用神经网络直接预测剪枝策略，实现毫秒级高效模型压缩。
LOP: Learning Optimal Pruning for Efficient On-Demand MLLMs Scaling
- 训练自回归神经网络直接预测每层剪枝比例，跳过耗时搜索
- 在多个任务上超越现有方法，速度提升最高达1000倍
- 适合需要快速适配不同硬件的多模态大模型部署场景
结构化剪枝技术对在从边缘设备到云服务器的各类硬件平台部署多模态大语言模型（MLLMs）至关重要。然而，当前剪枝方法通常通过迭代搜索确定最优策略，导致按需适配时计算开销巨大。为此，我们提出LOP，一种高效的神经剪枝框架，能从目标剪枝约束中学习最优剪枝策略，无需计算成本高昂的搜索过程。LOP通过训练自回归神经网络（NNs），直接预测与目标剪枝约束自适应的逐层剪枝策略，避免了耗时的迭代搜索。实验结果表明，在多个任务上，LOP在各项指标上均优于现有先进剪枝方法，同时实现高达三个数量级的速度提升。
原文摘要 · Abstract (English)
Structural pruning techniques are essential for deploying multimodal large language models (MLLMs) across various hardware platforms, from edge devices to cloud servers. However, current pruning methods typically determine optimal strategies through iterative search processes, resulting in substantial computational overhead for on-demand MLLMs adaptation. To address this challenge, we propose LOP, an efficient neural pruning framework that learns optimal pruning strategies from the target pruning constraint, eliminating the need for computationally expensive search-based methods. LOP approach trains autoregressive neural networks (NNs) to directly predict layer-wise pruning strategies adaptive to the target pruning constraint, eliminating the time-consuming iterative searches. Experimental results across multiple tasks show that LOP outperforms state-of-the-art pruning methods in various metrics while achieving up to three orders of magnitude speedup.
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