Fix/async chat serving (vllm-project#2727)

tests/async_engine/test_chat_template.py

@@ -60,12 +60,13 @@ class MockServingChat:
     tokenizer: MockTokenizer
 
 
-def test_load_chat_template():
+@pytest.mark.asyncio
+async def test_load_chat_template():
     # Testing chatml template
     tokenizer = MockTokenizer()
     mock_serving_chat = MockServingChat(tokenizer)
-    OpenAIServingChat._load_chat_template(mock_serving_chat,
-                                          chat_template=chatml_jinja_path)
+    await OpenAIServingChat._load_chat_template(
+        mock_serving_chat, chat_template=chatml_jinja_path)
 
     template_content = tokenizer.chat_template
 
@@ -76,26 +77,28 @@ def test_load_chat_template():
 {% if add_generation_prompt and messages[-1]['role'] != 'assistant' %}{{ '<|im_start|>assistant\\n' }}{% endif %}"""  # noqa: E501
 
 
-def test_no_load_chat_template_filelike():
+@pytest.mark.asyncio
+async def test_no_load_chat_template_filelike():
     # Testing chatml template
     template = "../../examples/does_not_exist"
     tokenizer = MockTokenizer()
 
     mock_serving_chat = MockServingChat(tokenizer)
 
     with pytest.raises(ValueError, match="looks like a file path"):
-        OpenAIServingChat._load_chat_template(mock_serving_chat,
-                                              chat_template=template)
+        await OpenAIServingChat._load_chat_template(mock_serving_chat,
+                                                    chat_template=template)
 
 
-def test_no_load_chat_template_literallike():
+@pytest.mark.asyncio
+async def test_no_load_chat_template_literallike():
     # Testing chatml template
     template = "{{ messages }}"
     tokenizer = MockTokenizer()
 
     mock_serving_chat = MockServingChat(tokenizer)
-    OpenAIServingChat._load_chat_template(mock_serving_chat,
-                                          chat_template=template)
+    await OpenAIServingChat._load_chat_template(mock_serving_chat,
+                                                chat_template=template)
     template_content = tokenizer.chat_template
 
     assert template_content == template
@@ -110,8 +113,8 @@ async def test_get_gen_prompt(model, template, add_generation_prompt,
     # Initialize the tokenizer
     tokenizer = get_tokenizer(tokenizer_name=model)
     mock_serving_chat = MockServingChat(tokenizer)
-    OpenAIServingChat._load_chat_template(mock_serving_chat,
-                                          chat_template=template)
+    await OpenAIServingChat._load_chat_template(mock_serving_chat,
+                                                chat_template=template)
 
     # Create a mock request object using keyword arguments
     mock_request = ChatCompletionRequest(

tests/entrypoints/openai/test_serving_chat.py

@@ -0,0 +1,37 @@
+import asyncio
+from dataclasses import dataclass
+
+from vllm.entrypoints.openai.serving_chat import OpenAIServingChat
+
+MODEL_NAME = "openai-community/gpt2"
+CHAT_TEMPLATE = "Dummy chat template for testing {}"
+
+
+@dataclass
+class MockModelConfig:
+    tokenizer = MODEL_NAME
+    trust_remote_code = False
+    tokenizer_mode = "auto"
+    max_model_len = 100
+    tokenizer_revision = None
+
+
+@dataclass
+class MockEngine:
+
+    async def get_model_config(self):
+        return MockModelConfig
+
+
+async def _async_serving_chat_init():
+    serving_completion = OpenAIServingChat(MockEngine(),
+                                           served_model_names=[MODEL_NAME],
+                                           response_role="assistant",
+                                           chat_template=CHAT_TEMPLATE)
+    return serving_completion
+
+
+def test_async_serving_chat_init():
+    serving_completion = asyncio.run(_async_serving_chat_init())
+    assert serving_completion.tokenizer is not None
+    assert serving_completion.tokenizer.chat_template == CHAT_TEMPLATE

tests/entrypoints/test_openai_server.py

@@ -150,7 +150,7 @@ def server(zephyr_lora_files):
     ray.shutdown()
 
 
-@pytest.fixture(scope="session")
+@pytest.fixture(scope="module")
 def client():
     client = openai.AsyncOpenAI(
         base_url="http://localhost:8000/v1",

vllm/entrypoints/openai/serving_chat.py

@@ -1,3 +1,4 @@
+import asyncio
 import codecs
 import time
 from typing import (AsyncGenerator, AsyncIterator, Awaitable, Iterable, List,
@@ -40,9 +41,11 @@ def __init__(self,
                  chat_template: Optional[str] = None):
         super().__init__(engine=engine,
                          served_model_names=served_model_names,
-                         lora_modules=lora_modules)
+                         lora_modules=lora_modules,
+                         await_post_init=self._load_chat_template(
+                             chat_template=chat_template))
+
         self.response_role = response_role
-        self._load_chat_template(chat_template)
 
     def _parse_chat_message_content(
         self,
@@ -356,7 +359,10 @@ async def chat_completion_full_generator(
 
         return response
 
-    def _load_chat_template(self, chat_template: Optional[str]):
+    async def _load_chat_template(self, chat_template: Optional[str]):
+        while self.tokenizer is None:
+            # Give the parent class time to load the tokenizer
+            await asyncio.sleep(0.1)
         tokenizer = self.tokenizer
 
         if chat_template is not None:

vllm/entrypoints/openai/serving_engine.py

@@ -2,7 +2,7 @@
 import json
 from dataclasses import dataclass
 from http import HTTPStatus
-from typing import Dict, List, Optional, Tuple, Union
+from typing import Any, Awaitable, Dict, List, Optional, Tuple, Union
 
 from pydantic import Field
 from transformers import PreTrainedTokenizer, PreTrainedTokenizerFast
@@ -29,8 +29,11 @@ class LoRAModulePath:
 
 class OpenAIServing:
 
-    def __init__(self, engine: AsyncLLMEngine, served_model_names: List[str],
-                 lora_modules: Optional[List[LoRAModulePath]]):
+    def __init__(self,
+                 engine: AsyncLLMEngine,
+                 served_model_names: List[str],
+                 lora_modules: Optional[List[LoRAModulePath]],
+                 await_post_init: Optional[Awaitable[Any]] = None):
         self.engine = engine
         self.served_model_names = served_model_names
         if lora_modules is None:
@@ -56,12 +59,12 @@ def __init__(self, engine: AsyncLLMEngine, served_model_names: List[str],
         if event_loop is not None and event_loop.is_running():
             # If the current is instanced by Ray Serve,
             # there is already a running event loop
-            event_loop.create_task(self._post_init())
+            event_loop.create_task(self._post_init(await_post_init))
         else:
             # When using single vLLM without engine_use_ray
-            asyncio.run(self._post_init())
+            asyncio.run(self._post_init(await_post_init))
 
-    async def _post_init(self):
+    async def _post_init(self, await_post_init):
         engine_model_config = await self.engine.get_model_config()
         self.max_model_len = engine_model_config.max_model_len
 
@@ -73,13 +76,8 @@ async def _post_init(self):
             trust_remote_code=engine_model_config.trust_remote_code,
             truncation_side="left")
 
-        if len(self.tokenizer) != engine_model_config.get_vocab_size():
-            logger.warning(
-                f"The tokenizer's vocabulary size {len(self.tokenizer)}"
-                f" does not match the model's vocabulary size "
-                f"{engine_model_config.get_vocab_size()}. This might "
-                f"cause an error in decoding. Please change config.json "
-                "to match the tokenizer's vocabulary size.")
+        if await_post_init is not None:
+            await await_post_init
 
     async def show_available_models(self) -> ModelList:
         """Show available models. Right now we only have one model."""