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import torch
import torch.ao.nn.quantized as nnq
import torch.ao.nn.intrinsic as nni
class LinearReLU(nnq.Linear):
r"""
A LinearReLU module fused from Linear and ReLU modules
We adopt the same interface as :class:`torch.ao.nn.quantized.Linear`.
Attributes:
Same as torch.ao.nn.quantized.Linear
Examples::
>>> # xdoctest: +SKIP
>>> m = nn.intrinsic.LinearReLU(20, 30)
>>> input = torch.randn(128, 20)
>>> output = m(input)
>>> print(output.size())
torch.Size([128, 30])
"""
_FLOAT_MODULE = nni.LinearReLU
def __init__(self, in_features, out_features, bias=True, dtype=torch.qint8):
super().__init__(in_features, out_features, bias, dtype)
def forward(self, x: torch.Tensor) -> torch.Tensor:
return torch.ops.quantized.linear_relu(
x, self._packed_params._packed_params, self.scale, self.zero_point)
def _get_name(self):
return 'QuantizedLinearReLU'
@classmethod
def from_float(cls, mod):
return super(LinearReLU, cls).from_float(mod)
@classmethod
def from_reference(cls, ref_linear_relu, output_scale, output_zero_point):
return super().from_reference(ref_linear_relu[0], output_scale, output_zero_point)
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