[quantization] how to quantize model which include not support to quantize layer

Hi, I have a model which include prelu layer, not support to quantize in current pytorch version, how to quantize this model for x86 CPU now? I try to define this model with the following format:

self.convbn1 = QuantizableConvBNBlock(xxx) (has defined)
self.prelu = nn.PReLU()
self.convbn2 = QuantizableConvBNBlock(xxx)        
self.quant = torch.quantization.QuantStub()
self.dequant = torch.quantization.DeQuantStub()
        
def forward(self, x):
    x = self.quant(x)
    x = self.convbn1(x) 
    x = self.dequant(x)
    x = self.prelu(x)
    x = self.quant(x)
    x = self.convbn2(x)
    ...

but I found after perform the quantization-aware training following tutorial, eval result is very terrible, what is the reason and how to solve it ?
Thanks

you’ll need to use different quant stub module for each usage because it is recording tensor statistics at that point.
e.g.

x = self.quant1(x)
...
x = self.dequant(x)
...
x = self.quant2(x)
...
x = self.dequant(x)