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license: apache-2.0 |
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[Optimum Habana](https://github.com/huggingface/optimum-habana) is the interface between the Transformers library and Habana's Gaudi processor (HPU). It provides a set of tools enabling easy and fast model loading and fine-tuning on single- and multi-HPU settings for different downstream tasks. |
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Learn more about how to take advantage of the power of Habana HPUs to train Transformers models at [hf.co/hardware/habana](https://huggingface.co/hardware/habana). |
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## BERT Large model HPU configuration |
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This model only contains the `GaudiConfig` file for running the [bert-large-uncased-whole-word-masking](https://huggingface.co/bert-large-uncased-whole-word-masking) model on Habana's Gaudi processors (HPU). |
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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 BERT Large with the following command: |
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```bash |
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python run_qa.py \ |
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--model_name_or_path bert-large-uncased-whole-word-masking \ |
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--gaudi_config_name gaudi_config_name_or_path \ |
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--dataset_name squad \ |
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--do_train \ |
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--do_eval \ |
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--per_device_train_batch_size 24 \ |
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--per_device_eval_batch_size 8 \ |
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--learning_rate 3e-5 \ |
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--num_train_epochs 2 \ |
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--max_seq_length 384 \ |
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--doc_stride 128 \ |
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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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