Mixed-Precision Quantization~(MQ) can achieve a competitive accuracy-complexity trade-off for models. Conventional training-based search methods require time-consuming candidate training to search optimized per-layer bit-width configurations in MQ. Recently, some training-free approaches have presented various MQ proxies and significantly improve search efficiency. However, the correlation between these proxies and quantization accuracy is poorly understood. To address the gap, we first build the MQ-Bench-101, which involves different bit configurations and quantization results. Then, we observe that the existing training-free proxies perform weak correlations on the MQ-Bench-101. To efficiently seek superior proxies, we develop an automatic search of proxies framework for MQ via evolving algorithms. In particular, we devise an elaborate search space involving the existing proxies and perform an evolution search to discover the best correlated MQ proxy. We proposed a diversity-prompting selection strategy and compatibility screening protocol to avoid premature convergence and improve search efficiency. In this way, our Evolving proxies for Mixed-precision Quantization~(EMQ) framework allows the auto-generation of proxies without heavy tuning and expert knowledge. Extensive experiments on ImageNet with various ResNet and MobileNet families demonstrate that our EMQ obtains superior performance than state-of-the-art mixed-precision methods at a significantly reduced cost. The code will be released.

翻译：混合精度量化能够为模型实现良好的精度-复杂度权衡。传统基于训练的搜索方法需要耗时训练候选模型以搜索优化后的逐层位宽配置。近年来，部分无训练方法提出了多种混合精度量化代理，显著提升了搜索效率。然而，这些代理与量化精度之间的相关性仍缺乏深入理解。为填补这一空白，我们首先构建了包含不同位配置与量化结果的MQ-Bench-101基准。随后发现现有无训练代理在该基准上呈现弱相关性。为高效寻找更优代理，我们开发了基于进化算法的代理自动搜索框架。具体而言，我们设计了包含现有代理的精细化搜索空间，通过进化搜索发现相关性最强的混合精度量化代理。同时提出多样性引导选择策略与兼容性筛选协议，以避免过早收敛并提升搜索效率。由此，我们提出的EMQ框架无需繁复调参和专家知识即可自动生成代理。在ImageNet上针对多种ResNet和MobileNet系列模型的广泛实验表明，EMQ能以显著更低的成本获得超越现有最优混合精度方法的性能。代码将开源。