History

Hongxin Liu b5f9e37c70 [legacy] clean up legacy code (#4743 ) * [legacy] remove outdated codes of pipeline (#4692) * [legacy] remove cli of benchmark and update optim (#4690) * [legacy] remove cli of benchmark and update optim * [doc] fix cli doc test * [legacy] fix engine clip grad norm * [legacy] remove outdated colo tensor (#4694) * [legacy] remove outdated colo tensor * [test] fix test import * [legacy] move outdated zero to legacy (#4696) * [legacy] clean up utils (#4700) * [legacy] clean up utils * [example] update examples * [legacy] clean up amp * [legacy] fix amp module * [legacy] clean up gpc (#4742) * [legacy] clean up context * [legacy] clean core, constants and global vars * [legacy] refactor initialize * [example] fix examples ci * [example] fix examples ci * [legacy] fix tests * [example] fix gpt example * [example] fix examples ci * [devops] fix ci installation * [example] fix examples ci		2023-09-18 16:31:06 +08:00
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saved_solution	[autoparallel] integrate device mesh initialization into autoparallelize (#2393 )	2023-01-11 14:03:49 +08:00
README.md	[auto] fix install cmd (#3772 )	2023-05-18 13:33:01 +08:00
auto_parallel_with_gpt.py	[legacy] clean up legacy code (#4743 )	2023-09-18 16:31:06 +08:00
gpt_modules.py	[example] upload auto parallel gpt2 demo (#2354 )	2023-01-06 11:38:38 +08:00
requirements.txt	[auto] fix requirements typo for issue #3125 (#3209 )	2023-03-23 10:22:08 +08:00

README.md

Auto-Parallelism with GPT2

Requirements

Before you can launch training, you need to install the following requirements.

Install PyTorch

#conda
conda install pytorch==1.12.0 torchvision==0.13.0 torchaudio==0.12.0 cudatoolkit=11.3 -c pytorch
#pip
pip install torch==1.12.0+cu113 torchvision==0.13.0+cu113 torchaudio==0.12.0 --extra-index-url https://download.pytorch.org/whl/cu113

Install Colossal-AI

pip install colossalai==0.2.0

Install transformers

pip install transformers

Install pulp and coin-or-cbc

pip install pulp
conda install -c conda-forge coin-or-cbc

Dataset

For simplicity, the input data is randomly generated here.

Training

#Run the auto parallel resnet example with 4 GPUs with a dummy dataset.
colossalai run --nproc_per_node 4 auto_parallel_with_gpt.py