Function load_model

external/ggml/tests/test-conv-transpose-1d.cpp:52–277

};

void load_model(test_model & model, bool use_gpu = false) {


    float adata_0[] = {1,2,3};
    float bdata_0[] = {1,2};

    float adata_1[] = {1,2,3,3,2,1};
    float bdata_1[] = {2,3,1,1,3,2};

    float adata_2[] =  {3,2,1,1,2,3,1,2,3,3,2,1};
    float bdata_2[] =  {2,3,1,1,3,2};

    float data[16*32*32];
    for (int i = 0; i < 16*32*32; ++i) {
        data[i] = (float)(i%1024);
    }




    size_t buffer_size = 0;
    {
        buffer_size += 3* ggml_type_size(GGML_TYPE_F32); // tensor a_0
        buffer_size += 2* ggml_type_size(GGML_TYPE_F32); // tensor b_0

        buffer_size += 6* ggml_type_size(GGML_TYPE_F32); // tensor a_1
        buffer_size += 6* ggml_type_size(GGML_TYPE_F32); // tensor b_1

        buffer_size += 12* ggml_type_size(GGML_TYPE_F32); // tensor a_2
        buffer_size += 6* ggml_type_size(GGML_TYPE_F32); // tensor b_2

        buffer_size += 2 * 3 * 2 * ggml_type_size(GGML_TYPE_F32); // tensor a_3
        buffer_size += 3 * 2* ggml_type_size(GGML_TYPE_F32); // tensor b_3

        buffer_size += 16 * 32 * 32 * ggml_type_size(GGML_TYPE_F32); // tensor a_4
        buffer_size += 197 * 32* ggml_type_size(GGML_TYPE_F32); // tensor b_4

        buffer_size += 1024;
    }

    printf("%s: ggml tensor size    = %d bytes\n", __func__, (int) sizeof(ggml_tensor));
    printf("%s: backend buffer size = %0.2f MB\n", __func__, (buffer_size/ 1024.f/ 1024.f));

    int num_tensors = 10;
    struct ggml_init_params params {
            /*.mem_size   =*/ ggml_tensor_overhead() * num_tensors,
            /*.mem_buffer =*/ NULL,
            /*.no_alloc   =*/ true,
    };

    ggml_log_set(ggml_log_callback_default, nullptr);

    // initialize the backend
#ifdef GGML_USE_CUDA
    if (use_gpu) {
        fprintf(stderr, "%s: using CUDA backend\n", __func__);
        model.backend = ggml_backend_cuda_init(0);
        if (!model.backend) {