arxiv:2503.07605

SEAP: Training-free Sparse Expert Activation Pruning Unlock the Brainpower of Large Language Models

Published on Mar 10

· Submitted by

UglyToilet on Mar 11

#2 Paper of the day

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Authors:

Hanyu Wang ,

Simin Niu ,

Shichao Song ,

Bo Tang ,

Abstract

Large Language Models have achieved remarkable success across various natural language processing tasks, yet their high computational cost during inference remains a major bottleneck. This paper introduces Sparse Expert Activation Pruning (SEAP), a training-free pruning method that selectively retains task-relevant parameters to reduce inference overhead. Inspired by the clustering patterns of hidden states and activations in LLMs, SEAP identifies task-specific expert activation patterns and prunes the model while preserving task performance and enhancing computational efficiency. Experimental results demonstrate that SEAP significantly reduces computational overhead while maintaining competitive accuracy. Notably, at 50% pruning, SEAP surpasses both WandA and FLAP by over 20%, and at 20% pruning, it incurs only a 2.2% performance drop compared to the dense model. These findings highlight SEAP's scalability and effectiveness, making it a promising approach for optimizing large-scale LLMs.

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UglyToilet

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🚀 SEAP: Task-Adaptive Expert Activation Pruning – A New Perspective on LLM Pruning

💡 Background: The Computational Bottleneck of LLMs
Large Language Models (LLMs) require massive computational resources for inference, limiting their deployment in edge devices and real-time systems. Traditional optimization techniques, such as quantization and Mixture of Experts (MoE), rely on static pruning, failing to account for task-specific variations, leading to inefficient resource utilization.

🧠 Inspiration: Dynamic Activation Inspired by the Brain
Research shows that the human brain selectively activates specific regions to optimize task efficiency. Can LLMs adopt a similar mechanism—activating only the most relevant neurons for a given task—to reduce redundant computations?

📌 Key Idea: Sparse Expert Activation Pruning (SEAP)
SEAP (Sparse Expert Activation Pruning) is an innovative training-free, task-adaptive pruning method that dynamically selects the most relevant neurons, significantly boosting inference efficiency while maintaining task performance.

🔍 SEAP Pruning Process:
1️⃣ Task-Specific Knowledge Base 📚: Collects activation data from different tasks.
2️⃣ Activation Pattern Modeling 📊: Analyzes how different tasks utilize model neurons.
3️⃣ Neuron Importance Evaluation 🧮: Measures each neuron's contribution per task.
4️⃣ Global Sparsity Allocation 📈: Dynamically adjusts pruning ratios across layers.
5️⃣ Task-Adaptive Pruning ✂️: Selectively removes less relevant neurons to enhance speed!

⚡ Why SEAP? Superior Performance, Efficient Pruning!
✅ Task-Aware Adaptability 🌎: Tailors pruning to each task, avoiding inefficient one-size-fits-all sparsity.
✅ High Pruning Ratios with Minimal Performance Loss 📉: Only 2.2% drop at 20% sparsity; at 50% pruning, surpasses baseline methods by 20%+!
✅ Significant Inference Speedup ⏩: Outperforms unstructured pruning in execution speed.
✅ No Additional Training Required 🆓: Enables seamless deployment without retraining overhead!

🎯 SEAP leverages task-adaptive pruning to enhance LLM inference efficiency, making large models lighter and more scalable for real-world applications! 🚀

🚀 SEAP：任务自适应专家激活剪枝，LLM剪枝新视角

💡 背景：LLM计算瓶颈
大规模语言模型（LLM）推理计算成本高昂，严重限制了其在边缘设备和实时系统中的应用。传统优化方法（如量化、MoE）大多是 静态剪枝，忽略了不同任务的个性化需求，导致计算资源浪费。

🧠 灵感：大脑启发的动态激活
研究发现，人脑会 选择性激活特定区域 以提高任务处理效率。那么，LLM是否也能按任务需求激活最相关的神经元，从而减少不必要的计算？

📌 核心思路：稀疏专家激活剪枝（SEAP）
SEAP (Sparse Expert Activation Pruning) 是一种全新的 免训练任务自适应剪枝方法，通过 动态选择最相关的神经元，在确保任务性能的同时 大幅提升推理效率。

🔍 SEAP 剪枝流程：
1️⃣ 任务特定知识库 📚：收集不同任务的隐藏层激活数据。
2️⃣ 激活模式建模 📊：分析不同任务的神经元使用模式。
3️⃣ 神经元重要性评估 🧮：计算任务特定的神经元贡献度。
4️⃣ 全局稀疏性分配 📈：动态调整不同层的剪枝比例。
5️⃣ 任务自适应剪枝 ✂️：根据任务剪枝不重要神经元，提高推理速度！

⚡ SEAP优势：表现优异，剪枝高效！
✅ 任务适应性强 🌎：根据任务需求剪枝，避免统一稀疏策略的低效问题。
✅ 高剪枝比仍能保持性能 📉：20%剪枝性能仅下降 2.2%，50%剪枝下性能超越现有方法 **20%+**！
✅ 推理加速明显 ⏩：相比非结构化剪枝，推理速度大幅提升！
✅ 无需额外训练 🆓：免去繁琐的训练过程，轻松落地应用！

🎯 SEAP通过 任务自适应剪枝，提升大语言模型推理效率，在 计算资源受限的环境下，让 LLM 变得更轻量、更高效！ 🚀