Repository: pytorch
Issue: The special size tensor containing batches has a difference of a few tens of thousands in calculation results between CPU and GPU
Labels: needs reproduction,module: cuda,triaged

### 🐛 Describe the bug

You can modify the comments to switch and run to view the changes in the results！

```
import torch.nn as nn
import torch.nn.functional as F 
import torch
BN_MOMENTUM = 0.1

def conv3x3(in_planes, out_planes, stride=1):
    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False)

class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(BasicBlock, self).__init__()
        self.conv1 = conv3x3(inplanes, planes, stride)
        self.bn1 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = conv3x3(planes, planes)
        self.bn2 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM)

        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)
        if self.downsample is not None:
            residual = self.downsample(x)

        out += residual
        out = self.relu(out)

        return out


class ConvInGPUError(nn.Module):
    def __init__(self):
        super(ConvInGPUError, self).__init__()
        self.block = BasicBlock
        self.inplanes = 256
        self.layer = self._make_layer(self.block, 256, 2, stride=2)
    
    def _make_layer(self, block, planes, blocks, stride=1):
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                nn.Conv2d(self.inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(planes * block.expansion, momentum=BN_MOMENTUM),
            )

        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample))
        self.inplanes = planes * block.expansion
        for i in range(1, blocks):
            layers.append(block(self.inplanes, planes))

        return nn.Sequential(*layers)

    def forward(self, x):
        out = self.layer(x.clone())  # B = 2
        out_1 = self.layer(x.clone()[:1])  
        print(f'in forward max diff: torch.max(out - out_1): {torch.max(out[:1] - out_1[:1]):.20f}')
        print(f'in forward min diff: torch.min(out - out_1): {torch.min(out[:1] - out_1[:1]):.20f}')
        print(f'in forward mean diff: torch.mean(out - out_1): {torch.mean(out[:1] - out_1[:1]):.20f}')
        return x

if __name__ == "__main__":

    torch.backends.cudnn.deterministic = True
    torch.backends.cudnn.benchmark = False
    torch.use_deterministic_algorithms(True)
    torch.manual_seed(42)
    
    model = ConvInGPUError()
    model.eval()
    
    # you can compare the result of gpu and cpu by using the same input, but the result is different
    # gpu
    model.cuda()
    input_data = torch.normal(0, 1, size=(2, 256, 48, 96)).cuda()
    # cpu
    # input_data = torch.normal(0, 1, size=(2, 256, 48, 96))
    
    
    with torch.no_grad():
        output = model(input_data)
    print(output.shape)
    
    
    """
    # input_data = torch.randn(2, 256, 96, 192)
    # input_data = torch.normal(0,1,(2, 256, 48, 160))
    # input_data = torch.normal(0,1,(2, 256, 40, 160))
    """
```

### Versions

![image](https://github.com/user-attachments/assets/f9e87459-d86b-46c7-8ec3-307a9b89c987)

The results on GPU and CPU are inconsistent. 
1. The current batch input is 2, and the complete inference yields A. 
2. Slice first and then infer to obtain B. The difference obtained by subtracting A and B is not completely zero.

date:20241227

cc @ptrblck @msaroufim @eqy