SNP: Structured Neuron-level Pruning to Preserve Attention Scores
This work addresses the deployment of Vision Transformers on resource-constrained devices like edge devices and server processors, offering an incremental improvement over existing pruning methods.
The paper tackles the high computational cost and memory footprint of Vision Transformers by proposing Structured Neuron-level Pruning (SNP), a method that prunes neurons to preserve attention scores and eliminate redundancy, resulting in models like DeiT-Small running 3.1× faster and achieving performance gains such as 1.12% higher accuracy than DeiT-Tiny.
Multi-head self-attention (MSA) is a key component of Vision Transformers (ViTs), which have achieved great success in various vision tasks. However, their high computational cost and memory footprint hinder their deployment on resource-constrained devices. Conventional pruning approaches can only compress and accelerate the MSA module using head pruning, although the head is not an atomic unit. To address this issue, we propose a novel graph-aware neuron-level pruning method, Structured Neuron-level Pruning (SNP). SNP prunes neurons with less informative attention scores and eliminates redundancy among heads. Specifically, it prunes graphically connected query and key layers having the least informative attention scores while preserving the overall attention scores. Value layers, which can be pruned independently, are pruned to eliminate inter-head redundancy. Our proposed method effectively compresses and accelerates Transformer-based models for both edge devices and server processors. For instance, the DeiT-Small with SNP runs 3.1$\times$ faster than the original model and achieves performance that is 21.94\% faster and 1.12\% higher than the DeiT-Tiny. Additionally, SNP combine successfully with conventional head or block pruning approaches. SNP with head pruning could compress the DeiT-Base by 80\% of the parameters and computational costs and achieve 3.85$\times$ faster inference speed on RTX3090 and 4.93$\times$ on Jetson Nano.