Add determinmistic kernel for reflection2d #136241
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@eqy sorry, this is ready for review now |
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@pytorchbot merge |
Merge failedReason: 1 jobs have failed, first few of them are: linux-binary-manywheel / manywheel-py3_9-cuda12_6-test / test Details for Dev Infra teamRaised by workflow job |
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@pytorchbot merge |
Merge startedYour change will be merged once all checks pass (ETA 0-4 Hours). Learn more about merging in the wiki. Questions? Feedback? Please reach out to the PyTorch DevX Team |
Merge failedReason: 3 mandatory check(s) failed. The first few are: Dig deeper by viewing the failures on hud |
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Looks like this PR hasn't been updated in a while so we're going to go ahead and mark this as |
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Ho sorry this fell through while I was away in Nov. |
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@pytorchbot merge |
Merge startedYour change will be merged once all checks pass (ETA 0-4 Hours). Learn more about merging in the wiki. Questions? Feedback? Please reach out to the PyTorch DevX Team |
Merge failedReason: 3 mandatory check(s) failed. The first few are: Dig deeper by viewing the failures on hud |
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@pytorchbot merge |
Merge startedYour change will be merged once all checks pass (ETA 0-4 Hours). Learn more about merging in the wiki. Questions? Feedback? Please reach out to the PyTorch DevX Team |
Merge failedReason: 1 mandatory check(s) failed. The first few are: Dig deeper by viewing the failures on hud |
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@pytorchbot merge -i lint failure is known flakyness in trunk. |
Merge startedYour change will be merged while ignoring the following 4 checks: Lint / lintrunner-noclang / linux-job, linux-binary-manywheel / manywheel-py3_9-cuda12_4-test / test, linux-binary-manywheel / manywheel-py3_9-cuda12_6-test / test, linux-binary-manywheel / manywheel-py3_9-cuda11_8-test / test Learn more about merging in the wiki. Questions? Feedback? Please reach out to the PyTorch DevX Team |
Adds feature for #98925
Tests pass for both existing reflectionpad2d and the new one I inserted.
Summary of the work:
Simple conditional check for deterministic mode that will dispatch to a different kernel. This kernel does not use any atomic operations, and will lead to deterministic results as instead of going from the output to input(1:1) relationship, I am doing the opposite. I am going from input -> all outputs, which is 1 to many. These operations are done in the same order every execution as I simply traverse the data set with a grid stride loop and use simple linearized indexing into the input tensor.
So each thread will compute the 4 conditionals, which are then used to see if the input has an output in the 8 regions. These 8 regions are top left, top, top right, left, right, bottom left, bottom, bottom right`.
I did not focus on performance for this PR as that would expand the scope heavily. If there are any performance questions though i can answer.