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
54 lines
1.3 KiB
C++
54 lines
1.3 KiB
C++
#pragma once
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/**
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* @file spectral.hpp
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* @brief Spectral Sensor and Controller — grokking awareness via rank tracking.
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*
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* Port of: packages/fces/core/spectral_sensor.py + spectral_controller.py
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*/
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#include <string>
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#include <unordered_map>
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#include <vector>
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#include <torch/torch.h>
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namespace fces {
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/**
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* SpectralSensor — tracks the effective rank of weight matrices.
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*
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* Used for grokking detection: when the model transitions from
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* memorization to generalization, the effective rank drops.
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*/
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class SpectralSensor {
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public:
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explicit SpectralSensor(torch::nn::Module& model);
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/// Track a layer's weight tensor
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void track_layer(const std::string& name, const torch::Tensor& weight);
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/// Get the global (average) effective rank
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float get_global_rank() const;
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/// Reset all tracked layers
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void reset();
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private:
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std::unordered_map<std::string, float> layer_ranks_;
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/// Compute effective rank via SVD
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static float compute_effective_rank(const torch::Tensor& weight);
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};
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/**
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* SpectralController — converts spectral rank into optimizer decisions.
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*/
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class SpectralController {
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public:
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/// Compute the spectral alpha (gating factor for rank-aware updates)
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float compute_alpha(float global_rank, float grokking_coefficient) const;
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};
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} // namespace fces
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