We currently only support conversion to ONNX for Caffe2 backend. This thread has additional context on what we currently support - ONNX export of quantized model
If you would like to add custom conversion logic to onnx operators for quantized pytorch ops you can follow the code in https://github.com/pytorch/pytorch/blob/master/torch/onnx/symbolic_caffe2.py which adds the mapping for the Caffe2 ops in ONNX.