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# mypy: allow-untyped-defs
import torch
import torch.ao.nn.intrinsic as nni
import torch.ao.nn.qat as nnqat
import torch.nn.functional as F
class LinearReLU(nnqat.Linear, nni._FusedModule):
r"""
A LinearReLU module fused from Linear and ReLU modules, attached with
FakeQuantize modules for weight, used in
quantization aware training.
We adopt the same interface as :class:`torch.nn.Linear`.
Similar to `torch.ao.nn.intrinsic.LinearReLU`, with FakeQuantize modules initialized to
default.
Attributes:
weight: fake quant module for weight
Examples::
>>> # xdoctest: +SKIP
>>> m = nn.qat.LinearReLU(20, 30)
>>> input = torch.randn(128, 20)
>>> output = m(input)
>>> print(output.size())
torch.Size([128, 30])
"""
_FLOAT_MODULE = nni.LinearReLU # type: ignore[assignment]
def __init__(self, in_features, out_features, bias=True, qconfig=None):
super().__init__(in_features, out_features, bias, qconfig)
def forward(self, input):
return F.relu(F.linear(input, self.weight_fake_quant(self.weight), self.bias))
@classmethod
def from_float(cls, mod, use_precomputed_fake_quant=False):
return super().from_float(mod, use_precomputed_fake_quant)
def to_float(self):
linear = torch.nn.Linear(
self.in_features, self.out_features, self.bias is not None
)
linear.weight = torch.nn.Parameter(self.weight.detach())
if self.bias is not None:
linear.bias = torch.nn.Parameter(self.bias.detach())
relu = torch.nn.ReLU()
return torch.ao.nn.intrinsic.LinearReLU(linear, relu)
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