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import operator_benchmark as op_bench
import torch
from typing import List
"""Microbenchmarks for as_strided operator"""
# Configs for PT as_strided operator
as_strided_configs_short = op_bench.config_list(
attr_names=["M", "N", "size", "stride", "storage_offset"],
attrs=[
[8, 8, (2, 2), (1, 1), 0],
[256, 256, (32, 32), (1, 1), 0],
[512, 512, (64, 64), (2, 2), 1],
],
cross_product_configs={
'device': ['cpu', 'cuda'],
},
tags=["short"],
)
as_strided_configs_long = op_bench.cross_product_configs(
M=[512],
N=[1024],
size=[(16, 16), (128, 128)],
stride=[(1, 1)],
storage_offset=[0, 1],
device=['cpu', 'cuda'],
tags=['long']
)
class As_stridedBenchmark(op_bench.TorchBenchmarkBase):
def init(self, M, N, size, stride, storage_offset, device):
self.inputs = {
"input_one": torch.rand(M, N, device=device),
"size": size,
"stride": stride,
"storage_offset": storage_offset
}
self.set_module_name('as_strided')
def forward(
self, input_one, size: List[int], stride: List[int], storage_offset: int
):
return torch.as_strided(
input_one, size, stride, storage_offset)
op_bench.generate_pt_test(as_strided_configs_short + as_strided_configs_long,
As_stridedBenchmark)
if __name__ == "__main__":
op_bench.benchmark_runner.main()
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