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
Stabilization Techniques for Routers
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- Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity, William Fedus, Barret Zoph, Noam Shazeer, 2022 Journal of Machine Learning Research, Vol. 23 (JMLR, Inc. and Microtome Publishing) DOI: 10.5555/3547466.3547565 - Introduces Router Z-Loss and discusses expert capacity, essential for training stable large-scale Mixture-of-Experts models.
- 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 International Conference on Learning Representations (ICLR 2017) DOI: 10.48550/arXiv.1701.06538 - A foundational paper for modern sparse Mixture-of-Experts layers, outlining early challenges and load balancing approaches for gating networks.
- Deep Learning, Ian Goodfellow, Yoshua Bengio, and Aaron Courville, 2016 (MIT Press) - Offers comprehensive details on regularization methods and gradient clipping, general techniques applicable to router stability.
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