  Vision-language pre-trained models have achieved impressive performance on
various downstream tasks. However, their large model sizes hinder their
utilization on platforms with limited computational resources. We find that
directly using smaller pre-trained models and applying magnitude-based pruning
on CLIP models leads to inflexibility and inferior performance. Recent efforts
for VLP compression either adopt uni-modal compression metrics resulting in
limited performance or involve costly mask-search processes with learnable
masks. In this paper, we first propose the Module-wise Pruning Error (MoPE)
metric, accurately assessing CLIP module importance by performance decline on
cross-modal tasks. Using the MoPE metric, we introduce a unified pruning
framework applicable to both pre-training and task-specific fine-tuning
compression stages. For pre-training, MoPE-CLIP effectively leverages knowledge
from the teacher model, significantly reducing pre-training costs while
maintaining strong zero-shot capabilities. For fine-tuning, consecutive pruning
from width to depth yields highly competitive task-specific models. Extensive
experiments in two stages demonstrate the effectiveness of the MoPE metric, and
MoPE-CLIP outperforms previous state-of-the-art VLP compression methods.
