HiAP: A Multi-Granular Stochastic Auto-Pruning Framework for Vision Transformers

Abstract

Vision Transformers require significant computational resources and memory bandwidth, severely limiting their deployment on edge devices. While recent structured pruning methods successfully reduce theoretical FLOPs, they typically operate at a single structural granularity and rely on complex, multi-stage pipelines with post-hoc thresholding to satisfy sparsity budgets. In this paper, we propose Hierarchical Auto-Pruning (HiAP), a continuous relaxation framework that discovers optimal sub-networks in a single end-to-end training phase without requiring manual importance heuristics or predefined per-layer sparsity targets. HiAP introduces stochastic Gumbel-Sigmoid gates at multiple granularities: macro-gates to prune entire attention heads and FFN blocks, and micro-gates to selectively prune intra-head dimensions and FFN neurons. By optimizing both levels simultaneously, HiAP addresses both the memory-bound overhead of loading large matrices and the compute-bound mathematical operations. HiAP naturally converges to stable sub-networks using a loss function that incorporates both structural feasibility penalties and analytical FLOPs. Extensive experiments on ImageNet demonstrate that HiAP organically discovers highly efficient architectures, and achieves a competitive accuracy-efficiency Pareto frontier for models like DeiT-Small, matching the performance of sophisticated multi-stage methods while significantly simplifying the deployment pipeline.