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https://github.com/zebrajr/pytorch.git
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6a866c3ed1
Summary: this mostly consisted of adding __all__ to files without them. A few functions in X.utils were made private too Test Plan: python test/test_public_bindings.py Reviewers: Subscribers: Tasks: Tags: Differential Revision: [D40814548](https://our.internmc.facebook.com/intern/diff/D40814548) Pull Request resolved: https://github.com/pytorch/pytorch/pull/87883 Approved by: https://github.com/jcaip, https://github.com/anjali411
82 lines
2.8 KiB
Python
82 lines
2.8 KiB
Python
import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from torch.ao.nn.intrinsic import LinearReLU
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from torch.nn.utils.parametrize import (
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is_parametrized,
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type_before_parametrizations,
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transfer_parametrizations_and_params,
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)
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__all__ = [
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"Linear"
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]
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class Linear(nn.Linear):
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r"""
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A linear module attached with FakeQuantize modules for weight,
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used for quantization aware training.
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We adopt the same interface as `torch.nn.Linear`, please see
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https://pytorch.org/docs/stable/nn.html#torch.nn.Linear
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for documentation.
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Similar to `torch.nn.Linear`, with FakeQuantize modules initialized to
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default.
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Attributes:
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weight: fake quant module for weight
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"""
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_FLOAT_MODULE = nn.Linear
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def __init__(self, in_features, out_features, bias=True,
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qconfig=None, device=None, dtype=None) -> None:
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factory_kwargs = {'device': device, 'dtype': dtype}
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super().__init__(in_features, out_features, bias, **factory_kwargs)
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assert qconfig, 'qconfig must be provided for QAT module'
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self.qconfig = qconfig
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self.weight_fake_quant = qconfig.weight(factory_kwargs=factory_kwargs)
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def forward(self, input):
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return F.linear(input, self.weight_fake_quant(self.weight), self.bias)
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@classmethod
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def from_float(cls, mod):
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r"""Create a qat module from a float module or qparams_dict
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Args: `mod` a float module, either produced by torch.ao.quantization utilities
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or directly from user
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"""
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assert type_before_parametrizations(mod) == cls._FLOAT_MODULE, (
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" qat."
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+ cls.__name__
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+ ".from_float only works for "
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+ cls._FLOAT_MODULE.__name__
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)
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assert hasattr(mod, "qconfig"), "Input float module must have qconfig defined"
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assert mod.qconfig, "Input float module must have a valid qconfig"
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if type_before_parametrizations(mod) == LinearReLU:
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mod = mod[0]
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qconfig = mod.qconfig
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qat_linear = cls(mod.in_features, mod.out_features, bias=mod.bias is not None, qconfig=qconfig)
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if is_parametrized(mod, "weight"):
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transfer_parametrizations_and_params(mod, qat_linear, "weight")
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else:
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qat_linear.weight = mod.weight
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if is_parametrized(mod, "bias"):
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transfer_parametrizations_and_params(mod, qat_linear, "bias")
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else:
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qat_linear.bias = mod.bias
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return qat_linear
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def to_float(self):
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linear = torch.nn.Linear(self.in_features, self.out_features, self.bias is not None)
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linear.weight = torch.nn.Parameter(self.weight.detach())
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if self.bias is not None:
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linear.bias = torch.nn.Parameter(self.bias.detach())
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linear.train(self.training)
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return linear
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