Adapt-Pruner: Adaptive Structural Pruning for Efficient Small Language Model Training
This work addresses the challenge of reducing computational costs for training SLMs on edge devices, offering a novel pruning approach that mitigates performance drops seen in existing methods.
The paper tackles the problem of efficiently training small language models (SLMs) by proposing Adapt-Pruner, an adaptive structural pruning method that interleaves pruning with training, achieving performance comparable to pre-training from scratch with significant computational savings. Experimental results show Adapt-Pruner outperforms conventional pruning methods by 1%-7% in accuracy on benchmarks and restores performance with 200× fewer tokens.
Small language models (SLMs) have attracted considerable attention from both academia and industry due to their broad range of applications in edge devices. To obtain SLMs with strong performance, conventional approaches either pre-train the models from scratch, which incurs substantial computational costs, or compress/prune existing large language models (LLMs), which results in performance drops and falls short in comparison to pre-training. In this paper, we investigate the family of acceleration methods that involve both structured pruning and model training. We found 1) layer-wise adaptive pruning (Adapt-Pruner) is extremely effective in LLMs and yields significant improvements over existing pruning techniques, 2) adaptive pruning equipped with further training leads to models comparable to those pre-training from scratch, 3) incremental pruning brings non-trivial performance gain by interleaving pruning with training and only removing a small portion of neurons ($\sim$5%) at a time. Experimental results on LLaMA-3.1-8B demonstrate that Adapt-Pruner outperforms conventional pruning methods, such as LLM-Pruner, FLAP, and SliceGPT, by an average of 1%-7% in accuracy on commonsense benchmarks. Additionally, Adapt-Pruner restores the performance of MobileLLM-125M to 600M on the MMLU benchmark with 200$\times$ fewer tokens via pruning from its larger counterparts, and discovers a new 1B model that surpasses LLaMA-3.2-1B in multiple benchmarks. The official code is released at https://github.com/research4pan/AdaptPruner.