Summary:
Removed skipCUDAIfRocm to re-enable tests for
ROCM platform.
Initially, Only 4799 cases were being run.
Out of those, 882 cases were being skipped.
After removing skipCUDAIfRocm from two places
in test_ops.py, now more than 8000 cases are
being executed, out of which only 282 cases
are bing skipped, which are FFT related tests.
Signed-off-by: Arindam Roy <rarindam@gmail.com>
Fixes #{issue number}
Pull Request resolved: https://github.com/pytorch/pytorch/pull/50500
Reviewed By: albanD
Differential Revision: D25920303
Pulled By: mrshenli
fbshipit-source-id: b2d17b7e2d1de4f9fdd6f1660fb4cad5841edaa0
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/50279
This allows different sample inputs to have different behavior for the same
operator. For example, `div(..., rounding_mode='true')` will promote but other
rounding modes don't. The current boolean flag is too restrictive to allow this.
Test Plan: Imported from OSS
Reviewed By: ngimel
Differential Revision: D25950011
Pulled By: mruberry
fbshipit-source-id: 7e82b82bedc626b2b6970d92d5b25676183ec384
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/50818
This PR does two things:
1. Add batched grad testing to OpInfo
2. Improve the error message from `gradcheck` if batched gradient
computation fails to include suggestions for workarounds.
To add batched grad testing to OpInfo, this PR:
- adds new `check_batched_grad=True` and `check_batched_gradgrad=True`
attributes to OpInfo. These are True by default because we expect most
operators to support batched gradient computation.
- If `check_batched_grad=True`, then `test_fn_grad` invokes gradcheck
with `check_batched_grad=True`.
- If `check_batched_gradgrad=True`, then `test_fn_gradgradgrad` invokes
gradgradcheck with `check_batched_grad=True`.
The improved gradcheck error message looks like the following when an
exception is thrown while computing batched gradients:
https://gist.github.com/zou3519/5a0f46f908ba036259ca5e3752fd642f
Future
- Sometime in the not-near future, we will separate out "batched grad
testing" from "gradcheck" for the purposes of OpInfo to make the
testing more granular and also so that we can test that the vmap
fallback doesn't get invoked (currently batched gradient testing only
tests that the output values are correct).
Test Plan: - run tests `pytest test/test_ops.py -v -k "Gradients"`
Reviewed By: ejguan
Differential Revision: D25997703
Pulled By: zou3519
fbshipit-source-id: 6d2d444d6348ae6cdc24c32c6c0622bd67b9eb7b
Summary:
This PR adds `torch.linalg.slogdet`.
Changes compared to the original torch.slogdet:
- Complex input now works as in NumPy
- Added out= variant (allocates temporary and makes a copy for now)
- Updated `slogdet_backward` to work with complex input
Ref. https://github.com/pytorch/pytorch/issues/42666
Pull Request resolved: https://github.com/pytorch/pytorch/pull/49194
Reviewed By: VitalyFedyunin
Differential Revision: D25916959
Pulled By: mruberry
fbshipit-source-id: cf9be8c5c044870200dcce38be48cd0d10e61a48
Summary:
Related https://github.com/pytorch/pytorch/issues/38349
Implement NumPy-like function `torch.broadcast_to` to broadcast the input tensor to a new shape.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/48997
Reviewed By: anjali411, ngimel
Differential Revision: D25663937
Pulled By: mruberry
fbshipit-source-id: 0415c03f92f02684983f412666d0a44515b99373
Summary:
These are redundant with the functional variant checks and can be very costly, as some grad and gradgrad testing takes minutes to run per variant. Maybe in the future we'll add them back for operations with divergent method implementations.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/49576
Reviewed By: albanD, ngimel
Differential Revision: D25631691
Pulled By: mruberry
fbshipit-source-id: 247f750979d9dafab2454cdbfa992a2aa6da724a
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/48627
Several changes to the OpInfo testing suite:
- Changed test_ops.py to support sample.inputs that are longer than a single element
- Changed OpInfo class to use custom sample_input generator functions, changed UnaryUfuncInfo to use new format
- Added mvp addmm op to operator database to test out sample.inputs with a length greater than a single element
Test Plan: Imported from OSS
Reviewed By: mruberry
Differential Revision: D25234178
Pulled By: Lilyjjo
fbshipit-source-id: cca2c60af7e6deb849a1cc3770c04ed88865016c
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/48426
Tests are run on the intersection of the dtypes requested and the types that are
supported by the operator (or are _not_ if `unsupported_dtypes_only` is used).
Test Plan: Imported from OSS
Reviewed By: ngimel
Differential Revision: D25205835
Pulled By: mruberry
fbshipit-source-id: 2c6318a1a3dc9836af7361f32caf9df28d8a792b
Summary:
PR opened just to run the CI tests
Pull Request resolved: https://github.com/pytorch/pytorch/pull/44465
Reviewed By: ngimel
Differential Revision: D23907565
Pulled By: mruberry
fbshipit-source-id: 620661667877f1e9a2bab17d19988e2dc986fc0f
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
