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
Router Architectures: Linear, Non-Linear, Attention-Based
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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 seminal paper introduced the modern sparsely-gated Mixture-of-Experts layer, detailing the use of a linear gating network with top-k selection and noise for load balancing, which forms the basis for linear routers.
- Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsely Activated Transformers, William Fedus, Barret Zoph, Noam Shazeer, 2022 Journal of Machine Learning Research, Vol. 23 DOI: 10.5555/3540277.3540449 - This work demonstrated the effectiveness of simple linear routers (often top-1 or top-2) in scaling Mixture-of-Experts models to billions of parameters, highlighting their computational efficiency and practical utility.
- Attention-based Experts Selection for Deep Neural Networks, Jung-Min Kim, Jong-Seok Lee, 2020 Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34 (Association for the Advancement of Artificial Intelligence (AAAI)) DOI: 10.1609/aaai.v34i04.5879 - This paper proposes an attention-based mechanism for selecting experts, where attention weights are learned to assign experts for each input, directly illustrating the concept of attention-based routers.