Method state_write_data

smallthinker/src/llama-kv-cache-unified.cpp:1314–1399

}

void llama_kv_cache_unified::state_write_data(llama_io_write_i & io, const std::vector<std::pair<uint32_t, uint32_t>> & cell_ranges) const {
    const uint32_t v_trans = this->v_trans ? 1 : 0;
    const uint32_t n_layer = layers.size();

    io.write(&v_trans, sizeof(v_trans));
    io.write(&n_layer, sizeof(n_layer));

    std::vector<uint8_t> tmp_buf;

    // Iterate and write all the keys first, each row is a cell
    // Get whole range at a time
    for (const auto & layer : layers) {
        const uint32_t il = layer.il;

        const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa(il) + hparams.n_embd_k_s();

        // Write key type
        const int32_t k_type_i = (int32_t)layer.k->type;
        io.write(&k_type_i, sizeof(k_type_i));

        // Write row size of key
        const uint64_t k_size_row = ggml_row_size(layer.k->type, n_embd_k_gqa);
        io.write(&k_size_row, sizeof(k_size_row));

        // Read each range of cells of k_size length each into tmp_buf and write out
        for (const auto & range : cell_ranges) {
            const size_t range_size = range.second - range.first;
            const size_t buf_size = range_size * k_size_row;
            io.write_tensor(layer.k, range.first * k_size_row, buf_size);
        }
    }

    if (!v_trans) {
        for (const auto & layer : layers) {
            const uint32_t il = layer.il;

            const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(il) + hparams.n_embd_v_s();

            // Write value type
            const int32_t v_type_i = (int32_t)layer.v->type;
            io.write(&v_type_i, sizeof(v_type_i));

            // Write row size of value
            const uint64_t v_size_row = ggml_row_size(layer.v->type, n_embd_v_gqa);
            io.write(&v_size_row, sizeof(v_size_row));

            // Read each range of cells of v_size length each into tmp_buf and write out
            for (const auto & range : cell_ranges) {
                const size_t range_size = range.second - range.first;
                const size_t buf_size = range_size * v_size_row;
                io.write_tensor(layer.v, range.first * v_size_row, buf_size);
            }
        }
    } else {
        // When v is transposed, we also need the element size and get the element ranges from each row
        const uint32_t kv_size = cells.size();

        for (const auto & layer : layers) {