Function effective_weight_norm_conv1d

src/models/vevo2/components.cpp:570–597

    int64_t rows,
    int64_t cols,
    const std::string & name) {
    if (rows <= 0 || cols <= 0 || static_cast<int64_t>(values.size()) != rows * cols) {
        throw std::runtime_error("Vevo2 L2 row normalization shape mismatch: " + name);
    }
    for (int64_t row = 0; row < rows; ++row) {
        double sum = 0.0;
        const int64_t base = row * cols;
        for (int64_t col = 0; col < cols; ++col) {
            const float value = values[static_cast<size_t>(base + col)];
            sum += static_cast<double>(value) * static_cast<double>(value);
        }
        const double norm = std::sqrt(sum);
        if (norm == 0.0) {
            throw std::runtime_error("Vevo2 L2 row normalization found zero norm: " + name);
        }
        const float inv = static_cast<float>(1.0 / norm);
        for (int64_t col = 0; col < cols; ++col) {
            values[static_cast<size_t>(base + col)] *= inv;
        }
    }
    return values;
}

std::pair<std::vector<float>, std::vector<float>> load_whisper_stats(
    const engine::assets::TensorSource & source,
    int64_t whisper_dim) {
    auto mean = source.require_f32("mean", {whisper_dim});
    auto std = source.require_f32("std", {whisper_dim});
    source.release_storage();
    return {std::move(mean), std::move(std)};
}