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Hongxin Liu
7f8b16635b
|
7 months ago | |
|---|---|---|
| .. | ||
| README.md | 1 year ago | |
| benchmark.py | 7 months ago | |
| benchmark.sh | 2 years ago | |
| benchmark_utils.py | 1 year ago | |
| data.py | 11 months ago | |
| finetune.py | 7 months ago | |
| requirements.txt | 2 years ago | |
| test_ci.sh | 11 months ago | |
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 |