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Merge branch 'main' into feature/shardformer
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@@ -7,6 +7,14 @@ This directory includes two parts: Using the Booster API finetune Huggingface Be
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bash test_ci.sh
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```
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### Results on 2-GPU
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| Plugin | Accuracy | F1-score |
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| -------------- | -------- | -------- |
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| torch_ddp | 84.4% | 88.6% |
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| torch_ddp_fp16 | 84.7% | 88.8% |
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| gemini | 84.0% | 88.4% |
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## Benchmark
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```
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bash benchmark.sh
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@@ -14,9 +22,9 @@ bash benchmark.sh
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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.
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## Results
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### Results
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### Bert
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#### Bert
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| | max cuda mem | throughput(sample/s) | params |
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| :-----| -----------: | :--------: | :----: |
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@@ -25,10 +33,10 @@ Now include these metrics in benchmark: CUDA mem occupy, throughput and the numb
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| gemini | 11.0 GB | 12.9 | 82M |
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| low_level_zero | 11.29 G | 14.7 | 82M |
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### AlBert
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#### AlBert
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| | max cuda mem | throughput(sample/s) | params |
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| :-----| -----------: | :--------: | :----: |
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| ddp | OOM | | |
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| ddp_fp16 | OOM | | |
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| gemini | 69.39 G | 1.3 | 208M |
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| low_level_zero | 56.89 G | 1.4 | 208M |
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| low_level_zero | 56.89 G | 1.4 | 208M |
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@@ -219,7 +219,7 @@ def main():
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if args.plugin.startswith('torch_ddp'):
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plugin = TorchDDPPlugin()
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elif args.plugin == 'gemini':
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plugin = GeminiPlugin(placement_policy='cuda', strict_ddp_mode=True, initial_scale=2**5)
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plugin = GeminiPlugin(initial_scale=2**5)
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elif args.plugin == 'low_level_zero':
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plugin = LowLevelZeroPlugin(initial_scale=2**5)
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elif args.plugin == 'hybrid_parallel':
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