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 Caching and Optimization
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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 (arXiv) DOI: 10.48550/arXiv.1701.06538 - Introduces the sparsely-gated Mixture-of-Experts layer, defining the router (gating network) and its function in directing tokens to experts. This paper provides a fundamental understanding of the MoE architecture.
- DeepSpeed-MoE: Advancing Mixture-of-Experts Inference and Training to Power Next-Generation AI Scale, Samyam Rajbhandari, Conglong Li, Zhewei Yao, Minjia Zhang, Reza Yazdani Aminabadi, Ammar Ahmad Awan, Jeff Rasley, Yuxiong He, 2022 ICML 2022 DOI: 10.48550/arXiv.2201.05596 - Presents methods for optimizing MoE inference and training, including efficient communication for routers and deployment strategies, directly relevant to the section's focus on router optimization.
- NVIDIA TensorRT Developer Guide, NVIDIA Corporation, 2023 (NVIDIA Corporation) - A guide on optimizing deep learning inference through methods like quantization, layer fusion, and kernel optimization, applicable to router components for performance gains.
- GShard: Scaling Giant Models with Automatic Partitioning and Parallelism, Dmitry Lepikhin, HyoukJoong Lee, Yuanzhong Xu, Dehao Chen, Orhan Firat, Yanping Huang, Maxim Krikun, Noam Shazeer, Zhifeng Chen, 2020 arXiv preprint arXiv:2006.16668 DOI: 10.48550/arXiv.2006.16668 - Discusses distributed training and inference of large MoE models, detailing challenges and solutions related to expert parallelism and communication patterns that impact router deployment.