Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

[reland][quant][fix] Add bias once in conv_fused (#48593) #48661

Closed
wants to merge 5 commits into from
Closed

[reland][quant][fix] Add bias once in conv_fused (#48593) #48661

Show file tree
Hide file tree
Changes from 2 commits
Commits
Show all changes
5 commits
Select commit Hold shift + click to select a range
6731ea7
[reland][quant][fix] Add bias once in conv_fused (#48593)
jerryzh168 Dec 1, 2020
118c96f
Update on "[reland][quant][fix] Add bias once in conv_fused (#48593)"
jerryzh168 Dec 1, 2020
a97f4ca
Update on "[reland][quant][fix] Add bias once in conv_fused (#48593)"
jerryzh168 Dec 2, 2020
24bf342
Update on "[reland][quant][fix] Add bias once in conv_fused (#48593)"
jerryzh168 Dec 2, 2020
c7b7a84
Update on "[reland][quant][fix] Add bias once in conv_fused (#48593)"
jerryzh168 Dec 2, 2020
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Jump to
Jump to file
Failed to load files.
Diff view
Diff view
6 changes: 5 additions & 1 deletion test/quantization/test_qat_module.py
View file
Open in desktop
Expand Up @@ -110,7 +110,11 @@ def _forward(self, input):
running_std = torch.sqrt(self.running_var + self.eps)
scale_factor = self.gamma / running_std
scaled_weight = self.weight * scale_factor.reshape([-1, 1, 1, 1])
conv = self._conv_forward(input, self.weight_fake_quant(scaled_weight))
if self.bias:
zero_bias = torch.zeros_like(self.bias)
else:
zero_bias = torch.zeros(self.out_channels, device=scaled_weight.device())
conv = self._conv_forward(input, self.weight_fake_quant(scaled_weight), zero_bias)

if self.training and not self.freeze_bn:
# recovering original conv to get original batch_mean and batch_var
Expand Down
8 changes: 6 additions & 2 deletions torch/nn/intrinsic/qat/modules/conv_fused.py
View file
Open in desktop
Expand Up @@ -94,7 +94,11 @@ def _forward(self, input):
bias_shape[1] = -1
scaled_weight = self.weight_fake_quant(self.weight * scale_factor.reshape(weight_shape))
# this does not include the conv bias
conv = self._conv_forward(input, scaled_weight)
if self.bias:
zero_bias = torch.zeros_like(self.bias)
else:
zero_bias = torch.zeros(self.out_channels, device=scaled_weight.device())
Copy link
Contributor

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

nice catch. Can we add a comment here with the motivation, similar to this PR summary?

Copy link
Contributor Author

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

sure

conv = self._conv_forward(input, scaled_weight, zero_bias)
conv_orig = conv / scale_factor.reshape(bias_shape)
if self.bias is not None:
conv_orig = conv_orig + self.bias.reshape(bias_shape)
Expand Down Expand Up @@ -402,7 +406,7 @@ def __init__(self, in_channels, out_channels, kernel_size, stride=1,

def forward(self, input):
return F.relu(
self._conv_forward(input, self.weight_fake_quant(self.weight)))
self._conv_forward(input, self.weight_fake_quant(self.weight), self.bias))

@classmethod
def from_float(cls, mod):
Expand Down
16 changes: 8 additions & 8 deletions torch/nn/modules/conv.py
View file
Open in desktop
Expand Up @@ -246,16 +246,16 @@ def __init__(
in_channels, out_channels, kernel_size, stride, padding, dilation,
False, _single(0), groups, bias, padding_mode)

def _conv_forward(self, input, weight):
def _conv_forward(self, input: Tensor, weight: Tensor, bias: Optional[Tensor]):
if self.padding_mode != 'zeros':
return F.conv1d(F.pad(input, self._reversed_padding_repeated_twice, mode=self.padding_mode),
weight, self.bias, self.stride,
weight, bias, self.stride,
_single(0), self.dilation, self.groups)
return F.conv1d(input, weight, self.bias, self.stride,
return F.conv1d(input, weight, bias, self.stride,
self.padding, self.dilation, self.groups)

def forward(self, input: Tensor) -> Tensor:
return self._conv_forward(input, self.weight)
return self._conv_forward(input, self.weight, self.bias)


class Conv2d(_ConvNd):
Expand Down Expand Up @@ -382,16 +382,16 @@ def __init__(
in_channels, out_channels, kernel_size, stride, padding, dilation,
False, _pair(0), groups, bias, padding_mode)

def _conv_forward(self, input, weight):
def _conv_forward(self, input: Tensor, weight: Tensor, bias: Optional[Tensor]):
if self.padding_mode != 'zeros':
return F.conv2d(F.pad(input, self._reversed_padding_repeated_twice, mode=self.padding_mode),
weight, self.bias, self.stride,
weight, bias, self.stride,
_pair(0), self.dilation, self.groups)
return F.conv2d(input, weight, self.bias, self.stride,
return F.conv2d(input, weight, bias, self.stride,
self.padding, self.dilation, self.groups)

def forward(self, input: Tensor) -> Tensor:
return self._conv_forward(input, self.weight)
return self._conv_forward(input, self.weight, self.bias)

class Conv3d(_ConvNd):
__doc__ = r"""Applies a 3D convolution over an input signal composed of several input
Expand Down
2 changes: 1 addition & 1 deletion torch/nn/qat/modules/conv.py
View file
Open in desktop
Expand Up @@ -29,7 +29,7 @@ def __init__(self, in_channels, out_channels, kernel_size, stride=1,
self.weight_fake_quant = qconfig.weight()

def forward(self, input):
return self._conv_forward(input, self.weight_fake_quant(self.weight))
return self._conv_forward(input, self.weight_fake_quant(self.weight), self.bias)

@classmethod
def from_float(cls, mod):
Expand Down