misc: Improvement to serving_chat.py and add more ut

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

@@ -14,7 +14,8 @@
 """Pydantic models for OpenAI API protocol"""
 
 import time
-from typing import Dict, List, Optional, Union
+from dataclasses import dataclass
+from typing import Any, Dict, List, Optional, Union
 
 from pydantic import (
     BaseModel,
@@ -587,3 +588,30 @@ class RerankResponse(BaseModel):
     ScoringRequest,
     V1RerankReqInput,
 ]
+
+
+@dataclass
+class MessageProcessingResult:
+    """Result of processing chat messages and applying templates.
+
+    This dataclass encapsulates all the outputs from message processing including
+    prompt generation, multimodal data extraction, and constraint preparation.
+    Used internally by OpenAIServingChat to pass processed data between methods.
+
+    Args:
+        prompt: The final text prompt after applying chat template
+        prompt_ids: Either the text prompt (str) or tokenized IDs (List[int])
+        image_data: Extracted image data from messages, if any
+        audio_data: Extracted audio data from messages, if any
+        modalities: List of modality types present in the messages
+        stop: Combined stop strings from template and request
+        tool_call_constraint: Optional constraint for structured tool calls
+    """
+
+    prompt: str
+    prompt_ids: Union[str, List[int]]
+    image_data: Optional[Any]
+    audio_data: Optional[Any]
+    modalities: List[str]
+    stop: List[str]
+    tool_call_constraint: Optional[Any] = None

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

@@ -22,6 +22,7 @@
     ErrorResponse,
     FunctionResponse,
     LogProbs,
+    MessageProcessingResult,
     ToolCall,
     TopLogprob,
 )
@@ -62,41 +63,33 @@ def _convert_to_internal_request(
         is_multimodal = self.tokenizer_manager.model_config.is_multimodal
 
         # Process messages and apply chat template
-        (
-            prompt,
-            prompt_ids,
-            image_data,
-            audio_data,
-            modalities,
-            stop,
-            tool_call_constraint,
-        ) = self._process_messages(request, is_multimodal)
+        processed_messages = self._process_messages(request, is_multimodal)
 
         # Build sampling parameters
         sampling_params = self._build_sampling_params(
-            request, stop, tool_call_constraint
+            request, processed_messages.stop, processed_messages.tool_call_constraint
         )
 
         # Handle single vs multiple requests
         if is_multimodal:
-            prompt_kwargs = {"text": prompt}
+            prompt_kwargs = {"text": processed_messages.prompt}
         else:
-            if isinstance(prompt_ids, str):
-                prompt_kwargs = {"text": prompt_ids}
+            if isinstance(processed_messages.prompt_ids, str):
+                prompt_kwargs = {"text": processed_messages.prompt_ids}
             else:
-                prompt_kwargs = {"input_ids": prompt_ids}
+                prompt_kwargs = {"input_ids": processed_messages.prompt_ids}
 
         adapted_request = GenerateReqInput(
             **prompt_kwargs,
-            image_data=image_data,
-            audio_data=audio_data,
+            image_data=processed_messages.image_data,
+            audio_data=processed_messages.audio_data,
             sampling_params=sampling_params,
             return_logprob=request.logprobs,
             logprob_start_len=-1,
             top_logprobs_num=request.top_logprobs or 0,
             stream=request.stream,
             return_text_in_logprobs=True,
-            modalities=modalities,
+            modalities=processed_messages.modalities,
             lora_path=request.lora_path,
             bootstrap_host=request.bootstrap_host,
             bootstrap_port=request.bootstrap_port,
@@ -108,74 +101,42 @@ def _convert_to_internal_request(
 
     def _process_messages(
         self, request: ChatCompletionRequest, is_multimodal: bool
-    ) -> tuple[
-        str,
-        Union[str, List[int]],
-        Optional[Any],
-        Optional[Any],
-        List[str],
-        List[str],
-        Optional[Any],
-    ]:
+    ) -> MessageProcessingResult:
         """Process chat messages and apply chat template"""
         tool_call_constraint = None
-        prompt = ""
-        prompt_ids = []
 
-        if not isinstance(request.messages, str):
-            # Apply chat template and its stop strings
-            tools = None
-            if request.tools and request.tool_choice != "none":
-                request.skip_special_tokens = False
-                if not isinstance(request.tool_choice, str):
-                    tools = [
-                        item.function.model_dump()
-                        for item in request.tools
-                        if item.function.name == request.tool_choice.function.name
-                    ]
-                else:
-                    tools = [item.function.model_dump() for item in request.tools]
+        # Apply chat template and its stop strings
+        tools = None
+        if request.tools and request.tool_choice != "none":
+            request.skip_special_tokens = False
+            if not isinstance(request.tool_choice, str):
+                tools = [
+                    item.function.model_dump()
+                    for item in request.tools
+                    if item.function.name == request.tool_choice.function.name
+                ]
+            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
-                )
+            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)
 
-            # Use chat template
-            if self.template_manager.chat_template_name is None:
-                prompt, prompt_ids, image_data, audio_data, modalities, stop = (
-                    self._apply_jinja_template(request, tools, is_multimodal)
-                )
-            else:
-                prompt, prompt_ids, image_data, audio_data, modalities, stop = (
-                    self._apply_conversation_template(request, is_multimodal)
-                )
+        # Use chat template
+        if self.template_manager.chat_template_name is None:
+            result = self._apply_jinja_template(request, tools, is_multimodal)
         else:
-            # Use raw prompt
-            prompt_ids = request.messages
-            stop = request.stop or []
-            image_data = None
-            audio_data = None
-            modalities = []
-            prompt = request.messages
-
-        return (
-            prompt,
-            prompt_ids,
-            image_data,
-            audio_data,
-            modalities,
-            stop,
-            tool_call_constraint,
-        )
+            result = self._apply_conversation_template(request, is_multimodal)
+
+        result.tool_call_constraint = tool_call_constraint
+        return result
 
     def _apply_jinja_template(
         self,
         request: ChatCompletionRequest,
         tools: Optional[List[Dict]],
         is_multimodal: bool,
-    ) -> tuple[str, List[int], Optional[Any], Optional[Any], List[str], List[str]]:
+    ) -> MessageProcessingResult:
         """Apply Jinja chat template"""
         prompt = ""
         prompt_ids = []
@@ -253,13 +214,20 @@ def _apply_jinja_template(
         image_data = image_data if image_data else None
         audio_data = audio_data if audio_data else None
         modalities = modalities if modalities else []
-        return prompt, prompt_ids, image_data, audio_data, modalities, stop
+        return MessageProcessingResult(
+            prompt=prompt,
+            prompt_ids=prompt_ids,
+            image_data=image_data,
+            audio_data=audio_data,
+            modalities=modalities,
+            stop=stop,
+        )
 
     def _apply_conversation_template(
         self,
         request: ChatCompletionRequest,
         is_multimodal: bool,
-    ) -> tuple[str, Optional[Any], Optional[Any], List[str], List[str], List[str]]:
+    ) -> MessageProcessingResult:
         """Apply conversation template"""
         prompt = ""
         prompt_ids = []
@@ -304,7 +272,14 @@ def _apply_conversation_template(
         if not is_multimodal:
             prompt_ids = self.tokenizer_manager.tokenizer.encode(prompt)
 
-        return prompt, prompt_ids, image_data, audio_data, modalities, stop
+        return MessageProcessingResult(
+            prompt=prompt,
+            prompt_ids=prompt_ids,
+            image_data=image_data,
+            audio_data=audio_data,
+            modalities=modalities,
+            stop=stop,
+        )
 
     def _build_sampling_params(
         self,

test/srt/openai_server/basic/test_serving_chat.py

@@ -13,7 +13,10 @@
 
 from fastapi import Request
 
-from sglang.srt.entrypoints.openai.protocol import ChatCompletionRequest
+from sglang.srt.entrypoints.openai.protocol import (
+    ChatCompletionRequest,
+    MessageProcessingResult,
+)
 from sglang.srt.entrypoints.openai.serving_chat import OpenAIServingChat
 from sglang.srt.managers.io_struct import GenerateReqInput
 
@@ -104,7 +107,7 @@ def test_convert_to_internal_request_single(self):
             conv_ins.stop_str = ["</s>"]
             conv_mock.return_value = conv_ins
 
-            proc_mock.return_value = (
+            proc_mock.return_value = MessageProcessingResult(
                 "Test prompt",
                 [1, 2, 3],
                 None,
@@ -119,6 +122,59 @@ def test_convert_to_internal_request_single(self):
             self.assertFalse(adapted.stream)
             self.assertEqual(processed, self.basic_req)
 
+    def test_stop_str_isolation_between_requests(self):
+        """Test that stop strings from one request don't affect subsequent requests.
+
+        This tests the fix for the bug where conv.stop_str was being mutated globally,
+        causing stop strings from one request to persist in subsequent requests.
+        """
+        # Mock conversation template with initial stop_str
+        initial_stop_str = ["\n"]
+
+        with patch(
+            "sglang.srt.entrypoints.openai.serving_chat.generate_chat_conv"
+        ) as conv_mock:
+            # Create a mock conversation object that will be returned by generate_chat_conv
+            conv_ins = Mock()
+            conv_ins.get_prompt.return_value = "Test prompt"
+            conv_ins.image_data = None
+            conv_ins.audio_data = None
+            conv_ins.modalities = []
+            conv_ins.stop_str = (
+                initial_stop_str.copy()
+            )  # Template's default stop strings
+            conv_mock.return_value = conv_ins
+
+            # First request with additional stop string
+            req1 = ChatCompletionRequest(
+                model="x",
+                messages=[{"role": "user", "content": "First request"}],
+                stop=["CUSTOM_STOP"],
+            )
+
+            # Call the actual _apply_conversation_template method (not mocked)
+            result1 = self.chat._apply_conversation_template(req1, is_multimodal=False)
+
+            # Verify first request has both stop strings
+            expected_stop1 = initial_stop_str + ["CUSTOM_STOP"]
+            self.assertEqual(result1.stop, expected_stop1)
+
+            # Verify the original template's stop_str wasn't mutated after first request
+            self.assertEqual(conv_ins.stop_str, initial_stop_str)
+
+            # Second request without additional stop string
+            req2 = ChatCompletionRequest(
+                model="x",
+                messages=[{"role": "user", "content": "Second request"}],
+                # No custom stop strings
+            )
+            result2 = self.chat._apply_conversation_template(req2, is_multimodal=False)
+
+            # Verify second request only has original stop strings (no CUSTOM_STOP from req1)
+            self.assertEqual(result2.stop, initial_stop_str)
+            self.assertNotIn("CUSTOM_STOP", result2.stop)
+            self.assertEqual(conv_ins.stop_str, initial_stop_str)
+
     # ------------- sampling-params -------------
     def test_sampling_param_build(self):
         req = ChatCompletionRequest(