Mixture of Experts: Core Concepts and Hands-on Implementation
Chapter 1: Foundations of Mixture of Experts Models
Overview of Sparsely-Gated MoE Architecture
The Gating Network: Formulation and Function
Expert Networks: Specialization and Capacity
Mathematical Formulation of the MoE Layer
Load Balancing and Auxiliary Losses
Challenges in MoE Training: Expert Collapse
Comparison with Dense Model Scaling
Hands-on: Implementing a Basic MoE Layer
Chapter 2: Advanced Routing Mechanisms
Analysis of Top-k Gating and its Variants
Noisy Top-k Gating for Load Balancing
Hash-based Routing for Deterministic Selection
Switch Transformers: Simplified Routing
Soft MoE: Differentiable Routing
Analyzing Routing Decisions and Specialization
Hands-on: Implementing Different Routing Strategies
Chapter 3: Training and Optimization of Large-Scale MoEs
Expert Parallelism for Distributed Training
Combining Model, Data, and Expert Parallelism
Capacity Factor and its Impact on Performance
Techniques for Mitigating Router Z-Loss Instability
Precision and its Effects: BFloat16 Training
Fine-tuning Strategies for Pre-trained MoE Models
Practice: Configuring a Distributed Training Job
Chapter 4: Efficient Inference with MoE Models
Inference Challenges: Memory and Latency
Expert Offloading to CPU or NVMe
Batching Strategies for Sparse Activation
Model Distillation for MoE Compression
Quantization Techniques for MoE Layers
Speculative Decoding with MoE Models
Hands-on: Building an Optimized Inference Pipeline
Chapter 5: Integrating MoE into Modern Architectures
Replacing FFNs with MoE Layers in Transformers
Placement of MoE Layers: Frequency and Location
MoE in Vision Transformers (ViT)
MoE in Multi-modal Models
Architectural Variants and their Properties
Analyzing Parameter vs. FLOPs Trade-offs
Practice: Modifying a Transformer to use MoE
Analyzing Routing Decisions and Specialization
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- Switch Transformers: Scaling to Trillion Parameter Models with Sparsely Activated Expert Layers, William Fedus, Barret Zoph, Noam Shazeer, 2022 Journal of Machine Learning Research, Vol. 23 (Microtome Publishing) DOI: 10.5555/3540306.3540316 - Describes an efficient Mixture of Experts architecture that introduced auxiliary losses (including load balancing and router z-loss) to improve training stability and expert utilization, which aids in analyzing routing behavior.
- Sparsely Gated Mixture of Experts Layers, Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc Le, Geoffrey Hinton, Jeff Dean, 2017 Advances in Neural Information Processing Systems, Vol. 30 (Neural Information Processing Systems Foundation, Inc. (NeurIPS)) DOI: 10.48550/arXiv.1701.06538 - A foundational work that introduced sparsely gated Mixture of Experts layers to deep learning, discussing the challenges in training MoE models and highlights expert utilization and balanced routing.
- GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding, Dmitry Lepikhin, Hieu Pham, Yanqi Zhou, Zhifeng Chen, Jeff Dean, 2021 International Conference on Learning Representations (ICLR) (ICLR) DOI: 10.48550/arXiv.2006.16668 - Presents an early large-scale MoE system that introduced several techniques for training conditional computation models, including auxiliary losses for load balancing and efficient distributed training.