Source code for

import torch
import torch.nn as nn
import torch.nn.functional as F
from import LinearReLU
from torch.nn.utils.parametrize import (

__all__ = [

[docs]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 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)
[docs] @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 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( return linear


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