[hotfix] disable problematic cuda tests on rocm builds#45435
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jaglinux
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If world_size is lesser than or equal to number of GPU's available then the rank can be directly mapped to corresponding GPU. This fixes the issue referenced in pytorch#45435 and pytorch#47629 For world_size = 3 and number of GPU's = 8, the rank to GPU mapping will be 0,2,4. This is due to the introduction of barrier, (refer pytorch#45181) the tensors in barrier is mapped to cuda0,1,2 and the tensors in the actual test cases are mapped to cuda0,2,4 resulting in different streams and leading to timeout. This issue is specific to default process group. Issue is not observed in new process group since the streams are created again after the initial barrier call. This patch maps the rank to corresponding GPU's when the world_size is less than or equal to the number of GPU's, in this case 0,1,2 Note: The barrier function in distributed_c10d.py should include new parameter to specify the tensor or rank to GPU mapping. In that case, this patch will be redundant but harmless since the tests can specify the tensors with appropriate GPU rankings.
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Summary: If world_size is lesser than or equal to number of GPU's available then the rank can be directly mapped to corresponding GPU. This fixes the issue referenced in #45435 and #47629 For world_size = 3 and number of GPU's = 8, the rank to GPU mapping will be 0,2,4. This is due to the introduction of barrier, (refer PR #45181) the tensors in barrier is mapped to cuda0,1,2 and the tensors in the actual test cases are mapped to cuda0,2,4 resulting in different streams and leading to timeout. This issue is specific to default process group. Issue is not observed in new process group since the streams are created again after the initial barrier call. This patch maps the rank to corresponding GPU's when the world_size is less than or equal to the number of GPU's, in this case 0,1,2 Note: The barrier function in distributed_c10d.py should include new parameter to specify the tensor or rank to GPU mapping. In that case, this patch will be redundant but harmless since the tests can specify the tensors with appropriate GPU rankings. Fixes #47629 Pull Request resolved: #47898 Reviewed By: smessmer Differential Revision: D24956021 Pulled By: rohan-varma fbshipit-source-id: a88257f22a7991ba36566329766c106d3360bb4e
laurentdupin
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Apr 24, 2026
Summary: Disable the recent 3 cuda tests on amd rocm build/tests Pull Request resolved: pytorch#45435 Reviewed By: malfet Differential Revision: D23962881 Pulled By: walterddr fbshipit-source-id: ad4ea1f835b4722cdbdce685806cfd64376cc16f
laurentdupin
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Apr 24, 2026
Summary: If world_size is lesser than or equal to number of GPU's available then the rank can be directly mapped to corresponding GPU. This fixes the issue referenced in pytorch#45435 and pytorch#47629 For world_size = 3 and number of GPU's = 8, the rank to GPU mapping will be 0,2,4. This is due to the introduction of barrier, (refer PR pytorch#45181) the tensors in barrier is mapped to cuda0,1,2 and the tensors in the actual test cases are mapped to cuda0,2,4 resulting in different streams and leading to timeout. This issue is specific to default process group. Issue is not observed in new process group since the streams are created again after the initial barrier call. This patch maps the rank to corresponding GPU's when the world_size is less than or equal to the number of GPU's, in this case 0,1,2 Note: The barrier function in distributed_c10d.py should include new parameter to specify the tensor or rank to GPU mapping. In that case, this patch will be redundant but harmless since the tests can specify the tensors with appropriate GPU rankings. Fixes pytorch#47629 Pull Request resolved: pytorch#47898 Reviewed By: smessmer Differential Revision: D24956021 Pulled By: rohan-varma fbshipit-source-id: a88257f22a7991ba36566329766c106d3360bb4e
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Disable the recent 3 problematic cuda tests with constant timeout issues on amd rocm build/tests