code : normalize enum names (#5697)

* coda : normalize enum names

ggml-ci

* code : cont

* code : cont

common/common.cpp

@@ -295,9 +295,9 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) {
                 break;
             }
             std::string value(argv[i]);
-            /**/ if (value == "none")   { params.rope_scaling_type = LLAMA_ROPE_SCALING_NONE; }
-            else if (value == "linear") { params.rope_scaling_type = LLAMA_ROPE_SCALING_LINEAR; }
-            else if (value == "yarn")   { params.rope_scaling_type = LLAMA_ROPE_SCALING_YARN; }
+            /**/ if (value == "none")   { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_NONE; }
+            else if (value == "linear") { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_LINEAR; }
+            else if (value == "yarn")   { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_YARN; }
             else { invalid_param = true; break; }
         } else if (arg == "--rope-scale") {
             if (++i >= argc) {
@@ -630,11 +630,11 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) {
             }
             std::string arg_next = argv[i];
             if (arg_next == "none") {
-                params.split_mode = LLAMA_SPLIT_NONE;
+                params.split_mode = LLAMA_SPLIT_MODE_NONE;
             } else if (arg_next == "layer") {
-                params.split_mode = LLAMA_SPLIT_LAYER;
+                params.split_mode = LLAMA_SPLIT_MODE_LAYER;
             } else if (arg_next == "row") {
-                params.split_mode = LLAMA_SPLIT_ROW;
+                params.split_mode = LLAMA_SPLIT_MODE_ROW;
             } else {
                 invalid_param = true;
                 break;
@@ -837,15 +837,15 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) {
             sep++;
             if (strncmp(sep, "int:", 4) == 0) {
                 sep += 4;
-                kvo.tag = LLAMA_KV_OVERRIDE_INT;
+                kvo.tag = LLAMA_KV_OVERRIDE_TYPE_INT;
                 kvo.int_value = std::atol(sep);
             } else if (strncmp(sep, "float:", 6) == 0) {
                 sep += 6;
-                kvo.tag = LLAMA_KV_OVERRIDE_FLOAT;
+                kvo.tag = LLAMA_KV_OVERRIDE_TYPE_FLOAT;
                 kvo.float_value = std::atof(sep);
             } else if (strncmp(sep, "bool:", 5) == 0) {
                 sep += 5;
-                kvo.tag = LLAMA_KV_OVERRIDE_BOOL;
+                kvo.tag = LLAMA_KV_OVERRIDE_TYPE_BOOL;
                 if (std::strcmp(sep, "true") == 0) {
                     kvo.bool_value = true;
                 } else if (std::strcmp(sep, "false") == 0) {

common/common.h

@@ -61,7 +61,7 @@ struct gpt_params {
     float   p_split               = 0.1f;  // speculative decoding split probability
     int32_t n_gpu_layers          = -1;    // number of layers to store in VRAM (-1 - use default)
     int32_t n_gpu_layers_draft    = -1;    // number of layers to store in VRAM for the draft model (-1 - use default)
-    llama_split_mode split_mode   = LLAMA_SPLIT_LAYER; // how to split the model across GPUs
+    llama_split_mode split_mode   = LLAMA_SPLIT_MODE_LAYER; // how to split the model across GPUs
     int32_t main_gpu              = 0;     // the GPU that is used for scratch and small tensors
     float   tensor_split[128]     = {0};   // how split tensors should be distributed across GPUs
     int32_t n_beams               = 0;     // if non-zero then use beam search of given width.
@@ -75,7 +75,7 @@ struct gpt_params {
     float   yarn_beta_fast        = 32.0f; // YaRN low correction dim
     float   yarn_beta_slow        = 1.0f;  // YaRN high correction dim
     int32_t yarn_orig_ctx         = 0;     // YaRN original context length
-    int32_t rope_scaling_type     = LLAMA_ROPE_SCALING_UNSPECIFIED;
+    int32_t rope_scaling_type     = LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED;
     ggml_numa_strategy numa       = GGML_NUMA_STRATEGY_DISABLED;
 
     // // sampling parameters

common/train.cpp

@@ -31,7 +31,7 @@ struct train_state  * init_train_state() {
 
     state->opt = new struct ggml_opt_context;
     state->opt->ctx = NULL;
-    state->opt->params = ggml_opt_default_params(GGML_OPT_ADAM);
+    state->opt->params = ggml_opt_default_params(GGML_OPT_TYPE_ADAM);
     state->opt->params.graph_size = LLAMA_TRAIN_MAX_NODES;
     state->opt->loss_after = 0.0f;
 
@@ -556,7 +556,7 @@ void load_opt_context_gguf(struct gguf_context * fctx, struct ggml_context * f_g
     std::string opt_type;
     GGUF_GET_KEY(fctx, opt_type, gguf_get_val_str, GGUF_TYPE_STRING, true, LLM_KV_OPTIMIZER_TYPE);
     if (opt_type == LLM_KV_OPTIMIZER_TYPE_ADAM) {
-        opt->params.type = GGML_OPT_ADAM;
+        opt->params.type = GGML_OPT_TYPE_ADAM;
 
         GGUF_GET_KEY(fctx, opt->adam.fx_best,          gguf_get_val_f32, GGUF_TYPE_FLOAT32, true, LLM_KV_OPTIMIZER_ADAM_BEST_LOSS);
         GGUF_GET_KEY(fctx, opt->adam.fx_prev,          gguf_get_val_f32, GGUF_TYPE_FLOAT32, true, LLM_KV_OPTIMIZER_ADAM_PREVIOUS_LOSS);
@@ -568,7 +568,7 @@ void load_opt_context_gguf(struct gguf_context * fctx, struct ggml_context * f_g
         copy_tensor_by_name(opt->adam.v,  f_ggml_ctx, LLM_TENSOR_OPTIMIZER_ADAM_SECOND_MOMENTS);
         copy_tensor_by_name(opt->adam.pf, f_ggml_ctx, LLM_TENSOR_OPTIMIZER_ADAM_PAST_LOSS_VALUES);
     } else if (opt_type == LLM_KV_OPTIMIZER_TYPE_LBFGS) {
-        opt->params.type = GGML_OPT_LBFGS;
+        opt->params.type = GGML_OPT_TYPE_LBFGS;
 
         GGUF_GET_KEY(fctx, opt->params.lbfgs.m,         gguf_get_val_u32, GGUF_TYPE_UINT32,  true, LLM_KV_OPTIMIZER_LBFGS_APPROX_HESSIAN_COUNT);
         GGUF_GET_KEY(fctx, opt->lbfgs.fx_best,          gguf_get_val_f32, GGUF_TYPE_FLOAT32, true, LLM_KV_OPTIMIZER_LBFGS_BEST_LOSS);
@@ -603,7 +603,7 @@ void save_opt_context_gguf(struct gguf_context * fctx, struct ggml_opt_context *
     gguf_set_val_bool(fctx, LLM_KV_OPTIMIZER_JUST_INITIALIZED, opt->just_initialized);
 
     switch (opt->params.type) {
-        case GGML_OPT_ADAM:
+        case GGML_OPT_TYPE_ADAM:
             {
                 gguf_set_val_str(fctx, LLM_KV_OPTIMIZER_TYPE, LLM_KV_OPTIMIZER_TYPE_ADAM);
                 gguf_set_val_f32(fctx, LLM_KV_OPTIMIZER_ADAM_BEST_LOSS,            opt->adam.fx_best);
@@ -622,7 +622,7 @@ void save_opt_context_gguf(struct gguf_context * fctx, struct ggml_opt_context *
                     gguf_add_tensor(fctx, opt->adam.pf);
                 }
             } break;
-        case GGML_OPT_LBFGS:
+        case GGML_OPT_TYPE_LBFGS:
             {
                 gguf_set_val_str(fctx, LLM_KV_OPTIMIZER_TYPE, LLM_KV_OPTIMIZER_TYPE_LBFGS);
                 gguf_set_val_u32(fctx, LLM_KV_OPTIMIZER_LBFGS_APPROX_HESSIAN_COUNT, opt->params.lbfgs.m);

examples/baby-llama/baby-llama.cpp

@@ -1547,7 +1547,7 @@ int main(int argc, char ** argv) {
 
         float error_before_opt = ggml_get_f32_1d(e, 0);
 
-        struct ggml_opt_params opt_params_lbfgs = ggml_opt_default_params(GGML_OPT_LBFGS);
+        struct ggml_opt_params opt_params_lbfgs = ggml_opt_default_params(GGML_OPT_TYPE_LBFGS);
         opt_params_lbfgs.print_forward_graph = false;
         opt_params_lbfgs.print_backward_graph = false;
         opt_params_lbfgs.lbfgs.n_iter = 16;

examples/finetune/finetune.cpp

@@ -1531,7 +1531,7 @@ int main(int argc, char ** argv) {
     lora.hparams.n_rank_output         = n_rank_output;
 
     // set opt params from command line
-    opt->params = ggml_opt_default_params(GGML_OPT_ADAM);
+    opt->params = ggml_opt_default_params(GGML_OPT_TYPE_ADAM);
     opt->params.print_forward_graph     = false;
     opt->params.print_backward_graph    = false;
     opt->params.graph_size              = LLAMA_TRAIN_MAX_NODES;

examples/llama-bench/llama-bench.cpp

@@ -157,9 +157,9 @@ static const char * output_format_str(output_formats format) {
 
 static const char * split_mode_str(llama_split_mode mode) {
     switch (mode) {
-        case LLAMA_SPLIT_NONE:  return "none";
-        case LLAMA_SPLIT_LAYER: return "layer";
-        case LLAMA_SPLIT_ROW:   return "row";
+        case LLAMA_SPLIT_MODE_NONE:  return "none";
+        case LLAMA_SPLIT_MODE_LAYER: return "layer";
+        case LLAMA_SPLIT_MODE_ROW:   return "row";
         default: GGML_ASSERT(!"invalid split mode");
     }
 }
@@ -193,7 +193,7 @@ static const cmd_params cmd_params_defaults = {
     /* type_v        */ {GGML_TYPE_F16},
     /* n_threads     */ {get_num_physical_cores()},
     /* n_gpu_layers  */ {99},
-    /* split_mode    */ {LLAMA_SPLIT_LAYER},
+    /* split_mode    */ {LLAMA_SPLIT_MODE_LAYER},
     /* main_gpu      */ {0},
     /* no_kv_offload */ {false},
     /* mul_mat_q     */ {true},
@@ -358,11 +358,11 @@ static cmd_params parse_cmd_params(int argc, char ** argv) {
             for (const auto & m : p) {
                 llama_split_mode mode;
                 if (m == "none") {
-                    mode = LLAMA_SPLIT_NONE;
+                    mode = LLAMA_SPLIT_MODE_NONE;
                 } else if (m == "layer") {
-                    mode = LLAMA_SPLIT_LAYER;
+                    mode = LLAMA_SPLIT_MODE_LAYER;
                 } else if (m == "row") {
-                    mode = LLAMA_SPLIT_ROW;
+                    mode = LLAMA_SPLIT_MODE_ROW;
                 } else {
                     invalid_param = true;
                     break;

examples/llava/llava.cpp

@@ -152,7 +152,7 @@ static bool clip_llava_handle_patches(clip_ctx * ctx_clip, std::vector<float *>
 
     ggml_tensor * newline_tmp = clip_get_newline_tensor(ctx_clip);
     model.newline = ggml_new_tensor_1d(model.ctx, GGML_TYPE_F32, newline_tmp->ne[0]);
-    if (newline_tmp->backend != GGML_BACKEND_CPU) {
+    if (newline_tmp->backend != GGML_BACKEND_TYPE_CPU) {
         if (newline_tmp->buffer == NULL) {
             printf("newline_tmp tensor buffer is NULL\n");
         }

examples/server/server.cpp

@@ -2086,9 +2086,9 @@ static void server_params_parse(int argc, char **argv, server_params &sparams,
                 break;
             }
             std::string value(argv[i]);
-            /**/ if (value == "none")   { params.rope_scaling_type = LLAMA_ROPE_SCALING_NONE; }
-            else if (value == "linear") { params.rope_scaling_type = LLAMA_ROPE_SCALING_LINEAR; }
-            else if (value == "yarn")   { params.rope_scaling_type = LLAMA_ROPE_SCALING_YARN; }
+            /**/ if (value == "none")   { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_NONE; }
+            else if (value == "linear") { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_LINEAR; }
+            else if (value == "yarn")   { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_YARN; }
             else { invalid_param = true; break; }
         }
         else if (arg == "--rope-freq-base")
@@ -2212,15 +2212,15 @@ static void server_params_parse(int argc, char **argv, server_params &sparams,
             std::string arg_next = argv[i];
             if (arg_next == "none")
             {
-                params.split_mode = LLAMA_SPLIT_NONE;
+                params.split_mode = LLAMA_SPLIT_MODE_NONE;
             }
             else if (arg_next == "layer")
             {
-                params.split_mode = LLAMA_SPLIT_LAYER;
+                params.split_mode = LLAMA_SPLIT_MODE_LAYER;
             }
             else if (arg_next == "row")
             {
-                params.split_mode = LLAMA_SPLIT_ROW;
+                params.split_mode = LLAMA_SPLIT_MODE_ROW;
             }
             else {
                 invalid_param = true;
@@ -2447,15 +2447,15 @@ static void server_params_parse(int argc, char **argv, server_params &sparams,
             sep++;
             if (strncmp(sep, "int:", 4) == 0) {
                 sep += 4;
-                kvo.tag = LLAMA_KV_OVERRIDE_INT;
+                kvo.tag = LLAMA_KV_OVERRIDE_TYPE_INT;
                 kvo.int_value = std::atol(sep);
             } else if (strncmp(sep, "float:", 6) == 0) {
                 sep += 6;
-                kvo.tag = LLAMA_KV_OVERRIDE_FLOAT;
+                kvo.tag = LLAMA_KV_OVERRIDE_TYPE_FLOAT;
                 kvo.float_value = std::atof(sep);
             } else if (strncmp(sep, "bool:", 5) == 0) {
                 sep += 5;
-                kvo.tag = LLAMA_KV_OVERRIDE_BOOL;
+                kvo.tag = LLAMA_KV_OVERRIDE_TYPE_BOOL;
                 if (std::strcmp(sep, "true") == 0) {
                     kvo.bool_value = true;
                 } else if (std::strcmp(sep, "false") == 0) {

examples/train-text-from-scratch/train-text-from-scratch.cpp

@@ -960,7 +960,7 @@ int main(int argc, char ** argv) {
     struct ggml_opt_context * opt   = train->opt;
 
     // set opt params from command line
-    opt->params = ggml_opt_default_params(GGML_OPT_ADAM);
+    opt->params = ggml_opt_default_params(GGML_OPT_TYPE_ADAM);
     opt->params.print_forward_graph     = false;
     opt->params.print_backward_graph    = false;
     opt->params.graph_size              = LLAMA_TRAIN_MAX_NODES;

ggml-cuda.cu

@@ -6369,11 +6369,11 @@ static __global__ void k_argsort_f32_i32(const float * x, int * dst, const int n
             int ixj = col ^ j;
             if (ixj > col) {
                 if ((col & k) == 0) {
-                    if (order == GGML_SORT_ASC ? x_row[dst_row[col]] > x_row[dst_row[ixj]] : x_row[dst_row[col]] < x_row[dst_row[ixj]]) {
+                    if (order == GGML_SORT_ORDER_ASC ? x_row[dst_row[col]] > x_row[dst_row[ixj]] : x_row[dst_row[col]] < x_row[dst_row[ixj]]) {
                         swap(dst_row[col], dst_row[ixj]);
                     }
                 } else {
-                    if (order == GGML_SORT_ASC ? x_row[dst_row[col]] < x_row[dst_row[ixj]] : x_row[dst_row[col]] > x_row[dst_row[ixj]]) {
+                    if (order == GGML_SORT_ORDER_ASC ? x_row[dst_row[col]] < x_row[dst_row[ixj]] : x_row[dst_row[col]] > x_row[dst_row[ixj]]) {
                         swap(dst_row[col], dst_row[ixj]);
                     }
                 }
@@ -7927,10 +7927,10 @@ static void argsort_f32_i32_cuda(const float * x, int * dst, const int ncols, co
 
     const dim3 block_dims(ncols, 1, 1);
     const dim3 block_nums(1, nrows, 1);
-    if (order == GGML_SORT_ASC) {
-        k_argsort_f32_i32<GGML_SORT_ASC><<<block_nums, block_dims, 0, stream>>>(x, dst, ncols);
-    } else if (order == GGML_SORT_DESC) {
-        k_argsort_f32_i32<GGML_SORT_DESC><<<block_nums, block_dims, 0, stream>>>(x, dst, ncols);
+    if (order == GGML_SORT_ORDER_ASC) {
+        k_argsort_f32_i32<GGML_SORT_ORDER_ASC><<<block_nums, block_dims, 0, stream>>>(x, dst, ncols);
+    } else if (order == GGML_SORT_ORDER_DESC) {
+        k_argsort_f32_i32<GGML_SORT_ORDER_DESC><<<block_nums, block_dims, 0, stream>>>(x, dst, ncols);
     } else {
         GGML_ASSERT(false);
     }
@@ -8362,11 +8362,11 @@ static cudaError_t ggml_cuda_cpy_tensor_2d(
 
     cudaMemcpyKind kind;
     char * src_ptr;
-    if (src->backend == GGML_BACKEND_CPU) {
+    if (src->backend == GGML_BACKEND_TYPE_CPU) {
         kind = cudaMemcpyHostToDevice;
         src_ptr = (char *) src->data;
-    } else if (src->backend == GGML_BACKEND_GPU || src->backend == GGML_BACKEND_GPU_SPLIT) {
-        GGML_ASSERT(src->backend != GGML_BACKEND_GPU_SPLIT || (i1_low == 0 && i1_high == src->ne[1]));
+    } else if (src->backend == GGML_BACKEND_TYPE_GPU || src->backend == GGML_BACKEND_TYPE_GPU_SPLIT) {
+        GGML_ASSERT(src->backend != GGML_BACKEND_TYPE_GPU_SPLIT || (i1_low == 0 && i1_high == src->ne[1]));
         kind = cudaMemcpyDeviceToDevice;
         ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) src->extra;
         int id;
@@ -8771,7 +8771,7 @@ static void ggml_cuda_op_mul_mat_q(
 
     // the main device has a larger memory buffer to hold the results from all GPUs
     // nrows_dst == nrows of the matrix that the kernel writes into
-    const int64_t nrows_dst = dst->backend == GGML_BACKEND_GPU && id == g_main_device ? ne0 : row_diff;
+    const int64_t nrows_dst = dst->backend == GGML_BACKEND_TYPE_GPU && id == g_main_device ? ne0 : row_diff;
 
     switch (src0->type) {
         case GGML_TYPE_Q4_0:
@@ -8920,7 +8920,7 @@ static void ggml_cuda_op_mul_mat_vec_q(
 
     // the main device has a larger memory buffer to hold the results from all GPUs
     // nrows_dst == nrows of the matrix that the kernel writes into
-    const int64_t nrows_dst = dst->backend == GGML_BACKEND_GPU && id == g_main_device ? ne0 : row_diff;
+    const int64_t nrows_dst = dst->backend == GGML_BACKEND_TYPE_GPU && id == g_main_device ? ne0 : row_diff;
 
     switch (src0->type) {
         case GGML_TYPE_Q4_0:
@@ -9096,7 +9096,7 @@ static void ggml_cuda_op_mul_mat_cublas(
 
     // the main device has a larger memory buffer to hold the results from all GPUs
     // ldc == nrows of the matrix that cuBLAS writes into
-    int ldc = dst->backend == GGML_BACKEND_GPU && id == g_main_device ? ne0 : row_diff;
+    int ldc = dst->backend == GGML_BACKEND_TYPE_GPU && id == g_main_device ? ne0 : row_diff;
 
     const int compute_capability = g_device_caps[id].cc;
 
@@ -9444,7 +9444,7 @@ static void ggml_cuda_op_soft_max(
     const bool use_src2 = src2 != nullptr;
 
     if (use_src2) {
-        const bool src2_on_device = src2->backend == GGML_BACKEND_GPU;
+        const bool src2_on_device = src2->backend == GGML_BACKEND_TYPE_GPU;
 
         if (src2_on_device) {
             ggml_tensor_extra_gpu * src2_extra = (ggml_tensor_extra_gpu *) src2->extra;
@@ -9502,16 +9502,16 @@ static void ggml_cuda_op_flatten(const ggml_tensor * src0, const ggml_tensor * s
     const bool use_src1 = src1 != nullptr;
     const int64_t nrows1 = use_src1 ? ggml_nrows(src1) : 1;
 
-    GGML_ASSERT(!use_src1 || src1->backend != GGML_BACKEND_GPU_SPLIT);
-    GGML_ASSERT(              dst->backend != GGML_BACKEND_GPU_SPLIT);
+    GGML_ASSERT(!use_src1 || src1->backend != GGML_BACKEND_TYPE_GPU_SPLIT);
+    GGML_ASSERT(              dst->backend != GGML_BACKEND_TYPE_GPU_SPLIT);
 
     ggml_tensor_extra_gpu * src0_extra =            (ggml_tensor_extra_gpu *) src0->extra;
     ggml_tensor_extra_gpu * src1_extra = use_src1 ? (ggml_tensor_extra_gpu *) src1->extra : nullptr;
     ggml_tensor_extra_gpu * dst_extra  =            (ggml_tensor_extra_gpu *)  dst->extra;
 
-    const bool src0_on_device =             src0->backend == GGML_BACKEND_GPU || src0->backend == GGML_BACKEND_GPU_SPLIT;
-    const bool src1_on_device = use_src1 && src1->backend == GGML_BACKEND_GPU;
-    const bool  dst_on_device =              dst->backend == GGML_BACKEND_GPU;
+    const bool src0_on_device =             src0->backend == GGML_BACKEND_TYPE_GPU || src0->backend == GGML_BACKEND_TYPE_GPU_SPLIT;
+    const bool src1_on_device = use_src1 && src1->backend == GGML_BACKEND_TYPE_GPU;
+    const bool  dst_on_device =              dst->backend == GGML_BACKEND_TYPE_GPU;
 
     // dd = data device
     float * src0_ddf = nullptr;
@@ -9555,7 +9555,7 @@ static void ggml_cuda_op_flatten(const ggml_tensor * src0, const ggml_tensor * s
         CUDA_CHECK(cudaMemcpyAsync(dst->data, dst_ddf, ggml_nbytes(dst), cudaMemcpyDeviceToHost, main_stream));
     }
 
-    if (dst->backend == GGML_BACKEND_CPU) {
+    if (dst->backend == GGML_BACKEND_TYPE_CPU) {
         CUDA_CHECK(cudaDeviceSynchronize());
     }
 }
@@ -9636,8 +9636,8 @@ static void ggml_cuda_op_mul_mat(
     const int nb2 = dst->nb[2];
     const int nb3 = dst->nb[3];
 
-    GGML_ASSERT(dst->backend != GGML_BACKEND_GPU_SPLIT);
-    GGML_ASSERT(src1->backend != GGML_BACKEND_GPU_SPLIT);
+    GGML_ASSERT(dst->backend != GGML_BACKEND_TYPE_GPU_SPLIT);
+    GGML_ASSERT(src1->backend != GGML_BACKEND_TYPE_GPU_SPLIT);
     GGML_ASSERT(src1->type == GGML_TYPE_F32 || (src1->ne[2] == 1 && src1->ne[3] == 1));
 
     GGML_ASSERT(ne12 >= ne02 && ne12 % ne02 == 0);
@@ -9653,20 +9653,20 @@ static void ggml_cuda_op_mul_mat(
     ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu *) src1->extra;
     ggml_tensor_extra_gpu *  dst_extra = (ggml_tensor_extra_gpu *)  dst->extra;
 
-    const bool src0_on_device = src0->backend == GGML_BACKEND_GPU || src0->backend == GGML_BACKEND_GPU_SPLIT;
+    const bool src0_on_device = src0->backend == GGML_BACKEND_TYPE_GPU || src0->backend == GGML_BACKEND_TYPE_GPU_SPLIT;
     const bool src0_is_contiguous = ggml_is_contiguous(src0);
     const bool src1_is_contiguous = ggml_is_contiguous(src1);
 
     const int64_t src1_padded_col_size = GGML_PAD(ne10, MATRIX_ROW_PADDING);
 
-    const bool split = src0->backend == GGML_BACKEND_GPU_SPLIT;
+    const bool split = src0->backend == GGML_BACKEND_TYPE_GPU_SPLIT;
     GGML_ASSERT(!(split && ne02 > 1));
     GGML_ASSERT(!(split && ne03 > 1));
     GGML_ASSERT(!(split && ne02 < ne12));
 
     std::array<float, GGML_CUDA_MAX_DEVICES> tensor_split;
     if (split) {
-        // TODO: check that src0->buffer->buft is a split buffer type, replace GGML_BACKEND_GPU_SPLIT check
+        // TODO: check that src0->buffer->buft is a split buffer type, replace GGML_BACKEND_TYPE_GPU_SPLIT check
         // GGML_ASSERT(src0->buffer != nullptr && src0->buffer->buft == ...);
         ggml_backend_cuda_split_buffer_type_context * buft_ctx = (ggml_backend_cuda_split_buffer_type_context *) src0->buffer->buft->context;
         tensor_split = buft_ctx->tensor_split;
@@ -9724,8 +9724,8 @@ static void ggml_cuda_op_mul_mat(
 
         used_devices++;
 
-        const bool src1_on_device = src1->backend == GGML_BACKEND_GPU && id == g_main_device;
-        const bool  dst_on_device =  dst->backend == GGML_BACKEND_GPU && id == g_main_device;
+        const bool src1_on_device = src1->backend == GGML_BACKEND_TYPE_GPU && id == g_main_device;
+        const bool  dst_on_device =  dst->backend == GGML_BACKEND_TYPE_GPU && id == g_main_device;
 
         ggml_cuda_set_device(id);
         cudaStream_t stream = g_cudaStreams[id][0];
@@ -9776,8 +9776,8 @@ static void ggml_cuda_op_mul_mat(
                 continue;
             }
 
-            const bool src1_on_device = src1->backend == GGML_BACKEND_GPU && id == g_main_device;
-            const bool  dst_on_device =  dst->backend == GGML_BACKEND_GPU && id == g_main_device;
+            const bool src1_on_device = src1->backend == GGML_BACKEND_TYPE_GPU && id == g_main_device;
+            const bool  dst_on_device =  dst->backend == GGML_BACKEND_TYPE_GPU && id == g_main_device;
             const int64_t row_diff = dev[id].row_high - dev[id].row_low;
 
             ggml_cuda_set_device(id);
@@ -9802,12 +9802,12 @@ static void ggml_cuda_op_mul_mat(
 
                 // the main device memory buffer can be on VRAM scratch, with space for all partial results
                 // in that case an offset on dst_ddf_i is needed
-                if (dst->backend == GGML_BACKEND_GPU && id == g_main_device) {
+                if (dst->backend == GGML_BACKEND_TYPE_GPU && id == g_main_device) {
                     dst_dd_i += dev[id].row_low; // offset is 0 if no tensor split
                 }
 
                 // copy src0, src1 to device if necessary
-                if (src1->backend == GGML_BACKEND_GPU && src1_is_contiguous) {
+                if (src1->backend == GGML_BACKEND_TYPE_GPU && src1_is_contiguous) {
                     if (id != g_main_device) {
                         if (convert_src1_to_q8_1) {
                             char * src1_ddq_i_source = dev[g_main_device].src1_ddq + src1_ddq_i_offset;
@@ -9820,14 +9820,14 @@ static void ggml_cuda_op_mul_mat(
                                                             src1_ncols*ne10*sizeof(float), stream));
                         }
                     }
-                } else if (src1->backend == GGML_BACKEND_CPU || (src1_on_device && !src1_is_contiguous)) {
+                } else if (src1->backend == GGML_BACKEND_TYPE_CPU || (src1_on_device && !src1_is_contiguous)) {
                     CUDA_CHECK(ggml_cuda_cpy_tensor_2d(
                                 src1_ddf_i, src1, i03, i02, src1_col_0, src1_col_0+src1_ncols, stream));
                 } else {
                     GGML_ASSERT(false);
                 }
 
-                if (convert_src1_to_q8_1 && (src1->backend == GGML_BACKEND_CPU || !src1_is_contiguous)) {
+                if (convert_src1_to_q8_1 && (src1->backend == GGML_BACKEND_TYPE_CPU || !src1_is_contiguous)) {
                     quantize_row_q8_1_cuda(src1_ddf_i, src1_ddq_i, ne10, src1_ncols, src1_padded_col_size, stream);
                     CUDA_CHECK(cudaGetLastError());
                 }
@@ -9845,10 +9845,10 @@ static void ggml_cuda_op_mul_mat(
                 if (!dst_on_device) {
                     void * dst_off_device;
                     cudaMemcpyKind kind;
-                    if (dst->backend == GGML_BACKEND_CPU) {
+                    if (dst->backend == GGML_BACKEND_TYPE_CPU) {
                         dst_off_device = dst->data;
                         kind = cudaMemcpyDeviceToHost;
-                    } else if (dst->backend == GGML_BACKEND_GPU) {
+                    } else if (dst->backend == GGML_BACKEND_TYPE_GPU) {
                         dst_off_device = dst_extra->data_device[g_main_device];
                         kind = cudaMemcpyDeviceToDevice;
                     } else {
@@ -9913,7 +9913,7 @@ static void ggml_cuda_op_mul_mat(
         }
     }
 
-    if (dst->backend == GGML_BACKEND_CPU) {
+    if (dst->backend == GGML_BACKEND_TYPE_CPU) {
         ggml_cuda_set_device(g_main_device);
         CUDA_CHECK(cudaDeviceSynchronize());
     }
@@ -10019,7 +10019,7 @@ GGML_CALL bool ggml_cuda_can_mul_mat(const struct ggml_tensor * src0, const stru
 
 static void ggml_cuda_mul_mat_vec_p021(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst){
     GGML_ASSERT(ggml_is_permuted(src0) && ggml_is_permuted(src1));
-    GGML_ASSERT(src0->backend != GGML_BACKEND_GPU_SPLIT);
+    GGML_ASSERT(src0->backend != GGML_BACKEND_TYPE_GPU_SPLIT);
     GGML_ASSERT(src0->nb[0] <= src0->nb[1] && src0->nb[2] <= src0->nb[3]); // 0213 permutation
     GGML_ASSERT(src1->nb[0] <= src1->nb[1] && src1->nb[2] <= src1->nb[3]); // 0213 permutation
     GGML_ASSERT(src0->type == GGML_TYPE_F16);
@@ -10050,7 +10050,7 @@ static void ggml_cuda_mul_mat_vec_nc(const ggml_tensor * src0, const ggml_tensor
     GGML_ASSERT(!ggml_is_transposed(src0));
     GGML_ASSERT(!ggml_is_transposed(src1));
     GGML_ASSERT(!ggml_is_permuted(src0));
-    GGML_ASSERT(src0->backend != GGML_BACKEND_GPU_SPLIT);
+    GGML_ASSERT(src0->backend != GGML_BACKEND_TYPE_GPU_SPLIT);
     GGML_ASSERT(src0->type == GGML_TYPE_F16);
     GGML_ASSERT(src1->type == GGML_TYPE_F32);
 
@@ -10109,7 +10109,7 @@ static void ggml_cuda_mul_mat_batched_cublas(const ggml_tensor * src0, const ggm
     GGML_ASSERT(!ggml_is_transposed(src0));
     GGML_ASSERT(!ggml_is_transposed(src1));
 
-    GGML_ASSERT(src0->backend != GGML_BACKEND_GPU_SPLIT);
+    GGML_ASSERT(src0->backend != GGML_BACKEND_TYPE_GPU_SPLIT);
     GGML_ASSERT(src0->type == GGML_TYPE_F16);
 
     GGML_TENSOR_BINARY_OP_LOCALS
@@ -10255,11 +10255,11 @@ static void ggml_cuda_mul_mat_batched_cublas(const ggml_tensor * src0, const ggm
 
 static void ggml_cuda_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
     const bool all_on_device =
-        (src0->backend == GGML_BACKEND_GPU || src0->backend == GGML_BACKEND_GPU_SPLIT) &&
-        (src1->backend == GGML_BACKEND_GPU) &&
-        ( dst->backend == GGML_BACKEND_GPU);
+        (src0->backend == GGML_BACKEND_TYPE_GPU || src0->backend == GGML_BACKEND_TYPE_GPU_SPLIT) &&
+        (src1->backend == GGML_BACKEND_TYPE_GPU) &&
+        ( dst->backend == GGML_BACKEND_TYPE_GPU);
 
-    const bool split = src0->backend == GGML_BACKEND_GPU_SPLIT;
+    const bool split = src0->backend == GGML_BACKEND_TYPE_GPU_SPLIT;
 
     int64_t min_compute_capability = INT_MAX;
 
@@ -10409,7 +10409,7 @@ static void ggml_cuda_mul_mat_id_cublas(ggml_tensor * dst) {
     GGML_ASSERT(!ggml_is_transposed(src00));
     GGML_ASSERT(!ggml_is_transposed(src1));
 
-    GGML_ASSERT(src00->backend != GGML_BACKEND_GPU_SPLIT);
+    GGML_ASSERT(src00->backend != GGML_BACKEND_TYPE_GPU_SPLIT);
     GGML_ASSERT(src1->type == GGML_TYPE_F32);
 
     const int64_t ne00 = src00->ne[0]; GGML_UNUSED(ne00);
@@ -10553,7 +10553,7 @@ static void ggml_cuda_mul_mat_id(const ggml_tensor * src0, const ggml_tensor * s
 
     cudaStream_t stream = g_cudaStreams[g_main_device][0];
 
-    if (ids->backend == GGML_BACKEND_GPU) {
+    if (ids->backend == GGML_BACKEND_TYPE_GPU) {
         const char * ids_dev = (const char *)((const ggml_tensor_extra_gpu *)ids->extra)->data_device[g_main_device];
         CUDA_CHECK(cudaMemcpyAsync(ids_host.data(), ids_dev, ggml_nbytes(ids), cudaMemcpyDeviceToHost, stream));
         CUDA_CHECK(cudaStreamSynchronize(stream));
@@ -10570,20 +10570,20 @@ static void ggml_cuda_mul_mat_id(const ggml_tensor * src0, const ggml_tensor * s
     ggml_tensor src1_row = *src1;
     ggml_tensor dst_row = *dst;
 
-    src1_row.backend = GGML_BACKEND_GPU;
-    dst_row.backend  = GGML_BACKEND_GPU;
+    src1_row.backend = GGML_BACKEND_TYPE_GPU;
+    dst_row.backend  = GGML_BACKEND_TYPE_GPU;
 
     src1_row.extra = &src1_row_extra;
     dst_row.extra = &dst_row_extra;
 
-    char * src1_original = src1->backend == GGML_BACKEND_CPU ?
+    char * src1_original = src1->backend == GGML_BACKEND_TYPE_CPU ?
         (char *) src1->data : (char *) src1_extra->data_device[g_main_device];
-    char * dst_original  =  dst->backend == GGML_BACKEND_CPU ?
+    char * dst_original  =  dst->backend == GGML_BACKEND_TYPE_CPU ?
         (char *)  dst->data : (char *)  dst_extra->data_device[g_main_device];
 
     if (src1->ne[1] == 1) {
-        GGML_ASSERT(src1->backend == GGML_BACKEND_GPU);
-        GGML_ASSERT(dst->backend  == GGML_BACKEND_GPU);
+        GGML_ASSERT(src1->backend == GGML_BACKEND_TYPE_GPU);
+        GGML_ASSERT(dst->backend  == GGML_BACKEND_TYPE_GPU);
 
         for (int64_t i01 = 0; i01 < ids->ne[1]; i01++) {
             //int32_t row_id;
@@ -10611,9 +10611,9 @@ static void ggml_cuda_mul_mat_id(const ggml_tensor * src0, const ggml_tensor * s
         src1_row_extra.data_device[g_main_device] = src1_contiguous.get();
         dst_row_extra.data_device[g_main_device]  =  dst_contiguous.get();
 
-        const cudaMemcpyKind src1_kind = src1->backend == GGML_BACKEND_CPU ?
+        const cudaMemcpyKind src1_kind = src1->backend == GGML_BACKEND_TYPE_CPU ?
             cudaMemcpyHostToDevice : cudaMemcpyDeviceToDevice;
-        const cudaMemcpyKind dst_kind  =  dst->backend == GGML_BACKEND_CPU ?
+        const cudaMemcpyKind dst_kind  =  dst->backend == GGML_BACKEND_TYPE_CPU ?
             cudaMemcpyDeviceToHost : cudaMemcpyDeviceToDevice;
 
         for (int32_t row_id = 0; row_id < n_as; ++row_id) {
@@ -10668,7 +10668,7 @@ static void ggml_cuda_mul_mat_id(const ggml_tensor * src0, const ggml_tensor * s
         }
     }
 
-    if (dst->backend == GGML_BACKEND_CPU) {
+    if (dst->backend == GGML_BACKEND_TYPE_CPU) {
         CUDA_CHECK(cudaStreamSynchronize(stream));
     }
 }
@@ -10685,8 +10685,8 @@ static void ggml_cuda_cpy(const ggml_tensor * src0, const ggml_tensor * src1, gg
     const int64_t ne = ggml_nelements(src0);
     GGML_ASSERT(ne == ggml_nelements(src1));
 
-    GGML_ASSERT(src0->backend == GGML_BACKEND_GPU);
-    GGML_ASSERT(src1->backend == GGML_BACKEND_GPU);
+    GGML_ASSERT(src0->backend == GGML_BACKEND_TYPE_GPU);
+    GGML_ASSERT(src1->backend == GGML_BACKEND_TYPE_GPU);
 
     GGML_ASSERT(ggml_nbytes(src0) <= INT_MAX);
     GGML_ASSERT(ggml_nbytes(src1) <= INT_MAX);
@@ -10817,9 +10817,9 @@ GGML_CALL bool ggml_cuda_compute_forward(struct ggml_compute_params * params, st
     if (!g_cublas_loaded) return false;
 
     ggml_cuda_func_t func;
-    const bool any_on_device = tensor->backend == GGML_BACKEND_GPU
-        || (tensor->src[0] != nullptr && (tensor->src[0]->backend == GGML_BACKEND_GPU || tensor->src[0]->backend == GGML_BACKEND_GPU_SPLIT))
-        || (tensor->src[1] != nullptr && tensor->src[1]->backend == GGML_BACKEND_GPU);
+    const bool any_on_device = tensor->backend == GGML_BACKEND_TYPE_GPU
+        || (tensor->src[0] != nullptr && (tensor->src[0]->backend == GGML_BACKEND_TYPE_GPU || tensor->src[0]->backend == GGML_BACKEND_TYPE_GPU_SPLIT))
+        || (tensor->src[1] != nullptr && tensor->src[1]->backend == GGML_BACKEND_TYPE_GPU);
 
     if (!any_on_device && tensor->op != GGML_OP_MUL_MAT && tensor->op != GGML_OP_MUL_MAT_ID) {
         return false;
@@ -10966,14 +10966,14 @@ GGML_CALL bool ggml_cuda_compute_forward(struct ggml_compute_params * params, st
             return false;
     }
 
-    if (tensor->src[0] != nullptr && tensor->src[0]->backend == GGML_BACKEND_GPU_SPLIT) {
+    if (tensor->src[0] != nullptr && tensor->src[0]->backend == GGML_BACKEND_TYPE_GPU_SPLIT) {
         ggml_cuda_set_peer_access(tensor->src[1]->ne[1]);
     }
 
     if (params->ith != 0) {
         return true;
     }
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return true;
     }
     func(tensor->src[0], tensor->src[1], tensor);
@@ -11072,7 +11072,7 @@ GGML_CALL static void ggml_backend_cuda_buffer_init_tensor(ggml_backend_buffer_t
 
     extra->data_device[ctx->device] = tensor->data;
 
-    tensor->backend = GGML_BACKEND_GPU;
+    tensor->backend = GGML_BACKEND_TYPE_GPU;
     tensor->extra = extra;
 
     if (ggml_is_quantized(tensor->type)) {
@@ -11087,7 +11087,7 @@ GGML_CALL static void ggml_backend_cuda_buffer_init_tensor(ggml_backend_buffer_t
 }
 
 GGML_CALL static void ggml_backend_cuda_buffer_set_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
-    GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU);
+    GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU);
 
     ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context;
 
@@ -11098,7 +11098,7 @@ GGML_CALL static void ggml_backend_cuda_buffer_set_tensor(ggml_backend_buffer_t
 }
 
 GGML_CALL static void ggml_backend_cuda_buffer_get_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * tensor, void * data, size_t offset, size_t size) {
-    GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU);
+    GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU);
 
     ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context;
 
@@ -11333,7 +11333,7 @@ GGML_CALL static void ggml_backend_cuda_split_buffer_init_tensor(ggml_backend_bu
             CUDA_CHECK(cudaEventCreateWithFlags(&extra->events[id][is], cudaEventDisableTiming));
         }
     }
-    tensor->backend = GGML_BACKEND_GPU_SPLIT;
+    tensor->backend = GGML_BACKEND_TYPE_GPU_SPLIT;
     tensor->extra = extra;
 }
 
@@ -11605,7 +11605,7 @@ GGML_CALL static void ggml_backend_cuda_set_tensor_async(ggml_backend_t backend,
     ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context;
 
     GGML_ASSERT(tensor->buffer->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device) && "unsupported buffer type");
-    GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU);
+    GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU);
 
     CUDA_CHECK(cudaMemcpyAsync((char *)tensor->data + offset, data, size, cudaMemcpyHostToDevice, g_cudaStreams[cuda_ctx->device][0]));
 }
@@ -11614,7 +11614,7 @@ GGML_CALL static void ggml_backend_cuda_get_tensor_async(ggml_backend_t backend,
     ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context;
 
     GGML_ASSERT(tensor->buffer->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device) && "unsupported buffer type");
-    GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU);
+    GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU);
 
     CUDA_CHECK(cudaMemcpyAsync(data, (const char *)tensor->data + offset, size, cudaMemcpyDeviceToHost, g_cudaStreams[cuda_ctx->device][0]));
 }
@@ -11644,7 +11644,7 @@ GGML_CALL static bool ggml_backend_cuda_graph_compute(ggml_backend_t backend, gg
     ggml_cuda_set_main_device(cuda_ctx->device);
 
     ggml_compute_params params = {};
-    params.type = GGML_TASK_COMPUTE;
+    params.type = GGML_TASK_TYPE_COMPUTE;
     params.ith = 0;
     for (int i = 0; i < cgraph->n_nodes; i++) {
         ggml_tensor * node = cgraph->nodes[i];
@@ -11654,13 +11654,13 @@ GGML_CALL static bool ggml_backend_cuda_graph_compute(ggml_backend_t backend, gg
         }
 
 #ifndef NDEBUG
-        assert(node->backend == GGML_BACKEND_GPU || node->backend == GGML_BACKEND_GPU_SPLIT);
+        assert(node->backend == GGML_BACKEND_TYPE_GPU || node->backend == GGML_BACKEND_TYPE_GPU_SPLIT);
         assert(node->buffer->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device));
         assert(node->extra != nullptr);
 
         for (int j = 0; j < GGML_MAX_SRC; j++) {
             if (node->src[j] != nullptr) {
-                assert(node->src[j]->backend == GGML_BACKEND_GPU || node->src[j]->backend == GGML_BACKEND_GPU_SPLIT);
+                assert(node->src[j]->backend == GGML_BACKEND_TYPE_GPU || node->src[j]->backend == GGML_BACKEND_TYPE_GPU_SPLIT);
                 assert(node->src[j]->buffer->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device) || ggml_backend_buffer_is_cuda_split(node->src[j]->buffer));
                 assert(node->src[j]->extra != nullptr);
             }

ggml-metal.m

@@ -2262,8 +2262,8 @@ static bool ggml_metal_graph_compute(
                         id<MTLComputePipelineState> pipeline = nil;
 
                         switch (order) {
-                            case GGML_SORT_ASC:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_ASC].pipeline;  break;
-                            case GGML_SORT_DESC: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_DESC].pipeline; break;
+                            case GGML_SORT_ORDER_ASC:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_ASC].pipeline;  break;
+                            case GGML_SORT_ORDER_DESC: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_DESC].pipeline; break;
                             default: GGML_ASSERT(false);
                         };
 

ggml-opencl.cpp

@@ -1354,7 +1354,7 @@ static void ggml_cl_pool_free(cl_mem mem, size_t size) {
 }
 
 void ggml_cl_free_data(const struct ggml_tensor* tensor) {
-    if (tensor->backend != GGML_BACKEND_GPU) {
+    if (tensor->backend != GGML_BACKEND_TYPE_GPU) {
         return;
     }
 
@@ -1412,7 +1412,7 @@ static cl_int ggml_cl_h2d_tensor_2d(cl_command_queue queue, cl_mem dst, size_t o
 }
 
 static void ggml_cl_mul_f32(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    GGML_ASSERT(src1->backend == GGML_BACKEND_GPU);
+    GGML_ASSERT(src1->backend == GGML_BACKEND_TYPE_GPU);
     const int64_t ne00 = src0->ne[0];
     const int64_t ne01 = src0->ne[1];
     const int64_t ne02 = src0->ne[2];
@@ -1476,7 +1476,7 @@ void ggml_cl_mul(const struct ggml_tensor * src0, const struct ggml_tensor * src
 }
 
 static void ggml_cl_add_f32(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    GGML_ASSERT(src1->backend == GGML_BACKEND_GPU);
+    GGML_ASSERT(src1->backend == GGML_BACKEND_TYPE_GPU);
     const int64_t ne00 = src0->ne[0];
     const int64_t ne01 = src0->ne[1];
     const int64_t ne02 = src0->ne[2];
@@ -1566,13 +1566,13 @@ static void ggml_cl_mul_mat_f32(const ggml_tensor * src0, const ggml_tensor * sr
     size_t y_size;
     size_t d_size;
     cl_mem d_X;
-    if (src0->backend == GGML_BACKEND_GPU) { // NOLINT
+    if (src0->backend == GGML_BACKEND_TYPE_GPU) { // NOLINT
         d_X = (cl_mem) src0->extra;
     } else {
         d_X = ggml_cl_pool_malloc(sizeof(float) * x_ne, &x_size);
     }
-    cl_mem d_Y = src1->backend == GGML_BACKEND_GPU ? (cl_mem) src1->extra : ggml_cl_pool_malloc(sizeof(float) * y_ne, &y_size);
-    cl_mem d_D =  dst->backend == GGML_BACKEND_GPU ? (cl_mem)  dst->extra : ggml_cl_pool_malloc(sizeof(float) * d_ne, &d_size);
+    cl_mem d_Y = src1->backend == GGML_BACKEND_TYPE_GPU ? (cl_mem) src1->extra : ggml_cl_pool_malloc(sizeof(float) * y_ne, &y_size);
+    cl_mem d_D =  dst->backend == GGML_BACKEND_TYPE_GPU ? (cl_mem)  dst->extra : ggml_cl_pool_malloc(sizeof(float) * d_ne, &d_size);
 
     size_t x_offset = 0;
 
@@ -1580,7 +1580,7 @@ static void ggml_cl_mul_mat_f32(const ggml_tensor * src0, const ggml_tensor * sr
         // TODO: copy src0 here when r3>1
         for (int64_t i13 = i03 * r3, e13 = i13 + r3; i13 < e13; i13++) {
             for (int64_t i02 = 0; i02 < ne02; i02++) {
-                if (src0->backend == GGML_BACKEND_GPU) {
+                if (src0->backend == GGML_BACKEND_TYPE_GPU) {
                     x_offset = (i03 * ne02 + i02) * x_ne;
                 } else {
                     // copy src0 to device
@@ -1589,7 +1589,7 @@ static void ggml_cl_mul_mat_f32(const ggml_tensor * src0, const ggml_tensor * sr
 
                 for (int64_t i12 = i02 * r2, e12 = i12 + r2; i12 < e12; i12++) {
                     // copy src1 to device
-                    if (src1->backend == GGML_BACKEND_CPU) {
+                    if (src1->backend == GGML_BACKEND_TYPE_CPU) {
                         CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_Y, 0, src1, i13, i12, NULL));
                     }
 
@@ -1612,7 +1612,7 @@ static void ggml_cl_mul_mat_f32(const ggml_tensor * src0, const ggml_tensor * sr
                     }
 
                     // copy dst to host
-                    if (dst->backend == GGML_BACKEND_CPU) {
+                    if (dst->backend == GGML_BACKEND_TYPE_CPU) {
                         float * d = (float *) ((char *) dst->data + i12*nb2 + i13*nb3);
                         CL_CHECK(clEnqueueReadBuffer(queue, d_D, true, 0, sizeof(float) * d_ne, d, 1, &ev_sgemm, NULL));
                     }
@@ -1621,13 +1621,13 @@ static void ggml_cl_mul_mat_f32(const ggml_tensor * src0, const ggml_tensor * sr
         }
     }
 
-    if (src0->backend != GGML_BACKEND_GPU) {
+    if (src0->backend != GGML_BACKEND_TYPE_GPU) {
         ggml_cl_pool_free(d_X, x_size);
     }
-    if (src1->backend != GGML_BACKEND_GPU) {
+    if (src1->backend != GGML_BACKEND_TYPE_GPU) {
         ggml_cl_pool_free(d_Y, y_size);
     }
-    if (dst->backend != GGML_BACKEND_GPU) {
+    if (dst->backend != GGML_BACKEND_TYPE_GPU) {
         ggml_cl_pool_free(d_D, d_size);
     }
 }
@@ -1670,7 +1670,7 @@ static void ggml_cl_mul_mat_f16(const ggml_tensor * src0, const ggml_tensor * sr
     size_t y_size;
     size_t d_size;
     cl_mem d_X;
-    if (src0->backend == GGML_BACKEND_GPU) { // NOLINT
+    if (src0->backend == GGML_BACKEND_TYPE_GPU) { // NOLINT
         d_X = (cl_mem) src0->extra;
     } else {
         d_X = ggml_cl_pool_malloc(sizeof(ggml_fp16_t) * x_ne, &x_size);
@@ -1687,7 +1687,7 @@ static void ggml_cl_mul_mat_f16(const ggml_tensor * src0, const ggml_tensor * sr
         // TODO: copy src0 here when r3>1
         for (int64_t i13 = i03 * r3, e13 = i13 + r3; i13 < e13; i13++) {
             for (int64_t i02 = 0; i02 < ne02; i02++) {
-                if (src0->backend == GGML_BACKEND_GPU) {
+                if (src0->backend == GGML_BACKEND_TYPE_GPU) {
                     x_offset = (i03 * ne02 + i02) * x_ne;
                 } else {
                     // copy src0 to device
@@ -1741,7 +1741,7 @@ static void ggml_cl_mul_mat_f16(const ggml_tensor * src0, const ggml_tensor * sr
                     }
 
                     // copy dst to host, then convert to float
-                    if (dst->backend == GGML_BACKEND_CPU) {
+                    if (dst->backend == GGML_BACKEND_TYPE_CPU) {
                         CL_CHECK(clEnqueueReadBuffer(queue, d_D, true, 0, sizeof(ggml_fp16_t) * d_ne, tmp, 1, &ev_sgemm, NULL));
                         float * d = (float *) ((char *) dst->data + i12*nb2 + i13*nb3);
                         ggml_fp16_to_fp32_row(tmp, d, d_ne);
@@ -1753,7 +1753,7 @@ static void ggml_cl_mul_mat_f16(const ggml_tensor * src0, const ggml_tensor * sr
         }
     }
 
-    if (src0->backend != GGML_BACKEND_GPU) {
+    if (src0->backend != GGML_BACKEND_TYPE_GPU) {
         ggml_cl_pool_free(d_X, x_size);
     }
     ggml_cl_pool_free(d_Y, y_size);
@@ -1798,7 +1798,7 @@ static void ggml_cl_mul_mat_q_f32(const ggml_tensor * src0, const ggml_tensor *
     cl_mem d_Y = ggml_cl_pool_malloc(sizeof(float) * y_ne, &y_size);
     cl_mem d_D = ggml_cl_pool_malloc(sizeof(float) * d_ne, &d_size);
     cl_mem d_Q;
-    if (src0->backend == GGML_BACKEND_CPU) {
+    if (src0->backend == GGML_BACKEND_TYPE_CPU) {
         d_Q = ggml_cl_pool_malloc(q_sz, &q_size);
     }
 
@@ -1817,10 +1817,10 @@ static void ggml_cl_mul_mat_q_f32(const ggml_tensor * src0, const ggml_tensor *
         for (int64_t i13 = i03 * r3, e13 = i13 + r3; i13 < e13; i13++) {
             for (int64_t i02 = 0; i02 < ne02; i02++) {
                 // copy src0 to device if necessary
-                if (src0->backend == GGML_BACKEND_CPU) {
+                if (src0->backend == GGML_BACKEND_TYPE_CPU) {
                     events.emplace_back();
                     CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_Q, 0, src0, i03, i02, events.data() + ev_idx++));
-                } else if (src0->backend == GGML_BACKEND_GPU) {
+                } else if (src0->backend == GGML_BACKEND_TYPE_GPU) {
                     d_Q = (cl_mem) src0->extra;
                 } else {
                     GGML_ASSERT(false);
@@ -1829,7 +1829,7 @@ static void ggml_cl_mul_mat_q_f32(const ggml_tensor * src0, const ggml_tensor *
                 if (!mul_mat_vec) {
                     // convert src0 to fp32 on device
                     const size_t global = x_ne / global_denom;
-                    const size_t offset = src0->backend == GGML_BACKEND_GPU ? (i03 * ne02 + i02) * x_bps : 0;
+                    const size_t offset = src0->backend == GGML_BACKEND_TYPE_GPU ? (i03 * ne02 + i02) * x_bps : 0;
                     CL_CHECK(clSetKernelArg(*to_fp32_cl, 0, sizeof(cl_mem), &d_Q));
                     CL_CHECK(clSetKernelArg(*to_fp32_cl, 1, sizeof(cl_mem), &d_X));
                     CL_CHECK(clEnqueueNDRangeKernel(queue, *to_fp32_cl, 1, &offset, &global, local > 0 ? &local : NULL, events.size(), !events.empty() ? events.data() : NULL, NULL));
@@ -1843,7 +1843,7 @@ static void ggml_cl_mul_mat_q_f32(const ggml_tensor * src0, const ggml_tensor *
 
                         // compute
                         const size_t global = ne01 * local;
-                        const size_t offset = src0->backend == GGML_BACKEND_GPU ? (i03 * ne02 + i02) * x_bps : 0;
+                        const size_t offset = src0->backend == GGML_BACKEND_TYPE_GPU ? (i03 * ne02 + i02) * x_bps : 0;
                         const cl_int ncols = ne00;
                         events.emplace_back();
                         CL_CHECK(clSetKernelArg(*dmmv, 0, sizeof(cl_mem), &d_Q));
@@ -1895,7 +1895,7 @@ static void ggml_cl_mul_mat_q_f32(const ggml_tensor * src0, const ggml_tensor *
     }
     ggml_cl_pool_free(d_Y, y_size);
     ggml_cl_pool_free(d_D, d_size);
-    if (src0->backend == GGML_BACKEND_CPU) {
+    if (src0->backend == GGML_BACKEND_TYPE_CPU) {
         ggml_cl_pool_free(d_Q, q_size);
     }
 }
@@ -1911,7 +1911,7 @@ bool ggml_cl_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tens
     if ((src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type)) &&
         src1->type == GGML_TYPE_F32 &&
         dst->type == GGML_TYPE_F32 &&
-        ((ne0 >= 32 && ne1 >= 32 && ne10 >= 32) || src0->backend == GGML_BACKEND_GPU)) {
+        ((ne0 >= 32 && ne1 >= 32 && ne10 >= 32) || src0->backend == GGML_BACKEND_TYPE_GPU)) {
         return true;
     }
 
@@ -1993,7 +1993,7 @@ void ggml_cl_transform_tensor(void * data, ggml_tensor * tensor) {
     CL_CHECK(clFinish(queue));
 
     tensor->extra = dst;
-    GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU);
+    GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU);
 }
 
 // ggml-backend
@@ -2045,7 +2045,7 @@ static void ggml_backend_opencl_buffer_init_tensor(ggml_backend_buffer_t buffer,
         ctx->sub_buffers.push_back(sub_buffer);
         tensor->extra = sub_buffer;
     }
-    tensor->backend = GGML_BACKEND_GPU;
+    tensor->backend = GGML_BACKEND_TYPE_GPU;
 }
 
 static void ggml_backend_opencl_buffer_set_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, const void * data, size_t offset, size_t size) {

ggml-sycl.cpp

@@ -3338,7 +3338,7 @@ void print_ggml_tensor(const char*name, struct ggml_tensor *src){
 
     size_t total_elements = ggml_nelements(src);
 
-    const bool src_on_device = src->backend == GGML_BACKEND_GPU || src->backend == GGML_BACKEND_GPU_SPLIT;
+    const bool src_on_device = src->backend == GGML_BACKEND_TYPE_GPU || src->backend == GGML_BACKEND_TYPE_GPU_SPLIT;
     float *src_data =NULL;
     if(src_on_device) {
         ggml_tensor_extra_gpu * src_extra = (ggml_tensor_extra_gpu *)  src->extra;
@@ -8086,11 +8086,11 @@ static void k_argsort_f32_i32(const float * x, int * dst, const int ncols,
             int ixj = col ^ j;
             if (ixj > col) {
                 if ((col & k) == 0) {
-                    if (order == GGML_SORT_ASC ? x_row[dst_row[col]] > x_row[dst_row[ixj]] : x_row[dst_row[col]] < x_row[dst_row[ixj]]) {
+                    if (order == GGML_SORT_ORDER_ASC ? x_row[dst_row[col]] > x_row[dst_row[ixj]] : x_row[dst_row[col]] < x_row[dst_row[ixj]]) {
                         swap(dst_row[col], dst_row[ixj]);
                     }
                 } else {
-                    if (order == GGML_SORT_ASC ? x_row[dst_row[col]] < x_row[dst_row[ixj]] : x_row[dst_row[col]] > x_row[dst_row[ixj]]) {
+                    if (order == GGML_SORT_ORDER_ASC ? x_row[dst_row[col]] < x_row[dst_row[ixj]] : x_row[dst_row[col]] > x_row[dst_row[ixj]]) {
                         swap(dst_row[col], dst_row[ixj]);
                     }
                 }
@@ -10825,7 +10825,7 @@ static void argsort_f32_i32_sycl(const float *x, int *dst, const int ncols,
 
     const sycl::range<3> block_dims(1, 1, ncols);
     const sycl::range<3> block_nums(1, nrows, 1);
-    if (order == GGML_SORT_ASC) {
+    if (order == GGML_SORT_ORDER_ASC) {
         /*
         DPCT1049:44: The work-group size passed to the SYCL kernel may exceed
         the limit. To get the device limit, query
@@ -10834,9 +10834,9 @@ static void argsort_f32_i32_sycl(const float *x, int *dst, const int ncols,
         stream->parallel_for(
             sycl::nd_range<3>(block_nums * block_dims, block_dims),
             [=](sycl::nd_item<3> item_ct1) {
-                k_argsort_f32_i32<GGML_SORT_ASC>(x, dst, ncols, item_ct1);
+                k_argsort_f32_i32<GGML_SORT_ORDER_ASC>(x, dst, ncols, item_ct1);
             });
-    } else if (order == GGML_SORT_DESC) {
+    } else if (order == GGML_SORT_ORDER_DESC) {
         /*
         DPCT1049:45: The work-group size passed to the SYCL kernel may exceed
         the limit. To get the device limit, query
@@ -10845,7 +10845,7 @@ static void argsort_f32_i32_sycl(const float *x, int *dst, const int ncols,
         stream->parallel_for(
             sycl::nd_range<3>(block_nums * block_dims, block_dims),
             [=](sycl::nd_item<3> item_ct1) {
-                k_argsort_f32_i32<GGML_SORT_DESC>(x, dst, ncols, item_ct1);
+                k_argsort_f32_i32<GGML_SORT_ORDER_DESC>(x, dst, ncols, item_ct1);
             });
     } else {
         GGML_ASSERT(false);
@@ -11407,12 +11407,12 @@ static dpct::err0 ggml_sycl_cpy_tensor_2d(void *dst,
 
     dpct::memcpy_direction kind;
     char * src_ptr;
-    if (src->backend == GGML_BACKEND_CPU) {
+    if (src->backend == GGML_BACKEND_TYPE_CPU) {
         kind = dpct::host_to_device;
         src_ptr = (char *) src->data;
-        // GGML_SYCL_DEBUG("ggml_sycl_cpy_tensor_2d  GGML_BACKEND_CPU src_ptr %p\n", src_ptr);
-    } else if (src->backend == GGML_BACKEND_GPU || src->backend == GGML_BACKEND_GPU_SPLIT) {
-        GGML_ASSERT(src->backend != GGML_BACKEND_GPU_SPLIT || (i1_low == 0 && i1_high == src->ne[1]));
+        // GGML_SYCL_DEBUG("ggml_sycl_cpy_tensor_2d  GGML_BACKEND_TYPE_CPU src_ptr %p\n", src_ptr);
+    } else if (src->backend == GGML_BACKEND_TYPE_GPU || src->backend == GGML_BACKEND_TYPE_GPU_SPLIT) {
+        GGML_ASSERT(src->backend != GGML_BACKEND_TYPE_GPU_SPLIT || (i1_low == 0 && i1_high == src->ne[1]));
         kind = dpct::device_to_device;
         ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) src->extra;
         int id;
@@ -11846,7 +11846,7 @@ inline void ggml_sycl_op_mul_mat_q(
 
     // the main device has a larger memory buffer to hold the results from all GPUs
     // nrows_dst == nrows of the matrix that the dequantize_mul_mat kernel writes into
-    const int64_t nrows_dst = dst->backend == GGML_BACKEND_GPU && device_id == g_main_device ? ne0 : row_diff;
+    const int64_t nrows_dst = dst->backend == GGML_BACKEND_TYPE_GPU && device_id == g_main_device ? ne0 : row_diff;
 
     switch (src0->type) {
         case GGML_TYPE_Q4_0:
@@ -12119,7 +12119,7 @@ inline void ggml_sycl_op_mul_mat_sycl(
 
     // the main device has a larger memory buffer to hold the results from all GPUs
     // ldc == nrows of the matrix that cuBLAS writes into
-    int ldc = dst->backend == GGML_BACKEND_GPU && device_id == g_main_device ? ne0 : row_diff;
+    int ldc = dst->backend == GGML_BACKEND_TYPE_GPU && device_id == g_main_device ? ne0 : row_diff;
 
 #ifdef GGML_SYCL_F16
     bool use_fp16 = true;  // TODO(Yu) SYCL capability check
@@ -12501,16 +12501,16 @@ static void ggml_sycl_op_flatten(const ggml_tensor *src0,
     const bool use_src1 = src1 != nullptr;
     const int64_t nrows1 = use_src1 ? ggml_nrows(src1) : 1;
 
-    GGML_ASSERT(!use_src1 || src1->backend != GGML_BACKEND_GPU_SPLIT);
-    GGML_ASSERT(              dst->backend != GGML_BACKEND_GPU_SPLIT);
+    GGML_ASSERT(!use_src1 || src1->backend != GGML_BACKEND_TYPE_GPU_SPLIT);
+    GGML_ASSERT(              dst->backend != GGML_BACKEND_TYPE_GPU_SPLIT);
 
     ggml_tensor_extra_gpu * src0_extra =            (ggml_tensor_extra_gpu *) src0->extra;
     ggml_tensor_extra_gpu * src1_extra = use_src1 ? (ggml_tensor_extra_gpu *) src1->extra : nullptr;
     ggml_tensor_extra_gpu * dst_extra  =            (ggml_tensor_extra_gpu *)  dst->extra;
 
-    const bool src0_on_device =             src0->backend == GGML_BACKEND_GPU || src0->backend == GGML_BACKEND_GPU_SPLIT;
-    const bool src1_on_device = use_src1 && src1->backend == GGML_BACKEND_GPU;
-    const bool  dst_on_device =              dst->backend == GGML_BACKEND_GPU;
+    const bool src0_on_device =             src0->backend == GGML_BACKEND_TYPE_GPU || src0->backend == GGML_BACKEND_TYPE_GPU_SPLIT;
+    const bool src1_on_device = use_src1 && src1->backend == GGML_BACKEND_TYPE_GPU;
+    const bool  dst_on_device =              dst->backend == GGML_BACKEND_TYPE_GPU;
 
     // dd = data device
     float * src0_ddf = nullptr;
@@ -12565,7 +12565,7 @@ static void ggml_sycl_op_flatten(const ggml_tensor *src0,
             main_stream->memcpy(dst->data, dst_ddf, ggml_nbytes(dst))));
     }
 
-    if (dst->backend == GGML_BACKEND_CPU) {
+    if (dst->backend == GGML_BACKEND_TYPE_CPU) {
         SYCL_CHECK(CHECK_TRY_ERROR(
             dpct::get_current_device().queues_wait_and_throw()));
     }
@@ -12640,8 +12640,8 @@ static void ggml_sycl_op_mul_mat(const ggml_tensor *src0,
     const int nb2 = dst->nb[2];
     const int nb3 = dst->nb[3];
 
-    GGML_ASSERT(dst->backend != GGML_BACKEND_GPU_SPLIT);
-    GGML_ASSERT(src1->backend != GGML_BACKEND_GPU_SPLIT);
+    GGML_ASSERT(dst->backend != GGML_BACKEND_TYPE_GPU_SPLIT);
+    GGML_ASSERT(src1->backend != GGML_BACKEND_TYPE_GPU_SPLIT);
 
     GGML_ASSERT(ne12 >= ne02 && ne12 % ne02 == 0);
 
@@ -12656,13 +12656,13 @@ static void ggml_sycl_op_mul_mat(const ggml_tensor *src0,
     ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu *) src1->extra;
     ggml_tensor_extra_gpu *  dst_extra = (ggml_tensor_extra_gpu *)  dst->extra;
 
-    const bool src0_on_device = src0->backend == GGML_BACKEND_GPU || src0->backend == GGML_BACKEND_GPU_SPLIT;
+    const bool src0_on_device = src0->backend == GGML_BACKEND_TYPE_GPU || src0->backend == GGML_BACKEND_TYPE_GPU_SPLIT;
     const bool src0_is_contiguous = ggml_is_contiguous(src0);
     const bool src1_is_contiguous = ggml_is_contiguous(src1);
 
     int64_t src1_padded_col_size = GGML_PAD(ne10, MATRIX_ROW_PADDING);
 
-    const bool split = src0->backend == GGML_BACKEND_GPU_SPLIT;
+    const bool split = src0->backend == GGML_BACKEND_TYPE_GPU_SPLIT;
     GGML_ASSERT(!(split && ne02 > 1));
     GGML_ASSERT(!(split && ne03 > 1));
     GGML_ASSERT(!(split && ne02 < ne12));
@@ -12717,8 +12717,8 @@ static void ggml_sycl_op_mul_mat(const ggml_tensor *src0,
 
         used_devices++;
 
-        const bool src1_on_device = src1->backend == GGML_BACKEND_GPU && id == g_main_device_index;
-        const bool  dst_on_device =  dst->backend == GGML_BACKEND_GPU && id == g_main_device_index;
+        const bool src1_on_device = src1->backend == GGML_BACKEND_TYPE_GPU && id == g_main_device_index;
+        const bool  dst_on_device =  dst->backend == GGML_BACKEND_TYPE_GPU && id == g_main_device_index;
 
         ggml_sycl_set_device(get_device_id_by_index(id));
         const dpct::queue_ptr stream = g_syclStreams[id][0];
@@ -12782,8 +12782,8 @@ static void ggml_sycl_op_mul_mat(const ggml_tensor *src0,
                 continue;
             }
 
-            const bool src1_on_device = src1->backend == GGML_BACKEND_GPU && id == g_main_device_index;
-            const bool  dst_on_device =  dst->backend == GGML_BACKEND_GPU && id == g_main_device_index;
+            const bool src1_on_device = src1->backend == GGML_BACKEND_TYPE_GPU && id == g_main_device_index;
+            const bool  dst_on_device =  dst->backend == GGML_BACKEND_TYPE_GPU && id == g_main_device_index;
             const int64_t row_diff = row_high[id] - row_low[id];
 
             ggml_sycl_set_device(get_device_id_by_index(id));
@@ -12809,12 +12809,12 @@ static void ggml_sycl_op_mul_mat(const ggml_tensor *src0,
 
                 // the main device memory buffer can be on VRAM scratch, with space for all partial results
                 // in that case an offset on dst_ddf_i is needed
-                if (dst->backend == GGML_BACKEND_GPU && id == g_main_device_index) {
+                if (dst->backend == GGML_BACKEND_TYPE_GPU && id == g_main_device_index) {
                     dst_dd_i += row_low[id]; // offset is 0 if no tensor split
                 }
 
                 // copy src0, src1 to device if necessary
-                if (src1->backend == GGML_BACKEND_GPU && src1_is_contiguous) {
+                if (src1->backend == GGML_BACKEND_TYPE_GPU && src1_is_contiguous) {
                     if (id != g_main_device_index) {
                         if (convert_src1_to_q8_1) {
                             char * src1_ddq_i_source = src1_ddq[g_main_device_index] + src1_ddq_i_offset;
@@ -12830,14 +12830,14 @@ static void ggml_sycl_op_mul_mat(const ggml_tensor *src0,
                                 src1_ncols * ne10 * sizeof(float))));
                         }
                     }
-                } else if (src1->backend == GGML_BACKEND_CPU || (src1_on_device && !src1_is_contiguous)) {
+                } else if (src1->backend == GGML_BACKEND_TYPE_CPU || (src1_on_device && !src1_is_contiguous)) {
                     SYCL_CHECK(ggml_sycl_cpy_tensor_2d(
                                    src1_ddf_i, src1, i03, i02, src1_col_0, src1_col_0+src1_ncols, stream));
                 } else {
                     GGML_ASSERT(false);
                 }
 
-                if (convert_src1_to_q8_1 && (src1->backend == GGML_BACKEND_CPU || !src1_is_contiguous)) {
+                if (convert_src1_to_q8_1 && (src1->backend == GGML_BACKEND_TYPE_CPU || !src1_is_contiguous)) {
                     quantize_row_q8_1_sycl(src1_ddf_i, src1_ddq_i, ne10, src1_ncols, src1_padded_col_size, stream);
                     /*
                     DPCT1010:92: SYCL uses exceptions to report errors and does
@@ -12867,10 +12867,10 @@ static void ggml_sycl_op_mul_mat(const ggml_tensor *src0,
                 if (!dst_on_device) {
                     void * dst_off_device;
                     dpct::memcpy_direction kind;
-                    if (dst->backend == GGML_BACKEND_CPU) {
+                    if (dst->backend == GGML_BACKEND_TYPE_CPU) {
                         dst_off_device = dst->data;
                         kind = dpct::device_to_host;
-                    } else if (dst->backend == GGML_BACKEND_GPU) {
+                    } else if (dst->backend == GGML_BACKEND_TYPE_GPU) {
                         dst_off_device = dst_extra->data_device[g_main_device_index];
                         kind = dpct::device_to_device;
                     } else {
@@ -12954,7 +12954,7 @@ static void ggml_sycl_op_mul_mat(const ggml_tensor *src0,
         }
     }
 
-    if (dst->backend == GGML_BACKEND_CPU) {
+    if (dst->backend == GGML_BACKEND_TYPE_CPU) {
         SYCL_CHECK(ggml_sycl_set_device(g_main_device));
         SYCL_CHECK(CHECK_TRY_ERROR(
             dpct::get_current_device().queues_wait_and_throw()));
@@ -13091,7 +13091,7 @@ static void ggml_sycl_mul_mat_vec_p021(const ggml_tensor *src0,
                                        const ggml_tensor *src1,
                                        ggml_tensor *dst) try {
     GGML_ASSERT(ggml_is_permuted(src0) && ggml_is_permuted(src1));
-    GGML_ASSERT(src0->backend != GGML_BACKEND_GPU_SPLIT);
+    GGML_ASSERT(src0->backend != GGML_BACKEND_TYPE_GPU_SPLIT);
     GGML_ASSERT(src0->nb[0] <= src0->nb[1] && src0->nb[2] <= src0->nb[3]); // 0213 permutation
     GGML_ASSERT(src1->nb[0] <= src1->nb[1] && src1->nb[2] <= src1->nb[3]); // 0213 permutation
     GGML_ASSERT(src0->type == GGML_TYPE_F16);
@@ -13129,7 +13129,7 @@ static void ggml_sycl_mul_mat_vec_nc(const ggml_tensor *src0,
     GGML_ASSERT(!ggml_is_transposed(src0));
     GGML_ASSERT(!ggml_is_transposed(src1));
     GGML_ASSERT(!ggml_is_permuted(src0));
-    GGML_ASSERT(src0->backend != GGML_BACKEND_GPU_SPLIT);
+    GGML_ASSERT(src0->backend != GGML_BACKEND_TYPE_GPU_SPLIT);
     GGML_ASSERT(src0->type == GGML_TYPE_F16);
     GGML_ASSERT(src1->type == GGML_TYPE_F32);
 
@@ -13196,7 +13196,7 @@ static void ggml_sycl_mul_mat_mat_batched_sycl(const ggml_tensor *src0,
     GGML_ASSERT(!ggml_is_transposed(src0));
     GGML_ASSERT(!ggml_is_transposed(src1));
 
-    GGML_ASSERT(src0->backend != GGML_BACKEND_GPU_SPLIT);
+    GGML_ASSERT(src0->backend != GGML_BACKEND_TYPE_GPU_SPLIT);
     GGML_ASSERT(src0->type == GGML_TYPE_F16);
     GGML_ASSERT(src1->type == GGML_TYPE_F32);
 
@@ -13372,11 +13372,11 @@ catch (sycl::exception const &exc) {
 
 static void ggml_sycl_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
     const bool all_on_device =
-        (src0->backend == GGML_BACKEND_GPU || src0->backend == GGML_BACKEND_GPU_SPLIT) &&
-        (src1->backend == GGML_BACKEND_GPU) &&
-        ( dst->backend == GGML_BACKEND_GPU);
+        (src0->backend == GGML_BACKEND_TYPE_GPU || src0->backend == GGML_BACKEND_TYPE_GPU_SPLIT) &&
+        (src1->backend == GGML_BACKEND_TYPE_GPU) &&
+        ( dst->backend == GGML_BACKEND_TYPE_GPU);
 
-    const bool split = src0->backend == GGML_BACKEND_GPU_SPLIT;
+    const bool split = src0->backend == GGML_BACKEND_TYPE_GPU_SPLIT;
 
     int64_t min_compute_capability = INT_MAX;
     for (int64_t id = 0; id < g_device_count; ++id) {
@@ -13505,7 +13505,7 @@ static void ggml_sycl_mul_mat_id_sycl(ggml_tensor * dst) {
     GGML_ASSERT(!ggml_is_transposed(src00));
     GGML_ASSERT(!ggml_is_transposed(src1));
 
-    GGML_ASSERT(src00->backend != GGML_BACKEND_GPU_SPLIT);
+    GGML_ASSERT(src00->backend != GGML_BACKEND_TYPE_GPU_SPLIT);
     GGML_ASSERT(src1->type == GGML_TYPE_F32);
 
     GGML_TENSOR_LOCALS(int64_t, ne0, src00, ne);
@@ -13643,7 +13643,7 @@ static void ggml_sycl_mul_mat_id(const ggml_tensor *src0,
 
     const dpct::queue_ptr stream = g_syclStreams[g_main_device_index][0];
 
-    if (ids->backend == GGML_BACKEND_GPU) {
+    if (ids->backend == GGML_BACKEND_TYPE_GPU) {
         const char * ids_dev = (const char *)((const ggml_tensor_extra_gpu *)ids->extra)->data_device[g_main_device_index];
         SYCL_CHECK(CHECK_TRY_ERROR(
             stream->memcpy(ids_host.data(), ids_dev, ggml_nbytes(ids))));
@@ -13661,20 +13661,20 @@ static void ggml_sycl_mul_mat_id(const ggml_tensor *src0,
     ggml_tensor src1_row = *src1;
     ggml_tensor dst_row = *dst;
 
-    src1_row.backend = GGML_BACKEND_GPU;
-    dst_row.backend  = GGML_BACKEND_GPU;
+    src1_row.backend = GGML_BACKEND_TYPE_GPU;
+    dst_row.backend  = GGML_BACKEND_TYPE_GPU;
 
     src1_row.extra = &src1_row_extra;
     dst_row.extra = &dst_row_extra;
 
-    char * src1_original = src1->backend == GGML_BACKEND_CPU ?
+    char * src1_original = src1->backend == GGML_BACKEND_TYPE_CPU ?
         (char *) src1->data : (char *) src1_extra->data_device[g_main_device_index];
-    char * dst_original  =  dst->backend == GGML_BACKEND_CPU ?
+    char * dst_original  =  dst->backend == GGML_BACKEND_TYPE_CPU ?
         (char *)  dst->data : (char *)  dst_extra->data_device[g_main_device_index];
 
     if (src1->ne[1] == 1) {
-        GGML_ASSERT(src1->backend == GGML_BACKEND_GPU);
-        GGML_ASSERT(dst->backend  == GGML_BACKEND_GPU);
+        GGML_ASSERT(src1->backend == GGML_BACKEND_TYPE_GPU);
+        GGML_ASSERT(dst->backend  == GGML_BACKEND_TYPE_GPU);
 
         for (int64_t i01 = 0; i01 < ids->ne[1]; i01++) {
             //int32_t row_id;
@@ -13756,7 +13756,7 @@ static void ggml_sycl_mul_mat_id(const ggml_tensor *src0,
         }
     }
 
-    if (dst->backend == GGML_BACKEND_CPU) {
+    if (dst->backend == GGML_BACKEND_TYPE_CPU) {
         SYCL_CHECK(CHECK_TRY_ERROR(stream->wait()));
     }
 }
@@ -13779,8 +13779,8 @@ static void ggml_sycl_cpy(const ggml_tensor *src0, const ggml_tensor *src1,
     const int64_t ne = ggml_nelements(src0);
     GGML_ASSERT(ne == ggml_nelements(src1));
 
-    GGML_ASSERT(src0->backend == GGML_BACKEND_GPU);
-    GGML_ASSERT(src1->backend == GGML_BACKEND_GPU);
+    GGML_ASSERT(src0->backend == GGML_BACKEND_TYPE_GPU);
+    GGML_ASSERT(src1->backend == GGML_BACKEND_TYPE_GPU);
 
     GGML_ASSERT(ggml_nbytes(src0) <= INT_MAX);
     GGML_ASSERT(ggml_nbytes(src1) <= INT_MAX);
@@ -13887,17 +13887,17 @@ void ggml_sycl_transform_tensor(void *data, struct ggml_tensor *tensor) try {
     memset(extra, 0, sizeof(*extra));
 
     for (int64_t id = 0; id < g_device_count; ++id) {
-        if (backend == GGML_BACKEND_GPU && id != g_main_device_index) {
+        if (backend == GGML_BACKEND_TYPE_GPU && id != g_main_device_index) {
             continue;
         }
         ggml_sycl_set_device(get_device_id_by_index(id));
         const dpct::queue_ptr stream = g_syclStreams[id][0];
 
         int64_t row_low, row_high;
-        if (backend == GGML_BACKEND_GPU) {
+        if (backend == GGML_BACKEND_TYPE_GPU) {
             row_low = 0;
             row_high = nrows;
-        } else if (backend == GGML_BACKEND_GPU_SPLIT) {
+        } else if (backend == GGML_BACKEND_TYPE_GPU_SPLIT) {
             const int64_t rounding = get_row_rounding(tensor->type);
 
             row_low = id == 0 ? 0 : nrows*g_tensor_split[id];
@@ -13946,7 +13946,7 @@ void ggml_sycl_transform_tensor(void *data, struct ggml_tensor *tensor) try {
 
         extra->data_device[id] = buf;
 
-        if (backend == GGML_BACKEND_GPU_SPLIT) {
+        if (backend == GGML_BACKEND_TYPE_GPU_SPLIT) {
             for (int64_t is = 0; is < MAX_STREAMS; ++is) {
                 SYCL_CHECK(CHECK_TRY_ERROR(extra->events[id][is] =
                                                 new sycl::event()));
@@ -13963,7 +13963,7 @@ catch (sycl::exception const &exc) {
 }
 
 void ggml_sycl_free_data(struct ggml_tensor *tensor) try {
-    if (!tensor || !tensor->extra || (tensor->backend != GGML_BACKEND_GPU && tensor->backend != GGML_BACKEND_GPU_SPLIT) ) {
+    if (!tensor || !tensor->extra || (tensor->backend != GGML_BACKEND_TYPE_GPU && tensor->backend != GGML_BACKEND_TYPE_GPU_SPLIT) ) {
         return;
     }
 
@@ -14016,15 +14016,15 @@ static void ggml_sycl_assign_buffers_impl(struct ggml_tensor *tensor,
         return;
     }
 
-    tensor->backend = GGML_BACKEND_GPU;
+    tensor->backend = GGML_BACKEND_TYPE_GPU;
 
-    if (tensor->src[0] != nullptr && tensor->src[0]->backend == GGML_BACKEND_CPU) {
+    if (tensor->src[0] != nullptr && tensor->src[0]->backend == GGML_BACKEND_TYPE_CPU) {
         const ggml_op src0_op = tensor->src[0]->op;
         if (src0_op == GGML_OP_RESHAPE || src0_op == GGML_OP_TRANSPOSE || src0_op == GGML_OP_VIEW || src0_op == GGML_OP_PERMUTE) {
             ggml_sycl_assign_buffers_impl(tensor->src[0], scratch, force_inplace, no_alloc);
         }
     }
-    if (tensor->op == GGML_OP_CPY && tensor->src[1]->backend == GGML_BACKEND_CPU) {
+    if (tensor->op == GGML_OP_CPY && tensor->src[1]->backend == GGML_BACKEND_TYPE_CPU) {
         ggml_sycl_assign_buffers_impl(tensor->src[1], scratch, force_inplace, no_alloc);
     }
 
@@ -14042,7 +14042,7 @@ static void ggml_sycl_assign_buffers_impl(struct ggml_tensor *tensor,
     SYCL_CHECK(ggml_sycl_set_device(g_main_device));
     const dpct::queue_ptr stream = g_syclStreams[g_main_device_index][0];
 
-    if (inplace && (tensor->src[0]->backend == GGML_BACKEND_GPU || tensor->src[0]->backend == GGML_BACKEND_GPU_SPLIT)) {
+    if (inplace && (tensor->src[0]->backend == GGML_BACKEND_TYPE_GPU || tensor->src[0]->backend == GGML_BACKEND_TYPE_GPU_SPLIT)) {
         ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu * ) tensor->src[0]->extra;
         char * src0_ddc = (char *) src0_extra->data_device[g_main_device_index];
         size_t offset = 0;
@@ -14111,7 +14111,7 @@ void ggml_sycl_assign_scratch_offset(struct ggml_tensor *tensor,
 
     const bool inplace = tensor->view_src != nullptr;
 
-    if (inplace && (tensor->view_src->backend == GGML_BACKEND_GPU || tensor->view_src->backend == GGML_BACKEND_GPU_SPLIT)) {
+    if (inplace && (tensor->view_src->backend == GGML_BACKEND_TYPE_GPU || tensor->view_src->backend == GGML_BACKEND_TYPE_GPU_SPLIT)) {
         ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu * ) tensor->view_src->extra;
         char * src0_ddc = (char *) src0_extra->data_device[g_main_device_index];
         size_t view_offset = 0;
@@ -14132,7 +14132,7 @@ catch (sycl::exception const &exc) {
 }
 
 void ggml_sycl_copy_to_device(struct ggml_tensor *tensor) try {
-    GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU);
+    GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU);
     GGML_ASSERT(ggml_is_contiguous(tensor));
 
     ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra;
@@ -14219,9 +14219,9 @@ bool ggml_sycl_compute_forward(struct ggml_compute_params * params, struct ggml_
     if (!g_sycl_loaded) return false;
 
     ggml_sycl_func_t func;
-    const bool any_on_device = tensor->backend == GGML_BACKEND_GPU
-        || (tensor->src[0] != nullptr && (tensor->src[0]->backend == GGML_BACKEND_GPU || tensor->src[0]->backend == GGML_BACKEND_GPU_SPLIT))
-        || (tensor->src[1] != nullptr && tensor->src[1]->backend == GGML_BACKEND_GPU);
+    const bool any_on_device = tensor->backend == GGML_BACKEND_TYPE_GPU
+        || (tensor->src[0] != nullptr && (tensor->src[0]->backend == GGML_BACKEND_TYPE_GPU || tensor->src[0]->backend == GGML_BACKEND_TYPE_GPU_SPLIT))
+        || (tensor->src[1] != nullptr && tensor->src[1]->backend == GGML_BACKEND_TYPE_GPU);
 
     if (!any_on_device && tensor->op != GGML_OP_MUL_MAT && tensor->op != GGML_OP_MUL_MAT_ID) {
         return false;
@@ -14359,14 +14359,14 @@ bool ggml_sycl_compute_forward(struct ggml_compute_params * params, struct ggml_
             return false;
     }
 
-    if (tensor->src[0] != nullptr && tensor->src[0]->backend == GGML_BACKEND_GPU_SPLIT) {
+    if (tensor->src[0] != nullptr && tensor->src[0]->backend == GGML_BACKEND_TYPE_GPU_SPLIT) {
         ggml_sycl_set_peer_access(tensor->src[1]->ne[1]);
     }
 
     if (params->ith != 0) {
         return true;
     }
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return true;
     }
     func(tensor->src[0], tensor->src[1], tensor);
@@ -14517,7 +14517,7 @@ static void ggml_backend_sycl_buffer_init_tensor(ggml_backend_buffer_t buffer,
 
     extra->data_device[ctx->device] = tensor->data;
 
-    tensor->backend = GGML_BACKEND_GPU;
+    tensor->backend = GGML_BACKEND_TYPE_GPU;
     tensor->extra = extra;
 
     if (ggml_is_quantized(tensor->type)) {
@@ -14548,7 +14548,7 @@ static void ggml_backend_sycl_buffer_set_tensor(ggml_backend_buffer_t buffer,
                                                 ggml_tensor *tensor,
                                                 const void *data, size_t offset,
                                                 size_t size) try {
-    GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU);
+    GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU);
 
      ggml_backend_sycl_buffer_context * ctx = ( ggml_backend_sycl_buffer_context *)buffer->context;
 
@@ -14573,7 +14573,7 @@ static void ggml_backend_sycl_buffer_get_tensor(ggml_backend_buffer_t buffer,
                                                 const ggml_tensor *tensor,
                                                 void *data, size_t offset,
                                                 size_t size) try {
-    GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU);
+    GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU);
 
      ggml_backend_sycl_buffer_context * ctx = ( ggml_backend_sycl_buffer_context *)buffer->context;
 
@@ -14809,7 +14809,7 @@ static void ggml_backend_sycl_set_tensor_async(ggml_backend_t backend,
     ggml_backend_sycl_context * sycl_ctx = (ggml_backend_sycl_context *)backend->context;
 
     GGML_ASSERT(tensor->buffer->buft == ggml_backend_sycl_buffer_type(sycl_ctx->device) && "unsupported buffer type");
-    GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU);
+    GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU);
 
     SYCL_CHECK(CHECK_TRY_ERROR(g_syclStreams[sycl_ctx->device][0]->memcpy(
         (char *)tensor->data + offset, data, size)));
@@ -14827,7 +14827,7 @@ static void ggml_backend_sycl_get_tensor_async(ggml_backend_t backend,
     ggml_backend_sycl_context * sycl_ctx = (ggml_backend_sycl_context *)backend->context;
 
     GGML_ASSERT(tensor->buffer->buft == ggml_backend_sycl_buffer_type(sycl_ctx->device) && "unsupported buffer type");
-    GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU);
+    GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU);
 
     SYCL_CHECK(CHECK_TRY_ERROR(g_syclStreams[sycl_ctx->device][0]->memcpy(
         data, (const char *)tensor->data + offset, size)));
@@ -14880,7 +14880,7 @@ static bool ggml_backend_sycl_graph_compute(ggml_backend_t backend, ggml_cgraph
     ggml_sycl_set_main_device(sycl_ctx->device);
 
     ggml_compute_params params = {};
-    params.type = GGML_TASK_COMPUTE;
+    params.type = GGML_TASK_TYPE_COMPUTE;
     params.ith = 0;
     for (int i = 0; i < cgraph->n_nodes; i++) {
         ggml_tensor * node = cgraph->nodes[i];
@@ -14888,13 +14888,13 @@ static bool ggml_backend_sycl_graph_compute(ggml_backend_t backend, ggml_cgraph
         if (node->op == GGML_OP_RESHAPE || node->op == GGML_OP_TRANSPOSE || node->op == GGML_OP_VIEW || node->op == GGML_OP_PERMUTE)
             continue;
 
-        assert(node->backend == GGML_BACKEND_GPU);
+        assert(node->backend == GGML_BACKEND_TYPE_GPU);
         assert(node->buffer->buft == ggml_backend_sycl_buffer_type(sycl_ctx->device));
         assert(node->extra != nullptr);
 
         for (int j = 0; j < GGML_MAX_SRC; j++) {
             if (node->src[j] != nullptr) {
-                assert(node->src[j]->backend == GGML_BACKEND_GPU);
+                assert(node->src[j]->backend == GGML_BACKEND_TYPE_GPU);
                 assert(node->src[j]->buffer->buft == ggml_backend_sycl_buffer_type(sycl_ctx->device));
                 assert(node->src[j]->extra != nullptr);
             }

ggml-vulkan.cpp

@@ -2320,8 +2320,8 @@ static void ggml_vk_mul_mat_q_f16(ggml_backend_vk_context * ctx, vk_context * su
         src1_uma = d_Qy != nullptr;
     }
 
-    const bool load_x = src0->backend != GGML_BACKEND_GPU && !src0_uma;
-    const bool load_y = src1->backend != GGML_BACKEND_GPU && !src1_uma;
+    const bool load_x = src0->backend != GGML_BACKEND_TYPE_GPU && !src0_uma;
+    const bool load_y = src1->backend != GGML_BACKEND_TYPE_GPU && !src1_uma;
 
     const bool x_non_contig = !load_x && !ggml_vk_dim01_contiguous(src0);
     const bool y_non_contig = !load_y && !ggml_vk_dim01_contiguous(src1);
@@ -2453,7 +2453,7 @@ static void ggml_vk_mul_mat_q_f16(ggml_backend_vk_context * ctx, vk_context * su
     // compute
     ggml_vk_matmul(ctx, subctx, *pipeline, { d_X, x_buf_offset, x_sz * ne02 * ne03 }, { d_Y, y_buf_offset, y_sz * ne12 * ne13 }, { d_D, d_buf_offset, d_sz * ne12 * ne13 }, { ctx->prealloc_split_k, 0, d_sz * ne12 * ne13 * split_k }, ne01, ne11, ne10, ne10, ne10, ne01, split_k, ne12*ne13, ne02, ne12, r2, r3, stride_batch_x, stride_batch_y, ne20*ne21);  // NOLINT
 
-    if (dst->backend == GGML_BACKEND_CPU) {
+    if (dst->backend == GGML_BACKEND_TYPE_CPU) {
         // copy dst to host
         float * d = (float *) ((char *) dst->data);
         ggml_vk_buffer_read_async(ctx, subctx, d_D, 0, d, sizeof(float) * d_ne * ne12 * ne13);
@@ -2506,8 +2506,8 @@ static void ggml_vk_mul_mat_vec_q_f16(ggml_backend_vk_context * ctx, vk_context
         src1_uma = d_Qy != nullptr;
     }
 
-    const bool load_x = src0->backend != GGML_BACKEND_GPU && !src0_uma;
-    const bool load_y = src1->backend != GGML_BACKEND_GPU && !src1_uma;
+    const bool load_x = src0->backend != GGML_BACKEND_TYPE_GPU && !src0_uma;
+    const bool load_y = src1->backend != GGML_BACKEND_TYPE_GPU && !src1_uma;
 
     const bool x_non_contig = !load_x && !ggml_vk_dim01_contiguous(src0);
     const bool y_non_contig = !load_y && !ggml_vk_dim01_contiguous(src1);
@@ -2630,7 +2630,7 @@ static void ggml_vk_mul_mat_vec_q_f16(ggml_backend_vk_context * ctx, vk_context
             ggml_vk_sync_buffers(subctx);
             ggml_vk_dispatch_pipeline(ctx, subctx, *dmmv, { { d_X, x_offset, x_sz }, { d_Y, y_buffer_offset, y_sz + y_shader_offset }, { d_D, d_buffer_offset, d_sz + d_shader_offset } }, 3 * sizeof(int), &pc, { (uint32_t)ne01, 1, 1});
 
-            if (dst->backend == GGML_BACKEND_CPU) {
+            if (dst->backend == GGML_BACKEND_TYPE_CPU) {
                 // copy dst to host
                 float * d = (float *) ((char *) dst->data + i12*nb2 + i13*nb3);
                 ggml_vk_sync_buffers(subctx);
@@ -2647,7 +2647,7 @@ static void ggml_vk_mul_mat_vec_p021_f16_f32(ggml_backend_vk_context * ctx, vk_c
     std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ",  backend=" << dst->backend << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3] << "),)" << std::endl;
 #endif
     GGML_ASSERT(ggml_is_permuted(src0) && ggml_is_permuted(src1));
-    GGML_ASSERT(src0->backend == GGML_BACKEND_GPU);
+    GGML_ASSERT(src0->backend == GGML_BACKEND_TYPE_GPU);
     GGML_ASSERT(src0->nb[0] <= src0->nb[1] && src0->nb[2] <= src0->nb[3]);  // NOLINT
     GGML_ASSERT(src1->nb[0] <= src1->nb[1] && src1->nb[2] <= src1->nb[3]);  // NOLINT
     GGML_ASSERT(src0->type == GGML_TYPE_F16);
@@ -2679,7 +2679,7 @@ static void ggml_vk_mul_mat_vec_p021_f16_f32(ggml_backend_vk_context * ctx, vk_c
         src1_uma = d_Qy != nullptr;
     }
 
-    const bool load_y = src1->backend != GGML_BACKEND_GPU && !src1_uma;
+    const bool load_y = src1->backend != GGML_BACKEND_TYPE_GPU && !src1_uma;
 
     const uint64_t x_ne = ne00 * ne01 * ne02;
     const uint64_t y_ne = ne10 * ne11 * ne12;
@@ -2721,7 +2721,7 @@ static void ggml_vk_mul_mat_vec_p021_f16_f32(ggml_backend_vk_context * ctx, vk_c
     ggml_vk_sync_buffers(subctx);
     ggml_vk_dispatch_pipeline(ctx, subctx, ctx->pipeline_mul_mat_vec_p021_f16_f32, { { d_Qx, qx_buf_offset, qx_sz }, { d_Qy, qy_buffer_offset, qy_sz + qy_shader_offset }, { d_D, d_buffer_offset, d_sz + d_shader_offset } }, 6 * sizeof(uint32_t), &pc, { 1, (uint32_t)ne01, (uint32_t)ne12 });
 
-    if (dst->backend == GGML_BACKEND_CPU) {
+    if (dst->backend == GGML_BACKEND_TYPE_CPU) {
         // copy dst to host
         float * d = (float *) dst->data;
         ggml_vk_sync_buffers(subctx);
@@ -2738,7 +2738,7 @@ static void ggml_vk_mul_mat_vec_nc_f16_f32(ggml_backend_vk_context * ctx, vk_con
     GGML_ASSERT(!ggml_is_transposed(src0));
     GGML_ASSERT(!ggml_is_transposed(src1));
     GGML_ASSERT(!ggml_is_permuted(src0));
-    GGML_ASSERT(src0->backend == GGML_BACKEND_GPU);
+    GGML_ASSERT(src0->backend == GGML_BACKEND_TYPE_GPU);
     GGML_ASSERT(src0->type == GGML_TYPE_F16);
     GGML_ASSERT(src1->type == GGML_TYPE_F32);
 
@@ -2771,7 +2771,7 @@ static void ggml_vk_mul_mat_vec_nc_f16_f32(ggml_backend_vk_context * ctx, vk_con
         src1_uma = d_Qy != nullptr;
     }
 
-    const bool load_y = src1->backend != GGML_BACKEND_GPU && !src1_uma;
+    const bool load_y = src1->backend != GGML_BACKEND_TYPE_GPU && !src1_uma;
 
     const uint64_t d_ne = ne01 * ne11 * ne12;
 
@@ -2814,7 +2814,7 @@ static void ggml_vk_mul_mat_vec_nc_f16_f32(ggml_backend_vk_context * ctx, vk_con
     ggml_vk_sync_buffers(subctx);
     ggml_vk_dispatch_pipeline(ctx, subctx, ctx->pipeline_mul_mat_vec_nc_f16_f32, { { d_Qx, qx_buf_offset, qx_sz }, { d_Qy, qy_buffer_offset, qy_sz + qy_shader_offset }, { d_D, d_buffer_offset, d_sz + d_shader_offset } }, 7 * sizeof(uint32_t), &pc, { 1, (uint32_t)ne01, (uint32_t)ne12 });
 
-    if (dst->backend == GGML_BACKEND_CPU) {
+    if (dst->backend == GGML_BACKEND_TYPE_CPU) {
         // copy dst to host
         float * d = (float *) dst->data;
         ggml_vk_sync_buffers(subctx);
@@ -2832,7 +2832,7 @@ static bool ggml_vk_can_mul_mat(const ggml_tensor * src0, const ggml_tensor * sr
     return (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type)) &&
            (src1->type == GGML_TYPE_F32 || src1->type == GGML_TYPE_F16 || ggml_is_quantized(src1->type)) &&
            dst->type == GGML_TYPE_F32 &&
-           ((ne0 >= 32 && ne1 >= 32 && ne10 >= 32) || src0->backend == GGML_BACKEND_GPU);
+           ((ne0 >= 32 && ne1 >= 32 && ne10 >= 32) || src0->backend == GGML_BACKEND_TYPE_GPU);
 }
 
 static void ggml_vk_mul_mat(ggml_backend_vk_context * ctx, vk_context * subctx, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) {
@@ -2880,8 +2880,8 @@ static void ggml_vk_op_repeat(ggml_backend_vk_context * ctx, vk_context * subctx
     // TODO: support for transposed / permuted tensors
     GGML_ASSERT(nb0  == sizeof(float));
     GGML_ASSERT(nb00 == sizeof(float));
-    GGML_ASSERT(src0->backend == GGML_BACKEND_GPU);
-    GGML_ASSERT(dst->backend == GGML_BACKEND_GPU);
+    GGML_ASSERT(src0->backend == GGML_BACKEND_TYPE_GPU);
+    GGML_ASSERT(dst->backend == GGML_BACKEND_TYPE_GPU);
 
     ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) dst->extra;
     ggml_tensor_extra_gpu * extra_src0 = (ggml_tensor_extra_gpu *) src0->extra;
@@ -3110,8 +3110,8 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, c
         }
     }
 
-    const bool transfer_src0 = src0->backend != GGML_BACKEND_GPU && !src0_uma;
-    const bool transfer_src1 = use_src1 && src1->backend != GGML_BACKEND_GPU && !src1_uma;
+    const bool transfer_src0 = src0->backend != GGML_BACKEND_TYPE_GPU && !src0_uma;
+    const bool transfer_src1 = use_src1 && src1->backend != GGML_BACKEND_TYPE_GPU && !src1_uma;
 
     uint64_t x_sz = ggml_vk_align_size(ggml_type_size(src0->type) * ne0, ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment);
     uint64_t y_sz = use_src1 ? ggml_vk_align_size(ggml_type_size(src1->type) * ne1, ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment) : 0;
@@ -3120,7 +3120,7 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, c
     vk_buffer d_D = extra->buffer_gpu.lock();
 
     // Workaround for tiny tensor inputs on ROPE
-    if (use_src1 && src1->backend == GGML_BACKEND_GPU && y_sz > d_D->size) {
+    if (use_src1 && src1->backend == GGML_BACKEND_TYPE_GPU && y_sz > d_D->size) {
         y_sz = VK_WHOLE_SIZE;
     }
 
@@ -3209,9 +3209,9 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, c
             ggml_vk_sync_buffers(subctx);
             ggml_vk_dispatch_pipeline(ctx, subctx, *pipeline, { { d_X, x_buf_offset, x_sz }, { d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements);
         }
-        if (dst->backend == GGML_BACKEND_CPU && op == GGML_OP_CPY) {
+        if (dst->backend == GGML_BACKEND_TYPE_CPU && op == GGML_OP_CPY) {
             ggml_vk_d2h_tensor_2d(ctx, subctx, d_D, 0, dst);
-        } else if(dst->backend == GGML_BACKEND_CPU) {
+        } else if(dst->backend == GGML_BACKEND_TYPE_CPU) {
             // copy dst to host
             float * d = (float *) dst->data;
             ggml_vk_buffer_read_async(ctx, subctx, d_D, 0, d, d_sz);
@@ -3253,7 +3253,7 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, c
                     ggml_vk_sync_buffers(subctx);
                     ggml_vk_dispatch_pipeline(ctx, subctx, *pipeline, { { d_X, x_buf_offset + x_offset, x_sz }, { d_D, d_buf_offset + d_offset, d_sz } }, sizeof(PC), &pc, elements);
                 }
-                if (dst->backend == GGML_BACKEND_CPU) {
+                if (dst->backend == GGML_BACKEND_TYPE_CPU) {
                     // copy dst to host
                     ggml_vk_buffer_read_async(ctx, subctx, d_D, d_buf_offset + d_offset, (char *) dst->data + i02*nb2 + i03*nb3, d_sz);
                 }
@@ -3359,7 +3359,7 @@ static void ggml_vk_rope(ggml_backend_vk_context * ctx, vk_context * subctx, con
 
 static void ggml_vk_nop(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, ggml_tensor * dst) {
     // If backend is CPU, data from src0 has to be copied off the device
-    if (dst->backend == GGML_BACKEND_CPU) {
+    if (dst->backend == GGML_BACKEND_TYPE_CPU) {
         ggml_tensor_extra_gpu * extra_src0 = (ggml_tensor_extra_gpu *) src0->extra;
         vk_buffer d_D = extra_src0->buffer_gpu.lock();
         ggml_vk_sync_buffers(subctx);
@@ -3994,9 +3994,9 @@ static void ggml_vk_preallocate_buffers_graph(ggml_backend_vk_context * ctx, ggm
 #ifdef GGML_VULKAN_DEBUG
     std::cerr << "ggml_vk_preallocate_buffers_graph(" << node << ")" << std::endl;
 #endif
-    const bool any_on_device = node->backend == GGML_BACKEND_GPU
-        || (node->src[0] != nullptr && (node->src[0]->backend == GGML_BACKEND_GPU || node->src[0]->backend == GGML_BACKEND_GPU_SPLIT))
-        || (node->src[1] != nullptr && (node->src[1]->backend == GGML_BACKEND_GPU));
+    const bool any_on_device = node->backend == GGML_BACKEND_TYPE_GPU
+        || (node->src[0] != nullptr && (node->src[0]->backend == GGML_BACKEND_TYPE_GPU || node->src[0]->backend == GGML_BACKEND_TYPE_GPU_SPLIT))
+        || (node->src[1] != nullptr && (node->src[1]->backend == GGML_BACKEND_TYPE_GPU));
 
     if (ctx->disable || (!any_on_device && node->op != GGML_OP_MUL_MAT)) {
         return;
@@ -4215,9 +4215,9 @@ static void ggml_vk_preallocate_buffers(ggml_backend_vk_context * ctx) {
 }
 
 static void ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * node, bool last_node){
-    const bool any_on_device = node->backend == GGML_BACKEND_GPU
-        || (node->src[0] != nullptr && (node->src[0]->backend == GGML_BACKEND_GPU || node->src[0]->backend == GGML_BACKEND_GPU_SPLIT))
-        || (node->src[1] != nullptr && node->src[1]->backend == GGML_BACKEND_GPU);
+    const bool any_on_device = node->backend == GGML_BACKEND_TYPE_GPU
+        || (node->src[0] != nullptr && (node->src[0]->backend == GGML_BACKEND_TYPE_GPU || node->src[0]->backend == GGML_BACKEND_TYPE_GPU_SPLIT))
+        || (node->src[1] != nullptr && node->src[1]->backend == GGML_BACKEND_TYPE_GPU);
 
     if (ctx->disable || (!any_on_device && node->op != GGML_OP_MUL_MAT) || (node->op == GGML_OP_MUL_MAT && !any_on_device && !ggml_vk_can_mul_mat(node->src[0], node->src[1], node))) {
         return;
@@ -4371,7 +4371,7 @@ static void ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod
     last_node = true;
 #endif
 
-    if (node->backend == GGML_BACKEND_CPU || last_node) {
+    if (node->backend == GGML_BACKEND_TYPE_CPU || last_node) {
         ggml_vk_ctx_end(ctx->compute_ctx);
         ctx->compute_ctx->exit_tensor = node;
         ctx->compute_ctx = nullptr;
@@ -4379,9 +4379,9 @@ static void ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod
 }
 
 static bool ggml_vk_compute_forward(ggml_backend_vk_context * ctx, ggml_compute_params * params, ggml_tensor * tensor){
-    const bool any_on_device = tensor->backend == GGML_BACKEND_GPU
-        || (tensor->src[0] != nullptr && (tensor->src[0]->backend == GGML_BACKEND_GPU || tensor->src[0]->backend == GGML_BACKEND_GPU_SPLIT))
-        || (tensor->src[1] != nullptr && tensor->src[1]->backend == GGML_BACKEND_GPU);
+    const bool any_on_device = tensor->backend == GGML_BACKEND_TYPE_GPU
+        || (tensor->src[0] != nullptr && (tensor->src[0]->backend == GGML_BACKEND_TYPE_GPU || tensor->src[0]->backend == GGML_BACKEND_TYPE_GPU_SPLIT))
+        || (tensor->src[1] != nullptr && tensor->src[1]->backend == GGML_BACKEND_TYPE_GPU);
 
     if (ctx->disable || (!any_on_device && tensor->op != GGML_OP_MUL_MAT)) {
         return false;
@@ -4442,7 +4442,7 @@ static bool ggml_vk_compute_forward(ggml_backend_vk_context * ctx, ggml_compute_
     if (params->ith != 0) {
         return true;
     }
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
         return true;
     }
 
@@ -4745,7 +4745,7 @@ GGML_CALL static void ggml_backend_vk_buffer_init_tensor(ggml_backend_buffer_t b
         extra->offset = (uint8_t *) tensor->data - (uint8_t *) vk_ptr_base;
     }
 
-    tensor->backend = GGML_BACKEND_GPU;
+    tensor->backend = GGML_BACKEND_TYPE_GPU;
     tensor->extra = extra;
 }
 
@@ -4753,7 +4753,7 @@ GGML_CALL static void ggml_backend_vk_buffer_set_tensor(ggml_backend_buffer_t bu
 #ifdef GGML_VULKAN_DEBUG
     std::cerr << "ggml_backend_vk_buffer_set_tensor(" << buffer << ", " << tensor << ", " << data << ", " << offset << ", " << size << ")" << std::endl;
 #endif
-    GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU);
+    GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU);
 
     ggml_backend_vk_buffer_context * ctx = (ggml_backend_vk_buffer_context *)buffer->context;
 
@@ -4768,7 +4768,7 @@ GGML_CALL static void ggml_backend_vk_buffer_get_tensor(ggml_backend_buffer_t bu
 #ifdef GGML_VULKAN_DEBUG
     std::cerr << "ggml_backend_vk_buffer_get_tensor(" << buffer << ", " << tensor << ", " << data << ", " << offset << ", " << size << ")" << std::endl;
 #endif
-    GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU);
+    GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU);
 
     ggml_backend_vk_buffer_context * ctx = (ggml_backend_vk_buffer_context *)buffer->context;
 
@@ -4999,7 +4999,7 @@ GGML_CALL static void ggml_backend_vk_set_tensor_async(ggml_backend_t backend, g
 #endif
     ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context;
     GGML_ASSERT((tensor->buffer->buft == ggml_backend_vk_buffer_type(ctx->idx) || tensor->buffer->buft == ggml_backend_vk_host_buffer_type()) && "unsupported buffer type");
-    GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU);
+    GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU);
 
     ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra;
 
@@ -5020,7 +5020,7 @@ GGML_CALL static void ggml_backend_vk_get_tensor_async(ggml_backend_t backend, c
 #endif
     ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context;
     GGML_ASSERT((tensor->buffer->buft == ggml_backend_vk_buffer_type(ctx->idx) || tensor->buffer->buft == ggml_backend_vk_host_buffer_type()) && "unsupported buffer type");
-    GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU);
+    GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU);
 
     ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra;
 
@@ -5097,7 +5097,7 @@ GGML_CALL static bool ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml
     int last_node = cgraph->n_nodes - 1;
 
     // If the last op in the cgraph isn't backend GPU, the command buffer doesn't get closed properly
-    while (last_node > 0 && cgraph->nodes[last_node]->backend != GGML_BACKEND_GPU) {
+    while (last_node > 0 && cgraph->nodes[last_node]->backend != GGML_BACKEND_TYPE_GPU) {
         last_node -= 1;
     }
 
@@ -5106,7 +5106,7 @@ GGML_CALL static bool ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml
     }
 
     ggml_compute_params params = {};
-    params.type = GGML_TASK_COMPUTE;
+    params.type = GGML_TASK_TYPE_COMPUTE;
     params.ith = 0;
     for (int i = 0; i < cgraph->n_nodes; i++) {
         ggml_tensor * node = cgraph->nodes[i];
@@ -5410,7 +5410,7 @@ static void ggml_vk_print_tensor_area(const ggml_tensor * tensor, const void * d
 static void ggml_vk_print_tensor(ggml_backend_vk_context * ctx, const ggml_tensor * tensor, const char * name) {
     void * tensor_data = tensor->data;
 
-    if (tensor->backend == GGML_BACKEND_GPU) {
+    if (tensor->backend == GGML_BACKEND_TYPE_GPU) {
         const size_t tensor_size = ggml_nbytes(tensor);
         tensor_data = malloc(tensor_size);
 
@@ -5436,14 +5436,14 @@ static void ggml_vk_print_tensor(ggml_backend_vk_context * ctx, const ggml_tenso
     std::vector<const ggml_tensor *> done;
     ggml_vk_print_graph_origin(tensor, done);
 
-    if (tensor->backend == GGML_BACKEND_GPU) {
+    if (tensor->backend == GGML_BACKEND_TYPE_GPU) {
         free(tensor_data);
     }
 }
 
 static void ggml_vk_check_tensor(const std::string& name, const ggml_tensor * tensor) {
     return;
-    GGML_ASSERT(tensor->backend == GGML_BACKEND_CPU);
+    GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_CPU);
     if (tensor->type != GGML_TYPE_F32 && tensor->type != GGML_TYPE_F16) {
         return;
     }
@@ -5481,7 +5481,7 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_
     if (params->ith != 0) {
         return;
     }
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE || tensor->op == GGML_OP_TRANSPOSE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE || tensor->op == GGML_OP_TRANSPOSE) {
         return;
     }
 
@@ -5518,10 +5518,10 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_
 
         src0_buffer = malloc(src0_size);
         src0_clone->data = src0_buffer;
-        if (src0->backend == GGML_BACKEND_CPU) {
+        if (src0->backend == GGML_BACKEND_TYPE_CPU) {
             memcpy(src0_clone->data, src0->data, src0_size);
             memcpy(src0_clone->nb, src0->nb, sizeof(size_t) * GGML_MAX_DIMS);
-        } else if (src0->backend == GGML_BACKEND_GPU) {
+        } else if (src0->backend == GGML_BACKEND_TYPE_GPU) {
             ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) src0->extra;
             uint64_t offset = extra->offset;
             if (!ggml_is_contiguous(src0) && ggml_vk_dim01_contiguous(src0)) {
@@ -5561,10 +5561,10 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_
 
         src1_buffer = malloc(src1_size);
         src1_clone->data = src1_buffer;
-        if (src1->backend == GGML_BACKEND_CPU) {
+        if (src1->backend == GGML_BACKEND_TYPE_CPU) {
             memcpy(src1_clone->data, src1->data, src1_size);
             memcpy(src1_clone->nb, src1->nb, sizeof(size_t) * GGML_MAX_DIMS);
-        } else if (src1->backend == GGML_BACKEND_GPU) {
+        } else if (src1->backend == GGML_BACKEND_TYPE_GPU) {
             ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) src1->extra;
             uint64_t offset = extra->offset;
             if (!ggml_is_contiguous(src1) && ggml_vk_dim01_contiguous(src1)) {
@@ -5723,7 +5723,7 @@ static void ggml_vk_check_results_1(ggml_backend_vk_context * ctx, ggml_compute_
     if (params->ith != 0) {
         return;
     }
-    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE || tensor->op == GGML_OP_TRANSPOSE) {
+    if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE || tensor->op == GGML_OP_TRANSPOSE) {
         return;
     }
     if (!(vk_output_tensor > 0 && vk_output_tensor == check_counter) && check_counter <= vk_skip_checks) {
@@ -5735,7 +5735,7 @@ static void ggml_vk_check_results_1(ggml_backend_vk_context * ctx, ggml_compute_
 
     void * tensor_data = tensor->data;
 
-    if (tensor->backend == GGML_BACKEND_GPU) {
+    if (tensor->backend == GGML_BACKEND_TYPE_GPU) {
         size_t tensor_size = ggml_nbytes(tensor);
         tensor_data = malloc(tensor_size);
 
@@ -5868,7 +5868,7 @@ static void ggml_vk_check_results_1(ggml_backend_vk_context * ctx, ggml_compute_
     comp_result = nullptr;
     comp_size = 0;
 
-    if (tensor->backend == GGML_BACKEND_GPU) {
+    if (tensor->backend == GGML_BACKEND_TYPE_GPU) {
         free(tensor_data);
     }
 }

ggml.h

@@ -364,9 +364,9 @@ extern "C" {
     };
 
     enum ggml_backend_type {
-        GGML_BACKEND_CPU = 0,
-        GGML_BACKEND_GPU = 10,
-        GGML_BACKEND_GPU_SPLIT = 20,
+        GGML_BACKEND_TYPE_CPU = 0,
+        GGML_BACKEND_TYPE_GPU = 10,
+        GGML_BACKEND_TYPE_GPU_SPLIT = 20,
     };
 
     // model file types
@@ -498,9 +498,9 @@ extern "C" {
     };
 
     enum ggml_object_type {
-        GGML_OBJECT_TENSOR,
-        GGML_OBJECT_GRAPH,
-        GGML_OBJECT_WORK_BUFFER
+        GGML_OBJECT_TYPE_TENSOR,
+        GGML_OBJECT_TYPE_GRAPH,
+        GGML_OBJECT_TYPE_WORK_BUFFER
     };
 
     enum ggml_log_level {
@@ -642,9 +642,9 @@ extern "C" {
     // NOTE: the INIT or FINALIZE pass is not scheduled unless explicitly enabled.
     // This behavior was changed since https://github.com/ggerganov/llama.cpp/pull/1995.
     enum ggml_task_type {
-        GGML_TASK_INIT = 0,
-        GGML_TASK_COMPUTE,
-        GGML_TASK_FINALIZE,
+        GGML_TASK_TYPE_INIT = 0,
+        GGML_TASK_TYPE_COMPUTE,
+        GGML_TASK_TYPE_FINALIZE,
     };
 
     struct ggml_compute_params {
@@ -1649,8 +1649,8 @@ extern "C" {
 
     // sort rows
     enum ggml_sort_order {
-        GGML_SORT_ASC,
-        GGML_SORT_DESC,
+        GGML_SORT_ORDER_ASC,
+        GGML_SORT_ORDER_DESC,
     };
 
     GGML_API struct ggml_tensor * ggml_argsort(
@@ -1943,8 +1943,8 @@ extern "C" {
 
     // optimization methods
     enum ggml_opt_type {
-        GGML_OPT_ADAM,
-        GGML_OPT_LBFGS,
+        GGML_OPT_TYPE_ADAM,
+        GGML_OPT_TYPE_LBFGS,
     };
 
     // linesearch methods
@@ -1958,12 +1958,12 @@ extern "C" {
 
     // optimization return values
     enum ggml_opt_result {
-        GGML_OPT_OK = 0,
-        GGML_OPT_DID_NOT_CONVERGE,
-        GGML_OPT_NO_CONTEXT,
-        GGML_OPT_INVALID_WOLFE,
-        GGML_OPT_FAIL,
-        GGML_OPT_CANCEL,
+        GGML_OPT_RESULT_OK = 0,
+        GGML_OPT_RESULT_DID_NOT_CONVERGE,
+        GGML_OPT_RESULT_NO_CONTEXT,
+        GGML_OPT_RESULT_INVALID_WOLFE,
+        GGML_OPT_RESULT_FAIL,
+        GGML_OPT_RESULT_CANCEL,
 
         GGML_LINESEARCH_FAIL = -128,
         GGML_LINESEARCH_MINIMUM_STEP,

llama.cpp

@@ -850,9 +850,9 @@ struct LLM_TN {
 //
 
 static std::map<int32_t, const char *> LLAMA_ROPE_SCALING_TYPES = {
-    { LLAMA_ROPE_SCALING_NONE,   "none"   },
-    { LLAMA_ROPE_SCALING_LINEAR, "linear" },
-    { LLAMA_ROPE_SCALING_YARN,   "yarn"   },
+    { LLAMA_ROPE_SCALING_TYPE_NONE,   "none"   },
+    { LLAMA_ROPE_SCALING_TYPE_LINEAR, "linear" },
+    { LLAMA_ROPE_SCALING_TYPE_YARN,   "yarn"   },
 };
 
 static int32_t llama_rope_scaling_type_from_string(const std::string & name) {
@@ -862,7 +862,7 @@ static int32_t llama_rope_scaling_type_from_string(const std::string & name) {
         }
     }
 
-    return LLAMA_ROPE_SCALING_UNSPECIFIED;
+    return LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED;
 }
 
 static std::string gguf_data_to_str(enum gguf_type type, const void * data, int i) {
@@ -1580,7 +1580,7 @@ struct llama_hparams {
     bool causal_attn = true;
     bool need_kq_pos = false;
 
-    uint32_t pooling_type = LLAMA_POOLING_NONE;
+    uint32_t pooling_type = LLAMA_POOLING_TYPE_NONE;
 
     bool operator!=(const llama_hparams & other) const {
         if (this->vocab_only    != other.vocab_only)    return true;
@@ -2345,9 +2345,9 @@ namespace GGUFMeta {
 
         static const char * override_type_to_str(const llama_model_kv_override_type ty) {
             switch (ty) {
-                case LLAMA_KV_OVERRIDE_BOOL:  return "bool";
-                case LLAMA_KV_OVERRIDE_INT:   return "int";
-                case LLAMA_KV_OVERRIDE_FLOAT: return "float";
+                case LLAMA_KV_OVERRIDE_TYPE_BOOL:  return "bool";
+                case LLAMA_KV_OVERRIDE_TYPE_INT:   return "int";
+                case LLAMA_KV_OVERRIDE_TYPE_FLOAT: return "float";
             }
             return "unknown";
         }
@@ -2358,13 +2358,13 @@ namespace GGUFMeta {
                 LLAMA_LOG_INFO("%s: Using metadata override (%5s) '%s' = ",
                     __func__, override_type_to_str(override->tag), override->key);
                 switch (override->tag) {
-                    case LLAMA_KV_OVERRIDE_BOOL:  {
+                    case LLAMA_KV_OVERRIDE_TYPE_BOOL:  {
                         LLAMA_LOG_INFO("%s\n", override->bool_value ? "true" : "false");
                     } break;
-                    case LLAMA_KV_OVERRIDE_INT:   {
+                    case LLAMA_KV_OVERRIDE_TYPE_INT:   {
                         LLAMA_LOG_INFO("%" PRId64 "\n", override->int_value);
                     } break;
-                    case LLAMA_KV_OVERRIDE_FLOAT: {
+                    case LLAMA_KV_OVERRIDE_TYPE_FLOAT: {
                         LLAMA_LOG_INFO("%.6f\n", override->float_value);
                     } break;
                     default:
@@ -2383,7 +2383,7 @@ namespace GGUFMeta {
         template<typename OT>
         static typename std::enable_if<std::is_same<OT, bool>::value, bool>::type
         try_override(OT & target, const struct llama_model_kv_override *override) {
-            if (validate_override(LLAMA_KV_OVERRIDE_BOOL, override)) {
+            if (validate_override(LLAMA_KV_OVERRIDE_TYPE_BOOL, override)) {
                 target = override->bool_value;
                 return true;
             }
@@ -2393,7 +2393,7 @@ namespace GGUFMeta {
         template<typename OT>
         static typename std::enable_if<!std::is_same<OT, bool>::value && std::is_integral<OT>::value, bool>::type
         try_override(OT & target, const struct llama_model_kv_override *override) {
-            if (validate_override(LLAMA_KV_OVERRIDE_INT, override)) {
+            if (validate_override(LLAMA_KV_OVERRIDE_TYPE_INT, override)) {
                 target = override->int_value;
                 return true;
             }
@@ -2403,7 +2403,7 @@ namespace GGUFMeta {
         template<typename OT>
         static typename std::enable_if<std::is_floating_point<OT>::value, bool>::type
         try_override(T & target, const struct llama_model_kv_override *override) {
-            if (validate_override(LLAMA_KV_OVERRIDE_FLOAT, override)) {
+            if (validate_override(LLAMA_KV_OVERRIDE_TYPE_FLOAT, override)) {
                 target = override->float_value;
                 return true;
             }
@@ -2999,7 +2999,7 @@ static void llm_load_hparams(
     std::string rope_scaling("linear");
     ml.get_key(LLM_KV_ROPE_SCALING_TYPE, rope_scaling, false);
     hparams.rope_scaling_type_train = llama_rope_scaling_type_from_string(rope_scaling);
-    GGML_ASSERT(hparams.rope_scaling_type_train != LLAMA_ROPE_SCALING_UNSPECIFIED);
+    GGML_ASSERT(hparams.rope_scaling_type_train != LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED);
 
     // rope_freq_scale (inverse of the kv) is optional
     float ropescale = 0.0f;
@@ -3643,7 +3643,7 @@ static bool llm_load_tensors(
         model.buft_layer[i] = llama_default_buffer_type_cpu(true);
     }
 
-    if (split_mode == LLAMA_SPLIT_LAYER) {
+    if (split_mode == LLAMA_SPLIT_MODE_LAYER) {
         // calculate the split points
         int device_count = llama_get_device_count();
         bool all_zero = tensor_split == nullptr || std::all_of(tensor_split, tensor_split + device_count, [](float x) { return x == 0.0f; });
@@ -3682,10 +3682,10 @@ static bool llm_load_tensors(
         }
     } else {
         ggml_backend_buffer_type_t split_buft;
-        if (split_mode == LLAMA_SPLIT_ROW) {
+        if (split_mode == LLAMA_SPLIT_MODE_ROW) {
             split_buft = llama_default_buffer_type_split(main_gpu, tensor_split);
         } else {
-            // LLAMA_SPLIT_NONE or LLAMA_SPLIT_LAYER in backends where it is not supported
+            // LLAMA_SPLIT_MODE_NONE or LLAMA_SPLIT_MODE_LAYER in backends where it is not supported
             split_buft = llama_default_buffer_type_offload(main_gpu);
         }
         // assign the repeating layers
@@ -5070,7 +5070,7 @@ struct llm_build_context {
         kv_head          (worst_case ? n_ctx - n_tokens : kv_self.head),
         n_orig_ctx       (cparams.n_yarn_orig_ctx),
         do_rope_shift    (worst_case || kv_self.has_shift),
-        pooling_type     (cparams.do_pooling ? hparams.pooling_type : (uint32_t)LLAMA_POOLING_NONE),
+        pooling_type     (cparams.do_pooling ? hparams.pooling_type : (uint32_t)LLAMA_POOLING_TYPE_NONE),
         cb               (cb),
         buf_compute_meta (lctx.buf_compute_meta) {
             // all initializations should be done in init()
@@ -6050,12 +6050,12 @@ struct llm_build_context {
         cur = inpL;
 
         // pooling layer
-        if (pooling_type == LLAMA_POOLING_MEAN) {
+        if (pooling_type == LLAMA_POOLING_TYPE_MEAN) {
             cur = ggml_mul_mat(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, cur)), inp_mean);
-        } else if (pooling_type == LLAMA_POOLING_CLS) {
+        } else if (pooling_type == LLAMA_POOLING_TYPE_CLS) {
             cur = ggml_get_rows(ctx0, cur, inp_cls);
         } else {
-            GGML_ASSERT(pooling_type == LLAMA_POOLING_NONE && "Invalid pooling type");
+            GGML_ASSERT(pooling_type == LLAMA_POOLING_TYPE_NONE && "Invalid pooling type");
         }
         cb(cur, "result_embd", -1);
 
@@ -7754,7 +7754,7 @@ static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) {
         }
     }
 
-    if (cparams.do_pooling && hparams.pooling_type == LLAMA_POOLING_MEAN) {
+    if (cparams.do_pooling && hparams.pooling_type == LLAMA_POOLING_TYPE_MEAN) {
         const int64_t n_tokens = batch.n_tokens;
 
         GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_mean->buffer));
@@ -7782,7 +7782,7 @@ static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) {
         }
     }
 
-    if (cparams.do_pooling && hparams.pooling_type == LLAMA_POOLING_CLS) {
+    if (cparams.do_pooling && hparams.pooling_type == LLAMA_POOLING_TYPE_CLS) {
         const int64_t n_tokens = batch.n_tokens;
 
         GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_cls->buffer));
@@ -11351,7 +11351,7 @@ static int llama_apply_lora_from_file_internal(
 struct llama_model_params llama_model_default_params() {
     struct llama_model_params result = {
         /*.n_gpu_layers                =*/ 0,
-        /*.split_mode                  =*/ LLAMA_SPLIT_LAYER,
+        /*.split_mode                  =*/ LLAMA_SPLIT_MODE_LAYER,
         /*.main_gpu                    =*/ 0,
         /*.tensor_split                =*/ nullptr,
         /*.progress_callback           =*/ nullptr,
@@ -11377,7 +11377,7 @@ struct llama_context_params llama_context_default_params() {
         /*.n_batch                     =*/ 512,
         /*.n_threads                   =*/ GGML_DEFAULT_N_THREADS, // TODO: better default
         /*.n_threads_batch             =*/ GGML_DEFAULT_N_THREADS,
-        /*.rope_scaling_type           =*/ LLAMA_ROPE_SCALING_UNSPECIFIED,
+        /*.rope_scaling_type           =*/ LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED,
         /*.rope_freq_base              =*/ 0.0f,
         /*.rope_freq_scale             =*/ 0.0f,
         /*.yarn_ext_factor             =*/ -1.0f,
@@ -11565,16 +11565,16 @@ struct llama_context * llama_new_context_with_model(
     cparams.cb_eval_user_data = params.cb_eval_user_data;
 
     auto rope_scaling_type = params.rope_scaling_type;
-    if (rope_scaling_type == LLAMA_ROPE_SCALING_UNSPECIFIED) {
+    if (rope_scaling_type == LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED) {
         rope_scaling_type = hparams.rope_scaling_type_train;
     }
 
-    if (rope_scaling_type == LLAMA_ROPE_SCALING_NONE) {
+    if (rope_scaling_type == LLAMA_ROPE_SCALING_TYPE_NONE) {
         cparams.rope_freq_scale = 1.0f; // never scale if scaling type is none
     }
 
     if (cparams.yarn_ext_factor < 0.0f) { // negative indicates 'not set'
-        cparams.yarn_ext_factor = rope_scaling_type == LLAMA_ROPE_SCALING_YARN ? 1.0f : 0.0f;
+        cparams.yarn_ext_factor = rope_scaling_type == LLAMA_ROPE_SCALING_TYPE_YARN ? 1.0f : 0.0f;
     }
 
     if (params.seed == LLAMA_DEFAULT_SEED) {
@@ -11608,8 +11608,8 @@ struct llama_context * llama_new_context_with_model(
         }
 #elif defined(GGML_USE_CUBLAS)
         if (model->n_gpu_layers > 0) {
-            // with split_mode LLAMA_SPLIT_NONE or LLAMA_SPLIT_ROW, only the main GPU backend is used
-            if (model->split_mode == LLAMA_SPLIT_NONE || model->split_mode == LLAMA_SPLIT_ROW) {
+            // with split_mode LLAMA_SPLIT_MODE_NONE or LLAMA_SPLIT_MODE_ROW, only the main GPU backend is used
+            if (model->split_mode == LLAMA_SPLIT_MODE_NONE || model->split_mode == LLAMA_SPLIT_MODE_ROW) {
                 ggml_backend_t backend = ggml_backend_cuda_init(model->main_gpu);
                 if (backend == nullptr) {
                     LLAMA_LOG_ERROR("%s: failed to initialize CUDA%d backend\n", __func__, model->main_gpu);
@@ -11618,7 +11618,7 @@ struct llama_context * llama_new_context_with_model(
                 }
                 ctx->backends.push_back(backend);
             } else {
-                // LLAMA_SPLIT_LAYER requires a backend for each GPU
+                // LLAMA_SPLIT_MODE_LAYER requires a backend for each GPU
                 for (int device = 0; device < ggml_backend_cuda_get_device_count(); ++device) {
                     ggml_backend_t backend = ggml_backend_cuda_init(device);
                     if (backend == nullptr) {

llama.h

@@ -109,23 +109,23 @@ extern "C" {
     };
 
     enum llama_rope_scaling_type {
-        LLAMA_ROPE_SCALING_UNSPECIFIED = -1,
-        LLAMA_ROPE_SCALING_NONE        = 0,
-        LLAMA_ROPE_SCALING_LINEAR      = 1,
-        LLAMA_ROPE_SCALING_YARN        = 2,
-        LLAMA_ROPE_SCALING_MAX_VALUE   = LLAMA_ROPE_SCALING_YARN,
+        LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED = -1,
+        LLAMA_ROPE_SCALING_TYPE_NONE        = 0,
+        LLAMA_ROPE_SCALING_TYPE_LINEAR      = 1,
+        LLAMA_ROPE_SCALING_TYPE_YARN        = 2,
+        LLAMA_ROPE_SCALING_TYPE_MAX_VALUE   = LLAMA_ROPE_SCALING_TYPE_YARN,
     };
 
     enum llama_pooling_type {
-        LLAMA_POOLING_NONE = 0,
-        LLAMA_POOLING_MEAN = 1,
-        LLAMA_POOLING_CLS  = 2,
+        LLAMA_POOLING_TYPE_NONE = 0,
+        LLAMA_POOLING_TYPE_MEAN = 1,
+        LLAMA_POOLING_TYPE_CLS  = 2,
     };
 
     enum llama_split_mode {
-        LLAMA_SPLIT_NONE    = 0, // single GPU
-        LLAMA_SPLIT_LAYER   = 1, // split layers and KV across GPUs
-        LLAMA_SPLIT_ROW     = 2, // split rows across GPUs
+        LLAMA_SPLIT_MODE_NONE    = 0, // single GPU
+        LLAMA_SPLIT_MODE_LAYER   = 1, // split layers and KV across GPUs
+        LLAMA_SPLIT_MODE_ROW     = 2, // split rows across GPUs
     };
 
     typedef struct llama_token_data {
@@ -173,9 +173,9 @@ extern "C" {
     } llama_batch;
 
     enum llama_model_kv_override_type {
-        LLAMA_KV_OVERRIDE_INT,
-        LLAMA_KV_OVERRIDE_FLOAT,
-        LLAMA_KV_OVERRIDE_BOOL,
+        LLAMA_KV_OVERRIDE_TYPE_INT,
+        LLAMA_KV_OVERRIDE_TYPE_FLOAT,
+        LLAMA_KV_OVERRIDE_TYPE_BOOL,
     };
 
     struct llama_model_kv_override {

tests/test-backend-ops.cpp

@@ -1264,7 +1264,7 @@ struct test_argsort : public test_case {
 
     test_argsort(ggml_type type = GGML_TYPE_F32,
             std::array<int64_t, 4> ne = {16, 10, 10, 10},
-            ggml_sort_order order = GGML_SORT_ASC)
+            ggml_sort_order order = GGML_SORT_ORDER_ASC)
         : type(type), ne(ne), order(order) {}
 
     ggml_tensor * build_graph(ggml_context * ctx) override {
@@ -2116,7 +2116,7 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op
     test_cases.emplace_back(new test_concat(GGML_TYPE_F32));
     test_cases.emplace_back(new test_concat(GGML_TYPE_I32));
 
-    for (ggml_sort_order order : {GGML_SORT_ASC, GGML_SORT_DESC}) {
+    for (ggml_sort_order order : {GGML_SORT_ORDER_ASC, GGML_SORT_ORDER_DESC}) {
         test_cases.emplace_back(new test_argsort(GGML_TYPE_F32, {8, 1, 1, 1}, order));
         test_cases.emplace_back(new test_argsort(GGML_TYPE_F32, {16, 10, 10, 10}, order));
     }

tests/test-opt.cpp

@@ -118,7 +118,7 @@ int main(void) {
     const float fe = ggml_get_f32_1d(e, 0);
     printf("%s: e = %.4f\n", __func__, fe);
 
-    struct ggml_opt_params opt_params = ggml_opt_default_params(GGML_OPT_ADAM);
+    struct ggml_opt_params opt_params = ggml_opt_default_params(GGML_OPT_TYPE_ADAM);
 
     ggml_opt(ctx, opt_params, e);