Fix torch's convert_to_tensor not respecting dtype when input is a Variable
#21452
+18
−15
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## master #21452 +/- ##
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Coverage 82.74% 82.74%
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Files 565 565
Lines 55300 55302 +2
Branches 8624 8625 +1
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+ Hits 45756 45761 +5
+ Misses 7439 7438 -1
+ Partials 2105 2103 -2
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fchollet
reviewed
Jul 4, 2025
keras/src/ops/nn.py
Outdated
| @@ -2816,7 +2816,8 @@ def _rms_normalization(x, scale=None, axis=-1, epsilon=None): | |||
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| x = backend.convert_to_tensor(x, dtype=compute_dtype) | |||
| if scale is not None: | |||
| scale = backend.convert_to_tensor(scale, x.dtype) | |||
| scale = backend.convert_to_tensor(scale) | |||
| scale = backend.cast(scale, x.dtype) | |||
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Why would 2 separate lines be necessary?
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No. I was testing the behavior and forgot to revert them.
Fixed. I also modified the tests in nn_test.py to use variables as inputs to verify the changes.
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There is a bug in torch's
convert_to_tensorwhen the input is aVariable.The root cause is that we return
x.variabledirectly without checking its dtype.This PR fixes the issue.