GitHub - getcognition-online/sovereign-metal: A zero-dependency, zero-copy Python-to-Metal GPGPU advection engine & local transformer. Runs FP16 text embeddings and parallel reductions entirely on macOS/iOS integrated GPUs. · GitHub
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⚜️ Sovereign-Metal
A zero-dependency, high-throughput Python-to-Metal GPGPU advection engine and local transformer pipeline. Program custom MSL (Metal Shading Language) kernels directly from Python with zero-copy unified memory alignment and zero PCIe bus latency.
🔱 Core Features
Zero-Copy Unified Memory: Directly maps NumPy arrays into shared CPU/GPU buffers (MTLResourceStorageModeShared), completely bypassing PCIe transfer bottlenecks.
Branchless Toroidal Boundary Wrapping: Implements hardware-level bitwise wrapping (x - 1u) & (dim - 1u) inside MSL shaders, avoiding thread divergence and division stalls on integrated Apple/Intel GPUs.
Zero-Latency Reduction Tree: Runs a dedicated Shannon entropy, localization intensity, and energy proxy reduction kernel entirely in threadgroup scratchpad memory.
Standalone GPGPU Transformer: Runs FP16 BERT-style embeddings (all-MiniLM-L6-v2) locally on Metal with strict GPGPU acceleration.
📐 Mathematical Framework
Toroidal Fluid Dynamics (Soliton Advection)
The advection advects a continuous scalar field $S$ through a density field $\rho$ on a conjoined toroidal manifold:
$$\frac{\partial S}{\partial t} = -\nu \nabla^2 S - \alpha (\nabla \rho) \cdot \nabla S + \kappa |S|^2 S$$
Where:
$\nu$ represents the dissipation/damping coefficient.
$\alpha$ is the advection coupling strength.
$\kappa$ is the non-linear soliton crystallization rate.
🚀 Quick Start
1. Installation
Clone the repository and install the dependencies:
cd sovereign-metal<br>pip install -e .
2. Run Semantic Embeddings Demo
Verify the local GPGPU transformer inference:
python examples/local_embeddings.py
3. Run Toroidal Advection Benchmark
Execute the soliton advection simulation and watch the real-time crystallization metrics stream from the GPU:
python examples/benchmark_advection.py
🤝 Acknowledgements & Credits
We stand on the shoulders of giants. A sincere thank you to:
The SentenceTransformers Team: For developing the incredibly efficient all-MiniLM-L6-v2 model weights architecture.
Hugging Face / Rust Tokenizers Team: For building the blazingly fast tokenization library that powers our input pipeline.
Apple Metal Team: For providing the hardware-native GPGPU framework that makes continuous cognitive advection possible.
⚜️ License
MIT License. Crafted for the GPGPU developer community.
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A zero-dependency, zero-copy Python-to-Metal GPGPU advection engine & local transformer. Runs FP16 text embeddings and parallel reductions entirely on macOS/iOS integrated GPUs.
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Python<br>74.1%
Metal<br>25.9%
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