Linear-MoE：线性注意力遇上混合专家的开源实践

Core Insights - The article highlights the rise of Linear-MoE architecture, which effectively combines linear sequence modeling and Mixture-of-Experts (MoE) for enhanced performance in large language models [1][10]. Group 1: Linear Sequence Modeling - Significant advancements in linear sequence modeling have been achieved over the past two years, characterized by linear time complexity in training and constant memory usage during inference [5]. - The main categories of linear sequence modeling include Linear Attention, State Space Models (SSM), and Linear RNN, with notable works such as Lightning Attention, GLA, Mamba2, and RWKV [5]. Group 2: Mixture-of-Experts (MoE) - MoE has become a standard in the industry, with various models like GPT-4, Gemini, and domestic models such as DeepSeek and Qwen all adopting MoE architectures [8]. - The importance of MoE in enhancing model capabilities is emphasized, although the article does not delve deeply into this aspect [8]. Group 3: Linear-MoE Architecture - Linear-MoE offers a complete system from modeling to training, allowing flexible combinations of linear sequence modeling layers and MoE layers, while also being compatible with traditional Softmax Attention Transformer layers [10]. - Key features include a modular architecture with support for various linear modeling methods and multiple MoE implementations, ensuring stability and scalability through the Megatron-Core framework [10]. Group 4: Performance and Future Prospects - Large-scale experiments validate the superiority of Linear-MoE, demonstrating faster inference speeds (2-5 times quicker than traditional architectures) and over 50% reduction in memory usage [12][13]. - The open-source nature of Linear-MoE fills a technical gap and provides reproducible training solutions, with future exploration planned for applications in long-context understanding and Vision-Language model architectures [13].