mirror of https://github.com/hpcaitech/ColossalAI
317 lines
12 KiB
Markdown
317 lines
12 KiB
Markdown
# Colossal-AI
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<div id="top" align="center">
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[](https://www.colossalai.org/)
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Colossal-AI: 一个面向大模型时代的通用深度学习系统
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<h3> <a href="https://arxiv.org/abs/2110.14883"> 论文 </a> |
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<a href="https://www.colossalai.org/"> 文档 </a> |
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<a href="https://github.com/hpcaitech/ColossalAI-Examples"> 例程 </a> |
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<a href="https://github.com/hpcaitech/ColossalAI/discussions"> 论坛 </a> |
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<a href="https://medium.com/@hpcaitech"> 博客 </a></h3>
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[](https://github.com/hpcaitech/ColossalAI/actions/workflows/build.yml)
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[](https://colossalai.readthedocs.io/en/latest/?badge=latest)
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[](https://www.codefactor.io/repository/github/hpcaitech/colossalai)
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[](https://huggingface.co/hpcai-tech)
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[](https://join.slack.com/t/colossalaiworkspace/shared_invite/zt-z7b26eeb-CBp7jouvu~r0~lcFzX832w)
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[](https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/WeChat.png)
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| [English](README.md) | [中文](README-zh-Hans.md) |
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</div>
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## 目录
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<ul>
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<li><a href="#为何选择-Colossal-AI">为何选择 Colossal-AI</a> </li>
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<li><a href="#特点">特点</a> </li>
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<li>
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<a href="#并行训练样例展示">并行训练样例展示</a>
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<ul>
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<li><a href="#ViT">ViT</a></li>
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<li><a href="#GPT-3">GPT-3</a></li>
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<li><a href="#GPT-2">GPT-2</a></li>
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<li><a href="#BERT">BERT</a></li>
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<li><a href="#PaLM">PaLM</a></li>
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<li><a href="#OPT">OPT</a></li>
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</ul>
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</li>
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<li>
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<a href="#单GPU训练样例展示">单GPU训练样例展示</a>
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<ul>
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<li><a href="#GPT-2-Single">GPT-2</a></li>
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<li><a href="#PaLM-Single">PaLM</a></li>
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</ul>
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</li>
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<li>
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<a href="#推理-Energon-AI-样例展示">推理 (Energon-AI) 样例展示</a>
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<ul>
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<li><a href="#GPT-3-Inference">GPT-3</a></li>
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<li><a href="#OPT-Serving">1750亿参数OPT在线推理服务</a></li>
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</ul>
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</li>
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<li>
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<a href="#Colossal-AI-in-the-Real-World">Colossal-AI 成功案例</a>
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<ul>
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<li><a href="#xTrimoMultimer">xTrimoMultimer: 蛋白质单体与复合物结构预测</a></li>
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</ul>
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</li>
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<li>
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<a href="#安装">安装</a>
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<ul>
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<li><a href="#PyPI">PyPI</a></li>
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<li><a href="#从源代码安装">从源代码安装</a></li>
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</ul>
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</li>
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<li><a href="#使用-Docker">使用 Docker</a></li>
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<li><a href="#社区">社区</a></li>
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<li><a href="#做出贡献">做出贡献</a></li>
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<li><a href="#快速预览">快速预览</a></li>
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<ul>
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<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>
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<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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</ul>
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<li><a href="#引用我们">引用我们</a></li>
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</ul>
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## 为何选择 Colossal-AI
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<div align="center">
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<a href="https://youtu.be/KnXSfjqkKN0">
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<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/JamesDemmel_Colossal-AI.png" width="600" />
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</a>
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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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</div>
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<p align="right">(<a href="#top">返回顶端</a>)</p>
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## 特点
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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。
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- 并行化策略
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- 数据并行
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- 流水线并行
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- 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
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- [序列并行](https://arxiv.org/abs/2105.13120)
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- [零冗余优化器 (ZeRO)](https://arxiv.org/abs/1910.02054)
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- 异构内存管理
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- [PatrickStar](https://arxiv.org/abs/2108.05818)
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- 使用友好
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- 基于参数文件的并行化
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- 推理
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- [Energon-AI](https://github.com/hpcaitech/EnergonAI)
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- Colossal-AI 成功案例
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- [xTrimoMultimer: 蛋白质单体与复合物结构预测](https://github.com/biomap-research/xTrimoMultimer)
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<p align="right">(<a href="#top">返回顶端</a>)</p>
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## 并行训练样例展示
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### ViT
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<p align="center">
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<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/ViT.png" width="450" />
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</p>
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- 14倍批大小和5倍训练速度（张量并行=64）
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### GPT-3
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<p align="center">
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<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/GPT3-v5.png" width=700/>
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</p>
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- 释放 50% GPU 资源占用, 或 10.7% 加速
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### GPT-2
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<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/GPT2.png" width=800/>
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- 降低11倍 GPU 显存占用，或超线性扩展（张量并行）
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<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/(updated)GPT-2.png" width=800>
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- 用相同的硬件训练24倍大的模型
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- 超3倍的吞吐量
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### BERT
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<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/BERT.png" width=800/>
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- 2倍训练速度，或1.5倍序列长度
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### PaLM
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- [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。
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### OPT
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<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/OPT_update.png" width=800/>
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- [Open Pretrained Transformer (OPT)](https://github.com/facebookresearch/metaseq), 由Meta发布的1750亿语言模型，由于完全公开了预训练参数权重，因此促进了下游任务和应用部署的发展。
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- 加速45%，仅用几行代码以低成本微调OPT。[[样例]](https://github.com/hpcaitech/ColossalAI-Examples/tree/main/language/opt) [[在线推理]](https://service.colossalai.org/opt)
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请访问我们的 [文档](https://www.colossalai.org/) 和 [例程](https://github.com/hpcaitech/ColossalAI-Examples) 以了解详情。
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<p align="right">(<a href="#top">返回顶端</a>)</p>
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## 单GPU训练样例展示
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### GPT-2
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<p id="GPT-2-Single" align="center">
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<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/GPT2-GPU1.png" width=450/>
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</p>
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- 用相同的硬件训练20倍大的模型
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<p id="GPT-2-NVME" align="center">
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<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/GPT2-NVME.png" width=800/>
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</p>
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- 用相同的硬件训练120倍大的模型 (RTX 3080)
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### PaLM
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<p id="PaLM-Single" align="center">
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<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/PaLM-GPU1.png" width=450/>
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</p>
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- 用相同的硬件训练34倍大的模型
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<p align="right">(<a href="#top">back to top</a>)</p>
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## 推理 (Energon-AI) 样例展示
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<p id="GPT-3-Inference" align="center">
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<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/inference_GPT-3.jpg" width=800/>
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</p>
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- [Energon-AI](https://github.com/hpcaitech/EnergonAI) ：用相同的硬件推理加速50%
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<p id="OPT-Serving" align="center">
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<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/OPT_serving.png" width=800/>
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</p>
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- [OPT推理服务](https://service.colossalai.org/opt): 无需注册，免费体验1750亿参数OPT在线推理服务
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<p align="right">(<a href="#top">back to top</a>)</p>
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## Colossal-AI 成功案例
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### xTrimoMultimer: 蛋白质单体与复合物结构预测
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<p id="xTrimoMultimer" align="center">
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<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/xTM_Prediction.jpg" width=380/>
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<p></p>
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<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/xTrimoMultimer_Table.jpg" width=800/>
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</p>
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- [xTrimoMultimer](https://github.com/biomap-research/xTrimoMultimer): 11倍加速蛋白质单体与复合物结构预测
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## 安装
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### 从官方安装
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您可以访问我们[下载](https://www.colossalai.org/download)页面来安装Colossal-AI，在这个页面上发布的版本都预编译了CUDA扩展。
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### 从源安装
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> 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 :)
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```shell
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git clone https://github.com/hpcaitech/ColossalAI.git
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cd ColossalAI
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# install dependency
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pip install -r requirements/requirements.txt
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# install colossalai
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pip install .
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```
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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）：
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```shell
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NO_CUDA_EXT=1 pip install .
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```
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<p align="right">(<a href="#top">返回顶端</a>)</p>
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## 使用 Docker
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### 从DockerHub获取镜像
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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。
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### 本地构建镜像
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运行以下命令从我们提供的 docker 文件中建立 docker 镜像。
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> 在Dockerfile里编译Colossal-AI需要有GPU支持，您需要将Nvidia Docker Runtime设置为默认的Runtime。更多信息可以点击[这里](https://stackoverflow.com/questions/59691207/docker-build-with-nvidia-runtime)。
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> 我们推荐从[项目主页](https://www.colossalai.org)直接下载Colossal-AI.
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```bash
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cd ColossalAI
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docker build -t colossalai ./docker
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```
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运行以下命令从以交互式启动 docker 镜像.
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```bash
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docker run -ti --gpus all --rm --ipc=host colossalai bash
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```
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<p align="right">(<a href="#top">返回顶端</a>)</p>
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## 社区
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欢迎通过[论坛](https://github.com/hpcaitech/ColossalAI/discussions),
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[Slack](https://join.slack.com/t/colossalaiworkspace/shared_invite/zt-z7b26eeb-CBp7jouvu~r0~lcFzX832w),
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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。
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## 做出贡献
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欢迎为该项目做出贡献，请参阅[贡献指南](./CONTRIBUTING.md)。
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真诚感谢所有贡献者！
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<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>
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*贡献者头像的展示顺序是随机的。*
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<p align="right">(<a href="#top">返回顶端</a>)</p>
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## 快速预览
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### 几行代码开启分布式训练
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```python
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parallel = dict(
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pipeline=2,
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tensor=dict(mode='2.5d', depth = 1, size=4)
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)
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```
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### 几行代码开启异构训练
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```python
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zero = dict(
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model_config=dict(
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tensor_placement_policy='auto',
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shard_strategy=TensorShardStrategy(),
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reuse_fp16_shard=True
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),
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optimizer_config=dict(initial_scale=2**5, gpu_margin_mem_ratio=0.2)
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)
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```
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<p align="right">(<a href="#top">返回顶端</a>)</p>
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## 引用我们
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```
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@article{bian2021colossal,
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title={Colossal-AI: A Unified Deep Learning System For Large-Scale Parallel Training},
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author={Bian, Zhengda and Liu, Hongxin and Wang, Boxiang and Huang, Haichen and Li, Yongbin and Wang, Chuanrui and Cui, Fan and You, Yang},
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journal={arXiv preprint arXiv:2110.14883},
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year={2021}
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}
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```
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<p align="right">(<a href="#top">返回顶端</a>)</p> |