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ngimel
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It's good we are getting rid of tolist()s.
| # which takes care of negative strides if present. | ||
| torch_fn, np_fn = funcs | ||
| self.compare_with_numpy(torch_fn, np_fn, data, device, dtype) | ||
| if dtype.is_floating_point or dtype.is_complex: |
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so this ignored device altogether? Nice.
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No. Historically it would create a list from a tensor and then compare_with_numpy would put that list into a tensor on the appropriate device.
The updated test just has fewer steps to get to the same place.
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Summary: Currently compare_with_numpy requires a device and dtype, but these arguments are ignored if a tensor is provided. This PR updates the function to only take device and dtype if a tensor-like object is given. This should prevent confusion that you could, for example, pass a CPU float tensor but provided a CUDA device and integer dtype. Several tests are updated to reflect this behavior. Pull Request resolved: pytorch#40064 Differential Revision: D22058072 Pulled By: mruberry fbshipit-source-id: b494bb759855977ce45b79ed3ffb0319a21c324c
laurentdupin
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Summary: Currently compare_with_numpy requires a device and dtype, but these arguments are ignored if a tensor is provided. This PR updates the function to only take device and dtype if a tensor-like object is given. This should prevent confusion that you could, for example, pass a CPU float tensor but provided a CUDA device and integer dtype. Several tests are updated to reflect this behavior. Pull Request resolved: pytorch#40064 Differential Revision: D22058072 Pulled By: mruberry fbshipit-source-id: b494bb759855977ce45b79ed3ffb0319a21c324c
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Currently compare_with_numpy requires a device and dtype, but these arguments are ignored if a tensor is provided. This PR updates the function to only take device and dtype if a tensor-like object is given. This should prevent confusion that you could, for example, pass a CPU float tensor but provided a CUDA device and integer dtype.
Several tests are updated to reflect this behavior.