MHA2MLA-VLM: Enabling DeepSeek's Economical Multi-Head Latent Attention across Vision-Language Models
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Researchers have developed MHA2MLA-VLM, a framework that efficiently converts existing vision-language models (VLMs) to utilize Multi-Head Latent Attention (MLA), addressing memory and computational challenges in inference. It employs a modality-adaptive partial-RoPE strategy and a low-rank approximation for KV spaces, allowing for effective compression. The method minimizes adaptation costs through fine-tuning, achieving performance restoration with limited data. Experiments show significant reductions in KV cache size while maintaining model effectiveness, facilitating better integration with KV quantization.
MHA2MLA-VLM: A Breakthrough in Vision-Language Model Efficiency
Researchers have unveiled MHA2MLA-VLM, a framework designed to enhance the efficiency of vision-language models (VLMs) through Multi-Head Latent Attention (MLA). This development addresses the memory and computational challenges associated with Key-Value (KV) caches in VLMs during inference.
The MHA2MLA-VLM framework introduces two innovative techniques aimed at optimizing the KV cache:
- Modality-Adaptive Partial-RoPE Strategy: This technique selectively masks nonessential dimensions for compatibility with various settings.
- Modality-Decoupled Low-Rank Approximation: This method compresses the visual and textual KV spaces independently, enhancing efficiency.
Extensive experiments on three VLMs demonstrate that MHA2MLA-VLM restores original model performance with minimal supervised data and significantly decreases the KV cache footprint.
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📰 Original Source: https://arxiv.org/abs/2601.11464v1
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