add qwen3

vllm-v0.6.2/tests/entrypoints/openai/test_serving_chat.py

@@ -0,0 +1,94 @@
+import asyncio
+from contextlib import suppress
+from dataclasses import dataclass
+from unittest.mock import MagicMock
+
+from vllm.config import MultiModalConfig
+from vllm.engine.multiprocessing.client import MQLLMEngineClient
+from vllm.entrypoints.openai.protocol import ChatCompletionRequest
+from vllm.entrypoints.openai.serving_chat import OpenAIServingChat
+from vllm.entrypoints.openai.serving_engine import BaseModelPath
+from vllm.transformers_utils.tokenizer import get_tokenizer
+
+MODEL_NAME = "openai-community/gpt2"
+CHAT_TEMPLATE = "Dummy chat template for testing {}"
+BASE_MODEL_PATHS = [BaseModelPath(name=MODEL_NAME, model_path=MODEL_NAME)]
+
+
+@dataclass
+class MockHFConfig:
+    model_type: str = "any"
+
+
+@dataclass
+class MockModelConfig:
+    task = "generate"
+    tokenizer = MODEL_NAME
+    trust_remote_code = False
+    tokenizer_mode = "auto"
+    chat_template_text_format = "string"
+    max_model_len = 100
+    tokenizer_revision = None
+    multimodal_config = MultiModalConfig()
+    hf_config = MockHFConfig()
+
+
+@dataclass
+class MockEngine:
+
+    async def get_model_config(self):
+        return MockModelConfig()
+
+
+async def _async_serving_chat_init():
+    engine = MockEngine()
+    model_config = await engine.get_model_config()
+
+    serving_completion = OpenAIServingChat(engine,
+                                           model_config,
+                                           BASE_MODEL_PATHS,
+                                           response_role="assistant",
+                                           chat_template=CHAT_TEMPLATE,
+                                           lora_modules=None,
+                                           prompt_adapters=None,
+                                           request_logger=None)
+    return serving_completion
+
+
+def test_async_serving_chat_init():
+    serving_completion = asyncio.run(_async_serving_chat_init())
+    assert serving_completion.chat_template == CHAT_TEMPLATE
+
+
+def test_serving_chat_should_set_correct_max_tokens():
+    mock_engine = MagicMock(spec=MQLLMEngineClient)
+    mock_engine.get_tokenizer.return_value = get_tokenizer(MODEL_NAME)
+    mock_engine.errored = False
+
+    serving_chat = OpenAIServingChat(mock_engine,
+                                     MockModelConfig(),
+                                     BASE_MODEL_PATHS,
+                                     response_role="assistant",
+                                     chat_template=CHAT_TEMPLATE,
+                                     lora_modules=None,
+                                     prompt_adapters=None,
+                                     request_logger=None)
+    req = ChatCompletionRequest(
+        model=MODEL_NAME,
+        messages=[{
+            "role": "user",
+            "content": "what is 1+1?"
+        }],
+        guided_decoding_backend="outlines",
+    )
+
+    with suppress(Exception):
+        asyncio.run(serving_chat.create_chat_completion(req))
+
+    assert mock_engine.generate.call_args.args[1].max_tokens == 93
+
+    req.max_tokens = 10
+    with suppress(Exception):
+        asyncio.run(serving_chat.create_chat_completion(req))
+
+    assert mock_engine.generate.call_args.args[1].max_tokens == 10