ExpertFlow: Optimized Expert Activation and Token Allocation for Efficient Mixture-of-Experts Inference (2410.17954v1)

Abstract: Sparse Mixture of Experts (MoE) models, while outperforming dense LLMs in terms of performance, face significant deployment challenges during inference due to their high memory demands. Existing offloading techniques, which involve swapping activated and idle experts between the GPU and CPU, often suffer from rigid expert caching mechanisms. These mechanisms fail to adapt to dynamic routing, leading to inefficient cache utilization, or incur prohibitive costs for prediction training. To tackle these inference-specific challenges, we introduce ExpertFlow, a comprehensive system specifically designed to enhance inference efficiency by accommodating flexible routing and enabling efficient expert scheduling between CPU and GPU. This reduces overhead and boosts system performance. Central to our approach is a predictive routing path-based offloading mechanism that utilizes a lightweight predictor to accurately forecast routing paths before computation begins. This proactive strategy allows for real-time error correction in expert caching, significantly increasing cache hit ratios and reducing the frequency of expert transfers, thereby minimizing I/O overhead. Additionally, we implement a dynamic token scheduling strategy that optimizes MoE inference by rearranging input tokens across different batches. This method not only reduces the number of activated experts per batch but also improves computational efficiency. Our extensive experiments demonstrate that ExpertFlow achieves up to 93.72\% GPU memory savings and enhances inference speed by 2 to 10 times compared to baseline methods, highlighting its effectiveness and utility as a robust solution for resource-constrained inference scenarios.

ExpertFlow: Optimized Expert Activation and Token Allocation for Efficient Mixture-of-Experts Inference

The paper "ExpertFlow: Optimized Expert Activation and Token Allocation for Efficient Mixture-of-Experts Inference" addresses the intricate challenge of deploying Sparse Mixture of Experts (MoE) models efficiently during inference. While MoE models typically outperform dense LLMs in terms of performance, they impose significant memory demands, leading to deployment challenges in resource-limited environments.

Overview of MoE Challenges

The primary issues with MoE models, which this paper highlights, revolve around the high computational and memory overhead necessitated by dynamic routing and expert activation. Standard offloading techniques fail to adapt flexibly to dynamic routing paths, resulting in underutilized cache performance or excessive I/O costs due to frequent expert transfers. Thus, a system capable of optimizing both expert activation and token allocation is sought.

ExpertFlow: Key Contributions

1. Predictive Routing Path-Based Offloading: The paper introduces a routing path predictor that anticipates routing paths before computation, allowing for real-time correction in expert caching. This proactive approach increases cache hit ratios and minimizes I/O overhead by reducing unnecessary expert transfers between CPU and GPU.
2. Dynamic Token Scheduling: ExpertFlow implements a token scheduling strategy that rearranges input tokens across batches. This strategy optimizes inference by minimizing the number of activated experts per batch and improving computational efficiency.
3. Enhanced GPU Memory Savings: Through comprehensive experiments, the paper demonstrates that ExpertFlow achieves substantial GPU memory savings, with up to 93.72% reduction, while enhancing inference speed by 2 to 10 times compared to baseline methods.

Experimental Validation

The system's efficacy is validated through experiments on various MoE models, including Mixtral and Switch Transformer series. The system shows profound improvements in both inference speed and memory usage across diverse natural language processing tasks such as summarization and translation.

Implications and Future Directions

The research emphasizes the optimization of resource allocation in MoE systems as a pragmatic approach to deploying advanced AI models in settings constrained by computational resources. The predictive mechanisms proposed could inspire further exploration into adaptive inference strategies, potentially broadening to other neural network architectures facing similar scalability challenges. Future explorations might consider integrating specialized hardware support to further enhance inference efficiency in MoE models.

In summary, ExpertFlow provides a robust system for efficiently managing and deploying MoE models, offering promising paths for future development in AI deployment strategies. The results and strategies discussed in this paper hold substantial potential for advancing the state-of-the-art in efficient large-scale AI inference.