# Colossal-AI
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[](https://www.colossalai.org/)
Colossal-AI: 一个面向大模型时代的通用深度学习系统
< h3 > < a href = "https://arxiv.org/abs/2110.14883" > 论文 < / a > |
< a href = "https://www.colossalai.org/" > 文档 < / a > |
< a href = "https://github.com/hpcaitech/ColossalAI-Examples" > 例程 < / a > |
< a href = "https://github.com/hpcaitech/ColossalAI/discussions" > 论坛 < / a > |
< a href = "https://medium.com/@hpcaitech" > 博客 < / a > < / h3 >
[](https://github.com/hpcaitech/ColossalAI/actions/workflows/build.yml)
[](https://colossalai.readthedocs.io/en/latest/?badge=latest)
[](https://www.codefactor.io/repository/github/hpcaitech/colossalai)
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[](https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/WeChat.png)
| [English ](README.md ) | [中文 ](README-zh-Hans.md ) |
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## 目录
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< li > < a href = "#为何选择-Colossal-AI" > 为何选择 Colossal-AI< / a > < / li >
< li > < a href = "#特点" > 特点< / a > < / li >
< li >
< a href = "#并行训练样例展示" > 并行训练样例展示< / a >
< ul >
< li > < a href = "#ViT" > ViT< / a > < / li >
< li > < a href = "#GPT-3" > GPT-3< / a > < / li >
< li > < a href = "#GPT-2" > GPT-2< / a > < / li >
< li > < a href = "#BERT" > BERT< / a > < / li >
< li > < a href = "#PaLM" > PaLM< / a > < / li >
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< li >
< a href = "#单GPU训练样例展示" > 单GPU训练样例展示< / a >
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< li > < a href = "#GPT-2-Single" > GPT-2< / a > < / li >
< li > < a href = "#PaLM-Single" > PaLM< / a > < / li >
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< li >
< a href = "#推理-Energon-AI-样例展示" > 推理 (Energon-AI) 样例展示< / a >
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< li > < a href = "#GPT-3-Inference" > GPT-3< / a > < / li >
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< li >
< a href = "#安装" > 安装< / a >
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< li > < a href = "#PyPI" > PyPI< / a > < / li >
< li > < a href = "#从源代码安装" > 从源代码安装< / a > < / li >
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< li > < a href = "#使用-Docker" > 使用 Docker< / a > < / li >
< li > < a href = "#社区" > 社区< / a > < / li >
< li > < a href = "#做出贡献" > 做出贡献< / a > < / li >
< li > < a href = "#快速预览" > 快速预览< / a > < / li >
< ul >
< li > < a href = "#几行代码开启分布式训练" > 几行代码开启分布式训练< / a > < / li >
< li > < a href = "#构建一个简单的2维并行模型" > 构建一个简单的2维并行模型< / a > < / li >
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< li > < a href = "#引用我们" > 引用我们< / a > < / li >
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## 为何选择 Colossal-AI
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< a href = "https://youtu.be/KnXSfjqkKN0" >
< img src = "https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/JamesDemmel_Colossal-AI.png" width = "600" / >
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James Demmel 教授 (加州大学伯克利分校): Colossal-AI 让分布式训练高效、易用、可扩展。
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< p align = "right" > (< a href = "#top" > 返回顶端< / a > )< / p >
## 特点
Colossal-AI 为您提供了一系列并行组件。我们的目标是让您的分布式 AI 模型像构建普通的单 GPU 模型一样简单。我们提供的友好工具可以让您在几行代码内快速开始分布式训练和推理。
- 并行化策略
- 数据并行
- 流水线并行
- 1维, [2维 ](https://arxiv.org/abs/2104.05343 ), [2.5维 ](https://arxiv.org/abs/2105.14500 ), [3维 ](https://arxiv.org/abs/2105.14450 ) 张量并行
- [序列并行 ](https://arxiv.org/abs/2105.13120 )
- [零冗余优化器 (ZeRO) ](https://arxiv.org/abs/1910.02054 )
- 异构内存管理
- [PatrickStar ](https://arxiv.org/abs/2108.05818 )
- 使用友好
- 基于参数文件的并行化
- 推理
- [Energon-AI ](https://github.com/hpcaitech/EnergonAI )
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## 并行训练样例展示
### ViT
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< img src = "https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/ViT.png" width = "450" / >
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- 14倍批大小和5倍训练速度（ ）
### GPT-3
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< img src = "https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/GPT3.png" width = 700/ >
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- 释放 50% GPU 资源占用, 或 10.7% 加速
### GPT-2
< img src = "https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/GPT2.png" width = 800/ >
- 降低11倍 GPU 显存占用，或超线性扩展（张量并行）
< img src = "https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/(updated)GPT-2.png" width = 800 >
- 用相同的硬件训练24倍大的模型
- 超3倍的吞吐量
### BERT
< img src = "https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/BERT.png" width = 800/ >
- 2倍训练速度，
### PaLM
- [PaLM-colossalai ](https://github.com/hpcaitech/PaLM-colossalai ): 可扩展的谷歌 Pathways Language Model ([PaLM](https://ai.googleblog.com/2022/04/pathways-language-model-palm-scaling-to.html)) 实现。
请访问我们的[文档和教程](https://www.colossalai.org/)以了解详情。
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## 单GPU训练样例展示
### GPT-2
< p id = "GPT-2-Single" align = "center" >
< img src = "https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/GPT2-GPU1.png" width = 450/ >
< / p >
- 用相同的硬件训练20倍大的模型
### PaLM
< p id = "PaLM-Single" align = "center" >
< img src = "https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/PaLM-GPU1.png" width = 450/ >
< / p >
- 用相同的硬件训练34倍大的模型
< p align = "right" > (< a href = "#top" > back to top< / a > )< / p >
## 推理 (Energon-AI) 样例展示
### GPT-3
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< img src = "https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/inference_GPT-3.jpg" width = 800/ >
< / p >
- [Energon-AI ](https://github.com/hpcaitech/EnergonAI ) ：
< p align = "right" > (< a href = "#top" > back to top< / a > )< / p >
## 安装
### 从官方安装
您可以访问我们[下载](https://www.colossalai.org/download)页面来安装Colossal-AI，
### 从源安装
> 此文档将与版本库的主分支保持一致。如果您遇到任何问题，欢迎给我们提 issue :)
```shell
git clone https://github.com/hpcaitech/ColossalAI.git
cd ColossalAI
# install dependency
pip install -r requirements/requirements.txt
# install colossalai
pip install .
```
如果您不想安装和启用 CUDA 内核融合（使用融合优化器时强制安装）：
```shell
NO_CUDA_EXT=1 pip install .
```
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## 使用 Docker
### 从DockerHub获取镜像
您可以直接从我们的[DockerHub主页](https://hub.docker.com/r/hpcaitech/colossalai)获取最新的镜像，每一次发布我们都会自动上传最新的镜像。
### 本地构建镜像
运行以下命令从我们提供的 docker 文件中建立 docker 镜像。
> 在Dockerfile里编译Colossal-AI需要有GPU支持，
> 我们推荐从[项目主页](https://www.colossalai.org)直接下载Colossal-AI.
```bash
cd ColossalAI
docker build -t colossalai ./docker
```
运行以下命令从以交互式启动 docker 镜像.
```bash
docker run -ti --gpus all --rm --ipc=host colossalai bash
```
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## 社区
欢迎通过[论坛](https://github.com/hpcaitech/ColossalAI/discussions),
[Slack ](https://join.slack.com/t/colossalaiworkspace/shared_invite/zt-z7b26eeb-CBp7jouvu~r0~lcFzX832w ),
或[微信](https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/WeChat.png "qrcode")加入 Colossal-AI 社区，与我们分享你的建议和问题。
## 做出贡献
欢迎为该项目做出贡献，请参阅[贡献指南](./CONTRIBUTING.md)。
真诚感谢所有贡献者！
< a href = "https://github.com/hpcaitech/ColossalAI/graphs/contributors" > < img src = "https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/contributor_avatar.png" width = "800px" > < / a >
*贡献者头像的展示顺序是随机的。*
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## 快速预览
### 几行代码开启分布式训练
```python
parallel = dict(
pipeline=2,
tensor=dict(mode='2.5d', depth = 1, size=4)
)
```
### 几行代码开启异构训练
```python
zero = dict(
model_config=dict(
tensor_placement_policy='auto',
shard_strategy=TensorShardStrategy(),
reuse_fp16_shard=True
),
optimizer_config=dict(initial_scale=2**5, gpu_margin_mem_ratio=0.2)
)
```
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## 引用我们
```
@article {bian2021colossal,
title={Colossal-AI: A Unified Deep Learning System For Large-Scale Parallel Training},
author={Bian, Zhengda and Liu, Hongxin and Wang, Boxiang and Huang, Haichen and Li, Yongbin and Wang, Chuanrui and Cui, Fan and You, Yang},
journal={arXiv preprint arXiv:2110.14883},
year={2021}
}
```
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