576a2f7b10
* [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 --------- 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 --------- 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 --------- Co-authored-by: yuehuayingxueluo <867460659@qq.com> * Revert "[inference]Re push async dynamic batching (#4901)" (#4905) This reverts commit |
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README.md
Colossal-AI
Colossal-AI: Making large AI models cheaper, faster, and more accessible
Paper | Documentation | Examples | Forum | Blog
Latest News
- [2023/09] One Half-Day of Training Using a Few Hundred Dollars Yields Similar Results to Mainstream Large Models, Open-Source and Commercial-Free Domain-Specific Llm Solution
- [2023/09] 70 Billion Parameter LLaMA2 Model Training Accelerated by 195%
- [2023/07] HPC-AI Tech Raises 22 Million USD in Series A Funding
- [2023/07] 65B Model Pretraining Accelerated by 38%, Best Practices for Building LLaMA-Like Base Models Open-Source
- [2023/03] ColossalChat: An Open-Source Solution for Cloning ChatGPT With a Complete RLHF Pipeline
- [2023/03] Intel and Colossal-AI Partner to Deliver Cost-Efficient Open-Source Solution for Protein Folding Structure Prediction
- [2023/03] AWS and Google Fund Colossal-AI with Startup Cloud Programs
- [2023/02] Open Source Solution Replicates ChatGPT Training Process! Ready to go with only 1.6GB GPU Memory
- [2023/01] Hardware Savings Up to 46 Times for AIGC and Automatic Parallelism
Table of Contents
- Why Colossal-AI
- Features
-
Colossal-AI for Real World Applications
- Colossal-LLaMA-2: One Half-Day of Training Using a Few Hundred Dollars Yields Similar Results to Mainstream Large Models, Open-Source and Commercial-Free Domain-Specific Llm Solution
- ColossalChat: An Open-Source Solution for Cloning ChatGPT With a Complete RLHF Pipeline
- AIGC: Acceleration of Stable Diffusion
- Biomedicine: Acceleration of AlphaFold Protein Structure
- Parallel Training Demo
- Single GPU Training Demo
- Inference (Energon-AI) Demo
- Installation
- Use Docker
- Community
- Contributing
- Cite Us
Why Colossal-AI
Prof. James Demmel (UC Berkeley): Colossal-AI makes training AI models efficient, easy, and scalable.
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
-
Friendly Usage
- Parallelism based on the configuration file
-
Inference
Colossal-AI in the Real World
Colossal-LLaMA-2
- One half-day of training using a few hundred dollars yields similar results to mainstream large models, open-source and commercial-free domain-specific LLM solution. [code] [blog] [HuggingFace model weights] [Modelscope model weights]
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
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.
Biomedicine
Acceleration of AlphaFold Protein Structure
- FastFold: Accelerating training and inference on GPU Clusters, faster data processing, inference sequence containing more than 10000 residues.
- FastFold with Intel: 3x inference acceleration and 39% cost reduce.
- xTrimoMultimer: accelerating structure prediction of protein monomers and multimer by 11x.
Parallel Training Demo
LLaMA2
LLaMA1
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 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.
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
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.
Installation
Requirements:
- PyTorch >= 1.11 (PyTorch 2.x in progress)
- Python >= 3.7
- CUDA >= 11.0
- NVIDIA GPU Compute Capability >= 7.0 (V100/RTX20 and higher)
- Linux OS
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 .
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
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:
- Leaving a Star ⭐ to show your like and support. Thanks!
- Posting an issue, or submitting a PR on GitHub follow the guideline in Contributing
- Send your official proposal to email contact@hpcaitech.com
Thanks so much to all of our amazing contributors!
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.