SegMo: Co-Designing Content-Aware Sparsity and Locally-Cohesive Segment Parallelism for Efficient VLM Inference

摘要

Video Large Language Models (VideoLLMs) face a fundamental performance bottleneck: the token explosion intrinsic to video inputs. The resulting O(N^2) prefill cost makes conventional Transformer inference prohibitively expensive at scale. Existing attempts fall into a hard accuracy-latency dilemma: naive sparsification risks losing essential temporal-spatial context, whereas naive parallelization introduces substantial communication and memory overhead. To overcome this impasse, we propose SegMo, a unified framework based on algorithm-system co-design that jointly optimizes what to compute (sparsification) and how to compute it (parallelism). Driven by the empirical discovery of Local Cohesion in VideoLLM attention, SegMo integrates two key components: (1) Content-Aware Sparsification (CAS): A lightweight, hierarchical algorithm that first employs Query Relevance for scene-level assessment, and then uses Temporal Redundancy for intra-scene static redundancy pruning, to generate a precise, non-uniform computation load, ensuring accuracy. (2) Locally-Cohesive Segment Parallelism (LSP): A novel paradigm that leverages attention locality to partition the video at scene boundaries, using a lightweight Global Context Injection mechanism to replace the massive communication and memory overheads of global attention. SegMo was validated across LVBench, LongVideoBench, and Video-MME. Our CAS module improved accuracy by up to 12.00%. When integrated with LSP, the full system (CAS + LSP) achieved a peak prefill acceleration of 3.55x, while still maintaining a significant accuracy gain of up to 8.31%.