Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/43208
This PR adds gradcheck for complex. The logic used for complex gradcheck is described in Section 3.5.3 here: https://arxiv.org/pdf/1701.00392.pdf
More concretely, this PR introduces the following changes:
1. Updates get_numerical_jacobian to take as input a scalar value for vector (v). Adds gradcheck logic for C -> C, C-> R, R -> C. For R -> C functions, only the real value of gradient is propagated.
2. Adds backward definition for `torch.complex` and also adds a test to verify the definition added.
3. Updates backward for `mul`, `sin`, `cos`, `sinh`, `cosh`.
4. Adds tests for all `torch.real`, `torch.imag`, `torch.view_as_real`, `torch.view_as_complex`, `torch.conj`.
Follow up tasks:
1. Add more thorough tests for R -> C cases. Specifically, add R->C test variants for functions. for e.g., `torch.mul(complex_tensor, real_tensor)`
2. Add back commented test in `common_methods_invocation.py`.
3. Add more special case checking for complex gradcheck to make debugging easier.
4. Update complex autograd note.
5. disable complex autograd for operators not tested for complex.
Test Plan: Imported from OSS
Reviewed By: zou3519
Differential Revision: D23655088
Pulled By: anjali411
fbshipit-source-id: caa75e09864b5f6ead0f988f6368dce64cf15deb
Summary:
This PR fixes three OpInfo-related bugs and moves some functions from TestTorchMathOps to be tested using the OpInfo pattern. The bugs are:
- A skip test path in test_ops.py incorrectly formatted its string argument
- Decorating the tests in common_device_type.py was incorrectly always applying decorators to the original test, not the op-specific variant of the test. This could cause the same decorator to be applied multiple times, overriding past applications.
- make_tensor was incorrectly constructing tensors in some cases
The functions moved are:
- asin
- asinh
- sinh
- acosh
- tan
- atan
- atanh
- tanh
- log
- log10
- log1p
- log2
In a follow-up PR more or all of the remaining functions in TestTorchMathOps will be refactored as OpInfo-based tests.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/44277
Reviewed By: mrshenli, ngimel
Differential Revision: D23617361
Pulled By: mruberry
fbshipit-source-id: edb292947769967de9383f6a84eb327f027509e0
Summary:
This PR fixes three OpInfo-related bugs and moves some functions from TestTorchMathOps to be tested using the OpInfo pattern. The bugs are:
- A skip test path in test_ops.py incorrectly formatted its string argument
- Decorating the tests in common_device_type.py was incorrectly always applying decorators to the original test, not the op-specific variant of the test. This could cause the same decorator to be applied multiple times, overriding past applications.
- make_tensor was incorrectly constructing tensors in some cases
The functions moved are:
- asin
- asinh
- sinh
- acosh
- tan
- atan
- atanh
- tanh
- log
- log10
- log1p
- log2
In a follow-up PR more or all of the remaining functions in TestTorchMathOps will be refactored as OpInfo-based tests.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/44277
Reviewed By: ngimel
Differential Revision: D23568330
Pulled By: mruberry
fbshipit-source-id: 03e69fccdbfd560217c34ce4e9a5f20e10d05a5e
Summary:
This PR adds a new test suite, test_ops.py, designed for generic tests across all operators with OpInfos. It currently has two kinds of tests:
- it validates that the OpInfo has the correct supported dtypes by verifying that unsupported dtypes throw an error and supported dtypes do not
- it runs grad and gradgrad checks on each op and its variants (method and inplace) that has an OpInfo
This is a significant expansion and simplification of the current autogenerated autograd tests, which spend considerable processing their inputs. As an alternative, this PR extends OpInfos with "SampleInputs" that are much easier to use. These sample inputs are analogous to the existing tuples in`method_tests()`.
Future PRs will extend OpInfo-based testing to other uses of `method_tests()`, like test_jit.py, to ensure that new operator tests can be implemented entirely using an OpInfo.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/43451
Reviewed By: albanD
Differential Revision: D23481723
Pulled By: mruberry
fbshipit-source-id: 0c2cdeacc1fdaaf8c69bcd060d623fa3db3d6459
