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pytorch - 💡(How to fix) Fix DISABLED test_opcheck_opinfo_NumpyMulCustomOp_cpu_float32 (__main__.TestCustomOpTestingCPU) [1 comments, 1 participants]

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pytorch/pytorch#180196•Fetched 2026-04-13 05:35:18
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Root Cause

This test was disabled because it is failing in CI. See recent examples and the most recent trunk workflow logs.

RAW_BUFFERClick to expand / collapse

Platforms: asan, linux

This test was disabled because it is failing in CI. See recent examples and the most recent trunk workflow logs.

Over the past 6 hours, it has been determined flaky in 60 workflow(s) with 120 failures and 60 successes.

Debugging instructions (after clicking on the recent samples link): DO NOT ASSUME THINGS ARE OKAY IF THE CI IS GREEN. We now shield flaky tests from developers so CI will thus be green but it will be harder to parse the logs. To find relevant log snippets:

  1. Click on the workflow logs linked above
  2. Click on the Test step of the job so that it is expanded. Otherwise, the grepping will not work.
  3. Grep for test_opcheck_opinfo_NumpyMulCustomOp_cpu_float32
  4. There should be several instances run (as flaky tests are rerun in CI) from which you can study the logs.

Test file path: test_custom_ops.py

For all disabled tests (by GitHub issue), see https://hud.pytorch.org/disabled.

cc @chauhang @penguinwu @bdhirsh @bobrenjc93 @aorenste

extent analysis

TL;DR

The test test_opcheck_opinfo_NumpyMulCustomOp_cpu_float32 in test_custom_ops.py is likely failing due to flakiness and can be investigated by analyzing the workflow logs.

Guidance

  • To investigate the issue, follow the provided debugging instructions: click on the workflow logs, expand the Test step, and grep for test_opcheck_opinfo_NumpyMulCustomOp_cpu_float32 to find relevant log snippets.
  • Study the logs from multiple instances of the test run to identify patterns or common errors that may be causing the flakiness.
  • Check the test file test_custom_ops.py for any potential issues or recent changes that may be contributing to the flakiness.
  • Review the list of disabled tests at https://hud.pytorch.org/disabled to see if there are any other related tests that may be experiencing similar issues.

Notes

The issue lacks specific technical details about the error or failure, so a more detailed analysis of the logs and test code is necessary to determine the root cause.

Recommendation

Apply workaround: Investigate and debug the test using the provided instructions to identify the cause of the flakiness and potentially develop a fix.

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