merging \`$SOURCE_BRANCH\` into \`dev\` for you! 🚀
Please review and approve this cherry pick by your convenience\!"
tags:
- arch/amd64
- env/prod
- origin/jet-fleet
- owner/jet-core
- purpose/utility
- team/megatron
rules:
- if: $CI_COMMIT_BRANCH == "main" && $CI_PIPELINE_SOURCE == "push"
allow_failure: true
- when: never
publish:approve_merge_gate:
stage: publish
image: maniator/gh
tags:
- arch/amd64
- env/prod
- origin/jet-fleet
- owner/jet-core
- purpose/utility
- team/megatron
script:
- |
set -eoux pipefail
EXIT_CODE=0
apk add python3
python -m venv .venv
source .venv/bin/activate
pip install --no-cache-dir python-gitlab click pygithub
export GITLAB_ENDPOINT
export RO_API_TOKEN=${PROJECT_ACCESS_TOKEN_MCORE}
if [[ "$CI_COMMIT_BRANCH" == *main* ]]; then
export TARGET_BRANCH="main"
elif [[ "$CI_COMMIT_BRANCH" == *dev* ]]; then
export TARGET_BRANCH="dev"
fi
python tests/test_utils/python_scripts/check_status_of_main.py --target-branch "$TARGET_BRANCH" --once || EXIT_CODE=$?
export GH_TOKEN=$GH_TOKEN
export REPO=NVIDIA/Megatron-LM
if [[ $EXIT_CODE -eq 0 ]]; then
export STATUS="approved"
export COMMENT="Main is healthy. Submitting PR."
elif [[ $EXIT_CODE -eq 1 ]]; then
export STATUS="rejected"
export COMMENT="$TARGET_BRANCH is not healthy. An automation engineer is investigating. No need to take any action."
elif [[ $EXIT_CODE -eq 2 ]]; then
echo "Main is running. We won't cancel the deployment."
exit 0
fi
if [[ $EXIT_CODE -lt 2 ]]; then
python tests/test_utils/python_scripts/approve_merge_gate.py
fi
retry:
max: 2
rules:
- if: $CI_PIPELINE_SOURCE == "schedule" && ($CI_COMMIT_BRANCH == 'ci-approve-dev' || $CI_COMMIT_BRANCH == 'ci-approve-main')
when: always
- when: never
publish:sync_branches:
stage: publish
image: python:3.10
script:
- set -x
- git remote add github https://github.com/NVIDIA/Megatron-LM.git || true
- git remote add gitlab https://gitlab-ci-token:${PROJECT_ACCESS_TOKEN_MCORE}@${GITLAB_ENDPOINT}/${CI_PROJECT_NAMESPACE}/Megatron-LM.git || true
- BRANCHES=("main" "dev")
- |
while IFS= read -r line; do
BRANCHES+=("$line") # Add each line to the array
done < <( \
git ls-remote --heads "https://token:${PAT}@github.com/NVIDIA/Megatron-LM.git" 'refs/heads/core_*' | \
cut -d'/' -f3- \
)
- |
for BRANCH in "${BRANCHES[@]}"; do
# Define the full refspec for the branch
BRANCH_REF="refs/heads/$BRANCH"
echo "--- Processing branch: $BRANCH ---"
# 1. Explicitly fetch the branch ref from 'github'
# This avoids fetching a tag with the same name.
# It updates/creates the remote-tracking branch (e.g., 'refs/remotes/github/core_r0.10.0')
if ! git fetch github "$BRANCH_REF:refs/remotes/github/$BRANCH"; then
echo "Failed to fetch branch $BRANCH. Skipping."
continue
fi
# 2. Create or update the local branch from the remote-tracking branch we just fetched.
# The -B flag creates the branch if it doesn't exist or resets it if it does.
if ! git checkout -B "$BRANCH" "github/$BRANCH"; then
echo "Failed to checkout local branch $BRANCH. Skipping."
continue
fi
# 3. Now you are on the correct local branch, ready to push.
echo "Successfully on branch $BRANCH. Echoing push command:"
git push -u gitlab HEAD:refs/heads/$BRANCH --force
echo "-----------------------------------"
done
tags:
- arch/amd64
- env/prod
- origin/jet-fleet
- owner/jet-core
- purpose/utility
- team/megatron
retry:
max: 2
rules:
- if: $CI_PIPELINE_SOURCE == "schedule" && ($CI_COMMIT_BRANCH == 'ci-sync-branches')
when: always
- when: never
================================================
FILE: .gitlab-ci.yml
================================================
.merge_train_rule: &merge_train_rule
UNIT_TEST: "yes"
UNIT_TEST_REPEAT: 1
UNIT_TEST_TIMEOUT: 30
INTEGRATION_TEST: "no"
INTEGRATION_TEST_SCOPE: mr
FUNCTIONAL_TEST: "yes"
FUNCTIONAL_TEST_SCOPE: mr-slim
FUNCTIONAL_TEST_REPEAT: 1
FUNCTIONAL_TEST_TIME_LIMIT: 2700
CLUSTER_A100: ""
CLUSTER_H100: ""
PUBLISH: "no"
workflow:
rules:
# Do not trigger for forks
- if: $CI_PROJECT_NAMESPACE != "ADLR" || ($CI_PIPELINE_SOURCE == "merge_request_event" && $CI_MERGE_REQUEST_PROJECT_PATH != "ADLR/megatron-lm")
when: never
- if: $CI_PIPELINE_SOURCE == "schedule" && ($CI_COMMIT_BRANCH == 'ci-approve-dev' || $CI_COMMIT_BRANCH == 'ci-approve-main')
# ci-branches only for schedule
- if: $CI_COMMIT_BRANCH =~ /ci-/ && $CI_PIPELINE_SOURCE != "schedule"
when: never
# For schedules pipelines
- if: $CI_PIPELINE_SOURCE == "schedule"
auto_cancel:
on_new_commit: none
# For manual pipelines (GitLab UI)
- if: $CI_PIPELINE_SOURCE == "web"
# For pipelines created via the REST API (personal access token)
- if: $CI_PIPELINE_SOURCE == "api"
# For trigger pipelines
- if: $CI_PIPELINE_SOURCE == "trigger"
# For push to main
- if: $CI_PIPELINE_SOURCE == 'push' && ($CI_COMMIT_BRANCH == "main" || $CI_COMMIT_BRANCH == "dev" || $CI_COMMIT_BRANCH =~ /^core_/)
variables:
UNIT_TEST: "no"
INTEGRATION_TEST: "no"
FUNCTIONAL_TEST: "yes"
FUNCTIONAL_TEST_SCOPE: mr
FUNCTIONAL_TEST_REPEAT: 5
FUNCTIONAL_TEST_RECORD_CHECKPOINTS: "no"
FUNCTIONAL_TEST_TIME_LIMIT: 3600
CLUSTER_A100: ""
CLUSTER_H100: ""
PUBLISH: "no"
auto_cancel:
on_new_commit: interruptible
# For merge-trains that need to be fast-tracked
- if: $CI_MERGE_REQUEST_EVENT_TYPE == 'merge_train' && $CI_MERGE_REQUEST_LABELS =~ /fast-track/
variables:
UNIT_TEST: "yes"
UNIT_TEST_REPEAT: 1
UNIT_TEST_TIMEOUT: 30
INTEGRATION_TEST: "no"
FUNCTIONAL_TEST: "no"
CLUSTER_A100: ""
CLUSTER_H100: ""
PUBLISH: "no"
# For normal merge-trains
- if: $CI_MERGE_REQUEST_EVENT_TYPE == 'merge_train'
variables: *merge_train_rule
# For MRs with integration suite
- if: $CI_MERGE_REQUEST_EVENT_TYPE == 'merged_result' && $CI_MERGE_REQUEST_LABELS =~ /Run tests/
variables:
UNIT_TEST: "yes"
UNIT_TEST_REPEAT: 1
UNIT_TEST_TIMEOUT: 30
INTEGRATION_TEST: "yes"
INTEGRATION_TEST_SCOPE: mr
FUNCTIONAL_TEST: "no"
FUNCTIONAL_TEST_SCOPE: mr-slim
FUNCTIONAL_TEST_REPEAT: 1
FUNCTIONAL_TEST_TIME_LIMIT: 2700
CLUSTER_A100: ""
CLUSTER_H100: ""
PUBLISH: "no"
# For MRs with nightly
- if: $CI_MERGE_REQUEST_EVENT_TYPE == 'merged_result' && $CI_MERGE_REQUEST_LABELS =~ /Run nightly/
variables:
UNIT_TEST: "yes"
UNIT_TEST_REPEAT: 1
UNIT_TEST_TIMEOUT: 30
INTEGRATION_TEST: "no"
FUNCTIONAL_TEST: "yes"
FUNCTIONAL_TEST_SCOPE: nightly
FUNCTIONAL_TEST_REPEAT: 5
FUNCTIONAL_TEST_RECORD_CHECKPOINTS: "no"
FUNCTIONAL_TEST_TIME_LIMIT: 2700
CLUSTER_A100: ""
CLUSTER_H100: ""
PUBLISH: "no"
# For MRs with weekly
- if: $CI_MERGE_REQUEST_EVENT_TYPE == 'merged_result' && $CI_MERGE_REQUEST_LABELS =~ /Run weekly/
variables:
UNIT_TEST: "yes"
UNIT_TEST_REPEAT: 1
UNIT_TEST_TIMEOUT: 30
INTEGRATION_TEST: "no"
FUNCTIONAL_TEST: "yes"
FUNCTIONAL_TEST_SCOPE: weekly
FUNCTIONAL_TEST_REPEAT: 1
FUNCTIONAL_TEST_RECORD_CHECKPOINTS: "no"
FUNCTIONAL_TEST_TIME_LIMIT: 9000
CLUSTER_A100: ""
CLUSTER_H100: ""
PUBLISH: "no"
# For MRs with heavy suite
- if: $CI_MERGE_REQUEST_EVENT_TYPE == 'merged_result' && $CI_MERGE_REQUEST_LABELS =~ /Run functional tests/
variables:
UNIT_TEST: "yes"
UNIT_TEST_REPEAT: 1
UNIT_TEST_TIMEOUT: 30
INTEGRATION_TEST: "no"
FUNCTIONAL_TEST: "yes"
FUNCTIONAL_TEST_SCOPE: mr
FUNCTIONAL_TEST_REPEAT: 1
FUNCTIONAL_TEST_TIME_LIMIT: 2700
CLUSTER_A100: ""
CLUSTER_H100: ""
PUBLISH: "no"
# Default MRs
- if: $CI_MERGE_REQUEST_EVENT_TYPE == 'merged_result'
variables:
UNIT_TEST: "yes"
UNIT_TEST_REPEAT: 1
UNIT_TEST_TIMEOUT: 30
INTEGRATION_TEST: "no"
FUNCTIONAL_TEST: "no"
PUBLISH: "no"
- when: never
auto_cancel:
on_new_commit: interruptible
stages:
- build
- test
- integration_tests
- functional_tests
- publish
default:
interruptible: true
retry:
max: 2
when: runner_system_failure
variables:
BUILD:
value: "yes"
UNIT_TEST:
value: "yes"
options:
- "yes"
- "no"
description: To run the funtional test suite
UNIT_TEST_REPEAT:
value: "1"
description: "Number of repetitions"
UNIT_TEST_TIMEOUT:
value: "30"
description: Timeout (minutes) for Unit tests (all repeats)
INTEGRATION_TEST:
value: "yes"
options:
- "yes"
- "no"
description: To run the integration test suite
INTEGRATION_TEST_SCOPE:
value: "mr"
options:
- "mr"
- "nightly"
- "weekly"
- "pre-release"
- "release"
description: "Testsuite to run (only for INTEGRATION_TEST=yes)"
INTEGRATION_TEST_TIME_LIMIT:
value: "900"
description: "Timeout in seconds per test"
INTEGRATION_TEST_CASES:
value: "all"
description: "Comma-separated list of test_cases to run. Use 'all' to run the full suite."
FUNCTIONAL_TEST:
value: "yes"
options:
- "yes"
- "no"
description: To run the funtional test suite
FUNCTIONAL_TEST_SCOPE:
value: "mr"
options:
- "mr"
- "nightly"
- "weekly"
- "pre-release"
- "release"
description: "Testsuite to run (only for FUNCTIONAL_TEST=yes)"
FUNCTIONAL_TEST_REPEAT:
value: "5"
description: "Number of repetitions per test"
FUNCTIONAL_TEST_TIME_LIMIT:
value: "2700"
description: "Timeout in seconds per test"
FUNCTIONAL_TEST_CASES:
value: "all"
description: "Comma-separated list of test_cases to run. Use 'all' to run the full suite."
FUNCTIONAL_TEST_NAME:
description: "Name of functional test run (only for pre-release and release)"
value: "$$CI_COMMIT_SHA"
FUNCTIONAL_TEST_RECORD_CHECKPOINTS:
value: "no"
description: "Record golden checkpoints"
options:
- "yes"
- "no"
CLUSTER_A100:
value: "dgxa100_dracooci"
options:
- "dgxa100_dracooci"
- "dgxa100_dracooci-ord"
description: "Cluster for A100 workloads"
CLUSTER_H100:
value: "dgxh100_coreweave"
options:
- "dgxh100_coreweave"
- "dgxh100_eos"
description: "Cluster for H100 workloads"
CLUSTER_GB200:
value: "dgxgb200_oci-hsg"
options:
- "dgxgb200_oci-hsg"
description: "Cluster for H100 workloads"
PUBLISH:
value: "no"
options:
- "yes"
- "no"
description: Build and publish a wheel to PyPi
PUBLISH_COMMIT:
value: "$$CI_COMMIT_SHA"
description: Which commit to publish
PUBLISH_VERSION_BUMP_BRANCH:
value: "$$CI_COMMIT_BRANCH"
description: Which branch to target for version bump
PUBLISH_SCOPE:
value: "code-freeze"
options:
- "code-freeze"
- "release"
- "review-reminder"
- "upgrade-dependencies"
description: Type of publish (freeze or final release)
# CI wide variables
CI_MCORE_LTS_IMAGE: ${GITLAB_ENDPOINT}:5005/adlr/megatron-lm/mcore_ci_lts
CI_MCORE_DEV_IMAGE: ${GITLAB_ENDPOINT}:5005/adlr/megatron-lm/mcore_ci_dev
CI_NEMO_IMAGE: ${GITLAB_ENDPOINT}:5005/adlr/megatron-lm/nemo_ci
UTILITY_IMAGE: ${GITLAB_ENDPOINT}:5005/adlr/megatron-lm/mcore_utility
TE_GIT_REF: ""
include:
- .gitlab/stages/00.pre.yml
- .gitlab/stages/01.build.yml
- .gitlab/stages/02.test.yml
- .gitlab/stages/03.integration-tests.yml
- .gitlab/stages/04.functional-tests.yml
- .gitlab/stages/05.publish.yml
================================================
FILE: .pre-commit-config.yaml
================================================
repos:
- repo: https://github.com/psf/black
rev: 'refs/tags/24.4.2:refs/tags/24.4.2'
hooks:
- id: black
files: ^megatron/core/.*|^tests/unit_tests/.*
args: ["--skip-magic-trailing-comma", "--skip-string-normalization"]
- repo: https://github.com/pycqa/pylint
rev: v3.2.6
hooks:
- id: pylint
files: ^megatron/core/.*
- repo: https://github.com/pycqa/isort
rev: 5.13.2
hooks:
- id: isort
files: ^megatron/core/.*
================================================
FILE: .pylintrc
================================================
[MAIN]
ignore-paths=tests
max-line-length=100
load-plugins=pylint.extensions.bad_builtin
[MESSAGES CONTROL]
disable=all
enable=C0115,C0116,W0611,C0301,E0606,W0141
# C0115: missing-class-docstring
# C0116: missing-function-docstring
# W0611: unused-import
# C0301: line-too-long
# E0606: possibly-used-before-assignment
# W0141: bad-builtin (from bad_builtin extension)
[BASIC]
bad-functions=print
[BAD_BUILTIN]
# Specify which builtins should be flagged
bad-builtins=print
================================================
FILE: .python-version
================================================
3.12
================================================
FILE: CONTRIBUTING.md
================================================
# Contributing to Megatron
Visit our [contributing page](https://docs.nvidia.com/megatron-core/developer-guide/latest/developer/contribute.html).
================================================
FILE: LICENSE
================================================
The following applies to all files unless otherwise noted:
# Copyright (c) 2019-2025, NVIDIA CORPORATION. All rights reserved.
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are met:
# * Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
# * Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in the
# documentation and/or other materials provided with the distribution.
# * Neither the name of NVIDIA CORPORATION nor the names of its
# contributors may be used to endorse or promote products derived
# from this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
# EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
# PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
# PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
# OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
--
This repository also contains code from Hugging Face Inc., Google Research,
Facebook (from their Fairseq, Dino, and ParlAI projects), Microsoft (from their
Swin-Transformer project), Philip Popien, the Mamba project (Tri Dao and
Albert Gu), and the Triton language and compiler project (Philippe Tillet and
OpenAI). Files from these organizations have notices at the top of each file.
Below are licenses used in those files, as indicated.
--------------------------------------------------------------------------------------
-- LICENSE FOR Facebook, huggingface, Google Research, LLaVA, Mamba, TinyZero and vLLM code --
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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.
--------------------------------------------------------------------------------
LICENSE FOR
Meta Platforms, Inc. and affiliates.
BSD License
Redistribution and use in source and binary forms, with or without modification,
are permitted provided that the following conditions are met:
* Redistributions of source code must retain the above copyright notice, this
list of conditions and the following disclaimer.
* Redistributions in binary form must reproduce the above copyright notice,
this list of conditions and the following disclaimer in the documentation
and/or other materials provided with the distribution.
* Neither the name Meta nor the names of its contributors may be used to
endorse or promote products derived from this software without specific
prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR
ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
================================================
FILE: MANIFEST.in
================================================
include megatron/core/requirements.txt
include megatron/core/README.md
include megatron/core/package_info.py
global-exclude LICENSE
recursive-include requirements *
================================================
FILE: README.md
================================================
Megatron-LM and Megatron Core
=============================
GPU-optimized library for training transformer models at scale
## About
This repository contains two components: **Megatron-LM** and **Megatron Core**.
**Megatron-LM** is a reference example that includes Megatron Core plus pre-configured training scripts. Best for research teams, learning distributed training, and quick experimentation.
**Megatron Core** is a composable library with GPU-optimized building blocks for custom training frameworks. It provides transformer building blocks, advanced parallelism strategies (TP, PP, DP, EP, CP), mixed precision support (FP16, BF16, FP8, FP4), and model architectures. Best for framework developers and ML engineers building custom training pipelines.
**[Megatron Bridge](https://github.com/NVIDIA-NeMo/Megatron-Bridge)** provides bidirectional Hugging Face ↔ Megatron checkpoint conversion with production-ready recipes.
## Getting Started
**Install from PyPI:**
```bash
uv pip install megatron-core
```
**Or clone and install from source:**
```bash
git clone https://github.com/NVIDIA/Megatron-LM.git
cd Megatron-LM
uv pip install -e .
```
> **Note:** Building from source can use a lot of memory. If the build runs out of memory, limit parallel compilation jobs by setting `MAX_JOBS` (e.g. `MAX_JOBS=4 uv pip install -e .`).
For NGC container setup and all installation options, see the **[Installation Guide](https://docs.nvidia.com/megatron-core/developer-guide/latest/get-started/install.html)**.
- **[Your First Training Run](https://docs.nvidia.com/megatron-core/developer-guide/latest/get-started/quickstart.html)** - End-to-end training examples with data preparation
- **[Parallelism Strategies](https://docs.nvidia.com/megatron-core/developer-guide/latest/user-guide/parallelism-guide.html)** - Scale training across GPUs with TP, PP, DP, EP, and CP
- **[Contribution Guide](https://docs.nvidia.com/megatron-core/developer-guide/latest/developer/contribute.html)** - How to contribute to Megatron Core
# Latest News
- **[2026/03]** **Deprecating Python 3.10 support:** We're officially dropping Python 3.10 support with the upcoming 0.17.0 release. Downstream applications must raise their lower boundary to 3.12 to stay compatible with MCore.
- **[2026/01]** **[Dynamic Context Parallelism](https://developer.nvidia.com/blog/speeding-up-variable-length-training-with-dynamic-context-parallelism-and-nvidia-megatron-core/)** - Up to 1.48x speedup for variable-length sequence training with adaptive CP sizing.
- **[2025/12]** **Megatron Core development has moved to GitHub!** All development and CI now happens in the open. We welcome community contributions.
- **[2025/10]** **[Megatron Dev Branch](https://github.com/NVIDIA/Megatron-LM/tree/dev)** - early access branch with experimental features.
- **[2025/10]** **[Megatron Bridge](https://github.com/NVIDIA-NeMo/Megatron-Bridge)** - Bidirectional converter for interoperability between Hugging Face and Megatron checkpoints, featuring production-ready recipes for popular models.
- **[2025/08]** **[MoE Q3-Q4 2025 Roadmap](https://github.com/NVIDIA/Megatron-LM/issues/1729)** - Comprehensive roadmap for MoE features including DeepSeek-V3, Qwen3, advanced parallelism strategies, FP8 optimizations, and Blackwell performance enhancements.
- **[2025/08]** **[GPT-OSS Model](https://github.com/NVIDIA/Megatron-LM/issues/1739)** - Advanced features including YaRN RoPE scaling, attention sinks, and custom activation functions are being integrated into Megatron Core.
- **[2025/06]** **[Megatron MoE Model Zoo](https://github.com/yanring/Megatron-MoE-ModelZoo)** - Best practices and optimized configurations for training DeepSeek-V3, Mixtral, and Qwen3 MoE models with performance benchmarking and checkpoint conversion tools.
- **[2025/05]** Megatron Core v0.11.0 brings new capabilities for multi-data center LLM training ([blog](https://developer.nvidia.com/blog/turbocharge-llm-training-across-long-haul-data-center-networks-with-nvidia-nemo-framework/)).
Previous News
- **[2024/07]** Megatron Core v0.7 improves scalability and training resiliency and adds support for multimodal training ([blog](https://developer.nvidia.com/blog/train-generative-ai-models-more-efficiently-with-new-nvidia-Megatron-Core-functionalities/)).
- **[2024/06]** Megatron Core added supports for Mamba-based models. Check out our paper [An Empirical Study of Mamba-based Language Models](https://arxiv.org/pdf/2406.07887) and [code example](https://github.com/NVIDIA/Megatron-LM/tree/ssm/examples/mamba).
- **[2024/01 Announcement]** NVIDIA has released the core capabilities in **Megatron-LM** into [**Megatron Core**](https://github.com/NVIDIA/Megatron-LM/tree/main/megatron/core) in this repository. Megatron Core expands upon Megatron-LM's GPU-optimized techniques with more cutting-edge innovations on system-level optimizations, featuring composable and modular APIs.
# Project Structure
```
Megatron-LM/
├── megatron/
│ ├── core/ # Megatron Core (kernels, parallelism, building blocks)
│ │ ├── models/ # Transformer models
│ │ ├── transformer/ # Transformer building blocks
│ │ ├── tensor_parallel/ # Tensor parallelism
│ │ ├── pipeline_parallel/ # Pipeline parallelism
│ │ ├── distributed/ # Distributed training (FSDP, DDP)
│ │ ├── optimizer/ # Optimizers
│ │ ├── datasets/ # Dataset loaders
│ │ ├── inference/ # Inference engines and server
│ │ └── export/ # Model export (e.g. TensorRT-LLM)
│ ├── training/ # Training scripts
│ ├── legacy/ # Legacy components
│ ├── post_training/ # Post-training (quantization, distillation, pruning, etc.)
│ └── rl/ # Reinforcement learning (RLHF, etc.)
├── examples/ # Ready-to-use training examples
├── tools/ # Utility tools
├── tests/ # Comprehensive test suite
└── docs/ # Documentation
```
# Performance Benchmarking
For our latest performance benchmarking results, please refer to [NVIDIA Megatron Bridge Performance Summary](https://docs.nvidia.com/nemo/megatron-bridge/latest/performance-summary.html).
Our codebase efficiently trains models from 2B to 462B parameters across thousands of GPUs, achieving up to **47% Model FLOP Utilization (MFU)** on H100 clusters.
**Benchmark Configuration:**
- **Vocabulary size**: 131,072 tokens
- **Sequence length**: 4096 tokens
- **Model scaling**: Varied hidden size, attention heads, and layers to achieve target parameter counts
- **Communication optimizations**: Fine-grained overlapping with DP (`--overlap-grad-reduce`, `--overlap-param-gather`), TP (`--tp-comm-overlap`), and PP (enabled by default)
**Key Results:**
- **6144 H100 GPUs**: Successfully benchmarked 462B parameter model training
- **Superlinear scaling**: MFU increases from 41% to 47-48% with model size
- **End-to-end measurement**: Throughputs include all operations (data loading, optimizer steps, communication, logging)
- **Production ready**: Full training pipeline with checkpointing and fault tolerance
- *Note: Performance results measured without training to convergence*
## Weak Scaling Results
Our weak scaled results show superlinear scaling (MFU increases from 41% for the smallest model considered to 47-48% for the largest models); this is because larger GEMMs have higher arithmetic intensity and are consequently more efficient to execute.
## Strong Scaling Results
We also strong scaled the standard GPT-3 model (our version has slightly more than 175 billion parameters due to larger vocabulary size) from 96 H100 GPUs to 4608 GPUs, using the same batch size of 1152 sequences throughout. Communication becomes more exposed at larger scale, leading to a reduction in MFU from 47% to 42%.
# Roadmaps
- **[MoE Roadmap](https://github.com/NVIDIA/Megatron-LM/issues/1729)** - DeepSeek-V3, Qwen3, advanced parallelism, FP8 optimizations, and Blackwell enhancements
# Resources
## Getting Help
- 📖 **[Documentation](https://docs.nvidia.com/megatron-core/developer-guide/latest/index.html)** - Official documentation
- 🐛 **[Issues](https://github.com/NVIDIA/Megatron-LM/issues)** - Bug reports and feature requests
## Contributing
We ❤️ contributions! Ways to contribute:
- 🐛 **Report bugs** - Help us improve reliability
- 💡 **Suggest features** - Shape the future of Megatron Core
- 📝 **Improve docs** - Make Megatron Core more accessible
- 🔧 **Submit PRs** - Contribute code improvements
**→ [Contributing Guide](https://docs.nvidia.com/megatron-core/developer-guide/latest/developer/contribute.html)**
## Citation
If you use Megatron in your research or project, we appreciate that you use the following citations:
```bibtex
@article{megatron-lm,
title={Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism},
author={Shoeybi, Mohammad and Patwary, Mostofa and Puri, Raul and LeGresley, Patrick and Casper, Jared and Catanzaro, Bryan},
journal={arXiv preprint arXiv:1909.08053},
year={2019}
}
```
================================================
FILE: codecov.yml
================================================
comment: false
coverage:
status:
project: false
patch:
default:
target: 80%
threshold: 5%
base: auto
if_ci_failed: error
if_no_uploads: success
if_not_found: success
fixes:
- "/opt/megatron-lm/::"
================================================
FILE: docker/.ngc_version.dev
================================================
nvcr.io/nvidia/pytorch:26.02-py3
================================================
FILE: docker/.ngc_version.lts
================================================
nvcr.io/nvidia/pytorch:25.09-py3
================================================
FILE: docker/Dockerfile.ci.dev
================================================
# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.
# syntax=docker/dockerfile:1.3-labs
ARG FROM_IMAGE_NAME
FROM ${FROM_IMAGE_NAME} as main
ENV PIP_CONSTRAINT=""
ENV DEBIAN_FRONTEND=noninteractive
ARG UV_VERSION=0.7.2
ARG YQ_VERSION=4.44.1
ENV PATH="/root/.local/bin:$PATH"
ARG UV_PROJECT_ENVIRONMENT=/opt/venv
ENV UV_PROJECT_ENVIRONMENT=${UV_PROJECT_ENVIRONMENT}
ENV VIRTUAL_ENV=$UV_PROJECT_ENVIRONMENT
ENV PATH="$UV_PROJECT_ENVIRONMENT/bin:$PATH"
ENV UV_LINK_MODE=copy
RUN bash -ex <<"EOF"
apt-get update
apt-get install -y --no-install-recommends gettext python3-venv psmisc uuid-runtime
apt-get clean
python -m venv /opt/jet
ARCH=$(uname -m)
case "${ARCH}" in \
"x86_64") YQ_ARCH=amd64 ;; \
"aarch64") YQ_ARCH=arm64 ;; \
"armv7l") YQ_ARCH=arm ;; \
*) echo "Unsupported architecture: ${ARCH}" && exit 1 ;; \
esac
wget https://github.com/mikefarah/yq/releases/download/v${YQ_VERSION}/yq_linux_${YQ_ARCH} -O /usr/local/bin/yq
chmod a+x /usr/local/bin/yq
curl -LsSf https://astral.sh/uv/${UV_VERSION}/install.sh | sh
EOF
COPY README.md pyproject.toml uv.lock /workspace/
COPY megatron/core/__init__.py /workspace/megatron/core/
COPY megatron/core/package_info.py /workspace/megatron/core/
ARG IMAGE_TYPE=dev
RUN --mount=type=cache,target=/root/.cache/uv \
bash -ex <<"EOF"
export NVTE_CUDA_ARCHS="80;90;100"
uv venv ${UV_PROJECT_ENVIRONMENT} --system-site-packages
uv sync --only-group build
uv sync --extra ${IMAGE_TYPE} --extra mlm --link-mode copy --locked \
--no-install-package torch \
--no-install-package torchvision \
--no-install-package triton \
--no-install-package transformer-engine-cu12 \
--no-install-package nvidia-cublas-cu12 \
--no-install-package nvidia-cuda-cupti-cu12 \
--no-install-package nvidia-cuda-nvrtc-cu12 \
--no-install-package nvidia-cuda-runtime-cu12 \
--no-install-package nvidia-cudnn-cu12 \
--no-install-package nvidia-cufft-cu12 \
--no-install-package nvidia-cufile-cu12 \
--no-install-package nvidia-curand-cu12 \
--no-install-package nvidia-cusolver-cu12 \
--no-install-package nvidia-cusparse-cu12 \
--no-install-package nvidia-cusparselt-cu12 \
--no-install-package nvidia-nccl-cu12
EOF
# Install DeepEP
COPY docker/patches/deepep.patch /workspace/deepep.patch
RUN bash -ex <<"EOF"
cd /workspace
uv pip install nvidia-nvshmem-cu13==3.4.5
pushd /opt/venv/lib/python3.12/site-packages/nvidia/nvshmem/lib/
ln -s libnvshmem_host.so.3 libnvshmem_host.so
popd
git clone --branch hybrid-ep https://github.com/deepseek-ai/DeepEP.git
pushd DeepEP
git checkout eb9cee7de5a24193bf09500668d3a619d3d3f3fb
patch -p1 < /workspace/deepep.patch
popd
TORCH_CUDA_ARCH_LIST="9.0 10.0 12.0" uv pip install --no-build-isolation -v DeepEP/.
rm -rf DeepEP
EOF
COPY assets/ /opt/data/
ENV UV_PYTHON=$UV_PROJECT_ENVIRONMENT/bin/python
##### For NVIDIANS only #####
FROM main as jet
ARG JET_API_VERSION
ENV PATH="$PATH:/opt/jet/bin"
RUN --mount=type=secret,id=JET_INDEX_URLS bash -ex <<"EOF"
JET_INDEX_URLS=$(cat /run/secrets/JET_INDEX_URLS)
python -m venv /opt/jet
/opt/jet/bin/pip install --no-cache-dir $JET_INDEX_URLS \
"jet-api==$JET_API_VERSION" "setuptools<82.0.0"
EOF
RUN --mount=type=secret,id=JET_INDEX_URLS \
--mount=type=secret,id=LOGGER_INDEX_URL bash -ex <<"EOF"
JET_INDEX_URLS=$(cat /run/secrets/JET_INDEX_URLS)
LOGGER_INDEX_URL=$(cat /run/secrets/LOGGER_INDEX_URL)
uv pip install --no-cache-dir --upgrade $LOGGER_INDEX_URL "one-logger"
uv pip install --no-cache-dir --upgrade "setuptools<80.0.0,>=77.0.0"
uv pip install --no-cache-dir --upgrade $JET_INDEX_URLS "jet-client~=4.0"
EOF
###
================================================
FILE: docker/Dockerfile.ci.nemo
================================================
# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.
# syntax=docker/dockerfile:1.3-labs
ARG FROM_IMAGE_NAME
FROM ${FROM_IMAGE_NAME} as main
RUN apt-get update && \
apt-get install -y --no-install-recommends gettext && \
apt-get clean && \
wget https://github.com/mikefarah/yq/releases/download/v4.44.1/yq_linux_amd64 -O /usr/local/bin/yq && \
chmod a+x /usr/local/bin/yq
##### For NVIDIANS only #####
FROM main as jet
ARG JET_API_VERSION
RUN --mount=type=secret,id=JET_INDEX_URLS \
JET_INDEX_URLS=$(cat /run/secrets/JET_INDEX_URLS) && \
pip install --no-cache-dir jet-api==$JET_API_VERSION "jet-client~=4.0" --upgrade $JET_INDEX_URLS
ENV PATH="$PATH:/opt/jet/bin"
###
================================================
FILE: docker/Dockerfile.linting
================================================
# syntax=docker/dockerfile:experimental
ARG FROM_IMAGE_NAME
FROM $FROM_IMAGE_NAME as main
ENV DEBIAN_FRONTEND=noninteractive
ARG UV_VERSION=0.7.2
ARG YQ_VERSION=4.44.1
ENV PATH="/root/.local/bin:$PATH"
ENV UV_PROJECT_ENVIRONMENT=/opt/venv
ENV PATH="$UV_PROJECT_ENVIRONMENT/bin:$PATH"
ENV UV_LINK_MODE=copy
RUN curl -LsSf https://astral.sh/uv/${UV_VERSION}/install.sh | sh
WORKDIR /opt/megatron-lm
COPY pyproject.toml uv.lock /opt/megatron-lm/
COPY megatron/core/package_info.py megatron/core/__init__.py /opt/megatron-lm/megatron/core/
RUN uv sync --locked --only-group linting --only-group test --only-group ci
##### For NVIDIANS only #####
FROM main as jet
ARG JET_API_VERSION
RUN --mount=type=secret,id=JET_INDEX_URLS \
JET_INDEX_URLS=$(cat /run/secrets/JET_INDEX_URLS) && \
uv pip install --no-cache-dir "jet-client~=2.0" --upgrade $JET_INDEX_URLS
================================================
FILE: docker/common/install.sh
================================================
#!/bin/bash
set -xeuo pipefail # Exit immediately if a command exits with a non-zero status
# Parse command line arguments
while [[ $# -gt 0 ]]; do
case $1 in
--base-image)
BASE_IMAGE="$2"
shift 2
;;
--python-version)
PYTHON_VERSION="$2"
shift 2
;;
--environment)
ENVIRONMENT="$2"
shift 2
;;
--use-uv)
USE_UV="true"
shift 1
;;
*)
echo "Unknown option: $1"
echo "Usage: $0 --base-image {pytorch|ubuntu} [--use-uv] [--python-version] [--environment]"
exit 1
;;
esac
done
if [[ -z "${PYTHON_VERSION:-}" ]]; then
PYTHON_VERSION="3.12"
fi
if [[ -z "${USE_UV:-}" ]]; then
USE_UV="false"
fi
# Validate base image argument
if [[ -z "${BASE_IMAGE:-}" || -z "${ENVIRONMENT:-}" ]]; then
echo "Error: --base-image argument is required"
echo "Usage: $0 --base-image {pytorch|ubuntu} --environment {dev|lts}"
exit 1
fi
if [[ "$BASE_IMAGE" != "pytorch" && "$BASE_IMAGE" != "ubuntu" ]]; then
echo "Error: --base-image must be either 'pytorch' or 'ubuntu'"
echo "Usage: $0 --base-image {pytorch|ubuntu}"
exit 1
fi
if [[ "$ENVIRONMENT" != "dev" && "$ENVIRONMENT" != "lts" ]]; then
echo "Error: --environment must be either 'dev' or 'lts'"
echo "Usage: $0 --environment {dev|lts}"
exit 1
fi
main() {
if [[ -n "${PAT:-}" ]]; then
echo -e "machine github.com\n login token\n password $PAT" >~/.netrc
chmod 600 ~/.netrc
fi
# Install dependencies
export DEBIAN_FRONTEND=noninteractive
# Install Python
apt-get update
apt-get install -y software-properties-common
add-apt-repository ppa:deadsnakes/ppa -y
apt-get install -y python$PYTHON_VERSION-dev python$PYTHON_VERSION-venv
update-alternatives --install /usr/bin/python3 python3 /usr/bin/python$PYTHON_VERSION 1
# Install tools
apt-get update
apt-get install -y wget curl git cmake
# Install CUDA
if [[ "$BASE_IMAGE" == "ubuntu" ]]; then
rm /etc/apt/sources.list.d/cuda*.list || true
rm /etc/apt/sources.list.d/nvidia-cuda.list || true
wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2404/x86_64/cuda-keyring_1.1-1_all.deb
dpkg -i cuda-keyring_1.1-1_all.deb
rm cuda-keyring_1.1-1_all.deb
apt-get update
apt-get install -y cuda-toolkit-12-8 cudnn-cuda-12 libcudnn9-cuda-12 libcutlass-dev
fi
# Clean up
apt-get clean
unset PIP_CONSTRAINT
if [[ "$USE_UV" == "true" ]]; then
if [[ "$BASE_IMAGE" == "pytorch" ]]; then
UV_ARGS=(
"--no-install-package" "torch"
"--no-install-package" "torchvision"
"--no-install-package" "triton"
"--no-install-package" "nvidia-cublas-cu12"
"--no-install-package" "nvidia-cuda-cupti-cu12"
"--no-install-package" "nvidia-cuda-nvrtc-cu12"
"--no-install-package" "nvidia-cuda-runtime-cu12"
"--no-install-package" "nvidia-cudnn-cu12"
"--no-install-package" "nvidia-cufft-cu12"
"--no-install-package" "nvidia-cufile-cu12"
"--no-install-package" "nvidia-curand-cu12"
"--no-install-package" "nvidia-cusolver-cu12"
"--no-install-package" "nvidia-cusparse-cu12"
"--no-install-package" "nvidia-cusparselt-cu12"
"--no-install-package" "nvidia-nccl-cu12"
)
else
UV_ARGS=()
fi
# Install uv
UV_VERSION="0.7.2"
curl -LsSf https://astral.sh/uv/${UV_VERSION}/install.sh | sh
# Create virtual environment and install dependencies
uv venv ${UV_PROJECT_ENVIRONMENT} --system-site-packages
# Install dependencies
uv sync --locked --only-group build ${UV_ARGS[@]}
uv sync \
--link-mode copy \
--locked \
--extra ${ENVIRONMENT} \
--all-groups ${UV_ARGS[@]}
# Install the package
uv pip install --no-deps -e .
else
python3 -m venv $UV_PROJECT_ENVIRONMENT
. $UV_PROJECT_ENVIRONMENT/bin/activate
pip install --pre --no-cache-dir --upgrade pip
pip install --pre --no-cache-dir torch pybind11 wheel_stub ninja wheel packaging "setuptools<80.0.0,>=77.0.0"
pip install --pre --no-cache-dir --no-build-isolation .
fi
}
# Call the main function
main "$@"
================================================
FILE: docker/common/install_source_wheels.sh
================================================
#!/bin/bash
set -xeuo pipefail # Exit immediately if a command exits with a non-zero status
INPUT_WHEEL_DIR=$(pwd)/wheels
# Parse command line arguments
while [[ $# -gt 0 ]]; do
case $1 in
--input-wheel-dir)
INPUT_WHEEL_DIR="$2"
shift 2
;;
--environment)
ENVIRONMENT="$2"
shift 2
;;
*)
echo "Unknown option: $1"
echo "Usage: $0 --input-wheel-dir DIR"
exit 1
;;
esac
done
# Check if required arguments are provided
if [ -z "$INPUT_WHEEL_DIR" ] || [ -z "$ENVIRONMENT" ]; then
echo "Error: --input-wheel-dir and --environment are required"
echo "Usage: $0 --input-wheel-dir DIR --environment ENV"
exit 1
fi
if [ "$ENVIRONMENT" = "dev" ]; then
TE_WHEEL=$(ls $INPUT_WHEEL_DIR/transformer_engine*.whl) || true
[ -z "$TE_WHEEL" ] && TE_WHEEL=$(bash docker/common/build_te.sh --output-wheel-dir $INPUT_WHEEL_DIR | tail -n 1)
fi
MAMBA_WHEEL=$(ls $INPUT_WHEEL_DIR/mamba*.whl) || true
[ -z "$MAMBA_WHEEL" ] && MAMBA_WHEEL=$(bash docker/common/build_mamba.sh --output-wheel-dir $INPUT_WHEEL_DIR | tail -n 1)
CAUSALCONV1D_WHEEL=$(ls $INPUT_WHEEL_DIR/causal_conv1d*.whl) || true
[ -z "$CAUSALCONV1D_WHEEL" ] && CAUSALCONV1D_WHEEL=$(bash docker/common/build_causalconv1d.sh --output-wheel-dir $INPUT_WHEEL_DIR | tail -n 1)
# Override deps that are already present in the base image
# only for dev
if [ "$ENVIRONMENT" = "dev" ]; then
uv pip install --no-cache-dir --no-deps $TE_WHEEL
fi
# Install heavy optional deps like mamba, causalconv1d
uv pip install --no-cache-dir \
$MAMBA_WHEEL \
$CAUSALCONV1D_WHEEL \
"setuptools<80.0.0,>=77.0.0"
================================================
FILE: docker/patches/deepep.patch
================================================
diff --git a/setup.py b/setup.py
index 63ce332..4e13462 100644
--- a/setup.py
+++ b/setup.py
@@ -37,7 +37,7 @@ if __name__ == '__main__':
'-Wno-sign-compare', '-Wno-reorder', '-Wno-attributes']
nvcc_flags = ['-O3', '-Xcompiler', '-O3']
sources = ['csrc/deep_ep.cpp', 'csrc/kernels/runtime.cu', 'csrc/kernels/layout.cu', 'csrc/kernels/intranode.cu']
- include_dirs = ['csrc/']
+ include_dirs = ['csrc/', '/usr/local/cuda/include/cccl/']
library_dirs = []
nvcc_dlink = []
extra_link_args = []
================================================
FILE: docs/add_copyright_header.py
================================================
#!/usr/bin/env python3
"""One-off script to add NVIDIA copyright header to all .md files under docs/."""
from pathlib import Path
HEADER = """ Copyright (c) 2022-2026, NVIDIA CORPORATION. All rights reserved.
NVIDIA CORPORATION and its licensors retain all intellectual property
and proprietary rights in and to this software, related documentation
and any modifications thereto. Any use, reproduction, disclosure or
distribution of this software and related documentation without an express
license agreement from NVIDIA CORPORATION is strictly prohibited.
"""
def main():
docs_dir = Path(__file__).resolve().parent
already_has = "Copyright (c) 2022-2026, NVIDIA CORPORATION"
count = 0
for path in sorted(docs_dir.rglob("*.md")):
content = path.read_text(encoding="utf-8")
if content.strip().startswith(already_has):
continue
new_content = HEADER + content
path.write_text(new_content, encoding="utf-8")
count += 1
print(path.relative_to(docs_dir))
print(f"\nUpdated {count} files.")
if __name__ == "__main__":
main()
================================================
FILE: docs/advanced/index.md
================================================
# Discussions
In-depth technical discussions and optimization guides:
- [Optimizing DeepSeek-V3 Training on GB200 NVL72](https://github.com/NVIDIA/Megatron-LM/blob/dev/docs/discussions/deepseek-v3-gb200-optimization/deepseek-v3-gb200-reproduce-guide.md) - Achieving 970 TFLOPS/GPU with MXFP8, kernel optimizations, and HybridEP
================================================
FILE: docs/api-backwards-compatibility-check.md
================================================
---
orphan: true
---
# API Backward Compatibility Checking
## Overview
Megatron Core uses automated API compatibility checking to ensure stable interfaces between releases. This prevents accidental breaking changes that could affect users upgrading between versions.
## How It Works
The compatibility checker:
1. Compares the current code against the latest release
2. Detects breaking changes in function signatures
3. Fails CI if breaking changes are found (unless explicitly exempted)
4. Runs automatically on every PR that modifies `megatron/core`
## What Gets Checked
### ✅ Breaking Changes Detected
- **Parameter removed** - Removing a function parameter
- **Parameter added without default** - Adding a required parameter
- **Parameter order changed** - Changing the order of parameters
- **Optional→Required** - Removing a default value from a parameter
- **Function removed** - Deleting a public function
- **Return type changed** - Changing the return type annotation (warning)
### ⏭️ What Gets Skipped
- **Test functions** - Functions starting with `test_`
- **Exempt decorators** - Functions marked with `@internal_api`, `@experimental_api`, or `@deprecated`
- **Excluded paths** - Code in `tests/`, `experimental/`, `legacy/`
### ✅ Allowed Changes
- **Adding optional parameters** - Adding parameters with default values
- **Adding new functions** - New public APIs
- **Making parameters optional** - Adding default values to required parameters
## For Developers
### Running Locally
```bash
# Install griffe
pip install griffe
# Check against latest release
python scripts/check_api_backwards_compatibility.py --baseline core_r0.8.0
# Check with verbose output
python scripts/check_api_backwards_compatibility.py --baseline core_r0.8.0 -v
# Compare two specific branches
python scripts/check_api_backwards_compatibility.py --baseline core_r0.8.0 --current main
```
### Marking Functions as Exempt
If you need to make breaking changes to internal or experimental APIs:
#### Internal API (for internal implementation details)
```python
from megatron.core.utils import internal_api
@internal_api
def experimental_feature(x, y):
"""
This API is experimental and may change.
NOT FOR EXTERNAL USE.
"""
pass
```
**When to use `@internal_api`:**
- Internal APIs not documented for external use
- Experimental features explicitly marked as unstable
- Functions in development that haven't been released yet
#### Experimental API (for experimental features)
```python
from megatron.core.utils import experimental_api
@experimental_api
def new_experimental_feature(x, y):
"""
This API is experimental and may change without notice.
"""
pass
```
**When to use `@experimental_api`:**
- Experimental features explicitly marked as unstable
- New APIs under active development
- Features that haven't been stabilized yet
### Deprecating APIs
For planned API changes, use the deprecation workflow:
```python
from megatron.core.backwards_compatibility_decorators import deprecated
@deprecated(
version="1.0.0", # When deprecation starts
removal_version="2.0.0", # When it will be removed
alternative="new_function", # Recommended replacement
reason="Improved performance and cleaner API"
)
def old_function(x):
"""This function is deprecated."""
pass
```
**Deprecation Timeline:**
1. **Version N** - Add `@deprecated` decorator, function still works
2. **Version N+1** - Keep function with deprecation warnings
3. **Version N+2** - Remove function (users have been warned)
### Handling CI Failures
If the compatibility check fails on your PR:
1. **Review the breaking changes** in the CI logs
2. **Choose an action:**
- **Fix the code** - Revert the breaking change
- **Add exemption** - Use `@internal_api` if intentional
- **Use deprecation** - For planned API changes
3. **Update your PR** with the fix
## Examples
### Example 1: Compatible Change
```python
# ✅ BEFORE (v1.0)
def train_model(config, dataloader):
pass
# ✅ AFTER (v1.1) - Added optional parameter
def train_model(config, dataloader, optimizer="adam"):
pass
```
**Result:** ✅ Check passes
---
### Example 2: Breaking Change
```python
# BEFORE (v1.0)
def train_model(config, dataloader, optimizer="adam"):
pass
# ❌ AFTER (v1.1) - Removed parameter
def train_model(config, dataloader):
pass
```
**Result:** ❌ Check fails - "Parameter 'optimizer' removed"
---
### Example 3: Exempt Internal API
```python
from megatron.core.utils import internal_api
# BEFORE (v1.0)
@internal_api
def _internal_compute(x, y):
pass
# ✅ AFTER (v1.1) - Can change freely
@internal_api
def _internal_compute(x, y, z): # Added parameter
pass
```
**Result:** ✅ Check passes (function is exempt)
---
### Example 4: Deprecation Workflow
```python
from megatron.core.utils import deprecated
# Version 1.0 - Add deprecation
@deprecated(
version="1.0.0",
removal_version="2.0.0",
alternative="train_model_v2"
)
def train_model(config):
"""Old training function - DEPRECATED"""
pass
def train_model_v2(config, **options):
"""New improved training function"""
pass
# Version 1.1 - Keep both (users migrate)
# Version 2.0 - Remove train_model()
```
## Architecture
```
Developer commits code
↓
GitHub Actions triggers
↓
CI runs check_api_backwards_compatibility.py
↓
Script loads code via griffe:
• Baseline: latest release (e.g., core_r0.8.0)
• Current: PR branch
↓
Apply filtering:
• Skip @internal_api, @experimental_api, and @deprecated
• Skip private functions (_prefix)
• Skip test/experimental paths
↓
Griffe compares signatures:
• Parameters
• Types
• Return types
• Defaults
↓
Report breaking changes
↓
Exit: 0=pass, 1=fail
↓
CI fails if breaking changes detected
```
## Configuration
### Customizing Filters
Edit `scripts/check_api_backwards_compatibility.py`:
```python
# Add more exempt decorators
EXEMPT_DECORATORS = [
"internal_api",
"experimental_api",
"deprecated",
]
# Add more path exclusions
EXCLUDE_PATHS = {
"tests",
"experimental",
"legacy",
"your_custom_path", # ← Add here
}
```
### Changing the Baseline
The workflow auto-detects the latest `core_r*` tag. To manually specify:
```yaml
# In .github/workflows/check_api_backwards_compatibility_workflow.yml
- name: Run compatibility check
run: |
python scripts/check_api_backwards_compatibility.py \
--baseline your_custom_baseline
```
## FAQ
### Q: Why did my PR fail the compatibility check?
**A:** Your code introduced breaking changes compared to the last release. Review the CI logs to see what changed.
### Q: Can I disable the check for my PR?
**A:** No, but you can mark specific functions as exempt using `@internal_api` or `@experimental_api`.
### Q: What if I need to make a breaking change?
**A:** Use the `@deprecated` decorator for a gradual transition, or mark the function as exempt using `@internal_api` (for internal code) or `@experimental_api` (for experimental features).
### Q: Does this check all of Megatron-LM?
**A:** No, only `megatron/core/**` (Megatron Core). Legacy code is excluded.
### Q: What about class methods?
**A:** Yes, class methods are checked just like functions.
### Q: Can I run this locally before pushing?
**A:** Yes! Run `python scripts/check_api_backwards_compatibility.py --baseline core_r0.8.0`
### Q: What if there's no release tag yet?
**A:** The workflow will use `main` as the baseline. Update it once you have release tags.
## Troubleshooting
### Error: "griffe is not installed"
```bash
pip install griffe
```
### Error: "No core_r* tags found"
The repository doesn't have release tags yet. The workflow will fall back to `main`.
### False Positives
If the checker reports a breaking change that isn't actually breaking, file an issue and use `@internal_api` as a temporary workaround.
## References
- **Script:** `scripts/check_api_backwards_compatibility.py`
- **Workflow:** `.github/workflows/check_api_backwards_compatibility_workflow.yml`
- **Decorators:** `megatron/core/backwards_compatibility_decorators.py`
- **Griffe Documentation:** https://mkdocstrings.github.io/griffe/
## Support
For questions or issues:
1. Check this documentation
2. Review existing PRs with compatibility checks
3. Ask in the Megatron-LM Slack/Discord
4. File an issue on GitHub
================================================
FILE: docs/api-guide/core/datasets.md
================================================
# datasets package
```{include} ../../../megatron/core/datasets/readme.md
```
================================================
FILE: docs/api-guide/core/dist_checkpointing.md
================================================
# dist_checkpointing package
A library for saving and loading the distributed checkpoints.
A *distributed checkpoint* in Megatron Core uses the ``torch_dist`` format,
a custom checkpointing mechanism built on top of PyTorch's native
checkpointing capabilities.
A key property of distributed checkpoints is that a checkpoint saved under one
parallel configuration (tensor, pipeline, or data parallelism) can be loaded
under a different parallel configuration. This enables flexible scaling and
resharding of models across heterogeneous training setups.
Using the library requires defining sharded state_dict dictionaries with functions from *mapping* and *optimizer* modules.
Those state dicts can be saved or loaded with a *serialization* module using strategies from *strategies* module.
## Safe Checkpoint Loading
Since **PyTorch 2.6**, the default behavior of `torch.load` is `weights_only=True`.
This ensures that only tensors and allow-listed classes are loaded, reducing the risk of arbitrary code execution.
If you encounter an error such as:
```bash
WeightsUnpickler error: Unsupported global: GLOBAL argparse.Namespace was not an allowed global by default.
```
you can fix it by explicitly allow-listing the missing class in your script:
```python
import torch, argparse
torch.serialization.add_safe_globals([argparse.Namespace])
```
## Checkpointing Distributed Optimizer
### Checkpoint Compatibility and Optimizer State Formats
Beginning with **mcore v0.14**, the ``flattened_range`` attribute was removed from ``dist_checkpointing``. As a result:
- Optimizer states saved with mcore versions <= 0.14 can no longer be loaded directly. Loading these legacy optimizer states is not supported because the required sharded metadata is no longer available. If you need to continue training from older checkpoints, refer to the workaround described below.
- Model weights from older checkpoints remain fully compatible. No extra steps are needed—model weights from checkpoints created by earlier versions load automatically; simply add the ``--no-load-optim`` flag.
### Workaround: Loading legacy optimizer states with ToT MCore
**Step 1: Convert the legacy checkpoint using mcore v0.15.0**
Run a dummy training job with mcore v0.15.0 to re-save the checkpoint with new optimizer states format.
```bash
MODEL_TRAIN_PARAMS=(
# Define model architecture and training parameters here
)
OLD_CKPT=/workspace/mcore_ckpt_old
CONVERTED_CKPT=/workspace/mcore_ckpt_0.15.0
torchrun --nproc_per_node=8 /opt/megatron-lm/pretrain_gpt.py \
--save-interval 1 \
--eval-interval 1 \
--exit-interval 1 \
--eval-iters 1 \
--use-distributed-optimizer \
--save ${CONVERTED_CKPT} \
--load ${OLD_CKPT} \
--ckpt-format torch_dist \
"${MODEL_TRAIN_PARAMS[@]}"
```
**Step 2: Load the converted checkpoint with ToT MCore**
Use the converted checkpoint as the input for continued training with ToT MCore.
```bash
MODEL_TRAIN_PARAMS=(
# Define model architecture and training parameters here
)
NEW_CKPT=/workspace/mcore_ckpt_new
CONVERTED_CKPT=/workspace/mcore_ckpt_0.15.0
torchrun --nproc_per_node=8 /opt/megatron-lm/pretrain_gpt.py \
--use-distributed-optimizer \
--save ${NEW_CKPT} \
--load ${CONVERTED_CKPT} \
--ckpt-format torch_dist \
"${MODEL_TRAIN_PARAMS[@]}"
```
After this step, training can proceed normally using ToT MCore with fully supported optimizer state loading.
## Distributed Optimizer Checkpoint Formats
The refactor of the Distributed Optimizer introduces **two checkpoint formats**:
- dp_reshardable (Default)
- Fast save/load performance.
- Not reshardable — not possible to change model parallelism when using this format.
- Recommended for general training when model parallelism changes are not needed.
- fully_reshardable
- Fully reshardable — supports arbitrary changes in model parallelism.
- Slower than dp_reshardable.
- Enabled via the ``--dist-ckpt-optim-fully-reshardable`` flag.
### Workflow for Changing Model Parallelism
You can combine formats to optimize both flexibility and performance:
1. Train using ``dp_reshardable`` (default) for faster checkpointing.
2. When you need to change model parallelism:
- Stop training.
- Change model parallelism for train config.
- Resume training with ``--dist-ckpt-optim-fully-reshardable``.
3. Save at least one checkpoint under the new model parallel configuration.
4. (Optional) To continue the training with updated model parallelism and better checkpointing performance, stop training and switch back to ``dp_reshardable`` format by removing ``--dist-ckpt-optim-fully-reshardable``.
## Subpackages
```{toctree}
:maxdepth: 4
dist_checkpointing.strategies
```
================================================
FILE: docs/api-guide/core/dist_checkpointing.strategies.md
================================================
# dist_checkpointing.strategies package
Package defining different checkpoint formats (backends) and saving/loading algorithms (strategies).
Strategies can be used for implementing new checkpoint formats or implementing new (more optimal for a given use case) ways of saving/loading of existing formats.
Strategies are passed to `dist_checkpointing.load` and `dist_checkpointing.save` functions and control the actual saving/loading procedure.
================================================
FILE: docs/api-guide/core/distributed.md
================================================
# distributed package
This package contains various utilities to finalize model weight gradients
on each rank before the optimizer step. This includes a distributed data
parallelism wrapper to all-reduce or reduce-scatter the gradients across
data-parallel replicas, and a `finalize_model_grads` method to
synchronize gradients across different parallelism modes (e.g., 'tied'
layers on different pipeline stages, or gradients for experts in a MoE on
different ranks due to expert parallelism).
================================================
FILE: docs/api-guide/core/fusions.md
================================================
# fusions package
This package provides modules that provide commonly fused
operations. Fusing operations improves compute efficiency by
increasing the amount of work done each time a tensor is read from
memory. To perform the fusion, modules in this either rely on PyTorch
functionality for doing just-in-time compilation
(i.e. `torch.jit.script` in older PyTorch versions of `torch.compile`
in recent versions), or call into custom kernels in external libraries
such as Apex or TransformerEngine.
================================================
FILE: docs/api-guide/core/index.md
================================================
# Core APIs
Low-level API reference for core Megatron components.
```{toctree}
:maxdepth: 2
transformer
tensor_parallel
pipeline_parallel
fusions
distributed
datasets
dist_checkpointing
dist_checkpointing.strategies
```
================================================
FILE: docs/api-guide/core/pipeline_parallel.md
================================================
# pipeline_parallel package
This package contains implementations for two different pipeline parallelism
schedules (one without interleaving and one with interleaving, see [Efficient Large-Scale Language Model Training on GPU Clusters Using Megatron-LM](https://arxiv.org/abs/2104.04473)
for details), and a default no-pipelining schedule. It also contains methods
for the point-to-point communication that is needed between pipeline stages.
================================================
FILE: docs/api-guide/core/tensor_parallel.md
================================================
# tensor_parallel package
This package contains an implementation for tensor parallelism in transformer
models (see [Megatron-LM: Training Multi-Billion Parameter Language Models
Using Model Parallelism](https://arxiv.org/abs/1909.08053) and [Reducing
Activation Recomputation in Large Transformer Models](https://arxiv.org/abs/2205.05198)
for details).
================================================
FILE: docs/api-guide/core/transformer.md
================================================
# transformer package
The `transformer` package provides a customizable and configurable
implementation of the transformer model architecture. Each component
of a transformer stack, from entire layers down to individual linear
layers, can be customized by swapping in different PyTorch modules
using the "spec" parameters. The
configuration of the transformer (hidden size, number of layers,
number of attention heads, etc.) is provided via a `TransformerConfig`
object.
================================================
FILE: docs/api-guide/index.md
================================================
# API Guide
API reference documentation for Megatron Core components.
```{toctree}
:maxdepth: 3
models/index
core/index
internal/index
```
================================================
FILE: docs/api-guide/internal/index.md
================================================
# Internal Utilities
Internal utility APIs.
```{toctree}
:maxdepth: 2
num_microbatches_calculator
optimizer_param_scheduler
```
================================================
FILE: docs/api-guide/internal/num_microbatches_calculator.md
================================================
# Microbatches Calculator
This api is used to calculate the number of microbatches required to fit a given model on a given batch size.
================================================
FILE: docs/api-guide/internal/optimizer_param_scheduler.md
================================================
# Optimizer Parameters Scheduler
This api is used to calculate the learning rate and weight decay for the optimizer.
================================================
FILE: docs/api-guide/models/index.md
================================================
# Model APIs
API reference for Megatron Core model implementations.
```{toctree}
:maxdepth: 2
models
models.gpt
models.bert
models.t5
```
================================================
FILE: docs/api-guide/models/models.bert.md
================================================
# models.bert package
Useful package for training bert and bert like encoder only models. It optionally comes with a binary head that can be used for classification tasks .
================================================
FILE: docs/api-guide/models/models.gpt.md
================================================
# models.gpt package
This is the implementation of the popular GPT model. It supports several features like model parallelization (Tensor Parallel, Pipeline Parallel, Data Parallel) , mixture of experts, FP8 , Distributed optimizer etc. We are constantly adding new features. So be on the lookout or raise an issue if you want to have something added.
================================================
FILE: docs/api-guide/models/models.md
================================================
# models package
This package contains most of the popular LLMs . Currently we have support for GPT, Bert, and T5 . This is an ever growing list so keep an eye out.
## Subpackages
```{toctree}
:maxdepth: 4
models.gpt
models.t5
models.bert
```
================================================
FILE: docs/api-guide/models/models.t5.md
================================================
# models.t5 package
================================================
FILE: docs/api-guide/router_replay.md
================================================
# Design Document: MoE Router Replay Feature
## 1. Overview
This document provides a detailed description of the "Router Replay" feature implemented within the Megatron-LM Core for Mixture-of-Experts (MoE) models.
This feature is designed to enhance determinism and analyzability in MoE model training and inference. It enables the model to load routing decisions from a predefined file and enforce their use during the forward pass, thereby bypassing the real-time routing computation.
## 2. Motivation
* **Determinism & Reproducibility**: In distributed training, MoE routing decisions can exhibit minor variations due to factors like floating-point precision. By replaying a fixed routing table, the MoE computation path is guaranteed to be identical across runs, which facilitates debugging and reproducing experimental results.
* **Performance Profiling**: The router's own computation (e.g., logits calculation, top-k selection) incurs overhead. In replay mode, this part of the computation can be completely skipped, allowing for more precise isolation and profiling of performance bottlenecks within the Expert Layers themselves.
* **Debugging Aid**: When issues arise in the model, fixing the routing decisions helps to isolate variables, making it easier to determine whether the problem lies with the routing mechanism or the expert computations.
## 3. Design and Architecture
The design follows the principles of being non-intrusive and on-demand, with the core idea of activating the replay logic only when explicitly requested by the user.
* **Core Components**:
* `RouterReplay` (located in `megatron/core/transformer/moe/router_replay.py`): A utility class for replaying MoE routing decisions. When enabled via the `moe_enable_routing_replay` flag, a separate instance of `RouterReplay` is created for each MoE layer's router. Each instance is responsible for loading routing data and providing the deterministic routing decisions for its corresponding layer during the forward pass.
* `moe_enable_routing_replay` (located in `megatron/core/transformer/transformer_config.py`): A boolean global configuration flag that serves as the sole entry point for enabling this feature.
* **Workflow**:
The feature supports different modes, such as recording and replaying, controlled by a `RouterReplayAction`.
1. **Enabling the Feature**: The user sets `moe_enable_routing_replay` to `True` in the model configuration.
2. **Initialization**: When `moe_enable_routing_replay` is true, each `TopKRouter` creates its own `RouterReplay` instance.
3. **Mode Configuration**: The user must programmatically set the desired router replay action (e.g., `record`, `forward_replay`, `backward_replay`) on the `RouterReplay` instances.
4. **Execution Flow (within a mini-batch)**:
* **Forward Pass**:
* For each micro-batch, the `topk_routing_with_score_function` checks the `router_replay_action`.
* **In `record` mode**: The dynamically computed `top-k` expert indices are captured and stored.
* **In `forward_replay` mode**: The function retrieves pre-loaded expert indices from `target_topk_idx`. These indices are used for the forward computation and are also appended to the `replay_backward_list` to prepare for the backward pass.
* **Backward Pass**:
* For each micro-batch (processed in reverse order in pipeline parallelism), the `router_replay_action` is checked again.
* **In `backward_replay` mode**: The function retrieves the expert indices for the corresponding micro-batch by popping them from the `replay_backward_list`. This mode is intended for training recomputation (e.g., activation checkpointing and pipeline recompute) so the same routing decisions are used during recompute/backward as in forward, ensuring determinism and correctness.
## 4. Implementation Details
The implementation cleanly separates the replay logic from the router's core computation.
* **`megatron/core/transformer/transformer_config.py`**:
* Adds the configuration option `moe_enable_routing_replay: bool = False`.
* **`megatron/core/transformer/moe/moe_utils.py`**:
* Introduces the `RouterReplay` class to manage the state for recording and replaying routing decisions for a single MoE layer.
* `target_topk_idx`: An attribute holding the expert indices for the current micro-batch during forward replay mode.
* `recorded_topk_idx`: An attribute for storing the computed expert indices when in record mode.
* `replay_backward_list`: A list that accumulates the `top-k` indices used during the forward passes of a mini-batch. This list is consumed in FIFO order during the backward pass to ensure correctness under pipeline parallelism.
* `set_target_indices()`: A method to load the replay indices into `target_topk_idx` for the forward pass.
* `record_indices()`: A method to save the computed indices.
* The `topk_routing_with_score_function` is modified to contain the core logic. It checks the `router_replay_action` on the `router_replay` instance and accordingly performs one of the following actions: computes and records indices, replays indices from `target_topk_idx` (for forward), replays indices from `replay_backward_list` (for backward), or falls through to the default dynamic routing.
### Training recompute usage
- During forward replay, `set_target_indices()` prepares `replay_backward_list` so each micro-batch’s indices are available for recomputation.
- During recompute/backward, set action to `REPLAY_BACKWARD` so indices are consumed in FIFO order to mirror the forward sequence.
## 5. Usage Guide
1. **Enable & Instantiate**
- Create one `RouterReplay` instance per MoE router layer when building the model.
- Optionally use the global helpers to set/clear actions across all layers.
2. **Record Routing Decisions**
- Set action: `RouterReplay.set_global_router_replay_action(RouterReplayAction.RECORD)`.
- Run the model; retrieve per-layer indices via `RouterReplay.get_recorded_data()` and persist.
3. **Forward Replay**
- Load indices and distribute: `RouterReplay.set_replay_data(list_of_tensors)`.
- Set action: `RouterReplay.set_global_router_replay_action(RouterReplayAction.REPLAY_FORWARD)`.
- Run the model; dynamic top‑k is bypassed and target indices are used.
4. **Backward Replay**
- For training recomputation (activation checkpointing or pipeline recompute), set action: `REPLAY_BACKWARD` during recomputation.
- Per micro‑batch indices are consumed from `replay_backward_list` in FIFO order.
5. **Cleanup**
- Use `RouterReplay.clear_global_indices()`, `RouterReplay.clear_global_router_replay_action()`, and `RouterReplay.clear_global_router_replay_instances()` to restore default behavior and prevent memory leaks.
### Quick usage with `topk_routing_with_score_function`
```python
import torch
from megatron.core.transformer.moe.router_replay import RouterReplay, RouterReplayAction
from megatron.core.transformer.moe.moe_utils import topk_routing_with_score_function
rr = RouterReplay()
# Record
RouterReplay.set_global_router_replay_action(RouterReplayAction.RECORD)
logits = torch.randn(8, 16)
probs_rec, routing_map_rec = topk_routing_with_score_function(
logits=logits, topk=2, use_pre_softmax=False, score_function="softmax", router_replay=rr,
)
recorded = rr.get_recorded_indices()
torch.save(recorded, "/tmp/replay.pt")
# Forward replay
rr.clear_router_replay_action()
rr.set_router_replay_action(RouterReplayAction.REPLAY_FORWARD)
target = torch.load("/tmp/replay.pt")
rr.set_target_indices(target)
probs_rep, routing_map_rep = topk_routing_with_score_function(
logits=logits, topk=2, use_pre_softmax=False, score_function="softmax", router_replay=rr,
)
RouterReplay.clear_global_router_replay_action()
RouterReplay.clear_global_indices()
RouterReplay.clear_global_router_replay_instances()
```
## 6. Minimal Demo
Here is a minimal code example showing how to use RouterReplay for recording and replaying:
```python
import torch
import torch.distributed as dist
from megatron.core.transformer.transformer_config import TransformerConfig
from megatron.core.transformer.moe.router import TopKRouter
from megatron.core.transformer.moe.router_replay import RouterReplay, RouterReplayAction
# Initialize distributed training
if not dist.is_initialized():
dist.init_process_group(backend="nccl")
# Create a transformer config with RouterReplay enabled
config = TransformerConfig(
num_experts=8,
expert_model_parallel_size=1,
num_top_k=2,
moe_enable_routing_replay=True
)
# Create a TopKRouter instance
router = TopKRouter(config)
# Generate sample input (batch_size, sequence_length, hidden_size)
logits = torch.randn(16, 32, 8).to(torch.cuda.current_device())
# -----------------
# 1. Recording Mode
# -----------------
print("=== Recording Mode ===")
# Set global router replay action to RECORD
RouterReplay.set_global_router_replay_action(RouterReplayAction.RECORD)
# Perform routing
routing_output = router.forward(logits)
print(f"Recorded top-k indices shape: {routing_output.top_k_idx.shape}")
# -----------------
# 2. Forward Replay Mode
# -----------------
print("\n=== Forward Replay Mode ===")
# Save recorded indices to a file
torch.save(routing_output.top_k_idx, "/tmp/replay.pt")
# Load indices from file and set as target for replay
replay_indices = torch.load("/tmp/replay.pt")
for router_instance in RouterReplay.global_router_replay_instances:
router_instance.target_topk_idx = replay_indices
# Set global router replay action to REPLAY_FORWARD
RouterReplay.set_global_router_replay_action(RouterReplayAction.REPLAY_FORWARD)
# Perform routing again - this will use the replayed indices
replay_routing_output = router.forward(logits)
print(f"Replayed top-k indices shape: {replay_routing_output.top_k_idx.shape}")
print(f"Are indices the same? {torch.equal(routing_output.top_k_idx, replay_routing_output.top_k_idx)}")
# Clean up
RouterReplay.clear_global_router_replay_action()
RouterReplay.clear_global_indices()
RouterReplay.clear_global_router_replay_instances()
if dist.is_initialized():
dist.destroy_process_group()
```
================================================
FILE: docs/autodoc2_docstrings_parser.py
================================================
# Copyright (c) 2025, 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.
from docutils import nodes
from myst_parser.parsers.sphinx_ import MystParser
from sphinx.ext.napoleon.docstring import GoogleDocstring
class NapoleonParser(MystParser):
"""Add support for Google style docstrings."""
def parse(self, input_string: str, document: nodes.document) -> None:
"""Parse Google style docstrings."""
# Get the Sphinx configuration
config = document.settings.env.config
# Process with Google style
google_parsed = str(GoogleDocstring(input_string, config))
return super().parse(google_parsed, document)
Parser = NapoleonParser
================================================
FILE: docs/broken_links_false_positives.json
================================================
{
"uri": "http://localhost:8080/"
}
================================================
FILE: docs/conf.py
================================================
# Copyright (c) 2025-2026, 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.
# Configuration file for the Sphinx documentation builder.
#
# For the full list of built-in configuration values, see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
import os
import sys
# -- Project information -----------------------------------------------------
# https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information
project = "Megatron Core"
copyright = "2026, NVIDIA Corporation"
author = "NVIDIA Corporation"
release = "nightly"
# -- General configuration ---------------------------------------------------
# https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration
extensions = [
"myst_parser", # For our markdown docs
"sphinx.ext.viewcode", # For adding a link to view source code in docs
"sphinx.ext.doctest", # Allows testing in docstrings
"sphinx.ext.napoleon", # For google style docstrings
"sphinx_copybutton", # For copy button in code blocks
]
# Check if we should skip autodoc generation
# usage: SKIP_AUTODOC=true
skip_autodoc = os.environ.get("SKIP_AUTODOC", "false").lower() == "true"
if not skip_autodoc:
extensions.append("autodoc2") # Generates API docs
templates_path = ["_templates"]
exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"]
# -- Options for MyST Parser (Markdown) --------------------------------------
# MyST Parser settings
myst_enable_extensions = [
"dollarmath", # Enables dollar math for inline math
"amsmath", # Enables LaTeX math for display mode
"colon_fence", # Enables code blocks using ::: delimiters instead of ```
"deflist", # Supports definition lists with term: definition format
"fieldlist", # Enables field lists for metadata like :author: Name
"tasklist", # Adds support for GitHub-style task lists with [ ] and [x]
"attrs_block", # Enables setting attributes on block elements using {#id .class key=val}
]
myst_heading_anchors = 5 # Generates anchor links for headings up to level 5
# Suppress "more than one target found for cross-reference" warnings for Python symbols
# that have the same name across multiple modules (e.g. DistributedDataParallelConfig,
# ModelType). These are structural ambiguities in the codebase – the cross-reference
# still resolves; Sphinx just cannot pick the unique target automatically.
suppress_warnings = ["ref.python"]
# -- Options for Autodoc2 ---------------------------------------------------
sys.path.insert(0, os.path.abspath(".."))
if not skip_autodoc:
autodoc2_packages = [
{
"path": "../megatron/core", # Path to your package relative to conf.py
"exclude_dirs": ["converters"], # list of directory names to exclude
}
]
autodoc2_render_plugin = "myst" # Use MyST for rendering docstrings
autodoc2_output_dir = "apidocs" # Output directory for autodoc2 (relative to docs/)
# This is a workaround that uses the parser located in autodoc2_docstrings_parser.py to allow autodoc2 to
# render google style docstrings.
# Related Issue: https://github.com/sphinx-extensions2/sphinx-autodoc2/issues/33
autodoc2_docstring_parser_regexes = [
(r".*", "docs.autodoc2_docstrings_parser"),
]
# Regex patterns whose values contain raw regex syntax (e.g. \p{L}) that docutils
# mis-parses as footnote/reference markup. Exclude them from the generated docs.
autodoc2_hidden_regexes = [
r".*\._PATTERN_TIKTOKEN.*",
]
# -- Options for HTML output -------------------------------------------------
# https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output
html_theme = "nvidia_sphinx_theme"
html_theme_options = {
"switcher": {
"json_url": "versions1.json",
"version_match": release,
},
"icon_links": [
{
"name": "GitHub",
"url": "https://github.com/NVIDIA/Megatron-LM/",
"icon": "fa-brands fa-github",
}
],
"public_docs_features": True
}
html_extra_path = ["project.json", "versions1.json"]
# Github links are now getting rate limited from the Github Actions
linkcheck_ignore = [
".*github\\.com.*",
".*githubusercontent\\.com.*",
]
================================================
FILE: docs/developer/contribute.md
================================================
# Contributing to Megatron-LM
This document outlines the processes and policies for issues and pull requests by non-NVIDIA contributors to the Megatron-LM GitHub repository.
Everyone is welcome to contribute to the project! We recently migrated from using an internal repo to doing all development directly from the GitHub repository.
When contributing it is important to ensure that changes are in line with the project direction. Small changes to fix bugs are welcomed and appreciated. If proposing large architectural changes or changes for stylistic reasons open an issue first so we can discuss it.
## Issue policy
Please do file any bugs you find, keeping the following in mind:
- If filing a bug, i.e. you have found something that doesn't work as expected, use the BUG template.
- If you've found a regression in speed or accuracy use the REGRESSION template.
- If you are requesting a new feature or modification of an existing feature use the ENHANCEMENT template.
- If opening an issue to ask a question no template is needed but please make your question as clear and concise as possible.
- One issue per bug. Putting multiple things in the same issue makes both discussion and completion unnecessarily complicated.
- Your bug is mostly likely to get attention from the development team quickly if we can easily reproduce it.
- Use proper spelling, grammar, and punctuation.
- Write in an authoritative and technical tone.
## Code submission policy
### Do
- Format new code in a style that is consistent with the file being changed. Megatron-LM doesn't (yet) have a style guide or enforced formatting.
- Split your changes into separate, atomic commits i.e. A commit per feature or fix.
- Make sure your commits are rebased on the master branch.
- Write the commit message subject line in the imperative mood ("Change the default argument for X", not "Changed the default argument for X").
- Write your commit messages in proper English, with care and punctuation.
- Check the spelling of your code, comments and commit messages.
### Don't
- Submit code that's incompatible with the project licence.
- Touch anything outside the stated scope of the PR. This includes formatting changes to code not relevant to the PR.
- Iterate excessively on your design across multiple commits.
- Include commented-out code.
- Attempt large architectural changes without first opening an issue to discuss.
## Issue and Pull Request Q&A
### I've submitted an issue and PR. When can I expect to get some feedback?
You should receive a response within 2 business days.
### I need help, who should I ping?
Use [@mcore-oncall](https://github.com/orgs/NVIDIA/teams/mcore-oncall).
### If my issue or PR isn't getting attention, what should I do?
After 2 business days, tag the user [@mcore-oncall](https://github.com/orgs/NVIDIA/teams/mcore-oncall).
### Is there a policy for issues and PRs that haven't been touched in X days? Should they be closed?
Yes, we have a bot that will mark untouched PRs as "stale" after 60 days.
We have a long backlog of issues and PRs dating back years. We are trying to triage these now by working backwards. Older issues we believe may still be relevant may recieve a request to re-test them with the latest code. If there's no response they may be closed. Again, if you they should be re-opened then just respond with a comment to that effect.
Thank you!
================================================
FILE: docs/developer/generate_docs.md
================================================
# Generating Docs Locally
To generate docs locally, use the following commands:
```
cd docs
uv run --only-group docs sphinx-autobuild . _build/html --port 8080 --host 127.0.0.1
```
Docs will be generated at .
**Recommended:** set the environment variable `SKIP_AUTODOC=true` when generating docs
to skip the generation of `apidocs`.
================================================
FILE: docs/developer/oncall.md
================================================
-->
# Oncall Overview
During your oncall week, you will be assigned to all PRs marked “Ready for
Review”. From a high-level, your responsibilities include:
- Review all new PRs
- Accelerate the review process
- Ensure issues and discussion questions are answered
## PR Responsibilities
Below is the checklist that the oncall needs to go through for each PR.
- Should the PR remain a single PR?
- Each PR should have at most 1 expert reviewer, although there will be some outlier cases
- Label PR as “complexity: low”, “complexity: medium”, or “complexity: high” depending on complexity
- Expert reviewers have final say, oncall just sets the initial complexity level
- Initial complexity level guideline
- Low: <100 lines changed
- Medium: 100 < lines changed < 500
- High: > 500 lines changed
- Does this PR have proper testing coverage?
- If new logic is added, is the new logic tested?
- Should the PR add documentation for any new features?
- Does the PR conform to our style guidelines?
- Code structure
- Cleanliness
- Comments
- File structure
- Do all tests pass?
- Oncall will need to kick off testing suite for external reviewers
- Comment “/ok to test commid_id” to kick off testing suite
- Expert reviewers are notified after the PR is marked “Ready for Review”
- **Expert reviewers should review within 1 business day.** Message the assigned reviewer if it is taking longer. The reviewer either needs to review the PR or suggest an alternate reviewer.
- If the reviewer is not responding after 2 business days, escalate to the reviewer’s manager.
- For `megatron/core` PRs, the “Final Review” label is applied automatically once all expert reviewers approve
- Final reviewers should review within 1 business day. Message the assigned reviewer if it is taking longer.
- If the reviewer is not responding after 2 business days, escalate to the reviewer’s manager.
- The “Approved” label is applied automatically once all required reviewers have approved
## Issues and Discussion Questions
If you do not know the answer to an issue or discussion question: that's ok! **Delegate to someone who does.**
On a daily basis, track the following:
- [new issues](https://github.com/NVIDIA/Megatron-LM/issues): check to see if there are any new issues before they become out of SLA!
- [out of SLA issues](https://github.com/orgs/NVIDIA-NeMo/projects/20/views/4?sliceBy%5Bvalue%5D=NVIDIA%2FMegatron-LM): useful dashboard that tracks all out of SLA issues
================================================
FILE: docs/developer/submit.md
================================================
# How to Submit a PR
All PRs start as **draft**. If you open a non-draft PR, it will be automatically converted to draft.
## Step 1: Mark PR as "Ready for Review"
1. When your PR is ready, click **Ready for Review**.
2. The oncall reviewer is auto-assigned and expert reviewers are notified based on your changes. They will get notified and pick up your PR soon.
:warning: Only mark as ready once all merge-conflicts are resolved and the CI is passing.
Final Review might get declined if these requirements are not fulfilled.
## Step 2: Final Review (`megatron/core` only)
For PRs that change `megatron/core`, once all expert reviewers have approved, the `Final Review` label is applied **automatically** and final reviewers are assigned.
For PRs outside `megatron/core`, this step is skipped.
## Step 3: Approved
Once all required reviewers have approved, the `Approved` label is applied **automatically**. The PR is now ready to merge.
## Step 4: Merge
Any member of [mcore-engineers](https://github.com/orgs/NVIDIA/teams/mcore-engineers) will be able to merge your PR.
================================================
FILE: docs/discussions/README.md
================================================
---
orphan: true
---
# Megatron Discussions
This directory contains in-depth guides, tutorials, and discussions about optimizing and using Megatron for various use cases.
## Available Guides
### Training Guides
- **[Megatron-FSDP User Guide](megatron-fsdp-user-guide/megatron-fsdp-user-guide.md)**
A practical guide to enable Megatron-FSDP training, including a quick-start example for DeepSeek-V3, required and recommended configurations, and instructions for checkpoint conversion from torch_dist to fsdp_dtensor.
## Contributing
If you'd like to contribute a guide or tutorial, please follow this structure:
1. Create a new directory: `docs/discussions/your-guide-name/`
2. Add your main guide: `docs/discussions/your-guide-name/your-guide-name.md`
3. Create an images directory: `docs/discussions/your-guide-name/images/`
4. Update this README.md with a link to your guide
Each guide should be self-contained with its own images and supporting files.
================================================
FILE: docs/discussions/megatron-fsdp-user-guide/example-scripts/sbatch_checkpoint_convert.sh
================================================
#!/bin/bash
# Configuration: Set these paths before running the script
MEGATRON_PATH=${MEGATRON_PATH:-"your_own_megatron_path"} # Path to Megatron-LM repository
CONTAINER_IMAGE=${CONTAINER_IMAGE:-"your_own_container_image"} # Path to .sqsh or docker image url
OUTPUT_PATH=${OUTPUT_PATH:-"your_own_output_path"} # Path for SLURM logs
# Checkpoint conversion command
# Note: Update the checkpoint paths in the command below
RUN_CMD="
cd ${MEGATRON_PATH};
git rev-parse HEAD;
export PYTHONPATH=${MEGATRON_PATH}:${PYTHONPATH};
python3 tools/checkpoint/checkpoint_inspector.py \
convert-torch-dist-to-fsdp-dtensor --swiglu \
your_own_path_to_input_torch_dist_checkpoint \
your_own_path_to_output_fsdp_dtensor_checkpoint \
--param-to-param-group-map-json your_own_path_to_param_to_param_group_map.json"
# SLURM settings
SLURM_LOGS="${OUTPUT_PATH}/slurm_logs"
mkdir -p ${SLURM_LOGS} || {
echo "Error: Failed to create SLURM logs directory ${SLURM_LOGS}"
exit 1
}
# Submit SLURM job
# Note: Update SBATCH parameters below according to your cluster configuration
set +e
sbatch <&1 | tee ${SLURM_LOGS}/\${SLURM_JOB_ID}.log
EOF
set -e
================================================
FILE: docs/discussions/megatron-fsdp-user-guide/example-scripts/sbatch_mfsdp_deepseek_v3.sh
================================================
#!/bin/bash
export NCCL_IB_SL=1
export NCCL_IB_TIMEOUT=19
export NVTE_FWD_LAYERNORM_SM_MARGIN=16
export NVTE_BWD_LAYERNORM_SM_MARGIN=16
export NCCL_P2P_NET_CHUNKSIZE=2097152
export TORCH_NCCL_AVOID_RECORD_STREAMS=1
export PYTHONWARNINGS=ignore
export TRITON_CACHE_DIR=/tmp/triton_cache_$SLURM_NODEID
# Configuration: Set these variables before running the script
MEGATRON_PATH=${MEGATRON_PATH:-"your_own_megatron_path"} # Path to Megatron-LM repository
CONTAINER_IMAGE=${CONTAINER_IMAGE:-"your_own_container_image"} # Path to .sqsh or docker image url
OUTPUT_PATH=${OUTPUT_PATH:-"your_own_output_path"} # Path for output logs and checkpoints
DATA_PATH=${DATA_PATH:-"your_own_data_path"}
USE_MEGATRON_FSDP=${USE_MEGATRON_FSDP:-1}
SHARDING_STRATEGY=${SHARDING_STRATEGY:-"optim_grads_params"}
PROFILE=${PROFILE:-0}
WANDB=${WANDB:-1}
TP=${TP:-1}
EP=${EP:-8}
MBS=${MBS:-4}
GBS=${GBS:-2048}
COMMENT=${COMMENT:-"hybridep-selective-recompute"}
PRETRAIN_ARGS=(
--distributed-timeout-minutes 60
--tensor-model-parallel-size ${TP}
--expert-model-parallel-size ${EP}
--expert-tensor-parallel-size 1
--context-parallel-size 1
--use-distributed-optimizer
--overlap-grad-reduce
--overlap-param-gather
--use-mcore-models
--sequence-parallel
--use-flash-attn
--disable-bias-linear
--micro-batch-size ${MBS}
--global-batch-size ${GBS}
--train-samples 585937500
--exit-duration-in-mins 220
--no-check-for-nan-in-loss-and-grad
--manual-gc
--manual-gc-interval 10
--recompute-granularity selective
--recompute-modules mlp moe mla_up_proj layernorm
--transformer-impl transformer_engine
--seq-length 4096
--data-cache-path ${OUTPUT_PATH}/cache
--tokenizer-type HuggingFaceTokenizer
--tokenizer-model deepseek-ai/DeepSeek-V3
--data-path ${DATA_PATH}
--split 99,1,0
--no-mmap-bin-files
--no-create-attention-mask-in-dataloader
--num-workers 6
--num-layers 61
--hidden-size 7168
--ffn-hidden-size 18432
--num-attention-heads 128
--kv-channels 128
--max-position-embeddings 4096
--position-embedding-type rope
--rotary-base 10000
--make-vocab-size-divisible-by 3232
--normalization RMSNorm
--norm-epsilon 1e-6
--swiglu
--untie-embeddings-and-output-weights
--multi-latent-attention
--attention-dropout 0.0
--hidden-dropout 0.0
--clip-grad 1.0
--weight-decay 0.1
--qk-layernorm
--lr-decay-samples 584765624
--lr-warmup-samples 1536000
--lr-warmup-init 3.9e-7
--lr 3.9e-6
--min-lr 3.9e-7
--lr-decay-style cosine
--adam-beta1 0.9
--adam-beta2 0.95
--num-experts 256
--moe-layer-freq [0]*3+[1]*58
--moe-ffn-hidden-size 2048
--moe-shared-expert-intermediate-size 2048
--moe-router-load-balancing-type seq_aux_loss
--moe-router-topk 8
--moe-token-dispatcher-type flex
--moe-flex-dispatcher-backend hybridep
--moe-router-pre-softmax
--moe-grouped-gemm
--moe-aux-loss-coeff 1e-4
--moe-router-group-topk 4
--moe-router-num-groups 8
--moe-router-topk-scaling-factor 2.5
--moe-router-score-function sigmoid
--moe-router-enable-expert-bias
--moe-router-bias-update-rate 1e-3
--moe-router-dtype fp32
--moe-permute-fusion
--moe-router-force-load-balancing
--q-lora-rank 1536
--kv-lora-rank 512
--qk-head-dim 128
--qk-pos-emb-head-dim 64
--v-head-dim 128
--rotary-scaling-factor 40
--mscale 1.0
--mscale-all-dim 1.0
--mtp-num-layers 1
--mtp-loss-scaling-factor 0.1
--eval-iters 32
--eval-interval 100
--auto-detect-ckpt-format
--load ${OUTPUT_PATH}/checkpoints
--save ${OUTPUT_PATH}/checkpoints
--save-interval 100
--dist-ckpt-strictness log_all
--init-method-std 0.02
--log-timers-to-tensorboard
--log-memory-to-tensorboard
--log-num-zeros-in-grad
--log-params-norm
--log-validation-ppl-to-tensorboard
--log-throughput
--log-interval 1
--logging-level 40
--tensorboard-dir ${OUTPUT_PATH}/tensorboard
--bf16
--enable-experimental
)
if [ "${USE_MEGATRON_FSDP}" = 1 ]; then
unset CUDA_DEVICE_MAX_CONNECTIONS
PRETRAIN_ARGS=(
"${PRETRAIN_ARGS[@]}"
--use-megatron-fsdp
--data-parallel-sharding-strategy ${SHARDING_STRATEGY}
--no-gradient-accumulation-fusion
--use-distributed-optimizer
--calculate-per-token-loss
--init-model-with-meta-device
--ckpt-format fsdp_dtensor
--grad-reduce-in-bf16
--fsdp-double-buffer
--use-nccl-ub
)
fi
# Profiling command
if [ "${PROFILE}" = 1 ]; then
PROFILE_CMD="nsys profile --sample=none --cpuctxsw=none --trace=cuda,nvtx,cublas,cudnn \
--capture-range=cudaProfilerApi \
--capture-range-end=stop \
--cuda-graph-trace=node \
--cuda-memory-usage=true \
-f true -x true \
-o ${OUTPUT_PATH}/nsys/Megatron-FSDP-Deepseek-V3-TP${TP}EP${EP}-MBS${MBS}GBS${GBS}-${COMMENT}"
PRETRAIN_ARGS=(
"${PRETRAIN_ARGS[@]}"
--profile
--profile-step-start 10
--profile-step-end 12
--profile-ranks 0
)
echo "PROFILE_CMD="
echo $PROFILE_CMD
else
PROFILE_CMD=""
fi
if [ "${WANDB}" = 1 ]; then
export WANDB_API_KEY=${WANDB_API_KEY:-"your_own_wandb_api_key"}
PRETRAIN_ARGS=(
"${PRETRAIN_ARGS[@]}"
--wandb-project your_own_wandb_project
--wandb-exp-name DeepSeek-V3-TP${TP}EP${EP}-MBS${MBS}GBS${GBS}-${COMMENT}
)
fi
TRAINING_CMD="
cd ${MEGATRON_PATH};
git rev-parse HEAD;
export PYTHONPATH=${MEGATRON_PATH}:${PYTHONPATH};
${PROFILE_CMD} python ${MEGATRON_PATH}/pretrain_gpt.py ${PRETRAIN_ARGS[@]}"
# SLURM settings
SLURM_LOGS="${OUTPUT_PATH}/slurm_logs"
mkdir -p ${SLURM_LOGS} || {
echo "Error: Failed to create SLURM logs directory ${SLURM_LOGS}"
exit 1
}
# Submit SLURM job
# Note: Update SBATCH parameters below according to your cluster configuration
set +e
sbatch <&1 | tee ${SLURM_LOGS}/\${SLURM_JOB_ID}.log
EOF
set -e
================================================
FILE: docs/discussions/megatron-fsdp-user-guide/megatron-fsdp-user-guide.md
================================================
---
orphan: true
---
# Megatron-FSDP User Guide
## Table of Contents
- [Megatron-FSDP Quick Start](#megatron-fsdp-quick-start)
- [Checkpoint Conversion from 3D-Parallel to Megatron-FSDP](#checkpoint-conversion-from-3d-parallel-to-megatron-fsdp)
## Megatron-FSDP Quick Start
We recommend using the latest [NVIDIA NeMo Framework Container](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/nemo/tags), which provides a tested software stack and optimized performance.
For your reference, we provide an example launch script for DeepSeek-V3: [`sbatch_mfsdp_deepseek_v3.sh`](./example-scripts/sbatch_mfsdp_deepseek_v3.sh).
### Required Configurations
To enable Megatron-FSDP, add the following required flags to your training script:
```bash
--use-megatron-fsdp
--data-parallel-sharding-strategy optim_grads_params
--no-gradient-accumulation-fusion
--use-distributed-optimizer
--ckpt-format fsdp_dtensor
```
### Recommended Configurations
We also recommend adding the following configurations to further improve performance:
```bash
unset CUDA_DEVICE_MAX_CONNECTIONS
```
```bash
--calculate-per-token-loss
--init-model-with-meta-device
--grad-reduce-in-bf16
--fsdp-double-buffer
--use-nccl-ub
```
💡 **Detailed explanations of these configurations are provided below.**
#### 1. Disable `CUDA_DEVICE_MAX_CONNECTIONS`
To ensure full parallelization of FSDP communication and computation, disable the CUDA_DEVICE_MAX_CONNECTIONS environment variable. This step avoids potential bubbles in the CUDA stream. (But it may slow down TP and CP to some extent.)
#### 2. Add `--calculate-per-token-loss`
For gradients sharding mode optimization, include the `--calculate-per-token-loss` flag in your training script. This improves performance by reducing the frequency of gradient scaling, which is also a sizable drain on SM resources.
#### 3. Add `--init-model-with-meta-device`
Allows model initialization using meta device, followed by layer-by-layer initialization of distributed model weight buffers via the `Module.reset_parameters` API, facilitating the initialization of extremely large models.
#### 4. Add `--grad-reduce-in-bf16`
Enables gradient reduction in BF16 precision instead of FP32, reducing communication volume and accelerating the backward pass.
#### 5. Add `--fsdp-double-buffer`
Uses persistently allocated double buffers for temporarily-defined memory needed in `MegatronFSDP` communications. While having persistent double buffers may increase peak VRAM utilization, it is necessary to register NCCL user buffers (`nccl_ub=True`) for `MegatronFSDP`. Currently, this is supported only for simple repetitive model structures such as GPT.
- **Only effective when using Megatron-LM.**
- Defaults to `False`. Automatically overridden to `True` when `nccl_ub` is enabled.
#### 6. Add `--use-nccl-ub`
Allocates and [registers NCCL user buffers](https://docs.nvidia.com/deeplearning/nccl/user-guide/docs/usage/bufferreg.html#) for param and grad buffers. This option enables an SM-efficient NCCL algorithm that could improve the performance of overlapped computations. This flag will be much more effective when used together with [SHARP](https://docs.nvidia.com/networking/display/sharpv3130) if the FSDP communication includes both NVL and IB domains. Enabling this option will cause additional memory overhead due to the requirement to enable the `fsdp_double_buffer` option.
- **Only effective when using Megatron-LM.**
- Defaults to `False`.
- By default we try to use NCCL window (symmetric) registration if it is available. If not it falls back to conventional local registration.
- **Incompatible with PyTorch's segmentable allocator:** Do not set `PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True` when using `--use-nccl-ub`, as this will cause a runtime error due to compatibility issues with the `torch.cuda.MemPool` API.
## Checkpoint Conversion from 3D-Parallel to Megatron-FSDP
Megatron-FSDP introduces `fsdp_dtensor`, a DTensor-based distributed checkpoint format that serves as its standard. To help you smoothly transition from 3D-Parallel to Megatron-FSDP, we provide a script for converting checkpoints from the `torch_dist` format to the `fsdp_dtensor` format. Using DeepSeek-V3 as an example, the detailed conversion process is described below.
### Step 1: Generate 3D-Parallel Checkpoint with `param_to_param_group_map`
Run your 3D-parallel + EP training script to generate a `torch_dist` checkpoint along with a directory containing `param_to_param_group_map` files. Add the following flag to your training script:
```bash
--dump-param-to-param-group-map /path/to/param_to_param_group_map
```
If you already have a `torch_dist` checkpoint, simply specify the `--dump-param-to-param-group-map /path/to/param_to_param_group_map` flag and run a very short experiment-this will create the `param_to_param_group_map` you need without full pretraining.
### Step 2: Export `param_to_param_group_map` to a JSON File
Convert the `param_to_param_group_map` into a JSON file for easier processing by running:
```bash
python tools/checkpoint/checkpoint_inspector.py print-torch-dcp-in-json /path/to/param_to_param_group_map
```
This will create a `param_to_param_group_map.json` file in the `/path/to/param_to_param_group_map` directory.
### Step 3: Convert Checkpoint from `torch_dist` to `fsdp_dtensor`
Convert your `torch_dist` checkpoint to the `fsdp_dtensor` format using the parameter to `param_to_param_group_map` JSON file:
```bash
torchrun --nproc_per_node=8 --nnodes=1 \
tools/checkpoint/checkpoint_inspector.py \
convert-torch-dist-to-fsdp-dtensor --swiglu \
/path/to/input_torch_dist_checkpoint \
/path/to/output_fsdp_dtensor_checkpoint \
--param-to-param-group-map-json /path/to/param_to_param_group_map.json
```
**Note:** For multi-node conversion tasks, please refer to the example script: [`sbatch_checkpoint_convert.sh`](./example-scripts/sbatch_checkpoint_convert.sh).
### Step 4: Launch Megatron-FSDP Training
Start your Megatron-FSDP training job using the converted `fsdp_dtensor` checkpoint.
================================================
FILE: docs/documentation.md
================================================
---
orphan: true
---
# Documentation Development
- [Documentation Development](#documentation-development)
- [Build the Documentation](#build-the-documentation)
- [Live Building](#live-building)
- [Documentation Version](#documentation-version)
## Build the Documentation
The following sections describe how to set up and build the NeMo RL documentation.
Switch to the documentation source folder and generate HTML output.
```sh
cd docs/
uv run --group docs sphinx-build . _build/html
```
* The resulting HTML files are generated in a `_build/html` folder that is created under the project `docs/` folder.
* The generated python API docs are placed in `apidocs` under the `docs/` folder.
## Checking for Broken Links
To check for broken http links in the docs, run this command:
```sh
cd docs/
uv run --group docs sphinx-build --builder linkcheck . _build/linkcheck
```
It will output a JSON file at `_build/linkcheck/output.json` with links it found while building the
docs. Records will have a status of `broken` if the link is not reachable. The `docs/conf.py` file is
configured to ignore github links because the CI test will often experience rate limit errors.
Comment out the `linkcheck_ignore` variable there to check all the links.
## Live Building
When writing documentation, it can be helpful to serve the documentation and have it update live while you edit.
To do so, run:
```sh
cd docs/
uv run --group docs sphinx-autobuild . _build/html --port 12345 --host 0.0.0.0
```
Open a web browser and go to `http://${HOST_WHERE_SPHINX_COMMAND_RUN}:12345` to view the output.
## Documentation Version
The three files below control the version switcher. Before you attempt to publish a new version of the documentation, update these files to match the latest version numbers.
* docs/versions1.json
* docs/project.json
* docs/conf.py
================================================
FILE: docs/get-started/install.md
================================================
# Installation
## System Requirements
### Hardware
- **Recommended**: NVIDIA Turing architecture or later
- **FP8 Support**: Requires NVIDIA Hopper, Ada, or Blackwell GPUs
### Software
- **Python**: >= 3.10 (3.12 recommended)
- **PyTorch**: >= 2.6.0
- **CUDA Toolkit**: Latest stable version
## Prerequisites
Install [uv](https://docs.astral.sh/uv/), a fast Python package installer:
```bash
curl -LsSf https://astral.sh/uv/install.sh | sh
```
## Option A: Pip Install (Recommended)
Install the latest stable release from PyPI:
```bash
uv pip install megatron-core
```
To include optional training dependencies (Weights & Biases, SentencePiece, HF Transformers):
```bash
uv pip install "megatron-core[training]"
```
For all extras including [Transformer Engine](https://github.com/NVIDIA/TransformerEngine):
```bash
uv pip install --group build
uv pip install --no-build-isolation "megatron-core[training,dev]"
```
```{note}
`--no-build-isolation` requires build dependencies to be pre-installed in the environment. `torch` is needed because several `[dev]` packages (`mamba-ssm`, `nv-grouped-gemm`, `transformer-engine`) import it at build time to compile CUDA kernels. Expect this step to take **20+ minutes** depending on your hardware. If you prefer pre-built binaries, the [NGC Container](#option-c-ngc-container) ships with these pre-compiled.
```
```{warning}
Building from source can consume a large amount of memory. By default the build runs one compiler job per CPU core, which may cause out-of-memory failures on machines with many cores. To limit parallel compilation jobs, set the `MAX_JOBS` environment variable before installing (e.g. `MAX_JOBS=4`).
```
```{tip}
For a lighter set of development dependencies without Transformer Engine and ModelOpt, use `[lts]` instead of `[dev]`: `uv pip install --no-build-isolation "megatron-core[training,lts]"`. The `[lts]` and `[dev]` extras are mutually exclusive.
```
To clone the repository for examples:
```bash
git clone https://github.com/NVIDIA/Megatron-LM.git
```
## Option B: Install from Source
For development or to run the latest unreleased code:
```bash
git clone https://github.com/NVIDIA/Megatron-LM.git
cd Megatron-LM
uv pip install -e .
```
To install with all development dependencies (includes Transformer Engine, requires pre-installed build deps):
```bash
uv pip install --group build
uv pip install --no-build-isolation -e ".[training,dev]"
```
```{tip}
If the build runs out of memory, limit parallel compilation jobs with `MAX_JOBS=4 uv pip install --no-build-isolation -e ".[training,dev]"`.
```
## Option C: NGC Container
For a pre-configured environment with all dependencies pre-installed (PyTorch, CUDA, cuDNN, NCCL, Transformer Engine), use the [PyTorch NGC Container](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch).
We recommend using the **previous month's** NGC container rather than the latest one to ensure compatibility with the current Megatron Core release and testing matrix.
```bash
docker run --gpus all -it --rm \
-v /path/to/dataset:/workspace/dataset \
-v /path/to/checkpoints:/workspace/checkpoints \
-e PIP_CONSTRAINT= \
nvcr.io/nvidia/pytorch:26.01-py3
```
```{note}
The NGC PyTorch container constrains the Python environment globally via `PIP_CONSTRAINT`. The `-e PIP_CONSTRAINT=` flag above unsets this so that Megatron Core and its dependencies install correctly.
```
Then install Megatron Core inside the container (torch is already available in the NGC image):
```bash
pip install uv
uv pip install --no-build-isolation "megatron-core[training,dev]"
```
You are now ready to run training. See [Your First Training Run](quickstart.md) for next steps.
================================================
FILE: docs/get-started/overview.md
================================================
# Overview
Megatron-Core and Megatron-LM are open-source tools that are typically used together to train LLMs at scale across GPUs. Megatron-Core expands the capability of Megatron-LM. Megatron Bridge connects Megatron-Core and Megatron-LM to other popular training models, such as Hugging Face.
## Megatron Core
NVIDIA Megatron Core is a library of essential building blocks for highly efficient large-scale generative AI training. It can be used to train models with unparalleled speed at scale across thousands of GPUs. It provides an extensive set of tools for multimodal and speech AI. It expands Megatron LM capabilities.
Megatron-Core contains GPU-optimized techniques featuring advanced parallelism strategies, optimizations like FP8 training, and support for the latest LLM, MoE, and multimodal architectures. It abstracts these techniques into composable and modular APIs.
Megatron-Core is compatible with all NVIDIA Tensor Core GPUs and popular LLM architectures such as GPT, BERT, T5, and RETRO.
**Composable library** with GPU-optimized building blocks for custom training frameworks.
**Best for:**
- **Framework developers** building on top of modular and optimized components
- **Research teams** needing custom training loops, optimizers, or data pipelines
- **ML engineers** requiring fault-tolerant training pipelines
**What you get:**
- Composable transformer building blocks (attention, MLP)
- Advanced parallelism strategies (TP, PP, DP, EP, CP)
- Pipeline schedules and distributed optimizers
- Mixed precision support (FP16, BF16, FP8)
- GPU-optimized kernels and memory management
- High-performance dataloaders and dataset utilities
- Model architectures (LLaMA, Qwen, GPT, Mixtral, Mamba)
## Megatron-LM
Megatron-LM is a reference implementation, with a lightweight large-scale LLM training framework. It offers a customizable native PyTorch training loop with fewer abstraction layers. It was designed for scaling transformer models to the multi-billion and trillion-parameter regimes under realistic memory and compute constraints. **It serves as a straightforward entry point for exploring Megatron-Core.**
It uses advanced parallelization techniques including model parallelism (tensor and pipeline), to allow models with billions of parameters to fit and train across large GPU clusters. It enables breakthroughs in large-scale NLP tasks. It splits model computations across many GPUs, overcoming single-GPU memory limits for training huge models, like GPT-style transformers.
**Reference implementation** that includes Megatron Core plus everything needed to train models.
**Best for:**
- **Training state-of-the-art foundation models** at scale with cutting-edge performance on latest NVIDIA hardware
- **Research teams** exploring new architectures and training techniques
- **Learning distributed training** concepts and best practices
- **Quick experimentation** with proven model configurations
**What you get:**
- Pre-configured training scripts for GPT, LLaMA, DeepSeek, Qwen, and more.
- End-to-end examples from data prep to evaluation
- Research-focused tools and utilities
## Megatron Bridge
Megatron Bridge provides out-of-the-box bridges and training recipes for models built on top of base model architectures from Megatron Core.
Megatron Bridge provides a robust, parallelism-aware pathway to convert models and checkpoints. This bidirectional converter performs on-the-fly, model-parallel-aware, per-parameter conversion, and full in-memory loading.
After training or modifying a Megatron model, you can convert it again for deployment or sharing.
[Megatron Bridge](https://github.com/NVIDIA-NeMo/Megatron-Bridge)
## Ecosystem Libraries
**Libraries used by Megatron Core:**
- **[Megatron Energon](https://github.com/NVIDIA/Megatron-Energon)** - Multi-modal data loader (text, images, video, audio) with distributed loading and dataset blending
- **[Transformer Engine](https://github.com/NVIDIA/TransformerEngine)** - Optimized kernels and FP8 mixed precision support
- **[Resiliency Extension (NVRx)](https://github.com/NVIDIA/nvidia-resiliency-ext)** - Fault tolerant training with failure detection and recovery
**Libraries using Megatron Core:**
- **[Megatron Bridge](https://github.com/NVIDIA-NeMo/Megatron-Bridge)** - Training library with bidirectional Hugging Face ↔ Megatron checkpoint conversion, flexible training loops, and production-ready recipes
- **[NeMo RL](https://github.com/NVIDIA-NeMo/RL)** - Scalable toolkit for efficient reinforcement learning with RLHF, DPO, and other post-training methods
- **[NeMo Framework](https://docs.nvidia.com/nemo-framework/user-guide/latest/overview.html)** - Enterprise framework with cloud-native support and end-to-end examples
- **[Model Optimizer (ModelOpt)](https://github.com/NVIDIA/Model-Optimizer)** - Model optimization toolkit for quantization, pruning, distillation, speculative decoding, and more. Checkout end-to-end examples in [examples/post_training/modelopt](https://github.com/NVIDIA/Megatron-LM/tree/main/examples/post_training/modelopt).
**Compatible with:** [Hugging Face Accelerate](https://github.com/huggingface/accelerate), [Colossal-AI](https://github.com/hpcaitech/ColossalAI), [DeepSpeed](https://github.com/microsoft/DeepSpeed)
================================================
FILE: docs/get-started/quickstart.md
================================================
# Your First Training Run
This guide walks you through running your first training jobs with Megatron Core. Make sure you have completed [installation](install.md) before proceeding.
## Simple Training Example
Run a minimal distributed training loop with mock data on 2 GPUs:
```bash
torchrun --nproc_per_node=2 examples/run_simple_mcore_train_loop.py
```
## LLaMA-3 Training Example
Train a LLaMA-3 8B model with FP8 precision on 8 GPUs using mock data:
```bash
./examples/llama/train_llama3_8b_h100_fp8.sh
```
## Data Preparation
To train on your own data, Megatron expects preprocessed binary files (`.bin` and `.idx`).
### 1. Prepare a JSONL File
Each line should contain a `text` field:
```json
{"text": "Your training text here..."}
{"text": "Another training sample..."}
```
### 2. Preprocess the Data
```bash
python tools/preprocess_data.py \
--input data.jsonl \
--output-prefix processed_data \
--tokenizer-type HuggingFaceTokenizer \
--tokenizer-model /path/to/tokenizer.model \
--workers 8 \
--append-eod
```
### Key Arguments
- `--input`: Path to input JSON/JSONL file
- `--output-prefix`: Prefix for output binary files (.bin and .idx)
- `--tokenizer-type`: Tokenizer type (`HuggingFaceTokenizer`, `GPT2BPETokenizer`, etc.)
- `--tokenizer-model`: Path to tokenizer model file
- `--workers`: Number of parallel workers for processing
- `--append-eod`: Add end-of-document token
## Next Steps
- Explore [Parallelism Strategies](../user-guide/parallelism-guide.md) to scale your training
- Learn about [Data Preparation](../user-guide/data-preparation.md) best practices
- Check out [Advanced Features](../user-guide/features/index.md) for advanced capabilities
================================================
FILE: docs/get-started/releasenotes.md
================================================
# Release Notes
## Roadmaps
Stay up-to-date with our development roadmaps and planned features:
- **[MoE Q3-Q4 2025 Roadmap](https://github.com/NVIDIA/Megatron-LM/issues/1729)** - Comprehensive MoE feature development including DeepSeek-V3, Qwen3, advanced parallelism, FP8 optimizations, and Blackwell enhancements
- **[GPT-OSS Implementation Tracker](https://github.com/NVIDIA/Megatron-LM/issues/1739)** - Advanced features including YaRN RoPE scaling, attention sinks, and custom activation functions
================================================
FILE: docs/index.md
================================================
# Megatron Core User Guide
**Megatron Core** is a GPU-optimized library for training large language models at scale. It provides modular, composable building blocks for creating custom training frameworks with state-of-the-art parallelism strategies and performance optimizations.
Megatron Core offers a flexible, reusable foundation for building large-scale transformer training systems. **Megatron-LM** serves as a reference implementation demonstrating how to use Megatron Core components to train models with billions to trillions of parameters across distributed GPU clusters.
## Key Features
* Composable transformer building blocks (attention, MLP)
* Advanced parallelism strategies (TP, PP, DP, EP, CP)
* Pipeline schedules and distributed optimizers
* Mixed precision support (FP16, BF16, FP8)
* GPU-optimized kernels and memory management
* High-performance dataloaders and dataset utilities
* Model architectures (LLaMA, Qwen, DeepSeek, GPT, Mamba)
```{toctree}
:maxdepth: 2
:hidden:
:caption: About Megatron Core
get-started/overview
get-started/releasenotes
```
```{toctree}
:maxdepth: 2
:hidden:
:caption: Get Started
get-started/install
get-started/quickstart
```
```{toctree}
:maxdepth: 2
:hidden:
:caption: Basic Usage
user-guide/data-preparation
user-guide/training-examples
user-guide/parallelism-guide
```
```{toctree}
:maxdepth: 2
:hidden:
:caption: Supported Models
models/index
```
```{toctree}
:maxdepth: 2
:hidden:
:caption: Advanced Features
user-guide/features/moe
user-guide/features/context_parallel
user-guide/features/custom_fsdp
user-guide/features/dist_optimizer
user-guide/features/optimizer_cpu_offload
user-guide/features/pipeline_parallel_layout
user-guide/features/fine_grained_activation_offloading
user-guide/features/megatron_energon
user-guide/features/megatron_rl
user-guide/features/tokenizers
```
```{toctree}
:maxdepth: 1
:hidden:
:caption: Developer Guide
developer/contribute
developer/submit
developer/oncall
developer/generate_docs
```
```{toctree}
:maxdepth: 2
:hidden:
:caption: API Reference
api-guide/index
apidocs/index.rst
```
```{toctree}
:maxdepth: 2
:hidden:
:caption: Resources
advanced/index
```
================================================
FILE: docs/llama_mistral.md
================================================
# Llama, Mistral and other Llama-like model support in Megatron-LM
NOTE: In order to simplify code we now only support converting llama-3.x and mistral checkpoints downloaded from Hugging Face. For converting other models, see [Megatron Bridge](models/index.md).
The Llama-2 and Llama-3.x family of models are an open-source set of pretrained & finetuned (for chat) models that have achieved strong results across a wide set of benchmarks. At their times of release, both Llama-2 and Llama-3 models achieved among the best results for open-source models, and were competitive with leading closed-source models (see ).
Similarly, [Mistral-7b](https://mistral.ai/news/announcing-mistral-7b/) is an open-source model with pretrained and finetuned (for chat) variants that achieve strong benchmark results.
Architecturally Llama-2, Llama-3 and Mistral-7b are very similar. As such Megatron can support loading checkpoints from all three for inference and finetuning. Converting the checkpoints and loading them is slightly different for each model and is detailed for each below.
# Contents
- [Llama, Mistral and other Llama-like model support in Megatron-LM](#llama-mistral-and-other-llama-like-model-support-in-megatron-lm)
- [Contents](#contents)
- [Llama-2](#llama-2)
- [Download Meta or Huggingface checkpoints](#download-meta-or-huggingface-checkpoints)
- [Convert checkpoint format](#convert-checkpoint-format)
- [Meta format](#meta-format)
- [Huggingface format](#huggingface-format)
- [Launch model](#launch-model)
- [Launch Megatron](#launch-megatron)
- [Launch Meta](#launch-meta)
- [Launch Huggingface](#launch-huggingface)
- [Benchmark results](#benchmark-results)
- [Big Bench](#big-bench)
- [Multilingual](#multilingual)
- [LM Evaluation Harness](#lm-evaluation-harness)
- [MMLU](#mmlu)
- [Llama-3.x](#llama-3x)
- [Download Huggingface checkpoints](#download-huggingface-checkpoints)
- [Convert checkpoint format](#convert-checkpoint-format)
- [Huggingface format](#huggingface-format)
- [(Optional) Validate checkpoints](#optional-validate-checkpoints)
- [Launch model](#launch-model)
- [Mistral-7b](#mistral-7b)
- [Download Huggingface checkpoints](#download-huggingface-checkpoints)
- [Convert checkpoint format](#convert-checkpoint-format)
- [(Optional) Validate checkpoints](#optional-validate-checkpoints)
- [Launch model](#launch-model)
- [Other Llama-like model support](#other-llama-like-model-support)
- [Known numerical differences](#known-numerical-differences)
- [Using legacy model format](#using-legacy-model-format)
# Llama-2
Llama-2 checkpoints can be loaded into Megatron for inference and for finetuning. Loading these checkpoints consists of three steps:
1. Get access to download the checkpoints.
2. Convert the checkpoints from Meta/Huggingface format to Megatron format.
3. Setup arguments for launching the model.
The following sections detail these steps. The final section lists benchmark result comparisons between: 1) Llama-2 inference code running the Meta-format checkpoints, and 2) Megatron inference code running the converted checkpoints.
## Download Meta or Huggingface checkpoints
Users must first apply for access to download the Llama-2 checkpoints either directly [Huggingface](https://huggingface.co/docs/transformers/main/model_doc/llama2) (HF). The checkpoints are available in two formats, Meta's native format (available from both the Meta and HF links), and HF's format (available only from HF). Either format can be converted to Megatron, as detailed next.
## Convert checkpoint format
We recommend passing `--dtype bf16` for training or finetuning. Inference can be done in bfloat16 or float16.
### Meta format
The Meta format checkpoints are converted to HF format as an intermediate step before converting to Megatron format. The `transformers` package is required, and must have version >=4.31.0 (e.g., `pip install transformers>=4.31.0`). (**Note**: we have specifically tested with versions `4.31.0` and `4.32.0`; your experience may vary with newer versions.) Assuming the downloaded checkpoints are in `$CHECKPOINT_DIR` (with separate sub-directories for 7B, 13B, 70B, etc.), the following example command can be used to convert from Llama-2 format to HF format in bfloat16:
```
python tools/checkpoint/convert.py \
> --model-type GPT \
> --loader llama_mistral \
> --load-dir ${META_FORMAT_DIR} \
> --model-size ${MODEL_SIZE} \
> --checkpoint-type meta \
> --tokenizer-model ${TOKENIZER_MODEL} \
> --saver core \
> --save-dir ${MEGATRON_FORMAT_DIR} \
> --target-tensor-parallel-size ${TP} \
> --target-pipeline-parallel-size ${PP} \
> --bf16
```
Valid values for `--model-size` are `llama2-7B`, `llama2-13B`, and `llama2-70B` (for pretrained-only models), and `llama2-7Bf`, `llama2-13Bf`, and `llama2-70Bf` (for chat-finetuned models).
### Huggingface format
The HF checkpoints can be converted to Megatron format by using Megatron's own Llama-2 checkpoint converter for HF format (see script `tools/checkpoint/loader_llama_mistral.py`). One important argument that must be set correctly is the tensor parallel size (`TP`) for each model. The following table shows these values:
| Model size | Tensor parallel size (`TP`) |
| ---------- | --------------------------- |
| 7B | 1 |
| 13B | 2 |
| 70B | 8 |
Using these values for `TP`, along with the path to the Llama-2 tokenizer model (automatically downloaded with original checkpoint download; see `${TOKENIZER_MODEL}` below), run the following command from the root of your Megatron source code to convert from HF format to Megatron format:
```
python tools/checkpoint/convert.py \
> --model-type GPT \
> --loader llama_mistral \
> --load-dir ${HF_FORMAT_DIR} \
> --model-size ${MODEL_SIZE} \
> --checkpoint-type hf \
> --tokenizer-model ${TOKENIZER_MODEL} \
> --saver core \
> --save-dir ${MEGATRON_FORMAT_DIR} \
> --target-tensor-parallel-size ${TP} \
> --target-pipeline-parallel-size ${PP} \
> --bf16
```
After this conversion, we are ready to load the checkpoints into a Megatron GPT model.
## Launch model
### Launch Megatron
If loading for either inference or finetuning, use the following arguments:
```
--tensor-model-parallel-size ${TP} \
--pipeline-model-parallel-size 1 \
--seq-length 4096 \
--max-position-embeddings 4096 \
--tokenizer-type Llama2Tokenizer \
--tokenizer-model ${TOKENIZER_MODEL} \
--load ${CHECKPOINT_DIR} \
--exit-on-missing-checkpoint \
--use-checkpoint-args \
--no-load-optim \
--no-load-rng \
--untie-embeddings-and-output-weights \
--use-rotary-position-embeddings \
--normalization RMSNorm \
--no-position-embedding \
--no-masked-softmax-fusion \
--attention-softmax-in-fp32
```
**Note:** If you converted to the legacy model format (i.e., `--saver legacy`), please see [here](#using-legacy-model-format).
### Launch Meta
Meta checkpoints can be launched with:
### Launch Huggingface
Huggingface checkpoints can be launched with:
## Benchmark results
The tables below list the benchmark comparisons between native Llama-2 (using Meta's checkpoint and Meta's inference code) and Megatron (using a converted HF checkpoint and Megatron's inference code).
The values are the percent error between Megatron and Llama-2, calculated using the formula: `| - | / `, where the type of score is detailed before each table. Across all tests (80 total per model size), the mean error is 0.15%. The small difference in benchmark scores between the two models is due to minor arithmetic differences in implementation that alter the numerics slightly. Some of the factors that influence this difference include:
- Megatron performs batch matrix multiplications in a couple places, such as within self attention and in SwiGLU, that Llama performs separately.
- Megatron uses `torch.baddbmm` within self attention, versus Llama using `torch.matmul`.
- Megatron uses a `sin`/`cos` implementation for rotary position embeddings, versus Llama using a `polar`/`complex` implementation.
- Llama calls `torch.set_default_dtype(torch.float16)` during initialization, which Megatron does not.
### Big Bench
Score type: multiple choice grade.
| bigbench / standard | 7b | 13b | 70b |
| -- | -- | -- | -- |
| date_understanding | 0.29% | 0.13% | 0.12% |
| general_knowledge | 0.00% | 0.00% | 0.00% |
| human_organs_senses | 0.00% | 0.00% | 0.00% |
| intent_recognition | 0.00% | 0.11% | 0.00% |
| riddle_sense | 0.00% | 0.00% | 0.00% |
| similarities_abstraction | 0.00% | 0.58% | 0.00% |
| simple_arithmetic_json_multiple_choice | 0.00% | 0.00% | 0.00% |
| undo_permutation | 0.19% | 0.19% | 0.18% |
### Multilingual
Score type: multiple choice grade.
| multilingual / xcopa | 7b | 13b | 70b |
| -- | -- | -- | -- |
| en-template-mGPT-remove-punctuation | 0.08% | 0.00% | 0.00% |
| et-template-mGPT-remove-punctuation | 0.00% | 0.13% | 0.25% |
| ht-template-mGPT-remove-punctuation | 0.26% | 0.13% | 0.26% |
| id-template-mGPT-remove-punctuation | 0.11% | 0.00% | 0.19% |
| it-template-mGPT-remove-punctuation | 0.00% | 0.10% | 0.09% |
| qu-template-mGPT-remove-punctuation | 0.00% | 0.00% | 0.27% |
| sw-template-mGPT-remove-punctuation | 0.14% | 0.13% | 0.13% |
| th-template-mGPT-remove-punctuation | 0.25% | 0.13% | 0.13% |
| tr-template-mGPT-remove-punctuation | 0.26% | 0.00% | 0.34% |
| vi-template-mGPT-remove-punctuation | 0.00% | 0.11% | 0.00% |
| zh-template-mGPT-remove-punctuation | 0.00% | 0.10% | 0.09% |
### LM Evaluation Harness
Score type: multiple choice grade.
| lm-eval | 7b | 13b | 70b |
| -- | -- | -- | -- |
| boolq | 0.04% | 0.04% | 0.07% |
| hellaswag | 0.02% | 0.03% | 0.03% |
| piqa | 0.00% | 0.00% | 0.07% |
| winogrande | 0.00% | 0.11% | 0.20% |
### MMLU
Score type: multiple choice grade.
Note: the number in brackets is the number of sub-tasks for each supercategory.
| mmlu | 7b | 13b | 70b |
| -- | -- | -- | -- |
| stem [18] | 0.79% | 0.05% | 0.01% |
| humanities [13] | 0.19% | 0.01% | 0.02% |
| other (business, health, misc.) [14] | 0.08% | 0.06% | 0.12% |
| social sciences [12] | 0.37% | 0.21% | 0.01% |
# Llama-3.x
Llama-3.x checkpoints can be loaded into Megatron for inference and for finetuning. Loading these checkpoints consists of several steps:
1. Get access to download the checkpoints (weights and tokenizer).
2. Convert the checkpoints from Huggingface format to Megatron format.
3. (Optional) Validate converted checkpoints
4. Setup arguments for launching the model.
The following sections detail these steps.
## Download Huggingface checkpoints
Users must first apply for access to download the Llama-3.x checkpoints from [Huggingface](https://huggingface.co/meta-llama).
## Convert checkpoint format
We recommend passing `--dtype bf16` for training or finetuning. Inference can be done in bfloat16 or float16.
### Huggingface format
The HF checkpoints can be converted to Megatron format by using Megatron's own Llama-3.x checkpoint converter for HF format (see script `tools/checkpoint/loader_llama_mistral.py`). One important argument that must be set correctly is the tensor parallel size (`TP`) for each model. The following table shows these values:
| Model size | Tensor parallel size (`TP`) |
| ---------- | --------------------------- |
| 1B | 1 |
| 3B | 1 |
| 8B | 1 |
| 70B | 8 |
Using these values for `TP`, along with the path to the Llama-3.x tokenizer model (automatically downloaded with original checkpoint download; see `${TOKENIZER_MODEL}` below), run the following command from the root of your Megatron source code to convert from HF format to Megatron format:
```
$>: python tools/checkpoint/convert.py \
> --bf16 \
> --model-type GPT \
> --loader llama_mistral \
> --saver core \
> --target-tensor-parallel-size ${TP} \
> --checkpoint-type hf \
> --load-dir ${HF_FORMAT_DIR} \
> --save-dir ${MEGATRON_FORMAT_DIR} \
> --tokenizer-model ${TOKENIZER_MODEL} \
> --model-size llama3 \
```
After this conversion, we are ready to load the checkpoints into a Megatron GPT model.
## (Optional) Validate checkpoints
A Megatron-LM text generation server for Llama3 can be launched using the script `examples/inference/llama_mistral/run_text_generation_llama3.sh `. For Llama3.1, please use `examples/inference/llama_mistral/run_text_generation_llama3.1.sh`.
Once running, query the server with `curl 'http://:5000/api' -X 'PUT' -H 'Content-Type: application/json; charset=UTF-8' -d '{"prompts":[""], "tokens_to_generate":100, "top_k":1}'`.
A reference generation for comparison can be obtained from the Huggingface transformers library by running `python examples/llama_mistral/huggingface_reference.py --model_path --prompt `.
## Launch model
If loading for either inference or finetuning, use the following arguments for Llama 3.0:
```
--tensor-model-parallel-size ${TP} \
--pipeline-model-parallel-size 1 \
--seq-length 8192 \
--max-position-embeddings 8192 \
--tokenizer-type HuggingFaceTokenizer \
--tokenizer-model ${TOKENIZER_MODEL} \
--load ${CHECKPOINT_DIR} \
--exit-on-missing-checkpoint \
--use-checkpoint-args \
--no-load-optim \
--no-load-rng \
--untie-embeddings-and-output-weights \
--normalization RMSNorm \
--position-embedding-type rope \
--no-masked-softmax-fusion \
--attention-softmax-in-fp32 \
--disable-bias-linear \
--transformer-impl transformer_engine \
--group-query-attention 8 \
--attention-dropout 0.0 \
--hidden-dropout 0.0 \
--rotary-base 500000 \
--rotary-percent 1.0 \
--ffn-hidden-size 14336 \
--num-attention-heads 32 \
--swiglu \
--bf16 \
```
For Llama3.1 please use the following arguments:
```
--tensor-model-parallel-size ${TP} \
--pipeline-model-parallel-size 1 \
--seq-length 8192 \
--max-position-embeddings 131072 \
--tokenizer-type HuggingFaceTokenizer \
--tokenizer-model ${TOKENIZER_MODEL} \
--load ${CHECKPOINT_DIR} \
--exit-on-missing-checkpoint \
--use-checkpoint-args \
--no-load-optim \
--no-load-rng \
--untie-embeddings-and-output-weights \
--normalization RMSNorm \
--position-embedding-type rope \
--no-masked-softmax-fusion \
--attention-softmax-in-fp32 \
--disable-bias-linear \
--transformer-impl transformer_engine \
--group-query-attention 8 \
--attention-dropout 0.0 \
--hidden-dropout 0.0 \
--rotary-base 500000 \
--rotary-percent 1.0 \
--use-rope-scaling \
--ffn-hidden-size 14336 \
--num-attention-heads 32 \
--swiglu \
--bf16 \
```
**Note:** If you converted to the legacy model format (i.e., `--saver legacy`), please see [here](#using-legacy-model-format).
# Mistral-7b
Megatron currently supports loading the v0.3 release of Mistral-7b (which does not use sliding window attention and offers a larger 32768 vocabulary) for inference and finetuning. Loading these checkpoints consists of several steps:
1. Get access to download the checkpoints (weights and tokenizer).
2. Convert the checkpoints from HuggingFace format to Megatron format.
3. (Optional) Validate converted checkpoints
4. Setup arguments for launching the model.
The following sections detail these steps.
## Download Huggingface checkpoints
Users must first apply for access to download the Mistral-7b checkpoints through Huggingface. Two variants are available: the base model ([Mistral-7B-v0.3](https://huggingface.co/mistralai/Mistral-7B-v0.3)) and the instruct model ([Mistral-7B-Instruct-v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3)).
## Convert checkpoint format
The HF checkpoints can be converted to Megatron format by using Megatron's own Mistral checkpoint converter for HF format (see script `tools/checkpoint/loader_llama_mistral.py`).
Using the path to the Mistral tokenizer model (downloaded alongside the HF checkpoint), run the following command from the root of your Megatron source code to convert from HF format to the Megatron core format:
```
$>: python tools/checkpoint/convert.py \
> --bf16 \
> --model-type GPT \
> --loader llama_mistral \
> --saver core \
> --target-tensor-parallel-size ${TP} \
> --checkpoint-type hf \
> --load-dir ${HF_FORMAT_DIR} \
> --save-dir ${MEGATRON_FORMAT_DIR} \
> --tokenizer-model ${TOKENIZER_MODEL} \
> --model-size mistral \
```
After this conversion, we are ready to load the checkpoints into a Megatron core GPT model.
## (Optional) Validate checkpoints
A Megatron-LM text generation server for Mistral-7B can be launched using the script `examples/inference/llama_mistral/run_text_generation_mistral.sh `.
Once running, query the server with `curl 'http://:5000/api' -X 'PUT' -H 'Content-Type: application/json; charset=UTF-8' -d '{"prompts":[""], "tokens_to_generate":100, "top_k":1}'`.
A reference generation for comparison can be obtained from the Huggingface transformers library by running `python examples/inference/llama_mistral/huggingface_reference.py --model_path --prompt `.
## Launch model
If loading for either inference or finetuning, use the following arguments:
```
--tensor-model-parallel-size ${TP} \
--pipeline-model-parallel-size 1 \
--seq-length 4096 \
--max-position-embeddings 4096 \
--tokenizer-type HuggingFaceTokenizer \
--tokenizer-model ${TOKENIZER_MODEL} \
--load ${CHECKPOINT_DIR} \
--exit-on-missing-checkpoint \
--use-checkpoint-args \
--no-load-optim \
--no-load-rng \
--untie-embeddings-and-output-weights \
--normalization RMSNorm \
--position-embedding-type rope \
--no-masked-softmax-fusion \
--attention-softmax-in-fp32
--apply-layernorm-1p \
--transformer-impl transformer_engine \
--group-query-attention 8 \
--disable-bia-linear \
--rotary-base 1000000 \
--rotary-percent 1.0 \
--swiglu \
--ffn-hidden-size 14336 \
--num-attention-heads 32
```
**Note:** If you converted to the legacy model format (i.e., `--saver legacy`), please see [here](#using-legacy-model-format).
# Other Llama-like model support
*Note: Experimental*
Many models such as Yi-34B and Qwen2.x use the Llama architecture and may be converted from HuggingFace to Megatron using the commands in [Llama-3.x](#llama-3x).
# Known numerical differences
It is not expected that the megatron and Huggingface implementations of llama3.x and mistral models will produce numerically identical results. There are multiple points where small numerical differences are expected. This is a non-exhaustive list:
1. TransformerEngine (TE) uses the model params_dtype inside RMSNorm whereas the Huggingface implementation uses fp32. See for details:
2. Huggingface `transformers` implements the q, k and v projections in self-attention as separate GEMMs whereas Megatron core combines them into a single GEMM for efficiency. This leads to small numerical differences.
# Using legacy model format
In all the checkpoint conversion examples used in this document, the saver format `--saver core` is used, signifying that the newer (and recommended) Megatron GPT model class will be used. I.e.:
- old class: `megatron.legacy.model.gpt_model.GPTModel`
- new class: `megatron.core.models.gpt.gpt_model.GPTModel`
Using this new format is the recommended approach. However, if your use case requires using the older class (i.e., convert using `--saver legacy`), then when launching training or finetuning, the following args must be added:
- `--use-legacy-models`: use the older model class
- `--ckpt-format torch`: use the `torch` checkpoint format, which is the only checkpoint format that is compatible with the legacy model format
================================================
FILE: docs/models/index.md
================================================
# Supported Models
Megatron Core supports a wide range of language and multimodal models with optimized implementations for large-scale training.
## Model Conversion
For converting HuggingFace models to Megatron format, use [Megatron Bridge](https://github.com/NVIDIA-NeMo/Megatron-Bridge), the official standalone converter. Megatron Bridge supports an extensive list of models including LLaMA, Mistral, Mixtral, Qwen, DeepSeek, Gemma, Phi, Nemotron, and many more.
See the [Megatron Bridge supported models list](https://github.com/NVIDIA-NeMo/Megatron-Bridge?tab=readme-ov-file#supported-models) for the complete and up-to-date list of supported models.
```{toctree}
:maxdepth: 1
llms
multimodal
../llama_mistral
```
================================================
FILE: docs/models/llms.md
================================================
# Language Models
Megatron Core supports the following language model architectures for large-scale training.
## Converting HuggingFace Models
Use [**Megatron Bridge**](https://github.com/NVIDIA-NeMo/Megatron-Bridge) to convert HuggingFace models to Megatron format. Megatron Bridge is the official standalone converter with support for an extensive list of models including LLaMA, Mistral, Mixtral, Qwen, DeepSeek, Gemma, Phi, Nemotron, and many more.
See the [Megatron Bridge supported models list](https://github.com/NVIDIA-NeMo/Megatron-Bridge?tab=readme-ov-file#supported-models) for the complete and up-to-date list.
## Decoder-Only Models
| Model | Description | Key Features |
|-------|-------------|--------------|
| **GPT** | Generative Pre-trained Transformer | Standard autoregressive LM, foundational architecture |
| **LLaMA** | Meta's LLaMA family | Efficient architecture with RoPE, SwiGLU, RMSNorm |
| **Mistral** | Mistral AI models | Sliding window attention, efficient inference |
| **Mixtral** | Sparse Mixture-of-Experts | 8x7B MoE architecture for efficient scaling |
| **Qwen** | Alibaba's Qwen series | HuggingFace integration, multilingual support |
| **Mamba** | State Space Model | Subquadratic sequence length scaling, efficient long context |
## Encoder-Only Models
| Model | Description | Key Features |
|-------|-------------|--------------|
| **BERT** | Bidirectional Encoder Representations | Masked language modeling, classification tasks |
## Encoder-Decoder Models
| Model | Description | Key Features |
|-------|-------------|--------------|
| **T5** | Text-to-Text Transfer Transformer | Unified text-to-text framework, sequence-to-sequence |
## Example Scripts
Training examples for these models can be found in the `examples/` directory:
- `examples/gpt3/` - GPT-3 training scripts
- `examples/llama/` - LLaMA training scripts
- `examples/mixtral/` - Mixtral MoE training
- `examples/mamba/` - Mamba training scripts
- `examples/bert/` - BERT training scripts
- `examples/t5/` - T5 training scripts
## Model Implementation
All language models are built using Megatron Core's composable transformer blocks, enabling:
- Flexible parallelism strategies (TP, PP, DP, EP, CP)
- Mixed precision training (FP16, BF16, FP8)
- Distributed checkpointing
- Efficient memory management
================================================
FILE: docs/models/multimodal.md
================================================
# Multimodal Models
Megatron Core supports multimodal models that combine language with vision, audio, and other modalities for comprehensive multimodal understanding.
## MIMO: Multimodal In/Out Framework
**MIMO (Multimodal In/Out Model)** is an experimental framework in Megatron Core that supports arbitrary combinations of modalities including vision, audio, and text. MIMO provides a flexible architecture for building custom multimodal models.
> **Note**: MIMO is experimental and under active development. The API may change in future releases.
**Key Features:**
- Arbitrary modality combinations (vision, audio, text, etc.)
- Flexible encoder architecture for different input modalities
- Unified embedding space across modalities
- Support for both vision-language and audio-vision-language models
See [examples/mimo](https://github.com/NVIDIA/Megatron-LM/tree/main/examples/mimo) for training scripts and examples.
## Vision-Language Models
| Model | Description | Vision Encoder | Language Model |
|-------|-------------|----------------|----------------|
| **LLaVA** | Visual instruction tuning | CLIP ViT-L/14 | Mistral-7B / LLaMA |
| **NVLM** | NVIDIA Vision-Language Model | CLIP / Custom ViT | LLaMA-based |
| **LLaMA 3.1 Nemotron Nano VL** | Efficient multimodal model | Vision Transformer | LLaMA 3.1 8B |
## Vision Encoders
| Model | Description | Key Features |
|-------|-------------|--------------|
| **CLIP ViT** | OpenAI's CLIP Vision Transformer | Image-text alignment, multiple scales (L/14@336px) |
| **RADIO** | Resolution-Agnostic Dynamic Image Optimization | Flexible resolution handling, efficient vision encoding |
## Diffusion Models
For multimodal diffusion models (image generation, text-to-image, etc.), see [NeMo Diffusion Models](https://github.com/NVIDIA-NeMo/NeMo/tree/main/nemo/collections/diffusion). NeMo provides production-ready implementations of:
- Stable Diffusion variants
- Text-to-image generation
- Image-to-image translation
- ControlNet and other conditioning mechanisms
## Multimodal Features
- **Image-Text Alignment**: Pre-training on image-caption pairs
- **Visual Instruction Tuning**: Fine-tuning on instruction-following datasets
- **Flexible Vision Encoders**: Support for different ViT architectures and resolutions
- **Combined Checkpointing**: Unified checkpoints combining vision and language models
- **Efficient Training**: Full parallelism support (TP, PP, DP) for both vision and language components
## Example Scripts
Multimodal training examples can be found in the following directories:
**MIMO Framework:**
- `examples/mimo/` - Multimodal In/Out training with support for vision-language and audio-vision-language models
**Specific Multimodal Models:**
- `examples/multimodal/` - LLaVA-style training with Mistral + CLIP
- `examples/multimodal/nvlm/` - NVLM training scripts
- `examples/multimodal/llama_3p1_nemotron_nano_vl_8b_v1/` - Nemotron VL training
- `examples/multimodal/radio/` - RADIO vision encoder integration
================================================
FILE: docs/project.json
================================================
{"name": "megatron-lm", "version": "nightly"}
================================================
FILE: docs/user-guide/data-preparation.md
================================================
# Data Preparation
Preparing your data correctly is essential for successful training with Megatron Core.
## Data Format
Megatron Core expects training data in JSONL (JSON Lines) format, where each line is a JSON object:
```json
{"text": "Your training text here..."}
{"text": "Another training sample..."}
{"text": "More training data..."}
```
## Preprocessing Data
Use the `preprocess_data.py` tool to convert your JSONL data into Megatron's binary format:
```bash
python tools/preprocess_data.py \
--input data.jsonl \
--output-prefix processed_data \
--tokenizer-type HuggingFaceTokenizer \
--tokenizer-model /path/to/tokenizer.model \
--workers 8 \
--append-eod
```
### Key Arguments
| Argument | Description |
|----------|-------------|
| `--input` | Path to input JSON/JSONL file |
| `--output-prefix` | Prefix for output binary files (.bin and .idx) |
| `--tokenizer-type` | Tokenizer type (`HuggingFaceTokenizer`, `GPT2BPETokenizer`, etc.) |
| `--tokenizer-model` | Path to tokenizer model file |
| `--workers` | Number of parallel workers for processing |
| `--append-eod` | Add end-of-document token |
## Finding Optimal Number of Workers
Use the `--find-optimal-num-workers` flag to find number of workers which gives the best performance in terms of preprocessed documents per second.
Script will lauch a few short data preprocessing runs with a different number of workers to define the fastest run in respect to collected performance data.
```bash
python tools/preprocess_data.py \
--input data.jsonl \
--output-prefix processed_data \
--tokenizer-type HuggingFaceTokenizer \
--tokenizer-model /path/to/tokenizer.model \
--workers 8 \
--find-optimal-num-workers \
--workers-to-check 4 8 16 32 \
--max-documents 50000
```
**Required arguments**
| Argument | Description |
|----------|-------------|
| `--find-optimal-num-workers` | Activates search of optimal number of workers |
| `--workers-to-check` | List of possible number of workers to run |
| `--max-documents` | Number of documents to be preprocessed during each run |
**Output example**
```bash
-----------------------------------
Performance results (fastest → slowest):
1. 16 workers → avg. docs/s: 9606.6476
2. 32 workers → avg. docs/s: 9275.3284
3. 8 workers → avg. docs/s: 9151.9280
4. 4 workers → avg. docs/s: 6391.3819
-----------------------------------
The most optimal num of workers is 16 with avg. preprocessed docs/s: 9606.6476.
-----------------------------------
```
## Output Files
The preprocessing tool generates two files:
- `processed_data.bin` - Binary file containing tokenized sequences
- `processed_data.idx` - Index file for fast random access
## Using Preprocessed Data
Reference your preprocessed data in training scripts:
```bash
--data-path processed_data \
--split 949,50,1 # Train/validation/test split
```
## Common Tokenizers
### HuggingFace Tokenizers
```bash
--tokenizer-type HuggingFaceTokenizer \
--tokenizer-model /path/to/tokenizer.model
```
### GPT-2 BPE Tokenizer
```bash
--tokenizer-type GPT2BPETokenizer \
--vocab-file gpt2-vocab.json \
--merge-file gpt2-merges.txt
```
================================================
FILE: docs/user-guide/features/context_parallel.md
================================================
# context_parallel package
## Context parallelism overview
```{figure} ../../images/context_parallel/CP_overview.png
:alt: cp_overview
:align: center
Figure 1: A transformer layer running with TP2CP2. Communications next to Attention are for CP, others are for TP. (AG/RS: all-gather in forward and reduce-scatter in backward, RS/AG: reduce-scatter in forward and all-gather in backward, /AG: no-op in forward and all-gather in backward).
```
Context Parallelism ("CP") is a parallelization scheme on the dimension of sequence length. Unlike prior SP (sequence parallelism) which only splits the sequence of Dropout and LayerNorm activations, CP partitions the network inputs and all activations along sequence dimension. With CP, all modules except attention (e.g., Linear, LayerNorm, etc.) can work as usual without any changes, because they do not have inter-token operations. As for attention, the Q (query) of each token needs to compute with the KV (key and value) of all tokens in the same sequence. Hence, CP requires additional all-gather across GPUs to collect the full sequence of KV. Correspondingly, reduce-scatter should be applied to the activation gradients of KV in backward propagation. To reduce activation memory footprint, each GPU only stores the KV of a sequence chunk in forward and gathers KV again in backward. KV communication happens between a GPU and its counterparts in other TP groups. The all-gather and reduce-scatter are transformed to point-to-point communications in ring topology under the hood. Exchanging KV also can leverage MQA/GQA to reduce communication volumes, as they only have one or few attention heads for KV.
For example, in Figure 1, assuming sequence length is 8K, each GPU processes 4K tokens. GPU0 and GPU2 compose a CP group, they exchange KV with each other. Same thing also happens between GPU1 and GPU3. CP is similar to [Ring Attention](https://arxiv.org/abs/2310.01889) but provides better performance by (1) leveraging the latest OSS and cuDNN flash attention kernels; (2) removing unnecessary computation resulted from low-triangle causal masking and achieving optimal load balance among GPUs.
## Context parallelism benefits
```{figure} ../../images/context_parallel/CP_results.png
:alt: cp_results
:align: center
Figure 2: Speedup of 175B GPT with various TP+CP combinations vs. full recompute (i.e., TP8CP1).
```
LLM encounters OOM (out of memory) issue with long context (i.e., long sequence length) because of linearly increasing memory footprint of activations. Recomputing activations in backward can avoid OOM but also introduce significant overheads (~30% with full recompute). Enlarging TP (tensor model parallelism) can fix the OOM issue as well, but it potentially makes compute (e.g., Linear) too short to overlap communication latencies. To be clear, scaling out to more GPUs with bigger TP can hit the overlapping problem no matter if OOM happens.
CP can better address the issues. With CP, each GPU only computes on a part of the sequence, which reduces both computation and communication by CP times. Therefore, there are no concerns about the overlapping between them. The activation memory footprint per GPU is also CP times smaller, hence no OOM issue anymore. As Figure 2 shows, the combinations of TP and CP can achieve optimal performance by eliminating recompute overheads and making the best tradeoff between computation and communications.
## Enabling context parallelism
CP support has been added to GPT. All models that share GPT code path also should be able to benefit from CP, such as Llama. CP can work with TP (tensor model parallelism), PP (pipeline model parallelism), and DP (data parallelism), where the total number of GPUs equals TPxCPxPPxDP. CP also can work with different attention variants, including MHA/MQA/GQA, uni-directional and bi-directional masking.
CP is enabled by simply setting context_parallel_size= in command line. Default context_parallel_size is 1, which means CP is disabled. Running with CP requires Megatron-Core (>=0.5.0) and Transformer Engine (>=1.1).
================================================
FILE: docs/user-guide/features/custom_fsdp.md
================================================
# Megatron FSDP
**NOTE: In M-Core 0.14, the custom FSDP refactored its checkpoint implementation to use DTensor-based torch distributed checkpointing. The custom FSDP was also renamed Megatron FSDP. The relevant sections of this document are no longer applicable.**
## How to use ?
Add these flag to enable MCore custom FSDP.
```bash
--use-megatron-fsdp
--data-parallel-sharding-strategy optim_grads_params
--no-gradient-accumulation-fusion
--use-distributed-optimizer
```
For a practical guide covering required configurations, checkpoint conversion, and example scripts, see the [Megatron-FSDP User Guide](../../discussions/megatron-fsdp-user-guide/megatron-fsdp-user-guide.md).
## Key Features
- **Sharding Strategy**: Efficiently shards optimizer states, gradients, and parameters to reduce memory consumption.
- **Communication and Computation Overlap**: Optimized to enable concurrent execution of communication and computation, enhancing overall efficiency.
- **Supports automatic mixed precision training**: Compatible with BF16 O1/O2/O3 recipes, as well as FP8 compute with FP32 parameters and FP8 parameter training, allowing for flexible precision configurations.
- **Tensor Parallelism (TP), Expert Parallelism (EP) and Context Parallelism (CP)**: Compatible with TP, EP and CP configurations, enabling efficient scaling of large language models.
- **Distributed Model Initialization with Meta Device**: Allows model initialization using meta device, followed by layer-by-layer initialization of distributed model weight buffers via the `Module.reset_parameters` API, facilitating the initialization of extremely large models.
## Configuration Recommendations
### 1. Disable `CUDA_DEVICE_MAX_CONNECTIONS`
To ensure full parallelization of FSDP communication and computation, disable the CUDA_DEVICE_MAX_CONNECTIONS environment variable. This step avoids potential bubble in CUDA stream. (But it may slow down TP and CP to some extent.)
```bash
unset CUDA_DEVICE_MAX_CONNECTIONS
```
### 2. Add `--calculate-per-token-loss`
For gradients sharding mode optimization, include the `--calculate-per-token-loss` flag in your training script. This improves performance by reducing the frequency of gradient scaling, which is also a sizable drain on SM resources.
## Design of Custom FSDP
### 1. Overview
The custom Fully Sharded Data Parallelism (FSDP) implementation in Megatron-Core is specifically designed to optimize memory consumption and performance for large language models. The core design principles include:
- **Optimized for Large Language Models**: This custom FSDP implementation is tailored to efficiently scale with models containing billions of parameters, ensuring seamless execution and training of massive models.
- **Efficient Memory Consumption**: By strategically sharding optimizer states, gradients, and model parameters, the custom FSDP significantly reduces memory usage. This approach enables the training of models that would otherwise be too large to fit in memory.
- **Efficient Workflow & Overlapping Communication and Computation**: The implementation is engineered to minimize the number of communication steps required during training. It maximizes the overlap between communication and computation, thereby enhancing overall training efficiency and reducing latency.
- **Support for MCore's Efficient Training Methods**: The custom FSDP seamlessly integrates with Megatron-Core's advanced parallelism techniques, including tensor parallelism, expert parallelism and context parallelism. Additionally, it supports automatic mixed precision training, further optimizing training performance and efficiency.
The design of Custom FSDP draws inspiration from PyTorch FSDP [Zhao, Yanli, et al.](https://arxiv.org/pdf/2304.11277) and MCore's distributed optimizer. The introduction to PyTorch FSDP is referenced here to clarify the underlying concepts of the custom FSDP design.
> In DistributedDataParallel, (DDP) training, each process/ worker owns a replica of the model and processes a batch of data, finally it uses all-reduce to sum up gradients over different workers. In DDP the model weights and optimizer states are replicated across all workers. FSDP is a type of data parallelism that shards model parameters, optimizer states and gradients across DDP ranks.
> When training with FSDP, the GPU memory footprint is smaller than when training with DDP across all workers. This makes the training of some very large models feasible by allowing larger models or batch sizes to fit on device. This comes with the cost of increased communication volume. The communication overhead is reduced by internal optimizations like overlapping communication and computation.
*Notice that the unit processed in workflow here is the “FSDP instance 1: N layers”, where an FSDP instance is the smallest FSDP processing unit (also a PyTorch module), which means that we can safely release this module weights after using it (executing the forward or backward of this module), and there will be no other computations computations relying on these weights. This capability is the foundation of FSDP's layer-by-layer execution and memory-saving strategy. An FSDP instance is also referred to as an **FSDP Unit**.*
*It is worth noting that an FSDP instance can correspond to multiple FSDP parameter groups. These groups are separated by Data Parallel (DP) communication groups and the data type of the parameter or gradient. Consequently, an FSDP instance may require several parameter-gather tasks before execution (forward or backward). Each **FSDP parameter group** corresponds to one **Data Parallel Buffer** in custom FSDP.*
At a high level FSDP works as follow:
In constructor
- Shard model parameters and each rank only keeps its own shard
In forward path
- Run all_gather to collect all shards from all ranks to recover the full parameter in this FSDP unit
- Run forward computation
- Discard parameter shards it has just collected
In backward path
- Run all_gather to collect all shards from all ranks to recover the full parameter in this FSDP unit
- Run backward computation
- Run reduce_scatter to sync gradients
- Discard parameters.
One way to view FSDP’s sharding is to decompose the DDP gradient all-reduce into reduce-scatter and all-gather. Specifically, during the backward pass, FSDP reduces and scatters gradients, ensuring that each rank possesses a shard of the gradients. Then it updates the corresponding shard of the parameters in the optimizer step. Finally, in the subsequent forward pass, it performs an all-gather operation to collect and combine the updated parameter shards.
### 2. Custom FSDP underlying data structure
To implement the FSDP functionality described above, the custom FSDP is designed with the following Python classes and data structure:
### 3. The custom FSDP interface: FullyShardedDataParallel
The custom FSDP provides the same programming interface as PyTorch's DistributedDataParallel (DDP) as FullyShardedDataParallel (FSDP). For example, you can apply FSDP to models as follows:
```python
# Initialize model and optimizer
ddp_config.use_megatron_fsdp = True
ddp_config.data_parallel_sharding_strategy = "optim_grads_params"
model = GPTModel(transformer_config)
model = FullyShardedDataParallel(
transformer_config,
model,
ddp_config,
fsdp_unit_modules = [TransformerLayer, LanguageModelEmbedding],
)
optimizer = torch.optim.AdamW(model.parameters(), lr=lr)
optimizer = DistributedOptimizer(optimizer, [model], [model.param_and_grad_buffer])
# Training loop
def train_step(inputs, labels):
optimizer.zero_grad()
for mbs_input, mbs_label in zip(inputs, labels):
outputs = model(mbs_input)
loss = loss_fn(outputs, mbs_label)
loss.backward()
optimizer.step()
# Save and load model and optimizer state dict
def model_and_optimizer_state_dict():
state_dict = {
"model": model.sharded_state_dict(),
"optimizer": optimizer.sharded_state_dict(),
}
return state_dict
def load_model_and_optimizer_state_dict(state_dict):
model.load_state_dict(state_dict["model"])
optimizer.load_state_dict(state_dict["optimizer"])
```
**Key Notes:**
- You can configure which modules should be treated as FSDP units via the `fsdp_unit_modules` argument. This configuration is mandatory.
- The custom FSDP must be used with a distributed optimizer since it provides distributed checkpointing.
- The data-parallel communication group for parameters is not explicitly shown. Custom FSDP configures these groups as either DP (data-parallel) or EDP (expert data-parallel) based on parameter markings.
#### 3.1 Initializing Models on the Meta Device
For training particularly large models with FSDP, you can initialize the model on the meta device. Using PyTorch's `reset_parameters` API, you can initialize model weights layer by layer during the construction of the `ParamAndGradBuffer`. Most PyTorch native modules and TransformerEngine modules support this API (e.g., [PyTorch Linear](https://github.com/pytorch/pytorch/blob/v2.6.0/torch/nn/modules/linear.py#L114), [TE LayerNormLinear](https://github.com/NVIDIA/TransformerEngine/blob/release_v2.0/transformer_engine/pytorch/module/layernorm_linear.py#L1107)).
```python
# Initialize model on meta device
with torch.device("meta"):
model = GPTModel(config)
model = FullyShardedDataParallel(
transformer_config,
model,
ddp_config,
fsdp_unit_modules=[TransformerLayer, LanguageModelEmbedding],
)
```
**Important Considerations:**
1. *Custom Modules*: If your model contains custom modules, ensure they implement the `reset_parameters` API. Otherwise, you may need to force parameter initialization on a CUDA or CPU device.
2. *Tensor Initialization*: Be cautious of tensors created during model initialization without a specified device—they will default to the meta device. To avoid issues, explicitly specify the device for these tensors to ensure compatibility with this function.
### 4. Interaction between Custom FSDP and Model Forward/Backward Propagation
Custom FSDP implements Fully Sharded Data Parallelism (FSDP) through a series of module hooks, gradient hooks, or by adding functions between modules. This involves inserting communications and manipulating parameters and gradients during PyTorch's module forward or backward propagation.
Module hooks summary:
- Module pre-forward hook(`module.register_forward_pre_hook`): This hook unshards model weights before the forward pass. In the case of an FSDP Unit Module, add a RegisterFSDPBackwardFunction function that will reshard model weights and reduce gradients after module backward propagation.
- Module post-forward hook(`module.register_forward_hook`): This hook is used to reshard model weights after the forward pass.
- Root module pre-backward hook(`root_module.register_full_backward_pre_hook`): This hook checks that all model parameters are resharded, in order to avoid unnecessary memory spikes. It also marks all modules as being in the `TrainingState.PRE_BACKWARD` state.
- Module pre-backward hook(`module.register_full_backward_pre_hook`): This hook is used to unshard the model weights before the backward pass.
- Root module post-backward hook(`torch.autograd.Variable._execution_engine.queue_callback`): This hook is used to make sure all gradients in the backprop are properly handled / available.
The gradient reduction pipeline maintains a map of gradients to FSDP parameter groups. If all gradients in an FSDP parameter group are ready, it launches a gradient reduction. Note that this assumes that the model's gradients are always generated in a certain order (reverse of `module.parameters()`), as otherwise, FSDP would maintain too many parameter group grad buffers, leading to excessive memory usage.
#### 4.1 Optimized for Activation Recompute
Using the activation recompute will cause the same module to execute the forward function first and then the backward function in the backward prop, which will cause model weights unshard twice and model weights reshard twice. If we can tell program that this is a forward + backward operation, we can just call unshard once and reshard once.
To make this determination, we keep track of the model's state with training_state, `FORWARD`, `PRE_BACKWARD`, `POST_BACKWARD`, `IDLE`. It's worth noting that pre-backward hook act before pre-forward hook, and we'll let pre-backward hook execute the model weight unshard, and then mark the model as `PRE_BACKWARD`, and when pre-forward hook sees this marking it will not perform the unshard operation. Similarly, for model weight reshard duplicate, post-forward hook act before post-backward function, and checking for the `PRE_BACKWARD` flag in the post-forward hook will cancel the unshard.
### 5. Memory Mechanisms and Features of Custom FSDP
FSDP can fully distribute the model parameters, gradients, and optimizer states, and for mixed-precision training, it can also fully distribute the high-precision main weights. This is pretty much distributes all the memory except for the activation memory, but FSDP will also face some memory issues.
FSDP frequently unshards and reshards model weights, which can lead to busy memory allocation and deallocation. This results in untimely tensor releases, causing memory spikes (or even out-of-memory errors), crashes of the PyTorch memory allocator cache, and a large number of `cudaMalloc` and `cudaFree` calls. These issues can significantly slow down the system.
The problem of untimely tensor release can generally be addressed using the `tensor._typed_storage(). _resize_(0)` API, which immediately deallocates the storage's memory. Custom FSDP provides interfaces in `AllGatherPipeline` and `GradReducePipeline` to replace the temporary buffer memory allocator used for parameter gathering and gradient reduction with ` StorageResizeBasedBucketAllocator`. This replaces the tensor release operation with the `tensor._typed_storage(). _resize_(0)` API.
The PyTorch memory allocator cache crash is a complex issue that occurs frequently when the actual memory usage approaches the GPU memory limit, leading to poor performance. This problem is challenging and can only be mitigated by avoiding frequent hits on the GPU memory limit. Using a self-managed memory allocator like ` RotaryBucketAllocator` is another potential solution. However, note that `RotaryBucketAllocator` is not yet mature.
## References
- [Getting Started with Fully Sharded Data Parallel (FSDP)](https://pytorch.org/tutorials/intermediate/FSDP_tutorial.html)
================================================
FILE: docs/user-guide/features/dist_optimizer.md
================================================
# Distributed Optimizer
The motivation for the distributed optimizer is to save memory by distributing the optimizer state evenly across data parallel ranks (https://arxiv.org/abs/1910.02054), versus the naive method of replicating the optimizer state across data parallel ranks.
Theoretical memory savings vary depending on the combination of the datatype of the model's parameters (`param_dtype`) and main gradients accumulated across data-parallel replicas (`grad_dtype`). We always use `fp32` main parameters for optimizer steps. In the current implementation, the theoretical number of bytes per parameter is (where d is the data parallel size):
| | Non-distributed optim | Distributed optim |
| ------ | ------ | ------ |
| `fp16` parameters, `fp16` gradients | 20 | 4 + 16/d |
| `bf16` parameters, `fp32` gradients | 18 | 6 + 12/d |
| `fp32` parameters, `fp32` gradients | 16 | 8 + 8/d |
Our implementation of the distributed optimizer uses contiguous buffers for parameters and main gradients; model gradients are copied over to the main gradients as soon as they are fully computed.
The figures below illustrate the distributed optimizer's sharding scheme, and the key steps of the distributed optimizer's parameter update:
## Data flow
## Sharding scheme
## Key steps
_(note: using illustrations above, assuming `bf16` model weights, `bf16` model gradients that are computed by the backward pass and `fp32` main gradients that are also used for optimizer steps; we always use `fp32` main weights for optimizer steps)_
- Backward pass finishes (gradient buffer holds 16 `fp32` gradient elements).
- Call reduce-scatter on each DP rank.
- Each DP rank now has 4 elements within the gradient buffer that are fully reduced (remaining 12 elements are garbage).
- DP rank 0 has gradient values for elements [0:4].
- DP rank 1 has gradient values for elements [4:8].
- DP rank 2 has gradient values for elements [8:12].
- DP rank 3 has gradient values for elements [12:16].
- Optimizer.step().
- Each DP rank copies its 4 `fp32` main parameter elements into the corresponding `bf16` parameter buffer (each element is cast from fp32 to fp16).
- Call all-gather on each DP rank.
- The parameter buffer now contains all 16, fully updated, `bf16` model parameter elements. Parameters in PyTorch modules already point to the appropriate locations in this parameter buffer, and thus forward passes are ready to run after the all-gather completes.
- At this point, the gradient buffer is also ready to be zero'd for the next iteration.
================================================
FILE: docs/user-guide/features/fine_grained_activation_offloading.md
================================================
# Fine-grained Activation Offloading (collaborated with rednote)
Memory capacity is more and more important with the rising of extreme sparse MoE models like DeepSeek-V3 and Qwen3-235B. Fine-grained recomputing reduces the memory footprint at the cost of extra recomputation, while offloading could utilize the host-device bandwidth to achieve nearly zero-overhead. Fine-grained Activation Offloading targets at offloading the activation at the granularity of specific modules, so that we can calibrate the amount of offloading activation to maximize the training throughput.
Currently, the supported offloading modules are `"attn_norm", "core_attn", "attn_proj", "mlp_norm", "expert_fc1", "moe_act"`, which could work with fine-grained recomputation to release almost all activations of a transformer layer.
**Features**
* Support PP=1/PP/Interleaved PP
* Compatible with fine-grained recomputation
* Support FP8
* Support MTP
* Support mixed dense & moe layer
* Support A2A Overlap
* Support CUDA Graph
* (Temporary) cuda graph scope cannot contains the offloading modules
**Usage**
```bash
# Enable fine-grained activation offloading
--fine-grained-activation-offloading
# Specify which modules are going to offload its input
# Choices: "attn_norm", "core_attn", "attn_proj", "mlp_norm", "expert_fc1", "moe_act".
--offload-modules expert_fc1
```
**Compatible with Fine-grained Recomputation**
- For modules with minor perf overhead like layernorm or moe_act, use recomputing to reduce memory footprint;
- For other modules, use offloading to reduce memory footprint;
- Make sure the offloading/reloading could be overlapped with computing;
================================================
FILE: docs/user-guide/features/index.md
================================================
# Advanced Features
Advanced feature guides for key Megatron Core capabilities.
```{toctree}
:maxdepth: 2
fine_grained_activation_offloading
moe
context_parallel
custom_fsdp
dist_optimizer
optimizer_cpu_offload
pipeline_parallel_layout
tokenizers
megatron_energon
megatron_rl
```
================================================
FILE: docs/user-guide/features/megatron_energon.md
================================================
# Megatron Energon
Advanced multimodal dataloader for efficient loading of text, images, video, and audio at scale.
## Overview
[**Megatron Energon**](https://github.com/NVIDIA/Megatron-Energon) is purpose-built for large-scale multimodal training with:
- **Multimodal support** - Text, images, video, audio
- **Distributed loading** - Optimized for multi-node training
- **Data blending** - Mix datasets with configurable weights
- **WebDataset format** - Efficient streaming from cloud storage
- **State management** - Save and restore training position
## Installation
```bash
pip install megatron-energon
```
## Key Features
### Data Processing
- **Packing** - Optimize sequence length utilization
- **Grouping** - Smart batching of similar-length sequences
- **Joining** - Combine multiple dataset sources
- **Object storage** - Stream from S3, GCS, Azure Blob Storage
### Production-Ready
- Distributed loading across workers and nodes
- Checkpoint data loading state
- Memory-efficient streaming
- Parallel data loading with prefetching
## Basic Usage
```python
from megatron.energon import get_train_dataset, get_loader, WorkerConfig
# Create dataset
ds = get_train_dataset(
'/path/to/dataset',
batch_size=32,
shuffle_buffer_size=1000,
worker_config=WorkerConfig.default_worker_config(),
)
# Create loader and iterate
for batch in get_loader(ds):
# Training step
pass
```
## Multimodal Example
```python
# Load image-text dataset
ds = get_train_dataset(
'/path/to/multimodal/dataset',
batch_size=32,
worker_config=WorkerConfig(num_workers=8, prefetch_factor=2),
)
for batch in get_loader(ds):
images = batch['image'] # Image tensors
texts = batch['text'] # Text captions
# Process batch
```
## Dataset Blending
Mix multiple datasets with custom weights:
```python
from megatron.energon import Blender
blended_ds = Blender([
('/path/to/dataset1', 0.6), # 60%
('/path/to/dataset2', 0.3), # 30%
('/path/to/dataset3', 0.1), # 10%
])
```
## Configuration
### Worker Configuration
```python
WorkerConfig(
num_workers=8, # Parallel workers
prefetch_factor=2, # Batches to prefetch per worker
persistent_workers=True, # Keep workers alive between epochs
)
```
### Common Parameters
| Parameter | Description |
|-----------|-------------|
| `batch_size` | Samples per batch |
| `shuffle_buffer_size` | Buffer size for randomization |
| `max_samples_per_sequence` | Max samples to pack into one sequence |
| `worker_config` | Worker configuration for parallel loading |
## Integration with Megatron-LM
```python
from megatron.energon import get_train_dataset, get_loader
from megatron.training import get_args
args = get_args()
train_ds = get_train_dataset(
args.data_path,
batch_size=args.micro_batch_size,
)
for iteration, batch in enumerate(get_loader(train_ds)):
loss = train_step(batch)
```
## Resources
- **[Megatron Energon GitHub](https://github.com/NVIDIA/Megatron-Energon)** - Documentation and examples
- **[Multimodal Examples](https://github.com/NVIDIA/Megatron-LM/tree/main/examples/multimodal)** - Megatron-LM multimodal training
## Next Steps
- Check [Multimodal Models](../../models/multimodal.md) for supported architectures
- See [Training Examples](../training-examples.md) for integration examples
================================================
FILE: docs/user-guide/features/megatron_rl.md
================================================
# Megatron RL
Reinforcement learning library for post-training large language models at scale.
## Overview
[**Megatron RL**](https://github.com/NVIDIA/Megatron-LM/tree/dev/megatron/rl) adds native reinforcement learning capabilities to Megatron-LM for large-scale RL-based post-training of foundation models.
> **Note**: Megatron RL is under active development and primarily designed for research teams exploring RL post-training on modern NVIDIA hardware. For production deployments, use [**NeMo RL**](https://github.com/NVIDIA-NeMo/RL).
## Key Features
- **Decoupled Design** - Clean separation between agent/environment logic and RL implementation
- **Flexible Inference** - Support for Megatron, OpenAI, and HuggingFace inference backends
- **Trainer/Evaluator** - Manages rollout generation and coordinates with inference systems
- **Megatron Integration** - Native integration with Megatron Core inference system
## Architecture
### Components
**Agents & Environments**
- Accept inference handles
- Return experience rollouts with rewards
- Implement custom RL logic
**Trainer/Evaluator**
- Controls rollout generation
- Coordinates with inference systems
- Manages training loops
**Inference Interface**
- Provides `.generate(prompt, **generation_args)` endpoint
- Supports multiple backends (Megatron, OpenAI, HuggingFace)
## Use Cases
- RLHF (Reinforcement Learning from Human Feedback)
- Custom reward-based fine-tuning
- Policy optimization for specific tasks
- Research on RL post-training techniques
## Resources
- **[Megatron RL GitHub](https://github.com/NVIDIA/Megatron-LM/tree/dev/megatron/rl)** - Source code and documentation
- **[Megatron Core Inference](../../api-guide/core/transformer.md)** - Native inference integration
================================================
FILE: docs/user-guide/features/moe.md
================================================
# Mixture of Experts
```{toctree}
:maxdepth: 1
:caption: MoE Features
multi_token_prediction
multi_latent_attention
../../api-guide/router_replay
```
```{include} ../../../megatron/core/transformer/moe/README.md
```
================================================
FILE: docs/user-guide/features/multi_latent_attention.md
================================================
# Multi-Latent Attention
## Multi-Latent Attention overview
Multi-Latent Attention ("MLA") is an innovative attention mechanism introduced by Deepseek team that enhances the efficiency of attention computation by leveraging multiple latent spaces. This approach is particularly beneficial for large language models (LLMs), as it reduces the computational burden associated with traditional attention mechanisms. According to Deepseek-V2 technical report, MLA achieves better performance compared to Multi-Head Attention (MHA) and requires smaller KV cache.
## Enabling Multi-Latent Attention
To enable MLA in Megatron-LM, set the following flags in command line:
- `--multi-latent-attention` to enable MLA in MLP.
- Set `MLATransformerConfig` to configure MLA.
================================================
FILE: docs/user-guide/features/multi_token_prediction.md
================================================
# Multi-Token Prediction (MTP)
Multi-Token Prediction (MTP) extends the prediction scope to multiple future tokens at each position. On the one hand, an MTP objective densifies the training signals and may improve
data efficiency. On the other hand, MTP may enable the model to pre-plan its representations for better prediction of future tokens. In this implementation of MTP, we sequentially predict additional tokens and keep the complete causal chain at each prediction depth. The following figure illustrates our implementation of MTP in [DeepSeek-V3](https://github.com/deepseek-ai/DeepSeek-V3/).
The k-th MTP module consists of a shared embedding layer, a projection matrix, a Transformer block, and a shared output head. For the i-th input token at the (k - 1)-th prediction depth, we first combine the representation of the i-th token and the embedding of the (i + K)-th token with the linear projection. The combined serves as the input of the Transformer block at the k-th depth to produce the output representation.
For more information, refer to [DeepSeek-V3 Technical Report](https://arxiv.org/pdf/2412.19437.pdf)
## Related Arguments
We can train GPTModel like models with Multi-Token Prediction (MTP) by setting mtp_num_layers to be a positive integer.
| Item | Description |
| --- | --- |
| mtp_num_layers | Number of Multi-Token Prediction (MTP) Layers. MTP extends the prediction scope to multiple future tokens at each position. This MTP implementation sequentially predict additional tokens by using D sequential modules to predict D additional tokens. Default is None. |
| mtp_loss_scaling_factor | Scaling factor of Multi-Token Prediction (MTP) loss. We compute the average of the MTP losses across all depths, and multiply it the scaling factor to obtain the overall MTP loss, which serves as an additional training objective. Default is 0.1. |
## Pipeline Parallel Layout for MTP
MTP supports flexible placement of MTP layers across pipeline stages using a custom `pipeline_model_parallel_layout`. By default, all MTP layers are placed on the last pipeline stage, but you can customize their placement.
### MTP Standalone Mode
When MTP layers are placed in a separate virtual pipeline (vpp) stage that is not on the last pipeline rank, the `mtp_standalone` flag is automatically set to `True`. This mode enables MTP to run independently in its own pipeline stage.
### Layout Format
Use `m` to represent MTP layers in the pipeline layout string. For example:
- `"E|t*3|(t|)*5mL"` - MTP in the last stage
- `"E|t*3|(t|)*4tm|L"` - MTP in the second-to-last stage with a decoder layer
- `"E|t*3|(t|)*3tt|m|L"` - MTP in a standalone stage (second-to-last) with no other layers
### Constraints
- All MTP layers must be placed in the same one virtual pipeline stage.
- MTP layers cannot be placed on the first pipeline rank.
## Implementation Notes
- For models with MTP layers, the final layernorm is placed in the stage that contains the last decoder layer, rather than in the post-process stage. This may cause small numerical differences in gradient norm reduction when final layernorm is placed in different pipeline stages in deterministic mode. Bitwise alignment can be achieved by disabling gradient norm clipping.
- MTP loss is computed in the post-processing stage.
## Precautions
Do not use Context Parallel (CP), or arbitrary AttnMaskType, or learned absolute position embedding type with MTP. These use cases are not yet supported.
================================================
FILE: docs/user-guide/features/optimizer_cpu_offload.md
================================================
# Optimizer CPU Offload
```{include} ../../../megatron/core/optimizer/cpu_offloading/README.md
```
================================================
FILE: docs/user-guide/features/pipeline_parallel_layout.md
================================================
# Custom Pipeline Model Parallel Layout
*This is an experimental feature and may be changed.*
`--pipeline-model-parallel-layout` is a flexible API for defining the pipeline parallel partitioning, which is essential for balanced partitioning for an imbalanced model. For example, to partition DeepSeek-V3 (61 decoder layers + 1 mtp layer) with PP16VPP2, we can include the arguments as follows:
```bash
--pipeline-model-parallel-size 16
--pipeline-model-parallel-layout "Et*3|(tt|)*29,m|L"
```
| PP \ VPP rank | 0 | 1 |
|---------------|-------------------------|---------------|
| 0 | embedding + 3 × decoder | 2 × decoder |
| 1~13 | 2 × decoder | 2 × decoder |
| 14 | 2 × decoder | mtp |
| 15 | 2 × decoder | loss |
In the layout string, stages are split by '|'. Replicated stages or layers can be described with multiplication. Commas can be used cosmetically. Symbol choices:
* `E` = embedding layer
* `t` = transformer decoder layer
* `m` = MTP layer
* `L` = loss calculation layer
Note that it is legal to have empty stages, e.g., `E||t|L` (the second stage is empty).
================================================
FILE: docs/user-guide/features/tokenizers.md
================================================
# Tokenizers
Megatron Core provides a unified tokenizer system with a HuggingFace-style API for easy tokenizer management and configuration.
## Overview
The `MegatronTokenizer` class offers a simple, familiar API for loading and managing tokenizers:
- **Automatic detection** - Load any tokenizer type without specifying the library
- **Metadata-based configuration** - Store tokenizer settings in JSON for easy reuse
- **HuggingFace-compatible API** - Familiar `.from_pretrained()` interface
- **Custom tokenizer support** - Extend with model-specific tokenization logic
## Key Features
### Unified API
Use the same API regardless of tokenizer backend (SentencePiece, HuggingFace, TikToken, etc.):
```python
from megatron.core.tokenizers import MegatronTokenizer
tokenizer = MegatronTokenizer.from_pretrained("/path/to/tokenizer")
```
### Tokenizer Metadata
Configuration is stored in a JSON metadata file containing:
- Tokenizer library (HuggingFace, SentencePiece, TikToken, etc.)
- Chat templates
- Custom tokenizer class
- Special token configurations
**Benefits:**
- Set configuration once, reuse everywhere
- No repeated CLI arguments
- Easy sharing - just copy the tokenizer directory
### Automatic Library Detection
The correct tokenizer implementation is automatically selected:
- No need to specify `SentencePieceTokenizer`, `HuggingFaceTokenizer`, etc.
- Library type detected from metadata
- Seamless switching between tokenizer backends
## Basic Usage
### Creating Tokenizer Metadata
Save tokenizer configuration for reuse:
```python
from megatron.core.tokenizers import MegatronTokenizer
# Create metadata for a SentencePiece tokenizer
MegatronTokenizer.write_metadata(
tokenizer_path="/path/to/tokenizer.model",
tokenizer_library="sentencepiece",
chat_template="{% for message in messages %}{{ message.content }}{% endfor %}",
)
```
The metadata is saved as `tokenizer_metadata.json` in the tokenizer directory.
### Loading a Tokenizer
Load from a directory with metadata:
```python
from megatron.core.tokenizers import MegatronTokenizer
# Load with auto-detected configuration
tokenizer = MegatronTokenizer.from_pretrained("/path/to/tokenizer.model")
```
### Loading with Custom Metadata Path
If metadata is stored separately:
```python
tokenizer = MegatronTokenizer.from_pretrained(
tokenizer_path="/path/to/tokenizer.model",
metadata_path="/path/to/custom/metadata.json",
)
```
### Loading with Inline Metadata
Pass metadata as a dictionary:
```python
tokenizer = MegatronTokenizer.from_pretrained(
tokenizer_path="GPT2BPETokenizer",
metadata_path={"library": "megatron"},
vocab_file="/path/to/vocab.txt",
)
```
## Advanced Usage
### Custom Tokenizer Classes
Create model-specific tokenization logic:
```python
from megatron.core.tokenizers.text import MegatronTokenizerText
class CustomTokenizer(MegatronTokenizerText):
def encode(self, text):
# Custom encoding logic
return super().encode(text)
def decode(self, tokens):
# Custom decoding logic
return super().decode(tokens)
# Save metadata with custom class
MegatronTokenizer.write_metadata(
tokenizer_path="/path/to/tokenizer.model",
tokenizer_library="sentencepiece",
tokenizer_class=CustomTokenizer,
)
```
### TikToken Tokenizers
Configure TikToken-based tokenizers:
```python
tokenizer = MegatronTokenizer.from_pretrained(
tokenizer_path="/path/to/tokenizer/model.json",
metadata_path={"library": "tiktoken"},
pattern="v2",
num_special_tokens=1000,
)
```
### Null Tokenizer
Use a null tokenizer for testing or non-text models:
```python
tokenizer = MegatronTokenizer.from_pretrained(
metadata_path={"library": "null-text"},
vocab_size=131072,
)
```
## Integration with Megatron-LM
### Using with Training Scripts
The tokenizer system integrates seamlessly with Megatron-LM training:
```bash
# Null tokenizer for testing
torchrun --nproc_per_node=8 pretrain_gpt.py \
--tokenizer-type NullTokenizer \
--vocab-size 131072 \
...
```
```bash
# HuggingFace tokenizer with metadata
torchrun --nproc_per_node=8 pretrain_gpt.py \
--tokenizer-type HuggingFaceTokenizer \
--tokenizer-model meta-llama/Meta-Llama-3-8B \
--tokenizer-metadata /path/to/metadata.json \
...
```
### Auto-Generated Metadata
If `--tokenizer-metadata` is not specified, a default metadata file is generated automatically based on the tokenizer type.
## Supported Tokenizer Libraries
| Library | Description | Use Case |
|---------|-------------|----------|
| **HuggingFace** | Transformers tokenizers | Most modern LLMs (LLaMA, Mistral, etc.) |
| **SentencePiece** | Google's tokenizer | GPT-style models, custom vocabularies |
| **TikToken** | OpenAI's tokenizer | GPT-3.5/GPT-4 style tokenization |
| **Megatron** | Built-in tokenizers | Legacy GPT-2 BPE |
| **Null** | No-op tokenizer | Testing, non-text modalities |
## Common Tokenizer Types
### LLaMA / Mistral
```python
MegatronTokenizer.write_metadata(
tokenizer_path="/path/to/llama/tokenizer.model",
tokenizer_library="sentencepiece",
)
```
### GPT-2
```python
MegatronTokenizer.write_metadata(
tokenizer_path="GPT2BPETokenizer",
tokenizer_library="megatron",
vocab_file="/path/to/gpt2-vocab.json",
merge_file="/path/to/gpt2-merges.txt",
)
```
## Best Practices
1. **Always save metadata** - Create metadata once, reuse across training runs
2. **Use HuggingFace tokenizers** - When possible, for modern LLM compatibility
3. **Test tokenization** - Verify encode/decode before starting training
4. **Version control metadata** - Include `tokenizer_metadata.json` in your experiment configs
5. **Share tokenizer directories** - Include both model files and metadata for reproducibility
## Next Steps
- **Prepare Data**: See [Data Preparation](../data-preparation.md) for preprocessing with tokenizers
- **Train Models**: Use tokenizers in [Training Examples](../training-examples.md)
- **Supported Models**: Check [Language Models](../../models/llms.md) for model-specific tokenizers
================================================
FILE: docs/user-guide/index.md
================================================
---
orphan: true
---
# User Guide
Comprehensive guides for using Megatron Core and Megatron-LM.
```{toctree}
:maxdepth: 2
msc_integration
data-preparation
training-examples
parallelism-guide
features/index
```
================================================
FILE: docs/user-guide/msc_integration.md
================================================
```{include} ../../megatron/core/MSC_Integration.md
```
================================================
FILE: docs/user-guide/parallelism-guide.md
================================================
# Parallelism Strategies Guide
Megatron Core supports multiple parallelism strategies that can be combined to efficiently train models from billions to trillions of parameters across thousands of GPUs.
## Overview
| Strategy | What it parallelizes | Best for |
|----------|---------------------|----------|
| **Data Parallelism (DP)** | Batch dimension | Standard training, most common |
| **Tensor Parallelism (TP)** | Individual layers | Large layers, GPU memory constraints |
| **Pipeline Parallelism (PP)** | Model depth | Very deep models |
| **Context Parallelism (CP)** | Sequence length | Long sequences (8K+ tokens) |
| **Expert Parallelism (EP)** | MoE experts | Mixture-of-Experts models |
## Data Parallelism (DP)
Replicate the model across GPUs and split the batch.
### Standard Data Parallel (DDP)
```bash
torchrun --nproc_per_node=8 pretrain_gpt.py \
--data-parallel-sharding-strategy no_shard
```
Each GPU has a full copy of the model and processes a portion of the batch.
### Fully Sharded Data Parallel (FSDP)
Shard model parameters, gradients, and optimizer states to reduce memory:
```bash
# Megatron FSDP (~15% faster than PyTorch FSDP2)
--use-megatron-fsdp \
--data-parallel-sharding-strategy optim_grads_params
```
**Sharding strategies:**
- `optim` - Shard optimizer states only (ZeRO-1)
- `optim_grads` - Shard gradients + optimizer (ZeRO-2)
- `optim_grads_params` - Shard parameters + gradients + optimizer (ZeRO-3)
## Tensor Parallelism (TP)
Split individual model layers across GPUs. Recommended for large hidden dimensions.
```bash
--tensor-model-parallel-size 4 # 4-way tensor parallelism
--sequence-parallel # Enable sequence parallelism (recommended)
```
**When to use:**
- Model layers don't fit on single GPU
- Large hidden dimensions (4096+)
- Usually combined with DP and PP
## Pipeline Parallelism (PP)
Split model layers across GPUs vertically (by depth).
```bash
--pipeline-model-parallel-size 8 # 8 pipeline stages
--num-layers-per-virtual-pipeline-stage 4 # Virtual pipeline for load balancing
```
**When to use:**
- Very deep models (50+ layers)
- Combine with TP for large models
- Helps distribute memory across GPUs
## Context Parallelism (CP)
Split long sequences across GPUs for efficient long-context training.
```bash
--context-parallel-size 2 # 2-way context parallelism
--cp-comm-type p2p # Communication type
```
**When to use:**
- Long sequences (8K+ tokens)
- Reduces activation memory
- Can combine with TP, PP, DP
**→ [Context Parallelism Deep Dive](features/context_parallel.md)** - Detailed guide with performance analysis
## Expert Parallelism (EP)
Distribute experts across GPUs in Mixture-of-Experts models.
```bash
--expert-model-parallel-size 8 # 8-way expert parallelism
--num-experts 64 # 64 experts per MoE layer
--moe-grouped-gemm # Optimize expert computation
```
**Important:** When combining EP with TP, you **must enable Sequence Parallelism**:
```bash
--tensor-model-parallel-size 4
--expert-model-parallel-size 8
--sequence-parallel # Required when using TP + EP
```
## Parallelism Selection Guide
Recommended configurations based on [NVIDIA NeMo production setups](https://github.com/NVIDIA/NeMo/tree/main/scripts/performance/recommended_model_configs):
### Language Models
| Model | Size | GPUs | TP | PP | CP | EP | Configuration Notes |
|-------|------|------|----|----|----|----|---------------------|
| **LLaMA-3** | 8B | 8 | 1 | 1 | 2 | 1 | CP=2 for long context (8K seqlen) |
| **LLaMA-3** | 70B | 64 | 4 | 4 | 2 | 1 | Balanced TP+PP for 70B scale |
| **LLaMA-3.1** | 405B | 1024 | 8 | 8 | 2 | 1 | 3D parallelism (TP+PP+CP) |
| **GPT-3** | 175B | 128-512 | 4 | 8 | 1 | 1 | Standard large model config |
### Mixture-of-Experts Models
| Model | Size | GPUs | TP | PP | CP | EP | Configuration Notes |
|-------|------|------|----|----|----|----|---------------------|
| **Mixtral** | 8x7B | 64 | 1 | 4 | 1 | 8 | EP=8 for 8 experts |
| **Mixtral** | 8x22B | 256 | 4 | 4 | 1 | 8 | TP+PP+EP for large MoE |
| **DeepSeek-V3** | 671B | 1024 | 2 | 16 | 1 | 64 | Massive MoE with 256 experts |
## Combining Strategies
### Total GPU Count
The total number of GPUs is calculated as:
```
Total GPUs = TP × PP × CP × EP × DP
```
### Example: LLaMA-3 70B on 64 GPUs
```bash
# TP=4, PP=4, CP=2, DP=2 => 4 × 4 × 2 × 2 = 64 GPUs
torchrun --nproc_per_node=8 pretrain_gpt.py \
--tensor-model-parallel-size 4 \
--pipeline-model-parallel-size 4 \
--context-parallel-size 2 \
--num-layers 80 \
--hidden-size 8192 \
--num-attention-heads 64 \
--seq-length 8192 \
--micro-batch-size 1 \
--global-batch-size 512 \
--bf16
```
## Performance Optimizations
### Communication Overlap
Enable overlapping of communication with computation:
```bash
--overlap-grad-reduce # Overlap gradient reduction with backward pass
--overlap-param-gather # Overlap parameter gathering with forward pass
--tp-comm-overlap # Overlap TP communication
```
### Distributed Optimizer
Recommended for all multi-GPU training:
```bash
--use-distributed-optimizer
```
Benefits:
- Faster checkpointing
- Reduced memory when combined with FSDP
- Better performance at scale
### Sequence Parallelism
Always enable when using TP:
```bash
--sequence-parallel
```
Reduces activation memory by sharding sequence dimension in LayerNorm and Dropout.
## Choosing the Right Strategy
### Start Simple
1. Begin with **Data Parallelism** (DP) only
2. Add **Tensor Parallelism** (TP) if model doesn't fit
3. Add **Pipeline Parallelism** (PP) for very large models
4. Add **Context Parallelism** (CP) for long sequences
### Memory Constraints
- Use **FSDP** to reduce memory per GPU
- Use **TP** to split large layers
- Use **PP** to split model depth
- Enable **activation checkpointing** for extreme cases
### Communication Bottlenecks
- Reduce **TP** degree (increases memory per GPU)
- Increase **PP** degree (may reduce efficiency)
- Use **CP** instead of larger TP for long sequences
## Next Steps
- **API Reference**: See [Tensor Parallel](../api-guide/core/tensor_parallel.md) and [Pipeline Parallel](../api-guide/core/pipeline_parallel.md) API documentation
- **Advanced Features**: Explore [Megatron FSDP](features/custom_fsdp.md) and [Distributed Optimizer](features/dist_optimizer.md)
- **Performance Tuning**: Check [NVIDIA NeMo Performance Guide](https://docs.nvidia.com/nemo-framework/user-guide/latest/performance/performance-guide.html)
================================================
FILE: docs/user-guide/training-examples.md
================================================
# Training Examples
Get started with Megatron Core training using these practical examples.
## Simple Training Example
The simplest way to get started is with the basic training loop using mock data:
```bash
# Distributed training on 2 GPUs with mock data
torchrun --nproc_per_node=2 examples/run_simple_mcore_train_loop.py
```
This example:
- Runs on 2 GPUs
- Uses generated mock data (no data preparation needed)
- Demonstrates basic distributed training setup
- Perfect for testing your installation
## LLaMA-3 Training Examples
### LLaMA-3 8B with FP8
Train LLaMA-3 8B model with FP8 mixed precision on 8 GPUs:
```bash
./examples/llama/train_llama3_8b_h100_fp8.sh
```
**Configuration:**
- 8 GPUs
- FP8 mixed precision (requires Hopper/Ada/Blackwell GPUs)
- Mock data for quick testing
### Custom LLaMA Training
For training with your own data:
```bash
torchrun --nproc_per_node=8 pretrain_gpt.py \
--tensor-model-parallel-size 1 \
--pipeline-model-parallel-size 1 \
--num-layers 32 \
--hidden-size 4096 \
--num-attention-heads 32 \
--seq-length 2048 \
--max-position-embeddings 2048 \
--micro-batch-size 4 \
--global-batch-size 32 \
--train-iters 100000 \
--lr 3.0e-4 \
--min-lr 3.0e-5 \
--lr-decay-style cosine \
--lr-warmup-iters 2000 \
--weight-decay 0.1 \
--clip-grad 1.0 \
--bf16 \
--data-path /path/to/your/preprocessed_data \
--split 949,50,1 \
--save /path/to/checkpoints \
--load /path/to/checkpoints \
--log-interval 10 \
--save-interval 1000 \
--eval-interval 1000
```
## GPT-3 Training Example
Train a GPT-3 style model:
```bash
torchrun --nproc_per_node=8 pretrain_gpt.py \
--tensor-model-parallel-size 2 \
--pipeline-model-parallel-size 2 \
--num-layers 24 \
--hidden-size 2048 \
--num-attention-heads 16 \
--seq-length 1024 \
--max-position-embeddings 1024 \
--micro-batch-size 2 \
--global-batch-size 16 \
--train-iters 100000 \
--lr 1.5e-4 \
--min-lr 1.0e-5 \
--lr-decay-style cosine \
--lr-warmup-iters 1000 \
--weight-decay 0.1 \
--clip-grad 1.0 \
--fp16 \
--data-path /path/to/preprocessed_data \
--split 949,50,1 \
--save /path/to/checkpoints \
--load /path/to/checkpoints
```
## Key Training Arguments
### Model Architecture
| Argument | Description |
|----------|-------------|
| `--num-layers` | Number of transformer layers |
| `--hidden-size` | Hidden dimension size |
| `--num-attention-heads` | Number of attention heads |
| `--seq-length` | Sequence length for training |
### Training Configuration
| Argument | Description |
|----------|-------------|
| `--micro-batch-size` | Batch size per GPU |
| `--global-batch-size` | Total batch size across all GPUs |
| `--train-iters` | Number of training iterations |
### Learning Rate
| Argument | Description |
|----------|-------------|
| `--lr` | Peak learning rate |
| `--min-lr` | Minimum learning rate |
| `--lr-decay-style` | LR schedule (cosine, linear, constant) |
| `--lr-warmup-iters` | Warmup iterations |
### Mixed Precision
| Argument | Description |
|----------|-------------|
| `--fp16` | FP16 mixed precision |
| `--bf16` | BF16 mixed precision (recommended) |
| `--fp8-hybrid` | FP8 mixed precision (Hopper/Ada/Blackwell) |
### Data and Checkpointing
| Argument | Description |
|----------|-------------|
| `--data-path` | Path to preprocessed data |
| `--split` | Train/validation/test split (e.g., 949,50,1) |
| `--save` | Checkpoint save directory |
| `--load` | Checkpoint load directory |
| `--save-interval` | Save checkpoint every N iterations |
## Next Steps
- **Optimize Performance**: See [Advanced Features](features/index.md) for FSDP, distributed optimizer, and other optimizations
- **Scale Up**: Learn about [Parallelism Strategies](parallelism-guide.md) to train larger models across more GPUs
- **Prepare Data**: Follow the [Data Preparation](data-preparation.md) guide to process your own datasets
================================================
FILE: docs/versions1.json
================================================
[
{
"name": "nightly",
"version": "nightly",
"url": "https://docs.nvidia.com/megatron-core/developer-guide/nightly/"
},
{
"name": "0.16.0 (latest)",
"version": "0.16.0",
"url": "https://docs.nvidia.com/megatron-core/developer-guide/latest/"
},
{
"name": "0.15.0",
"version": "0.15.0",
"url": "https://docs.nvidia.com/megatron-core/developer-guide/0.15.0/"
}
]
================================================
FILE: examples/__init__.py
================================================
================================================
FILE: examples/academic_paper_scripts/detxoify_lm/README.md
================================================
# SGEAT: Detoxify Larger-scale Language Models
This is the official code base for our NeurIPS 2022 paper:
[Exploring the Limits of Domain-Adaptive Training for Detoxifying Large-Scale Language Models](https://arxiv.org/abs/2202.04173)
Boxin Wang, Wei Ping, Chaowei Xiao, Peng Xu, Mostofa Patwary, Mohammad Shoeybi, Bo Li, Anima Anandkumar, Bryan Catanzaro
## Citation
```
@article{WangExp2022,
title={Exploring the Limits of Domain-Adaptive Training for Detoxifying Large-Scale Language Models},
author={Wang, Boxin and Ping, Wei and Xiao, Chaowei and Xu, Peng and Patwary, Mostofa and Shoeybi, Mohammad and and Li, Bo and Anandkumar, Anima and Catanzaro, Bryan},
journal={NeurIPS},
year={2022}
}
```
## Usage
### Prepare your environment
The project environment is based on the standard [nvcr docker](nvcr.io/nvidia/pytorch:21.12-py3) of version `nvcr.io/nvidia/pytorch:21.12-py3`.
To run Perspective API, you need to install `google-api-python-client`
```bash
pip install --upgrade google-api-python-client
```
### Self Generation
#### SGEAT (Standard)
To perform unconditional generation for a Megatron LM, we provide an example script for 1.3B LM.
```bash
# [num of samples] [model checkpoint] [random seed]
bash examples/detxoify_lm/self_generation/selfgenerate-1.3b-unconditional.sh 1000 checkpoints/gpt3/gpt3-1.3b/ 2333
```
This will generate a jsonl file of 1000 generated text (as a toy example) at `selfgeneration/unconditional_generation_gpt3-1.3b/2333.out`.
Note that you may want to set your own gpt2 vocab and merge file dir, as well as your output data dir in `selfgenerate-1.3b-unconditional.sh`.
### Annotation
We then use Perspective API to annotate the self generated corpus. Note that you need to fill in your own Perspective API key in the `examples/detoxify_lm/perspective_api_annotate.py`.
```bash
python examples/detxoify_lm/perspective_api_annotate.py --data-path [input-data-path] --out-path [output-data-path] --workers 70
```
For example,
```bash
python examples/detxoify_lm/annotations/perspective_api_annotate.py --data-path selfgeneration/unconditional_generation_gpt3-1.3b/2333.out --out-path selfgeneration/unconditional_generation_gpt3-1.3b/2333.annotated.out --workers 70
```
### Filtering
We then filter the self annotated generated corpus to get the most nontoxic 50% of the corus.
For example,
```bash
python examples/detxoify_lm/annotations/filter-selfgeneration.py --data-path selfgeneration/unconditional_generation_gpt3-1.3b/2333.annotated.out --out-path selfgeneration/unconditional_generation_gpt3-1.3b/2333.annotated.nontoxic.out
```
This will generate a jsonl file of 500 text of the lowest toxicity (as a toy example) at `selfgeneration/unconditional_generation_gpt3-1.3b/2333.annotated.nontoxic.out`.
### Preprocess
We then preprocess the dataset so that Megatron LM can use the dumped dataset to fine-tune.
```
bash examples/detxoify_lm/annotations/preprocess.sh selfgeneration/unconditional_generation_gpt3-1.3b/2333.annotated.nontoxic.out selfgeneration/unconditional_generation_gpt3-1.3b/2333.annotated.nontoxic
```
This will generate two files as follows
```bash
selfgeneration/unconditional_generation_gpt3-1.3b/2333.annotated.nontoxic_text_document.idx
selfgeneration/unconditional_generation_gpt3-1.3b/2333.annotated.nontoxic_text_document.bin
```
which will be used in the following domain-adative training step.
### Fine-tuning
We then use the preprocess dataset as input to fine-tune our Megatron-LM.
```bash
# [fine-tuning dataset] [output-dir] [lr] [bs] [train-iters] [load checkpoint]
bash examples/detxoify_lm/finetune_gpt_distributed-1.3b.sh selfgeneration/unconditional_generation_gpt3-1.3b/2333.annotated.nontoxic_text_document gpt3-1.3b-toy-example-lr-2e-5-bs-512 2e-5 512 78 checkpoints/gpt3/gpt3-1.3b
```
This will dump the final checkpoint in `$SHARE_DATA/gpt3-1.3b-toy-example-lr-2e-5-bs-512`. (`$SHARE_DATA` is your current work dir, default to `$PWD`)
### Evaluation
We then use the fine-tuned checkpoint to perform conditional generation given RealToxicityPrompts:
```bash
# [input-prompts] [model-checkpoint]
bash examples/detxoify_lm/generate-1.3b.sh augmented_prompts.jsonl $SHARE_DATA/gpt3-1.3b-toy-example-lr-2e-5-bs-512
```
For example, this will generate the continuations in the file `augmented_prompts.jsonl_output_gpt3-1.3b-toy-example-lr-2e-5-bs-512_seed_31846.jsonl` (seed is a random generated number).
Note that the input prompts are augmented so that each prompts appear 25 times to calculate the Expected Maximum Toxicity over 25 generations and Toxicity Probability,
We then use Perspective API to evaluate the Expected Maximum Toxicity and Toxicity Probability.
```bash
python examples/detxoify_lm/perspective_api.py --data-path "augmented_prompts.jsonl_output_gpt3-1.3b-toy-example-lr-2e-5-bs-512_seed_31846.jsonl" --prompt-path prompts.jsonl --workers 30
```
================================================
FILE: examples/academic_paper_scripts/detxoify_lm/annotations/filter-selfgeneration.py
================================================
import json
import time
from typing import Dict, Optional, List
import joblib
from googleapiclient import discovery
from googleapiclient.errors import HttpError
import argparse
from tqdm import tqdm
parser = argparse.ArgumentParser(description='Process some integers.')
parser.add_argument('--data-path', type=str, default='',
help='data path to load the jsonl')
parser.add_argument('--out-path', type=str, default='',
help='data path to load the jsonl')
parser.add_argument('--prompt-path', type=str, default='datasets/realprompts/prompts.jsonl',
help='data path to load the prompt jsonl')
parser.add_argument('--workers', type=int, default=10,
help='Number of worker processes to launch')
def get_corpus_scores(lines):
scores = []
corpus = []
for line in tqdm(lines):
data = json.loads(line)
score = data['score']
if score is not None:
scores.append(score['toxicity'])
corpus.append(data)
return scores, corpus
def main():
args = parser.parse_args()
with open(args.data_path) as f:
lines = f.readlines()
print(f"total line number: {len(lines)}")
scores, corpus = get_corpus_scores(lines)
import numpy as np
scores = np.array(scores)
indices = np.argsort(scores)
print(f"total valid samples: {len(scores)}")
print(f"Avg toxicity (all): {np.mean(scores)} +- {np.std(scores)}")
print(f"Avg toxicity (toxic): {np.mean(scores[scores > 0.5])} +- {np.std(scores[scores > 0.5])}")
print(f"Toxic Percentage {sum(scores > 0.5) / len(scores)}")
print(f"Avg toxicity (nontoxic): {np.mean(scores[scores <= 0.5])} +- {np.std(scores[scores <= 0.5])}")
print(f"Nontoxic Percentage {sum(scores <= 0.5) / len(scores)}")
samples_left = len(lines) // 2
print(f"After filtering: {samples_left} of samples are left")
nontoxic_indices = indices[:samples_left]
print(f"Avg toxicity (filtered): {np.mean(scores[nontoxic_indices])} +- {np.std(scores[nontoxic_indices])}")
print(f"Toxicity Range (filtered): {np.min(scores[nontoxic_indices])} ~ {np.max(scores[nontoxic_indices])}")
nontoxic_data = [corpus[ind] for ind in nontoxic_indices]
print(f"Total samples after filtering: {len(nontoxic_data)}")
print(f"Examples: {nontoxic_data[:3]}")
from sklearn.utils import shuffle
nontoxic_data = shuffle(nontoxic_data)
with open(args.out_path, 'w') as f:
for x in nontoxic_data:
f.write(json.dumps(x) + '\n')
main()
================================================
FILE: examples/academic_paper_scripts/detxoify_lm/annotations/perspective_api_annotate.py
================================================
import json
import time
from typing import Dict, Optional, List
import joblib
from googleapiclient import discovery
from googleapiclient.errors import HttpError
import argparse
from tqdm import tqdm
parser = argparse.ArgumentParser(description='Process some integers.')
parser.add_argument('--data-path', type=str, default='',
help='data path to load the jsonl')
parser.add_argument('--out-path', type=str, default='',
help='data path to load the jsonl')
parser.add_argument('--total', type=int, default=-1,
help='Total number of data')
parser.add_argument('--workers', type=int, default=1,
help='Number of worker processes to launch')
class PerspectiveApiScorer:
"""
This class provides a method for accessing Perspective API using the Google API Client to obtain attribute scores for generated texts.
"""
DEFAULT_ATTRIBUTES = ['toxicity', 'severe_toxicity', 'sexually_explicit', 'threat', 'profanity', 'identity_attack']
def __init__(self):
"""
:param api_key: the API key to use. For details, see https://support.perspectiveapi.com/s/docs-get-started
"""
api_key = ''
self._service = discovery.build(
"commentanalyzer",
"v1alpha1",
developerKey=api_key,
discoveryServiceUrl="https://commentanalyzer.googleapis.com/$discovery/rest?version=v1alpha1",
static_discovery=False,
)
def get_scores(self, input_text: str, requested_attributes: Optional[List[str]] = None) -> Dict[str, float]:
"""
Get attribute scores for a given text via Perspective API.
:param input_text: the input text
:param requested_attributes: the attributes for which to compute scores
:return: a mapping from attribute names to scores
"""
requested_attributes = requested_attributes if requested_attributes else PerspectiveApiScorer.DEFAULT_ATTRIBUTES
analyze_request = {
'comment': {'text': input_text},
'requestedAttributes': {attribute.upper(): {} for attribute in requested_attributes},
'spanAnnotations': False,
'languages': ['en'],
}
response = None
while not response:
try:
response = self._service.comments().analyze(body=analyze_request).execute()
except Exception as e:
print(f'Perspective API threw an error: {e}\n Retrying in 5 seconds...')
print(input_text)
time.sleep(1)
return {attribute: response['attributeScores'][attribute.upper()]['summaryScore']['value'] for attribute in
requested_attributes}
def test():
scorer = PerspectiveApiScorer()
for i in range(1):
print(scorer.get_scores("toxic behaviors and nasty negro"))
def split_lines(lines, split):
tot = len(lines)
each = tot // split
return [lines[i:i+each] for i in range(0, tot, each)]
from joblib import Parallel, delayed
scorer = PerspectiveApiScorer()
def get_score(line):
data = json.loads(line)
text = data['text']
text = text.replace("<|endoftext|>", "")
data['text'] = text
if not text.strip():
data['score'] = None
return json.dumps(data)
encoded_text = text.encode('utf8')
encoded_text = encoded_text[:20480]
try:
decoded_text = encoded_text.decode('utf8')
except UnicodeDecodeError:
try:
decoded_text = encoded_text[:20479].decode('utf8')
except UnicodeDecodeError:
try:
decoded_text = encoded_text[:20478].decode('utf8')
except UnicodeDecodeError:
try:
decoded_text = encoded_text[:20476].decode('utf8')
except Exception:
print("Error occurred")
data['score'] = None
return json.dumps(data)
data['score'] = scorer.get_scores(decoded_text)
return json.dumps(data)
def get_scores(lines):
scorer = PerspectiveApiScorer()
all_data = []
for i, line in enumerate(tqdm(lines)):
data = json.loads(line)
text = data['text']
if not text.strip():
data['score'] = None
all_data.append(json.dumps(data))
continue
encoded_text = text.encode('utf8')
encoded_text = encoded_text[:20480]
try:
decoded_text = encoded_text.decode('utf8')
except UnicodeDecodeError:
try:
decoded_text = encoded_text[:20479].decode('utf8')
except UnicodeDecodeError:
try:
decoded_text = encoded_text[:20478].decode('utf8')
except UnicodeDecodeError:
try:
decoded_text = encoded_text[:20476].decode('utf8')
except Exception:
print("Error occurred")
data['score'] = None
all_data.append(json.dumps(data))
continue
data['score'] = scorer.get_scores(decoded_text)
all_data.append(json.dumps(data))
return all_data
def get_annotated_datasets(lines, threads=10):
sub_lines = lines
splitted_lines = split_lines(sub_lines, threads)
print(len(sub_lines))
final = Parallel(n_jobs=threads)(delayed(get_score)(l) for l in splitted_lines)
import itertools
finals = list(itertools.chain.from_iterable(final))
return finals
def main():
args = parser.parse_args()
path = args.data_path
out = args.out_path if args.out_path else path + '-annotated.jsonl'
print(out)
fin = open(path, 'r', encoding='utf-8')
import multiprocessing
pool = multiprocessing.Pool(args.workers)
annotated = pool.imap(get_score, fin, 25)
with open(out, "w") as f:
if args.total > 0:
for x in tqdm(annotated, total=args.total):
f.write(x + '\n')
else:
for x in tqdm(annotated):
f.write(x + '\n')
if __name__ == '__main__':
main()
================================================
FILE: examples/academic_paper_scripts/detxoify_lm/annotations/preprocess.sh
================================================
VOCAB_FILE=pt2-vocab.json
MERGE_FILE=gpt2-merges.txt
python3 tools/preprocess_data.py \
--input $1 \
--output-prefix $2 \
--vocab-file $VOCAB_FILE \
--merge-file $MERGE_FILE \
--tokenizer-type GPT2BPETokenizer \
--append-eod --workers 20 --chunk-size 25
================================================
FILE: examples/academic_paper_scripts/detxoify_lm/finetune_gpt.py
================================================
# coding=utf-8
# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
"""Fine-tune GPT"""
import torch
from functools import partial
import os
import sys
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__),
os.path.pardir, os.path.pardir)))
from megatron.training import get_args
from megatron.training import get_timers
from megatron.training import get_tokenizer
from megatron.training import print_rank_0
from megatron.core import mpu
from megatron.core.datasets.blended_megatron_dataset_builder import BlendedMegatronDatasetBuilder
from megatron.core.datasets.blended_megatron_dataset_config import GPTDatasetConfig
from megatron.core.datasets.gpt_dataset import GPTDataset
from megatron.core.datasets.utils import get_blend_from_list
from megatron.legacy.model import GPTModel
from megatron.core.enums import ModelType
from megatron.training import pretrain
from megatron.training.utils import get_ltor_masks_and_position_ids
from megatron.training.utils import average_losses_across_data_parallel_group
def model_provider(pre_process=True, post_process=True):
"""Build the model."""
print_rank_0('building GPT model ...')
model = GPTModel(
num_tokentypes=0,
parallel_output=True,
pre_process=pre_process,
post_process=post_process
)
return model
def get_batch(data_iterator):
"""Generate a batch"""
args = get_args()
tokenizer = get_tokenizer()
# Items and their type.
keys = ['text']
datatype = torch.int64
# Broadcast data.
if data_iterator is not None:
data = next(data_iterator)
else:
data = None
data_b = mpu.broadcast_data(keys, data, datatype)
# Unpack.
tokens_ = data_b['text'].long()
labels = tokens_[:, 1:].contiguous()
tokens = tokens_[:, :-1].contiguous()
# Get the masks and postition ids.
attention_mask, loss_mask, position_ids = get_ltor_masks_and_position_ids(
tokens,
tokenizer.eod,
args.reset_position_ids,
args.reset_attention_mask,
args.eod_mask_loss)
return tokens, labels, loss_mask, attention_mask, position_ids
def loss_func(loss_mask, output_tensor):
losses = output_tensor.float()
loss_mask = loss_mask.view(-1).float()
loss = torch.sum(losses.view(-1) * loss_mask) / loss_mask.sum()
# Reduce loss for logging.
averaged_loss = average_losses_across_data_parallel_group([loss])
return loss, {'lm loss': averaged_loss[0]}
def forward_step(data_iterator, model):
"""Forward step."""
args = get_args()
timers = get_timers()
# Get the batch.
timers('batch-generator').start()
tokens, labels, loss_mask, attention_mask, position_ids = get_batch(
data_iterator)
timers('batch-generator').stop()
output_tensor = model(tokens, position_ids, attention_mask,
labels=labels)
return output_tensor, partial(loss_func, loss_mask)
def train_valid_test_datasets_provider(train_val_test_num_samples):
"""Build train, valid, and test datasets."""
args = get_args()
print_rank_0('> building train, validation, and test datasets '
'for GPT ...')
train_ds, _, test_ds = BlendedMegatronDatasetBuilder(
GPTDataset,
train_val_test_num_samples,
lambda: True,
GPTDatasetConfig(
blend=get_blend_from_list(args.data_path),
split=args.split,
random_seed=args.seed,
sequence_length=args.seq_length,
path_to_cache=args.data_cache_path,
return_document_ids=False,
mid_level_dataset_surplus=args.mid_level_dataset_surplus,
)
).build()
print_rank_0("> finished creating finetuning GPT datasets ...")
_, valid_ds, _ = BlendedMegatronDatasetBuilder(
GPTDataset,
train_val_test_num_samples,
lambda: True,
GPTDatasetConfig(
blend=get_blend_from_list(args.data_path2),
split="98,2,0",
random_seed=1234,
sequence_length=2048,
path_to_cache=args.data_cache_path,
return_document_ids=False,
mid_level_dataset_surplus=args.mid_level_dataset_surplus,
)
).build()
print_rank_0("> finished creating pretrained GPT datasets ...")
return train_ds, valid_ds, test_ds
def add_validation_args(parser):
"""Text generation arguments."""
group = parser.add_argument_group(title='validation set')
group.add_argument('--data-path2', nargs='*', default=None,
help='Path to the validation dataset. Accepted format:'
'1) a single data path, 2) multiple datasets in the'
'form: dataset1-weight dataset1-path dataset2-weight '
'dataset2-path ...')
group.add_argument('--eval-ppl', action='store_true', default=False)
group.add_argument('--stored_params', type=dict, default=dict())
return parser
if __name__ == "__main__":
pretrain(train_valid_test_datasets_provider, model_provider,
ModelType.encoder_or_decoder,
forward_step, args_defaults={'tokenizer_type': 'GPT2BPETokenizer'},
extra_args_provider=add_validation_args,)
================================================
FILE: examples/academic_paper_scripts/detxoify_lm/finetune_gpt_distributed-1.3b.sh
================================================
#! /bin/bash
# Change for multinode config
GPUS_PER_NODE=16
MASTER_ADDR=localhost
MASTER_PORT=$(($RANDOM + 1024))
NNODES=1
NODE_RANK=0
WORLD_SIZE=$(($GPUS_PER_NODE*$NNODES))
# input
DATA_PATH=$1
SHARE_DATA=$PWD # current work dir
FINETUNED_PATH="$SHARE_DATA/$2"
lr=$3
bs=$4
iter=$5
CHECKPOINT_PATH=$6
# vocab
VOCAB_FILE=gpt2-vocab.json # Your gpt-2 vocab
MERGE_FILE=gpt2-merges.txt # Your gpt-2 merge file
# tensorboard
TENSORBOARD_DIR="$SHARE_DATA/tensorboard/$2"
mkdir -p ${TENSORBOARD_DIR}
DISTRIBUTED_ARGS="--nproc_per_node $GPUS_PER_NODE --nnodes $NNODES --node_rank $NODE_RANK --master_addr $MASTER_ADDR --master_port $MASTER_PORT"
python -m torch.distributed.run $DISTRIBUTED_ARGS \
examples/detxoify_lm/finetune_gpt.py \
--num-layers 24 \
--hidden-size 2048 \
--num-attention-heads 32 \
--micro-batch-size 4 \
--global-batch-size $bs \
--seq-length 2048 \
--max-position-embeddings 2048 \
--train-iters $iter \
--save $FINETUNED_PATH \
--load $CHECKPOINT_PATH \
--data-path $DATA_PATH \
--data-path2 ${DATA_BLEND} \
--vocab-file $VOCAB_FILE \
--merge-file $MERGE_FILE \
--split 100,0,0 \
--distributed-backend nccl \
--lr-decay-style constant \
--lr $lr \
--clip-grad 1.0 \
--weight-decay 0.1 \
--adam-beta1 0.9 \
--adam-beta2 0.95 \
--checkpoint-activations \
--log-interval 1 \
--save-interval 78 \
--eval-interval 78 \
--eval-iters 50 \
--fp16 \
--DDP-impl local \
--finetune --no-load-optim \
--log-validation-ppl-to-tensorboard \
--tensorboard-dir ${TENSORBOARD_DIR}
================================================
FILE: examples/academic_paper_scripts/detxoify_lm/generate-1.3b.sh
================================================
#!/bin/bash
CHECKPOINT_PATH=$2 # Your model ckpt
VOCAB_FILE=gpt2-vocab.json
MERGE_FILE=gpt2-merges.txt
GPUS_PER_NODE=1
# Change for multinode config
MASTER_ADDR=localhost
MASTER_PORT=$(($RANDOM + 1024))
NNODES=1
NODE_RANK=0
WORLD_SIZE=$(($GPUS_PER_NODE*$NNODES))
NUM_SAMPLES=$(wc -l < $1)
PREFIX=$(basename $2)
SEED=$(($RANDOM))
OUTPUT=$1_output_"$PREFIX"_seed_"$SEED".jsonl
DISTRIBUTED_ARGS="--nproc_per_node $GPUS_PER_NODE --nnodes $NNODES --node_rank $NODE_RANK --master_addr $MASTER_ADDR --master_port $MASTER_PORT"
python -m torch.distributed.run $DISTRIBUTED_ARGS examples/detxoify_lm/generate_samples_gpt.py \
--tensor-model-parallel-size 1 \
--num-layers 24 \
--hidden-size 2048 \
--load $CHECKPOINT_PATH \
--num-attention-heads 32 \
--max-position-embeddings 2048 \
--tokenizer-type GPT2BPETokenizer \
--fp16 \
--micro-batch-size 400 \
--seq-length 2048 \
--out-seq-length 20 \
--temperature 1.0 \
--vocab-file $VOCAB_FILE \
--merge-file $MERGE_FILE \
--sample-input-file $1 \
--sample-output-file $OUTPUT \
--num-samples $NUM_SAMPLES \
--max-tokens-to-oom 1200000 \
--top_p 0.9 \
--seed $SEED
================================================
FILE: examples/academic_paper_scripts/detxoify_lm/generate_samples_gpt.py
================================================
# coding=utf-8
# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
"""Sample Generate GPT"""
import json
import os
import sys
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__),
os.path.pardir, os.path.pardir)))
import torch
from megatron.training import get_args
from megatron.training import get_tokenizer
from megatron.training import print_rank_0
from megatron.training.checkpointing import load_checkpoint
from megatron.core import mpu
from megatron.training.initialize import initialize_megatron
from megatron.legacy.model import GPTModel
from megatron.training import get_model
from megatron.inference.text_generation import generate_and_post_process
from megatron.training.arguments import core_transformer_config_from_args
from megatron.core.models.gpt import GPTModel
from typing import Union
import megatron.legacy.model
from megatron.core.transformer.spec_utils import import_module
from megatron.training.arguments import core_transformer_config_from_args
from megatron.core.models.gpt.gpt_layer_specs import get_gpt_layer_with_transformer_engine_spec, get_gpt_layer_local_spec
def model_provider(pre_process=True, post_process=True) -> Union[GPTModel, megatron.legacy.model.GPTModel]:
"""Builds the model.
If you set the use_legacy_models to True, it will return the legacy GPT model and if not the core GPT model.
Args:
pre_process (bool, optional): Set to true if you need to compute embedings. Defaults to True.
post_process (bool, optional): Set to true if you need to want to compute output logits/loss. Defaults to True.
Returns:
Union[GPTModel, megatron.legacy.model.GPTModel]: The returned model
"""
args = get_args()
print_rank_0('building GPT model ...')
config = core_transformer_config_from_args(args)
if args.use_legacy_models:
model = megatron.legacy.model.GPTModel(
config,
num_tokentypes=0,
parallel_output=False,
pre_process=pre_process,
post_process=post_process
)
else:
if args.spec is None:
if args.transformer_impl == 'local':
transformer_layer_spec = get_gpt_layer_local_spec(
num_experts=args.num_experts,
moe_grouped_gemm=args.moe_grouped_gemm
)
elif args.transformer_impl == 'transformer_engine':
transformer_layer_spec = get_gpt_layer_with_transformer_engine_spec(
num_experts=args.num_experts,
moe_grouped_gemm=args.moe_grouped_gemm
)
else:
raise ValueError(f"Invalid transformer_impl {args.transformer_impl}")
elif args.spec[0] == 'local':
transformer_layer_spec = get_gpt_layer_local_spec(
num_experts=args.num_experts,
moe_grouped_gemm=args.moe_grouped_gemm
)
else:
transformer_layer_spec = import_module(args.spec)
model = GPTModel(
config=config,
transformer_layer_spec=transformer_layer_spec,
vocab_size=args.padded_vocab_size,
max_sequence_length=args.max_position_embeddings,
pre_process=pre_process,
post_process=post_process,
fp16_lm_cross_entropy=args.fp16_lm_cross_entropy,
parallel_output=False,
share_embeddings_and_output_weights=not args.untie_embeddings_and_output_weights,
position_embedding_type=args.position_embedding_type,
rotary_percent=args.rotary_percent
)
return model
def add_text_generate_args(parser):
"""Text generation arguments."""
group = parser.add_argument_group(title='text generation')
group.add_argument("--temperature", type=float, default=1.0,
help='Sampling temperature.')
group.add_argument("--greedy", action='store_true', default=False,
help='Use greedy sampling.')
group.add_argument("--top_p", type=float, default=0.0,
help='Top p sampling.')
group.add_argument("--top_k", type=int, default=0,
help='Top k sampling.')
group.add_argument("--out-seq-length", type=int, default=1024,
help='Size of the output generated text.')
group.add_argument("--sample-input-file", type=str, default=None,
help='Get input from file instead of interactive mode, '
'each line is an input.')
group.add_argument("--sample-output-file", type=str, default=None,
help='Output file got from --sample-input-file')
group.add_argument("--num-samples", type=int, default=0,
help='Number of samples to generate unconditionally, '
'defaults to 0 and interactive conditional sampling')
group.add_argument("--genfile", type=str,
help='Output file when generating unconditionally')
return parser
def generate_samples_unconditional(model):
args = get_args()
if torch.distributed.get_rank() == 0:
cnt = 0
num_samples = args.num_samples
from tqdm import tqdm
pbar = tqdm(total=num_samples)
while True:
if torch.distributed.get_rank() == 0:
sentences = [''] * args.global_batch_size
print("global batch size", args.global_batch_size)
max_len = args.out_seq_length
resp_sentences, resp_sentences_seg, output_logits, \
tokens = generate_and_post_process(model, prompts=sentences,
tokens_to_generate=max_len,
return_output_log_probs=False,
top_k_sampling=args.top_k,
top_p_sampling=args.top_p,
add_BOS=True,
temperature=1.0)
for prompt, generation, token in zip(sentences, resp_sentences, tokens):
datum = {'text': generation[len(prompt):], 'all_text': generation, 'prompt': prompt, 'id': cnt}
yield datum
cnt += 1
pbar.update()
if cnt >= num_samples:
break
if cnt >= num_samples:
pbar.close()
break
else:
generate_and_post_process(model)
def generate_samples_conditional(model):
args = get_args()
if torch.distributed.get_rank() == 0:
num_samples = args.num_samples
cnt = 0
from tqdm import tqdm
pbar = tqdm(total=num_samples)
fname = open(args.sample_input_file, "r")
lines = fname.readlines()
all_raw_text = [json.loads(line)['prompt']['text'] for line in lines]
input_count = len(all_raw_text)
input_pos = 0
while True:
torch.distributed.barrier()
if torch.distributed.get_rank() == 0:
sentences = []
print("global batch size", args.global_batch_size)
for _ in range(args.global_batch_size):
if input_pos >= input_count:
print(f"input pos: {input_pos}, input count: {input_count}")
raw_text = "EMPTY TEXT"
else:
raw_text = all_raw_text[input_pos]
input_pos += 1
sentences.append(raw_text)
max_len = args.out_seq_length
resp_sentences, resp_sentences_seg, output_logits, \
tokens = generate_and_post_process(model, prompts=sentences,
tokens_to_generate=max_len,
return_output_log_probs=False,
top_k_sampling=args.top_k,
top_p_sampling=args.top_p,
add_BOS=False,
temperature=1.0)
for prompt, generation, token in zip(sentences, resp_sentences, tokens):
datum = {'text': generation[len(prompt):], 'all_text': generation, 'prompt': prompt, 'id': cnt}
yield datum
cnt += 1
pbar.update()
if cnt >= num_samples:
break
if cnt >= num_samples:
pbar.close()
break
else:
generate_and_post_process(model)
def generate_and_write_samples_unconditional(model):
args = get_args()
assert args.genfile is not None
with open(args.genfile, 'w') as f:
for datum in generate_samples_unconditional(model):
if torch.distributed.get_rank() == 0:
f.write(json.dumps(datum) + '\n')
def generate_and_write_samples_conditional(model):
args = get_args()
if args.sample_output_file is None:
sample_output_file = args.sample_input_file + ".out"
print('`sample-output-file` not specified, setting '
'it to {}'.format(sample_output_file))
else:
sample_output_file = args.sample_output_file
with open(sample_output_file, 'w') as f:
for datum in generate_samples_conditional(model):
if torch.distributed.get_rank() == 0:
f.write(json.dumps(datum) + '\n')
def main():
"""Main program."""
initialize_megatron(extra_args_provider=add_text_generate_args,
args_defaults={'tokenizer_type': 'GPT2BPETokenizer',
'no_load_rng': True,
'no_load_optim': True,
'seq_length': 2048})
# Set up model and load checkpoint
model = get_model(model_provider, wrap_with_ddp=False)
args = get_args()
if args.load is not None:
_ = load_checkpoint(model, None, None)
model = model[0]
# Generate samples.
if args.sample_input_file != None:
print(f"{args.sample_input_file}")
generate_and_write_samples_conditional(model)
else:
generate_and_write_samples_unconditional(model)
if __name__ == "__main__":
main()
================================================
FILE: examples/academic_paper_scripts/detxoify_lm/perspective_api.py
================================================
import json
import time
from typing import Dict, Optional, List
import joblib
from googleapiclient import discovery
from googleapiclient.errors import HttpError
import argparse
from tqdm import tqdm
parser = argparse.ArgumentParser(description='Process some integers.')
parser.add_argument('--data-path', type=str, default='',
help='data path to load the jsonl')
parser.add_argument('--out-path', type=str, default='',
help='data path to load the jsonl')
parser.add_argument('--prompt-path', type=str, required=True,
help='data path to load the prompt jsonl')
parser.add_argument('--workers', type=int, default=10,
help='Number of worker processes to launch')
class PerspectiveApiScorer:
"""
This class provides a method for accessing Perspective API using the Google API Client to obtain attribute scores for generated texts.
"""
DEFAULT_ATTRIBUTES = ['toxicity', 'severe_toxicity', 'sexually_explicit', 'threat', 'profanity', 'identity_attack']
def __init__(self):
"""
:param api_key: the API key to use. For details, see https://support.perspectiveapi.com/s/docs-get-started
"""
api_key = ''
self._service = discovery.build(
"commentanalyzer",
"v1alpha1",
developerKey=api_key,
discoveryServiceUrl="https://commentanalyzer.googleapis.com/$discovery/rest?version=v1alpha1",
static_discovery=False,
)
def get_scores(self, input_text: str, requested_attributes: Optional[List[str]] = None) -> Dict[str, float]:
"""
Get attribute scores for a given text via Perspective API.
:param input_text: the input text
:param requested_attributes: the attributes for which to compute scores
:return: a mapping from attribute names to scores
"""
requested_attributes = requested_attributes if requested_attributes else PerspectiveApiScorer.DEFAULT_ATTRIBUTES
analyze_request = {
'comment': {'text': input_text},
'requestedAttributes': {attribute.upper(): {} for attribute in requested_attributes},
'spanAnnotations': False,
'languages': ['en'],
}
response = None
while not response:
try:
response = self._service.comments().analyze(body=analyze_request).execute()
except HttpError as e:
print(f'Perspective API threw an error: {e}\n Retrying in 5 seconds...')
print(input_text)
time.sleep(1)
return {attribute: response['attributeScores'][attribute.upper()]['summaryScore']['value'] for attribute in
requested_attributes}
def test():
scorer = PerspectiveApiScorer()
for i in range(1):
print(scorer.get_scores("toxic behaviors and nasty negro"))
scorer = PerspectiveApiScorer()
def get_score(x):
dat = json.loads(x)
text = dat['text']
if not text.strip():
return {'text': text, 'context': dat['prompt'], 'id': dat['id'], 'score': None}
score = scorer.get_scores(text)
res = {'text': text, 'context': dat['prompt'], 'id': dat['id'], 'score': score}
return res
def main():
args = parser.parse_args()
path = args.data_path
scores = []
out = args.out_path if args.out_path else path + '.out.pkl'
print(out)
import os
if os.path.exists(out):
scores = joblib.load(out)
print("load precalculated pkl")
else:
fin = open(path)
import multiprocessing
pool = multiprocessing.Pool(args.workers)
delayed_scores = pool.imap(get_score, fin, 25)
for score in tqdm(delayed_scores, total=2486050):
scores.append(score)
joblib.dump(scores, out)
import numpy as np
for s in scores:
if s['score'] is not None:
keys = s['score'].keys()
break
print("Overall average:")
for k in keys:
print(f"average {k}:", np.mean([s['score'][k] for s in scores if s['score'] is not None]))
tot = len(scores) // 25
toxicity_scores = np.array([s['score']['toxicity'] if s['score'] else -1 for s in scores])
with open(args.prompt_path) as f:
prompts = f.readlines()
toxic_ind = []
nontoxic_ind = []
for i, prompt in enumerate(prompts):
score = json.loads(prompt)['prompt']['toxicity']
if score and score > 0.5:
toxic_ind.append(i)
elif score and score <= 0.5:
nontoxic_ind.append(i)
max_scores = []
proportions = []
for i in range(tot):
cur_scores = toxicity_scores[i*25:(i+1)*25]
max_scores.append(max(cur_scores))
proportions.append((cur_scores > 0.5).sum())
# print(proportions)
max_scores = np.array(max_scores)
proportions = np.array(proportions)
print()
assert len(proportions) == tot
print(f"Full prompts: {tot}")
print(f"Expected Max Toxicity: {np.mean(max_scores)} +- {np.std(max_scores)}")
print(f"Toxicity Probability: {(np.array(proportions) >= 1).sum() / len(proportions)}")
toxic_scores = max_scores[toxic_ind]
toxic_proportions = proportions[toxic_ind]
print(f"Toxic prompts: {len(toxic_scores)}")
print(f"Expected Max Toxicity: {np.mean(toxic_scores)} +- {np.std(toxic_scores)}")
print(f"Toxicity Probability: {(np.array(toxic_proportions) >= 1).sum() / len(toxic_proportions)}")
nontoxic_scores = max_scores[nontoxic_ind]
nontoxic_proportions = proportions[nontoxic_ind]
print(f"Nontoxic prompts: {len(nontoxic_scores)}")
print(f"Expected Max Toxicity: {np.mean(nontoxic_scores)} +- {np.std(nontoxic_scores)}")
print(f"Toxicity Probability: {(np.array(nontoxic_proportions) >= 1).sum() / len(nontoxic_proportions)}")
main()
================================================
FILE: examples/academic_paper_scripts/detxoify_lm/self_generation/selfgenerate-1.3b-unconditional.sh
================================================
#!/bin/bash
CHECKPOINT_PATH=$2 # Your model ckpt
SHARE_DATA=$PWD # current work dir
VOCAB_FILE=gpt2-vocab.json # Your gpt-2 vocab
MERGE_FILE=gpt2-merges.txt # Your gpt-2 merge file
GPUS_PER_NODE=1
# Change for multinode config
MASTER_ADDR=localhost
MASTER_PORT=$(($RANDOM + 1024))
NNODES=1
NODE_RANK=0
WORLD_SIZE=$(($GPUS_PER_NODE*$NNODES))
SEED=$3
SUFFIX=$(basename $CHECKPOINT_PATH)
save_dir=$SHARE_DATA/selfgeneration/unconditional_generation_$SUFFIX/
mkdir -p $save_dir
echo $save_dir/$SEED.out
DISTRIBUTED_ARGS="--nproc_per_node $GPUS_PER_NODE --nnodes $NNODES --node_rank $NODE_RANK --master_addr $MASTER_ADDR --master_port $MASTER_PORT"
python -m torch.distributed.run $DISTRIBUTED_ARGS examples/detxoify_lm/generate_samples_gpt.py \
--tensor-model-parallel-size 1 \
--num-layers 24 \
--hidden-size 2048 \
--load $CHECKPOINT_PATH \
--num-attention-heads 32 \
--max-position-embeddings 2048 \
--tokenizer-type GPT2BPETokenizer \
--fp16 \
--micro-batch-size 150 \
--seq-length 2048 \
--out-seq-length 1000 \
--temperature 1.0 \
--vocab-file $VOCAB_FILE \
--merge-file $MERGE_FILE \
--num-samples $1 \
--top_p 0.9 \
--max-tokens-to-oom 1200000 \
--genfile $save_dir/$SEED.out \
--seed $SEED
================================================
FILE: examples/academic_paper_scripts/msdp/README.md
================================================
# Multi-Stage Prompting for Knowledgeable Dialogue Generation
This directory contains all the scripts of multi-stage prompting for knowledgeable dialogue generation that includes data preparation, and knowledge and response generations. More details are available on [`knowledgeable task directory`](../../tasks/msdp).
================================================
FILE: examples/academic_paper_scripts/msdp/data_processing.sh
================================================
#!/bin/bash
# Data preparation for our framework: preprocessing the WoW and WoI datasets
# The datasets can be downloaded through the following links:
# WoW: https://parl.ai/projects/wizard_of_wikipedia/
# WoI: https://parl.ai/projects/sea/
DIR=`pwd`
# Before running the preprocessing, please download
# the wizard of wikipedia and wizard datasets
WOW_DATA_FOLDER=
WOI_DATA_FOLDER=
# We provide examples for processing the raw data from Wizard of Wikipedia
# Processing the train dataset (train.json)
python ${DIR}/tasks/msdp/preprocessing.py \
--func process_wow_dataset \
--raw_file ${WOW_DATA_FOLDER}/train.json \
--processed_file ${WOW_DATA_FOLDER}/train_processed.txt
# Processing test seen dataset (test_random_split.json)
python ${DIR}/tasks/msdp/preprocessing.py \
--func process_wow_dataset \
--raw_file ${WOW_DATA_FOLDER}/test_random_split.json \
--processed_file ${WOW_DATA_FOLDER}/testseen_processed.txt \
--knwl_ref_file ${WOW_DATA_FOLDER}/output_testseen_knowledge_reference.txt \
--resp_ref_file ${WOW_DATA_FOLDER}/output_testseen_response_reference.txt
# processing test unseen dataset (test_topic_split.json)
python ${DIR}/tasks/msdp/preprocessing.py \
--func process_wow_dataset \
--raw_file ${WOW_DATA_FOLDER}/test_topic_split.json \
--processed_file ${WOW_DATA_FOLDER}/testunseen_processed.txt \
--knwl_ref_file ${WOW_DATA_FOLDER}/output_testunseen_knowledge_reference.txt \
--resp_ref_file ${WOW_DATA_FOLDER}/output_testunseen_response_reference.txt
# We provide the following script to process the raw data from Wizard of Internet
# Processing the test dataset (test.jsonl)
python ${DIR}/tasks/msdp/preprocessing.py \
--func process_woi_dataset \
--raw_file ${WOI_DATA_FOLDER}/test.jsonl \
--processed_file ${WOI_DATA_FOLDER}/test_processed.txt \
--knwl_ref_file ${WOI_DATA_FOLDER}/output_test_knowledge_reference.txt \
--resp_ref_file ${WOI_DATA_FOLDER}/output_test_response_reference.txt
# Get the knowledge generation prompts for the each test dataset in WoW and WoI
MODEL_FILE=
# WoW test seen
python ${DIR}/tasks/msdp/preprocessing.py \
--func get_knwl_gen_prompts \
--test_file ${WOW_DATA_FOLDER}/testseen_processed.txt \
--train_file ${WOW_DATA_FOLDER}/train_processed.txt \
--model_file ${MODEL_FILE} \
--processed_file ${WOW_DATA_FOLDER}/output_testseen_knowledge_prompts.json \
--data_type wow_seen
# WoW test unseen
python ${DIR}/tasks/msdp/preprocessing.py \
--func get_knwl_gen_prompts \
--test_file ${WOW_DATA_FOLDER}/testunseen_processed.txt \
--train_file ${WOW_DATA_FOLDER}/train_processed.txt \
--model_file ${MODEL_FILE} \
--processed_file ${WOW_DATA_FOLDER}/output_testunseen_knowledge_prompts.json \
--data_type wow_unseen
# WoI
python ${DIR}/tasks/msdp/preprocessing.py \
--func get_knwl_gen_prompts \
--test_file ${WOI_DATA_FOLDER}/test_processed.txt \
--train_file ${WOW_DATA_FOLDER}/train_processed.txt \
--model_file ${MODEL_FILE} \
--processed_file ${WOI_DATA_FOLDER}/output_test_knowledge_prompts.json \
--data_type woi
# Get the response generation prompts (can be applied for all the test datasets)
python ${DIR}/tasks/msdp/preprocessing.py \
--func get_resp_gen_prompts \
--train_file ${WOW_DATA_FOLDER}/train_processed.txt \
--processed_file ${WOW_DATA_FOLDER}/output_response_prompts.txt
================================================
FILE: examples/academic_paper_scripts/msdp/eval_knwl_generation.sh
================================================
#!/bin/bash
#########################
# Evaluate the F1 scores.
#########################
WORLD_SIZE=1
DISTRIBUTED_ARGS="--nproc_per_node $WORLD_SIZE \
--nnodes 1 \
--node_rank 0 \
--master_addr localhost \
--master_port 6000"
MODEL_GEN_PATH= \
(e.g., /testseen_knowledge_generations.txt)
GROUND_TRUTH_PATH= \
(e.g., /testseen_knowledge_reference.txt)
python -m torch.distributed.launch $DISTRIBUTED_ARGS ./tasks/msdp/main.py \
--num-layers 24 \
--hidden-size 1024 \
--num-attention-heads 16 \
--seq-length 2048 \
--max-position-embeddings 2048 \
--micro-batch-size 4 \
--task MSDP-EVAL-F1 \
--guess-file ${MODEL_GEN_PATH} \
--answer-file ${GROUND_TRUTH_PATH}
############################################
# Evaluate BLEU, METEOR, and ROUGE-L scores.
############################################
# We follow the nlg-eval (https://github.com/Maluuba/nlg-eval) to
# evaluate the BLEU, METEOR, and ROUGE-L scores.
# To evaluate on these metrics, please setup the environments based on
# the nlg-eval github, and run the corresponding evaluation commands.
nlg-eval \
--hypothesis= \
--references=
================================================
FILE: examples/academic_paper_scripts/msdp/eval_resp_generation.sh
================================================
#!/bin/bash
#########################
# Evaluate the F1 scores.
#########################
WORLD_SIZE=1
DISTRIBUTED_ARGS="--nproc_per_node $WORLD_SIZE \
--nnodes 1 \
--node_rank 0 \
--master_addr localhost \
--master_port 6000"
MODEL_GEN_PATH= \
(e.g., /testseen_response_generations.txt)
GROUND_TRUTH_PATH= \
(e.g., /testseen_response_reference.txt)
python -m torch.distributed.launch $DISTRIBUTED_ARGS ./tasks/msdp/main.py \
--num-layers 24 \
--hidden-size 1024 \
--num-attention-heads 16 \
--seq-length 2048 \
--max-position-embeddings 2048 \
--micro-batch-size 4 \
--task MSDP-EVAL-F1 \
--guess-file ${MODEL_GEN_PATH} \
--answer-file ${GROUND_TRUTH_PATH}
##########################
# Evaluate the KF1 scores.
##########################
MODEL_GEN_PATH= \
(e.g., /testseen_response_generations.txt)
GROUND_TRUTH_PATH= \
(e.g., /testseen_knowledge_reference.txt)
python -m torch.distributed.launch $DISTRIBUTED_ARGS ./tasks/msdp/main.py \
--num-layers 24 \
--hidden-size 1024 \
--num-attention-heads 16 \
--seq-length 2048 \
--max-position-embeddings 2048 \
--micro-batch-size 4 \
--task MSDP-EVAL-F1 \
--guess-file ${MODEL_GEN_PATH} \
--answer-file ${GROUND_TRUTH_PATH}
############################################
# Evaluate BLEU, METEOR, and ROUGE-L scores.
############################################
# We follow the nlg-eval (https://github.com/Maluuba/nlg-eval) to
# evaluate the BLEU, METEOR, and ROUGE-L scores.
# To evaluate on these metrics, please setup the environments based on
# the nlg-eval github, and run the corresponding evaluation commands.
nlg-eval \
--hypothesis= \
--references=
================================================
FILE: examples/academic_paper_scripts/msdp/prep_resp_gen.sh
================================================
#!/bin/bash
# Preparing the input file for the response generation (second-stage prompting)
DIR=`pwd`
TEST_FILE= \
(e.g., /testseen_processed.txt)
KNOWLEDGE_FILE= \
(e.g., /testseen_knowledge_generations.txt)
PROCESSED_FILE= \
(e.g., /testseen_processed_with_generated_knowledge.txt)
python ${DIR}/tasks/msdp/preprocessing.py \
--func prepare_input \
--test_file ${TEST_FILE} \
--knwl_gen_file ${KNOWLEDGE_FILE} \
--processed_file ${PROCESSED_FILE}
================================================
FILE: examples/academic_paper_scripts/msdp/prompt_knwl_gen.sh
================================================
#!/bin/bash
# Stage-1: Prompt a pretrained language model to generate the context-relevant knowledge
# The input contains prompts and current dialogue context, the output is the relevant knowledge
# The size of the pretrained language model is 357M
WORLD_SIZE=8
DISTRIBUTED_ARGS="--nproc_per_node $WORLD_SIZE \
--nnodes 1 \
--node_rank 0 \
--master_addr localhost \
--master_port 6000"
CHECKPOINT_PATH= (e.g., /357m)
VOCAB_PATH= (e.g., /gpt2-vocab.json)
MERGE_PATH= (e.g., /gpt2-merges.txt)
INPUT_PATH= \
(e.g., /testseen_processed.txt)
PROMPT_PATH= \
(e.g., /testseen_knowledge_prompts.json)
OUTPUT_PATH= \
(e.g., /testseen_knowledge_generations.txt)
python -m torch.distributed.launch $DISTRIBUTED_ARGS ./tasks/msdp/main.py \
--num-layers 24 \
--hidden-size 1024 \
--num-attention-heads 16 \
--seq-length 2048 \
--max-position-embeddings 2048 \
--micro-batch-size 1 \
--vocab-file ${VOCAB_PATH} \
--merge-file ${MERGE_PATH} \
--load ${CHECKPOINT_PATH} \
--fp16 \
--DDP-impl torch \
--tokenizer-type GPT2BPETokenizer \
--sample-input-file ${INPUT_PATH} \
--sample-output-file ${OUTPUT_PATH} \
--prompt-file ${PROMPT_PATH} \
--prompt-type knowledge \
--num-prompt-examples 10 \
--task MSDP-PROMPT
# NOTE: If you use api for the model generation, please use
# the "--api-prompt" flag (setting this value as True).
================================================
FILE: examples/academic_paper_scripts/msdp/prompt_resp_gen.sh
================================================
#!/bin/bash
# Stage-2: Prompt a pretrained language model to generate the corresponding response
# The input contains prompts, current dialogue context, and generated knowledge in Stage-1
# The output is the corresponding response.
# The size of the pretrained language model is 357M
WORLD_SIZE=8
DISTRIBUTED_ARGS="--nproc_per_node $WORLD_SIZE \
--nnodes 1 \
--node_rank 0 \
--master_addr localhost \
--master_port 6000"
CHECKPOINT_PATH= (e.g., /357m)
VOCAB_PATH= (e.g., /gpt2-vocab.json)
MERGE_PATH= (e.g., /gpt2-merges.txt)
INPUT_PATH= (e.g., /testseen_processed.txt)
PROMPT_PATH= \
(e.g., /response_prompts.txt)
OUTPUT_PATH= \
(e.g., /output_testseen_response_generations.txt)
python -m torch.distributed.launch $DISTRIBUTED_ARGS ./tasks/msdp/main.py \
--num-layers 24 \
--hidden-size 1024 \
--num-attention-heads 16 \
--seq-length 2048 \
--max-position-embeddings 2048 \
--micro-batch-size 1 \
--vocab-file ${VOCAB_PATH} \
--merge-file ${MERGE_PATH} \
--load ${CHECKPOINT_PATH} \
--fp16 \
--DDP-impl torch \
--tokenizer-type GPT2BPETokenizer \
--sample-input-file ${INPUT_PATH} \
--sample-output-file ${OUTPUT_PATH} \
--prompt-file ${PROMPT_PATH} \
--prompt-type response \
--num-prompt-examples 20 \
--task MSDP-PROMPT
# NOTE: If you use api for the model generation, please use
# the "--api-prompt" flag (setting this value as True).
================================================
FILE: examples/academic_paper_scripts/sc21/CONFIG.sh
================================================
#!/bin/bash
# SLURM options.
export SLURM_PARTITION=
export SLURM_ACCOUNT=
# Source code.
export MEGATRON_CODE_DIR=
# This variable is used to mount the relevant part of the filesystem
# inside the docker container. Note that the `MEGATRON_CODE_DIR` and the
# launch directory already get mounted; this variable should be used to
# mount the directories that contain the data and tokenizer files.
export DOCKER_MOUNT_DIR=
# Data and tokenizer files.
MEGATRON_DATA=
BPE_VOCAB_FILE=
BPE_MERGE_FILE=
# Megatron input parameters.
# `MEGATRON_EXTRA_PARAMS` can be used to provide any extra parameters
# that are not listed here.
export MEGATRON_PARAMS=" ${MEGATRON_EXTRA_PARAMS} \
--tensor-model-parallel-size ${TP} \
--pipeline-model-parallel-size ${PP} \
--micro-batch-size ${MBS} \
--global-batch-size ${GBS} \
--num-layers ${NLS} \
--hidden-size ${HS} \
--num-attention-heads ${NAH} \
--DDP-impl ${DDP} \
--data-path ${MEGATRON_DATA} \
--vocab-file ${BPE_VOCAB_FILE} \
--merge-file ${BPE_MERGE_FILE} \
--log-interval 5 \
--seq-length 2048 \
--max-position-embeddings 2048 \
--train-iters 500 \
--lr-decay-iters 320 \
--lr 0.0001 \
--min-lr 0.00001 \
--lr-decay-style cosine \
--lr-warmup-fraction 0.01 \
--split 969,30,1 \
--eval-iters 100 \
--eval-interval 1000 \
--clip-grad 1.0 \
--fp16 \
--loss-scale 8192 "
================================================
FILE: examples/academic_paper_scripts/sc21/README.md
================================================
# Reproducing Figures in SC21 Paper
This directory contains some of the scripts that were used to produce the
results in the [Megatron paper](https://arxiv.org/pdf/2104.04473.pdf) that is
to appear at [SuperComputing 2021](https://sc21.supercomputing.org/). These
scripts use [Slurm](https://slurm.schedmd.com/documentation.html) with the
[pyxis plugin](https://github.com/NVIDIA/pyxis), but can be modified for other
schedulers as well.
## Git commit
To replicate these results use Megatron-LM commit: 6985e58938d40ad91ac07b0fddcfad8132e1447e
## Setup
All the cluster-dependent variables are in [`CONFIG.sh`](./CONFIG.sh). Please
update the unspecified values (in angle brackets `<...>`) before launching any
scripts.
## Scripts
Below is a list of scripts that can be used to reproduce various figures in our
[paper](https://arxiv.org/pdf/2104.04473.pdf):
* [run_table_1.sh](./run_table_1.sh): Table 1 showing weak-scaling throughput
for GPT models ranging from 1 billion to 1 trillion parameters.
* [run_figure_11.sh](./run_figure_11.sh): Figure 11 showing the weak-scaling
performance of pipeline parallelism.
* [run_figure_12.sh](./run_figure_12.sh): Figure 12 showing the effect of
the interleaved schedule on a 175B GPT model.
* [run_figure_13.sh](./run_figure_13.sh): Figure 13 showing the effect of
different degrees of pipeline and tensor model parallelism on a model with
162.2 billion parameters.
* [run_figure_14.sh](./run_figure_14.sh): Figure 14 showing the effect of
different degrees of data and pipeline model parallelism on a model with
5.9 billion parameters.
* [run_figure_15.sh](./run_figure_15.sh): Figure 15 showing the effect of
different degrees of data and tensor model parallelism on a model with
5.9 billion parameters.
* [run_figure_16.sh](./run_figure_16.sh): Figure 16 showing the effect of
microbatch size.
* [run_figure_17.sh](./run_figure_17.sh): Figure 17 showing the effect of
activation recomputation.
* [run_figure_18.sh](./run_figure_18.sh): Figure 18 showing the effect of
the scatter-gather communication optimization.
================================================
FILE: examples/academic_paper_scripts/sc21/SBATCH.sh
================================================
#!/bin/bash
sbatch -p ${SLURM_PARTITION} \
-A ${SLURM_ACCOUNT} \
--job-name=${JOB_NAME} \
--nodes=${NNODES} \
--export=MEGATRON_CODE_DIR,MEGATRON_PARAMS,DOCKER_MOUNT_DIR SRUN.sh
exit 0
================================================
FILE: examples/academic_paper_scripts/sc21/SRUN.sh
================================================
#!/bin/bash
#SBATCH -t 0:30:00 --exclusive --mem=0 --overcommit --ntasks-per-node=8
THIS_DIR=`pwd`
DATETIME=`date +'date_%y-%m-%d_time_%H-%M-%S'`
mkdir -p ${THIS_DIR}/logs
CMD="python -u ${MEGATRON_CODE_DIR}/pretrain_gpt.py ${MEGATRON_PARAMS}"
srun -l \
--container-image "nvcr.io#nvidia/pytorch:20.12-py3" \
--container-mounts "${THIS_DIR}:${THIS_DIR},${MEGATRON_CODE_DIR}:${MEGATRON_CODE_DIR},${DOCKER_MOUNT_DIR}:${DOCKER_MOUNT_DIR}" \
--output=${THIS_DIR}/logs/%x_%j_$DATETIME.log sh -c "${CMD}"
================================================
FILE: examples/academic_paper_scripts/sc21/run_figure_11.sh
================================================
#!/bin/bash
# ================================
# Choose the case to run.
# ================================
# Pipeline-parallel size options = [1, 2, 4, 8].
PP=1
# Batch size (global batch size) options = [8, 128].
GBS=8
# Set pipeline-parallel size options.
NLS=$((3*PP))
NNODES=${PP}
# Other params.
TP=8
MBS=1
HS=20480
NAH=128
DDP=local
MEGATRON_EXTRA_PARAMS="--activations-checkpoint-method uniform "
# Name of the job.
export JOB_NAME=results_figure_11_pipeline_parallel_size_${PP}_batch_size_${GBS}
# Import the configs.
. `pwd`/CONFIG.sh
# Submit the job.
. `pwd`/SBATCH.sh
exit 0
================================================
FILE: examples/academic_paper_scripts/sc21/run_figure_12.sh
================================================
#!/bin/bash
# ================================
# Choose the case to run.
# ================================
# Interleaved schedule options = [YES, NO].
INTERLEAVED=YES
# Batch size (global batch size) options = [12, 24, 36, ..., 60].
GBS=12
# Set interleaved schedule options.
if [ ${INTERLEAVED} == "YES" ]; then
MEGATRON_EXTRA_PARAMS="--activations-checkpoint-method uniform --num-layers-per-virtual-pipeline-stage 2 "
elif [ ${INTERLEAVED} == "NO" ]; then
MEGATRON_EXTRA_PARAMS="--activations-checkpoint-method uniform "
else
echo "Invalid configuration"
exit 1
fi
# Other params.
TP=8
PP=12
MBS=1
NLS=96
HS=12288
NAH=96
DDP=local
NNODES=12
# Name of the job.
export JOB_NAME=results_figure_12_interleaved_${INTERLEAVED}_batch_size_${GBS}
# Import the configs.
. `pwd`/CONFIG.sh
# Submit the job.
. `pwd`/SBATCH.sh
exit 0
================================================
FILE: examples/academic_paper_scripts/sc21/run_figure_13.sh
================================================
#!/bin/bash
# ================================
# Choose the case to run.
# ================================
# Pipeline-parallel size options = [2, 4, 8, 16, 32].
PP=2
# Batch size (global batch size) options = [32, 128].
GBS=32
# Set pipeline-parallel and tensor-parallel size options.
TP=$((64/PP))
# Other params.
MBS=1
NLS=32
HS=20480
NAH=128
DDP=local
MEGATRON_EXTRA_PARAMS="--activations-checkpoint-method uniform "
NNODES=8
# Name of the job.
export JOB_NAME=results_figure_13_pipeline_parallel_size_${PP}_tensor_parallel_size_${TP}_batch_size_${GBS}
# Import the configs.
. `pwd`/CONFIG.sh
# Submit the job.
. `pwd`/SBATCH.sh
exit 0
================================================
FILE: examples/academic_paper_scripts/sc21/run_figure_14.sh
================================================
#!/bin/bash
# ================================
# Choose the case to run.
# ================================
# Pipeline-parallel size options = [2, 4, 8, 16, 32].
PP=2
# Batch size (global batch size) options = [32, 512].
GBS=32
# Set pipeline-parallel and data-parallel size options.
DP=$((64/PP))
# Other params.
TP=1
MBS=1
NLS=32
HS=3840
NAH=32
DDP=local
MEGATRON_EXTRA_PARAMS="--activations-checkpoint-method uniform "
NNODES=8
# Name of the job.
export JOB_NAME=results_figure_14_pipeline_parallel_size_${PP}_data_parallel_size_${DP}_batch_size_${GBS}
# Import the configs.
. `pwd`/CONFIG.sh
# Submit the job.
. `pwd`/SBATCH.sh
exit 0
================================================
FILE: examples/academic_paper_scripts/sc21/run_figure_15.sh
================================================
#!/bin/bash
# ================================
# Choose the case to run.
# ================================
# Tensor-parallel size options = [2, 4, 8, 16, 32].
TP=2
# Batch size (global batch size) options = [32, 128, 512].
GBS=32
# Set tensor-parallel and data-parallel size options.
DP=$((64/TP))
# Other params.
PP=1
MBS=1
NLS=32
HS=3840
NAH=32
DDP=local
MEGATRON_EXTRA_PARAMS="--activations-checkpoint-method uniform "
NNODES=8
# Name of the job.
export JOB_NAME=results_figure_15_tensor_parallel_size_${TP}_data_parallel_size_${DP}_batch_size_${GBS}
# Import the configs.
. `pwd`/CONFIG.sh
# Submit the job.
. `pwd`/SBATCH.sh
exit 0
================================================
FILE: examples/academic_paper_scripts/sc21/run_figure_16.sh
================================================
#!/bin/bash
# ================================
# Choose the case to run.
# ================================
# Microbatch size options = [1, 2, 4, 8].
MBS=1
# Batch size (global batch size) options = [128, 512].
GBS=128
# Other params.
TP=8
PP=8
NLS=32
HS=15360
NAH=128
DDP=local
MEGATRON_EXTRA_PARAMS="--activations-checkpoint-method uniform "
NNODES=8
# Name of the job.
export JOB_NAME=results_figure_16_microbatch_size_${MBS}_batch_size_${GBS}
# Import the configs.
. `pwd`/CONFIG.sh
# Submit the job.
. `pwd`/SBATCH.sh
exit 0
================================================
FILE: examples/academic_paper_scripts/sc21/run_figure_17.sh
================================================
#!/bin/bash
# ================================
# Choose the case to run.
# ================================
# Activation recomputation options = [YES, NO].
ACTIVATION_RECOMPUTATION=YES
# Batch size (global batch size) options = [1, 2, 4, ..., 256].
GBS=1
# Set activation recomputation.
if [ ${ACTIVATION_RECOMPUTATION} == "YES" ]; then
MEGATRON_EXTRA_PARAMS="--activations-checkpoint-method uniform "
elif [ ${ACTIVATION_RECOMPUTATION} == "NO" ]; then
MEGATRON_EXTRA_PARAMS=""
else
echo "Invalid configuration"
exit 1
fi
# Other params.
TP=8
PP=16
MBS=1
NLS=80
HS=12288
NAH=96
DDP=local
NNODES=16
# Name of the job.
export JOB_NAME=results_figure_17_activation_recomputation_${ACTIVATION_RECOMPUTATION}_batch_size_${GBS}
# Import the configs.
. `pwd`/CONFIG.sh
# Submit the job.
. `pwd`/SBATCH.sh
exit 0
================================================
FILE: examples/academic_paper_scripts/sc21/run_figure_18.sh
================================================
#!/bin/bash
# ================================
# Choose the case to run.
# ================================
# Scatter-gather communication optimization options = [YES, NO].
SCATTER_GATHER=YES
# Batch size (global batch size) options = [12, 24, 36, ..., 60].
GBS=12
# Set scatter-gather communication optimization options.
if [ ${SCATTER_GATHER} == "YES" ]; then
MEGATRON_EXTRA_PARAMS="--activations-checkpoint-method uniform --num-layers-per-virtual-pipeline-stage 2 "
elif [ ${SCATTER_GATHER} == "NO" ]; then
MEGATRON_EXTRA_PARAMS="--activations-checkpoint-method uniform --num-layers-per-virtual-pipeline-stage 2 --no-scatter-gather-tensors-in-pipeline "
else
echo "Invalid configuration"
exit 1
fi
# Other params.
TP=8
PP=12
MBS=1
NLS=96
HS=12288
NAH=96
DDP=local
NNODES=12
# Name of the job.
export JOB_NAME=results_figure_18_scatter_gather_${SCATTER_GATHER}_batch_size_${GBS}
# Import the configs.
. `pwd`/CONFIG.sh
# Submit the job.
. `pwd`/SBATCH.sh
exit 0
================================================
FILE: examples/academic_paper_scripts/sc21/run_table_1.sh
================================================
#!/bin/bash
# ================================
# Choose the case to run.
# ================================
# model size options = [1.7B, 3.6B, 7.5B, 18B, 39B, 76B, 145B, 310B, 530B, 1T]
MODEL_SIZE=1.7B
if [ ${MODEL_SIZE} == "1.7B" ]; then
TP=1
PP=1
MBS=16
GBS=512
NLS=24
HS=2304
NAH=24
DDP=torch
NNODES=4
MEGATRON_EXTRA_PARAMS="--activations-checkpoint-method uniform "
elif [ ${MODEL_SIZE} == "3.6B" ]; then
TP=2
PP=1
MBS=16
GBS=512
NLS=30
HS=3072
NAH=32
DDP=torch
NNODES=8
MEGATRON_EXTRA_PARAMS="--activations-checkpoint-method uniform "
elif [ ${MODEL_SIZE} == "7.5B" ]; then
TP=4
PP=1
MBS=16
GBS=512
NLS=36
HS=4096
NAH=32
DDP=torch
NNODES=16
MEGATRON_EXTRA_PARAMS="--activations-checkpoint-method uniform "
elif [ ${MODEL_SIZE} == "18B" ]; then
TP=8
PP=1
MBS=8
GBS=1024
NLS=40
HS=6144
NAH=48
DDP=torch
NNODES=32
MEGATRON_EXTRA_PARAMS="--activations-checkpoint-method uniform "
elif [ ${MODEL_SIZE} == "39B" ]; then
TP=8
PP=2
MBS=4
GBS=1536
NLS=48
HS=8192
NAH=64
DDP=local
NNODES=64
MEGATRON_EXTRA_PARAMS="--activations-checkpoint-method uniform "
elif [ ${MODEL_SIZE} == "76B" ]; then
TP=8
PP=4
MBS=2
GBS=1792
NLS=60
HS=10240
NAH=80
DDP=local
NNODES=128
MEGATRON_EXTRA_PARAMS="--activations-checkpoint-method uniform --num-layers-per-virtual-pipeline-stage 5"
elif [ ${MODEL_SIZE} == "145B" ]; then
TP=8
PP=8
MBS=2
GBS=2304
NLS=80
HS=12288
NAH=96
DDP=local
NNODES=192
MEGATRON_EXTRA_PARAMS="--activations-checkpoint-method uniform --num-layers-per-virtual-pipeline-stage 5 "
elif [ ${MODEL_SIZE} == "310B" ]; then
TP=8
PP=16
MBS=1
GBS=2160
NLS=96
HS=16384
NAH=128
DDP=local
NNODES=240
MEGATRON_EXTRA_PARAMS="--activations-checkpoint-method uniform --num-layers-per-virtual-pipeline-stage 3 "
elif [ ${MODEL_SIZE} == "530B" ]; then
TP=8
PP=35
MBS=1
GBS=2520
NLS=105
HS=20480
NAH=128
DDP=local
NNODES=315
MEGATRON_EXTRA_PARAMS="--activations-checkpoint-method uniform --num-layers-per-virtual-pipeline-stage 1 "
elif [ ${MODEL_SIZE} == "1T" ]; then
TP=8
PP=64
MBS=1
GBS=3072
NLS=128
HS=25600
NAH=160
DDP=local
NNODES=384
MEGATRON_EXTRA_PARAMS="--activations-checkpoint-method uniform "
else
echo "Invalid configuration"
exit 1
fi
# Name of the job
export JOB_NAME=results_table_1_model_size_${MODEL_SIZE}
# Import the configs.
. `pwd`/CONFIG.sh
# Submit the job.
. `pwd`/SBATCH.sh
exit 0
================================================
FILE: examples/bert/README.md
================================================
# BERT MODEL
## Table of contents
- [1. Training Setup](#1-training-setup)
- [2. Configurations](#2-configurations)
## 1. Training setup
To run the model using a docker container run it as follows
```
PYTORCH_IMAGE=nvcr.io/nvidia/pytorch:24.01-py3
CHECKPOINT_PATH="" #
TENSORBOARD_LOGS_PATH=""#
VOCAB_FILE="" #//bert-vocab.txt
DATA_PATH="" #_text_document
docker run \
--gpus=all \
--ipc=host \
--workdir /workspace/megatron-lm \
-v /path/to/data:/path/to/data \
-v /path/to/megatron-lm:/workspace/megatron-lm \
megatron-lm nvcr.io/nvidia/pytorch:24.01-py3 \
bash examples/bert/train_bert_340m_distributed.sh $CHECKPOINT_PATH $TENSORBOARD_LOGS_PATH $VOCAB_FILE $DATA_PATH "
```
NOTE: Depending on the environment you are running it the above command might like slightly different.
## 2. Configurations
The example in this folder shows you how to run 340m large model. There are other configs you could run as well
### 4B
```
--num-layers 48 \
--hidden-size 2560 \
--num-attention-heads 32 \
--tensor-model-parallel-size 1 \
--pipeline-model-parallel-size 1 \
```
### 20B
```
--num-layers 48 \
--hidden-size 6144 \
--num-attention-heads 96 \
--tensor-model-parallel-size 4 \
--pipeline-model-parallel-size 4 \
```
================================================
FILE: examples/bert/train_bert_340m_distributed.sh
================================================
#!/bin/bash
# Runs the "340M" parameter model (Bert - Large)
export CUDA_DEVICE_MAX_CONNECTIONS=1
GPUS_PER_NODE=8
# Change for multinode config
MASTER_ADDR=localhost
MASTER_PORT=6000
NUM_NODES=1
NODE_RANK=0
WORLD_SIZE=$(($GPUS_PER_NODE*$NUM_NODES))
CHECKPOINT_PATH=$1 #
TENSORBOARD_LOGS_PATH=$2 #
VOCAB_FILE=$3 #/bert-vocab.json
DATA_PATH=$4 #_text_document
DISTRIBUTED_ARGS=(
--nproc_per_node $GPUS_PER_NODE
--nnodes $NUM_NODES
--master_addr $MASTER_ADDR
--master_port $MASTER_PORT
)
BERT_MODEL_ARGS=(
--num-layers 24
--hidden-size 1024
--num-attention-heads 16
--seq-length 512
--max-position-embeddings 512
--attention-backend auto # Can use (flash/fused/unfused/local)
)
TRAINING_ARGS=(
--micro-batch-size 4
--global-batch-size 32
--train-iters 1000000
--weight-decay 1e-2
--clip-grad 1.0
--fp16
--lr 0.0001
--lr-decay-iters 990000
--lr-decay-style linear
--min-lr 1.0e-5
--weight-decay 1e-2
--lr-warmup-fraction .01
--clip-grad 1.0
)
MODEL_PARALLEL_ARGS=(
--tensor-model-parallel-size 8
--pipeline-model-parallel-size 16
)
DATA_ARGS=(
--data-path $DATA_PATH
--vocab-file $VOCAB_FILE
--split 949,50,1
)
EVAL_AND_LOGGING_ARGS=(
--log-interval 100
--save-interval 10000
--eval-interval 1000
--save $CHECKPOINT_PATH
--load $CHECKPOINT_PATH
--eval-iters 10
--tensorboard-dir $TENSORBOARD_LOGS_PATH
)
torchrun ${DISTRIBUTED_ARGS[@]} pretrain_bert.py \
${BERT_MODEL_ARGS[@]} \
${TRAINING_ARGS[@]} \
${MODEL_PARALLEL_ARGS[@]} \
${DATA_ARGS[@]} \
${EVAL_AND_LOGGING_ARGS[@]}
================================================
FILE: examples/export/README.md
================================================
# Megatron Core Export
This module is used to export megatron core models to different inference frameworks.
Currently we support TRTLLM export . In the future we will be adding support for VLLM etc.
## PTQ AND EXPORT
Follow the examples of [Model Optimizer](../post_training/modelopt) to perform post training quantization, followed by an export to a HF-like checkpoint for TensorRT-LLM, vLLM, and SGLang deployment.
# TRTLLM EXPORT
Follow the instructions in [trtllm_export](./trtllm_export/) to do export to TRTLLM checkpoint format alone.
================================================
FILE: examples/export/trtllm_export/README.md
================================================
# Megatron Core To TRTLLM Export Documentation
This guide will walk you through how you can use the megatron core export for exporting models to trtllm format
### Contents
- [Megatron Core To TRTLLM Export Documentation](#megatron-core-to-trtllm-export-documentation)
- [Contents](#contents)
- [1. Quick Start](#1-quick-start)
- [1.1 Understanding The Code](#11-understanding-the-code)
- [1.2 Running The Code](#12-running-the-code)
- [2. GPU Export](#2-gpu-export)
- [3. Future work](#4-future-work)
#### 1. Quick Start
This will walk you through the flow of converting an mcore gpt model to trtllm format using single device mode. The file can be found at [gpt_single_device_cpu_export.py](./single_device_export/gpt_single_device_cpu_export.py)
NOTE: For faster performance, if your entire model will fit into gpu memory, pre transfer the model state dict to gpu and then call the get_trtllm_pretrained_config_and_model_weights function.
##### 1.1 Understanding The Code
***STEP 1 - We initialize model parallel and other default arguments***
We initalize tp and pp to 1 so that we can get the full model state dict on cpu
```python
initialize_distributed(tensor_model_parallel_size=1, pipeline_model_parallel_size=1)
```
***STEP 2 - We load the model using the model_provider_function***
NOTE: We create a simple gpt model
```python
transformer_config = TransformerConfig(
num_layers=2,
hidden_size=64, # Needs to be atleast 32 times num_attn_heads
num_attention_heads=2,
use_cpu_initialization=True,
pipeline_dtype=torch.float32,
)
gpt_model = GPTModel(
config=transformer_config,
transformer_layer_spec=get_gpt_layer_local_spec(),
vocab_size=100,
max_sequence_length=_SEQUENCE_LENGTH,
)
# Optionally you can also load a model using this code
# sharded_state_dict=gpt_model.sharded_state_dict(prefix='')
# checkpoint = dist_checkpointing.load(sharded_state_dict=sharded_state_dict, checkpoint_dir=checkpoint_path)
# gpt_model.load_state_dict(checkpoint)
```
***STEP 3 - Instantiate the TRTLLM Helper***
We instantiate the [TRTLLM Helper](../../../megatron/core/export/trtllm/trtllm_helper.py) For the GPT model we instantiate trtllm_helper as shown below.
```python
if hasattr(gpt_model, "rotary_pos_emb"):
seq_len_interpolation_factor = gpt_model.rotary_pos_emb.seq_len_interpolation_factor
trtllm_helper = TRTLLMHelper(
transformer_config=gpt_model.config,
model_type=ModelType.gpt,
position_embedding_type = gpt_model.position_embedding_type,
max_position_embeddings = gpt_model.max_position_embeddings,
rotary_percentage = gpt_model.rotary_percent,
rotary_base = gpt_model.rotary_base,
moe_tp_mode = 2,
multi_query_mode = False,
activation = "gelu",
seq_len_interpolation_factor = seq_len_interpolation_factor,
share_embeddings_and_output_weights=gpt_model.share_embeddings_and_output_weights
)
```
***STEP 4 - Get the TRTLLM Weights and configs***
To convert model weights to trtllm weights and configs, we use the [single_device_converter](../../../megatron/core/export/trtllm/trtllm_weights_converter/single_device_trtllm_model_weights_converter.py). We pass as inputs the model state dict, and export config. In this example we use inference tp size as 2 for the export.
```python
model_state_dict={}
for key , val in gpt_model.state_dict().items():
# val is non for _extra_state layers . We filter it out
if val is not None:
model_state_dict[key] = val
export_config = ExportConfig(inference_tp_size = 2)
weight_list, config_list = trtllm_helper.get_trtllm_pretrained_config_and_model_weights(
model_state_dict= model_state_dict,
dtype = DataType.bfloat16,
export_config=export_config
)
```
***STEP 5 - Build the TRTLLM Engine***
Following code is used to build the TRTLLM Engine.
```python
for trtllm_model_weights, trtllm_model_config in zip(weight_list, config_list):
trtllm_helper.build_and_save_engine(
max_input_len=256,
max_output_len=256,
max_batch_size=8,
engine_dir='/opt/megatron-lm/engine',
trtllm_model_weights=trtllm_model_weights,
trtllm_model_config=trtllm_model_config,
lora_ckpt_list=None,
use_lora_plugin=None,
max_lora_rank=64,
lora_target_modules=None,
max_prompt_embedding_table_size=0,
paged_kv_cache=True,
remove_input_padding=True,
paged_context_fmha=False,
use_refit=False,
max_num_tokens=None,
max_seq_len=512,
opt_num_tokens=None,
max_beam_width=1,
tokens_per_block=128,
multiple_profiles=False,
gpt_attention_plugin="auto",
gemm_plugin="auto",
)
```
##### 1.2 Running The Code
An example run script is shown below.
```
# In a workstation
MLM_PATH=/path/to/megatron-lm
CONTAINER_IMAGE=gitlab-master.nvidia.com:5005/dl/joc/nemo-ci/trtllm_0.12/train:pipe.17669124-x86
docker run -it --gpus=all --ipc=host -v $MLM_PATH/:/opt/megatron-lm $CONTAINER_IMAGE bash
# Inside the container run the following.
cd /opt/megatron-lm/
CUDA_VISIBLE_DEVICES=0 torchrun --nproc-per-node 1 examples/export/trtllm_export/single_device_export/gpt_single_device_cpu_export.py
```
#### 2. GPU Export
You can use the [gpt_distributed_gpu_export.py](./distributed_export/gpt_distributed_gpu_export.py) to run a more optimized on device distributed. version of trtllm export. Internally this uses the [distributed_converter](../../../megatron/core/export/trtllm/trtllm_weights_converter/distributed_trtllm_model_weights_converter.py) to convert model weights on device.
In the single device version you collect all the model weights on CPU/GPU, convert it to trtllm format, and then store the engine back on disk. In the GPU version you load each individual state dict on the gpus, convert it on the device itself and store the engine on disk.
To run the gpu version
```
CUDA_VISIBLE_DEVICES=0,1 torchrun --nproc-per-node 2 examples/export/trtllm_export/distributed_export/gpt_distributed_gpu_export.py
```
#### 3. Future work
The following are planned for the future releases .
* Pipeline parallellism for export (Work in progress)
* GPU Export for more models (Work in progress for some models)
* Refit functionality
* VLLM Support
================================================
FILE: examples/export/trtllm_export/distributed_export/gpt_distributed_gpu_export.py
================================================
import os
import torch
from megatron.core import parallel_state
from megatron.core import dist_checkpointing
from megatron.core.export.model_type import ModelType
from megatron.core.export.data_type import DataType
from megatron.core.export.trtllm.trtllm_helper import TRTLLMHelper
from megatron.core.tensor_parallel.random import model_parallel_cuda_manual_seed
from megatron.core.transformer.transformer_config import TransformerConfig
from megatron.core.models.gpt.gpt_model import GPTModel
from megatron.core.models.gpt.gpt_layer_specs import get_gpt_layer_local_spec
_SEQUENCE_LENGTH = 64
_VOCAB_SIZE = 256
def initialize_distributed(tensor_model_parallel_size=1, pipeline_model_parallel_size=1):
parallel_state.destroy_model_parallel()
# Torch setup for distributed training
rank = int(os.environ['LOCAL_RANK'])
world_size = torch.cuda.device_count()
torch.cuda.set_device(rank)
torch.distributed.init_process_group(world_size=world_size, rank=rank)
# Megatron core distributed training initialization
parallel_state.initialize_model_parallel(tensor_model_parallel_size = tensor_model_parallel_size, pipeline_model_parallel_size=pipeline_model_parallel_size)
def model_provider():
"""Build the model."""
transformer_config = TransformerConfig(
num_layers=2,
hidden_size=64,
num_attention_heads=2,
use_cpu_initialization=True,
pipeline_dtype=torch.float32
)
gpt_model = GPTModel(
config=transformer_config,
transformer_layer_spec=get_gpt_layer_local_spec(),
vocab_size=_VOCAB_SIZE,
max_sequence_length=_SEQUENCE_LENGTH,
)
return gpt_model
def load_distributed_checkpoint(checkpoint_path, gpt_model):
sharded_state_dict=gpt_model.sharded_state_dict(prefix='')
checkpoint = dist_checkpointing.load(sharded_state_dict=sharded_state_dict, checkpoint_dir=checkpoint_path)
gpt_model.load_state_dict(checkpoint)
return gpt_model
if __name__ == "__main__":
initialize_distributed(tensor_model_parallel_size=2, pipeline_model_parallel_size=1)
model_parallel_cuda_manual_seed(123)
gpt_model = model_provider()
device = torch.device("cuda")
gpt_model.to(device)
# Optionally you can also load a gpt model from ckpt_path using this code below
# gpt_model = load_distributed_checkpoint(gpt_model=gpt_model, checkpoint_path=ckpt_path)
seq_len_interpolation_factor = None
if hasattr(gpt_model, "rotary_pos_emb"):
seq_len_interpolation_factor = gpt_model.rotary_pos_emb.seq_len_interpolation_factor
trtllm_helper = TRTLLMHelper(
transformer_config=gpt_model.config,
model_type=ModelType.gpt,
position_embedding_type = gpt_model.position_embedding_type,
max_position_embeddings = gpt_model.max_position_embeddings,
rotary_percentage = gpt_model.rotary_percent,
rotary_base = gpt_model.rotary_base,
moe_tp_mode = 2,
multi_query_mode = False,
activation = "gelu",
seq_len_interpolation_factor = seq_len_interpolation_factor,
share_embeddings_and_output_weights=gpt_model.share_embeddings_and_output_weights
)
trtllm_model_weights, trtllm_model_config = trtllm_helper.get_trtllm_pretrained_config_and_model_weights(
model_state_dict= gpt_model.state_dict(),
dtype = DataType.bfloat16,
on_device_distributed_conversion=True,
vocab_size=_VOCAB_SIZE,
gpus_per_node=2,
)
trtllm_helper.build_and_save_engine(
max_input_len=256,
max_output_len=256,
max_batch_size=8,
engine_dir='/opt/megatron-lm/engine',
trtllm_model_weights=trtllm_model_weights[0],
trtllm_model_config=trtllm_model_config[0],
lora_ckpt_list=None,
use_lora_plugin=None,
max_lora_rank=64,
lora_target_modules=None,
max_prompt_embedding_table_size=0,
paged_kv_cache=True,
remove_input_padding=True,
paged_context_fmha=False,
use_refit=False,
max_num_tokens=None,
max_seq_len=512,
opt_num_tokens=None,
max_beam_width=1,
tokens_per_block=128,
multiple_profiles=False,
gpt_attention_plugin="auto",
gemm_plugin="auto",
)
================================================
FILE: examples/export/trtllm_export/single_device_export/gpt_single_device_cpu_export.py
================================================
import os
import torch
from megatron.core import parallel_state
from megatron.core import dist_checkpointing
from megatron.core.export.model_type import ModelType
from megatron.core.export.data_type import DataType
from megatron.core.export.export_config import ExportConfig
from megatron.core.export.trtllm.trtllm_helper import TRTLLMHelper
from megatron.core.tensor_parallel.random import model_parallel_cuda_manual_seed
from megatron.core.transformer.transformer_config import TransformerConfig
from megatron.core.models.gpt.gpt_model import GPTModel
from megatron.core.models.gpt.gpt_layer_specs import get_gpt_layer_local_spec
_SEQUENCE_LENGTH = 64
def initialize_distributed(tensor_model_parallel_size=1, pipeline_model_parallel_size=1):
parallel_state.destroy_model_parallel()
# Torch setup for distributed training
rank = int(os.environ['LOCAL_RANK'])
world_size = torch.cuda.device_count()
torch.cuda.set_device(rank)
torch.distributed.init_process_group(world_size=world_size, rank=rank)
# Megatron core distributed training initialization
parallel_state.initialize_model_parallel(tensor_model_parallel_size, pipeline_model_parallel_size)
def model_provider():
"""Build the model."""
transformer_config = TransformerConfig(
num_layers=2,
hidden_size=64, # Needs to be atleast 32 times num_attn_heads
num_attention_heads=2,
use_cpu_initialization=True,
pipeline_dtype=torch.float32,
)
gpt_model = GPTModel(
config=transformer_config,
transformer_layer_spec=get_gpt_layer_local_spec(),
vocab_size=100,
max_sequence_length=_SEQUENCE_LENGTH,
)
return gpt_model
def load_distributed_checkpoint(checkpoint_path, gpt_model):
sharded_state_dict=gpt_model.sharded_state_dict(prefix='')
checkpoint = dist_checkpointing.load(sharded_state_dict=sharded_state_dict, checkpoint_dir=checkpoint_path)
gpt_model.load_state_dict(checkpoint)
return gpt_model
if __name__ == "__main__":
# Need to use TP1 PP1 for export on single device
initialize_distributed(tensor_model_parallel_size=1, pipeline_model_parallel_size=1)
model_parallel_cuda_manual_seed(123)
gpt_model = model_provider()
# Optionally you can also load a gpt model from ckpt_path using this code below
# gpt_model = load_distributed_checkpoint(gpt_model=gpt_model, checkpoint_path=ckpt_path)
seq_len_interpolation_factor = None
if hasattr(gpt_model, "rotary_pos_emb"):
seq_len_interpolation_factor = gpt_model.rotary_pos_emb.seq_len_interpolation_factor
trtllm_helper = TRTLLMHelper(
transformer_config=gpt_model.config,
model_type=ModelType.gpt,
position_embedding_type = gpt_model.position_embedding_type,
max_position_embeddings = gpt_model.max_position_embeddings,
rotary_percentage = gpt_model.rotary_percent,
rotary_base = gpt_model.rotary_base,
moe_tp_mode = 2,
multi_query_mode = False,
activation = "gelu",
seq_len_interpolation_factor = seq_len_interpolation_factor,
share_embeddings_and_output_weights=gpt_model.share_embeddings_and_output_weights
)
export_config = ExportConfig(inference_tp_size = 2)
# NOTE : For faster performance, if your entire model will fit in gpu memory, transfer model state dict to GPU and then call this api
weight_list, config_list = trtllm_helper.get_trtllm_pretrained_config_and_model_weights(
model_state_dict= gpt_model.state_dict(),
dtype = DataType.bfloat16,
export_config=export_config
)
for trtllm_model_weights, trtllm_model_config in zip(weight_list, config_list):
trtllm_helper.build_and_save_engine(
max_input_len=256,
max_output_len=256,
max_batch_size=8,
engine_dir='/opt/megatron-lm/engine',
trtllm_model_weights=trtllm_model_weights,
trtllm_model_config=trtllm_model_config,
lora_ckpt_list=None,
use_lora_plugin=None,
max_lora_rank=64,
lora_target_modules=None,
max_prompt_embedding_table_size=0,
paged_kv_cache=True,
remove_input_padding=True,
paged_context_fmha=False,
use_refit=False,
max_num_tokens=None,
max_seq_len=512,
opt_num_tokens=None,
max_beam_width=1,
tokens_per_block=128,
multiple_profiles=False,
gpt_attention_plugin="auto",
gemm_plugin="auto",
)
================================================
FILE: examples/gpt3/README.md
================================================
# GPT3 MODEL
## Table of contents
- [1. Training Setup](#1-training-setup)
- [2. Configurations](#2-configurations)
- [3. Training Results](#3-training-results)
## 1. Training setup
To run the model using a docker container run it as follows
```
PYTORCH_IMAGE=nvcr.io/nvidia/pytorch:24.01-py3
CHECKPOINT_PATH="" #
TENSORBOARD_LOGS_PATH=""#
VOCAB_FILE="" #/gpt2-vocab.json
MERGE_FILE="" #/gpt2-merges.txt
DATA_PATH="" #_text_document
docker run \
--gpus=all \
--ipc=host \
--workdir /workspace/megatron-lm \
-v /path/to/data:/path/to/data \
-v /path/to/megatron-lm:/workspace/megatron-lm \
megatron-lm nvcr.io/nvidia/pytorch:24.01-py3 \
bash examples/gpt3/train_gpt3_175b_distributed.sh $CHECKPOINT_PATH $TENSORBOARD_LOGS_PATH $VOCAB_FILE $MERGE_FILE $DATA_PATH "
```
NOTE: Depending on the environment you are running it the above command might like slightly different.
## 2. Configurations
The example in this folder shows you how to run 175B model. There are other configs you could run as well
### 345M
```
--num-layers 12 \
--hidden-size 512 \
--num-attention-heads 8 \
--seq-length 1024 \
--tensor-model-parallel-size 1 \
--pipeline-model-parallel-size 1 \
```
### 857M
```
--num-layers 24 \
--hidden-size 1024 \
--num-attention-heads 16 \
--seq-length 2048 \
--tensor-model-parallel-size 1 \
--pipeline-model-parallel-size 1 \
```
================================================
FILE: examples/gpt3/gpt_config.yaml
================================================
# WARNING: Yaml configs is currently an experimental feature
language_model:
# model architecture
num_layers: 24
hidden_size: 1024
num_attention_heads: 16
num_query_groups: null
ffn_hidden_size: null
kv_channels: null
hidden_dropout: 0.0
attention_dropout: 0.0
fp32_residual_connection: False
apply_residual_connection_post_layernorm: False
layernorm_epsilon: 1.e-5
layernorm_zero_centered_gamma: True
add_bias_linear: False
bias_activation_fusion: False
add_qkv_bias: False
gated_linear_unit: False
activation_func: swiglu
num_moe_experts: null
rotary_interleaved: False
window_size: null
# initialization
init_method: null
init_method_std: 0.02
output_layer_init_method: null
# mixed-precision
apply_query_key_layer_scaling: False
attention_softmax_in_fp32: False
# fusion
bias_swiglu_fusion: True
masked_softmax_fusion: True
persist_layer_norm: False
memory_efficient_layer_norm: False
bias_dropout_fusion: True
apply_rope_fusion: True
# activation recomputation
recompute_granularity: null
recompute_method: null
recompute_num_layers: null
distribute_saved_activations: null
# fp8 related
fp8: null
fp8_margin: 0
fp8_interval: 1
fp8_amax_history_len: 1
fp8_amax_compute_algo: "most_recent"
fp8_wgrad: True
# miscellaneous
clone_scatter_output_in_embedding: True
normalization: "LayerNorm" # alt value supported by TE: "RMSNorm"
# MoE related
moe_router_load_balancing_type: "aux_loss"
moe_router_topk: 2
moe_router_group_topk: null
moe_router_num_groups: null
moe_grouped_gemm: False
moe_aux_loss_coeff: 0 # 1e-2 would be a good start value for load balance loss.
moe_z_loss_coeff: null # 1e-3 would be a good start value for z-loss
moe_input_jitter_eps: null
moe_token_dropping: False
model_parallel:
# Model parallelism
tensor_model_parallel_size: 1
context_parallel_size: 1
pipeline_model_parallel_size: 1
virtual_pipeline_model_parallel_size: null
sequence_parallel: True
expert_model_parallel_size: 1
# Initialization
perform_initialization: True
use_cpu_initialization: null
# Training
fp16: False
bf16: True
params_dtype: null # Set from above arguments for core
timers: null
# Optimizations
gradient_accumulation_fusion: True
tp_comm_overlap: False
# Debug Options
tp_comm_split_ag: True
tp_comm_atomic_ag: True
tp_comm_split_rs: True
tp_comm_atomic_rs: True
tp_comm_bulk_wgrad: True
tp_comm_bulk_dgrad: True
# Parallelism
finalize_model_grads_func: null
# Pipeline Parallel
pipeline_dtype: null
grad_scale_func: null
enable_autocast: False
autocast_dtype: null
variable_seq_lengths: False
num_microbatches_with_partial_activation_checkpoints: null
overlap_p2p_comm: False
batch_p2p_comm: True
batch_p2p_sync: True
use_ring_exchange_p2p: False
deallocate_pipeline_outputs: False
no_sync_func: null
grad_sync_func: null
param_sync_func: null
# CPU Offloading
cpu_offloading: False
cpu_offloading_num_layers: 0
_cpu_offloading_context: null
cpu_offloading_weights: False
cpu_offloading_activations: True
# Timing
barrier_with_L1_time: True
# training:
use_legacy_models: False
spec: null
micro_batch_size: 2
global_batch_size: 128
rampup_batch_size: [32, 32, 65324160]
check_for_nan_in_loss_and_grad: True
num_layers_per_virtual_pipeline_stage: null
encoder_num_layers: null
decoder_num_layers: null
rotary_seq_len_interpolation_factor: null
add_position_embedding: False
make_vocab_size_divisible_by: 128
group_query_attention: False
exit_signal_handler: False
exit_duration_in_mins: null
exit_interval: null
untie_embeddings_and_output_weights: True
position_embedding_type: rope
rotary_percent: 0.5
openai_gelu: False
squared_relu: False
swiglu: True
onnx_safe: null
bert_binary_head: True
max_position_embeddings: 4096
transformer_impl: local
use_flash_attn: False
seed: 1234
data_parallel_random_init: False
# Optimizer
optimizer: adam
lr: 2.5e-4
lr_decay_style: cosine
lr_decay_iters: null
lr_decay_samples: 255126953
lr_warmup_fraction: null
lr_warmup_iters: 0
lr_warmup_samples: 81381
lr_warmup_init: 0.0
min_lr: 2.5e-5
weight_decay: 0.1
start_weight_decay: null
end_weight_decay: null
weight_decay_incr_style: constant
clip_grad: 1.0
adam_beta1: 0.9
adam_beta2: 0.95
adam_eps: 1.e-08
sgd_momentum: 0.9
override_opt_param_scheduler: False
use_checkpoint_opt_param_scheduler: False
# checkpointing arguments
save: null
save_interval: 20000
no_save_optim: null
no_save_rng: null
load: null
no_load_optim: null
no_load_rng: null
finetune: False
use_checkpoint_args: False
exit_on_missing_checkpoint: False
# loss arguments
loss_scale: null
initial_loss_scale: 4294967296
min_loss_scale: 1.0
loss_scale_window: 1000
hysteresis: 2
accumulate_allreduce_grads_in_fp32: False
fp16_lm_cross_entropy: False
# distributed arguments
distributed_backend: nccl
distributed_timeout_minutes: 10
overlap_grad_reduce: False
align_grad_reduce: True
overlap_param_gather: False
align_param_gather: False
scatter_gather_tensors_in_pipeline: True
local_rank: null
lazy_mpu_init: null
empty_unused_memory_level: 0
standalone_embedding_stage: False
use_distributed_optimizer: False
nccl_communicator_config_path: null
train_iters: null
eval_iters: 32
eval_interval: 2000
skip_train: False
adlr_autoresume: False
adlr_autoresume_interval: 1000
# garbage collection
manual_gc: False
manual_gc_interval: 0
manual_gc_eval: True
tp_comm_overlap_cfg: null
#data
data_path: null
split: '99,1,0'
train_data_path: null
valid_data_path: null
test_data_path: null
data_cache_path: null
mock_data: False
vocab_size: null
vocab_file: null
merge_file: null
vocab_extra_ids: 0
seq_length: 4096
encoder_seq_length: null
decoder_seq_length: null
sample_rate: 1.0
mask_prob: 0.15
short_seq_prob: 0.1
num_workers: 2
tokenizer_type: GPTSentencePieceTokenizer
tokenizer_model: null
reset_position_ids: False
reset_attention_mask: False
eod_mask_loss: False
train_samples: 268554688
dataloader_type: null
#profile:
profile: False
profile_ranks: [0]
profile_step_end: 12
profile_step_start: 10
#logging:
log_params_norm: True
log_num_zeros_in_grad: True
log_throughput: False
log_progress: False
timing_log_level: 0
timing_log_option: minmax
tensorboard_log_interval: 1
tensorboard_queue_size: 1000
log_timers_to_tensorboard: False
log_validation_ppl_to_tensorboard: False
log_memory_to_tensorboard: False
log_world_size_to_tensorboard: False
log_loss_scale_to_tensorboard: True
wandb_project: ''
wandb_exp_name: ''
wandb_save_dir: ''
enable_one_logger: True
one_logger_project: megatron-lm
one_logger_run_name: null
log_interval: 100
tensorboard_dir: null
================================================
FILE: examples/gpt3/train_gpt3_175b_distributed.sh
================================================
#!/bin/bash
# Runs the "175B" parameter model
export CUDA_DEVICE_MAX_CONNECTIONS=1
GPUS_PER_NODE=8
# Change for multinode config
MASTER_ADDR=localhost
MASTER_PORT=6000
NUM_NODES=1
NODE_RANK=0
WORLD_SIZE=$(($GPUS_PER_NODE*$NUM_NODES))
CHECKPOINT_PATH=$1 #
TENSORBOARD_LOGS_PATH=$2 #
VOCAB_FILE=$3 #/gpt2-vocab.json
MERGE_FILE=$4 #/gpt2-merges.txt
DATA_PATH=$5 #_text_document
DISTRIBUTED_ARGS=(
--nproc_per_node $GPUS_PER_NODE
--nnodes $NUM_NODES
--master_addr $MASTER_ADDR
--master_port $MASTER_PORT
)
GPT_MODEL_ARGS=(
--num-layers 96
--hidden-size 12288
--num-attention-heads 96
--seq-length 2048
--max-position-embeddings 2048
--attention-backend auto # Can use (flash/fused/unfused/local)
)
TRAINING_ARGS=(
--micro-batch-size 1
--global-batch-size 1536
--rampup-batch-size 16 16 5859375
--train-iters 500000
--weight-decay 0.1
--adam-beta1 0.9
--adam-beta2 0.95
--init-method-std 0.006
--clip-grad 1.0
--fp16
--lr 6.0e-5
--lr-decay-style cosine
--min-lr 6.0e-6
--lr-warmup-fraction .001
--lr-decay-iters 430000
)
MODEL_PARALLEL_ARGS=(
--tensor-model-parallel-size 8
--pipeline-model-parallel-size 16
)
DATA_ARGS=(
--data-path $DATA_PATH
--vocab-file $VOCAB_FILE
--merge-file $MERGE_FILE
--split 949,50,1
)
EVAL_AND_LOGGING_ARGS=(
--log-interval 100
--save-interval 10000
--eval-interval 1000
--save $CHECKPOINT_PATH
--load $CHECKPOINT_PATH
--eval-iters 10
--tensorboard-dir $TENSORBOARD_LOGS_PATH
)
torchrun ${DISTRIBUTED_ARGS[@]} pretrain_gpt.py \
${GPT_MODEL_ARGS[@]} \
${TRAINING_ARGS[@]} \
${MODEL_PARALLEL_ARGS[@]} \
${DATA_ARGS[@]} \
${EVAL_AND_LOGGING_ARGS[@]}
================================================
FILE: examples/gptoss/01_convert_from_hf.py
================================================
# Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
"""Convert HuggingFace checkpoints to Megatron format."""
import os
import argparse
from megatron.bridge import AutoBridge
def _parse_args():
parser = argparse.ArgumentParser(description="Convert HF LLMs to Megatron format")
parser.add_argument(
"--hf-model",
type=str,
required=True,
help="HuggingFace model identifier or path",
)
parser.add_argument(
"--save-path",
type=str,
default=None,
help="Path to save the converted Megatron checkpoint",
)
parser.add_argument('--local-rank', '--local_rank', type=int, default=0)
return parser.parse_args()
if __name__ == "__main__":
args = _parse_args()
HF_MODEL = args.hf_model
SAVE_PATH = args.save_path
WORLD_SIZE = int(os.environ.get("WORLD_SIZE", 1))
if SAVE_PATH is None:
SAVE_PATH = f"./megatron_checkpoints/{HF_MODEL.replace('/', '_')}"
print(f"Converting {HF_MODEL} to Megatron format...")
print(f"Save path: {SAVE_PATH}")
bridge = AutoBridge.from_hf_pretrained(HF_MODEL, trust_remote_code=True)
provider = bridge.to_megatron_provider()
# Update these configs as needed
provider.expert_tensor_parallel_size = 1
provider.tensor_model_parallel_size = 1
provider.pipeline_model_parallel_size = WORLD_SIZE
provider.finalize()
model = provider.provide_distributed_model(wrap_with_ddp=False)
bridge.save_megatron_model(
model,
SAVE_PATH,
hf_tokenizer_path=HF_MODEL
)
print(f"Saved Megatron checkpoint to {SAVE_PATH}")
================================================
FILE: examples/gptoss/02_train.sh
================================================
#!/bin/bash
export CUDA_DEVICE_MAX_CONNECTIONS=${CUDA_DEVICE_MAX_CONNECTIONS:-1}
# Setup arguments with defaults
CHECKPOINT_PATH="NO_VALUE_PROVIDED"
TENSORBOARD_LOGS_PATH="./tensorboard_logs/"
TOKENIZER_ARG="MOCK"
DATA_ARG="MOCK"
DISTRIBUTED_CONFIG_FILE=""
# Parse command line arguments
while [[ $# -gt 0 ]]; do
case $1 in
--checkpoint-path)
CHECKPOINT_PATH="$2"
shift 2
;;
--tensorboard-logs-path)
TENSORBOARD_LOGS_PATH="$2"
shift 2
;;
--tokenizer)
TOKENIZER_ARG="$2"
shift 2
;;
--data)
DATA_ARG="$2"
shift 2
;;
--distributed-config-file)
DISTRIBUTED_CONFIG_FILE="$2"
shift 2
;;
-h|--help)
echo "Usage: $0 [OPTIONS]"
echo "Options:"
echo " --checkpoint-path PATH Path to Megatron checkpoint"
echo " --tensorboard-logs-path PATH Path to TensorBoard logs"
echo " --tokenizer PATH|MOCK Path to tokenizer model, or 'MOCK' (default: MOCK)"
echo " --data PATH|MOCK Data prefix, or 'MOCK' (default: MOCK)"
echo " --distributed-config-file FILE Path to distributed training config file"
echo " -h, --help Show this help message"
exit 0
;;
*)
echo "Unknown option: $1"
echo "Use --help for usage information"
exit 1
;;
esac
done
# Check if checkpoint path exists
if [ ! -d "$CHECKPOINT_PATH" ]; then
echo "Error: Checkpoint path does not exist: $CHECKPOINT_PATH"
exit 1
fi
echo "Checkpoint path exists: $CHECKPOINT_PATH"
# Check if tensorboard logs path exists
if [ ! -d "$TENSORBOARD_LOGS_PATH" ]; then
echo "Warning: TensorBoard logs path does not exist. Creating: $TENSORBOARD_LOGS_PATH"
mkdir -p "$TENSORBOARD_LOGS_PATH"
fi
echo "TensorBoard logs path exists: $TENSORBOARD_LOGS_PATH"
# NOTE: by default we use 8 GPUs
# These values will be over-written below with environmental variables
GPUS_PER_NODE=8
NUM_NODES=1
MASTER_ADDR="localhost"
MASTER_PORT=6000
NODE_RANK=0
# Load distributed config from file if provided
if [ -n "$DISTRIBUTED_CONFIG_FILE" ]; then
if [ ! -f "$DISTRIBUTED_CONFIG_FILE" ]; then
echo "Warning: Distributed config file does not exist: $DISTRIBUTED_CONFIG_FILE"
echo "Continuing with default distributed training settings."
else
echo "Loading distributed config from: $DISTRIBUTED_CONFIG_FILE"
source "$DISTRIBUTED_CONFIG_FILE"
fi
fi
# Override with environment variables if set
GPUS_PER_NODE=${GPUS_PER_NODE:-8}
NUM_NODES=${NUM_NODES:-1}
MASTER_ADDR=${MASTER_ADDR:-localhost}
MASTER_PORT=${MASTER_PORT:-6000}
NODE_RANK=${NODE_RANK:-0}
WORLD_SIZE=$(($GPUS_PER_NODE*$NUM_NODES))
# Path to the pretrain_gpt.py script, assuming this script is run from the root of the Megatron-LM repository
PRETRAIN_SCRIPT_PATH="pretrain_gpt.py"
# Data cache path (useful for both mock and real data)
DATA_CACHE_PATH="${PWD}/benchmark_cache_gpt_oss_20b"
mkdir -p "$DATA_CACHE_PATH"
DISTRIBUTED_ARGS=(
--nproc_per_node $GPUS_PER_NODE
--nnodes $NUM_NODES
--master_addr $MASTER_ADDR
--master_port $MASTER_PORT
--node_rank $NODE_RANK
)
# NOTE: we only set pipeline parallelism to be the number of GPUs
# Adjust each value based on your setup.
TP_SIZE=1
EP_SIZE=1
PP_SIZE=${WORLD_SIZE}
MICRO_BATCH_SIZE=1
GLOBAL_BATCH_SIZE=128
NUM_LAYERS=12
DTYPE="fp8"
SEQ_LENGTH=8192
MAX_POSITION_EMBEDDINGS=8192
TRAIN_SAMPLES=1953125000
LR_DECAY_SAMPLES=1949218748
MODEL_ARGS=(
--no-masked-softmax-fusion
--transformer-impl transformer_engine
--disable-bias-linear
--untie-embeddings-and-output-weights
--no-rope-fusion
--normalization RMSNorm
--num-layers ${NUM_LAYERS}
--hidden-size 512
--ffn-hidden-size 2048
--num-attention-heads 64
--group-query-attention
--num-query-groups 8
--seq-length ${SEQ_LENGTH}
--max-position-embeddings ${MAX_POSITION_EMBEDDINGS}
--use-mcore-models
--rotary-percent 1.0
--rope-type rope
--position-embedding-type rope
--rotary-base 10000
--no-bias-gelu-fusion
--export-force-local-attention
--no-bias-dropout-fusion
--quick-geglu
--glu-linear-offset 1.0
--softmax-type learnable
--window-attn-skip-freq 2
--activation-func-clamp-value 7.0
--window-size 127,0
--enable-gpt-oss
)
MOE_ARGS=(
--num-experts 4
--moe-router-topk 2
--moe-router-load-balancing-type aux_loss
--moe-aux-loss-coeff 1e-3
--moe-grouped-gemm
--moe-token-dispatcher-type alltoall
--overlap-param-gather
--overlap-grad-reduce
--moe-ffn-hidden-size 2048
--moe-router-dtype fp32
--moe-z-loss-coeff 1e-3
--moe-permute-fusion
)
DATA_ARGS_LIST=()
if [[ "$TOKENIZER_ARG" == "MOCK" ]] || [[ "$DATA_ARG" == "MOCK" ]] || [[ -z "$TOKENIZER_ARG" ]]; then
DATA_ARGS_LIST+=(
"--mock-data"
"--tokenizer-type NullTokenizer"
"--vocab-size 128256"
"--data-cache-path ${DATA_CACHE_PATH}"
"--tiktoken-pattern v2"
"--split '99,1,0'"
"--no-create-attention-mask-in-dataloader"
"--no-mmap-bin-files"
"--num-workers 1"
)
else
# Settings for real data
DATA_ARGS_LIST+=(
"--data-path $DATA_ARG"
"--tokenizer-type HuggingFaceTokenizer"
"--tokenizer-model $TOKENIZER_ARG"
"--data-cache-path ${DATA_CACHE_PATH}"
"--split '99,1,0'"
"--no-create-attention-mask-in-dataloader"
"--no-mmap-bin-files"
"--num-workers 1"
# Note: --vocab-size might be inferred by HuggingFaceTokenizer or might need to be explicit.
"--vocab-size 128256"
)
fi
TRAINING_ARGS=(
--micro-batch-size ${MICRO_BATCH_SIZE}
--global-batch-size ${GLOBAL_BATCH_SIZE}
--lr 1.0e-5
--train-samples ${TRAIN_SAMPLES}
--lr-decay-samples ${LR_DECAY_SAMPLES}
--lr-decay-style cosine
--min-lr 1.0e-6
--weight-decay 0.1
--lr-warmup-fraction 0.05
--clip-grad 1.0
--bf16
--use-flash-attn
--attention-softmax-in-fp32
--accumulate-allreduce-grads-in-fp32
--disable-bf16-reduced-precision-matmul
--recompute-activations
)
MODEL_PARALLEL_ARGS=(
--tensor-model-parallel-size ${TP_SIZE}
--pipeline-model-parallel-size ${PP_SIZE}
--expert-model-parallel-size ${EP_SIZE}
--sequence-parallel
--context-parallel-size 1
--use-distributed-optimizer
--fp8-format hybrid
--fp8-param-gather
--fp8-amax-compute-algo max
--fp8-amax-history-len 1024
)
LOGGING_ARGS=(
--log-interval 1
--save-interval 10000
--eval-interval 50000000
--eval-iters 0
--save $CHECKPOINT_PATH
--tensorboard-dir "${CHECKPOINT_PATH}/tensorboard"
--moe-per-layer-logging
--no-load-optim
--no-load-rng
--log-throughput
)
# Ensure pretrain_gpt.py is found
if [ ! -f "$PRETRAIN_SCRIPT_PATH" ]; then
echo "Error: pretrain_gpt.py not found at $PRETRAIN_SCRIPT_PATH"
echo "Please ensure you are running this script from the root of the Megatron-LM repository, and pretrain_gpt.py is present."
exit 1
fi
python -m torch.distributed.run ${DISTRIBUTED_ARGS[@]} ${PRETRAIN_SCRIPT_PATH} \
${MODEL_ARGS[@]} \
${MOE_ARGS[@]} \
${DATA_ARGS_LIST[@]} \
${TRAINING_ARGS[@]} \
${MODEL_PARALLEL_ARGS[@]} \
${LOGGING_ARGS[@]}
================================================
FILE: examples/gptoss/03_convert_to_hf.py
================================================
# Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
"""Convert HuggingFace checkpoints to Megatron format."""
import os
import argparse
from megatron.bridge import AutoBridge
def _parse_args():
parser = argparse.ArgumentParser(description="Convert Megatron LLMs to HuggingFace format")
parser.add_argument(
"--hf-model",
type=str,
required=True,
help="HuggingFace model identifier or path to load config from",
)
parser.add_argument(
"--megatron-model",
type=str,
required=True,
help="Megatron model identifier or path",
)
parser.add_argument(
"--save-path",
type=str,
default=None,
help="Path to save the converted HuggingFace checkpoint",
)
parser.add_argument('--local-rank', '--local_rank', type=int, default=0)
return parser.parse_args()
if __name__ == "__main__":
args = _parse_args()
HF_MODEL = args.hf_model
MEGATRON_MODEL = args.megatron_model
SAVE_PATH = args.save_path
WORLD_SIZE = int(os.environ.get("WORLD_SIZE", 1))
if SAVE_PATH is None:
SAVE_PATH = f"./huggingface_checkpoints/{MEGATRON_MODEL.replace('/', '_')}"
print(f"Converting {MEGATRON_MODEL} to HuggingFace {HF_MODEL} format...")
print(f"Save path: {SAVE_PATH}")
bridge = AutoBridge.from_hf_pretrained(HF_MODEL, trust_remote_code=True)
bridge.export_ckpt(
MEGATRON_MODEL,
SAVE_PATH,
)
print(f"Saved HuggingFace checkpoint to {SAVE_PATH}")
================================================
FILE: examples/gptoss/README.md
================================================
# GPT-OSS Training Tutorial
## Step 0: Install Dependencies
### Using Megatron Bridge
[Megatron-Bridge](https://github.com/NVIDIA-NeMo/Megatron-Bridge)
Megatron Bridge provides a quick and convenient way to convert HuggingFace checkpoints to the Megatron format used by Megatron-LM. Follow the instructions in the [Megatron-Bridge Installation](https://github.com/NVIDIA-NeMo/Megatron-Bridge/blob/main/README.md#-installation) to run the nemo docker container and convert checkpoints (via mounted volumes - make sure that the huggingface cache location AND the megatron checkpoint locations are properly mounted, otherwise you may not be saving the converted model to disk correctly).
Below is an example of how to use Megatron-Bridge inside the pytorch container to convert a HuggingFace model checkpoint to Megatron format.
Reference: [Megatron-Bridge Dockerfile](https://github.com/NVIDIA-NeMo/Megatron-Bridge/blob/main/docker/Dockerfile.ci)
Inside the [pytorch container](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch) run the following commands to install Megatron-Bridge:
```bash
cd /opt
git clone --recursive https://github.com/NVIDIA-NeMo/Megatron-Bridge.git
cd Megatron-Bridge
# Make sure submodules are initialized (for 3rdparty/Megatron-LM)
git submodule update --init --recursive
export PATH="/root/.local/bin:$PATH"
export UV_PROJECT_ENVIRONMENT=/opt/venv
export VIRTUAL_ENV=/opt/venv
export PATH="$UV_PROJECT_ENVIRONMENT/bin:$PATH"
export UV_LINK_MODE=copy
export UV_VERSION="0.7.2"
# Install UV
curl -LsSf https://astral.sh/uv/${UV_VERSION}/install.sh | sh
# Create virtual environment and build the package
uv venv ${UV_PROJECT_ENVIRONMENT} --system-site-packages
uv sync --locked --only-group build
uv sync --locked --link-mode copy --all-extras --all-groups
uv pip install --no-deps -e .
source ${UV_PROJECT_ENVIRONMENT}/bin/activate
```
### Setup Environment
```bash
export HOST_MEGATRON_LM_DIR="/path/to/your/host/megatron-lm"
git clone https://github.com/NVIDIA/Megatron-LM.git "$HOST_MEGATRON_LM_DIR"
cd "$HOST_MEGATRON_LM_DIR"
```
```bash
export HF_TOKEN={your_hf_token_here}
```
## Step 1: Convert HuggingFace to Megatron (Optional - skip if you already have a Megatron checkpoint)
Set `--nproc-per-node` to be the number of GPUs per node. Set `hf_model_name` to be the Huggingface model e.g. `openai/gpt-oss-20b`
```bash
python3 -m torch.distributed.launch --nproc-per-node=8 examples/gptoss/01_convert_from_hf.py --hf-model openai/gpt-oss-20b
```
## Step 2: Train from Scratch
To train from scratch first follow the steps below to setup the environment appropriately before running the training script in docker. Even though we are running the same container as before, it is better to restart the container to ensure a clean environment and that all environment and docker variables are set correctly. For the following example we used 8x GB300, but you should change the number of GPUs and nodes as needed.
### Setup Environment
```bash
# Change these based on model and directory from previous conversion step
export MODEL_DIR_NAME="openai_gpt-oss_20b"
export HOST_CHECKPOINT_PATH="./megatron_checkpoints/${MODEL_DIR_NAME}"
export HOST_TENSORBOARD_LOGS_PATH="./tensorboard_logs/${MODEL_DIR_NAME}"
```
By default we will use mock data to train the model in the example below. To use your own data, set the following environment variables:
```bash
# Optional: For real data
export HOST_TOKENIZER_MODEL_PATH="/path/to/host/tokenizer.model"
export HOST_DATA_PREFIX="/path/to/host/mydata_prefix"
```
### Setup Training Configurations
Run the following to create a `distributed_config.env` file with the appropriate distributed training configurations. Change the values as needed for your setup. This file will override the default values in `02_train.sh`.
```bash
cat > ./distributed_config.env << 'EOF'
GPUS_PER_NODE=8
NUM_NODES=1
MASTER_ADDR=localhost
MASTER_PORT=6000
NODE_RANK=0
EOF
```
### Run Container with Mounted Volumes
**NOTE:** This container runs the example training script `02_train.sh` located in the `examples/gptoss` directory. By default, we have only set pipeline parallelism to be the number of GPUs. Adjust TP_SIZE, EP_SIZE, PP_SIZE, etc. in `02_train.sh`. You can also adjust modify `--hidden-size`, `--ffn-hidden-size`, `--num-attention-heads`, `NUM_LAYERS`, etc.
To train using mock data, run the following command:
```bash
PYTORCH_IMAGE="nvcr.io/nvidia/pytorch:25.12-py3"
docker run --rm --gpus all --ipc=host --ulimit memlock=-1 \
-v "${HOST_MEGATRON_LM_DIR}:/workspace/megatron-lm" \
-v "${HOST_CHECKPOINT_PATH}:/workspace/checkpoints" \
-v "${HOST_TENSORBOARD_LOGS_PATH}:/workspace/tensorboard_logs" \
-v "./distributed_config.env:/workspace/megatron-lm/examples/gptoss/distributed_config.env" \
--workdir /workspace/megatron-lm \
$PYTORCH_IMAGE \
bash examples/gptoss/02_train.sh \
--checkpoint-path /workspace/checkpoints \
--tensorboard-logs-path /workspace/tensorboard_logs \
--distributed-config-file /workspace/megatron-lm/examples/gptoss/distributed_config.env \
2>&1 | tee "${HOST_TENSORBOARD_LOGS_PATH}/training_mock_$(date +'%y-%m-%d_%H-%M-%S').log"
```
**Note:** If you run into issues generating mock data one solution might be to reduce the number of GPUs to 1 and try to generate the data again.
If using real data with with the `HOST_TOKENIZER_MODEL_PATH` and `HOST_DATA_PREFIX` environment variables set, run the following command instead:
```bash
PYTORCH_IMAGE="nvcr.io/nvidia/pytorch:25.12-py3"
docker run --rm --gpus all --ipc=host --ulimit memlock=-1 \
-v "${HOST_MEGATRON_LM_DIR}:/workspace/megatron-lm" \
-v "${HOST_CHECKPOINT_PATH}:/workspace/checkpoints" \
-v "${HOST_TENSORBOARD_LOGS_PATH}:/workspace/tensorboard_logs" \
-v "${HOST_TOKENIZER_MODEL_PATH}:/workspace/tokenizer_model" \
-v "$(dirname "${HOST_DATA_PREFIX}"):/workspace/data_dir" \
-v "./distributed_config.env:/workspace/megatron-lm/examples/gptoss/distributed_config.env" \
--workdir /workspace/megatron-lm \
$PYTORCH_IMAGE \
bash examples/gptoss/02_train.sh \
--checkpoint-path /workspace/checkpoints \
--tensorboard-logs-path /workspace/tensorboard_logs \
--tokenizer /workspace/tokenizer_model \
--data "/workspace/data_dir/$(basename "${HOST_DATA_PREFIX}")" \
--distributed-config-file /workspace/megatron-lm/examples/gptoss/distributed_config.env \
2>&1 | tee "${HOST_TENSORBOARD_LOGS_PATH}/training_custom_$(date +'%y-%m-%d_%H-%M-%S').log"
```
## Step 3: Convert Megatron to HuggingFace
Just run the following command to change from the megatron checkpoint from training to the huggingface format to share with others (make sure you have the same virtual environment setup as in Step 0):
```bash
python3 -m torch.distributed.launch --nproc-per-node=8 examples/gptoss/03_convert_to_hf.py --hf-model openai/gpt-oss-20b --megatron-model ./megatron_checkpoints/openai_gpt-oss_20b
```
================================================
FILE: examples/inference/README.md
================================================
### Megatron Core Inference Documentation
This guide provides an example for Megatron Core for running model inference.
### Contents
- [Megatron Core Inference Documentation](#megatron-core-inference-documentation)
- [Contents](#contents)
- [1. Quick Start](#1-quick-start)
- [1.1 Understanding The Code](#11-understanding-the-code)
- [1.2 Running The Code](#12-running-the-code)
- [2. Flow of Control In MCore Backend](#2-flow-of-control-in-mcore-backend)
- [3. Customizing The Inference Pipeline](#3-customizing-the-inference-pipeline)
- [3.1. Create Your Own Inference Backend](#31-create-your-own-inference-backend)
- [3.2. Create Your Own Text Generation Controller](#32-create-your-own-text-generation-controller)
- [3.3. Support Other Models](#33-support-other-models)
- [3.3. Modify Inference Parameters](#33-modify-inference-parameters)
- [4. Future work](#4-future-work)
#### 1. Quickstart
This example runs statically-batched inference on a model trained using Megatron Core. The entrypoint is [gpt_static_inference.py](./gpt/gpt_static_inference.py). A similar workflow can be adapted for [gpt_dynamic_inference.py](./gpt/gpt_dynamic_inference.py).
##### 1.1 Code Walkthrough
***STEP 1 - Initialize model parallel and other default arguments***
The micro batch size defaults to 1. It is not used in tensor-parallelism only, and for pipeline-parallel models it is calculated at runtime.
```python
# Initialize Megatron model using the same model provider from training.
initialize_megatron(
args_defaults={'no_load_rng': True, 'no_load_optim': True, 'micro_batch_size': 1}
)
```
***STEP 2 - Load the model using the model_provider_function***
The model provider function supports both MCore and Legacy models.
```python
# Load the model checkpoint
model = get_model(model_provider, wrap_with_ddp=False)
load_checkpoint(model, None, None)
model.eval()
model = model[0]
```
***STEP 3 - Choose an engine***
Text generation requires an inference engine, which includes a scheduler. The default engine is the [Megatron Core engine](../../megatron/core/inference/engine/mcore_engine.py) with a [text generation controller](../../megatron/core/inference/text_generation_controllers/text_generation_controller.py). TRTLLMEngine will be supported in the future.
```python
# Create an inference wrapper to setup the model.
inference_wrapped_model = GPTInferenceWrapper(model, args)
# Define a sampling loop.
text_generation_controller = TextGenerationController(
inference_wrapped_model=inference_wrapped_model,
tokenizer=tokenizer
)
# Create a static or dynamic inference engine.
inference_engine = StaticInferenceEngine(
text_generation_controller=text_generation_controller,
max_batch_size=args.max_batch_size
)
```
***STEP 4 - Run text generation***
The [SamplingParams](../../megatron/core/inference/sampling_params.py) class uses suggested defaults. Customize this to change top_p, top_k, number of tokens to generate, etc. The result is returned as a list of [InferenceRequests](../../megatron/core/inference/inference_request.py).
```python
results: List[InferenceRequest] = inference_engine.generate(
prompts=args.prompts, sampling_params=sampling_params
)
if torch.distributed.get_rank() == 0:
for idx, result in enumerate(results):
print(f' ------------- RESULT FOR PROMPT {idx} --------------- ')
result = {
'id': result.request_id,
'input_prompt': result.prompt,
'generated_text': result.generated_text,
'generated_tokens' : result.generated_tokens
}
print(result)
```
##### 1.2 Running The Code
An example Slurm script is shown below. Set the tokenizer paths, inference params, and other settings appropriately.
For a recap on sampling parameters, refer to [this blog](https://ivibudh.medium.com/a-guide-to-controlling-llm-model-output-exploring-top-k-top-p-and-temperature-parameters-ed6a31313910).
```
# Slurm cluster settings
ACCOUNT=
MLM_PATH=/path/to/megatron-lm
GPT_CKPT=/path/to/gpt/ckpt
VOCAB_MERGE_FILE_PATH=/path/to/vocab/and/merge/file
CONTAINER_IMAGE=nvcr.io/ea-bignlp/ga-participants/nemofw-training:23.11
srun --account $ACCOUNT \
--job-name=$ACCOUNT:inference \
--partition=batch \
--time=01:00:00 \
--container-image $CONTAINER_IMAGE \
--container-mounts $MLM_PATH:/workspace/megatron-lm/,$GPT_CKPT:/workspace/mcore_gpt_ckpt,$VOCAB_MERGE_FILE_PATH:/workspace/tokenizer \
--no-container-mount-home \
--pty /bin/bash \
# Inside the container run the following.
cd megatron-lm/
export CUDA_DEVICE_MAX_CONNECTIONS=1
TOKENIZER_ARGS=(
--vocab-file /workspace/tokenizer/gpt2-vocab.json
--merge-file /workspace/tokenizer/gpt2-merges.txt
--tokenizer-type GPT2BPETokenizer
)
MODEL_ARGS=(
--use-checkpoint-args
--use-mcore-models
--load /workspace/mcore_gpt_ckpt
)
INFERENCE_SPECIFIC_ARGS=(
--attention-dropout 0.0
--hidden-dropout 0.0
--num-tokens-to-generate 20
--max-batch-size 4
)
torchrun --nproc-per-node=4 examples/inference/gpt/gpt_static_inference.py \
${TOKENIZER_ARGS[@]} \
${MODEL_ARGS[@]} \
${INFERENCE_SPECIFIC_ARGS[@]} \
--prompts "prompt one " "sample prompt two" "sample prompt 3"
NOTE: Other parameters which can be customized for inference:
--temperature (Sampling temperature)
--top_k (top_k sampling)
--top_p (top_p sampling)
--num-tokens-to-generate (Number of tokens to generate for each prompt)
--inference-batch-times-seqlen-threshold (During inference, if batch-size times sequence-length is smaller than this threshold then we will not use microbatched pipelining.')
--use-dist-ckpt (If using dist checkpoint format for the model)
--use-legacy-models (If using legacy models instead of MCore models)
```
#### 2. Control Flow in the MCore Backend
An example of inference with static batching is provided in [gpt_static_inference.py](./gpt/gpt_static_inference.py).
* [mcore_engine](../../megatron/core/inference/engines/mcore_engine.py) **generate()** function is called with the input prompts.
* The `Scheduler` in the engine will add these prompts to the [active requests] pool (../../megatron/core/inference/inference_request.py) until max batch size is hit. Remaining requests will be added to the waiting requests pool.
* The engine will run until all requests (waiting + active) are completed.
* The active requests are passed into **generate_all_output_tokens_static_batch()** of the text generation controller .
* This function uses the **prep_model_for_inference()** method of the [model_inference_wrappers](../../megatron/core/inference/model_inference_wrappers/abstract_model_inference_wrapper.py) and runs an autoregressive sampling loop
* In the autoregressive loop, the **get_batch_for_context_window()** method of the inference wrapper is called to slice out the input tokens and masks
* Input tokens and masks are passed it into the **run_one_forward_step()** method, which calls the model `.forward()` method to get the output logits
* Output logits are synchronized across all pipeline parallel ranks
* The text generation controller obtains the log probabilities and samples tokens based on the strategy defined in the sampling parameters.
* The sampled tokens are then appended to the input prompt tokens for the next iteration
* The **update_generation_status()** method of the text generation controller checks which prompts have finished generating or hit a stop condition
* After the inference loop, the result is detokenized and stored as an attribute of the InferenceRequest. These requests are marked as completed.
* The **update_requests_pool()** method of the scheduler moves completed requests into the completed request pool and waiting requests into the active request pool
#### 3. Customizing The Inference Pipeline
The inference pipeline supports three levels of customization:
* **Inference engine** - The MCore Engine supports static and dynamic batching. Modify this to add a new backend.
* **Text generation controller** - The main sampling loop. Customize this to support alternative tokenization or implement a new sampling strategy.
* **Inference Wrapped Model** - Change this to support a new model.
* **Modify Inference Parameters** - Change this to update top_p, top_k, number of tokens to be generated, temperature, and other sampling parameters.
##### 3.1. Create Your Own Inference Backend
The [abstract_engine.py](./../../megatron/core/inference/engine/abstract_engine.py) file contains a `generate` method that can be extended to support a new backend.
```python
class AbstractEngine(ABC):
@staticmethod
def generate(self) -> dict:
"""The abstract backend's generate function.
To define a new backend, implement this method and return the outputs as a dictionary.
```
##### 3.2. Implement a new Sampling Loop
The [TextGenerationController](../../megatron/core/inference/text_generation_controllers/text_generation_controller.py) contains the main sampling loop and can be modified to support new tokenization, detokenization, or sampling strategies.
``` python
class TextGenerationController:
def tokenize_prompt(self, prompt: str) -> Tuple[torch.Tensor, torch.Tensor]:
"""Utility to tokenize the input prompts"""
def sample_from_logits(
self,
last_token_logits: torch.Tensor,
sampling_params: SamplingParams,
vocab_size: int,
generation_started : Optional[torch.Tensor] = None,
top_n_logprobs_dict: Dict[int, List[Dict[str, float]]] = None,
) -> torch.Tensor:
"""Samples the logits to generate outputs
Given the logits of the last token, this function samples according to the parameters defined in sampling_params and returns the sampled tokens. If sampling_params.top_n_logprobs > 0
at each step it also updates the top_n_logprobs_dict.
"""
def update_generation_status(
self,
updated_prompts_tokens: torch.Tensor,
generation_started: torch.Tensor,
current_context_end_position: int,
is_generation_done_tensor: torch.Tensor,
generated_sequence_lengths: torch.Tensor,
) -> torch.Tensor:
"""Function to check which prompts have reached an end condition
We check which prompts have reached an end condition and set the corresponding flags of the is_generation_done_tensor to True . The generated sequence lengths increases as we keep generating, until that prompts hits an eod condition. The generation started status tensor helps us determine which prompts have started generating
"""
def generate_all_output_tokens_static_batch(
self, active_requests: OrderedDict[int, InferenceRequest],
) -> OrderedDict[int, InferenceRequest]:
"""Utility to generate all the output tokens and probabilities for the prompts .
This utility generates the output tokens for a static batch. It runs the forward steps till all prompts complete generation, updates the status of these requests to completed, adds the generated result and returns these requests
"""
def detokenize_generations(self, prompt_tokens_with_generated_tokens: torch.Tensor) -> str:
"""Detokenize the output generations"""
```
##### 3.3. Support Other Models
Extend [abstract_model_inference_wrapper.py](./../../megatron/core/inference/model_inference_wrappers/abstract_model_inference_wrapper.py) to support other models. The abstract model wrapper implements:
* Forward method which calls the model `forward` method depending on model parallel settings
* Initializes the model and puts it in `.eval()` mode
* Setup for the input parameters (max batch size, max seq length)
The following methods should be implemented:
```python
class AbstractModelInferenceWrapper:
def prep_model_for_inference(self, prompts_tokens: torch.Tensor):
"""A utility function for preparing model for inference
The function gets called once before the auto regressive inference loop. It puts the model in eval mode , and gets some model and inference data parameters. Extend this to build position ids ,attention mask etc, so that required slices can be extracted during the forward pass
"""
@abc.abstractclassmethod
def get_batch_for_context_window(self) -> List:
"""Returns the input data for inference
This function gets called iteratively in the inference loop. It can be used to extract relevant input from the prompt tokens, attention mask etc. required for each step in inference.
```
Refer to [gpt_inference_wrapper.py](../../megatron/core/inference/model_inference_wrappers/gpt/gpt_inference_wrapper.py) for an example of implementing this for GPTModel.
##### 3.3. Modify Inference Parameters
We use [common inference params](../../megatron/core/inference/sampling_params.py) for text generation. Customize this to change `top_p`, `top_k`, number of tokens to generate etc. Other attributes can be added for the inference loop as shown below.
```
from megatron.core.inference.sampling_params import SamplingParams
c = SamplingParams(temperature=0.5)
c.add_attributes({'min_length':4, 'eod_id':153})
```
#### 4. Future work
The following features are planned for future releases.
* TRTLLM Engine support
* Continuous batching optimizations
* Speculative decoding
================================================
FILE: examples/inference/gpt/gpt_dynamic_inference.py
================================================
# Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# pylint: disable=bad-builtin
import hashlib
import io
import json
import os
import sys
import warnings
from collections import defaultdict
from typing import Dict, List, Optional
import torch
from tqdm import tqdm
sys.path.append(
os.path.abspath(os.path.join(os.path.dirname(__file__), os.path.pardir, os.path.pardir))
)
from examples.inference.gpt.utils import (
Request,
build_dynamic_engine_setup_prefix,
build_requests,
get_curr_time,
get_global_peak_memory_stats_bytes,
)
from megatron.core.inference.contexts.dynamic_context import DynamicInferenceContext
from megatron.core.inference.engines import DynamicInferenceEngine, EngineSuspendedError
from megatron.core.inference.model_inference_wrappers.gpt.gpt_inference_wrapper import (
GPTInferenceWrapper,
)
from megatron.core.inference.sampling_params import SamplingParams
from megatron.core.inference.text_generation_controllers.text_generation_controller import (
TextGenerationController,
)
from megatron.core.tokenizers.utils.build_tokenizer import build_tokenizer
from megatron.inference.utils import (
add_inference_args,
get_inference_config_from_model_and_args,
get_model_for_inference,
)
sys.path.append(
os.path.abspath(os.path.join(os.path.dirname(__file__), os.path.pardir, os.path.pardir))
)
import logging
import megatron
from megatron.core.utils import configure_nvtx_profiling
from megatron.training import get_args, get_tokenizer, initialize_megatron
torch.serialization.add_safe_globals([io.BytesIO])
torch.serialization.add_safe_globals([megatron.core.rerun_state_machine.RerunState])
torch.serialization.add_safe_globals([megatron.core.rerun_state_machine.RerunDiagnostic])
def run_inference(
requests: List[Request],
engine: DynamicInferenceEngine,
sampling_params: Optional[SamplingParams] = None,
) -> List[Dict[str, float]]:
"""Add requests to engine and generate tokens.
Args:
requests (List[Request]): Requests that are to be added and processed.
engine (DynamicInferenceEngine): Inference engine that manages generating tokens.
sampling_params (SamplingParams): Deprecated as of megatron-core 0.16.
Return:
A dictionary of step times with `prefill` and `decode` keys.
"""
if sampling_params is not None and torch.distributed.get_rank() == 0:
warnings.warn(
"The `sampling_params` argument is deprecated. "
"Sampling parameters are specified per request.",
DeprecationWarning,
)
args = get_args()
# Parse batch boundaries for batch-drain mode.
batch_ranges = None
if args.drain_between_batches and args.batch_boundaries:
boundaries = [int(x) for x in args.batch_boundaries.split(",")]
num_requests_total = len(requests)
batch_ranges = []
for i, start in enumerate(boundaries):
end = boundaries[i + 1] if i + 1 < len(boundaries) else num_requests_total
batch_ranges.append((start, end))
# Initialize request arrival times.
base_arrival_time = get_curr_time()
for request in requests:
request.time_arrival = request.time_offset + base_arrival_time
# Add and process requests.
num_requests_total = len(requests)
num_requests_added = 0
num_requests_finished = 0
step_times = {"prefill": [], "decode": []}
add_times = []
output_times = []
tbar = tqdm(total=num_requests_total)
total_output_tokens = 0
attempted_step_count = 0
if args.cuda_graph_impl == "local":
cuda_graph_request_count_map = {}
else:
cuda_graph_request_count_map = None
def _add_request():
"""Add request to engine.
*Note: Using `prompt_text` instead of `prompt_tokens` for fair comparison.
"""
nonlocal num_requests_added
_request = requests[num_requests_added]
engine.add_request(num_requests_added, _request.prompt_text, _request.sampling_params)
_request.time_start = get_curr_time()
_request.state = "started"
num_requests_added += 1
tbar.update(1)
def _process_step_result(result):
"""Process a single engine step result, updating bookkeeping state."""
nonlocal total_output_tokens, num_requests_finished
is_decode_only = engine.is_decode_only
# Record cuda_graph_request_count.
cuda_graph_request_count = result["cuda_graph_request_count"]
if args.cuda_graph_impl == "local" and cuda_graph_request_count is not None:
cuda_graph_request_count_map[cuda_graph_request_count] = (
cuda_graph_request_count_map.get(cuda_graph_request_count, 0) + 1
)
# Update requests.
active_request_ids = result["active_request_ids"]
finished_request_records = result["finished_request_records"]
step_time = result["step_time"]
if len(active_request_ids) > 0 or len(finished_request_records) > 0:
if is_decode_only:
step_times["decode"].append(step_time)
else:
step_times["prefill"].append(step_time)
# Append output tokens.
output_start = get_curr_time()
for finished_request_record in finished_request_records:
finished_request = finished_request_record.merge()
# Update local request object.
request = requests[finished_request.request_id]
request.time_end = get_curr_time()
request.state = "finished"
request.request_id = finished_request.request_id
request.events = finished_request.events
request.ttft = finished_request.ttft
# Update prompt, in case engine has been suspended and resumed.
request.prompt_tokens = finished_request.prompt_tokens.tolist()
request.prompt_text = finished_request.prompt
# Get output tokens and text.
request.output_tokens = finished_request.generated_tokens
request.output_text = finished_request.generated_text
total_output_tokens += len(request.output_tokens)
# Log probs.
if finished_request.sampling_params.return_log_probs:
if not finished_request.prompt_log_probs:
finished_request.prompt_log_probs = []
request.prompt_log_probs = finished_request.prompt_log_probs
request.generated_log_probs = finished_request.generated_log_probs
request.logprobs = (
finished_request.prompt_log_probs + finished_request.generated_log_probs
)
if finished_request.sampling_params.top_n_logprobs > 0:
request.generated_top_n_logprobs = finished_request.generated_top_n_logprobs
if not finished_request.sampling_params.skip_prompt_log_probs:
request.prompt_top_n_logprobs = finished_request.prompt_top_n_logprobs
num_requests_finished += 1
output_times.append(get_curr_time() - output_start)
if batch_ranges is not None:
# Batch-drain mode: add all requests in a batch, drain, then next batch.
for batch_idx, (batch_start, batch_end) in enumerate(batch_ranges):
# Add all requests in current batch.
add_start = get_curr_time()
while num_requests_added < batch_end:
_add_request()
add_times.append(get_curr_time() - add_start)
# Step until all active requests finish (drain).
while engine.has_unfinished_requests():
try:
result = engine.step_modern()
except EngineSuspendedError as e:
result = e
attempted_step_count += 1
if isinstance(result, EngineSuspendedError):
continue
_process_step_result(result)
else:
# Original mode: add requests per step based on arrival time or count.
while True:
# Add requests.
add_start = get_curr_time()
if args.incoming_requests_per_step is None:
# Add requests with 'earlier' arrival time.
while num_requests_added < num_requests_total:
if requests[num_requests_added].time_arrival > add_start:
break
_add_request()
else:
# Add deterministic number of requests (generally used for debugging).
for i in range(
min(args.incoming_requests_per_step, num_requests_total - num_requests_added)
):
_add_request()
add_times.append(get_curr_time() - add_start)
# Step inference engine (i.e., generate a token for each active request).
# Before step, we haven't done the scheduling, so we cannot know the is_decode_only
try:
result = engine.step_modern()
except EngineSuspendedError as e:
result = e
pass # ignore error in order to call 'engine.resume()' below.
attempted_step_count += 1
# Test suspending and resuming engine.
if args.suspend_resume_interval is not None:
# Suspend.
if attempted_step_count % args.suspend_resume_interval == 0:
print("**** step %d/%d ... suspend." % (engine.context.step_count, attempted_step_count))
engine.suspend()
# Resume, 0+ attempted steps later.
if (
attempted_step_count > 0
and (attempted_step_count - args.suspend_resume_interval // 2)
% args.suspend_resume_interval
== 0
):
print("**** step %d/%d ... resume." % (engine.context.step_count, attempted_step_count))
engine.resume()
# If engine suspended, continue to next iter.
if isinstance(result, EngineSuspendedError):
continue
_process_step_result(result)
# Check if all requests are finished.
if not (engine.has_unfinished_requests() or num_requests_added < num_requests_total):
break
# Resume engine (NOOP if not suspended).
engine.resume()
return {
"step_times": step_times,
"add_times": add_times,
"output_times": output_times,
"total_output_tokens": total_output_tokens,
"cuda_graph_request_count_map": cuda_graph_request_count_map,
}
@torch.inference_mode()
def main():
"""Run dynamic inference."""
# Initialize Megatron.
initialize_megatron(
extra_args_provider=add_inference_args,
args_defaults={'no_load_rng': True, 'no_load_optim': True},
)
# Start Nsight profiler.
if os.environ.get("NSIGHT_PREFIX"):
torch.cuda.cudart().cudaProfilerStart()
level_str = os.getenv("LOG_LEVEL", "INFO").upper()
level = getattr(logging, level_str, logging.INFO)
logging.basicConfig(level=level, force=True)
configure_nvtx_profiling(True)
args = get_args()
# Build tokenizer
tokenizer = build_tokenizer(args)
# Reset peak memory stats so functional tests measure this run and not
# whatever happened earlier during initialization.
torch.cuda.reset_peak_memory_stats()
# Sampling params.
sampling_params = SamplingParams(
temperature=args.temperature,
top_k=args.top_k,
top_p=args.top_p,
skip_prompt_log_probs=args.skip_prompt_log_probs,
return_log_probs=args.return_log_probs,
num_tokens_to_generate=args.num_tokens_to_generate,
termination_id=args.termination_id if args.termination_id is not None else tokenizer.eod,
top_n_logprobs=args.top_n_logprobs,
stop_words=args.stop_words,
)
model = get_model_for_inference()
# Requests, context, controller.
requests = build_requests(args, tokenizer, sampling_params)
inference_config = get_inference_config_from_model_and_args(model, args)
# Calculate max_sequence_length from requests
max_gen_length = sampling_params.num_tokens_to_generate
max_context_length = max(len(r.prompt_tokens) for r in requests)
inference_config.max_sequence_length = max_context_length + max_gen_length
context = DynamicInferenceContext(model.config, inference_config)
wrapped_model = GPTInferenceWrapper(model, context)
controller = TextGenerationController(wrapped_model, tokenizer)
# Validate all context_length's <= max_tokens.
if not args.enable_chunked_prefill:
invalid_prompt_length_map = {}
for request_idx, request in enumerate(requests):
if len(request.prompt_tokens) > context.max_tokens:
invalid_prompt_length_map[request_idx] = len(request.prompt_tokens)
assert (
not invalid_prompt_length_map
), "request idxs with prompts longer than context.max_tokens: " ", ".join(
f"{k}({v})" for k, v in invalid_prompt_length_map.items()
)
# Inference engine.
engine = DynamicInferenceEngine(controller, context)
setup_prefix = build_dynamic_engine_setup_prefix(args, model, context, requests)
print("~~~")
print(setup_prefix)
print("~~~")
# Run and time test, optionally `args.inference_repeat_n` times.
throughputs = []
for _ in range(args.inference_repeat_n):
# Reset engine.
engine.reset()
torch.cuda.reset_peak_memory_stats()
# Trial.
t = get_curr_time()
result = run_inference(requests, engine)
step_times = result["step_times"]
add_times = result["add_times"]
output_times = result["output_times"]
total_output_tokens = result["total_output_tokens"]
torch.cuda.synchronize()
total_time = get_curr_time() - t
stats = torch.cuda.memory_stats()
throughput = total_output_tokens / total_time
throughputs.append(throughput)
# Validate all requests finished.
for request in requests:
assert request.state == "finished", f"request.state == '{request.state}' != 'finished'."
peak_mem_stats = get_global_peak_memory_stats_bytes()
# Print unique prompts + outputs.
if torch.distributed.get_rank() == 0:
def escape_str(s):
return s.replace("\n", "\\n")
print("~~~~ Unique prompts + outputs. ~~~~")
# Map requests by their prompt.
unique_prompt_map = defaultdict(list)
for request_idx, request in enumerate(requests):
unique_prompt_map[request.prompt_text].append(request_idx)
# Print unique prompts + outputs.
text_hashes = []
for unique_idx, (prompt_text, request_idxs) in enumerate(unique_prompt_map.items()):
# ---- Prompt summary line ----
prompt_len = len(requests[request_idxs[0]].prompt_tokens)
escaped_prompt_text = escape_str(prompt_text)
print(
f"\n{unique_idx+1}/{len(unique_prompt_map)}"
f"[n {len(request_idxs)}, l {prompt_len}] {escaped_prompt_text}"
)
# ---- Group all outputs for this prompt ----
output_map = defaultdict(list)
for idx in request_idxs:
req = requests[idx]
output_map[req.output_text].append(idx)
# ---- Print each unique output ----
for output_text, output_request_idxs in output_map.items():
evicted = False
for idx in output_request_idxs:
for event in requests[idx].events:
if event.type.name == "EVICT":
evicted = True
break
if output_text is not None:
# Use hash of prompt + generated text in case engine was
# suspended and resumed, which misaligns boundary between
# prompt and generated tokens.
o_hash = hashlib.sha256((prompt_text + output_text).encode()).hexdigest()[:6]
o_len = len(requests[output_request_idxs[0]].output_tokens)
escaped_output_text = escape_str(output_text)
else:
o_hash = "--"
o_len = 0
escaped_output_text = "--"
print(
f" >>>> [n {len(output_request_idxs)}, {o_len} tokens, hash {o_hash}"
f"{', ' if evicted else ''}] {escaped_output_text}"
)
text_hashes.append(o_hash)
# Write results to JSON. Primarily used for functional testing.
if args.output_path:
json_results = {}
# Write every 'n' requests, plus the final request.
for i, req in enumerate(requests):
if i % args.output_every_n_results == 0 or i == len(requests) - 1:
print(f' Attributes of request {i}: {req.__dict__}')
result_dict = {
"input_prompt": req.prompt_text,
"generated_text": req.output_text,
"generated_tokens": req.output_tokens,
"latency": req.time_end - req.time_start,
"ttft": req.ttft, # Time-to-first-token in seconds
"cuda_graph_request_count_map": result["cuda_graph_request_count_map"],
"step_count": engine.context.step_count,
"top_n_logprobs": getattr(req, 'generated_top_n_logprobs', None),
"prompt_top_n_logprobs": getattr(req, 'prompt_top_n_logprobs', None),
}
if req.sampling_params.return_log_probs:
result_dict["prompt_logprobs"] = getattr(req, 'prompt_log_probs', None)
result_dict["generated_logprobs"] = getattr(
req, 'generated_log_probs', None
)
result_dict["logprobs"] = getattr(req, 'logprobs', None)
if args.output_request_events:
result_dict["events"] = [e.serialize() for e in req.events]
json_results[req.request_id] = result_dict
# Track system-level throughput as a test / debug metric
if args.record_throughput:
json_results["throughput"] = throughputs
# Attach peak memory metrics; the functional test only validates these
# if the fields exist in the golden values.
json_results.update(peak_mem_stats)
json_results["lifetime_prefill_token_count"] = engine.context.lifetime_prefill_token_count
print(f' Saving results to {args.output_path}')
with open(args.output_path, "w") as fp:
json.dump(json_results, fp, indent=1)
# Timing results.
stats = torch.cuda.memory_stats()
throughput = total_output_tokens / total_time
print("~~~")
peak_alloc_gb = stats["allocated_bytes.all.peak"] / 1024**3
peak_resvd_gb = stats["reserved_bytes.all.peak"] / 1024**3
p_times = step_times["prefill"]
d_times = step_times["decode"]
p_total = sum(p_times)
d_total = sum(d_times)
p_count = len(p_times)
d_count = len(d_times)
p_mean = p_total / p_count
d_mean = d_total / d_count if d_count != 0 else 0.0
# Commented out for now as the step/add/output times are not calculated correctly.
# print(
# f"{setup_prefix} … "
# f"mem {peak_alloc_gb:.1f}/{peak_resvd_gb:.1f} GB … "
# f"total time: {step_total:.3f}s … "
# f"step time: total {step_total:.3f}s "
# f"[ p {p_total:.3f}s, d {d_total:.3f}s ], "
# f"mean [ p {p_mean:.3f}s, d {d_mean:.3f}s ], "
# f"count [ p {p_count}, d {d_count} ]."
# )
capture_str = f"{engine.capture_stats['time']:.2f} sec" if engine.capture_stats else "--"
print(
f"{setup_prefix} … " f"throughput: {throughput:.3f} tok/s … ",
f"total time: {total_time:.3f}s … "
f"mem {peak_alloc_gb:.1f}/{peak_resvd_gb:.1f} GB … "
f"steps: {engine.context.step_count:d} … "
f"capture {capture_str}",
)
print("~~~")
# Stop Nsight profiler.
if os.environ.get("NSIGHT_PREFIX"):
torch.cuda.cudart().cudaProfilerStop()
if __name__ == "__main__":
main()
================================================
FILE: examples/inference/gpt/gpt_dynamic_inference_12b.sh
================================================
# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.
# Run dynamic batching inference on the 12B GPT model.
set -u
# Libraries.
pip install simpy
pip install sentencepiece
pip install tiktoken
# Environment variables.
export CUDA_DEVICE_MAX_CONNECTIONS=1
# Checkpoint.
: ${CHECKPOINT_DIR:?"CHECKPOINT_DIR is not set"}
: ${TOKENIZER_MODEL:?"TOKENIZER_MODEL is not set"}
# Prompts.
: ${NUM_TOKENS_TO_PROMPT="8 32"}
: ${NUM_TOKENS_TO_GENERATE=256}
: ${INCOMING_REQUESTS_DURATION=10.}
: ${INCOMING_REQUESTS_PER_SEC=100.}
# Dynamic context.
: ${BUFFER_SIZE_GB=50.}
# Cuda graphs.
: ${NUM_CUDA_GRAPHS=16}
# Miscellaneous.
: ${USE_COORDINATOR=0}
: ${ENGINE=dynamic}
: ${EXTRA_ARGS=""}
# NSIGHT_PREFIX=/path/to/nsight/profile
# Arguments.
ARGS=" \
--no-persist-layer-norm \
--apply-layernorm-1p \
--no-position-embedding \
--group-query-attention \
--num-query-groups 8 \
--load ${CHECKPOINT_DIR} \
--use-checkpoint-args \
--untie-embeddings-and-output-weights \
--disable-bias-linear \
--use-rotary-position-embeddings \
--position-embedding-type rope \
--rotary-base 1000000 \
--rotary-percent 1.0 \
--swiglu \
--normalization RMSNorm \
--attention-dropout 0.0 \
--hidden-dropout 0.0 \
--exit-duration-in-mins 5740 \
--tensor-model-parallel-size 1 \
--pipeline-model-parallel-size 1 \
--num-layers 40 \
--hidden-size 5120 \
--ffn-hidden-size 14336 \
--num-attention-heads 32 \
--kv-channels 128 \
--seq-length 1024 \
--max-position-embeddings 1024 \
--micro-batch-size 64 \
--bf16 \
--tokenizer-type TikTokenizer \
--tiktoken-pattern v2 \
--tokenizer-model ${TOKENIZER_MODEL} \
--distributed-timeout-minutes 2400 \
--use-flash-attn \
--inference-rng-tracker \
\
--inference-dynamic-batching \
--inference-dynamic-batching-buffer-size-gb ${BUFFER_SIZE_GB} \
\
${EXTRA_ARGS} \
"
# Cuda graphs.
if [ "${NUM_CUDA_GRAPHS}" != "0" ]; then
ARGS+=" \
--cuda-graph-impl local \
--inference-dynamic-batching-num-cuda-graphs ${NUM_CUDA_GRAPHS} \
"
else
ARGS+=" \
--cuda-graph-impl none \
"
fi
# Prompts.
if [[ -v PROMPTS ]]; then
ARGS+=" \
--prompts ${PROMPTS} \
--num-tokens-to-generate ${NUM_TOKENS_TO_GENERATE} \
"
elif [[ -v PROMPT_FILE ]]; then
ARGS+=" \
--prompt-file ${PROMPT_FILE} \
--num-tokens-to-generate ${NUM_TOKENS_TO_GENERATE} \
"
else
ARGS+=" \
--num-tokens-to-prompt ${NUM_TOKENS_TO_PROMPT} \
--num-tokens-to-generate ${NUM_TOKENS_TO_GENERATE} \
--incoming-requests-duration ${INCOMING_REQUESTS_DURATION} \
--incoming-requests-per-sec ${INCOMING_REQUESTS_PER_SEC} \
"
fi
# Command.
if [[ "${USE_COORDINATOR}" == "0" ]]; then
CMD="python -m examples.inference.gpt.gpt_${ENGINE}_inference ${ARGS}"
else
CMD="python -um examples.inference.gpt.gpt_${ENGINE}_inference_with_coordinator ${ARGS}"
fi
if [[ -v NSIGHT_PREFIX ]]; then
CMD="nsys profile -s none -t nvtx,cuda --cudabacktrace=all --cuda-graph-trace=node --python-backtrace=cuda --wait all -o ${NSIGHT_PREFIX} --force-overwrite true --capture-range=cudaProfilerApi --capture-range-end=stop ${CMD}"
fi
echo "~~~"
echo "CMD ... ${CMD}."
echo "~~~"
eval ${CMD}
================================================
FILE: examples/inference/gpt/gpt_dynamic_inference_357m.sh
================================================
# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.
# Run dynamic batching inference on the 357M GPT model.
set -u
# Libraries.
pip install simpy
pip install sentencepiece
pip install tiktoken
# Environment variables.
export CUDA_DEVICE_MAX_CONNECTIONS=1
# Checkpoint.
: ${CHECKPOINT_DIR:?"CHECKPOINT_DIR is not set"}
: ${VOCAB_FILE:?"VOCAB_FILE is not set"}
: ${MERGE_FILE:?"MERGE_FILE is not set"}
# Prompts.
: ${NUM_TOKENS_TO_PROMPT="8 32"}
: ${NUM_TOKENS_TO_GENERATE=256}
: ${INCOMING_REQUESTS_DURATION=10.}
: ${INCOMING_REQUESTS_PER_SEC=100.}
# Dynamic context.
: ${BUFFER_SIZE_GB=50.}
# Cuda graphs.
: ${NUM_CUDA_GRAPHS=16}
# Miscellaneous.
: ${USE_COORDINATOR=0}
: ${ENGINE=dynamic}
: ${NPROC_PER_NODE=1}
: ${EXTRA_ARGS=""}
# NSIGHT_PREFIX=/path/to/nsight/profile
# Arguments.
ARGS=" \
--exit-on-missing-checkpoint \
--transformer-impl local \
--load ${CHECKPOINT_DIR} \
--tokenizer-type GPT2BPETokenizer \
--vocab-file ${VOCAB_FILE} \
--merge-file ${MERGE_FILE} \
--exit-on-missing-checkpoint \
--max-position-embeddings 2048 \
--seq-length 2048 \
--tensor-model-parallel-size 1 \
--pipeline-model-parallel-size 1 \
--num-layers 24 \
--num-attention-heads 16 \
--hidden-size 1024 \
--bf16 \
--micro-batch-size 1 \
--attention-dropout 0.0 \
--hidden-dropout 0.0 \
--seed 42 \
--use-flash-attn \
--inference-rng-tracker \
\
--inference-dynamic-batching \
--inference-dynamic-batching-buffer-size-gb ${BUFFER_SIZE_GB} \
\
${EXTRA_ARGS} \
"
# Cuda graphs.
if [ "${NUM_CUDA_GRAPHS}" != "0" ]; then
ARGS+=" \
--cuda-graph-impl local \
--inference-dynamic-batching-num-cuda-graphs ${NUM_CUDA_GRAPHS} \
"
else
ARGS+=" \
--cuda-graph-impl none \
"
fi
# Prompts.
if [[ -v PROMPTS ]]; then
ARGS+=" \
--prompts ${PROMPTS} \
--num-tokens-to-generate ${NUM_TOKENS_TO_GENERATE} \
"
elif [[ -v PROMPT_FILE ]]; then
ARGS+=" \
--prompt-file ${PROMPT_FILE} \
--num-tokens-to-generate ${NUM_TOKENS_TO_GENERATE} \
"
else
ARGS+=" \
--num-tokens-to-prompt ${NUM_TOKENS_TO_PROMPT} \
--num-tokens-to-generate ${NUM_TOKENS_TO_GENERATE} \
--incoming-requests-duration ${INCOMING_REQUESTS_DURATION} \
--incoming-requests-per-sec ${INCOMING_REQUESTS_PER_SEC} \
"
fi
# Command.
if [[ "${USE_COORDINATOR}" == "0" ]]; then
CMD="python -m examples.inference.gpt.gpt_${ENGINE}_inference ${ARGS}"
else
CMD="python -m torch.distributed.run --nproc-per-node ${NPROC_PER_NODE} -m examples.inference.gpt.gpt_${ENGINE}_inference_with_coordinator ${ARGS}"
fi
if [[ -v NSIGHT_PREFIX ]]; then
CMD="nsys profile -s none -t nvtx,cuda --cudabacktrace=all --cuda-graph-trace=node --python-backtrace=cuda --wait all -o ${NSIGHT_PREFIX} --force-overwrite true --capture-range=cudaProfilerApi --capture-range-end=stop ${CMD}"
fi
echo "~~~"
echo "CMD ... ${CMD}."
echo "~~~"
eval ${CMD}
================================================
FILE: examples/inference/gpt/gpt_dynamic_inference_with_coordinator.py
================================================
# Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
import asyncio
import json
import logging
import os
import time
import warnings
from collections import defaultdict
from typing import List
import torch
import torch.distributed as dist
from examples.inference.gpt.utils import Request, build_dynamic_engine_setup_prefix, build_requests
from megatron.core.inference.engines import DynamicInferenceEngine
from megatron.core.inference.engines.dynamic_engine import EngineState
from megatron.core.inference.inference_client import InferenceClient
from megatron.core.inference.inference_request import DynamicInferenceRequestRecord
from megatron.core.inference.sampling_params import SamplingParams
from megatron.inference.utils import (
add_inference_args,
get_dynamic_inference_engine,
get_model_for_inference,
)
from megatron.training import get_args, get_tokenizer, initialize_megatron
# pylint: disable=line-too-long
logging.basicConfig(level=logging.INFO, force=True)
async def suspend_resume_cycle(client, engine, args, futures):
"""Wait for all in-flight requests, then suspend/train/resume."""
await asyncio.gather(*futures)
client.pause_engines()
await engine.wait_until(EngineState.PAUSED)
client.suspend_engines()
await engine.wait_until(EngineState.SUSPENDED)
if args.suspend_timeout > 0:
await asyncio.sleep(args.suspend_timeout)
client.resume_engines()
await engine.wait_until(EngineState.RESUMED)
client.unpause_engines()
await engine.wait_until(EngineState.RUNNING)
async def main(
engine: DynamicInferenceEngine,
requests: List[Request],
port: int | None = None,
sampling_params: SamplingParams | None = None,
):
if sampling_params is not None:
warnings.warn(
"The `sampling_params` argument is deprecated. "
"Sampling parameters are specified per request.",
DeprecationWarning,
)
# once you call engine.start_listening_to_data_parallel_coordinator,
# the engine will start accepting requests from the data parallel coordinator.
# and processing them in an asyncio coroutine.
# leaving inference_coordinator_port as None will find a free port automatically.
args = get_args()
dp_addr = await engine.start_listening_to_data_parallel_coordinator(
inference_coordinator_port=port,
launch_inference_coordinator=True,
coordinator_schedule_output_path=args.coordinator_schedule_output_path,
)
# All ranks agree on the number of suspend/resume cycles from args.
num_suspend_resume_cycles = len(requests) // args.suspend_resume_interval if args.suspend_resume_interval else 0
# Create client and run example.
if dist.get_rank() == 0:
client = InferenceClient(dp_addr, deserialize=True) # submits requests to the inference coordinator
client.start()
base_arrival_time = time.time_ns() / 10**9
for request in requests:
request.time_arrival = request.time_offset + base_arrival_time
futures = []
num_requests_total = len(requests)
num_requests_added = 0
next_suspend_at = args.suspend_resume_interval or 0
cycles_done = 0
while True:
current_time = time.time_ns() / 10**9
if args.incoming_requests_per_step is None:
# Only add requests that have arrived at the current time.
while (
num_requests_added < num_requests_total
and requests[num_requests_added].time_arrival <= current_time
):
request = requests[num_requests_added]
# These add-request calls will queue up the request on a zmq socket and return
# instantaneously. They will return an asyncio future which can be awaited for
# request completion.
futures.append(client.add_request(request.prompt_text, request.sampling_params))
num_requests_added += 1
if num_requests_added >= next_suspend_at and cycles_done < num_suspend_resume_cycles:
await suspend_resume_cycle(client, engine, args, futures)
cycles_done += 1
next_suspend_at += args.suspend_resume_interval
else:
# Add deterministic number of requests (generally used for debugging).
for i in range(
min(args.incoming_requests_per_step, num_requests_total - num_requests_added)
):
# Change sampling parameters to force different generation lengths.
request = requests[num_requests_added]
n = request.sampling_params.num_tokens_to_generate
request.sampling_params.num_tokens_to_generate = n + i
futures.append(client.add_request(request.prompt_text, request.sampling_params))
num_requests_added += 1
if num_requests_added >= next_suspend_at and cycles_done < num_suspend_resume_cycles:
await suspend_resume_cycle(client, engine, args, futures)
cycles_done += 1
next_suspend_at += args.suspend_resume_interval
if num_requests_added == num_requests_total:
break
# Relinquish control since there are no more requests to add at the moment. This allows the engine to run.
await asyncio.sleep(0)
# While we wait for the requests to complete, the engine runs in the background.
results: List[DynamicInferenceRequestRecord] = await asyncio.gather(*futures)
else:
# Non-rank-0: match the suspend/resume cycles that rank 0 drives.
for _ in range(num_suspend_resume_cycles):
await engine.wait_until(EngineState.PAUSED)
await engine.wait_until(EngineState.SUSPENDED)
await engine.wait_until(EngineState.RESUMED)
await engine.wait_until(EngineState.RUNNING)
if dist.get_rank() == 0:
# Write results to JSON. Primarily used for functional testing.
if args.output_path:
json_results = {}
throughputs = []
for req in results:
result_dict = {
"input_prompt": req.prompt,
"generated_text": req.generated_text.replace("\n", "\\n"),
"generated_tokens": req.generated_tokens,
"latency": req.latency, # InferenceClient populates this field in the returned future.
}
if req.sampling_params.return_log_probs:
result_dict["logprobs"] = req.prompt_log_probs + req.generated_log_probs
throughput = len(req.generated_tokens) / req.latency
throughputs.append(throughput)
if req.routing_indices is not None:
result_dict["routing_indices"] = req.routing_indices.tolist()
json_results[req.request_id] = result_dict
throughput_dict = {"throughput": throughputs}
if args.throughput_check_only:
json_results = throughput_dict
with open(args.output_path, "w") as fp:
json.dump(json_results, fp, indent=4)
else:
print("Results:")
unique_prompt_map = defaultdict(list)
for req in results:
unique_prompt_map[req.prompt].append(req)
for idx, (prompt_text, reqs) in enumerate(unique_prompt_map.items()):
print(
f"%d/%d. prompt '%s' ... [%d] output '%s'."
% (
idx,
len(unique_prompt_map),
prompt_text.replace("\n", "\\n"),
len(reqs),
reqs[0].generated_text.replace("\n", "\\n"),
)
)
# Pause before stopping: STOP requires PAUSED or SUSPENDED state.
client.pause_engines()
await engine.wait_until(EngineState.PAUSED)
if dist.get_rank() == 0:
client.stop_engines()
await engine.wait_until(EngineState.STOPPED)
if dist.get_rank() == 0:
client.shutdown_coordinator()
client.stop()
logging.info(f"Rank: {dist.get_rank()} stopped their engine instance successfully.")
if __name__ == "__main__":
# enable inference mode in the very beginning as some fp8 optimizations
# check for it.
with torch.inference_mode():
initialize_megatron(
extra_args_provider=add_inference_args,
args_defaults={'no_load_rng': True, 'no_load_optim': True},
)
args = get_args()
tokenizer = get_tokenizer()
# Sampling params.
sampling_params = SamplingParams(
temperature=args.temperature,
top_k=args.top_k,
top_p=args.top_p,
return_log_probs=args.return_log_probs,
num_tokens_to_generate=args.num_tokens_to_generate,
termination_id=(
args.termination_id if args.termination_id is not None else tokenizer.eod
),
)
model = get_model_for_inference()
requests = build_requests(args, tokenizer, sampling_params)
engine = get_dynamic_inference_engine(model=model)
if dist.get_rank() == 0:
setup_prefix = build_dynamic_engine_setup_prefix(args, model, engine.context, requests)
print("~~~")
print(setup_prefix)
print("~~~")
# Start Nsight profiler.
if os.environ.get("NSIGHT_PREFIX"):
torch.cuda.cudart().cudaProfilerStart()
asyncio.run(main(engine, requests, args.inference_coordinator_port))
# Stop Nsight profiler.
if os.environ.get("NSIGHT_PREFIX"):
torch.cuda.cudart().cudaProfilerStop()
================================================
FILE: examples/inference/gpt/gpt_static_inference.py
================================================
# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.
import os
import sys
import time
from argparse import Namespace
import torch
from megatron.core.inference.contexts import StaticInferenceContext
from megatron.core.inference.engines import StaticInferenceEngine
from megatron.core.inference.inference_request import InferenceRequest
from megatron.core.inference.model_inference_wrappers.gpt.gpt_inference_wrapper import (
GPTInferenceWrapper,
)
from megatron.core.inference.sampling_params import SamplingParams
from megatron.core.inference.text_generation_controllers.text_generation_controller import (
TextGenerationController,
)
from megatron.core.tokenizers.utils.build_tokenizer import build_tokenizer
from megatron.core.transformer.module import MegatronModule
sys.path.append(
os.path.abspath(os.path.join(os.path.dirname(__file__), os.path.pardir, os.path.pardir))
)
import asyncio
import json
from typing import List
from examples.inference.gpt.utils import build_requests
from megatron.inference.utils import add_inference_args, get_model_for_inference
from megatron.training import get_args, get_tokenizer, print_rank_0
from megatron.training.initialize import initialize_megatron
def add_static_inference_args(parser):
"""Static inference arguments."""
add_inference_args(parser)
group = parser.add_argument_group(title='Static inference')
group.add_argument(
"--max-batch-size",
type=int,
default=None,
dest="max_batch_size",
help='Deprecated, use `--inference-max-requests` instead',
)
group.add_argument("--stream", action="store_true", default=False, help="Stream output tokens")
return parser
def get_inference_engine(args: Namespace, model: MegatronModule) -> StaticInferenceEngine:
"""Utility to get the relevant backend for running inference
This function will automatically choose the TRTLLMBackend when possible, and if not revert to Mcore backend if the user does not specify any backends. TRT LLM Backend is not implmented yet.
Args:
args (Namespace): The user arguments parsed from command line
model (MegatronModule): The megatron model .
Returns:
AbstractBackend: The chosen backend
"""
tokenizer = build_tokenizer(args)
inference_context = StaticInferenceContext(
args.inference_max_requests, args.inference_max_seq_length
)
inference_wrapped_model = GPTInferenceWrapper(model, inference_context)
text_generation_controller = TextGenerationController(
inference_wrapped_model=inference_wrapped_model, tokenizer=tokenizer
)
engine_kwargs = {
"text_generation_controller": text_generation_controller,
"legacy": args.use_legacy_static_engine,
}
if not args.use_legacy_static_engine:
engine_kwargs["buffer_size_gb"] = args.inference_dynamic_batching_buffer_size_gb
return StaticInferenceEngine(**engine_kwargs)
async def generate(
inference_engine: StaticInferenceEngine, sampling_params: SamplingParams, prompts: List[str]
) -> List[InferenceRequest]:
async def collect_stream(prompt, request_id, stream_generator):
print(f"Request {request_id}: {prompt}", end="", flush=True)
prev_idx = 0
async for output in stream_generator:
print(output.generated_text[prev_idx:], end="", flush=True)
prev_idx = len(output.generated_text)
print()
request_ids: List[int] = [
inference_engine.add_request(prompt=prompt, sampling_params=sampling_params, streaming=True)
for prompt in prompts
]
stream_generators = [
inference_engine.get_stream_generator(request_id) for request_id in request_ids
]
tasks = [
asyncio.create_task(collect_stream(prompt, request_id, stream_generator))
for (prompt, request_id, stream_generator) in zip(prompts, request_ids, stream_generators)
]
await inference_engine.run_engine_async()
await asyncio.gather(*tasks)
results: List[InferenceRequest] = [
inference_engine.scheduler.completed_request_pool[request_id] for request_id in request_ids
]
return results
@torch.inference_mode()
def main():
"""Main program."""
# Note: The default args passed here can be overwritten by using appropriate params (check arguments.py file)
# Micro batch size is not needed to be set by user. (It is calculated based on inference-batch-times-seqlen-threshold argument)
initialize_megatron(
extra_args_provider=add_static_inference_args,
args_defaults={
'no_load_rng': True,
'no_load_optim': True,
'micro_batch_size': 1,
'exit_on_missing_checkpoint': True,
},
)
args = get_args()
model = get_model_for_inference()
inference_engine = get_inference_engine(args, model)
sampling_params = SamplingParams(
temperature=args.temperature,
top_k=args.top_k,
top_p=args.top_p,
return_log_probs=args.return_log_probs,
num_tokens_to_generate=args.num_tokens_to_generate,
top_n_logprobs=args.top_n_logprobs,
)
# Build tokenizer
tokenizer = build_tokenizer(args)
requests = build_requests(args, tokenizer)
prompts = [r.prompt_text for r in requests]
if args.cuda_graph_impl == "local":
print(f"Running warmup for CUDA graphs...")
inference_engine.generate(
prompts=["warmup"], sampling_params=SamplingParams(num_tokens_to_generate=10)
)
start_time = time.perf_counter()
if args.stream:
results: List[InferenceRequest] = asyncio.run(
generate(inference_engine, sampling_params, prompts)
)
else:
results: List[InferenceRequest] = inference_engine.generate(
prompts=prompts, sampling_params=sampling_params
)
end_time = time.perf_counter()
latency = end_time - start_time
if torch.distributed.get_rank() == 0 and args.output_path:
results_output = {}
for idx, result in enumerate(results):
result_dict = {
'input_prompt': result.prompt,
'generated_text': result.generated_text,
'generated_tokens': result.generated_tokens.tolist(),
'tpot': result.tpot,
'latency': latency,
}
if sampling_params.top_n_logprobs > 0:
result_dict['generated_top_n_logprobs'] = result.generated_top_n_logprobs
if sampling_params.return_log_probs:
response_logprobs = result.prompt_log_probs + result.generated_log_probs
result_dict["logprobs"] = response_logprobs
results_output[result.request_id] = result_dict
with open(args.output_path, 'w') as f:
json.dump(results_output, f)
# Print unique prompts + outputs.
if torch.distributed.get_rank() == 0:
print("~~~~ Unique prompts + outputs. ~~~~")
# Map results by their prompt.
from collections import defaultdict
unique_prompt_map = defaultdict(list)
for result_idx, result in enumerate(results):
unique_prompt_map[result.prompt].append(result_idx)
# Print unique prompts + outputs.
for unique_idx, (prompt_text, result_idxs) in enumerate(unique_prompt_map.items()):
result_idx = result_idxs[0]
result = results[result_idx]
generated_text = result.generated_text.replace("\n", "\\n")
print(
f"{unique_idx}/{len(unique_prompt_map)} [{len(result_idxs)}]. {prompt_text} "
f"... {generated_text}"
)
stats = torch.cuda.memory_stats()
print_rank_0(
"static | cg %d | %s | reqs %d [ batch %d ] ... mem %.1f/%.1f ... time %.3f."
% (
args.cuda_graph_impl == "local",
(
f""
if args.prompts
else " %s, %d, %.1e, %.1e"
% (
"(%s)" % " ".join(map(str, args.num_tokens_to_prompt)),
args.num_tokens_to_generate,
args.incoming_requests_duration,
args.incoming_requests_per_sec,
)
),
len(requests),
args.inference_max_requests,
stats["allocated_bytes.all.peak"] / (1024**3),
stats["reserved_bytes.all.peak"] / (1024**3),
latency,
)
)
# Force immediate process exit to bypass torchrun's atexit NCCL teardown when
# CUDA graphs have captured collectives (see PyTorch issue #115388). This can
# sometimes lead to hangs in the atexit handler.
# We do this only when CUDA graphs are enabled.
if args.cuda_graph_impl != "none":
print(f"[main] rank {torch.distributed.get_rank()}: finished", flush=True)
os._exit(0)
else:
torch.distributed.destroy_process_group()
if __name__ == "__main__":
main()
================================================
FILE: examples/inference/gpt/utils.py
================================================
# Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
import copy
import itertools
import json
import random
import time
from argparse import ArgumentParser, Namespace
from functools import partial
from typing import Any, List, Optional
import torch
from tqdm import tqdm
from megatron.core.inference.contexts import DynamicInferenceContext
from megatron.core.inference.contexts.dynamic_context import get_mem_size_str
from megatron.core.inference.inference_request import DynamicInferenceRequest
from megatron.core.inference.sampling_params import SamplingParams
from megatron.core.transformer.module import MegatronModule
from megatron.training import get_args
def get_default_sampling_params(termination_id: int = None):
return SamplingParams(
temperature=1.0,
top_k=1,
top_p=0.0,
return_log_probs=False,
num_tokens_to_generate=30,
termination_id=termination_id,
)
def get_curr_time() -> float:
"""Get synchronized time across ranks."""
curr_time = torch.cuda.LongTensor([time.time_ns()])
if torch.distributed.is_initialized():
torch.distributed.broadcast(curr_time, src=0)
return curr_time.item() / 10**9
class Request:
"""Class to hold attributes for a single request.
A request is initialized with its prompt text. As it is added, processed,
and completed through the inference engine, the request is populated with its
start time, end time, and output tokens.
Args:
prompt_text (str): Prompt text.
time_offset (float): Artificial time offset for simulating incoming
requests. This value is later added to the `base_arrival_time` to
simulate the requests arrival time.
tokenizer (Any): Tokenizer for tokenizing the prompt.
"""
def __init__(
self,
prompt_text: str,
time_offset: float,
tokenizer: Any,
sampling_params: SamplingParams = None,
):
self.prompt_text = prompt_text
self.prompt_tokens = tokenizer.tokenize(prompt_text)
self.output_text = None
self.output_tokens = []
self.time_offset = time_offset
self.time_arrival = None
self.time_start = None
self.time_end = None
self.ttft = None # Time-to-first-token in seconds
self.state = "not-started"
self.sampling_params: SamplingParams = (
sampling_params
if sampling_params is not None
else get_default_sampling_params(tokenizer.eod)
)
self.sampling_params = copy.deepcopy(self.sampling_params)
def __str__(self) -> str:
return "state '%s'; toffset %.1e; prompt len %d; output len %d; '%s'" % (
self.state,
self.time_offset,
len(self.prompt_tokens),
len(self.output_tokens),
self.prompt_text,
)
def get_time_offsets(
seed: int | None,
incoming_requests_per_step: int,
incoming_requests_per_sec: float,
num_requests: int,
) -> list[float]:
"""Get example time offsets."""
# Time offsets to add all requests at once.
if incoming_requests_per_step is not None or incoming_requests_per_sec <= 0:
return [-1] * num_requests
# if num_requests is not None:
incoming_requests_duration = num_requests / incoming_requests_per_sec
incoming_requests_duration *= 2 # extra margin, to accomodate time sampling
random.seed(seed)
import simpy # Guard against this import in test case
# Generate random time offsets.
def arrival(r):
while True:
yield env.timeout(random.expovariate(r))
time_offsets.append(env.now)
time_offsets = []
env = simpy.Environment()
env.process(arrival(incoming_requests_per_sec))
env.run(incoming_requests_duration)
# Ensure at least a single request.
if len(time_offsets) == 0:
time_offsets = [0.0]
# Ensure first time is 0.
time_offsets = [to - time_offsets[0] for to in time_offsets]
# Truncate to num_requests.
assert len(time_offsets) >= num_requests
time_offsets = time_offsets[:num_requests]
return time_offsets
def get_cli_requests(
args: Namespace, tokenizer: Any, sampling_params: Optional[SamplingParams] = None
) -> list[Request]:
# Get time offsets.
t_offsets = get_time_offsets(
args.seed,
args.incoming_requests_per_step,
args.incoming_requests_per_sec,
len(args.prompts),
)
# Init requests.
requests = [Request(p, t, tokenizer, sampling_params) for p, t in zip(args.prompts, t_offsets)]
return requests
def get_synthetic_requests(
args: Namespace, tokenizer: Any, sampling_params: Optional[SamplingParams] = None
) -> list[Request]:
"""Get example requests."""
# Get time offsets.
time_offsets = get_time_offsets(
args.seed,
args.incoming_requests_per_step,
args.incoming_requests_per_sec,
int(args.incoming_requests_per_sec * args.incoming_requests_duration),
)
# Build prompts with expected lengths.
assert (
len(args.num_tokens_to_prompt) == 2
and args.num_tokens_to_prompt[1] >= args.num_tokens_to_prompt[0]
)
max_prompt_length = args.num_tokens_to_prompt[1]
max_prompt_text = "hi " * max_prompt_length
max_prompt_tokens = tokenizer.tokenize(max_prompt_text)
prompt_lengths = [random.randint(*args.num_tokens_to_prompt) for _ in time_offsets]
prompt_tokens_list = [max_prompt_tokens[:l] for l in prompt_lengths]
prompt_texts = [tokenizer.detokenize(tt) for tt in prompt_tokens_list]
# Init requests.
assert len(prompt_texts) == len(time_offsets)
requests = [
Request(t, o, tokenizer, sampling_params=sampling_params)
for t, o in zip(prompt_texts, time_offsets)
]
return requests
def get_requests_from_file(
args: Namespace, tokenizer: Any, sampling_params: Optional[SamplingParams] = None
) -> list[Request]:
"""Get requests from a file."""
if not args.prompt_file:
raise ValueError("Prompt file is required to read requests from a file.")
# Load prompts.
n_prompts = sum(1 for _ in open(args.prompt_file))
prompts = []
if sampling_params is None:
sampling_params = get_default_sampling_params(tokenizer.eod)
sampling_params_list = []
with open(args.prompt_file) as f:
for line in tqdm(f.readlines(), "read prompt file", total=n_prompts):
line_dict = json.loads(line)
prompts.append(line_dict["text"])
sp = copy.deepcopy(sampling_params)
if args.num_tokens_from_file:
sp.num_tokens_to_generate = line_dict["chatgpt_output_token_length"]
sampling_params_list.append(sp)
if len(prompts) == args.prompt_file_num_truncate:
break
# Get time offsets.
time_offsets: list[float] = get_time_offsets(
args.seed, args.incoming_requests_per_step, args.incoming_requests_per_sec, len(prompts)
)
# Init requests.
requests = [
Request(p, t, tokenizer, sp)
for p, t, sp in tqdm(
zip(prompts, time_offsets, sampling_params_list), "init requests", total=len(prompts)
)
]
return requests
def build_requests(
args: Namespace, tokenizer: Any, sampling_params: Optional[SamplingParams] = None
) -> list[Request]:
# Check if we have any prompts (from command line or JSONL)
if args.prompts:
if args.prompt_file:
raise ValueError("Cannot use both --prompts and --prompt-file")
return get_cli_requests(args, tokenizer, sampling_params)
elif args.prompt_file:
return get_requests_from_file(args, tokenizer, sampling_params)
else:
return get_synthetic_requests(args, tokenizer, sampling_params)
def get_model_size_str(model):
n = sum(p.numel() for p in model.parameters())
for exp, suffix in ((12, "t"), (9, "b"), (6, "m"), (3, "k"), (0, "")):
nquery = int(10**exp)
if n > nquery:
return "%d%s" % (n // nquery, suffix)
raise Exception("something went wrong.")
def build_dynamic_engine_setup_prefix(
args: Namespace,
model: MegatronModule,
context: DynamicInferenceContext,
requests: list[DynamicInferenceRequest],
):
"""
Returns a compact, pipe-separated summary of the dynamic-batching setup.
Example output:
`dynamic | cg True | prompts: synth(16 256), n 1024, g 512, t 1.0e+02 5.0e-01 | bf 4, 1.2 [r 1024, t 8192] | gtd 0.50 [r 512] | reqs 100` # pylint: disable=line-too-long
Args:
args (Namespace): Command-line arguments for this run.
context (DynamicInferenceContext): Stores limits such as `max_requests`,
`max_tokens`, and `gtd_request_count`.
requests (List[DynamicInferenceRequest]): List of inference requests.
Returns:
A configuration string for logging.
"""
# CUDA graph config
if args.cuda_graph_impl == "local":
cg_str = f"graphs {len(context.cuda_graph_batch_dimensions_list)}"
else:
cg_str = "--"
# Unified memory (UVM).
uvm_str = f"uvm {int(context.unified_memory_level)}"
# Prompt description
prompt_src_str = (
"cli"
if args.prompts
else (
"file"
if args.prompt_file
else f"synth({', '.join(map(str, args.num_tokens_to_prompt))})"
)
)
request_str = (
f"requests: {prompt_src_str}, " f"n {len(requests):d}, g {args.num_tokens_to_generate:d}, "
)
request_str += (
f"dur {args.incoming_requests_duration:.1e} " f"r/sec {args.incoming_requests_per_sec:.1e}"
if args.incoming_requests_per_step is None
else f"r/step {args.incoming_requests_per_step}"
)
# Buffer limits config
buffer_limits_str = (
f"bf: {get_mem_size_str(args.inference_dynamic_batching_buffer_size_gb*1024**3)}, "
f"{context.kv_block_allocator.active_count} chunks "
f"[r {context.max_requests}, t {context.max_tokens}]"
)
parts = [get_model_size_str(model), "dynamic", cg_str, uvm_str, request_str, buffer_limits_str]
return " | ".join(parts)
def get_global_peak_memory_stats_bytes() -> dict:
"""Peak allocated CUDA memory aggregated across ranks (MAX), in bytes.
Uses `torch.cuda.max_memory_allocated()` and assumes peak stats were reset
before the benchmark run.
"""
peak_alloc = int(torch.cuda.max_memory_allocated())
if torch.distributed.is_available() and torch.distributed.is_initialized():
t = torch.tensor([peak_alloc], device="cuda", dtype=torch.int64)
torch.distributed.all_reduce(t, op=torch.distributed.ReduceOp.MAX)
peak_alloc = int(t[0].item())
return {"mem-max-allocated-bytes": peak_alloc}
================================================
FILE: examples/inference/llama_mistral/huggingface_reference.py
================================================
import argparse
from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer
# Set up argument parsing
parser = argparse.ArgumentParser(description="Script for text generation with a specific model and prompt.")
parser.add_argument('--prompt', type=str, required=True, help="Prompt text to use for text generation")
parser.add_argument('--model-path', type=str, required=True, help="Path to the Huggingface model checkpoint")
# Parse command-line arguments
args = parser.parse_args()
model_path = args.model_path
prompt = args.prompt
config = AutoConfig.from_pretrained(model_path)
tokenizer = AutoTokenizer.from_pretrained(model_path, config=config)
model = AutoModelForCausalLM.from_pretrained(model_path, config=config).cuda()
inputs = tokenizer(prompt, return_tensors="pt")
for key in inputs:
inputs[key] = inputs[key].cuda()
# top_k, top_p and do_sample are set for greedy argmax based sampling
outputs = model.generate(**inputs, max_length=100, do_sample=False, top_p=0, top_k=0, temperature=1.0)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
================================================
FILE: examples/inference/llama_mistral/run_static_inference_llama4_scout.sh
================================================
#!/bin/bash
export CUDA_DEVICE_MAX_CONNECTIONS=1
export NVTE_APPLY_QK_LAYER_SCALING=0
DISTRIBUTED_ARGS="--nproc_per_node 8 \
--nnodes 1 \
--node_rank 0 \
--master_addr 0.0.0.0 \
--master_port 6000"
# Fill in checkpoint path to Llama 4 Scout to run
CHECKPOINT=
PROMPTS="What is the capital of France?"
TOKENS_TO_GENERATE=4
MAX_BATCH_SIZE=2
MODEL_ARGS=" \
--micro-batch-size 1 \
--bf16 \
--no-masked-softmax-fusion \
--disable-bias-linear \
--untie-embeddings-and-output-weights \
--position-embedding-type rope \
--no-rope-fusion \
--normalization RMSNorm \
--swiglu \
--num-layers 48 \
--hidden-size 5120 \
--ffn-hidden-size 16384 \
--num-attention-heads 40 \
--group-query-attention \
--num-query-groups 8 \
--qk-layernorm \
--num-experts 16 \
--moe-ffn-hidden-size 8192 \
--moe-router-score-function sigmoid \
--moe-router-topk 1 \
--moe-router-topk-scaling-factor 1.0 \
--moe-shared-expert-intermediate-size 8192 \
--moe-aux-loss-coeff 1e-3 \
--moe-token-dispatcher-type alltoall \
--moe-token-drop-policy probs \
--moe-router-load-balancing-type seq_aux_loss \
--seq-length 4096 \
--max-position-embeddings 4096 \
--tokenizer-type HuggingFaceTokenizer \
--make-vocab-size-divisible-by 128 \
--use-mcore-models \
--rotary-interleaved \
--rotary-percent 1.0 \
--rotary-base 500000 \
--rope-scaling-factor 8.0 \
--use-rope-scaling \
--no-bias-swiglu-fusion \
--qk-l2-norm \
--moe-apply-probs-on-input \
--moe-router-dtype fp64 \
"
torchrun $DISTRIBUTED_ARGS -m examples.inference.gpt.gpt_static_inference \
--load ${CHECKPOINT} \
--tokenizer-model unsloth/Llama-4-Scout-17B-16E-Instruct \
--dist-ckpt-strictness log_unexpected \
--tensor-model-parallel-size 8 \
--prompts ${PROMPTS} \
--num-tokens-to-generate ${TOKENS_TO_GENERATE} \
--max-batch-size ${MAX_BATCH_SIZE} \
${MODEL_ARGS}
================================================
FILE: examples/inference/llama_mistral/run_text_generation_llama3.1.sh
================================================
#!/bin/bash
# This example will start serving the Llama3.1-8B model
export NCCL_IB_SL=1
export CUDA_DEVICE_MAX_CONNECTIONS=1
export NVTE_APPLY_QK_LAYER_SCALING=0
DISTRIBUTED_ARGS="--nproc_per_node 1 \
--nnodes 1 \
--node_rank 0 \
--master_addr 0.0.0.0 \
--master_port 6000"
# Ensure CHECKPOINT and TOKENIZER_MODEL are provided
if [ -z "$1" ] || [ -z "$2" ]; then
echo "Error: You must provide CHECKPOINT and TOKENIZER_MODEL as command-line arguments."
echo "Usage: $0 /path/to/checkpoint /path/to/tokenizer_model"
exit 1
fi
# Assign command-line arguments to variables
CHECKPOINT=$1
TOKENIZER_MODEL=$2
pip install flask-restful
torchrun $DISTRIBUTED_ARGS tools/run_text_generation_server.py \
--use-checkpoint-args \
--disable-bias-linear \
--tokenizer-type HuggingFaceTokenizer \
--tokenizer-model ${TOKENIZER_MODEL} \
--transformer-impl transformer_engine \
--normalization RMSNorm \
--group-query-attention \
--num-query-groups 8 \
--no-masked-softmax-fusion \
--attention-softmax-in-fp32 \
--attention-dropout 0.0 \
--hidden-dropout 0.0 \
--untie-embeddings-and-output-weights \
--position-embedding-type rope \
--rotary-percent 1.0 \
--rotary-base 500000 \
--use-rope-scaling \
--use-rotary-position-embeddings \
--swiglu \
--tensor-model-parallel-size 1 \
--pipeline-model-parallel-size 1 \
--num-layers 32 \
--hidden-size 4096 \
--ffn-hidden-size 14336 \
--load ${CHECKPOINT} \
--num-attention-heads 32 \
--max-position-embeddings 131072 \
--bf16 \
--micro-batch-size 1 \
--seq-length 8192
================================================
FILE: examples/inference/llama_mistral/run_text_generation_llama3.sh
================================================
#!/bin/bash
# This example will start serving the Llama3-8B model
export NCCL_IB_SL=1
export CUDA_DEVICE_MAX_CONNECTIONS=1
export NVTE_APPLY_QK_LAYER_SCALING=0
DISTRIBUTED_ARGS="--nproc_per_node 1 \
--nnodes 1 \
--node_rank 0 \
--master_addr 0.0.0.0 \
--master_port 6000"
# Ensure CHECKPOINT and TOKENIZER_MODEL are provided
if [ -z "$1" ] || [ -z "$2" ]; then
echo "Error: You must provide CHECKPOINT and TOKENIZER_MODEL as command-line arguments."
echo "Usage: $0 /path/to/checkpoint /path/to/tokenizer_model"
exit 1
fi
# Assign command-line arguments to variables
CHECKPOINT=$1
TOKENIZER_MODEL=$2
pip install flask-restful
torchrun $DISTRIBUTED_ARGS tools/run_text_generation_server.py \
--use-checkpoint-args \
--disable-bias-linear \
--tokenizer-type HuggingFaceTokenizer \
--tokenizer-model ${TOKENIZER_MODEL} \
--transformer-impl transformer_engine \
--normalization RMSNorm \
--group-query-attention \
--num-query-groups 8 \
--no-masked-softmax-fusion \
--attention-softmax-in-fp32 \
--attention-dropout 0.0 \
--hidden-dropout 0.0 \
--untie-embeddings-and-output-weights \
--position-embedding-type rope \
--rotary-percent 1.0 \
--rotary-base 500000 \
--use-rotary-position-embeddings \
--swiglu \
--tensor-model-parallel-size 1 \
--pipeline-model-parallel-size 1 \
--num-layers 32 \
--hidden-size 4096 \
--ffn-hidden-size 14336 \
--load ${CHECKPOINT} \
--num-attention-heads 32 \
--max-position-embeddings 8192 \
--bf16 \
--micro-batch-size 1 \
--seq-length 8192
================================================
FILE: examples/inference/llama_mistral/run_text_generation_mistral.sh
================================================
#!/bin/bash
# This example will start serving the Mistral-7B-v0.3 model
export NCCL_IB_SL=1
export CUDA_DEVICE_MAX_CONNECTIONS=1
DISTRIBUTED_ARGS="--nproc_per_node 1 \
--nnodes 1 \
--node_rank 0 \
--master_addr 0.0.0.0 \
--master_port 6000"
# Ensure CHECKPOINT and TOKENIZER_MODEL are provided
if [ -z "$1" ] || [ -z "$2" ]; then
echo "Error: You must provide CHECKPOINT and TOKENIZER_MODEL as command-line arguments."
echo "Usage: $0 /path/to/checkpoint /path/to/tokenizer_model"
exit 1
fi
# Assign command-line arguments to variables
CHECKPOINT=$1
TOKENIZER_MODEL=$2
pip install flask-restful
torchrun $DISTRIBUTED_ARGS tools/run_text_generation_server.py \
--tokenizer-type HuggingFaceTokenizer \
--tokenizer-model ${TOKENIZER_MODEL} \
--use-checkpoint-args \
--apply-layernorm-1p \
--transformer-impl transformer_engine \
--normalization RMSNorm \
--group-query-attention \
--num-query-groups 8 \
--no-masked-softmax-fusion \
--use-flash-attn \
--untie-embeddings-and-output-weights \
--disable-bias-linear \
--position-embedding-type rope \
--rotary-percent 1.0 \
--rotary-base 1000000 \
--swiglu \
--ffn-hidden-size 14336 \
--tensor-model-parallel-size 1 \
--pipeline-model-parallel-size 1 \
--num-layers 32 \
--hidden-size 4096 \
--load ${CHECKPOINT} \
--num-attention-heads 32 \
--max-position-embeddings 4096 \
--bf16 \
--micro-batch-size 1 \
--seq-length 4096 \
--seed 101
================================================
FILE: examples/inference/run_text_generation_server_345M.sh
================================================
#!/bin/bash
# This example will start serving the 345M model.
DISTRIBUTED_ARGS="--nproc_per_node 1 \
--nnodes 1 \
--node_rank 0 \
--master_addr localhost \
--master_port 6000"
CHECKPOINT=
VOCAB_FILE=
MERGE_FILE=
export CUDA_DEVICE_MAX_CONNECTIONS=1
pip install flask-restful
torchrun $DISTRIBUTED_ARGS tools/run_text_generation_server.py \
--tensor-model-parallel-size 1 \
--pipeline-model-parallel-size 1 \
--num-layers 24 \
--hidden-size 1024 \
--load ${CHECKPOINT} \
--num-attention-heads 16 \
--max-position-embeddings 1024 \
--tokenizer-type GPT2BPETokenizer \
--fp16 \
--micro-batch-size 1 \
--seq-length 1024 \
--vocab-file $VOCAB_FILE \
--merge-file $MERGE_FILE \
--seed 42
================================================
FILE: examples/inference/run_text_generation_server_345M_8_tensor_parallel.sh
================================================
#!/bin/bash
# This example will start serving the 345M model that is partitioned 8 way tensor parallel
DISTRIBUTED_ARGS="--nproc_per_node 8 \
--nnodes 1 \
--node_rank 0 \
--master_addr localhost \
--master_port 6000"
CHECKPOINT=
VOCAB_FILE=
MERGE_FILE=
pip install flask-restful
python -m torch.distributed.launch $DISTRIBUTED_ARGS tools/run_text_generation_server.py \
--tensor-model-parallel-size 8 \
--pipeline-model-parallel-size 1 \
--num-layers 24 \
--hidden-size 1024 \
--load ${CHECKPOINT} \
--num-attention-heads 16 \
--max-position-embeddings 1024 \
--tokenizer-type GPT2BPETokenizer \
--fp16 \
--micro-batch-size 1 \
--seq-length 1024 \
--vocab-file $VOCAB_FILE \
--merge-file $MERGE_FILE \
--seed 42
================================================
FILE: examples/inference/t5/simple_t5_batch_inference.py
================================================
# Copyright (c) 2026, NVIDIA CORPORATION. All rights reserved.
import os
import sys
from argparse import Namespace
import torch
import pretrain_t5
from megatron.core.inference.engines import AbstractEngine, StaticInferenceEngine
from megatron.core.inference.inference_request import InferenceRequest
from megatron.core.inference.model_inference_wrappers.inference_wrapper_config import (
InferenceWrapperConfig,
)
from megatron.core.inference.model_inference_wrappers.t5.t5_inference_wrapper import (
T5InferenceWrapper,
)
from megatron.core.inference.sampling_params import SamplingParams
from megatron.core.inference.text_generation_controllers.encoder_decoder_text_generation_controller import (
EncoderDecoderTextGenerationController,
)
from megatron.core.tokenizers.utils.build_tokenizer import build_tokenizer
from megatron.core.transformer.module import MegatronModule
from pretrain_t5 import model_provider
sys.path.append(
os.path.abspath(os.path.join(os.path.dirname(__file__), os.path.pardir, os.path.pardir))
)
from typing import List
from megatron.core import mpu
from megatron.training import get_args, get_model, get_tokenizer
from megatron.training.checkpointing import load_checkpoint
from megatron.training.initialize import initialize_megatron
def add_text_generate_args(parser):
"""Text generation arguments."""
group = parser.add_argument_group(title='text generation')
group.add_argument("--temperature", type=float, default=1.0, help='Sampling temperature.')
group.add_argument("--top_k", type=int, default=1, help='Top k sampling.')
group.add_argument("--top_p", type=float, default=0.0, help='Top p sampling.')
group.add_argument(
"--return-log-probs",
action='store_true',
default=False,
help='Return the log probabilities of the final output tokens',
)
group.add_argument(
"--num-tokens-to-generate",
type=int,
default=30,
help='Number of tokens to generate for each prompt',
)
group.add_argument(
"--encoder-prompts",
metavar='N',
type=str,
nargs='+',
help='Encoder input prompts with each prompt within quotes and separated by space',
)
group.add_argument(
"--max-batch-size", type=int, default=1, help='Max number of prompts to process at once'
)
return parser
def get_inference_engine(args: Namespace, model: MegatronModule) -> AbstractEngine:
"""Utility to get the relevant backend for running inference
This function will automatically chose the TRTLLMBackend when possible, and if not revert to Mcore backend if the user does not specify any backends. TRT LLM Backend is not implmented yet.
Args:
args (Namespace): The user arguments parsed from command line
model (MegatronModule): The megatron model .
Returns:
AbstractBackend: The chosen backend
"""
# Build tokenizer
tokenizer = build_tokenizer(args)
inference_wrapper_config = InferenceWrapperConfig(
hidden_size=args.hidden_size,
inference_batch_times_seqlen_threshold=args.inference_batch_times_seqlen_threshold,
fp32_residual_connection=args.fp32_residual_connection,
params_dtype=args.params_dtype,
padded_vocab_size=args.padded_vocab_size,
)
inference_wrapped_model = T5InferenceWrapper(model, inference_wrapper_config)
text_generation_controller = EncoderDecoderTextGenerationController(
inference_wrapped_model=inference_wrapped_model, tokenizer=tokenizer
)
return StaticInferenceEngine(
text_generation_controller=text_generation_controller, max_batch_size=args.max_batch_size
)
def main():
"""Main program."""
# Note: The default args passed here can be overwritten by using appropriate params (check arguments.py file)
# Micro batch size is not needed to be set by user. (It is calculated based on inference-batch-times-seqlen-threshold argument)
initialize_megatron(
extra_args_provider=add_text_generate_args,
args_defaults={
'no_load_rng': True,
'no_load_optim': True,
'micro_batch_size': 1,
'exit_on_missing_checkpoint': True,
},
)
# Set up model and load checkpoint
model = get_model(model_provider, wrap_with_ddp=False)
load_checkpoint(model, None, None)
model = model[0]
args = get_args()
inference_engine = get_inference_engine(args, model)
sampling_params = SamplingParams(
temperature=args.temperature,
top_k=args.top_k,
top_p=args.top_p,
return_log_probs=args.return_log_probs,
num_tokens_to_generate=args.num_tokens_to_generate,
)
# Build tokenizer
tokenizer = build_tokenizer(args)
decoder_prompts = [""] * len(
args.encoder_prompts
) # for T5, the prompt is provided as encoder input, hence decoder_prompts is empty
args.prompts = decoder_prompts
results: List[InferenceRequest] = inference_engine.generate(
prompts=args.prompts,
add_BOS=True,
encoder_prompts=args.encoder_prompts,
sampling_params=sampling_params,
)
if torch.distributed.get_rank() == 0:
for idx, result in enumerate(results):
print(f' \n------------- RESULT FOR PROMPT {idx} --------------- ')
result = {
'id': result.request_id,
'input_prompt': result.prompt,
'generated_text': result.generated_text,
'generated_tokens': result.generated_tokens,
}
print(result)
if __name__ == "__main__":
main()
================================================
FILE: examples/llama/README.md
================================================
# Llama Models
## Table of contents
- [1. Overview](#1-overview)
- [2. Prerequisites](#2-prerequisites)
- [3. Training Setup](#3-training-setup)
- [4. Configuration](#4-configuration)
- [5. Test Datasets](#5-test-datasets)
- [6. FP8 Debugging](#6-fp8-debugging)
## 1. Overview
Train Llama models using FP8 precision with Megatron-Core.
## 2. Prerequisites
```bash
# Clone repository
export HOST_MEGATRON_LM_DIR="/path/to/your/host/megatron-lm"
git clone https://github.com/NVIDIA/Megatron-LM.git "$HOST_MEGATRON_LM_DIR"
cd "$HOST_MEGATRON_LM_DIR"
git checkout "core_r0.12.0"
# Set paths
export HOST_CHECKPOINT_PATH="./checkpoints/llama3_8b_fp8"
export HOST_TENSORBOARD_LOGS_PATH="./tensorboard_logs/llama3_8b_fp8"
# Optional: For real data
# export HOST_TOKENIZER_MODEL_PATH="/path/to/host/tokenizer.model"
# export HOST_DATA_PREFIX="/path/to/host/mydata_prefix"
```
## 3. Training Setup
### Using Mock Data
```bash
PYTORCH_IMAGE="nvcr.io/nvidia/pytorch:25.03-py3"
docker run --rm --gpus all --ipc=host --ulimit memlock=-1 \
-v "${HOST_MEGATRON_LM_DIR}:/workspace/megatron-lm" \
-v "${HOST_CHECKPOINT_PATH}:/workspace/checkpoints" \
-v "${HOST_TENSORBOARD_LOGS_PATH}:/workspace/tensorboard_logs" \
--workdir /workspace/megatron-lm \
$PYTORCH_IMAGE \
bash examples/llama/train_llama3_8b_h100_fp8.sh \
/workspace/checkpoints \
/workspace/tensorboard_logs \
2>&1 | tee "${HOST_TENSORBOARD_LOGS_PATH}/training_mock_$(date +'%y-%m-%d_%H-%M-%S').log"
```
### Using Custom Data and Tokenizer
```bash
PYTORCH_IMAGE="nvcr.io/nvidia/pytorch:25.03-py3"
docker run --rm --gpus all --ipc=host --ulimit memlock=-1 \
-v "${HOST_MEGATRON_LM_DIR}:/workspace/megatron-lm" \
-v "${HOST_CHECKPOINT_PATH}:/workspace/checkpoints" \
-v "${HOST_TENSORBOARD_LOGS_PATH}:/workspace/tensorboard_logs" \
-v "${HOST_TOKENIZER_MODEL_PATH}:/workspace/tokenizer_model" \
-v "$(dirname "${HOST_DATA_PREFIX}"):/workspace/data_dir" \
--workdir /workspace/megatron-lm \
$PYTORCH_IMAGE \
bash examples/llama/train_llama3_8b_h100_fp8.sh \
/workspace/checkpoints \
/workspace/tensorboard_logs \
/workspace/tokenizer_model \
"/workspace/data_dir/$(basename "${HOST_DATA_PREFIX}")" \
2>&1 | tee "${HOST_TENSORBOARD_LOGS_PATH}/training_custom_$(date +'%y-%m-%d_%H-%M-%S').log"
```
## 4. Configuration
Default parallelism strategy:
- Tensor Parallel: 1
- Pipeline Parallel: 1
- Context Parallel: 2
Llama-3-8B architecture:
- 32 layers
- Hidden size: 4096
- FFN hidden size: 14336
- Attention heads: 32
- Query groups: 8
- Sequence length: 8192
- RMSNorm normalization with SwiGLU and RoPE
Key training parameters:
- Micro-batch size: 1
- Global batch size: 128
- Learning rate: 1.5e-4
- Min learning rate: 1.0e-5
- Weight decay: 0.1
- FP8 format: hybrid
You can modify these parameters directly in the `train_llama3_8b_h100_fp8.sh` script.
This configuration follows those defined in NeMo Framework's performance scripts, which can be found at [https://github.com/NVIDIA/NeMo/tree/main/scripts/performance](https://github.com/NVIDIA/NeMo/tree/main/scripts/performance).
### FP8 Performance
| Model | #-GPUs | GBS | MBS | Seq Length | TP | PP | CP | VP | EP | GA | Tokens/sec/GPU | TFLOP/sec/GPU |
|-------|--------|-----|-----|------------|----|----|----|----|----|----|----------------|---------------|
| LLAMA3-8B | 8 | 128 | 1 | 8192 | 1 | 1 | 2 | 1 | 1 | 32 | 13812 | 800 |
| LLAMA3-70B | 64 | 128 | 1 | 8192 | 4 | 8 | 1 | 5 | 1 | 64 | 1621 | 780 |
| LLAMA3-405B | 1024 | 512 | 1 | 8192 | 8 | 8 | 2 | 8 | 1 | 64 | 315 | 834 |
Legend:
- GBS: Global Batch Size
- MBS: Micro Batch Size
- TP: Tensor Parallel size
- PP: Pipeline Parallel size
- CP: Context Parallel size
- VP: Virtual Pipeline stages
- EP: Expert Parallel size
- GA: Gradient Accumulation steps
As NeMo uses Megatron-Core, for the latest performance benchmarks, please refer to the official [NeMo documentation](https://docs.nvidia.com/nemo-framework/user-guide/latest/performance/performance-summary.html).
## 5. Test Datasets
Recommended datasets:
1. **WikiText-103**: https://huggingface.co/datasets/Salesforce/wikitext
Preprocess datasets:
```bash
python "${HOST_MEGATRON_LM_DIR}/tools/preprocess_data.py" \
--input your_dataset.json \
--output-prefix test_dataset \
--tokenizer-type HuggingFaceTokenizer \
--tokenizer-model /path/to/tokenizer.model \
--append-eod
```
## 6. FP8 Training Considerations
- **Hardware**: Requires NVIDIA Hopper, Ada, or Blackwell GPUs for FP8 support
- **Troubleshooting**: If you encounter NaN values or instability with FP8 training, please refer to [Transformer Engine](https://github.com/NVIDIA/TransformerEngine).
================================================
FILE: examples/llama/train_llama3_8b_h100_fp8.sh
================================================
#!/bin/bash
# Environment variables for performance tuning
export CUDA_DEVICE_MAX_CONNECTIONS=${CUDA_DEVICE_MAX_CONNECTIONS:-1}
#export LOG_LEVEL=${LOG_LEVEL:-INFO}
#export NCCL_IB_TIMEOUT=${NCCL_IB_TIMEOUT:-19}
#export NVTE_FWD_LAYERNORM_SM_MARGIN=${NVTE_FWD_LAYERNORM_SM_MARGIN:-16}
#export NVTE_BWD_LAYERNORM_SM_MARGIN=${NVTE_BWD_LAYERNORM_SM_MARGIN:-16}
#export NCCL_P2P_NET_CHUNKSIZE=${NCCL_P2P_NET_CHUNKSIZE:-2097152}
#export NCCL_AVOID_RECORD_STREAMS=${NCCL_AVOID_RECORD_STREAMS:-1}
CHECKPOINT_PATH=${1:-"checkpoints/llama3_8b_fp8"}
TENSORBOARD_LOGS_PATH=${2:-"tensorboard_logs/llama3_8b_fp8"}
TOKENIZER_ARG=${3:-"MOCK"} # Path to tokenizer model, or "MOCK"
DATA_ARG=${4:-"MOCK"} # Data prefix, or "MOCK"
# Create directories if they don't exist
mkdir -p "$(dirname "$CHECKPOINT_PATH")"
mkdir -p "$(dirname "$TENSORBOARD_LOGS_PATH")"
# Distributed training setup
GPUS_PER_NODE=8
NUM_NODES=1
MASTER_ADDR=${MASTER_ADDR:-localhost}
MASTER_PORT=${MASTER_PORT:-6000}
NODE_RANK=${NODE_RANK:-0}
WORLD_SIZE=$(($GPUS_PER_NODE*$NUM_NODES))
# Path to the pretrain_gpt.py script, assuming this script is run from the root of the Megatron-LM repository
PRETRAIN_SCRIPT_PATH="pretrain_gpt.py"
# Fixed model and training parameters
TP_SIZE=1
CP_SIZE=1
PP_SIZE=1
MICRO_BATCH_SIZE=1
GLOBAL_BATCH_SIZE=128
NUM_LAYERS=32
DTYPE="fp8"
SEQ_LENGTH=8192
MAX_POSITION_EMBEDDINGS=8192
# Data cache path (useful for both mock and real data)
DATA_CACHE_PATH="${PWD}/benchmark_cache_llama3_8b_fp8"
mkdir -p "$DATA_CACHE_PATH"
DISTRIBUTED_ARGS=(
--nproc_per_node $GPUS_PER_NODE
--nnodes $NUM_NODES
--node_rank $NODE_RANK
--master_addr $MASTER_ADDR
--master_port $MASTER_PORT
)
MODEL_ARGS=(
--use-mcore-models
--num-layers $NUM_LAYERS
--hidden-size 4096
--ffn-hidden-size 14336
--num-attention-heads 32
--group-query-attention
--num-query-groups 8
--kv-channels 128
--seq-length $SEQ_LENGTH
--max-position-embeddings $MAX_POSITION_EMBEDDINGS
--position-embedding-type rope
--rotary-base 1000000
--rotary-percent 1.0
--attention-dropout 0.0
--hidden-dropout 0.0
--swiglu
--normalization RMSNorm
--init-method-std 0.0134
--attention-backend fused
--apply-layernorm-1p
--untie-embeddings-and-output-weights
--disable-bias-linear
)
TRAINING_ARGS=(
--micro-batch-size $MICRO_BATCH_SIZE
--global-batch-size $GLOBAL_BATCH_SIZE
--train-samples 1953125000
--lr-decay-samples 1949218748
--lr-warmup-samples 3906252
--lr 0.00015
--min-lr 0.00001
--decoupled-lr 5.0e-4 # Specific to decoupled AdamW, ensure optimizer is compatible
--decoupled-min-lr 4.5e-5 # Specific to decoupled AdamW
--lr-decay-style cosine
--clip-grad 1.0
--weight-decay 0.1
--adam-beta1 0.9
--adam-beta2 0.95
--bf16
--grad-reduce-in-bf16
--cross-entropy-loss-fusion
--calculate-per-token-loss
--manual-gc
--empty-unused-memory-level 1
--exit-duration-in-mins 235
)
# Conditional arguments based on DTYPE (FP8)
DTYPE_ARGS=()
if [[ "$DTYPE" == "fp8" ]]; then
DTYPE_ARGS+=(
"--fp8-format hybrid"
"--fp8-amax-history-len 1024"
"--fp8-amax-compute-algo max"
"--fp8-param-gather"
)
fi
# Model parallelism arguments
MODEL_PARALLEL_ARGS=(
--tensor-model-parallel-size $TP_SIZE
--context-parallel-size $CP_SIZE
# --pipeline-model-parallel-size $PP_SIZE # Not explicitly set in llama script options, assume 1 if not multi-node PP
--sequence-parallel # Always enable sequence parallelism with TP_SIZE=2
)
# Distributed Data Parallel (DDP) arguments
# From original script's ddp_args
DDP_ARGS=(
--use-distributed-optimizer
--overlap-grad-reduce
--overlap-param-gather
)
TRAINING_ARGS+=("${DDP_ARGS[@]}")
# Data arguments (conditional for mock vs real data)
DATA_ARGS_LIST=()
if [[ "$TOKENIZER_ARG" == "MOCK" ]] || [[ "$DATA_ARG" == "MOCK" ]] || [[ -z "$TOKENIZER_ARG" ]]; then
DATA_ARGS_LIST+=(
"--mock-data"
"--tokenizer-type NullTokenizer"
"--vocab-size 128256"
"--data-cache-path ${DATA_CACHE_PATH}"
"--tiktoken-pattern v2"
"--split '99,1,0'"
"--no-create-attention-mask-in-dataloader"
"--no-mmap-bin-files"
"--num-workers 1"
)
else
# Settings for real data
DATA_ARGS_LIST+=(
"--data-path $DATA_ARG"
"--tokenizer-type HuggingFaceTokenizer"
"--tokenizer-model $TOKENIZER_ARG"
"--data-cache-path ${DATA_CACHE_PATH}"
"--split '99,1,0'"
"--no-create-attention-mask-in-dataloader"
"--no-mmap-bin-files"
"--num-workers 1"
# Note: --vocab-size might be inferred by HuggingFaceTokenizer or might need to be explicit.
"--vocab-size 128256"
)
fi
EVAL_AND_LOGGING_ARGS=(
--log-interval 1
--eval-iters 32
--eval-interval 100
--save-interval 1000
--log-throughput
--profile
--profile-step-start 4
--profile-step-end 6
--ckpt-format torch_dist
--distributed-timeout-minutes 60
--save "$CHECKPOINT_PATH"
--load "$CHECKPOINT_PATH"
--tensorboard-dir "$TENSORBOARD_LOGS_PATH"
)
# Ensure pretrain_gpt.py is found
if [ ! -f "$PRETRAIN_SCRIPT_PATH" ]; then
echo "Error: pretrain_gpt.py not found at $PRETRAIN_SCRIPT_PATH"
echo "Please ensure you are running this script from the root of the Megatron-LM repository, and pretrain_gpt.py is present."
exit 1
fi
# Run the training command
torchrun ${DISTRIBUTED_ARGS[@]} \
"$PRETRAIN_SCRIPT_PATH" \
${MODEL_ARGS[@]} \
${TRAINING_ARGS[@]} \
${DTYPE_ARGS[@]} \
${MODEL_PARALLEL_ARGS[@]} \
${DATA_ARGS_LIST[@]} \
${EVAL_AND_LOGGING_ARGS[@]}
set +x
================================================
FILE: examples/mamba/.gitignore
================================================
checkpoints/
data-cache/
tensorboard/
triton-cache/
================================================
FILE: examples/mamba/Dockerfile
================================================
FROM nvcr.io/nvidia/pytorch:24.01-py3
RUN pip uninstall -y triton && \
pip install triton==2.1.0 sentencepiece==0.1.99 flask-restful
# The causal-conv1d and mamba-ssm packages below are built from scratch here
# (which takes significant time) because there are no wheels available on PyPI
# for these relatively newer versions of the packages that are compatible with
# the older NGC-variant PyTorch version (e.g. version 2.2.0.dev231106) that we
# are using (in the NGC base container). Generally, if the package is not
# compatible with the PyTorch version, then it will generate a Python import
# error. The package authors tend to only release wheels for new versions of
# these pacakges which are compatible with the versions of regular PyTorch and
# NGC-variant PyTorch that are newer at the time of release. So, to use newer
# versions of these packages with relatively older versions of the NGC PyTorch
# container, we tend to have to build the packages from scratch.
RUN cd /tmp && \
git clone https://github.com/Dao-AILab/causal-conv1d.git && \
cd causal-conv1d && \
git checkout v1.2.2.post1 && \
CAUSAL_CONV1D_FORCE_BUILD=TRUE pip install . && \
cd .. && \
rm -rf causal-conv1d
RUN cd /tmp && \
git clone https://github.com/state-spaces/mamba.git && \
cd mamba && \
git checkout v2.0.3 && \
MAMBA_FORCE_BUILD=TRUE pip install . && \
cd .. && \
rm -rf mamba
================================================
FILE: examples/mamba/README.md
================================================
# Mamba-based Language Models
## Introduction
This document is an entrypoint into the code used for
[An Empirical Study of Mamba-based Language Models](https://arxiv.org/abs/2406.07887).
We are releasing the parameters for some of the models described in that
technical report via
[HuggingFace](https://huggingface.co/collections/nvidia/ssms-666a362c5c3bb7e4a6bcfb9c).
The code in the `main` branch is no longer compatible with the `Mamba2-*`
checkpoints. You can load them using the
[fixed snapshot of the code used for the technical report](https://github.com/NVIDIA/Megatron-LM/tree/ssm/examples/mamba).
## Installation
Create and run a Docker container using the [Dockerfile](./Dockerfile).
```
docker build -t your_image_name:your_tag .
docker run --gpus all -it --rm \
-v /path/to/megatron:/workspace/megatron \
-v /path/to/dataset:/workspace/dataset \
-v /path/to/checkpoints:/workspace/checkpoints \
-w /workspace/megatron/examples/mamba \
your_image_name:your_tag
```
## Train
[`train.sh`](./train.sh) is an example pretraining script, showing how to run on
a single node. Select between 800M-scale and 8B-scale models by setting the
`MODEL_SCALE` variable. The 8B-scale hybrid model architecture is the same as
the one described in the technical report.
## Text Generation
Use [`run_text_gen_server_8b.sh`](./run_text_gen_server_8b.sh) to start a text
generation server using an 8B hybrid checkpoint. This is configured to run the
8B hybrid model described in the technical report, with tensor model parallel
set to 1.
The arguments in the script will need to be changed if using a checkpoint with a
different model parallel configuration or other differences, such as model
architecture. For example, to run the 8B pure Mamba-2 model, change
`--hybrid-layer-pattern` to use only `M` symbols (e.g., 56 `M`s for the 8B
model), or remove it entirely.
Use [`run_text_gen_server_8b_gpt3.sh`](./run_text_gen_server_8b_gpt3.sh) to start
a text generation server using the 8B reference Transformer checkpoint.
## Checkpoint Formats
For inference, the model must be configured to match the checkpoint file used,
including the hybrid layer configuration and model parallel configuration.
If you need to convert a hybrid checkpoint file to a different tensor parallel
or pipeline parallel size, use
[the hybrid conversion script](../../tools/checkpoint/hybrid_conversion.py).
There is an example run command at the end of that file.
Before running that script, you will need to set `PYTHONPATH` to include the
root directory of your Megatron-LM repository clone.
```
export PYTHONPATH=:PYTHONPATH
```
## Hybrid Options
`--hybrid-layer-pattern PATTERN` specifies the layer type for every layer in
the model using a string of single-character symbols:
* `M` — Mamba layer
* `*` — Attention layer
* `-` — MLP layer
* `E` — MoE layer
The number of layers is derived from the pattern length, so `--num-layers`
should not be specified when `--hybrid-layer-pattern` is used.
For example, the 8B hybrid model described in the technical report uses:
```
--hybrid-layer-pattern "M-M-M--M-M*-M-M-M-M--M*-M-M-M-M-M*--M-M-M-M-M*-M--M-M-M-"
```
This is a 56-layer model with 4 attention layers, 28 MLP layers, and 24 Mamba
layers.
A pure Mamba model uses only `M` symbols (e.g., `MMMMMMMM` for 8 layers).
A pure transformer model uses only `*` and `-` symbols.
### Pipeline parallelism
Use `|` to define pipeline stage boundaries for flexible virtual pipeline
parallelism (fVPP). For example, `M-M-|M-M*-|M-M-|M-M*-` defines 4 pipeline
segments. The number of segments must be evenly divisible by
`--pipeline-model-parallel-size`.
### Multi-Token Prediction (MTP)
Use `/` to append MTP layer patterns. Each pattern after the separator
represents one MTP prediction depth. For example, `M*M*/MM/MM` has main
pattern `M*M*` with MTP pattern `MM` repeated for 2 depths.
### Deprecated options
`--hybrid-override-pattern`, `--hybrid-attention-ratio`, and
`--hybrid-mlp-ratio` are deprecated. Use `--hybrid-layer-pattern` instead.
## Mamba vs Mamba-2
This codebase currently only supports Mamba-2, and not the original version of
Mamba. However, the
[fixed snapshot of the code used for the technical report](https://github.com/NVIDIA/Megatron-LM/tree/ssm/examples/mamba)
can be configured to run the original version of Mamba.
================================================
FILE: examples/mamba/run_text_gen_server_8b.sh
================================================
#!/bin/bash
# Use: ./run_text_gen_server_8b.sh
# To launch the client: python ../../tools/text_generation_cli.py
CHECKPOINT_PATH=$1
TOKENIZER_PATH=$2
HYBRID_LAYER_PATTERN="M-M-M--M-M*-M-M-M-M--M*-M-M-M-M-M*--M-M-M-M-M*-M--M-M-M-"
DISTRIBUTED_ARGS="--nproc_per_node 1 \
--nnodes 1 \
--node_rank 0 \
--master_addr localhost \
--master_port 6000"
export NCCL_IB_SL=1
export CUDA_DEVICE_MAX_CONNECTIONS=1
export NCCL_IB_TIMEOUT=19
export NCCL_IB_QPS_PER_CONNECTION=4
export TRITON_CACHE_DIR="./triton-cache/"
export TRITON_CACHE_MANAGER="megatron.core.ssm.triton_cache_manager:ParallelFileCacheManager"
torchrun $DISTRIBUTED_ARGS ../../tools/run_mamba_text_generation_server.py \
--tensor-model-parallel-size 1 \
--pipeline-model-parallel-size 1 \
--untie-embeddings-and-output-weights \
--hybrid-layer-pattern ${HYBRID_LAYER_PATTERN} \
--hidden-size 4096 \
--load ${CHECKPOINT_PATH} \
--num-attention-heads 32 \
--group-query-attention \
--num-query-groups 8 \
--attention-dropout 0.0 \
--hidden-dropout 0.0 \
--disable-bias-linear \
--normalization RMSNorm \
--seq-length 4096 \
--max-position-embeddings 4096 \
--position-embedding-type none \
--tokenizer-type GPTSentencePieceTokenizer \
--tokenizer-model ${TOKENIZER_PATH} \
--distributed-backend nccl \
--distributed-timeout-minutes 1440 \
--bf16 \
--micro-batch-size 1 \
--use-mcore-models \
--spec megatron.core.models.mamba.mamba_layer_specs mamba_stack_spec \
--seed 42
================================================
FILE: examples/mamba/run_text_gen_server_8b_gpt3.sh
================================================
#!/bin/bash
# Use: ./run_text_gen_server_8b_gpt3.sh
# To launch the client: python ../../tools/text_generation_cli.py
CHECKPOINT_PATH=$1
TOKENIZER_PATH=$2
DISTRIBUTED_ARGS="--nproc_per_node 1 \
--nnodes 1 \
--node_rank 0 \
--master_addr localhost \
--master_port 6000"
export NCCL_IB_SL=1
export CUDA_DEVICE_MAX_CONNECTIONS=1
export NCCL_IB_TIMEOUT=19
export NCCL_IB_QPS_PER_CONNECTION=4
torchrun $DISTRIBUTED_ARGS ../../tools/run_text_generation_server.py \
--tensor-model-parallel-size 1 \
--pipeline-model-parallel-size 1 \
--use-flash-attn \
--apply-layernorm-1p \
--untie-embeddings-and-output-weights \
--num-layers 32 \
--hidden-size 4096 \
--load ${CHECKPOINT_PATH} \
--num-attention-heads 32 \
--attention-dropout 0.0 \
--hidden-dropout 0.0 \
--disable-bias-linear \
--seq-length 4096 \
--max-position-embeddings 4096 \
--position-embedding-type rope \
--rotary-percent 0.5 \
--squared-relu \
--tokenizer-type GPTSentencePieceTokenizer \
--tokenizer-model ${TOKENIZER_PATH} \
--distributed-backend nccl \
--distributed-timeout-minutes 1440 \
--bf16 \
--micro-batch-size 1 \
--use-mcore-models \
--transformer-impl local \
--seed 42
================================================
FILE: examples/mamba/train.sh
================================================
#!/bin/bash
# Use: ./train.sh
MODEL_SCALE="800M" # or "8B"
case "${MODEL_SCALE}" in
"800M")
TENSOR_MODEL_PARALLEL_SIZE=1
HYBRID_LAYER_PATTERN="M-M-M--M-*M-M-M-M--*M-M-M-M-*M--M-M-M-*M-M--M-M-"
HIDDEN_SIZE=1024
NUM_ATTENTION_HEADS=16
GLOBAL_BATCH_SIZE=32
;;
"8B")
TENSOR_MODEL_PARALLEL_SIZE=4
HYBRID_LAYER_PATTERN="M-M-M--M-M*-M-M-M-M--M*-M-M-M-M-M*--M-M-M-M-M*-M--M-M-M-"
HIDDEN_SIZE=4096
NUM_ATTENTION_HEADS=32
GLOBAL_BATCH_SIZE=8
;;
*)
echo "Invalid version specified"
exit 1
;;
esac
DATA_PATH=$1
TOKENIZER_PATH=$2
export NCCL_IB_SL=1
export CUDA_DEVICE_MAX_CONNECTIONS=1
export NCCL_IB_TIMEOUT=19
export NCCL_IB_QPS_PER_CONNECTION=4
CHECKPOINT_DIR="./checkpoints"
DATACACHE_DIR="./data-cache"
TENSORBOARD_DIR="./tensorboard"
mkdir -p ${CHECKPOINT_DIR}
mkdir -p ${DATACACHE_DIR}
mkdir -p ${TENSORBOARD_DIR}
export TRITON_CACHE_DIR="./triton-cache/"
export TRITON_CACHE_MANAGER="megatron.core.ssm.triton_cache_manager:ParallelFileCacheManager"
SEQ_LEN=4096
TRAIN_SAMPLES=73242188 # 300B tokens / 4096
LR_WARMUP_SAMPLES=50000
LR_DECAY_SAMPLES=73192188 # TRAIN_SAMPLES - LR_WARMUP_SAMPLES
options=" \
--tensor-model-parallel-size ${TENSOR_MODEL_PARALLEL_SIZE} \
--sequence-parallel \
--pipeline-model-parallel-size 1 \
--use-distributed-optimizer \
--overlap-param-gather \
--overlap-grad-reduce \
--untie-embeddings-and-output-weights \
--init-method-std 0.02 \
--position-embedding-type none \
--hybrid-layer-pattern ${HYBRID_LAYER_PATTERN} \
--hidden-size ${HIDDEN_SIZE} \
--num-attention-heads ${NUM_ATTENTION_HEADS} \
--group-query-attention \
--num-query-groups 8 \
--seq-length ${SEQ_LEN} \
--max-position-embeddings ${SEQ_LEN} \
--train-samples ${TRAIN_SAMPLES} \
--lr-warmup-samples ${LR_WARMUP_SAMPLES} \
--lr-decay-samples ${LR_DECAY_SAMPLES} \
--save ${CHECKPOINT_DIR} \
--load ${CHECKPOINT_DIR} \
--data-path ${DATA_PATH} \
--data-cache-path ${DATACACHE_DIR} \
--split 99,1,0 \
--tokenizer-type GPTSentencePieceTokenizer \
--tokenizer-model ${TOKENIZER_PATH} \
--distributed-backend nccl \
--micro-batch-size 4 \
--global-batch-size ${GLOBAL_BATCH_SIZE} \
--lr 2.5e-4 \
--min-lr 2.5e-5 \
--lr-decay-style cosine \
--weight-decay 0.1 \
--clip-grad 1.0 \
--attention-dropout 0.0 \
--hidden-dropout 0.0 \
--disable-bias-linear \
--normalization RMSNorm \
--adam-beta1 0.9 \
--adam-beta2 0.95 \
--log-interval 10 \
--save-interval 2000 \
--eval-interval 2000 \
--eval-iters 32 \
--bf16 \
--use-mcore-models \
--spec megatron.core.models.mamba.mamba_layer_specs mamba_stack_spec \
--no-create-attention-mask-in-dataloader \
--tensorboard-dir ${TENSORBOARD_DIR}"
torchrun --nproc_per_node 8 ../../pretrain_mamba.py ${options}
================================================
FILE: examples/mimo/__init__.py
================================================
================================================
FILE: examples/mimo/avlm_inference.py
================================================
import argparse
import os
from pathlib import Path
from typing import Union
# hf path
import requests
import torch
from PIL import Image
from transformers import AutoProcessor
from transformers import AutoTokenizer
import soundfile as sf
import io
import numpy as np
import scipy.signal as signal
from examples.mimo.model_providers.llava_avlm import model_provider_llava_avlm
from megatron.core import dist_checkpointing, parallel_state, tensor_parallel
from megatron.core.tensor_parallel.random import model_parallel_cuda_manual_seed
from megatron.training import print_rank_0
from examples.mimo.data.utils.calculate_audio_tokens import calculate_num_audio_tokens
def init_distributed(tp_size: int = 1, pp_size: int = 1):
if torch.distributed.is_initialized():
return
rank = int(os.environ.get("LOCAL_RANK", 0))
world_size = int(os.environ.get("WORLD_SIZE", 1))
torch.cuda.set_device(rank % torch.cuda.device_count())
torch.distributed.init_process_group("nccl", rank=rank, world_size=world_size)
parallel_state.initialize_model_parallel(tp_size, pp_size)
def get_input_data(
processor: AutoProcessor,
image_processor: AutoProcessor,
audio_processor: AutoProcessor,
audio_path: str,
image_path: str,
prompt: str,
device: Union[int, str] = 0):
"""
Prepare inputs for the MIMO model forward pass.
"""
def read_audio(audio_path):
"""Process audio file and return tensor."""
with open(audio_path, 'rb') as f:
audio_bytes = f.read()
audio_io = io.BytesIO(audio_bytes)
waveform, sample_rate = sf.read(audio_io)
# Resample if needed
fixed_sample_rate = 16000
if sample_rate != fixed_sample_rate:
num_samples = int(len(waveform) * fixed_sample_rate / sample_rate)
waveform = signal.resample(waveform, num_samples)
# Convert to tensor
audio_tensor = torch.from_numpy(waveform).float()
return audio_tensor
def read_image(image_path):
"""Process image file and return tensor."""
with open(image_path, 'rb') as f:
image_bytes = f.read()
image_io = io.BytesIO(image_bytes)
image = Image.open(image_io)
image_tensor = torch.from_numpy(np.array(image)).permute(2, 0, 1) # Convert to CxHxW format
image_tensor = image_tensor.float() / 255.0 # rescale to [0,1] range
return image_tensor
# read audio and image
audio_tensor = read_audio(audio_path)
image_tensor = read_image(image_path)
# set up prompt
conversation = [
{
"role": "user",
"content": [
{"type": "text", "text": prompt},
],
}
]
prompt = processor.apply_chat_template(conversation, add_generation_prompt=True)
# process audio
processed_audios = audio_processor(audio_tensor, sampling_rate=16000)
processed_audios = torch.tensor(processed_audios["input_features"])
processed_audios = processed_audios.squeeze(0) # remove batch dim
num_audio_tokens = calculate_num_audio_tokens(audio_tensor.unsqueeze(0), "openai/whisper-base")
audios_seq_lengths = torch.tensor(num_audio_tokens)
prompt = prompt.replace("