ColossalAI

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wukong1992 a55fb00c18 [booster] update bert example, using booster api (#3885 )		2023-06-07 15:51:00 +08:00
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README.md	[booster] update bert example, using booster api (#3885 )	2023-06-07 15:51:00 +08:00
benchmark.py	[booster] update bert example, using booster api (#3885 )	2023-06-07 15:51:00 +08:00
benchmark.sh	[booster] update bert example, using booster api (#3885 )	2023-06-07 15:51:00 +08:00
benchmark_utils.py	[booster] update bert example, using booster api (#3885 )	2023-06-07 15:51:00 +08:00
data.py	[booster] update bert example, using booster api (#3885 )	2023-06-07 15:51:00 +08:00
finetune.py	[booster] update bert example, using booster api (#3885 )	2023-06-07 15:51:00 +08:00
requirements.txt	[booster] update bert example, using booster api (#3885 )	2023-06-07 15:51:00 +08:00
test_ci.sh	[booster] update bert example, using booster api (#3885 )	2023-06-07 15:51:00 +08:00

README.md

Overview

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

Finetune

bash test_ci.sh

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