test_serving_chat.py

"""
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 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"
        )


if __name__ == "__main__":
    unittest.main(verbosity=2)