mirror of https://github.com/hpcaitech/ColossalAI
You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
273 lines
8.6 KiB
273 lines
8.6 KiB
# Colossal-AI
|
|
<div id="top" align="center">
|
|
|
|
[](https://www.colossalai.org/)
|
|
|
|
An integrated large-scale model training system with efficient parallelization techniques.
|
|
|
|
<h3> <a href="https://arxiv.org/abs/2110.14883"> Paper </a> |
|
|
<a href="https://www.colossalai.org/"> Documentation </a> |
|
|
<a href="https://github.com/hpcaitech/ColossalAI-Examples"> Examples </a> |
|
|
<a href="https://github.com/hpcaitech/ColossalAI/discussions"> Forum </a> |
|
|
<a href="https://medium.com/@hpcaitech"> Blog </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)
|
|
[](https://join.slack.com/t/colossalaiworkspace/shared_invite/zt-z7b26eeb-CBp7jouvu~r0~lcFzX832w)
|
|
[](https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/WeChat.png)
|
|
|
|
|
|
| [English](README.md) | [中文](README-zh-Hans.md) |
|
|
|
|
</div>
|
|
|
|
## Table of Contents
|
|
<ul>
|
|
<li><a href="#Features">Features</a> </li>
|
|
<li>
|
|
<a href="#Demo">Demo</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>
|
|
</ul>
|
|
</li>
|
|
|
|
<li>
|
|
<a href="#Installation">Installation</a>
|
|
<ul>
|
|
<li><a href="#PyPI">PyPI</a></li>
|
|
<li><a href="#Install-From-Source">Install From Source</a></li>
|
|
</ul>
|
|
</li>
|
|
<li><a href="#Use-Docker">Use Docker</a></li>
|
|
<li><a href="#Community">Community</a></li>
|
|
<li><a href="#contributing">Contributing</a></li>
|
|
<li><a href="#Quick-View">Quick View</a></li>
|
|
<ul>
|
|
<li><a href="#Start-Distributed-Training-in-Lines">Start Distributed Training in Lines</a></li>
|
|
<li><a href="#Write-a-Simple-2D-Parallel-Model">Write a Simple 2D Parallel Model</a></li>
|
|
</ul>
|
|
<li><a href="#Cite-Us">Cite Us</a></li>
|
|
</ul>
|
|
|
|
## Features
|
|
|
|
Colossal-AI provides a collection of parallel training components for you. We aim to support you to write your
|
|
distributed deep learning models just like how you write your model on your laptop. We provide user-friendly tools to kickstart
|
|
distributed training in a few lines.
|
|
|
|
- Data Parallelism
|
|
- Pipeline Parallelism
|
|
- 1D, 2D, 2.5D, 3D tensor parallelism
|
|
- Sequence parallelism
|
|
- Friendly trainer and engine
|
|
- Extensible for new parallelism
|
|
- Mixed Precision Training
|
|
- Zero Redundancy Optimizer (ZeRO)
|
|
|
|
<p align="right">(<a href="#top">back to top</a>)</p>
|
|
|
|
## Demo
|
|
### ViT
|
|
<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/ViT.png" width="450" />
|
|
|
|
- 14x larger batch size, and 5x faster training for Tensor Parallelism = 64
|
|
|
|
### GPT-3
|
|
<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/GPT3.png" width=700/>
|
|
|
|
- Save 50% GPU resources, and 10.7% acceleration
|
|
|
|
### GPT-2
|
|
<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/GPT2.png" width=800/>
|
|
|
|
- 11x lower GPU memory consumption, and superlinear scaling efficiency with Tensor Parallelism
|
|
|
|
<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/Colossal-AI%20with%20ZeRO.jpg" width=393>
|
|
|
|
- 10.7x larger model size on the same hardware
|
|
### BERT
|
|
<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/BERT.png" width=800/>
|
|
|
|
- 2x faster training, or 50% longer sequence length
|
|
|
|
Please visit our [documentation and tutorials](https://www.colossalai.org/) for more details.
|
|
|
|
<p align="right">(<a href="#top">back to top</a>)</p>
|
|
|
|
## Installation
|
|
|
|
### PyPI
|
|
|
|
```bash
|
|
pip install colossalai
|
|
```
|
|
This command will install CUDA extension if your have installed CUDA, NVCC and torch.
|
|
|
|
If you don't want to install CUDA extension, you should add `--global-option="--no_cuda_ext"`, like:
|
|
```bash
|
|
pip install colossalai --global-option="--no_cuda_ext"
|
|
```
|
|
|
|
If you want to use `ZeRO`, you can run:
|
|
```bash
|
|
pip install colossalai[zero]
|
|
```
|
|
|
|
### Install From Source
|
|
|
|
> The version of Colossal-AI will be in line with the main branch of the repository. Feel free to create an issue if you encounter any problems. :-)
|
|
|
|
```shell
|
|
git clone https://github.com/hpcaitech/ColossalAI.git
|
|
cd ColossalAI
|
|
# install dependency
|
|
pip install -r requirements/requirements.txt
|
|
|
|
# install colossalai
|
|
pip install .
|
|
```
|
|
|
|
If you don't want to install and enable CUDA kernel fusion (compulsory installation when using fused optimizer):
|
|
|
|
```shell
|
|
pip install --global-option="--no_cuda_ext" .
|
|
```
|
|
|
|
<p align="right">(<a href="#top">back to top</a>)</p>
|
|
|
|
## Use Docker
|
|
|
|
Run the following command to build a docker image from Dockerfile provided.
|
|
|
|
```bash
|
|
cd ColossalAI
|
|
docker build -t colossalai ./docker
|
|
```
|
|
|
|
Run the following command to start the docker container in interactive mode.
|
|
|
|
```bash
|
|
docker run -ti --gpus all --rm --ipc=host colossalai bash
|
|
```
|
|
|
|
<p align="right">(<a href="#top">back to top</a>)</p>
|
|
|
|
## Community
|
|
|
|
Join the Colossal-AI community on [Forum](https://github.com/hpcaitech/ColossalAI/discussions),
|
|
[Slack](https://join.slack.com/t/colossalaiworkspace/shared_invite/zt-z7b26eeb-CBp7jouvu~r0~lcFzX832w),
|
|
and [WeChat](https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/WeChat.png "qrcode") to share your suggestions, feedback, and questions with our engineering team.
|
|
|
|
## Contributing
|
|
|
|
If you wish to contribute to this project, please follow the guideline in [Contributing](./CONTRIBUTING.md).
|
|
|
|
Thanks so much to all of our amazing contributors!
|
|
|
|
<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>
|
|
|
|
*The order of contributor avatars is randomly shuffled.*
|
|
|
|
<p align="right">(<a href="#top">back to top</a>)</p>
|
|
|
|
## Quick View
|
|
|
|
### Start Distributed Training in Lines
|
|
|
|
```python
|
|
import colossalai
|
|
from colossalai.utils import get_dataloader
|
|
|
|
|
|
# my_config can be path to config file or a dictionary obj
|
|
# 'localhost' is only for single node, you need to specify
|
|
# the node name if using multiple nodes
|
|
colossalai.launch(
|
|
config=my_config,
|
|
rank=rank,
|
|
world_size=world_size,
|
|
backend='nccl',
|
|
port=29500,
|
|
host='localhost'
|
|
)
|
|
|
|
# build your model
|
|
model = ...
|
|
|
|
# build you dataset, the dataloader will have distributed data
|
|
# sampler by default
|
|
train_dataset = ...
|
|
train_dataloader = get_dataloader(dataset=dataset,
|
|
shuffle=True
|
|
)
|
|
|
|
|
|
# build your optimizer
|
|
optimizer = ...
|
|
|
|
# build your loss function
|
|
criterion = ...
|
|
|
|
# initialize colossalai
|
|
engine, train_dataloader, _, _ = colossalai.initialize(
|
|
model=model,
|
|
optimizer=optimizer,
|
|
criterion=criterion,
|
|
train_dataloader=train_dataloader
|
|
)
|
|
|
|
# start training
|
|
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()
|
|
|
|
```
|
|
|
|
### Write a Simple 2D Parallel Model
|
|
|
|
Let's say we have a huge MLP model and its very large hidden size makes it difficult to fit into a single GPU. We can
|
|
then distribute the model weights across GPUs in a 2D mesh while you still write your model in a familiar way.
|
|
|
|
```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
|
|
|
|
```
|
|
|
|
<p align="right">(<a href="#top">back to top</a>)</p>
|
|
|
|
## Cite Us
|
|
|
|
```
|
|
@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}
|
|
}
|
|
```
|
|
|
|
<p align="right">(<a href="#top">back to top</a>)</p> |