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import operator_benchmark as op_bench
import torch
from torch.testing._internal.common_device_type import get_all_device_types
"""Microbenchmark for Fill_ operator."""
fill_short_configs = op_bench.config_list(
attr_names=["N"],
attrs=[
[1],
[1024],
[2048],
],
cross_product_configs={
'device': ['cpu', 'cuda'],
'dtype': [torch.int32],
},
tags=["short"],
)
fill_long_configs = op_bench.cross_product_configs(
N=[10, 1000],
device=get_all_device_types(),
dtype=[torch.bool, torch.int8, torch.uint8, torch.int16, torch.int32,
torch.int64, torch.half, torch.float, torch.double],
tags=["long"]
)
class Fill_Benchmark(op_bench.TorchBenchmarkBase):
def init(self, N, device, dtype):
self.inputs = {
"input_one": torch.zeros(N, device=device).type(dtype)
}
self.set_module_name("fill_")
def forward(self, input_one):
return input_one.fill_(10)
op_bench.generate_pt_test(fill_short_configs + fill_long_configs,
Fill_Benchmark)
if __name__ == "__main__":
op_bench.benchmark_runner.main()
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