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Remove hmp from gaudi_config.json and README (#1)
Browse files- Update gaudi_config.json (474b4fa63edcf7423d63531f352aca6338eb91f3)
- Update README.md (64740b86e118649a61b9a6a4b2502f5f70ed64d8)
- Update README.md (488c8fa2d7f4daf8adad16cc341662ddd18f5e58)
- Update gaudi_config.json (22ac3adc2ad07616b3daa8985866b4692ea5453f)
- Update README.md (d0b8563ec87ef4e8564618e01bc6d791ede265a9)
- Update README.md (98d260447830ee1346ceb07c3e7367f7e7ed1fc9)
Co-authored-by: Jan Wieczorek <jwieczorekhabana@users.noreply.huggingface.co>
- README.md +5 -7
- gaudi_config.json +1 -24
README.md
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@@ -13,18 +13,15 @@ This model only contains the `GaudiConfig` file for running the [distilbert-base
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**This model contains no model weights, only a GaudiConfig.**
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This enables to specify:
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- `use_habana_mixed_precision`: whether to use Habana Mixed Precision (HMP)
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- `hmp_opt_level`: optimization level for HMP, see [here](https://docs.habana.ai/en/latest/PyTorch/PyTorch_Mixed_Precision/PT_Mixed_Precision.html#configuration-options) for a detailed explanation
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- `hmp_bf16_ops`: list of operators that should run in bf16
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- `hmp_fp32_ops`: list of operators that should run in fp32
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- `hmp_is_verbose`: verbosity
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- `use_fused_adam`: whether to use Habana's custom AdamW implementation
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- `use_fused_clip_norm`: whether to use Habana's fused gradient norm clipping operator
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## Usage
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The model is instantiated the same way as in the Transformers library.
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The only difference is that there are a few new training arguments specific to HPUs
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[Here](https://github.com/huggingface/optimum-habana/blob/main/examples/question-answering/run_qa.py) is a question-answering example script to fine-tune a model on SQuAD. You can run it with DistilBERT with the following command:
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```bash
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--output_dir /tmp/squad/ \
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--use_habana \
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--use_lazy_mode \
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--throughput_warmup_steps 2
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```
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Check the [documentation](https://huggingface.co/docs/optimum/habana/index) out for more advanced usage and examples.
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**This model contains no model weights, only a GaudiConfig.**
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This enables to specify:
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- `use_fused_adam`: whether to use Habana's custom AdamW implementation
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- `use_fused_clip_norm`: whether to use Habana's fused gradient norm clipping operator
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- `use_torch_autocast`: whether to use Torch Autocast for managing mixed precision
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## Usage
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The model is instantiated the same way as in the Transformers library.
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The only difference is that there are a few new training arguments specific to HPUs.\
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It is strongly recommended to train this model doing bf16 mixed-precision training for optimal performance and accuracy.
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[Here](https://github.com/huggingface/optimum-habana/blob/main/examples/question-answering/run_qa.py) is a question-answering example script to fine-tune a model on SQuAD. You can run it with DistilBERT with the following command:
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```bash
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--output_dir /tmp/squad/ \
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--use_habana \
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--use_lazy_mode \
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--throughput_warmup_steps 2 \
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--bf16
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```
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Check the [documentation](https://huggingface.co/docs/optimum/habana/index) out for more advanced usage and examples.
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gaudi_config.json
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{
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"use_habana_mixed_precision": true,
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"hmp_is_verbose": false,
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"use_fused_adam": true,
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"use_fused_clip_norm": true,
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"
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"add",
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"addmm",
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"bmm",
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"div",
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"dropout",
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"gelu",
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"iadd",
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"linear",
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"layer_norm",
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"matmul",
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"mm",
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"rsub",
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"softmax",
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"truediv"
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],
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"hmp_fp32_ops": [
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"embedding",
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"nll_loss",
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"log_softmax",
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"cross_entropy"
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]
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}
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{
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"use_fused_adam": true,
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"use_fused_clip_norm": true,
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"use_torch_autocast": true
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}
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