memory_manager.cpp

#include <pybind11/pybind11.h>
#include <vector>
#include <numeric>

namespace py = pybind11;

/**
 * Simulates a low-level memory buffer optimization.
 * In a production LLM environment, this would represent operations like
 * weight normalization, quantization adjustments, or KV cache management.
 */
float optimize_buffer(std::vector<float> input_buffer) {
    if (input_buffer.empty()) {
        return 0.0f;
    }

    // Simulating a low-level computation (e.g., calculating average activation)
    float sum = std::accumulate(input_buffer.begin(), input_buffer.end(), 0.0f);
    return sum / input_buffer.size();
}

/**
 * Pybind11 module definition.
 * This bridges the C++ performance-critical logic with the Python workflow.
 */
PYBIND11_MODULE(performance_lib, m) {
    m.doc() = "High-performance helper for LLM memory buffer management";
    
    m.def("optimize_buffer", &optimize_buffer, 
          "Processes and optimizes memory buffers for inference efficiency");
}