Quantization Aware Training (QAT) with Custom Bitwidth below INT8 using FakeQuantize

username: andrea.migliorati
cooked: <p>Hi everyone, I’m trying to implement QAT as reported in this tutorial <a href="https://pytorch.org/docs/stable/quantization.html#quantization-aware-training-for-static-quantization" class="inline-onebox" rel="noopener nofollow ugc">Quantization — PyTorch 1.12 documentation</a>. I’m working with a ResNet18 implementation I found online with the CIFAR10 dataset. I can make the QAT fine-tuning work easily but only as long as I use the standard “<strong>fbgemm</strong>” Qconfig (8 bits QAT). If I try to go below 8 bits by using a custom <strong>FakeQuantize</strong> Qconfig, the QAT never converges (loss stuck at the same value every fine-tuning epoch, eval accuracy stuck at 10%). I tried as dummy check to use my custom Qconfig with B = 8, and QAT with <strong>FakeQuantize</strong> works just fine so the problem is only for B &lt; 8.</p>
<p>This is how I’m doing QAT. What am I doing wrong? I will attach the ResNet18 implementation in the comments, I’m not quite sure where exactly to put the <strong>quant</strong> and <strong>dequant</strong> stubs.</p>
<pre><code># CREATE FP32 MODEL
model = resnet18(num_classes=10)
train_loader, test_loader = prepare_dataloader(num_workers=8, train_batch_size=200, 
eval_batch_size=100)

# LOAD A PRETRAINED FP32 MODEL
model = load_model(model=model, model_filepath=model_filepath, device=cuda_device)
model.train()

# CUSTOM QCONFIG, B BITS

B = 7  # 6, 5, 4, 3, 2

act = FakeQuantize.with_args(observer=MovingAverageMinMaxObserver, quant_min=0, 
quant_max=int(2 ** B - 1), dtype=torch.quint8, qscheme=torch.per_tensor_affine, 
reduce_range=False)

weights = FakeQuantize.with_args(observer=MovingAveragePerChannelMinMaxObserver, 
quant_min=int(-(2 ** B) / 2), quant_max=int((2 ** B) / 2 - 1), dtype=torch.qint8, 
qscheme=torch.per_channel_symmetric, reduce_range=False)

model.qconfig = QConfig(activation=act, weight=weights)

# MODEL FUSION
fused_model = torch.quantization.fuse_modules(model, [["conv1", "bn1", "relu"]])
for module_name, module in model.named_children():
    if "layer" in module_name:
        for basic_block_name, basic_block in module.named_children():
            torch.quantization.fuse_modules(basic_block, [["conv1", "bn1", "relu1"], ["conv2", "bn2"]])
            for sub_block_name, sub_block in basic_block.named_children():
                if sub_block_name == "downsample":
                    torch.quantization.fuse_modules(sub_block, [["0", "1"]], inplace=True)

# QAT PREPARATIONS ON THE FUSED
model_prepared = torch.quantization.prepare_qat(fused_model)

# CALIBRATION
calibrate_model(model=model_prepared, loader=train_loader, device=cuda_device)

# QAT TRAINING OF THE PREPARED MODEL
train_model(model=model_prepared, train_loader=train_loader, test_loader=test_loader,     device=cuda_device, learning_rate=1e-4, num_epochs=100)

# CONVERT THE QAT FINE-TUNED MODEL
model_prepared.eval()
quantized_model = torch.quantization.convert(model_prepared)

# EVALUATION ACCURACY
fp32_eval_acc = evaluate_model(model=model, test_loader=test_loader)
qat_eval_acc = evaluate_model(model=quantized_model, test_loader=test_loader)
</code></pre>

username: andrea.migliorati
cooked: <p><strong>And here’s my ResNet18 implementation:</strong></p>
<pre><code>def conv3x3(in_planes: int, out_planes: int, stride: int = 1, groups: int = 1, dilation: int = 1) -&gt; nn.Conv2d:
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:
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")
    self.conv1 = conv3x3(inplanes, planes, stride)
    self.bn1 = norm_layer(planes)
    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()
    self.quant = torch.quantization.QuantStub()
    self.deqnt = torch.quantization.DeQuantStub()
    self.relu2 = nn.ReLU(inplace=True)

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

    out = self.skip_add.add(identity, out)
    out = self.relu2(out)
    out = self.deqnt(out)
    return out


class Bottleneck(nn.Module):

    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
        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.quant = torch.quantization.QuantStub()
        self.deqnt = torch.quantization.DeQuantStub()
        self.relu2 = nn.ReLU(inplace=True)

    def forward(self, x: Tensor) -&gt; Tensor:
        identity = x
        x = self.quant(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 = self.skip_add.add(identity, out)
        out = self.relu2(out)
        out = self.deqnt(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:
            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)

        self.quant = torch.quantization.QuantStub()
        self.deqnt = torch.quantization.DeQuantStub()

        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)

        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:

        x = self.quant(x)
        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)
        x = self.deqnt(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:
    return _resnet('resnet18', BasicBlock, [2, 2, 2, 2], pretrained, progress, **kwargs)
</code></pre>

username: jerryzh168
cooked: <p>can you try initialize a quantizable resnet18 from <a href="https://github.com/pytorch/vision/blob/main/torchvision/models/quantization/resnet.py" class="inline-onebox" rel="noopener nofollow ugc">vision/resnet.py at main · pytorch/vision · GitHub</a>, it already did all the modifications needed for resnet18</p>
<p>here is some reference script for qat training as well: <a href="https://github.com/pytorch/vision/blob/main/references/classification/train_quantization.py" class="inline-onebox" rel="noopener nofollow ugc">vision/train_quantization.py at main · pytorch/vision · GitHub</a></p>