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import operator_benchmark as op_bench
import torch
intraop_bench_configs = op_bench.config_list(
attrs=[
[8, 16],
],
attr_names=["M", "N"],
tags=["short"],
)
@torch.jit.script
def torch_sumall(a, iterations):
# type: (Tensor, int)
result = 0.0
for _ in range(iterations):
result += float(torch.sum(a))
a[0][0] += 0.01
return result
class TorchSumBenchmark(op_bench.TorchBenchmarkBase):
def init(self, M, N):
self.input_one = torch.rand(M, N)
self.set_module_name("sum")
# This is a very temporary method and will be removed soon, so
# don't use this method in your benchmark
# TODO(mingzhe): use one forward method for both JIT and Eager
def jit_forward(self, iters):
return torch_sumall(self.input_one, iters)
op_bench.generate_pt_test(intraop_bench_configs, TorchSumBenchmark)
if __name__ == "__main__":
op_bench.benchmark_runner.main()
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