os.getenv('QWEN_AGENT_DEFAULT_MAX_REF_TOKEN', 20000)) # The window size reserved for RAG materials DEFAULT_PARSER_PAGE_SIZE: int = int(os.getenv('QWEN_AGENT_DEFAULT_PARSER_PAGE_SIZE', 500)) # Max tokens per chunk when doing RAG DEFAULT_RAG_KEYGEN_STRATEGY: Literal['None', 'GenKeyword', 'SplitQueryThenGenKeyword', 'GenKeywordWithKnowledge', 'SplitQueryThenGenKeywordWithKnowledge'] = os.getenv( 'QWEN_AGENT_DEFAULT_RAG_KEYGEN_STRATEGY', 'GenKeyword') DEFAULT_RAG_SEARCHERS: List[str] = ast.literal_eval( os.getenv('QWEN_AGENT_DEFAULT_RAG_SEARCHERS', "['keyword_search', 'front_page_search']")) # Sub-searchers for hybrid retrieval ================================================ FILE: verl/workers/rollout/vllm_rollout/qwen_agent/tools/base.py ================================================ import json import os from abc import ABC, abstractmethod from typing import Dict, List, Optional, Union from qwen_agent.llm.schema import ContentItem from qwen_agent.settings import DEFAULT_WORKSPACE from qwen_agent.utils.utils import has_chinese_chars, json_loads, logger, print_traceback, save_url_to_local_work_dir TOOL_REGISTRY = {} class ToolServiceError(Exception): def __init__(self, exception: Optional[Exception] = None, code: Optional[str] = None, message: Optional[str] = None, extra: Optional[dict] = None): if exception is not None: super().__init__(exception) else: super().__init__(f'\nError code: {code}. Error message: {message}') self.exception = exception self.code = code self.message = message self.extra = extra def register_tool(name, allow_overwrite=False): def decorator(cls): if name in TOOL_REGISTRY: if allow_overwrite: logger.warning(f'Tool `{name}` already exists! Overwriting with class {cls}.') else: raise ValueError(f'Tool `{name}` already exists! Please ensure that the tool name is unique.') if cls.name and (cls.name != name): raise ValueError(f'{cls.__name__}.name="{cls.name}" conflicts with @register_tool(name="{name}").') cls.name = name TOOL_REGISTRY[name] = cls return cls return decorator def is_tool_schema(obj: dict) -> bool: """ Check if obj is a valid JSON schema describing a tool compatible with OpenAI's tool calling. Example valid schema: { "name": "get_current_weather", "description": "Get the current weather in a given location", "parameters": { "type": "object", "properties": { "location": { "type": "string", "description": "The city and state, e.g. San Francisco, CA" }, "unit": { "type": "string", "enum": ["celsius", "fahrenheit"] } }, "required": ["location"] } } """ import jsonschema try: assert set(obj.keys()) == {'name', 'description', 'parameters'} assert isinstance(obj['name'], str) assert obj['name'].strip() assert isinstance(obj['description'], str) assert isinstance(obj['parameters'], dict) assert set(obj['parameters'].keys()) == {'type', 'properties', 'required'} assert obj['parameters']['type'] == 'object' assert isinstance(obj['parameters']['properties'], dict) assert isinstance(obj['parameters']['required'], list) assert set(obj['parameters']['required']).issubset(set(obj['parameters']['properties'].keys())) except AssertionError: return False try: jsonschema.validate(instance={}, schema=obj['parameters']) except jsonschema.exceptions.SchemaError: return False except jsonschema.exceptions.ValidationError: pass return True class BaseTool(ABC): name: str = '' description: str = '' parameters: Union[List[dict], dict] = [] def __init__(self, cfg: Optional[dict] = None): self.cfg = cfg or {} if not self.name: raise ValueError( f'You must set {self.__class__.__name__}.name, either by @register_tool(name=...) or explicitly setting {self.__class__.__name__}.name' ) if isinstance(self.parameters, dict): if not is_tool_schema({'name': self.name, 'description': self.description, 'parameters': self.parameters}): raise ValueError( 'The parameters, when provided as a dict, must confirm to a valid openai-compatible JSON schema.') @abstractmethod def call(self, params: Union[str, dict], **kwargs) -> Union[str, list, dict, List[ContentItem]]: """The interface for calling tools. Each tool needs to implement this function, which is the workflow of the tool. Args: params: The parameters of func_call. kwargs: Additional parameters for calling tools. Returns: The result returned by the tool, implemented in the subclass. """ raise NotImplementedError def _verify_json_format_args(self, params: Union[str, dict], strict_json: bool = False) -> dict: """Verify the parameters of the function call""" if isinstance(params, str): try: if strict_json: params_json: dict = json.loads(params) else: params_json: dict = json_loads(params) except json.decoder.JSONDecodeError: raise ValueError('Parameters must be formatted as a valid JSON!') else: params_json: dict = params if isinstance(self.parameters, list): for param in self.parameters: if 'required' in param and param['required']: if param['name'] not in params_json: raise ValueError('Parameters %s is required!' % param['name']) elif isinstance(self.parameters, dict): import jsonschema jsonschema.validate(instance=params_json, schema=self.parameters) else: raise ValueError return params_json @property def function(self) -> dict: # Bad naming. It should be `function_info`. return { 'name_for_human': self.name_for_human, 'name': self.name, 'description': self.description, 'parameters': self.parameters, 'args_format': self.args_format } @property def name_for_human(self) -> str: return self.cfg.get('name_for_human', self.name) @property def args_format(self) -> str: fmt = self.cfg.get('args_format') if fmt is None: if has_chinese_chars([self.name_for_human, self.name, self.description, self.parameters]): fmt = '此工具的输入应为JSON对象。' else: fmt = 'Format the arguments as a JSON object.' return fmt @property def file_access(self) -> bool: return False class BaseToolWithFileAccess(BaseTool, ABC): def __init__(self, cfg: Optional[Dict] = None): super().__init__(cfg) assert self.name default_work_dir = os.path.join(DEFAULT_WORKSPACE, 'tools', self.name) self.work_dir: str = self.cfg.get('work_dir', default_work_dir) @property def file_access(self) -> bool: return True def call(self, params: Union[str, dict], files: List[str] = None, **kwargs) -> str: # Copy remote files to the working directory: if files: os.makedirs(self.work_dir, exist_ok=True) for file in files: try: save_url_to_local_work_dir(file, self.work_dir) except Exception: print_traceback() # Then do something with the files: # ... ================================================ FILE: verl/workers/rollout/vllm_rollout/qwen_agent/tools/code_interpreter.py ================================================ import asyncio import atexit import base64 import glob import io import json import os import queue import re import shutil import signal import stat import subprocess import sys import time import uuid import threading from pathlib import Path from typing import Dict, List, Optional, Union import json5 from qwen_agent.log import logger from qwen_agent.tools.base import BaseToolWithFileAccess, register_tool from qwen_agent.utils.utils import append_signal_handler, extract_code, has_chinese_chars, print_traceback LAUNCH_KERNEL_PY = """ from ipykernel import kernelapp as app app.launch_new_instance() """ INIT_CODE_FILE = str(Path(__file__).absolute().parent / 'resource' / 'code_interpreter_init_kernel.py') ALIB_FONT_FILE = str(Path(__file__).absolute().parent / 'resource' / 'AlibabaPuHuiTi-3-45-Light.ttf') _KERNEL_CLIENTS: dict = {} _MISC_SUBPROCESSES: Dict[str, subprocess.Popen] = {} def _kill_kernels_and_subprocesses(_sig_num=None, _frame=None): for v in _KERNEL_CLIENTS.values(): v.shutdown() for k in list(_KERNEL_CLIENTS.keys()): del _KERNEL_CLIENTS[k] for v in _MISC_SUBPROCESSES.values(): v.terminate() for k in list(_MISC_SUBPROCESSES.keys()): del _MISC_SUBPROCESSES[k] # Make sure all subprocesses are terminated even if killed abnormally: # If not running in the main thread, (for example run in streamlit) # register a signal would cause a RuntimeError if threading.current_thread() is threading.main_thread(): atexit.register(_kill_kernels_and_subprocesses) append_signal_handler(signal.SIGTERM, _kill_kernels_and_subprocesses) append_signal_handler(signal.SIGINT, _kill_kernels_and_subprocesses) @register_tool('code_interpreter') class CodeInterpreter(BaseToolWithFileAccess): description = 'Python代码沙盒，可用于执行Python代码。' parameters = [{'name': 'code', 'type': 'string', 'description': '待执行的代码', 'required': True}] def __init__(self, cfg: Optional[Dict] = None): super().__init__(cfg) self.work_dir: str = os.getenv('M6_CODE_INTERPRETER_WORK_DIR', self.work_dir) self.work_dir: str = self.cfg.get('work_dir', self.work_dir) self.instance_id: str = str(uuid.uuid4()) _check_deps_for_code_interpreter() @property def args_format(self) -> str: fmt = self.cfg.get('args_format') if fmt is None: if has_chinese_chars([self.name_for_human, self.name, self.description, self.parameters]): fmt = '此工具的输入应为Markdown代码块。' else: fmt = 'Enclose the code within triple backticks (`) at the beginning and end of the code.' return fmt def call(self, params: Union[str, dict], files: List[str] = None, timeout: Optional[int] = 1, **kwargs) -> str: super().call(params=params, files=files) # copy remote files to work_dir try: params = json5.loads(params) code = params['code'] except Exception: code = extract_code(params) if not code.strip(): return '' kernel_id: str = f'{self.instance_id}_{os.getpid()}' if kernel_id in _KERNEL_CLIENTS: kc = _KERNEL_CLIENTS[kernel_id] else: _fix_matplotlib_cjk_font_issue() self._fix_secure_write_for_code_interpreter() kc, subproc = self._start_kernel(kernel_id) with open(INIT_CODE_FILE) as fin: start_code = fin.read() start_code = start_code.replace('{{M6_FONT_PATH}}', repr(ALIB_FONT_FILE)[1:-1]) start_code += '\n%xmode Minimal' self._execute_code(kc, start_code) # logger.info(self._execute_code(kc, start_code)) _KERNEL_CLIENTS[kernel_id] = kc _MISC_SUBPROCESSES[kernel_id] = subproc if timeout: code = f'_M6CountdownTimer.start({timeout})\n{code}' fixed_code = [] for line in code.split('\n'): fixed_code.append(line) if line.startswith('sns.set_theme('): fixed_code.append('plt.rcParams["font.family"] = _m6_font_prop.get_name()') fixed_code = '\n'.join(fixed_code) fixed_code += '\n\n' # Prevent code not executing in notebook due to no line breaks at the end result = self._execute_code(kc, fixed_code) if timeout: self._execute_code(kc, '_M6CountdownTimer.cancel()') return result if result.strip() else 'Finished execution.' def __del__(self): # Recycle the jupyter subprocess: k: str = f'{self.instance_id}_{os.getpid()}' if k in _KERNEL_CLIENTS: _KERNEL_CLIENTS[k].shutdown() del _KERNEL_CLIENTS[k] if k in _MISC_SUBPROCESSES: _MISC_SUBPROCESSES[k].terminate() del _MISC_SUBPROCESSES[k] def _fix_secure_write_for_code_interpreter(self): if 'linux' in sys.platform.lower(): os.makedirs(self.work_dir, exist_ok=True) fname = os.path.join(self.work_dir, f'test_file_permission_{os.getpid()}.txt') if os.path.exists(fname): os.remove(fname) with os.fdopen(os.open(fname, os.O_CREAT | os.O_WRONLY | os.O_TRUNC, 0o0600), 'w') as f: f.write('test') file_mode = stat.S_IMODE(os.stat(fname).st_mode) & 0o6677 if file_mode != 0o0600: os.environ['JUPYTER_ALLOW_INSECURE_WRITES'] = '1' if os.path.exists(fname): os.remove(fname) def _start_kernel(self, kernel_id: str): connection_file = os.path.join(self.work_dir, f'kernel_connection_file_{kernel_id}.json') launch_kernel_script = os.path.join(self.work_dir, f'launch_kernel_{kernel_id}.py') for f in [connection_file, launch_kernel_script]: if os.path.exists(f): logger.info(f'WARNING: {f} already exists') os.remove(f) os.makedirs(self.work_dir, exist_ok=True) with open(launch_kernel_script, 'w') as fout: fout.write(LAUNCH_KERNEL_PY) kernel_process = subprocess.Popen( [ sys.executable, os.path.abspath(launch_kernel_script), '--IPKernelApp.connection_file', os.path.abspath(connection_file), '--matplotlib=inline', '--quiet', ], cwd=os.path.abspath(self.work_dir), ) logger.info(f"INFO: kernel process's PID = {kernel_process.pid}") # Wait for kernel connection file to be written while True: if not os.path.isfile(connection_file): time.sleep(0.1) else: # Keep looping if JSON parsing fails, file may be partially written try: with open(connection_file, 'r') as fp: json.load(fp) break except json.JSONDecodeError: pass # Client from jupyter_client import BlockingKernelClient kc = BlockingKernelClient(connection_file=connection_file) asyncio.set_event_loop_policy(AnyThreadEventLoopPolicy()) kc.load_connection_file() kc.start_channels() kc.wait_for_ready() return kc, kernel_process def _execute_code(self, kc, code: str) -> str: kc.wait_for_ready() kc.execute(code) result = '' image_idx = 0 while True: text = '' image = '' finished = False msg_type = 'error' try: msg = kc.get_iopub_msg() msg_type = msg['msg_type'] if msg_type == 'status': if msg['content'].get('execution_state') == 'idle': finished = True elif msg_type == 'execute_result': text = msg['content']['data'].get('text/plain', '') if 'image/png' in msg['content']['data']: image_b64 = msg['content']['data']['image/png'] image_url = self._serve_image(image_b64) image_idx += 1 image = '![fig-%03d](%s)' % (image_idx, image_url) elif msg_type == 'display_data': if 'image/png' in msg['content']['data']: image_b64 = msg['content']['data']['image/png'] image_url = self._serve_image(image_b64) image_idx += 1 image = '![fig-%03d](%s)' % (image_idx, image_url) else: text = msg['content']['data'].get('text/plain', '') elif msg_type == 'stream': msg_type = msg['content']['name'] # stdout, stderr text = msg['content']['text'] elif msg_type == 'error': text = _escape_ansi('\n'.join(msg['content']['traceback'])) if 'M6_CODE_INTERPRETER_TIMEOUT' in text: text = 'Timeout: Code execution exceeded the time limit.' except queue.Empty: text = 'Timeout: Code execution exceeded the time limit.' finished = True except Exception: text = 'The code interpreter encountered an unexpected error.' print_traceback() finished = True if text: result += f'\n\n{msg_type}:\n\n```\n{text}\n```' if image: result += f'\n\n{image}' if finished: break result = result.lstrip('\n') return result def _serve_image(self, image_base64: str) -> str: import PIL.Image image_file = f'{uuid.uuid4()}.png' local_image_file = os.path.join(self.work_dir, image_file) png_bytes = base64.b64decode(image_base64) assert isinstance(png_bytes, bytes) bytes_io = io.BytesIO(png_bytes) PIL.Image.open(bytes_io).save(local_image_file, 'png') image_server_url = os.getenv('M6_CODE_INTERPRETER_STATIC_URL', '') if image_server_url: return f'{image_server_url}/{image_file}' return local_image_file def _check_deps_for_code_interpreter(): try: import matplotlib # noqa import matplotlib.pyplot as plt # noqa import numpy as np # noqa import pandas as pd # noqa import PIL.Image # noqa import seaborn as sns # noqa from jupyter_client import BlockingKernelClient # noqa from sympy import Eq, solve, symbols # noqa except ImportError as e: raise ImportError( 'The dependencies for Code Interpreter support are not installed. ' 'Please install the required dependencies by running: pip install "qwen-agent[code_interpreter]"') from e def _fix_matplotlib_cjk_font_issue(): import matplotlib ttf_name = os.path.basename(ALIB_FONT_FILE) local_ttf = os.path.join(os.path.abspath(os.path.join(matplotlib.matplotlib_fname(), os.path.pardir)), 'fonts', 'ttf', ttf_name) if not os.path.exists(local_ttf): try: shutil.copy(ALIB_FONT_FILE, local_ttf) font_list_cache = os.path.join(matplotlib.get_cachedir(), 'fontlist-*.json') for cache_file in glob.glob(font_list_cache): with open(cache_file) as fin: cache_content = fin.read() if ttf_name not in cache_content: os.remove(cache_file) except Exception: print_traceback() def _escape_ansi(line: str) -> str: ansi_escape = re.compile(r'(?:\x1B[@-_]|[\x80-\x9F])[0-?]*[ -/]*[@-~]') return ansi_escape.sub('', line) # # The _BasePolicy and AnyThreadEventLoopPolicy below are borrowed from Tornado. # Ref: https://www.tornadoweb.org/en/stable/_modules/tornado/platform/asyncio.html#AnyThreadEventLoopPolicy # if sys.platform == 'win32' and hasattr(asyncio, 'WindowsSelectorEventLoopPolicy'): _BasePolicy = asyncio.WindowsSelectorEventLoopPolicy # type: ignore else: _BasePolicy = asyncio.DefaultEventLoopPolicy class AnyThreadEventLoopPolicy(_BasePolicy): # type: ignore """Event loop policy that allows loop creation on any thread. The default `asyncio` event loop policy only automatically creates event loops in the main threads. Other threads must create event loops explicitly or `asyncio.get_event_loop` (and therefore `.IOLoop.current`) will fail. Installing this policy allows event loops to be created automatically on any thread. Usage:: asyncio.set_event_loop_policy(AnyThreadEventLoopPolicy()) """ def get_event_loop(self) -> asyncio.AbstractEventLoop: try: return super().get_event_loop() except RuntimeError: # "There is no current event loop in thread %r" loop = self.new_event_loop() self.set_event_loop(loop) return loop ================================================ FILE: verl/workers/rollout/vllm_rollout/qwen_agent/tools/python_executor.py ================================================ import copy import datetime import io import os import pickle import traceback from concurrent.futures import TimeoutError from contextlib import redirect_stdout from functools import partial from typing import Any, Dict, List, Optional, Union from multiprocessing import Pool, cpu_count import json5 import regex from tqdm import tqdm from qwen_agent.tools.base import BaseTool from qwen_agent.utils.utils import extract_code class GenericRuntime: GLOBAL_DICT = {} LOCAL_DICT = None HEADERS = [] def __init__(self): self._global_vars = copy.copy(self.GLOBAL_DICT) self._local_vars = copy.copy(self.LOCAL_DICT) if self.LOCAL_DICT else None for c in self.HEADERS: self.exec_code(c) def exec_code(self, code_piece: str) -> None: if regex.search(r'(\s|^)?input\(', code_piece) or regex.search(r'(\s|^)?os.system\(', code_piece): raise RuntimeError() exec(code_piece, self._global_vars) def eval_code(self, expr: str) -> Any: return eval(expr, self._global_vars) def inject(self, var_dict: Dict[str, Any]) -> None: for k, v in var_dict.items(): self._global_vars[k] = v @property def answer(self): return self._global_vars['answer'] class DateRuntime(GenericRuntime): import dateutil.relativedelta GLOBAL_DICT = { 'datetime': datetime.datetime, 'timedelta': dateutil.relativedelta.relativedelta, 'relativedelta': dateutil.relativedelta.relativedelta } class CustomDict(dict): def __iter__(self): return list(super().__iter__()).__iter__() class ColorObjectRuntime(GenericRuntime): GLOBAL_DICT = {'dict': CustomDict} def _check_deps_for_python_executor(): try: import dateutil.relativedelta # noqa import multiprocess # noqa from multiprocess import Pool # noqa from pebble import ProcessPool # noqa from timeout_decorator import timeout # noqa except ImportError as e: raise ImportError( 'The dependencies for Python Executor support are not installed. ' 'Please install the required dependencies by running: pip install "qwen-agent[python_executor]"') from e # @register_tool('python_executor') # Do not register this tool by default because it is dangerous. class PythonExecutor(BaseTool): name = 'python_executor' description = 'For executing python code. Not sandboxed. Do not use it for production purposes.' parameters = [{'name': 'code', 'type': 'string', 'description': '待执行的代码', 'required': True}] def __init__(self, cfg: Optional[Dict] = None): _check_deps_for_python_executor() import multiprocess from multiprocess import Pool super().__init__(cfg) runtime: Optional[Any] = self.cfg.get('runtime', None) get_answer_symbol: Optional[str] = self.cfg.get('get_answer_symbol', None) get_answer_expr: Optional[str] = self.cfg.get('get_answer_expr', None) get_answer_from_stdout: bool = self.cfg.get('get_answer_from_stdout', True) timeout_length: int = self.cfg.get('timeout_length', 20) self.runtime = runtime if runtime else GenericRuntime() self.answer_symbol = get_answer_symbol self.answer_expr = get_answer_expr self.get_answer_from_stdout = get_answer_from_stdout self.pool = Pool(multiprocess.cpu_count()) self.timeout_length = timeout_length def call(self, params: Union[str, dict], **kwargs) -> list: try: params = json5.loads(params) code = params['code'] except Exception: code = extract_code(params) if not code.strip(): return ['', ''] predictions = self.apply(code) return predictions def apply(self, code: str) -> list: return self.batch_apply([code])[0] def process_generation_to_code(self, gens: str): return [g.split('\n') for g in gens] @staticmethod def execute( code, get_answer_from_stdout=None, runtime=None, answer_symbol=None, answer_expr=None, timeout_length=20, ): from timeout_decorator import timeout try: if get_answer_from_stdout: program_io = io.StringIO() with redirect_stdout(program_io): timeout(timeout_length)(runtime.exec_code)('\n'.join(code)) program_io.seek(0) result = program_io.read() elif answer_symbol: timeout(timeout_length)(runtime.exec_code)('\n'.join(code)) result = runtime._global_vars[answer_symbol] elif answer_expr: timeout(timeout_length)(runtime.exec_code)('\n'.join(code)) result = timeout(timeout_length)(runtime.eval_code)(answer_expr) else: timeout(timeout_length)(runtime.exec_code)('\n'.join(code[:-1])) result = timeout(timeout_length)(runtime.eval_code)(code[-1]) report = 'Done' str(result) pickle.dumps(result) # serialization check except Exception: result = '' report = traceback.format_exc().split('\n')[-2] return result, report @staticmethod def truncate(s, max_length=256): half = max_length // 2 if len(s) > max_length: s = s[:half] + '...' + s[-half:] return s def batch_apply(self, batch_code: List[str]) -> list: from pebble import ProcessPool all_code_snippets = self.process_generation_to_code(batch_code) timeout_cnt = 0 all_exec_results = [] with ProcessPool(max_workers=min(len(all_code_snippets), os.cpu_count())) as pool: executor = partial( self.execute, get_answer_from_stdout=self.get_answer_from_stdout, runtime=self.runtime, answer_symbol=self.answer_symbol, answer_expr=self.answer_expr, timeout_length=self.timeout_length, # this timeout not work ) future = pool.map(executor, all_code_snippets, timeout=self.timeout_length) iterator = future.result() if len(all_code_snippets) > 100: progress_bar = tqdm(total=len(all_code_snippets), desc='Execute') else: progress_bar = None while True: try: result = next(iterator) all_exec_results.append(result) except StopIteration: break except TimeoutError as error: print(error) all_exec_results.append(('', 'Timeout Error')) timeout_cnt += 1 except Exception as error: print(error) exit() if progress_bar is not None: progress_bar.update(1) if progress_bar is not None: progress_bar.close() batch_results = [] for code, (res, report) in zip(all_code_snippets, all_exec_results): # post processing res, report = str(res).strip(), str(report).strip() res, report = self.truncate(res), self.truncate(report) batch_results.append((res, report)) return batch_results def _test(): batch_code = ["""import math print(math.pi*4)"""] executor = PythonExecutor() predictions = executor.apply(batch_code[0]) print(predictions) if __name__ == '__main__': _test() ================================================ FILE: verl/workers/rollout/vllm_rollout/qwen_agent/utils/utils.py ================================================ import base64 import copy import hashlib import json import os import re import shutil import signal import socket import sys import time import traceback import urllib.parse from io import BytesIO from typing import Any, List, Literal, Optional, Tuple, Union import json5 import requests from pydantic import BaseModel from qwen_agent.llm.schema import ASSISTANT, DEFAULT_SYSTEM_MESSAGE, FUNCTION, SYSTEM, USER, ContentItem, Message from qwen_agent.log import logger def append_signal_handler(sig, handler): """ Installs a new signal handler while preserving any existing handler. If an existing handler is present, it will be called _after_ the new handler. """ old_handler = signal.getsignal(sig) if not callable(old_handler): old_handler = None if sig == signal.SIGINT: def old_handler(*args, **kwargs): raise KeyboardInterrupt elif sig == signal.SIGTERM: def old_handler(*args, **kwargs): raise SystemExit def new_handler(*args, **kwargs): handler(*args, **kwargs) if old_handler is not None: old_handler(*args, **kwargs) signal.signal(sig, new_handler) def get_local_ip() -> str: s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM) try: # doesn't even have to be reachable s.connect(('10.255.255.255', 1)) ip = s.getsockname()[0] except Exception: ip = '127.0.0.1' finally: s.close() return ip def hash_sha256(text: str) -> str: hash_object = hashlib.sha256(text.encode()) key = hash_object.hexdigest() return key def print_traceback(is_error: bool = True): tb = ''.join(traceback.format_exception(*sys.exc_info(), limit=3)) if is_error: logger.error(tb) else: logger.warning(tb) CHINESE_CHAR_RE = re.compile(r'[\u4e00-\u9fff]') def has_chinese_chars(data: Any) -> bool: text = f'{data}' return bool(CHINESE_CHAR_RE.search(text)) def has_chinese_messages(messages: List[Union[Message, dict]], check_roles: Tuple[str] = (SYSTEM, USER)) -> bool: for m in messages: if m['role'] in check_roles: if has_chinese_chars(m['content']): return True return False def get_basename_from_url(path_or_url: str) -> str: if re.match(r'^[A-Za-z]:\\', path_or_url): # "C:\\a\\b\\c" -> "C:/a/b/c" path_or_url = path_or_url.replace('\\', '/') # "/mnt/a/b/c" -> "c" # "https://github.com/here?k=v" -> "here" # "https://github.com/" -> "" basename = urllib.parse.urlparse(path_or_url).path basename = os.path.basename(basename) basename = urllib.parse.unquote(basename) basename = basename.strip() # "https://github.com/" -> "" -> "github.com" if not basename: basename = [x.strip() for x in path_or_url.split('/') if x.strip()][-1] return basename def is_http_url(path_or_url: str) -> bool: if path_or_url.startswith('https://') or path_or_url.startswith('http://'): return True return False def is_image(path_or_url: str) -> bool: filename = get_basename_from_url(path_or_url).lower() for ext in ['jpg', 'jpeg', 'png', 'webp']: if filename.endswith(ext): return True return False def sanitize_chrome_file_path(file_path: str) -> str: if os.path.exists(file_path): return file_path # Dealing with "file:///...": new_path = urllib.parse.urlparse(file_path) new_path = urllib.parse.unquote(new_path.path) new_path = sanitize_windows_file_path(new_path) if os.path.exists(new_path): return new_path return sanitize_windows_file_path(file_path) def sanitize_windows_file_path(file_path: str) -> str: # For Linux and macOS. if os.path.exists(file_path): return file_path # For native Windows, drop the leading '/' in '/C:/' win_path = file_path if win_path.startswith('/'): win_path = win_path[1:] if os.path.exists(win_path): return win_path # For Windows + WSL. if re.match(r'^[A-Za-z]:/', win_path): wsl_path = f'/mnt/{win_path[0].lower()}/{win_path[3:]}' if os.path.exists(wsl_path): return wsl_path # For native Windows, replace / with \. win_path = win_path.replace('/', '\\') if os.path.exists(win_path): return win_path return file_path def save_url_to_local_work_dir(url: str, save_dir: str, save_filename: str = '') -> str: if not save_filename: save_filename = get_basename_from_url(url) new_path = os.path.join(save_dir, save_filename) if os.path.exists(new_path): os.remove(new_path) logger.info(f'Downloading {url} to {new_path}...') start_time = time.time() if not is_http_url(url): url = sanitize_chrome_file_path(url) shutil.copy(url, new_path) else: headers = { 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.3' } response = requests.get(url, headers=headers) if response.status_code == 200: with open(new_path, 'wb') as file: file.write(response.content) else: raise ValueError('Can not download this file. Please check your network or the file link.') end_time = time.time() logger.info(f'Finished downloading {url} to {new_path}. Time spent: {end_time - start_time} seconds.') return new_path def save_text_to_file(path: str, text: str) -> None: with open(path, 'w', encoding='utf-8') as fp: fp.write(text) def read_text_from_file(path: str) -> str: try: with open(path, 'r', encoding='utf-8') as file: file_content = file.read() except UnicodeDecodeError: print_traceback(is_error=False) from charset_normalizer import from_path results = from_path(path) file_content = str(results.best()) return file_content def contains_html_tags(text: str) -> bool: pattern = r'<(p|span|div|li|html|script)[^>]*?' return bool(re.search(pattern, text)) def get_content_type_by_head_request(path: str) -> str: try: response = requests.head(path, timeout=5) content_type = response.headers.get('Content-Type', '') return content_type except requests.RequestException: return 'unk' def get_file_type(path: str) -> Literal['pdf', 'docx', 'pptx', 'txt', 'html', 'csv', 'tsv', 'xlsx', 'xls', 'unk']: f_type = get_basename_from_url(path).split('.')[-1].lower() if f_type in ['pdf', 'docx', 'pptx', 'csv', 'tsv', 'xlsx', 'xls']: # Specially supported file types return f_type if is_http_url(path): # The HTTP header information for the response is obtained by making a HEAD request to the target URL, # where the Content-type field usually indicates the Type of Content to be returned content_type = get_content_type_by_head_request(path) if 'application/pdf' in content_type: return 'pdf' elif 'application/msword' in content_type: return 'docx' # Assuming that the URL is HTML by default, # because the file downloaded by the request may contain html tags return 'html' else: # Determine by reading local HTML file try: content = read_text_from_file(path) except Exception: print_traceback() return 'unk' if contains_html_tags(content): return 'html' else: return 'txt' def extract_urls(text: str) -> List[str]: pattern = re.compile(r'https?://\S+') urls = re.findall(pattern, text) return urls def extract_markdown_urls(md_text: str) -> List[str]: pattern = r'!?\[[^\]]*\]\(([^\)]+)\)' urls = re.findall(pattern, md_text) return urls def extract_code(text: str) -> str: # Match triple backtick blocks first triple_match = re.search(r'```[^\n]*\n(.+?)```', text, re.DOTALL) if triple_match: text = triple_match.group(1) else: try: text = json5.loads(text)['code'] except Exception: print_traceback(is_error=False) # If no code blocks found, return original text return text def json_loads(text: str) -> dict: text = text.strip('\n') if text.startswith('```') and text.endswith('\n```'): text = '\n'.join(text.split('\n')[1:-1]) try: return json.loads(text) except json.decoder.JSONDecodeError as json_err: try: return json5.loads(text) except ValueError: raise json_err class PydanticJSONEncoder(json.JSONEncoder): def default(self, obj): if isinstance(obj, BaseModel): return obj.model_dump() return super().default(obj) def json_dumps_pretty(obj: dict, ensure_ascii=False, indent=2, **kwargs) -> str: return json.dumps(obj, ensure_ascii=ensure_ascii, indent=indent, cls=PydanticJSONEncoder, **kwargs) def json_dumps_compact(obj: dict, ensure_ascii=False, indent=None, **kwargs) -> str: return json.dumps(obj, ensure_ascii=ensure_ascii, indent=indent, cls=PydanticJSONEncoder, **kwargs) def format_as_multimodal_message( msg: Message, add_upload_info: bool, add_multimodel_upload_info: bool, lang: Literal['auto', 'en', 'zh'] = 'auto', ) -> Message: assert msg.role in (USER, ASSISTANT, SYSTEM, FUNCTION) content: List[ContentItem] = [] if isinstance(msg.content, str): # if text content if msg.content: content = [ContentItem(text=msg.content)] elif isinstance(msg.content, list): # if multimodal content files = [] for item in msg.content: k, v = item.get_type_and_value() if k in ('text', 'image', 'audio', 'video'): content.append(item) if k == 'file': # Move 'file' out of 'content' since it's not natively supported by models files.append(v) if add_multimodel_upload_info and k == 'image': # Indicate the image name # Not considering audio and video for now files.append(v) if add_upload_info and files and (msg.role in (SYSTEM, USER)): if lang == 'auto': has_zh = has_chinese_chars(msg) else: has_zh = (lang == 'zh') upload = [] for f in [get_basename_from_url(f) for f in files]: if is_image(f): if has_zh: upload.append(f'![图片]({f})') else: upload.append(f'![image]({f})') else: if has_zh: upload.append(f'[文件]({f})') else: upload.append(f'[file]({f})') upload = ' '.join(upload) if has_zh: upload = f'（上传了 {upload}）\n\n' else: upload = f'(Uploaded {upload})\n\n' # Check and avoid adding duplicate upload info upload_info_already_added = False for item in content: if item.text and (upload in item.text): upload_info_already_added = True if not upload_info_already_added: content = [ContentItem(text=upload)] + content else: raise TypeError msg = Message(role=msg.role, content=content, name=msg.name if msg.role == FUNCTION else None, function_call=msg.function_call, extra=msg.extra) return msg def format_as_text_message( msg: Message, add_upload_info: bool, lang: Literal['auto', 'en', 'zh'] = 'auto', ) -> Message: msg = format_as_multimodal_message(msg, add_upload_info=add_upload_info, add_multimodel_upload_info=add_upload_info, lang=lang) text = '' for item in msg.content: if item.type == 'text': text += item.value msg.content = text return msg def extract_text_from_message( msg: Message, add_upload_info: bool, lang: Literal['auto', 'en', 'zh'] = 'auto', ) -> str: if isinstance(msg.content, list): text = format_as_text_message(msg, add_upload_info=add_upload_info, lang=lang).content elif isinstance(msg.content, str): text = msg.content else: raise TypeError(f'List of str or str expected, but received {type(msg.content).__name__}.') return text.strip() def extract_files_from_messages(messages: List[Message], include_images: bool) -> List[str]: files = [] for msg in messages: if isinstance(msg.content, list): for item in msg.content: if item.file and item.file not in files: files.append(item.file) if include_images and item.image and item.image not in files: files.append(item.image) return files def merge_generate_cfgs(base_generate_cfg: Optional[dict], new_generate_cfg: Optional[dict]) -> dict: generate_cfg: dict = copy.deepcopy(base_generate_cfg or {}) if new_generate_cfg: for k, v in new_generate_cfg.items(): if k == 'stop': stop = generate_cfg.get('stop', []) stop = stop + [s for s in v if s not in stop] generate_cfg['stop'] = stop else: generate_cfg[k] = v return generate_cfg def build_text_completion_prompt( messages: List[Message], allow_special: bool = False, default_system: str = DEFAULT_SYSTEM_MESSAGE, ) -> str: im_start = '<|im_start|>' im_end = '<|im_end|>' if messages[0].role == SYSTEM: sys = messages[0].content assert isinstance(sys, str) prompt = f'{im_start}{SYSTEM}\n{sys}{im_end}' messages = messages[1:] else: prompt = f'{im_start}{SYSTEM}\n{default_system}{im_end}' # Make sure we are completing the chat in the tone of the assistant if messages[-1].role != ASSISTANT: messages = messages + [Message(ASSISTANT, '')] for msg in messages: assert isinstance(msg.content, str) content = msg.content if allow_special: assert msg.role in (USER, ASSISTANT, SYSTEM, FUNCTION) if msg.function_call: assert msg.role == ASSISTANT tool_call = msg.function_call.arguments try: tool_call = {'name': msg.function_call.name, 'arguments': json.loads(tool_call)} tool_call = json.dumps(tool_call, ensure_ascii=False, indent=2) except json.decoder.JSONDecodeError: tool_call = '{"name": "' + msg.function_call.name + '", "arguments": ' + tool_call + '}' if content: content += '\n' content += f'\n{tool_call}\n' else: assert msg.role in (USER, ASSISTANT) assert msg.function_call is None prompt += f'\n{im_start}{msg.role}\n{content}{im_end}' assert prompt.endswith(im_end) prompt = prompt[:-len(im_end)] return prompt def encode_image_as_base64(path: str, max_short_side_length: int = -1) -> str: from PIL import Image image = Image.open(path) if (max_short_side_length > 0) and (min(image.size) > max_short_side_length): ori_size = image.size image = resize_image(image, short_side_length=max_short_side_length) logger.debug(f'Image "{path}" resized from {ori_size} to {image.size}.') image = image.convert(mode='RGB') buffered = BytesIO() image.save(buffered, format='JPEG') return 'data:image/jpeg;base64,' + base64.b64encode(buffered.getvalue()).decode('utf-8') def load_image_from_base64(image_base64: Union[bytes, str]): from PIL import Image image = Image.open(BytesIO(base64.b64decode(image_base64))) image.load() return image def resize_image(img, short_side_length: int = 1080): from PIL import Image assert isinstance(img, Image.Image) width, height = img.size if width <= height: new_width = short_side_length new_height = int((short_side_length / width) * height) else: new_height = short_side_length new_width = int((short_side_length / height) * width) resized_img = img.resize((new_width, new_height), resample=Image.Resampling.BILINEAR) return resized_img def get_last_usr_msg_idx(messages: List[Union[dict, Message]]) -> int: i = len(messages) - 1 while (i >= 0) and (messages[i]['role'] != 'user'): i -= 1 assert i >= 0, messages assert messages[i]['role'] == 'user' return i ================================================ FILE: verl/workers/rollout/vllm_rollout/vllm_rollout.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ The vllm_rollout that can be applied in different backend When working with FSDP: - Use DTensor weight loader (recommended) or HF weight loader - Utilize state_dict from the FSDP to synchronize the weights among tp ranks in vLLM When working with Megatron: - Use Megatron weight loader - During training, only the current pp stage holds the parameters - Before inference, broadcast the parameters of the current pp rank to all other pp ranks (all pp ranks holds all the parameters) - Bind the parameters to the inference engine - Do inference in tp. pp is treated as additional dp - After inference, all the parameters that doesn't belong to this pp rank is freed. """ from typing import List from contextlib import contextmanager from omegaconf import DictConfig import torch import torch.distributed from tensordict import TensorDict from torch import nn from verl import DataProto from verl.utils.torch_functional import get_eos_mask, pad_sequence_to_length from verl.workers.rollout.base import BaseRollout from verl.third_party.vllm import LLM, vllm_version from verl.third_party.vllm import parallel_state as vllm_ps from vllm import SamplingParams # TODO # 1. support pp in vllm # 2. passing tokenizer is not necessary? no encoding/decoding is happending here # 3. simplify init logics # NOTE(sgm): add for verl. We can optimize it by making the dataloader yield List[int] without padding. def _pre_process_inputs(pad_token_id, prompt_token_ids: torch.Tensor) -> List[int]: # remove the left padding in the prompt token_id # pad_token_id = self.llm_engine.tokenizer.pad_token_id if self.llm_engine.tokenizer.pad_token_id is not None else self.llm_engine.tokenizer.eos_token_id non_pad_index = torch.nonzero(prompt_token_ids != pad_token_id, as_tuple=False)[0][0] token_ids = prompt_token_ids[non_pad_index:].tolist() return token_ids class vLLMRollout(BaseRollout): def __init__(self, actor_module: nn.Module, config: DictConfig, tokenizer, model_hf_config, **kwargs): """A vLLM rollout. It requires the module is supported by the vllm. Args: module: module here follows huggingface APIs config: DictConfig tokenizer: the task/model tokenizer model_hf_config: the huggingface config to initiallize the generating model in vllm **kwargs: train_tp, for Megatron Backend to initialize hybrid engine (zero redundancy) process group """ super().__init__() self.config = config assert not (not config.enforce_eager and config.free_cache_engine), \ "disable CUDA graph (enforce_eager = False) if free cache engine" tensor_parallel_size = self.config.get('tensor_model_parallel_size', 1) assert tensor_parallel_size <= torch.distributed.get_world_size(), \ "tensor parallel size should be less than or equal to the world size" max_num_batched_tokens = int(self.config.get('max_num_batched_tokens', 8192)) if kwargs.get('train_tp', None) is not None: # deployed with megatron import os os.environ['CUDA_TIMER_STREAM_KAFKA_ENABLE'] = '0' os.environ['MEGATRON_IMPORT_TIMERS'] = '0' train_tp = kwargs.get('train_tp', None) num_tp_per_train_tp = train_tp // tensor_parallel_size if vllm_version in ('0.4.2', '0.5.4', '0.6.3'): vllm_ps.initialize_parallel_state(tensor_model_parallel_size=tensor_parallel_size, num_tp_per_train_tp=num_tp_per_train_tp) assert model_hf_config.max_position_embeddings >= config.prompt_length + config.response_length, \ "model context length should be greater than total sequence length" max_model_len = self.config.max_model_len if self.config.max_model_len \ else config.prompt_length + config.response_length max_model_len = int(max_model_len) if max_num_batched_tokens < max_model_len and self.config.enable_chunked_prefill: raise ValueError('Enable chunked prefill, max_num_batched_tokens is smaller than max_model_len, \ please increase max_num_batched_tokens or disable chunked prefill') self.inference_engine = LLM( actor_module, tokenizer=tokenizer, model_hf_config=model_hf_config, tensor_parallel_size=tensor_parallel_size, dtype=config.dtype, enforce_eager=config.enforce_eager, gpu_memory_utilization=config.gpu_memory_utilization, skip_tokenizer_init=False, max_model_len=max_model_len, load_format=config.load_format, disable_log_stats=config.disable_log_stats, max_num_batched_tokens=max_num_batched_tokens, enable_chunked_prefill=config.enable_chunked_prefill, ) # Offload vllm model to reduce peak memory usage self.inference_engine.offload_model_weights() kwargs = dict( n=1, logprobs=1, # can be set to 0 and let actor to recompute max_tokens=config.response_length, ) # we may detokenize the result all together later if vllm_version in ('0.4.2', '0.5.4', '0.6.3'): kwargs['detokenize'] = False # supporting adding any sampling params from the config file for k in config.keys(): if hasattr(SamplingParams(), str(k)): kwargs[k] = config.get(k) print(f"kwargs: {kwargs}") self.sampling_params = SamplingParams(**kwargs) self.pad_token_id = tokenizer.pad_token_id @contextmanager def update_sampling_params(self, **kwargs): # update sampling params old_sampling_params_args = {} if kwargs: for key, value in kwargs.items(): if hasattr(self.sampling_params, key): old_value = getattr(self.sampling_params, key) old_sampling_params_args[key] = old_value setattr(self.sampling_params, key, value) yield # roll back to previous sampling params # if len(old_sampling_params_args): for key, value in old_sampling_params_args.items(): setattr(self.sampling_params, key, value) @torch.no_grad() def generate_sequences(self, prompts: DataProto, **kwargs) -> DataProto: # rebuild vllm cache engine if self.config.free_cache_engine: self.inference_engine.init_cache_engine() idx = prompts.batch['input_ids'] # (bs, prompt_length) # left-padded attention_mask attention_mask = prompts.batch['attention_mask'] position_ids = prompts.batch['position_ids'] # used to construct attention_mask eos_token_id = prompts.meta_info['eos_token_id'] batch_size = idx.size(0) idx_list = [] # parse idx from torch.Tensor to List[List[str]] for i in range(batch_size): idx_list.append(_pre_process_inputs(self.pad_token_id, idx[i])) do_sample = prompts.meta_info.get('do_sample', True) if not do_sample: kwargs = { 'best_of': 1, 'top_p': 1.0, 'top_k': -1, 'min_p': 0.0, 'temperature': 0, 'n': 1 # if greedy, only 1 response } # users can customize different sampling_params at different run with self.update_sampling_params(**kwargs): output = self.inference_engine.generate( prompts=None, # because we have already convert it to prompt token id sampling_params=self.sampling_params, prompt_token_ids=idx_list, use_tqdm=False) # TODO(sgm): disable logprob when recompute_log_prob is enable # if n = 1: (bs, response_length) ; if n > 1: (bs * n, response_length) response = output[0].to(idx.device) log_probs = output[1].to(idx.device) if response.shape[1] < self.config.response_length: response = pad_sequence_to_length(response, self.config.response_length, self.pad_token_id) log_probs = pad_sequence_to_length(log_probs, self.config.response_length, self.pad_token_id) if self.config.n > 1 and do_sample: idx = idx.repeat_interleave(self.config.n, dim=0) attention_mask = attention_mask.repeat_interleave(self.config.n, dim=0) position_ids = position_ids.repeat_interleave(self.config.n, dim=0) batch_size = batch_size * self.config.n seq = torch.cat([idx, response], dim=-1) response_length = response.size(1) delta_position_id = torch.arange(1, response_length + 1, device=position_ids.device) delta_position_id = delta_position_id.unsqueeze(0).repeat(batch_size, 1) # TODO(sgm): fix position_ids on right_pad # prompt: left pad + response: right pad # attention_mask: [0,0,0,0,1,1,1,1, | 1,1,1,0,0,0,0,0] # position_ids: [0,0,0,0,0,1,2,3, | 4,5,6,7,8,9,10,11] response_position_ids = position_ids[:, -1:] + delta_position_id position_ids = torch.cat([position_ids, response_position_ids], dim=-1) response_attention_mask = get_eos_mask(response_id=response, eos_token=eos_token_id, dtype=attention_mask.dtype) attention_mask = torch.cat((attention_mask, response_attention_mask), dim=-1) # all the tp ranks should contain the same data here. data in all ranks are valid batch = TensorDict( { 'prompts': idx, 'responses': response, 'input_ids': seq, # here input_ids become the whole sentences # 'old_log_probs': log_probs, # we will recompute old log prob with actor 'attention_mask': attention_mask, 'position_ids': position_ids }, batch_size=batch_size) # free vllm cache engine if self.config.free_cache_engine: self.inference_engine.free_cache_engine() return DataProto(batch=batch) ================================================ FILE: verl/workers/rollout/vllm_rollout/vllm_rollout_spmd.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ The vllm_rollout that can be applied in different backend When working with FSDP: - Use DTensor weight loader (recommended) or HF weight loader - Utilize state_dict from the FSDP to synchronize the weights among tp ranks in vLLM When working with Megatron: - Use Megatron weight loader - During training, only the current pp stage holds the parameters - Before inference, broadcast the parameters of the current pp rank to all other pp ranks (all pp ranks holds all the parameters) - Bind the parameters to the inference engine - Do inference in tp. pp is treated as additional dp - After inference, all the parameters that doesn't belong to this pp rank is freed. """ import numpy as np from typing import List from contextlib import contextmanager from omegaconf import DictConfig import os import torch import torch.distributed from tensordict import TensorDict import requests from multiprocessing import Pool from functools import partial from torch import nn from typing import Any, Union from verl import DataProto from verl.utils.torch_functional import get_eos_mask, pad_2d_list_to_length from verl.workers.rollout.base import BaseRollout from vllm.distributed import parallel_state as vllm_ps from vllm import LLM, SamplingParams from verl.third_party.vllm import vllm_version from qwen_agent.tools.python_executor import PythonExecutor from qwen_agent.tools.code_interpreter import CodeInterpreter from qwen_agent.utils.utils import print_traceback from typing import Tuple import json5 import pdb import json import copy from concurrent.futures import ThreadPoolExecutor, as_completed, TimeoutError # TODO # 1. support pp in vllm # 2. passing tokenizer is not necessary? no encoding/decoding is happending here # 3. simplify init logics # NOTE(sgm): add for verl. We can optimize it by making the dataloader yield List[int] without padding. def _pre_process_inputs(pad_token_id, prompt_token_ids: torch.Tensor) -> List[int]: # remove the left padding in the prompt token_id # pad_token_id = self.llm_engine.tokenizer.pad_token_id if self.llm_engine.tokenizer.pad_token_id is not None else self.llm_engine.tokenizer.eos_token_id non_pad_index = torch.nonzero(prompt_token_ids != pad_token_id, as_tuple=False)[0][0] token_ids = prompt_token_ids[non_pad_index:].tolist() return token_ids def _repeat_interleave(value: Union[torch.Tensor, np.ndarray], repeats: int) -> Union[torch.Tensor, List[Any]]: if isinstance(value, torch.Tensor): return value.repeat_interleave(repeats, dim=0) else: return np.repeat(value, repeats, axis=0) OBS_START = '```output' OBS_END = '\n```\n' def extract_program(result: str, last_only=True): """ extract the program after "```python", and before "```" """ program = '' start = False for line in result.split('\n'): if line.startswith('```python') or line.endswith('```python'): if last_only: program = '' # only extract the last program else: program += '\n# ========\n' start = True elif line.startswith('```'): start = False elif start: program += line + '\n' if start: # the code is incomplete program = '' return program def _detect_tool(text: str) -> Tuple[bool, str, str, str]: program = extract_program(text) if program: program = json.dumps({'code': program}, ensure_ascii=False) return (program != ''), PythonExecutor.name, program, text def send_request(json_data): try: url = 'sandbox_url' response = requests.post(url, json=json_data, timeout=10) return response.json() # 返回响应的 JSON 数据 except: print("sanbox timeout") return {"error": "unknown"} class vLLMRollout(BaseRollout): def __init__(self, model_path: str, config: DictConfig, tokenizer, model_hf_config, **kwargs): """A vLLM rollout. It requires the module is supported by the vllm. Args: module: module here follows huggingface APIs config: DictConfig tokenizer: the task/model tokenizer model_hf_config: the huggingface config to initiallize the generating model in vllm **kwargs: train_tp, for Megatron Backend to initialize hybrid engine (zero redundancy) process group """ super().__init__() self.config = config assert not (not config.enforce_eager and config.free_cache_engine), \ "disable CUDA graph (enforce_eager = False) if free cache engine" tensor_parallel_size = self.config.get('tensor_model_parallel_size', 1) assert tensor_parallel_size <= torch.distributed.get_world_size(), \ "tensor parallel size should be less than or equal to the world size" max_num_batched_tokens = self.config.get('max_num_batched_tokens', 8192) if kwargs.get('train_tp', None) is not None: # deployed with megatron import os os.environ['CUDA_TIMER_STREAM_KAFKA_ENABLE'] = '0' os.environ['MEGATRON_IMPORT_TIMERS'] = '0' train_tp = kwargs.get('train_tp', None) num_tp_per_train_tp = train_tp // tensor_parallel_size vllm_ps.initialize_parallel_state(tensor_model_parallel_size=tensor_parallel_size, num_tp_per_train_tp=num_tp_per_train_tp) assert model_hf_config.max_position_embeddings >= config.prompt_length + config.response_length, \ "model context length should be greater than total sequence length" self.inference_engine = LLM( model=model_path, enable_sleep_mode=True, tensor_parallel_size=tensor_parallel_size, distributed_executor_backend="external_launcher", dtype=config.dtype, enforce_eager=config.enforce_eager, gpu_memory_utilization=config.gpu_memory_utilization, disable_custom_all_reduce=True, skip_tokenizer_init=False, max_model_len=config.prompt_length + config.response_length, disable_log_stats=config.disable_log_stats, max_num_batched_tokens=max_num_batched_tokens, enable_chunked_prefill=config.enable_chunked_prefill, enable_prefix_caching=True, ) # Offload vllm model to reduce peak memory usage self.inference_engine.sleep(level=1) kwargs = dict( n=1, logprobs=0, # can be set to 0 and let actor to recompute max_tokens=config.response_length, ) # # we may detokenize the result all together later if vllm_version != '0.3.1': kwargs['detokenize'] = False # supporting adding any sampling params from the config file for k in config.keys(): if hasattr(SamplingParams(), str(k)): kwargs[k] = config.get(k) print(f"kwargs: {kwargs}") self.sampling_params = SamplingParams(**kwargs) self.pad_token_id = tokenizer.pad_token_id self.tokenizer = tokenizer self.executor=PythonExecutor() # self.code_interpreter=CodeInterpreter() def _get_prompts_and_indices(self, samples_info): prompts, indices=[], [] for index, info in enumerate(samples_info): if not info['stop']: prompts.append(info['sequence']) indices.append(info['index']) return prompts, indices # def code_interpreter_batch_call(self, tool_inputs): # with Pool(processes=min(len(tool_inputs),os.cpu_count(), 32)) as pool: # results = pool.map(self.code_interpreter.call, tool_inputs) # def postproc(result): # report=result.split("```")[0].strip() # output=result.split("```")[-1].split("```")[-1].strip() # if report=="stdout:": report="Done" # return (output, report) # results=[postproc(result) for result in results] # return results def code_interpreter_batch_call(self, tool_inputs, timeout=20): tool_inputs=[{'code': tool_input,'language': 'python'} for tool_input in tool_inputs] results = [None] * len(tool_inputs) with ThreadPoolExecutor(max_workers=max(min(len(tool_inputs), os.cpu_count(), 64), 1)) as executor: future_to_index = {executor.submit(send_request, input): i for i, input in enumerate(tool_inputs)} for future in as_completed(future_to_index): index = future_to_index[future] try: result = future.result(timeout=timeout) results[index] = result except: results[index] = {"run_result": {"stdout": "Error", "stderr": "TimeoutError"}} def postproc(output): try: if str(output['run_result']['return_code'])=='0' or len(str(output['run_result']['stdout'])) != 0: return output['run_result']['stdout'], "Done" else: return output['run_result']['stdout'], output['run_result']['stderr'].strip() except Exception: return "Error", "UnknownError" results=[postproc(result) for result in results] return results def _tokenize_and_find_mask_token_indices(self, sample_info): response=sample_info['response'] mask_str_ranges=sample_info['mask_info'] encoding=self.tokenizer(response, add_special_tokens=False, return_offsets_mapping=True) response_token_ids=encoding['input_ids'] offset_mapping_tensor=torch.tensor(encoding['offset_mapping'], dtype=torch.long) token_starts = offset_mapping_tensor[:,0] token_ends = offset_mapping_tensor[:,1] mask_tensor=torch.ones(len(response_token_ids)) for mask_str_range in mask_str_ranges: start_index, end_index=mask_str_range[0], mask_str_range[1] mask = (token_starts < end_index) & (token_ends > start_index) & (token_starts >= start_index) mask_tensor[mask]=0 return response_token_ids, mask_tensor def _tir_generate(self, prompts=None, sampling_params=None, prompt_token_ids=None, use_tqdm=False): sampling_params=copy.deepcopy(sampling_params) # prompts=self.tokenizer.batch_decode(prompt_token_ids, skip_special_tokens=True) prompts=[self.tokenizer.decode(prompt['prompt_token_ids'], skip_special_tokens=False) for prompt in prompts] prompts=[prompt for prompt in prompts for _ in range(sampling_params.n) ] sampling_params.n=1 sampling_params.detokenize=True sampling_params.stop=["```output"] samples_info=[{"prompt": prompt, "sequence": prompt, "response": "", "stop": False, "finish_reason": None,"index": index, "mask_info": [], "execution_pass": 0} for index, prompt in enumerate(prompts)] program2output=[] num_llm_calls_available=copy.deepcopy(self.config.num_llm_calls_available) while num_llm_calls_available >= 0: if num_llm_calls_available==0: sampling_params.stop=None num_llm_calls_available-=1 # llm generate response, stop at eos token or ```output input_prompts, indices=self._get_prompts_and_indices(samples_info) input_prompts = [{ 'prompt_token_ids': self.tokenizer.encode(x, add_special_tokens=False)[:self.config.prompt_length+self.config.response_length]} for x in input_prompts] outputs = self.inference_engine.generate(prompts=input_prompts, sampling_params=sampling_params, use_tqdm=use_tqdm) sorted_outputs = sorted(outputs, key=lambda output: int(output.request_id)) responses=[x.outputs[0].text for x in sorted_outputs] finish_reason=[x.outputs[0].finish_reason for x in sorted_outputs] stop_reason=[x.outputs[0].stop_reason for x in sorted_outputs] if num_llm_calls_available==-1: for i ,index in enumerate(indices): samples_info[index]['response']+=responses[i] samples_info[index]['sequence']+=responses[i] samples_info[index]['stop']=True samples_info[index]['finish_reason']=finish_reason[i] break def _python_execution(finish_reason, stop_reason): if finish_reason=='stop' and stop_reason==None: return False if finish_reason=='stop' and stop_reason=='```output': return True if finish_reason=='length': False return False is_execution=[_python_execution(finish_reason[i], stop_reason[i]) for i in range(len(finish_reason))] # check if all samples are finished if all([not x for x in is_execution]): break # prepare for python execution tool_infos=[ _detect_tool(response) for response in responses] tool_indices=[] tool_inputs=[] for i, tool_info in enumerate(tool_infos): if tool_info[0] and is_execution[i]: tool_indices.append(i) tool_inputs.append(tool_info[2]) def postproc_observation(observation): execution_pass=0 try: observation_list=observation if observation_list[-1] == 'Done': observation = observation_list[0] execution_pass=1 else: observation = observation_list[-1] except Exception: observation="Error" if "Error" in observation: observation=observation.strip().split("\n")[-1] if len(observation.strip())==0: observation="timeout_decorator.timeout_decorator.TimeoutError: 'Timed Out'" observation = observation.strip() if len(observation)>=256: observation = observation[:128]+"..."+observation[-128:] observation = f'{OBS_START}\n{observation}{OBS_END}' return observation, execution_pass # execute python code # observations=self.executor.batch_apply([json5.loads(x)['code'] for x in tool_inputs]) observations=self.code_interpreter_batch_call([json5.loads(x)['code'] for x in tool_inputs]) # construction responses from observations responses=[response+"\n" if not response.endswith('\n') else response for response in responses] responses_w_res=copy.deepcopy(responses) execution_passes=[0 for _ in range(len(responses))] for i, index in enumerate(tool_indices): processed_observation=postproc_observation(observations[i]) responses_w_res[index]+=processed_observation[0] execution_passes[index]=processed_observation[1] # program2output.append([{"code": tool_input, "answer": postproc_observation(observations[idx])} for idx, tool_input in enumerate(tool_inputs)]) # update samples_info for i ,index in enumerate(indices): mask=[ len(responses[i]) + len('```output'), len(responses_w_res[i]) ] samples_info[index]['mask_info'].append(mask) samples_info[index]['response']+=responses_w_res[i] samples_info[index]['sequence']+=responses_w_res[i] samples_info[index]['stop']=not is_execution[i] samples_info[index]['finish_reason']=finish_reason[i] samples_info[index]['execution_pass']=execution_passes[i] for i, line in enumerate(samples_info): if samples_info[i]['finish_reason']!='length': samples_info[i]['response']+=self.tokenizer.eos_token responses_ids=[] tool_output_masks=[] execution_passes=[] for idx, sample_info in enumerate(samples_info): response_id, tool_output_mask = self._tokenize_and_find_mask_token_indices(sample_info) responses_ids.append(response_id[:self.config.response_length]) tool_output_masks.append(tool_output_mask[:self.config.response_length]) execution_passes.append(sample_info['execution_pass']) # save id and mask to check correctness # samples_info[idx]['responses_id']=response_id[:self.config.response_length] # samples_info[idx]['tool_output_mask']=tool_output_mask[:self.config.response_length].tolist() # with open("/mnt/bn/seedllm3-lixuefeng-2/code/o1/verl-tir/sample_infos.json", 'w', encoding='utf-8') as f: # json.dump(samples_info, f, ensure_ascii=False, indent=2) return responses_ids, tool_output_masks, torch.tensor(execution_passes, dtype=torch.long) @contextmanager def update_sampling_params(self, **kwargs): # update sampling params old_sampling_params_args = {} if kwargs: for key, value in kwargs.items(): if hasattr(self.sampling_params, key): old_value = getattr(self.sampling_params, key) old_sampling_params_args[key] = old_value setattr(self.sampling_params, key, value) yield # roll back to previous sampling params # if len(old_sampling_params_args): for key, value in old_sampling_params_args.items(): setattr(self.sampling_params, key, value) @torch.no_grad() def generate_sequences(self, prompts: DataProto, **kwargs) -> DataProto: # rebuild vllm cache engine if vllm_version in ('0.3.1', '0.4.2', '0.5.4', '0.6.3') and self.config.free_cache_engine: self.inference_engine.init_cache_engine() idx = prompts.batch['input_ids'] # (bs, prompt_length) # left-padded attention_mask attention_mask = prompts.batch['attention_mask'] position_ids = prompts.batch['position_ids'] # used to construct attention_mask eos_token_id = prompts.meta_info['eos_token_id'] batch_size = idx.size(0) non_tensor_batch = prompts.non_tensor_batch if 'raw_prompt_ids' not in non_tensor_batch: non_tensor_batch['raw_prompt_ids'] = np.array( [_pre_process_inputs(self.pad_token_id, idx[i]) for i in range(batch_size)], dtype=object) if batch_size != len(non_tensor_batch['raw_prompt_ids']): raise RuntimeError('vllm sharding manager is not work properly.') if 'multi_modal_data' in non_tensor_batch: vllm_inputs = [] for raw_prompt_ids, multi_modal_data in zip(non_tensor_batch.pop('raw_prompt_ids'), non_tensor_batch.pop('multi_modal_data')): vllm_inputs.append({'prompt_token_ids': raw_prompt_ids, 'multi_modal_data': multi_modal_data}) else: vllm_inputs = [{ 'prompt_token_ids': raw_prompt_ids } for raw_prompt_ids in non_tensor_batch.pop('raw_prompt_ids')] do_sample = prompts.meta_info.get('do_sample', True) if not do_sample: kwargs = { 'best_of': 1, 'top_p': 1.0, 'top_k': -1, 'min_p': 0.0, 'temperature': 0, 'n': 1 # if greedy, only 1 response } # users can customize different sampling_params at different run with self.update_sampling_params(**kwargs): response, tool_output_masks, execution_passes = self._tir_generate( prompts=vllm_inputs, # because we have already convert it to prompt token id sampling_params=self.sampling_params, use_tqdm=False) # TODO(sgm): disable logprob when recompute_log_prob is enable # if n = 1: (bs, response_length) ; if n > 1: (bs * n, response_length) # response = [] # for output in outputs: # for sample_id in range(len(output.outputs)): # response.append(output.outputs[sample_id].token_ids) response = pad_2d_list_to_length(response, self.pad_token_id, max_length=self.config.response_length).to(idx.device) tool_output_masks = pad_2d_list_to_length(tool_output_masks, 1, max_length=self.config.response_length).to(idx.device).int() execution_passes = execution_passes.to(idx.device).int() if self.config.n > 1 and do_sample: idx = _repeat_interleave(idx, self.config.n) attention_mask = _repeat_interleave(attention_mask, self.config.n) position_ids = _repeat_interleave(position_ids, self.config.n) batch_size = batch_size * self.config.n if 'multi_modal_inputs' in non_tensor_batch.keys(): non_tensor_batch['multi_modal_inputs'] = _repeat_interleave(non_tensor_batch['multi_modal_inputs'], self.config.n) seq = torch.cat([idx, response], dim=-1) response_length = response.size(1) delta_position_id = torch.arange(1, response_length + 1, device=position_ids.device) delta_position_id = delta_position_id.unsqueeze(0).expand(batch_size, -1) if position_ids.dim() == 3: # qwen2vl mrope delta_position_id = delta_position_id.view(batch_size, 1, -1).expand(batch_size, 3, -1) # TODO(sgm): fix position_ids on right_pad # prompt: left pad + response: right pad # attention_mask: [0,0,0,0,1,1,1,1, | 1,1,1,0,0,0,0,0] # position_ids: [0,0,0,0,0,1,2,3, | 4,5,6,7,8,9,10,11] response_position_ids = position_ids[:, -1:] + delta_position_id position_ids = torch.cat([position_ids, response_position_ids], dim=-1) response_attention_mask = get_eos_mask(response_id=response, eos_token=eos_token_id, dtype=attention_mask.dtype) # response_attention_mask = response_attention_mask & tool_output_masks attention_mask = torch.cat((attention_mask, response_attention_mask), dim=-1) # all the tp ranks should contain the same data here. data in all ranks are valid batch = TensorDict( { 'prompts': idx, 'responses': response, 'input_ids': seq, # here input_ids become the whole sentences # 'old_log_probs': log_probs, # we will recompute old log prob with actor 'attention_mask': attention_mask, 'tool_output_masks': tool_output_masks, 'position_ids': position_ids, 'execution_passes': execution_passes, }, batch_size=batch_size) # free vllm cache engine if vllm_version in ('0.3.1', '0.4.2', '0.5.4', '0.6.3') and self.config.free_cache_engine: self.inference_engine.free_cache_engine() return DataProto(batch=batch, non_tensor_batch=non_tensor_batch) ================================================ FILE: verl/workers/sharding_manager/__init__.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from verl.utils.import_utils import is_vllm_available, is_megatron_core_available from .base import BaseShardingManager from .fsdp_ulysses import FSDPUlyssesShardingManager AllGatherPPModel = None if is_megatron_core_available() and is_vllm_available(): from .megatron_vllm import AllGatherPPModel, MegatronVLLMShardingManager elif AllGatherPPModel is not None: pass else: AllGatherPPModel = None MegatronVLLMShardingManager = None if is_vllm_available(): from .fsdp_vllm import FSDPVLLMShardingManager else: FSDPVLLMShardingManager = None ================================================ FILE: verl/workers/sharding_manager/base.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Sharding manager to implement HybridEngine """ from verl import DataProto class BaseShardingManager: def __enter__(self): pass def __exit__(self, exc_type, exc_value, traceback): pass def preprocess_data(self, data: DataProto) -> DataProto: return data def postprocess_data(self, data: DataProto) -> DataProto: return data ================================================ FILE: verl/workers/sharding_manager/fsdp_ulysses.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Contains a resharding manager that binds weights from FSDP zero3 to XPerfGPT """ from .base import BaseShardingManager from torch.distributed.device_mesh import DeviceMesh from verl.utils.torch_functional import allgather_dict_tensors from verl.utils.ulysses import set_ulysses_sequence_parallel_group, get_ulysses_sequence_parallel_group import numpy as np import torch import torch.distributed from verl import DataProto class FSDPUlyssesShardingManager(BaseShardingManager): """ Sharding manager to support data resharding when using FSDP + Ulysses """ def __init__(self, device_mesh: DeviceMesh): super().__init__() self.device_mesh = device_mesh self.seed_offset = 12345 def __enter__(self): if self.device_mesh is not None: # We have a global SP group # so we have to change to use model-specific sp group self.prev_sp_group = get_ulysses_sequence_parallel_group() set_ulysses_sequence_parallel_group(self.device_mesh['sp'].get_group()) # TODO: check how to set seed for each model def __exit__(self, exc_type, exc_value, traceback): # restore random states if self.device_mesh is not None: # revert to previous sp group set_ulysses_sequence_parallel_group(self.prev_sp_group) # TODO: check how to set seed for each model def preprocess_data(self, data: DataProto) -> DataProto: """ AllGather data from sp region This is because the data is first sharded along the FSDP dimension as we utilize the DP_COMPUTE In Ulysses, we need to make sure the same data is used across a SP group """ if self.device_mesh is not None: sp_size = self.device_mesh['sp'].size() group = self.device_mesh['sp'].get_group() prev_device = data.batch.device data.batch = data.batch.cuda(device=torch.cuda.current_device()) data.batch = allgather_dict_tensors(data.batch.contiguous(), size=sp_size, group=group, dim=0) data.batch = data.batch.to(prev_device) # all gather non_tensor_batch all_non_tensor_batch = [None for _ in range(sp_size)] torch.distributed.all_gather_object(all_non_tensor_batch, data.non_tensor_batch, group=group) data.non_tensor_batch = { k: np.concatenate([d[k] for d in all_non_tensor_batch]) for k in data.non_tensor_batch } return data def postprocess_data(self, data: DataProto) -> DataProto: """ Split the data to follow FSDP partition """ if self.device_mesh is not None: sp_size = self.device_mesh['sp'].size() sp_rank = self.device_mesh['sp'].get_local_rank() data = data.chunk(chunks=sp_size)[sp_rank] return data ================================================ FILE: verl/workers/sharding_manager/fsdp_vllm.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import logging import torch import numpy as np from torch.distributed.fsdp.fully_sharded_data_parallel import FullyShardedDataParallel as FSDP from torch.distributed.fsdp.api import ShardingStrategy, ShardedStateDictConfig, StateDictType, FullStateDictConfig from torch.distributed.device_mesh import DeviceMesh from verl.third_party.vllm import LLM from verl.third_party.vllm import parallel_state as vllm_ps from verl import DataProto from verl.utils.torch_functional import (broadcast_dict_tensor, allgather_dict_tensors) from verl.utils.debug import log_gpu_memory_usage from verl.third_party.vllm import vllm_version from .base import BaseShardingManager logger = logging.getLogger(__file__) logger.setLevel(os.getenv('VERL_PPO_LOGGING_LEVEL', 'WARN')) class FSDPVLLMShardingManager(BaseShardingManager): def __init__(self, module: FSDP, inference_engine: LLM, model_config, full_params: bool = False, device_mesh: DeviceMesh = None): self.module = module self.inference_engine = inference_engine self.model_config = model_config self.device_mesh = device_mesh # Full params self.full_params = full_params if full_params: FSDP.set_state_dict_type(self.module, state_dict_type=StateDictType.FULL_STATE_DICT, state_dict_config=FullStateDictConfig()) else: FSDP.set_state_dict_type(self.module, state_dict_type=StateDictType.SHARDED_STATE_DICT, state_dict_config=ShardedStateDictConfig()) # Note that torch_random_states may be different on each dp rank self.torch_random_states = torch.cuda.get_rng_state() # get a random rng states if self.device_mesh is not None: gen_dp_rank = self.device_mesh['dp'].get_local_rank() torch.cuda.manual_seed(gen_dp_rank + 1000) # make sure all tp ranks have the same random states self.gen_random_states = torch.cuda.get_rng_state() torch.cuda.set_rng_state(self.torch_random_states) else: self.gen_random_states = None def __enter__(self): log_gpu_memory_usage('Before state_dict() in sharding manager memory', logger=logger) params = self.module.state_dict() log_gpu_memory_usage('After state_dict() in sharding manager memory', logger=logger) # Copy, not share memory load_format = 'hf' if self.full_params else 'dtensor' if vllm_version in ('0.4.2', '0.5.4', '0.6.3'): self.inference_engine.sync_model_weights(params, load_format=load_format) else: self.inference_engine.wake_up() # TODO(ZSL): deal with 'hf' format if load_format == 'dtensor': from verl.third_party.vllm import load_dtensor_weights load_dtensor_weights( params, self.inference_engine.llm_engine.model_executor.driver_worker.worker.model_runner.model) else: raise NotImplementedError(f'load_format {load_format} not implemented') log_gpu_memory_usage('After sync model weights in sharding manager', logger=logger) del params torch.cuda.empty_cache() log_gpu_memory_usage('After del state_dict and empty_cache in sharding manager', logger=logger) # TODO: offload FSDP model weights # self.module.cpu() # torch.cuda.empty_cache() # if torch.distributed.get_rank() == 0: # print(f'after model to cpu in sharding manager memory allocated: {torch.cuda.memory_allocated() / 1e9}GB, reserved: {torch.cuda.memory_reserved() / 1e9}GB') # important: need to manually set the random states of each tp to be identical. if self.device_mesh is not None: self.torch_random_states = torch.cuda.get_rng_state() torch.cuda.set_rng_state(self.gen_random_states) def __exit__(self, exc_type, exc_value, traceback): log_gpu_memory_usage('Before vllm offload in sharding manager', logger=logger) # TODO(ZSL): check this if vllm_version in ('0.4.2', '0.5.4', '0.6.3'): self.inference_engine.offload_model_weights() else: self.inference_engine.sleep(level=1) log_gpu_memory_usage('After vllm offload in sharding manager', logger=logger) # self.module.to('cuda') # if torch.distributed.get_rank() == 0: # print(f'after actor module to cuda in sharding manager memory allocated: {torch.cuda.memory_allocated() / 1e9}GB, reserved: {torch.cuda.memory_reserved() / 1e9}GB') self.module.train() # add empty cache after each compute torch.cuda.empty_cache() # restore random states if self.device_mesh is not None: self.gen_random_states = torch.cuda.get_rng_state() torch.cuda.set_rng_state(self.torch_random_states) def preprocess_data(self, data: DataProto) -> DataProto: # TODO: Current impl doesn't consider FSDP with torch micro-dp tp_size = vllm_ps.get_tensor_model_parallel_world_size() if vllm_version in ('0.3.1', '0.4.2', '0.5.4', '0.6.3'): group = vllm_ps.get_tensor_model_parallel_group() else: group = vllm_ps.get_tensor_model_parallel_group().device_group prev_device = data.batch.device data.batch = data.batch.cuda(device=torch.cuda.current_device()) data.batch = allgather_dict_tensors(data.batch.contiguous(), size=tp_size, group=group, dim=0) data.batch = data.batch.to(prev_device) # all gather non_tensor_batch all_non_tensor_batch = [None for _ in range(tp_size)] torch.distributed.all_gather_object(all_non_tensor_batch, data.non_tensor_batch, group=group) data.non_tensor_batch = {k: np.concatenate([d[k] for d in all_non_tensor_batch]) for k in data.non_tensor_batch} return data def postprocess_data(self, data: DataProto) -> DataProto: # TODO: Current impl doesn't consider FSDP with torch micro-dp local_world_size = vllm_ps.get_tensor_model_parallel_world_size() src_rank = (torch.distributed.get_rank() // local_world_size) * local_world_size if vllm_version in ('0.3.1', '0.4.2', '0.5.4', '0.6.3'): broadcast_dict_tensor(data.batch, src=src_rank, group=vllm_ps.get_tensor_model_parallel_group()) else: broadcast_dict_tensor(data.batch, src=src_rank, group=vllm_ps.get_tensor_model_parallel_group().device_group) dp_rank = torch.distributed.get_rank() dp_size = torch.distributed.get_world_size() # not consider torch micro-dp tp_size = vllm_ps.get_tensor_model_parallel_world_size() if tp_size > 1: # TODO: shall we build a micro_dp group for vllm when integrating with vLLM? local_prompts = data.chunk(chunks=tp_size) data = local_prompts[dp_rank % tp_size] return data ================================================ FILE: verl/workers/sharding_manager/megatron_vllm.py ================================================ # Copyright 2024 Bytedance Ltd. and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ This file contains a Megatron style Hybrid Engine that shares the weights of the actor with the inference engine. """ import importlib from packaging.version import Version import torch import torch.distributed as dist from torch import nn from megatron.core import parallel_state as mpu from megatron.core import DistributedDataParallel as LocalDDP from megatron.core.transformer.module import Float16Module from torch.nn.parallel.distributed import DistributedDataParallel as torchDDP from verl.utils.megatron_utils import get_model, unwrap_model from verl.utils.memory_buffer import ( build_memory_buffer, build_memory_reference_from_module, get_weight_buffer_meta_from_module, ) class AllGatherPPModel: def __init__(self, model_provider) -> None: self._pp_group = mpu.get_pipeline_model_parallel_group() self._pp_rank = mpu.get_pipeline_model_parallel_rank() self._pp_size = mpu.get_pipeline_model_parallel_world_size() self._vpp_size = mpu.get_virtual_pipeline_model_parallel_world_size() self._model_chunk_size = self._vpp_size or 1 # each one holds a list of model_chunks in this pp stage self._pp_models = [None] * self.pp_size rank_list = list(range(self.pp_size)) # make current rank the last one to initialize rank_list[self.pp_rank], rank_list[-1] = rank_list[-1], rank_list[self.pp_rank] self._this_rank_models = None # store the parameter of each pp stage self.memory_buffers = [None] * self.pp_size for cur_pp_rank in rank_list: print( f'create pp model', f'torch allocated {torch.cuda.memory_allocated() / 1e9:.4f} GB, ' f'reserved {torch.cuda.memory_reserved() / 1e9:.4f} GB') # since the last initialized rank is the current pp rank, after init, the pp rank is still correct mpu.set_pipeline_model_parallel_rank(cur_pp_rank) if cur_pp_rank != self.pp_rank: models = get_model(model_provider, wrap_with_ddp=False) models = nn.ModuleList(models) assert len(models) == self._model_chunk_size, f"{len(models)} != {self._model_chunk_size}" self.pp_models[cur_pp_rank] = models else: # for regular model, we wrapped it with DDP models = get_model(model_provider) assert len(models) == self._model_chunk_size, f"{len(models)} != {self._model_chunk_size}" self._this_rank_models = nn.ModuleList(models) self.pp_models[cur_pp_rank] = nn.ModuleList(unwrap_model(models, (torchDDP, LocalDDP))) self._build_param_buffer(cur_pp_rank) self._build_param_references(cur_pp_rank, maintain_weight=cur_pp_rank == self.pp_rank) # TODO: after binding to the memory buffer, we can load the checkpoint here if cur_pp_rank != self.pp_rank: for model in self.pp_models[cur_pp_rank]: model.eval() self._offload_params_to_cpu(cur_pp_rank) def _build_param_buffer(self, pp_rank): """Build the parameter buffer in each pp rank""" if pp_rank == self._pp_rank: from verl.utils.memory_buffer import MemoryBuffer # The code here is very hard-coded, based on the following assumptions: # 1. `len(_this_rank_models) == 1` # 2. `_this_rank_models[0]` is a instance of `DistributedDataParallel` and `use_distributed_optimizer=True` # 3. Only bfloat16 data type is used in parameters source = self._this_rank_models[0].buffers[0].param_data self.memory_buffers[pp_rank] = { torch.bfloat16: MemoryBuffer(source.numel(), source.numel(), torch.bfloat16, source) } else: model = self.pp_models[pp_rank] weight_buffer_meta = get_weight_buffer_meta_from_module(model) self.memory_buffers[pp_rank] = build_memory_buffer(weight_buffer_meta) def _build_param_references(self, pp_rank, maintain_weight=False): if pp_rank == self._pp_rank: return model = self.pp_models[pp_rank] build_memory_reference_from_module(model, self.memory_buffers[pp_rank], maintain_weight=maintain_weight) def _load_params_to_cuda(self, pp_rank, to_empty=False): assert pp_rank != self.pp_rank, f"unexpected to load current pp rank [{pp_rank}] back to cuda" for buffer in self.memory_buffers[pp_rank].values(): if not to_empty: buffer.data = buffer.data.to(torch.cuda.current_device(), non_blocking=True) else: buffer.data = torch.empty_like(buffer.data, device='cuda') # rebuild reference after loading to CUDA self._build_param_references(pp_rank) def _offload_params_to_cpu(self, pp_rank, to_empty=False): assert pp_rank != self.pp_rank, f"unexpected to offload current pp rank [{pp_rank}] to cpu" for buffer in self.memory_buffers[pp_rank].values(): if not to_empty: # offload the whole memory buffer to CPU buffer.data = buffer.data.to('cpu', non_blocking=True) else: buffer.data = torch.empty_like(buffer.data, device='cpu') self._build_param_references(pp_rank) def load_params_to_cuda(self, to_empty=False): """load all model params to cuda""" for cur_pp_rank in range(self.pp_size): if cur_pp_rank != self.pp_rank: self._load_params_to_cuda(cur_pp_rank, to_empty=to_empty) def allgather_params(self): """allgather params of all pp ranks. Return a list of handles""" for cur_pp_rank in range(self.pp_size): global_src = dist.get_global_rank(group=self.pp_group, group_rank=cur_pp_rank) # NOTE(sgm): the async op may cause memory leakage of the memory_buffer/pp_models for _, param in sorted(self.pp_models[cur_pp_rank].named_parameters()): dist.broadcast(tensor=param.data, src=global_src, group=self.pp_group, async_op=False) def forward(self, *inputs, **kwargs): try: prev_output = None for cur_chunk_rank in range(self._model_chunk_size): if self._vpp_size: mpu.set_virtual_pipeline_model_parallel_rank(cur_chunk_rank) for cur_pp_rank in range(self.pp_size): mpu.set_pipeline_model_parallel_rank(cur_pp_rank) self.pp_models[cur_pp_rank][cur_chunk_rank].set_input_tensor(prev_output) ret = self.pp_models[cur_pp_rank][cur_chunk_rank](*inputs, **kwargs) self.pp_models[cur_pp_rank][cur_chunk_rank].set_input_tensor(None) prev_output = ret finally: if self._vpp_size: mpu.set_virtual_pipeline_model_parallel_rank(0) mpu.set_pipeline_model_parallel_rank(self.pp_rank) return ret def __call__(self, *inputs, **kwargs): return self.forward(*inputs, **kwargs) def eval(self): for model in self.pp_models[self.pp_rank]: model.eval() def train(self): for model in self.pp_models[self.pp_rank]: model.train() def offload_params_to_cpu(self, to_empty=False): """offload params of models that are not of current pp rank to cpu""" for cur_pp_rank in range(self.pp_size): if cur_pp_rank != self.pp_rank: self._offload_params_to_cpu(cur_pp_rank, to_empty=to_empty) def get_all_params(self): """Get all the parameters of the models in all pp ranks Returns: params: List[List[Dict[str, Tensor]]]: a list of parameters in all pp, where each is a list of dict tensors of each model chunk """ params = [] for pp_rank in range(self.pp_size): params.append([]) for model_chunk_idx in range(len(self.pp_models[pp_rank])): params[pp_rank].append({}) pp_model = self.pp_models[pp_rank][model_chunk_idx] pp_model = unwrap_model(pp_model, ((torchDDP, LocalDDP, Float16Module))) # not use Float16Module for name, param in pp_model.named_parameters(): # NOTE(gh) workaround: should not get lora params for inference if 'lora' in name: continue params[pp_rank][model_chunk_idx][name] = param return params def update_this_rank_models(self, new_models): self._this_rank_models = new_models self._pp_models[self.pp_rank] = unwrap_model(new_models, (torchDDP, LocalDDP)) @property def this_rank_models(self): return self._this_rank_models @property def pp_size(self): return self._pp_size @property def pp_rank(self): return self._pp_rank @property def pp_group(self): return self._pp_group @property def pp_models(self): return self._pp_models """ Megatron Hybrid Engine: - During training, only the current pp stage holds the parameters - Before inference, broadcast the parameters of the current pp rank to all other pp ranks (all pp ranks holds all the parameters) - Bind the parameters to the inference engine - Do inference in tp. pp is treated as additional dp - After inference, all the parameters that doesn't belong to this pp rank is freed. """ from .base import BaseShardingManager import torch from torch import nn import torch.distributed from torch.distributed import new_group from verl import DataProto from verl.utils.torch_functional import (broadcast_dict_tensor, allgather_dict_tensors) import verl.utils.megatron.tensor_parallel as tp_utils from verl.third_party.vllm import parallel_state as vllm_ps from verl.third_party.vllm import LLM from verl.utils.model import normalize_pp_vpp_params # Micro Data parallel group. Micro data parallel group is additional dp group that origins from splitting training tp # into infer_tp and micro_tp. By default, we use order micro_dp - tp _MICRO_DATA_PARALLEL_GROUP = None class MegatronVLLMShardingManager(BaseShardingManager): def __init__(self, module: AllGatherPPModel, inference_engine: LLM, model_config, layer_name_mapping): self.module = module self.inference_engine = inference_engine self.model_config = model_config self.layer_name_mapping = layer_name_mapping # initialize micro_dp group for vllm inference global _MICRO_DATA_PARALLEL_GROUP world_size = torch.distributed.get_world_size() rank = torch.distributed.get_rank() train_tensor_parallel_size = mpu.get_tensor_model_parallel_world_size() infer_tensor_parallel_size = vllm_ps.get_tensor_model_parallel_world_size() # TODO(sgm): this may not be true for FSDP -> vLLM assert infer_tensor_parallel_size <= train_tensor_parallel_size, \ 'Not implemented for infer_tp > train_tp' assert train_tensor_parallel_size % infer_tensor_parallel_size == 0 micro_dp_size = train_tensor_parallel_size // infer_tensor_parallel_size num_micro_dp_groups = world_size // micro_dp_size assert _MICRO_DATA_PARALLEL_GROUP is None, ("micro data parallel group is already initialized") for i in range(num_micro_dp_groups): ranks = range(i * micro_dp_size, (i + 1) * micro_dp_size) group = new_group(ranks=ranks) if rank in ranks: _MICRO_DATA_PARALLEL_GROUP = group def default_tp_concat_fn(self, name, param, infer_params, model_config): """ name: name of the parameter param: training parameters infer_params (List[torch.Tensor]): a list of parameters all-gathered from micro_dp_group model_config: huggingface model_config TODO(zhangchi.usc1992): currently, the implementation is adhoc. We can move this function to the model definition so that it is model-agnostic. If the model doesn't implement this function, we can throw an error to force user disable TP HybridEngine. """ if self.layer_name_mapping.get("qkv_layer_name") in name: # if the tensor is qkv, for each param on tp, split into q, k, v # concat q, k, v separately. q_lst = [] k_lst = [] v_lst = [] assert model_config.num_attention_heads % model_config.num_key_value_heads == 0 num_q_per_kv = model_config.num_attention_heads // model_config.num_key_value_heads assert infer_params[0].shape[0] % (num_q_per_kv + 2) == 0 kv_size_per_tp = infer_params[0].shape[0] // (num_q_per_kv + 2) split_size = [kv_size_per_tp * num_q_per_kv, kv_size_per_tp, kv_size_per_tp] for infer_param in infer_params: q, k, v = infer_param.split(split_size) q_lst.append(q) k_lst.append(k) v_lst.append(v) q = torch.cat(q_lst, dim=0) k = torch.cat(k_lst, dim=0) v = torch.cat(v_lst, dim=0) infer_params = torch.cat((q, k, v), dim=0) elif self.layer_name_mapping.get("gate_proj_layer_name") in name: # if the tensor is gate and proj gate_lst = [] up_lst = [] for infer_param in infer_params: gate, up = infer_param.chunk(2) gate_lst.append(gate) up_lst.append(up) gate = torch.cat(gate_lst, dim=0) up = torch.cat(up_lst, dim=0) infer_params = torch.cat((gate, up), dim=0) else: # concat tensor infer_params = torch.cat(infer_params, dim=tp_utils.get_tensor_parallel_partition_dim(param)) return infer_params def _post_process_params(self, params): """ For each param, if it is a tp-splited param, we all-gather from micro_dp group. """ # here the params are in train tp format. we iterate params and all-gather # TODO(zhangchi.usc1992) We can consider copy non-tp weight to another infer buffer. # In this way, all the params in the original memory_buffers and can be offload. micro_dp_size = get_micro_data_parallel_world_size() micro_dp_group = get_micro_data_parallel_group() if micro_dp_size <= 1: return origin_params = {} for name in params.keys(): param = params[name] if tp_utils.is_tensor_parallel_param(param): # allocate a new tensor with proper size infer_params = [torch.empty_like(param) for _ in range(micro_dp_size)] torch.distributed.all_gather(infer_params, param, group=micro_dp_group) infer_params = self.default_tp_concat_fn(name, param, infer_params, self.model_config) # replace with original param params[name] = infer_params origin_params[name] = param return origin_params def __enter__(self): # create a new cuda space for parameters not in this pp rank self.module.load_params_to_cuda() # broadcast the parameters from pp rank to other ranks self.module.allgather_params() # obtain name to parameters in pp/vpp params = self.module.get_all_params() # bind the params to inference engine self.params = normalize_pp_vpp_params(params=params, num_hidden_layers=self.model_config.num_hidden_layers, layer_name='layers') self.origin_params = self._post_process_params(self.params) self.inference_engine.sync_model_weights(self.params, load_format='megatron') def __exit__(self, exc_type, exc_value, traceback): # offload parameters doesn't belong to this pp rank self.module.offload_params_to_cpu() # FIXME(sgm): the best practice is to delete the cuda tensor # rebind the model weights, can be any cpu tensor if get_micro_data_parallel_world_size() > 1: for name in self.params.keys(): self.params[name] = self.origin_params[name] # self.inference_engine.sync_model_weights(params) self.inference_engine.offload_model_weights() self.module.train() # add empty cache after each compute torch.cuda.empty_cache() def preprocess_data(self, data: DataProto) -> DataProto: # prompts are identical for each training tp. We select for each inference tp micro_dp_size = get_micro_data_parallel_world_size() micro_dp_rank = get_micro_data_parallel_rank() # broadcast from tp=0 to other tp ranks broadcast_dict_tensor(data.batch, src=mpu.get_tensor_model_parallel_src_rank(), group=mpu.get_tensor_model_parallel_group()) if micro_dp_size > 1: local_prompts = data.chunk(chunks=micro_dp_size) data = local_prompts[micro_dp_rank] return data def postprocess_data(self, data: DataProto) -> DataProto: meta_info = data.meta_info # all gather batch among micro-dp groups micro_dp_size = get_micro_data_parallel_world_size() if micro_dp_size > 1: data.batch = allgather_dict_tensors(data.batch.contiguous(), size=get_micro_data_parallel_world_size(), group=get_micro_data_parallel_group(), dim=0) # all gather batch among pp group if meta_info.get('allgather_pp_output', True): data.batch = allgather_dict_tensors(data.batch.contiguous(), size=mpu.get_pipeline_model_parallel_world_size(), group=mpu.get_pipeline_model_parallel_group(), dim=0) return data """ Micro Data parallel group """ def get_micro_data_parallel_group(): assert _MICRO_DATA_PARALLEL_GROUP is not None return _MICRO_DATA_PARALLEL_GROUP def get_micro_data_parallel_world_size(): return torch.distributed.get_world_size(group=get_micro_data_parallel_group()) def get_micro_data_parallel_rank(): return torch.distributed.get_rank(group=get_micro_data_parallel_group())