feat: Add device consistency support to BatchFeature and ProcessorMixin #42722 #42892
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feat: Add device consistency support to BatchFeature and ProcessorMixin
leejianwoo-collab
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View the CircleCI Test Summary for this PR: https://huggingface.co/spaces/transformers-community/circle-ci-viz?pr=42892&sha=3a5eeb |
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zucchini-nlp
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zucchini-nlp
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Thanks for the PR @leejianwoo-collab ! I am not sure this is a good issue to choose if it's your first contribution. I would love for someone from transformers to work on it
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| if device is not None: | ||
| self.to(device) |
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Imo instead of doing this, it needs to be passed to convert_to_tensors. And then we can use if tensor_type is 'pt' as torch.tensor(value, device=device)
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| # Extract device parameter if present | ||
| device = kwargs.get("device") | ||
| return BatchFeature(outputs, device=device) |
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will not really work with structured inputs. It needs to be in common kwargs which will structure it properly in self.merge_kwargs. Then we can re-use it from merged_inputs
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feat: Add device consistency support to BatchFeature and ProcessorMixin
fix : #42722
What does this PR do?
feat: Add device consistency support to BatchFeature and ProcessorMixin
This change addresses the device inconsistency issue in OneFormerProcessor and provides
a general solution for all processors that use BatchFeature. The implementation
follows community feedback to centralize device handling in BatchFeature rather than
individual processors.
Usage: processor(image, ["semantic"], return_tensors="pt", device="cuda")
Fixes #42722
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