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💊 CI failures summary and remediationsAs of commit 9ebb4e4 (more details on the Dr. CI page):
🕵️ 1 new failure recognized by patternsThe following CI failures do not appear to be due to upstream breakages:
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Summary: This PR adds the `torch.linalg` namespace as part of our continued effort to be more compatible with NumPy. The namespace is tested by adding a single function, `torch.linalg.outer`, and testing it in a new test suite, test_linalg.py. It follows the same pattern that #41911, which added the `torch.fft` namespace, did. Future PRs will likely: - add more functions to torch.linalg - expand the testing done in test_linalg.py, including legacy functions, like torch.ger - deprecate existing linalg functions outside of `torch.linalg` in preference to the new namespace Pull Request resolved: #42664 Reviewed By: ngimel Differential Revision: D22991019 Pulled By: mruberry fbshipit-source-id: 39258d9b116a916817b3588f160b141f956e5d0b
Summary: This PR creates a new namespace, torch.fft (torch::fft) and puts a single function, fft, in it. This function is analogous to is a simplified version of NumPy's [numpy.fft.fft](https://numpy.org/doc/1.18/reference/generated/numpy.fft.fft.html?highlight=fft#numpy.fft.fft) that accepts no optional arguments. It is intended to demonstrate how to add and document functions in the namespace, and is not intended to deprecate the existing torch.fft function. Adding this namespace was complicated by the existence of the torch.fft function in Python. Creating a torch.fft Python module makes this name ambiguous: does it refer to a function or module? If the JIT didn't exist, a solution to this problem would have been to make torch.fft refer to a callable class that mimicked both the function and module. The JIT, however, cannot understand this pattern. As a workaround it's required to explicitly `import torch.fft` to access the torch.fft.fft function in Python: ``` import torch.fft t = torch.randn(128, dtype=torch.cdouble) torch.fft.fft(t) ``` See pytorch#42175 for future work. Another possible future PR is to get the JIT to understand torch.fft as a callable class so it need not be imported explicitly to be used. Pull Request resolved: pytorch#41911 Reviewed By: glaringlee Differential Revision: D22941894 Pulled By: mruberry fbshipit-source-id: c8e0b44cbe90d21e998ca3832cf3a533f28dbe8d
Summary: This PR adds the `torch.linalg` namespace as part of our continued effort to be more compatible with NumPy. The namespace is tested by adding a single function, `torch.linalg.outer`, and testing it in a new test suite, test_linalg.py. It follows the same pattern that pytorch#41911, which added the `torch.fft` namespace, did. Future PRs will likely: - add more functions to torch.linalg - expand the testing done in test_linalg.py, including legacy functions, like torch.ger - deprecate existing linalg functions outside of `torch.linalg` in preference to the new namespace Pull Request resolved: pytorch#42664 Reviewed By: ngimel Differential Revision: D22991019 Pulled By: mruberry fbshipit-source-id: 39258d9b116a916817b3588f160b141f956e5d0b
This PR creates a new namespace, torch.fft (torch::fft) and puts a single function, fft, in it. This function is analogous to is a simplified version of NumPy's numpy.fft.fft that accepts no optional arguments. It is intended to demonstrate how to add and document functions in the namespace, and is not intended to deprecate the existing torch.fft function.
Adding this namespace was complicated by the existence of the torch.fft function in Python. Creating a torch.fft Python module makes this name ambiguous: does it refer to a function or module? If the JIT didn't exist, a solution to this problem would have been to make torch.fft refer to a callable class that mimicked both the function and module. The JIT, however, cannot understand this pattern. As a workaround it's required to explicitly
import torch.fftto access the torch.fft.fft function in Python:See #42175 for future work. Another possible future PR is to get the JIT to understand torch.fft as a callable class so it need not be imported explicitly to be used.