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[gemini] gemini support tensor parallelism. (#4942)
* [colossalai]fix typo

* [inference] Add smmoothquant for llama (#4904)

* [inference] add int8 rotary embedding kernel for smoothquant (#4843)

* [inference] add smoothquant llama attention (#4850)

* add smoothquant llama attention

* remove uselss code

* remove useless code

* fix import error

* rename file name

* [inference] add silu linear fusion for smoothquant llama mlp  (#4853)

* add silu linear

* update skip condition

* catch smoothquant cuda lib exception

* prcocess exception for tests

* [inference] add llama mlp for smoothquant (#4854)

* add llama mlp for smoothquant

* fix down out scale

* remove duplicate lines

* add llama mlp check

* delete useless code

* [inference] add smoothquant llama (#4861)

* add smoothquant llama

* fix attention accuracy

* fix accuracy

* add kv cache and save pretrained

* refactor example

* delete smooth

* refactor code

* [inference] add smooth function and delete useless code for smoothquant (#4895)

* add smooth function and delete useless code

* update datasets

* remove duplicate import

* delete useless file

* refactor codes (#4902)

* rafactor code

* add license

* add torch-int and smoothquant license

* Update flash_attention_patch.py

To be compatible with the new change in the Transformers library, where a new argument 'padding_mask' was added to forward function of attention layer.
https://github.com/huggingface/transformers/pull/25598

* [kernel] support pure fp16 for cpu adam and update gemini optim tests (#4921)

* [kernel] support pure fp16 for cpu adam (#4896)

* [kernel] fix cpu adam kernel for pure fp16 and update tests (#4919)

* [kernel] fix cpu adam

* [test] update gemini optim test

* [format] applied code formatting on changed files in pull request 4908 (#4918)

Co-authored-by: github-actions <github-actions@github.com>

* [gemini] support gradient accumulation (#4869)

* add test

* fix no_sync bug in low level zero plugin

* fix test

* add argument for grad accum

* add grad accum in backward hook for gemini

* finish implementation, rewrite tests

* fix test

* skip stuck model in low level zero test

* update doc

* optimize communication & fix gradient checkpoint

* modify doc

* cleaning codes

* update cpu adam fp16 case

* [hotfix] fix torch 2.0 compatibility (#4936)

* [hotfix] fix launch

* [test] fix test gemini optim

* [shardformer] fix vit

* [test] add no master test for low level zero plugin (#4934)

* [format] applied code formatting on changed files in pull request 4820 (#4886)

Co-authored-by: github-actions <github-actions@github.com>

* [nfc] fix some typo with colossalai/ docs/ etc. (#4920)

* [Refactor] Integrated some lightllm kernels into token-attention  (#4946)

* add some req for inference

* clean codes

* add codes

* add some lightllm deps

* clean codes

* hello

* delete rms files

* add some comments

* add comments

* add doc

* add lightllm deps

* add lightllm cahtglm2 kernels

* add lightllm cahtglm2 kernels

* replace rotary embedding with lightllm kernel

* add some commnets

* add some comments

* add some comments

* add

* replace fwd kernel att1

* fix a arg

* add

* add

* fix token attention

* add some comments

* clean codes

* modify comments

* fix readme

* fix bug

* fix bug

---------

Co-authored-by: cuiqing.li <lixx336@gmail.com>
Co-authored-by: CjhHa1 <cjh18671720497@outlook.com>

* [test] merge old components to test to model zoo (#4945)

* [test] add custom models in model zoo

* [test] update legacy test

* [test] update model zoo

* [test] update gemini test

* [test] remove components to test

* [inference] add reference and fix some bugs (#4937)

* add reference and fix some bugs

* update gptq init

---------

Co-authored-by: Xu Kai <xukai16@foxamil.com>

* [Inference]ADD Bench Chatglm2 script (#4963)

* add bench chatglm

* fix bug and make utils

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Co-authored-by: CjhHa1 <cjh18671720497outlook.com>

* [Pipeline inference] Combine kvcache with pipeline inference (#4938)

* merge kvcache with pipeline inference and refactor the code structure

* support ppsize > 2

* refactor pipeline code

* do pre-commit

* modify benchmark

* fix bench mark

* polish code

* add docstring and update readme

* refactor the code

* fix some logic bug of ppinfer

* polish readme

* fix typo

* skip infer test

* updated c++17 compiler flags (#4983)

* [Inference] Dynamic Batching Inference, online and offline (#4953)

* [inference] Dynamic Batching for Single and Multiple GPUs (#4831)

* finish batch manager

* 1

* first

* fix

* fix dynamic batching

* llama infer

* finish test

* support different lengths generating

* del prints

* del prints

* fix

* fix bug

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Co-authored-by: CjhHa1 <cjh18671720497outlook.com>

* [inference] Async dynamic batching  (#4894)

* finish input and output logic

* add generate

* test forward

* 1

* [inference]Re push async dynamic batching (#4901)

* adapt to ray server

* finish async

* finish test

* del test

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Co-authored-by: yuehuayingxueluo <867460659@qq.com>

* Revert "[inference]Re push async dynamic batching (#4901)" (#4905)

This reverts commit fbf3c09e67.

* Revert "[inference] Async dynamic batching  (#4894)"

This reverts commit fced140250.

* Revert "[inference] Async dynamic batching  (#4894)" (#4909)

This reverts commit fced140250.

* Add Ray Distributed Environment Init Scripts

* support DynamicBatchManager base function

* revert _set_tokenizer version

* add driver async generate

* add async test

* fix bugs in test_ray_dist.py

* add get_tokenizer.py

* fix code style

* fix bugs about No module named 'pydantic' in ci test

* fix bugs in ci test

* fix bugs in ci test

* fix bugs in ci test

* [infer]Add Ray Distributed Environment Init Scripts (#4911)

* Revert "[inference] Async dynamic batching  (#4894)"

This reverts commit fced140250.

* Add Ray Distributed Environment Init Scripts

* support DynamicBatchManager base function

* revert _set_tokenizer version

* add driver async generate

* add async test

* fix bugs in test_ray_dist.py

* add get_tokenizer.py

* fix code style

* fix bugs about No module named 'pydantic' in ci test

* fix bugs in ci test

* fix bugs in ci test

* fix bugs in ci test

* support dynamic batch for bloom model and is_running function

* [Inference]Test for new Async engine (#4935)

* infer engine

* infer engine

* test engine

* test engine

* new manager

* change step

* add

* test

* fix

* fix

* finish test

* finish test

* finish test

* finish test

* add license

---------

Co-authored-by: yuehuayingxueluo <867460659@qq.com>

* add assertion for config (#4947)

* [Inference] Finish dynamic batching offline test (#4948)

* test

* fix test

* fix quant

* add default

* fix

* fix some bugs

* fix some bugs

* fix

* fix bug

* fix bugs

* reset param

---------

Co-authored-by: yuehuayingxueluo <867460659@qq.com>
Co-authored-by: Cuiqing Li <lixx3527@gmail.com>
Co-authored-by: CjhHa1 <cjh18671720497outlook.com>

* [Kernels]Updated Triton kernels into 2.1.0 and adding flash-decoding for llama token attention  (#4965)

* adding flash-decoding

* clean

* adding kernel

* adding flash-decoding

* add integration

* add

* adding kernel

* adding kernel

* adding triton 2.1.0 features for inference

* update bloom triton kernel

* remove useless vllm kernels

* clean codes

* fix

* adding files

* fix readme

* update llama flash-decoding

---------

Co-authored-by: cuiqing.li <lixx336@gmail.com>

* fix ColossalEval (#4992)

Co-authored-by: Xu Yuanchen <yuanchen.xu00@gmail.com>

* [doc]Update doc for colossal-inference (#4989)

* update doc

* Update README.md

---------

Co-authored-by: cuiqing.li <lixx336@gmail.com>

* [hotfix] Fix the bug where process groups were not being properly released. (#4940)

* Fix the bug where process groups were not being properly released.

* test

* Revert "test"

This reverts commit 479900c139.

* [hotfix] fix the bug of repeatedly storing param group (#4951)

* [doc] add supported feature diagram for hybrid parallel plugin (#4996)

* [Pipeline Inference] Merge pp with tp (#4993)

* refactor pipeline into new CaiInferEngine

* updata llama modeling forward

* merge tp with pp

* update docstring

* optimize test workflow and example

* fix typo

* add assert and todo

* [release] update version (#4995)

* [release] update version

* [hotfix] fix ci

* [gemini] gemini support tp

[gemini] gemini support tp

[gemini] gemini support tp

[gemini] gemini support tp

[gemini] gemini support tp

* fix

fix

fix

* update checkpointIO

update checkpointIO

update checkpointIO

update checkpointIO

update checkpointIO

update checkpointIO

update checkpointIO

update checkpointIO

update checkpointIO

* support fused layernorm

support fused layernorm

support fused layernorm

* update fusedlayernorm

update fusedlayernorm

update fusedlayernorm

* add sequence parallel to gemini

add sequence parallel to gemini

* fix

* fix comments

fix comments

fix comments

* fix

* fix t5

* clear cache

* fix

* activate ci

* activate ci

* fix

* fix

* fix

* fix

* revert

* modify tp gather method

modify tp gather method

modify tp gather method

modify tp gather method

* fix test

---------

Co-authored-by: Xu Kai <xukai16@foxmail.com>
Co-authored-by: Zian(Andy) Zheng <62330719+Orion-Zheng@users.noreply.github.com>
Co-authored-by: Hongxin Liu <lhx0217@gmail.com>
Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
Co-authored-by: github-actions <github-actions@github.com>
Co-authored-by: Baizhou Zhang <eddiezhang@pku.edu.cn>
Co-authored-by: Zhongkai Zhao <kanezz620@gmail.com>
Co-authored-by: digger yu <digger-yu@outlook.com>
Co-authored-by: Cuiqing Li <lixx3527@gmail.com>
Co-authored-by: cuiqing.li <lixx336@gmail.com>
Co-authored-by: CjhHa1 <cjh18671720497@outlook.com>
Co-authored-by: Xu Kai <xukai16@foxamil.com>
Co-authored-by: Jianghai <72591262+CjhHa1@users.noreply.github.com>
Co-authored-by: Bin Jia <45593998+FoolPlayer@users.noreply.github.com>
Co-authored-by: アマデウス <kurisusnowdeng@users.noreply.github.com>
Co-authored-by: yuehuayingxueluo <867460659@qq.com>
Co-authored-by: Yuanchen <70520919+chengeharrison@users.noreply.github.com>
Co-authored-by: Xu Yuanchen <yuanchen.xu00@gmail.com>
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2023-11-10 10:15:16 +08:00
.github [misc] add code owners (#5024) 2023-11-08 15:18:51 +08:00
applications Support mtbench (#5025) 2023-11-09 13:41:50 +08:00
colossalai [gemini] gemini support tensor parallelism. (#4942) 2023-11-10 10:15:16 +08:00
docker Update Dockerfile (#4499) 2023-09-01 18:12:34 +08:00
docs [doc] add supported feature diagram for hybrid parallel plugin (#4996) 2023-10-31 19:56:42 +08:00
examples [moe]: fix ep/tp tests, add hierarchical all2all (#4982) 2023-11-09 06:31:00 +00:00
inference@56b35f3c06 Automated submodule synchronization (#3062) 2023-03-09 11:22:56 +08:00
op_builder updated c++17 compiler flags (#4983) 2023-10-27 18:19:56 +08:00
requirements [Inference] Dynamic Batching Inference, online and offline (#4953) 2023-10-30 10:52:19 +08:00
tests [gemini] gemini support tensor parallelism. (#4942) 2023-11-10 10:15:16 +08:00
.clang-format [revert] recover "[refactor] restructure configuration files (#2977)" (#3022) 2023-03-07 13:31:23 +08:00
.compatibility [devops] update torch version in compability test (#3919) 2023-06-08 09:29:32 +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
.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 [lazy] support torch 2.0 (#4763) 2023-09-21 16:30:23 +08:00
.pre-commit-config.yaml [misc] update pre-commit and run all files (#4752) 2023-09-19 14:20:26 +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 [doc] add a note about unit-testing to CONTRIBUTING.md (#3970) 2023-06-14 16:32:39 +08:00
LICENSE [inference] Add smmoothquant for llama (#4904) 2023-10-16 11:28:44 +08:00
MANIFEST.in
README.md Update main README.md 2023-10-10 23:19:34 +08:00
pytest.ini [moe] merge moe into main (#4978) 2023-11-02 02:21:24 +00:00
setup.py [misc] update pre-commit and run all files (#4752) 2023-09-19 14:20:26 +08:00
version.txt [release] update version (#4995) 2023-11-01 13:41:22 +08:00

README.md

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

Colossal-LLaMA-2

Backbone Tokens Consumed MMLU CMMLU AGIEval GAOKAO CEval
- 5-shot 5-shot 5-shot 0-shot 5-shot
Baichuan-7B - 1.2T 42.32 (42.30) 44.53 (44.02) 38.72 36.74 42.80
Baichuan-13B-Base - 1.4T 50.51 (51.60) 55.73 (55.30) 47.20 51.41 53.60
Baichuan2-7B-Base - 2.6T 46.97 (54.16) 57.67 (57.07) 45.76 52.60 54.00
Baichuan2-13B-Base - 2.6T 54.84 (59.17) 62.62 (61.97) 52.08 58.25 58.10
ChatGLM-6B - 1.0T 39.67 (40.63) 41.17 (-) 40.10 36.53 38.90
ChatGLM2-6B - 1.4T 44.74 (45.46) 49.40 (-) 46.36 45.49 51.70
InternLM-7B - 1.6T 46.70 (51.00) 52.00 (-) 44.77 61.64 52.80
Qwen-7B - 2.2T 54.29 (56.70) 56.03 (58.80) 52.47 56.42 59.60
Llama-2-7B - 2.0T 44.47 (45.30) 32.97 (-) 32.60 25.46 -
Linly-AI/Chinese-LLaMA-2-7B-hf Llama-2-7B 1.0T 37.43 29.92 32.00 27.57 -
wenge-research/yayi-7b-llama2 Llama-2-7B - 38.56 31.52 30.99 25.95 -
ziqingyang/chinese-llama-2-7b Llama-2-7B - 33.86 34.69 34.52 25.18 34.2
TigerResearch/tigerbot-7b-base Llama-2-7B 0.3T 43.73 42.04 37.64 30.61 -
LinkSoul/Chinese-Llama-2-7b Llama-2-7B - 48.41 38.31 38.45 27.72 -
FlagAlpha/Atom-7B Llama-2-7B 0.1T 49.96 41.10 39.83 33.00 -
IDEA-CCNL/Ziya-LLaMA-13B-v1.1 Llama-13B 0.11T 50.25 40.99 40.04 30.54 -
Colossal-LLaMA-2-7b-base Llama-2-7B 0.0085T 53.06 49.89 51.48 58.82 50.2

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

LLaMA2

  • 70 billion parameter LLaMA2 model training accelerated by 195% [code] [blog]

LLaMA1

  • 65-billion-parameter large model pretraining accelerated by 38% [code] [blog]

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 NeurIPS, SC, AAAI, PPoPP, CVPR, ISC, NVIDIA GTC ,etc.

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