llama_chat_apply_template: use term "chat" everywhere

llama.cpp

@@ -12459,7 +12459,10 @@ int32_t llama_token_to_piece(const struct llama_model * model, llama_token token
     return 0;
 }
 
-int32_t llama_chat_apply_template_internal(std::string &dest, std::string chat_template, std::vector<const llama_chat_message *> conversation, bool add_ass);
+int32_t llama_chat_apply_template_internal(
+    const std::string & chat_template,
+    const std::vector<const llama_chat_message *> & chat, 
+    std::string & dest, bool add_ass);
 
 // trim whitespace from the beginning and end of a string
 static std::string trim(const std::string & str) {
@@ -12476,12 +12479,15 @@ static std::string trim(const std::string & str) {
 
 // Simple version of "llama_apply_chat_template" that only works with strings
 // This function uses heuristic checks to determine commonly used template. It is not a jinja parser.
-int32_t llama_chat_apply_template_internal(std::string &dest, std::string chat_template, std::vector<const llama_chat_message *> conversation, bool add_ass) {
+int32_t llama_chat_apply_template_internal(
+    const std::string & chat_template,
+    const std::vector<const llama_chat_message *> & chat, 
+    std::string & dest, bool add_ass) {
     // Taken from the research: https://github.com/ggerganov/llama.cpp/issues/5527
     std::stringstream ss;
     if (chat_template.find("<|im_start|>") != std::string::npos) {
         // chatml template
-        for (auto message : conversation) {
+        for (auto message : chat) {
             ss << "<|im_start|>" << message->role << "\n" << message->content << "<|im_end|>\n";
         }
         if (add_ass) {
@@ -12500,7 +12506,7 @@ int32_t llama_chat_apply_template_internal(std::string &dest, std::string chat_t
         // construct the prompt
         bool is_inside_turn = true; // skip BOS at the beginning
         ss << "[INST] ";
-        for (auto message : conversation) {
+        for (auto message : chat) {
             std::string content = strip_message ? trim(message->content) : message->content;
             std::string role(message->role);
             if (!is_inside_turn) {
@@ -12524,7 +12530,7 @@ int32_t llama_chat_apply_template_internal(std::string &dest, std::string chat_t
         // llama2 templates seem to not care about "add_generation_prompt"
     } else if (chat_template.find("<|user|>") != std::string::npos) {
         // zephyr template
-        for (auto message : conversation) {
+        for (auto message : chat) {
             ss << "<|" << message->role << "|>" << "\n" << message->content << "<|endoftext|>\n";
         }
         if (add_ass) {
@@ -12541,7 +12547,7 @@ int32_t llama_chat_apply_template_internal(std::string &dest, std::string chat_t
 LLAMA_API int32_t llama_chat_apply_template(
                 const struct llama_model * model,
                               const char * custom_template,
-         const struct llama_chat_message * msg,
+         const struct llama_chat_message * chat,
                                   size_t   n_msg,
                                     bool   add_ass,
                                     char * buf,
@@ -12560,14 +12566,14 @@ LLAMA_API int32_t llama_chat_apply_template(
             current_template = std::string(model_template.data(), model_template.size());
         }
     }
-    // format the conversation to string
-    std::vector<const llama_chat_message *> conversation_vec;
-    conversation_vec.resize(n_msg);
+    // format the chat to string
+    std::vector<const llama_chat_message *> chat_vec;
+    chat_vec.resize(n_msg);
     for (size_t i = 0; i < n_msg; i++) {
-        conversation_vec[i] = &msg[i];
+        chat_vec[i] = &chat[i];
     }
     std::string formatted_chat;
-    int32_t res = llama_chat_apply_template_internal(formatted_chat, current_template, conversation_vec, add_ass);
+    int32_t res = llama_chat_apply_template_internal(current_template, chat_vec, formatted_chat, add_ass);
     if (res < 0) {
         return res;
     }

llama.h

@@ -704,18 +704,20 @@ extern "C" {
                                   char * buf,
                                int32_t   length);
 
-    /// Apply chat template and maybe tokenize it. Inspired by hf apply_chat_template() on python.
+    /// 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.
-    /// @param custom_template A Jinja template to use for this conversion. If this is nullptr, the model’s default chat template will be used instead.
-    /// @param msg Pointer to a list of multiple llama_chat_message
+    /// @param custom_template 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
     /// @param add_ass Whether to end the prompt with the token(s) that indicate the start of an assistant message.
     /// @param buf A buffer to hold the output formatted prompt. The recommended alloc size is 2 * (total number of characters of all messages)
+    /// @param length The size of the allocated buffer
     /// @return The total number of bytes of the formatted prompt. If is it larger than the size of buffer, you may need to re-alloc it and then re-apply the template.
     LLAMA_API int32_t llama_chat_apply_template(
               const struct llama_model * model,
                             const char * custom_template,
-       const struct llama_chat_message * msg,
+       const struct llama_chat_message * chat,
                                 size_t   n_msg,
                                   bool   add_ass,
                                   char * buf,