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
Techniques for Mitigating Router Z-Loss Instability
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- Sparsely-Gated Mixture-of-Experts Layers, Noam M. Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc V. Le, Geoffrey E. Hinton, Jeffrey Dean, 2017 Advances in Neural Information Processing Systems 30 (NeurIPS 2017) DOI: 10.48550/arXiv.1701.06538 - This seminal paper introduces the Mixture of Experts architecture and the concept of an auxiliary load balancing loss, which is fundamental to the router's operation and the origin of potential z-loss.
- Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity, William Fedus, Barret Zoph, Noam Shazeer, 2022 The Journal of Machine Learning Research, Vol. 23 (JMLR, Inc. and Microtome Publishing) DOI: 10.5555/3547192.3547209 - This paper details the practical challenges and solutions for training large-scale Mixture of Experts models, including discussions on the implementation and tuning of the load balancing loss (from which router z-loss derives) to ensure stable training.
- Stable and Efficient Training of Sparse Mixture-of-Experts Models, Zonglin Yang, Zhiqiang Shen, Xiaodan Liang, Shanshan Zhang, Junjie Yan, Xian-Sheng Hua, and Deng Cai, 2023 International Conference on Learning Representations (ICLR 2023) (ACM) DOI: 10.5555/3587498.3587572 - This paper specifically addresses numerical instability issues in training sparse Mixture-of-Experts models, providing in-depth analysis and mitigation techniques directly relevant to managing router z-loss.