Colossal-AI

Colossal-AI: A Unified Deep Learning System for Big Model Era

Paper | Documentation | Examples | Forum | Blog

| English | 中文 |

Latest News

• [2023/01] Hardware Savings Up to 46 Times for AIGC and Automatic Parallelism
• [2022/11] Diffusion Pretraining and Hardware Fine-Tuning Can Be Almost 7X Cheaper
• [2022/10] Use a Laptop to Analyze 90% of Proteins, With a Single-GPU Inference Sequence Exceeding 10,000
• [2022/10] Embedding Training With 1% GPU Memory and 100 Times Less Budget for Super-Large Recommendation Model
• [2022/09] HPC-AI Tech Completes $6 Million Seed and Angel Round Fundraising

Table of Contents

• Why Colossal-AI
• Features
• Parallel Training Demo
  • GPT-3
  • GPT-2
  • BERT
  • PaLM
  • OPT
  • ViT
  • Recommendation System Models
• Single GPU Training Demo
  • GPT-2
  • PaLM
• Inference (Energon-AI) Demo
  • GPT-3
  • OPT-175B Online Serving for Text Generation
  • 175B BLOOM
• Colossal-AI for Real World Applications
  • AIGC: Acceleration of Stable Diffusion
  • Biomedicine: Acceleration of AlphaFold Protein Structure
• Installation
  • PyPI
  • Install From Source
• Use Docker
• Community
• Contributing
• Cite Us

Why Colossal-AI

Prof. James Demmel (UC Berkeley): Colossal-AI makes training AI models efficient, easy, and scalable.

(back to top)

Features

Colossal-AI provides a collection of parallel 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 and inference in a few lines.

• Parallelism strategies

  • Data Parallelism
  • Pipeline Parallelism
  • 1D, 2D, 2.5D, 3D Tensor Parallelism
  • Sequence Parallelism
  • Zero Redundancy Optimizer (ZeRO)
  • Auto-Parallelism

• Heterogeneous Memory Management

  • PatrickStar

• Friendly Usage

  • Parallelism based on configuration file

• Inference

  • Energon-AI

• Colossal-AI in the Real World

  • Biomedicine: FastFold accelerates training and inference of AlphaFold protein structure

(back to top)

Parallel Training Demo

GPT-3

• Save 50% GPU resources, and 10.7% acceleration

GPT-2

• 11x lower GPU memory consumption, and superlinear scaling efficiency with Tensor Parallelism

• 24x larger model size on the same hardware
• over 3x acceleration

BERT

• 2x faster training, or 50% longer sequence length

PaLM

• PaLM-colossalai: Scalable implementation of Google's Pathways Language Model (PaLM).

OPT

• Open Pretrained Transformer (OPT), 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.
• 45% speedup fine-tuning OPT at low cost in lines. [Example] [Online Serving]

Please visit our documentation and examples for more details.

ViT

• 14x larger batch size, and 5x faster training for Tensor Parallelism = 64

Recommendation System Models

• Cached Embedding, utilize software cache to train larger embedding tables with a smaller GPU memory budget.

(back to top)

Single GPU Training Demo

GPT-2

• 20x larger model size on the same hardware

• 120x larger model size on the same hardware (RTX 3080)

PaLM

• 34x larger model size on the same hardware

(back to top)

Inference (Energon-AI) Demo

• Energon-AI: 50% inference acceleration on the same hardware

• OPT Serving: Try 175-billion-parameter OPT online services for free, without any registration whatsoever.

• BLOOM: Reduce hardware deployment costs of 175-billion-parameter BLOOM by more than 10 times.

(back to top)

Colossal-AI in the Real World

AIGC

Acceleration of AIGC (AI-Generated Content) models such as Stable Diffusion v1 and Stable Diffusion v2.

• Training: Reduce Stable Diffusion memory consumption by up to 5.6x and hardware cost by up to 46x (from A100 to RTX3060).

• DreamBooth Fine-tuning: Personalize your model using just 3-5 images of the desired subject.

• Inference: Reduce inference GPU memory consumption by 2.5x.

(back to top)

Biomedicine

Acceleration of AlphaFold Protein Structure

• FastFold: accelerating training and inference on GPU Clusters, faster data processing, inference sequence containing more than 10000 residues.

• xTrimoMultimer: accelerating structure prediction of protein monomers and multimer by 11x.

(back to top)

Installation

Download From Official Releases

You can visit the Download page to download Colossal-AI with pre-built CUDA extensions.

Download From Source

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. :)

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):

NO_CUDA_EXT=1 pip install .

(back to top)

Use Docker

Pull from DockerHub

You can directly pull the docker image from our DockerHub page. The image is automatically uploaded upon release.

Build On Your Own

Run the following command to build a docker image from Dockerfile provided.

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. We recommend you install Colossal-AI from our project page directly.

cd ColossalAI
docker build -t colossalai ./docker

Run the following command to start the docker container in interactive mode.

docker run -ti --gpus all --rm --ipc=host colossalai bash

(back to top)

Community

Join the Colossal-AI community on Forum, Slack, and WeChat 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.

Thanks so much to all of our amazing contributors!

The order of contributor avatars is randomly shuffled.

(back to top)

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}
}

(back to top)