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 hf.co/hardware/habana.
ALBERT Large model HPU configuration
This model only contains the GaudiConfig file for running the albert-large-v2 model on Habana's Gaudi processors (HPU).
This model contains no model weights, only a GaudiConfig.
This enables to specify:
use_torch_autocast: whether to use PyTorch's autocast mixed precisionuse_fused_adam: whether to use Habana's custom AdamW implementationuse_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 ALBERT Large with the following command:
python run_qa.py \
--model_name_or_path albert-large-v2 \
--gaudi_config_name Habana/albert-large-v2 \
--dataset_name squad \
--do_train \
--do_eval \
--per_device_train_batch_size 32 \
--per_device_eval_batch_size 4 \
--learning_rate 5e-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.