Auto-cast and pytorch 2 export quantization

Jorge_Lamperez: <p>Hi, I am following this tutorial, <a href="https://pytorch.org/tutorials/prototype/pt2e_quant_qat.html" class="inline-onebox" rel="noopener nofollow ugc">(prototype) PyTorch 2 Export Quantization-Aware Training (QAT) — PyTorch Tutorials 2.5.0+cu124 documentation</a> for doing model quantization.</p>
<p>I have a module that uses autocast in the forward method,</p>
<pre><code class="lang-auto">    def forward(self, x):
        with torch.cuda.amp.autocast(self.fp16):
            x = self.conv1(x)
            x = self.bn1(x)
            x = self.prelu(x)
            x = self.layer1(x)
            x = self.layer2(x)
            x = self.layer3(x)
            x = self.layer4(x)
            x = self.bn2(x)
            x = torch.flatten(x, 1)
            x = self.dropout(x)
            x = self.fc(x.float() if self.fp16 else x)
            x = self.features(x)
        return x
</code></pre>
<p>When I try to export it with</p>
<pre><code class="lang-auto">m = torch.export.export_for_training(model, x).module()
</code></pre>
<p>I have got,</p>
<pre><code class="lang-auto">  File "/usr/local/lib/python3.10/dist-packages/torch/_export/verifier.py", line 155, in check
    self._check_graph_module(ep.graph_module)
  File "/usr/local/lib/python3.10/dist-packages/torch/_export/verifier.py", line 223, in _check_graph_module
    _check_val(node)
  File "/usr/local/lib/python3.10/dist-packages/torch/_export/verifier.py", line 64, in _check_val
    raise SpecViolationError(f"Node.meta {node.name} is missing val field.")
torch._export.verifier.SpecViolationError: Node.meta _enter_autocast is missing val field.

</code></pre>
<p>Removing the autocast for the forward method <code>torch.export.export</code>_for_training works well.</p>
<p>My question is how can I use mixed-precision, auto-cast, when doing quantization?</p>
<p>Do I need to export for training, get a model with aten operations and then do auto-cast before training?</p>
<p>Thank you!</p>

HDCharles: <p>this error doesn’t look related to quantization, it looks like a bug with autocast + export. Does the code work without quantization?</p>

Jorge_Lamperez: <p>This worked for Pytorch 1.13 without quantization. I haven’t tried Pytorch 2.5, but I understand that there should be no problems.</p>

HDCharles: <p>can you confirm everything works on 2.5:</p>
<ol>
<li>without autocast with quantization</li>
<li>without quantization, with autocast</li>
</ol>
<p>if so, if you give a repro i can look into it</p>

Jorge_Lamperez: <p>Hi <a class="mention" href="/u/hdcharles">@HDCharles</a>,</p>
<p>I appreciate your help,</p>
<p>I have been able to train using Pytorch 2.5. So I can confirm  <code>2. without quantization, with autocast</code>.</p>
<p>The problem is with autocast and quantization,</p>
<p>Here the code you can use to replicate the problem,</p>
<pre><code class="lang-auto">import torch
from torch import nn
from torch.utils.checkpoint import checkpoint

__all__ = ['iresnet50']
using_ckpt = False


def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
    """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, out_planes, stride=1):
    """1x1 convolution"""
    return nn.Conv2d(in_planes,
                     out_planes,
                     kernel_size=1,
                     stride=stride,
                     bias=False)


class IBasicBlock(nn.Module):
    expansion = 1

    def __init__(self, inplanes, planes, stride=1, downsample=None,
                 groups=1, base_width=64, dilation=1):
        super(IBasicBlock, self).__init__()
        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.bn1 = nn.BatchNorm2d(inplanes, eps=1e-05,)
        self.conv1 = conv3x3(inplanes, planes)
        self.bn2 = nn.BatchNorm2d(planes, eps=1e-05,)
        self.prelu = nn.PReLU(planes)
        self.conv2 = conv3x3(planes, planes, stride)
        self.bn3 = nn.BatchNorm2d(planes, eps=1e-05,)
        self.downsample = downsample
        self.stride = stride

    def forward_impl(self, x):
        identity = x
        out = self.bn1(x)
        out = self.conv1(out)
        out = self.bn2(out)
        out = self.prelu(out)
        out = self.conv2(out)
        out = self.bn3(out)

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

        return out

    def forward(self, x):
        if self.training and using_ckpt:
            return checkpoint(self.forward_impl, x)
        else:
            return self.forward_impl(x)


class IResNet(nn.Module):
    fc_scale = 7 * 7

    def __init__(self,
                 block,
                 layers,
                 dropout=0,
                 num_features=512,
                 zero_init_residual=False,
                 groups=1,
                 width_per_group=64,
                 replace_stride_with_dilation=None,
                 fp16=False):
        super(IResNet, self).__init__()
        self.extra_gflops = 0.0
        self.fp16 = fp16
        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=3,
                               stride=1, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(self.inplanes, eps=1e-05)
        self.prelu = nn.PReLU(self.inplanes)
        self.layer1 = self._make_layer(block, 64, layers[0], stride=2)
        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.bn2 = nn.BatchNorm2d(512 * block.expansion, eps=1e-05,)
        self.dropout = nn.Dropout(p=dropout, inplace=True)
        self.fc = nn.Linear(512 * block.expansion * self.fc_scale, num_features)
        self.features = nn.BatchNorm1d(num_features, eps=1e-05)
        nn.init.constant_(self.features.weight, 1.0)
        self.features.weight.requires_grad = False

        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.normal_(m.weight, 0, 0.1)
            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, IBasicBlock):
                    nn.init.constant_(m.bn2.weight, 0)

    def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
        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),
                nn.BatchNorm2d(planes * block.expansion, eps=1e-05, ),
            )
        layers = []
        layers.append(
            block(self.inplanes, planes, stride, downsample, self.groups,
                  self.base_width, previous_dilation))
        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))

        return nn.Sequential(*layers)

    def forward(self, x):
        # with torch.cuda.amp.autocast(self.fp16):
        with torch.amp.autocast(
            'cuda', dtype=torch.float16 if self.fp16 else torch.float32):
            x = self.conv1(x)
            x = self.bn1(x)
            x = self.prelu(x)
            x = self.layer1(x)
            x = self.layer2(x)
            x = self.layer3(x)
            x = self.layer4(x)
            x = self.bn2(x)
            x = torch.flatten(x, 1)
            x = self.dropout(x)
        x = self.fc(x.float() if self.fp16 else x)
        x = self.features(x)
        return x


def _iresnet(arch, block, layers, pretrained, progress, **kwargs):
    model = IResNet(block, layers, **kwargs)
    if pretrained:
        raise ValueError()
    return model


def iresnet50(pretrained=False, progress=True, **kwargs):
    return _iresnet('iresnet50', IBasicBlock, [3, 4, 14, 3], pretrained,
                    progress, **kwargs)


if __name__ == '__main__':
    backbone = iresnet50()
    # print(backbone)
    x = (torch.randn(64, 3, 112, 112),)
    m = torch.export.export_for_training(backbone, x).module()
</code></pre>
<pre><code class="lang-auto">python3 iresnet.py
</code></pre>
<p>If you want a Dockerfile to replicate the environment,</p>
<pre><code class="lang-auto">FROM nvidia/cuda:12.1.0-devel-ubuntu22.04

RUN apt-get update &amp;&amp; apt-get install -y python3-pip python3
RUN pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121

RUN useradd -ms /bin/bash -u 1000 python-dev &amp;&amp; \
    echo "python-dev ALL=(ALL:ALL) NOPASSWD:ALL" &gt;&gt; /etc/sudoers
# Default configuration
WORKDIR /home/python-dev
USER 1000
</code></pre>
<p>Thanks!</p>

jerryzh168: <p>yeah we haven’t tested how pt2e quant compose with amp autocast before, in this case, looks like export does not support it, I can route this to export team</p>

Jorge_Lamperez: <p>Yes, that sounds great! Thanks for taking care of this, and greetings!</p>

jerryzh168: <aside class="quote no-group" data-username="Jorge_Lamperez" data-post="5" data-topic="212660">
<div class="title">
<div class="quote-controls"></div>
<img loading="lazy" alt="" width="24" height="24" src="https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/jorge_lamperez/48/73831_2.png" class="avatar"> Jorge_Lamperez:</div>
<blockquote>
<pre><code class="lang-auto">import torch
from torch import nn
from torch.utils.checkpoint import checkpoint

__all__ = ['iresnet50']
using_ckpt = False


def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
    """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, out_planes, stride=1):
    """1x1 convolution"""
    return nn.Conv2d(in_planes,
                     out_planes,
                     kernel_size=1,
                     stride=stride,
                     bias=False)


class IBasicBlock(nn.Module):
    expansion = 1

    def __init__(self, inplanes, planes, stride=1, downsample=None,
                 groups=1, base_width=64, dilation=1):
        super(IBasicBlock, self).__init__()
        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.bn1 = nn.BatchNorm2d(inplanes, eps=1e-05,)
        self.conv1 = conv3x3(inplanes, planes)
        self.bn2 = nn.BatchNorm2d(planes, eps=1e-05,)
        self.prelu = nn.PReLU(planes)
        self.conv2 = conv3x3(planes, planes, stride)
        self.bn3 = nn.BatchNorm2d(planes, eps=1e-05,)
        self.downsample = downsample
        self.stride = stride

    def forward_impl(self, x):
        identity = x
        out = self.bn1(x)
        out = self.conv1(out)
        out = self.bn2(out)
        out = self.prelu(out)
        out = self.conv2(out)
        out = self.bn3(out)

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

        return out

    def forward(self, x):
        if self.training and using_ckpt:
            return checkpoint(self.forward_impl, x)
        else:
            return self.forward_impl(x)


class IResNet(nn.Module):
    fc_scale = 7 * 7

    def __init__(self,
                 block,
                 layers,
                 dropout=0,
                 num_features=512,
                 zero_init_residual=False,
                 groups=1,
                 width_per_group=64,
                 replace_stride_with_dilation=None,
                 fp16=False):
        super(IResNet, self).__init__()
        self.extra_gflops = 0.0
        self.fp16 = fp16
        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=3,
                               stride=1, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(self.inplanes, eps=1e-05)
        self.prelu = nn.PReLU(self.inplanes)
        self.layer1 = self._make_layer(block, 64, layers[0], stride=2)
        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.bn2 = nn.BatchNorm2d(512 * block.expansion, eps=1e-05,)
        self.dropout = nn.Dropout(p=dropout, inplace=True)
        self.fc = nn.Linear(512 * block.expansion * self.fc_scale, num_features)
        self.features = nn.BatchNorm1d(num_features, eps=1e-05)
        nn.init.constant_(self.features.weight, 1.0)
        self.features.weight.requires_grad = False

        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.normal_(m.weight, 0, 0.1)
            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, IBasicBlock):
                    nn.init.constant_(m.bn2.weight, 0)

    def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
        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),
                nn.BatchNorm2d(planes * block.expansion, eps=1e-05, ),
            )
        layers = []
        layers.append(
            block(self.inplanes, planes, stride, downsample, self.groups,
                  self.base_width, previous_dilation))
        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))

        return nn.Sequential(*layers)

    def forward(self, x):
        # with torch.cuda.amp.autocast(self.fp16):
        with torch.amp.autocast(
            'cuda', dtype=torch.float16 if self.fp16 else torch.float32):
            x = self.conv1(x)
            x = self.bn1(x)
            x = self.prelu(x)
            x = self.layer1(x)
            x = self.layer2(x)
            x = self.layer3(x)
            x = self.layer4(x)
            x = self.bn2(x)
            x = torch.flatten(x, 1)
            x = self.dropout(x)
        x = self.fc(x.float() if self.fp16 else x)
        x = self.features(x)
        return x


def _iresnet(arch, block, layers, pretrained, progress, **kwargs):
    model = IResNet(block, layers, **kwargs)
    if pretrained:
        raise ValueError()
    return model


def iresnet50(pretrained=False, progress=True, **kwargs):
    return _iresnet('iresnet50', IBasicBlock, [3, 4, 14, 3], pretrained,
                    progress, **kwargs)


if __name__ == '__main__':
    backbone = iresnet50()
    # print(backbone)
    x = (torch.randn(64, 3, 112, 112),)
    m = torch.export.export_for_training(backbone, x).module()
</code></pre>
</blockquote>
</aside>
<p>Hi Jorge,</p>
<p>I can’t repro the issue with pytorch nightly actually, can you try again with pytorch nightly?</p>
<pre><code class="lang-auto">pip3 install --pre torch --index-url https://download.pytorch.org/whl/nightly/cu121
</code></pre>

Jorge_Lamperez: <p>Hi <a class="mention" href="/u/jerryzh168">@jerryzh168</a> ,</p>
<p>I have updated the docker image to have cudnn and as you said with pytorch nightly the issue can’t be reproduced and is working for me.</p>
<p>Thank you very much!</p>