I can talk about how to place quantstub/dequantstub in general

QuantStub should be placed at the point when you want to quantize a floating point Tensor to a quantized Tensor, the module following QuantStub is also expected to be quantized (to int8 quantized module)
DeQuantStub should be placed at the point when you want to dequantize a int8 tensor back to a floating point Tensor.
e.g.
class M(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.conv = torch.nn.Conv2d(3, 3, 3)
        self.quant = QuantStub
        self.dequant = DeQuantStub()

    def forward(self, x):
        # original input assumed to be fp32
        x = self.quant(x)
        # after quant, x is quantized to int8 tensor
        x = self.conv(x)
        # we also need to quantize conv module to be a int8 quantized conv module
        # which takes int8 Tensor as input and outputs a int8 quantized Tensor
        x = self.dequant(x)
        # dequant would dequantize a int8 quantized Tensor back to a fp32 Tensor
        return x

Please post the exact model if you need more specific help on the model.

Can you include the actual model implementation? in the meantime, you can find all supported fusions here: pytorch/fuser_method_mappings.py at master · pytorch/pytorch · GitHub