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import numpy
import operator_benchmark as op_bench
import torch
"""Microbenchmarks for index_select operator."""
# An example input from this configuration is M=4, N=4, dim=0.
index_select_configs_short = op_bench.config_list(
attr_names=["M", "N", "K", "dim"],
attrs=[
[8, 8, 1, 1],
[256, 512, 1, 1],
[512, 512, 1, 1],
[8, 8, 2, 1],
[256, 512, 2, 1],
[512, 512, 2, 1],
],
cross_product_configs={
"device": ["cpu", "cuda"],
},
tags=["short"],
)
index_select_configs_long = op_bench.cross_product_configs(
M=[128, 1024],
N=[128, 1024],
K=[1, 2],
dim=[1],
device=["cpu", "cuda"],
tags=["long"],
)
class IndexSelectBenchmark(op_bench.TorchBenchmarkBase):
def init(self, M, N, K, dim, device):
max_val = N
numpy.random.seed((1 << 32) - 1)
index_dim = numpy.random.randint(0, N)
self.inputs = {
"input_one": torch.rand(M, N, K, device=device),
"dim": dim,
"index": torch.tensor(
numpy.random.randint(0, max_val, index_dim), device=device
),
}
self.set_module_name("index_select")
def forward(self, input_one, dim, index):
return torch.index_select(input_one, dim, index)
op_bench.generate_pt_test(
index_select_configs_short + index_select_configs_long, IndexSelectBenchmark
)
if __name__ == "__main__":
op_bench.benchmark_runner.main()
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