What are the different types of quantization in PyTorch?

What are the different types of quantization in PyTorch?

In PyTorch, there are mainly three types of quantization: dynamic quantization, static quantization, and quantization-aware training.

Can you explain dynamic quantization?

Dynamic quantization applies quantization during inference, converting weights to lower precision while keeping activations in float.

What about static quantization?

Static quantization involves calibrating the model with representative data to determine the scale and zero-point for weights and activations.

And what is quantization-aware training?

Quantization-aware training simulates quantization during training, allowing the model to learn to minimize the accuracy loss due to quantization.