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import operator_benchmark as op_bench
import torch
import numpy
"""Microbenchmarks for gather operator."""
# An example input from this configuration is M=4, N=4, dim=0.
gather_configs_short = op_bench.config_list(
attr_names=["M", "N", "dim"],
attrs=[
[256, 512, 0],
[512, 512, 1],
],
cross_product_configs={
'device': ['cpu', 'cuda'],
},
tags=["short"]
)
gather_configs_long = op_bench.cross_product_configs(
M=[128, 1024],
N=[128, 1024],
dim=[0, 1],
device=['cpu', 'cuda'],
tags=["long"]
)
class GatherBenchmark(op_bench.TorchBenchmarkBase):
def init(self, M, N, dim, device):
min_val = M if dim == 0 else N
numpy.random.seed((1 << 32) - 1)
self.inputs = {
"input_one": torch.rand(M, N, device=device),
"dim": dim,
"index": torch.tensor(numpy.random.randint(0, min_val, (M, N)), device=device)
}
self.set_module_name("gather")
def forward(self, input_one, dim: int, index):
return torch.gather(input_one, dim, index)
op_bench.generate_pt_test(gather_configs_short + gather_configs_long,
GatherBenchmark)
if __name__ == "__main__":
op_bench.benchmark_runner.main()
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