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Refactor torch.*solve tests#25733

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vishwakftw wants to merge 8 commits intopytorch:masterfrom
vishwakftw:refactor-linalg-tests
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Refactor torch.*solve tests#25733
vishwakftw wants to merge 8 commits intopytorch:masterfrom
vishwakftw:refactor-linalg-tests

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@vishwakftw vishwakftw commented Sep 5, 2019

Changelog:

  • De-duplicate the code in tests for torch.solve, torch.cholesky_solve, torch.triangular_solve
  • Skip tests explicitly if requirements aren't met for e.g., if NumPy / SciPy aren't available in the environment
  • Add generic helpers for these tests in test/common_utils.py

Test Plan:

  • All tests should pass to confirm that the change is not erroneous

Clears one point specified in the discussion in #24333.

@vishwakftw
Refactor torch.*solve tests
76d31d9 
Changelog:
- De-duplicate the code in tests for torch.solve, torch.cholesky_solve, torch.triangular_solve
- Skip tests explicitly if requirements aren't met for e.g., if NumPy / SciPy aren't available in the environment
- Add generic helpers for these tests in test/common_utils.py

Test Plan:
- All tests should pass to confirm that the change is not erroneous
@pytorchbot pytorchbot added module: cuda Related to torch.cuda, and CUDA support in general module: operators module: tests Issues related to tests (not the torch.testing module) labels Sep 5, 2019
@vishwakftw vishwakftw requested a review from zou3519 September 5, 2019 22:53
@vishwakftw vishwakftw force-pushed the refactor-linalg-tests branch from b049ba7 to ad763e0 Compare September 5, 2019 23:03
@vishwakftw vishwakftw force-pushed the refactor-linalg-tests branch from ad763e0 to e740e98 Compare September 5, 2019 23:17
zou3519
zou3519 reviewed Sep 9, 2019
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Comment thread test/test_torch.py Outdated
b, A, L = cholesky_solve_test_helper((n,), (n, k), cast, upper)
x = torch.cholesky_solve(b, L, upper=upper)
b_ = torch.matmul(A, x)
self.assertEqual(b_, b)
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In the original test we are checking that the delta is less than 1e-12, should we keep that?

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I don't think it would matter all that much.

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I don't have context on why the original error bound was 1e-12 so I'm a little hesitant to remove it. Does the test still pass with the 1e-12?

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I’ll re-add it to the tests, push and await CI signal. Hope that is fine.

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Comment thread test/test_torch.py Outdated
@jerryzh168 jerryzh168 added the triaged This issue has been looked at a team member, and triaged and prioritized into an appropriate module label Sep 11, 2019
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vishwakftw commented Sep 11, 2019 •
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Failures are unrelated, and previously failing ROCm test has passed now

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@zou3519 has imported this pull request. If you are a Facebook employee, you can view this diff on Phabricator.

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facebook-github-bot commented Sep 11, 2019

@zou3519 merged this pull request in eee58f8.

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gchanan commented Sep 12, 2019

Any chance this has caused spurious failures? I see things like https://circleci.com/gh/pytorch/pytorch/2760063?utm_campaign=vcs-integration-link&utm_medium=referral&utm_source=github-build-link/console:

Sep 12 16:13:22 ======================================================================
Sep 12 16:13:22 FAIL: test_triangular_solve_batched (__main__.TestCuda)
Sep 12 16:13:22 ----------------------------------------------------------------------
Sep 12 16:13:22 Traceback (most recent call last):
Sep 12 16:13:22   File "/var/lib/jenkins/workspace/test/common_utils.py", line 570, in wrapper
Sep 12 16:13:22     method(*args, **kwargs)
Sep 12 16:13:22   File "/var/lib/jenkins/workspace/test/common_utils.py", line 570, in wrapper
Sep 12 16:13:22     method(*args, **kwargs)
Sep 12 16:13:22   File "test_cuda.py", line 2786, in test_triangular_solve_batched
Sep 12 16:13:22     _TestTorchMixin._test_triangular_solve_batched(self, lambda t: t.cuda())
Sep 12 16:13:22   File "/var/lib/jenkins/workspace/test/test_torch.py", line 5995, in _test_triangular_solve_batched
Sep 12 16:13:22     upper, unitriangular, transpose)
Sep 12 16:13:22   File "/var/lib/jenkins/workspace/test/test_torch.py", line 5987, in triangular_solve_batch_helper
Sep 12 16:13:22     self.assertEqual(x_act, x_exp)  # Equality check
Sep 12 16:13:22   File "/var/lib/jenkins/workspace/test/common_utils.py", line 734, in assertEqual
Sep 12 16:13:22     assertTensorsEqual(x, y)
Sep 12 16:13:22   File "/var/lib/jenkins/workspace/test/common_utils.py", line 714, in assertTensorsEqual
Sep 12 16:13:22     self.assertLessEqual(max_err, prec, message)
Sep 12 16:13:22 AssertionError: tensor(62.7710, device='cuda:0') not less than or equal to 1e-05 : 
Sep 12 16:13:22 
Sep 12 16:13:22 ----------------------------------------------------------------------

not sure if it's related.

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vishwakftw commented Sep 12, 2019

I see no reason why that test would fail. I'll re-open the PR, rebase on master and wait on CI.

@vishwakftw vishwakftw reopened this Sep 12, 2019
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vishwakftw commented Sep 12, 2019

I found out the source of the error: it's 276bde3 which enable non default stream testing.

I'll skip the test appropriately.

@vishwakftw vishwakftw closed this Sep 12, 2019
@vishwakftw vishwakftw deleted the refactor-linalg-tests branch September 12, 2019 20:03
laurentdupin pushed a commit to laurentdupin/pytorch that referenced this pull request Apr 24, 2026
@vishwakftw @facebook-github-bot
Refactor torch.*solve tests (pytorch#25733)
05864d6 
Summary:
Changelog:
- De-duplicate the code in tests for torch.solve, torch.cholesky_solve, torch.triangular_solve
- Skip tests explicitly if requirements aren't met for e.g., if NumPy / SciPy aren't available in the environment
- Add generic helpers for these tests in test/common_utils.py
Pull Request resolved: pytorch#25733

Test Plan:
- All tests should pass to confirm that the change is not erroneous

Clears one point specified in the discussion in pytorch#24333.

Differential Revision: D17315330

Pulled By: zou3519

fbshipit-source-id: c72a793e89af7e2cdb163521816d56747fd70a0e
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Merged module: cuda Related to torch.cuda, and CUDA support in general module: tests Issues related to tests (not the torch.testing module) open source triaged This issue has been looked at a team member, and triaged and prioritized into an appropriate module

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