ReMoE: Boosting Expert Reuse through Router Fine-Tuning in Memory-Constrained MoE LLM Inference
ReMoE boosts expert reuse by 26% through router fine-tuning in memory-constrained MoE inference.
Key Findings
Methodology
ReMoE is a router fine-tuning framework designed to enhance expert reuse, reducing storage fetches. It biases the router towards recently selected experts, producing temporally stable routing that aligns with cache locality constraints. By increasing short-horizon expert reuse, ReMoE reduces storage fetches without adding inference-time computation.
Key Results
- Experiments show ReMoE improves expert reuse by 26% on DeepSeek and Qwen models while maintaining downstream task performance.
- Improves output throughput by 8.4% under vLLM GPU-CPU expert offloading and reduces TPOT by 43.6-49.8% on Jetson Orin NX.
- Achieves 1.77-1.99× decode speedup across diverse workloads.
Significance
ReMoE provides an effective solution for memory-constrained MoE inference, reducing I/O overhead caused by frequent expert switching. It enhances inference efficiency without altering model architecture, making it significant for deploying LLMs on edge devices.
Technical Contribution
ReMoE offers a lightweight post-training adaptation method by fine-tuning the router instead of modifying model architecture or hardware. It optimizes routing trace to enhance cache-friendliness without adding runtime computation burden.
Novelty
ReMoE is the first method to enhance expert reuse via router fine-tuning, differing from existing architecture modification or pretraining methods by only adjusting gate parameters of an already-trained MoE checkpoint.
Limitations
- ReMoE's effectiveness is limited when cache capacity is small, potentially unable to handle long-tail expert misses.
- The method relies on the quality of pretrained models and may perform poorly in certain contexts.
Future Work
Future research could explore ReMoE's application on different model architectures and larger datasets, as well as its integration with other cache optimization techniques.
AI Executive Summary
In memory-constrained inference scenarios, Mixture-of-Experts (MoE) models face the challenge of frequent expert switching, leading to increased I/O overhead. Existing solutions often attempt to hide this latency through system-level techniques like prefetching or caching algorithms, but these methods fail to address the mismatch between the routing trace produced by the router and cache locality constraints. ReMoE introduces a router fine-tuning framework that biases the router towards recently selected experts, producing temporally stable routing that aligns with cache locality constraints. Experimental results demonstrate that ReMoE improves expert reuse by 26% on DeepSeek and Qwen models while maintaining downstream task performance. It enhances output throughput by 8.4% under vLLM GPU-CPU expert offloading and reduces TPOT by 43.6-49.8% on Jetson Orin NX. This method provides an effective solution for deploying LLMs on edge devices, reducing I/O overhead caused by frequent expert switching. Future research could explore ReMoE's application on different model architectures and larger datasets, as well as its integration with other cache optimization techniques.
Deep Analysis
Background
As the demand for deploying large language models (LLMs) on edge devices increases, Mixture-of-Experts (MoE) models have become a crucial architectural choice due to their sparse activation feature. MoE models maintain high model capacity by activating only a subset of experts, reducing computation. However, in memory-constrained inference scenarios, frequent expert switching leads to increased I/O overhead, affecting inference efficiency.
Core Problem
In memory-constrained inference scenarios, MoE models face the challenge of frequent expert switching, leading to increased I/O overhead. Existing solutions fail to address the mismatch between the routing trace produced by the router and cache locality constraints, resulting in increased inference latency.
Innovation
ReMoE introduces a router fine-tuning framework that biases the router towards recently selected experts, producing temporally stable routing that aligns with cache locality constraints. Unlike existing architecture modification or pretraining methods, ReMoE only requires adjusting gate parameters of an already-trained MoE checkpoint.
Methodology
- �� ReMoE fine-tunes the router to favor recently selected experts, increasing short-horizon expert reuse.
- �� Utilizes two objectives: temporal locality loss and Trust-KL loss, to encourage expert reuse and limit distribution drift.
- �� Does not modify model architecture, hardware, or inference kernels, enhancing inference efficiency.
Experiments
Experiments were conducted on DeepSeek and Qwen models to evaluate the improvement in expert reuse and maintenance of downstream task performance. Real-system evaluations were performed using vLLM GPU-CPU expert offloading and Jetson Orin NX to observe changes in output throughput and TPOT.
Results
ReMoE improves expert reuse by 26% on DeepSeek and Qwen models while maintaining downstream task performance. Enhances output throughput by 8.4% under vLLM GPU-CPU expert offloading and reduces TPOT by 43.6-49.8% on Jetson Orin NX.
Applications
ReMoE is suitable for deploying LLMs on edge devices, reducing I/O overhead caused by frequent expert switching, and enhancing inference efficiency. It provides an effective solution for memory-constrained inference scenarios.
Limitations & Outlook
ReMoE's effectiveness is limited when cache capacity is small, potentially unable to handle long-tail expert misses. The method relies on the quality of pretrained models and may perform poorly in certain contexts.
Plain Language Accessible to non-experts
Imagine a kitchen where chefs are experts, each specializing in different dishes. During busy dinner hours, chefs need to quickly switch to different dishes, similar to expert switching in MoE models. ReMoE acts like a smart kitchen manager, ensuring chefs reuse their skills within a short time rather than frequently switching to new dishes. This reduces kitchen chaos and improves efficiency.
ELI14 Explained like you're 14
Hey there! Imagine you're playing a super cool game where each level has different tasks. You need to choose the best character to complete the tasks, just like choosing experts to solve problems. ReMoE is like a smart game assistant that helps you reuse the best characters in a short time instead of picking new ones each time. This makes the game faster and more fun!
Glossary
Mixture-of-Experts
An architectural model that reduces computation by activating only a subset of experts while maintaining high model capacity.
Used in the paper to improve inference efficiency.
ReMoE
A router fine-tuning framework that enhances expert reuse to reduce I/O overhead.
Used to optimize expert selection sequences.
TPOT
A metric for inference time that measures system efficiency in processing tasks.
Used to evaluate inference efficiency.
Trust-KL loss
A loss function used to limit router distribution drift.
Used in ReMoE to maintain model quality.
Temporal locality loss
A loss function that encourages expert reuse and reduces frequent switching.
Used in ReMoE to optimize routing trace.
Open Questions Unanswered questions from this research
- 1 How to apply ReMoE on larger datasets? Current methods perform poorly in certain contexts, requiring further optimization.
- 2 ReMoE's effectiveness is limited when cache capacity is small; how to address long-tail expert misses?
Applications
Immediate Applications
LLM deployment on edge devices
ReMoE can be used for deploying LLMs on edge devices, reducing I/O overhead caused by frequent expert switching and enhancing inference efficiency.
Long-term Vision
Application on large datasets
Explore ReMoE's application on larger datasets, integrate with other cache optimization techniques to enhance inference efficiency.
Abstract
Fine-grained Mixture-of-Experts (MoE) models sparsely activate only a subset of experts per token, reducing activated computation while maintaining high model capacity. However, in memory-constrained inference scenarios, only a small set of experts can be cached. Experts not in the cache must be fetched from slow external storage (e.g., UFS), leading to frequent evictions and substantial I/O overhead. We propose ReMoE, a router fine-tuning framework designed to boost token-wise expert reuse. ReMoE biases the router toward recently selected experts, producing temporally stable routing that better matches cache locality constraints. By increasing short-horizon expert reuse, ReMoE reduces expert fetches from storage without adding inference-time computation. Experiments on DeepSeek and Qwen models show that ReMoE improves expert reuse by 26% while maintaining downstream task performance. Real-system evaluations further confirm these benefits, improving output throughput by 8.4% under vLLM GPU-CPU expert offloading and reducing TPOT by 43.6-49.8% under llama.cpp on Jetson Orin NX, corresponding to a 1.77-1.99$\times$ decode speedup across diverse workloads. Checkpoints and usage instructions are available at https://github.com/BUAA-OSCAR/ReMoE.