ColossalAI

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benchmark_utils.py

[Device]Support npu (#6159 )

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benchmark.py

[misc] refactor launch API and tensor constructor (#5666 )

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benchmark.sh

[booster] update bert example, using booster api (#3885 )

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data.py

[pipeline]: support arbitrary batch size in forward_only mode (#5201 )

2024-01-02 23:41:12 +08:00

finetune.py

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README.md

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requirements.txt

[booster] update bert example, using booster api (#3885 )

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test_ci.sh

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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