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Changelog

All notable changes to the Mixture-of-Recursions (MoR) project will be documented in this file.

The format is based on Keep a Changelog, and this project adheres to Semantic Versioning.

[1.0.0] - 2024-07-19

🎉 Initial Release

Added

  • Core MoR Architecture

    • Recursive transformer layers with parameter sharing
    • Adaptive token-level routing for dynamic recursion depths
    • Selective attention masking for efficiency
    • KV caching optimization for memory efficiency

  • Advanced Features

    • Learned threshold routing with dynamic depth assignment
    • Multi-scale attention mechanisms
    • Efficiency-aware routing with computational cost feedback
    • Adaptive caching for key-value pairs
    • Hierarchical recursion patterns

  • Model Configurations

    • Small model: 33M parameters, depth 3
    • Medium model: 90M parameters, depth 4
    • Large model: 288M parameters, depth 6
    • Configurable architectures for research

  • Training & Evaluation

    • Complete training pipeline with multiple dataset support
    • Evaluation scripts with efficiency metrics
    • Integration with Weights & Biases and TensorBoard
    • Hydra configuration management

  • Analysis Tools

    • Comprehensive MoRAnalyzer for pattern analysis
    • Recursion depth visualization and statistics
    • Token complexity correlation analysis
    • Throughput and memory benchmarking
    • Efficiency metrics and trade-off analysis

  • Demonstrations

    • Simple MoR demo showcasing router functionality
    • Advanced demo with all features enabled
    • Interactive Jupyter notebook for exploration
    • Command-line experiment runner

  • Professional Development Setup

    • Docker containerization for reproducibility
    • GitHub Actions CI/CD pipeline
    • Pre-commit hooks for code quality
    • Comprehensive test suite with pytest
    • Professional documentation and contributing guidelines

Performance

  • Efficiency Gains: 30-50% reduction in computation vs. standard transformers
  • Memory Optimization: Selective attention and KV caching reduce memory usage
  • Adaptive Processing: Dynamic depth assignment based on token complexity
  • Scalability: Tested on models from 33M to 288M parameters

Documentation

  • Complete README with usage examples and quick start guide
  • API documentation with docstrings
  • Research paper implementation details
  • Contributing guidelines for open-source collaboration
  • Docker setup for easy reproduction

Testing

  • Unit tests for all core components
  • Integration tests for complete pipeline
  • Performance benchmarks and regression tests
  • Automated testing with multiple Python versions

🔧 Technical Details

  • Python: 3.8+ support
  • PyTorch: 2.0+ compatibility
  • Dependencies: Pinned versions for reproducibility
  • Hardware: GPU acceleration with optional Flash Attention
  • Datasets: WikiText, OpenWebText, and custom dataset support

🎯 Research Impact

  • First comprehensive open-source MoR implementation
  • Reproducible research with exact version specifications
  • Extensible architecture for future research directions
  • Community-ready with professional development practices

[Unreleased]

Planned Features

  • Hardware-specific optimizations for different GPU architectures
  • Multimodal extensions for vision-language models
  • Production deployment optimizations
  • Advanced routing strategies research
  • Integration with popular ML frameworks

Research Directions

  • Scaling laws for MoR architectures
  • Meta-learning for routing strategies
  • Hybrid MoR + MoE architectures
  • Neural architecture search for optimal configurations

Contributing

See CONTRIBUTING.md for details on how to contribute to this changelog and the project.

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