regisss's picture
HF staff
license: apache-2.0
[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 [](
## 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_habana_mixed_precision`: whether to use Habana Mixed Precision (HMP)
- `hmp_opt_level`: optimization level for HMP, see [here]( for a detailed explanation
- `hmp_bf16_ops`: list of operators that should run in bf16
- `hmp_fp32_ops`: list of operators that should run in fp32
- `hmp_is_verbose`: verbosity
- `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
## Usage
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.
[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 \
--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 2
Check the [documentation]( out for more advanced usage and examples.