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321 lines
12 KiB
321 lines
12 KiB
# 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: A Unified Deep Learning System for Big Model Era
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<h3> <a href="https://arxiv.org/abs/2110.14883"> Paper </a> |
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<a href="https://www.colossalai.org/"> Documentation </a> |
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<a href="https://github.com/hpcaitech/ColossalAI-Examples"> Examples </a> |
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<a href="https://github.com/hpcaitech/ColossalAI/discussions"> Forum </a> |
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<a href="https://medium.com/@hpcaitech"> Blog </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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## Table of Contents
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<ul>
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<li><a href="#Why-Colossal-AI">Why Colossal-AI</a> </li>
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<li><a href="#Features">Features</a> </li>
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<li>
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<a href="#Parallel-Training-Demo">Parallel Training Demo</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="#Single-GPU-Training-Demo">Single GPU Training Demo</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="#Inference-Energon-AI-Demo">Inference (Energon-AI) Demo</a>
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<ul>
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<li><a href="#GPT-3-Inference">GPT-3</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 for Real World Applications</a>
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<ul>
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<li><a href="#xTrimoMultimer">xTrimoMultimer: Accelerating Protein Monomer and Multimer Structure Prediction</a></li>
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</ul>
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</li>
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<li>
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<a href="#Installation">Installation</a>
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<ul>
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<li><a href="#PyPI">PyPI</a></li>
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<li><a href="#Install-From-Source">Install From Source</a></li>
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</ul>
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</li>
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<li><a href="#Use-Docker">Use Docker</a></li>
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<li><a href="#Community">Community</a></li>
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<li><a href="#contributing">Contributing</a></li>
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<li><a href="#Quick-View">Quick View</a></li>
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<ul>
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<li><a href="#Start-Distributed-Training-in-Lines">Start Distributed Training in Lines</a></li>
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<li><a href="#Write-a-Simple-2D-Parallel-Model">Write a Simple 2D Parallel Model</a></li>
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</ul>
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<li><a href="#Cite-Us">Cite Us</a></li>
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</ul>
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## Why 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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Prof. James Demmel (UC Berkeley): Colossal-AI makes training AI models efficient, easy, and scalable.
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</div>
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<p align="right">(<a href="#top">back to top</a>)</p>
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## Features
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Colossal-AI provides a collection of parallel components for you. We aim to support you to write your
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distributed deep learning models just like how you write your model on your laptop. We provide user-friendly tools to kickstart
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distributed training and inference in a few lines.
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- Parallelism strategies
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- Data Parallelism
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- Pipeline Parallelism
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- 1D, [2D](https://arxiv.org/abs/2104.05343), [2.5D](https://arxiv.org/abs/2105.14500), [3D](https://arxiv.org/abs/2105.14450) Tensor Parallelism
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- [Sequence Parallelism](https://arxiv.org/abs/2105.13120)
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- [Zero Redundancy Optimizer (ZeRO)](https://arxiv.org/abs/1910.02054)
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- Heterogeneous Memory Management
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- [PatrickStar](https://arxiv.org/abs/2108.05818)
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- Friendly Usage
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- Parallelism based on configuration file
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- Inference
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- [Energon-AI](https://github.com/hpcaitech/EnergonAI)
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- Colossal-AI in the Real World
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- [xTrimoMultimer](https://github.com/biomap-research/xTrimoMultimer): Accelerating Protein Monomer and Multimer Structure Prediction
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<p align="right">(<a href="#top">back to top</a>)</p>
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## Parallel Training Demo
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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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- 14x larger batch size, and 5x faster training for Tensor Parallelism = 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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- Save 50% GPU resources, and 10.7% acceleration
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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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- 11x lower GPU memory consumption, and superlinear scaling efficiency with Tensor Parallelism
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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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- 24x larger model size on the same hardware
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- over 3x acceleration
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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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- 2x faster training, or 50% longer sequence length
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### PaLM
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- [PaLM-colossalai](https://github.com/hpcaitech/PaLM-colossalai): Scalable implementation of Google's Pathways Language Model ([PaLM](https://ai.googleblog.com/2022/04/pathways-language-model-palm-scaling-to.html)).
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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), a 175-Billion parameter AI language model released by Meta, which stimulates AI programmers to perform various downstream tasks and application deployments because public pretrained model weights.
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- 45% speedup fine-tuning OPT at low cost in lines. [[Example]](https://github.com/hpcaitech/ColossalAI-Examples/tree/main/language/opt)
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Please visit our [documentation](https://www.colossalai.org/) and [examples](https://github.com/hpcaitech/ColossalAI-Examples) for more details.
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<p align="right">(<a href="#top">back to top</a>)</p>
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## Single GPU Training Demo
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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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- 20x larger model size on the same hardware
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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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- 120x larger model size on the same hardware (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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- 34x larger model size on the same hardware
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<p align="right">(<a href="#top">back to top</a>)</p>
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## Inference (Energon-AI) Demo
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### GPT-3
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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): 50% inference acceleration on the same hardware
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<p align="right">(<a href="#top">back to top</a>)</p>
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## Colossal-AI in the Real World
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### xTrimoMultimer: Accelerating Protein Monomer and Multimer Structure Prediction
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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): accelerating structure prediction of protein monomers and multimer by 11x
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<p align="right">(<a href="#top">back to top</a>)</p>
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## Installation
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### Download From Official Releases
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You can visit the [Download](https://www.colossalai.org/download) page to download Colossal-AI with pre-built CUDA extensions.
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### Download From Source
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> The version of Colossal-AI will be in line with the main branch of the repository. Feel free to raise an issue if you encounter any problem. :)
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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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If you don't want to install and enable CUDA kernel fusion (compulsory installation when using fused optimizer):
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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">back to top</a>)</p>
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## Use Docker
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### Pull from DockerHub
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You can directly pull the docker image from our [DockerHub page](https://hub.docker.com/r/hpcaitech/colossalai). The image is automatically uploaded upon release.
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### Build On Your Own
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Run the following command to build a docker image from Dockerfile provided.
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> Building Colossal-AI from scratch requires GPU support, you need to use Nvidia Docker Runtime as the default when doing `docker build`. More details can be found [here](https://stackoverflow.com/questions/59691207/docker-build-with-nvidia-runtime).
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> We recommend you install Colossal-AI from our [project page](https://www.colossalai.org) directly.
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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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Run the following command to start the docker container in interactive mode.
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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">back to top</a>)</p>
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## Community
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Join the Colossal-AI community on [Forum](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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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.
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## Contributing
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If you wish to contribute to this project, please follow the guideline in [Contributing](./CONTRIBUTING.md).
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Thanks so much to all of our amazing contributors!
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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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*The order of contributor avatars is randomly shuffled.*
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<p align="right">(<a href="#top">back to top</a>)</p>
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## Quick View
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### Start Distributed Training in Lines
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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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### Start Heterogeneous Training in Lines
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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">back to top</a>)</p>
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## Cite Us
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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">back to top</a>)</p>
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