server: clean up oai parsing function

examples/server/server.cpp

@@ -847,7 +847,14 @@ struct server_context {
         slot.sparams.penalize_nl       = json_value(data, "penalize_nl",       default_sparams.penalize_nl);
         slot.params.n_keep             = json_value(data, "n_keep",            slot.params.n_keep);
         slot.params.seed               = json_value(data, "seed",              default_params.seed);
-        if (data.contains("json_schema") && !data.contains("grammar")) {
+        slot.sparams.n_probs           = json_value(data, "n_probs",           default_sparams.n_probs);
+        slot.sparams.min_keep          = json_value(data, "min_keep",          default_sparams.min_keep);
+
+        // process "json_schema" and "grammar"
+        if (data.contains("json_schema") && data.contains("grammar")) {
+            send_error(task, "Either \"json_schema\" or \"grammar\" can be specified, but not both", ERROR_TYPE_INVALID_REQUEST);
+            return false;
+        } else if (data.contains("json_schema") && !data.contains("grammar")) {
             try {
                 auto schema                = json_value(data, "json_schema", json::object());
                 slot.sparams.grammar       = json_schema_to_grammar(schema);
@@ -858,8 +865,6 @@ struct server_context {
         } else {
             slot.sparams.grammar       = json_value(data, "grammar",           default_sparams.grammar);
         }
-        slot.sparams.n_probs           = json_value(data, "n_probs",           default_sparams.n_probs);
-        slot.sparams.min_keep          = json_value(data, "min_keep",          default_sparams.min_keep);
 
         if (slot.params.cache_prompt && slot.ga_n != 1) {
             LOG_WARNING("cache_prompt is not supported with group-attention", {});

examples/server/utils.hpp

@@ -49,6 +49,34 @@ extern bool server_log_json;
 #define LOG_WARNING(MSG, ...) server_log("WARN", __func__, __LINE__, MSG, __VA_ARGS__)
 #define LOG_INFO(   MSG, ...) server_log("INFO", __func__, __LINE__, MSG, __VA_ARGS__)
 
+// GRAMMAR_JSON is used by OAI "response_format" field
+static const std::string GRAMMAR_JSON = R"(root   ::= object
+value  ::= object | array | string | number | ("true" | "false" | "null") ws
+
+object ::=
+  "{" ws (
+            string ":" ws value
+    ("," ws string ":" ws value)*
+  )? "}" ws
+
+array  ::=
+  "[" ws (
+            value
+    ("," ws value)*
+  )? "]" ws
+
+string ::=
+  "\"" (
+    [^"\\\x7F\x00-\x1F] |
+    "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F]) # escapes
+  )* "\"" ws
+
+number ::= ("-"? ([0-9] | [1-9] [0-9]*)) ("." [0-9]+)? ([eE] [-+]? [0-9]+)? ws
+
+# Optional space: by convention, applied in this grammar after literal chars when allowed
+ws ::= ([ \t\n] ws)?
+)";
+
 template <typename T>
 static T json_value(const json &body, const std::string &key, const T &default_value) {
     // Fallback null to default value
@@ -352,52 +380,65 @@ 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"]            = 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);
-    llama_params["top_p"]             = json_value(body,   "top_p",             1.0);
-    llama_params["n_predict"]         = json_value(body,   "max_tokens",        -1);
-    llama_params["logit_bias"]        = json_value(body,   "logit_bias",        json::object());
     llama_params["frequency_penalty"] = json_value(body,   "frequency_penalty", 0.0);
+    llama_params["logit_bias"]        = json_value(body,   "logit_bias",        json::object());
+    llama_params["n_predict"]         = json_value(body,   "max_tokens",        -1);
     llama_params["presence_penalty"]  = json_value(body,   "presence_penalty",  0.0);
     llama_params["seed"]              = json_value(body,   "seed",              LLAMA_DEFAULT_SEED);
     llama_params["stream"]            = json_value(body,   "stream",            false);
-    llama_params["mirostat"]          = json_value(body,   "mirostat",          default_sparams.mirostat);
+    llama_params["temperature"]       = json_value(body,   "temperature",       0.0);
-    llama_params["mirostat_tau"]      = json_value(body,   "mirostat_tau",      default_sparams.mirostat_tau);
+    llama_params["top_p"]             = json_value(body,   "top_p",             1.0);
-    llama_params["mirostat_eta"]      = json_value(body,   "mirostat_eta",      default_sparams.mirostat_eta);
-    llama_params["penalize_nl"]       = json_value(body,   "penalize_nl",       default_sparams.penalize_nl);
-    llama_params["typical_p"]         = json_value(body,   "typical_p",         default_sparams.typical_p);
-    llama_params["repeat_last_n"]     = json_value(body,   "repeat_last_n",     default_sparams.penalty_last_n);
-    llama_params["ignore_eos"]        = json_value(body,   "ignore_eos",        false);
-    llama_params["tfs_z"]             = json_value(body,   "tfs_z",             default_sparams.tfs_z);
-    llama_params["n_keep"]            = json_value(body,   "n_keep",            0);
 
-    if (body.contains("grammar")) {
+    // Apply chat template to the list of messages
-        llama_params["grammar"] = json_value(body, "grammar", json::object());
+    llama_params["prompt"] = format_chat(model, chat_template, body["messages"]);
-    }
 
-    if (body.contains("response_format")) {
+    // Handle "stop" field
-        auto response_format = json_value(body, "response_format", json::object());
-        if (response_format.contains("type")) {
-            if (response_format["type"] == "json_object") {
-                llama_params["json_schema"] = json_value(response_format, "schema", json::object());
-            } else {
-                throw std::runtime_error("response_format type not supported: " + response_format["type"].dump());
-            }
-        }
-    }
-
-    // Handle 'stop' field
     if (body.contains("stop") && body["stop"].is_string()) {
         llama_params["stop"] = json::array({body["stop"].get<std::string>()});
     } else {
         llama_params["stop"] = json_value(body, "stop", json::array());
     }
-
     // Ensure there is ChatML-specific end sequence among stop words
     llama_params["stop"].push_back("<|im_end|>");
 
+    // Handle "response_format" field
+    if (body.contains("response_format")) {
+        json response_format      = json_value(body, "response_format", json::object());
+        std::string response_type = json_value(response_format, "type", std::string());
+        if (response_type == "json_object") {
+            // "json_object" guarantees the message the model generates is valid JSON.
+            llama_params["grammar"] = GRAMMAR_JSON;
+        } else {
+            throw std::runtime_error("response_format type not supported: " + response_type);
+        }
+    }
+
+    // Handle "n" field
+    int n_choices = json_value(body, "n", 1);
+    if (n_choices != 1) {
+        throw std::runtime_error("Only one completion choice is supported");
+    }
+
+    // Params supported by OAI but unsupported by llama.cpp
+    static const std::vector<std::string> unsupported_params{
+        "logprobs", "top_logprobs", "tools", "tool_choice"
+    };
+    for (auto & param : unsupported_params) {
+        if (llama_params.contains(param)) {
+            throw std::runtime_error("Unsupported param: " + param);
+        }
+    }
+
+    // Copy remaining properties to llama_params
+    // This allows user to use llama.cpp-specific params like "mirostat", "tfs_z",... via OAI endpoint.
+    // See "launch_slot_with_task()" for a complete list of params supported by llama.cpp
+    for (const auto & item : body.items()) {
+        // Exception: if "n_predict" is present, we overwrite the value specified by "max_tokens"
+        if (!llama_params.contains(item.key()) || item.key() == "n_predict") {
+            llama_params[item.key()] = item.value();
+        }
+    }
+
     return llama_params;
 }