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
Inference Challenges with Sparse Models
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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 International Conference on Learning Representations (ICLR) DOI: 10.48550/arXiv.1701.06538 - Foundational work introducing the Mixture of Experts architecture, discussing the need for sparsity to scale models and hinting at its computational and memory implications.
- DeepSpeed-MoE: Advancing Mixture-of-Experts Inference and Training for Large Language Models, Samyam Rajbhandari, Conglong Li, Zhewei Yao, Minjia Zhang, Reza Yazdani Aminabadi, Ammar Ahmad Awan, Jeff Rasley, Yuxiong He, 2022 Proceedings of the 39th International Conference on Machine Learning (ICML 2022), Vol. 162 (PMLR) DOI: 10.48550/arXiv.2201.05596 - Describes specific inference challenges of MoE models and presents solutions for optimizing latency, throughput, and memory utilization, including techniques for load balancing and communication.
- Tutel: Adaptive Mixture-of-Experts Training for Faster Convergence and Better Quality, Zhengda Chen, Xiaoxia Wu, Minjia Zhang, Yuxiong He, 2023 IEEE Transactions on Parallel and Distributed Systems, Vol. 34 (IEEE) DOI: 10.1109/TPDS.2023.3283259 - Details a system for MoE, with a focus on optimizing execution, including techniques to manage communication overhead and load imbalance during inference.
- GLaM: Efficient Scaling of Language Models with MoE, Nan Du, Yanping Huang, Andrew M. Dai, Simon Tong, Dmitry Lepikhin, Yuanzhong Xu, Maxim Krikun, Yanqi Zhou, Adams Wei Yu, Orhan Firat, Barret Zoph, Liam Fedus, Maarten Bosma, Zongwei Zhou, Tao Wang, Yu Emma Wang, Kellie Webster, Marie Pellat, Kevin Robinson, Kathleen Meier-Hellstern, Toju Duke, Lucas Dixon, Kun Zhang, Quoc V Le, Yonghui Wu, Zhifeng Chen, Claire Cui, 2022 International Conference on Machine Learning (ICML) 2022 DOI: 10.48550/arXiv.2112.06905 - Presents a large-scale MoE language model, discussing the practical aspects of its efficiency during inference, including memory and computation considerations at scale.