vulkan: support GGML_OP_SUM

Author: remyoudompheng

ggml/src/ggml-vulkan/ggml-vulkan.cpp

@@ -5276,6 +5276,7 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const
             return ctx->device->pipeline_argsort_f32;
         }
         return nullptr;
+    case GGML_OP_SUM:
     case GGML_OP_SUM_ROWS:
         if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
             return ctx->device->pipeline_sum_rows_f32;
@@ -5554,6 +5555,10 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context& subctx, co
                 elements = { nr, 1, 1 };
             }
         } break;
+    case GGML_OP_SUM:
+        // We use GGML_OP_SUM_ROWS with 1 row.
+        elements = { 1, 1, 1 };
+        break;
     case GGML_OP_GROUP_NORM:
         {
             const uint32_t num_groups = dst->op_params[0];
@@ -6136,6 +6141,10 @@ static void ggml_vk_argsort(ggml_backend_vk_context * ctx, vk_context& subctx, c
     }, dryrun);
 }
 
+static void ggml_vk_sum(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst, bool dryrun = false) {
+    ggml_vk_op_f32<vk_op_push_constants>(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_SUM, { (uint32_t)ggml_nelements(src0), 0, 0.0f, 0.0f }, dryrun);
+}
+
 static void ggml_vk_sum_rows(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst, bool dryrun = false) {
     ggml_vk_op_f32<vk_op_push_constants>(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_SUM_ROWS, { (uint32_t)src0->ne[0], 0, 0.0f, 0.0f }, dryrun);
 }
@@ -7029,6 +7038,7 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod
     case GGML_OP_MUL_MAT:
     case GGML_OP_MUL_MAT_ID:
     case GGML_OP_ARGSORT:
+    case GGML_OP_SUM:
     case GGML_OP_SUM_ROWS:
     case GGML_OP_IM2COL:
     case GGML_OP_TIMESTEP_EMBEDDING:
@@ -7080,6 +7090,7 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod
         case GGML_OP_SOFT_MAX:
         case GGML_OP_ROPE:
         case GGML_OP_ARGSORT:
+        case GGML_OP_SUM:
         case GGML_OP_SUM_ROWS:
         case GGML_OP_IM2COL:
         case GGML_OP_TIMESTEP_EMBEDDING:
@@ -7200,6 +7211,10 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod
     case GGML_OP_ARGSORT:
         ggml_vk_argsort(ctx, compute_ctx, src0, node, dryrun);
 
+        break;
+    case GGML_OP_SUM:
+        ggml_vk_sum(ctx, compute_ctx, src0, node, dryrun);
+
         break;
     case GGML_OP_SUM_ROWS:
         ggml_vk_sum_rows(ctx, compute_ctx, src0, node, dryrun);
@@ -7314,6 +7329,7 @@ static bool ggml_vk_compute_forward(ggml_backend_vk_context * ctx, ggml_tensor *
     case GGML_OP_TRANSPOSE:
     case GGML_OP_NONE:
     case GGML_OP_ARGSORT:
+    case GGML_OP_SUM:
     case GGML_OP_SUM_ROWS:
     case GGML_OP_IM2COL:
     case GGML_OP_TIMESTEP_EMBEDDING:
@@ -8248,6 +8264,7 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm
         case GGML_OP_DIAG_MASK_INF:
         case GGML_OP_SOFT_MAX:
         case GGML_OP_ARGSORT:
+        case GGML_OP_SUM:
         case GGML_OP_SUM_ROWS:
         case GGML_OP_IM2COL:
         case GGML_OP_TIMESTEP_EMBEDDING:
@@ -8819,6 +8836,8 @@ static void ggml_vk_check_results_0(ggml_tensor * tensor) {
         tensor_clone = ggml_get_rows(ggml_ctx, src0_clone, src1_clone);
     } else if (tensor->op == GGML_OP_ARGSORT) {
         tensor_clone = ggml_argsort(ggml_ctx, src0_clone, (ggml_sort_order) *(int *)tensor->op_params);
+    } else if (tensor->op == GGML_OP_SUM) {
+        tensor_clone = ggml_sum(ggml_ctx, src0_clone);
     } else if (tensor->op == GGML_OP_SUM_ROWS) {
         tensor_clone = ggml_sum_rows(ggml_ctx, src0_clone);
     } else if (tensor->op == GGML_OP_IM2COL) {