The Evolution of Mixture Of Experts: From Basics To Breakthroughs

Author(s): Arpita Vats

Originally published on Towards AI.

Introduction

This recently released study is a comprehensive survey of 80+ Mixture Of Experts (MoE) models from foundational concepts to cutting-edge innovations.

In the ever-evolving landscape of artificial intelligence (AI) and deep learning, new architectures and techniques are constantly emerging to enhance the performance and efficiency of models. One such groundbreaking approach is the Mixture of Experts (MoE) architecture, which has garnered significant attention in recent years. MoE is a powerful method for scaling models and distributing computational resources in ways that maximize efficiency, especially in handling large datasets or complex tasks.

At its core, MoE allows AI models to work smarter, not harder, by leveraging multiple specialized “experts” that each focus on solving specific parts of a problem. Rather than relying on a single, monolithic model to handle all tasks, MoE dynamically selects which expert — or set of experts — should process a given input, enabling more efficient use of computational power.

With the rise of large language models (LLMs), computer vision, and other machine learning domains, MoE has become a crucial tool for researchers and practitioners alike. In this article, we’ll explore how MoE works, the innovations driving its recent popularity, and the potential it holds for the future of AI.

What is Mixture of Experts (MoE)?

At a high level, Mixture of Experts (MoE) is an advanced architecture that divides a complex problem into smaller, manageable sub-tasks, assigning each of these sub-tasks to specialized models called experts. Instead of a single model attempting to master an entire dataset, the MoE approach uses several expert models, each focused on a specific type of data or problem.

The key innovation in MoE is the gating network, which acts like a smart coordinator. When an input comes in, the gating network decides which experts are most suitable to handle it. This allows the system to dynamically activate only the relevant experts for each task, making the model more efficient and scalable.

Here’s how MoE operates in more detail:

• Expert Specialization: Each expert model within the MoE system is trained to excel at solving a specific part of the task or processing a specific subset of the data. For example, one expert might focus on handling complex linguistic structures, while another might be fine-tuned to process numerical data.
• Gating Network: The gating network acts as a router. It analyzes each input and selects the best experts to process it based on the data characteristics. The selection is dynamic, meaning that different inputs can be assigned to different experts, depending on the specific needs of the task.
• Collaboration of Experts: By leveraging multiple experts with different specializations, MoE allows the system to break down complex tasks into simpler ones. This divide-and-conquer approach leads to improved performance, as each expert focuses only on what it does best, rather than the model being stretched thin across every task.

In essence, MoE is all about maximizing efficiency by ensuring that the right expert handles the right task. This not only boosts the performance of the model but also allows for scaling up the model’s capacity without the corresponding increase in computational costs.

Basic concepts of Mixture of Experts (MoE)

The following are some of the essential concepts in Mixture Of Experts models.

Expert Choice Routing

In Mixture of Experts (MoE), expert choice routing refers to the process by which the gating network decides which experts should handle each input. Each expert has a limited capacity, meaning the routing process ensures that the right experts are selected based on the nature of the input data. The gate assigns inputs to the top-performing experts for the task, ensuring that each expert focuses on a subset of the data it’s best suited for. This routing system is critical for balancing computational load and improving task performance.

Load Balancing

Load balancing in MoE is crucial to ensure that the experts are utilized evenly. Without proper load balancing, some experts might handle a disproportionate amount of work while others remain underutilized, leading to inefficiency. MoE models typically include regularization terms in the loss function to promote even distribution of tasks among experts. The goal is to maximize performance while preventing any single expert from becoming overwhelmed with tasks.

MegaBlocks Handling of Load Balancing

MegaBlocks is a technique designed to handle load balancing more efficiently in MoE systems. It uses block-wise parallelism, dividing the model into smaller, manageable blocks that can be processed in parallel. By leveraging structured sparsity, MegaBlocks ensures that only a subset of the network is activated for each input, which reduces the computational cost while ensuring that all experts are used evenly. This dynamic capacity adjustment helps distribute workloads in real-time, improving overall model efficiency.

Expert Specialization

In MoE, expert specialization refers to the process by which each expert model becomes particularly good at handling a specific subset of tasks or data. As the model trains, experts specialize based on the types of data they process, allowing them to focus on specific patterns or relationships. This specialization improves the accuracy and performance of the model, as each expert becomes highly efficient at solving a particular part of the overall task, leading to better overall results.

Shown below is a neat visualization from Towards Understanding the Mixture-of-Experts Layer in Deep Learning by Chen et al, which shows how a 4-expert MoE model learns to solve a binary classification problem on a toy dataset that’s segmented into 4 clusters.

Gated architectures of Mixture of Experts (MoE)

The Mixture of Experts (MoE) architecture is built around the collaboration of multiple specialized models (experts) and a gating network that decides which experts should handle specific inputs. This architecture enables efficient handling of complex tasks by delegating different parts of the task to the most suitable expert models.

Gate Functionality

At the heart of MoE lies the gating network (also referred to as the gate or router). The gate plays two key roles:

• Clustering Data: The gate organizes incoming data into clusters, grouping similar data points together. This clustering is learned during training, enabling the model to identify patterns in the data and decide which cluster the input belongs to.
• Expert Selection: Once the gate has organized the data, it maps each cluster to the most appropriate expert model. The gate dynamically assigns each data point to the expert best suited for processing it, ensuring efficient and accurate handling of the input.

In summary, the gate controls how data is routed to different experts, optimizing the model’s ability to specialize and collaborate.

Sparsely Gated

One of the key innovations in modern MoE architectures is sparse gating. Instead of activating all experts for every input, the gating network selects only a few experts to handle each input. This sparsely gated approach dramatically improves the efficiency of the model by reducing computational costs while still maintaining high performance.

For each input, only a small subset of experts is active, allowing the model to scale up its capacity without a proportional increase in computational demands. This mechanism is especially important in large language models (LLMs) and other tasks requiring extensive computation, as it helps maintain performance without overwhelming resource requirements.

Why MoE Matters: Load Balancing and Efficiency

One of the most important aspects of the Mixture of Experts (MoE) architecture is its ability to efficiently manage computational resources, especially when dealing with large and complex datasets. MoE achieves this through effective load balancing and efficient computation, making it a game-changer in AI model scaling.

• Load Balancing: In an MoE system, each expert handles only a portion of the overall task. Without proper load balancing, some experts might become overworked while others are underutilized, leading to inefficiencies. To prevent this, MoE includes techniques that evenly distribute computational workloads across all experts. This ensures that no single expert is overwhelmed, allowing for smooth and efficient processing.
• Efficiency through Sparse Gating: Unlike traditional deep learning models where all parameters are activated for every input, MoE dynamically selects only a few relevant experts for each input. This sparsely gated approach ensures that only a small subset of experts is active at any given time, significantly reducing the computational overhead. The inactive experts remain idle, conserving resources while still allowing the model to scale up its capacity when needed.

By dynamically routing tasks to the appropriate experts and balancing the load across the network, MoE models are not only more efficient but also highly scalable. This makes them particularly useful for tasks like large language modeling and computer vision, where the balance between performance and computational cost is critical.

Notable contributions in MoE

Over the years, the Mixture of Experts (MoE) architecture has inspired numerous developments and extensions in both research and practical applications. Below are some key works that have advanced the field.

Sparsely Gated MoE by Shazeer et al. (2017)

One of the pivotal contributions to the MoE architecture was made by Noam Shazeer et al., who introduced the concept of Sparsely Gated MoE in 2017. This approach allowed for the scaling of models by activating only a few experts per input, significantly reducing the computational burden. Their work laid the foundation for MoE to be applied in large-scale deep learning models, enabling a thousandfold increase in model capacity without proportional increases in computational cost.

Vision Mixture of Experts (V-MoE) by Google Brain

In the field of computer vision, Google Brain introduced Vision MoE (V-MoE), a sparse version of the Vision Transformer (ViT). This approach routes each image patch to a subset of experts, allowing models to scale up to 15 billion parameters while maintaining performance levels comparable to state-of-the-art dense models. V-MoE demonstrated the potential of MoE for handling large, high-dimensional data in vision tasks.

Multi-gate MoE (MMoE) by Jiaqi et al. (2018)

MMoE, introduced by Jiaqi et al. in 2018, extended the MoE framework to the realm of multi-task learning. The MMoE model employs multiple experts that are shared across tasks, with separate gating networks for each task. This allows the model to dynamically allocate shared resources, making it more efficient for applications where tasks vary in their level of relatedness. MMoE has been successfully applied to large-scale recommendation systems.

Switch Transformer by Fedus et al. (2021)

The Switch Transformer, developed by Fedus et al., is a notable large-scale implementation of the MoE architecture in the context of language modeling. This model utilizes sparse gating to scale up to 1.6 trillion parameters, activating only one expert per input, making it one of the most efficient MoE models in terms of balancing performance and computational resources. Switch Transformer has significantly impacted the scaling of large language models (LLMs).

GLaM (Generalist Language Model) by Du et al. (2022)

The GLaM model introduced a sparsely activated MoE architecture to language modeling, achieving similar performance to dense models but with significantly lower computational cost. GLaM’s architecture allows it to scale to 1.2 trillion parameters while using only a fraction of the energy needed for dense models like GPT-3. This work demonstrated MoE’s capability to improve performance in NLP tasks without the associated computational burdens of dense models.

These related works illustrate the widespread adoption and versatility of MoE across different domains, from vision to language and multi-task learning. Each of these advancements has pushed the boundaries of what is possible with MoE, making it a critical tool in the ongoing evolution of machine learning.

If this work has been of assistance to you, please feel free to cite the paper.

@article{article,
author = {Vats, Arpita and Raja, Rahul and Jain, Vinija and Chadha, Aman},
year = {2024},
month = {08},
pages = {12},
title = {THE EVOLUTION OF MIXTURE OF EXPERTS: A SURVEY FROM BASICS TO BREAKTHROUGHS}
}

Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.

Published via Towards AI