Probe Pruning: Accelerating LLMs through Dynamic Pruning via Model-Probing

• URL: http://arxiv.org/abs/2502.15618v1
• Date: Fri, 21 Feb 2025 17:41:21 GMT
• Title: Probe Pruning: Accelerating LLMs through Dynamic Pruning via Model-Probing
• Authors: Qi Le, Enmao Diao, Ziyan Wang, Xinran Wang, Jie Ding, Li Yang, Ali Anwar,
• Abstract summary: Probe Pruning is a novel framework for online, dynamic, structured pruning of Large Language Models.<n>It comprises three main stages: probing, history-informed pruning, and full inference.<n>It operates without requiring additional neural network modules or fine-tuning.
• Score: 28.694253577030135
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce Probe Pruning (PP), a novel framework for online, dynamic, structured pruning of Large Language Models (LLMs) applied in a batch-wise manner. PP leverages the insight that not all samples and tokens contribute equally to the model's output, and probing a small portion of each batch effectively identifies crucial weights, enabling tailored dynamic pruning for different batches. It comprises three main stages: probing, history-informed pruning, and full inference. In the probing stage, PP selects a small yet crucial set of hidden states, based on residual importance, to run a few model layers ahead. During the history-informed pruning stage, PP strategically integrates the probing states with historical states. Subsequently, it structurally prunes weights based on the integrated states and the PP importance score, a metric developed specifically to assess the importance of each weight channel in maintaining performance. In the final stage, full inference is conducted on the remaining weights. A major advantage of PP is its compatibility with existing models, as it operates without requiring additional neural network modules or fine-tuning. Comprehensive evaluations of PP on LLaMA-2/3 and OPT models reveal that even minimal probing-using just 1.5% of FLOPs-can substantially enhance the efficiency of structured pruning of LLMs. For instance, when evaluated on LLaMA-2-7B with WikiText2, PP achieves a 2.56 times lower ratio of performance degradation per unit of runtime reduction compared to the state-of-the-art method at a 40% pruning ratio. Our code is available at https://github.com/Qi-Le1/Probe_Pruning.

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