Quantization Aware Training (QAT)

The process of converting continuous to discrete values (Quantization) and vice-versa (Dequantization), requires scale and zeroPt (zero-point) parameters to be set. There are two quantization methods based on how these two parameters are calculated:

  1. Post Training Quantization (PTQ)

    Post Training Quantization computes scale after network has been trained. A representative dataset is used to capture the distribution of activations for each activation tensor, then this distribution data is used to compute the scale value for each tensor. Each weight's distribution is used to compute weight scale.

    TensorRT provides a workflow for PTQ, called calibration.

    flowchart LR id1(Calibration data) --> id2(Pre-trained model) --> id3(Capture layer distribution) --> id4(Compute 'scale') --> id5(Quantize model)
  2. Quantization Aware Training (QAT)

    Quantization Aware Training aims at computing scale factors during training. Once the network is fully trained, Quantize (Q) and Dequantize (DQ) nodes are inserted into the graph following a specific set of rules. The network is then further trained for few epochs in a process called Fine-Tuning. Q/DQ nodes simulate quantization loss and add it to the training loss during fine-tuning, making the network more resilient to quantization. In other words, QAT is able to better preserve accuracy when compared to PTQ.

    flowchart LR id1(Pre-trained model) --> id2(Add Q/DQ nodes) --> id3(Finetune model) --> id4(Store 'scale') --> id5(Quantize model)


This toolkit supports only QAT as a quantization method. Note that we follow the quantization algorithm implemented by TensorRT™ when inserting Q/DQ nodes in a model. This leads to a quantized network with optimal layer fusion during the TensorRT™ engine building step.


Since TensorRT™ only supports symmetric quantization, we assume zeroPt = 0.