PyramidDrop: Accelerating Your Large Vision-Language Models via Pyramid Visual Redundancy Reduction (2410.17247v1)

Abstract: In large vision-LLMs (LVLMs), images serve as inputs that carry a wealth of information. As the idiom "A picture is worth a thousand words" implies, representing a single image in current LVLMs can require hundreds or even thousands of tokens. This results in significant computational costs, which grow quadratically as input image resolution increases, thereby severely impacting the efficiency of both training and inference. Previous approaches have attempted to reduce the number of image tokens either before or within the early layers of LVLMs. However, these strategies inevitably result in the loss of crucial image information, ultimately diminishing model performance. To address this challenge, we conduct an empirical study revealing that all visual tokens are necessary for LVLMs in the shallow layers, and token redundancy progressively increases in the deeper layers of the model. To this end, we propose PyramidDrop, a visual redundancy reduction strategy for LVLMs to boost their efficiency in both training and inference with neglectable performance loss. Specifically, we partition the LVLM into several stages and drop part of the image tokens at the end of each stage with a pre-defined ratio, creating pyramid-like visual tokens across model layers. The dropping is based on a lightweight similarity calculation with a negligible time overhead. Extensive experiments demonstrate that PyramidDrop can achieve a 40% training time and 55% inference FLOPs acceleration of LLaVA-NeXT with comparable performance. Besides, the PyramidDrop could also serve as a plug-and-play strategy for inference acceleration without training, with better performance and lower inference cost than counterparts. We hope that the insights and approach introduced by PyramidDrop will inspire future research to further investigate the role of image tokens in LVLMs.

PyramidDrop: Efficient Visual Redundancy Reduction in LVLMs

The paper "PyramidDrop: Accelerating Your Large Vision-LLMs via Pyramid Visual Redundancy Reduction" addresses a pressing challenge in the domain of Large Vision-LLMs (LVLMs)—the substantial computational cost associated with visual token processing. LVLMs have become integral in various applications due to their ability to process and understand multimodal data, such as text and images. However, the inefficiencies arising from handling high-resolution images, which demand processing thousands of visual tokens, exacerbate the computational burden. This work proposes PyramidDrop, a novel approach designed to mitigate these inefficiencies by reducing visual token redundancy through a structured, layer-wise approach.

Overview

PyramidDrop leverages a key insight into the runtime behavior of LVLMs: not all visual tokens are necessary at all layers. The approach systematically reduces the number of visual tokens processed in deeper layers of the model using a pyramid-like structure. This reduction is achieved by employing a lightweight attention mechanism that ranks image tokens based on their relevance to the textual input, dropping less relevant tokens at predefined stages throughout the model. Such a strategy retains all tokens in the initial layers to avoid information loss and progressively reduces them in deeper layers, thereby optimizing the balance between computational efficiency and model performance.

Empirical Validation

The authors empirically validate the efficacy of PyramidDrop through extensive experimentation, demonstrating a 40% reduction in training time and a 55% decrease in FLOPs during inference on models like LLaVA-NeXT. This is achieved without compromising the performance on a variety of vision-language tasks. Importantly, the strategy ensures that LVLMs maintain their ability to understand fine-grained information crucial for tasks like DocVQA and TextVQA, where dense and detailed image comprehension is necessary.

Implications and Future Directions

The practical implications of PyramidDrop are significant, enabling the deployment of LVLMs on systems with limited computational resources by reducing their operational costs through a scalable token reduction framework. Theoretically, this method invites further investigation into the adaptive management of token redundancy across different model architectures, potentially inspiring new algorithms that further explore the layer-wise processing dynamics in multimodal models.

PyramidDrop acts as a plug-and-play strategy that can be applied at inference time without requiring retraining, showcasing its potential for widespread applicability. This characteristic makes it especially viable for existing models where retraining may not be feasible due to resource constraints.

Conclusion

In conclusion, PyramidDrop represents a thoughtful and empirically grounded approach to the challenge of computational inefficiency in LVLMs due to visual token redundancy. This paper contributes not only a methodological advancement but also provides a robust empirical foundation that could catalyze further research into efficient model design and deployment strategies in multi-modal AI systems. As the demand for high-capacity vision-language processing grows, solutions like PyramidDrop will be critical in balancing the scales between computational cost and performance efficacy, guiding future developments in the field.