aed20fb2df
* [feat] support zbv in mixtral benchmark; * [fix] MixtralForCausalLMPolicy get_held_layer support zbv; * [feat] update MixtralPipelineForwards --> mixtral_model_forward; support zbv; * [feat] support MixtralPipelineForwards--> mixtral_for_causal_lm_forward for zbv * [fix] fix llama, mixtral benchmark zbv loss none bug; update mixtral & llama policy and modeling; * [feat] Linear1D_COL/ROW support zbv WeightGradStore; * [feat] support use_zbv in llama, mixtral modeling; only replace Linear1D_Col/Row policy; * [fix] fix test case; moe error in second iter * [feat]EPMixtralSparseMoeBlock (op in MOE) support zbv; * [fix] fix bwd b; now bwd w only for Layer replaced by Linear1D_Col/Row; other layer perform a fully bwd; * [fix] debug zbv llama test; * [fix] rm use_zbv flag in Shardconfig; rm debug info; * [fix] add & fix llama test * [feat] support meta cache, meta_grad_send, meta_tensor_send; fix runtime too long in Recv Bwd; benchmark for llama + Hybrid(tp+pp); * [fix\ fix fail case test_shard_llama * [fix] fix test_shard_llama * [fix] fix llama modeling policy; * [fix] fix test_shard_llama ci; * [fix] fix test zerobubble * [fix] fix handle name; rm useless comments; * [fix] fix send recv signature; * [fix] fix comment in llama & benchmark * [feat] support no tensor parallel Linear in shardformer; Add test for use weightGradStore and not use WeightGradStore * [fix] fix linear (no tp) ops func name;
Colossal-AI Examples
Table of Contents
Overview
This folder provides several examples accelerated by Colossal-AI.
Folders such as images and language include a wide range of deep learning tasks and applications.
The community folder aim to create a collaborative platform for developers to contribute exotic features built on top of Colossal-AI.
The tutorial folder is for everyone to quickly try out the different features in Colossal-AI.
You can find applications such as Chatbot, AIGC and Biomedicine in the Applications directory.
Folder Structure
└─ examples
└─ images
└─ vit
└─ test_ci.sh
└─ train.py
└─ README.md
└─ ...
└─ ...
Invitation to open-source contribution
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.
- Join the Colossal-AI community on Slack, and WeChat(微信) to share your ideas.
- Send your official proposal to email contact@hpcaitech.com
Thanks so much to all of our amazing contributors!
Integrate Your Example With Testing
Regular checks are important to ensure that all examples run without apparent bugs and stay compatible with the latest API. Colossal-AI runs workflows to check for examples on a on-pull-request and weekly basis. When a new example is added or changed, the workflow will run the example to test whether it can run. Moreover, Colossal-AI will run testing for examples every week.
Therefore, it is essential for the example contributors to know how to integrate your example with the testing workflow. Simply, you can follow the steps below.
- Create a script called
test_ci.shin your example folder - Configure your testing parameters such as number steps, batch size in
test_ci.sh, e.t.c. Keep these parameters small such that each example only takes several minutes. - Export your dataset path with the prefix
/dataand make sure you have a copy of the dataset in the/data/scratch/examples-datadirectory on the CI machine. Community contributors can contact us via slack to request for downloading the dataset on the CI machine. - Implement the logic such as dependency setup and example execution
Community Dependency
We are happy to introduce the following nice community dependency repos that are powered by Colossal-AI: