AccKV: Towards Efficient Audio-Video LLMs Inference via Adaptive-Focusing and Cross-Calibration KV Cache Optimization
Abstract
Recent advancements in Audio-Video Large Language Models (AV-LLMs) have enhanced their capabilities in tasks like audio-visual question answering and multimodal dialog systems. Video and audio introduce an extended temporal dimension, resulting in a larger key-value (KV) cache compared to static image embedding. A naive optimization strategy is to selectively focus on and retain KV caches of audio or video based on task. However, in the experiment, we observed that the attention of AV-LLMs to various modalities in the high layers is not strictly dependent on the task. In higher layers, the attention of AV-LLMs shifts more towards the video modality. In addition, we also found that directly integrating temporal KV of audio and spatial-temporal KV of video may lead to information confusion and significant performance degradation of AV-LLMs. If audio and video are processed indiscriminately, it may also lead to excessive compression or reservation of a certain modality, thereby disrupting the alignment between modalities. To address these challenges, we propose AccKV, an Adaptive-Focusing and Cross-Calibration KV cache optimization framework designed specifically for efficient AV-LLMs inference. Our method is based on layer adaptive focusing technology, selectively focusing on key modalities according to the characteristics of different layers, and enhances the recognition of heavy hitter tokens through attention redistribution. In addition, we propose a Cross-Calibration technique that first integrates inefficient KV caches within the audio and video modalities, and then aligns low-priority modalities with high-priority modalities to selectively evict KV cache of low-priority modalities. The experimental results show that AccKV can significantly improve the computational efficiency of AV-LLMs while maintaining accuracy.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The paper presents AccKV, a novel framework for optimizing KV cache in AV-LLMs, addressing critical challenges in multimodal data processing. The technical contributions are substantial, with a well-structured methodology and comprehensive experimental validation, although improvements in reproducibility and broader applicability could enhance its impact in the field.
Methodology
The proposed AccKV framework introduces innovative techniques such as Adaptive-Focusing and Cross-Calibration for optimizing KV cache in AV-LLMs. The methodology is well-structured, addressing specific challenges in multimodal data processing, particularly the unique attention patterns observed in AV-LLMs. The attention redistribution mechanism is a notable enhancement that compensates for biases in traditional attention score calculations, making the approach more robust. However, the paper could benefit from clearer definitions of the algorithms and more explicit connections to existing methods.
Results & evaluation
The experimental validation of AccKV is comprehensive, utilizing multiple datasets and comparing against established KV caching strategies. The results demonstrate significant improvements in both computational efficiency and model accuracy, validating the effectiveness of the proposed methods. The use of ablation studies to assess the contribution of individual components adds rigor to the evaluation. However, more details on the experimental setup and hyperparameter tuning would enhance the clarity of the results.
Reproducibility
The paper lacks sufficient implementation details that would facilitate reproducibility. While the methodology is described, specific algorithmic steps, hyperparameter settings, and dataset configurations are not sufficiently detailed. Providing a code repository or supplementary materials would greatly aid in this aspect.
Limitations
One limitation is the reliance on specific AV-LLMs for validation, which may limit the generalizability of the findings to other models. Additionally, while the paper identifies the challenges of merging audio and video modalities, it does not explore alternative strategies that could be employed in different contexts. The performance metrics could also be expanded to include more qualitative assessments of the model's outputs.
Broader impact
The work has significant implications for the deployment of AV-LLMs in real-world applications, particularly in areas such as video understanding, multimodal interaction, and AI-driven content generation. By improving the efficiency of AV-LLMs, the proposed methods could facilitate more responsive and capable systems in various domains, including education, entertainment, and accessibility technologies. The paper presents AccKV, a novel framework for optimizing KV cache in AV-LLMs, addressing critical challenges in multimodal data processing. The technical contributions are substantial, with a well-structured methodology and comprehensive experimental validation, although improvements in reproducibility and broader applicability could enhance its impact in the field.
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