Server: use llama_chat_apply_template

examples/server/oai.hpp

@@ -15,13 +15,11 @@
 using json = nlohmann::json;
 
 inline static json oaicompat_completion_params_parse(
+    const struct llama_model * model,
     const json &body, /* openai api json semantics */
     const std::string &chat_template)
 {
     json llama_params;
-    std::string formatted_prompt = chat_template == "chatml"
-        ? format_chatml(body["messages"])  // OpenAI 'messages' to chatml (with <|im_start|>,...)
-        : format_llama2(body["messages"]); // OpenAI 'messages' to llama2 (with [INST],...)
 
     llama_params["__oaicompat"] = true;
 
@@ -34,7 +32,7 @@ inline static json oaicompat_completion_params_parse(
     // https://platform.openai.com/docs/api-reference/chat/create
     llama_sampling_params default_sparams;
     llama_params["model"]             = json_value(body, "model", std::string("unknown"));
-    llama_params["prompt"]            = formatted_prompt;
+    llama_params["prompt"]            = format_chat(model, chat_template, body["messages"]);
     llama_params["cache_prompt"]      = json_value(body, "cache_prompt", false);
     llama_params["temperature"]       = json_value(body, "temperature", 0.0);
     llama_params["top_k"]             = json_value(body, "top_k", default_sparams.top_k);

examples/server/server.cpp

@@ -37,7 +37,7 @@ struct server_params
     std::string hostname = "127.0.0.1";
     std::vector<std::string> api_keys;
     std::string public_path = "examples/server/public";
-    std::string chat_template = "chatml";
+    std::string chat_template = "";
     int32_t port = 8080;
     int32_t read_timeout = 600;
     int32_t write_timeout = 600;
@@ -1937,8 +1937,9 @@ static void server_print_usage(const char *argv0, const gpt_params &params,
     printf("                            types: int, float, bool. example: --override-kv tokenizer.ggml.add_bos_token=bool:false\n");
     printf("  -gan N, --grp-attn-n N    set the group attention factor to extend context size through self-extend(default: 1=disabled), used together with group attention width `--grp-attn-w`");
     printf("  -gaw N, --grp-attn-w N    set the group attention width to extend context size through self-extend(default: 512), used together with group attention factor `--grp-attn-n`");
-    printf("  --chat-template FORMAT_NAME");
-    printf("                            set chat template, possible value is: llama2, chatml (default %s)", sparams.chat_template.c_str());
+    printf("  --chat-template JINJA_TEMPLATE");
+    printf("                            set custom jinja chat template (default: template taken from model's metadata)");
+    printf("                            Note: only commonly used templates are accepted, since we don't have jinja parser");
     printf("\n");
 }
 
@@ -2390,12 +2391,13 @@ static void server_params_parse(int argc, char **argv, server_params &sparams,
                 break;
             }
             std::string value(argv[i]);
-            if (value != "chatml" && value != "llama2") {
-                fprintf(stderr, "error: chat template can be \"llama2\" or \"chatml\", but got: %s\n", value.c_str());
+            if (!verify_custom_template(argv[i])) {
+                fprintf(stderr, "error: the supplied chat template is not supported: %s\n", argv[i]);
+                fprintf(stderr, "note: llama.cpp does not use jinja parser, we only support commonly used templates\n");
                 invalid_param = true;
                 break;
             }
-            sparams.chat_template = value;
+            sparams.chat_template = argv[i];
         }
         else if (arg == "--override-kv")
         {
@@ -2913,7 +2915,7 @@ int main(int argc, char **argv)
                 if (!validate_api_key(req, res)) {
                     return;
                 }
-                json data = oaicompat_completion_params_parse(json::parse(req.body), sparams.chat_template);
+                json data = oaicompat_completion_params_parse(llama.model, json::parse(req.body), sparams.chat_template);
 
                 const int task_id = llama.queue_tasks.get_new_id();
                 llama.queue_results.add_waiting_task_id(task_id);

examples/server/utils.hpp

@@ -167,50 +167,47 @@ static T json_value(const json &body, const std::string &key, const T &default_v
         : default_value;
 }
 
-inline std::string format_llama2(std::vector<json> messages)
-{
-    std::ostringstream output;
-    bool is_inside_turn = false;
+// Check if the template supplied via "--chat-template" is supported or not. Returns true if it's valid
+inline bool verify_custom_template(std::string tmpl) {
+    llama_chat_message chat[] = {{"user", "test"}};
+    std::vector<char> buf(1);
+    int res = llama_chat_apply_template(nullptr, tmpl.c_str(), chat, 1, true, buf.data(), buf.size());
+    return res >= 0;
+}
 
-    for (auto it = messages.begin(); it != messages.end(); ++it) {
-        if (!is_inside_turn) {
-            output << "[INST] ";
-        }
-        std::string role    = json_value(*it, "role", std::string("user"));
-        std::string content = json_value(*it, "content", std::string(""));
-        if (role == "system") {
-            output << "<<SYS>>\n" << content << "\n<<SYS>>\n\n";
-            is_inside_turn = true;
-        } else if (role == "user") {
-            output << content << " [/INST]";
-            is_inside_turn = true;
-        } else {
-            output << " " << content << " </s>";
-            is_inside_turn = false;
-        }
+// Format given chat. If tmpl is empty, we take the template from model metadata
+inline std::string format_chat(const struct llama_model * model, const std::string tmpl, std::vector<json> messages)
+{
+    size_t alloc_size = 0;
+    // vector holding all allocated string to be passed to llama_chat_apply_template
+    std::vector<std::string> str(messages.size() * 2);
+    std::vector<llama_chat_message> chat(messages.size());
+
+    for (size_t i = 0; i < messages.size(); ++i) {
+        auto &curr_msg = messages[i];
+        str[i]          = json_value(curr_msg, "role",    std::string(""));
+        str[i + 1]      = json_value(curr_msg, "content", std::string(""));
+        alloc_size     += str[i + 1].length();
+        chat[i].role    = str[i].c_str();
+        chat[i].content = str[i + 1].c_str();
     }
 
-    LOG_VERBOSE("format_llama2", {{"text", output.str()}});
+    const char * ptr_tmpl = tmpl.empty() ? nullptr : tmpl.c_str();
+    std::vector<char> buf(alloc_size * 2);
 
-    return output.str();
-}
-
-inline std::string format_chatml(std::vector<json> messages)
-{
-    std::ostringstream chatml_msgs;
+    // run the first time to get the total output length
+    int32_t res = llama_chat_apply_template(model, ptr_tmpl, chat.data(), chat.size(), true, buf.data(), buf.size());
 
-    for (auto it = messages.begin(); it != messages.end(); ++it) {
-        chatml_msgs << "<|im_start|>"
-                    << json_value(*it, "role",    std::string("user")) << '\n';
-        chatml_msgs << json_value(*it, "content", std::string(""))
-                    << "<|im_end|>\n";
+    // if it turns out that our buffer is too small, we resize it
+    if ((size_t) res > buf.size()) {
+        buf.resize(res);
+        res = llama_chat_apply_template(model, ptr_tmpl, chat.data(), chat.size(), true, buf.data(), buf.size());
     }
 
-    chatml_msgs << "<|im_start|>assistant" << '\n';
-
-    LOG_VERBOSE("format_chatml", {{"text", chatml_msgs.str()}});
+    std::string formatted_chat(buf.data(), buf.size());
+    LOG_VERBOSE("formatted_chat", {{"text", formatted_chat.c_str()}});
 
-    return chatml_msgs.str();
+    return formatted_chat;
 }
 
 //

llama.cpp

@@ -12602,7 +12602,7 @@ LLAMA_API int32_t llama_chat_apply_template(
         // load template from model
         std::vector<char> model_template(2048, 0); // longest known template is about 1200 bytes
         std::string template_key = "tokenizer.chat_template";
-        int32_t res = llama_model_meta_val_str(model, template_key.c_str(), model_template.data(), curr_tmpl.size());
+        int32_t res = llama_model_meta_val_str(model, template_key.c_str(), model_template.data(), model_template.size());
         if (res < 0) {
             // worst case: there is no information about template, we will use chatml by default
             curr_tmpl = "<|im_start|>"; // see llama_chat_apply_template_internal