[Frontend] Move async logic outside of constructor (#4674)

tests/async_engine/test_chat_template.py

@@ -60,13 +60,12 @@ class MockServingChat:
     tokenizer: MockTokenizer
 
 
-@pytest.mark.asyncio
-async def test_load_chat_template():
+def test_load_chat_template():
     # Testing chatml template
     tokenizer = MockTokenizer()
     mock_serving_chat = MockServingChat(tokenizer)
-    await OpenAIServingChat._load_chat_template(
-        mock_serving_chat, chat_template=chatml_jinja_path)
+    OpenAIServingChat._load_chat_template(mock_serving_chat,
+                                          chat_template=chatml_jinja_path)
 
     template_content = tokenizer.chat_template
 
@@ -77,8 +76,7 @@ async def test_load_chat_template():
 {% if add_generation_prompt and messages[-1]['role'] != 'assistant' %}{{ '<|im_start|>assistant\\n' }}{% endif %}"""  # noqa: E501
 
 
-@pytest.mark.asyncio
-async def test_no_load_chat_template_filelike():
+def test_no_load_chat_template_filelike():
     # Testing chatml template
     template = "../../examples/does_not_exist"
     tokenizer = MockTokenizer()
@@ -86,34 +84,32 @@ async def test_no_load_chat_template_filelike():
     mock_serving_chat = MockServingChat(tokenizer)
 
     with pytest.raises(ValueError, match="looks like a file path"):
-        await OpenAIServingChat._load_chat_template(mock_serving_chat,
+        OpenAIServingChat._load_chat_template(mock_serving_chat,
                                               chat_template=template)
 
 
-@pytest.mark.asyncio
-async def test_no_load_chat_template_literallike():
+def test_no_load_chat_template_literallike():
     # Testing chatml template
     template = "{{ messages }}"
     tokenizer = MockTokenizer()
 
     mock_serving_chat = MockServingChat(tokenizer)
-    await OpenAIServingChat._load_chat_template(mock_serving_chat,
+    OpenAIServingChat._load_chat_template(mock_serving_chat,
                                           chat_template=template)
     template_content = tokenizer.chat_template
 
     assert template_content == template
 
 
-@pytest.mark.asyncio
 @pytest.mark.parametrize(
     "model,template,add_generation_prompt,expected_output",
     MODEL_TEMPLATE_GENERATON_OUTPUT)
-async def test_get_gen_prompt(model, template, add_generation_prompt,
+def test_get_gen_prompt(model, template, add_generation_prompt,
                         expected_output):
     # Initialize the tokenizer
     tokenizer = get_tokenizer(tokenizer_name=model)
     mock_serving_chat = MockServingChat(tokenizer)
-    await OpenAIServingChat._load_chat_template(mock_serving_chat,
+    OpenAIServingChat._load_chat_template(mock_serving_chat,
                                           chat_template=template)
 
     # Create a mock request object using keyword arguments

tests/entrypoints/openai/test_serving_chat.py

@@ -20,11 +20,15 @@ class MockModelConfig:
 class MockEngine:
 
     async def get_model_config(self):
-        return MockModelConfig
+        return MockModelConfig()
 
 
 async def _async_serving_chat_init():
-    serving_completion = OpenAIServingChat(MockEngine(),
+    engine = MockEngine()
+    model_config = await engine.get_model_config()
+
+    serving_completion = OpenAIServingChat(engine,
+                                           model_config,
                                            served_model_names=[MODEL_NAME],
                                            response_role="assistant",
                                            chat_template=CHAT_TEMPLATE)

vllm/engine/arg_utils.py

@@ -516,7 +516,7 @@ class EngineArgs:
         return parser
 
     @classmethod
-    def from_cli_args(cls, args: argparse.Namespace) -> 'EngineArgs':
+    def from_cli_args(cls, args: argparse.Namespace):
         # Get the list of attributes of this dataclass.
         attrs = [attr.name for attr in dataclasses.fields(cls)]
         # Set the attributes from the parsed arguments.

vllm/entrypoints/openai/api_server.py

@@ -4,7 +4,7 @@ import inspect
 import re
 from contextlib import asynccontextmanager
 from http import HTTPStatus
-from typing import Set
+from typing import Optional, Set
 
 import fastapi
 import uvicorn
@@ -164,15 +164,32 @@ if __name__ == "__main__":
         served_model_names = args.served_model_name
     else:
         served_model_names = [args.model]
+
     engine_args = AsyncEngineArgs.from_cli_args(args)
     engine = AsyncLLMEngine.from_engine_args(
         engine_args, usage_context=UsageContext.OPENAI_API_SERVER)
-    openai_serving_chat = OpenAIServingChat(engine, served_model_names,
+
+    event_loop: Optional[asyncio.AbstractEventLoop]
+    try:
+        event_loop = asyncio.get_running_loop()
+    except RuntimeError:
+        event_loop = None
+
+    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
+        model_config = event_loop.run_until_complete(engine.get_model_config())
+    else:
+        # When using single vLLM without engine_use_ray
+        model_config = asyncio.run(engine.get_model_config())
+
+    openai_serving_chat = OpenAIServingChat(engine, model_config,
+                                            served_model_names,
                                             args.response_role,
                                             args.lora_modules,
                                             args.chat_template)
     openai_serving_completion = OpenAIServingCompletion(
-        engine, served_model_names, args.lora_modules)
+        engine, model_config, served_model_names, args.lora_modules)
 
     app.root_path = args.root_path
     uvicorn.run(app,

vllm/entrypoints/openai/serving_chat.py

-import asyncio
 import codecs
 import time
 from typing import (AsyncGenerator, AsyncIterator, Awaitable, Iterable, List,
@@ -8,6 +7,7 @@ from fastapi import Request
 from openai.types.chat import (ChatCompletionContentPartParam,
                                ChatCompletionRole)
 
+from vllm.config import ModelConfig
 from vllm.engine.async_llm_engine import AsyncLLMEngine
 from vllm.entrypoints.openai.protocol import (
     ChatCompletionRequest, ChatCompletionResponse,
@@ -35,17 +35,47 @@ class OpenAIServingChat(OpenAIServing):
 
     def __init__(self,
                  engine: AsyncLLMEngine,
+                 model_config: ModelConfig,
                  served_model_names: List[str],
                  response_role: str,
                  lora_modules: Optional[List[LoRAModulePath]] = None,
                  chat_template: Optional[str] = None):
         super().__init__(engine=engine,
+                         model_config=model_config,
                          served_model_names=served_model_names,
-                         lora_modules=lora_modules,
-                         await_post_init=self._load_chat_template(
-                             chat_template=chat_template))
+                         lora_modules=lora_modules)
 
         self.response_role = response_role
+        self._load_chat_template(chat_template)
+
+    def _load_chat_template(self, chat_template: Optional[str]):
+        tokenizer = self.tokenizer
+
+        if chat_template is not None:
+            try:
+                with open(chat_template, "r") as f:
+                    tokenizer.chat_template = f.read()
+            except OSError as e:
+                JINJA_CHARS = "{}\n"
+                if not any(c in chat_template for c in JINJA_CHARS):
+                    msg = (f"The supplied chat template ({chat_template}) "
+                           f"looks like a file path, but it failed to be "
+                           f"opened. Reason: {e}")
+                    raise ValueError(msg) from e
+
+                # If opening a file fails, set chat template to be args to
+                # ensure we decode so our escape are interpreted correctly
+                tokenizer.chat_template = codecs.decode(
+                    chat_template, "unicode_escape")
+
+            logger.info("Using supplied chat template:\n%s",
+                        tokenizer.chat_template)
+        elif tokenizer.chat_template is not None:
+            logger.info("Using default chat template:\n%s",
+                        tokenizer.chat_template)
+        else:
+            logger.warning(
+                "No chat template provided. Chat API will not work.")
 
     def _parse_chat_message_content(
         self,
@@ -358,35 +388,3 @@ class OpenAIServingChat(OpenAIServing):
         )
 
         return response
\ No newline at end of file
-
-    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:
-            try:
-                with open(chat_template, "r") as f:
-                    tokenizer.chat_template = f.read()
-            except OSError as e:
-                JINJA_CHARS = "{}\n"
-                if not any(c in chat_template for c in JINJA_CHARS):
-                    msg = (f"The supplied chat template ({chat_template}) "
-                           f"looks like a file path, but it failed to be "
-                           f"opened. Reason: {e}")
-                    raise ValueError(msg) from e
-
-                # If opening a file fails, set chat template to be args to
-                # ensure we decode so our escape are interpreted correctly
-                tokenizer.chat_template = codecs.decode(
-                    chat_template, "unicode_escape")
-
-            logger.info("Using supplied chat template:\n%s",
-                        tokenizer.chat_template)
-        elif tokenizer.chat_template is not None:
-            logger.info("Using default chat template:\n%s",
-                        tokenizer.chat_template)
-        else:
-            logger.warning(
-                "No chat template provided. Chat API will not work.")

vllm/entrypoints/openai/serving_completion.py

@@ -4,6 +4,7 @@ from typing import (AsyncGenerator, AsyncIterator, Callable, Dict, List,
 
 from fastapi import Request
 
+from vllm.config import ModelConfig
 from vllm.engine.async_llm_engine import AsyncLLMEngine
 from vllm.entrypoints.openai.protocol import (CompletionRequest,
                                               CompletionResponse,
@@ -52,11 +53,11 @@ def parse_prompt_format(prompt) -> Tuple[bool, list]:
 
 class OpenAIServingCompletion(OpenAIServing):
 
-    def __init__(self,
-                 engine: AsyncLLMEngine,
+    def __init__(self, engine: AsyncLLMEngine, model_config: ModelConfig,
                  served_model_names: List[str],
-                 lora_modules: Optional[List[LoRAModulePath]] = None):
+                 lora_modules: Optional[List[LoRAModulePath]]):
         super().__init__(engine=engine,
+                         model_config=model_config,
                          served_model_names=served_model_names,
                          lora_modules=lora_modules)
 

vllm/entrypoints/openai/serving_engine.py

-import asyncio
 import json
 from dataclasses import dataclass
 from http import HTTPStatus
-from typing import Any, Awaitable, Dict, List, Optional, Tuple, Union
+from typing import Dict, List, Optional, Tuple, Union
 
 from pydantic import Field
-from transformers import PreTrainedTokenizer, PreTrainedTokenizerFast
 from typing_extensions import Annotated
 
+from vllm.config import ModelConfig
 from vllm.engine.async_llm_engine import AsyncLLMEngine
 from vllm.entrypoints.openai.protocol import (ChatCompletionRequest,
                                               CompletionRequest, ErrorResponse,
@@ -29,13 +28,24 @@ class LoRAModulePath:
 
 class OpenAIServing:
 
-    def __init__(self,
-                 engine: AsyncLLMEngine,
+    def __init__(self, engine: AsyncLLMEngine, model_config: ModelConfig,
                  served_model_names: List[str],
-                 lora_modules: Optional[List[LoRAModulePath]],
-                 await_post_init: Optional[Awaitable[Any]] = None):
+                 lora_modules: Optional[List[LoRAModulePath]]):
+        super().__init__()
+
         self.engine = engine
+        self.max_model_len = model_config.max_model_len
+
+        # A separate tokenizer to map token IDs to strings.
+        self.tokenizer = get_tokenizer(
+            model_config.tokenizer,
+            tokenizer_mode=model_config.tokenizer_mode,
+            tokenizer_revision=model_config.tokenizer_revision,
+            trust_remote_code=model_config.trust_remote_code,
+            truncation_side="left")
+
         self.served_model_names = served_model_names
+
         if lora_modules is None:
             self.lora_requests = []
         else:
@@ -47,38 +57,6 @@ class OpenAIServing:
                 ) for i, lora in enumerate(lora_modules, start=1)
             ]
 
-        self.max_model_len = 0
-        # Lazy initialized
-        self.tokenizer: Union[PreTrainedTokenizer, PreTrainedTokenizerFast]
-
-        try:
-            event_loop = asyncio.get_running_loop()
-        except RuntimeError:
-            event_loop = None
-
-        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(await_post_init))
-        else:
-            # When using single vLLM without engine_use_ray
-            asyncio.run(self._post_init(await_post_init))
-
-    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
-
-        # A separate tokenizer to map token IDs to strings.
-        self.tokenizer = get_tokenizer(
-            engine_model_config.tokenizer,
-            tokenizer_mode=engine_model_config.tokenizer_mode,
-            tokenizer_revision=engine_model_config.tokenizer_revision,
-            trust_remote_code=engine_model_config.trust_remote_code,
-            truncation_side="left")
-
-        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."""
         model_cards = [