Mixture of Experts: Advanced Architecture, Training, and Scaling
Chapter 1: Foundations of Sparse Expert Models
Conditional Computation Principles
The Sparse MoE Approach
Contrasting Dense vs. Sparse Activation
Mathematical Formulation of Basic MoE Layers
Chapter 2: Advanced MoE Architectures
Designing Effective Gating Networks
Hierarchical MoE Structures
Router Architectures: Linear, Non-Linear, Attention-Based
Expert Capacity and Sizing Considerations
Stabilization Techniques for Routers
Hands-on Practical: Implementing Custom Gating Mechanisms
Chapter 3: Training Dynamics and Optimization
The Load Balancing Problem in MoE
Auxiliary Loss Functions for Load Balancing
Router Optimization Strategies
Handling Dropped Tokens
Expert Specialization Collapse and Prevention
Impact of Optimizer Choice and Hyperparameters
Hands-on Practical: Implementing and Tuning Load Balancing Losses
Chapter 4: Scaling MoE Models: Distributed Training
Challenges in Distributed MoE Training
Expert Parallelism: Distributing Experts Across Devices
Integrating Expert Parallelism with Data Parallelism
All-to-All Communication Patterns
Pipeline Parallelism for MoE Models
Communication Optimization Techniques (e.g., Overlapping)
Frameworks and Libraries for Distributed MoE (e.g., DeepSpeed, Tutel)
Practice: Configuring Distributed MoE Training
Chapter 5: Inference Optimization and Deployment
Inference Challenges with Sparse Models
Batching Strategies for MoE Inference
Model Compression Techniques for MoE
Hardware Acceleration Considerations
Router Caching and Optimization
Deployment Patterns for Large Sparse Models
Hands-on Practical: Profiling MoE Inference
Mathematical Formulation of Basic MoE Layers
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- Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer, Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc Le, Geoffrey Hinton, Jeff Dean, 2017 arXiv preprint arXiv:1701.06538 DOI: 10.48550/arXiv.1701.06538 - This foundational paper introduces the sparsely-gated Mixture-of-Experts layer, detailing the gating network, Top-k routing, and weighted output combination, forming the basis for modern MoE architectures.
- Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity, William Fedus, Barret Zoph, Noam Shazeer, 2021 Journal of Machine Learning Research DOI: 10.48550/arXiv.2101.03961 - This paper expands on the practical implementation and scaling of MoE layers in large Transformer models, discussing architectural details, routing mechanisms, and training challenges such as expert load balancing.
- Learning with Experts, Robert A. Jacobs, Michael I. Jordan, Steven J. Nowlan, Geoffrey E. Hinton, 1991 Neural Computation, Vol. 3 (MIT Press) DOI: 10.1162/neco.1991.3.1.79 - This seminal work introduces the concept of learning with mixtures of experts, laying much of the theoretical groundwork for combining multiple specialized networks with a gating network to process inputs.