ColossalAI

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Hongxin Liu 079bf3cb26 [misc] update pre-commit and run all files (#4752 ) * [misc] update pre-commit * [misc] run pre-commit * [misc] remove useless configuration files * [misc] ignore cuda for clang-format		2023-09-19 14:20:26 +08:00
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README.md	…
benchmark.py	[misc] update pre-commit and run all files (#4752 )	2023-09-19 14:20:26 +08:00
benchmark.sh	…
benchmark_utils.py	[misc] update pre-commit and run all files (#4752 )	2023-09-19 14:20:26 +08:00
data.py	[misc] update pre-commit and run all files (#4752 )	2023-09-19 14:20:26 +08:00
finetune.py	[misc] update pre-commit and run all files (#4752 )	2023-09-19 14:20:26 +08:00
requirements.txt	…
test_ci.sh	…

README.md

Overview

This directory includes two parts: Using the Booster API finetune Huggingface Bert and AlBert models and benchmarking Bert and AlBert models with different Booster Plugin.

Finetune

bash test_ci.sh

Bert-Finetune Results

Plugin	Accuracy	F1-score	GPU number
torch_ddp	84.4%	88.6%	2
torch_ddp_fp16	84.7%	88.8%	2
gemini	84.0%	88.4%	2
hybrid_parallel	84.5%	88.6%	4

Benchmark

bash benchmark.sh

Now include these metrics in benchmark: CUDA mem occupy, throughput and the number of model parameters. If you have custom metrics, you can add them to benchmark_util.

Results

Bert

	max cuda mem	throughput(sample/s)	params
ddp	21.44 GB	3.0	82M
ddp_fp16	16.26 GB	11.3	82M
gemini	11.0 GB	12.9	82M
low_level_zero	11.29 G	14.7	82M

AlBert

	max cuda mem	throughput(sample/s)	params
ddp	OOM
ddp_fp16	OOM
gemini	69.39 G	1.3	208M
low_level_zero	56.89 G	1.4	208M