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
The Load Balancing Problem in MoE
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- Sparsely-Gated Mixture-of-Experts Layers, Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc Le, Geoffrey Hinton, and Jeff Dean, 2017 International Conference on Learning Representations (ICLR) DOI: 10.48550/arXiv.1701.06538 - This foundational paper introduced the concept of sparsely-gated Mixture of Experts and proposed an auxiliary loss for balancing expert load, directly addressing the problem described.
- GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding, Dmitry Lepikhin, Hieu Pham, Orhan Firat, Michele Catasta, Zhifeng Chen, George Tucker, Azade Nova, Andre Barreto, Max Dean, and Jeff Dean, 2020 arXiv preprint arXiv:2006.16668 DOI: 10.48550/arXiv.2006.16668 - This work demonstrates the practical scaling of MoE models to massive sizes, emphasizing the necessity of efficient load distribution and automatic sharding strategies which are intrinsically tied to the load balancing problem.
- Router Argumentation for Mixture-of-Experts, Koustuv Sinha, Michael Noukhovitch, Subhabrata Roy, Karthik Srinivasan, William Fedus, Michael Ryoo, and Yoshua Bengio, 2022 International Conference on Learning Representations (ICLR) DOI: 10.48550/arXiv.2202.04944 - This paper proposes methods to improve the gating network's routing decisions, directly contributing to better load balance and expert specialization by making the routing more robust.