First commit

vllm/entrypoints/api_server.py

@@ -0,0 +1,163 @@
+"""
+NOTE: This API server is used only for demonstrating usage of AsyncEngine
+and simple performance benchmarks. It is not intended for production use.
+For production use, we recommend using our OpenAI compatible server.
+We are also not going to accept PRs modifying this file, please
+change `vllm/entrypoints/openai/api_server.py` instead.
+"""
+import asyncio
+import json
+import ssl
+from argparse import Namespace
+from typing import Any, AsyncGenerator, Optional
+
+from fastapi import FastAPI, Request
+from fastapi.responses import JSONResponse, Response, StreamingResponse
+
+from vllm.engine.arg_utils import AsyncEngineArgs
+from vllm.engine.async_llm_engine import AsyncLLMEngine
+from vllm.entrypoints.launcher import serve_http
+from vllm.logger import init_logger
+from vllm.sampling_params import SamplingParams
+from vllm.usage.usage_lib import UsageContext
+from vllm.utils import (FlexibleArgumentParser, iterate_with_cancellation,
+                        random_uuid)
+from vllm.version import __version__ as VLLM_VERSION
+
+logger = init_logger("vllm.entrypoints.api_server")
+
+TIMEOUT_KEEP_ALIVE = 5  # seconds.
+app = FastAPI()
+engine = None
+
+
+@app.get("/health")
+async def health() -> Response:
+    """Health check."""
+    return Response(status_code=200)
+
+
+@app.post("/generate")
+async def generate(request: Request) -> Response:
+    """Generate completion for the request.
+
+    The request should be a JSON object with the following fields:
+    - prompt: the prompt to use for the generation.
+    - stream: whether to stream the results or not.
+    - other fields: the sampling parameters (See `SamplingParams` for details).
+    """
+    request_dict = await request.json()
+    prompt = request_dict.pop("prompt")
+    stream = request_dict.pop("stream", False)
+    sampling_params = SamplingParams(**request_dict)
+    request_id = random_uuid()
+
+    assert engine is not None
+    results_generator = engine.generate(prompt, sampling_params, request_id)
+    results_generator = iterate_with_cancellation(
+        results_generator, is_cancelled=request.is_disconnected)
+
+    # Streaming case
+    async def stream_results() -> AsyncGenerator[bytes, None]:
+        async for request_output in results_generator:
+            prompt = request_output.prompt
+            assert prompt is not None
+            text_outputs = [
+                prompt + output.text for output in request_output.outputs
+            ]
+            ret = {"text": text_outputs}
+            yield (json.dumps(ret) + "\0").encode("utf-8")
+
+    if stream:
+        return StreamingResponse(stream_results())
+
+    # Non-streaming case
+    final_output = None
+    try:
+        async for request_output in results_generator:
+            final_output = request_output
+    except asyncio.CancelledError:
+        return Response(status_code=499)
+
+    assert final_output is not None
+    prompt = final_output.prompt
+    assert prompt is not None
+    text_outputs = [prompt + output.text for output in final_output.outputs]
+    ret = {"text": text_outputs}
+    return JSONResponse(ret)
+
+
+def build_app(args: Namespace) -> FastAPI:
+    global app
+
+    app.root_path = args.root_path
+    return app
+
+
+async def init_app(
+    args: Namespace,
+    llm_engine: Optional[AsyncLLMEngine] = None,
+) -> FastAPI:
+    app = build_app(args)
+
+    global engine
+
+    engine_args = AsyncEngineArgs.from_cli_args(args)
+    engine = (llm_engine
+              if llm_engine is not None else AsyncLLMEngine.from_engine_args(
+                  engine_args, usage_context=UsageContext.API_SERVER))
+
+    return app
+
+
+async def run_server(args: Namespace,
+                     llm_engine: Optional[AsyncLLMEngine] = None,
+                     **uvicorn_kwargs: Any) -> None:
+    logger.info("vLLM API server version %s", VLLM_VERSION)
+    logger.info("args: %s", args)
+
+    app = await init_app(args, llm_engine)
+    assert engine is not None
+
+    shutdown_task = await serve_http(
+        app,
+        host=args.host,
+        port=args.port,
+        log_level=args.log_level,
+        timeout_keep_alive=TIMEOUT_KEEP_ALIVE,
+        ssl_keyfile=args.ssl_keyfile,
+        ssl_certfile=args.ssl_certfile,
+        ssl_ca_certs=args.ssl_ca_certs,
+        ssl_cert_reqs=args.ssl_cert_reqs,
+        **uvicorn_kwargs,
+    )
+
+    await shutdown_task
+
+
+if __name__ == "__main__":
+    parser = FlexibleArgumentParser()
+    parser.add_argument("--host", type=str, default=None)
+    parser.add_argument("--port", type=int, default=8000)
+    parser.add_argument("--ssl-keyfile", type=str, default=None)
+    parser.add_argument("--ssl-certfile", type=str, default=None)
+    parser.add_argument("--ssl-ca-certs",
+                        type=str,
+                        default=None,
+                        help="The CA certificates file")
+    parser.add_argument(
+        "--ssl-cert-reqs",
+        type=int,
+        default=int(ssl.CERT_NONE),
+        help="Whether client certificate is required (see stdlib ssl module's)"
+    )
+    parser.add_argument(
+        "--root-path",
+        type=str,
+        default=None,
+        help="FastAPI root_path when app is behind a path based routing proxy")
+    parser.add_argument("--log-level", type=str, default="debug")
+    parser = AsyncEngineArgs.add_cli_args(parser)
+    args = parser.parse_args()
+
+    asyncio.run(run_server(args))

vllm/entrypoints/chat_utils.py

@@ -0,0 +1,581 @@
+import asyncio
+import codecs
+import json
+from abc import ABC, abstractmethod
+from collections import defaultdict
+from functools import lru_cache, partial
+from pathlib import Path
+from typing import (Any, Awaitable, Dict, Generic, Iterable, List, Literal,
+                    Mapping, Optional, Tuple, TypeVar, Union, cast)
+
+# yapf conflicts with isort for this block
+# yapf: disable
+from openai.types.chat import (ChatCompletionAssistantMessageParam,
+                               ChatCompletionContentPartImageParam)
+from openai.types.chat import (
+    ChatCompletionContentPartParam as OpenAIChatCompletionContentPartParam)
+from openai.types.chat import (ChatCompletionContentPartRefusalParam,
+                               ChatCompletionContentPartTextParam)
+from openai.types.chat import (
+    ChatCompletionMessageParam as OpenAIChatCompletionMessageParam)
+from openai.types.chat import (ChatCompletionMessageToolCallParam,
+                               ChatCompletionToolMessageParam)
+# yapf: enable
+# pydantic needs the TypedDict from typing_extensions
+from pydantic import ConfigDict
+from transformers import PreTrainedTokenizer, PreTrainedTokenizerFast
+from typing_extensions import Required, TypeAlias, TypedDict
+
+from vllm.config import ModelConfig
+from vllm.logger import init_logger
+from vllm.multimodal import MultiModalDataDict
+from vllm.multimodal.utils import (async_get_and_parse_audio,
+                                   async_get_and_parse_image,
+                                   get_and_parse_audio, get_and_parse_image)
+from vllm.transformers_utils.tokenizer import AnyTokenizer, MistralTokenizer
+
+logger = init_logger(__name__)
+
+
+class AudioURL(TypedDict, total=False):
+    url: Required[str]
+    """
+    Either a URL of the audio or a data URL with base64 encoded audio data.
+    """
+
+
+class ChatCompletionContentPartAudioParam(TypedDict, total=False):
+    audio_url: Required[AudioURL]
+
+    type: Required[Literal["audio_url"]]
+    """The type of the content part."""
+
+
+class CustomChatCompletionContentPartParam(TypedDict, total=False):
+    __pydantic_config__ = ConfigDict(extra="allow")  # type: ignore
+
+    type: Required[str]
+    """The type of the content part."""
+
+
+ChatCompletionContentPartParam: TypeAlias = Union[
+    OpenAIChatCompletionContentPartParam, ChatCompletionContentPartAudioParam,
+    ChatCompletionContentPartRefusalParam,
+    CustomChatCompletionContentPartParam]
+
+
+class CustomChatCompletionMessageParam(TypedDict, total=False):
+    """Enables custom roles in the Chat Completion API."""
+    role: Required[str]
+    """The role of the message's author."""
+
+    content: Union[str, List[ChatCompletionContentPartParam]]
+    """The contents of the message."""
+
+    name: str
+    """An optional name for the participant.
+
+    Provides the model information to differentiate between participants of the
+    same role.
+    """
+
+    tool_call_id: Optional[str]
+    """Tool call that this message is responding to."""
+
+    tool_calls: Optional[Iterable[ChatCompletionMessageToolCallParam]]
+    """The tool calls generated by the model, such as function calls."""
+
+
+ChatCompletionMessageParam = Union[OpenAIChatCompletionMessageParam,
+                                   CustomChatCompletionMessageParam]
+
+
+# TODO: Make fields ReadOnly once mypy supports it
+class ConversationMessage(TypedDict, total=False):
+    role: Required[str]
+    """The role of the message's author."""
+
+    content: Optional[str]
+    """The contents of the message"""
+
+    tool_call_id: Optional[str]
+    """Tool call that this message is responding to."""
+
+    name: Optional[str]
+    """The name of the function to call"""
+
+    tool_calls: Optional[Iterable[ChatCompletionMessageToolCallParam]]
+    """The tool calls generated by the model, such as function calls."""
+
+
+ModalityStr = Literal["image", "audio", "video"]
+_T = TypeVar("_T")
+
+
+class BaseMultiModalItemTracker(ABC, Generic[_T]):
+    """
+    Tracks multi-modal items in a given request and ensures that the number
+    of multi-modal items in a given request does not exceed the configured
+    maximum per prompt.
+    """
+
+    def __init__(self, model_config: ModelConfig, tokenizer: AnyTokenizer):
+        super().__init__()
+
+        self._model_config = model_config
+        self._tokenizer = tokenizer
+        self._allowed_items = (model_config.multimodal_config.limit_per_prompt
+                               if model_config.multimodal_config else {})
+        self._consumed_items = {k: 0 for k in self._allowed_items}
+
+        self._items: List[_T] = []
+
+    @staticmethod
+    @lru_cache(maxsize=None)
+    def _cached_token_str(tokenizer: AnyTokenizer, token_index: int) -> str:
+        return tokenizer.decode(token_index)
+
+    def _placeholder_str(self, modality: ModalityStr,
+                         current_count: int) -> Optional[str]:
+        # TODO: Let user specify how to insert image tokens into prompt
+        # (similar to chat template)
+        hf_config = self._model_config.hf_config
+        model_type = hf_config.model_type
+
+        if modality == "image":
+            if model_type == "phi3_v":
+                # Workaround since this token is not defined in the tokenizer
+                return f"<|image_{current_count}|>"
+            if model_type == "minicpmv":
+                return "(<image>./</image>)"
+            if model_type in ("blip-2", "chatglm", "fuyu", "paligemma",
+                              "pixtral"):
+                # These models do not use image tokens in the prompt
+                return None
+            if model_type == "qwen":
+                return f"Picture {current_count}: <img></img>"
+            if model_type.startswith("llava"):
+                return self._cached_token_str(self._tokenizer,
+                                              hf_config.image_token_index)
+            if model_type in ("chameleon", "internvl_chat", "NVLM_D"):
+                return "<image>"
+            if model_type == "mllama":
+                return "<|image|>"
+            if model_type == "qwen2_vl":
+                return "<|vision_start|><|image_pad|><|vision_end|>"
+            if model_type == "molmo":
+                return ""
+
+            raise TypeError(f"Unknown model type: {model_type}")
+        elif modality == "audio":
+            if model_type == "ultravox":
+                return "<|reserved_special_token_0|>"
+            raise TypeError(f"Unknown model type: {model_type}")
+        elif modality == "video":
+            if model_type == "qwen2_vl":
+                return "<|vision_start|><|video_pad|><|vision_end|>"
+            raise TypeError(f"Unknown model type: {model_type}")
+        else:
+            raise TypeError(f"Unknown modality: {modality}")
+
+    @staticmethod
+    def _combine(items: List[MultiModalDataDict]) -> MultiModalDataDict:
+        mm_lists: Mapping[str, List[object]] = defaultdict(list)
+
+        # Merge all the multi-modal items
+        for single_mm_data in items:
+            for mm_key, mm_item in single_mm_data.items():
+                if isinstance(mm_item, list):
+                    mm_lists[mm_key].extend(mm_item)
+                else:
+                    mm_lists[mm_key].append(mm_item)
+
+        # Unpack any single item lists for models that don't expect multiple.
+        return {
+            mm_key: mm_list[0] if len(mm_list) == 1 else mm_list
+            for mm_key, mm_list in mm_lists.items()
+        }
+
+    def add(self, modality: ModalityStr, item: _T) -> Optional[str]:
+        """
+        Add a multi-modal item to the current prompt and returns the
+        placeholder string to use, if any.
+        """
+        allowed_count = self._allowed_items.get(modality, 1)
+        current_count = self._consumed_items.get(modality, 0) + 1
+        if current_count > allowed_count:
+            raise ValueError(
+                f"At most {allowed_count} {modality}(s) may be provided in "
+                "one request.")
+
+        self._consumed_items[modality] = current_count
+        self._items.append(item)
+
+        return self._placeholder_str(modality, current_count)
+
+    @abstractmethod
+    def create_parser(self) -> "BaseMultiModalContentParser":
+        raise NotImplementedError
+
+
+class MultiModalItemTracker(BaseMultiModalItemTracker[MultiModalDataDict]):
+
+    def all_mm_data(self) -> Optional[MultiModalDataDict]:
+        return self._combine(self._items) if self._items else None
+
+    def create_parser(self) -> "BaseMultiModalContentParser":
+        return MultiModalContentParser(self)
+
+
+class AsyncMultiModalItemTracker(
+        BaseMultiModalItemTracker[Awaitable[MultiModalDataDict]]):
+
+    async def all_mm_data(self) -> Optional[MultiModalDataDict]:
+        if self._items:
+            items = await asyncio.gather(*self._items)
+            return self._combine(items)
+
+        return None
+
+    def create_parser(self) -> "BaseMultiModalContentParser":
+        return AsyncMultiModalContentParser(self)
+
+
+class BaseMultiModalContentParser(ABC):
+
+    def __init__(self) -> None:
+        super().__init__()
+
+        # multimodal placeholder_string : count
+        self._placeholder_counts: Dict[str, int] = defaultdict(lambda: 0)
+
+    def _add_placeholder(self, placeholder: Optional[str]):
+        if placeholder:
+            self._placeholder_counts[placeholder] += 1
+
+    def mm_placeholder_counts(self) -> Dict[str, int]:
+        return dict(self._placeholder_counts)
+
+    @abstractmethod
+    def parse_image(self, image_url: str) -> None:
+        raise NotImplementedError
+
+    @abstractmethod
+    def parse_audio(self, audio_url: str) -> None:
+        raise NotImplementedError
+
+
+class MultiModalContentParser(BaseMultiModalContentParser):
+
+    def __init__(self, tracker: MultiModalItemTracker) -> None:
+        super().__init__()
+
+        self._tracker = tracker
+
+    def parse_image(self, image_url: str) -> None:
+        image = get_and_parse_image(image_url)
+
+        placeholder = self._tracker.add("image", image)
+        self._add_placeholder(placeholder)
+
+    def parse_audio(self, audio_url: str) -> None:
+        audio = get_and_parse_audio(audio_url)
+
+        placeholder = self._tracker.add("audio", audio)
+        self._add_placeholder(placeholder)
+
+
+class AsyncMultiModalContentParser(BaseMultiModalContentParser):
+
+    def __init__(self, tracker: AsyncMultiModalItemTracker) -> None:
+        super().__init__()
+
+        self._tracker = tracker
+
+    def parse_image(self, image_url: str) -> None:
+        image_coro = async_get_and_parse_image(image_url)
+
+        placeholder = self._tracker.add("image", image_coro)
+        self._add_placeholder(placeholder)
+
+    def parse_audio(self, audio_url: str) -> None:
+        audio_coro = async_get_and_parse_audio(audio_url)
+
+        placeholder = self._tracker.add("audio", audio_coro)
+        self._add_placeholder(placeholder)
+
+
+def validate_chat_template(chat_template: Optional[Union[Path, str]]):
+    """Raises if the provided chat template appears invalid."""
+    if chat_template is None:
+        return
+
+    elif isinstance(chat_template, Path) and not chat_template.exists():
+        raise FileNotFoundError(
+            "the supplied chat template path doesn't exist")
+
+    elif isinstance(chat_template, str):
+        JINJA_CHARS = "{}\n"
+        if not any(c in chat_template
+                   for c in JINJA_CHARS) and not Path(chat_template).exists():
+            raise ValueError(
+                f"The supplied chat template string ({chat_template}) "
+                f"appears path-like, but doesn't exist!")
+
+    else:
+        raise TypeError(
+            f"{type(chat_template)} is not a valid chat template type")
+
+
+def load_chat_template(
+        chat_template: Optional[Union[Path, str]]) -> Optional[str]:
+    if chat_template is None:
+        return None
+    try:
+        with open(chat_template, "r") as f:
+            resolved_chat_template = f.read()
+    except OSError as e:
+        if isinstance(chat_template, Path):
+            raise
+
+        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
+        resolved_chat_template = codecs.decode(chat_template, "unicode_escape")
+
+    logger.info("Using supplied chat template:\n%s", resolved_chat_template)
+    return resolved_chat_template
+
+
+# TODO: Let user specify how to insert multimodal tokens into prompt
+# (similar to chat template)
+def _get_full_multimodal_text_prompt(placeholder_counts: Dict[str, int],
+                                     text_prompt: str) -> str:
+    """Combine multimodal prompts for a multimodal language model."""
+
+    # Look through the text prompt to check for missing placeholders
+    missing_placeholders: List[str] = []
+    for placeholder in placeholder_counts:
+
+        # For any existing placeholder in the text prompt, we leave it as is
+        placeholder_counts[placeholder] -= text_prompt.count(placeholder)
+
+        if placeholder_counts[placeholder] < 0:
+            raise ValueError(
+                f"Found more '{placeholder}' placeholders in input prompt than "
+                "actual multimodal data items.")
+
+        missing_placeholders.extend([placeholder] *
+                                    placeholder_counts[placeholder])
+
+    # NOTE: For now we always add missing placeholders at the front of
+    # the prompt. This may change to be customizable in the future.
+    return "\n".join(missing_placeholders + [text_prompt])
+
+
+# No need to validate using Pydantic again
+_TextParser = partial(cast, ChatCompletionContentPartTextParam)
+_ImageParser = partial(cast, ChatCompletionContentPartImageParam)
+_AudioParser = partial(cast, ChatCompletionContentPartAudioParam)
+_RefusalParser = partial(cast, ChatCompletionContentPartRefusalParam)
+MODEL_KEEP_MULTI_MODAL_CONTENT = {'mllama'}
+
+
+def _parse_chat_message_content_parts(
+    role: str,
+    parts: Iterable[ChatCompletionContentPartParam],
+    mm_tracker: BaseMultiModalItemTracker,
+) -> List[ConversationMessage]:
+    texts: List[str] = []
+
+    mm_parser = mm_tracker.create_parser()
+    keep_multimodal_content = \
+        mm_tracker._model_config.hf_config.model_type in \
+            MODEL_KEEP_MULTI_MODAL_CONTENT
+
+    has_image = False
+    for part in parts:
+        part_type = part["type"]
+        if part_type == "text":
+            text = _TextParser(part)["text"]
+            texts.append(text)
+        elif part_type == "image_url":
+            image_url = _ImageParser(part)["image_url"]
+
+            if image_url.get("detail", "auto") != "auto":
+                logger.warning(
+                    "'image_url.detail' is currently not supported and "
+                    "will be ignored.")
+
+            mm_parser.parse_image(image_url["url"])
+            has_image = True
+        elif part_type == "audio_url":
+            audio_url = _AudioParser(part)["audio_url"]
+
+            mm_parser.parse_audio(audio_url["url"])
+        elif part_type == "refusal":
+            text = _RefusalParser(part)["refusal"]
+            texts.append(text)
+        else:
+            raise NotImplementedError(f"Unknown part type: {part_type}")
+
+    text_prompt = "\n".join(texts)
+    if keep_multimodal_content:
+        text_prompt = "\n".join(texts)
+        role_content = [{'type': 'text', 'text': text_prompt}]
+
+        if has_image:
+            role_content = [{'type': 'image'}] + role_content
+        return [ConversationMessage(role=role,
+                                    content=role_content)]  # type: ignore
+    else:
+        mm_placeholder_counts = mm_parser.mm_placeholder_counts()
+        if mm_placeholder_counts:
+            text_prompt = _get_full_multimodal_text_prompt(
+                mm_placeholder_counts, text_prompt)
+        return [ConversationMessage(role=role, content=text_prompt)]
+
+
+# No need to validate using Pydantic again
+_AssistantParser = partial(cast, ChatCompletionAssistantMessageParam)
+_ToolParser = partial(cast, ChatCompletionToolMessageParam)
+
+
+def _parse_chat_message_content(
+    message: ChatCompletionMessageParam,
+    mm_tracker: BaseMultiModalItemTracker,
+) -> List[ConversationMessage]:
+    role = message["role"]
+    content = message.get("content")
+
+    if content is None:
+        content = []
+    elif isinstance(content, str):
+        content = [
+            ChatCompletionContentPartTextParam(type="text", text=content)
+        ]
+
+    result = _parse_chat_message_content_parts(
+        role,
+        content,  # type: ignore
+        mm_tracker,
+    )
+
+    for result_msg in result:
+        if role == 'assistant':
+            parsed_msg = _AssistantParser(message)
+
+            if "tool_calls" in parsed_msg:
+                result_msg["tool_calls"] = list(parsed_msg["tool_calls"])
+        elif role == "tool":
+            parsed_msg = _ToolParser(message)
+            if "tool_call_id" in parsed_msg:
+                result_msg["tool_call_id"] = parsed_msg["tool_call_id"]
+
+        if "name" in message and isinstance(message["name"], str):
+            result_msg["name"] = message["name"]
+
+    return result
+
+
+def _postprocess_messages(messages: List[ConversationMessage]) -> None:
+    # per the Transformers docs & maintainers, tool call arguments in
+    # assistant-role messages with tool_calls need to be dicts not JSON str -
+    # this is how tool-use chat templates will expect them moving forwards
+    # so, for messages that have tool_calls, parse the string (which we get
+    # from openAI format) to dict
+    for message in messages:
+        if (message["role"] == "assistant" and "tool_calls" in message
+                and isinstance(message["tool_calls"], list)):
+
+            for item in message["tool_calls"]:
+                item["function"]["arguments"] = json.loads(
+                    item["function"]["arguments"])
+
+
+def parse_chat_messages(
+    messages: List[ChatCompletionMessageParam],
+    model_config: ModelConfig,
+    tokenizer: AnyTokenizer,
+) -> Tuple[List[ConversationMessage], Optional[MultiModalDataDict]]:
+    conversation: List[ConversationMessage] = []
+    mm_tracker = MultiModalItemTracker(model_config, tokenizer)
+
+    for msg in messages:
+        sub_messages = _parse_chat_message_content(msg, mm_tracker)
+
+        conversation.extend(sub_messages)
+
+    _postprocess_messages(conversation)
+
+    return conversation, mm_tracker.all_mm_data()
+
+
+def parse_chat_messages_futures(
+    messages: List[ChatCompletionMessageParam],
+    model_config: ModelConfig,
+    tokenizer: AnyTokenizer,
+) -> Tuple[List[ConversationMessage], Awaitable[Optional[MultiModalDataDict]]]:
+    conversation: List[ConversationMessage] = []
+    mm_tracker = AsyncMultiModalItemTracker(model_config, tokenizer)
+
+    for msg in messages:
+        sub_messages = _parse_chat_message_content(msg, mm_tracker)
+
+        conversation.extend(sub_messages)
+
+    _postprocess_messages(conversation)
+
+    return conversation, mm_tracker.all_mm_data()
+
+
+def apply_hf_chat_template(
+    tokenizer: Union[PreTrainedTokenizer, PreTrainedTokenizerFast],
+    conversation: List[ConversationMessage],
+    chat_template: Optional[str],
+    *,
+    tokenize: bool = False,  # Different from HF's default
+    **kwargs: Any,
+) -> str:
+    if chat_template is None and tokenizer.chat_template is None:
+        raise ValueError(
+            "As of transformers v4.44, default chat template is no longer "
+            "allowed, so you must provide a chat template if the tokenizer "
+            "does not define one.")
+
+    return tokenizer.apply_chat_template(
+        conversation=conversation,  # type: ignore[arg-type]
+        chat_template=chat_template,
+        tokenize=tokenize,
+        **kwargs,
+    )
+
+
+def apply_mistral_chat_template(
+    tokenizer: MistralTokenizer,
+    messages: List[ChatCompletionMessageParam],
+    chat_template: Optional[str] = None,
+    **kwargs: Any,
+) -> List[int]:
+    if chat_template is not None:
+        logger.warning(
+            "'chat_template' cannot be overridden for mistral tokenizer.")
+    if "add_generation_prompt" in kwargs:
+        logger.warning(
+            "'add_generation_prompt' is not supported for mistral tokenizer, "
+            "so it will be ignored.")
+    if "continue_final_message" in kwargs:
+        logger.warning(
+            "'continue_final_message' is not supported for mistral tokenizer, "
+            "so it will be ignored.")
+
+    return tokenizer.apply_chat_template(
+        messages=messages,
+        **kwargs,
+    )

vllm/entrypoints/launcher.py

@@ -0,0 +1,103 @@
+import asyncio
+import signal
+from http import HTTPStatus
+from typing import Any
+
+import uvicorn
+from fastapi import FastAPI, Request, Response
+
+from vllm import envs
+from vllm.engine.async_llm_engine import AsyncEngineDeadError
+from vllm.engine.multiprocessing import MQEngineDeadError
+from vllm.logger import init_logger
+from vllm.utils import find_process_using_port
+
+logger = init_logger(__name__)
+
+
+async def serve_http(app: FastAPI, **uvicorn_kwargs: Any):
+    logger.info("Available routes are:")
+    for route in app.routes:
+        methods = getattr(route, "methods", None)
+        path = getattr(route, "path", None)
+
+        if methods is None or path is None:
+            continue
+
+        logger.info("Route: %s, Methods: %s", path, ', '.join(methods))
+
+    config = uvicorn.Config(app, **uvicorn_kwargs)
+    server = uvicorn.Server(config)
+    _add_shutdown_handlers(app, server)
+
+    loop = asyncio.get_running_loop()
+
+    server_task = loop.create_task(server.serve())
+
+    def signal_handler() -> None:
+        # prevents the uvicorn signal handler to exit early
+        server_task.cancel()
+
+    async def dummy_shutdown() -> None:
+        pass
+
+    loop.add_signal_handler(signal.SIGINT, signal_handler)
+    loop.add_signal_handler(signal.SIGTERM, signal_handler)
+
+    try:
+        await server_task
+        return dummy_shutdown()
+    except asyncio.CancelledError:
+        port = uvicorn_kwargs["port"]
+        process = find_process_using_port(port)
+        if process is not None:
+            logger.debug(
+                "port %s is used by process %s launched with command:\n%s",
+                port, process, " ".join(process.cmdline()))
+        logger.info("Shutting down FastAPI HTTP server.")
+        return server.shutdown()
+
+
+def _add_shutdown_handlers(app: FastAPI, server: uvicorn.Server) -> None:
+    """Adds handlers for fatal errors that should crash the server"""
+
+    @app.exception_handler(RuntimeError)
+    async def runtime_error_handler(request: Request, __):
+        """On generic runtime error, check to see if the engine has died.
+        It probably has, in which case the server will no longer be able to
+        handle requests. Trigger a graceful shutdown with a SIGTERM."""
+        engine = request.app.state.engine_client
+        if (not envs.VLLM_KEEP_ALIVE_ON_ENGINE_DEATH and engine.errored
+                and not engine.is_running):
+            logger.fatal("AsyncLLMEngine has failed, terminating server "
+                         "process")
+            # See discussions here on shutting down a uvicorn server
+            # https://github.com/encode/uvicorn/discussions/1103
+            # In this case we cannot await the server shutdown here because
+            # this handler must first return to close the connection for
+            # this request.
+            server.should_exit = True
+
+        return Response(status_code=HTTPStatus.INTERNAL_SERVER_ERROR)
+
+    @app.exception_handler(AsyncEngineDeadError)
+    async def async_engine_dead_handler(_, __):
+        """Kill the server if the async engine is already dead. It will
+        not handle any further requests."""
+        if not envs.VLLM_KEEP_ALIVE_ON_ENGINE_DEATH:
+            logger.fatal("AsyncLLMEngine is already dead, terminating server "
+                         "process")
+            server.should_exit = True
+
+        return Response(status_code=HTTPStatus.INTERNAL_SERVER_ERROR)
+
+    @app.exception_handler(MQEngineDeadError)
+    async def mq_engine_dead_handler(_, __):
+        """Kill the server if the mq engine is already dead. It will
+        not handle any further requests."""
+        if not envs.VLLM_KEEP_ALIVE_ON_ENGINE_DEATH:
+            logger.fatal("MQLLMEngine is already dead, terminating server "
+                         "process")
+            server.should_exit = True
+
+        return Response(status_code=HTTPStatus.INTERNAL_SERVER_ERROR)

vllm/entrypoints/llm.py

@@ -0,0 +1,909 @@
+import itertools
+import warnings
+from contextlib import contextmanager
+from typing import (Any, ClassVar, Dict, List, Optional, Sequence, Tuple,
+                    Union, cast, overload)
+
+from tqdm import tqdm
+
+from vllm.beam_search import (BeamSearchInstance, BeamSearchOutput,
+                              BeamSearchSequence, get_beam_search_score)
+from vllm.engine.arg_utils import EngineArgs
+from vllm.engine.llm_engine import LLMEngine
+from vllm.entrypoints.chat_utils import (ChatCompletionMessageParam,
+                                         apply_hf_chat_template,
+                                         apply_mistral_chat_template,
+                                         parse_chat_messages)
+from vllm.inputs import PromptType, TextPrompt, TokensPrompt
+from vllm.inputs.parse import parse_and_batch_prompt
+from vllm.logger import init_logger
+from vllm.lora.request import LoRARequest
+from vllm.model_executor.guided_decoding.guided_fields import (
+    GuidedDecodingRequest, LLMGuidedOptions)
+from vllm.outputs import EmbeddingRequestOutput, RequestOutput
+from vllm.pooling_params import PoolingParams
+from vllm.prompt_adapter.request import PromptAdapterRequest
+from vllm.sampling_params import (BeamSearchParams, GuidedDecodingParams,
+                                  RequestOutputKind, SamplingParams)
+from vllm.transformers_utils.tokenizer import (AnyTokenizer, MistralTokenizer,
+                                               get_cached_tokenizer)
+from vllm.transformers_utils.tokenizer_group import TokenizerGroup
+from vllm.usage.usage_lib import UsageContext
+from vllm.utils import Counter, deprecate_kwargs, is_list_of
+
+logger = init_logger(__name__)
+
+
+class LLM:
+    """An LLM for generating texts from given prompts and sampling parameters.
+
+    This class includes a tokenizer, a language model (possibly distributed
+    across multiple GPUs), and GPU memory space allocated for intermediate
+    states (aka KV cache). Given a batch of prompts and sampling parameters,
+    this class generates texts from the model, using an intelligent batching
+    mechanism and efficient memory management.
+
+    Args:
+        model: The name or path of a HuggingFace Transformers model.
+        tokenizer: The name or path of a HuggingFace Transformers tokenizer.
+        tokenizer_mode: The tokenizer mode. "auto" will use the fast tokenizer
+            if available, and "slow" will always use the slow tokenizer.
+        skip_tokenizer_init: If true, skip initialization of tokenizer and
+            detokenizer. Expect valid prompt_token_ids and None for prompt
+            from the input.
+        trust_remote_code: Trust remote code (e.g., from HuggingFace) when
+            downloading the model and tokenizer.
+        tensor_parallel_size: The number of GPUs to use for distributed
+            execution with tensor parallelism.
+        dtype: The data type for the model weights and activations. Currently,
+            we support `float32`, `float16`, and `bfloat16`. If `auto`, we use
+            the `torch_dtype` attribute specified in the model config file.
+            However, if the `torch_dtype` in the config is `float32`, we will
+            use `float16` instead.
+        quantization: The method used to quantize the model weights. Currently,
+            we support "awq", "gptq", and "fp8" (experimental).
+            If None, we first check the `quantization_config` attribute in the
+            model config file. If that is None, we assume the model weights are
+            not quantized and use `dtype` to determine the data type of
+            the weights.
+        revision: The specific model version to use. It can be a branch name,
+            a tag name, or a commit id.
+        tokenizer_revision: The specific tokenizer version to use. It can be a
+            branch name, a tag name, or a commit id.
+        seed: The seed to initialize the random number generator for sampling.
+        gpu_memory_utilization: The ratio (between 0 and 1) of GPU memory to
+            reserve for the model weights, activations, and KV cache. Higher
+            values will increase the KV cache size and thus improve the model's
+            throughput. However, if the value is too high, it may cause out-of-
+            memory (OOM) errors.
+        swap_space: The size (GiB) of CPU memory per GPU to use as swap space.
+            This can be used for temporarily storing the states of the requests
+            when their `best_of` sampling parameters are larger than 1. If all
+            requests will have `best_of=1`, you can safely set this to 0.
+            Otherwise, too small values may cause out-of-memory (OOM) errors.
+        cpu_offload_gb: The size (GiB) of CPU memory to use for offloading
+            the model weights. This virtually increases the GPU memory space
+            you can use to hold the model weights, at the cost of CPU-GPU data
+            transfer for every forward pass.
+        enforce_eager: Whether to enforce eager execution. If True, we will
+            disable CUDA graph and always execute the model in eager mode.
+            If False, we will use CUDA graph and eager execution in hybrid.
+        max_context_len_to_capture: Maximum context len covered by CUDA graphs.
+            When a sequence has context length larger than this, we fall back
+            to eager mode (DEPRECATED. Use `max_seq_len_to_capture` instead).
+        max_seq_len_to_capture: Maximum sequence len covered by CUDA graphs.
+            When a sequence has context length larger than this, we fall back
+            to eager mode. Additionally for encoder-decoder models, if the
+            sequence length of the encoder input is larger than this, we fall
+            back to the eager mode.
+        disable_custom_all_reduce: See ParallelConfig
+        **kwargs: Arguments for :class:`~vllm.EngineArgs`. (See
+            :ref:`engine_args`)
+
+    Note:
+        This class is intended to be used for offline inference. For online
+        serving, use the :class:`~vllm.AsyncLLMEngine` class instead.
+    """
+
+    DEPRECATE_LEGACY: ClassVar[bool] = False
+    """A flag to toggle whether to deprecate the legacy generate/encode API."""
+
+    @classmethod
+    @contextmanager
+    def deprecate_legacy_api(cls):
+        cls.DEPRECATE_LEGACY = True
+
+        yield
+
+        cls.DEPRECATE_LEGACY = False
+
+    def __init__(
+        self,
+        model: str,
+        tokenizer: Optional[str] = None,
+        tokenizer_mode: str = "auto",
+        skip_tokenizer_init: bool = False,
+        trust_remote_code: bool = False,
+        tensor_parallel_size: int = 1,
+        dtype: str = "auto",
+        quantization: Optional[str] = None,
+        revision: Optional[str] = None,
+        tokenizer_revision: Optional[str] = None,
+        seed: int = 0,
+        gpu_memory_utilization: float = 0.9,
+        swap_space: float = 4,
+        cpu_offload_gb: float = 0,
+        enforce_eager: Optional[bool] = None,
+        max_context_len_to_capture: Optional[int] = None,
+        max_seq_len_to_capture: int = 8192,
+        disable_custom_all_reduce: bool = False,
+        disable_async_output_proc: bool = False,
+        mm_processor_kwargs: Optional[Dict[str, Any]] = None,
+        **kwargs,
+    ) -> None:
+        '''
+        LLM constructor.
+
+        Note: if enforce_eager is unset (enforce_eager is None)
+        it defaults to False.
+        '''
+
+        if "disable_log_stats" not in kwargs:
+            kwargs["disable_log_stats"] = True
+
+        engine_args = EngineArgs(
+            model=model,
+            tokenizer=tokenizer,
+            tokenizer_mode=tokenizer_mode,
+            skip_tokenizer_init=skip_tokenizer_init,
+            trust_remote_code=trust_remote_code,
+            tensor_parallel_size=tensor_parallel_size,
+            dtype=dtype,
+            quantization=quantization,
+            revision=revision,
+            tokenizer_revision=tokenizer_revision,
+            seed=seed,
+            gpu_memory_utilization=gpu_memory_utilization,
+            swap_space=swap_space,
+            cpu_offload_gb=cpu_offload_gb,
+            enforce_eager=enforce_eager,
+            max_context_len_to_capture=max_context_len_to_capture,
+            max_seq_len_to_capture=max_seq_len_to_capture,
+            disable_custom_all_reduce=disable_custom_all_reduce,
+            disable_async_output_proc=disable_async_output_proc,
+            mm_processor_kwargs=mm_processor_kwargs,
+            **kwargs,
+        )
+        self.llm_engine = LLMEngine.from_engine_args(
+            engine_args, usage_context=UsageContext.LLM_CLASS)
+        self.request_counter = Counter()
+
+    def get_tokenizer(self) -> AnyTokenizer:
+        return self.llm_engine.get_tokenizer_group(TokenizerGroup).tokenizer
+
+    def set_tokenizer(self, tokenizer: AnyTokenizer) -> None:
+        tokenizer_group = self.llm_engine.get_tokenizer_group(TokenizerGroup)
+
+        # While CachedTokenizer is dynamic, have no choice but
+        # compare class name. Misjudgment will arise from
+        # user-defined tokenizer started with 'Cached'
+        if tokenizer.__class__.__name__.startswith("Cached"):
+            tokenizer_group.tokenizer = tokenizer
+        else:
+            tokenizer_group.tokenizer = get_cached_tokenizer(tokenizer)
+
+    @overload  # LEGACY: single (prompt + optional token ids)
+    def generate(
+        self,
+        prompts: str,
+        sampling_params: Optional[Union[SamplingParams,
+                                        List[SamplingParams]]] = None,
+        prompt_token_ids: Optional[List[int]] = None,
+        use_tqdm: bool = True,
+        lora_request: Optional[Union[List[LoRARequest], LoRARequest]] = None,
+    ) -> List[RequestOutput]:
+        ...
+
+    @overload  # LEGACY: multi (prompt + optional token ids)
+    def generate(
+        self,
+        prompts: List[str],
+        sampling_params: Optional[Union[SamplingParams,
+                                        List[SamplingParams]]] = None,
+        prompt_token_ids: Optional[List[List[int]]] = None,
+        use_tqdm: bool = True,
+        lora_request: Optional[Union[List[LoRARequest], LoRARequest]] = None,
+    ) -> List[RequestOutput]:
+        ...
+
+    @overload  # LEGACY: single (token ids + optional prompt)
+    def generate(
+        self,
+        prompts: Optional[str] = None,
+        sampling_params: Optional[Union[SamplingParams,
+                                        List[SamplingParams]]] = None,
+        *,
+        prompt_token_ids: List[int],
+        use_tqdm: bool = True,
+        lora_request: Optional[Union[List[LoRARequest], LoRARequest]] = None,
+    ) -> List[RequestOutput]:
+        ...
+
+    @overload  # LEGACY: multi (token ids + optional prompt)
+    def generate(
+        self,
+        prompts: Optional[List[str]] = None,
+        sampling_params: Optional[Union[SamplingParams,
+                                        List[SamplingParams]]] = None,
+        *,
+        prompt_token_ids: List[List[int]],
+        use_tqdm: bool = True,
+        lora_request: Optional[Union[List[LoRARequest], LoRARequest]] = None,
+    ) -> List[RequestOutput]:
+        ...
+
+    @overload  # LEGACY: single or multi token ids [pos-only]
+    def generate(
+        self,
+        prompts: None,
+        sampling_params: None,
+        prompt_token_ids: Union[List[int], List[List[int]]],
+        use_tqdm: bool = True,
+        lora_request: Optional[Union[List[LoRARequest], LoRARequest]] = None,
+    ) -> List[RequestOutput]:
+        ...
+
+    @overload
+    def generate(
+        self,
+        prompts: Union[PromptType, Sequence[PromptType]],
+        /,
+        *,
+        sampling_params: Optional[Union[SamplingParams,
+                                        Sequence[SamplingParams]]] = None,
+        use_tqdm: bool = True,
+        lora_request: Optional[Union[List[LoRARequest], LoRARequest]] = None,
+    ) -> List[RequestOutput]:
+        ...
+
+    @deprecate_kwargs(
+        "prompt_token_ids",
+        is_deprecated=lambda: LLM.DEPRECATE_LEGACY,
+        additional_message="Please use the 'prompts' parameter instead.",
+    )
+    def generate(
+        self,
+        prompts: Union[Union[PromptType, Sequence[PromptType]],
+                       Optional[Union[str, List[str]]]] = None,
+        sampling_params: Optional[Union[SamplingParams,
+                                        Sequence[SamplingParams]]] = None,
+        prompt_token_ids: Optional[Union[List[int], List[List[int]]]] = None,
+        use_tqdm: bool = True,
+        lora_request: Optional[Union[List[LoRARequest], LoRARequest]] = None,
+        prompt_adapter_request: Optional[PromptAdapterRequest] = None,
+        guided_options_request: Optional[Union[LLMGuidedOptions,
+                                               GuidedDecodingRequest]] = None,
+        priority: Optional[List[int]] = None,
+    ) -> List[RequestOutput]:
+        """Generates the completions for the input prompts.
+
+        This class automatically batches the given prompts, considering
+        the memory constraint. For the best performance, put all of your prompts
+        into a single list and pass it to this method.
+
+        Args:
+            prompts: The prompts to the LLM. You may pass a sequence of prompts
+                for batch inference. See :class:`~vllm.inputs.PromptType`
+                for more details about the format of each prompts.
+            sampling_params: The sampling parameters for text generation. If
+                None, we use the default sampling parameters.
+                When it is a single value, it is applied to every prompt.
+                When it is a list, the list must have the same length as the
+                prompts and it is paired one by one with the prompt.
+            use_tqdm: Whether to use tqdm to display the progress bar.
+            lora_request: LoRA request to use for generation, if any.
+            prompt_adapter_request: Prompt Adapter request to use for
+                generation, if any.
+            priority: The priority of the requests, if any.
+                Only applicable when priority scheduling policy is enabled.
+
+        Returns:
+            A list of ``RequestOutput`` objects containing the
+            generated completions in the same order as the input prompts.
+
+        Note:
+            Using ``prompts`` and ``prompt_token_ids`` as keyword parameters is
+            considered legacy and may be deprecated in the future. You should
+            instead pass them via the ``inputs`` parameter.
+        """
+        if self.llm_engine.model_config.embedding_mode:
+            raise ValueError(
+                "LLM.generate() is only supported for (conditional) generation "
+                "models (XForCausalLM, XForConditionalGeneration).")
+
+        if prompt_token_ids is not None:
+            parsed_prompts = self._convert_v1_inputs(
+                prompts=cast(Optional[Union[str, List[str]]], prompts),
+                prompt_token_ids=prompt_token_ids,
+            )
+        else:
+            parsed_prompts = cast(Union[PromptType, Sequence[PromptType]],
+                                  prompts)
+
+        if isinstance(guided_options_request, dict):
+            if len(guided_options_request) > 1:
+                raise ValueError(
+                    "You can only use one guided decoding but multiple is "
+                    f"specified: {guided_options_request}")
+            guided_options_request = GuidedDecodingRequest(
+                **guided_options_request)
+
+        if sampling_params is None:
+            # Use default sampling params.
+            sampling_params = SamplingParams()
+
+        self._validate_and_add_requests(
+            prompts=parsed_prompts,
+            params=sampling_params,
+            lora_request=lora_request,
+            prompt_adapter_request=prompt_adapter_request,
+            guided_options=guided_options_request,
+            priority=priority)
+
+        outputs = self._run_engine(use_tqdm=use_tqdm)
+        return LLMEngine.validate_outputs(outputs, RequestOutput)
+
+    def beam_search(
+        self,
+        prompts: List[Union[str, List[int]]],
+        params: BeamSearchParams,
+    ) -> List[BeamSearchOutput]:
+        """
+        Generate sequences using beam search.
+
+        Args:
+            prompts: A list of prompts. Each prompt can be a string or a list
+                of token IDs.
+            params: The beam search parameters.
+
+        TODO: how does beam search work together with length penalty, frequency
+        penalty, and stopping criteria, etc.?
+        """
+
+        beam_width = params.beam_width
+        max_tokens = params.max_tokens
+        temperature = params.temperature
+        ignore_eos = params.ignore_eos
+        length_penalty = params.length_penalty
+
+        def sort_beams_key(x: BeamSearchSequence) -> float:
+            return get_beam_search_score(x.tokens, x.cum_logprob,
+                                         tokenizer.eos_token_id,
+                                         length_penalty)
+
+        tokenizer = self.get_tokenizer()
+        # generate 2 * beam_width candidates at each step
+        # following the huggingface transformers implementation
+        # at https://github.com/huggingface/transformers/blob/e15687fffe5c9d20598a19aeab721ae0a7580f8a/src/transformers/generation/beam_search.py#L534 # noqa
+        beam_search_params = SamplingParams(logprobs=2 * beam_width,
+                                            max_tokens=1,
+                                            temperature=temperature)
+        instances: List[BeamSearchInstance] = []
+
+        for prompt in prompts:
+            prompt_tokens = prompt if isinstance(
+                prompt, list) else tokenizer.encode(prompt)
+            instances.append(BeamSearchInstance(prompt_tokens))
+
+        for _ in range(max_tokens):
+            all_beams: List[BeamSearchSequence] = list(
+                sum((instance.beams for instance in instances), []))
+            pos = [0] + list(
+                itertools.accumulate(
+                    len(instance.beams) for instance in instances))
+            instance_start_and_end: List[Tuple[int, int]] = list(
+                zip(pos[:-1], pos[1:]))
+
+            if len(all_beams) == 0:
+                break
+
+            prompts_batch = [
+                TokensPrompt(prompt_token_ids=beam.tokens)
+                for beam in all_beams
+            ]
+
+            # only runs for one step
+            # we don't need to use tqdm here
+            output = self.generate(prompts_batch,
+                                   sampling_params=beam_search_params,
+                                   use_tqdm=False)
+
+            for (start, end), instance in zip(instance_start_and_end,
+                                              instances):
+                instance_new_beams = []
+                for i in range(start, end):
+                    current_beam = all_beams[i]
+                    result = output[i]
+
+                    if result.outputs[0].logprobs is not None:
+                        # if `result.outputs[0].logprobs` is None, it means
+                        # the sequence is completed because of the max-model-len
+                        # or abortion. we don't need to add it to the new beams.
+                        logprobs = result.outputs[0].logprobs[0]
+                        for token_id, logprob_obj in logprobs.items():
+                            new_beam = BeamSearchSequence(
+                                tokens=current_beam.tokens + [token_id],
+                                cum_logprob=current_beam.cum_logprob +
+                                logprob_obj.logprob)
+
+                            if token_id == tokenizer.eos_token_id and \
+                                not ignore_eos:
+                                instance.completed.append(new_beam)
+                            else:
+                                instance_new_beams.append(new_beam)
+                sorted_beams = sorted(instance_new_beams,
+                                      key=sort_beams_key,
+                                      reverse=True)
+                instance.beams = sorted_beams[:beam_width]
+
+        outputs = []
+        for instance in instances:
+            instance.completed.extend(instance.beams)
+            sorted_completed = sorted(instance.completed,
+                                      key=sort_beams_key,
+                                      reverse=True)
+            best_beams = sorted_completed[:beam_width]
+
+            for beam in best_beams:
+                beam.text = tokenizer.decode(beam.tokens)
+            outputs.append(BeamSearchOutput(sequences=best_beams))
+
+        return outputs
+
+    def chat(
+        self,
+        messages: Union[List[ChatCompletionMessageParam],
+                        List[List[ChatCompletionMessageParam]]],
+        sampling_params: Optional[Union[SamplingParams,
+                                        List[SamplingParams]]] = None,
+        use_tqdm: bool = True,
+        lora_request: Optional[LoRARequest] = None,
+        chat_template: Optional[str] = None,
+        add_generation_prompt: bool = True,
+        continue_final_message: bool = False,
+        tools: Optional[List[Dict[str, Any]]] = None,
+        mm_processor_kwargs: Optional[Dict[str, Any]] = None,
+    ) -> List[RequestOutput]:
+        """
+        Generate responses for a chat conversation.
+
+        The chat conversation is converted into a text prompt using the
+        tokenizer and calls the :meth:`generate` method to generate the
+        responses.
+
+        Multi-modal inputs can be passed in the same way you would pass them
+        to the OpenAI API.
+
+        Args:
+            messages: A list of conversations or a single conversation. 
+                - Each conversation is represented as a list of messages.
+                - Each message is a dictionary with 'role' and 'content' keys.
+            sampling_params: The sampling parameters for text generation.
+                If None, we use the default sampling parameters. When it
+                is a single value, it is applied to every prompt. When it
+                is a list, the list must have the same length as the
+                prompts and it is paired one by one with the prompt.
+            use_tqdm: Whether to use tqdm to display the progress bar.
+            lora_request: LoRA request to use for generation, if any.
+            chat_template: The template to use for structuring the chat.
+              If not provided, the model's default chat template will be used.
+            add_generation_prompt: If True, adds a generation template
+                to each message.
+            continue_final_message: If True, continues the final message in
+                the conversation instead of starting a new one. Cannot be `True`
+                if `add_generation_prompt` is also `True`.
+            mm_processor_kwargs: Multimodal processor kwarg overrides for this
+                chat request. Only used for offline requests.
+
+        Returns:
+            A list of ``RequestOutput`` objects containing the generated
+            responses in the same order as the input messages.
+        """
+        list_of_messages: List[List[ChatCompletionMessageParam]]
+
+        # Handle multi and single conversations
+        if is_list_of(messages, list):
+            # messages is List[List[...]]
+            list_of_messages = cast(List[List[ChatCompletionMessageParam]],
+                                    messages)
+        else:
+            # messages is List[...]
+            list_of_messages = [
+                cast(List[ChatCompletionMessageParam], messages)
+            ]
+
+        prompts: List[Union[TokensPrompt, TextPrompt]] = []
+
+        for msgs in list_of_messages:
+            tokenizer = self.get_tokenizer()
+            model_config = self.llm_engine.get_model_config()
+
+            # NOTE: _parse_chat_message_content_parts() currently doesn't
+            # handle mm_processor_kwargs, since there is no implementation in
+            # the chat message parsing for it.
+            conversation, mm_data = parse_chat_messages(
+                msgs, model_config, tokenizer)
+
+            prompt_data: Union[str, List[int]]
+            if isinstance(tokenizer, MistralTokenizer):
+                prompt_data = apply_mistral_chat_template(
+                    tokenizer,
+                    messages=msgs,
+                    chat_template=chat_template,
+                    add_generation_prompt=add_generation_prompt,
+                    continue_final_message=continue_final_message,
+                    tools=tools,
+                )
+            else:
+                prompt_data = apply_hf_chat_template(
+                    tokenizer,
+                    conversation=conversation,
+                    chat_template=chat_template,
+                    add_generation_prompt=add_generation_prompt,
+                    continue_final_message=continue_final_message,
+                    tools=tools,
+                )
+
+            prompt: Union[TokensPrompt, TextPrompt]
+            if is_list_of(prompt_data, int):
+                prompt = TokensPrompt(prompt_token_ids=prompt_data)
+            else:
+                prompt = TextPrompt(prompt=prompt_data)
+
+            if mm_data is not None:
+                prompt["multi_modal_data"] = mm_data
+
+            if mm_processor_kwargs is not None:
+                prompt["mm_processor_kwargs"] = mm_processor_kwargs
+
+            prompts.append(prompt)
+
+        return self.generate(
+            prompts,
+            sampling_params=sampling_params,
+            use_tqdm=use_tqdm,
+            lora_request=lora_request,
+        )
+
+    @overload  # LEGACY: single (prompt + optional token ids)
+    def encode(
+        self,
+        prompts: str,
+        pooling_params: Optional[Union[PoolingParams,
+                                       Sequence[PoolingParams]]] = None,
+        prompt_token_ids: Optional[List[int]] = None,
+        use_tqdm: bool = True,
+        lora_request: Optional[Union[List[LoRARequest], LoRARequest]] = None,
+    ) -> List[EmbeddingRequestOutput]:
+        ...
+
+    @overload  # LEGACY: multi (prompt + optional token ids)
+    def encode(
+        self,
+        prompts: List[str],
+        pooling_params: Optional[Union[PoolingParams,
+                                       Sequence[PoolingParams]]] = None,
+        prompt_token_ids: Optional[List[List[int]]] = None,
+        use_tqdm: bool = True,
+        lora_request: Optional[Union[List[LoRARequest], LoRARequest]] = None,
+    ) -> List[EmbeddingRequestOutput]:
+        ...
+
+    @overload  # LEGACY: single (token ids + optional prompt)
+    def encode(
+        self,
+        prompts: Optional[str] = None,
+        pooling_params: Optional[Union[PoolingParams,
+                                       Sequence[PoolingParams]]] = None,
+        *,
+        prompt_token_ids: List[int],
+        use_tqdm: bool = True,
+        lora_request: Optional[Union[List[LoRARequest], LoRARequest]] = None,
+    ) -> List[EmbeddingRequestOutput]:
+        ...
+
+    @overload  # LEGACY: multi (token ids + optional prompt)
+    def encode(
+        self,
+        prompts: Optional[List[str]] = None,
+        pooling_params: Optional[Union[PoolingParams,
+                                       Sequence[PoolingParams]]] = None,
+        *,
+        prompt_token_ids: List[List[int]],
+        use_tqdm: bool = True,
+        lora_request: Optional[Union[List[LoRARequest], LoRARequest]] = None,
+    ) -> List[EmbeddingRequestOutput]:
+        ...
+
+    @overload  # LEGACY: single or multi token ids [pos-only]
+    def encode(
+        self,
+        prompts: None,
+        pooling_params: None,
+        prompt_token_ids: Union[List[int], List[List[int]]],
+        use_tqdm: bool = True,
+        lora_request: Optional[Union[List[LoRARequest], LoRARequest]] = None,
+    ) -> List[EmbeddingRequestOutput]:
+        ...
+
+    @overload
+    def encode(
+        self,
+        prompts: Union[PromptType, Sequence[PromptType]],
+        /,
+        *,
+        pooling_params: Optional[Union[PoolingParams,
+                                       Sequence[PoolingParams]]] = None,
+        use_tqdm: bool = True,
+        lora_request: Optional[Union[List[LoRARequest], LoRARequest]] = None,
+    ) -> List[EmbeddingRequestOutput]:
+        ...
+
+    @deprecate_kwargs(
+        "prompt_token_ids",
+        is_deprecated=lambda: LLM.DEPRECATE_LEGACY,
+        additional_message="Please use the 'prompts' parameter instead.",
+    )
+    def encode(
+        self,
+        prompts: Union[Union[PromptType, Sequence[PromptType]],
+                       Optional[Union[str, List[str]]]] = None,
+        pooling_params: Optional[Union[PoolingParams,
+                                       Sequence[PoolingParams]]] = None,
+        prompt_token_ids: Optional[Union[List[int], List[List[int]]]] = None,
+        use_tqdm: bool = True,
+        lora_request: Optional[Union[List[LoRARequest], LoRARequest]] = None,
+        prompt_adapter_request: Optional[PromptAdapterRequest] = None,
+    ) -> List[EmbeddingRequestOutput]:
+        """Generates the completions for the input prompts.
+
+        This class automatically batches the given prompts, considering
+        the memory constraint. For the best performance, put all of your prompts
+        into a single list and pass it to this method.
+
+        Args:
+            prompts: The prompts to the LLM. You may pass a sequence of prompts
+                for batch inference. See :class:`~vllm.inputs.PromptType`
+                for more details about the format of each prompts.
+            pooling_params: The pooling parameters for pooling. If None, we
+                use the default pooling parameters.
+            use_tqdm: Whether to use tqdm to display the progress bar.
+            lora_request: LoRA request to use for generation, if any.
+            prompt_adapter_request: Prompt Adapter request to use for
+                generation, if any.
+
+        Returns:
+            A list of `EmbeddingRequestOutput` objects containing the
+            generated embeddings in the same order as the input prompts.
+
+        Note:
+            Using ``prompts`` and ``prompt_token_ids`` as keyword parameters is
+            considered legacy and may be deprecated in the future. You should
+            instead pass them via the ``inputs`` parameter.
+        """
+        if not self.llm_engine.model_config.embedding_mode:
+            raise ValueError(
+                "LLM.encode() is only supported for embedding models (XModel)."
+            )
+
+        if prompt_token_ids is not None:
+            parsed_prompts = self._convert_v1_inputs(
+                prompts=cast(Optional[Union[str, List[str]]], prompts),
+                prompt_token_ids=prompt_token_ids,
+            )
+        else:
+            parsed_prompts = cast(Union[PromptType, Sequence[PromptType]],
+                                  prompts)
+
+        if pooling_params is None:
+            # Use default pooling params.
+            pooling_params = PoolingParams()
+
+        self._validate_and_add_requests(
+            prompts=parsed_prompts,
+            params=pooling_params,
+            lora_request=lora_request,
+            prompt_adapter_request=prompt_adapter_request,
+        )
+
+        outputs = self._run_engine(use_tqdm=use_tqdm)
+        return LLMEngine.validate_outputs(outputs, EmbeddingRequestOutput)
+
+    def start_profile(self) -> None:
+        self.llm_engine.start_profile()
+
+    def stop_profile(self) -> None:
+        self.llm_engine.stop_profile()
+
+    # LEGACY
+    def _convert_v1_inputs(
+        self,
+        prompts: Optional[Union[str, List[str]]],
+        prompt_token_ids: Optional[Union[List[int], List[List[int]]]],
+    ):
+        # skip_tokenizer_init is now checked in engine
+
+        if prompts is not None:
+            prompts = [p["content"] for p in parse_and_batch_prompt(prompts)]
+        if prompt_token_ids is not None:
+            prompt_token_ids = [
+                p["content"] for p in parse_and_batch_prompt(prompt_token_ids)
+            ]
+
+        num_requests = None
+        if prompts is not None:
+            num_requests = len(prompts)
+        if prompt_token_ids is not None:
+            if (num_requests is not None
+                    and num_requests != len(prompt_token_ids)):
+                raise ValueError("The lengths of prompts and prompt_token_ids "
+                                 "must be the same.")
+
+            num_requests = len(prompt_token_ids)
+        if num_requests is None:
+            raise ValueError("Either prompts or prompt_token_ids must be "
+                             "provided.")
+
+        parsed_prompts: List[PromptType] = []
+        for i in range(num_requests):
+            item: PromptType
+
+            if prompts is not None:
+                item = TextPrompt(prompt=prompts[i])
+            elif prompt_token_ids is not None:
+                item = TokensPrompt(prompt_token_ids=prompt_token_ids[i])
+            else:
+                raise AssertionError
+
+            parsed_prompts.append(item)
+
+        return parsed_prompts
+
+    def _validate_and_add_requests(
+        self,
+        prompts: Union[PromptType, Sequence[PromptType]],
+        params: Union[SamplingParams, Sequence[SamplingParams], PoolingParams,
+                      Sequence[PoolingParams]],
+        lora_request: Optional[Union[Sequence[LoRARequest], LoRARequest]],
+        prompt_adapter_request: Optional[PromptAdapterRequest],
+        guided_options: Optional[GuidedDecodingRequest] = None,
+        priority: Optional[List[int]] = None,
+    ) -> None:
+        if guided_options is not None:
+            warnings.warn(
+                "guided_options_request is deprecated, use "
+                "SamplingParams.guided_decoding instead",
+                DeprecationWarning,
+                stacklevel=2,
+            )
+
+        if isinstance(prompts, (str, dict)):
+            # Convert a single prompt to a list.
+            prompts = [prompts]
+
+        num_requests = len(prompts)
+        if isinstance(params, list) and len(params) != num_requests:
+            raise ValueError("The lengths of prompts and params "
+                             "must be the same.")
+        if isinstance(lora_request,
+                      list) and len(lora_request) != num_requests:
+            raise ValueError("The lengths of prompts and lora_request "
+                             "must be the same.")
+
+        for sp in params if isinstance(params, list) else (params, ):
+            if isinstance(sp, SamplingParams):
+                self._add_guided_params(sp, guided_options)
+
+                # We only care about the final output
+                sp.output_kind = RequestOutputKind.FINAL_ONLY
+
+        # Add requests to the engine.
+        for i, prompt in enumerate(prompts):
+            self._add_request(
+                prompt,
+                params[i] if isinstance(params, Sequence) else params,
+                lora_request=lora_request[i] if isinstance(
+                    lora_request, Sequence) else lora_request,
+                prompt_adapter_request=prompt_adapter_request,
+                priority=priority[i] if priority else 0,
+            )
+
+    def _add_request(
+        self,
+        prompt: PromptType,
+        params: Union[SamplingParams, PoolingParams],
+        lora_request: Optional[LoRARequest] = None,
+        prompt_adapter_request: Optional[PromptAdapterRequest] = None,
+        priority: int = 0,
+    ) -> None:
+        request_id = str(next(self.request_counter))
+        self.llm_engine.add_request(
+            request_id,
+            prompt,
+            params,
+            lora_request=lora_request,
+            prompt_adapter_request=prompt_adapter_request,
+            priority=priority,
+        )
+
+    def _add_guided_params(
+            self,
+            params: SamplingParams,
+            guided_options: Optional[GuidedDecodingRequest] = None):
+        if guided_options is None:
+            return params
+
+        if params.guided_decoding is not None:
+            raise ValueError("Cannot set both guided_options_request and"
+                             "params.guided_decoding.")
+
+        params.guided_decoding = GuidedDecodingParams(
+            json=guided_options.guided_json,
+            regex=guided_options.guided_regex,
+            choice=guided_options.guided_choice,
+            grammar=guided_options.guided_grammar,
+            json_object=guided_options.guided_json_object,
+            backend=guided_options.guided_decoding_backend,
+            whitespace_pattern=guided_options.guided_whitespace_pattern)
+        return params
+
+    def _run_engine(
+            self, *, use_tqdm: bool
+    ) -> List[Union[RequestOutput, EmbeddingRequestOutput]]:
+        # Initialize tqdm.
+        if use_tqdm:
+            num_requests = self.llm_engine.get_num_unfinished_requests()
+            pbar = tqdm(
+                total=num_requests,
+                desc="Processed prompts",
+                dynamic_ncols=True,
+                postfix=(f"est. speed input: {0:.2f} toks/s, "
+                         f"output: {0:.2f} toks/s"),
+            )
+
+        # Run the engine.
+        outputs: List[Union[RequestOutput, EmbeddingRequestOutput]] = []
+        total_in_toks = 0
+        total_out_toks = 0
+        while self.llm_engine.has_unfinished_requests():
+            step_outputs = self.llm_engine.step()
+            for output in step_outputs:
+                if output.finished:
+                    outputs.append(output)
+                    if use_tqdm:
+                        if isinstance(output, RequestOutput):
+                            # Calculate tokens only for RequestOutput
+                            assert output.prompt_token_ids is not None
+                            total_in_toks += len(output.prompt_token_ids)
+                            in_spd = total_in_toks / pbar.format_dict["elapsed"]
+                            total_out_toks += sum(
+                                len(stp.token_ids) for stp in output.outputs)
+                            out_spd = (total_out_toks /
+                                       pbar.format_dict["elapsed"])
+                            pbar.postfix = (
+                                f"est. speed input: {in_spd:.2f} toks/s, "
+                                f"output: {out_spd:.2f} toks/s")
+                        pbar.update(1)
+
+        if use_tqdm:
+            pbar.close()
+        # Sort the outputs by request ID.
+        # This is necessary because some requests may be finished earlier than
+        # its previous requests.
+        return sorted(outputs, key=lambda x: int(x.request_id))
+
+    def _is_encoder_decoder_model(self):
+        return self.llm_engine.is_encoder_decoder_model()
+
+    def _is_embedding_model(self):
+        return self.llm_engine.is_embedding_model()

vllm/entrypoints/logger.py

@@ -0,0 +1,42 @@
+from typing import List, Optional, Union
+
+from vllm.logger import init_logger
+from vllm.lora.request import LoRARequest
+from vllm.pooling_params import PoolingParams
+from vllm.prompt_adapter.request import PromptAdapterRequest
+from vllm.sampling_params import BeamSearchParams, SamplingParams
+
+logger = init_logger(__name__)
+
+
+class RequestLogger:
+
+    def __init__(self, *, max_log_len: Optional[int]) -> None:
+        super().__init__()
+
+        self.max_log_len = max_log_len
+
+    def log_inputs(
+        self,
+        request_id: str,
+        prompt: Optional[str],
+        prompt_token_ids: Optional[List[int]],
+        params: Optional[Union[SamplingParams, PoolingParams,
+                               BeamSearchParams]],
+        lora_request: Optional[LoRARequest],
+        prompt_adapter_request: Optional[PromptAdapterRequest],
+    ) -> None:
+        max_log_len = self.max_log_len
+        if max_log_len is not None:
+            if prompt is not None:
+                prompt = prompt[:max_log_len]
+
+            if prompt_token_ids is not None:
+                prompt_token_ids = prompt_token_ids[:max_log_len]
+
+        logger.info(
+            "Received request %s: prompt: %r, "
+            "params: %s, prompt_token_ids: %s, "
+            "lora_request: %s, prompt_adapter_request: %s.", request_id,
+            prompt, params, prompt_token_ids, lora_request,
+            prompt_adapter_request)

vllm/entrypoints/openai/api_server.py

@@ -0,0 +1,585 @@
+import asyncio
+import importlib
+import inspect
+import multiprocessing
+import os
+import re
+import signal
+import socket
+import tempfile
+from argparse import Namespace
+from contextlib import asynccontextmanager
+from functools import partial
+from http import HTTPStatus
+from typing import AsyncIterator, Set
+
+import uvloop
+from fastapi import APIRouter, FastAPI, Request
+from fastapi.exceptions import RequestValidationError
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.responses import JSONResponse, Response, StreamingResponse
+from starlette.datastructures import State
+from starlette.routing import Mount
+from typing_extensions import assert_never
+
+import vllm.envs as envs
+from vllm.config import ModelConfig
+from vllm.engine.arg_utils import AsyncEngineArgs
+from vllm.engine.async_llm_engine import AsyncLLMEngine
+from vllm.engine.multiprocessing.client import MQLLMEngineClient
+from vllm.engine.multiprocessing.engine import run_mp_engine
+from vllm.engine.protocol import EngineClient
+from vllm.entrypoints.launcher import serve_http
+from vllm.entrypoints.logger import RequestLogger
+from vllm.entrypoints.openai.cli_args import (make_arg_parser,
+                                              validate_parsed_serve_args)
+# yapf conflicts with isort for this block
+# yapf: disable
+from vllm.entrypoints.openai.protocol import (ChatCompletionRequest,
+                                              ChatCompletionResponse,
+                                              CompletionRequest,
+                                              CompletionResponse,
+                                              DetokenizeRequest,
+                                              DetokenizeResponse,
+                                              EmbeddingRequest,
+                                              EmbeddingResponse, ErrorResponse,
+                                              LoadLoraAdapterRequest,
+                                              TokenizeRequest,
+                                              TokenizeResponse,
+                                              UnloadLoraAdapterRequest)
+# yapf: enable
+from vllm.entrypoints.openai.serving_chat import OpenAIServingChat
+from vllm.entrypoints.openai.serving_completion import OpenAIServingCompletion
+from vllm.entrypoints.openai.serving_embedding import OpenAIServingEmbedding
+from vllm.entrypoints.openai.serving_engine import BaseModelPath
+from vllm.entrypoints.openai.serving_tokenization import (
+    OpenAIServingTokenization)
+from vllm.entrypoints.openai.tool_parsers import ToolParserManager
+from vllm.logger import init_logger
+from vllm.usage.usage_lib import UsageContext
+from vllm.utils import FlexibleArgumentParser, get_open_zmq_ipc_path
+from vllm.version import __version__ as VLLM_VERSION
+
+TIMEOUT_KEEP_ALIVE = 5  # seconds
+
+prometheus_multiproc_dir: tempfile.TemporaryDirectory
+
+# Cannot use __name__ (https://github.com/vllm-project/vllm/pull/4765)
+logger = init_logger('vllm.entrypoints.openai.api_server')
+
+_running_tasks: Set[asyncio.Task] = set()
+
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    try:
+        if app.state.log_stats:
+            engine_client: EngineClient = app.state.engine_client
+
+            async def _force_log():
+                while True:
+                    await asyncio.sleep(10.)
+                    await engine_client.do_log_stats()
+
+            task = asyncio.create_task(_force_log())
+            _running_tasks.add(task)
+            task.add_done_callback(_running_tasks.remove)
+        else:
+            task = None
+        try:
+            yield
+        finally:
+            if task is not None:
+                task.cancel()
+    finally:
+        # Ensure app state including engine ref is gc'd
+        del app.state
+
+
+@asynccontextmanager
+async def build_async_engine_client(
+        args: Namespace) -> AsyncIterator[EngineClient]:
+
+    # Context manager to handle engine_client lifecycle
+    # Ensures everything is shutdown and cleaned up on error/exit
+    engine_args = AsyncEngineArgs.from_cli_args(args)
+
+    async with build_async_engine_client_from_engine_args(
+            engine_args, args.disable_frontend_multiprocessing) as engine:
+        yield engine
+
+
+@asynccontextmanager
+async def build_async_engine_client_from_engine_args(
+    engine_args: AsyncEngineArgs,
+    disable_frontend_multiprocessing: bool = False,
+) -> AsyncIterator[EngineClient]:
+    """
+    Create EngineClient, either:
+        - in-process using the AsyncLLMEngine Directly
+        - multiprocess using AsyncLLMEngine RPC
+
+    Returns the Client or None if the creation failed.
+    """
+
+    # Fall back
+    # TODO: fill out feature matrix.
+    if (MQLLMEngineClient.is_unsupported_config(engine_args)
+            or disable_frontend_multiprocessing):
+        engine_config = engine_args.create_engine_config()
+        uses_ray = getattr(AsyncLLMEngine._get_executor_cls(engine_config),
+                           "uses_ray", False)
+
+        build_engine = partial(AsyncLLMEngine.from_engine_args,
+                               engine_args=engine_args,
+                               engine_config=engine_config,
+                               usage_context=UsageContext.OPENAI_API_SERVER)
+        if uses_ray:
+            # Must run in main thread with ray for its signal handlers to work
+            engine_client = build_engine()
+        else:
+            engine_client = await asyncio.get_running_loop().run_in_executor(
+                None, build_engine)
+
+        yield engine_client
+        return
+
+    # Otherwise, use the multiprocessing AsyncLLMEngine.
+    else:
+        if "PROMETHEUS_MULTIPROC_DIR" not in os.environ:
+            # Make TemporaryDirectory for prometheus multiprocessing
+            # Note: global TemporaryDirectory will be automatically
+            #   cleaned up upon exit.
+            global prometheus_multiproc_dir
+            prometheus_multiproc_dir = tempfile.TemporaryDirectory()
+            os.environ[
+                "PROMETHEUS_MULTIPROC_DIR"] = prometheus_multiproc_dir.name
+        else:
+            logger.warning(
+                "Found PROMETHEUS_MULTIPROC_DIR was set by user. "
+                "This directory must be wiped between vLLM runs or "
+                "you will find inaccurate metrics. Unset the variable "
+                "and vLLM will properly handle cleanup.")
+
+        # Select random path for IPC.
+        ipc_path = get_open_zmq_ipc_path()
+        logger.info("Multiprocessing frontend to use %s for IPC Path.",
+                    ipc_path)
+
+        # Start RPCServer in separate process (holds the LLMEngine).
+        # the current process might have CUDA context,
+        # so we need to spawn a new process
+        context = multiprocessing.get_context("spawn")
+
+        engine_process = context.Process(target=run_mp_engine,
+                                         args=(engine_args,
+                                               UsageContext.OPENAI_API_SERVER,
+                                               ipc_path))
+        engine_process.start()
+        logger.info("Started engine process with PID %d", engine_process.pid)
+
+        # Build RPCClient, which conforms to EngineClient Protocol.
+        # NOTE: Actually, this is not true yet. We still need to support
+        # embedding models via RPC (see TODO above)
+        engine_config = engine_args.create_engine_config()
+        mp_engine_client = MQLLMEngineClient(ipc_path, engine_config)
+
+        try:
+            while True:
+                try:
+                    await mp_engine_client.setup()
+                    break
+                except TimeoutError:
+                    if not engine_process.is_alive():
+                        raise RuntimeError(
+                            "Engine process failed to start") from None
+
+            yield mp_engine_client  # type: ignore[misc]
+        finally:
+            # Ensure rpc server process was terminated
+            engine_process.terminate()
+
+            # Close all open connections to the backend
+            mp_engine_client.close()
+
+            # Wait for engine process to join
+            engine_process.join(4)
+            if engine_process.exitcode is None:
+                # Kill if taking longer than 5 seconds to stop
+                engine_process.kill()
+
+            # Lazy import for prometheus multiprocessing.
+            # We need to set PROMETHEUS_MULTIPROC_DIR environment variable
+            # before prometheus_client is imported.
+            # See https://prometheus.github.io/client_python/multiprocess/
+            from prometheus_client import multiprocess
+            multiprocess.mark_process_dead(engine_process.pid)
+
+
+router = APIRouter()
+
+
+def mount_metrics(app: FastAPI):
+    # Lazy import for prometheus multiprocessing.
+    # We need to set PROMETHEUS_MULTIPROC_DIR environment variable
+    # before prometheus_client is imported.
+    # See https://prometheus.github.io/client_python/multiprocess/
+    from prometheus_client import (CollectorRegistry, make_asgi_app,
+                                   multiprocess)
+
+    prometheus_multiproc_dir_path = os.getenv("PROMETHEUS_MULTIPROC_DIR", None)
+    if prometheus_multiproc_dir_path is not None:
+        logger.info("vLLM to use %s as PROMETHEUS_MULTIPROC_DIR",
+                    prometheus_multiproc_dir_path)
+        registry = CollectorRegistry()
+        multiprocess.MultiProcessCollector(registry)
+
+        # Add prometheus asgi middleware to route /metrics requests
+        metrics_route = Mount("/metrics", make_asgi_app(registry=registry))
+    else:
+        # Add prometheus asgi middleware to route /metrics requests
+        metrics_route = Mount("/metrics", make_asgi_app())
+
+    # Workaround for 307 Redirect for /metrics
+    metrics_route.path_regex = re.compile("^/metrics(?P<path>.*)$")
+    app.routes.append(metrics_route)
+
+
+def chat(request: Request) -> OpenAIServingChat:
+    return request.app.state.openai_serving_chat
+
+
+def completion(request: Request) -> OpenAIServingCompletion:
+    return request.app.state.openai_serving_completion
+
+
+def tokenization(request: Request) -> OpenAIServingTokenization:
+    return request.app.state.openai_serving_tokenization
+
+
+def embedding(request: Request) -> OpenAIServingEmbedding:
+    return request.app.state.openai_serving_embedding
+
+
+def engine_client(request: Request) -> EngineClient:
+    return request.app.state.engine_client
+
+
+@router.get("/health")
+async def health(raw_request: Request) -> Response:
+    """Health check."""
+    await engine_client(raw_request).check_health()
+    return Response(status_code=200)
+
+
+@router.post("/tokenize")
+async def tokenize(request: TokenizeRequest, raw_request: Request):
+    generator = await tokenization(raw_request).create_tokenize(request)
+    if isinstance(generator, ErrorResponse):
+        return JSONResponse(content=generator.model_dump(),
+                            status_code=generator.code)
+    elif isinstance(generator, TokenizeResponse):
+        return JSONResponse(content=generator.model_dump())
+
+    assert_never(generator)
+
+
+@router.post("/detokenize")
+async def detokenize(request: DetokenizeRequest, raw_request: Request):
+    generator = await tokenization(raw_request).create_detokenize(request)
+    if isinstance(generator, ErrorResponse):
+        return JSONResponse(content=generator.model_dump(),
+                            status_code=generator.code)
+    elif isinstance(generator, DetokenizeResponse):
+        return JSONResponse(content=generator.model_dump())
+
+    assert_never(generator)
+
+
+@router.get("/v1/models")
+async def show_available_models(raw_request: Request):
+    models = await completion(raw_request).show_available_models()
+    return JSONResponse(content=models.model_dump())
+
+
+@router.get("/version")
+async def show_version():
+    ver = {"version": VLLM_VERSION}
+    return JSONResponse(content=ver)
+
+
+@router.post("/v1/chat/completions")
+async def create_chat_completion(request: ChatCompletionRequest,
+                                 raw_request: Request):
+
+    generator = await chat(raw_request).create_chat_completion(
+        request, raw_request)
+
+    if isinstance(generator, ErrorResponse):
+        return JSONResponse(content=generator.model_dump(),
+                            status_code=generator.code)
+
+    elif isinstance(generator, ChatCompletionResponse):
+        return JSONResponse(content=generator.model_dump())
+
+    return StreamingResponse(content=generator, media_type="text/event-stream")
+
+
+@router.post("/v1/completions")
+async def create_completion(request: CompletionRequest, raw_request: Request):
+    generator = await completion(raw_request).create_completion(
+        request, raw_request)
+    if isinstance(generator, ErrorResponse):
+        return JSONResponse(content=generator.model_dump(),
+                            status_code=generator.code)
+    elif isinstance(generator, CompletionResponse):
+        return JSONResponse(content=generator.model_dump())
+
+    return StreamingResponse(content=generator, media_type="text/event-stream")
+
+
+@router.post("/v1/embeddings")
+async def create_embedding(request: EmbeddingRequest, raw_request: Request):
+    generator = await embedding(raw_request).create_embedding(
+        request, raw_request)
+    if isinstance(generator, ErrorResponse):
+        return JSONResponse(content=generator.model_dump(),
+                            status_code=generator.code)
+    elif isinstance(generator, EmbeddingResponse):
+        return JSONResponse(content=generator.model_dump())
+
+    assert_never(generator)
+
+
+if envs.VLLM_TORCH_PROFILER_DIR:
+    logger.warning(
+        "Torch Profiler is enabled in the API server. This should ONLY be "
+        "used for local development!")
+
+    @router.post("/start_profile")
+    async def start_profile(raw_request: Request):
+        logger.info("Starting profiler...")
+        await engine_client(raw_request).start_profile()
+        logger.info("Profiler started.")
+        return Response(status_code=200)
+
+    @router.post("/stop_profile")
+    async def stop_profile(raw_request: Request):
+        logger.info("Stopping profiler...")
+        await engine_client(raw_request).stop_profile()
+        logger.info("Profiler stopped.")
+        return Response(status_code=200)
+
+
+if envs.VLLM_ALLOW_RUNTIME_LORA_UPDATING:
+    logger.warning(
+        "Lora dynamic loading & unloading is enabled in the API server. "
+        "This should ONLY be used for local development!")
+
+    @router.post("/v1/load_lora_adapter")
+    async def load_lora_adapter(request: LoadLoraAdapterRequest,
+                                raw_request: Request):
+        response = await chat(raw_request).load_lora_adapter(request)
+        if isinstance(response, ErrorResponse):
+            return JSONResponse(content=response.model_dump(),
+                                status_code=response.code)
+
+        response = await completion(raw_request).load_lora_adapter(request)
+        if isinstance(response, ErrorResponse):
+            return JSONResponse(content=response.model_dump(),
+                                status_code=response.code)
+
+        return Response(status_code=200, content=response)
+
+    @router.post("/v1/unload_lora_adapter")
+    async def unload_lora_adapter(request: UnloadLoraAdapterRequest,
+                                  raw_request: Request):
+        response = await chat(raw_request).unload_lora_adapter(request)
+        if isinstance(response, ErrorResponse):
+            return JSONResponse(content=response.model_dump(),
+                                status_code=response.code)
+
+        response = await completion(raw_request).unload_lora_adapter(request)
+        if isinstance(response, ErrorResponse):
+            return JSONResponse(content=response.model_dump(),
+                                status_code=response.code)
+
+        return Response(status_code=200, content=response)
+
+
+def build_app(args: Namespace) -> FastAPI:
+    if args.disable_fastapi_docs:
+        app = FastAPI(openapi_url=None,
+                      docs_url=None,
+                      redoc_url=None,
+                      lifespan=lifespan)
+    else:
+        app = FastAPI(lifespan=lifespan)
+    app.include_router(router)
+    app.root_path = args.root_path
+
+    mount_metrics(app)
+
+    app.add_middleware(
+        CORSMiddleware,
+        allow_origins=args.allowed_origins,
+        allow_credentials=args.allow_credentials,
+        allow_methods=args.allowed_methods,
+        allow_headers=args.allowed_headers,
+    )
+
+    @app.exception_handler(RequestValidationError)
+    async def validation_exception_handler(_, exc):
+        chat = app.state.openai_serving_chat
+        err = chat.create_error_response(message=str(exc))
+        return JSONResponse(err.model_dump(),
+                            status_code=HTTPStatus.BAD_REQUEST)
+
+    if token := envs.VLLM_API_KEY or args.api_key:
+
+        @app.middleware("http")
+        async def authentication(request: Request, call_next):
+            root_path = "" if args.root_path is None else args.root_path
+            if request.method == "OPTIONS":
+                return await call_next(request)
+            if not request.url.path.startswith(f"{root_path}/v1"):
+                return await call_next(request)
+            if request.headers.get("Authorization") != "Bearer " + token:
+                return JSONResponse(content={"error": "Unauthorized"},
+                                    status_code=401)
+            return await call_next(request)
+
+    for middleware in args.middleware:
+        module_path, object_name = middleware.rsplit(".", 1)
+        imported = getattr(importlib.import_module(module_path), object_name)
+        if inspect.isclass(imported):
+            app.add_middleware(imported)
+        elif inspect.iscoroutinefunction(imported):
+            app.middleware("http")(imported)
+        else:
+            raise ValueError(f"Invalid middleware {middleware}. "
+                             f"Must be a function or a class.")
+
+    return app
+
+
+def init_app_state(
+    engine_client: EngineClient,
+    model_config: ModelConfig,
+    state: State,
+    args: Namespace,
+) -> None:
+    if args.served_model_name is not None:
+        served_model_names = args.served_model_name
+    else:
+        served_model_names = [args.model]
+
+    if args.disable_log_requests:
+        request_logger = None
+    else:
+        request_logger = RequestLogger(max_log_len=args.max_log_len)
+
+    base_model_paths = [
+        BaseModelPath(name=name, model_path=args.model)
+        for name in served_model_names
+    ]
+
+    state.engine_client = engine_client
+    state.log_stats = not args.disable_log_stats
+
+    state.openai_serving_chat = OpenAIServingChat(
+        engine_client,
+        model_config,
+        base_model_paths,
+        args.response_role,
+        lora_modules=args.lora_modules,
+        prompt_adapters=args.prompt_adapters,
+        request_logger=request_logger,
+        chat_template=args.chat_template,
+        return_tokens_as_token_ids=args.return_tokens_as_token_ids,
+        enable_auto_tools=args.enable_auto_tool_choice,
+        tool_parser=args.tool_call_parser)
+    state.openai_serving_completion = OpenAIServingCompletion(
+        engine_client,
+        model_config,
+        base_model_paths,
+        lora_modules=args.lora_modules,
+        prompt_adapters=args.prompt_adapters,
+        request_logger=request_logger,
+        return_tokens_as_token_ids=args.return_tokens_as_token_ids,
+    )
+    state.openai_serving_embedding = OpenAIServingEmbedding(
+        engine_client,
+        model_config,
+        base_model_paths,
+        request_logger=request_logger,
+    )
+    state.openai_serving_tokenization = OpenAIServingTokenization(
+        engine_client,
+        model_config,
+        base_model_paths,
+        lora_modules=args.lora_modules,
+        request_logger=request_logger,
+        chat_template=args.chat_template,
+    )
+
+
+async def run_server(args, **uvicorn_kwargs) -> None:
+    logger.info("vLLM API server version %s", VLLM_VERSION)
+    logger.info("args: %s", args)
+
+    if args.tool_parser_plugin and len(args.tool_parser_plugin) > 3:
+        ToolParserManager.import_tool_parser(args.tool_parser_plugin)
+
+    valide_tool_parses = ToolParserManager.tool_parsers.keys()
+    if args.enable_auto_tool_choice \
+        and args.tool_call_parser not in valide_tool_parses:
+        raise KeyError(f"invalid tool call parser: {args.tool_call_parser} "
+                       f"(chose from {{ {','.join(valide_tool_parses)} }})")
+
+    # workaround to make sure that we bind the port before the engine is set up.
+    # This avoids race conditions with ray.
+    # see https://github.com/vllm-project/vllm/issues/8204
+    sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
+    sock.bind(("", args.port))
+
+    def signal_handler(*_) -> None:
+        # Interrupt server on sigterm while initializing
+        raise KeyboardInterrupt("terminated")
+
+    signal.signal(signal.SIGTERM, signal_handler)
+
+    async with build_async_engine_client(args) as engine_client:
+        app = build_app(args)
+
+        model_config = await engine_client.get_model_config()
+        init_app_state(engine_client, model_config, app.state, args)
+
+        shutdown_task = await serve_http(
+            app,
+            host=args.host,
+            port=args.port,
+            log_level=args.uvicorn_log_level,
+            timeout_keep_alive=TIMEOUT_KEEP_ALIVE,
+            ssl_keyfile=args.ssl_keyfile,
+            ssl_certfile=args.ssl_certfile,
+            ssl_ca_certs=args.ssl_ca_certs,
+            ssl_cert_reqs=args.ssl_cert_reqs,
+            fd=sock.fileno(),
+            **uvicorn_kwargs,
+        )
+
+    # NB: Await server shutdown only after the backend context is exited
+    await shutdown_task
+
+
+if __name__ == "__main__":
+    # NOTE(simon):
+    # This section should be in sync with vllm/scripts.py for CLI entrypoints.
+    parser = FlexibleArgumentParser(
+        description="vLLM OpenAI-Compatible RESTful API server.")
+    parser = make_arg_parser(parser)
+    args = parser.parse_args()
+    validate_parsed_serve_args(args)
+
+    uvloop.run(run_server(args))

vllm/entrypoints/openai/cli_args.py

@@ -0,0 +1,252 @@
+"""
+This file contains the command line arguments for the vLLM's
+OpenAI-compatible server. It is kept in a separate file for documentation
+purposes.
+"""
+
+import argparse
+import json
+import ssl
+from typing import List, Optional, Sequence, Union
+
+from vllm.engine.arg_utils import AsyncEngineArgs, nullable_str
+from vllm.entrypoints.chat_utils import validate_chat_template
+from vllm.entrypoints.openai.serving_engine import (LoRAModulePath,
+                                                    PromptAdapterPath)
+from vllm.entrypoints.openai.tool_parsers import ToolParserManager
+from vllm.utils import FlexibleArgumentParser
+
+
+class LoRAParserAction(argparse.Action):
+
+    def __call__(
+        self,
+        parser: argparse.ArgumentParser,
+        namespace: argparse.Namespace,
+        values: Optional[Union[str, Sequence[str]]],
+        option_string: Optional[str] = None,
+    ):
+        if values is None:
+            values = []
+        if isinstance(values, str):
+            raise TypeError("Expected values to be a list")
+
+        lora_list: List[LoRAModulePath] = []
+        for item in values:
+            if item in [None, '']:  # Skip if item is None or empty string
+                continue
+            if '=' in item and ',' not in item:  # Old format: name=path
+                name, path = item.split('=')
+                lora_list.append(LoRAModulePath(name, path))
+            else:  # Assume JSON format
+                try:
+                    lora_dict = json.loads(item)
+                    lora = LoRAModulePath(**lora_dict)
+                    lora_list.append(lora)
+                except json.JSONDecodeError:
+                    parser.error(
+                        f"Invalid JSON format for --lora-modules: {item}")
+                except TypeError as e:
+                    parser.error(
+                        f"Invalid fields for --lora-modules: {item} - {str(e)}"
+                    )
+        setattr(namespace, self.dest, lora_list)
+
+
+class PromptAdapterParserAction(argparse.Action):
+
+    def __call__(
+        self,
+        parser: argparse.ArgumentParser,
+        namespace: argparse.Namespace,
+        values: Optional[Union[str, Sequence[str]]],
+        option_string: Optional[str] = None,
+    ):
+        if values is None:
+            values = []
+        if isinstance(values, str):
+            raise TypeError("Expected values to be a list")
+
+        adapter_list: List[PromptAdapterPath] = []
+        for item in values:
+            name, path = item.split('=')
+            adapter_list.append(PromptAdapterPath(name, path))
+        setattr(namespace, self.dest, adapter_list)
+
+
+def make_arg_parser(parser: FlexibleArgumentParser) -> FlexibleArgumentParser:
+    parser.add_argument("--host",
+                        type=nullable_str,
+                        default=None,
+                        help="host name")
+    parser.add_argument("--port", type=int, default=8000, help="port number")
+    parser.add_argument(
+        "--uvicorn-log-level",
+        type=str,
+        default="info",
+        choices=['debug', 'info', 'warning', 'error', 'critical', 'trace'],
+        help="log level for uvicorn")
+    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("--api-key",
+                        type=nullable_str,
+                        default=None,
+                        help="If provided, the server will require this key "
+                        "to be presented in the header.")
+    parser.add_argument(
+        "--lora-modules",
+        type=nullable_str,
+        default=None,
+        nargs='+',
+        action=LoRAParserAction,
+        help="LoRA module configurations in either 'name=path' format"
+        "or JSON format. "
+        "Example (old format): 'name=path' "
+        "Example (new format): "
+        "'{\"name\": \"name\", \"local_path\": \"path\", "
+        "\"base_model_name\": \"id\"}'")
+    parser.add_argument(
+        "--prompt-adapters",
+        type=nullable_str,
+        default=None,
+        nargs='+',
+        action=PromptAdapterParserAction,
+        help="Prompt adapter configurations in the format name=path. "
+        "Multiple adapters can be specified.")
+    parser.add_argument("--chat-template",
+                        type=nullable_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=nullable_str,
+                        default="assistant",
+                        help="The role name to return if "
+                        "`request.add_generation_prompt=true`.")
+    parser.add_argument("--ssl-keyfile",
+                        type=nullable_str,
+                        default=None,
+                        help="The file path to the SSL key file")
+    parser.add_argument("--ssl-certfile",
+                        type=nullable_str,
+                        default=None,
+                        help="The file path to the SSL cert file")
+    parser.add_argument("--ssl-ca-certs",
+                        type=nullable_str,
+                        default=None,
+                        help="The CA certificates file")
+    parser.add_argument(
+        "--ssl-cert-reqs",
+        type=int,
+        default=int(ssl.CERT_NONE),
+        help="Whether client certificate is required (see stdlib ssl module's)"
+    )
+    parser.add_argument(
+        "--root-path",
+        type=nullable_str,
+        default=None,
+        help="FastAPI root_path when app is behind a path based routing proxy")
+    parser.add_argument(
+        "--middleware",
+        type=nullable_str,
+        action="append",
+        default=[],
+        help="Additional ASGI middleware to apply to the app. "
+        "We accept multiple --middleware arguments. "
+        "The value should be an import path. "
+        "If a function is provided, vLLM will add it to the server "
+        "using @app.middleware('http'). "
+        "If a class is provided, vLLM will add it to the server "
+        "using app.add_middleware(). ")
+    parser.add_argument(
+        "--return-tokens-as-token-ids",
+        action="store_true",
+        help="When --max-logprobs is specified, represents single tokens as "
+        "strings of the form 'token_id:{token_id}' so that tokens that "
+        "are not JSON-encodable can be identified.")
+    parser.add_argument(
+        "--disable-frontend-multiprocessing",
+        action="store_true",
+        help="If specified, will run the OpenAI frontend server in the same "
+        "process as the model serving engine.")
+
+    parser.add_argument(
+        "--enable-auto-tool-choice",
+        action="store_true",
+        default=False,
+        help=
+        "Enable auto tool choice for supported models. Use --tool-call-parser"
+        "to specify which parser to use")
+
+    valid_tool_parsers = ToolParserManager.tool_parsers.keys()
+    parser.add_argument(
+        "--tool-call-parser",
+        type=str,
+        metavar="{" + ",".join(valid_tool_parsers) + "} or name registered in "
+        "--tool-parser-plugin",
+        default=None,
+        help=
+        "Select the tool call parser depending on the model that you're using."
+        " This is used to parse the model-generated tool call into OpenAI API "
+        "format. Required for --enable-auto-tool-choice.")
+
+    parser.add_argument(
+        "--tool-parser-plugin",
+        type=str,
+        default="",
+        help=
+        "Special the tool parser plugin write to parse the model-generated tool"
+        " into OpenAI API format, the name register in this plugin can be used "
+        "in --tool-call-parser.")
+
+    parser = AsyncEngineArgs.add_cli_args(parser)
+
+    parser.add_argument('--max-log-len',
+                        type=int,
+                        default=None,
+                        help='Max number of prompt characters or prompt '
+                        'ID numbers being printed in log.'
+                        '\n\nDefault: Unlimited')
+
+    parser.add_argument(
+        "--disable-fastapi-docs",
+        action='store_true',
+        default=False,
+        help="Disable FastAPI's OpenAPI schema, Swagger UI, and ReDoc endpoint"
+    )
+
+    return parser
+
+
+def validate_parsed_serve_args(args: argparse.Namespace):
+    """Quick checks for model serve args that raise prior to loading."""
+    if hasattr(args, "subparser") and args.subparser != "serve":
+        return
+
+    # Ensure that the chat template is valid; raises if it likely isn't
+    validate_chat_template(args.chat_template)
+
+    # Enable auto tool needs a tool call parser to be valid
+    if args.enable_auto_tool_choice and not args.tool_call_parser:
+        raise TypeError("Error: --enable-auto-tool-choice requires "
+                        "--tool-call-parser")
+
+
+def create_parser_for_docs() -> FlexibleArgumentParser:
+    parser_for_docs = FlexibleArgumentParser(
+        prog="-m vllm.entrypoints.openai.api_server")
+    return make_arg_parser(parser_for_docs)

vllm/entrypoints/openai/logits_processors.py

@@ -0,0 +1,86 @@
+from functools import lru_cache, partial
+from typing import Dict, FrozenSet, Iterable, List, Optional, Union
+
+import torch
+
+from vllm.sampling_params import LogitsProcessor
+from vllm.transformers_utils.tokenizer import AnyTokenizer
+
+
+class AllowedTokenIdsLogitsProcessor:
+    """Logits processor for constraining generated tokens to a
+    specific set of token ids."""
+
+    def __init__(self, allowed_ids: Iterable[int]):
+        self.allowed_ids: Optional[List[int]] = list(allowed_ids)
+        self.mask: Optional[torch.Tensor] = None
+
+    def __call__(self, token_ids: List[int],
+                 logits: torch.Tensor) -> torch.Tensor:
+        if self.mask is None:
+            self.mask = torch.ones((logits.shape[-1], ),
+                                   dtype=torch.bool,
+                                   device=logits.device)
+            self.mask[self.allowed_ids] = False
+            self.allowed_ids = None
+        logits.masked_fill_(self.mask, float("-inf"))
+        return logits
+
+
+@lru_cache(maxsize=32)
+def _get_allowed_token_ids_logits_processor(
+    allowed_token_ids: FrozenSet[int],
+    vocab_size: int,
+) -> LogitsProcessor:
+    if not allowed_token_ids:
+        raise ValueError("Empty allowed_token_ids provided")
+    if not all(0 <= tid < vocab_size for tid in allowed_token_ids):
+        raise ValueError("allowed_token_ids contains "
+                         "out-of-vocab token id")
+    return AllowedTokenIdsLogitsProcessor(allowed_token_ids)
+
+
+def logit_bias_logits_processor(
+    logit_bias: Dict[int, float],
+    token_ids: List[int],
+    logits: torch.Tensor,
+) -> torch.Tensor:
+    for token_id, bias in logit_bias.items():
+        logits[token_id] += bias
+    return logits
+
+
+def get_logits_processors(
+    logit_bias: Optional[Union[Dict[int, float], Dict[str, float]]],
+    allowed_token_ids: Optional[List[int]],
+    tokenizer: AnyTokenizer,
+) -> List[LogitsProcessor]:
+    logits_processors: List[LogitsProcessor] = []
+    if logit_bias:
+        try:
+            # Convert token_id to integer
+            # Clamp the bias between -100 and 100 per OpenAI API spec
+            clamped_logit_bias: Dict[int, float] = {
+                int(token_id): min(100.0, max(-100.0, bias))
+                for token_id, bias in logit_bias.items()
+            }
+        except ValueError as exc:
+            raise ValueError(
+                "Found token_id in logit_bias that is not "
+                "an integer or string representing an integer") from exc
+
+        # Check if token_id is within the vocab size
+        for token_id, bias in clamped_logit_bias.items():
+            if token_id < 0 or token_id >= tokenizer.vocab_size:
+                raise ValueError(f"token_id {token_id} in logit_bias contains "
+                                 "out-of-vocab token id")
+
+        logits_processors.append(
+            partial(logit_bias_logits_processor, clamped_logit_bias))
+
+    if allowed_token_ids is not None:
+        logits_processors.append(
+            _get_allowed_token_ids_logits_processor(
+                frozenset(allowed_token_ids), tokenizer.vocab_size))
+
+    return logits_processors

vllm/entrypoints/openai/protocol.py

@@ -0,0 +1,992 @@
+# Adapted from
+# https://github.com/lm-sys/FastChat/blob/168ccc29d3f7edc50823016105c024fe2282732a/fastchat/protocol/openai_api_protocol.py
+import time
+from argparse import Namespace
+from typing import Any, Dict, List, Literal, Optional, Union
+
+import torch
+from openai.types.chat import ChatCompletionContentPartParam
+from pydantic import BaseModel, ConfigDict, Field, model_validator
+from typing_extensions import Annotated, Required, TypedDict
+
+from vllm.entrypoints.chat_utils import ChatCompletionMessageParam
+from vllm.pooling_params import PoolingParams
+from vllm.sampling_params import (BeamSearchParams, GuidedDecodingParams,
+                                  RequestOutputKind, SamplingParams)
+from vllm.sequence import Logprob
+from vllm.utils import random_uuid
+
+# torch is mocked during docs generation,
+# so we have to provide the values as literals
+_MOCK_LONG_INFO = Namespace(min=-9223372036854775808, max=9223372036854775807)
+_LONG_INFO: Union["torch.iinfo", Namespace]
+
+try:
+    from sphinx.ext.autodoc.mock import _MockModule
+
+    if isinstance(torch, _MockModule):
+        _LONG_INFO = _MOCK_LONG_INFO
+    else:
+        _LONG_INFO = torch.iinfo(torch.long)
+except ModuleNotFoundError:
+    _LONG_INFO = torch.iinfo(torch.long)
+
+assert _LONG_INFO.min == _MOCK_LONG_INFO.min
+assert _LONG_INFO.max == _MOCK_LONG_INFO.max
+
+
+class CustomChatCompletionMessageParam(TypedDict, total=False):
+    """Enables custom roles in the Chat Completion API."""
+    role: Required[str]
+    """The role of the message's author."""
+
+    content: Union[str, List[ChatCompletionContentPartParam]]
+    """The contents of the message."""
+
+    name: str
+    """An optional name for the participant.
+
+    Provides the model information to differentiate between participants of the
+    same role.
+    """
+
+    tool_call_id: Optional[str]
+
+    tool_calls: Optional[List[dict]]
+
+
+class OpenAIBaseModel(BaseModel):
+    # OpenAI API does not allow extra fields
+    model_config = ConfigDict(extra="forbid")
+
+
+class ErrorResponse(OpenAIBaseModel):
+    object: str = "error"
+    message: str
+    type: str
+    param: Optional[str] = None
+    code: int
+
+
+class ModelPermission(OpenAIBaseModel):
+    id: str = Field(default_factory=lambda: f"modelperm-{random_uuid()}")
+    object: str = "model_permission"
+    created: int = Field(default_factory=lambda: int(time.time()))
+    allow_create_engine: bool = False
+    allow_sampling: bool = True
+    allow_logprobs: bool = True
+    allow_search_indices: bool = False
+    allow_view: bool = True
+    allow_fine_tuning: bool = False
+    organization: str = "*"
+    group: Optional[str] = None
+    is_blocking: bool = False
+
+
+class ModelCard(OpenAIBaseModel):
+    id: str
+    object: str = "model"
+    created: int = Field(default_factory=lambda: int(time.time()))
+    owned_by: str = "vllm"
+    root: Optional[str] = None
+    parent: Optional[str] = None
+    max_model_len: Optional[int] = None
+    permission: List[ModelPermission] = Field(default_factory=list)
+
+
+class ModelList(OpenAIBaseModel):
+    object: str = "list"
+    data: List[ModelCard] = Field(default_factory=list)
+
+
+class UsageInfo(OpenAIBaseModel):
+    prompt_tokens: int = 0
+    total_tokens: int = 0
+    completion_tokens: Optional[int] = 0
+
+
+class RequestResponseMetadata(BaseModel):
+    request_id: str
+    final_usage_info: Optional[UsageInfo] = None
+
+
+class JsonSchemaResponseFormat(OpenAIBaseModel):
+    name: str
+    description: Optional[str] = None
+    # schema is the field in openai but that causes conflicts with pydantic so
+    # instead use json_schema with an alias
+    json_schema: Optional[Dict[str, Any]] = Field(default=None, alias='schema')
+    strict: Optional[bool] = None
+
+
+class ResponseFormat(OpenAIBaseModel):
+    # type must be "json_schema", "json_object" or "text"
+    type: Literal["text", "json_object", "json_schema"]
+    json_schema: Optional[JsonSchemaResponseFormat] = None
+
+
+class StreamOptions(OpenAIBaseModel):
+    include_usage: Optional[bool] = True
+    continuous_usage_stats: Optional[bool] = True
+
+
+class FunctionDefinition(OpenAIBaseModel):
+    name: str
+    description: Optional[str] = None
+    parameters: Optional[Dict[str, Any]] = None
+
+
+class ChatCompletionToolsParam(OpenAIBaseModel):
+    type: Literal["function"] = "function"
+    function: FunctionDefinition
+
+
+class ChatCompletionNamedFunction(OpenAIBaseModel):
+    name: str
+
+
+class ChatCompletionNamedToolChoiceParam(OpenAIBaseModel):
+    function: ChatCompletionNamedFunction
+    type: Literal["function"] = "function"
+
+
+class ChatCompletionRequest(OpenAIBaseModel):
+    # Ordered by official OpenAI API documentation
+    # https://platform.openai.com/docs/api-reference/chat/create
+    messages: List[ChatCompletionMessageParam]
+    model: str
+    frequency_penalty: Optional[float] = 0.0
+    logit_bias: Optional[Dict[str, float]] = None
+    logprobs: Optional[bool] = False
+    top_logprobs: Optional[int] = 0
+    max_tokens: Optional[int] = None
+    n: Optional[int] = 1
+    presence_penalty: Optional[float] = 0.0
+    response_format: Optional[ResponseFormat] = None
+    seed: Optional[int] = Field(None, ge=_LONG_INFO.min, le=_LONG_INFO.max)
+    stop: Optional[Union[str, List[str]]] = Field(default_factory=list)
+    stream: Optional[bool] = False
+    stream_options: Optional[StreamOptions] = None
+    temperature: Optional[float] = 0.7
+    top_p: Optional[float] = 1.0
+    tools: Optional[List[ChatCompletionToolsParam]] = None
+    tool_choice: Optional[Union[Literal["none"], Literal["auto"],
+                                ChatCompletionNamedToolChoiceParam]] = "none"
+
+    # NOTE this will be ignored by VLLM -- the model determines the behavior
+    parallel_tool_calls: Optional[bool] = False
+    user: Optional[str] = None
+
+    # doc: begin-chat-completion-sampling-params
+    best_of: Optional[int] = None
+    use_beam_search: bool = False
+    top_k: int = -1
+    min_p: float = 0.0
+    repetition_penalty: float = 1.0
+    length_penalty: float = 1.0
+    stop_token_ids: Optional[List[int]] = Field(default_factory=list)
+    include_stop_str_in_output: bool = False
+    ignore_eos: bool = False
+    min_tokens: int = 0
+    skip_special_tokens: bool = True
+    spaces_between_special_tokens: bool = True
+    truncate_prompt_tokens: Optional[Annotated[int, Field(ge=1)]] = None
+    prompt_logprobs: Optional[int] = None
+    # doc: end-chat-completion-sampling-params
+
+    # doc: begin-chat-completion-extra-params
+    echo: bool = Field(
+        default=False,
+        description=(
+            "If true, the new message will be prepended with the last message "
+            "if they belong to the same role."),
+    )
+    add_generation_prompt: bool = Field(
+        default=True,
+        description=
+        ("If true, the generation prompt will be added to the chat template. "
+         "This is a parameter used by chat template in tokenizer config of the "
+         "model."),
+    )
+    continue_final_message: bool = Field(
+        default=False,
+        description=
+        ("If this is set, the chat will be formatted so that the final "
+         "message in the chat is open-ended, without any EOS tokens. The "
+         "model will continue this message rather than starting a new one. "
+         "This allows you to \"prefill\" part of the model's response for it. "
+         "Cannot be used at the same time as `add_generation_prompt`."),
+    )
+    add_special_tokens: bool = Field(
+        default=False,
+        description=(
+            "If true, special tokens (e.g. BOS) will be added to the prompt "
+            "on top of what is added by the chat template. "
+            "For most models, the chat template takes care of adding the "
+            "special tokens so this should be set to false (as is the "
+            "default)."),
+    )
+    documents: Optional[List[Dict[str, str]]] = Field(
+        default=None,
+        description=
+        ("A list of dicts representing documents that will be accessible to "
+         "the model if it is performing RAG (retrieval-augmented generation)."
+         " If the template does not support RAG, this argument will have no "
+         "effect. We recommend that each document should be a dict containing "
+         "\"title\" and \"text\" keys."),
+    )
+    chat_template: Optional[str] = Field(
+        default=None,
+        description=(
+            "A Jinja template to use for this conversion. "
+            "As of transformers v4.44, default chat template is no longer "
+            "allowed, so you must provide a chat template if the tokenizer "
+            "does not define one."),
+    )
+    chat_template_kwargs: Optional[Dict[str, Any]] = Field(
+        default=None,
+        description=("Additional kwargs to pass to the template renderer. "
+                     "Will be accessible by the chat template."),
+    )
+    guided_json: Optional[Union[str, dict, BaseModel]] = Field(
+        default=None,
+        description=("If specified, the output will follow the JSON schema."),
+    )
+    guided_regex: Optional[str] = Field(
+        default=None,
+        description=(
+            "If specified, the output will follow the regex pattern."),
+    )
+    guided_choice: Optional[List[str]] = Field(
+        default=None,
+        description=(
+            "If specified, the output will be exactly one of the choices."),
+    )
+    guided_grammar: Optional[str] = Field(
+        default=None,
+        description=(
+            "If specified, the output will follow the context free grammar."),
+    )
+    guided_decoding_backend: Optional[str] = Field(
+        default=None,
+        description=(
+            "If specified, will override the default guided decoding backend "
+            "of the server for this specific request. If set, must be either "
+            "'outlines' / 'lm-format-enforcer'"))
+    guided_whitespace_pattern: Optional[str] = Field(
+        default=None,
+        description=(
+            "If specified, will override the default whitespace pattern "
+            "for guided json decoding."))
+    priority: int = Field(
+        default=0,
+        description=(
+            "The priority of the request (lower means earlier handling; "
+            "default: 0). Any priority other than 0 will raise an error "
+            "if the served model does not use priority scheduling."))
+
+    # doc: end-chat-completion-extra-params
+
+    def to_beam_search_params(self,
+                              default_max_tokens: int) -> BeamSearchParams:
+        max_tokens = self.max_tokens
+        if max_tokens is None:
+            max_tokens = default_max_tokens
+
+        n = self.n if self.n is not None else 1
+        temperature = self.temperature if self.temperature is not None else 0.0
+
+        return BeamSearchParams(
+            beam_width=n,
+            max_tokens=max_tokens,
+            ignore_eos=self.ignore_eos,
+            temperature=temperature,
+            length_penalty=self.length_penalty,
+        )
+
+    def to_sampling_params(self, default_max_tokens: int) -> SamplingParams:
+        max_tokens = self.max_tokens
+        if max_tokens is None:
+            max_tokens = default_max_tokens
+
+        prompt_logprobs = self.prompt_logprobs
+        if prompt_logprobs is None and self.echo:
+            prompt_logprobs = self.top_logprobs
+
+        guided_json_object = None
+        if (self.response_format is not None
+                and self.response_format.type == "json_object"):
+            guided_json_object = True
+
+        guided_decoding = GuidedDecodingParams.from_optional(
+            json=self._get_guided_json_from_tool() or self.guided_json,
+            regex=self.guided_regex,
+            choice=self.guided_choice,
+            grammar=self.guided_grammar,
+            json_object=guided_json_object,
+            backend=self.guided_decoding_backend,
+            whitespace_pattern=self.guided_whitespace_pattern)
+
+        return SamplingParams.from_optional(
+            n=self.n,
+            best_of=self.best_of,
+            presence_penalty=self.presence_penalty,
+            frequency_penalty=self.frequency_penalty,
+            repetition_penalty=self.repetition_penalty,
+            temperature=self.temperature,
+            top_p=self.top_p,
+            top_k=self.top_k,
+            min_p=self.min_p,
+            seed=self.seed,
+            stop=self.stop,
+            stop_token_ids=self.stop_token_ids,
+            logprobs=self.top_logprobs if self.logprobs else None,
+            prompt_logprobs=prompt_logprobs,
+            ignore_eos=self.ignore_eos,
+            max_tokens=max_tokens,
+            min_tokens=self.min_tokens,
+            skip_special_tokens=self.skip_special_tokens,
+            spaces_between_special_tokens=self.spaces_between_special_tokens,
+            include_stop_str_in_output=self.include_stop_str_in_output,
+            truncate_prompt_tokens=self.truncate_prompt_tokens,
+            output_kind=RequestOutputKind.DELTA if self.stream \
+                else RequestOutputKind.FINAL_ONLY,
+            guided_decoding=guided_decoding,
+            logit_bias=self.logit_bias)
+
+    def _get_guided_json_from_tool(
+            self) -> Optional[Union[str, dict, BaseModel]]:
+        # user has chosen to not use any tool
+        if self.tool_choice == "none" or self.tools is None:
+            return None
+
+        # user has chosen to use a named tool
+        if type(self.tool_choice) is ChatCompletionNamedToolChoiceParam:
+            tool_name = self.tool_choice.function.name
+            tools = {tool.function.name: tool.function for tool in self.tools}
+            if tool_name not in tools:
+                raise ValueError(
+                    f"Tool '{tool_name}' has not been passed in `tools`.")
+            tool = tools[tool_name]
+            return tool.parameters
+
+        return None
+
+    @model_validator(mode="before")
+    @classmethod
+    def validate_stream_options(cls, data):
+        if data.get("stream_options") and not data.get("stream"):
+            raise ValueError(
+                "Stream options can only be defined when `stream=True`.")
+
+        return data
+
+    @model_validator(mode="before")
+    @classmethod
+    def check_logprobs(cls, data):
+        if (prompt_logprobs := data.get("prompt_logprobs")) is not None:
+            if data.get("stream") and prompt_logprobs > 0:
+                raise ValueError(
+                    "`prompt_logprobs` are not available when `stream=True`.")
+
+            if prompt_logprobs < 0:
+                raise ValueError("`prompt_logprobs` must be a positive value.")
+
+        if (top_logprobs := data.get("top_logprobs")) is not None:
+            if top_logprobs < 0:
+                raise ValueError("`top_logprobs` must be a positive value.")
+
+            if not data.get("logprobs"):
+                raise ValueError(
+                    "when using `top_logprobs`, `logprobs` must be set to true."
+                )
+
+        return data
+
+    @model_validator(mode="before")
+    @classmethod
+    def check_guided_decoding_count(cls, data):
+        if isinstance(data, ValueError):
+            raise data
+
+        guide_count = sum([
+            "guided_json" in data and data["guided_json"] is not None,
+            "guided_regex" in data and data["guided_regex"] is not None,
+            "guided_choice" in data and data["guided_choice"] is not None
+        ])
+        # you can only use one kind of guided decoding
+        if guide_count > 1:
+            raise ValueError(
+                "You can only use one kind of guided decoding "
+                "('guided_json', 'guided_regex' or 'guided_choice').")
+        # you can only either use guided decoding or tools, not both
+        if guide_count > 1 and data.get("tool_choice",
+                                        "none") not in ("none", "auto"):
+            raise ValueError(
+                "You can only either use guided decoding or tools, not both.")
+        return data
+
+    @model_validator(mode="before")
+    @classmethod
+    def check_tool_usage(cls, data):
+
+        # if "tool_choice" is not specified but tools are provided,
+        # default to "auto" tool_choice
+        if "tool_choice" not in data and data.get("tools"):
+            data["tool_choice"] = "auto"
+
+        # if "tool_choice" is specified -- validation
+        if "tool_choice" in data:
+
+            # ensure that if "tool choice" is specified, tools are present
+            if "tools" not in data or data["tools"] is None:
+                raise ValueError(
+                    "When using `tool_choice`, `tools` must be set.")
+
+            # make sure that tool choice is either a named tool
+            # OR that it's set to "auto"
+            if data["tool_choice"] != "auto" and not isinstance(
+                    data["tool_choice"], dict):
+                raise ValueError(
+                    "`tool_choice` must either be a named tool or \"auto\". "
+                    "`tool_choice=\"none\" is not supported.")
+
+            # ensure that if "tool_choice" is specified as an object,
+            # it matches a valid tool
+            if isinstance(data["tool_choice"], dict):
+                valid_tool = False
+                specified_function = data["tool_choice"]["function"]
+                if not specified_function:
+                    raise ValueError(
+                        "Incorrectly formatted `tool_choice`. Should be like "
+                        "`{\"type\": \"function\","
+                        " \"function\": {\"name\": \"my_function\"}}`")
+                specified_function_name = specified_function["name"]
+                if not specified_function_name:
+                    raise ValueError(
+                        "Incorrectly formatted `tool_choice`. Should be like "
+                        "`{\"type\": \"function\", "
+                        "\"function\": {\"name\": \"my_function\"}}`")
+                for tool in data["tools"]:
+                    if tool["function"]["name"] == specified_function_name:
+                        valid_tool = True
+                        break
+                if not valid_tool:
+                    raise ValueError(
+                        "The tool specified in `tool_choice` does not match any"
+                        " of the specified `tools`")
+        return data
+
+    @model_validator(mode="before")
+    @classmethod
+    def check_generation_prompt(cls, data):
+        if data.get("continue_final_message") and data.get(
+                "add_generation_prompt"):
+            raise ValueError("Cannot set both `continue_final_message` and "
+                             "`add_generation_prompt` to True.")
+        return data
+
+
+class CompletionRequest(OpenAIBaseModel):
+    # Ordered by official OpenAI API documentation
+    # https://platform.openai.com/docs/api-reference/completions/create
+    model: str
+    prompt: Union[List[int], List[List[int]], str, List[str]]
+    best_of: Optional[int] = None
+    echo: Optional[bool] = False
+    frequency_penalty: Optional[float] = 0.0
+    logit_bias: Optional[Dict[str, float]] = None
+    logprobs: Optional[int] = None
+    max_tokens: Optional[int] = 16
+    n: int = 1
+    presence_penalty: Optional[float] = 0.0
+    seed: Optional[int] = Field(None, ge=_LONG_INFO.min, le=_LONG_INFO.max)
+    stop: Optional[Union[str, List[str]]] = Field(default_factory=list)
+    stream: Optional[bool] = False
+    stream_options: Optional[StreamOptions] = None
+    suffix: Optional[str] = None
+    temperature: Optional[float] = 1.0
+    top_p: Optional[float] = 1.0
+    user: Optional[str] = None
+
+    # doc: begin-completion-sampling-params
+    use_beam_search: bool = False
+    top_k: int = -1
+    min_p: float = 0.0
+    repetition_penalty: float = 1.0
+    length_penalty: float = 1.0
+    stop_token_ids: Optional[List[int]] = Field(default_factory=list)
+    include_stop_str_in_output: bool = False
+    ignore_eos: bool = False
+    min_tokens: int = 0
+    skip_special_tokens: bool = True
+    spaces_between_special_tokens: bool = True
+    truncate_prompt_tokens: Optional[Annotated[int, Field(ge=1)]] = None
+    allowed_token_ids: Optional[List[int]] = None
+    prompt_logprobs: Optional[int] = None
+    # doc: end-completion-sampling-params
+
+    # doc: begin-completion-extra-params
+    add_special_tokens: bool = Field(
+        default=True,
+        description=(
+            "If true (the default), special tokens (e.g. BOS) will be added to "
+            "the prompt."),
+    )
+    response_format: Optional[ResponseFormat] = Field(
+        default=None,
+        description=
+        ("Similar to chat completion, this parameter specifies the format of "
+         "output. Only {'type': 'json_object'} or {'type': 'text' } is "
+         "supported."),
+    )
+    guided_json: Optional[Union[str, dict, BaseModel]] = Field(
+        default=None,
+        description="If specified, the output will follow the JSON schema.",
+    )
+    guided_regex: Optional[str] = Field(
+        default=None,
+        description=(
+            "If specified, the output will follow the regex pattern."),
+    )
+    guided_choice: Optional[List[str]] = Field(
+        default=None,
+        description=(
+            "If specified, the output will be exactly one of the choices."),
+    )
+    guided_grammar: Optional[str] = Field(
+        default=None,
+        description=(
+            "If specified, the output will follow the context free grammar."),
+    )
+    guided_decoding_backend: Optional[str] = Field(
+        default=None,
+        description=(
+            "If specified, will override the default guided decoding backend "
+            "of the server for this specific request. If set, must be one of "
+            "'outlines' / 'lm-format-enforcer'"))
+    guided_whitespace_pattern: Optional[str] = Field(
+        default=None,
+        description=(
+            "If specified, will override the default whitespace pattern "
+            "for guided json decoding."))
+    priority: int = Field(
+        default=0,
+        description=(
+            "The priority of the request (lower means earlier handling; "
+            "default: 0). Any priority other than 0 will raise an error "
+            "if the served model does not use priority scheduling."))
+
+    # doc: end-completion-extra-params
+
+    def to_beam_search_params(self,
+                              default_max_tokens: int) -> BeamSearchParams:
+        max_tokens = self.max_tokens
+        if max_tokens is None:
+            max_tokens = default_max_tokens
+
+        n = self.n if self.n is not None else 1
+        temperature = self.temperature if self.temperature is not None else 0.0
+
+        return BeamSearchParams(
+            beam_width=n,
+            max_tokens=max_tokens,
+            ignore_eos=self.ignore_eos,
+            temperature=temperature,
+            length_penalty=self.length_penalty,
+        )
+
+    def to_sampling_params(self, default_max_tokens: int) -> SamplingParams:
+        max_tokens = self.max_tokens
+        if max_tokens is None:
+            max_tokens = default_max_tokens
+
+        prompt_logprobs = self.prompt_logprobs
+        if prompt_logprobs is None and self.echo:
+            prompt_logprobs = self.logprobs
+
+        echo_without_generation = self.echo and self.max_tokens == 0
+
+        guided_json_object = None
+        if (self.response_format is not None
+                and self.response_format.type == "json_object"):
+            guided_json_object = True
+
+        guided_decoding = GuidedDecodingParams.from_optional(
+            json=self.guided_json,
+            regex=self.guided_regex,
+            choice=self.guided_choice,
+            grammar=self.guided_grammar,
+            json_object=guided_json_object,
+            backend=self.guided_decoding_backend,
+            whitespace_pattern=self.guided_whitespace_pattern)
+
+        return SamplingParams.from_optional(
+            n=self.n,
+            best_of=self.best_of,
+            presence_penalty=self.presence_penalty,
+            frequency_penalty=self.frequency_penalty,
+            repetition_penalty=self.repetition_penalty,
+            temperature=self.temperature,
+            top_p=self.top_p,
+            top_k=self.top_k,
+            min_p=self.min_p,
+            seed=self.seed,
+            stop=self.stop,
+            stop_token_ids=self.stop_token_ids,
+            logprobs=self.logprobs,
+            ignore_eos=self.ignore_eos,
+            max_tokens=max_tokens if not echo_without_generation else 1,
+            min_tokens=self.min_tokens,
+            prompt_logprobs=prompt_logprobs,
+            skip_special_tokens=self.skip_special_tokens,
+            spaces_between_special_tokens=self.spaces_between_special_tokens,
+            include_stop_str_in_output=self.include_stop_str_in_output,
+            truncate_prompt_tokens=self.truncate_prompt_tokens,
+            output_kind=RequestOutputKind.DELTA if self.stream \
+                else RequestOutputKind.FINAL_ONLY,
+            guided_decoding=guided_decoding,
+            logit_bias=self.logit_bias,
+            allowed_token_ids=self.allowed_token_ids)
+
+    @model_validator(mode="before")
+    @classmethod
+    def check_guided_decoding_count(cls, data):
+        guide_count = sum([
+            "guided_json" in data and data["guided_json"] is not None,
+            "guided_regex" in data and data["guided_regex"] is not None,
+            "guided_choice" in data and data["guided_choice"] is not None
+        ])
+        if guide_count > 1:
+            raise ValueError(
+                "You can only use one kind of guided decoding "
+                "('guided_json', 'guided_regex' or 'guided_choice').")
+        return data
+
+    @model_validator(mode="before")
+    @classmethod
+    def check_logprobs(cls, data):
+        if (prompt_logprobs := data.get("prompt_logprobs")) is not None:
+            if data.get("stream") and prompt_logprobs > 0:
+                raise ValueError(
+                    "`prompt_logprobs` are not available when `stream=True`.")
+
+            if prompt_logprobs < 0:
+                raise ValueError("`prompt_logprobs` must be a positive value.")
+
+        if (logprobs := data.get("logprobs")) is not None and logprobs < 0:
+            raise ValueError("`logprobs` must be a positive value.")
+
+        return data
+
+    @model_validator(mode="before")
+    @classmethod
+    def validate_stream_options(cls, data):
+        if data.get("stream_options") and not data.get("stream"):
+            raise ValueError(
+                "Stream options can only be defined when `stream=True`.")
+
+        return data
+
+
+class EmbeddingRequest(OpenAIBaseModel):
+    # Ordered by official OpenAI API documentation
+    # https://platform.openai.com/docs/api-reference/embeddings
+    model: str
+    input: Union[List[int], List[List[int]], str, List[str]]
+    encoding_format: Literal["float", "base64"] = "float"
+    dimensions: Optional[int] = None
+    user: Optional[str] = None
+    truncate_prompt_tokens: Optional[Annotated[int, Field(ge=1)]] = None
+
+    # doc: begin-embedding-pooling-params
+    additional_data: Optional[Any] = None
+
+    # doc: end-embedding-pooling-params
+
+    # doc: begin-embedding-extra-params
+    priority: int = Field(
+        default=0,
+        description=(
+            "The priority of the request (lower means earlier handling; "
+            "default: 0). Any priority other than 0 will raise an error "
+            "if the served model does not use priority scheduling."))
+
+    # doc: end-embedding-extra-params
+
+    def to_pooling_params(self):
+        return PoolingParams(additional_data=self.additional_data)
+
+
+class CompletionLogProbs(OpenAIBaseModel):
+    text_offset: List[int] = Field(default_factory=list)
+    token_logprobs: List[Optional[float]] = Field(default_factory=list)
+    tokens: List[str] = Field(default_factory=list)
+    top_logprobs: List[Optional[Dict[str,
+                                     float]]] = Field(default_factory=list)
+
+
+class CompletionResponseChoice(OpenAIBaseModel):
+    index: int
+    text: str
+    logprobs: Optional[CompletionLogProbs] = None
+    finish_reason: Optional[str] = None
+    stop_reason: Optional[Union[int, str]] = Field(
+        default=None,
+        description=(
+            "The stop string or token id that caused the completion "
+            "to stop, None if the completion finished for some other reason "
+            "including encountering the EOS token"),
+    )
+    prompt_logprobs: Optional[List[Optional[Dict[int, Logprob]]]] = None
+
+
+class CompletionResponse(OpenAIBaseModel):
+    id: str = Field(default_factory=lambda: f"cmpl-{random_uuid()}")
+    object: str = "text_completion"
+    created: int = Field(default_factory=lambda: int(time.time()))
+    model: str
+    choices: List[CompletionResponseChoice]
+    usage: UsageInfo
+
+
+class CompletionResponseStreamChoice(OpenAIBaseModel):
+    index: int
+    text: str
+    logprobs: Optional[CompletionLogProbs] = None
+    finish_reason: Optional[str] = None
+    stop_reason: Optional[Union[int, str]] = Field(
+        default=None,
+        description=(
+            "The stop string or token id that caused the completion "
+            "to stop, None if the completion finished for some other reason "
+            "including encountering the EOS token"),
+    )
+
+
+class CompletionStreamResponse(OpenAIBaseModel):
+    id: str = Field(default_factory=lambda: f"cmpl-{random_uuid()}")
+    object: str = "text_completion"
+    created: int = Field(default_factory=lambda: int(time.time()))
+    model: str
+    choices: List[CompletionResponseStreamChoice]
+    usage: Optional[UsageInfo] = Field(default=None)
+
+
+class EmbeddingResponseData(OpenAIBaseModel):
+    index: int
+    object: str = "embedding"
+    embedding: Union[List[float], str]
+
+
+class EmbeddingResponse(OpenAIBaseModel):
+    id: str = Field(default_factory=lambda: f"cmpl-{random_uuid()}")
+    object: str = "list"
+    created: int = Field(default_factory=lambda: int(time.time()))
+    model: str
+    data: List[EmbeddingResponseData]
+    usage: UsageInfo
+
+
+class FunctionCall(OpenAIBaseModel):
+    name: str
+    arguments: str
+
+
+class ToolCall(OpenAIBaseModel):
+    id: str = Field(default_factory=lambda: f"chatcmpl-tool-{random_uuid()}")
+    type: Literal["function"] = "function"
+    function: FunctionCall
+
+
+class DeltaFunctionCall(BaseModel):
+    name: Optional[str] = None
+    arguments: Optional[str] = None
+
+
+# a tool call delta where everything is optional
+class DeltaToolCall(OpenAIBaseModel):
+    id: str = Field(default_factory=lambda: f"chatcmpl-tool-{random_uuid()}")
+    type: Literal["function"] = "function"
+    index: int
+    function: Optional[DeltaFunctionCall] = None
+
+
+class ExtractedToolCallInformation(BaseModel):
+    # indicate if tools were called
+    tools_called: bool
+
+    # extracted tool calls
+    tool_calls: List[ToolCall]
+
+    # content - per OpenAI spec, content AND tool calls can be returned rarely
+    # But some models will do this intentionally
+    content: Optional[str] = None
+
+
+class ChatMessage(OpenAIBaseModel):
+    role: str
+    content: Optional[str] = None
+    tool_calls: List[ToolCall] = Field(default_factory=list)
+
+
+class ChatCompletionLogProb(OpenAIBaseModel):
+    token: str
+    logprob: float = -9999.0
+    bytes: Optional[List[int]] = None
+
+
+class ChatCompletionLogProbsContent(ChatCompletionLogProb):
+    top_logprobs: List[ChatCompletionLogProb] = Field(default_factory=list)
+
+
+class ChatCompletionLogProbs(OpenAIBaseModel):
+    content: Optional[List[ChatCompletionLogProbsContent]] = None
+
+
+class ChatCompletionResponseChoice(OpenAIBaseModel):
+    index: int
+    message: ChatMessage
+    logprobs: Optional[ChatCompletionLogProbs] = None
+    # per OpenAI spec this is the default
+    finish_reason: Optional[str] = "stop"
+    # not part of the OpenAI spec but included in vLLM for legacy reasons
+    stop_reason: Optional[Union[int, str]] = None
+
+
+class ChatCompletionResponse(OpenAIBaseModel):
+    id: str = Field(default_factory=lambda: f"chatcmpl-{random_uuid()}")
+    object: Literal["chat.completion"] = "chat.completion"
+    created: int = Field(default_factory=lambda: int(time.time()))
+    model: str
+    choices: List[ChatCompletionResponseChoice]
+    usage: UsageInfo
+    prompt_logprobs: Optional[List[Optional[Dict[int, Logprob]]]] = None
+
+
+class DeltaMessage(OpenAIBaseModel):
+    role: Optional[str] = None
+    content: Optional[str] = None
+    tool_calls: List[DeltaToolCall] = Field(default_factory=list)
+
+
+class ChatCompletionResponseStreamChoice(OpenAIBaseModel):
+    index: int
+    delta: DeltaMessage
+    logprobs: Optional[ChatCompletionLogProbs] = None
+    finish_reason: Optional[str] = None
+    stop_reason: Optional[Union[int, str]] = None
+
+
+class ChatCompletionStreamResponse(OpenAIBaseModel):
+    id: str = Field(default_factory=lambda: f"chatcmpl-{random_uuid()}")
+    object: Literal["chat.completion.chunk"] = "chat.completion.chunk"
+    created: int = Field(default_factory=lambda: int(time.time()))
+    model: str
+    choices: List[ChatCompletionResponseStreamChoice]
+    usage: Optional[UsageInfo] = Field(default=None)
+
+
+class BatchRequestInput(OpenAIBaseModel):
+    """
+    The per-line object of the batch input file.
+
+    NOTE: Currently only the `/v1/chat/completions` endpoint is supported.
+    """
+
+    # A developer-provided per-request id that will be used to match outputs to
+    # inputs. Must be unique for each request in a batch.
+    custom_id: str
+
+    # The HTTP method to be used for the request. Currently only POST is
+    # supported.
+    method: str
+
+    # The OpenAI API relative URL to be used for the request. Currently
+    # /v1/chat/completions is supported.
+    url: str
+
+    # The parameters of the request.
+    body: Union[ChatCompletionRequest, EmbeddingRequest]
+
+
+class BatchResponseData(OpenAIBaseModel):
+    # HTTP status code of the response.
+    status_code: int = 200
+
+    # An unique identifier for the API request.
+    request_id: str
+
+    # The body of the response.
+    body: Optional[Union[ChatCompletionResponse, EmbeddingResponse]] = None
+
+
+class BatchRequestOutput(OpenAIBaseModel):
+    """
+    The per-line object of the batch output and error files
+    """
+
+    id: str
+
+    # A developer-provided per-request id that will be used to match outputs to
+    # inputs.
+    custom_id: str
+
+    response: Optional[BatchResponseData]
+
+    # For requests that failed with a non-HTTP error, this will contain more
+    # information on the cause of the failure.
+    error: Optional[Any]
+
+
+class TokenizeCompletionRequest(OpenAIBaseModel):
+    model: str
+    prompt: str
+
+    add_special_tokens: bool = Field(default=True)
+
+
+class TokenizeChatRequest(OpenAIBaseModel):
+    model: str
+    messages: List[ChatCompletionMessageParam]
+
+    add_generation_prompt: bool = Field(default=True)
+    continue_final_message: bool = Field(default=False)
+    add_special_tokens: bool = Field(default=False)
+
+    @model_validator(mode="before")
+    @classmethod
+    def check_generation_prompt(cls, data):
+        if data.get("continue_final_message") and data.get(
+                "add_generation_prompt"):
+            raise ValueError("Cannot set both `continue_final_message` and "
+                             "`add_generation_prompt` to True.")
+        return data
+
+
+TokenizeRequest = Union[TokenizeCompletionRequest, TokenizeChatRequest]
+
+
+class TokenizeResponse(OpenAIBaseModel):
+    count: int
+    max_model_len: int
+    tokens: List[int]
+
+
+class DetokenizeRequest(OpenAIBaseModel):
+    model: str
+    tokens: List[int]
+
+
+class DetokenizeResponse(OpenAIBaseModel):
+    prompt: str
+
+
+class LoadLoraAdapterRequest(BaseModel):
+    lora_name: str
+    lora_path: str
+
+
+class UnloadLoraAdapterRequest(BaseModel):
+    lora_name: str
+    lora_int_id: Optional[int] = Field(default=None)

vllm/entrypoints/openai/run_batch.py

@@ -0,0 +1,285 @@
+import asyncio
+from http import HTTPStatus
+from io import StringIO
+from typing import Awaitable, Callable, List, Optional
+
+import aiohttp
+import torch
+from prometheus_client import start_http_server
+from tqdm import tqdm
+
+from vllm.engine.arg_utils import AsyncEngineArgs, nullable_str
+from vllm.engine.async_llm_engine import AsyncLLMEngine
+from vllm.entrypoints.logger import RequestLogger, logger
+# yapf: disable
+from vllm.entrypoints.openai.protocol import (BatchRequestInput,
+                                              BatchRequestOutput,
+                                              BatchResponseData,
+                                              ChatCompletionResponse,
+                                              EmbeddingResponse, ErrorResponse)
+# yapf: enable
+from vllm.entrypoints.openai.serving_chat import OpenAIServingChat
+from vllm.entrypoints.openai.serving_embedding import OpenAIServingEmbedding
+from vllm.entrypoints.openai.serving_engine import BaseModelPath
+from vllm.usage.usage_lib import UsageContext
+from vllm.utils import FlexibleArgumentParser, random_uuid
+from vllm.version import __version__ as VLLM_VERSION
+
+
+def parse_args():
+    parser = FlexibleArgumentParser(
+        description="vLLM OpenAI-Compatible batch runner.")
+    parser.add_argument(
+        "-i",
+        "--input-file",
+        required=True,
+        type=str,
+        help=
+        "The path or url to a single input file. Currently supports local file "
+        "paths, or the http protocol (http or https). If a URL is specified, "
+        "the file should be available via HTTP GET.")
+    parser.add_argument(
+        "-o",
+        "--output-file",
+        required=True,
+        type=str,
+        help="The path or url to a single output file. Currently supports "
+        "local file paths, or web (http or https) urls. If a URL is specified,"
+        " the file should be available via HTTP PUT.")
+    parser.add_argument("--response-role",
+                        type=nullable_str,
+                        default="assistant",
+                        help="The role name to return if "
+                        "`request.add_generation_prompt=True`.")
+
+    parser = AsyncEngineArgs.add_cli_args(parser)
+
+    parser.add_argument('--max-log-len',
+                        type=int,
+                        default=None,
+                        help='Max number of prompt characters or prompt '
+                        'ID numbers being printed in log.'
+                        '\n\nDefault: Unlimited')
+
+    parser.add_argument("--enable-metrics",
+                        action="store_true",
+                        help="Enable Prometheus metrics")
+    parser.add_argument(
+        "--url",
+        type=str,
+        default="0.0.0.0",
+        help="URL to the Prometheus metrics server "
+        "(only needed if enable-metrics is set).",
+    )
+    parser.add_argument(
+        "--port",
+        type=int,
+        default=8000,
+        help="Port number for the Prometheus metrics server "
+        "(only needed if enable-metrics is set).",
+    )
+
+    return parser.parse_args()
+
+
+# explicitly use pure text format, with a newline at the end
+# this makes it impossible to see the animation in the progress bar
+# but will avoid messing up with ray or multiprocessing, which wraps
+# each line of output with some prefix.
+_BAR_FORMAT = "{desc}: {percentage:3.0f}% Completed | {n_fmt}/{total_fmt} [{elapsed}<{remaining}, {rate_fmt}]\n"  # noqa: E501
+
+
+class BatchProgressTracker:
+
+    def __init__(self):
+        self._total = 0
+        self._pbar: Optional[tqdm] = None
+
+    def submitted(self):
+        self._total += 1
+
+    def completed(self):
+        if self._pbar:
+            self._pbar.update()
+
+    def pbar(self) -> tqdm:
+        enable_tqdm = not torch.distributed.is_initialized(
+        ) or torch.distributed.get_rank() == 0
+        self._pbar = tqdm(total=self._total,
+                          unit="req",
+                          desc="Running batch",
+                          mininterval=5,
+                          disable=not enable_tqdm,
+                          bar_format=_BAR_FORMAT)
+        return self._pbar
+
+
+async def read_file(path_or_url: str) -> str:
+    if path_or_url.startswith("http://") or path_or_url.startswith("https://"):
+        async with aiohttp.ClientSession() as session, \
+                   session.get(path_or_url) as resp:
+            return await resp.text()
+    else:
+        with open(path_or_url, "r", encoding="utf-8") as f:
+            return f.read()
+
+
+async def write_file(path_or_url: str, data: str) -> None:
+    if path_or_url.startswith("http://") or path_or_url.startswith("https://"):
+        async with aiohttp.ClientSession() as session, \
+                   session.put(path_or_url, data=data.encode("utf-8")):
+            pass
+    else:
+        # We should make this async, but as long as this is always run as a
+        # standalone program, blocking the event loop won't effect performance
+        # in this particular case.
+        with open(path_or_url, "w", encoding="utf-8") as f:
+            f.write(data)
+
+
+def make_error_request_output(request: BatchRequestInput,
+                              error_msg: str) -> BatchRequestOutput:
+    batch_output = BatchRequestOutput(
+        id=f"vllm-{random_uuid()}",
+        custom_id=request.custom_id,
+        response=BatchResponseData(
+            status_code=HTTPStatus.BAD_REQUEST,
+            request_id=f"vllm-batch-{random_uuid()}",
+        ),
+        error=error_msg,
+    )
+    return batch_output
+
+
+async def make_async_error_request_output(
+        request: BatchRequestInput, error_msg: str) -> BatchRequestOutput:
+    return make_error_request_output(request, error_msg)
+
+
+async def run_request(serving_engine_func: Callable,
+                      request: BatchRequestInput,
+                      tracker: BatchProgressTracker) -> BatchRequestOutput:
+    response = await serving_engine_func(request.body)
+
+    if isinstance(response, (ChatCompletionResponse, EmbeddingResponse)):
+        batch_output = BatchRequestOutput(
+            id=f"vllm-{random_uuid()}",
+            custom_id=request.custom_id,
+            response=BatchResponseData(
+                body=response, request_id=f"vllm-batch-{random_uuid()}"),
+            error=None,
+        )
+    elif isinstance(response, ErrorResponse):
+        batch_output = BatchRequestOutput(
+            id=f"vllm-{random_uuid()}",
+            custom_id=request.custom_id,
+            response=BatchResponseData(
+                status_code=response.code,
+                request_id=f"vllm-batch-{random_uuid()}"),
+            error=response,
+        )
+    else:
+        batch_output = make_error_request_output(
+            request, error_msg="Request must not be sent in stream mode")
+
+    tracker.completed()
+    return batch_output
+
+
+async def main(args):
+    if args.served_model_name is not None:
+        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_BATCH_RUNNER)
+
+    model_config = await engine.get_model_config()
+    base_model_paths = [
+        BaseModelPath(name=name, model_path=args.model)
+        for name in served_model_names
+    ]
+
+    if args.disable_log_requests:
+        request_logger = None
+    else:
+        request_logger = RequestLogger(max_log_len=args.max_log_len)
+
+    # Create the openai serving objects.
+    openai_serving_chat = OpenAIServingChat(
+        engine,
+        model_config,
+        base_model_paths,
+        args.response_role,
+        lora_modules=None,
+        prompt_adapters=None,
+        request_logger=request_logger,
+        chat_template=None,
+    )
+    openai_serving_embedding = OpenAIServingEmbedding(
+        engine,
+        model_config,
+        base_model_paths,
+        request_logger=request_logger,
+    )
+
+    tracker = BatchProgressTracker()
+    logger.info("Reading batch from %s...", args.input_file)
+
+    # Submit all requests in the file to the engine "concurrently".
+    response_futures: List[Awaitable[BatchRequestOutput]] = []
+    for request_json in (await read_file(args.input_file)).strip().split("\n"):
+        # Skip empty lines.
+        request_json = request_json.strip()
+        if not request_json:
+            continue
+
+        request = BatchRequestInput.model_validate_json(request_json)
+
+        # Determine the type of request and run it.
+        if request.url == "/v1/chat/completions":
+            response_futures.append(
+                run_request(openai_serving_chat.create_chat_completion,
+                            request, tracker))
+            tracker.submitted()
+        elif request.url == "/v1/embeddings":
+            response_futures.append(
+                run_request(openai_serving_embedding.create_embedding, request,
+                            tracker))
+            tracker.submitted()
+        else:
+            response_futures.append(
+                make_async_error_request_output(
+                    request,
+                    error_msg="Only /v1/chat/completions and "
+                    "/v1/embeddings are supported in the batch endpoint.",
+                ))
+
+    with tracker.pbar():
+        responses = await asyncio.gather(*response_futures)
+
+    output_buffer = StringIO()
+    for response in responses:
+        print(response.model_dump_json(), file=output_buffer)
+
+    output_buffer.seek(0)
+    await write_file(args.output_file, output_buffer.read().strip())
+
+
+if __name__ == "__main__":
+    args = parse_args()
+
+    logger.info("vLLM batch processing API version %s", VLLM_VERSION)
+    logger.info("args: %s", args)
+
+    # Start the Prometheus metrics server. LLMEngine uses the Prometheus client
+    # to publish metrics at the /metrics endpoint.
+    if args.enable_metrics:
+        logger.info("Prometheus metrics enabled")
+        start_http_server(port=args.port, addr=args.url)
+    else:
+        logger.info("Prometheus metrics disabled")
+
+    asyncio.run(main(args))

vllm/entrypoints/openai/serving_chat.py

@@ -0,0 +1,891 @@
+import asyncio
+import json
+import time
+from typing import (AsyncGenerator, AsyncIterator, Callable, Dict, Final, List,
+                    Optional)
+from typing import Sequence as GenericSequence
+from typing import Union
+
+from fastapi import Request
+
+from vllm.config import ModelConfig
+from vllm.engine.async_llm_engine import AsyncLLMEngine
+from vllm.engine.multiprocessing.client import MQLLMEngineClient
+from vllm.engine.protocol import EngineClient
+from vllm.entrypoints.chat_utils import (ConversationMessage,
+                                         apply_hf_chat_template,
+                                         apply_mistral_chat_template,
+                                         load_chat_template,
+                                         parse_chat_messages_futures)
+from vllm.entrypoints.logger import RequestLogger
+from vllm.entrypoints.openai.protocol import (
+    ChatCompletionLogProb, ChatCompletionLogProbs,
+    ChatCompletionLogProbsContent, ChatCompletionNamedToolChoiceParam,
+    ChatCompletionRequest, ChatCompletionResponse,
+    ChatCompletionResponseChoice, ChatCompletionResponseStreamChoice,
+    ChatCompletionStreamResponse, ChatMessage, DeltaFunctionCall, DeltaMessage,
+    DeltaToolCall, ErrorResponse, FunctionCall, RequestResponseMetadata,
+    ToolCall, UsageInfo)
+from vllm.entrypoints.openai.serving_engine import (BaseModelPath,
+                                                    LoRAModulePath,
+                                                    OpenAIServing,
+                                                    PromptAdapterPath,
+                                                    TextTokensPrompt)
+from vllm.entrypoints.openai.tool_parsers import ToolParser, ToolParserManager
+from vllm.inputs import TokensPrompt
+from vllm.logger import init_logger
+from vllm.outputs import CompletionOutput, RequestOutput
+from vllm.sampling_params import BeamSearchParams, SamplingParams
+from vllm.sequence import Logprob
+from vllm.tracing import (contains_trace_headers, extract_trace_headers,
+                          log_tracing_disabled_warning)
+from vllm.transformers_utils.tokenizer import AnyTokenizer, MistralTokenizer
+from vllm.utils import iterate_with_cancellation, random_uuid
+
+logger = init_logger(__name__)
+
+
+class OpenAIServingChat(OpenAIServing):
+
+    def __init__(self,
+                 engine_client: EngineClient,
+                 model_config: ModelConfig,
+                 base_model_paths: List[BaseModelPath],
+                 response_role: str,
+                 *,
+                 lora_modules: Optional[List[LoRAModulePath]],
+                 prompt_adapters: Optional[List[PromptAdapterPath]],
+                 request_logger: Optional[RequestLogger],
+                 chat_template: Optional[str],
+                 return_tokens_as_token_ids: bool = False,
+                 enable_auto_tools: bool = False,
+                 tool_parser: Optional[str] = None):
+        super().__init__(engine_client=engine_client,
+                         model_config=model_config,
+                         base_model_paths=base_model_paths,
+                         lora_modules=lora_modules,
+                         prompt_adapters=prompt_adapters,
+                         request_logger=request_logger,
+                         return_tokens_as_token_ids=return_tokens_as_token_ids)
+
+        self.response_role = response_role
+        self.use_tool_use_model_template = False
+        self.chat_template = load_chat_template(chat_template)
+
+        # set up tool use
+        self.enable_auto_tools: bool = enable_auto_tools
+        if self.enable_auto_tools:
+            logger.info(
+                "\"auto\" tool choice has been enabled please note that while"
+                " the parallel_tool_calls client option is preset for "
+                "compatibility reasons, it will be ignored.")
+
+        self.tool_parser: Optional[Callable[[AnyTokenizer], ToolParser]] = None
+        if self.enable_auto_tools:
+            try:
+                self.tool_parser = ToolParserManager.get_tool_parser(
+                    tool_parser)
+            except Exception as e:
+                raise TypeError("Error: --enable-auto-tool-choice requires "
+                                f"tool_parser:'{tool_parser}' which has not "
+                                "been registered") from e
+
+    async def create_chat_completion(
+        self,
+        request: ChatCompletionRequest,
+        raw_request: Optional[Request] = None,
+    ) -> Union[AsyncGenerator[str, None], ChatCompletionResponse,
+               ErrorResponse]:
+        """Completion API similar to OpenAI's API.
+
+        See https://platform.openai.com/docs/api-reference/chat/create
+        for the API specification. This API mimics the OpenAI
+        ChatCompletion API.
+
+        """
+        error_check_ret = await self._check_model(request)
+        if error_check_ret is not None:
+            logger.error("Error with model %s", error_check_ret)
+            return error_check_ret
+
+        # If the engine is dead, raise the engine's DEAD_ERROR.
+        # This is required for the streaming case, where we return a
+        # success status before we actually start generating text :).
+        if self.engine_client.errored:
+            raise self.engine_client.dead_error
+
+        try:
+            (
+                lora_request,
+                prompt_adapter_request,
+            ) = self._maybe_get_adapters(request)
+
+            model_config = self.model_config
+            tokenizer = await self.engine_client.get_tokenizer(lora_request)
+
+            conversation, mm_data_future = parse_chat_messages_futures(
+                request.messages, model_config, tokenizer)
+
+            tool_dicts = None if request.tools is None else [
+                tool.model_dump() for tool in request.tools
+            ]
+
+            prompt: Union[str, List[int]]
+            is_mistral_tokenizer = isinstance(tokenizer, MistralTokenizer)
+            if is_mistral_tokenizer:
+                prompt = apply_mistral_chat_template(
+                    tokenizer,
+                    messages=request.messages,
+                    chat_template=request.chat_template or self.chat_template,
+                    add_generation_prompt=request.add_generation_prompt,
+                    continue_final_message=request.continue_final_message,
+                    tools=tool_dicts,
+                    documents=request.documents,
+                    **(request.chat_template_kwargs or {}),
+                )
+            else:
+                prompt = apply_hf_chat_template(
+                    tokenizer,
+                    conversation=conversation,
+                    chat_template=request.chat_template or self.chat_template,
+                    add_generation_prompt=request.add_generation_prompt,
+                    continue_final_message=request.continue_final_message,
+                    tools=tool_dicts,
+                    documents=request.documents,
+                    **(request.chat_template_kwargs or {}),
+                )
+        except Exception as e:
+            logger.exception("Error in applying chat template from request")
+            return self.create_error_response(str(e))
+
+        try:
+            mm_data = await mm_data_future
+        except Exception as e:
+            logger.exception("Error in loading multi-modal data")
+            return self.create_error_response(str(e))
+
+        # validation for OpenAI tools
+        # tool_choice = "required" is not supported
+        if request.tool_choice == "required":
+            return self.create_error_response(
+                "tool_choice = \"required\" is not supported!")
+
+        if not is_mistral_tokenizer and request.tool_choice == "auto" and not (
+                self.enable_auto_tools and self.tool_parser is not None):
+            # for hf tokenizers, "auto" tools requires
+            # --enable-auto-tool-choice and --tool-call-parser
+            return self.create_error_response(
+                "\"auto\" tool choice requires "
+                "--enable-auto-tool-choice and --tool-call-parser to be set")
+
+        request_id = f"chat-{random_uuid()}"
+
+        request_metadata = RequestResponseMetadata(request_id=request_id)
+        if raw_request:
+            raw_request.state.request_metadata = request_metadata
+
+        try:
+            if self.enable_auto_tools and self.tool_parser:
+                request = self.tool_parser(tokenizer).adjust_request(
+                    request=request)
+
+            if isinstance(prompt, str):
+                prompt_inputs = self._tokenize_prompt_input(
+                    request,
+                    tokenizer,
+                    prompt,
+                    truncate_prompt_tokens=request.truncate_prompt_tokens,
+                    add_special_tokens=request.add_special_tokens,
+                )
+            else:
+                assert isinstance(prompt, list) and isinstance(
+                    prompt[0], int
+                ), "Prompt has to be either a string or a list of token ids"
+                prompt_inputs = TextTokensPrompt(
+                    prompt=tokenizer.decode(prompt), prompt_token_ids=prompt)
+
+            assert prompt_inputs is not None
+
+            sampling_params: Union[SamplingParams, BeamSearchParams]
+            default_max_tokens = self.max_model_len - len(
+                prompt_inputs["prompt_token_ids"])
+            if request.use_beam_search:
+                sampling_params = request.to_beam_search_params(
+                    default_max_tokens)
+            else:
+                sampling_params = request.to_sampling_params(
+                    default_max_tokens)
+
+            self._log_inputs(request_id,
+                             prompt_inputs,
+                             params=sampling_params,
+                             lora_request=lora_request,
+                             prompt_adapter_request=prompt_adapter_request)
+
+            engine_inputs = TokensPrompt(
+                prompt_token_ids=prompt_inputs["prompt_token_ids"])
+            if mm_data is not None:
+                engine_inputs["multi_modal_data"] = mm_data
+
+            is_tracing_enabled = (await
+                                  self.engine_client.is_tracing_enabled())
+            trace_headers = None
+            if is_tracing_enabled and raw_request:
+                trace_headers = extract_trace_headers(raw_request.headers)
+            if (not is_tracing_enabled and raw_request
+                    and contains_trace_headers(raw_request.headers)):
+                log_tracing_disabled_warning()
+
+            if isinstance(sampling_params, BeamSearchParams):
+                assert isinstance(self.engine_client,
+                                    (AsyncLLMEngine,
+                                    MQLLMEngineClient)), \
+                    "Beam search is only supported with" \
+                    "AsyncLLMEngine and MQLLMEngineClient."
+                result_generator = self.engine_client.beam_search(
+                    engine_inputs['prompt_token_ids'],
+                    request_id,
+                    sampling_params,
+                )
+            else:
+                result_generator = self.engine_client.generate(
+                    engine_inputs,
+                    sampling_params,
+                    request_id,
+                    lora_request=lora_request,
+                    trace_headers=trace_headers,
+                    prompt_adapter_request=prompt_adapter_request,
+                    priority=request.priority,
+                )
+        except ValueError as e:
+            # TODO: Use a vllm-specific Validation Error
+            return self.create_error_response(str(e))
+
+        if raw_request:
+            result_generator = iterate_with_cancellation(
+                result_generator, raw_request.is_disconnected)
+
+        # Streaming response
+        if request.stream:
+            return self.chat_completion_stream_generator(
+                request, result_generator, request_id, conversation, tokenizer,
+                request_metadata)
+
+        try:
+            return await self.chat_completion_full_generator(
+                request, result_generator, request_id, conversation, tokenizer,
+                request_metadata)
+        except ValueError as e:
+            # TODO: Use a vllm-specific Validation Error
+            return self.create_error_response(str(e))
+
+    def get_chat_request_role(self, request: ChatCompletionRequest) -> str:
+        if request.add_generation_prompt:
+            return self.response_role
+        return request.messages[-1]["role"]
+
+    async def chat_completion_stream_generator(
+        self,
+        request: ChatCompletionRequest,
+        result_generator: AsyncIterator[RequestOutput],
+        request_id: str,
+        conversation: List[ConversationMessage],
+        tokenizer: AnyTokenizer,
+        request_metadata: RequestResponseMetadata,
+    ) -> AsyncGenerator[str, None]:
+        model_name = self.base_model_paths[0].name
+        created_time = int(time.time())
+        chunk_object_type: Final = "chat.completion.chunk"
+        first_iteration = True
+
+        # Send response for each token for each request.n (index)
+        num_choices = 1 if request.n is None else request.n
+        previous_num_tokens = [0] * num_choices
+        finish_reason_sent = [False] * num_choices
+        num_prompt_tokens = 0
+
+        if isinstance(request.tool_choice, ChatCompletionNamedToolChoiceParam):
+            tool_choice_function_name = request.tool_choice.function.name
+        else:
+            tool_choice_function_name = None
+
+        # Determine whether tools are in use with "auto" tool choice
+        tool_choice_auto = (
+            not tool_choice_function_name
+            and self._should_stream_with_auto_tool_parsing(request))
+
+        all_previous_token_ids: Optional[List[List[int]]]
+        if tool_choice_auto:
+            # These are only required in "auto" tool choice case
+            previous_texts = [""] * num_choices
+            all_previous_token_ids = [[]] * num_choices
+        else:
+            previous_texts, all_previous_token_ids = None, None
+
+        # Prepare the tool parser if it's needed
+        try:
+            if tool_choice_auto and self.tool_parser:
+                tool_parsers: List[Optional[ToolParser]] = [
+                    self.tool_parser(tokenizer)
+                ] * num_choices
+            else:
+                tool_parsers = [None] * num_choices
+        except RuntimeError as e:
+            logger.error("Error in tool parser creation: %s", e)
+            data = self.create_streaming_error_response(str(e))
+            yield f"data: {data}\n\n"
+            yield "data: [DONE]\n\n"
+            return
+
+        try:
+            async for res in result_generator:
+                if res.prompt_token_ids is not None:
+                    num_prompt_tokens = len(res.prompt_token_ids)
+                    if res.encoder_prompt_token_ids is not None:
+                        num_prompt_tokens += len(res.encoder_prompt_token_ids)
+
+                # We need to do it here, because if there are exceptions in
+                # the result_generator, it needs to be sent as the FIRST
+                # response (by the try...catch).
+                if first_iteration:
+                    # Send first response for each request.n (index) with
+                    # the role
+                    role = self.get_chat_request_role(request)
+
+                    # NOTE num_choices defaults to 1 so this usually executes
+                    # once per request
+                    for i in range(num_choices):
+                        tool_parser = tool_parsers[i]
+                        choice_data = ChatCompletionResponseStreamChoice(
+                            index=i,
+                            delta=DeltaMessage(
+                                role=role,
+                                content="",
+                            ),
+                            logprobs=None,
+                            finish_reason=None)
+                        chunk = ChatCompletionStreamResponse(
+                            id=request_id,
+                            object=chunk_object_type,
+                            created=created_time,
+                            choices=[choice_data],
+                            model=model_name)
+
+                        # if usage should be included
+                        if (request.stream_options
+                                and request.stream_options.include_usage):
+                            # if continuous usage stats are requested, add it
+                            if request.stream_options.continuous_usage_stats:
+                                usage = UsageInfo(
+                                    prompt_tokens=num_prompt_tokens,
+                                    completion_tokens=0,
+                                    total_tokens=num_prompt_tokens)
+                                chunk.usage = usage
+                            # otherwise don't
+                            else:
+                                chunk.usage = None
+
+                        data = chunk.model_dump_json(exclude_unset=True)
+                        yield f"data: {data}\n\n"
+
+                    # Send response to echo the input portion of the
+                    # last message
+                    if request.echo or request.continue_final_message:
+                        last_msg_content: str = ""
+                        if conversation and "content" in conversation[
+                                -1] and conversation[-1].get("role") == role:
+                            last_msg_content = conversation[-1]["content"] or ""
+
+                        if last_msg_content:
+                            for i in range(num_choices):
+                                choice_data = (
+                                    ChatCompletionResponseStreamChoice(
+                                        index=i,
+                                        delta=DeltaMessage(
+                                            content=last_msg_content),
+                                        logprobs=None,
+                                        finish_reason=None))
+                                chunk = ChatCompletionStreamResponse(
+                                    id=request_id,
+                                    object=chunk_object_type,
+                                    created=created_time,
+                                    choices=[choice_data],
+                                    model=model_name)
+                                if (request.stream_options and
+                                        request.stream_options.include_usage):
+                                    if (request.stream_options.
+                                            continuous_usage_stats):
+                                        usage = UsageInfo(
+                                            prompt_tokens=num_prompt_tokens,
+                                            completion_tokens=0,
+                                            total_tokens=num_prompt_tokens)
+                                        chunk.usage = usage
+                                    else:
+                                        chunk.usage = None
+
+                                data = chunk.model_dump_json(
+                                    exclude_unset=True)
+                                yield f"data: {data}\n\n"
+                    first_iteration = False
+
+                for output in res.outputs:
+                    i = output.index
+                    tool_parser = tool_parsers[i]
+
+                    if finish_reason_sent[i]:
+                        continue
+
+                    if request.logprobs and request.top_logprobs is not None:
+                        assert output.logprobs is not None, (
+                            "Did not output logprobs")
+                        logprobs = self._create_chat_logprobs(
+                            token_ids=output.token_ids,
+                            top_logprobs=output.logprobs,
+                            tokenizer=tokenizer,
+                            num_output_top_logprobs=request.top_logprobs,
+                        )
+                    else:
+                        logprobs = None
+
+                    delta_text = output.text
+                    delta_message: Optional[DeltaMessage]
+
+                    # handle streaming deltas for tools with named tool_choice
+                    if tool_choice_function_name:
+                        delta_message = DeltaMessage(tool_calls=[
+                            DeltaToolCall(function=DeltaFunctionCall(
+                                name=tool_choice_function_name,
+                                arguments=delta_text),
+                                          index=i)
+                        ])
+
+                    # handle streaming deltas for tools with "auto" tool choice
+                    elif tool_choice_auto:
+                        assert previous_texts is not None
+                        assert all_previous_token_ids is not None
+                        assert tool_parser is not None
+                        #TODO optimize manipulation of these lists
+                        previous_text = previous_texts[i]
+                        previous_token_ids = all_previous_token_ids[i]
+                        current_text = previous_text + delta_text
+                        current_token_ids = previous_token_ids + list(
+                            output.token_ids)
+
+                        delta_message = (
+                            tool_parser.extract_tool_calls_streaming(
+                                previous_text=previous_text,
+                                current_text=current_text,
+                                delta_text=delta_text,
+                                previous_token_ids=previous_token_ids,
+                                current_token_ids=current_token_ids,
+                                delta_token_ids=output.token_ids,
+                                request=request))
+
+                        # update the previous values for the next iteration
+                        previous_texts[i] = current_text
+                        all_previous_token_ids[i] = current_token_ids
+
+                    # handle streaming just a content delta
+                    else:
+                        delta_message = DeltaMessage(content=delta_text)
+
+                    # set the previous values for the next iteration
+                    previous_num_tokens[i] += len(output.token_ids)
+
+                    # if the message delta is None (e.g. because it was a
+                    # "control token" for tool calls or the parser otherwise
+                    # wasn't ready to send a token, then
+                    #   get the next token without streaming a chunk
+                    if delta_message is None:
+                        continue
+
+                    if output.finish_reason is None:
+                        # Send token-by-token response for each request.n
+
+                        choice_data = ChatCompletionResponseStreamChoice(
+                            index=i,
+                            delta=delta_message,
+                            logprobs=logprobs,
+                            finish_reason=None)
+                        chunk = ChatCompletionStreamResponse(
+                            id=request_id,
+                            object=chunk_object_type,
+                            created=created_time,
+                            choices=[choice_data],
+                            model=model_name)
+
+                        # handle usage stats if requested & if continuous
+                        if (request.stream_options
+                                and request.stream_options.include_usage):
+                            if request.stream_options.continuous_usage_stats:
+                                completion_tokens = len(output.token_ids)
+                                usage = UsageInfo(
+                                    prompt_tokens=num_prompt_tokens,
+                                    completion_tokens=completion_tokens,
+                                    total_tokens=num_prompt_tokens +
+                                    completion_tokens,
+                                )
+                                chunk.usage = usage
+                            else:
+                                chunk.usage = None
+
+                        data = chunk.model_dump_json(exclude_unset=True)
+                        yield f"data: {data}\n\n"
+
+                    # if the model is finished generating
+                    else:
+                        # check to make sure we haven't "forgotten" to stream
+                        #   any tokens that were generated but previously
+                        #   matched by partial json parsing
+                        # only happens if we are NOT using guided decoding
+                        auto_tools_called = False
+                        if tool_parser:
+                            auto_tools_called = len(
+                                tool_parser.prev_tool_call_arr) > 0
+                            index = len(tool_parser.prev_tool_call_arr
+                                        ) - 1 if auto_tools_called else 0
+                        else:
+                            index = 0
+
+                        if self._should_check_for_unstreamed_tool_arg_tokens(
+                                delta_message, output) and tool_parser:
+                            # get the expected call based on partial JSON
+                            # parsing which "autocompletes" the JSON
+                            expected_call = json.dumps(
+                                tool_parser.prev_tool_call_arr[index].get(
+                                    "arguments", {}))
+
+                            # get what we've streamed so far for arguments
+                            # for the current tool
+                            actual_call = tool_parser.streamed_args_for_tool[
+                                index]
+
+                            # check to see if there's anything left to stream
+                            remaining_call = expected_call.replace(
+                                actual_call, "", 1)
+
+                            # set that as a delta message
+                            delta_message = DeltaMessage(tool_calls=[
+                                DeltaToolCall(index=index,
+                                              function=DeltaFunctionCall(
+                                                  arguments=remaining_call).
+                                              model_dump(exclude_none=True))
+                            ])
+
+                        # Send the finish response for each request.n only once
+                        choice_data = ChatCompletionResponseStreamChoice(
+                            index=i,
+                            delta=delta_message,
+                            logprobs=logprobs,
+                            finish_reason=output.finish_reason
+                            if not auto_tools_called else "tool_calls",
+                            stop_reason=output.stop_reason)
+                        chunk = ChatCompletionStreamResponse(
+                            id=request_id,
+                            object=chunk_object_type,
+                            created=created_time,
+                            choices=[choice_data],
+                            model=model_name)
+                        if (request.stream_options
+                                and request.stream_options.include_usage):
+                            if request.stream_options.continuous_usage_stats:
+                                completion_tokens = len(output.token_ids)
+                                usage = UsageInfo(
+                                    prompt_tokens=num_prompt_tokens,
+                                    completion_tokens=completion_tokens,
+                                    total_tokens=num_prompt_tokens +
+                                    completion_tokens,
+                                )
+                                chunk.usage = usage
+                            else:
+                                chunk.usage = None
+                        data = chunk.model_dump_json(exclude_unset=True)
+                        yield f"data: {data}\n\n"
+                        finish_reason_sent[i] = True
+
+            # once the final token is handled, if stream_options.include_usage
+            # is sent, send the usage
+            if (request.stream_options
+                    and request.stream_options.include_usage):
+                completion_tokens = previous_num_tokens[i]
+                final_usage = UsageInfo(
+                    prompt_tokens=num_prompt_tokens,
+                    completion_tokens=completion_tokens,
+                    total_tokens=num_prompt_tokens + completion_tokens,
+                )
+
+                final_usage_chunk = ChatCompletionStreamResponse(
+                    id=request_id,
+                    object=chunk_object_type,
+                    created=created_time,
+                    choices=[],
+                    model=model_name,
+                    usage=final_usage)
+                final_usage_data = (final_usage_chunk.model_dump_json(
+                    exclude_unset=True, exclude_none=True))
+                yield f"data: {final_usage_data}\n\n"
+
+            # report to FastAPI middleware aggregate usage across all choices
+            num_completion_tokens = sum(previous_num_tokens)
+            request_metadata.final_usage_info = UsageInfo(
+                prompt_tokens=num_prompt_tokens,
+                completion_tokens=num_completion_tokens,
+                total_tokens=num_prompt_tokens + num_completion_tokens)
+
+        except ValueError as e:
+            # TODO: Use a vllm-specific Validation Error
+            logger.error("error in chat completion stream generator: %s", e)
+            data = self.create_streaming_error_response(str(e))
+            yield f"data: {data}\n\n"
+        # Send the final done message after all response.n are finished
+        yield "data: [DONE]\n\n"
+
+    async def chat_completion_full_generator(
+        self,
+        request: ChatCompletionRequest,
+        result_generator: AsyncIterator[RequestOutput],
+        request_id: str,
+        conversation: List[ConversationMessage],
+        tokenizer: AnyTokenizer,
+        request_metadata: RequestResponseMetadata,
+    ) -> Union[ErrorResponse, ChatCompletionResponse]:
+
+        model_name = self.base_model_paths[0].name
+        created_time = int(time.time())
+        final_res: Optional[RequestOutput] = None
+
+        try:
+            async for res in result_generator:
+                final_res = res
+        except asyncio.CancelledError:
+            return self.create_error_response("Client disconnected")
+
+        assert final_res is not None
+
+        choices: List[ChatCompletionResponseChoice] = []
+
+        role = self.get_chat_request_role(request)
+        for output in final_res.outputs:
+            token_ids = output.token_ids
+            out_logprobs = output.logprobs
+
+            if request.logprobs and request.top_logprobs is not None:
+                assert out_logprobs is not None, "Did not output logprobs"
+                logprobs = self._create_chat_logprobs(
+                    token_ids=token_ids,
+                    top_logprobs=out_logprobs,
+                    num_output_top_logprobs=request.top_logprobs,
+                    tokenizer=tokenizer,
+                )
+            else:
+                logprobs = None
+
+            # In the OpenAI API the finish_reason is "tools_called"
+            # if the tool choice is auto and the model produced a tool
+            # call. The same is not true for named function calls
+            auto_tools_called = False
+
+            # if auto tools are not enabled, and a named tool choice using
+            #   outlines is not being used
+            if (not self.enable_auto_tools
+                    or not self.tool_parser) and not isinstance(
+                        request.tool_choice,
+                        ChatCompletionNamedToolChoiceParam):
+                message = ChatMessage(role=role, content=output.text)
+
+            # if the request uses tools and specified a tool choice
+            elif request.tool_choice and type(
+                    request.tool_choice) is ChatCompletionNamedToolChoiceParam:
+
+                message = ChatMessage(
+                    role=role,
+                    content="",
+                    tool_calls=[
+                        ToolCall(function=FunctionCall(
+                            name=request.tool_choice.function.name,
+                            arguments=output.text))
+                    ])
+
+            # if the request doesn't use tool choice
+            # OR specifies to not use a tool
+            elif not request.tool_choice or request.tool_choice == "none":
+
+                message = ChatMessage(role=role, content=output.text)
+
+            # handle when there are tools and tool choice is auto
+            elif request.tools and (
+                    request.tool_choice == "auto"
+                    or request.tool_choice is None) and self.enable_auto_tools \
+                    and self.tool_parser:
+
+                try:
+                    tool_parser = self.tool_parser(tokenizer)
+                except RuntimeError as e:
+                    logger.error("Error in tool parser creation: %s", e)
+                    return self.create_error_response(str(e))
+
+                tool_call_info = tool_parser.extract_tool_calls(
+                    output.text, request=request)
+                # In the OpenAI API the finish_reason is "tools_called"
+                # if the tool choice is auto and the model produced a tool
+                # call. The same is not true for named function calls
+                auto_tools_called = tool_call_info.tools_called
+                if tool_call_info.tools_called:
+                    message = ChatMessage(role=role,
+                                          content=tool_call_info.content,
+                                          tool_calls=tool_call_info.tool_calls)
+
+                else:
+                    # FOR NOW make it a chat message; we will have to detect
+                    # the type to make it later.
+                    message = ChatMessage(role=role, content=output.text)
+
+            # undetermined case that is still important to handle
+            else:
+                logger.error(
+                    "Error in chat_completion_full_generator - cannot determine"
+                    " if tools should be extracted. Returning a standard chat "
+                    "completion.")
+                message = ChatMessage(role=role, content=output.text)
+
+            choice_data = ChatCompletionResponseChoice(
+                index=output.index,
+                message=message,
+                logprobs=logprobs,
+                finish_reason="tool_calls" if auto_tools_called else
+                output.finish_reason if output.finish_reason else "stop",
+                stop_reason=output.stop_reason)
+            choices.append(choice_data)
+
+        if request.echo or request.continue_final_message:
+            last_msg_content = ""
+            if conversation and "content" in conversation[-1] and conversation[
+                    -1].get("role") == role:
+                last_msg_content = conversation[-1]["content"] or ""
+
+            for choice in choices:
+                full_message = last_msg_content + (choice.message.content
+                                                   or "")
+                choice.message.content = full_message
+
+        assert final_res.prompt_token_ids is not None
+        num_prompt_tokens = len(final_res.prompt_token_ids)
+        if final_res.encoder_prompt_token_ids is not None:
+            num_prompt_tokens += len(final_res.encoder_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,
+        )
+
+        request_metadata.final_usage_info = usage
+
+        response = ChatCompletionResponse(
+            id=request_id,
+            created=created_time,
+            model=model_name,
+            choices=choices,
+            usage=usage,
+            prompt_logprobs=final_res.prompt_logprobs,
+        )
+
+        return response
+
+    def _get_top_logprobs(
+            self, logprobs: Dict[int, Logprob], top_logprobs: Optional[int],
+            tokenizer: AnyTokenizer) -> List[ChatCompletionLogProb]:
+        return [
+            ChatCompletionLogProb(token=(token := self._get_decoded_token(
+                p[1],
+                p[0],
+                tokenizer,
+                return_as_token_id=self.return_tokens_as_token_ids)),
+                                  logprob=max(p[1].logprob, -9999.0),
+                                  bytes=list(
+                                      token.encode("utf-8", errors="replace")))
+            for i, p in enumerate(logprobs.items())
+            if top_logprobs and i < top_logprobs
+        ]
+
+    def _create_chat_logprobs(
+        self,
+        token_ids: GenericSequence[int],
+        top_logprobs: GenericSequence[Optional[Dict[int, Logprob]]],
+        tokenizer: AnyTokenizer,
+        num_output_top_logprobs: Optional[int] = None,
+    ) -> ChatCompletionLogProbs:
+        """Create OpenAI-style logprobs."""
+        logprobs_content: List[ChatCompletionLogProbsContent] = []
+
+        for i, token_id in enumerate(token_ids):
+            step_top_logprobs = top_logprobs[i]
+            if step_top_logprobs is None:
+                token = tokenizer.decode(token_id)
+                if self.return_tokens_as_token_ids:
+                    token = f"token_id:{token_id}"
+
+                logprobs_content.append(
+                    ChatCompletionLogProbsContent(
+                        token=token,
+                        bytes=list(token.encode("utf-8", errors="replace")),
+                    ))
+            else:
+                step_token = step_top_logprobs[token_id]
+                step_decoded = step_token.decoded_token
+
+                logprobs_content.append(
+                    ChatCompletionLogProbsContent(
+                        token=self._get_decoded_token(
+                            step_token,
+                            token_id,
+                            tokenizer,
+                            self.return_tokens_as_token_ids,
+                        ),
+                        logprob=max(step_token.logprob, -9999.0),
+                        bytes=None if step_decoded is None else list(
+                            step_decoded.encode("utf-8", errors="replace")),
+                        top_logprobs=self._get_top_logprobs(
+                            step_top_logprobs,
+                            num_output_top_logprobs,
+                            tokenizer,
+                        ),
+                    ))
+
+        return ChatCompletionLogProbs(content=logprobs_content)
+
+    def _should_stream_with_auto_tool_parsing(self,
+                                              request: ChatCompletionRequest):
+        """
+        Utility function to check if streamed tokens should go through the tool
+        call parser that was configured.
+
+        We only want to do this IF user-provided tools are set, a tool parser
+        is configured, "auto" tool choice is enabled, and the request's tool
+        choice field indicates that "auto" tool choice should be used.
+        """
+        return (request.tools and self.tool_parser and self.enable_auto_tools
+                and request.tool_choice in ['auto', None])
+
+    def _should_check_for_unstreamed_tool_arg_tokens(
+        self,
+        delta_message: Optional[DeltaMessage],
+        output: CompletionOutput,
+    ) -> bool:
+        """
+        Check to see if we should check for unstreamed tool arguments tokens.
+        This is only applicable when auto tool parsing is enabled, the delta
+        is a tool call with arguments.
+        """
+
+        # yapf: disable
+        return bool(
+            # if there is a delta message that includes tool calls which
+            # include a function that has arguments
+            output.finish_reason is not None
+            and self.enable_auto_tools and self.tool_parser and delta_message
+            and delta_message.tool_calls and delta_message.tool_calls[0]
+            and delta_message.tool_calls[0].function
+            and delta_message.tool_calls[0].function.arguments is not None
+        )

vllm/entrypoints/openai/serving_completion.py

@@ -0,0 +1,554 @@
+import asyncio
+import time
+from typing import (AsyncGenerator, AsyncIterator, Callable, Dict, List,
+                    Optional)
+from typing import Sequence as GenericSequence
+from typing import Tuple, Union, cast
+
+from fastapi import Request
+
+from vllm.config import ModelConfig
+from vllm.engine.async_llm_engine import AsyncLLMEngine
+from vllm.engine.multiprocessing.client import MQLLMEngineClient
+from vllm.engine.protocol import EngineClient
+from vllm.entrypoints.logger import RequestLogger
+# yapf conflicts with isort for this block
+# yapf: disable
+from vllm.entrypoints.openai.protocol import (CompletionLogProbs,
+                                              CompletionRequest,
+                                              CompletionResponse,
+                                              CompletionResponseChoice,
+                                              CompletionResponseStreamChoice,
+                                              CompletionStreamResponse,
+                                              ErrorResponse,
+                                              RequestResponseMetadata,
+                                              UsageInfo)
+# yapf: enable
+from vllm.entrypoints.openai.serving_engine import (BaseModelPath,
+                                                    LoRAModulePath,
+                                                    OpenAIServing,
+                                                    PromptAdapterPath)
+from vllm.logger import init_logger
+from vllm.outputs import RequestOutput
+from vllm.sampling_params import BeamSearchParams, SamplingParams
+from vllm.sequence import Logprob
+from vllm.tracing import (contains_trace_headers, extract_trace_headers,
+                          log_tracing_disabled_warning)
+from vllm.transformers_utils.tokenizer import AnyTokenizer
+from vllm.utils import merge_async_iterators, random_uuid
+
+logger = init_logger(__name__)
+
+TypeTokenIDs = List[int]
+TypeTopLogProbs = List[Optional[Dict[int, float]]]
+TypeCreateLogProbsFn = Callable[
+    [TypeTokenIDs, TypeTopLogProbs, Optional[int], int], CompletionLogProbs]
+
+
+class OpenAIServingCompletion(OpenAIServing):
+
+    def __init__(
+        self,
+        engine_client: EngineClient,
+        model_config: ModelConfig,
+        base_model_paths: List[BaseModelPath],
+        *,
+        lora_modules: Optional[List[LoRAModulePath]],
+        prompt_adapters: Optional[List[PromptAdapterPath]],
+        request_logger: Optional[RequestLogger],
+        return_tokens_as_token_ids: bool = False,
+    ):
+        super().__init__(engine_client=engine_client,
+                         model_config=model_config,
+                         base_model_paths=base_model_paths,
+                         lora_modules=lora_modules,
+                         prompt_adapters=prompt_adapters,
+                         request_logger=request_logger,
+                         return_tokens_as_token_ids=return_tokens_as_token_ids)
+
+    async def create_completion(
+        self,
+        request: CompletionRequest,
+        raw_request: Request,
+    ) -> Union[AsyncGenerator[str, None], CompletionResponse, ErrorResponse]:
+        """Completion API similar to OpenAI's API.
+
+        See https://platform.openai.com/docs/api-reference/completions/create
+        for the API specification. This API mimics the OpenAI Completion API.
+
+        NOTE: Currently we do not support the following feature:
+            - suffix (the language models we currently support do not support
+            suffix)
+        """
+        error_check_ret = await self._check_model(request)
+        if error_check_ret is not None:
+            return error_check_ret
+
+        # If the engine is dead, raise the engine's DEAD_ERROR.
+        # This is required for the streaming case, where we return a
+        # success status before we actually start generating text :).
+        if self.engine_client.errored:
+            raise self.engine_client.dead_error
+
+        # Return error for unsupported features.
+        if request.suffix is not None:
+            return self.create_error_response(
+                "suffix is not currently supported")
+
+        model_name = self.base_model_paths[0].name
+        request_id = f"cmpl-{random_uuid()}"
+        created_time = int(time.time())
+
+        request_metadata = RequestResponseMetadata(request_id=request_id)
+        if raw_request:
+            raw_request.state.request_metadata = request_metadata
+
+        # Schedule the request and get the result generator.
+        generators: List[AsyncGenerator[RequestOutput, None]] = []
+        try:
+            (
+                lora_request,
+                prompt_adapter_request,
+            ) = self._maybe_get_adapters(request)
+
+            tokenizer = await self.engine_client.get_tokenizer(lora_request)
+
+            prompts = list(
+                self._tokenize_prompt_input_or_inputs(
+                    request,
+                    tokenizer,
+                    request.prompt,
+                    truncate_prompt_tokens=request.truncate_prompt_tokens,
+                    add_special_tokens=request.add_special_tokens,
+                ))
+
+            for i, prompt_inputs in enumerate(prompts):
+                sampling_params: Union[SamplingParams, BeamSearchParams]
+                default_max_tokens = self.max_model_len - len(
+                    prompt_inputs["prompt_token_ids"])
+                if request.use_beam_search:
+                    sampling_params = request.to_beam_search_params(
+                        default_max_tokens)
+                else:
+                    sampling_params = request.to_sampling_params(
+                        default_max_tokens)
+
+                request_id_item = f"{request_id}-{i}"
+
+                self._log_inputs(request_id_item,
+                                 prompt_inputs,
+                                 params=sampling_params,
+                                 lora_request=lora_request,
+                                 prompt_adapter_request=prompt_adapter_request)
+
+                is_tracing_enabled = (await
+                                      self.engine_client.is_tracing_enabled())
+                trace_headers = None
+                if is_tracing_enabled:
+                    trace_headers = extract_trace_headers(raw_request.headers)
+                if not is_tracing_enabled and contains_trace_headers(
+                        raw_request.headers):
+                    log_tracing_disabled_warning()
+
+                if isinstance(sampling_params, BeamSearchParams):
+                    assert isinstance(self.engine_client,
+                                    (AsyncLLMEngine,
+                                    MQLLMEngineClient)), \
+                    "Beam search is only supported with" \
+                    "AsyncLLMEngine and MQLLMEngineClient."
+                    generator = self.engine_client.beam_search(
+                        prompt_inputs["prompt_token_ids"],
+                        request_id_item,
+                        sampling_params,
+                    )
+                else:
+                    generator = self.engine_client.generate(
+                        {
+                            "prompt_token_ids":
+                            prompt_inputs["prompt_token_ids"]
+                        },
+                        sampling_params,
+                        request_id_item,
+                        lora_request=lora_request,
+                        prompt_adapter_request=prompt_adapter_request,
+                        trace_headers=trace_headers,
+                        priority=request.priority,
+                    )
+
+                generators.append(generator)
+        except ValueError as e:
+            # TODO: Use a vllm-specific Validation Error
+            return self.create_error_response(str(e))
+
+        result_generator = merge_async_iterators(
+            *generators, is_cancelled=raw_request.is_disconnected)
+
+        # Similar to the OpenAI API, when n != best_of, we do not stream the
+        # results. In addition, we do not stream the results when use
+        # beam search.
+        stream = (request.stream
+                  and (request.best_of is None or request.n == request.best_of)
+                  and not request.use_beam_search)
+
+        # Streaming response
+        if stream:
+            return self.completion_stream_generator(
+                request,
+                result_generator,
+                request_id,
+                created_time,
+                model_name,
+                num_prompts=len(prompts),
+                tokenizer=tokenizer,
+                request_metadata=request_metadata)
+
+        # Non-streaming response
+        final_res_batch: List[Optional[RequestOutput]] = [None] * len(prompts)
+        try:
+            async for i, res in result_generator:
+                final_res_batch[i] = res
+
+            for i, final_res in enumerate(final_res_batch):
+                assert final_res is not None
+
+                # The output should contain the input text
+                # We did not pass it into vLLM engine to avoid being redundant
+                # with the inputs token IDs
+                if final_res.prompt is None:
+                    final_res.prompt = prompts[i]["prompt"]
+
+            final_res_batch_checked = cast(List[RequestOutput],
+                                           final_res_batch)
+
+            response = self.request_output_to_completion_response(
+                final_res_batch_checked,
+                request,
+                request_id,
+                created_time,
+                model_name,
+                tokenizer,
+                request_metadata,
+            )
+        except asyncio.CancelledError:
+            return self.create_error_response("Client disconnected")
+        except ValueError as e:
+            # TODO: Use a vllm-specific Validation Error
+            return self.create_error_response(str(e))
+
+        # When user requests streaming but we don't stream, we still need to
+        # return a streaming response with a single event.
+        if request.stream:
+            response_json = response.model_dump_json()
+
+            async def fake_stream_generator() -> AsyncGenerator[str, None]:
+                yield f"data: {response_json}\n\n"
+                yield "data: [DONE]\n\n"
+
+            return fake_stream_generator()
+
+        return response
+
+    async def completion_stream_generator(
+        self,
+        request: CompletionRequest,
+        result_generator: AsyncIterator[Tuple[int, RequestOutput]],
+        request_id: str,
+        created_time: int,
+        model_name: str,
+        num_prompts: int,
+        tokenizer: AnyTokenizer,
+        request_metadata: RequestResponseMetadata,
+    ) -> AsyncGenerator[str, None]:
+        num_choices = 1 if request.n is None else request.n
+        previous_text_lens = [0] * num_choices * num_prompts
+        previous_num_tokens = [0] * num_choices * num_prompts
+        has_echoed = [False] * num_choices * num_prompts
+        num_prompt_tokens = [0] * num_prompts
+
+        try:
+            async for prompt_idx, res in result_generator:
+                prompt_token_ids = res.prompt_token_ids
+                prompt_logprobs = res.prompt_logprobs
+                prompt_text = res.prompt
+
+                # Prompt details are excluded from later streamed outputs
+                if res.prompt_token_ids is not None:
+                    num_prompt_tokens[prompt_idx] = len(res.prompt_token_ids)
+
+                delta_token_ids: GenericSequence[int]
+                out_logprobs: Optional[GenericSequence[Optional[Dict[
+                    int, Logprob]]]]
+
+                for output in res.outputs:
+                    i = output.index + prompt_idx * num_choices
+                    # TODO(simon): optimize the performance by avoiding full
+                    # text O(n^2) sending.
+
+                    assert request.max_tokens is not None
+                    if request.echo and request.max_tokens == 0:
+                        assert prompt_token_ids is not None
+                        assert prompt_text is not None
+                        # only return the prompt
+                        delta_text = prompt_text
+                        delta_token_ids = prompt_token_ids
+                        out_logprobs = prompt_logprobs
+                        has_echoed[i] = True
+                    elif (request.echo and request.max_tokens > 0
+                          and not has_echoed[i]):
+                        assert prompt_token_ids is not None
+                        assert prompt_text is not None
+                        assert prompt_logprobs is not None
+                        # echo the prompt and first token
+                        delta_text = prompt_text + output.text
+                        delta_token_ids = [
+                            *prompt_token_ids, *output.token_ids
+                        ]
+                        out_logprobs = [
+                            *prompt_logprobs,
+                            *(output.logprobs or []),
+                        ]
+                        has_echoed[i] = True
+                    else:
+                        # return just the delta
+                        delta_text = output.text
+                        delta_token_ids = output.token_ids
+                        out_logprobs = output.logprobs
+
+                    if request.logprobs is not None:
+                        assert out_logprobs is not None, (
+                            "Did not output logprobs")
+                        logprobs = self._create_completion_logprobs(
+                            token_ids=delta_token_ids,
+                            top_logprobs=out_logprobs,
+                            num_output_top_logprobs=request.logprobs,
+                            tokenizer=tokenizer,
+                            initial_text_offset=previous_text_lens[i],
+                        )
+                    else:
+                        logprobs = None
+
+                    previous_text_lens[i] += len(output.text)
+                    previous_num_tokens[i] += len(output.token_ids)
+                    finish_reason = output.finish_reason
+                    stop_reason = output.stop_reason
+
+                    chunk = CompletionStreamResponse(
+                        id=request_id,
+                        created=created_time,
+                        model=model_name,
+                        choices=[
+                            CompletionResponseStreamChoice(
+                                index=i,
+                                text=delta_text,
+                                logprobs=logprobs,
+                                finish_reason=finish_reason,
+                                stop_reason=stop_reason,
+                            )
+                        ])
+                    if (request.stream_options
+                            and request.stream_options.include_usage):
+                        if (request.stream_options.continuous_usage_stats
+                                or output.finish_reason is not None):
+                            prompt_tokens = num_prompt_tokens[prompt_idx]
+                            completion_tokens = previous_num_tokens[i]
+                            usage = UsageInfo(
+                                prompt_tokens=prompt_tokens,
+                                completion_tokens=completion_tokens,
+                                total_tokens=prompt_tokens + completion_tokens,
+                            )
+                        if request.stream_options.continuous_usage_stats:
+                            chunk.usage = usage
+                        else:
+                            chunk.usage = None
+
+                    response_json = chunk.model_dump_json(exclude_unset=False)
+                    yield f"data: {response_json}\n\n"
+
+            if (request.stream_options
+                    and request.stream_options.include_usage):
+                final_usage_chunk = CompletionStreamResponse(
+                    id=request_id,
+                    created=created_time,
+                    model=model_name,
+                    choices=[],
+                    usage=usage,
+                )
+                final_usage_data = (final_usage_chunk.model_dump_json(
+                    exclude_unset=False, exclude_none=True))
+                yield f"data: {final_usage_data}\n\n"
+
+            # report to FastAPI middleware aggregate usage across all choices
+            total_prompt_tokens = sum(num_prompt_tokens)
+            total_completion_tokens = sum(previous_num_tokens)
+            request_metadata.final_usage_info = UsageInfo(
+                prompt_tokens=total_prompt_tokens,
+                completion_tokens=total_completion_tokens,
+                total_tokens=total_prompt_tokens + total_completion_tokens)
+
+        except ValueError as e:
+            # TODO: Use a vllm-specific Validation Error
+            data = self.create_streaming_error_response(str(e))
+            yield f"data: {data}\n\n"
+        yield "data: [DONE]\n\n"
+
+    def request_output_to_completion_response(
+        self,
+        final_res_batch: List[RequestOutput],
+        request: CompletionRequest,
+        request_id: str,
+        created_time: int,
+        model_name: str,
+        tokenizer: AnyTokenizer,
+        request_metadata: RequestResponseMetadata,
+    ) -> CompletionResponse:
+        choices: List[CompletionResponseChoice] = []
+        num_prompt_tokens = 0
+        num_generated_tokens = 0
+
+        for final_res in final_res_batch:
+            prompt_token_ids = final_res.prompt_token_ids
+            assert prompt_token_ids is not None
+            prompt_logprobs = final_res.prompt_logprobs
+            prompt_text = final_res.prompt
+
+            token_ids: GenericSequence[int]
+            out_logprobs: Optional[GenericSequence[Optional[Dict[int,
+                                                                 Logprob]]]]
+
+            for output in final_res.outputs:
+                assert request.max_tokens is not None
+                if request.echo and request.max_tokens == 0:
+                    assert prompt_text is not None
+                    token_ids = prompt_token_ids
+                    out_logprobs = prompt_logprobs
+                    output_text = prompt_text
+                elif request.echo and request.max_tokens > 0:
+                    assert prompt_text is not None
+                    token_ids = [*prompt_token_ids, *output.token_ids]
+
+                    if request.logprobs is None:
+                        out_logprobs = None
+                    else:
+                        assert prompt_logprobs is not None
+                        assert output.logprobs is not None
+                        out_logprobs = [
+                            *prompt_logprobs,
+                            *output.logprobs,
+                        ]
+
+                    output_text = prompt_text + output.text
+                else:
+                    token_ids = output.token_ids
+                    out_logprobs = output.logprobs
+                    output_text = output.text
+
+                if request.logprobs is not None:
+                    assert out_logprobs is not None, "Did not output logprobs"
+                    logprobs = self._create_completion_logprobs(
+                        token_ids=token_ids,
+                        top_logprobs=out_logprobs,
+                        tokenizer=tokenizer,
+                        num_output_top_logprobs=request.logprobs,
+                    )
+                else:
+                    logprobs = None
+
+                choice_data = CompletionResponseChoice(
+                    index=len(choices),
+                    text=output_text,
+                    logprobs=logprobs,
+                    finish_reason=output.finish_reason,
+                    stop_reason=output.stop_reason,
+                    prompt_logprobs=final_res.prompt_logprobs,
+                )
+                choices.append(choice_data)
+
+                num_generated_tokens += len(output.token_ids)
+
+            num_prompt_tokens += len(prompt_token_ids)
+
+        usage = UsageInfo(
+            prompt_tokens=num_prompt_tokens,
+            completion_tokens=num_generated_tokens,
+            total_tokens=num_prompt_tokens + num_generated_tokens,
+        )
+
+        request_metadata.final_usage_info = usage
+
+        return CompletionResponse(
+            id=request_id,
+            created=created_time,
+            model=model_name,
+            choices=choices,
+            usage=usage,
+        )
+
+    def _create_completion_logprobs(
+        self,
+        token_ids: GenericSequence[int],
+        top_logprobs: GenericSequence[Optional[Dict[int, Logprob]]],
+        num_output_top_logprobs: int,
+        tokenizer: AnyTokenizer,
+        initial_text_offset: int = 0,
+    ) -> CompletionLogProbs:
+        """Create logprobs for OpenAI Completion API."""
+        out_text_offset: List[int] = []
+        out_token_logprobs: List[Optional[float]] = []
+        out_tokens: List[str] = []
+        out_top_logprobs: List[Optional[Dict[str, float]]] = []
+
+        last_token_len = 0
+
+        for i, token_id in enumerate(token_ids):
+            step_top_logprobs = top_logprobs[i]
+            if step_top_logprobs is None:
+                token = tokenizer.decode(token_id)
+                if self.return_tokens_as_token_ids:
+                    token = f"token_id:{token_id}"
+
+                out_tokens.append(token)
+                out_token_logprobs.append(None)
+                out_top_logprobs.append(None)
+            else:
+                step_token = step_top_logprobs[token_id]
+
+                token = self._get_decoded_token(
+                    step_token,
+                    token_id,
+                    tokenizer,
+                    return_as_token_id=self.return_tokens_as_token_ids,
+                )
+                token_logprob = max(step_token.logprob, -9999.0)
+
+                out_tokens.append(token)
+                out_token_logprobs.append(token_logprob)
+
+                # makes sure to add the top num_output_top_logprobs + 1
+                # logprobs, as defined in the openai API
+                # (cf. https://github.com/openai/openai-openapi/blob/
+                # 893ba52242dbd5387a97b96444ee1c742cfce9bd/openapi.yaml#L7153)
+                out_top_logprobs.append({
+                    # Convert float("-inf") to the
+                    # JSON-serializable float that OpenAI uses
+                    self._get_decoded_token(
+                        top_lp[1],
+                        top_lp[0],
+                        tokenizer,
+                        return_as_token_id=self.return_tokens_as_token_ids):
+                    max(top_lp[1].logprob, -9999.0)
+                    for i, top_lp in enumerate(step_top_logprobs.items())
+                    if num_output_top_logprobs >= i
+                })
+
+            if len(out_text_offset) == 0:
+                out_text_offset.append(initial_text_offset)
+            else:
+                out_text_offset.append(out_text_offset[-1] + last_token_len)
+            last_token_len = len(token)
+
+        return CompletionLogProbs(
+            text_offset=out_text_offset,
+            token_logprobs=out_token_logprobs,
+            tokens=out_tokens,
+            top_logprobs=out_top_logprobs,
+        )

vllm/entrypoints/openai/serving_embedding.py

@@ -0,0 +1,203 @@
+import asyncio
+import base64
+import time
+from typing import AsyncGenerator, List, Literal, Optional, Union, cast
+
+import numpy as np
+from fastapi import Request
+from typing_extensions import assert_never
+
+from vllm.config import ModelConfig
+from vllm.engine.protocol import EngineClient
+from vllm.entrypoints.logger import RequestLogger
+from vllm.entrypoints.openai.protocol import (EmbeddingRequest,
+                                              EmbeddingResponse,
+                                              EmbeddingResponseData,
+                                              ErrorResponse, UsageInfo)
+from vllm.entrypoints.openai.serving_engine import BaseModelPath, OpenAIServing
+from vllm.logger import init_logger
+from vllm.outputs import EmbeddingOutput, EmbeddingRequestOutput
+from vllm.utils import merge_async_iterators, random_uuid
+
+logger = init_logger(__name__)
+
+TypeTokenIDs = List[int]
+
+
+def _get_embedding(
+    output: EmbeddingOutput,
+    encoding_format: Literal["float", "base64"],
+) -> Union[List[float], str]:
+    if encoding_format == "float":
+        return output.embedding
+    elif encoding_format == "base64":
+        # Force to use float32 for base64 encoding
+        # to match the OpenAI python client behavior
+        embedding_bytes = np.array(output.embedding, dtype="float32").tobytes()
+        return base64.b64encode(embedding_bytes).decode("utf-8")
+
+    assert_never(encoding_format)
+
+
+def request_output_to_embedding_response(
+        final_res_batch: List[EmbeddingRequestOutput], request_id: str,
+        created_time: int, model_name: str,
+        encoding_format: Literal["float", "base64"]) -> EmbeddingResponse:
+    data: List[EmbeddingResponseData] = []
+    num_prompt_tokens = 0
+    for idx, final_res in enumerate(final_res_batch):
+        prompt_token_ids = final_res.prompt_token_ids
+        embedding = _get_embedding(final_res.outputs, encoding_format)
+        embedding_data = EmbeddingResponseData(index=idx, embedding=embedding)
+        data.append(embedding_data)
+
+        num_prompt_tokens += len(prompt_token_ids)
+
+    usage = UsageInfo(
+        prompt_tokens=num_prompt_tokens,
+        total_tokens=num_prompt_tokens,
+    )
+
+    return EmbeddingResponse(
+        id=request_id,
+        created=created_time,
+        model=model_name,
+        data=data,
+        usage=usage,
+    )
+
+
+class OpenAIServingEmbedding(OpenAIServing):
+
+    def __init__(
+        self,
+        engine_client: EngineClient,
+        model_config: ModelConfig,
+        base_model_paths: List[BaseModelPath],
+        *,
+        request_logger: Optional[RequestLogger],
+    ):
+        super().__init__(engine_client=engine_client,
+                         model_config=model_config,
+                         base_model_paths=base_model_paths,
+                         lora_modules=None,
+                         prompt_adapters=None,
+                         request_logger=request_logger)
+        self._enabled = self._check_embedding_mode(model_config.embedding_mode)
+
+    async def create_embedding(
+        self,
+        request: EmbeddingRequest,
+        raw_request: Optional[Request] = None,
+    ) -> Union[EmbeddingResponse, ErrorResponse]:
+        """Completion API similar to OpenAI's API.
+
+        See https://platform.openai.com/docs/api-reference/embeddings/create
+        for the API specification. This API mimics the OpenAI Embedding API.
+        """
+        if not self._enabled:
+            return self.create_error_response("Embedding API disabled")
+        error_check_ret = await self._check_model(request)
+        if error_check_ret is not None:
+            return error_check_ret
+
+        encoding_format = request.encoding_format
+        if request.dimensions is not None:
+            return self.create_error_response(
+                "dimensions is currently not supported")
+
+        model_name = request.model
+        request_id = f"embd-{random_uuid()}"
+        created_time = int(time.monotonic())
+
+        truncate_prompt_tokens = None
+
+        if request.truncate_prompt_tokens is not None:
+            if request.truncate_prompt_tokens <= self.max_model_len:
+                truncate_prompt_tokens = request.truncate_prompt_tokens
+            else:
+                return self.create_error_response(
+                    "truncate_prompt_tokens value is "
+                    "greater than max_model_len."
+                    " Please, select a smaller truncation size.")
+
+        # Schedule the request and get the result generator.
+        generators: List[AsyncGenerator[EmbeddingRequestOutput, None]] = []
+        try:
+            (
+                lora_request,
+                prompt_adapter_request,
+            ) = self._maybe_get_adapters(request)
+
+            tokenizer = await self.engine_client.get_tokenizer(lora_request)
+
+            pooling_params = request.to_pooling_params()
+
+            prompts = list(
+                self._tokenize_prompt_input_or_inputs(request, tokenizer,
+                                                      request.input,
+                                                      truncate_prompt_tokens))
+
+            for i, prompt_inputs in enumerate(prompts):
+                request_id_item = f"{request_id}-{i}"
+
+                self._log_inputs(request_id_item,
+                                 prompt_inputs,
+                                 params=pooling_params,
+                                 lora_request=lora_request,
+                                 prompt_adapter_request=prompt_adapter_request)
+
+                if prompt_adapter_request is not None:
+                    raise NotImplementedError(
+                        "Prompt adapter is not supported "
+                        "for embedding models")
+
+                generator = self.engine_client.encode(
+                    {"prompt_token_ids": prompt_inputs["prompt_token_ids"]},
+                    pooling_params,
+                    request_id_item,
+                    lora_request=lora_request,
+                    priority=request.priority,
+                )
+
+                generators.append(generator)
+        except ValueError as e:
+            # TODO: Use a vllm-specific Validation Error
+            return self.create_error_response(str(e))
+
+        result_generator = merge_async_iterators(
+            *generators,
+            is_cancelled=raw_request.is_disconnected if raw_request else None,
+        )
+
+        # Non-streaming response
+        final_res_batch: List[Optional[EmbeddingRequestOutput]]
+        final_res_batch = [None] * len(prompts)
+        try:
+            async for i, res in result_generator:
+                final_res_batch[i] = res
+
+            for final_res in final_res_batch:
+                assert final_res is not None
+
+            final_res_batch_checked = cast(List[EmbeddingRequestOutput],
+                                           final_res_batch)
+
+            response = request_output_to_embedding_response(
+                final_res_batch_checked, request_id, created_time, model_name,
+                encoding_format)
+        except asyncio.CancelledError:
+            return self.create_error_response("Client disconnected")
+        except ValueError as e:
+            # TODO: Use a vllm-specific Validation Error
+            return self.create_error_response(str(e))
+
+        return response
+
+    def _check_embedding_mode(self, embedding_mode: bool) -> bool:
+        if not embedding_mode:
+            logger.warning(
+                "embedding_mode is False. Embedding API will not work.")
+        else:
+            logger.info("Activating the server engine with embedding enabled.")
+        return embedding_mode

vllm/entrypoints/openai/serving_engine.py

@@ -0,0 +1,487 @@
+import json
+import pathlib
+from dataclasses import dataclass
+from http import HTTPStatus
+from typing import Iterable, Iterator, List, Optional, Tuple, TypedDict, Union
+
+from pydantic import Field
+from typing_extensions import Annotated
+
+from vllm.config import ModelConfig
+from vllm.engine.protocol import EngineClient
+from vllm.entrypoints.logger import RequestLogger
+# yapf conflicts with isort for this block
+# yapf: disable
+from vllm.entrypoints.openai.protocol import (ChatCompletionRequest,
+                                              CompletionRequest,
+                                              DetokenizeRequest,
+                                              EmbeddingRequest, ErrorResponse,
+                                              LoadLoraAdapterRequest,
+                                              ModelCard, ModelList,
+                                              ModelPermission,
+                                              TokenizeChatRequest,
+                                              TokenizeCompletionRequest,
+                                              TokenizeRequest,
+                                              UnloadLoraAdapterRequest)
+# yapf: enable
+from vllm.inputs.parse import parse_and_batch_prompt
+from vllm.logger import init_logger
+from vllm.lora.request import LoRARequest
+from vllm.pooling_params import PoolingParams
+from vllm.prompt_adapter.request import PromptAdapterRequest
+from vllm.sampling_params import BeamSearchParams, SamplingParams
+from vllm.sequence import Logprob
+from vllm.transformers_utils.tokenizer import AnyTokenizer
+from vllm.utils import AtomicCounter
+
+logger = init_logger(__name__)
+
+
+@dataclass
+class BaseModelPath:
+    name: str
+    model_path: str
+
+
+@dataclass
+class PromptAdapterPath:
+    name: str
+    local_path: str
+
+
+@dataclass
+class LoRAModulePath:
+    name: str
+    path: str
+    base_model_name: Optional[str] = None
+
+
+AnyRequest = Union[ChatCompletionRequest, CompletionRequest, DetokenizeRequest,
+                   EmbeddingRequest, TokenizeRequest]
+
+
+class TextTokensPrompt(TypedDict):
+    prompt: str
+    prompt_token_ids: List[int]
+
+
+class OpenAIServing:
+
+    def __init__(
+        self,
+        engine_client: EngineClient,
+        model_config: ModelConfig,
+        base_model_paths: List[BaseModelPath],
+        *,
+        lora_modules: Optional[List[LoRAModulePath]],
+        prompt_adapters: Optional[List[PromptAdapterPath]],
+        request_logger: Optional[RequestLogger],
+        return_tokens_as_token_ids: bool = False,
+    ):
+        super().__init__()
+
+        self.engine_client = engine_client
+        self.model_config = model_config
+        self.max_model_len = model_config.max_model_len
+
+        self.base_model_paths = base_model_paths
+
+        self.lora_id_counter = AtomicCounter(0)
+        self.lora_requests = []
+        if lora_modules is not None:
+            self.lora_requests = [
+                LoRARequest(lora_name=lora.name,
+                            lora_int_id=i,
+                            lora_path=lora.path,
+                            base_model_name=lora.base_model_name
+                            if lora.base_model_name
+                            and self._is_model_supported(lora.base_model_name)
+                            else self.base_model_paths[0].name)
+                for i, lora in enumerate(lora_modules, start=1)
+            ]
+
+        self.prompt_adapter_requests = []
+        if prompt_adapters is not None:
+            for i, prompt_adapter in enumerate(prompt_adapters, start=1):
+                with pathlib.Path(prompt_adapter.local_path,
+                                  "adapter_config.json").open() as f:
+                    adapter_config = json.load(f)
+                    num_virtual_tokens = adapter_config["num_virtual_tokens"]
+                self.prompt_adapter_requests.append(
+                    PromptAdapterRequest(
+                        prompt_adapter_name=prompt_adapter.name,
+                        prompt_adapter_id=i,
+                        prompt_adapter_local_path=prompt_adapter.local_path,
+                        prompt_adapter_num_virtual_tokens=num_virtual_tokens))
+
+        self.request_logger = request_logger
+        self.return_tokens_as_token_ids = return_tokens_as_token_ids
+
+    async def show_available_models(self) -> ModelList:
+        """Show available models. Right now we only have one model."""
+        model_cards = [
+            ModelCard(id=base_model.name,
+                      max_model_len=self.max_model_len,
+                      root=base_model.model_path,
+                      permission=[ModelPermission()])
+            for base_model in self.base_model_paths
+        ]
+        lora_cards = [
+            ModelCard(id=lora.lora_name,
+                      root=lora.local_path,
+                      parent=lora.base_model_name if lora.base_model_name else
+                      self.base_model_paths[0].name,
+                      permission=[ModelPermission()])
+            for lora in self.lora_requests
+        ]
+        prompt_adapter_cards = [
+            ModelCard(id=prompt_adapter.prompt_adapter_name,
+                      root=self.base_model_paths[0].name,
+                      permission=[ModelPermission()])
+            for prompt_adapter in self.prompt_adapter_requests
+        ]
+        model_cards.extend(lora_cards)
+        model_cards.extend(prompt_adapter_cards)
+        return ModelList(data=model_cards)
+
+    def create_error_response(
+            self,
+            message: str,
+            err_type: str = "BadRequestError",
+            status_code: HTTPStatus = HTTPStatus.BAD_REQUEST) -> ErrorResponse:
+        return ErrorResponse(message=message,
+                             type=err_type,
+                             code=status_code.value)
+
+    def create_streaming_error_response(
+            self,
+            message: str,
+            err_type: str = "BadRequestError",
+            status_code: HTTPStatus = HTTPStatus.BAD_REQUEST) -> str:
+        json_str = json.dumps({
+            "error":
+            self.create_error_response(message=message,
+                                       err_type=err_type,
+                                       status_code=status_code).model_dump()
+        })
+        return json_str
+
+    async def _check_model(
+        self,
+        request: AnyRequest,
+    ) -> Optional[ErrorResponse]:
+        if self._is_model_supported(request.model):
+            return None
+        if request.model in [lora.lora_name for lora in self.lora_requests]:
+            return None
+        if request.model in [
+                prompt_adapter.prompt_adapter_name
+                for prompt_adapter in self.prompt_adapter_requests
+        ]:
+            return None
+        return self.create_error_response(
+            message=f"The model `{request.model}` does not exist.",
+            err_type="NotFoundError",
+            status_code=HTTPStatus.NOT_FOUND)
+
+    def _maybe_get_adapters(
+        self, request: AnyRequest
+    ) -> Union[Tuple[None, None], Tuple[LoRARequest, None], Tuple[
+            None, PromptAdapterRequest]]:
+        if self._is_model_supported(request.model):
+            return None, None
+        for lora in self.lora_requests:
+            if request.model == lora.lora_name:
+                return lora, None
+        for prompt_adapter in self.prompt_adapter_requests:
+            if request.model == prompt_adapter.prompt_adapter_name:
+                return None, prompt_adapter
+        # if _check_model has been called earlier, this will be unreachable
+        raise ValueError(f"The model `{request.model}` does not exist.")
+
+    def _normalize_prompt_text_to_input(
+        self,
+        request: AnyRequest,
+        tokenizer: AnyTokenizer,
+        prompt: str,
+        truncate_prompt_tokens: Optional[Annotated[int, Field(ge=1)]],
+        add_special_tokens: bool,
+    ) -> TextTokensPrompt:
+        if truncate_prompt_tokens is None:
+            encoded = tokenizer(prompt, add_special_tokens=add_special_tokens)
+        else:
+            encoded = tokenizer(prompt,
+                                add_special_tokens=add_special_tokens,
+                                truncation=True,
+                                max_length=truncate_prompt_tokens)
+
+        input_ids = encoded.input_ids
+
+        input_text = prompt
+
+        return self._validate_input(request, input_ids, input_text)
+
+    def _normalize_prompt_tokens_to_input(
+        self,
+        request: AnyRequest,
+        tokenizer: AnyTokenizer,
+        prompt_ids: List[int],
+        truncate_prompt_tokens: Optional[Annotated[int, Field(ge=1)]],
+    ) -> TextTokensPrompt:
+        if truncate_prompt_tokens is None:
+            input_ids = prompt_ids
+        else:
+            input_ids = prompt_ids[-truncate_prompt_tokens:]
+
+        input_text = tokenizer.decode(input_ids)
+
+        return self._validate_input(request, input_ids, input_text)
+
+    def _validate_input(
+        self,
+        request: AnyRequest,
+        input_ids: List[int],
+        input_text: str,
+    ) -> TextTokensPrompt:
+        token_num = len(input_ids)
+
+        # Note: EmbeddingRequest doesn't have max_tokens
+        if isinstance(request, EmbeddingRequest):
+            if token_num > self.max_model_len:
+                raise ValueError(
+                    f"This model's maximum context length is "
+                    f"{self.max_model_len} tokens. However, you requested "
+                    f"{token_num} tokens in the input for embedding "
+                    f"generation. Please reduce the length of the input.")
+            return TextTokensPrompt(prompt=input_text,
+                                    prompt_token_ids=input_ids)
+
+        # Note: TokenizeRequest and DetokenizeRequest doesn't have max_tokens
+        # and does not require model context length validation
+        if isinstance(request, (TokenizeCompletionRequest, TokenizeChatRequest,
+                                DetokenizeRequest)):
+            return TextTokensPrompt(prompt=input_text,
+                                    prompt_token_ids=input_ids)
+
+        if request.max_tokens is None:
+            if token_num >= self.max_model_len:
+                raise ValueError(
+                    f"This model's maximum context length is "
+                    f"{self.max_model_len} tokens. However, you requested "
+                    f"{token_num} tokens in the messages, "
+                    f"Please reduce the length of the messages.")
+        elif token_num + request.max_tokens > self.max_model_len:
+            raise ValueError(
+                f"This model's maximum context length is "
+                f"{self.max_model_len} tokens. However, you requested "
+                f"{request.max_tokens + token_num} tokens "
+                f"({token_num} in the messages, "
+                f"{request.max_tokens} in the completion). "
+                f"Please reduce the length of the messages or completion.")
+
+        return TextTokensPrompt(prompt=input_text, prompt_token_ids=input_ids)
+
+    def _tokenize_prompt_input(
+        self,
+        request: AnyRequest,
+        tokenizer: AnyTokenizer,
+        prompt_input: Union[str, List[int]],
+        truncate_prompt_tokens: Optional[Annotated[int, Field(ge=1)]] = None,
+        add_special_tokens: bool = True,
+    ) -> TextTokensPrompt:
+        """
+        A simpler implementation of :meth:`_tokenize_prompt_input_or_inputs`
+        that assumes single input.
+        """
+        return next(
+            self._tokenize_prompt_inputs(
+                request,
+                tokenizer,
+                [prompt_input],
+                truncate_prompt_tokens=truncate_prompt_tokens,
+                add_special_tokens=add_special_tokens,
+            ))
+
+    def _tokenize_prompt_inputs(
+        self,
+        request: AnyRequest,
+        tokenizer: AnyTokenizer,
+        prompt_inputs: Iterable[Union[str, List[int]]],
+        truncate_prompt_tokens: Optional[Annotated[int, Field(ge=1)]] = None,
+        add_special_tokens: bool = True,
+    ) -> Iterator[TextTokensPrompt]:
+        """
+        A simpler implementation of :meth:`_tokenize_prompt_input_or_inputs`
+        that assumes multiple inputs.
+        """
+        for text in prompt_inputs:
+            if isinstance(text, str):
+                yield self._normalize_prompt_text_to_input(
+                    request,
+                    tokenizer,
+                    prompt=text,
+                    truncate_prompt_tokens=truncate_prompt_tokens,
+                    add_special_tokens=add_special_tokens,
+                )
+            else:
+                yield self._normalize_prompt_tokens_to_input(
+                    request,
+                    tokenizer,
+                    prompt_ids=text,
+                    truncate_prompt_tokens=truncate_prompt_tokens,
+                )
+
+    def _tokenize_prompt_input_or_inputs(
+        self,
+        request: AnyRequest,
+        tokenizer: AnyTokenizer,
+        input_or_inputs: Union[str, List[str], List[int], List[List[int]]],
+        truncate_prompt_tokens: Optional[Annotated[int, Field(ge=1)]] = None,
+        add_special_tokens: bool = True,
+    ) -> Iterator[TextTokensPrompt]:
+        """
+        Tokenize/detokenize depending on the input format.
+
+        According to `OpenAI API <https://platform.openai.com/docs/api-reference/embeddings/create>`_
+        , each input can be a string or array of tokens. Note that each request
+        can pass one or more inputs.
+        """
+        for prompt_input in parse_and_batch_prompt(input_or_inputs):
+            # Although our type checking is based on mypy,
+            # VSCode Pyright extension should still work properly
+            # "is True" is required for Pyright to perform type narrowing
+            # See: https://github.com/microsoft/pyright/issues/7672
+            if prompt_input["is_tokens"] is False:
+                yield self._normalize_prompt_text_to_input(
+                    request,
+                    tokenizer,
+                    prompt=prompt_input["content"],
+                    truncate_prompt_tokens=truncate_prompt_tokens,
+                    add_special_tokens=add_special_tokens,
+                )
+            else:
+                yield self._normalize_prompt_tokens_to_input(
+                    request,
+                    tokenizer,
+                    prompt_ids=prompt_input["content"],
+                    truncate_prompt_tokens=truncate_prompt_tokens,
+                )
+
+    def _log_inputs(
+        self,
+        request_id: str,
+        inputs: Union[str, List[int], TextTokensPrompt],
+        params: Optional[Union[SamplingParams, PoolingParams,
+                               BeamSearchParams]],
+        lora_request: Optional[LoRARequest],
+        prompt_adapter_request: Optional[PromptAdapterRequest],
+    ) -> None:
+        if self.request_logger is None:
+            return
+
+        if isinstance(inputs, str):
+            prompt = inputs
+            prompt_token_ids = None
+        elif isinstance(inputs, list):
+            prompt = None
+            prompt_token_ids = inputs
+        else:
+            prompt = inputs["prompt"]
+            prompt_token_ids = inputs["prompt_token_ids"]
+
+        self.request_logger.log_inputs(
+            request_id,
+            prompt,
+            prompt_token_ids,
+            params=params,
+            lora_request=lora_request,
+            prompt_adapter_request=prompt_adapter_request,
+        )
+
+    @staticmethod
+    def _get_decoded_token(logprob: Logprob,
+                           token_id: int,
+                           tokenizer: AnyTokenizer,
+                           return_as_token_id: bool = False) -> str:
+        if return_as_token_id:
+            return f"token_id:{token_id}"
+
+        if logprob.decoded_token is not None:
+            return logprob.decoded_token
+        return tokenizer.decode(token_id)
+
+    async def _check_load_lora_adapter_request(
+            self, request: LoadLoraAdapterRequest) -> Optional[ErrorResponse]:
+        # Check if both 'lora_name' and 'lora_path' are provided
+        if not request.lora_name or not request.lora_path:
+            return self.create_error_response(
+                message="Both 'lora_name' and 'lora_path' must be provided.",
+                err_type="InvalidUserInput",
+                status_code=HTTPStatus.BAD_REQUEST)
+
+        # Check if the lora adapter with the given name already exists
+        if any(lora_request.lora_name == request.lora_name
+               for lora_request in self.lora_requests):
+            return self.create_error_response(
+                message=
+                f"The lora adapter '{request.lora_name}' has already been"
+                "loaded.",
+                err_type="InvalidUserInput",
+                status_code=HTTPStatus.BAD_REQUEST)
+
+        return None
+
+    async def _check_unload_lora_adapter_request(
+            self,
+            request: UnloadLoraAdapterRequest) -> Optional[ErrorResponse]:
+        # Check if either 'lora_name' or 'lora_int_id' is provided
+        if not request.lora_name and not request.lora_int_id:
+            return self.create_error_response(
+                message=
+                "either 'lora_name' and 'lora_int_id' needs to be provided.",
+                err_type="InvalidUserInput",
+                status_code=HTTPStatus.BAD_REQUEST)
+
+        # Check if the lora adapter with the given name exists
+        if not any(lora_request.lora_name == request.lora_name
+                   for lora_request in self.lora_requests):
+            return self.create_error_response(
+                message=
+                f"The lora adapter '{request.lora_name}' cannot be found.",
+                err_type="InvalidUserInput",
+                status_code=HTTPStatus.BAD_REQUEST)
+
+        return None
+
+    async def load_lora_adapter(
+            self,
+            request: LoadLoraAdapterRequest) -> Union[ErrorResponse, str]:
+        error_check_ret = await self._check_load_lora_adapter_request(request)
+        if error_check_ret is not None:
+            return error_check_ret
+
+        lora_name, lora_path = request.lora_name, request.lora_path
+        unique_id = self.lora_id_counter.inc(1)
+        self.lora_requests.append(
+            LoRARequest(lora_name=lora_name,
+                        lora_int_id=unique_id,
+                        lora_path=lora_path))
+        return f"Success: LoRA adapter '{lora_name}' added successfully."
+
+    async def unload_lora_adapter(
+            self,
+            request: UnloadLoraAdapterRequest) -> Union[ErrorResponse, str]:
+        error_check_ret = await self._check_unload_lora_adapter_request(request
+                                                                        )
+        if error_check_ret is not None:
+            return error_check_ret
+
+        lora_name = request.lora_name
+        self.lora_requests = [
+            lora_request for lora_request in self.lora_requests
+            if lora_request.lora_name != lora_name
+        ]
+        return f"Success: LoRA adapter '{lora_name}' removed successfully."
+
+    def _is_model_supported(self, model_name):
+        return any(model.name == model_name for model in self.base_model_paths)

vllm/entrypoints/openai/serving_tokenization.py

@@ -0,0 +1,157 @@
+from typing import List, Optional, Union
+
+from vllm.config import ModelConfig
+from vllm.engine.protocol import EngineClient
+from vllm.entrypoints.chat_utils import (apply_hf_chat_template,
+                                         apply_mistral_chat_template,
+                                         load_chat_template,
+                                         parse_chat_messages_futures)
+from vllm.entrypoints.logger import RequestLogger
+# yapf conflicts with isort for this block
+# yapf: disable
+from vllm.entrypoints.openai.protocol import (DetokenizeRequest,
+                                              DetokenizeResponse,
+                                              ErrorResponse,
+                                              TokenizeChatRequest,
+                                              TokenizeRequest,
+                                              TokenizeResponse)
+# yapf: enable
+from vllm.entrypoints.openai.serving_engine import (BaseModelPath,
+                                                    LoRAModulePath,
+                                                    OpenAIServing)
+from vllm.logger import init_logger
+from vllm.transformers_utils.tokenizer import MistralTokenizer
+from vllm.utils import random_uuid
+
+logger = init_logger(__name__)
+
+
+class OpenAIServingTokenization(OpenAIServing):
+
+    def __init__(
+        self,
+        engine_client: EngineClient,
+        model_config: ModelConfig,
+        base_model_paths: List[BaseModelPath],
+        *,
+        lora_modules: Optional[List[LoRAModulePath]],
+        request_logger: Optional[RequestLogger],
+        chat_template: Optional[str],
+    ):
+        super().__init__(engine_client=engine_client,
+                         model_config=model_config,
+                         base_model_paths=base_model_paths,
+                         lora_modules=lora_modules,
+                         prompt_adapters=None,
+                         request_logger=request_logger)
+
+        # If this is None we use the tokenizer's default chat template
+        # the list of commonly-used chat template names for HF named templates
+        hf_chat_templates: List[str] = ['default', 'tool_use']
+        self.chat_template = chat_template \
+            if chat_template in hf_chat_templates \
+            else load_chat_template(chat_template)
+
+    async def create_tokenize(
+        self,
+        request: TokenizeRequest,
+    ) -> Union[TokenizeResponse, ErrorResponse]:
+        error_check_ret = await self._check_model(request)
+        if error_check_ret is not None:
+            return error_check_ret
+
+        request_id = f"tokn-{random_uuid()}"
+
+        (
+            lora_request,
+            prompt_adapter_request,
+        ) = self._maybe_get_adapters(request)
+
+        tokenizer = await self.engine_client.get_tokenizer(lora_request)
+
+        prompt: Union[str, List[int]]
+        if isinstance(request, TokenizeChatRequest):
+            model_config = self.model_config
+
+            conversation, mm_data_future = parse_chat_messages_futures(
+                request.messages, model_config, tokenizer)
+
+            mm_data = await mm_data_future
+            if mm_data:
+                logger.warning(
+                    "Multi-modal inputs are ignored during tokenization")
+
+            if isinstance(tokenizer, MistralTokenizer):
+                prompt = apply_mistral_chat_template(
+                    tokenizer,
+                    messages=request.messages,
+                    chat_template=self.chat_template,
+                    add_generation_prompt=request.add_generation_prompt,
+                    continue_final_message=request.continue_final_message,
+                )
+            else:
+                prompt = apply_hf_chat_template(
+                    tokenizer,
+                    conversation=conversation,
+                    chat_template=self.chat_template,
+                    add_generation_prompt=request.add_generation_prompt,
+                    continue_final_message=request.continue_final_message,
+                )
+        else:
+            prompt = request.prompt
+
+        self._log_inputs(request_id,
+                         prompt,
+                         params=None,
+                         lora_request=lora_request,
+                         prompt_adapter_request=prompt_adapter_request)
+
+        # Silently ignore prompt adapter since it does not affect tokenization
+
+        prompt_input = self._tokenize_prompt_input(
+            request,
+            tokenizer,
+            prompt,
+            add_special_tokens=request.add_special_tokens,
+        )
+        input_ids = prompt_input["prompt_token_ids"]
+
+        return TokenizeResponse(tokens=input_ids,
+                                count=len(input_ids),
+                                max_model_len=self.max_model_len)
+
+    async def create_detokenize(
+        self,
+        request: DetokenizeRequest,
+    ) -> Union[DetokenizeResponse, ErrorResponse]:
+        error_check_ret = await self._check_model(request)
+        if error_check_ret is not None:
+            return error_check_ret
+
+        request_id = f"tokn-{random_uuid()}"
+
+        (
+            lora_request,
+            prompt_adapter_request,
+        ) = self._maybe_get_adapters(request)
+
+        tokenizer = await self.engine_client.get_tokenizer(lora_request)
+
+        self._log_inputs(request_id,
+                         request.tokens,
+                         params=None,
+                         lora_request=lora_request,
+                         prompt_adapter_request=prompt_adapter_request)
+
+        if prompt_adapter_request is not None:
+            raise NotImplementedError("Prompt adapter is not supported "
+                                      "for tokenization")
+
+        prompt_input = self._tokenize_prompt_input(
+            request,
+            tokenizer,
+            request.tokens,
+        )
+        input_text = prompt_input["prompt"]
+
+        return DetokenizeResponse(prompt=input_text)

vllm/entrypoints/openai/tool_parsers/__init__.py

@@ -0,0 +1,10 @@
+from .abstract_tool_parser import ToolParser, ToolParserManager
+from .hermes_tool_parser import Hermes2ProToolParser
+from .internlm2_tool_parser import Internlm2ToolParser
+from .llama_tool_parser import Llama3JsonToolParser
+from .mistral_tool_parser import MistralToolParser
+
+__all__ = [
+    "ToolParser", "ToolParserManager", "Hermes2ProToolParser",
+    "MistralToolParser", "Internlm2ToolParser", "Llama3JsonToolParser"
+]

vllm/entrypoints/openai/tool_parsers/abstract_tool_parser.py

@@ -0,0 +1,161 @@
+import importlib
+import importlib.util
+import os
+from functools import cached_property
+from typing import Callable, Dict, List, Optional, Sequence, Type, Union
+
+from vllm.entrypoints.openai.protocol import (ChatCompletionRequest,
+                                              DeltaMessage,
+                                              ExtractedToolCallInformation)
+from vllm.logger import init_logger
+from vllm.transformers_utils.tokenizer import AnyTokenizer
+from vllm.utils import is_list_of
+
+logger = init_logger(__name__)
+
+
+class ToolParser:
+    """
+    Abstract ToolParser class that should not be used directly. Provided
+    properties and methods should be used in
+    derived classes.
+    """
+
+    def __init__(self, tokenizer: AnyTokenizer):
+        self.prev_tool_call_arr: List[Dict] = []
+        # the index of the tool call that is currently being parsed
+        self.current_tool_id: int = -1
+        self.current_tool_name_sent: bool = False
+        self.streamed_args_for_tool: List[str] = []
+
+        self.model_tokenizer = tokenizer
+
+    @cached_property
+    def vocab(self) -> Dict[str, int]:
+        # NOTE: Only PreTrainedTokenizerFast is guaranteed to have .vocab
+        # whereas all tokenizers have .get_vocab()
+        return self.model_tokenizer.get_vocab()
+
+    def adjust_request(
+            self, request: ChatCompletionRequest) -> ChatCompletionRequest:
+        """
+        Static method that used to adjust the request parameters.
+        """
+        return request
+
+    def extract_tool_calls(
+            self, model_output: str,
+            request: ChatCompletionRequest) -> ExtractedToolCallInformation:
+        """
+        Static method that should be implemented for extracting tool calls from
+        a complete model-generated string.
+        Used for non-streaming responses where we have the entire model response
+        available before sending to the client.
+        Static because it's stateless.
+        """
+        raise NotImplementedError(
+            "AbstractToolParser.extract_tool_calls has not been implemented!")
+
+    def extract_tool_calls_streaming(
+        self,
+        previous_text: str,
+        current_text: str,
+        delta_text: str,
+        previous_token_ids: Sequence[int],
+        current_token_ids: Sequence[int],
+        delta_token_ids: Sequence[int],
+        request: ChatCompletionRequest,
+    ) -> Union[DeltaMessage, None]:
+        """
+        Instance method that should be implemented for extracting tool calls
+        from an incomplete response; for use when handling tool calls and
+        streaming. Has to be an instance method because  it requires state -
+        the current tokens/diffs, but also the information about what has
+        previously been parsed and extracted (see constructor)
+        """
+        raise NotImplementedError(
+            "AbstractToolParser.extract_tool_calls_streaming has not been "
+            "implemented!")
+
+
+class ToolParserManager:
+    tool_parsers: Dict[str, Type] = {}
+
+    @classmethod
+    def get_tool_parser(cls, name) -> Type:
+        """
+        Get tool parser by name which is registered by `register_module`.
+
+        Raise a KeyError exception if the name is not registered.
+        """
+        if name in cls.tool_parsers:
+            return cls.tool_parsers[name]
+
+        raise KeyError(f"tool helper: '{name}' not found in tool_parsers")
+
+    @classmethod
+    def _register_module(cls,
+                         module: Type,
+                         module_name: Optional[Union[str, List[str]]] = None,
+                         force: bool = True) -> None:
+        if not issubclass(module, ToolParser):
+            raise TypeError(
+                f'module must be subclass of ToolParser, but got {type(module)}'
+            )
+        if module_name is None:
+            module_name = module.__name__
+        if isinstance(module_name, str):
+            module_name = [module_name]
+        for name in module_name:
+            if not force and name in cls.tool_parsers:
+                existed_module = cls.tool_parsers[name]
+                raise KeyError(f'{name} is already registered '
+                               f'at {existed_module.__module__}')
+            cls.tool_parsers[name] = module
+
+    @classmethod
+    def register_module(
+            cls,
+            name: Optional[Union[str, List[str]]] = None,
+            force: bool = True,
+            module: Union[Type, None] = None) -> Union[type, Callable]:
+        """
+        Register module with the given name or name list. it can be used as a
+        decoder(with module as None) or normal function(with module as not 
+        None).
+        """
+        if not isinstance(force, bool):
+            raise TypeError(f'force must be a boolean, but got {type(force)}')
+
+        # raise the error ahead of time
+        if not (name is None or isinstance(name, str)
+                or is_list_of(name, str)):
+            raise TypeError(
+                'name must be None, an instance of str, or a sequence of str, '
+                f'but got {type(name)}')
+
+        # use it as a normal method: x.register_module(module=SomeClass)
+        if module is not None:
+            cls._register_module(module=module, module_name=name, force=force)
+            return module
+
+        # use it as a decorator: @x.register_module()
+        def _register(module):
+            cls._register_module(module=module, module_name=name, force=force)
+            return module
+
+        return _register
+
+    @classmethod
+    def import_tool_parser(cls, plugin_path: str) -> None:
+        """
+        Import a user defined tool parser by the path of the tool parser define
+        file.
+        """
+        module_name = os.path.splitext(os.path.basename(plugin_path))[0]
+        spec = importlib.util.spec_from_file_location(module_name, plugin_path)
+        if spec is None or spec.loader is None:
+            logger.error("load %s from %s failed.", module_name, plugin_path)
+            return
+        module = importlib.util.module_from_spec(spec)
+        spec.loader.exec_module(module)

vllm/entrypoints/openai/tool_parsers/hermes_tool_parser.py

@@ -0,0 +1,338 @@
+import json
+import re
+from typing import Dict, List, Sequence, Union
+
+import partial_json_parser
+from partial_json_parser.core.options import Allow
+
+from vllm.entrypoints.openai.protocol import (ChatCompletionRequest,
+                                              DeltaFunctionCall, DeltaMessage,
+                                              DeltaToolCall,
+                                              ExtractedToolCallInformation,
+                                              FunctionCall, ToolCall)
+from vllm.entrypoints.openai.tool_parsers.abstract_tool_parser import (
+    ToolParser, ToolParserManager)
+from vllm.entrypoints.openai.tool_parsers.utils import (
+    extract_intermediate_diff)
+from vllm.logger import init_logger
+from vllm.transformers_utils.tokenizer import AnyTokenizer, MistralTokenizer
+from vllm.utils import random_uuid
+
+logger = init_logger(__name__)
+
+
+@ToolParserManager.register_module("hermes")
+class Hermes2ProToolParser(ToolParser):
+
+    def __init__(self, tokenizer: AnyTokenizer):
+        super().__init__(tokenizer)
+
+        if isinstance(self.model_tokenizer, MistralTokenizer):
+            logger.error(
+                "Detected Mistral tokenizer when using a Hermes model")
+            self.model_tokenizer = self.model_tokenizer.tokenizer
+
+        self.current_tool_name_sent: bool = False
+        self.prev_tool_call_arr: List[Dict] = []
+        self.current_tool_id: int = -1
+        self.streamed_args_for_tool: List[str] = [
+        ]  # map what has been streamed for each tool so far to a list
+
+        self.tool_call_start_token: str = "<tool_call>"
+        self.tool_call_end_token: str = "</tool_call>"
+
+        self.tool_call_regex = re.compile(
+            r"<tool_call>(.*?)</tool_call>|<tool_call>(.*)", re.DOTALL)
+        self.scratch_pad_regex = re.compile(
+            r"<scratch_pad>(.*?)</scratch_pad>", re.DOTALL)
+
+        if not self.model_tokenizer:
+            raise ValueError(
+                "The model tokenizer must be passed to the ToolParser "
+                "constructor during construction.")
+        self.tool_call_start_token_id = self.vocab.get(
+            self.tool_call_start_token)
+        self.tool_call_end_token_id = self.vocab.get(self.tool_call_end_token)
+        if not self.tool_call_start_token_id or not self.tool_call_end_token_id:
+            raise RuntimeError(
+                "Hermes 2 Pro Tool parser could not locate tool call start/end "
+                "tokens in the tokenizer!")
+
+    def extract_tool_calls(
+        self,
+        model_output: str,
+        request: ChatCompletionRequest,
+    ) -> ExtractedToolCallInformation:
+
+        # sanity check; avoid unnecessary processing
+        if self.tool_call_start_token not in model_output:
+            return ExtractedToolCallInformation(tools_called=False,
+                                                tool_calls=[],
+                                                content=model_output)
+
+        else:
+
+            try:
+                # there are two possible captures - between tags, or between a
+                # tag and end-of-string so the result of
+                # findall is an array of tuples where one is a function call and
+                # the other is None
+                function_call_tuples = (
+                    self.tool_call_regex.findall(model_output))
+
+                # load the JSON, and then use it to build the Function and
+                # Tool Call
+                raw_function_calls = [
+                    json.loads(match[0] if match[0] else match[1])
+                    for match in function_call_tuples
+                ]
+                tool_calls = [
+                    ToolCall(
+                        type="function",
+                        function=FunctionCall(
+                            name=function_call["name"],
+                            # function call args are JSON but as a string
+                            arguments=json.dumps(function_call["arguments"])))
+                    for function_call in raw_function_calls
+                ]
+
+                content = model_output[:model_output.
+                                       find(self.tool_call_start_token)]
+                return ExtractedToolCallInformation(
+                    tools_called=True,
+                    tool_calls=tool_calls,
+                    content=content if content else None)
+
+            except Exception as e:
+                logger.error("Error in extracting tool call from response %s",
+                             e)
+                return ExtractedToolCallInformation(tools_called=False,
+                                                    tool_calls=[],
+                                                    content=model_output)
+
+    def extract_tool_calls_streaming(
+        self,
+        previous_text: str,
+        current_text: str,
+        delta_text: str,
+        previous_token_ids: Sequence[int],
+        current_token_ids: Sequence[int],
+        delta_token_ids: Sequence[int],
+        request: ChatCompletionRequest,
+    ) -> Union[DeltaMessage, None]:
+
+        logger.debug("delta_text: %s", delta_text)
+        logger.debug("delta_token_ids: %s", delta_token_ids)
+        # check to see if we should be streaming a tool call - is there a
+        if self.tool_call_start_token_id not in current_token_ids:
+            logger.debug("No tool call tokens found!")
+            return DeltaMessage(content=delta_text)
+
+        try:
+
+            # figure out where we are in the parsing by counting tool call
+            # start & end tags
+            prev_tool_start_count = previous_token_ids.count(
+                self.tool_call_start_token_id)
+            prev_tool_end_count = previous_token_ids.count(
+                self.tool_call_end_token_id)
+            cur_tool_start_count = current_token_ids.count(
+                self.tool_call_start_token_id)
+            cur_tool_end_count = current_token_ids.count(
+                self.tool_call_end_token_id)
+
+            # case: if we're generating text, OR rounding out a tool call
+            if (cur_tool_start_count == cur_tool_end_count
+                    and prev_tool_end_count == cur_tool_end_count):
+                logger.debug("Generating text content! skipping tool parsing.")
+                if delta_text != self.tool_call_end_token:
+                    return DeltaMessage(content=delta_text)
+
+            # case: if tool open & close tag counts don't match, we're doing
+            # imaginary "else" block here
+            # something with tools with this diff.
+            # flags for partial JSON parting. exported constants from
+            # "Allow" are handled via BIT MASK
+            flags = Allow.ALL if self.current_tool_name_sent \
+                else Allow.ALL & ~Allow.STR
+
+            # case -- we're starting a new tool call
+            if (cur_tool_start_count > cur_tool_end_count
+                    and cur_tool_start_count > prev_tool_start_count):
+                if len(delta_token_ids) > 1:
+                    tool_call_portion = current_text.split(
+                        self.tool_call_start_token)[-1]
+                else:
+                    tool_call_portion = None
+                    delta = None
+
+                text_portion = None
+
+                # set cursors and state appropriately
+                self.current_tool_id += 1
+                self.current_tool_name_sent = False
+                self.streamed_args_for_tool.append("")
+                logger.debug("Starting on a new tool %s", self.current_tool_id)
+
+            # case -- we're updating an existing tool call
+            elif (cur_tool_start_count > cur_tool_end_count
+                  and cur_tool_start_count == prev_tool_start_count):
+
+                # get the portion of the text that's the tool call
+                tool_call_portion = current_text.split(
+                    self.tool_call_start_token)[-1]
+                text_portion = None
+
+            # case -- the current tool call is being closed.
+            elif (cur_tool_start_count == cur_tool_end_count
+                  and cur_tool_end_count > prev_tool_end_count):
+                diff = self.prev_tool_call_arr[self.current_tool_id].get(
+                    "arguments")
+                if diff:
+                    diff = json.dumps(diff).replace(
+                        self.streamed_args_for_tool[self.current_tool_id], "")
+                    logger.debug(
+                        "Finishing tool and found diff that had not "
+                        "been streamed yet: %s", diff)
+                    self.streamed_args_for_tool[self.current_tool_id] \
+                        += diff
+                    return DeltaMessage(tool_calls=[
+                        DeltaToolCall(index=self.current_tool_id,
+                                      function=DeltaFunctionCall(
+                                          arguments=diff).model_dump(
+                                              exclude_none=True))
+                    ])
+
+            # case -- otherwise we're just generating text
+            else:
+                text = delta_text.replace(self.tool_call_start_token, "")
+                text = text.replace(self.tool_call_end_token, "")
+                delta = DeltaMessage(tool_calls=[], content=text)
+                return delta
+
+            try:
+
+                current_tool_call = partial_json_parser.loads(
+                    tool_call_portion or "{}",
+                    flags) if tool_call_portion else None
+                logger.debug("Parsed tool call %s", current_tool_call)
+            except partial_json_parser.core.exceptions.MalformedJSON:
+                logger.debug('not enough tokens to parse into JSON yet')
+                return None
+
+            # case - we haven't sent the tool name yet. If it's available, send
+            #   it. otherwise, wait until it's available.
+            if not self.current_tool_name_sent:
+                function_name: Union[str, None] = current_tool_call.get("name")
+                if function_name:
+                    self.current_tool_name_sent = True
+                    return DeltaMessage(tool_calls=[
+                        DeltaToolCall(index=self.current_tool_id,
+                                      type="function",
+                                      id=f"chatcmpl-tool-{random_uuid()}",
+                                      function=DeltaFunctionCall(
+                                          name=function_name).model_dump(
+                                              exclude_none=True))
+                    ])
+                else:
+                    return None
+            # case -- otherwise, send the tool call delta
+
+            # if the tool call portion is None, send the delta as text
+            if tool_call_portion is None:
+                # if there's text but not tool calls, send that -
+                # otherwise None to skip chunk
+                delta = DeltaMessage(content=delta_text) \
+                    if text_portion is not None else None
+                return delta
+
+            # now, the nitty-gritty of tool calls
+            # now we have the portion to parse as tool call.
+
+            logger.debug("Trying to parse current tool call with ID %s",
+                         self.current_tool_id)
+
+            # if we're starting a new tool call, push an empty object in as
+            #   a placeholder for the arguments
+            if len(self.prev_tool_call_arr) <= self.current_tool_id:
+                self.prev_tool_call_arr.append({})
+
+            # main logic for tool parsing here - compare prev. partially-parsed
+            #   JSON to the current partially-parsed JSON
+            prev_arguments = (
+                self.prev_tool_call_arr[self.current_tool_id].get("arguments"))
+            cur_arguments = current_tool_call.get("arguments")
+
+            logger.debug("diffing old arguments: %s", prev_arguments)
+            logger.debug("against new ones: %s", cur_arguments)
+
+            # case -- no arguments have been created yet. skip sending a delta.
+            if not cur_arguments and not prev_arguments:
+                logger.debug("Skipping text %s - no arguments", delta_text)
+                delta = None
+
+            # case -- prev arguments are defined, but non are now.
+            #   probably impossible, but not a fatal error - just keep going
+            elif not cur_arguments and prev_arguments:
+                logger.error("should be impossible to have arguments reset "
+                             "mid-call. skipping streaming anything.")
+                delta = None
+
+            # case -- we now have the first info about arguments available from
+            #   autocompleting the JSON
+            elif cur_arguments and not prev_arguments:
+
+                cur_arguments_json = json.dumps(cur_arguments)
+                logger.debug("finding %s in %s", delta_text,
+                             cur_arguments_json)
+
+                # get the location where previous args differ from current
+                args_delta_start_loc = cur_arguments_json.index(delta_text) \
+                                       + len(delta_text)
+
+                # use that to find the actual delta
+                arguments_delta = cur_arguments_json[:args_delta_start_loc]
+                logger.debug("First tokens in arguments received: %s",
+                             arguments_delta)
+
+                delta = DeltaMessage(tool_calls=[
+                    DeltaToolCall(index=self.current_tool_id,
+                                  function=DeltaFunctionCall(
+                                      arguments=arguments_delta).model_dump(
+                                          exclude_none=True))
+                ])
+                self.streamed_args_for_tool[self.current_tool_id] \
+                    += arguments_delta
+
+            # last case -- we have an update to existing arguments.
+            elif cur_arguments and prev_arguments:
+
+                cur_args_json = json.dumps(cur_arguments)
+                prev_args_json = json.dumps(prev_arguments)
+                logger.debug("Searching for diff between\n%s", cur_args_json)
+                logger.debug("and\n%s", prev_args_json)
+                argument_diff = extract_intermediate_diff(
+                    cur_args_json, prev_args_json)
+                logger.debug("got argument diff %s", argument_diff)
+                delta = DeltaMessage(tool_calls=[
+                    DeltaToolCall(index=self.current_tool_id,
+                                  function=DeltaFunctionCall(
+                                      arguments=argument_diff).model_dump(
+                                          exclude_none=True))
+                ])
+                self.streamed_args_for_tool[self.current_tool_id] \
+                    += argument_diff
+
+            # handle saving the state for the current tool into
+            # the "prev" list for use in diffing for the next iteration
+            if self.current_tool_id == len(self.prev_tool_call_arr) - 1:
+                self.prev_tool_call_arr[self.current_tool_id] = \
+                    current_tool_call
+            else:
+                self.prev_tool_call_arr.append(current_tool_call)
+
+            return delta
+
+        except Exception as e:
+            logger.error("Error trying to handle streaming tool call: %s", e)
+            return None  # do not stream a delta. skip this token ID.

vllm/entrypoints/openai/tool_parsers/internlm2_tool_parser.py

@@ -0,0 +1,208 @@
+import json
+from typing import Dict, Sequence, Union
+
+import partial_json_parser
+from partial_json_parser.core.options import Allow
+
+from vllm.entrypoints.openai.protocol import (ChatCompletionRequest,
+                                              DeltaFunctionCall, DeltaMessage,
+                                              DeltaToolCall,
+                                              ExtractedToolCallInformation,
+                                              FunctionCall, ToolCall)
+from vllm.entrypoints.openai.tool_parsers.abstract_tool_parser import (
+    ToolParser, ToolParserManager)
+from vllm.entrypoints.openai.tool_parsers.utils import (
+    extract_intermediate_diff)
+from vllm.logger import init_logger
+from vllm.transformers_utils.tokenizer import AnyTokenizer
+from vllm.utils import random_uuid
+
+logger = init_logger(__name__)
+
+
+@ToolParserManager.register_module(["internlm"])
+class Internlm2ToolParser(ToolParser):
+
+    def __init__(self, tokenizer: AnyTokenizer):
+        super().__init__(tokenizer)
+        self.position = 0
+
+    def adjust_request(
+            self, request: ChatCompletionRequest) -> ChatCompletionRequest:
+        if request.tools and request.tool_choice != 'none':
+            # do not skip special tokens because internlm use the special
+            # tokens to indicated the start and end of the tool calls
+            # information.
+            request.skip_special_tokens = False
+        return request
+
+    def get_argments(self, obj):
+        if "parameters" in obj:
+            return obj.get("parameters")
+        elif "arguments" in obj:
+            return obj.get("arguments")
+        return None
+
+    def extract_tool_calls_streaming(
+        self,
+        previous_text: str,
+        current_text: str,
+        delta_text: str,
+        previous_token_ids: Sequence[int],
+        current_token_ids: Sequence[int],
+        delta_token_ids: Sequence[int],
+        request: ChatCompletionRequest,
+    ) -> Union[DeltaMessage, None]:
+        if '<|action_start|>' not in current_text:
+            self.position = len(current_text)
+            return DeltaMessage(content=delta_text)
+        # if the tool call is sended, return a empty delta message
+        # to make sure the finish_reason will be send correctly.
+        if self.current_tool_id > 0:
+            return DeltaMessage(content='')
+
+        last_pos = self.position
+        if '<|action_start|><|plugin|>' not in current_text[last_pos:]:
+            return None
+
+        new_delta = current_text[last_pos:]
+        text, action = new_delta.split('<|action_start|><|plugin|>')
+
+        if len(text) > 0:
+            self.position = self.position + len(text)
+            return DeltaMessage(content=text)
+
+        action = action.strip()
+        action = action.split('<|action_end|>'.strip())[0]
+
+        # bit mask flags for partial JSON parsing. If the name hasn't been
+        # sent yet, don't allow sending
+        # an incomplete string since OpenAI only ever (as far as I have
+        # seen) allows sending the entire tool/ function name at once.
+        flags = Allow.ALL if self.current_tool_name_sent \
+            else Allow.ALL & ~Allow.STR
+
+        try:
+            parsable_arr = action
+
+            # tool calls are generated in an object in inernlm2
+            # it's not support parallel tool calls
+            try:
+                tool_call_arr: Dict = partial_json_parser.loads(
+                    parsable_arr, flags)
+            except partial_json_parser.core.exceptions.MalformedJSON:
+                logger.debug('not enough tokens to parse into JSON yet')
+                return None
+
+            # if the current tool name hasn't been sent, send if available
+            # - otherwise send nothing
+            if not self.current_tool_name_sent:
+                function_name = tool_call_arr.get("name")
+                if function_name:
+                    self.current_tool_id = self.current_tool_id + 1
+                    delta = DeltaMessage(tool_calls=[
+                        DeltaToolCall(index=self.current_tool_id,
+                                      type="function",
+                                      id=f"chatcmpl-tool-{random_uuid()}",
+                                      function=DeltaFunctionCall(
+                                          name=function_name).model_dump(
+                                              exclude_none=True))
+                    ])
+                    self.current_tool_name_sent = True
+                    self.streamed_args_for_tool.append("")
+                else:
+                    delta = None
+            # now we know we're on the same tool call and we're streaming
+            # arguments
+            else:
+                prev_arguments = self.get_argments(
+                    self.prev_tool_call_arr[self.current_tool_id])
+                cur_arguments = self.get_argments(tool_call_arr)
+
+                # not arguments generated
+                if not cur_arguments and not prev_arguments:
+                    delta = None
+                # will never happen
+                elif not cur_arguments and prev_arguments:
+                    logger.error(
+                        "INVARIANT - impossible to have arguments reset "
+                        "mid-arguments")
+                    delta = None
+                # first time to get parameters
+                elif cur_arguments and not prev_arguments:
+                    cur_arguments_json = json.dumps(cur_arguments)
+
+                    arguments_delta = cur_arguments_json[:cur_arguments_json.
+                                                         index(delta_text) +
+                                                         len(delta_text)]
+                    delta = DeltaMessage(tool_calls=[
+                        DeltaToolCall(index=self.current_tool_id,
+                                      function=DeltaFunctionCall(
+                                          arguments=arguments_delta).
+                                      model_dump(exclude_none=True))
+                    ])
+                    self.streamed_args_for_tool[
+                        self.current_tool_id] += arguments_delta
+                # both prev and cur parameters, send the increase parameters
+                elif cur_arguments and prev_arguments:
+                    cur_args_json = json.dumps(cur_arguments)
+                    prev_args_json = json.dumps(prev_arguments)
+
+                    argument_diff = extract_intermediate_diff(
+                        cur_args_json, prev_args_json)
+
+                    delta = DeltaMessage(tool_calls=[
+                        DeltaToolCall(index=self.current_tool_id,
+                                      function=DeltaFunctionCall(
+                                          arguments=argument_diff).model_dump(
+                                              exclude_none=True))
+                    ])
+                    self.streamed_args_for_tool[
+                        self.current_tool_id] += argument_diff
+
+            # check to see if the name is defined and has been sent. if so,
+            # stream the name - otherwise keep waiting
+            # finish by setting old and returning None as base case
+            tool_call_arr["arguments"] = self.get_argments(tool_call_arr)
+            self.prev_tool_call_arr = [tool_call_arr]
+            return delta
+        except Exception as e:
+            logger.error("Error trying to handle streaming tool call: %s", e)
+            logger.debug(
+                "Skipping chunk as a result of tool streaming extraction "
+                "error")
+            return None
+
+    def extract_tool_calls(
+        self,
+        model_output: str,
+        request: ChatCompletionRequest,
+    ) -> ExtractedToolCallInformation:
+        text = model_output
+        tools = request.tools
+        if '<|action_start|><|plugin|>' in text:
+            text, action = text.split('<|action_start|><|plugin|>')
+            action = action.split('<|action_end|>'.strip())[0]
+            action = action[action.find('{'):]
+            action_dict = json.loads(action)
+            name, parameters = action_dict['name'], json.dumps(
+                action_dict.get('parameters', action_dict.get('arguments',
+                                                              {})))
+
+            if not tools or name not in [t.function.name for t in tools]:
+                ExtractedToolCallInformation(tools_called=False,
+                                             tool_calls=[],
+                                             content=text)
+
+            tool_calls = [
+                ToolCall(
+                    function=FunctionCall(name=name, arguments=parameters))
+            ]
+            return ExtractedToolCallInformation(
+                tools_called=True,
+                tool_calls=tool_calls,
+                content=text if len(text) > 0 else None)
+
+        return ExtractedToolCallInformation(tools_called=False,
+                                            tool_calls=[],
+                                            content=text)

vllm/entrypoints/openai/tool_parsers/llama_tool_parser.py

@@ -0,0 +1,277 @@
+import json
+import re
+from json import JSONDecodeError, JSONDecoder
+from typing import Dict, List, Sequence, Union
+
+import partial_json_parser
+from partial_json_parser.core.options import Allow
+from transformers import PreTrainedTokenizerBase
+
+from vllm.entrypoints.openai.protocol import (ChatCompletionRequest,
+                                              DeltaFunctionCall, DeltaMessage,
+                                              DeltaToolCall,
+                                              ExtractedToolCallInformation,
+                                              FunctionCall, ToolCall)
+from vllm.entrypoints.openai.tool_parsers.abstract_tool_parser import (
+    ToolParser, ToolParserManager)
+from vllm.entrypoints.openai.tool_parsers.utils import find_common_prefix
+from vllm.logger import init_logger
+from vllm.utils import random_uuid
+
+logger = init_logger(__name__)
+
+
+# partial_json_parser doesn't support extra data and
+# JSONDecorder.raw_decode doesn't support partial JSON
+def partial_json_loads(input_str, flags):
+    try:
+        return (partial_json_parser.loads(input_str, flags), len(input_str))
+    except JSONDecodeError as e:
+        if "Extra data" in e.msg:
+            dec = JSONDecoder()
+            return dec.raw_decode(input_str)
+        else:
+            raise
+
+
+def is_complete_json(input_str):
+    try:
+        json.loads(input_str)
+        return True
+    except JSONDecodeError:
+        return False
+
+
+@ToolParserManager.register_module("llama3_json")
+class Llama3JsonToolParser(ToolParser):
+    """
+    Tool call parser for Llama 3.1 models intended for use with the
+    examples/tool_chat_template_llama.jinja template.
+
+    Used when --enable-auto-tool-choice --tool-call-parser mistral are all set
+    """
+
+    def __init__(self, tokenizer: PreTrainedTokenizerBase):
+        super().__init__(tokenizer)
+
+        # initialize properties used for state when parsing tool calls in
+        # streaming mode
+        self.prev_tool_call_arr: List[Dict] = []
+        self.current_tool_id: int = -1
+        self.current_tool_name_sent: bool = False
+        self.streamed_args_for_tool: List[str] = [
+        ]  # map what has been streamed for each tool so far to a list
+        self.bot_token = "<|python_tag|>"
+        self.bot_token_id = tokenizer.encode(self.bot_token,
+                                             add_special_tokens=False)[0]
+        self.tool_call_regex = re.compile(r"\[{.*?}\]", re.DOTALL)
+
+    def extract_tool_calls(
+            self, model_output: str,
+            request: ChatCompletionRequest) -> ExtractedToolCallInformation:
+        """
+        Extract the tool calls from a complete model response.
+        """
+        # case -- if a tool call token is not present, return a text response
+        if not (model_output.startswith(self.bot_token)
+                or model_output.startswith('{')):
+            return ExtractedToolCallInformation(tools_called=False,
+                                                tool_calls=[],
+                                                content=model_output)
+
+        try:
+            # load the JSON, and then use it to build the Function and
+            # Tool Call
+            dec = JSONDecoder()
+            function_call_arr = []
+
+            # depending on the prompt format the Llama model may or may not
+            # prefix the output with the <|python_tag|> token
+            start_idx = len(self.bot_token) if model_output.startswith(
+                self.bot_token) else 0
+            while start_idx < len(model_output):
+                (obj, end_idx) = dec.raw_decode(model_output[start_idx:])
+                start_idx += end_idx + len('; ')
+                function_call_arr.append(obj)
+
+            tool_calls: List[ToolCall] = [
+                ToolCall(
+                    type="function",
+                    function=FunctionCall(
+                        name=raw_function_call["name"],
+                        # function call args are JSON but as a string
+                        arguments=json.dumps(raw_function_call["arguments"] \
+                                if "arguments" in raw_function_call \
+                                else raw_function_call["parameters"])))
+                for raw_function_call in function_call_arr
+            ]
+
+            # get any content before  the tool call
+            ret = ExtractedToolCallInformation(tools_called=True,
+                                               tool_calls=tool_calls,
+                                               content=None)
+            return ret
+
+        except Exception as e:
+            logger.error("Error in extracting tool call from response: %s", e)
+            print("ERROR", e)
+            # return information to just treat the tool call as regular JSON
+            return ExtractedToolCallInformation(tools_called=False,
+                                                tool_calls=[],
+                                                content=model_output)
+
+    def extract_tool_calls_streaming(
+        self,
+        previous_text: str,
+        current_text: str,
+        delta_text: str,
+        previous_token_ids: Sequence[int],
+        current_token_ids: Sequence[int],
+        delta_token_ids: Sequence[int],
+        request: ChatCompletionRequest,
+    ) -> Union[DeltaMessage, None]:
+
+        if not (current_text.startswith(self.bot_token)
+                or current_text.startswith('{')):
+            return DeltaMessage(content=delta_text)
+
+        # bit mask flags for partial JSON parsing. If the name hasn't been
+        # sent yet, don't allow sending
+        # an incomplete string since OpenAI only ever (as far as I have
+        # seen) allows sending the entire tool/ function name at once.
+        flags = Allow.ALL if self.current_tool_name_sent \
+            else Allow.ALL & ~Allow.STR
+        try:
+            tool_call_arr = []
+            is_complete = []
+            try:
+                # depending on the prompt format the Llama model may or may not
+                # prefix the output with the <|python_tag|> token
+                start_idx = len(self.bot_token) if current_text.startswith(
+                    self.bot_token) else 0
+                while start_idx < len(current_text):
+                    (obj,
+                     end_idx) = partial_json_loads(current_text[start_idx:],
+                                                   flags)
+                    is_complete.append(
+                        is_complete_json(current_text[start_idx:start_idx +
+                                                      end_idx]))
+                    start_idx += end_idx + len('; ')
+                    # depending on the prompt Llama can use
+                    # either arguments or parameters
+                    if "parameters" in obj:
+                        assert "arguments" not in obj, \
+                            "model generated both parameters and arguments"
+                        obj["arguments"] = obj["parameters"]
+                    tool_call_arr.append(obj)
+            except partial_json_parser.core.exceptions.MalformedJSON:
+                logger.debug('not enough tokens to parse into JSON yet')
+                return None
+
+            # select as the current tool call the one we're on the state at
+            current_tool_call: Dict = tool_call_arr[self.current_tool_id] \
+                if len(tool_call_arr) > 0 else {}
+
+            # case -- if no tokens have been streamed for the tool, e.g.
+            #   only the array brackets, stream nothing
+            if len(tool_call_arr) == 0:
+                return None
+
+            # case: we are starting a new tool in the array
+            #   -> array has > 0 length AND length has moved past cursor
+            elif (len(tool_call_arr) > 0
+                  and len(tool_call_arr) > self.current_tool_id + 1):
+
+                # if we're moving on to a new call, first make sure we
+                # haven't missed anything in the previous one that was
+                # auto-generated due to JSON completions, but wasn't
+                # streamed to the client yet.
+                if self.current_tool_id >= 0:
+                    cur_arguments = current_tool_call.get("arguments")
+                    if cur_arguments:
+                        cur_args_json = json.dumps(cur_arguments)
+                        sent = len(
+                            self.streamed_args_for_tool[self.current_tool_id])
+                        argument_diff = cur_args_json[sent:]
+
+                        logger.debug("got arguments diff: %s", argument_diff)
+                        delta = DeltaMessage(tool_calls=[
+                            DeltaToolCall(index=self.current_tool_id,
+                                          function=DeltaFunctionCall(
+                                              arguments=argument_diff).
+                                          model_dump(exclude_none=True))
+                        ])
+                        self.streamed_args_for_tool[
+                            self.current_tool_id] += argument_diff
+                    else:
+                        delta = None
+                else:
+                    delta = None
+                # re-set stuff pertaining to progress in the current tool
+                self.current_tool_id = len(tool_call_arr) - 1
+                self.current_tool_name_sent = False
+                self.streamed_args_for_tool.append("")
+                logger.debug("starting on new tool %d", self.current_tool_id)
+                return delta
+
+            # if the current tool name hasn't been sent, send if available
+            # - otherwise send nothing
+            elif not self.current_tool_name_sent:
+                function_name = current_tool_call.get("name")
+                if function_name:
+
+                    delta = DeltaMessage(tool_calls=[
+                        DeltaToolCall(index=self.current_tool_id,
+                                      type="function",
+                                      id=f"chatcmpl-tool-{random_uuid()}",
+                                      function=DeltaFunctionCall(
+                                          name=function_name).model_dump(
+                                              exclude_none=True))
+                    ])
+                    self.current_tool_name_sent = True
+                else:
+                    delta = None
+
+            # now we know we're on the same tool call and we're streaming
+            # arguments
+            else:
+                cur_arguments = current_tool_call.get("arguments")
+                delta = None
+
+                if cur_arguments:
+                    sent = len(
+                        self.streamed_args_for_tool[self.current_tool_id])
+                    cur_args_json = json.dumps(cur_arguments)
+                    prev_arguments = self.prev_tool_call_arr[
+                        self.current_tool_id].get("arguments")
+
+                    argument_diff = None
+                    if is_complete[self.current_tool_id]:
+                        argument_diff = cur_args_json[sent:]
+                    elif prev_arguments:
+                        prev_args_json = json.dumps(prev_arguments)
+                        if cur_args_json != prev_args_json:
+
+                            prefix = find_common_prefix(
+                                prev_args_json, cur_args_json)
+                            argument_diff = prefix[sent:]
+
+                    if argument_diff is not None:
+                        delta = DeltaMessage(tool_calls=[
+                            DeltaToolCall(index=self.current_tool_id,
+                                          function=DeltaFunctionCall(
+                                              arguments=argument_diff).
+                                          model_dump(exclude_none=True))
+                        ])
+                        self.streamed_args_for_tool[
+                            self.current_tool_id] += argument_diff
+
+            self.prev_tool_call_arr = tool_call_arr
+            return delta
+
+        except Exception as e:
+            logger.error("Error trying to handle streaming tool call: %s", e)
+            logger.debug(
+                "Skipping chunk as a result of tool streaming extraction "
+                "error")
+            return None

vllm/entrypoints/openai/tool_parsers/mistral_tool_parser.py

@@ -0,0 +1,306 @@
+import json
+import re
+from random import choices
+from string import ascii_letters, digits
+from typing import Dict, List, Sequence, Union
+
+import partial_json_parser
+from partial_json_parser.core.options import Allow
+from pydantic import Field
+
+from vllm.entrypoints.openai.protocol import (ChatCompletionRequest,
+                                              DeltaFunctionCall, DeltaMessage,
+                                              DeltaToolCall,
+                                              ExtractedToolCallInformation,
+                                              FunctionCall, ToolCall)
+from vllm.entrypoints.openai.tool_parsers.abstract_tool_parser import (
+    ToolParser, ToolParserManager)
+from vllm.entrypoints.openai.tool_parsers.utils import (
+    extract_intermediate_diff)
+from vllm.logger import init_logger
+from vllm.transformers_utils.tokenizer import AnyTokenizer, MistralTokenizer
+from vllm.utils import random_uuid
+
+logger = init_logger(__name__)
+
+ALPHANUMERIC = ascii_letters + digits
+
+
+class MistralToolCall(ToolCall):
+    id: str = Field(
+        default_factory=lambda: MistralToolCall.generate_random_id())
+
+    @staticmethod
+    def generate_random_id():
+        # Mistral Tool Call Ids must be alphanumeric with a maximum length of 9.
+        # https://github.com/mistralai/mistral-common/blob/21ee9f6cee3441e9bb1e6ed2d10173f90bd9b94b/src/mistral_common/protocol/instruct/validator.py#L299
+        return "".join(choices(ALPHANUMERIC, k=9))
+
+
+@ToolParserManager.register_module("mistral")
+class MistralToolParser(ToolParser):
+    """
+    Tool call parser for Mistral 7B Instruct v0.3, intended for use with the
+    examples/tool_chat_template_mistral.jinja template.
+
+    Used when --enable-auto-tool-choice --tool-call-parser mistral are all set
+    """
+
+    def __init__(self, tokenizer: AnyTokenizer):
+        super().__init__(tokenizer)
+
+        if not isinstance(self.model_tokenizer, MistralTokenizer):
+            logger.info("Non-Mistral tokenizer detected when using a Mistral "
+                        "model...")
+
+        # initialize properties used for state when parsing tool calls in
+        # streaming mode
+        self.prev_tool_call_arr: List[Dict] = []
+        self.current_tool_id: int = -1
+        self.current_tool_name_sent: bool = False
+        self.streamed_args_for_tool: List[str] = [
+        ]  # map what has been streamed for each tool so far to a list
+        self.bot_token = "[TOOL_CALLS]"
+        self.bot_token_id = self.vocab.get(self.bot_token)
+        self.tool_call_regex = re.compile(r"\[{.*?}\]", re.DOTALL)
+        if not self.bot_token_id:
+            raise RuntimeError(
+                "Mistral Tool Parser could not locate the tool call token in "
+                "the tokenizer!")
+
+    def extract_tool_calls(
+        self,
+        model_output: str,
+        request: ChatCompletionRequest,
+    ) -> ExtractedToolCallInformation:
+        """
+        Extract the tool calls from a complete model response. Requires
+        find-and-replacing single quotes with double quotes for JSON parsing,
+        make sure your tool call arguments don't ever include quotes!
+        """
+
+        # case -- if a tool call token is not present, return a text response
+        if self.bot_token not in model_output:
+            return ExtractedToolCallInformation(tools_called=False,
+                                                tool_calls=[],
+                                                content=model_output)
+        try:
+
+            # use a regex to find the tool call. remove the BOT token
+            #   and make sure to replace single quotes with double quotes
+            raw_tool_call = self.tool_call_regex.findall(
+                model_output.replace(self.bot_token, ""))[0]
+
+            # load the JSON, and then use it to build the Function and
+            # Tool Call
+            function_call_arr = json.loads(raw_tool_call)
+            tool_calls: List[MistralToolCall] = [
+                MistralToolCall(
+                    type="function",
+                    function=FunctionCall(
+                        name=raw_function_call["name"],
+                        # function call args are JSON but as a string
+                        arguments=json.dumps(raw_function_call["arguments"])))
+                for raw_function_call in function_call_arr
+            ]
+
+            # get any content before  the tool call
+            content = model_output.split(self.bot_token)[0]
+            return ExtractedToolCallInformation(
+                tools_called=True,
+                tool_calls=tool_calls,
+                content=content if len(content) > 0 else None)
+
+        except Exception as e:
+            logger.error("Error in extracting tool call from response: %s", e)
+            # return information to just treat the tool call as regular JSON
+            return ExtractedToolCallInformation(tools_called=False,
+                                                tool_calls=[],
+                                                content=model_output)
+
+    def extract_tool_calls_streaming(
+        self,
+        previous_text: str,
+        current_text: str,
+        delta_text: str,
+        previous_token_ids: Sequence[int],
+        current_token_ids: Sequence[int],
+        delta_token_ids: Sequence[int],
+        request: ChatCompletionRequest,
+    ) -> Union[DeltaMessage, None]:
+
+        # if the tool call token is not in the tokens generated so far, append
+        # output to contents since it's not a tool
+        if self.bot_token not in current_text:
+            return DeltaMessage(content=delta_text)
+
+        # if the tool call token ID IS in the tokens generated so far, that
+        # means we're parsing as tool calls now
+
+        # handle if we detected the BOT token which means the start of tool
+        # calling
+        if (self.bot_token_id in delta_token_ids
+                and len(delta_token_ids) == 1):
+            # if it's the only token, return None, so we don't send a chat
+            # completion any don't send a control token
+            return None
+
+        # bit mask flags for partial JSON parsing. If the name hasn't been
+        # sent yet, don't allow sending
+        # an incomplete string since OpenAI only ever (as far as I have
+        # seen) allows sending the entire tool/ function name at once.
+        flags = Allow.ALL if self.current_tool_name_sent \
+            else Allow.ALL & ~Allow.STR
+        try:
+
+            # replace BOT token with empty string, and convert single quotes
+            # to double to allow parsing as JSON since mistral uses single
+            # quotes instead of double for tool calls
+            parsable_arr = current_text.split(self.bot_token)[-1]
+
+            # tool calls are generated in an array, so do partial JSON
+            # parsing on the entire array
+            try:
+                tool_call_arr: List[Dict] = partial_json_parser.loads(
+                    parsable_arr, flags)
+            except partial_json_parser.core.exceptions.MalformedJSON:
+                logger.debug('not enough tokens to parse into JSON yet')
+                return None
+
+            # select as the current tool call the one we're on the state at
+
+            current_tool_call: Dict = tool_call_arr[self.current_tool_id] \
+                if len(tool_call_arr) > 0 else {}
+
+            # case -- if no tokens have been streamed for the tool, e.g.
+            #   only the array brackets, stream nothing
+            if len(tool_call_arr) == 0:
+                return None
+
+            # case: we are starting a new tool in the array
+            #   -> array has > 0 length AND length has moved past cursor
+            elif (len(tool_call_arr) > 0
+                  and len(tool_call_arr) > self.current_tool_id + 1):
+
+                # if we're moving on to a new call, first make sure we
+                # haven't missed anything in the previous one that was
+                # auto-generated due to JSON completions, but wasn't
+                # streamed to the client yet.
+                if self.current_tool_id >= 0:
+                    diff: Union[str, None] = current_tool_call.get("arguments")
+
+                    if diff:
+                        diff = json.dumps(diff).replace(
+                            self.streamed_args_for_tool[self.current_tool_id],
+                            "")
+                        delta = DeltaMessage(tool_calls=[
+                            DeltaToolCall(index=self.current_tool_id,
+                                          function=DeltaFunctionCall(
+                                              arguments=diff).model_dump(
+                                                  exclude_none=True))
+                        ])
+                        self.streamed_args_for_tool[
+                            self.current_tool_id] += diff
+                    else:
+                        delta = None
+                else:
+                    delta = None
+                # re-set stuff pertaining to progress in the current tool
+                self.current_tool_id = len(tool_call_arr) - 1
+                self.current_tool_name_sent = False
+                self.streamed_args_for_tool.append("")
+                logger.debug("starting on new tool %d", self.current_tool_id)
+                return delta
+
+            # case: update an existing tool - this is handled below
+
+            # if the current tool name hasn't been sent, send if available
+            # - otherwise send nothing
+            if not self.current_tool_name_sent:
+                function_name = current_tool_call.get("name")
+                if function_name:
+
+                    delta = DeltaMessage(tool_calls=[
+                        DeltaToolCall(index=self.current_tool_id,
+                                      type="function",
+                                      id=f"chatcmpl-tool-{random_uuid()}",
+                                      function=DeltaFunctionCall(
+                                          name=function_name).model_dump(
+                                              exclude_none=True))
+                    ])
+                    self.current_tool_name_sent = True
+                else:
+                    delta = None
+
+            # now we know we're on the same tool call and we're streaming
+            # arguments
+            else:
+
+                prev_arguments = self.prev_tool_call_arr[
+                    self.current_tool_id].get("arguments")
+                cur_arguments = current_tool_call.get("arguments")
+
+                new_text = delta_text.replace("\'", "\"")
+
+                if not cur_arguments and not prev_arguments:
+
+                    delta = None
+                elif not cur_arguments and prev_arguments:
+                    logger.error(
+                        "INVARIANT - impossible to have arguments reset "
+                        "mid-arguments")
+                    delta = None
+                elif cur_arguments and not prev_arguments:
+                    cur_arguments_json = json.dumps(cur_arguments)
+                    logger.debug("finding %s in %s", new_text,
+                                 cur_arguments_json)
+
+                    arguments_delta = cur_arguments_json[:cur_arguments_json.
+                                                         index(new_text) +
+                                                         len(new_text)]
+                    logger.debug("First tokens in arguments received: %s",
+                                 arguments_delta)
+                    delta = DeltaMessage(tool_calls=[
+                        DeltaToolCall(index=self.current_tool_id,
+                                      function=DeltaFunctionCall(
+                                          arguments=arguments_delta).
+                                      model_dump(exclude_none=True))
+                    ])
+                    self.streamed_args_for_tool[
+                        self.current_tool_id] += arguments_delta
+
+                elif cur_arguments and prev_arguments:
+                    cur_args_json = json.dumps(cur_arguments)
+                    prev_args_json = json.dumps(prev_arguments)
+                    logger.debug("Searching for diff between \n%s\n%s",
+                                 cur_args_json, prev_args_json)
+
+                    argument_diff = extract_intermediate_diff(
+                        cur_args_json, prev_args_json)
+                    logger.debug("got arguments diff: %s", argument_diff)
+                    delta = DeltaMessage(tool_calls=[
+                        DeltaToolCall(index=self.current_tool_id,
+                                      function=DeltaFunctionCall(
+                                          arguments=argument_diff).model_dump(
+                                              exclude_none=True))
+                    ])
+                    self.streamed_args_for_tool[
+                        self.current_tool_id] += argument_diff
+                else:
+                    # try parsing it with regular JSON - if it works we're
+                    # at the end, and we need to send the difference between
+                    # tokens streamed so far and the valid JSON
+                    delta = None
+
+            # check to see if the name is defined and has been sent. if so,
+            # stream the name - otherwise keep waiting
+            # finish by setting old and returning None as base case
+            self.prev_tool_call_arr = tool_call_arr
+            return delta
+
+        except Exception as e:
+            logger.error("Error trying to handle streaming tool call: %s", e)
+            logger.debug(
+                "Skipping chunk as a result of tool streaming extraction "
+                "error")
+            return None

vllm/entrypoints/openai/tool_parsers/utils.py

@@ -0,0 +1,87 @@
+def find_common_prefix(s1: str, s2: str) -> str:
+    """
+    Finds a common prefix that is shared between two strings, if there is one.
+    Order of arguments is NOT important.
+
+    This function is provided as a UTILITY for extracting information from JSON
+    generated by partial_json_parser, to help in ensuring that the right tokens
+    are returned in streaming, so that close-quotes, close-brackets and
+    close-braces are not returned prematurely.
+
+    e.g. find_common_prefix('{"fruit": "ap"}', '{"fruit": "apple"}') ->
+    '{"fruit": "ap'
+    """
+    prefix = ''
+    min_length = min(len(s1), len(s2))
+    for i in range(0, min_length):
+        if s1[i] == s2[i]:
+            prefix += s1[i]
+        else:
+            break
+    return prefix
+
+
+def find_common_suffix(s1: str, s2: str) -> str:
+    """
+    Finds a common suffix shared between two strings, if there is one. Order of
+    arguments is NOT important.
+    Stops when the suffix ends OR it hits an alphanumeric character
+
+    e.g. find_common_suffix('{"fruit": "ap"}', '{"fruit": "apple"}') -> '"}'
+    """
+    suffix = ''
+    min_length = min(len(s1), len(s2))
+    for i in range(1, min_length + 1):
+        if s1[-i] == s2[-i] and not s1[-i].isalnum():
+            suffix = s1[-i] + suffix
+        else:
+            break
+    return suffix
+
+
+def extract_intermediate_diff(curr: str, old: str) -> str:
+    """
+    Given two strings, extract the difference in the middle between two strings
+    that are known to have a common prefix and/or suffix.
+
+    This function is provided as a UTILITY for extracting information from JSON
+    generated by partial_json_parser, to help in ensuring that the right tokens
+    are returned in streaming, so that close-quotes, close-brackets and
+    close-braces are not returned prematurely. The order of arguments IS
+    important - the new version of the partially-parsed JSON must be the first
+    argument, and the secnod argument must be from the previous generation.
+
+    What it returns, is tokens that should be streamed to the client.
+
+    e.g. extract_intermediate_diff('{"fruit": "apple"}', '{"fruit": "ap"}')
+        -> 'ple'
+
+    """
+    suffix = find_common_suffix(curr, old)
+
+    old = old[::-1].replace(suffix[::-1], '', 1)[::-1]
+    prefix = find_common_prefix(curr, old)
+    diff = curr
+    if len(suffix):
+        diff = diff[::-1].replace(suffix[::-1], '', 1)[::-1]
+
+    if len(prefix):
+        # replace the prefix only once in case it's mirrored
+        diff = diff.replace(prefix, '', 1)
+
+    return diff
+
+
+def find_all_indices(string, substring):
+    """
+    Find all (starting) indices of a substring in a given string. Useful for
+    tool call extraction
+    """
+    indices = []
+    index = -1
+    while True:
+        index = string.find(substring, index + 1)
+        if index == -1:
+            break
+        indices.append(index)
+    return indices