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license: apache-2.0 |
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--- |
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[Optimum Habana](https://github.com/huggingface/optimum-habana) is the interface between the Hugging Face Transformers and Diffusers libraries and Habana's Gaudi processor (HPU). |
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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. |
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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](https://huggingface.co/hardware/habana). |
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## CLIP model HPU configuration |
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This model only contains the `GaudiConfig` file for running CLIP-like models (e.g. [this one](https://huggingface.co/openai/clip-vit-large-patch14)) on Habana's Gaudi processors (HPU). |
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**This model contains no model weights, only a GaudiConfig.** |
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This enables to specify: |
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- `use_fused_adam`: whether to use Habana's custom AdamW implementation |
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- `use_fused_clip_norm`: whether to use Habana's fused gradient norm clipping operator |
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- `use_torch_autocast`: whether to use Torch Autocast for managing mixed precision |
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## Usage |
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The model is instantiated the same way as in the Transformers library. |
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The only difference is that there are a few new training arguments specific to HPUs.\ |
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It is strongly recommended to train this model doing bf16 mixed-precision training for optimal performance and accuracy. |
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[Here](https://github.com/huggingface/optimum-habana/blob/main/examples/contrastive-image-text) is an example script to fine-tune a model on COCO. |
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Use it as follows: |
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1. You first need to download the dataset: |
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```bash |
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mkdir data |
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cd data |
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wget http://images.cocodataset.org/zips/train2017.zip |
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wget http://images.cocodataset.org/zips/val2017.zip |
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wget http://images.cocodataset.org/zips/test2017.zip |
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wget http://images.cocodataset.org/annotations/annotations_trainval2017.zip |
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wget http://images.cocodataset.org/annotations/image_info_test2017.zip |
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cd .. |
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``` |
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2. Then, you can create a model from pretrained vision and text decoder models: |
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```python |
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from transformers import ( |
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VisionTextDualEncoderModel, |
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VisionTextDualEncoderProcessor, |
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AutoTokenizer, |
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AutoImageProcessor |
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) |
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model = VisionTextDualEncoderModel.from_vision_text_pretrained( |
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"openai/clip-vit-large-patch14", "roberta-large" |
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) |
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tokenizer = AutoTokenizer.from_pretrained("roberta-large") |
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image_processor = AutoImageProcessor.from_pretrained("openai/clip-vit-large-patch14") |
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processor = VisionTextDualEncoderProcessor(image_processor, tokenizer) |
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# save the model and processor |
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model.save_pretrained("clip-roberta") |
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processor.save_pretrained("clip-roberta") |
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``` |
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3. Finally, you can run it with the following command: |
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```bash |
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python run_clip.py \ |
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--output_dir ./clip-roberta-finetuned \ |
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--model_name_or_path ./clip-roberta \ |
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--data_dir $PWD/data \ |
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--dataset_name ydshieh/coco_dataset_script \ |
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--dataset_config_name=2017 \ |
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--image_column image_path \ |
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--caption_column caption \ |
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--remove_unused_columns=False \ |
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--do_train --do_eval \ |
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--per_device_train_batch_size="16" \ |
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--per_device_eval_batch_size="16" \ |
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--learning_rate="5e-5" --warmup_steps="0" --weight_decay 0.1 \ |
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--overwrite_output_dir \ |
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--save_strategy epoch \ |
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--use_habana \ |
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--use_lazy_mode \ |
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--use_hpu_graphs \ |
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--gaudi_config_name Habana/clip \ |
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--throughput_warmup_steps 2 \ |
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--bf16 |
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``` |
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Check the [documentation](https://huggingface.co/docs/optimum/habana/index) out for more advanced usage and examples. |
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