# Colossal-AI
[![logo](https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/Colossal-AI_logo.png)](https://www.colossalai.org/) Colossal-AI: A Unified Deep Learning System for Big Model Era

Paper | Documentation | Examples | Forum | Blog

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

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## 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](https://arxiv.org/abs/2104.05343), [2.5D](https://arxiv.org/abs/2105.14500), [3D](https://arxiv.org/abs/2105.14450) Tensor Parallelism - [Sequence Parallelism](https://arxiv.org/abs/2105.13120) - [Zero Redundancy Optimizer (ZeRO)](https://arxiv.org/abs/1910.02054) - Heterogeneous Memory Management - [PatrickStar](https://arxiv.org/abs/2108.05818) - Friendly Usage - Parallelism based on configuration file - Inference - [Energon-AI](https://github.com/hpcaitech/EnergonAI)

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## Parallel Training Demo ### ViT

- 14x larger batch size, and 5x faster training for Tensor Parallelism = 64 ### 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](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)). ### OPT - [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. - 40% speedup fine-tuning OPT at low cost in lines. [[Example]](https://github.com/hpcaitech/ColossalAI-Examples/tree/main/language/opt) Please visit our [documentation](https://www.colossalai.org/) and [examples](https://github.com/hpcaitech/ColossalAI-Examples) for more details.

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## Single GPU Training Demo ### GPT-2

- 20x larger model size on the same hardware ### PaLM

- 34x larger model size on the same hardware

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## Inference (Energon-AI) Demo ### GPT-3

- [Energon-AI](https://github.com/hpcaitech/EnergonAI): 50% inference acceleration on the same hardware

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## Installation ### Download From Official Releases You can visit the [Download](https://www.colossalai.org/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. :) ```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 NO_CUDA_EXT=1 pip install . ```

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## Use Docker ### Pull from DockerHub 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. ### 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](https://stackoverflow.com/questions/59691207/docker-build-with-nvidia-runtime). > We recommend you install Colossal-AI from our [project page](https://www.colossalai.org) directly. ```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 ```

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## 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! *The order of contributor avatars is randomly shuffled.*

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## Quick View ### Start Distributed Training in Lines ```python parallel = dict( pipeline=2, tensor=dict(mode='2.5d', depth = 1, size=4) ) ``` ### Start Heterogeneous Training in Lines ```python zero = dict( model_config=dict( tensor_placement_policy='auto', shard_strategy=TensorShardStrategy(), reuse_fp16_shard=True ), optimizer_config=dict(initial_scale=2**5, gpu_margin_mem_ratio=0.2) ) ```

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## 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} } ```

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