Zafar:

Currently, the ConvTranspose is only supported using the QNNPACK. The FBGEMM version is planned, but there is no specific date specified for it. Meanwhile, you have two options for the eager mode: replace the ConvTranspose: 1) Replace the instances of the ConvTranspose with dequant->ConvTranspose->quant construct 2) Set the torch.backends.quantized.engine = 'qnnpack' before running your model. You also might need to set the qconfig = torch.quantization.get_default_qconfig('qnnpack') or qconfig = torch.quantization.get_default_qat_qconfig('qnnpack')


hi @Zafar: I am running into this error when trying to use a ConvTranspose2d with a FBGEMM backend. I am trying to implement workaround (1) that you suggested, i.e. to wrap the ConvTranspose2d with dequant and quant steps, but am struggling to get it right. That is, even if I run those steps, the error (‘FBGEMM doesn’t support transpose packing yet!’) appears. Do you know of an example of how to implement such wrapping that would allow quantization to go through? Thank you!
What I did was to create QuantStub and DeQuantStub instances during initialization, and then during the forward() did
        x = self.dequant(x)
        x = self.transpose_conv2d(x)
        x = self.quant(x)

… but this still gives the same error.