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
Pipeline Parallelism for MoE Models
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- GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism, Yanping Huang, Youlong Cheng, Ankur Bapna, Orhan Firat, Dehao Chen, Mia Chen, HyoukJoong Lee, Jiquan Ngiam, Quoc V Le, Yonghui Wu, Zhifeng Chen, 2019 Advances in Neural Information Processing Systems (NeurIPS), Vol. 32 DOI: 10.5555/3454287.3455171 - Introduces pipeline parallelism with micro-batching for training large neural networks, a foundational work on the topic.
- Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism, Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper, and Bryan Catanzaro, 2019 arXiv preprint arXiv:1909.08053 DOI: 10.48550/arXiv.1909.08053 - Describes model parallelism techniques, including pipeline parallelism, for training multi-billion parameter language models efficiently.
- Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity, William Fedus, Barret Zoph, and Noam Shazeer, 2022 Journal of Machine Learning Research, Vol. 23 (Journal of Machine Learning Research) DOI: 10.48550/arXiv.2101.03961 - Details the architecture and distributed training strategies for Switch Transformers, a sparse Mixture-of-Experts model scaled to trillions of parameters.