Use explicit templates in gpu_kernel_with_scalars#40992
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malfet wants to merge 1 commit intopytorch:masterfrom
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Use explicit templates in gpu_kernel_with_scalars#40992malfet wants to merge 1 commit intopytorch:masterfrom
gpu_kernel_with_scalars#40992malfet wants to merge 1 commit intopytorch:masterfrom
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This trick should have no effect on performance, but it reduces size of kernels using the template by 10% For example, sizeofBinaryMulDivKernel.cu.o) compiled by CUDA-10.1 toolchain for sm_75 before the change was 4.2Mb, after 3.8Mb
zasdfgbnm
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Why is the binary size reduced?
ngimel
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@malfet is landing this pull request. If you are a Facebook employee, you can view this diff on Phabricator.
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@zasdfgbnm I'm not entirely sure, to tell the truth, but my guess is that too many lambdas confuse both host and GPU compiler to have multiple identical instances of the same template. |
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csarofeen
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Jul 7, 2020
Summary: This trick should have no effect on performance, but it reduces size of kernels using the template by 10% For example, sizeof(BinaryMulDivKernel.cu.o) compiled by CUDA-10.1 toolchain for sm_75 before the change was 4.2Mb, after 3.8Mb Pull Request resolved: pytorch#40992 Differential Revision: D22398733 Pulled By: malfet fbshipit-source-id: 6576f4da00dc5fc2575b2313577f52c6571d5e6f
facebook-github-bot
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Jul 9, 2020
Summary: Follow up after #40992 Use explicit templates instead of lambdas to reduce binary size without affecting the perf by 100-200Kb per arch per CU, namely: BinaryMulDivKernel.cu 3.8Mb -> 3.5Mb CompareEQKernel.cu 1.8Mb -> 1.7Mb BinaryAddSubKernel.cu 2.0Mb -> 1.8Mb BinaryBitwiseOpsKernels.cu 2.6Mb -> 2.3Mb Pull Request resolved: #41059 Differential Revision: D22458928 Pulled By: malfet fbshipit-source-id: cca623bb6e769cfe372977b08463d98b1a02dd14
laurentdupin
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Apr 24, 2026
Summary: This trick should have no effect on performance, but it reduces size of kernels using the template by 10% For example, sizeof(BinaryMulDivKernel.cu.o) compiled by CUDA-10.1 toolchain for sm_75 before the change was 4.2Mb, after 3.8Mb Pull Request resolved: pytorch#40992 Differential Revision: D22398733 Pulled By: malfet fbshipit-source-id: 6576f4da00dc5fc2575b2313577f52c6571d5e6f
laurentdupin
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to laurentdupin/pytorch
that referenced
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Apr 24, 2026
Summary: Follow up after pytorch#40992 Use explicit templates instead of lambdas to reduce binary size without affecting the perf by 100-200Kb per arch per CU, namely: BinaryMulDivKernel.cu 3.8Mb -> 3.5Mb CompareEQKernel.cu 1.8Mb -> 1.7Mb BinaryAddSubKernel.cu 2.0Mb -> 1.8Mb BinaryBitwiseOpsKernels.cu 2.6Mb -> 2.3Mb Pull Request resolved: pytorch#41059 Differential Revision: D22458928 Pulled By: malfet fbshipit-source-id: cca623bb6e769cfe372977b08463d98b1a02dd14
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This trick should have no effect on performance, but it reduces size of kernels using the template by 10%
For example, sizeof(BinaryMulDivKernel.cu.o) compiled by CUDA-10.1 toolchain for sm_75 before the change was 4.2Mb, after 3.8Mb