Markov-PQ: Joint Pruning-Quantization via Learnable Markov Chain

Various network compression methods, such as pruning and quantization, have been proposed to synergistically reduce resource requirements. However, existing joint compression works are based on black-box optimization and do not interpret the interaction mechanism between these two compression techniques, leading to a slow and unstable convergence of compression strategy. To address this issue, we present Markov-PQ, the first interpretable pruning-quantization co-compression framework using a Markov Chain. In Markov-PQ, the joint strategy search is modeled as a Markov Chain and decoupled with Bayes Rule into pruning and quantization strategy searching. Specifically, the quantization state accounts for the co-compression state from the last time and is updated by a learnable transition probability matrix. To ensure differentiability, we design a forward-hard and backward-soft quantization. The pruning state is influenced not only by the last co-compression state but also by the concurrent quantization state. In addition, to perceive the current layer-wise bit sensitivity and alleviate the long-tail problem, a complexity-aware regularizer is devised to re-evaluate the filter importance. Extensive experiments demonstrate the superiority of Markov-PQ. For example, with an accuracy loss of only 0.33%, we can achieve a 56.12× acceleration for ResNet-18 on ImageNet2012.