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
Comparison with Dense Model Scaling
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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 preprint arXiv:1701.06538 DOI: 10.48550/arXiv.1701.06538 - Introduces the sparsely-gated Mixture-of-Experts (MoE) layer for neural networks, enabling models with a vast number of parameters while maintaining a constant computational cost per example. This is foundational to the concept of decoupling parameters from computation.
- Scaling Laws for Neural Language Models, Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B. Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, Dario Amodei, 2020 arXiv preprint arXiv:2001.08361 DOI: 10.48550/arXiv.2001.08361 - A seminal paper exploring how the performance of dense neural language models scales with model size, dataset size, and computational budget, providing the context for why MoE's parameter efficiency is so beneficial.