I run your code and it prints 0, 1, 2, 3, 4, 5, 6, 7, 8, 9.
import torch.nn as nn
import torch.nn.functional as F

class Conv2d(nn.Conv2d):
    def __init__(self, in_channels, out_channels, kernel_size, stride=1,
             padding=0, dilation=1, groups=1, bias=True):
        super(Conv2d, self).__init__(in_channels, out_channels, kernel_size, stride,
             padding, dilation, groups, bias)

        self.count = 0

    def forward(self, x):
        weight = self.weight
        print(self.count)  # always prints 0
        self.count += 1
        return F.conv2d(x, weight, self.bias, self.stride,
                    self.padding, self.dilation, self.groups)
        
net = Conv2d(3, 3, 3)
for _ in range(10):
    x = torch.rand(3, 3, 3, 3)
    net(x)