from ... import functional as F
from .. import linear as Float
from .module import QATModule
[docs]class Linear(Float.Linear, QATModule):
r"""A :class:`~.QATModule` version of :class:`~.module.Linear`.
Could be applied with :class:`~.Observer` and :class:`~.quantization.fake_quant.FakeQuantize`.
Args:
in_features: size of each input sample.
out_features: size of each output sample.
bias: If set to ``False``, the layer will not learn an additive bias.
Default: True
"""
def calc_linear_qat(self, inp):
w_qat = self.apply_quant_weight(self.weight)
b_qat = self.apply_quant_bias(self.bias, inp, w_qat)
linear = self.calc_linear(inp, w_qat, b_qat)
return linear
def forward(self, inp):
w_qat = self.apply_quant_weight(self.weight)
b_qat = self.apply_quant_bias(self.bias, inp, w_qat)
return self.apply_quant_activation(self.calc_linear(inp, w_qat, b_qat))
[docs] @classmethod
def from_float_module(cls, float_module: Float.Linear):
r"""
Return a :class:`~.QATModule` instance converted from
a float :class:`~.Module` instance.
"""
qmod = cls(
float_module.in_features, float_module.out_features, name=float_module.name
)
qmod.weight = float_module.weight
qmod.bias = float_module.bias
return qmod
class LinearRelu(Linear):
r"""A :class:`~.QATModule` include :class:`~.module.Linear` and :func:`~.relu` with QAT support.
Could be applied with :class:`~.Observer` and :class:`~.quantization.fake_quant.FakeQuantize`.
"""
def forward(self, inp):
return self.apply_quant_activation(F.relu(self.calc_linear_qat(inp)))