9bbe253810
- CMakeLists.txt with libtorch, GoogleTest, GoogleBenchmark, OpenMP, pybind11 - Header files: config, controller, population, fitness, evolution, spectral, oscillation, telemetry, optimizer - Source implementations: controller (full micro-MLP forward pass, mutation, crossover), fitness (Welford's algorithm), oscillation (DFT), spectral (SVD rank), optimizer (sign-SGD stub) - Tests: controller, population, fitness, optimizer (Google Test) - Benchmarks: evolve throughput, optimizer step (Google Benchmark) - Examples: simple optimization, PyTorch/libtorch integration - Python extension: pybind11 bindings with setup.py - README with architecture diagram and build instructions
115 lines
4.5 KiB
Markdown
115 lines
4.5 KiB
Markdown
# FCES-native
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**High-performance C++ reimplementation of the Fuzzy Controlled Evolutionary Search (FCES) optimizer.**
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FCES is a zero-memory evolutionary optimizer that replaces AdamW for neural network training, saving 100% of optimizer VRAM. This repository provides a native C++ implementation with libtorch integration for maximum performance.
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## Features
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- **Zero-State Overhead**: No per-parameter momentum/variance buffers (unlike Adam)
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- **Neuro-Evolutionary Search**: Population of fuzzy controllers evolved via genetic algorithms
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- **Spectral Sensing**: Grokking-aware rank minimization
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- **Born Quantized**: Quantization-aware evolutionary training
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- **libtorch Integration**: Drop-in replacement for PyTorch optimizers
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- **Python Extension**: pip-installable via pybind11 for seamless PyTorch interop
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## Architecture
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```
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┌──────────────────────────────────────────────────────┐
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│ FCESOptimizer │
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│ ┌────────────────┐ ┌─────────────────────────────┐ │
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│ │ EvolutionMgr │ │ Parameter Update (libtorch) │ │
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│ │ ┌────────────┐ │ │ • sign(grad) │ │
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│ │ │ Population │ │ │ • trust region clipping │ │
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│ │ │ ┌────────┐ │ │ │ • weight decay │ │
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│ │ │ │Ctrl[0] │ │ │ └─────────────────────────────┘ │
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│ │ │ │Ctrl[1] │ │ │ ┌─────────────────────────────┐ │
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│ │ │ │ ... │ │ │ │ SpectralSensor │ │
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│ │ │ │Ctrl[N] │ │ │ │ • rank tracking │ │
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│ │ │ └────────┘ │ │ │ • grokking detection │ │
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│ │ └────────────┘ │ └─────────────────────────────┘ │
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│ │ • crossover │ ┌─────────────────────────────┐ │
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│ │ • mutation │ │ OscillationDetector (FFT) │ │
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│ │ • selection │ └─────────────────────────────┘ │
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│ └────────────────┘ │
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└──────────────────────────────────────────────────────┘
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```
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## Building
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### Prerequisites
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- C++17 compiler (GCC 9+, Clang 10+, MSVC 2019+)
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- CMake 3.18+
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- libtorch (PyTorch C++ distribution)
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### Build Steps
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```bash
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# Download libtorch (Linux, CUDA 12.1)
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wget https://download.pytorch.org/libtorch/cu121/libtorch-cxx11-abi-shared-with-deps-2.3.0%2Bcu121.zip
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unzip libtorch-*.zip
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# Configure & Build
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mkdir build && cd build
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cmake .. -DCMAKE_PREFIX_PATH=/path/to/libtorch
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cmake --build . --config Release -j$(nproc)
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# Run tests
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ctest --output-on-failure
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```
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### Windows (MSVC)
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```powershell
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mkdir build; cd build
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cmake .. -DCMAKE_PREFIX_PATH="C:/path/to/libtorch" -G "Visual Studio 17 2022"
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cmake --build . --config Release
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ctest -C Release --output-on-failure
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```
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## Python Extension
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```bash
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cd python
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pip install .
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```
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```python
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import torch
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from fces_native import FCESOptimizer
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model = MyModel()
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optimizer = FCESOptimizer(model.parameters(), lr=1.6e-3, population_size=200)
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for batch in dataloader:
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loss = model(batch)
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loss.backward()
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optimizer.step()
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optimizer.update_fitness(loss.item())
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optimizer.zero_grad()
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```
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## Performance
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Expected speedup over the Python implementation:
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| Component | Python | C++ | Speedup |
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|-----------|--------|-----|---------|
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| `evolve()` (200 controllers) | ~2ms | ~20μs | ~100x |
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| `decide_update()` | ~0.5ms | ~5μs | ~100x |
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| End-to-end `step()` overhead | ~4ms | ~0.15ms | ~25x |
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## Scientific Background
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See [FCES SCIENCE.md](https://git.zky.de/sven/FCES/src/branch/main/SCIENCE.md) for the full scientific log documenting the evolution from V1.0 (hardcoded fuzzy rules) through V49.0 (born-quantized training).
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## License
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MIT License — See [LICENSE](LICENSE) for details.
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## Related
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- [FCES (Python)](https://git.zky.de/sven/FCES) — Original Python implementation
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