# Colossal-AI
- 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. - 45% speedup fine-tuning OPT at low cost in lines. [[Example]](https://github.com/hpcaitech/ColossalAI/tree/main/examples/language/opt) [[Online Serving]](https://colossalai.org/docs/advanced_tutorials/opt_service) Please visit our [documentation](https://www.colossalai.org/) and [examples](https://github.com/hpcaitech/ColossalAI/tree/main/examples) for more details. ### ViT
- 14x larger batch size, and 5x faster training for Tensor Parallelism = 64 ### Recommendation System Models - [Cached Embedding](https://github.com/hpcaitech/CachedEmbedding), 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](https://github.com/hpcaitech/EnergonAI): 50% inference acceleration on the same hardware
- [OPT Serving](https://colossalai.org/docs/advanced_tutorials/opt_service): Try 175-billion-parameter OPT online services
- [BLOOM](https://github.com/hpcaitech/EnergonAI/tree/main/examples/bloom): Reduce hardware deployment costs of 176-billion-parameter BLOOM by more than 10 times. ## Colossal-AI in the Real World ### ChatGPT A low-cost [ChatGPT](https://openai.com/blog/chatgpt/) equivalent implementation process. [[code]](https://github.com/hpcaitech/ColossalAI/tree/main/applications/ChatGPT) [[blog]](https://www.hpc-ai.tech/blog/colossal-ai-chatgpt)
- 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 in a sufficiently high running speed ### AIGC Acceleration of AIGC (AI-Generated Content) models such as [Stable Diffusion v1](https://github.com/CompVis/stable-diffusion) and [Stable Diffusion v2](https://github.com/Stability-AI/stablediffusion).
- [Training](https://github.com/hpcaitech/ColossalAI/tree/main/examples/images/diffusion): Reduce Stable Diffusion memory consumption by up to 5.6x and hardware cost by up to 46x (from A100 to RTX3060).
- [DreamBooth Fine-tuning](https://github.com/hpcaitech/ColossalAI/tree/main/examples/images/dreambooth): Personalize your model using just 3-5 images of the desired subject.
- [Inference](https://github.com/hpcaitech/ColossalAI/tree/main/examples/images/diffusion): Reduce inference GPU memory consumption by 2.5x. ### Biomedicine Acceleration of [AlphaFold Protein Structure](https://alphafold.ebi.ac.uk/)
- [FastFold](https://github.com/hpcaitech/FastFold): Accelerating training and inference on GPU Clusters, faster data processing, inference sequence containing more than 10000 residues.
- [FastFold with Intel](https://github.com/hpcaitech/FastFold): 3x inference acceleration and 39% cost reduce.
- [xTrimoMultimer](https://github.com/biomap-research/xTrimoMultimer): accelerating structure prediction of protein monomers and multimer by 11x. ## Installation Requirements: - PyTorch >= 1.11 (PyTorch 2.x in progress) - Python >= 3.7 - CUDA >= 11.0 If you encounter any problem about installation, you may want to raise an [issue](https://github.com/hpcaitech/ColossalAI/issues/new/choose) 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.** ```bash 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`. ```bash CUDA_EXT=1 pip install colossalai ``` **Otherwise, CUDA kernels will be built during runtime when you actually need it.** We also keep release the nightly version to PyPI on a weekly basis. This allows you to access the unreleased features and bug fixes in the main branch. Installation can be made via ```bash 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 problem. :) ```shell 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): ```shell CUDA_EXT=1 pip install . ``` ## 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 ``` ## 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(微信)](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 Referring to the successful attempts of [BLOOM](https://bigscience.huggingface.co/) and [Stable Diffusion](https://en.wikipedia.org/wiki/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 ⭐](https://github.com/hpcaitech/ColossalAI/stargazers) to show your like and support. Thanks! 2. Posting an [issue](https://github.com/hpcaitech/ColossalAI/issues/new/choose), or submitting a PR on GitHub follow the guideline in [Contributing](https://github.com/hpcaitech/ColossalAI/blob/main/CONTRIBUTING.md) 3. Send your official proposal to email contact@hpcaitech.com Thanks so much to all of our amazing contributors! *The order of contributor avatars is randomly shuffled.* ## CI/CD We leverage the power of [GitHub Actions](https://github.com/features/actions) to automate our development, release and deployment workflows. Please check out this [documentation](.github/workflows/README.md) 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](./docs/REFERENCE.md). 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 tutorials by top conference [SC](https://sc22.supercomputing.org/), [AAAI](https://aaai.org/Conferences/AAAI-23/), [PPoPP](https://ppopp23.sigplan.org/), [CVPR](https://cvpr2023.thecvf.com/), [ISC](https://www.isc-hpc.com/), etc.