This project implements a Mixture of Experts (MoE) model with a focus on efficient routing and expert utilization. The implementation includes:

1. A flexible MoE router that can handle different numbers of experts and routing parameters
2. Expert layers implemented as feed-forward networks
3. A complete MoE layer that combines routing and expert computation
4. A transformer block that uses MoE for the feed-forward layer
5. Comprehensive benchmarking and visualization utilities

.
├── src/                    # Main source code
│   ├── models/            # Model implementations
│   ├── utils/             # Utility functions
│   └── triton/            # Triton optimizations
├── data/                  # Dataset and data processing
├── configs/               # Configuration files
├── docs/                  # Documentation
├── assets/                # Visualizations and diagrams
└── tests/                 # Test suite

• Efficient Routing Algorithm

  • Top-k expert selection
  • Capacity factor for load balancing
  • Optional router jitter for improved training
  • Sparse expert selection for computational efficiency

• Expert Layers

  • Configurable dimensions
  • Flexible activation functions
  • Batch processing support

• Visualization and Analysis

  • Expert utilization tracking
  • Performance benchmarking
  • Parameter comparison analysis
  • Memory usage profiling

Expert utilization patterns with k=2 and capacity factor=1.5

Performance comparison across different configurations

Impact of different parameters on model performance

1. Create a virtual environment:

python -m venv venv
source venv/bin/activate  # On Linux/Mac
# or
.\venv\Scripts\activate  # On Windows

2. Install dependencies:

pip install -r requirements.txt

python src/benchmark.py

This will:

1. Run comprehensive benchmarks with different configurations
2. Generate expert utilization plots
3. Create performance comparison charts
4. Save results in the assets/ directory

The project includes several visualization tools:

1. Expert Utilization Plotter

   • Tracks expert selection patterns
   • Visualizes load balancing
   • Generates heatmaps of expert usage

2. Performance Analyzer

   • Creates comparative charts
   • Tracks memory usage
   • Analyzes computational efficiency

3. Parameter Comparison Tool

   • Compares different configurations
   • Visualizes trade-offs
   • Helps optimize hyperparameters

python src/profile_moe.py

This will:

1. Profile memory usage and performance
2. Generate detailed reports
3. Create visualization of bottlenecks
4. Update profiling documentation

1. ✅ Core Implementation

   • ✅ Router implementation
   • ✅ Expert implementation
   • ✅ MoE layer implementation
   • ✅ Visualization tools

2. ✅ Evaluation Framework

   • ✅ Benchmarking utilities
   • ✅ Expert utilization analysis
   • ✅ Performance profiling
   • ✅ Visualization suite

3. 🔄 Advanced Features (In Progress)

   • 🔄 Memory optimization
   • 🔄 Quantization support
   • 🔄 Multi-GPU support
   • 🔄 Enhanced visualization tools

Contributions are welcome! Please feel free to submit issues and enhancement requests.

This project is licensed under the MIT License - see the LICENSE file for details.