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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 [shardformer] update shardformer readme (#4617) 2023-09-05 13:14:41 +08:00
benchmark.py [misc] update pre-commit and run all files (#4752) 2023-09-19 14:20:26 +08:00
benchmark.sh [booster] update bert example, using booster api (#3885) 2023-06-07 15:51:00 +08:00
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 [booster] update bert example, using booster api (#3885) 2023-06-07 15:51:00 +08:00
test_ci.sh [shardformer] update bert finetune example with HybridParallelPlugin (#4584) 2023-09-04 21:46:29 +08:00

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