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