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.
CLIP model HPU configuration
This model only contains the GaudiConfig
file for running CLIP-like models (e.g. this one) 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 example script to fine-tune a model on COCO. Use it as follows:
- You first need to download the dataset:
mkdir data
cd data
wget http://images.cocodataset.org/zips/train2017.zip
wget http://images.cocodataset.org/zips/val2017.zip
wget http://images.cocodataset.org/zips/test2017.zip
wget http://images.cocodataset.org/annotations/annotations_trainval2017.zip
wget http://images.cocodataset.org/annotations/image_info_test2017.zip
cd ..
- Then, you can create a model from pretrained vision and text decoder models:
from transformers import (
VisionTextDualEncoderModel,
VisionTextDualEncoderProcessor,
AutoTokenizer,
AutoImageProcessor
)
model = VisionTextDualEncoderModel.from_vision_text_pretrained(
"openai/clip-vit-large-patch14", "roberta-large"
)
tokenizer = AutoTokenizer.from_pretrained("roberta-large")
image_processor = AutoImageProcessor.from_pretrained("openai/clip-vit-large-patch14")
processor = VisionTextDualEncoderProcessor(image_processor, tokenizer)
# save the model and processor
model.save_pretrained("clip-roberta")
processor.save_pretrained("clip-roberta")
- Finally, you can run it with the following command:
python run_clip.py \
--output_dir ./clip-roberta-finetuned \
--model_name_or_path ./clip-roberta \
--data_dir $PWD/data \
--dataset_name ydshieh/coco_dataset_script \
--dataset_config_name=2017 \
--image_column image_path \
--caption_column caption \
--remove_unused_columns=False \
--do_train --do_eval \
--per_device_train_batch_size="16" \
--per_device_eval_batch_size="16" \
--learning_rate="5e-5" --warmup_steps="0" --weight_decay 0.1 \
--overwrite_output_dir \
--save_strategy epoch \
--use_habana \
--use_lazy_mode \
--use_hpu_graphs \
--gaudi_config_name Habana/clip \
--throughput_warmup_steps 2 \
--bf16
Check the documentation out for more advanced usage and examples.