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* halfway * fix cross-PP-stage position id length diff bug * fix typo * fix typo * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * unified cross entropy func for all shardformer models * remove redundant lines * add basic ring attn; debug cross entropy * fwd bwd logic complete * fwd bwd logic complete; add experimental triton rescale * precision tests passed * precision tests passed * fix typos and remove misc files * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * add sp_mode to benchmark; fix varlen interface * update softmax_lse shape by new interface * change tester name * remove buffer clone; support packed seq layout * add varlen tests * fix typo * all tests passed * add dkv_group; fix mask * remove debug statements --------- Co-authored-by: Edenzzzz <wtan45@wisc.edu> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> |
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README.md
Colossal-AI Tutorial Hands-on
This path is an abbreviated tutorial prepared for specific activities and may not be maintained in real time. For use of Colossal-AI, please refer to other examples and documents.
Introduction
Welcome to the Colossal-AI tutorial, which has been accepted as official tutorials by top conference NeurIPS, SC, AAAI, PPoPP, CVPR, ISC, NVIDIA GTC ,etc.
Colossal-AI, a unified deep learning system for the big model era, integrates many advanced technologies such as multi-dimensional tensor parallelism, sequence parallelism, heterogeneous memory management, large-scale optimization, adaptive task scheduling, etc. By using Colossal-AI, we could help users to efficiently and quickly deploy large AI model training and inference, reducing large AI model training budgets and scaling down the labor cost of learning and deployment.
🚀 Quick Links
Colossal-AI | Paper | Documentation | Issue | Slack
Table of Content
- Multi-dimensional Parallelism [code] [video]
- Sequence Parallelism [code] [video]
- Large Batch Training Optimization [code] [video]
- Automatic Parallelism [code] [video]
- Fine-tuning and Inference for OPT [code] [video]
- Optimized AlphaFold [code] [video]
- Optimized Stable Diffusion [code] [video]
- ColossalChat: Cloning ChatGPT with a Complete RLHF Pipeline [code] [blog] [demo] [video]
Discussion
Discussion about the Colossal-AI project is always welcomed! We would love to exchange ideas with the community to better help this project grow. If you think there is a need to discuss anything, you may jump to our Slack.
If you encounter any problem while running these tutorials, you may want to raise an issue in this repository.
🛠️ Setup environment
[video] You should use conda to create a virtual environment, we recommend python 3.8, e.g. conda create -n colossal python=3.8. This installation commands are for CUDA 11.3, if you have a different version of CUDA, please download PyTorch and Colossal-AI accordingly.
You can refer to the Installation to set up your environment.
You can run colossalai check -i to verify if you have correctly set up your environment 🕹️.
If you encounter messages like please install with cuda_ext, do let me know as it could be a problem of the distribution wheel. 😥
Then clone the Colossal-AI repository from GitHub.
git clone https://github.com/hpcaitech/ColossalAI.git
cd ColossalAI/examples/tutorial