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import operator_benchmark as op_bench
import torch
import torch.ao.nn.quantized as nnq
import torch.ao.nn.quantized.dynamic as nnqd
from pt import configs
"""
Microbenchmarks for Quantized Linear operators.
"""
class _QLinearBenchmarkBase(op_bench.TorchBenchmarkBase):
def init(self, N, IN, OUT, linear_under_test):
scale = torch.tensor(1.0 / 255)
zero_point = torch.tensor(0)
self.X = torch.randn(N, IN, dtype=torch.float32)
self.qX = torch.quantize_per_tensor(self.X, scale=scale, zero_point=zero_point, dtype=torch.quint8)
W = torch.randn(OUT, IN, dtype=torch.float32)
qW = torch.quantize_per_tensor(W, scale=scale, zero_point=0, dtype=torch.qint8)
# Assume that the `self.qlinear` is set in the child
self.qlinear = linear_under_test
self.qlinear.weight = qW
self.qlinear.scale = scale
self.qlinear.zero_point = zero_point
def forward(self, input):
# Assume that the `self.input` is set in the child
return self.qlinear(input)
class QLinearBenchmark(_QLinearBenchmarkBase):
def init(self, N, IN, OUT, device):
super(QLinearBenchmark, self).init(N, IN, OUT, nnq.Linear(IN, OUT))
self.inputs = {
"input": self.qX
}
self.set_module_name("QLinear")
class QDynamicLinearBenchmark(_QLinearBenchmarkBase):
def init(self, N, IN, OUT, device):
super(QDynamicLinearBenchmark, self).init(N, IN, OUT, nnqd.Linear(IN, OUT))
self.inputs = {
"input": self.X
}
self.set_module_name("QDynamicLinear")
op_bench.generate_pt_test(configs.remove_cuda(configs.linear_configs_short + configs.linear_configs_long), QLinearBenchmark)
op_bench.generate_pt_test(configs.remove_cuda(configs.linear_configs_short + configs.linear_configs_long), QDynamicLinearBenchmark)
if __name__ == "__main__":
op_bench.benchmark_runner.main()
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