# Colossal-AI
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[](https://www.colossalai.org/)
一个整合高效并行技术的AI大模型训练系统。
< 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)
[](https://huggingface.co/hpcai-tech)
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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
< ul >
< li > < a href = "#特点" > 特点< / a > < / li >
< li >
< a href = "#展示样例" > 展示样例< / 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" > BERT< / a > < / li >
< / ul >
< / li >
< li >
< a href = "#安装" > 安装< / a >
< ul >
< li > < a href = "#PyPI" > PyPI< / a > < / li >
< li > < a href = "#从源代码安装" > 从源代码安装< / a > < / li >
< / ul >
< / li >
< 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 >
< / ul >
< li > < a href = "#引用我们" > 引用我们< / a > < / li >
< / ul >
## 特点
Colossal-AI为您提供了一系列并行训练组件。我们的目标是让您的分布式AI模型训练像普通的单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。
- 数据并行
- 流水线并行
- 1维, 2维, 2.5维, 3维张量并行
- 序列并行
- 友好的trainer和engine
- 可扩展新的并行方式
- 混合精度
- 零冗余优化器 (ZeRO)
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## 展示样例
### ViT
< img src = "https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/ViT.png" width = "450" / >
- 14倍批大小和5倍训练速度（张量并行=64）
### GPT-3
< img src = "https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/GPT3.png" width = 700/ >
- 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 >
- 用相同的硬件条件训练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，或1.5倍序列长度
### 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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## 安装
### PyPI
```bash
pip install colossalai
```
该命令将会安装CUDA extension，如果你已安装CUDA, NVCC和torch。
如果你不想安装CUDA extension, 可在命令中添加`--global-option="--no_cuda_ext"`, 例如:
```bash
pip install colossalai --global-option="--no_cuda_ext"
```
如果你想使用`ZeRO`, 你可以使用:
```bash
pip install colossalai[zero]
```
### 从源代码安装
> Colossal-AI的版本将与该项目的主分支保持一致。欢迎通过issue反馈你遇到的任何问题 :)
```shell
git clone https://github.com/hpcaitech/ColossalAI.git
cd ColossalAI
# 安装依赖
pip install -r requirements/requirements.txt
# 安装 colossalai
pip install .
```
如果你不想安装和使用CUDA kernel fusion (使用fused优化器需安装):
```shell
pip install --global-option="--no_cuda_ext" .
```
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## 使用 Docker
运行以下命令从我们提供的docker文件中建立docker镜像。
```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
import colossalai
from colossalai.utils import get_dataloader
# my_config可以是config文件的路径或字典对象
# 'localhost' 仅适用于单节点，在多节点时需指明节点名
colossalai.launch(
config=my_config,
rank=rank,
world_size=world_size,
backend='nccl',
port=29500,
host='localhost'
)
# 构建模型
model = ...
# 构建数据集, dataloader会默认处理分布式数据sampler
train_dataset = ...
train_dataloader = get_dataloader(dataset=dataset,
shuffle=True
)
# 构建优化器
optimizer = ...
# 构建损失函数
criterion = ...
# 初始化colossalai
engine, train_dataloader, _, _ = colossalai.initialize(
model=model,
optimizer=optimizer,
criterion=criterion,
train_dataloader=train_dataloader
)
# 开始训练
engine.train()
for epoch in range(NUM_EPOCHS):
for data, label in train_dataloader:
engine.zero_grad()
output = engine(data)
loss = engine.criterion(output, label)
engine.backward(loss)
engine.step()
```
### 构建一个简单的2维并行模型
假设我们有一个非常巨大的MLP模型，它巨大的hidden size使得它难以被单个GPU容纳。我们可以将该模型的权重以二维网格的形式分配到多个GPU上，且保持你熟悉的模型构建方式。
```python
from colossalai.nn import Linear2D
import torch.nn as nn
class MLP_2D(nn.Module):
def __init__ (self):
super().__init__()
self.linear_1 = Linear2D(in_features=1024, out_features=16384)
self.linear_2 = Linear2D(in_features=16384, out_features=1024)
def forward(self, x):
x = self.linear_1(x)
x = self.linear_2(x)
return x
```
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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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