sglangv0.5.2 & support Qwen3-Next-80B-A3B-Instruct

test/srt/openai_server/basic/test_serving_chat.py

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+"""
+Unit-tests for OpenAIServingChat — rewritten to use only the std-lib 'unittest'.
+Run with either:
+    python tests/test_serving_chat_unit.py -v
+or
+    python -m unittest discover -s tests -p "test_*unit.py" -v
+"""
+
+import asyncio
+import json
+import unittest
+import uuid
+from typing import Optional
+from unittest.mock import Mock, patch
+
+from fastapi import Request
+
+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
+
+
+class _MockTokenizerManager:
+    """Minimal mock that satisfies OpenAIServingChat."""
+
+    def __init__(self):
+        self.model_config = Mock(is_multimodal=False)
+        self.server_args = Mock(
+            enable_cache_report=False,
+            tool_call_parser="hermes",
+            reasoning_parser=None,
+        )
+        self.chat_template_name: Optional[str] = "llama-3"
+
+        # tokenizer stub
+        self.tokenizer = Mock()
+        self.tokenizer.encode.return_value = [1, 2, 3, 4, 5]
+        self.tokenizer.decode.return_value = "Test response"
+        self.tokenizer.chat_template = None
+        self.tokenizer.bos_token_id = 1
+
+        # async generator stub for generate_request
+        async def _mock_generate():
+            yield {
+                "text": "Test response",
+                "meta_info": {
+                    "id": f"chatcmpl-{uuid.uuid4()}",
+                    "prompt_tokens": 10,
+                    "completion_tokens": 5,
+                    "cached_tokens": 0,
+                    "finish_reason": {"type": "stop", "matched": None},
+                    "output_token_logprobs": [(0.1, 1, "Test"), (0.2, 2, "response")],
+                    "output_top_logprobs": None,
+                },
+                "index": 0,
+            }
+
+        self.generate_request = Mock(return_value=_mock_generate())
+        self.create_abort_task = Mock()
+
+
+class _MockTemplateManager:
+    """Minimal mock for TemplateManager."""
+
+    def __init__(self):
+        self.chat_template_name: Optional[str] = "llama-3"
+        self.jinja_template_content_format: Optional[str] = None
+        self.completion_template_name: Optional[str] = None
+
+
+class ServingChatTestCase(unittest.TestCase):
+    # ------------- common fixtures -------------
+    def setUp(self):
+        self.tm = _MockTokenizerManager()
+        self.template_manager = _MockTemplateManager()
+        self.chat = OpenAIServingChat(self.tm, self.template_manager)
+
+        # frequently reused requests
+        self.basic_req = ChatCompletionRequest(
+            model="x",
+            messages=[{"role": "user", "content": "Hi?"}],
+            temperature=0.7,
+            max_tokens=100,
+            stream=False,
+        )
+        self.stream_req = ChatCompletionRequest(
+            model="x",
+            messages=[{"role": "user", "content": "Hi?"}],
+            temperature=0.7,
+            max_tokens=100,
+            stream=True,
+        )
+
+        self.fastapi_request = Mock(spec=Request)
+        self.fastapi_request.headers = {}
+
+    # ------------- conversion tests -------------
+    def test_convert_to_internal_request_single(self):
+        with patch(
+            "sglang.srt.entrypoints.openai.serving_chat.generate_chat_conv"
+        ) as conv_mock, patch.object(self.chat, "_process_messages") as proc_mock:
+            conv_ins = Mock()
+            conv_ins.get_prompt.return_value = "Test prompt"
+            conv_ins.image_data = conv_ins.audio_data = None
+            conv_ins.modalities = []
+            conv_ins.stop_str = ["</s>"]
+            conv_mock.return_value = conv_ins
+
+            proc_mock.return_value = MessageProcessingResult(
+                "Test prompt",
+                [1, 2, 3],
+                None,
+                None,
+                [],
+                ["</s>"],
+                None,
+            )
+
+            adapted, processed = self.chat._convert_to_internal_request(self.basic_req)
+            self.assertIsInstance(adapted, GenerateReqInput)
+            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(
+            model="x",
+            messages=[{"role": "user", "content": "Hi"}],
+            temperature=0.8,
+            max_tokens=150,
+            min_tokens=5,
+            top_p=0.9,
+            stop=["</s>"],
+        )
+        with patch.object(
+            self.chat,
+            "_process_messages",
+            return_value=("Prompt", [1], None, None, [], ["</s>"], None),
+        ):
+            params = self.chat._build_sampling_params(req, ["</s>"], None)
+            self.assertEqual(params["temperature"], 0.8)
+            self.assertEqual(params["max_new_tokens"], 150)
+            self.assertEqual(params["min_new_tokens"], 5)
+            self.assertEqual(params["stop"], ["</s>"])
+
+    async def test_unstreamed_tool_args_completion(self):
+        """Test that remaining tool call arguments are sent when generation finishes."""
+
+        # Mock FunctionCallParser with detector that has partial tool call data
+        mock_parser = Mock()
+        mock_detector = Mock()
+
+        # Simulate a tool call that was partially streamed
+        mock_detector.prev_tool_call_arr = [
+            {
+                "name": "get_weather",
+                "arguments": {"location": "San Francisco", "unit": "celsius"},
+            }
+        ]
+        mock_detector.streamed_args_for_tool = [
+            '{"location": "San Francisco"'  # Partial arguments streamed so far
+        ]
+        mock_parser.detector = mock_detector
+
+        content = {
+            "meta_info": {
+                "id": "chatcmpl-test123",
+            }
+        }
+
+        request = ChatCompletionRequest(
+            model="test",
+            messages=[{"role": "user", "content": "What's the weather?"}],
+            tools=[{"type": "function", "function": {"name": "get_weather"}}],
+        )
+
+        # Test the completion method
+        result = self.chat._check_for_unstreamed_tool_args(
+            parser=mock_parser,
+            content=content,
+            request=request,
+            finish_reason_type="stop",
+            index=0,
+        )
+
+        # Should return a chunk with remaining arguments
+        self.assertIsNotNone(result, "Should return chunk with remaining arguments")
+        self.assertIn('"arguments":', result, "Should contain arguments field")
+        self.assertIn(
+            ', "unit": "celsius"}', result, "Should contain remaining arguments"
+        )
+        self.assertIn(
+            '"finish_reason":null',
+            result,
+            "Should not include finish_reason in completion chunk",
+        )
+
+    async def test_unstreamed_tool_args_no_completion_needed(self):
+        """Test that no completion chunk is sent when all arguments were already streamed."""
+
+        # Mock FunctionCallParser with detector that has complete tool call data
+        mock_parser = Mock()
+        mock_detector = Mock()
+
+        # Simulate a tool call that was completely streamed
+        mock_detector.prev_tool_call_arr = [
+            {"name": "get_weather", "arguments": {"location": "San Francisco"}}
+        ]
+        mock_detector.streamed_args_for_tool = [
+            '{"location": "San Francisco"}'  # All arguments already streamed
+        ]
+        mock_parser.detector = mock_detector
+
+        content = {
+            "meta_info": {
+                "id": "chatcmpl-test123",
+            }
+        }
+
+        request = ChatCompletionRequest(
+            model="test",
+            messages=[{"role": "user", "content": "What's the weather?"}],
+            tools=[{"type": "function", "function": {"name": "get_weather"}}],
+        )
+
+        # Test the completion method
+        result = self.chat._check_for_unstreamed_tool_args(
+            parser=mock_parser,
+            content=content,
+            request=request,
+            finish_reason_type="stop",
+            index=0,
+        )
+
+        # Should return None since no completion is needed
+        self.assertIsNone(result, "Should return None when no completion is needed")
+
+    async def test_unstreamed_tool_args_no_parser_data(self):
+        """Test that no completion chunk is sent when parser has no tool call data."""
+
+        # Mock FunctionCallParser with empty detector
+        mock_parser = Mock()
+        mock_detector = Mock()
+        mock_detector.prev_tool_call_arr = []
+        mock_detector.streamed_args_for_tool = []
+        mock_parser.detector = mock_detector
+
+        content = {
+            "meta_info": {
+                "id": "chatcmpl-test123",
+            }
+        }
+
+        request = ChatCompletionRequest(
+            model="test",
+            messages=[{"role": "user", "content": "What's the weather?"}],
+            tools=[{"type": "function", "function": {"name": "get_weather"}}],
+        )
+
+        # Test the completion method
+        result = self.chat._check_for_unstreamed_tool_args(
+            parser=mock_parser,
+            content=content,
+            request=request,
+            finish_reason_type="stop",
+            index=0,
+        )
+
+        # Should return None since there's no parser data
+        self.assertIsNone(
+            result, "Should return None when parser has no tool call data"
+        )
+
+    # ------------- kimi_k2 tool_call_id formatting -------------
+    def test_kimi_k2_non_streaming_tool_call_id_format(self):
+        """Ensure non-streaming tool_call.id matches functions.{name}:{index} for kimi_k2 parser."""
+
+        # Force kimi_k2 parser
+        self.chat.tool_call_parser = "kimi_k2"
+
+        # Mock FunctionCallParser.parse_non_stream to return one tool call
+        with patch(
+            "sglang.srt.entrypoints.openai.serving_chat.FunctionCallParser"
+        ) as ParserMock:
+            parser_instance = ParserMock.return_value
+
+            # Build a mock ToolCallItem-like object
+            call_info = Mock()
+            call_info.name = "get_weather"
+            call_info.parameters = '{"city":"Paris"}'
+            call_info.tool_index = 0
+
+            parser_instance.has_tool_call.return_value = True
+            parser_instance.parse_non_stream.return_value = ("", [call_info])
+
+            finish_reason = {"type": "stop", "matched": None}
+            tools = [
+                {"type": "function", "function": {"name": "get_weather"}},
+            ]
+
+            tool_calls, remaining_text, _ = self.chat._process_tool_calls(
+                text="<|tool_calls_section_begin|>...",
+                tools=tools,
+                finish_reason=finish_reason,
+            )
+
+            self.assertIsNotNone(tool_calls)
+            self.assertEqual(len(tool_calls), 1)
+            self.assertEqual(tool_calls[0].id, "functions.get_weather:0")
+            self.assertEqual(tool_calls[0].function.name, "get_weather")
+
+    def test_kimi_k2_streaming_tool_call_id_format(self):
+        """Ensure streaming first chunk tool_call.id matches functions.{name}:{index} for kimi_k2 parser."""
+
+        # Force kimi_k2 parser
+        self.chat.tool_call_parser = "kimi_k2"
+
+        # Prepare request with tools
+        req = ChatCompletionRequest(
+            model="x",
+            messages=[{"role": "user", "content": "Hi?"}],
+            tools=[{"type": "function", "function": {"name": "get_weather"}}],
+            stream=True,
+        )
+
+        # Patch FunctionCallParser used inside _process_tool_call_stream
+        with patch(
+            "sglang.srt.entrypoints.openai.serving_chat.FunctionCallParser"
+        ) as ParserMock:
+            parser_instance = ParserMock.return_value
+
+            # First call returns one ToolCallItem-like chunk (with name)
+            first_chunk_call = Mock()
+            first_chunk_call.tool_index = 0
+            first_chunk_call.name = "get_weather"
+            first_chunk_call.parameters = ""
+            parser_instance.parse_stream_chunk.side_effect = [
+                ("", [first_chunk_call]),
+                ("", []),
+            ]
+
+            async def collect_first_tool_chunk():
+                gen = self.chat._process_tool_call_stream(
+                    index=0,
+                    delta="irrelevant",
+                    parser_dict={},
+                    content={"meta_info": {"id": "chatcmpl-test"}},
+                    request=req,
+                    has_tool_calls={},
+                )
+                # Get first yielded SSE line
+                line = None
+                async for emitted in gen:
+                    line = emitted
+                    break
+                return line
+
+            loop = asyncio.get_event_loop()
+            line = loop.run_until_complete(collect_first_tool_chunk())
+            self.assertIsNotNone(line)
+            self.assertTrue(line.startswith("data: "))
+
+            payload = json.loads(line[len("data: ") :])
+            tool_calls = payload["choices"][0]["delta"]["tool_calls"]
+            self.assertEqual(tool_calls[0]["id"], "functions.get_weather:0")
+
+
+if __name__ == "__main__":
+    unittest.main(verbosity=2)