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