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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()
|
