disable tensorify floats when cuda graphs is on #140983
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/140983
Note: Links to docs will display an error until the docs builds have been completed. ❗ 1 Active SEVsThere are 1 currently active SEVs. If your PR is affected, please view them below: ✅ No FailuresAs of commit 3b3fcf3 with merge base c3fbec7 ( This comment was automatically generated by Dr. CI and updates every 15 minutes. |
This was referenced Nov 18, 2024
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Fixes `python test/inductor/test_fused_attention.py SDPAPatternRewriterCpuTests.test_pattern_fails_with_unsupported_mask_cpu` when `specialize_float=False`. You might wonder how it's related, it's because there is a "negative" test that expects us not to match. Previously it would fail on isinstance(param, Tensor), but now that we tensorify the float, it did match and caused a failure. This check ensures the mask has the same shape to ensure this negative test case actually fails. Pull Request resolved: #141003 Approved by: https://github.com/ezyang ghstack dependencies: #140983
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…nputs (#140346) Fixes a bunch of benchmarks that failed with cudagraph errors including `tlp python benchmarks/dynamo/timm_models.py --device cuda --inductor --accuracy --amp --training --only resmlp_12_224` when `specialize_float=False` Also brings down number of overall failures (with keep-going) from 108 => 62. I'd estimate >80% of those 62 are wobbly expect tests. Pull Request resolved: #140346 Approved by: https://github.com/ezyang ghstack dependencies: #140983, #141003
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…41003) Fixes `python test/inductor/test_fused_attention.py SDPAPatternRewriterCpuTests.test_pattern_fails_with_unsupported_mask_cpu` when `specialize_float=False`. You might wonder how it's related, it's because there is a "negative" test that expects us not to match. Previously it would fail on isinstance(param, Tensor), but now that we tensorify the float, it did match and caused a failure. This check ensures the mask has the same shape to ensure this negative test case actually fails. Pull Request resolved: pytorch#141003 Approved by: https://github.com/ezyang ghstack dependencies: pytorch#140983
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…nputs (pytorch#140346) Fixes a bunch of benchmarks that failed with cudagraph errors including `tlp python benchmarks/dynamo/timm_models.py --device cuda --inductor --accuracy --amp --training --only resmlp_12_224` when `specialize_float=False` Also brings down number of overall failures (with keep-going) from 108 => 62. I'd estimate >80% of those 62 are wobbly expect tests. Pull Request resolved: pytorch#140346 Approved by: https://github.com/ezyang ghstack dependencies: pytorch#140983, pytorch#141003
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cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @chauhang @amjames