https://github.com/oneflow-inc/libai

LiBai(李白): A Toolbox for Large-Scale Distributed Parallel Training
https://github.com/oneflow-inc/libai

data-parallelism deep-learning distributed-training large-scale model-parallelism nlp oneflow pipeline-parallelism self-supervised-learning transformer vision-transformer

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LiBai(李白): A Toolbox for Large-Scale Distributed Parallel Training

• Host: GitHub
• URL: https://github.com/oneflow-inc/libai
• Owner: Oneflow-Inc
• License: apache-2.0
• Created: 2021-10-25T07:50:21.000Z (about 3 years ago)
• Default Branch: main
• Last Pushed: 2024-10-17T13:14:12.000Z (27 days ago)
• Last Synced: 2024-10-19T01:02:16.769Z (25 days ago)
• Topics: data-parallelism, deep-learning, distributed-training, large-scale, model-parallelism, nlp, oneflow, pipeline-parallelism, self-supervised-learning, transformer, vision-transformer
• Language: Python
• Homepage: https://libai.readthedocs.io
• Size: 34.8 MB
• Stars: 391
• Watchers: 42
• Forks: 55
• Open Issues: 80
• Metadata Files:
  • Readme: README.md
  • Changelog: changelog.md
  • Contributing: CONTRIBUTING.md
  • License: LICENSE

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README

        


LiBai



    

        

    

    

        

    

    

        

    

    

        

    

    

        

    

    

        

    



## Introduction

**English** | [简体中文](/README_zh-CN.md)

LiBai is a large-scale open-source model training toolbox based on OneFlow. The main branch works with OneFlow 0.7.0.

  Highlights  

- **Support a collection of parallel training components**

    LiBai provides multiple parallelisms such as Data Parallelism, Tensor Parallelism, and Pipeline Parallelism. It's also extensible for other new parallelisms.

- **Varied training techniques**

    LiBai provides many out-of-the-box training techniques such as Distributed Training, Mixed Precision Training, Activation Checkpointing, Recomputation, Gradient Accumulation, and Zero Redundancy Optimizer(ZeRO).

- **Support for both CV and NLP tasks**

    LiBai has predefined data process for both CV and NLP datasets such as CIFAR, ImageNet, and BERT Dataset.

- **Easy to use**

    LiBai's components are designed to be modular for easier usage as follows:

    - LazyConfig system for more flexible syntax and no predefined structures 

    - Friendly trainer and engine

    - Used as a library to support building research projects on it. See [projects/](/projects) for some projects that are built based on LiBai

- **High Efficiency**

## Installation

See [Installation instructions](https://libai.readthedocs.io/en/latest/tutorials/get_started/Installation.html).

## Getting Started

See [Quick Run](https://libai.readthedocs.io/en/latest/tutorials/get_started/quick_run.html) for the basic usage of LiBai.

## Documentation

See LiBai's [documentation](https://libai.readthedocs.io/en/latest/index.html) for full API documentation and tutorials.

## ChangeLog

**Beta 0.3.0** was released in 03/11/2024, the general changes in **0.3.0** version are as follows:

**Features:**

- Support mock transformers, see [Mock transformers](https://github.com/Oneflow-Inc/libai/tree/main/projects/mock_transformers#readme)

- Support lm-evaluation-harness for model evaluation

- User Experience Optimization

**New Supported Models:**

- These models are natively supported by libai

  

    

       Models 

       2D(tp+pp) Inference

       3D Parallel Training 

    

    

        BLOOM  

      ✔

      -

    

    

        ChatGLM  

      ✔

      ✔

    

    

        Couplets  

      ✔

      ✔

    

    

        DALLE2  

      ✔

      -

    

    

        Llama2  

      ✔

      ✔

    

    

        MAE  

      ✔

      ✔

    

    

        Stable_Diffusion  

      -

      -

    

  

**New Mock Models:**

- These models are extended and implemented by libai through mocking transformers.

  

    

       Models 

      Tensor Parallel

      Pipeline Parallel

    

    

        BLOOM  

      ✔

      -

    

    

        GPT2  

      ✔

      -

    

    

        LLAMA  

      ✔

      -

    

    

        LLAMA2  

      ✔

      -

    

    

        Baichuan  

      ✔

      -

    

    

        OPT  

      ✔

      -

    

  

See [changelog](./changelog.md) for details and release history.

## Contributing

We appreciate all contributions to improve LiBai. See [CONTRIBUTING](./CONTRIBUTING.md) for the contributing guideline.

## License

This project is released under the [Apache 2.0 license](LICENSE).

## Citation

If you find this project useful for your research, consider cite:

```BibTeX

@misc{of2021libai,

  author =       {Xingyu Liao and Peng Cheng and Tianhe Ren and Depeng Liang and

                  Kai Dang and Yi Wang and Xiaoyu Xu},

  title =        {LiBai},

  howpublished = {\url{https://github.com/Oneflow-Inc/libai}},

  year =         {2021}

}

```

## Join the WeChat group

![LiBai_Wechat_QRcode](./docs/source/tutorials/assets/LiBai_Wechat.png)