用背包算法统一剪枝深度与宽度，精准达标压缩率。
Unifying Depth and Width Pruning for LLMs via Binary Knapsack Optimization
- 通过粗粒度组件的背包优化分配参数，再精细剪枝满足预算。
- 压缩率匹配度达98%，远超现有方法最高33%的偏差。
- 跨架构通用性强，在18项任务中平均排名1.25，适合工业级部署。
结构化剪枝是压缩大语言模型的有效方法，但现有方法多依赖贪婪启发式策略，常无法精确达成目标压缩预算。本文提出SNIPER，一种两阶段结构化剪枝框架：首先在粗粒度组件上求解背包优化问题，基于固定重要性估计实现条件最优参数分配；随后进行细粒度剪枝以严格满足预算约束。引入压缩比吻合因子（CRAFT）量化预算匹配度，结果显示，现有剪枝器压缩比偏差最高达33%，而SNIPER达到0.98的接近完美匹配。在四种不同架构、18个任务（涵盖五个领域）上的评估表明，相较于六种先进剪枝方法，SNIPER在平均性能保留和任务级稳定性上均有显著提升。所有剪枝配置下，其平均排名仅为1.25，体现出卓越的跨架构泛化能力与可靠性。
原文摘要 · Abstract (English)
Structured pruning is a promising approach for compressing large language models (LLMs), yet existing methods rely heavily on greedy heuristics that produce myopic decisions, and often fail to precisely meet target compression budgets. We present SNIPER, a two-stage structured pruning framework that solves a knapsack optimization over coarse-granularity components to yield conditionally optimal parameter allocations with respect to fixed importance estimates, followed by a fine-grained pruning stage to meet strict budget constraints. We introduce the Compression Ratio Adherence Factor (CRAFT) to quantify budget fidelity, showing that while existing pruners deviate from target compression ratios by up to 33%, SNIPER achieves near-exact adherence with a CRAFT score of 0.98. Evaluations across four diverse architectures over a set of 18 tasks spanning five domains demonstrate SNIPER's consistent improvements in average performance retention and task-level stability over six state-of-the-art pruners. Across all pruning configurations, SNIPER achieves an excellent mean rank of 1.25, indicating its robust cross-architectural generalizability and excellent reliability.
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