What is the sense of parameter qconfig_spec in quantize_dynamic

Hello all.

I have begun to learn about Quantization with “dynamic quantization” as a first try.
According to several tutorials like this or this, it catches my attention that torch.nn.Relu is used in this example from the official tutorials in the qconfig_spec argument of the function quantize_dynamic:

quantized_model = torch.quantization.quantize_dynamic(
model, {torch.nn.Linear}, dtype=torch.qint8

The tutorial says: “We specify that we want the torch.nn.Linear modules in our model to be quantized”. But to me, it is obvious that you want them to be converted to int8. You probably want everything you can convert to int8 converted.

With this already being said, I would like to know if it is necessary to specify the Linear module.

HI @Miguel_Campos

Are you talking about nn.Linear? I don’t see nn.Relu anywhere in the tutorial you linked. You need to specify the Linear.

Sometimes you have more than one type of module you want to quantize, for example in the case of LSTM + Linear, seen here: Dynamic Quantization — PyTorch Tutorials 2.0.1+cu117 documentation

Different layers of a network have a different effect on the accuracy so thats why you may want some quantized and some not.

Sorry my bad, I mean nn.Linear.

Ok I see, thanks for your answer