ECoFLaP: Efficient Coarse-to-Fine Layer-Wise Pruning for Vision-Language Models

University of North Carolina at Chapel Hill
Computer Science

ECoFLaP is a unified coarse-to-fine approach to first efficiently compute sparsity ratios for each layer with zeroth-order gradients, and then prune the model in a layer-wise manner with the obtained sparsity.


Large Vision-Language Models (LVLMs) can understand the world comprehensively by integrating rich information from different modalities, achieving remarkable performance improvements on various multimodal downstream tasks. However, deploying LVLMs is often problematic due to their massive computational/energy costs and carbon consumption. Such issues make it infeasible to adopt conventional iterative global pruning, which is costly due to computing the Hessian matrix of the entire large model for sparsification. Alternatively, several studies have recently proposed layer-wise pruning approaches to avoid the expensive computation of global pruning and efficiently compress model weights according to their importance within a layer. However, these methods often suffer from suboptimal model compression due to their lack of a global perspective. To address this limitation in recent efficient pruning methods for large models, we propose Efficient Coarse-to-Fine Layer-Wise Pruning (ECoFLaP), a two-stage coarse-to-fine weight pruning approach for LVLMs. We first determine the sparsity ratios of different layers or blocks by leveraging the global importance score, which is efficiently computed based on the zeroth-order approximation of the global model gradients. Then, the multimodal model performs local layer-wise unstructured weight pruning based on globally-informed sparsity ratios. We validate our proposed method across various multimodal and unimodal models and datasets, demonstrating significant performance improvements over prevalent pruning techniques in the high-sparsity regime.






          author = {Yi-Lin Sung, Jaehong Yoon, Mohit Bansal},
          title = {ECoFLaP: Efficient Coarse-to-Fine Layer-Wise Pruning for Vision-Language Models},
          booktitle = {arXiv:2310.02998},
          year = {2023},