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See https://github.com/pytorch/pytorch/pull/129751#issue-2380881501. Most changes are auto-generated by linter. You can review these PRs via: ```bash git diff --ignore-all-space --ignore-blank-lines HEAD~1 ``` Pull Request resolved: https://github.com/pytorch/pytorch/pull/129754 Approved by: https://github.com/ezyang
55 lines
1.3 KiB
Python
55 lines
1.3 KiB
Python
import operator_benchmark as op_bench
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import torch
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"""Microbenchmarks for MatMul operator"""
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# Configs for PT Matmul operator
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mm_short_configs = op_bench.config_list(
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attr_names=["M", "N", "K", "trans_a", "trans_b"],
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attrs=[
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[1, 1, 1, True, False],
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[128, 128, 128, True, False],
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[256, 256, 256, False, True],
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],
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cross_product_configs={
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"device": ["cpu", "cuda"],
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},
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tags=["short"],
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)
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mm_long_configs = op_bench.cross_product_configs(
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M=[32],
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N=[512, 128],
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K=[64],
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trans_a=[False, True],
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trans_b=[True, False],
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device=["cpu", "cuda"],
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tags=["long"],
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)
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class MatMulBenchmark(op_bench.TorchBenchmarkBase):
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def init(self, M, N, K, trans_a, trans_b, device):
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self.inputs = {
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"input_one": torch.rand(M, N, device=device)
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if trans_a
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else torch.rand(N, M, device=device).t(),
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"input_two": torch.rand(N, K, device=device)
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if trans_b
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else torch.rand(K, N, device=device).t(),
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}
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self.set_module_name("matmul")
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def forward(self, input_one, input_two):
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return torch.matmul(input_one, input_two)
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op_bench.generate_pt_test(mm_long_configs + mm_short_configs, MatMulBenchmark)
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if __name__ == "__main__":
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op_bench.benchmark_runner.main()
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