Mixed Precision Quantization (MPQ) has become an essential technique for optimizing neural network by determining the optimal bitwidth per layer. Existing MPQ methods, however, face a major hurdle: they require a computationally expensive search for quantization policies on large-scale datasets. To resolve this issue, we introduce a novel approach that first searches for quantization policies on small datasets and then generalizes them to large-scale datasets. This approach simplifies the process, eliminating the need for large-scale quantization fine-tuning and only necessitating model weight adjustment. Our method is characterized by three key techniques: sharpness-aware minimization for enhanced quantization generalization, implicit gradient direction alignment to handle gradient conflicts among different optimization objectives, and an adaptive perturbation radius to accelerate optimization. Both theoretical analysis and experimental results validate our approach. Using the CIFAR10 dataset (just 0.5\% the size of ImageNet training data) for MPQ policy search, we achieved equivalent accuracy on ImageNet with a significantly lower computational cost, while improving efficiency by up to 150% over the baselines.

本研究解决了现有混合精度量化（MPQ）方法在大规模数据集上需要昂贵计算搜索量化策略的问题。我们提出了一种新方法，首先在小数据集上搜索量化策略，然后将其推广到大规模数据集，从而简化了流程并减少了计算开销。实验表明，该方法在CIFAR10数据集上的量化策略搜索达到了与ImageNet相当的准确度，同时计算成本显著降低，效率提高了150%。

从损失景观中学习：通过自适应敏感度表面梯度对齐实现通用化混合精度量化