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💊 CI failures summary and remediationsAs of commit 6c0052a (more details on the Dr. CI page): 💚 💚 Looks good so far! There are no failures yet. 💚 💚 This comment was automatically generated by Dr. CI (expand for details).Follow this link to opt-out of these comments for your Pull Requests.Please report bugs/suggestions to the (internal) Dr. CI Users group. |
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@@ Coverage Diff @@
## master #52941 +/- ##
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- Coverage 80.76% 80.44% -0.33%
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Files 1975 1975
Lines 216701 216701
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- Hits 175025 174321 -704
- Misses 41676 42380 +704 |
mrshenli
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Mar 11, 2021
| DistributedTest, TestDistBackend | ||
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| torch.backends.cuda.matmul.allow_tf32 = False |
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I am not familiar with the impact of this configuration, but this looks in general OK to me and tests suggests this is fine at least in our CI.
@ngimel please correct me if I was wrong. Thanks!
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xsacha
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Mar 31, 2021
Summary: When a system has an ampere and a non-ampere card, lots of tests will fail, because results on different cards are differnet. Pull Request resolved: pytorch#52941 Reviewed By: albanD Differential Revision: D26994287 Pulled By: mrshenli fbshipit-source-id: 287537495fc13361104a4460f5bcd79a208b5d8d
facebook-github-bot
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May 20, 2021
Summary: This PR does several things to relax test tolerance - Do not use TF32 in cuda matmul in test_c10d. See #52941. - Do not use TF32 in cuda matmul in test_linalg. Increase atol for float and cfloat. See #50453 The tolerance is increased because most linear algebra operators are not that stable in single precision. Pull Request resolved: #56114 Reviewed By: ailzhang Differential Revision: D28554467 Pulled By: ngimel fbshipit-source-id: 90416be8e4c048bedb16903b01315584d344ecdf
laurentdupin
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Apr 24, 2026
Summary: When a system has an ampere and a non-ampere card, lots of tests will fail, because results on different cards are differnet. Pull Request resolved: pytorch#52941 Reviewed By: albanD Differential Revision: D26994287 Pulled By: mrshenli fbshipit-source-id: 287537495fc13361104a4460f5bcd79a208b5d8d
laurentdupin
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Apr 25, 2026
Summary: This PR does several things to relax test tolerance - Do not use TF32 in cuda matmul in test_c10d. See pytorch#52941. - Do not use TF32 in cuda matmul in test_linalg. Increase atol for float and cfloat. See pytorch#50453 The tolerance is increased because most linear algebra operators are not that stable in single precision. Pull Request resolved: pytorch#56114 Reviewed By: ailzhang Differential Revision: D28554467 Pulled By: ngimel fbshipit-source-id: 90416be8e4c048bedb16903b01315584d344ecdf
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When a system has an ampere and a non-ampere card, lots of tests will fail, because results on different cards are differnet.