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Mixture of Experts: Advanced Architecture, Training, and Scaling

Chapter 1: Foundations of Sparse Expert Models

Conditional Computation Principles

The Sparse MoE Paradigm

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

Mixture of Experts: Advanced Architecture, Training, and Scaling

Prerequisites: Advanced Deep Learning knowledge

Level: Expert

What You'll Learn

Advanced MoE Architectures
Analyze and implement sophisticated MoE architectural variants beyond basic designs.
Expert Routing Mechanisms
Understand and implement advanced routing algorithms and gating networks for conditional computation.
MoE Training Dynamics
Address challenges in MoE training, including load balancing, router optimization, and expert specialization.
Distributed Training Optimization
Apply advanced distributed training techniques specifically tailored for sparse MoE models.
Scaling Strategies for MoE
Implement efficient scaling strategies combining model, data, and pipeline parallelism for MoEs.
Performance Analysis and Tuning
Profile, analyze, and tune the performance of large-scale MoE models in distributed environments.

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