ggml-cuda : fix f16 mul mat (#3961)

* ggml-cuda : fix f16 mul mat

ggml-ci

* silence common.cpp warning (bonus)

diff --git a/common/common.cpp b/common/common.cpp
index 37e3ace8ac5d9291bccd13b43d25cbb6189188fc..6a711420004b485391bac38dbacc1f104f9ce71a 100644 (file)
--- a/common/common.cpp
+++ b/common/common.cpp
@@ -101,8 +101,8 @@ void process_escapes(std::string& input) {
                             input[output_idx++] = char(val);
                             break;
                         }
-                        // Intentionally fall through to default.
                     }
+                    // fall through
                 default:   input[output_idx++] = '\\';
                            input[output_idx++] = input[input_idx]; break;
             }

diff --git a/ggml-cuda.cu b/ggml-cuda.cu
index 9f873035ad0c054b5d716965485236ab88037656..2d9ffffbf7496677f75a5ad1a8e449803a4fdb77 100644 (file)
--- a/ggml-cuda.cu
+++ b/ggml-cuda.cu
@@ -7414,6 +7414,8 @@ static void ggml_cuda_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1
         (src1->backend == GGML_BACKEND_GPU) &&
         ( dst->backend == GGML_BACKEND_GPU);
 
+    const bool split = src0->backend == GGML_BACKEND_GPU_SPLIT;
+
     int64_t min_compute_capability = INT_MAX;
     for (int64_t id = 0; id < g_device_count; ++id) {
         if (min_compute_capability > g_compute_capabilities[id] && g_tensor_split[id] < (id + 1 < g_device_count ? g_tensor_split[id + 1] : 1.0f)) {
@@ -7435,13 +7437,13 @@ static void ggml_cuda_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1
     //printf("src0 is contiguous %d, transposed %d, type = %s, name = %s\n", ggml_is_contiguous(src0), ggml_is_transposed(src0), ggml_type_name(src0->type), src0->name);
     //printf("src1 is contiguous %d, transposed %d, type = %s, name = %s\n", ggml_is_contiguous(src1), ggml_is_transposed(src1), ggml_type_name(src1->type), src1->name);
 
-    if (all_on_device && !use_tensor_cores && src0->type == GGML_TYPE_F16 && ggml_is_permuted(src0) && ggml_is_permuted(src1) && src1->ne[1] == 1) {
+    if (!split && all_on_device && !use_tensor_cores && src0->type == GGML_TYPE_F16 && ggml_is_permuted(src0) && ggml_is_permuted(src1) && src1->ne[1] == 1) {
         // KQ single-batch
         ggml_cuda_mul_mat_vec_p021(src0, src1, dst);
-    } else if (all_on_device && !use_tensor_cores && src0->type == GGML_TYPE_F16 && !ggml_is_contiguous(src0) && !ggml_is_transposed(src1) && src1->ne[1] == 1) {
+    } else if (!split && all_on_device && !use_tensor_cores && src0->type == GGML_TYPE_F16 && !ggml_is_contiguous(src0) && !ggml_is_transposed(src1) && src1->ne[1] == 1) {
         // KQV single-batch
         ggml_cuda_mul_mat_vec_nc(src0, src1, dst);
-    } else if (all_on_device && use_tensor_cores && src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F32 && !ggml_is_transposed(src0) && !ggml_is_transposed(src1)) {
+    } else if (!split && all_on_device && use_tensor_cores && src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F32 && !ggml_is_transposed(src0) && !ggml_is_transposed(src1)) {
         // KQ + KQV multi-batch
         ggml_cuda_mul_mat_mat_batched_cublas(src0, src1, dst);
     } else if (src0->type == GGML_TYPE_F32) {