// 모델 최적화 파이프라인 - ONNX → TensorRT → Pruning

class ModelOptimizer {
public:
    // Step 1: ONNX 변환 + Graph Optimization
    OnnxModel convert_to_onnx(const TrainedModel& model) {
        OnnxExporter exporter;
        exporter.set_opset_version(17);
        exporter.set_dynamic_axes({"input", {0}});
        auto onnx = exporter.export(model);

        // Graph optimization: operator fusion, constant folding
        OnnxOptimizer opt;
        opt.add_pass("fuse_bn_into_conv");
        opt.add_pass("eliminate_nop_transpose");
        opt.add_pass("constant_folding");
        return opt.optimize(onnx);
    }

    // Step 2: TensorRT INT8 양자화
    TRTEngine quantize_tensorrt(const OnnxModel& onnx,
                              const CalibrationData& calib) {
        TRTBuilder builder;
        builder.set_precision(Precision::INT8);
        builder.set_calibrator(
            std::make_unique<EntropyCalibrator>(calib));
        builder.set_max_batch_size(64);
        builder.set_max_workspace(1 << 30); // 1GB
        return builder.build(onnx);
    }

    // Step 3: Structured Pruning
    PrunedModel prune(TrainedModel& model, double ratio = 0.3) {
        // L1-norm 기반 중요도 낮은 필터 제거
        auto importance = compute_l1_importance(model);
        auto threshold = percentile(importance, ratio * 100);

        for (auto& layer : model.layers()) {
            if (importance[layer] < threshold) {
                layer.mask(); // 뉴런 마스킹
            }
        }
        // Fine-tune: 프루닝 후 정확도 복원
        model.fine_tune(5); // 5 epochs
        return model.compact();
    }
};