重新审视基于向量化的重参数化适应性取整用于大语言模型量化
Revisiting Adaptive Rounding with Vectorized Reparameterization for LLM Quantization
AI总结：
VQRound通过向量化的重参数化方法，实现了在大语言模型量化中的高效适应性取整，以提升收敛速度并减少参数使用。
摘要：
适应性取整作为一种替代四舍五入（RTN）的方法，在训练后量化中通过实现跨元素误差抵消而崭露头角。然而，密集且逐元素的取整矩阵对于十亿参数的大语言模型（LLMs）来说成本过高。我们从效率的角度重新审视适应性取整，并提出VQRound，一种参数高效的优化框架，将取整矩阵重新参数化为一个紧凑的代码本。与低秩替代方案不同，VQRound在L∞范数下最小化逐元素最坏情况误差，这对于处理LLMs中的重尾权重分布至关重要。除了重参数化之外，我们识别出取整初始化是关键因素，并开发了一种轻量级的端到端微调流程，仅使用128个样本即可在所有层上优化代码本。在OPT、LLaMA、LLaMA2和Qwen3模型上的广泛实验表明，VQRound在相同步骤数下比传统适应性取整具有更好的收敛性，同时仅使用0.2%的可训练参数。我们的结果表明，适应性取整可以变得既可扩展又快速适应。代码可在https://github.com/zhoustan/VQRound上获取。
英文摘要：
Adaptive Rounding has emerged as an alternative to round-to-nearest (RTN) for post-training quantization by enabling cross-element error cancellation. Yet, dense and element-wise rounding matrices are prohibitively expensive for billion-parameter large language models (LLMs). We revisit adaptive rounding from an efficiency perspective and propose VQRound, a parameter-efficient optimization framework that reparameterizes the rounding matrix into a compact codebook. Unlike low-rank alternatives, VQRound minimizes the element-wise worst-case error under $L_\infty$ norm, which is critical for handling heavy-tailed weight distributions in LLMs. Beyond reparameterization, we identify rounding initialization as a decisive factor and develop a lightweight end-to-end finetuning pipeline that optimizes codebooks across all layers using only 128 samples. Extensive experiments on OPT, LLaMA, LLaMA2, and Qwen3 models demonstrate that VQRound achieves better convergence than traditional adaptive rounding at the same number of steps while using as little as 0.2% of the trainable parameters. Our results show that adaptive rounding can be made both scalable and fast-fitting. The code is available at https://github.com/zhoustan/VQRound.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
