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Optimum Habana is the interface between the Hugging Face Transformers and Diffusers libraries and Habana's Gaudi processor (HPU). It provides a set of tools enabling easy and fast model loading, training and inference on single- and multi-HPU settings for different downstream tasks. Learn more about how to take advantage of the power of Habana HPUs to train and deploy Transformers and Diffusers models at hf.co/hardware/habana.

BERT Large model HPU configuration

This model only contains the GaudiConfig file for running the bert-large-uncased-whole-word-masking model on Habana's Gaudi processors (HPU).

This model contains no model weights, only a GaudiConfig.

This enables to specify:

  • use_fused_adam: whether to use Habana's custom AdamW implementation
  • use_fused_clip_norm: whether to use Habana's fused gradient norm clipping operator
  • use_torch_autocast: whether to use Torch Autocast for managing mixed precision


The model is instantiated the same way as in the Transformers library. The only difference is that there are a few new training arguments specific to HPUs.
It is strongly recommended to train this model doing bf16 mixed-precision training for optimal performance and accuracy.

Here is a question-answering example script to fine-tune a model on SQuAD. You can run it with BERT Large with the following command:

python run_qa.py \
  --model_name_or_path bert-large-uncased-whole-word-masking \
  --gaudi_config_name gaudi_config_name_or_path \
  --dataset_name squad \
  --do_train \
  --do_eval \
  --per_device_train_batch_size 24 \
  --per_device_eval_batch_size 8 \
  --learning_rate 3e-5 \
  --num_train_epochs 2 \
  --max_seq_length 384 \
  --doc_stride 128 \
  --output_dir /tmp/squad/ \
  --use_habana \
  --use_lazy_mode \
  --throughput_warmup_steps 3 \

Check the documentation out for more advanced usage and examples.

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