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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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