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module: ciRelated to continuous integrationRelated to continuous integrationmodule: linear algebraIssues related to specialized linear algebra operations in PyTorch; includes matrix multiply matmulIssues related to specialized linear algebra operations in PyTorch; includes matrix multiply matmulmodule: testsIssues related to tests (not the torch.testing module)Issues related to tests (not the torch.testing module)triagedThis issue has been looked at a team member, and triaged and prioritized into an appropriate moduleThis issue has been looked at a team member, and triaged and prioritized into an appropriate module
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Hi torch team,
Starting from einops 0.8.1, you can test torch against einops with:
# install numpy, einops, pytest and torch
python -m einops.tests.run_tests numpy torchand I suggest having this in torch's CI.
There are a couple of motivations:
- einops tests actually reveal regressions in frameworks (happened several times, not in torch)
- it is hard within einops to test against more advanced features like torch.compile, because most of engineering/regressions happen on torch side. If tests fail within einops - 1) that's late, problem should be caught earlier 2) not much I can do. See this issue for a motivational example. Should simplify work on your side too.
Ready to answer questions / discuss concerns. Tests currently take ~12 seconds.
cc @seemethere @malfet @pytorch/pytorch-dev-infra @mruberry @ZainRizvi @jianyuh @nikitaved @pearu @walterddr @xwang233 @lezcano
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module: ciRelated to continuous integrationRelated to continuous integrationmodule: linear algebraIssues related to specialized linear algebra operations in PyTorch; includes matrix multiply matmulIssues related to specialized linear algebra operations in PyTorch; includes matrix multiply matmulmodule: testsIssues related to tests (not the torch.testing module)Issues related to tests (not the torch.testing module)triagedThis issue has been looked at a team member, and triaged and prioritized into an appropriate moduleThis issue has been looked at a team member, and triaged and prioritized into an appropriate module