Making big AI models cheaper, easier, and scalable
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Hongxin Liu b5f0566363
[chat] add distributed PPO trainer (#3740)
* Detached ppo (#9)

* run the base

* working on dist ppo

* sync

* detached trainer

* update detached trainer. no maker update function

* facing init problem

* 1 maker 1 trainer detached run. but no model update

* facing cuda problem

* fix save functions

* verified maker update

* nothing

* add ignore

* analyize loss issue

* remove some debug codes

* facing 2m1t stuck issue

* 2m1t verified

* do not use torchrun

* working on 2m2t

* working on 2m2t

* initialize strategy in ray actor env

* facing actor's init order issue

* facing ddp model update issue (need unwarp ddp)

* unwrap ddp actor

* checking 1m2t stuck problem

* nothing

* set timeout for trainer choosing. It solves the stuck problem!

* delete some debug output

* rename to sync with upstream

* rename to sync with upstream

* coati rename

* nothing

* I am going to detach the replaybuffer from trainer and make it a Ray Actor. Two benefits: 1. support TP trainer. 2. asynchronized buffer operations

* experience_maker_holder performs target-revolving _send_experience() instead of length comparison.

* move code to ray subfolder

* working on pipeline inference

* apply comments

* working on pipeline strategy. in progress.

* remove pipeline code. clean this branch

* update remote parameters by state_dict. no test

* nothing

* state_dict sharding transfer

* merge debug branch

* gemini _unwrap_model fix

* simplify code

* simplify code & fix LoRALinear AttributeError

* critic unwrapped state_dict

---------

Co-authored-by: csric <richcsr256@gmail.com>

* [chat] add perfomance evaluator and fix bugs (#10)

* [chat] add performance evaluator for ray

* [chat] refactor debug arg

* [chat] support hf config

* [chat] fix generation

* [chat] add 1mmt dummy example

* [chat] fix gemini ckpt

* split experience to send (#11)

Co-authored-by: csric <richcsr256@gmail.com>

* [chat] refactor trainer and maker (#12)

* [chat] refactor experience maker holder

* [chat] refactor model init

* [chat] refactor trainer args

* [chat] refactor model init

* [chat] refactor trainer

* [chat] refactor experience sending logic and training loop args (#13)

* [chat] refactor experience send logic

* [chat] refactor trainer

* [chat] refactor trainer

* [chat] refactor experience maker

* [chat] refactor pbar

* [chat] refactor example folder (#14)

* [chat] support quant (#15)

* [chat] add quant

* [chat] add quant example

* prompt example (#16)

* prompt example

* prompt load csv data

* remove legacy try

---------

Co-authored-by: csric <richcsr256@gmail.com>

* [chat] add mmmt dummy example and refactor experience sending (#17)

* [chat] add mmmt dummy example

* [chat] refactor naive strategy

* [chat] fix struck problem

* [chat] fix naive strategy

* [chat] optimize experience maker sending logic

* [chat] refactor sending assignment

* [chat] refactor performance evaluator (#18)

* Prompt Example & requires_grad state_dict & sharding state_dict (#19)

* prompt example

* prompt load csv data

* remove legacy try

* maker models require_grad set to False

* working on zero redundancy update

* mmmt_prompt example; naive strategy requires_grad state_dict & sharding; maker model requires_no_grad.

* remove legacy examples

* remove legacy examples

* remove replay buffer tp state. bad design

---------

Co-authored-by: csric <richcsr256@gmail.com>

* state_dict sending adapts to new unwrap function (#20)

* prompt example

* prompt load csv data

* remove legacy try

* maker models require_grad set to False

* working on zero redundancy update

* mmmt_prompt example; naive strategy requires_grad state_dict & sharding; maker model requires_no_grad.

* remove legacy examples

* remove legacy examples

* remove replay buffer tp state. bad design

* opt benchmark

* better script

* nothing

* [chat] strategy refactor unwrap model

* [chat] strategy refactor save model

* [chat] add docstr

* [chat] refactor trainer save model

* [chat] fix strategy typing

* [chat] refactor trainer save model

* [chat] update readme

* [chat] fix unit test

* working on lora reconstruction

* state_dict sending adapts to new unwrap function

* remove comments

---------

Co-authored-by: csric <richcsr256@gmail.com>
Co-authored-by: ver217 <lhx0217@gmail.com>

* [chat-ray] add readme (#21)

* add readme

* transparent graph

* add note background

---------

Co-authored-by: csric <richcsr256@gmail.com>

* [chat] get images from url (#22)

* Refactor/chat ray (#23)

* [chat] lora add todo

* [chat] remove unused pipeline strategy

* [chat] refactor example structure

* [chat] setup ci for ray

* [chat-ray] Support LoRA trainer. LoRA weights reconstruction. (#24)

* lora support prototype

* lora support

* 1mmt lora & remove useless code

---------

Co-authored-by: csric <richcsr256@gmail.com>

* [chat] fix test ci for ray

* [chat] fix test ci requirements for ray

* [chat] fix ray runtime env

* [chat] fix ray runtime env

* [chat] fix example ci docker args

* [chat] add debug info in trainer

* [chat] add nccl debug info

* [chat] skip ray test

* [doc] fix typo

---------

Co-authored-by: csric <59389055+CsRic@users.noreply.github.com>
Co-authored-by: csric <richcsr256@gmail.com>
2023-06-07 10:41:16 +08:00
.github [chat] add distributed PPO trainer (#3740) 2023-06-07 10:41:16 +08:00
applications [chat] add distributed PPO trainer (#3740) 2023-06-07 10:41:16 +08:00
colossalai [nfc]fix typo colossalai/pipeline tensor nn (#3899) 2023-06-06 14:07:36 +08:00
docker [Docker] Fix a couple of build issues (#3691) 2023-05-24 10:22:51 +08:00
docs [doc] fix docs about booster api usage (#3898) 2023-06-06 13:36:11 +08:00
examples [example] update gemini examples (#3868) 2023-05-30 18:41:41 +08:00
inference@56b35f3c06 Automated submodule synchronization (#3062) 2023-03-09 11:22:56 +08:00
op_builder [Docker] Fix a couple of build issues (#3691) 2023-05-24 10:22:51 +08:00
requirements [workflow] fixed testmon cache in build CI (#3806) 2023-05-24 14:59:40 +08:00
tests [bf16] add bf16 support (#3882) 2023-06-05 15:58:31 +08:00
.clang-format [revert] recover "[refactor] restructure configuration files (#2977)" (#3022) 2023-03-07 13:31:23 +08:00
.compatibility [workflow] automated the compatiblity test (#2453) 2023-01-11 23:40:16 +08:00
.coveragerc [devops] update torch version of CI (#3725) 2023-05-15 17:20:56 +08:00
.cuda_ext.json [workflow] added cuda extension build test before release (#2598) 2023-02-06 17:07:41 +08:00
.flake8
.gitignore [workflow] fixed testmon cache in build CI (#3806) 2023-05-24 14:59:40 +08:00
.gitmodules [tutorial] update fastfold tutorial (#2565) 2023-02-03 16:54:28 +08:00
.isort.cfg [revert] recover "[refactor] restructure configuration files (#2977)" (#3022) 2023-03-07 13:31:23 +08:00
.pre-commit-config.yaml [revert] recover "[refactor] restructure configuration files (#2977)" (#3022) 2023-03-07 13:31:23 +08:00
.style.yapf [revert] recover "[refactor] restructure configuration files (#2977)" (#3022) 2023-03-07 13:31:23 +08:00
CHANGE_LOG.md [doc] updated the CHANGE_LOG.md for github release page (#2552) 2023-02-03 10:47:27 +08:00
CONTRIBUTING.md [revert] recover "[refactor] restructure configuration files (#2977)" (#3022) 2023-03-07 13:31:23 +08:00
LICENSE updated flash attention usage 2023-03-20 17:57:04 +08:00
MANIFEST.in [builder] reconfig op_builder for pypi install (#2314) 2023-01-04 16:32:32 +08:00
pytest.ini [booster] gemini plugin support shard checkpoint (#3610) 2023-05-05 14:37:21 +08:00
README.md [workflow] supported test on CUDA 10.2 (#3841) 2023-05-25 14:14:34 +08:00
setup.py [doc] fix setup.py typo (#3603) 2023-04-19 17:28:15 +08:00
version.txt [release] bump to v0.3.0 (#3830) 2023-05-25 16:20:07 +08:00

Colossal-AI

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Colossal-AI: Making large AI models cheaper, faster, and more accessible

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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.

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Colossal-AI in the Real World

ColossalChat

ColossalChat: An open-source solution for cloning ChatGPT with a complete RLHF pipeline. [code] [blog] [demo] [tutorial]

  • Up to 10 times faster for RLHF PPO Stage3 Training

  • Up to 7.73 times faster for single server training and 1.42 times faster for single-GPU inference

  • Up to 10.3x growth in model capacity on one GPU
  • A mini demo training process requires only 1.62GB of GPU memory (any consumer-grade GPU)

  • Increase the capacity of the fine-tuning model by up to 3.7 times on a single GPU
  • Keep at a sufficiently high running speed

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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).

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

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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.

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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

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 of public pre-trained 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.

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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

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Inference (Energon-AI) Demo

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

  • OPT Serving: Try 175-billion-parameter OPT online services

  • BLOOM: Reduce hardware deployment costs of 176-billion-parameter BLOOM by more than 10 times.

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Installation

Requirements:

If you encounter any problem with installation, you may want to raise an issue in this repository.

Install from PyPI

You can easily install Colossal-AI with the following command. By default, we do not build PyTorch extensions during installation.

pip install colossalai

Note: only Linux is supported for now.

However, if you want to build the PyTorch extensions during installation, you can set CUDA_EXT=1.

CUDA_EXT=1 pip install colossalai

Otherwise, CUDA kernels will be built during runtime when you actually need them.

We also keep releasing the nightly version to PyPI every week. This allows you to access the unreleased features and bug fixes in the main branch. Installation can be made via

pip install colossalai-nightly

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 problems. :)

git clone https://github.com/hpcaitech/ColossalAI.git
cd ColossalAI

# install colossalai
pip install .

By default, we do not compile CUDA/C++ kernels. ColossalAI will build them during runtime. If you want to install and enable CUDA kernel fusion (compulsory installation when using fused optimizer):

CUDA_EXT=1 pip install .

For Users with CUDA 10.2, you can still build ColossalAI from source. However, you need to manually download the cub library and copy it to the corresponding directory.

# clone the repository
git clone https://github.com/hpcaitech/ColossalAI.git
cd ColossalAI

# download the cub library
wget https://github.com/NVIDIA/cub/archive/refs/tags/1.8.0.zip
unzip 1.8.0.zip
cp -r cub-1.8.0/cub/ colossalai/kernel/cuda_native/csrc/kernels/include/

# install
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. 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

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Community

Join the Colossal-AI community on Forum, Slack, and WeChat(微信) to share your suggestions, feedback, and questions with our engineering team.

Contributing

Referring to the successful attempts of BLOOM and Stable Diffusion, any and all developers and partners with computing powers, datasets, models are welcome to join and build the Colossal-AI community, making efforts towards the era of big AI models!

You may contact us or participate in the following ways:

  1. Leaving a Star to show your like and support. Thanks!
  2. Posting an issue, or submitting a PR on GitHub follow the guideline in Contributing
  3. Send your official proposal to email contact@hpcaitech.com

Thanks so much to all of our amazing contributors!

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CI/CD

We leverage the power of GitHub Actions to automate our development, release and deployment workflows. Please check out this documentation on how the automated workflows are operated.

Cite Us

This project is inspired by some related projects (some by our team and some by other organizations). We would like to credit these amazing projects as listed in the Reference List.

To cite this project, you can use the following BibTeX citation.

@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}
}

Colossal-AI has been accepted as official tutorial by top conferences SC, AAAI, PPoPP, CVPR, ISC, etc.

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