Summary of Dynamic-llava: Efficient Multimodal Large Language Models Via Dynamic Vision-language Context Sparsification, by Wenxuan Huang et al.
Dynamic-LLaVA: Efficient Multimodal Large Language Models via Dynamic Vision-language Context Sparsification
by Wenxuan Huang, Zijie Zhai, Yunhang Shen, Shaosheng Cao, Fei Zhao, Xiangfeng Xu, Zheyu Ye, Shaohui Lin
First submitted to arxiv on: 1 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes Dynamic-LLaVA, a dynamic vision-language context sparsification framework designed to efficiently infer Multimodal Large Language Models (MLLMs). MLLMs excel in vision understanding, reasoning, and interaction but suffer from increasing computation and memory demands during decoding. Existing methods reduce vision context redundancy, but this benefit is lost during decoding. Dynamic-LLaVA addresses this issue by dynamically reducing vision context redundancy in the prefill stage and decreasing memory and computation overhead during decoding. The framework incorporates a tailored sparsification inference scheme for different inference modes (prefill, decoding with/without KV cache). Experimental results show that Dynamic-LLaVA reduces computation consumption by 75% in the prefill stage, 50% overall under decoding without KV cache, and saves 50% GPU memory overhead when decoding with KV cache. Moreover, Dynamic-LLaVA achieves efficient inference for MLLMs with negligible understanding and generation ability degradation or even performance gains compared to full-context baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about a new way to make computers better at understanding and generating text that is connected to images. The problem is that these computers need more and more power and memory as they generate more text, which makes them slow and uses up too much space. The researchers created a new framework called Dynamic-LLaVA that helps solve this problem by making the computer use less energy and memory while still being able to understand and generate text well. They tested their idea and found that it worked really well, reducing the amount of power needed by 75% in some cases and saving space on computers’ hard drives. |
Keywords
* Artificial intelligence * Inference
