提出分层自适应状态剪枝方法，显著降低深度状态空间模型计算开销。
Layer-Adaptive State Pruning for Deep State Space Models
- 基于子系统H∞范数与层内能量归一化，实现跨层状态评分
- 平均剪枝33%状态，仅损失0.52%准确率且无需重新训练
- 适用于需压缩状态空间的多输入多输出状态空间模型
由于缺乏状态维度优化方法，深度状态空间模型（SSMs）不得不牺牲模型容量、训练搜索空间或稳定性来缓解高状态维度带来的计算负担。本文提出一种结构化剪枝方法——分层自适应状态剪枝（LAST），通过扩展单系统模态截断，以最小化模型级输出能量损失为目标，对每一层的状态维度进行剪枝。LAST利用子系统的$\/mathcal{H}_{\infty}$范数和层间能量归一化计算得分，作为全局剪枝标准，支持跨层状态比较与分层自适应剪枝。在多个序列基准测试中，LAST优化了现有SSMs，揭示其状态空间存在冗余与可压缩性。值得注意的是，平均剪枝33%状态后，多输入多输出SSMs仍保持性能，仅产生0.52%准确率下降，且无需重新训练。代码已开源：https://github.com/msgwak/LAST。
原文摘要 · Abstract (English)
Due to the lack of state dimension optimization methods, deep state space models (SSMs) have sacrificed model capacity, training search space, or stability to alleviate computational costs caused by high state dimensions. In this work, we provide a structured pruning method for SSMs, Layer-Adaptive STate pruning (LAST), which reduces the state dimension of each layer in minimizing model-level output energy loss by extending modal truncation for a single system. LAST scores are evaluated using the $\mathcal{H}_{\infty}$ norms of subsystems and layer-wise energy normalization. The scores serve as global pruning criteria, enabling cross-layer comparison of states and layer-adaptive pruning. Across various sequence benchmarks, LAST optimizes previous SSMs, revealing the redundancy and compressibility of their state spaces. Notably, we demonstrate that, on average, pruning 33% of states still maintains performance with 0.52% accuracy loss in multi-input multi-output SSMs without retraining. Code is available at https://github.com/msgwak/LAST.
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