Summary:
Fixes https://github.com/pytorch/pytorch/issues/44378 by providing a wider range of drivers similar to what SciPy is doing.
The supported CPU drivers are `gels, gelsy, gelsd, gelss`.
The CUDA interface has only `gels` implemented but only for overdetermined systems.
The current state of this PR:
- [x] CPU interface
- [x] CUDA interface
- [x] CPU tests
- [x] CUDA tests
- [x] Memory-efficient batch-wise iteration with broadcasting which fixes https://github.com/pytorch/pytorch/issues/49252
- [x] docs
Pull Request resolved: https://github.com/pytorch/pytorch/pull/49093
Reviewed By: albanD
Differential Revision: D26991788
Pulled By: mruberry
fbshipit-source-id: 8af9ada979240b255402f55210c0af1cba6a0a3c
Summary:
Context: https://github.com/pytorch/pytorch/pull/53299#discussion_r587882857
These are the only hand-written parts of this diff:
- the addition to `.github/workflows/lint.yml`
- the file endings changed in these four files (to appease FB-internal land-blocking lints):
- `GLOSSARY.md`
- `aten/src/ATen/core/op_registration/README.md`
- `scripts/README.md`
- `torch/csrc/jit/codegen/fuser/README.md`
The rest was generated by running this command (on macOS):
```
git grep -I -l ' $' -- . ':(exclude)**/contrib/**' ':(exclude)third_party' | xargs gsed -i 's/ *$//'
```
I looked over the auto-generated changes and didn't see anything that looked problematic.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/53406
Test Plan:
This run (after adding the lint but before removing existing trailing spaces) failed:
- https://github.com/pytorch/pytorch/runs/2043032377
This run (on the tip of this PR) succeeded:
- https://github.com/pytorch/pytorch/runs/2043296348
Reviewed By: walterddr, seemethere
Differential Revision: D26856620
Pulled By: samestep
fbshipit-source-id: 3f0de7f7c2e4b0f1c089eac9b5085a58dd7e0d97
Summary:
Fixes https://github.com/pytorch/pytorch/issues/44378 by providing a wider range of drivers similar to what SciPy is doing.
The supported CPU drivers are `gels, gelsy, gelsd, gelss`.
The CUDA interface has only `gels` implemented but only for overdetermined systems.
The current state of this PR:
- [x] CPU interface
- [x] CUDA interface
- [x] CPU tests
- [x] CUDA tests
- [x] Memory-efficient batch-wise iteration with broadcasting which fixes https://github.com/pytorch/pytorch/issues/49252
- [x] docs
Pull Request resolved: https://github.com/pytorch/pytorch/pull/49093
Reviewed By: H-Huang
Differential Revision: D26723384
Pulled By: mruberry
fbshipit-source-id: c9866a95f14091955cf42de22f4ac9e2da009713
Summary:
This way, we can have a mapping from the test files we directly execute (the tests [here](https://github.com/pytorch/pytorch/blob/master/test/run_test.py#L20)) to the test suites that we store data for in XML reports.
This will come in use later for categorizing the tests we run in CI.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/52791
Reviewed By: samestep
Differential Revision: D26655086
Pulled By: janeyx99
fbshipit-source-id: 94be32f80d7bc0ea1a7a11d4c4b1d3d8e774c5ea
Summary:
Take 2 of https://github.com/pytorch/pytorch/issues/50914
This change moves the early termination logic into common_utils.TestCase class.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/52126
Test Plan: CI with ci-all tag
Reviewed By: malfet
Differential Revision: D26391762
Pulled By: walterddr
fbshipit-source-id: a149ecc47ccda7f2795e107fb95915506ae060b4
Summary:
This is a follow up on https://github.com/pytorch/pytorch/issues/49869.
Previously CUDA early termination only happens for generic test classes that extends from `DeviceTypeTestBase`. However, JIT test cases which extends from common_utils.TestCase cannot benefit from the early termination.
This change moves the early termination logic into common_utils.TestCase class.
- all tests extended from common_utils.TestCase now should early terminate if CUDA assert occurs.
- For TestCases that extends from common_device_type.DeviceTypeTestBase, still only do torch.cuda.synchronize() when RTE is thrown.
- For TestCases extends common_utils.TestCase, regardless of whether a test case uses GPU or not, it will always synchronize CUDA as long as `torch.cuda.is_initialize()` returns true.
- Disabling this on common_distributed.py
Pull Request resolved: https://github.com/pytorch/pytorch/pull/50914
Reviewed By: malfet
Differential Revision: D26019289
Pulled By: walterddr
fbshipit-source-id: ddc7c1c0d00db4d073a6c8bc5b7733637a7e77d1
Summary:
Toward fixing https://github.com/pytorch/pytorch/issues/47624
~Step 1: add `TORCH_WARN_MAYBE` which can either warn once or every time in c++, and add a c++ function to toggle the value.
Step 2 will be to expose this to python for tests. Should I continue in this PR or should we take a different approach: add the python level exposure without changing any c++ code and then over a series of PRs change each call site to use the new macro and change the tests to make sure it is being checked?~
Step 1: add a python and c++ toggle to convert TORCH_WARN_ONCE into TORCH_WARN so the warnings can be caught in tests
Step 2: add a python-level decorator to use this toggle in tests
Step 3: (in future PRs): use the decorator to catch the warnings instead of `maybeWarnsRegex`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/48560
Reviewed By: ngimel
Differential Revision: D26171175
Pulled By: mruberry
fbshipit-source-id: d83c18f131d282474a24c50f70a6eee82687158f
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/51706
Pull Request resolved: https://github.com/pytorch/pytorch/pull/50280
As mentioned in gh-43874, this adds a `rounding_mode={'true', 'trunc', 'floor'}`
argument so `torch.div` can be used as a replacement for `floor_divide` during
the transitional period.
I've included dedicated kernels for truncated and floor division which
aren't strictly necessary for float, but do perform significantly better (~2x) than
doing true division followed by a separate rounding kernel.
Note: I introduce new overloads for `aten::div` instead of just adding a default
`rounding_mode` because various JIT passes rely on the exact operator schema.
Test Plan: Imported from OSS
Reviewed By: ngimel
Differential Revision: D26123271
Pulled By: mruberry
fbshipit-source-id: 51a83717602114597ec9c4d946e35a392eb01d46
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/50739
This does not turn on batched grad testing for autogenerated NewModuleTest
tests and CriterionTest tests. Those are coming later.
Test Plan: - run tests
Reviewed By: ejguan
Differential Revision: D25997677
Pulled By: zou3519
fbshipit-source-id: b4b2d68e0f99c3d573faf237e1e531d0b3fced40
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:
Building on top of the work of anjali411 (https://github.com/pytorch/pytorch/issues/46640)
Things added in this PR:
1. Modify backward and double-backward formulas
2. Add complex support for `new module tests` and criterion tests (and add complex tests for L1)
3. Modify some existing tests to support complex
Pull Request resolved: https://github.com/pytorch/pytorch/pull/49912
Reviewed By: zhangguanheng66
Differential Revision: D25853036
Pulled By: soulitzer
fbshipit-source-id: df619f1b71c450ab2818eb17804e0c55990aa8ad
Summary:
Used to temporarily change working directory, but restore it even if exception is raised
Use it in test_type_hints and during code coverage collection
Pull Request resolved: https://github.com/pytorch/pytorch/pull/49657
Reviewed By: walterddr
Differential Revision: D25660543
Pulled By: malfet
fbshipit-source-id: 77f08d57e4b60b95daa4068d0dacf7c25f978526
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:
This replaces the narrow character set APIs with the wide character set ones in `THAllocator.cpp`. This fixes the potential crashes caused by passing non-ASCII characters in `torch::from_file` on Windows.
See: https://github.com/pytorch/pytorch/issues/47422
Pull Request resolved: https://github.com/pytorch/pytorch/pull/47905
Reviewed By: zhangguanheng66
Differential Revision: D25399146
Pulled By: ezyang
fbshipit-source-id: 0a183b65de171c48ed1718fa71e773224eaf196f
Summary:
should fixes https://github.com/pytorch/pytorch/issues/48879.
To test the effect of the messages: make test break, such as add `self.assertEqual(1, 2, "user_msg")` to any test
* Before:
```
AssertionError: False is not true : user_msg
```
* After
```
AssertionError: False is not true : Scalars failed to compare as equal! Comparing 1 and 2 gives a difference of 1, but the allowed difference with rtol=0 and atol=0 is only 0!
user_msg;
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/48935
Reviewed By: samestep
Differential Revision: D25382153
Pulled By: walterddr
fbshipit-source-id: 95633a9f664f4b05a28801786b12a10bd21ff431
Summary:
`torch.cholesky_solve` now works for complex inputs on GPU.
I moved the existing tests to `test_linalg.py` and modified them to test complex and float32 dtypes.
Differentiation also works correctly with complex inputs now.
Ref. https://github.com/pytorch/pytorch/issues/33152
Pull Request resolved: https://github.com/pytorch/pytorch/pull/47047
Reviewed By: ngimel
Differential Revision: D24730020
Pulled By: mruberry
fbshipit-source-id: 95402da5789c56e5a682019790985207fa28fa1f
Summary:
I think these can be safely removed since the min version of supported Python is now 3.6
Pull Request resolved: https://github.com/pytorch/pytorch/pull/47822
Reviewed By: smessmer
Differential Revision: D24954936
Pulled By: ezyang
fbshipit-source-id: 5d4b2aeb78fc97d7ee4abaf5fb2aae21bf765e8b
Summary:
checks sizes of sparse tensors when comparing them in assertEqual.
Removes additional checks in safeCoalesce, safeCoalesce should not be a test for `.coalesce()` function.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/47148
Reviewed By: mruberry
Differential Revision: D24823127
Pulled By: ngimel
fbshipit-source-id: 9303a6ff74aa3c9d9207803d05c0be2325fe392a
Summary:
The `IN_CIRCLECI` variable is a misnomer since the flag really indicates when we enable XML reporting because we want to run the test in CI. Since this doesn't necessarily mean CircleCI in particular, IN_CI is more accurate and general.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/46567
Reviewed By: walterddr
Differential Revision: D24407642
Pulled By: janeyx99
fbshipit-source-id: 5e141a0571b914310a174a58ac0fde58e9521c6b
Summary:
According to https://app.circleci.com/pipelines/github/pytorch/pytorch/228154/workflows/31951076-b633-4391-bd0d-b2953c940876/jobs/8290059
TestFFTCUDA.test_fftn_backward_cuda_complex128 takes 242 seconds to finish, where most of the time spent checking 2nd gradient
Refactor common part of test_fft_backward and test_fftn_backward into _fft_grad_check_helper
Introduce `slowAwareTest` decorator
Split test into fast and slow parts by checking 2nd degree gradient only during the slow part
Pull Request resolved: https://github.com/pytorch/pytorch/pull/46509
Reviewed By: walterddr
Differential Revision: D24378901
Pulled By: malfet
fbshipit-source-id: 606670c2078480219905f63b9b278b835e760a66
Summary:
This PR adds support for complex-valued input for `torch.pinverse`.
Fixed cuda SVD implementation to return singular values with real dtype.
Fixes https://github.com/pytorch/pytorch/issues/45385.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/45819
Reviewed By: heitorschueroff
Differential Revision: D24306539
Pulled By: anjali411
fbshipit-source-id: 2fe19bc630de528e0643132689e1bc5ffeaa162a
Summary:
Rename jobs for testing GraphExecutor configurations to something a little bit more sensical.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/45715
Reviewed By: ezyang, anjali411
Differential Revision: D24114344
Pulled By: Krovatkin
fbshipit-source-id: 89e5f54aaebd88f8c5878e060e983c6f1f41b9bb
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/45776
Splitting out backend and custom class registration into their own library is
not currently implemented in fbcode, so detect that we are running tests in
fbcode and disable those tests.
Test Plan: buck test mode/no-gpu mode/dev caffe2/test:jit
Reviewed By: smessmer
Differential Revision: D24085871
fbshipit-source-id: 1fcc0547880bc4be59428e2810b6a7f6e50ef798
Summary:
This PR adds support for complex-valued input for `torch.symeig`.
TODO:
- [ ] complex cuda tests raise `RuntimeError: _th_bmm_out not supported on CUDAType for ComplexFloat`
Update: Added xfailing tests for complex dtypes on CUDA. Once support for complex `bmm` is added these tests will work.
Fixes https://github.com/pytorch/pytorch/issues/45061.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/45121
Reviewed By: mrshenli
Differential Revision: D24049649
Pulled By: anjali411
fbshipit-source-id: 2cd11f0e47d37c6ad96ec786762f2da57f25dac5
