DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models (2401.06066v1)

Abstract: In the era of LLMs, Mixture-of-Experts (MoE) is a promising architecture for managing computational costs when scaling up model parameters. However, conventional MoE architectures like GShard, which activate the top-$K$ out of $N$ experts, face challenges in ensuring expert specialization, i.e. each expert acquires non-overlapping and focused knowledge. In response, we propose the DeepSeekMoE architecture towards ultimate expert specialization. It involves two principal strategies: (1) finely segmenting the experts into $mN$ ones and activating $mK$ from them, allowing for a more flexible combination of activated experts; (2) isolating $K_s$ experts as shared ones, aiming at capturing common knowledge and mitigating redundancy in routed experts. Starting from a modest scale with 2B parameters, we demonstrate that DeepSeekMoE 2B achieves comparable performance with GShard 2.9B, which has 1.5 times the expert parameters and computation. In addition, DeepSeekMoE 2B nearly approaches the performance of its dense counterpart with the same number of total parameters, which set the upper bound of MoE models. Subsequently, we scale up DeepSeekMoE to 16B parameters and show that it achieves comparable performance with LLaMA2 7B, with only about 40% of computations. Further, our preliminary efforts to scale up DeepSeekMoE to 145B parameters consistently validate its substantial advantages over the GShard architecture, and show its performance comparable with DeepSeek 67B, using only 28.5% (maybe even 18.2%) of computations.

• The paper introduces fine-grained expert segmentation and shared expert isolation to enhance specialization in Mixture-of-Experts language models.
• It demonstrates that models with 2B parameters can rival larger models, while scaling up to 16B parameters maintains strong performance with reduced computation.
• The study promotes practical research by releasing checkpoints and lowering computational demands, making advanced language modeling more accessible.

Understanding DeepSeekMoE: A Leap in LLM Efficiency

Introduction

The landscape of AI LLMs is rapidly changing, with the development of ever-larger models achieving state-of-the-art results. A key innovation in this area is the Mixture-of-Experts (MoE) architecture, which has shown to be a cost-effective strategy for scaling up models. DeepSeekMoE is an advanced iteration of this architecture, aiming to enhance the specialization of experts—individual neural networks within the MoE model, each refining its skillset on specialized tasks.

A Novel Expert Specialization Approach

Unlike typical MoE models that activate a fixed top set of experts for each input, DeepSeekMoE introduces two strategic optimizations to induce high specialization:

1. Fine-Grained Expert Segmentation: By dividing existing expert networks into smaller segments, DeepSeekMoE enables a more nuanced routing of tokens. This granulation presents a more targeted and precise approach to learning, allowing for a flexible and adaptive response to varying inputs and a high level of expert specialization.
2. Shared Expert Isolation: In typical MoE architectures, the overlap of required knowledge across experts leads to inefficiencies. DeepSeekMoE's structure dedicates certain experts to holding this common knowledge, thereby reducing redundancy and improving overall parameter efficiency.

Empirical Validation

The effectiveness of the innovative design of DeepSeekMoE is well-supported by empirical results. The model, with only 2 billion parameters, rivals or surpasses the performance of larger and more computationally expensive models. These results are not confined to small scale; as DeepSeekMoE scales up to 16 billion parameters, it continues to demonstrate strong performance across various benchmarks, while requiring considerably less computation.

Scalability and Performance

When scaled to 16 billion parameters, DeepSeekMoE notably matches the performance of the 7 billion parameter model DeepSeek and the much-cited model LLaMA2, with roughly 40% of their computational requirements. Moreover, preliminary studies suggest that a larger 145 billion parameter version of DeepSeekMoE marks significant performance improvements over GShard, a conventional MoE, while consuming only a fraction of the computational resources.

Impact and Accessibility

The significance of DeepSeekMoE extends beyond its impressive technical achievements. By releasing the model checkpoint for the 16 billion parameter version, which can operate on a single 40GB GPU, the developers encourage widespread exploration and application. This initiative opens doors for researchers and practitioners with limited computational resources to engage with one of the most efficient large-scale LLMs to date.

Conclusion

The advancements introduced by DeepSeekMoE contribute to solving a critical challenge in the AI field—the trade-off between model size, performance, and computational cost. The paper's insights on expert specialization provide a blueprint for future developments, sharing the potential to make large-scale LLMs more sustainable and accessible, spurring innovation and research in various AI applications.