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import operator_benchmark as op_bench
import torch
"""Microbenchmarks for linear_unpack_fp16_ operator. Supports both Caffe2/PyTorch."""
# Configs for PT linear_unpack_fp16 operator
linear_unpack_fp16_long_configs = op_bench.cross_product_configs(
M=[8, 128],
N=[32, 64],
K=[256, 512],
device=['cpu'],
tags=["long"]
)
linear_unpack_fp16_short_configs = op_bench.config_list(
attr_names=["M", "N", "K"],
attrs=[
[1, 1, 1],
[64, 64, 64],
[64, 64, 128],
],
cross_product_configs={
'device': ['cpu'],
},
tags=["short"],
)
class LinearUnpackFP16Benchmark(op_bench.TorchBenchmarkBase):
def init(self, M, N, K, device):
# input to unpack operator must be what the output is for prepack operator
self.inputs = {
"input_one": torch.ops.quantized.linear_prepack_fp16(torch.rand(M, N, K, device=device,
requires_grad=False,
dtype=torch.float32))
}
self.set_module_name("linear_unpack_fp16")
def forward(self, input_one):
return torch.ops.quantized.linear_unpack_fp16(input_one)
# The generated test names based on linear_unpack_fp16_short_configs will be in the following pattern:
# linear_unpack_fp16_M8_N16_K32_devicecpu
op_bench.generate_pt_test(linear_unpack_fp16_long_configs + linear_unpack_fp16_short_configs, LinearUnpackFP16Benchmark)
if __name__ == "__main__":
op_bench.benchmark_runner.main()
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