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.
GPT2 model HPU configuration
This model only contains the GaudiConfig file for running the GPT2 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 implementationuse_fused_clip_norm: whether to use Habana's fused gradient norm clipping operatoruse_torch_autocast: whether to use PyTorch's autocast mixed precision
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 causal language modeling example script to pre-train/fine-tune a model. You can run it with GPT2 with the following command:
python run_clm.py \
--model_name_or_path gpt2 \
--dataset_name wikitext \
--dataset_config_name wikitext-2-raw-v1 \
--per_device_train_batch_size 4 \
--per_device_eval_batch_size 4 \
--do_train \
--do_eval \
--output_dir /tmp/test-clm \
--gaudi_config_name Habana/gpt2 \
--use_habana \
--use_lazy_mode \
--throughput_warmup_steps 2
Check the documentation out for more advanced usage and examples.