| Welcome to verl's documentation! | |
| ================================================ | |
| verl is a flexible, efficient and production-ready RL training framework designed for large language models (LLMs) post-training. It is an open source implementation of the `HybridFlow <https://arxiv.org/pdf/2409.19256>`_ paper. | |
| verl is flexible and easy to use with: | |
| - **Easy extension of diverse RL algorithms**: The hybrid programming model combines the strengths of single-controller and multi-controller paradigms to enable flexible representation and efficient execution of complex Post-Training dataflows. Allowing users to build RL dataflows in a few lines of code. | |
| - **Seamless integration of existing LLM infra with modular APIs**: Decouples computation and data dependencies, enabling seamless integration with existing LLM frameworks, such as PyTorch FSDP, Megatron-LM, vLLM and SGLang. Moreover, users can easily extend to other LLM training and inference frameworks. | |
| - **Flexible device mapping and parallelism**: Supports various placement of models onto different sets of GPUs for efficient resource utilization and scalability across different cluster sizes. | |
| - Ready integration with popular HuggingFace models | |
| verl is fast with: | |
| - **State-of-the-art throughput**: By seamlessly integrating existing SOTA LLM training and inference frameworks, verl achieves high generation and training throughput. | |
| - **Efficient actor model resharding with 3D-HybridEngine**: Eliminates memory redundancy and significantly reduces communication overhead during transitions between training and generation phases. | |
| -------------------------------------------- | |
| .. _Contents: | |
| .. toctree:: | |
| :maxdepth: 2 | |
| :caption: Quickstart | |
| start/install | |
| start/quickstart | |
| start/multinode | |
| start/ray_debug_tutorial | |
| .. toctree:: | |
| :maxdepth: 2 | |
| :caption: Programming guide | |
| hybrid_flow | |
| single_controller | |
| .. toctree:: | |
| :maxdepth: 1 | |
| :caption: Data Preparation | |
| preparation/prepare_data | |
| preparation/reward_function | |
| .. toctree:: | |
| :maxdepth: 2 | |
| :caption: Configurations | |
| examples/config | |
| .. toctree:: | |
| :maxdepth: 1 | |
| :caption: PPO Example | |
| examples/ppo_code_architecture | |
| examples/gsm8k_example | |
| examples/multi_modal_example | |
| .. toctree:: | |
| :maxdepth: 1 | |
| :caption: Algorithms | |
| algo/ppo.md | |
| algo/grpo.md | |
| algo/dapo.md | |
| algo/spin.md | |
| algo/sppo.md | |
| algo/entropy.md | |
| algo/opo.md | |
| algo/baseline.md | |
| .. toctree:: | |
| :maxdepth: 1 | |
| :caption: PPO Trainer and Workers | |
| workers/ray_trainer | |
| workers/fsdp_workers | |
| workers/megatron_workers | |
| workers/sglang_worker | |
| .. toctree:: | |
| :maxdepth: 1 | |
| :caption: Performance Tuning Guide | |
| perf/dpsk.md | |
| perf/perf_tuning | |
| README_vllm0.8.md | |
| perf/device_tuning | |
| perf/nsight_profiling.md | |
| .. toctree:: | |
| :maxdepth: 1 | |
| :caption: Adding new models | |
| advance/fsdp_extension | |
| advance/megatron_extension | |
| .. toctree:: | |
| :maxdepth: 1 | |
| :caption: Advanced Features | |
| advance/checkpoint | |
| advance/rope | |
| advance/ppo_lora.rst | |
| sglang_multiturn/multiturn.rst | |
| sglang_multiturn/interaction_system.rst | |
| advance/placement | |
| advance/dpo_extension | |
| examples/sandbox_fusion_example | |
| .. toctree:: | |
| :maxdepth: 1 | |
| :caption: Hardware Support | |
| amd_tutorial/amd_build_dockerfile_page.rst | |
| amd_tutorial/amd_vllm_page.rst | |
| ascend_tutorial/ascend_quick_start.rst | |
| .. toctree:: | |
| :maxdepth: 1 | |
| :caption: API References | |
| api/data | |
| api/single_controller.rst | |
| api/trainer.rst | |
| api/utils.rst | |
| .. toctree:: | |
| :maxdepth: 2 | |
| :caption: FAQ | |
| faq/faq | |
| .. toctree:: | |
| :maxdepth: 1 | |
| :caption: Development Notes | |
| sglang_multiturn/sandbox_fusion.rst | |
| Contribution | |
| ------------- | |
| verl is free software; you can redistribute it and/or modify it under the terms | |
| of the Apache License 2.0. We welcome contributions. | |
| Join us on `GitHub <https://github.com/volcengine/verl>`_, `Slack <https://join.slack.com/t/verlgroup/shared_invite/zt-2w5p9o4c3-yy0x2Q56s_VlGLsJ93A6vA>`_ and `Wechat <https://raw.githubusercontent.com/eric-haibin-lin/verl-community/refs/heads/main/WeChat.JPG>`_ for discussions. | |
| Contributions from the community are welcome! Please check out our `project roadmap <https://github.com/volcengine/verl/issues/710>`_ and `good first issues <https://github.com/volcengine/verl/issues?q=is%3Aissue%20state%3Aopen%20label%3A%22good%20first%20issue%22>`_ to see where you can contribute. | |
| Code Linting and Formatting | |
| ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ | |
| We use pre-commit to help improve code quality. To initialize pre-commit, run: | |
| .. code-block:: bash | |
| pip install pre-commit | |
| pre-commit install | |
| To resolve CI errors locally, you can also manually run pre-commit by: | |
| .. code-block:: bash | |
| pre-commit run | |
| Adding CI tests | |
| ^^^^^^^^^^^^^^^^^^^^^^^^ | |
| If possible, please add CI test(s) for your new feature: | |
| 1. Find the most relevant workflow yml file, which usually corresponds to a ``hydra`` default config (e.g. ``ppo_trainer``, ``ppo_megatron_trainer``, ``sft_trainer``, etc). | |
| 2. Add related path patterns to the ``paths`` section if not already included. | |
| 3. Minimize the workload of the test script(s) (see existing scripts for examples). | |
| We are HIRING! Send us an `email <mailto:[email protected]>`_ if you are interested in internship/FTE opportunities in MLSys/LLM reasoning/multimodal alignment. | |