HoliTom: Holistic Token Merging for Fast Video Large Language Models

TL;DR

HoliTom introduces a comprehensive token merging framework for video LLMs that significantly reduces computational costs by over 90% while maintaining high performance, through global temporal segmentation and spatial-temporal merging strategies.

Contribution

This work presents a training-free, holistic token merging method combining outer-LLM and inner-LLM pruning, addressing both spatial and temporal redundancies in video LLMs.

Findings

Reduces FLOPs to 6.9% of original while maintaining 99.1% performance.

Achieves 2.28x faster Time-To-First-Token (TTFT).

Attains 1.32x acceleration in decoding throughput.

Abstract

Video large language models (video LLMs) excel at video comprehension but face significant computational inefficiency due to redundant video tokens. Existing token pruning methods offer solutions. However, approaches operating within the LLM (inner-LLM pruning), such as FastV, incur intrinsic computational overhead in shallow layers. In contrast, methods performing token pruning before the LLM (outer-LLM pruning) primarily address spatial redundancy within individual frames or limited temporal windows, neglecting the crucial global temporal dynamics and correlations across longer video sequences. This leads to sub-optimal spatio-temporal reduction and does not leverage video compressibility fully. Crucially, the synergistic potential and mutual influence of combining these strategies remain unexplored. To further reduce redundancy, we introduce HoliTom, a novel training-free holistic token merging framework. HoliTom employs outer-LLM pruning through global redundancy-aware temporal segmentation, followed by spatial-temporal merging to reduce visual tokens by over 90%, significantly alleviating the LLM's computational burden. Complementing this, we introduce a robust inner-LLM token similarity-based merging approach, designed for superior performance and compatibility with outer-LLM pruning. Evaluations demonstrate our method's promising efficiency-performance trade-off on LLaVA-OneVision-7B, reducing computational costs to 6.9% of FLOPs while maintaining 99.1% of the original performance. Furthermore, we achieve a 2.28x reduction in Time-To-First-Token (TTFT) and a 1.32x acceleration in decoding throughput, highlighting the practical benefits of our integrated pruning approach for efficient video LLMs inference.