XMoE: Sparse Models with Fine-grained and Adaptive Expert Selection (2403.18926v2)

Abstract: Sparse models, including sparse Mixture-of-Experts (MoE) models, have emerged as an effective approach for scaling Transformer models. However, they often suffer from computational inefficiency since a significant number of parameters are unnecessarily involved in computations via multiplying values by zero or low activation values. To address this issue, we present \tool, a novel MoE designed to enhance both the efficacy and efficiency of sparse MoE models. \tool leverages small experts and a threshold-based router to enable tokens to selectively engage only essential parameters. Our extensive experiments on LLMing and machine translation tasks demonstrate that \tool can enhance model performance while decreasing the computation load at MoE layers by over 50\% without sacrificing performance. Furthermore, we present the versatility of \tool by applying it to dense models, enabling sparse computation during inference. We provide a comprehensive analysis and make our code available at https://github.com/ysngki/XMoE.

Enhancing Efficiency in Sparse Models with Sparser Selection

Introduction to XMoE

Sparse Mixture-of-Experts (MoE) models have been identified as a promising avenue for scaling Transformer models without proportionally increasing computational costs. A critical issue with existing MoE implementations, however, is the under-utilization of parameters, resulting from substantial computations involving zero or negligibly small values. Addressing this inefficiency, the paper introduces the novel MoE design, XMoE, which employs smaller experts and a threshold-based router, demarcating a significant stride towards computational efficiency and efficacy in MoE models.

Key Contributions

The proposed methodology consists of the following primary elements:

• Small Experts Utilization: By embracing small experts, XMoE permits a more granular parameter selection process. This adaptability ensures that only the most relevant parameters are engaged during computations, thereby enhancing the model's efficiency.
• Adaptive Threshold-based Router: Unlike the static, top-$k$ selection routine, XMoE's adaptive router dynamically determines the number of experts each token should engage with. This methodology stands on the premise that tokens vary in the complexity they introduce, necessitating a flexible approach to expert allocation.
• Performance Demonstration: Through extensive evaluation on LLMing and machine translation tasks, XMoE showcases the potential to significantly reduce computational overhead (by over 50% in MoE layers) without compromising on model performance. Additionally, the approach's versatility is highlighted by its applicability to dense models for inference-time computational savings.
• Analytical Insights: The paper further explores a comprehensive analysis, highlighting operative insights into the computational inefficiencies extant in sparse MoE models and delineating the pathways through which XMoE addresses these inefficiencies.

Theoretical and Practical Implications

1. On Theoretical Grounds: The paper's findings elucidate the computational redundancy prevalent in MoE models, challenging the prevalent notion that larger models with a greater number of parameters directly correlate with enhanced performance.
2. In Practical Realms: XMoE not only establishes a method for significantly reducing computational costs but also sets a precedent for further research into the development of more efficient and effective sparse models. The adaptability introduced by the threshold-based router paves the way for models that dynamically adjust their computational strategies based on token complexity—a feature that could revolutionize processing efficiency in large-scale models.
3. Speculations on Future Developments: Looking ahead, the insights garnered from XMoE's implementation could inspire the development of hardware specifically designed to optimize the execution of sparse computational tasks. Furthermore, extending XMoE's principles to a broader array of tasks and exploring its scalability to even larger models present promising avenues for future research.

Conclusion

In sum, XMoE heralds a significant step forward in enhancing the efficiency of sparse models through the strategic employment of smaller experts and an adaptive, threshold-based routing mechanism. The model's demonstrated efficacy across various tasks, coupled with its potential to markedly reduce computational costs, underscores the pivotal role that such innovations could play in the ongoing advancement of MoE models and generative AI at large. The research also lays the groundwork for future investigations aimed at further refining and extending the capabilities of sparse computational models.

Limitations and Future Work

While XMoE marks a notable advancement in sparse model efficiency, its exploration remains restricted to specific NLP tasks and a relatively smaller model scale due to computational resource constraints. Future studies are encouraged to evaluate XMoE's effectiveness across a wider range of tasks and at the scale of larger model architectures. Moreover, the optimal size of experts within XMoE begs for further exploration to balance the trade-off between computational efficiency and performance effectively.