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import operator_benchmark as op_bench
import torch
add_configs = op_bench.cross_product_configs(
M=[8, 1],
N=[8, 2],
K=[8, 4],
tags=["short"]
)
# This benchmark uses the auto_set to automatically set requires_grad
# for both inputs. The test name can also be used for filtering.
class AddBenchmark(op_bench.TorchBenchmarkBase):
def init(self, M, N, K):
self.input_one = torch.rand(M, N, K, requires_grad=self.auto_set())
self.input_two = torch.rand(M, N, K, requires_grad=self.auto_set())
self.set_module_name("add")
def forward(self):
return torch.add(self.input_one, self.input_two)
op_bench.generate_pt_test(add_configs, AddBenchmark)
op_bench.generate_pt_gradient_test(add_configs, AddBenchmark)
if __name__ == "__main__":
op_bench.benchmark_runner.main()
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