PTMQ: Post-training Multi-Bit Quantization of Neural Networks

The ability of model quantization with arbitrary bit-width to dynamically meet diverse bit-width requirements during runtime has attracted significant attention. Recent research has focused on optimizing large-scale training methods to achieve robust bit-width adaptation, which is a time-consuming process requiring hundreds of GPU hours. Furthermore, converting bit-widths requires recalculating statistical parameters of the norm layers, thereby impeding real-time switching of the bit-width. To overcome these challenges, we propose an efficient Post-Training Multi-bit Quantization (PTMQ) scheme that requires only a small amount of calibration data to perform block-wise reconstruction of multi-bit quantization errors. It eliminates the influence of statistical parameters by fusing norm layers, and supports real-time switching bit-widths in uniform quantization and mixed-precision quantization. To improve quantization accuracy and robustness, we propose a Multi-bit Feature Mixer technique (MFM) for fusing features of different bit-widths to enhance robustness across varying bit-widths. Moreover, we introduced the Group-wise Distillation Loss (GD-Loss) to enhance the correlation between different bit-width groups and further improve the overall performance of PTMQ. Extensive experiments demonstrate that PTMQ achieves comparable performance to existing state-of-the-art post-training quantization methods, while optimizing it speeds up by 100$\times$ compared to recent multi-bit quantization works. Code can be available at https://github.com/xuke225/PTMQ.