QAT（Quantization Aware Training）量化感知训练（二）【详解】
1、QAT（Quantization Aware Training）的建议
Quantization Aware Training is based on Straight Through Estimator (STE) derivative approximation. It is some time known as “quantization aware training”. We don’t use the name because it doesn’t reflect the underneath assumption. If anything, it makes training being “unaware” of quantization because of the STE approximation.
After calibration is done, Quantization Aware Training is simply select a training schedule and continue training the calibrated model. Usually, it doesn’t need to fine tune very long. We usually use around 10% of the original training schedule, starting at 1% of the initial training learning rate, and a cosine annealing learning rate schedule that follows the decreasing half of a cosine period, down to 1% of the initial fine tuning learning rate (0.01% of the initial training learning rate).
Some recommendations:
Quantization Aware Training (Essentially a discrete numerical optimization problem) is not a solved problem mathematically. Based on our experience, here are some recommendations:
For STE approximation to work well, it is better to use small learning rate. Large learning rate is more likely to enlarge the variance introduced by STE approximation and destroy the trained network.
Do not change quantization representation (scale) during training, at least not too frequently. Changing scale every step, it is effectively like changing data format (e8m7, e5m10, e3m4, et.al) every step, which will easily affect convergence.