Can this be represented using the Dequantize-Add-Quantize pattern?

No. Dequantizing would turn them back into floats. So you'd loose both the memory savings and performance improvements of integer math on the operation. You'd also have to recalculate scales to requantize which would add time. Also using the QLinear* Ops are for models that were quantized at training time. So dequantizing and requantizing would incur accuracy hits hat would harm the point of the "quantization aware" training of the the original model.

Also from what I've seen, when you have QLinear* Ops you start with a QuantizeLinear. Do a series of QLinear* Ops on the values and then afterward there's a DequantizeLinear at the end. Everything between the QuantizeLinear and DequantizeLinear should stay quantized and use scales and offsets set at training time.

Just as a background explanation: there has been a shift towards using the pattern Justin describes: at the model level, an op on quantized tensor(s) is expressed as "Dequantize => op => Quantize" first. Then, a backend can rewrite this pattern into a "CustomQuantizedOp" if it has support for doing so.

The reason was to avoid introducing QLinearX for many different X ops (like Add, Mul, Sub, Div, Relu, etc.). Which would be very disruptive. However, if the industry converges on ops that are worth explicitly supporting in quantized form, they may be worth adding at some point. I am not sure we are there yet. But opinions welcome.