p-MoD: Building Mixture-of-Depths MLLMs via Progressive Ratio Decay

• URL: http://arxiv.org/abs/2412.04449v1
• Date: Thu, 05 Dec 2024 18:58:03 GMT
• Title: p-MoD: Building Mixture-of-Depths MLLMs via Progressive Ratio Decay
• Authors: Jun Zhang, Desen Meng, Ji Qi, Zhenpeng Huang, Tao Wu, Limin Wang,
• Abstract summary: We propose to build efficient multimodal large language models (MLLMs) by leveraging the Mixture-of-Depths (MoD) mechanism. We adapt the MoD module with two novel designs: tanh-gated weight normalization (TanhNorm) and symmetric token reweighting (STRing) Our model, p-MoD, matches or even surpasses the performance of the baseline models, with only 55.6% TFLOPs and 53.8% KV cache storage during inference, and 77.7% GPU hours during training.
• Score: 18.958138693220704
• License:
• Abstract: Despite the remarkable performance of multimodal large language models (MLLMs) across diverse tasks, the substantial training and inference costs impede their advancement. The majority of computation stems from the overwhelming volume of vision tokens processed by the transformer decoder. In this paper, we propose to build efficient MLLMs by leveraging the Mixture-of-Depths (MoD) mechanism, where each transformer decoder layer selects essential vision tokens to process while skipping redundant ones. However, integrating MoD into MLLMs is non-trivial. To address the challenges of training and inference stability as well as limited training data, we adapt the MoD module with two novel designs: tanh-gated weight normalization (TanhNorm) and symmetric token reweighting (STRing). Moreover, we observe that vision tokens exhibit higher redundancy in deeper layer and thus design a progressive ratio decay (PRD) strategy, which gradually reduces the token retention ratio layer by layer, employing a shifted cosine schedule. This crucial design fully unleashes the potential of MoD, significantly boosting the efficiency and performance of our models. To validate the effectiveness of our approach, we conduct extensive experiments with two baseline models across 14 benchmarks. Our model, p-MoD, matches or even surpasses the performance of the baseline models, with only 55.6% TFLOPs and 53.8% KV cache storage during inference, and 77.7% GPU hours during training.

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