Feat: support disable tool parser (#10184)

python/sglang/srt/entrypoints/openai/serving_chat.py

@@ -53,6 +53,7 @@ class OpenAIServingChat(OpenAIServingBase):
     ):
         super().__init__(tokenizer_manager)
         self.template_manager = template_manager
+        self.tool_call_parser = self.tokenizer_manager.server_args.tool_call_parser
 
     def _request_id_prefix(self) -> str:
         return "chatcmpl-"
@@ -172,10 +173,11 @@ class OpenAIServingChat(OpenAIServingBase):
                 ]
             else:
                 tools = [item.function.model_dump() for item in request.tools]
-
-            tool_call_parser = self.tokenizer_manager.server_args.tool_call_parser
-            parser = FunctionCallParser(request.tools, tool_call_parser)
-            tool_call_constraint = parser.get_structure_constraint(request.tool_choice)
+            if self.tool_call_parser:
+                parser = FunctionCallParser(request.tools, self.tool_call_parser)
+                tool_call_constraint = parser.get_structure_constraint(
+                    request.tool_choice
+                )
 
         # Use chat template
         if self.template_manager.chat_template_name is None:
@@ -537,7 +539,11 @@ class OpenAIServingChat(OpenAIServingBase):
                         yield f"data: {chunk.model_dump_json()}\n\n"
 
                 # Handle tool calls
-                if request.tool_choice != "none" and request.tools:
+                if (
+                    request.tool_choice != "none"
+                    and request.tools
+                    and self.tool_call_parser
+                ):
                     async for chunk in self._process_tool_call_stream(
                         index,
                         delta,
@@ -727,10 +733,13 @@ class OpenAIServingChat(OpenAIServingBase):
 
             # Handle tool calls
             tool_calls = None
-            if request.tool_choice != "none" and request.tools:
-                tool_call_parser = self.tokenizer_manager.server_args.tool_call_parser
+            if (
+                request.tool_choice != "none"
+                and request.tools
+                and self.tool_call_parser
+            ):
                 tool_calls, text, finish_reason = self._process_tool_calls(
-                    text, request.tools, tool_call_parser, finish_reason
+                    text, request.tools, finish_reason
                 )
 
             choice_data = ChatCompletionResponseChoice(
@@ -824,11 +833,10 @@ class OpenAIServingChat(OpenAIServingBase):
         self,
         text: str,
         tools: List[Any],
-        tool_call_parser: Optional[str],
         finish_reason: Dict[str, Any],
     ) -> tuple[Optional[List[ToolCall]], str, Dict[str, Any]]:
         """Process tool calls in the response"""
-        parser = FunctionCallParser(tools, tool_call_parser)
+        parser = FunctionCallParser(tools, self.tool_call_parser)
         if parser.has_tool_call(text):
             if finish_reason["type"] == "stop":
                 finish_reason["type"] = "tool_calls"
@@ -838,7 +846,10 @@ class OpenAIServingChat(OpenAIServingBase):
                 tool_calls = []
                 for call_info in call_info_list:
                     # For Kimi-K2, align tool_call_id with the model format: functions.{name}:{index}
-                    if tool_call_parser == "kimi_k2" and call_info.name is not None:
+                    if (
+                        self.tool_call_parser == "kimi_k2"
+                        and call_info.name is not None
+                    ):
                         tool_id = f"functions.{call_info.name}:{call_info.tool_index}"
                     else:
                         tool_id = f"call_{uuid.uuid4().hex[:24]}"
@@ -933,7 +944,7 @@ class OpenAIServingChat(OpenAIServingBase):
         if index not in parser_dict:
             parser_dict[index] = FunctionCallParser(
                 tools=request.tools,
-                tool_call_parser=self.tokenizer_manager.server_args.tool_call_parser,
+                tool_call_parser=self.tool_call_parser,
             )
         parser = parser_dict[index]
 
@@ -962,7 +973,7 @@ class OpenAIServingChat(OpenAIServingBase):
             # Tool call ID should be generated only once per tool call
             if call_item.name:
                 # First chunk: include ID and function name
-                if self.tokenizer_manager.server_args.tool_call_parser == "kimi_k2":
+                if self.tool_call_parser == "kimi_k2":
                     # Align with Kimi-K2 format: functions.{name}:{index}
                     tool_call_id = f"functions.{call_item.name}:{call_item.tool_index}"
                 else:

test/srt/openai_server/basic/test_serving_chat.py

@@ -332,7 +332,7 @@ class ServingChatTestCase(unittest.TestCase):
         """Ensure non-streaming tool_call.id matches functions.{name}:{index} for kimi_k2 parser."""
 
         # Force kimi_k2 parser
-        self.tm.server_args.tool_call_parser = "kimi_k2"
+        self.chat.tool_call_parser = "kimi_k2"
 
         # Mock FunctionCallParser.parse_non_stream to return one tool call
         with patch(
@@ -357,7 +357,6 @@ class ServingChatTestCase(unittest.TestCase):
             tool_calls, remaining_text, _ = self.chat._process_tool_calls(
                 text="<|tool_calls_section_begin|>...",
                 tools=tools,
-                tool_call_parser="kimi_k2",
                 finish_reason=finish_reason,
             )
 
@@ -370,7 +369,7 @@ class ServingChatTestCase(unittest.TestCase):
         """Ensure streaming first chunk tool_call.id matches functions.{name}:{index} for kimi_k2 parser."""
 
         # Force kimi_k2 parser
-        self.tm.server_args.tool_call_parser = "kimi_k2"
+        self.chat.tool_call_parser = "kimi_k2"
 
         # Prepare request with tools
         req = ChatCompletionRequest(