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import operator_benchmark as op_bench
import torch
tensor_conversion_short_configs = op_bench.cross_product_configs(
M=(8, 16, 32,),
N=(16, 64, 128,),
device=['cpu', 'cuda'],
tags=['short'],
)
tensor_conversion_long_configs = op_bench.cross_product_configs(
M=(64, 128, 256, 512,),
N=(256, 512, 1024, 2048,),
device=['cpu', 'cuda'],
tags=['long'],
)
class FloatToHalfTensorConversionBenchmark(op_bench.TorchBenchmarkBase):
def init(self, M, N, device):
self.inputs = {
"input": torch.rand(M, N, device=device, requires_grad=False, dtype=torch.float)
}
def forward(self, input):
return input.to(torch.half)
class HalfToFloatTensorConversionBenchmark(op_bench.TorchBenchmarkBase):
def init(self, M, N, device):
self.inputs = {
"input": torch.rand(M, N, device=device, requires_grad=False, dtype=torch.half)
}
def forward(self, input):
return input.to(torch.float)
op_bench.generate_pt_test(tensor_conversion_short_configs, FloatToHalfTensorConversionBenchmark)
op_bench.generate_pt_test(tensor_conversion_long_configs, FloatToHalfTensorConversionBenchmark)
op_bench.generate_pt_test(tensor_conversion_short_configs, HalfToFloatTensorConversionBenchmark)
op_bench.generate_pt_test(tensor_conversion_long_configs, HalfToFloatTensorConversionBenchmark)
if __name__ == "__main__":
op_bench.benchmark_runner.main()
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