Adds reference vs. noncontiguous OpInfo test (#67434)
Summary:
Fixes https://github.com/pytorch/pytorch/issues/63341.
This PR adds a new test, `test_noncontigous_samples`, that runs ops forward and backward and compares their outputs and grads between "normal" contiguous SampleInputs and noncontiguous SampleInputs. This test should preclude the need for noncontiguous SampleInputs going forward.
The test was added by generalizing the `.numpy()` transform on SampleInputs to support a new `.noncontiguous()` transform and copying forward/backward patterns from other tests in test_ops.py. It also discovered that many SampleInputs were incorrectly reusing tensors, so those have been revised. SampleInputs creating noncontiguous tensors for testing have also been altered to no longer do so.
In addition, this test discovered the following high priority silent correctness issues:
- https://github.com/pytorch/pytorch/issues/67432
- https://github.com/pytorch/pytorch/issues/67517
- https://github.com/pytorch/pytorch/issues/67513
- https://github.com/pytorch/pytorch/issues/67512
- https://github.com/pytorch/pytorch/issues/67470
It also identified the following issues:
- https://github.com/pytorch/pytorch/issues/67539
The pow OpInfo also incorrectly specified that pow supported the bool datatype, and this has been fixed. Its SampleInputs were written in a way that made requests for boolean SampleInputs return type promoting inputs that never actually tried to compute pow in bool.
This PR suggests we should add the following guidance for writing SampleInputs:
- ensure that all SampleInputs are independent of each other (don't reuse tensors)
- ensure that all SampleInput tensors have no grad or backward functions (no autograd history) -- they should be leaves
- prefer keeping sample inputs simple where possible, a good set of handwritten samples that test interesting cases may be better than an exhaustive but hard to read and maintain programmatic enumeration
- keep code readable by using functools.partial and writing simple inline helpers; break up large statements into a more readable series of smaller statements; especially don't write complicated generator expressions with a `for` at the end!
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Pull Request resolved: https://github.com/pytorch/pytorch/pull/67434
Reviewed By: ngimel
Differential Revision: D32014557
Pulled By: mruberry
fbshipit-source-id: b17e19adc1d41e24441f0765af13d381fef5e3c1
