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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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## DistilBERT Base model HPU configuration |
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This model contains just the `GaudiConfig` file for running the [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) 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_User_Guide/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 the Gaudi configuration has to be loaded and provided to the trainer: |
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``` |
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from optimum.habana import GaudiConfig, GaudiTrainer, GaudiTrainingArguments |
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from transformers import DistilBertTokenizer, DistilBertModel |
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tokenizer = DistilBertTokenizer.from_pretrained("distilbert-base-uncased") |
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model = DistilBertModel.from_pretrained("distilbert-base-uncased") |
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gaudi_config = GaudiConfig.from_pretrained("Habana/distilbert-base-uncased") |
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args = GaudiTrainingArguments( |
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output_dir="/tmp/output_dir", |
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use_habana=True, |
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use_lazy_mode=True, |
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) |
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trainer = GaudiTrainer( |
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model=model, |
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gaudi_config=gaudi_config, |
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args=args, |
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tokenizer=tokenizer, |
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) |
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trainer.train() |
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``` |
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