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1import operator_benchmark as op_bench
2
3import torch
4from torch.testing._internal.common_device_type import get_all_device_types
5
6
7"""Microbenchmark for Fill_ operator."""
8
9fill_short_configs = op_bench.config_list(
10    attr_names=["N"],
11    attrs=[
12        [1],
13        [1024],
14        [2048],
15    ],
16    cross_product_configs={
17        "device": ["cpu", "cuda"],
18        "dtype": [torch.int32],
19    },
20    tags=["short"],
21)
22
23fill_long_configs = op_bench.cross_product_configs(
24    N=[10, 1000],
25    device=get_all_device_types(),
26    dtype=[
27        torch.bool,
28        torch.int8,
29        torch.uint8,
30        torch.int16,
31        torch.int32,
32        torch.int64,
33        torch.half,
34        torch.float,
35        torch.double,
36    ],
37    tags=["long"],
38)
39
40
41class Fill_Benchmark(op_bench.TorchBenchmarkBase):
42    def init(self, N, device, dtype):
43        self.inputs = {"input_one": torch.zeros(N, device=device).type(dtype)}
44        self.set_module_name("fill_")
45
46    def forward(self, input_one):
47        return input_one.fill_(10)
48
49
50op_bench.generate_pt_test(fill_short_configs + fill_long_configs, Fill_Benchmark)
51
52
53if __name__ == "__main__":
54    op_bench.benchmark_runner.main()
55