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In-depth Analysis on Caching and Pre-fetching in Mixture of Experts Offloading

Shuning Lin, Yifan He, Yitong Chen

TL;DR

The paper analyzes caching and pre-fetching for Mixture of Experts (MoE) offloading to address memory constraints on limited hardware. It combines detailed activation traces with a comparative study of LRU and LFU caching, demonstrating that LFU yields notable speedups and that expert distribution is skewed, informing caching choices. It also implements speculative preloading to forecast next-layer expert usage, reporting high precision/recall and highlighting substantial speedup potential alongside bandwidth trade-offs. The work provides practical guidance for MoE inference efficiency on edge-like devices and offers architectural insights and future directions for pruning and interpretation.

Abstract

In today's landscape, Mixture of Experts (MoE) is a crucial architecture that has been used by many of the most advanced models. One of the major challenges of MoE models is that they usually require much more memory than their dense counterparts due to their unique architecture, and hence are harder to deploy in environments with limited GPU memory, such as edge devices. MoE offloading is a promising technique proposed to overcome this challenge, especially if it is enhanced with caching and pre-fetching, but prior work stopped at suboptimal caching algorithm and offered limited insights. In this work, we study MoE offloading in depth and make the following contributions: 1. We analyze the expert activation and LRU caching behavior in detail and provide traces. 2. We propose LFU caching optimization based on our analysis and obtain strong improvements from LRU. 3. We implement and experiment speculative expert pre-fetching, providing detailed trace showing its huge potential . 4. In addition, our study extensively covers the behavior of the MoE architecture itself, offering information on the characteristic of the gating network and experts. This can inspire future work on the interpretation of MoE models and the development of pruning techniques for MoE architecture with minimal performance loss.

In-depth Analysis on Caching and Pre-fetching in Mixture of Experts Offloading

TL;DR

The paper analyzes caching and pre-fetching for Mixture of Experts (MoE) offloading to address memory constraints on limited hardware. It combines detailed activation traces with a comparative study of LRU and LFU caching, demonstrating that LFU yields notable speedups and that expert distribution is skewed, informing caching choices. It also implements speculative preloading to forecast next-layer expert usage, reporting high precision/recall and highlighting substantial speedup potential alongside bandwidth trade-offs. The work provides practical guidance for MoE inference efficiency on edge-like devices and offers architectural insights and future directions for pruning and interpretation.

Abstract

In today's landscape, Mixture of Experts (MoE) is a crucial architecture that has been used by many of the most advanced models. One of the major challenges of MoE models is that they usually require much more memory than their dense counterparts due to their unique architecture, and hence are harder to deploy in environments with limited GPU memory, such as edge devices. MoE offloading is a promising technique proposed to overcome this challenge, especially if it is enhanced with caching and pre-fetching, but prior work stopped at suboptimal caching algorithm and offered limited insights. In this work, we study MoE offloading in depth and make the following contributions: 1. We analyze the expert activation and LRU caching behavior in detail and provide traces. 2. We propose LFU caching optimization based on our analysis and obtain strong improvements from LRU. 3. We implement and experiment speculative expert pre-fetching, providing detailed trace showing its huge potential . 4. In addition, our study extensively covers the behavior of the MoE architecture itself, offering information on the characteristic of the gating network and experts. This can inspire future work on the interpretation of MoE models and the development of pruning techniques for MoE architecture with minimal performance loss.

Paper Structure

This paper contains 19 sections, 14 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Mixture of Experts Background
  4. MoE Offloading
  5. Methodology
  6. Temporal Locality and Caching
  7. Speculative Expert Loading
  8. Experiments
  9. Experiment Setup
  10. LFU
  11. Speculative Preloading
  12. Analysis
  13. LRU Cache Performance
  14. LRU Cache Experts Distribution
  15. LFU Cache Performance
  16. ...and 4 more sections

Figures (14)

  • Figure 1: The trace of LRU cache performance. The small gray squares(cached experts) tend to repeat history rather than predict the selected experts in the future.
  • Figure 2: The trace of expert activation and LRU cache with cache size=4 for the 1st layer.
  • Figure 3: The trace of expert activation and LRU cache with cache size=4 for the 8th layer.
  • Figure 4: The trace of expert activation and LRU cache with cache size=4 for the 16th layer.
  • Figure 5: The trace of expert activation and LRU cache with cache size=4 for the 24th layer.
  • ...and 9 more figures