import torch

import operator_benchmark as op_bench


"""Microbenchmarks for diag operator"""


# Configs for PT diag operator
diag_configs_short = op_bench.config_list(
    attr_names=["dim", "M", "N", "diagonal", "out"],
    attrs=[
        [1, 64, 64, 0, True],
        [2, 128, 128, -10, False],
        [1, 256, 256, 20, True],
    ],
    cross_product_configs={
        "device": ["cpu", "cuda"],
    },
    tags=["short"],
)


class DiagBenchmark(op_bench.TorchBenchmarkBase):
    def init(self, dim, M, N, diagonal, out, device):
        self.inputs = {
            "input": torch.rand(M, N, device=device)
            if dim == 2
            else torch.rand(M, device=device),
            "diagonal": diagonal,
            "out": out,
            "out_tensor": torch.tensor(
                (),
            ),
        }
        self.set_module_name("diag")

    def forward(self, input, diagonal: int, out: bool, out_tensor):
        if out:
            return torch.diag(input, diagonal=diagonal, out=out_tensor)
        else:
            return torch.diag(input, diagonal=diagonal)


op_bench.generate_pt_test(diag_configs_short, DiagBenchmark)


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

3d688aa49850772411018777cbc0f508a49c0e9d
<mask token>


class DiagBenchmark(op_bench.TorchBenchmarkBase):

    def init(self, dim, M, N, diagonal, out, device):
        self.inputs = {'input': torch.rand(M, N, device=device) if dim == 2
             else torch.rand(M, device=device), 'diagonal': diagonal, 'out':
            out, 'out_tensor': torch.tensor(())}
        self.set_module_name('diag')
    <mask token>


<mask token>

<mask token>


class DiagBenchmark(op_bench.TorchBenchmarkBase):

    def init(self, dim, M, N, diagonal, out, device):
        self.inputs = {'input': torch.rand(M, N, device=device) if dim == 2
             else torch.rand(M, device=device), 'diagonal': diagonal, 'out':
            out, 'out_tensor': torch.tensor(())}
        self.set_module_name('diag')

    def forward(self, input, diagonal: int, out: bool, out_tensor):
        if out:
            return torch.diag(input, diagonal=diagonal, out=out_tensor)
        else:
            return torch.diag(input, diagonal=diagonal)


op_bench.generate_pt_test(diag_configs_short, DiagBenchmark)
if __name__ == '__main__':
    op_bench.benchmark_runner.main()

<mask token>
diag_configs_short = op_bench.config_list(attr_names=['dim', 'M', 'N',
    'diagonal', 'out'], attrs=[[1, 64, 64, 0, True], [2, 128, 128, -10, 
    False], [1, 256, 256, 20, True]], cross_product_configs={'device': [
    'cpu', 'cuda']}, tags=['short'])


class DiagBenchmark(op_bench.TorchBenchmarkBase):

    def init(self, dim, M, N, diagonal, out, device):
        self.inputs = {'input': torch.rand(M, N, device=device) if dim == 2
             else torch.rand(M, device=device), 'diagonal': diagonal, 'out':
            out, 'out_tensor': torch.tensor(())}
        self.set_module_name('diag')

    def forward(self, input, diagonal: int, out: bool, out_tensor):
        if out:
            return torch.diag(input, diagonal=diagonal, out=out_tensor)
        else:
            return torch.diag(input, diagonal=diagonal)


op_bench.generate_pt_test(diag_configs_short, DiagBenchmark)
if __name__ == '__main__':
    op_bench.benchmark_runner.main()

import torch
import operator_benchmark as op_bench
<mask token>
diag_configs_short = op_bench.config_list(attr_names=['dim', 'M', 'N',
    'diagonal', 'out'], attrs=[[1, 64, 64, 0, True], [2, 128, 128, -10, 
    False], [1, 256, 256, 20, True]], cross_product_configs={'device': [
    'cpu', 'cuda']}, tags=['short'])


class DiagBenchmark(op_bench.TorchBenchmarkBase):

    def init(self, dim, M, N, diagonal, out, device):
        self.inputs = {'input': torch.rand(M, N, device=device) if dim == 2
             else torch.rand(M, device=device), 'diagonal': diagonal, 'out':
            out, 'out_tensor': torch.tensor(())}
        self.set_module_name('diag')

    def forward(self, input, diagonal: int, out: bool, out_tensor):
        if out:
            return torch.diag(input, diagonal=diagonal, out=out_tensor)
        else:
            return torch.diag(input, diagonal=diagonal)


op_bench.generate_pt_test(diag_configs_short, DiagBenchmark)
if __name__ == '__main__':
    op_bench.benchmark_runner.main()

import torch

import operator_benchmark as op_bench


"""Microbenchmarks for diag operator"""


# Configs for PT diag operator
diag_configs_short = op_bench.config_list(
    attr_names=["dim", "M", "N", "diagonal", "out"],
    attrs=[
        [1, 64, 64, 0, True],
        [2, 128, 128, -10, False],
        [1, 256, 256, 20, True],
    ],
    cross_product_configs={
        "device": ["cpu", "cuda"],
    },
    tags=["short"],
)


class DiagBenchmark(op_bench.TorchBenchmarkBase):
    def init(self, dim, M, N, diagonal, out, device):
        self.inputs = {
            "input": torch.rand(M, N, device=device)
            if dim == 2
            else torch.rand(M, device=device),
            "diagonal": diagonal,
            "out": out,
            "out_tensor": torch.tensor(
                (),
            ),
        }
        self.set_module_name("diag")

    def forward(self, input, diagonal: int, out: bool, out_tensor):
        if out:
            return torch.diag(input, diagonal=diagonal, out=out_tensor)
        else:
            return torch.diag(input, diagonal=diagonal)


op_bench.generate_pt_test(diag_configs_short, DiagBenchmark)


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