Inference Research: Log-Linear Attention & MesaNet Optimizations

A research repository demonstrating breakthrough algorithmic speedups for video AI inference, featuring Log-Linear Attention and MesaNet optimizations with real A100 GPU benchmarks.

Key Achievements

• Linear Attention: Achieved 6.55× speedup with true O(T) complexity vs O(T²)
• Log-Linear Attention: O(T log T) complexity using λ(ℓ) gating mechanisms
• MesaNet: Efficient first-order optimization with Conjugate Gradient
• Thin VAE: 38.2× parameter reduction while maintaining quality
• Memory-efficient attention: Chunking for O(T) memory vs O(T²)

Benchmark Results

Real speedups that scale with sequence length:

• 256 tokens: 1.56× faster
• 512 tokens: 3.52× faster
• 1024 tokens: 6.55× faster
• Average speedup: 3.88× across all tests

Research Implementations

Core Algorithms

• final_working_speedups.py - Main benchmark with all optimizations
• mesanet_log_linear_benchmark.py - MesaNet and Log-Linear attention
• linear_attention_implementations.py - Kernel trick implementations

Production Benchmarks

• extended_a100_benchmark.py - Comprehensive A100 GPU benchmarks
• simple_working_benchmark.py - Minimal working examples
• performance_diagnosis.py - Performance analysis tools

Advanced Implementations

• triton_production_seedance.py - Triton kernel optimizations
• production_scaling_480p_1080p.py - High-resolution scaling tests
• complete_seedance_killer.py - Full SOTA comparison

Setup

# Clone the repository
git clone git@github.com:ry2009/-intro-Inference-research.git
cd -intro-Inference-research

# Install dependencies (requires CUDA-capable GPU)
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
pip install triton flash-attn xformers

# Run basic benchmark
python final_working_speedups.py

Documentation

• atual_inf_goal/cookbook.md - Comprehensive technical documentation
• SOTA_BENCHMARK_REPORT.md - Detailed benchmark analysis
• RESULTS_SUMMARY.md - Key findings and results

Research Focus

This repository validates theoretical complexity improvements with real-world measurements:

1. Algorithmic Breakthroughs: Moving from O(T²) to O(T) and O(T log T) complexities
2. Parameter Efficiency: Massive model size reductions without quality loss
3. Memory Optimization: Scaling to long sequences with limited GPU memory
4. Production Ready: Real A100 benchmarks showing practical speedups

SOTA Comparisons

Our implementations achieve speedups comparable to major research papers while using simple PyTorch code, proving that fundamental algorithmic improvements can deliver dramatic performance gains without complex engineering.

📄 License

MIT License - see LICENSE file for details.

🤝 Contributing

This is active research code. Feel free to open issues or submit PRs for improvements.