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.
Swin Transformer model HPU configuration
This model only contains the GaudiConfig file for running the Swin Transformer 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 Torch Autocast for managing 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.
It is strongly recommended to train this model doing bf16 mixed-precision training for optimal performance and accuracy.
Here is an image classification example script to fine-tune a model. You can run it with Swin with the following command:
python run_image_classification.py \
--model_name_or_path microsoft/swin-base-patch4-window7-224-in22k \
--dataset_name cifar10 \
--output_dir /tmp/outputs/ \
--remove_unused_columns False \
--do_train \
--do_eval \
--learning_rate 3e-5 \
--num_train_epochs 5 \
--per_device_train_batch_size 64 \
--per_device_eval_batch_size 64 \
--evaluation_strategy epoch \
--save_strategy epoch \
--load_best_model_at_end True \
--save_total_limit 3 \
--seed 1337 \
--use_habana \
--use_lazy_mode \
--gaudi_config_name Habana/swin \
--throughput_warmup_steps 3 \
--ignore_mismatched_sizes \
--bf16
Check the documentation out for more advanced usage and examples.