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import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.ao.nn.intrinsic import LinearReLU
from torch.nn.utils.parametrize import (
is_parametrized,
type_before_parametrizations,
transfer_parametrizations_and_params,
)
class Linear(nn.Linear):
r"""
A linear module attached with FakeQuantize modules for weight,
used for quantization aware training.
We adopt the same interface as `torch.nn.Linear`, please see
https://pytorch.org/docs/stable/nn.html#torch.nn.Linear
for documentation.
Similar to `torch.nn.Linear`, with FakeQuantize modules initialized to
default.
Attributes:
weight: fake quant module for weight
"""
_FLOAT_MODULE = nn.Linear
def __init__(self, in_features, out_features, bias=True,
qconfig=None, device=None, dtype=None) -> None:
factory_kwargs = {'device': device, 'dtype': dtype}
super().__init__(in_features, out_features, bias, **factory_kwargs)
assert qconfig, 'qconfig must be provided for QAT module'
self.qconfig = qconfig
self.weight_fake_quant = qconfig.weight(factory_kwargs=factory_kwargs)
def forward(self, input):
return F.linear(input, self.weight_fake_quant(self.weight), self.bias)
@classmethod
def from_float(cls, mod):
r"""Create a qat module from a float module or qparams_dict
Args: `mod` a float module, either produced by torch.ao.quantization utilities
or directly from user
"""
assert type_before_parametrizations(mod) == cls._FLOAT_MODULE, (
" qat."
+ cls.__name__
+ ".from_float only works for "
+ cls._FLOAT_MODULE.__name__
)
assert hasattr(mod, "qconfig"), "Input float module must have qconfig defined"
assert mod.qconfig, "Input float module must have a valid qconfig"
if type_before_parametrizations(mod) == LinearReLU:
mod = mod[0]
qconfig = mod.qconfig
qat_linear = cls(mod.in_features, mod.out_features, bias=mod.bias is not None, qconfig=qconfig)
if is_parametrized(mod, "weight"):
transfer_parametrizations_and_params(mod, qat_linear, "weight")
else:
qat_linear.weight = mod.weight
if is_parametrized(mod, "bias"):
transfer_parametrizations_and_params(mod, qat_linear, "bias")
else:
qat_linear.bias = mod.bias
return qat_linear
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())
linear.train(self.training)
return linear
|
