Support chat template and `echo` for chat API (#1756)

docs/source/getting_started/quickstart.rst

@@ -107,6 +107,7 @@ OpenAI-Compatible Server
 ------------------------
 
 vLLM can be deployed as a server that mimics the OpenAI API protocol. This allows vLLM to be used as a drop-in replacement for applications using OpenAI API.
+By default, it starts the server at ``http://localhost:8000``. You can specify the address with ``--host`` and ``--port`` arguments. The server currently hosts one model at a time (OPT-125M in the above command) and implements `list models <https://platform.openai.com/docs/api-reference/models/list>`_, `create chat completion <https://platform.openai.com/docs/api-reference/chat/completions/create>`_, and `create completion <https://platform.openai.com/docs/api-reference/completions/create>`_ endpoints. We are actively adding support for more endpoints.
 
 Start the server:
 
@@ -122,7 +123,13 @@ Use model from www.modelscope.cn
     $ VLLM_USE_MODELSCOPE=True python -m vllm.entrypoints.openai.api_server \
     $     --model="qwen/Qwen-7B-Chat" --revision="v1.1.8" --trust-remote-code
 
-By default, it starts the server at ``http://localhost:8000``. You can specify the address with ``--host`` and ``--port`` arguments. The server currently hosts one model at a time (OPT-125M in the above command) and implements `list models <https://platform.openai.com/docs/api-reference/models/list>`_ and `create completion <https://platform.openai.com/docs/api-reference/completions/create>`_ endpoints. We are actively adding support for more endpoints.
+By default, the server uses a predefined chat template stored in the tokenizer. You can override this template by using the ``--chat-template`` argument:
+
+.. code-block:: console
+
+   $ python -m vllm.entrypoints.openai.api_server \
+   $     --model facebook/opt-125m \
+   $     --chat-template ./examples/template_chatml.json
 
 This server can be queried in the same format as OpenAI API. For example, list the models:
 
@@ -130,6 +137,9 @@ This server can be queried in the same format as OpenAI API. For example, list t
 
     $ curl http://localhost:8000/v1/models
 
+Using OpenAI Completions API with vLLM
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
 Query the model with input prompts:
 
 .. code-block:: console
@@ -156,3 +166,45 @@ Since this server is compatible with OpenAI API, you can use it as a drop-in rep
     print("Completion result:", completion)
 
 For a more detailed client example, refer to `examples/openai_completion_client.py <https://github.com/vllm-project/vllm/blob/main/examples/openai_completion_client.py>`_.
+
+Using OpenAI Chat API with vLLM
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+The vLLM server is designed to support the OpenAI Chat API, allowing you to engage in dynamic conversations with the model. The chat interface is a more interactive way to communicate with the model, allowing back-and-forth exchanges that can be stored in the chat history. This is useful for tasks that require context or more detailed explanations.
+
+Querying the model using OpenAI Chat API:
+
+You can use the `create chat completion <https://platform.openai.com/docs/api-reference/chat/completions/create>`_ endpoint to communicate with the model in a chat-like interface:
+
+.. code-block:: console
+
+    $ curl http://localhost:8000/v1/chat/completions \
+    $     -H "Content-Type: application/json" \
+    $     -d '{
+    $         "model": "facebook/opt-125m",
+    $         "messages": [
+    $             {"role": "system", "content": "You are a helpful assistant."},
+    $             {"role": "user", "content": "Who won the world series in 2020?"}
+    $         ]
+    $     }'
+
+Python Client Example:
+
+Using the `openai` python package, you can also communicate with the model in a chat-like manner:
+
+.. code-block:: python
+
+    import openai
+    # Set OpenAI's API key and API base to use vLLM's API server.
+    openai.api_key = "EMPTY"
+    openai.api_base = "http://localhost:8000/v1"
+    chat_response = openai.ChatCompletion.create(
+        model="facebook/opt-125m",
+        messages=[
+            {"role": "system", "content": "You are a helpful assistant."},
+            {"role": "user", "content": "Tell me a joke."},
+        ]
+    )
+    print("Chat response:", chat_response)
+
+For more in-depth examples and advanced features of the chat API, you can refer to the official OpenAI documentation.

examples/template_alpaca.jinja

@@ -0,0 +1,29 @@
+{{ (messages|selectattr('role', 'equalto', 'system')|list|last).content|trim if (messages|selectattr('role', 'equalto', 'system')|list) else '' }}
+
+{% for message in messages %}
+{% if message['role'] == 'user' %}
+### Instruction:
+{{ message['content']|trim -}}
+{% if not loop.last %}
+
+
+{% endif %}
+{% elif message['role'] == 'assistant' %}
+### Response:
+{{ message['content']|trim -}}
+{% if not loop.last %}
+
+
+{% endif %}
+{% elif message['role'] == 'user_context' %}
+### Input:
+{{ message['content']|trim -}}
+{% if not loop.last %}
+
+
+{% endif %}
+{% endif %}
+{% endfor %}
+{% if add_generation_prompt and messages[-1]['role'] != 'assistant' %}
+### Response:
+{% endif %}

examples/template_chatml.jinja

@@ -0,0 +1,2 @@
+{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content']}}{% if (loop.last and add_generation_prompt) or not loop.last %}{{ '<|im_end|>' + '\n'}}{% endif %}{% endfor %}
+{% if add_generation_prompt and messages[-1]['role'] != 'assistant' %}{{ '<|im_start|>assistant\n' }}{% endif %}

examples/template_inkbot.jinja

@@ -0,0 +1,30 @@
+<#meta#>
+- Date: {{ (messages|selectattr('role', 'equalto', 'meta-current_date')|list|last).content|trim if (messages|selectattr('role', 'equalto', 'meta-current_date')|list) else '' }}
+- Task: {{ (messages|selectattr('role', 'equalto', 'meta-task_name')|list|last).content|trim if (messages|selectattr('role', 'equalto', 'meta-task_name')|list) else '' }}
+<#system#>
+{{ (messages|selectattr('role', 'equalto', 'system')|list|last).content|trim if (messages|selectattr('role', 'equalto', 'system')|list) else '' }}
+<#chat#>
+{% for message in messages %}
+{% if message['role'] == 'user' %}
+<#user#>
+{{ message['content']|trim -}}
+{% if not loop.last %}
+
+{% endif %}
+{% elif message['role'] == 'assistant' %}
+<#bot#>
+{{ message['content']|trim -}}
+{% if not loop.last %}
+
+{% endif %}
+{% elif message['role'] == 'user_context' %}
+<#user_context#>
+{{ message['content']|trim -}}
+{% if not loop.last %}
+
+{% endif %}
+{% endif %}
+{% endfor %}
+{% if add_generation_prompt and messages[-1]['role'] != 'assistant' %}
+<#bot#>
+{% endif %}

tests/async_engine/test_openai_server.py

@@ -0,0 +1,119 @@
+from argparse import Namespace
+from dataclasses import dataclass
+
+import pytest
+from fastapi.testclient import TestClient
+
+from vllm.entrypoints.openai.api_server import *
+
+# Define models, templates, and their corresponding expected outputs
+MODEL_TEMPLATE_GENERATON_OUTPUT = [
+    ("facebook/opt-125m", None, True,
+     "Hello</s>Hi there!</s>What is the capital of</s>"),
+    ("facebook/opt-125m", None, False,
+     "Hello</s>Hi there!</s>What is the capital of</s>"),
+    ("facebook/opt-125m", "../../examples/template_chatml.jinja", True,
+     """<|im_start|>user
+Hello<|im_end|>
+<|im_start|>assistant
+Hi there!<|im_end|>
+<|im_start|>user
+What is the capital of<|im_end|>
+<|im_start|>assistant
+"""),
+    ("facebook/opt-125m", "../../examples/template_chatml.jinja", False,
+     """<|im_start|>user
+Hello<|im_end|>
+<|im_start|>assistant
+Hi there!<|im_end|>
+<|im_start|>user
+What is the capital of""")
+]
+
+TEST_MESSAGES = [
+    {
+        'role': 'user',
+        'content': 'Hello'
+    },
+    {
+        'role': 'assistant',
+        'content': 'Hi there!'
+    },
+    {
+        'role': 'user',
+        'content': 'What is the capital of'
+    },
+]
+client = TestClient(app)
+
+
+@dataclass
+class MockTokenizer:
+    chat_template = None
+
+
+def test_load_chat_template():
+    # Testing chatml template
+    template = "../../examples/template_chatml.jinja"
+    mock_args = Namespace(chat_template=template)
+    tokenizer = MockTokenizer()
+
+    # Call the function with the mocked args
+    load_chat_template(mock_args, tokenizer)
+
+    template_content = tokenizer.chat_template
+
+    # Test assertions
+    assert template_content is not None
+    # Hard coded value for template_chatml.jinja
+    assert template_content == """{% for message in messages %}{{'<|im_start|>' + message['role'] + '\\n' + message['content']}}{% if (loop.last and add_generation_prompt) or not loop.last %}{{ '<|im_end|>' + '\\n'}}{% endif %}{% endfor %}
+{% if add_generation_prompt and messages[-1]['role'] != 'assistant' %}{{ '<|im_start|>assistant\\n' }}{% endif %}"""
+
+
+def test_no_load_chat_template():
+    # Testing chatml template
+    template = "../../examples/does_not_exist"
+    mock_args = Namespace(chat_template=template)
+    tokenizer = MockTokenizer()
+
+    # Call the function with the mocked args
+    load_chat_template(mock_args, tokenizer=tokenizer)
+    template_content = tokenizer.chat_template
+
+    # Test assertions
+    assert template_content is not None
+    # Hard coded value for template_chatml.jinja
+    assert template_content == """../../examples/does_not_exist"""
+
+
+@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,
+                              expected_output):
+    # Initialize the tokenizer
+    tokenizer = get_tokenizer(tokenizer_name=model)
+
+    mock_args = Namespace(chat_template=template)
+    load_chat_template(mock_args, tokenizer)
+
+    # Create a mock request object using keyword arguments
+    mock_request = ChatCompletionRequest(
+        model=model,
+        messages=TEST_MESSAGES,
+        add_generation_prompt=add_generation_prompt)
+
+    # Call the function and get the result
+    result = tokenizer.apply_chat_template(
+        conversation=mock_request.messages,
+        tokenize=False,
+        add_generation_prompt=mock_request.add_generation_prompt)
+
+    # Test assertion
+    assert result == expected_output, f"The generated prompt does not match the expected output for model {model} and template {template}"
+
+
+def test_health_endpoint():
+    response = client.get("/health")
+    assert response.status_code == 200

vllm/entrypoints/openai/api_server.py

@@ -3,6 +3,7 @@
 
 import argparse
 import asyncio
+import codecs
 import json
 import time
 from http import HTTPStatus
@@ -14,7 +15,6 @@ from fastapi import Request
 from fastapi.exceptions import RequestValidationError
 from fastapi.middleware.cors import CORSMiddleware
 from fastapi.responses import JSONResponse, StreamingResponse, Response
-from packaging import version
 
 from vllm.engine.arg_utils import AsyncEngineArgs
 from vllm.engine.async_llm_engine import AsyncLLMEngine
@@ -31,20 +31,55 @@ from vllm.sampling_params import SamplingParams
 from vllm.transformers_utils.tokenizer import get_tokenizer
 from vllm.utils import random_uuid
 
-try:
-    import fastchat
-    from fastchat.conversation import Conversation, SeparatorStyle
-    from fastchat.model.model_adapter import get_conversation_template
-    _fastchat_available = True
-except ImportError:
-    _fastchat_available = False
-
 TIMEOUT_KEEP_ALIVE = 5  # seconds
 
 logger = init_logger(__name__)
 served_model = None
 app = fastapi.FastAPI()
 engine = None
+response_role = None
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description="vLLM OpenAI-Compatible RESTful API server.")
+    parser.add_argument("--host", type=str, default=None, help="host name")
+    parser.add_argument("--port", type=int, default=8000, help="port number")
+    parser.add_argument("--allow-credentials",
+                        action="store_true",
+                        help="allow credentials")
+    parser.add_argument("--allowed-origins",
+                        type=json.loads,
+                        default=["*"],
+                        help="allowed origins")
+    parser.add_argument("--allowed-methods",
+                        type=json.loads,
+                        default=["*"],
+                        help="allowed methods")
+    parser.add_argument("--allowed-headers",
+                        type=json.loads,
+                        default=["*"],
+                        help="allowed headers")
+    parser.add_argument("--served-model-name",
+                        type=str,
+                        default=None,
+                        help="The model name used in the API. If not "
+                        "specified, the model name will be the same as "
+                        "the huggingface name.")
+    parser.add_argument("--chat-template",
+                        type=str,
+                        default=None,
+                        help="The file path to the chat template, "
+                        "or the template in single-line form "
+                        "for the specified model")
+    parser.add_argument("--response-role",
+                        type=str,
+                        default="assistant",
+                        help="The role name to return if "
+                        "`request.add_generation_prompt=true`.")
+
+    parser = AsyncEngineArgs.add_cli_args(parser)
+    return parser.parse_args()
 
 
 def create_error_response(status_code: HTTPStatus,
@@ -54,6 +89,25 @@ def create_error_response(status_code: HTTPStatus,
                         status_code=status_code.value)
 
 
+def load_chat_template(args, tokenizer):
+    if args.chat_template is not None:
+        try:
+            with open(args.chat_template, "r") as f:
+                chat_template = f.read()
+        except OSError:
+            # If opening a file fails, set chat template to be args to
+            # ensure we decode so our escape are interpreted correctly
+            chat_template = codecs.decode(args.chat_template, "unicode_escape")
+
+        tokenizer.chat_template = chat_template
+        logger.info(
+            f"Using supplied chat template:\n{tokenizer.chat_template}")
+    elif tokenizer.chat_template is not None:
+        logger.info(f"Using default chat template:\n{tokenizer.chat_template}")
+    else:
+        logger.warning("No chat template provided. Chat API will not work.")
+
+
 @app.exception_handler(RequestValidationError)
 async def validation_exception_handler(_, exc):
     return create_error_response(HTTPStatus.BAD_REQUEST, str(exc))
@@ -69,53 +123,6 @@ async def check_model(request) -> Optional[JSONResponse]:
     return ret
 
 
-async def get_gen_prompt(request) -> str:
-    if not _fastchat_available:
-        raise ModuleNotFoundError(
-            "fastchat is not installed. Please install fastchat to use "
-            "the chat completion and conversation APIs: `$ pip install fschat`"
-        )
-    if version.parse(fastchat.__version__) < version.parse("0.2.23"):
-        raise ImportError(
-            f"fastchat version is low. Current version: {fastchat.__version__} "
-            "Please upgrade fastchat to use: `$ pip install -U fschat`")
-
-    conv = get_conversation_template(request.model)
-    conv = Conversation(
-        name=conv.name,
-        system_template=conv.system_template,
-        system_message=conv.system_message,
-        roles=conv.roles,
-        messages=list(conv.messages),  # prevent in-place modification
-        offset=conv.offset,
-        sep_style=SeparatorStyle(conv.sep_style),
-        sep=conv.sep,
-        sep2=conv.sep2,
-        stop_str=conv.stop_str,
-        stop_token_ids=conv.stop_token_ids,
-    )
-
-    if isinstance(request.messages, str):
-        prompt = request.messages
-    else:
-        for message in request.messages:
-            msg_role = message["role"]
-            if msg_role == "system":
-                conv.system_message = message["content"]
-            elif msg_role == "user":
-                conv.append_message(conv.roles[0], message["content"])
-            elif msg_role == "assistant":
-                conv.append_message(conv.roles[1], message["content"])
-            else:
-                raise ValueError(f"Unknown role: {msg_role}")
-
-        # Add a blank message for the assistant.
-        conv.append_message(conv.roles[1], None)
-        prompt = conv.get_prompt()
-
-    return prompt
-
-
 async def check_length(
     request: Union[ChatCompletionRequest, CompletionRequest],
     prompt: Optional[str] = None,
@@ -207,7 +214,6 @@ async def create_chat_completion(request: ChatCompletionRequest,
         - function_call (Users should implement this by themselves)
         - logit_bias (to be supported by vLLM engine)
     """
-
     error_check_ret = await check_model(request)
     if error_check_ret is not None:
         return error_check_ret
@@ -217,7 +223,15 @@ async def create_chat_completion(request: ChatCompletionRequest,
         return create_error_response(HTTPStatus.BAD_REQUEST,
                                      "logit_bias is not currently supported")
 
-    prompt = await get_gen_prompt(request)
+    try:
+        prompt = tokenizer.apply_chat_template(
+            conversation=request.messages,
+            tokenize=False,
+            add_generation_prompt=request.add_generation_prompt)
+    except Exception as e:
+        logger.error(f"Error in applying chat template from request: {str(e)}")
+        return create_error_response(HTTPStatus.BAD_REQUEST, str(e))
+
     token_ids, error_check_ret = await check_length(request, prompt=prompt)
     if error_check_ret is not None:
         return error_check_ret
@@ -225,6 +239,7 @@ async def create_chat_completion(request: ChatCompletionRequest,
     model_name = request.model
     request_id = f"cmpl-{random_uuid()}"
     created_time = int(time.monotonic())
+    chunk_object_type = "chat.completion.chunk"
     try:
         spaces_between_special_tokens = request.spaces_between_special_tokens
         sampling_params = SamplingParams(
@@ -249,128 +264,162 @@ async def create_chat_completion(request: ChatCompletionRequest,
     result_generator = engine.generate(prompt, sampling_params, request_id,
                                        token_ids)
 
-    def create_stream_response_json(
-        index: int,
-        text: str,
-        finish_reason: Optional[str] = None,
-        usage: Optional[UsageInfo] = None,
-    ) -> str:
-        choice_data = ChatCompletionResponseStreamChoice(
-            index=index,
-            delta=DeltaMessage(content=text),
-            finish_reason=finish_reason,
-        )
-        response = ChatCompletionStreamResponse(
-            id=request_id,
-            created=created_time,
-            model=model_name,
-            choices=[choice_data],
-        )
-        if usage is not None:
-            response.usage = usage
-        # exclude unset to leave details out of each sse
-        response_json = response.json(exclude_unset=True, ensure_ascii=False)
-
-        return response_json
+    def get_role() -> str:
+        if request.add_generation_prompt:
+            return response_role
+        else:
+            return request.messages[-1]["role"]
 
     async def completion_stream_generator() -> AsyncGenerator[str, None]:
-        # First chunk with role
+        # Send first response for each request.n (index) with the role
+        role = get_role()
         for i in range(request.n):
             choice_data = ChatCompletionResponseStreamChoice(
-                index=i,
-                delta=DeltaMessage(role="assistant"),
-                finish_reason=None,
-            )
+                index=i, delta=DeltaMessage(role=role), finish_reason=None)
             chunk = ChatCompletionStreamResponse(id=request_id,
+                                                 object=chunk_object_type,
+                                                 created=created_time,
                                                  choices=[choice_data],
                                                  model=model_name)
             data = chunk.json(exclude_unset=True, ensure_ascii=False)
             yield f"data: {data}\n\n"
 
+        # Send response to echo the input portion of the last message
+        if request.echo:
+            last_msg_content = ""
+            if request.messages and isinstance(
+                    request.messages, list) and request.messages[-1].get(
+                        "content") and request.messages[-1].get(
+                            "role") == role:
+                last_msg_content = request.messages[-1]["content"]
+            if last_msg_content:
+                for i in range(request.n):
+                    choice_data = ChatCompletionResponseStreamChoice(
+                        index=i,
+                        delta=DeltaMessage(content=last_msg_content),
+                        finish_reason=None)
+                    chunk = ChatCompletionStreamResponse(
+                        id=request_id,
+                        object=chunk_object_type,
+                        created=created_time,
+                        choices=[choice_data],
+                        model=model_name)
+                    data = chunk.json(exclude_unset=True, ensure_ascii=False)
+                    yield f"data: {data}\n\n"
+
+        # Send response for each token for each request.n (index)
         previous_texts = [""] * request.n
         previous_num_tokens = [0] * request.n
+        finish_reason_sent = [False] * request.n
         async for res in result_generator:
             res: RequestOutput
             for output in res.outputs:
                 i = output.index
-                delta_text = output.text[len(previous_texts[i]):]
-                previous_texts[i] = output.text
-                completion_tokens = len(output.token_ids)
-                previous_num_tokens[i] = completion_tokens
-                response_json = create_stream_response_json(
-                    index=i,
-                    text=delta_text,
-                )
-                yield f"data: {response_json}\n\n"
-                if output.finish_reason is not None:
+
+                if finish_reason_sent[i]:
+                    continue
+
+                if output.finish_reason is None:
+                    # Send token-by-token response for each request.n
+                    delta_text = output.text[len(previous_texts[i]):]
+                    previous_texts[i] = output.text
+                    completion_tokens = len(output.token_ids)
+                    previous_num_tokens[i] = completion_tokens
+                    choice_data = ChatCompletionResponseStreamChoice(
+                        index=i,
+                        delta=DeltaMessage(content=delta_text),
+                        finish_reason=None)
+                    chunk = ChatCompletionStreamResponse(
+                        id=request_id,
+                        object=chunk_object_type,
+                        created=created_time,
+                        choices=[choice_data],
+                        model=model_name)
+                    data = chunk.json(exclude_unset=True, ensure_ascii=False)
+                    yield f"data: {data}\n\n"
+                else:
+                    # Send the finish response for each request.n only once
                     prompt_tokens = len(res.prompt_token_ids)
                     final_usage = UsageInfo(
                         prompt_tokens=prompt_tokens,
                         completion_tokens=completion_tokens,
                         total_tokens=prompt_tokens + completion_tokens,
                     )
-                    response_json = create_stream_response_json(
-                        index=i,
-                        text="",
-                        finish_reason=output.finish_reason,
-                        usage=final_usage,
-                    )
-                    yield f"data: {response_json}\n\n"
+                    choice_data = ChatCompletionResponseStreamChoice(
+                        index=i, delta=[], finish_reason=output.finish_reason)
+                    chunk = ChatCompletionStreamResponse(
+                        id=request_id,
+                        object=chunk_object_type,
+                        created=created_time,
+                        choices=[choice_data],
+                        model=model_name)
+                    if final_usage is not None:
+                        chunk.usage = final_usage
+                    data = chunk.json(exclude_unset=True,
+                                      exclude_none=True,
+                                      ensure_ascii=False)
+                    yield f"data: {data}\n\n"
+                    finish_reason_sent[i] = True
+        # Send the final done message after all response.n are finished
         yield "data: [DONE]\n\n"
 
+    async def completion_full_generator():
+        final_res: RequestOutput = None
+        async for res in result_generator:
+            if await raw_request.is_disconnected():
+                # Abort the request if the client disconnects.
+                await engine.abort(request_id)
+                return create_error_response(HTTPStatus.BAD_REQUEST,
+                                             "Client disconnected")
+            final_res = res
+        assert final_res is not None
+
+        choices = []
+        role = get_role()
+        for output in final_res.outputs:
+            choice_data = ChatCompletionResponseChoice(
+                index=output.index,
+                message=ChatMessage(role=role, content=output.text),
+                finish_reason=output.finish_reason,
+            )
+            choices.append(choice_data)
+
+        if request.echo:
+            last_msg_content = ""
+            if request.messages and isinstance(
+                    request.messages, list) and request.messages[-1].get(
+                        "content") and request.messages[-1].get(
+                            "role") == role:
+                last_msg_content = request.messages[-1]["content"]
+
+            for choice in choices:
+                full_message = last_msg_content + choice.message.content
+                choice.message.content = full_message
+
+        num_prompt_tokens = len(final_res.prompt_token_ids)
+        num_generated_tokens = sum(
+            len(output.token_ids) for output in final_res.outputs)
+        usage = UsageInfo(
+            prompt_tokens=num_prompt_tokens,
+            completion_tokens=num_generated_tokens,
+            total_tokens=num_prompt_tokens + num_generated_tokens,
+        )
+        response = ChatCompletionResponse(
+            id=request_id,
+            created=created_time,
+            model=model_name,
+            choices=choices,
+            usage=usage,
+        )
+
+        return response
+
     # Streaming response
     if request.stream:
         return StreamingResponse(completion_stream_generator(),
                                  media_type="text/event-stream")
-
-    # Non-streaming response
-    final_res: RequestOutput = None
-    async for res in result_generator:
-        if await raw_request.is_disconnected():
-            # Abort the request if the client disconnects.
-            await engine.abort(request_id)
-            return create_error_response(HTTPStatus.BAD_REQUEST,
-                                         "Client disconnected")
-        final_res = res
-    assert final_res is not None
-    choices = []
-    for output in final_res.outputs:
-        choice_data = ChatCompletionResponseChoice(
-            index=output.index,
-            message=ChatMessage(role="assistant", content=output.text),
-            finish_reason=output.finish_reason,
-        )
-        choices.append(choice_data)
-
-    num_prompt_tokens = len(final_res.prompt_token_ids)
-    num_generated_tokens = sum(
-        len(output.token_ids) for output in final_res.outputs)
-    usage = UsageInfo(
-        prompt_tokens=num_prompt_tokens,
-        completion_tokens=num_generated_tokens,
-        total_tokens=num_prompt_tokens + num_generated_tokens,
-    )
-    response = ChatCompletionResponse(
-        id=request_id,
-        created=created_time,
-        model=model_name,
-        choices=choices,
-        usage=usage,
-    )
-
-    if request.stream:
-        # When user requests streaming but we don't stream, we still need to
-        # return a streaming response with a single event.
-        response_json = response.json(ensure_ascii=False)
-
-        async def fake_stream_generator() -> AsyncGenerator[str, None]:
-            yield f"data: {response_json}\n\n"
-            yield "data: [DONE]\n\n"
-
-        return StreamingResponse(fake_stream_generator(),
-                                 media_type="text/event-stream")
-
-    return response
+    else:
+        return await completion_full_generator()
 
 
 @app.post("/v1/completions")
@@ -642,34 +691,7 @@ async def create_completion(request: CompletionRequest, raw_request: Request):
 
 
 if __name__ == "__main__":
-    parser = argparse.ArgumentParser(
-        description="vLLM OpenAI-Compatible RESTful API server.")
-    parser.add_argument("--host", type=str, default=None, help="host name")
-    parser.add_argument("--port", type=int, default=8000, help="port number")
-    parser.add_argument("--allow-credentials",
-                        action="store_true",
-                        help="allow credentials")
-    parser.add_argument("--allowed-origins",
-                        type=json.loads,
-                        default=["*"],
-                        help="allowed origins")
-    parser.add_argument("--allowed-methods",
-                        type=json.loads,
-                        default=["*"],
-                        help="allowed methods")
-    parser.add_argument("--allowed-headers",
-                        type=json.loads,
-                        default=["*"],
-                        help="allowed headers")
-    parser.add_argument("--served-model-name",
-                        type=str,
-                        default=None,
-                        help="The model name used in the API. If not "
-                        "specified, the model name will be the same as "
-                        "the huggingface name.")
-
-    parser = AsyncEngineArgs.add_cli_args(parser)
-    args = parser.parse_args()
+    args = parse_args()
 
     app.add_middleware(
         CORSMiddleware,
@@ -686,6 +708,8 @@ if __name__ == "__main__":
     else:
         served_model = args.model
 
+    response_role = args.response_role
+
     engine_args = AsyncEngineArgs.from_cli_args(args)
     engine = AsyncLLMEngine.from_engine_args(engine_args)
     engine_model_config = asyncio.run(engine.get_model_config())
@@ -696,6 +720,7 @@ if __name__ == "__main__":
         engine_model_config.tokenizer,
         tokenizer_mode=engine_model_config.tokenizer_mode,
         trust_remote_code=engine_model_config.trust_remote_code)
+    load_chat_template(args, tokenizer)
 
     uvicorn.run(app,
                 host=args.host,

vllm/entrypoints/openai/protocol.py

@@ -73,6 +73,8 @@ class ChatCompletionRequest(BaseModel):
     stop_token_ids: Optional[List[int]] = Field(default_factory=list)
     skip_special_tokens: Optional[bool] = True
     spaces_between_special_tokens: Optional[bool] = True
+    add_generation_prompt: Optional[bool] = True
+    echo: Optional[bool] = False
 
 
 class CompletionRequest(BaseModel):