kornia: List of crashing tests in M1 GPU (mps)
Describe the bug
This issue is created to list all the tests cases, which are crashing on M1 GPU (torch.device(‘mps’)), so we can at least run all the tests, which are running, failing or not.
- test/test_metrics.py::TestPsnr::test_metric
- test/feature/test_laf.py::TestAngleToRotationMatrix::test_angles
- test/geometry/camera/test_perspective.py::TestProjectPoints::test_project_and_unproject
- test/geometry/calibration/test_pnp.py::TestNormalization
- test/geometry/epipolar/test_epipolar_metrics.py::TestSymmetricalEpipolarDistance::test_shift
Reproduction steps
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Expected behavior
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Environment
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Additional context
No response
About this issue
- Original URL
- State: open
- Created 2 years ago
- Comments: 20 (18 by maintainers)
Hi @gau-nernst ,
Thank you for your interest! Options you mentioned are exactly what would be helpful 😃 Semi-ordered list:
Crashes. It crashes python altogether.
Pick the crash from the list above, find out the minimal reproducing example, report at PyTorch forum, similarly to https://github.com/pytorch/pytorch/issues/86107
If you are experienced with Apple Metal, you may try to fix that in PyTorch code.
Failures: it doesn’t crash, but throws an error, or gives incorrect results (https://github.com/kornia/kornia/issues/2224). In this case main options would be
a) pick the test, which gives an incorrect result, provide minimal example and report to PyTorch core repo. b) For test, which fail because of the unsupported operations, first check if it is in the list https://github.com/pytorch/pytorch/issues/77764 and if there any work going. c) if it is unlikely to be fixed in PyTorch core, write custom PyTorch code. Instead of multiple dispatch we can just check the device, and if it is ops, call the custom operation.
And yes, we are targeting pytorch 2.0
https://github.com/pytorch/pytorch/issues/86107
@edgarriba spatial gradient is fixed here https://github.com/kornia/kornia/pull/1898 There is also “spatial_gradient3d”, which is not fixed, but there 3d convolution is actually correct alg and it is much rarery used
@edgarriba I will do the things in separate PRs once the basic for testing https://github.com/kornia/kornia/pull/1716 be merged
https://github.com/pytorch/pytorch/issues/77958