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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
137 lines
4.2 KiB
Python
137 lines
4.2 KiB
Python
from pt import configs
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import operator_benchmark as op_bench
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import torch
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import torch.nn as nn
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"""
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Microbenchmarks for Conv1d and ConvTranspose1d operators.
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"""
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class Conv1dBenchmark(op_bench.TorchBenchmarkBase):
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def init(self, IC, OC, kernel, stride, N, L, device):
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self.inputs = {
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"input": torch.rand(N, IC, L, device=device, requires_grad=self.auto_set())
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}
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self.conv1d = nn.Conv1d(IC, OC, kernel, stride=stride).to(device=device)
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self.set_module_name("Conv1d")
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def forward(self, input):
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return self.conv1d(input)
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class ConvTranspose1dBenchmark(op_bench.TorchBenchmarkBase):
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def init(self, IC, OC, kernel, stride, N, L, device):
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self.inputs = {"input": torch.rand(N, IC, L, device=device)}
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self.convtranspose1d = nn.ConvTranspose1d(IC, OC, kernel, stride=stride).to(
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device=device
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)
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self.set_module_name("ConvTranspose1d")
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def forward(self, input):
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return self.convtranspose1d(input)
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op_bench.generate_pt_test(
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configs.conv_1d_configs_short + configs.conv_1d_configs_long, Conv1dBenchmark
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)
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op_bench.generate_pt_test(
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configs.convtranspose_1d_configs_short
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+ configs.conv_1d_configs_short
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+ configs.conv_1d_configs_long,
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ConvTranspose1dBenchmark,
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)
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"""
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Microbenchmarks for Conv2d, ConvTranspose2d, and Conv2dPointwise operators.
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"""
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class Conv2dBenchmark(op_bench.TorchBenchmarkBase):
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def init(self, IC, OC, kernel, stride, N, H, W, G, pad, device):
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self.inputs = {"input": torch.rand(N, IC, H, W, device=device)}
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self.conv2d = nn.Conv2d(
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IC, OC, kernel, stride=stride, groups=G, padding=pad
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).to(device=device)
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self.set_module_name("Conv2d")
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def forward(self, input):
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return self.conv2d(input)
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class ConvTranspose2dBenchmark(op_bench.TorchBenchmarkBase):
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def init(self, IC, OC, kernel, stride, N, H, W, G, pad, device):
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self.inputs = {"input": torch.rand(N, IC, H, W, device=device)}
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self.convtranspose2d = nn.ConvTranspose2d(
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IC, OC, kernel, stride=stride, groups=G, padding=pad
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).to(device=device)
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self.set_module_name("ConvTranspose2d")
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def forward(self, input):
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return self.convtranspose2d(input)
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class Conv2dPointwiseBenchmark(op_bench.TorchBenchmarkBase):
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def init(self, IC, OC, stride, N, H, W, G, pad, device):
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self.inputs = {"input": torch.rand(N, IC, H, W, device=device)}
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# Use 1 as kernel for pointwise convolution
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self.conv2d = nn.Conv2d(IC, OC, 1, stride=stride, groups=G, padding=pad).to(
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device=device
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)
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self.set_module_name("Conv2dPointwise")
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def forward(self, input):
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return self.conv2d(input)
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op_bench.generate_pt_test(
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configs.conv_2d_configs_short + configs.conv_2d_configs_long, Conv2dBenchmark
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)
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op_bench.generate_pt_test(
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configs.conv_2d_configs_short + configs.conv_2d_configs_long,
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ConvTranspose2dBenchmark,
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)
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op_bench.generate_pt_test(
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configs.conv_2d_pw_configs_short + configs.conv_2d_pw_configs_long,
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Conv2dPointwiseBenchmark,
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)
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"""
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Microbenchmarks for Conv3d and ConvTranspose3d operators.
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"""
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class Conv3dBenchmark(op_bench.TorchBenchmarkBase):
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def init(self, IC, OC, kernel, stride, N, D, H, W, device):
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self.inputs = {"input": torch.rand(N, IC, D, H, W, device=device)}
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self.conv3d = nn.Conv3d(IC, OC, kernel, stride=stride).to(device=device)
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self.set_module_name("Conv3d")
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def forward(self, input):
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return self.conv3d(input)
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class ConvTranspose3dBenchmark(op_bench.TorchBenchmarkBase):
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def init(self, IC, OC, kernel, stride, N, D, H, W, device):
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self.inputs = {"input": torch.rand(N, IC, D, H, W, device=device)}
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self.convtranspose3d = nn.ConvTranspose3d(IC, OC, kernel, stride=stride).to(
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device=device
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)
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self.set_module_name("ConvTranspose3d")
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def forward(self, input):
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return self.convtranspose3d(input)
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op_bench.generate_pt_test(configs.conv_3d_configs_short, Conv3dBenchmark)
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op_bench.generate_pt_test(configs.conv_3d_configs_short, ConvTranspose3dBenchmark)
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if __name__ == "__main__":
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op_bench.benchmark_runner.main()
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