Transformers GPU Support

[danielgural]

What changes are proposed in this pull request?

Adding GPU support to transformers utils

How is this patch tested? If it is not, please explain why.

Run this notebook tutorial with gpu https://docs.voxel51.com/integrations/huggingface.html#batch-inference

Release Notes

Is this a user-facing change that should be mentioned in the release notes?

• [ ] No. You can skip the rest of this section.
• [ ] Yes. Give a description of this change to be included in the release notes for FiftyOne users.

(Details in 1-2 sentences. You can just refer to another PR with a description if this PR is part of a larger change.)

What areas of FiftyOne does this PR affect?

• [ ] App: FiftyOne application changes
• [ ] Build: Build and test infrastructure changes
• [ ] Core: Core fiftyone Python library changes
• [ ] Documentation: FiftyOne documentation changes
• [ ] Other

Summary by CodeRabbit

• New Features

  • Improved device management for model and processor allocation, ensuring models load on the appropriate device (CUDA or CPU) seamlessly.

• Bug Fixes

  • Streamlined logic for device assignment during model loading, enhancing consistency across different transformer classes.

[coderabbitai[bot]]

Walkthrough

The changes primarily involve modifications to device allocation handling for models and processors in the fiftyone/utils/transformers.py file. The device determination logic has been simplified to utilize torch.cuda.is_available(), ensuring models and processors are assigned to "cuda" if available, or defaulting to "cpu". The _load_model methods across various transformer classes have been updated to explicitly move models to the appropriate device upon loading, enhancing consistency in device management.

Changes

File Path	Change Summary
fiftyone/utils/transformers.py	- Updated device allocation logic in FiftyOneTransformer, FiftyOneZeroShotTransformer, and other transformer classes to use torch.cuda.is_available(). - Modified _load_model methods to ensure models and processors are moved to the designated device upon loading. - Adjusted _get_detector_from_processor and _get_model_for_image_text_retrieval functions for direct model loading on specified device.

Possibly related PRs

• #4587: The changes in this PR also focus on device management for the FiftyOneTransformer class, specifically enhancing the handling of device allocation for tensor operations, which aligns closely with the modifications made in the main PR regarding device assignment logic.

Suggested reviewers

• jacobmarks

🐰 In the land of code, where models reside,
A hop for the device, we take in our stride.
With CUDA now checked, and tensors in place,
Our transformers are ready, they quicken their pace.
So let’s load them with care, on devices they’ll thrive,
In the world of FiftyOne, our models come alive! 🌟

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