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import operator_benchmark as op_bench
import torch
# Configs for pointwise and reduction unary ops
qmethods_configs_short = op_bench.config_list(
attr_names=["M", "N"],
attrs=[
[32, 32],
],
cross_product_configs={
"dtype": [torch.quint8],
"contig": [False, True],
},
tags=["short"],
)
qmethods_configs_long = op_bench.cross_product_configs(
M=[256, 1024],
N=[256, 1024],
dtype=[torch.qint8, torch.qint32],
contig=[False, True],
tags=["long"],
)
class _QMethodBenchmarkBase(op_bench.TorchBenchmarkBase):
def init(self, M, N, dtype, contig):
f_input = torch.rand(M, N)
scale = 1.0
zero_point = 0
self.q_input = torch.quantize_per_tensor(
f_input, scale=scale, zero_point=zero_point, dtype=dtype
)
if not contig:
permute_dims = list(range(self.q_input.ndim))[::-1]
self.q_input = self.q_input.permute(permute_dims)
self.inputs = {
"q_input": self.q_input,
}
class QMethodTensorInputCopyBenchmark(_QMethodBenchmarkBase):
def forward(self, q_input):
return q_input.copy_(q_input)
op_bench.generate_pt_test(
qmethods_configs_short + qmethods_configs_long, QMethodTensorInputCopyBenchmark
)
if __name__ == "__main__":
op_bench.benchmark_runner.main()
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