Add sum reduction operator to TritonBench #2282
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This pull request was exported from Phabricator. Differential Revision: D58048782 |
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Summary: Add a Triton reduction kernel for the `sum` operator where `dim=None` to TritonBench, following the [TritonBench guide](https://fb.workplace.com/notes/953949486404240). This implementation works for all matrices being reduced to a scalar value. To measure accuracy of Triton reduction kernel, add accuracy metric to sum kernel in TritonBench in order to test accuracy of Triton implementation against baseline PyTorch implementation, referencing [`torchbenchmark/operators/gemm/operator.py`](https://www.internalfb.com/code/fbsource/[767bb6faa353685b84f08a39f36fdcf6ca170c85]/fbcode/pytorch/benchmark/torchbenchmark/operators/gemm/operator.py?lines=236). Reset output registers per run of the Triton kernel for accurate Triton output. Referenced the existing [vector_add](https://www.internalfb.com/code/fbsource/fbcode/pytorch/benchmark/torchbenchmark/operators/vector_add/) and [grouped_gemm](https://www.internalfb.com/code/fbsource/fbcode/pytorch/benchmark/torchbenchmark/operators/grouped_gemm/) TritonBench operators as frameworks for implementation. See the [TritonBench Operator Coverage Tracker](https://docs.google.com/spreadsheets/d/1091POOPSPsUnlNVEKaz2X_DQXdIwFv-fGOH_g9by-Zo/edit#gid=0) for current operator coverage in TritonBench. Reviewed By: xuzhao9, davidberard98 Differential Revision: D58048782
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Summary: Add a Triton reduction kernel for the `sum` operator where `dim=None` to TritonBench, following the [TritonBench guide](https://fb.workplace.com/notes/953949486404240). This implementation works for all matrices being reduced to a scalar value. To measure accuracy of Triton reduction kernel, add accuracy metric to sum kernel in TritonBench in order to test accuracy of Triton implementation against baseline PyTorch implementation, referencing [`torchbenchmark/operators/gemm/operator.py`](https://www.internalfb.com/code/fbsource/[767bb6faa353685b84f08a39f36fdcf6ca170c85]/fbcode/pytorch/benchmark/torchbenchmark/operators/gemm/operator.py?lines=236). Reset output registers per run of the Triton kernel for accurate Triton output. To measure performance of the Triton reduction kernel against PyTorch, add gbps metric, referencing [`torchbenchmark/operators/vector_add/operator.py`](https://www.internalfb.com/code/fbsource/[858eda681c7618f9427ba55cef8d4aba712cb26e]/fbcode/pytorch/benchmark/torchbenchmark/operators/vector_add/operator.py?lines=19). Referenced the existing [vector_add](https://www.internalfb.com/code/fbsource/fbcode/pytorch/benchmark/torchbenchmark/operators/vector_add/) and [grouped_gemm](https://www.internalfb.com/code/fbsource/fbcode/pytorch/benchmark/torchbenchmark/operators/grouped_gemm/) TritonBench operators as frameworks for implementation. See the [TritonBench Operator Coverage Tracker](https://docs.google.com/spreadsheets/d/1091POOPSPsUnlNVEKaz2X_DQXdIwFv-fGOH_g9by-Zo/edit#gid=0) for current operator coverage in TritonBench. Reviewed By: xuzhao9, davidberard98 Differential Revision: D58048782
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Summary:
Add a Triton reduction kernel for the sum operator where dim=None to TritonBench, following the TritonBench guide. This implementation works for all matrices being reduced to a scalar value.
To measure accuracy of Triton reduction kernel, add accuracy metric to sum kernel in TritonBench in order to test accuracy of Triton implementation against baseline PyTorch implementation, referencing torchbenchmark/operators/gemm/operator.py. Reset output registers per run of the Triton kernel for accurate Triton output.
To measure performance of the Triton reduction kernel against PyTorch, add gbps metric, referencing torchbenchmark/operators/vector_add/operator.py.
Referenced the existing vector_add and grouped_gemm TritonBench operators as frameworks for implementation.
See the TritonBench Operator Coverage Tracker for current operator coverage in TritonBench.
Reviewed By: xuzhao9, davidberard98
Differential Revision: D58048782