hpcaitech / ColossalAI-Examples

Examples of training models with hybrid parallelism using ColossalAI

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ColossalAI-Examples

2022.01.05

This repo is deprecated. Use our timely maintained example at ColossalAI/example.

Introduction

This repository provides various examples for Colossal-AI. For each feature of Colossal-AI, you can find a simple example in the feature folder and a corresponding tutorial in feature section of the documentation. For more complex examples for domain-specific models, you can find them in this repository as well. Some of them are covered in the advanced tutorials of the documentation.

This repository is built upon Colossal-AI and Titans.

🚀 Quick Links

Colossal-AI | Titans Paper | Documentation | Forum | Blog

Setup

  1. Install Colossal-AI

You can download Colossal-AI here.

  1. Install dependencies
pip install -r requirements.txt

Table of Content

This repository contains examples of training models with ColossalAI. These examples fall under three categories:

  1. Computer Vision

    • ResNet
    • SimCLR
    • Vision Transformer
      • Data Parallel
      • Pipeline Parallel
      • Hybrid Parallel
    • WideNet
      • Mixture of experts
  2. Natural Language Processing

    • BERT
      • Sequence Parallel
    • GPT-2
      • Hybrid Parallel
    • GPT-3
      • Hybrid Parallel
    • Knowledge Graph Embedding
  3. Features

    • Mixed Precision Training
    • Gradient Accumulation
    • Gradient Clipping
    • Tensor Parallel
    • Pipeline Parallel
    • ZeRO

The image and language folders are for complex model applications. The features folder is for demonstration of Colossal-AI. The features folder aims to be simple so that users can execute in minutes. Each example in the features folder relates to a tutorial in the Official Documentation.

If you wish to make contribution to this repository, please read the Contributing section below.

Discussion

Discussion about the Colossal-AI project and examples is always welcomed! We would love to exchange ideas with the community to better help this project grow. If you think there is a need to discuss anything, you may jump to our discussion forum and create a topic there.

If you encounter any problem while running these examples, you may want to raise an issue in this repository.

Contributing

This project welcomes constructive ideas and implementations from the community.

Update an Example

If you find that an example is broken (not working) or not user-friendly, you may put up a pull request to this repository and update this example.

Add a New Example

If you wish to add an example for a specific application, please follow the steps below.

  1. create a folder in the image, language or features folders. Generally we do not accept new examples for features as one example is often enough. We encourage contribution with hybrid parallel or models of different domains (e.g. GAN, self-supervised, detection, video understanding, text classification, text generation)
  2. Prepare configuration files and train.py
  3. Prepare a detailed readme on environment setup, dataset preparation, code execution, etc. in your example folder
  4. Update the table of content (first section above) in this readme file

If your PR is accepted, we may invite you to put up a tutorial or blog in ColossalAI Documentation.

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Examples of training models with hybrid parallelism using ColossalAI

License:Apache License 2.0


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