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[shardformer] adding fine-tuned result of bert to readme. #4617

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Sep 5, 2023
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[shardformer] adding fine-tuned result of bert to readme. #4617

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11 changes: 6 additions & 5 deletions colossalai/shardformer/README.md
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Expand Up @@ -429,12 +429,13 @@ As shown in the figures above, when the sequence length is around 1000 or greate
### Convergence


To validate that training the model using shardformers does not impact its convergence. We [fine-tuned the BERT model](./examples/convergence_benchmark.py) using both shardformer and non-shardformer approaches. We compared the accuracy, loss, F1 score of the training results.
To validate that training the model using shardformers does not impact its convergence. We [fine-tuned the BERT model](../../examples/language/bert/finetune.py) using both shardformer and non-shardformer approaches. The example that utilizes Shardformer simultaneously with Pipeline Parallelism and Data Parallelism (Zero1). We then compared the accuracy, loss, and F1 score of the training results.

| accuracy | f1 | loss | GPU number | model shard |

| accuracy | f1 | loss | GPU number | model sharded |
| :------: | :-----: | :-----: | :--------: | :---------: |
| 0.82594 | 0.87441 | 0.09913 | 4 | True |
| 0.81884 | 0.87299 | 0.10120 | 2 | True |
| 0.81855 | 0.87124 | 0.10357 | 1 | False |
| 0.84589 | 0.88613 | 0.43414 | 4 | True |
| 0.83594 | 0.88064 | 0.43298 | 1 | False |


Overall, the results demonstrate that using shardformers during model training does not affect the convergence.
14 changes: 8 additions & 6 deletions examples/language/bert/README.md
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Expand Up @@ -7,13 +7,15 @@ This directory includes two parts: Using the Booster API finetune Huggingface Be
bash test_ci.sh
```

### Results on 2-GPU
### 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 |

| Plugin | Accuracy | F1-score |
| -------------- | -------- | -------- |
| torch_ddp | 84.4% | 88.6% |
| torch_ddp_fp16 | 84.7% | 88.8% |
| gemini | 84.0% | 88.4% |

## Benchmark
```
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