RuntimeError: Unsupported qscheme: per_channel_affine

manudwd: <p>I’m following a tutorial on quantization aware training <a href="https://leimao.github.io/blog/PyTorch-Quantization-Aware-Training/" rel="noopener nofollow ugc">here</a> for a modified Resnet18 model which is here:</p>
<pre><code class="lang-auto">#!/usr/bin/env python
# coding: utf-8

# In[ ]:


# Modified from
# https://github.com/pytorch/vision/blob/release/0.8.0/torchvision/models/resnet.py

import torch
from torch import Tensor
import torch.nn as nn
from torch.hub import load_state_dict_from_url
from typing import Type, Any, Callable, Union, List, Optional


__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
           'resnet152', 'resnext50_32x4d', 'resnext101_32x8d',
           'wide_resnet50_2', 'wide_resnet101_2']


model_urls = {
    'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
    'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
    'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
    'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
    'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
    'resnext50_32x4d': 'https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth',
    'resnext101_32x8d': 'https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.pth',
    'wide_resnet50_2': 'https://download.pytorch.org/models/wide_resnet50_2-95faca4d.pth',
    'wide_resnet101_2': 'https://download.pytorch.org/models/wide_resnet101_2-32ee1156.pth',
}


def conv3x3(in_planes: int, out_planes: int, stride: int = 1, groups: int = 1, dilation: int = 1) -&gt; nn.Conv2d:
    """3x3 convolution with padding"""
    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
                     padding=dilation, groups=groups, bias=False, dilation=dilation)


def conv1x1(in_planes: int, out_planes: int, stride: int = 1) -&gt; nn.Conv2d:
    """1x1 convolution"""
    return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)


class BasicBlock(nn.Module):
    expansion: int = 1

    def __init__(
        self,
        inplanes: int,
        planes: int,
        stride: int = 1,
        downsample: Optional[nn.Module] = None,
        groups: int = 1,
        base_width: int = 64,
        dilation: int = 1,
        norm_layer: Optional[Callable[..., nn.Module]] = None
    ) -&gt; None:
        super(BasicBlock, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        if groups != 1 or base_width != 64:
            raise ValueError('BasicBlock only supports groups=1 and base_width=64')
        if dilation &gt; 1:
            raise NotImplementedError("Dilation &gt; 1 not supported in BasicBlock")
        # Both self.conv1 and self.downsample layers downsample the input when stride != 1
        self.conv1 = conv3x3(inplanes, planes, stride)
        self.bn1 = norm_layer(planes)
        # Rename relu to relu1
        self.relu1 = nn.ReLU(inplace=True)
        self.conv2 = conv3x3(planes, planes)
        self.bn2 = norm_layer(planes)
        self.downsample = downsample
        self.stride = stride
        self.skip_add = nn.quantized.FloatFunctional()
        # Remember to use two independent ReLU for layer fusion.
        self.relu2 = nn.ReLU(inplace=True)

    def forward(self, x: Tensor) -&gt; Tensor:
        identity = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu1(out)

        out = self.conv2(out)
        out = self.bn2(out)

        if self.downsample is not None:
            identity = self.downsample(x)
        
        # Use FloatFunctional for addition for quantization compatibility
        # out += identity
        out = self.skip_add.add(identity, out)
        out = self.relu2(out)

        return out


class Bottleneck(nn.Module):
    # Bottleneck in torchvision places the stride for downsampling at 3x3 convolution(self.conv2)
    # while original implementation places the stride at the first 1x1 convolution(self.conv1)
    # according to "Deep residual learning for image recognition"https://arxiv.org/abs/1512.03385.
    # This variant is also known as ResNet V1.5 and improves accuracy according to
    # https://ngc.nvidia.com/catalog/model-scripts/nvidia:resnet_50_v1_5_for_pytorch.

    expansion: int = 4

    def __init__(
        self,
        inplanes: int,
        planes: int,
        stride: int = 1,
        downsample: Optional[nn.Module] = None,
        groups: int = 1,
        base_width: int = 64,
        dilation: int = 1,
        norm_layer: Optional[Callable[..., nn.Module]] = None
    ) -&gt; None:
        super(Bottleneck, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        width = int(planes * (base_width / 64.)) * groups
        # Both self.conv2 and self.downsample layers downsample the input when stride != 1
        self.conv1 = conv1x1(inplanes, width)
        self.bn1 = norm_layer(width)
        self.conv2 = conv3x3(width, width, stride, groups, dilation)
        self.bn2 = norm_layer(width)
        self.conv3 = conv1x1(width, planes * self.expansion)
        self.bn3 = norm_layer(planes * self.expansion)
        self.relu1 = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride
        self.skip_add = nn.quantized.FloatFunctional()
        self.relu2 = nn.ReLU(inplace=True)

    def forward(self, x: Tensor) -&gt; Tensor:
        identity = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu1(out)

        out = self.conv2(out)
        out = self.bn2(out)
        out = self.relu(out)

        out = self.conv3(out)
        out = self.bn3(out)

        if self.downsample is not None:
            identity = self.downsample(x)

        # out += identity
        out = self.skip_add.add(identity, out)
        out = self.relu2(out)

        return out


class ResNet(nn.Module):

    def __init__(
        self,
        block: Type[Union[BasicBlock, Bottleneck]],
        layers: List[int],
        num_classes: int = 1000,
        zero_init_residual: bool = False,
        groups: int = 1,
        width_per_group: int = 64,
        replace_stride_with_dilation: Optional[List[bool]] = None,
        norm_layer: Optional[Callable[..., nn.Module]] = None
    ) -&gt; None:
        super(ResNet, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        self._norm_layer = norm_layer

        self.inplanes = 64
        self.dilation = 1
        if replace_stride_with_dilation is None:
            # each element in the tuple indicates if we should replace
            # the 2x2 stride with a dilated convolution instead
            replace_stride_with_dilation = [False, False, False]
        if len(replace_stride_with_dilation) != 3:
            raise ValueError("replace_stride_with_dilation should be None "
                             "or a 3-element tuple, got {}".format(replace_stride_with_dilation))
        self.groups = groups
        self.base_width = width_per_group
        self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3,
                               bias=False)
        self.bn1 = norm_layer(self.inplanes)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.layer1 = self._make_layer(block, 64, layers[0])
        self.layer2 = self._make_layer(block, 128, layers[1], stride=2,
                                       dilate=replace_stride_with_dilation[0])
        self.layer3 = self._make_layer(block, 256, layers[2], stride=2,
                                       dilate=replace_stride_with_dilation[1])
        self.layer4 = self._make_layer(block, 512, layers[3], stride=2,
                                       dilate=replace_stride_with_dilation[2])
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(512 * block.expansion, num_classes)

        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
            elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)

        # Zero-initialize the last BN in each residual branch,
        # so that the residual branch starts with zeros, and each residual block behaves like an identity.
        # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
        if zero_init_residual:
            for m in self.modules():
                if isinstance(m, Bottleneck):
                    nn.init.constant_(m.bn3.weight, 0)  # type: ignore[arg-type]
                elif isinstance(m, BasicBlock):
                    nn.init.constant_(m.bn2.weight, 0)  # type: ignore[arg-type]

    def _make_layer(self, block: Type[Union[BasicBlock, Bottleneck]], planes: int, blocks: int,
                    stride: int = 1, dilate: bool = False) -&gt; nn.Sequential:
        norm_layer = self._norm_layer
        downsample = None
        previous_dilation = self.dilation
        if dilate:
            self.dilation *= stride
            stride = 1
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                conv1x1(self.inplanes, planes * block.expansion, stride),
                norm_layer(planes * block.expansion),
            )

        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample, self.groups,
                            self.base_width, previous_dilation, norm_layer))
        self.inplanes = planes * block.expansion
        for _ in range(1, blocks):
            layers.append(block(self.inplanes, planes, groups=self.groups,
                                base_width=self.base_width, dilation=self.dilation,
                                norm_layer=norm_layer))

        return nn.Sequential(*layers)

    def _forward_impl(self, x: Tensor) -&gt; Tensor:
        # See note [TorchScript super()]
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)

        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)

        x = self.avgpool(x)
        x = torch.flatten(x, 1)
        x = self.fc(x)

        return x

    def forward(self, x: Tensor) -&gt; Tensor:
        return self._forward_impl(x)


def _resnet(
    arch: str,
    block: Type[Union[BasicBlock, Bottleneck]],
    layers: List[int],
    pretrained: bool,
    progress: bool,
    **kwargs: Any
) -&gt; ResNet:
    model = ResNet(block, layers, **kwargs)
    if pretrained:
        state_dict = load_state_dict_from_url(model_urls[arch],
                                              progress=progress)
        model.load_state_dict(state_dict)
    return model


def resnet18(pretrained: bool = False, progress: bool = True, **kwargs: Any) -&gt; ResNet:
    r"""ResNet-18 model from
    `"Deep Residual Learning for Image Recognition" &lt;https://arxiv.org/pdf/1512.03385.pdf&gt;`_.

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
        progress (bool): If True, displays a progress bar of the download to stderr
    """
    return _resnet('resnet18', BasicBlock, [2, 2, 2, 2], pretrained, progress,
                   **kwargs)


def resnet34(pretrained: bool = False, progress: bool = True, **kwargs: Any) -&gt; ResNet:
    r"""ResNet-34 model from
    `"Deep Residual Learning for Image Recognition" &lt;https://arxiv.org/pdf/1512.03385.pdf&gt;`_.

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
        progress (bool): If True, displays a progress bar of the download to stderr
    """
    return _resnet('resnet34', BasicBlock, [3, 4, 6, 3], pretrained, progress,
                   **kwargs)


def resnet50(pretrained: bool = False, progress: bool = True, **kwargs: Any) -&gt; ResNet:
    r"""ResNet-50 model from
    `"Deep Residual Learning for Image Recognition" &lt;https://arxiv.org/pdf/1512.03385.pdf&gt;`_.

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
        progress (bool): If True, displays a progress bar of the download to stderr
    """
    return _resnet('resnet50', Bottleneck, [3, 4, 6, 3], pretrained, progress,
                   **kwargs)


def resnet101(pretrained: bool = False, progress: bool = True, **kwargs: Any) -&gt; ResNet:
    r"""ResNet-101 model from
    `"Deep Residual Learning for Image Recognition" &lt;https://arxiv.org/pdf/1512.03385.pdf&gt;`_.

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
        progress (bool): If True, displays a progress bar of the download to stderr
    """
    return _resnet('resnet101', Bottleneck, [3, 4, 23, 3], pretrained, progress,
                   **kwargs)


def resnet152(pretrained: bool = False, progress: bool = True, **kwargs: Any) -&gt; ResNet:
    r"""ResNet-152 model from
    `"Deep Residual Learning for Image Recognition" &lt;https://arxiv.org/pdf/1512.03385.pdf&gt;`_.

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
        progress (bool): If True, displays a progress bar of the download to stderr
    """
    return _resnet('resnet152', Bottleneck, [3, 8, 36, 3], pretrained, progress,
                   **kwargs)


def resnext50_32x4d(pretrained: bool = False, progress: bool = True, **kwargs: Any) -&gt; ResNet:
    r"""ResNeXt-50 32x4d model from
    `"Aggregated Residual Transformation for Deep Neural Networks" &lt;https://arxiv.org/pdf/1611.05431.pdf&gt;`_.

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
        progress (bool): If True, displays a progress bar of the download to stderr
    """
    kwargs['groups'] = 32
    kwargs['width_per_group'] = 4
    return _resnet('resnext50_32x4d', Bottleneck, [3, 4, 6, 3],
                   pretrained, progress, **kwargs)


def resnext101_32x8d(pretrained: bool = False, progress: bool = True, **kwargs: Any) -&gt; ResNet:
    r"""ResNeXt-101 32x8d model from
    `"Aggregated Residual Transformation for Deep Neural Networks" &lt;https://arxiv.org/pdf/1611.05431.pdf&gt;`_.

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
        progress (bool): If True, displays a progress bar of the download to stderr
    """
    kwargs['groups'] = 32
    kwargs['width_per_group'] = 8
    return _resnet('resnext101_32x8d', Bottleneck, [3, 4, 23, 3],
                   pretrained, progress, **kwargs)


def wide_resnet50_2(pretrained: bool = False, progress: bool = True, **kwargs: Any) -&gt; ResNet:
    r"""Wide ResNet-50-2 model from
    `"Wide Residual Networks" &lt;https://arxiv.org/pdf/1605.07146.pdf&gt;`_.

    The model is the same as ResNet except for the bottleneck number of channels
    which is twice larger in every block. The number of channels in outer 1x1
    convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048
    channels, and in Wide ResNet-50-2 has 2048-1024-2048.

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
        progress (bool): If True, displays a progress bar of the download to stderr
    """
    kwargs['width_per_group'] = 64 * 2
    return _resnet('wide_resnet50_2', Bottleneck, [3, 4, 6, 3],
                   pretrained, progress, **kwargs)


def wide_resnet101_2(pretrained: bool = False, progress: bool = True, **kwargs: Any) -&gt; ResNet:
    r"""Wide ResNet-101-2 model from
    `"Wide Residual Networks" &lt;https://arxiv.org/pdf/1605.07146.pdf&gt;`_.

    The model is the same as ResNet except for the bottleneck number of channels
    which is twice larger in every block. The number of channels in outer 1x1
    convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048
    channels, and in Wide ResNet-50-2 has 2048-1024-2048.

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
        progress (bool): If True, displays a progress bar of the download to stderr
    """
    kwargs['width_per_group'] = 64 * 2
    return _resnet('wide_resnet101_2', Bottleneck, [3, 4, 23, 3],
                   pretrained, progress, **kwargs)
</code></pre>
<p>I am training it on a custom data using similar dataloaders as in the tutorial. Training seems to go fine in the normal phase however following the tutorial when fused models are applied, the quantized models seems to fail after giving out</p>
<pre><code class="lang-auto">QConfig(activation=functools.partial(&lt;class 'torch.ao.quantization.observer.HistogramObserver'&gt;, reduce_range=True){}, weight=functools.partial(&lt;class 'torch.ao.quantization.observer.PerChannelMinMaxObserver'&gt;, dtype=torch.qint8, qscheme=torch.per_channel_symmetric){})
Training QAT Model...

/usr/local/lib/python3.7/dist-packages/torch/ao/quantization/observer.py:178: UserWarning: Please use quant_min and quant_max to specify the range for observers.                     reduce_range will be deprecated in a future release of PyTorch.
  reduce_range will be deprecated in a future release of PyTorch."

Epoch: -1 Eval Loss: 1.123 Eval Acc: 0.675
Epoch: 000 Train Loss: 0.445 Train Acc: 0.851 Eval Loss: 1.160 Eval Acc: 0.650
Epoch: 001 Train Loss: 0.475 Train Acc: 0.834 Eval Loss: 1.216 Eval Acc: 0.624
Epoch: 002 Train Loss: 0.491 Train Acc: 0.820 Eval Loss: 1.422 Eval Acc: 0.619
Epoch: 003 Train Loss: 0.545 Train Acc: 0.799 Eval Loss: 1.145 Eval Acc: 0.635
Epoch: 004 Train Loss: 0.554 Train Acc: 0.808 Eval Loss: 1.239 Eval Acc: 0.655
Epoch: 005 Train Loss: 0.471 Train Acc: 0.838 Eval Loss: 1.223 Eval Acc: 0.629
Epoch: 006 Train Loss: 0.513 Train Acc: 0.811 Eval Loss: 1.289 Eval Acc: 0.624
Epoch: 007 Train Loss: 0.496 Train Acc: 0.819 Eval Loss: 1.210 Eval Acc: 0.640
Epoch: 008 Train Loss: 0.510 Train Acc: 0.818 Eval Loss: 1.198 Eval Acc: 0.665
Epoch: 009 Train Loss: 0.514 Train Acc: 0.818 Eval Loss: 1.170 Eval Acc: 0.660

---------------------------------------------------------------------------

RuntimeError                              Traceback (most recent call last)

&lt;ipython-input-5-16fdb7751f14&gt; in &lt;module&gt;()
    121 if __name__ == "__main__":
    122 
--&gt; 123     main()

10 frames

/usr/local/lib/python3.7/dist-packages/torch/_ops.py in __call__(self, *args, **kwargs)
    141         # We save the function ptr as the `op` attribute on
    142         # OpOverloadPacket to access it here.
--&gt; 143         return self._op(*args, **kwargs or {})
    144 
    145     # TODO: use this to make a __dir__

RuntimeError: Unsupported qscheme: per_channel_affine
</code></pre>
<p>I explored the source code for utils.py for quantization module which can be found <a href="https://github.com/pytorch/pytorch/blob/master/torch/ao/big%20/utils.py" rel="noopener nofollow ugc">here</a>:</p>
<pre><code class="lang-auto">def get_qparam_dict(observer_or_fake_quant):
    qscheme = observer_or_fake_quant.qscheme if hasattr(observer_or_fake_quant, "qscheme") else None
    dtype = observer_or_fake_quant.dtype
    qparams = {"qscheme": qscheme, "dtype": dtype}

    if not qscheme:
        return qparams

    if is_per_tensor(qscheme):
        qscheme = torch.per_tensor_affine
    elif is_per_channel(qscheme):
        # change symmetric to affine since we do not have symmetric
        # quantized Tensor
        if qscheme == torch.per_channel_symmetric:
            qscheme = torch.per_channel_affine
        qparams["axis"] = observer_or_fake_quant.ch_axis
    else:
        raise RuntimeError(f"Unrecognized qscheme: {qscheme}")
    # update qscheme, since we don't have symmetric quant qscheme
    # in quantized Tensor
    qparams["qscheme"] = qscheme

    scale, zero_point = observer_or_fake_quant.calculate_qparams()
    qparams["scale"] = scale
    qparams["zero_point"] = zero_point

    return qparams
</code></pre>
<p>Which doesnt seem to make sense since following the line</p>
<pre><code class="lang-auto">if qscheme == torch.per_channel_symmetric:
                qscheme = torch.per_channel_affine
            qparams["axis"] = observer_or_fake_quant.ch_axis
        else:
            raise RuntimeError(f"Unrecognized qscheme: {qscheme}")
</code></pre>
<p>The qscheme is passed as torch.per_channel_affine which is the qscheme it seems to raise the error again in the else clause, looking for any possible ideas on working around this.</p>

HDCharles: <p>the issue likely has less to do with symmetric vs affine and more to do with the per_channel piece. Perhaps with a clearer repro I could say more. However in general that tutorial was made by someone unassociated with the quantization team and it may not be updated regularly.</p>
<p>Our internal tutorial that goes into qat can be found here: <a href="https://pytorch.org/tutorials/advanced/static_quantization_tutorial.html" class="inline-onebox" rel="noopener nofollow ugc">(beta) Static Quantization with Eager Mode in PyTorch — PyTorch Tutorials 1.12.0+cu102 documentation</a></p>