CUDA GPU acceleration for LoRAs + f16 models (#1970)

1 files changed, 35 insertions, 1 deletions

diff --git a/llama.cpp b/llama.cpp
index 5a142ab..5f3761b 100644
--- a/llama.cpp
+++ b/llama.cpp
@@ -2976,7 +2976,7 @@ int llama_apply_lora_from_file_internal(const struct llama_model & model, const
                         return false;
                     }
         }
-        ggml_tensor* lora_tensor;
+        ggml_tensor * lora_tensor;
         if (n_dims == 2) {
             lora_tensor = ggml_new_tensor_2d(lora_ctx, wtype, ne[0], ne[1]);
         }
@@ -2984,6 +2984,7 @@ int llama_apply_lora_from_file_internal(const struct llama_model & model, const
             fprintf(stderr, "%s: unsupported tensor dimension %d\n", __func__, n_dims);
             return 1;
         }
+        ggml_set_name(lora_tensor, "lora_tensor");
 
         // load tensor data
         size_t offset = fin.tellg();
@@ -2999,6 +3000,21 @@ int llama_apply_lora_from_file_internal(const struct llama_model & model, const
             lora_tensors.find(base_name + ".loraB") != lora_tensors.end()) {
 
             ggml_tensor * dest_t = model_tensors[base_name];
+
+            offload_func_t offload_func = llama_nop;
+            offload_func_t offload_func_force_inplace = llama_nop;
+
+#ifdef GGML_USE_CUBLAS
+            if (dest_t->backend == GGML_BACKEND_GPU || dest_t->backend == GGML_BACKEND_GPU_SPLIT) {
+                if (dest_t->type != GGML_TYPE_F16) {
+                    throw std::runtime_error(format(
+                        "%s: error: the simultaneous use of LoRAs and GPU acceleration is only supported for f16 models", __func__));
+                }
+                offload_func = ggml_cuda_assign_buffers;
+                offload_func_force_inplace = ggml_cuda_assign_buffers_force_inplace;
+            }
+#endif // GGML_USE_CUBLAS
+
             ggml_tensor * base_t;
             if (model_loader) {
                 // load from base model
@@ -3026,7 +3042,12 @@ int llama_apply_lora_from_file_internal(const struct llama_model & model, const
             }
 
             ggml_tensor * loraA = lora_tensors[base_name + ".loraA"];
+            GGML_ASSERT(loraA->type == GGML_TYPE_F32);
+            ggml_set_name(loraA, "loraA");
+
             ggml_tensor * loraB = lora_tensors[base_name + ".loraB"];
+            GGML_ASSERT(loraB->type == GGML_TYPE_F32);
+            ggml_set_name(loraB, "loraB");
 
             if (base_t->ne[0] != loraA->ne[1] || base_t->ne[1] != loraB->ne[1]) {
                 fprintf(stderr, "%s: incompatible tensor dimensions (%" PRId64 " and %" PRId64 ");"
@@ -3036,19 +3057,32 @@ int llama_apply_lora_from_file_internal(const struct llama_model & model, const
 
             // w = w + BA*s
             ggml_tensor * BA = ggml_mul_mat(lora_ctx, loraA, loraB);
+            offload_func(BA);
+            ggml_set_name(BA, "BA");
 
             if (scaling != 1.0f) {
                 ggml_tensor * scale_tensor = ggml_new_f32(lora_ctx, scaling);
+                ggml_set_name(scale_tensor, "scale_tensor");
+
                 BA = ggml_scale_inplace(lora_ctx, BA, scale_tensor);
+                offload_func(BA);
+                ggml_set_name(BA, "BA_scaled");
             }
 
             ggml_tensor * r;
             if (base_t == dest_t) {
                 r = ggml_add_inplace(lora_ctx, dest_t, BA);
+                offload_func_force_inplace(r);
+                ggml_set_name(r, "r_add_inplace");
             }
             else {
                 r = ggml_add(lora_ctx, base_t, BA);
+                offload_func(r);
+                ggml_set_name(r, "r_add");
+
                 r = ggml_cpy(lora_ctx, r, dest_t);
+                offload_func(r);
+                ggml_set_name(r, "r_cpy");
             }
 
             struct ggml_cgraph gf = ggml_build_forward(r);