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Add _apply_fn_to_data method to TorchAOBaseTensor base class #2365

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Add _apply_fn_to_data method to TorchAOBaseTensor base class #2365

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8 changes: 8 additions & 0 deletions test/test_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -49,6 +49,14 @@ def __init__(self, data):
with self.assertRaisesRegex(NotImplementedError, "arg_types"):
l.weight = torch.nn.Parameter(MyTensor(l.weight))

def test_apply_fn_to_data(self):
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@jerryzh168 jerryzh168 Jun 13, 2025 •
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might be good to add a small tensor and use apply_fn_to_data to implement some common methods like clone, and make sure this works functionally

self.assertTrue(hasattr(TorchAOBaseTensor, "_apply_fn_to_data"))
self.assertTrue(callable(getattr(TorchAOBaseTensor, "_apply_fn_to_data")))

method = getattr(TorchAOBaseTensor, "_apply_fn_to_data")
self.assertFalse(isinstance(method, classmethod))
self.assertFalse(isinstance(method, staticmethod))


if __name__ == "__main__":
unittest.main()
13 changes: 13 additions & 0 deletions torchao/utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -577,6 +577,19 @@ def __tensor_unflatten__(
):
raise NotImplementedError("Subclasses must implement __tensor_unflatten__")

def _apply_fn_to_data(self, fn: Callable):
"""Applies a fn to all tensor components stored on this class"""
tensor_names, ctx = self.__tensor_flatten__()
new_tensors = {}
for name in tensor_names:
new_tensors[name] = fn(getattr(self, name))
return self.__class__.__tensor_unflatten__(
new_tensors,
ctx,
None,
None,
)

def __repr__(self):
raise NotImplementedError("Subclasses must implement __repr__")

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