English

LPCD: Unified Framework from Layer-Wise to Submodule Quantization

Machine Learning 2025-12-02 v1 Artificial Intelligence Computation and Language Machine Learning

Abstract

Post-training quantization (PTQ) aims to preserve model-level behavior; however, most methods focus on individual linear layers. Even recent extensions, such as QEP and LoaQ, which mitigate error propagation or target specific submodules, still rely on layer-wise formulations and fail to capture the behavior of larger submodules. We introduce Layer-Projected Coordinate Descent (LPCD), a unified framework that extends PTQ beyond layers by optimizing relaxed objectives across arbitrary submodules and projecting the solutions with layer-wise quantizers. LPCD generalizes existing methods and provides a principled approach to quantizing complex submodules while maintaining the efficiency and compatibility of layer-wise PTQ pipelines. Across diverse LLM architectures and bit-widths, LPCD-based submodule quantization consistently enhances both layer-wise PTQ methods and existing submodule approaches.

Cite

@article{arxiv.2512.01546,
  title  = {LPCD: Unified Framework from Layer-Wise to Submodule Quantization},
  author = {Yuma Ichikawa and Yudai Fujimoto and Akira Sakai},
  journal= {arXiv preprint arXiv:2512.01546},
  year   = {2025}
}

Comments

21 pages, 4 figures

R2 v1 2026-07-01T08:03:31.686Z