LiBai

Introduction

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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/ for some projects that are built based on LiBai

• High Efficiency

Installation

See Installation instructions.

Getting Started

See Quick Run for the basic usage of LiBai.

Documentation

See LiBai's documentation 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
• 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 for details and release history.

Contributing

We appreciate all contributions to improve LiBai. See CONTRIBUTING for the contributing guideline.

License

This project is released under the Apache 2.0 license.

Citation

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

@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}
}

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