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.
T5 model HPU configuration
This model only contains the GaudiConfig file for running the T5 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 explanationhmp_bf16_ops: list of operators that should run in bf16hmp_fp32_ops: list of operators that should run in fp32hmp_is_verbose: verbosity
use_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 summarization example script to fine-tune a model. You can run it with T5-small with the following command:
python run_summarization.py \
--model_name_or_path t5-small \
--do_train \
--do_eval \
--dataset_name cnn_dailymail \
--dataset_config "3.0.0" \
--source_prefix "summarize: " \
--output_dir /tmp/tst-summarization \
--per_device_train_batch_size 4 \
--per_device_eval_batch_size 4 \
--overwrite_output_dir \
--predict_with_generate \
--use_habana \
--use_lazy_mode \
--gaudi_config_name Habana/t5 \
--ignore_pad_token_for_loss False \
--pad_to_max_length \
--save_strategy epoch \
--throughput_warmup_steps 2
Check the documentation out for more advanced usage and examples.