llama : fix loading models with shared tok_embd and output (#5651)

ggml-ci

examples/server/README.md

@@ -41,6 +41,7 @@ see https://github.com/ggerganov/llama.cpp/issues/1437
 - `--grp-attn-w`: Set the group attention width to extend context size through self-extend(default: 512), used together with group attention factor `--grp-attn-n`
 - `-n, --n-predict`: Set the maximum tokens to predict (default: -1)
 - `--slots-endpoint-disable`: To disable slots state monitoring endpoint. Slots state may contain user data, prompts included.
+- `--chat-template JINJA_TEMPLATE`: Set custom jinja chat template. This parameter accepts a string, not a file name (default: template taken from model's metadata). We only support [some pre-defined templates](https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template)
 
 ## Build
 

llama.cpp

@@ -2791,13 +2791,7 @@ struct llama_model_loader {
 
         std::vector<no_init<uint8_t>> read_buf;
 
-        for (int i = 0; i < gguf_get_n_tensors(ctx_gguf); i++) {
-            struct ggml_tensor * cur = ggml_get_tensor(ctx, gguf_get_tensor_name(ctx_gguf, i));
-            if (!cur) {
-                // some tensors may be allocated in a different context
-                continue;
-            }
-
+        for (struct ggml_tensor * cur = ggml_get_first_tensor(ctx); cur != NULL; cur = ggml_get_next_tensor(ctx, cur)) {
             if (progress_callback) {
                 if (!progress_callback((float) size_done / size_data, progress_callback_user_data)) {
                     return false;
@@ -3722,7 +3716,7 @@ static bool llm_load_tensors(
     }
 
     // create one context per buffer type
-    size_t ctx_size = ggml_tensor_overhead()*ml.n_tensors;
+    size_t ctx_size = ggml_tensor_overhead()*(ml.n_tensors + 1); // +1 for models where tok_embd is duplicated as output
     std::map<ggml_backend_buffer_type_t, ggml_context *> ctx_map;
     for (auto & it : buft_layer_count) {
         struct ggml_init_params params = {
@@ -3860,6 +3854,7 @@ static bool llm_load_tensors(
                         } else {
                             model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); // needs to be on GPU
                             ml.n_created--; // artificial tensor
+                            ml.size_data += ggml_nbytes(model.output);
                         }
                     }
 
@@ -4396,6 +4391,7 @@ static bool llm_load_tensors(
                     model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
                     model.output      = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD,  "weight"), {n_embd, n_vocab}); // same as tok_embd, duplicated to allow offloading
                     ml.n_created--; // artificial tensor
+                    ml.size_data += ggml_nbytes(model.output);
 
                     const int64_t n_ff          = hparams.n_ff;
                     const int64_t n_embd_head_k = hparams.n_embd_head_k;

llama.h

@@ -708,7 +708,7 @@ extern "C" {
 
     /// Apply chat template. Inspired by hf apply_chat_template() on python.
     /// Both "model" and "custom_template" are optional, but at least one is required. "custom_template" has higher precedence than "model"
-    /// NOTE: This function only support some known jinja templates. It is not a jinja parser.
+    /// NOTE: This function does not use a jinja parser. It only support a pre-defined list of template. See more: https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template
     /// @param tmpl A Jinja template to use for this chat. If this is nullptr, the model’s default chat template will be used instead.
     /// @param chat Pointer to a list of multiple llama_chat_message
     /// @param n_msg Number of llama_chat_message in this chat