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from pt import configs
import operator_benchmark as op_bench
import torch
import torch.ao.nn.quantized as nnq
"""
Microbenchmarks for qConv operators.
"""
class QConv1dBenchmark(op_bench.TorchBenchmarkBase):
# def init(self, N, IC, OC, L, G, kernel, stride, pad):
def init(self, IC, OC, kernel, stride, N, L, device):
G = 1
pad = 0
self.scale = 1.0 / 255
self.zero_point = 0
X = torch.randn(N, IC, L, dtype=torch.float32)
qX = torch.quantize_per_tensor(
X, scale=self.scale, zero_point=self.zero_point, dtype=torch.quint8
)
# Convert the tensor to NHWC format
W = torch.randn(OC, IC // G, kernel, dtype=torch.float32)
self.qW = torch.quantize_per_tensor(
W, scale=self.scale, zero_point=0, dtype=torch.qint8
)
self.inputs = {"input": qX}
self.qconv1d = nnq.Conv1d(IC, OC, kernel, stride=stride, padding=pad, groups=G)
self.qconv1d.set_weight_bias(self.qW, None)
self.qconv1d.scale = torch.tensor(self.scale, dtype=torch.double)
self.qconv1d.zero_point = torch.tensor(self.zero_point, dtype=torch.int)
self.set_module_name("QConv1d")
def forward(self, input):
return self.qconv1d(input)
class QConv2dBenchmark(op_bench.TorchBenchmarkBase):
# def init(self, N, IC, OC, H, W, G, kernel, stride, pad):
def init(self, IC, OC, kernel, stride, N, H, W, G, pad, device):
# super().init(N, IC, OC, (H, W), G, (kernel, kernel), stride, pad)
self.scale = 1.0 / 255
self.zero_point = 0
X = torch.randn(N, IC, H, W, dtype=torch.float32)
qX = torch.quantize_per_tensor(
X, scale=self.scale, zero_point=self.zero_point, dtype=torch.quint8
)
# Convert the tensor to NHWC format
W = torch.randn(OC, IC // G, kernel, kernel, dtype=torch.float32)
self.qW = torch.quantize_per_tensor(
W, scale=self.scale, zero_point=0, dtype=torch.qint8
)
self.inputs = {"input": qX}
self.qconv2d = nnq.Conv2d(IC, OC, kernel, stride=stride, padding=pad, groups=G)
self.qconv2d.set_weight_bias(self.qW, None)
self.qconv2d.scale = torch.tensor(self.scale, dtype=torch.double)
self.qconv2d.zero_point = torch.tensor(self.zero_point, dtype=torch.int)
self.set_module_name("QConv2d")
def forward(self, input):
return self.qconv2d(input)
op_bench.generate_pt_test(
configs.remove_cuda(configs.conv_1d_configs_short + configs.conv_1d_configs_long),
QConv1dBenchmark,
)
op_bench.generate_pt_test(
configs.remove_cuda(configs.conv_2d_configs_short + configs.conv_2d_configs_long),
QConv2dBenchmark,
)
if __name__ == "__main__":
op_bench.benchmark_runner.main()
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