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c0ed38e644
See https://github.com/pytorch/pytorch/pull/129751#issue-2380881501. Most changes are auto-generated by linter. You can review these PRs via: ```bash git diff --ignore-all-space --ignore-blank-lines HEAD~1 ``` Pull Request resolved: https://github.com/pytorch/pytorch/pull/129754 Approved by: https://github.com/ezyang
63 lines
1.5 KiB
Python
63 lines
1.5 KiB
Python
import numpy
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import operator_benchmark as op_bench
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import torch
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"""Microbenchmarks for index_select operator."""
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# An example input from this configuration is M=4, N=4, dim=0.
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index_select_configs_short = op_bench.config_list(
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attr_names=["M", "N", "K", "dim"],
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attrs=[
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[8, 8, 1, 1],
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[256, 512, 1, 1],
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[512, 512, 1, 1],
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[8, 8, 2, 1],
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[256, 512, 2, 1],
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[512, 512, 2, 1],
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],
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cross_product_configs={
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"device": ["cpu", "cuda"],
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},
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tags=["short"],
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)
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index_select_configs_long = op_bench.cross_product_configs(
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M=[128, 1024],
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N=[128, 1024],
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K=[1, 2],
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dim=[1],
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device=["cpu", "cuda"],
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tags=["long"],
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)
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class IndexSelectBenchmark(op_bench.TorchBenchmarkBase):
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def init(self, M, N, K, dim, device):
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max_val = N
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numpy.random.seed((1 << 32) - 1)
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index_dim = numpy.random.randint(0, N)
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self.inputs = {
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"input_one": torch.rand(M, N, K, device=device),
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"dim": dim,
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"index": torch.tensor(
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numpy.random.randint(0, max_val, index_dim), device=device
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),
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}
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self.set_module_name("index_select")
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def forward(self, input_one, dim, index):
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return torch.index_select(input_one, dim, index)
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op_bench.generate_pt_test(
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index_select_configs_short + index_select_configs_long, IndexSelectBenchmark
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)
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if __name__ == "__main__":
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op_bench.benchmark_runner.main()
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