cu-lengths-attention-flow

What problem does it solve?

This skill provides a bilingual, in-depth guide to how cu_lengths controls attention boundaries across the ViT and LLM stages in LLaVA-OneVision2, clarifying why patch_positions grouping affects ViT but not LLM attention.

Core Features & Use Cases

• Clarified attention semantics: explains non-packed full causal attention in the LLM and how packed cu_seqlens creates block-diagonal attention across sub-samples.
• Practical debugging guidance: helps reason about offline packed vs. non-packed data, and how sample boundaries affect attention isolation.
• Cross-modality reasoning: describes how ViT-level patch_positions interacts with LLM-level attention.

Quick Start

Inspect the forward-pass attention flow in LLaVA-OneVision2 and identify where cu_lengths and cu_seqlens determine attention domain.