Layerwise Error Attribution for Fast and Robust Mixed-Precision Post-Training Quantization

2026-10-07

AI Summary

This study addresses the combinatorial complexity of bit-width allocation and the sensitivity to calibration data in mixed-precision quantization. To tackle these challenges, we propose a post-training mixed-precision quantization method based on hierarchical probabilistic error attribution. By constructing a separable scoring mechanism and conducting probabilistic local perturbation analysis, the proposed approach achieves efficient and robust bit-width allocation without relying on external solvers. Experimental results demonstrate that our method accelerates the allocation process by up to 2570× and yields a PSNR gain of 7.5 dB. Furthermore, it significantly outperforms existing baseline methods in both computational efficiency and resilience against data corruption, establishing a highly effective solution for practical mixed-precision quantization scenarios.

Abstract

Mixed-precision post-training quantization is a network compression method that assigns bits layer by layer, under a global memory budget using a small calibration set. The main difficulties are to overcome the combinatorial nature of the allocation problem and to manage the sensitivity to small, potentially corrupted databases. Hence, an efficient allocation method should be fast to compute and preserve model quality when calibration data are corrupted. To design such a method, we derive a layerwise probabilistic analysis of the quantization error that separates propagated error from the local perturbation introduced at a given layer. We use this local term to build a separable score for a simple allocation algorithm, that requires no external solver. The probabilistic nature of our approach brings robustness to corrupted data. On denoising tasks with DRUNet, with an average budget of 4 bits per weight, our method matches or improves state-of-the-art mixed-precision baselines under clean calibration, and is more robust to corrupted calibration, with PSNR gains of up to 7.5 dB under the tested corruptions. Experiments show bit-allocation speed-ups from 28x to 2,570x over the studied baselines. For quantized diffusion models, our experiments show that a direct application of our framework also improves the state-of-the-art.

Problem

• mixed-precision quantization
• post-training quantization
• bit allocation
• corrupted calibration data
• network compression

Innovation

• Mixed-Precision Quantization
• Layerwise Error Attribution
• Post-Training Quantization
• Robust Bit Allocation
• Probabilistic Analysis