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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
46 lines
1.1 KiB
Python
46 lines
1.1 KiB
Python
import operator_benchmark as op_bench
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import torch
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"""Microbenchmarks for add_ operator. Supports both Caffe2/PyTorch."""
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class BmmBenchmark(op_bench.TorchBenchmarkBase):
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def init(self, B, M, N, K, device, op):
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self.inputs = {
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"batch1": torch.rand(
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(B, M, K), device=device, requires_grad=self.auto_set()
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),
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"batch2": torch.rand(
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(
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B,
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K,
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N,
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),
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device=device,
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requires_grad=self.auto_set(),
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),
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}
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self.set_module_name(f"bmm (actual op={op}")
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self.op = torch.bmm if op == "bmm" else torch.matmul
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def forward(self, batch1, batch2):
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return self.op(batch1, batch2)
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bmm_configs = op_bench.cross_product_configs(
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B=[2, 100],
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M=[8, 256],
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N=[256, 16],
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K=[16, 32],
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device=["cpu"],
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tags=["short"],
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op=["bmm", "matmul"],
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)
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op_bench.generate_pt_test(bmm_configs, BmmBenchmark)
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if __name__ == "__main__":
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op_bench.benchmark_runner.main()
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