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[CUDA][Compex] test_reference_numerics_large_jiterator_unary_cuda_complex64 broken after updating to numpy >= 1.25.0 #125198

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eqy opened this issue Apr 29, 2024 · 3 comments
Open

[CUDA][Compex] test_reference_numerics_large_jiterator_unary_cuda_complex64 broken after updating to numpy >= 1.25.0 #125198

eqy opened this issue Apr 29, 2024 · 3 comments
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module: complex Related to complex number support in PyTorch module: cuda Related to torch.cuda, and CUDA support in general module: jiterator module: numpy Related to numpy support, and also numpy compatibility of our operators triaged This issue has been looked at a team member, and triaged and prioritized into an appropriate module

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@eqy
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eqy commented Apr 29, 2024 •
edited by pytorch-bot bot

馃悰 Describe the bug

Unsure why this hasn't surfaced in upstream CI as we've observed it on sm90, sm86, sm80, sm60, ...

Doesn't surface on e.g., numpy==1.24.4 but starts appearing at least as early as numpy==1.25.0

  File "/usr/local/lib/python3.10/dist-packages/torch/testing/_internal/common_utils.py", line 3622, in assertEqual
    raise error_metas.pop()[0].to_error(
AssertionError: Tensor-likes are not close!

Mismatched elements: 400 / 10000 (4.0%)
Greatest absolute difference: nan at index (8800,) (up to 1e-05 allowed)
Greatest relative difference: nan at index (8800,) (up to 1.3e-06 allowed)

To execute this test, run the following from the base repo dir:
     python test/test_unary_ufuncs.py -k test_reference_numerics_large_jiterator_unary_cuda_complex64

This message can be suppressed by setting PYTORCH_PRINT_REPRO_ON_FAILURE=0

CC @nWEIdia @tinglvv @malfet @atalman

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Source build as of 4/29

cc @ptrblck @ezyang @anjali411 @dylanbespalko @mruberry @lezcano @nikitaved @amjames @rgommers
@eqy eqy added module: cuda Related to torch.cuda, and CUDA support in general module: complex Related to complex number support in PyTorch module: numpy Related to numpy support, and also numpy compatibility of our operators module: jiterator labels Apr 29, 2024
@ezyang
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ezyang commented Apr 30, 2024

Is it a Numpy problem or a PyTorch problem 馃

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@cpuhrsch cpuhrsch added the triaged This issue has been looked at a team member, and triaged and prioritized into an appropriate module label Apr 30, 2024
@eqy
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eqy commented Apr 30, 2024 •
edited

Looking at an example mismatch, previously numpy returned nan+infj and now it returns -infj+infj

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module: complex Related to complex number support in PyTorch module: cuda Related to torch.cuda, and CUDA support in general module: jiterator module: numpy Related to numpy support, and also numpy compatibility of our operators triaged This issue has been looked at a team member, and triaged and prioritized into an appropriate module
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