import operator_benchmark as op_bench
import torch

from torch.testing._internal.common_device_type import get_all_device_types

"""Microbenchmark for Fill_ operator."""

fill_short_configs = op_bench.config_list(
    attr_names=["N"],
    attrs=[
        [1],
        [1024],
        [2048],
    ],
    cross_product_configs={
        'device': ['cpu', 'cuda'],
        'dtype': [torch.int32],
    },
    tags=["short"],
)

fill_long_configs = op_bench.cross_product_configs(
    N=[10, 1000],
    device=get_all_device_types(),
    dtype=[torch.bool, torch.int8, torch.uint8, torch.int16, torch.int32,
           torch.int64, torch.half, torch.float, torch.double],
    tags=["long"]
)


class Fill_Benchmark(op_bench.TorchBenchmarkBase):
    def init(self, N, device, dtype):
        self.inputs = {
            "input_one": torch.zeros(N, device=device).type(dtype)
        }
        self.set_module_name("fill_")

    def forward(self, input_one):
        return input_one.fill_(10)


op_bench.generate_pt_test(fill_short_configs + fill_long_configs,
                          Fill_Benchmark)


if __name__ == "__main__":
    op_bench.benchmark_runner.main()