llama : code style fixes + progress print fix

.gitignore

@@ -1,5 +1,15 @@
-*.o
+/*.o
-*.a
+/*.a
+/*.sh
+/*.log
+/*.org
+
+/ppl-*.txt
+/qnt-*.txt
+/perf-*.txt
+
+*.bin
+
 .DS_Store
 .build/
 .cache/
@@ -21,8 +31,9 @@ build-no-accel/
 build-sanitize-addr/
 build-sanitize-thread/
 
+prompts/
 models/*
-*.bin
+wikitext-2-raw/
 
 /main
 /quantize
@@ -33,6 +44,7 @@ models/*
 /benchmark-matmult
 /vdot
 /Pipfile
+/libllama.so
 
 build-info.h
 arm_neon.h
@@ -43,17 +55,4 @@ __pycache__
 zig-out/
 zig-cache/
 
-ppl-*.txt
-qnt-*.txt
-perf-*.txt
-
 examples/jeopardy/results.txt
-
-/prompts
-*.sh
-*.log
-*.py
-*.txt
-/wikitext-2-raw/
-*.org
-/libllama.so

llama.cpp

@@ -1011,20 +1011,27 @@ static void llama_model_load_internal(
 
         model.tok_embeddings = ml->get_tensor("tok_embeddings.weight", {n_embd, n_vocab}, GGML_BACKEND_CPU);
         model.norm           = ml->get_tensor("norm.weight",           {n_embd},          GGML_BACKEND_CPU);
+
+        // "output" tensor
+        {
             ggml_backend backend_output;
             if (n_gpu_layers > int(n_layer)) {
                 backend_output = GGML_BACKEND_CUDA;
             } else {
                 backend_output = GGML_BACKEND_CPU;
             }
+
             model.output = ml->get_tensor("output.weight",         {n_embd, n_vocab}, backend_output);
+        }
+
+        const int i_gpu_start = n_layer - n_gpu_layers;
 
         model.layers.resize(n_layer);
-        const int i_gpu_start = n_layer - n_gpu_layers;
         for (uint32_t i = 0; i < n_layer; ++i) {
-            auto & layer = model.layers[i];
             const ggml_backend backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : GGML_BACKEND_CUDA;
 
+            auto & layer = model.layers[i];
+
             std::string layers_i = "layers." + std::to_string(i);
 
             layer.attention_norm = ml->get_tensor(layers_i + ".attention_norm.weight", {n_embd}, backend);
@@ -1039,10 +1046,12 @@ static void llama_model_load_internal(
             layer.w1 = ml->get_tensor(layers_i + ".feed_forward.w1.weight", {n_embd,   n_ff},   backend);
             layer.w2 = ml->get_tensor(layers_i + ".feed_forward.w2.weight", {  n_ff,   n_embd}, backend);
             layer.w3 = ml->get_tensor(layers_i + ".feed_forward.w3.weight", {n_embd,   n_ff},   backend);
+
             if (backend == GGML_BACKEND_CUDA) {
-                vram_total += ggml_nbytes(layer.attention_norm) + ggml_nbytes(layer.wq) + ggml_nbytes(layer.wk)
+                vram_total +=
-                    + ggml_nbytes(layer.wv) + ggml_nbytes(layer.wo) + ggml_nbytes(layer.attention_norm)
+                    ggml_nbytes(layer.attention_norm) + ggml_nbytes(layer.wq) + ggml_nbytes(layer.wk)             +
-                    + ggml_nbytes(layer.w1) + ggml_nbytes(layer.w2) + ggml_nbytes(layer.w3);
+                    ggml_nbytes(layer.wv)             + ggml_nbytes(layer.wo) + ggml_nbytes(layer.attention_norm) +
+                    ggml_nbytes(layer.w1)             + ggml_nbytes(layer.w2) + ggml_nbytes(layer.w3);
             }
         }
     }
@@ -2192,7 +2201,7 @@ struct llama_context * llama_init_from_file(
             unsigned * cur_percentage_p = (unsigned *) ctx;
             unsigned percentage = (unsigned) (100 * progress);
             while (percentage > *cur_percentage_p) {
-                ++*cur_percentage_p;
+                *cur_percentage_p = percentage;
                 fprintf(stderr, ".");
                 fflush(stderr);
                 if (percentage >= 100) {
@@ -2442,8 +2451,7 @@ int llama_apply_lora_from_file_internal(struct llama_context * ctx, const char *
                 }
                 size_t idx = model_loader->tensors_map.name_to_idx[base_name];
                 llama_load_tensor & lt = model_loader->tensors_map.tensors[idx];
-                base_t = model_loader->get_tensor(
+                base_t = model_loader->get_tensor(base_name, { (uint32_t)dest_t->ne[0], (uint32_t)dest_t->ne[1] }, GGML_BACKEND_CPU);
-                    base_name, { (uint32_t)dest_t->ne[0], (uint32_t)dest_t->ne[1] }, GGML_BACKEND_CPU);
                 lt.data = (uint8_t *) lt.ggml_tensor->data;
                 model_loader->load_data_for(lt);
                 lt.ggml_tensor->data = lt.data;