Multimodal Mamba: Decoder-only Multimodal State Space Model via Quadratic to Linear Distillation

↗ arXiv ↗ Hugging Face

TL;DR

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Key Takeaways

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Why does it matter?

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This paper is important because it addresses the crucial limitations of current multimodal large language models (MLLMs): quadratic complexity and reliance on separate vision encoders. By introducing mmMamba, a novel framework for developing linear-complexity multimodal state space models, it significantly improves computational efficiency without sacrificing performance. This opens new avenues for deploying MLLMs on resource-constrained devices and for developing more scalable and efficient multimodal AI systems. The proposed distillation-based approach is also highly significant, as it circumvents the need for pre-trained linear-complexity LLMs, making the development of such models more accessible.

Visual Insights

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🔼 Figure 1 provides a comprehensive comparison of the proposed multimodal Mamba model. Subfigure (a) illustrates the architecture of mmMamba, highlighting its linear-complexity and hybrid decoder-only VLM design achieved through knowledge distillation from Transformer to Mamba-2. Subfigure (b) presents a performance comparison showing that mmMamba-linear achieves competitive results against existing models, while mmMamba-hybrid outperforms them across various benchmarks. Subfigures (c) and (d) compare the speed and memory usage of mmMamba with the teacher model HoVLE on an NVIDIA 4090 GPU. mmMamba-linear exhibits significantly lower latency and memory consumption than HoVLE, while mmMamba-hybrid scales more efficiently with increasing sequence length. Specifically, at 103K tokens, mmMamba-linear achieves a 20.6x speedup and 75.8% memory saving, whereas mmMamba-hybrid shows a 13.5x speedup and 60.2% memory saving compared to HoVLE.

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Figure 1: Comprehensive comparison of mmMamba. (a) Our mmMamba can build linear-complexity and hybrid decoder-only VLM by distilling the knowledge in Transformer to Mamba-2. (b) By distilling from the quadratic-complexity decoder-only VLM HoVLE, our mmMamba-linear achieves competitive performance against existing linear and quadratic-complexity VLMs with fewer parameters (e.g., 2×\times× fewer than EVE-7B), while mmMamba-hybrid surpasses them across all benchmarks and approaches the teacher model HoVLE’s performance. (c)-(d) We compare the speed and memory of mmMamba-linear and mmMamba-hybrid with the teacher model HoVLE on the same single NVIDIA 4090 GPU. mmMamba-linear maintains consistently low latency and memory usage, while mmMamba-hybrid’s resource consumption scales significantly better than HoVLE. At 103K tokens, mmMamba-linear demonstrates 20.6×\times× speedup compared to HoVLE and saves 75.8% GPU memory, while mmMamba-hybrid achieves 13.5×\times× speedup and saves 60.2% GPU memory.

🔼 This table compares various Vision-Language Models (VLMs) on their performance across several benchmark datasets. It contrasts different model architectures (encoder-based vs. decoder-only) and training methods (pre-training, supervised fine-tuning, and distillation). Key metrics include the overall performance on each benchmark, model size (# of parameters), the proportion of trainable parameters, and the computational complexity (quadratic or linear) in relation to the input sequence length. The best performing model for each benchmark is highlighted in bold, while the second-best is underlined. This allows for a direct comparison of the trade-offs between performance, model size, and computational efficiency among different VLMs.

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Table 1: Comparison with existing VLMs on general VLM benchmarks. “Recipe” denotes the adopted training recipe. “PT”, “SFT”, and “DT” denote the pre-training, supervised fine-tuning, and distillation training, respectively. “Complexity” denotes the model computation complexity with respect to the number of tokens. “# P.” denotes the number of total parameters. “# T.P.” denotes the percentage of trainable parameters (trainable paramterstotal parameterstrainable paramterstotal parameters\frac{\text{trainable paramters}}{\text{total parameters}}divide start_ARG trainable paramters end_ARG start_ARG total parameters end_ARG). The best performance is highlighted in bold and the second-best result is underlined.

In-depth insights

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Quadratic to Linear

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The core concept of “Quadratic to Linear” in the context of the research paper likely revolves around optimizing the computational complexity of large multimodal language models (MLLMs). Traditional transformer-based models exhibit quadratic complexity, making them computationally expensive, especially for long sequences. This section likely details the techniques used to distill knowledge from these quadratic models into more efficient linear models. This involves transferring the core capabilities and understanding from a computationally expensive architecture to a more resource-friendly one, significantly reducing the computational burden without sacrificing performance too drastically. The process probably involves architectural modifications, potentially introducing linear-complexity mechanisms like the Mamba-2 state space model, and sophisticated distillation strategies to effectively transfer knowledge between the two architectures. The result is a MLLM that maintains performance while boasting a significant advantage in terms of speed and memory efficiency, making it more practical for real-world applications.

Decoder-only VLMs

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Decoder-only Vision Language Models (VLMs) represent a significant shift in multimodal architecture design. Unlike encoder-decoder models, which process visual and textual information separately before combining them, decoder-only VLMs process both modalities within a unified decoder network. This approach simplifies the architecture, potentially reducing computational complexity and improving efficiency. However, decoder-only VLMs often present challenges in effectively integrating heterogeneous data types. Successfully training and deploying effective decoder-only VLMs requires careful consideration of pre-training strategies, the ability to handle long sequences of visual and textual information, and the inherent trade-offs between model size and performance. While some approaches address these challenges through innovative training techniques or architectural modifications, there remains a need for improved methods to enhance their performance, efficiency, and generalizability to various vision-language tasks. The advantages of a unified approach, especially concerning inference speed and reduced resource requirements, make decoder-only VLMs an active area of research with promising potential for real-world applications.

Progressive Distillation

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Progressive distillation, as described in the context of the research paper, is a multi-stage training strategy designed to effectively transfer knowledge from a complex, quadratic-complexity model (like a Transformer-based VLM) to a simpler, linear-complexity model (like a Mamba-2 based VLM). This approach is crucial because it tackles the challenge of directly converting a pre-trained, high-performing model into a more efficient architecture without significant performance degradation. The methodology appears to involve three main stages: initializing the simpler model with parameters from the complex model, followed by layer-wise training and alignment, and finally, end-to-end fine-tuning to optimize overall performance. This stepwise refinement process mitigates the difficulties associated with a direct transfer, allowing for a gradual adaptation and stabilization of the linear model. The use of different loss functions at each stage, such as MSE and KL-divergence, also suggests a tailored approach to address varying aspects of model behavior. This progressive method is key to obtaining a linear-complexity model that maintains the multimodal capabilities of its teacher model. By incrementally transferring knowledge, the method overcomes the challenges of simply initializing the target model with the source’s parameters. The three-stage approach, therefore, ensures the success of the distillation while addressing the complexities of adapting between different model architectures.

Efficiency Analysis

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The efficiency analysis section of the paper focuses on evaluating the computational performance gains achieved by the proposed mmMamba model. The key aspect is the comparison of inference speed and memory usage between mmMamba and existing models, particularly HoVLE, a quadratic-complexity model. Experiments demonstrate that mmMamba significantly outperforms HoVLE in terms of speed and memory efficiency, especially when processing longer sequences, highlighting its suitability for real-world applications demanding high throughput and low latency. The analysis also explores architectural variations of mmMamba, comparing a purely linear version (mmMamba-linear) with a hybrid version (mmMamba-hybrid). The results show a trade-off between performance and resource consumption, where the hybrid model offers a balance between speed and accuracy. The substantial speedup and memory savings achieved by mmMamba underscore its potential for deploying large-scale multimodal language models efficiently on resource-constrained devices. This is a significant contribution, as it tackles the major bottleneck of high computational costs typically associated with large, transformer-based models.

Hybrid Architecture

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The research explores a hybrid architecture combining the strengths of Transformer and Mamba-2 layers. This approach offers a flexible trade-off between computational efficiency and model capability. By strategically interleaving Transformer layers (which excel in complex feature representation) with Mamba-2 layers (known for linear computational complexity), the model adapts to diverse deployment needs. The number of interleaved Transformer layers can be adjusted to fine-tune this balance, making it highly adaptable to resource constraints. Experiments demonstrate that a hybrid model incorporating both architecture types significantly improves performance over a purely linear model while maintaining computational benefits compared to full Transformer models, showing the hybrid model’s effectiveness in balancing performance and efficiency.

More visual insights

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Full paper

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