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.venv/lib/python3.11/site-packages/vllm/entrypoints/api_server.py

@@ -0,0 +1,169 @@
+# SPDX-License-Identifier: Apache-2.0
+"""
+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.entrypoints.utils import with_cancellation
+from vllm.logger import init_logger
+from vllm.sampling_params import SamplingParams
+from vllm.usage.usage_lib import UsageContext
+from vllm.utils import FlexibleArgumentParser, random_uuid, set_ulimit
+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()
+    return await _generate(request_dict, raw_request=request)
+
+
+@with_cancellation
+async def _generate(request_dict: dict, raw_request: Request) -> Response:
+    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)
+
+    # 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) + "\n").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)
+
+    set_ulimit()
+
+    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))

.venv/lib/python3.11/site-packages/vllm/entrypoints/chat_utils.py

@@ -0,0 +1,1007 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import asyncio
+import codecs
+import json
+from abc import ABC, abstractmethod
+from collections import defaultdict, deque
+from functools import cache, lru_cache, partial
+from pathlib import Path
+from typing import (Any, Awaitable, Callable, Dict, Generic, Iterable, List,
+                    Literal, Optional, Tuple, TypeVar, Union, cast)
+
+import jinja2.nodes
+import transformers.utils.chat_template_utils as hf_chat_utils
+# yapf conflicts with isort for this block
+# yapf: disable
+from openai.types.chat import (ChatCompletionAssistantMessageParam,
+                               ChatCompletionContentPartImageParam,
+                               ChatCompletionContentPartInputAudioParam)
+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)
+from openai.types.chat.chat_completion_content_part_input_audio_param import (
+    InputAudio)
+# yapf: enable
+# pydantic needs the TypedDict from typing_extensions
+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 MediaConnector
+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 VideoURL(TypedDict, total=False):
+    url: Required[str]
+    """
+    Either a URL of the video or a data URL with base64 encoded video data.
+    """
+
+
+class ChatCompletionContentPartVideoParam(TypedDict, total=False):
+    video_url: Required[VideoURL]
+
+    type: Required[Literal["video_url"]]
+    """The type of the content part."""
+
+
+class CustomChatCompletionContentSimpleImageParam(TypedDict, total=False):
+    """A simpler version of the param that only accepts a plain image_url.
+    This is supported by OpenAI API, although it is not documented.
+
+    Example:
+    {
+        "image_url": "https://example.com/image.jpg"
+    }
+    """
+    image_url: Required[str]
+
+
+class CustomChatCompletionContentSimpleAudioParam(TypedDict, total=False):
+    """A simpler version of the param that only accepts a plain audio_url.
+
+    Example:
+    {
+        "audio_url": "https://example.com/audio.mp3"
+    }
+    """
+    audio_url: Required[str]
+
+
+class CustomChatCompletionContentSimpleVideoParam(TypedDict, total=False):
+    """A simpler version of the param that only accepts a plain audio_url.
+
+    Example:
+    {
+        "video_url": "https://example.com/video.mp4"
+    }
+    """
+    video_url: Required[str]
+
+
+ChatCompletionContentPartParam: TypeAlias = Union[
+    OpenAIChatCompletionContentPartParam, ChatCompletionContentPartAudioParam,
+    ChatCompletionContentPartInputAudioParam,
+    ChatCompletionContentPartVideoParam, ChatCompletionContentPartRefusalParam,
+    CustomChatCompletionContentSimpleImageParam,
+    CustomChatCompletionContentSimpleAudioParam,
+    CustomChatCompletionContentSimpleVideoParam, str]
+
+
+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: Union[Optional[str], List[Dict[str, 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."""
+
+
+# Passed in by user
+ChatTemplateContentFormatOption = Literal["auto", "string", "openai"]
+
+# Used internally
+_ChatTemplateContentFormat = Literal["string", "openai"]
+
+
+def _is_var_access(node: jinja2.nodes.Node, varname: str) -> bool:
+    if isinstance(node, jinja2.nodes.Name):
+        return node.ctx == "load" and node.name == varname
+
+    return False
+
+
+def _is_attr_access(node: jinja2.nodes.Node, varname: str, key: str) -> bool:
+    if isinstance(node, jinja2.nodes.Getitem):
+        return (_is_var_access(node.node, varname)
+                and isinstance(node.arg, jinja2.nodes.Const)
+                and node.arg.value == key)
+
+    if isinstance(node, jinja2.nodes.Getattr):
+        return _is_var_access(node.node, varname) and node.attr == key
+
+    return False
+
+
+def _is_var_or_elems_access(
+    node: jinja2.nodes.Node,
+    varname: str,
+    key: Optional[str] = None,
+) -> bool:
+    if isinstance(node, jinja2.nodes.Filter):
+        return (node.node is not None
+                and _is_var_or_elems_access(node.node, varname, key))
+    if isinstance(node, jinja2.nodes.Test):
+        return _is_var_or_elems_access(node.node, varname, key)
+
+    if (isinstance(node, jinja2.nodes.Getitem)
+            and isinstance(node.arg, jinja2.nodes.Slice)):
+        return _is_var_or_elems_access(node.node, varname, key)
+
+    # yapf: disable
+    return (
+        _is_attr_access(node, varname, key) if key
+        else _is_var_access(node, varname)
+    ) # yapf: enable
+
+
+def _iter_nodes_assign_var_or_elems(root: jinja2.nodes.Node, varname: str):
+    # Global variable that is implicitly defined at the root
+    yield root, varname
+
+    # Iterative BFS
+    related_varnames = deque([varname])
+    while related_varnames:
+        related_varname = related_varnames.popleft()
+
+        for assign_ast in root.find_all(jinja2.nodes.Assign):
+            lhs = assign_ast.target
+            rhs = assign_ast.node
+
+            if _is_var_or_elems_access(rhs, related_varname):
+                assert isinstance(lhs, jinja2.nodes.Name)
+                yield assign_ast, lhs.name
+
+                # Avoid infinite looping for self-assignment
+                if lhs.name != related_varname:
+                    related_varnames.append(lhs.name)
+
+
+# NOTE: The proper way to handle this is to build a CFG so that we can handle
+# the scope in which each variable is defined, but that is too complicated
+def _iter_nodes_assign_messages_item(root: jinja2.nodes.Node):
+    messages_varnames = [
+        varname
+        for _, varname in _iter_nodes_assign_var_or_elems(root, "messages")
+    ]
+
+    # Search for {%- for message in messages -%} loops
+    for loop_ast in root.find_all(jinja2.nodes.For):
+        loop_iter = loop_ast.iter
+        loop_target = loop_ast.target
+
+        for varname in messages_varnames:
+            if _is_var_or_elems_access(loop_iter, varname):
+                assert isinstance(loop_target, jinja2.nodes.Name)
+                yield loop_ast, loop_target.name
+                break
+
+
+def _iter_nodes_assign_content_item(root: jinja2.nodes.Node):
+    message_varnames = [
+        varname for _, varname in _iter_nodes_assign_messages_item(root)
+    ]
+
+    # Search for {%- for content in message['content'] -%} loops
+    for loop_ast in root.find_all(jinja2.nodes.For):
+        loop_iter = loop_ast.iter
+        loop_target = loop_ast.target
+
+        for varname in message_varnames:
+            if _is_var_or_elems_access(loop_iter, varname, "content"):
+                assert isinstance(loop_target, jinja2.nodes.Name)
+                yield loop_ast, loop_target.name
+                break
+
+
+def _try_extract_ast(chat_template: str) -> Optional[jinja2.nodes.Template]:
+    try:
+        jinja_compiled = hf_chat_utils._compile_jinja_template(chat_template)
+        return jinja_compiled.environment.parse(chat_template)
+    except Exception:
+        logger.exception("Error when compiling Jinja template")
+        return None
+
+
+def _detect_content_format(
+    chat_template: str,
+    *,
+    default: _ChatTemplateContentFormat,
+) -> _ChatTemplateContentFormat:
+    jinja_ast = _try_extract_ast(chat_template)
+    if jinja_ast is None:
+        return default
+
+    try:
+        next(_iter_nodes_assign_content_item(jinja_ast))
+    except StopIteration:
+        return "string"
+    except Exception:
+        logger.exception("Error when parsing AST of Jinja template")
+        return default
+    else:
+        return "openai"
+
+
+def _resolve_chat_template_content_format(
+    chat_template: Optional[str],
+    given_format: ChatTemplateContentFormatOption,
+    tokenizer: AnyTokenizer,
+) -> _ChatTemplateContentFormat:
+    if isinstance(tokenizer, (PreTrainedTokenizer, PreTrainedTokenizerFast)):
+        tokenizer_chat_template = tokenizer.chat_template
+    else:
+        tokenizer_chat_template = None
+
+    jinja_text: Optional[str]
+    if isinstance(tokenizer_chat_template, str) and chat_template is None:
+        jinja_text = tokenizer_chat_template
+    elif (isinstance(tokenizer_chat_template, dict)
+            and chat_template in tokenizer_chat_template):
+        jinja_text = tokenizer_chat_template[chat_template]
+    else:
+        jinja_text = load_chat_template(chat_template, is_literal=True)
+
+    detected_format = ("string" if jinja_text is None else
+                       _detect_content_format(jinja_text, default="string"))
+
+    return detected_format if given_format == "auto" else given_format
+
+
+@lru_cache
+def resolve_chat_template_content_format(
+    chat_template: Optional[str],
+    given_format: ChatTemplateContentFormatOption,
+    tokenizer: AnyTokenizer,
+) -> _ChatTemplateContentFormat:
+    detected_format = _resolve_chat_template_content_format(
+        chat_template,
+        given_format,
+        tokenizer,
+    )
+
+    logger.info(
+        "Detected the chat template content format to be '%s'. "
+        "You can set `--chat-template-content-format` to override this.",
+        detected_format,
+    )
+
+    if given_format != "auto" and given_format != detected_format:
+        logger.warning(
+            "You specified `--chat-template-content-format %s` "
+            "which is different from the detected format '%s'. "
+            "If our automatic detection is incorrect, please consider "
+            "opening a GitHub issue so that we can improve it: "
+            "https://github.com/vllm-project/vllm/issues/new/choose",
+            given_format,
+            detected_format,
+        )
+
+    return detected_format
+
+
+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._items_by_modality = defaultdict[str, list[_T]](list)
+
+    @property
+    def model_config(self) -> ModelConfig:
+        return self._model_config
+
+    @property
+    def allowed_local_media_path(self):
+        return self._model_config.allowed_local_media_path
+
+    @staticmethod
+    @cache
+    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 in ("minicpmo", "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", "deepseek_vl_v2", "internvl_chat",
+                              "NVLM_D", "h2ovl_chat"):
+                return "<image>"
+            if model_type == "mllama":
+                return "<|image|>"
+            if model_type in ("qwen2_vl", "qwen2_5_vl"):
+                return "<|vision_start|><|image_pad|><|vision_end|>"
+            if model_type == "molmo":
+                return ""
+            if model_type == "idefics3":
+                return "<image>"
+            if model_type == "aria":
+                return "<|fim_prefix|><|img|><|fim_suffix|>"
+
+            raise TypeError(f"Unknown {modality} model type: {model_type}")
+        elif modality == "audio":
+            if model_type == "ultravox":
+                return "<|audio|>"
+            if model_type == "qwen2_audio":
+                return (f"Audio {current_count}: "
+                        f"<|audio_bos|><|AUDIO|><|audio_eos|>")
+            if model_type == "minicpmo":
+                return "(<audio>./</audio>)"
+            raise TypeError(f"Unknown model type: {model_type}")
+        elif modality == "video":
+            if model_type in ("qwen2_vl", "qwen2_5_vl"):
+                return "<|vision_start|><|video_pad|><|vision_end|>"
+            if model_type in ("minicpmo", "minicpmv"):
+                return "(<video>./</video>)"
+            if model_type.startswith("llava"):
+                return self._cached_token_str(self._tokenizer,
+                                              hf_config.video_token_index)
+            raise TypeError(f"Unknown {modality} model type: {model_type}")
+        else:
+            raise TypeError(f"Unknown modality: {modality}")
+
+    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 = len(self._items_by_modality[modality]) + 1
+        if current_count > allowed_count:
+            raise ValueError(
+                f"At most {allowed_count} {modality}(s) may be provided in "
+                "one request.")
+
+        self._items_by_modality[modality].append(item)
+
+        return self._placeholder_str(modality, current_count)
+
+    @abstractmethod
+    def create_parser(self) -> "BaseMultiModalContentParser":
+        raise NotImplementedError
+
+
+class MultiModalItemTracker(BaseMultiModalItemTracker[object]):
+
+    def all_mm_data(self) -> Optional[MultiModalDataDict]:
+        if self._items_by_modality:
+            return dict(self._items_by_modality)
+
+        return None
+
+    def create_parser(self) -> "BaseMultiModalContentParser":
+        return MultiModalContentParser(self)
+
+
+class AsyncMultiModalItemTracker(BaseMultiModalItemTracker[Awaitable[object]]):
+
+    async def all_mm_data(self) -> Optional[MultiModalDataDict]:
+        if self._items_by_modality:
+            return {
+                modality: await asyncio.gather(*items)
+                for modality, items in self._items_by_modality.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
+
+    @abstractmethod
+    def parse_input_audio(self, input_audio: InputAudio) -> None:
+        raise NotImplementedError
+
+    @abstractmethod
+    def parse_video(self, video_url: str) -> None:
+        raise NotImplementedError
+
+
+class MultiModalContentParser(BaseMultiModalContentParser):
+
+    def __init__(self, tracker: MultiModalItemTracker) -> None:
+        super().__init__()
+
+        self._tracker = tracker
+
+        self._connector = MediaConnector(
+            allowed_local_media_path=tracker.allowed_local_media_path,
+        )
+
+    def parse_image(self, image_url: str) -> None:
+        image = self._connector.fetch_image(image_url)
+
+        placeholder = self._tracker.add("image", image)
+        self._add_placeholder(placeholder)
+
+    def parse_audio(self, audio_url: str) -> None:
+        audio = self._connector.fetch_audio(audio_url)
+
+        placeholder = self._tracker.add("audio", audio)
+        self._add_placeholder(placeholder)
+
+    def parse_input_audio(self, input_audio: InputAudio) -> None:
+        audio_data = input_audio.get("data", "")
+        audio_format = input_audio.get("format", "")
+        audio_url = f"data:audio/{audio_format};base64,{audio_data}"
+
+        return self.parse_audio(audio_url)
+
+    def parse_video(self, video_url: str) -> None:
+        video = self._connector.fetch_video(video_url)
+
+        placeholder = self._tracker.add("video", video)
+        self._add_placeholder(placeholder)
+
+
+class AsyncMultiModalContentParser(BaseMultiModalContentParser):
+
+    def __init__(self, tracker: AsyncMultiModalItemTracker) -> None:
+        super().__init__()
+
+        self._tracker = tracker
+        self._connector = MediaConnector(
+            allowed_local_media_path=tracker.allowed_local_media_path,
+        )
+
+    def parse_image(self, image_url: str) -> None:
+        image_coro = self._connector.fetch_image_async(image_url)
+
+        placeholder = self._tracker.add("image", image_coro)
+        self._add_placeholder(placeholder)
+
+    def parse_audio(self, audio_url: str) -> None:
+        audio_coro = self._connector.fetch_audio_async(audio_url)
+
+        placeholder = self._tracker.add("audio", audio_coro)
+        self._add_placeholder(placeholder)
+
+    def parse_input_audio(self, input_audio: InputAudio) -> None:
+        audio_data = input_audio.get("data", "")
+        audio_format = input_audio.get("format", "")
+        audio_url = f"data:audio/{audio_format};base64,{audio_data}"
+
+        return self.parse_audio(audio_url)
+
+    def parse_video(self, video_url: str) -> None:
+        video = self._connector.fetch_video_async(video_url)
+
+        placeholder = self._tracker.add("video", video)
+        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]],
+    *,
+    is_literal: bool = False,
+) -> Optional[str]:
+    if chat_template is None:
+        return None
+
+    if is_literal:
+        if isinstance(chat_template, Path):
+            raise TypeError("chat_template is expected to be read directly "
+                            "from its value")
+
+        return codecs.decode(chat_template, "unicode_escape")
+
+    try:
+        with open(chat_template) as f:
+            return 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
+        return load_chat_template(chat_template, is_literal=True)
+
+
+# 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)
+_InputAudioParser = partial(cast, ChatCompletionContentPartInputAudioParam)
+_RefusalParser = partial(cast, ChatCompletionContentPartRefusalParam)
+_VideoParser = partial(cast, ChatCompletionContentPartVideoParam)
+
+_ContentPart: TypeAlias = Union[str, Dict[str, str], InputAudio]
+
+# Define a mapping from part types to their corresponding parsing functions.
+MM_PARSER_MAP: Dict[
+    str,
+    Callable[[ChatCompletionContentPartParam], _ContentPart],
+] = {
+    "text":
+    lambda part: _TextParser(part).get("text", ""),
+    "image_url":
+    lambda part: _ImageParser(part).get("image_url", {}).get("url", ""),
+    "audio_url":
+    lambda part: _AudioParser(part).get("audio_url", {}).get("url", ""),
+    "input_audio":
+    lambda part: _InputAudioParser(part).get("input_audio", {}),
+    "refusal":
+    lambda part: _RefusalParser(part).get("refusal", ""),
+    "video_url":
+    lambda part: _VideoParser(part).get("video_url", {}).get("url", ""),
+}
+
+
+def _parse_chat_message_content_mm_part(
+        part: ChatCompletionContentPartParam) -> tuple[str, _ContentPart]:
+    """
+    Parses a given multi-modal content part based on its type.
+
+    Args:
+        part: A dict containing the content part, with a potential 'type' field.
+
+    Returns:
+        A tuple (part_type, content) where:
+        - part_type: Type of the part (e.g., 'text', 'image_url').
+        - content: Parsed content (e.g., text, image URL).
+
+    Raises:
+        ValueError: If the 'type' field is missing and no direct URL is found.
+    """
+    assert isinstance(
+        part, dict)  # This is needed to avoid mypy errors: part.get() from str
+    part_type = part.get("type", None)
+
+    if isinstance(part_type, str) and part_type in MM_PARSER_MAP:
+        content = MM_PARSER_MAP[part_type](part)
+
+        # Special case for 'image_url.detail'
+        # We only support 'auto', which is the default
+        if part_type == "image_url" and part.get("detail", "auto") != "auto":
+            logger.warning("'image_url.detail' is currently not supported "
+                           "and will be ignored.")
+
+        return part_type, content
+
+    # Handle missing 'type' but provided direct URL fields.
+    # 'type' is required field by pydantic
+    if part_type is None:
+        if part.get("image_url") is not None:
+            image_params = cast(CustomChatCompletionContentSimpleImageParam,
+                                part)
+            return "image_url", image_params.get("image_url", "")
+        if part.get("audio_url") is not None:
+            audio_params = cast(CustomChatCompletionContentSimpleAudioParam,
+                                part)
+            return "audio_url", audio_params.get("audio_url", "")
+        if part.get("input_audio") is not None:
+            input_audio_params = cast(Dict[str, str], part)
+            return "input_audio", input_audio_params
+        if part.get("video_url") is not None:
+            video_params = cast(CustomChatCompletionContentSimpleVideoParam,
+                                part)
+            return "video_url", video_params.get("video_url", "")
+        # Raise an error if no 'type' or direct URL is found.
+        raise ValueError("Missing 'type' field in multimodal part.")
+
+    if not isinstance(part_type, str):
+        raise ValueError("Invalid 'type' field in multimodal part.")
+    return part_type, "unknown part_type content"
+
+
+VALID_MESSAGE_CONTENT_MM_PART_TYPES = ("text", "refusal", "image_url",
+                                       "audio_url", "input_audio", "video_url")
+
+
+def _parse_chat_message_content_parts(
+    role: str,
+    parts: Iterable[ChatCompletionContentPartParam],
+    mm_tracker: BaseMultiModalItemTracker,
+    *,
+    wrap_dicts: bool,
+) -> List[ConversationMessage]:
+    content = list[_ContentPart]()
+
+    mm_parser = mm_tracker.create_parser()
+
+    for part in parts:
+        parse_res = _parse_chat_message_content_part(
+            part,
+            mm_parser,
+            wrap_dicts=wrap_dicts,
+        )
+        if parse_res:
+            content.append(parse_res)
+
+    if wrap_dicts:
+        # Parsing wraps images and texts as interleaved dictionaries
+        return [ConversationMessage(role=role,
+                                    content=content)]  # type: ignore
+    texts = cast(List[str], content)
+    text_prompt = "\n".join(texts)
+    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)]
+
+
+def _parse_chat_message_content_part(
+    part: ChatCompletionContentPartParam,
+    mm_parser: BaseMultiModalContentParser,
+    *,
+    wrap_dicts: bool,
+) -> Optional[_ContentPart]:
+    """Parses a single part of a conversation. If wrap_dicts is True,
+    structured dictionary pieces for texts and images will be
+    wrapped in dictionaries, i.e., {"type": "text", "text", ...} and
+    {"type": "image"}, respectively. Otherwise multimodal data will be
+    handled by mm_parser, and texts will be returned as strings to be joined
+    with multimodal placeholders.
+    """
+    if isinstance(part, str):  # Handle plain text parts
+        return part
+
+    # Handle structured dictionary parts
+    part_type, content = _parse_chat_message_content_mm_part(part)
+
+    # if part_type is text/refusal/image_url/audio_url/video_url/input_audio but
+    # content is empty, log a warning and skip
+    if part_type in VALID_MESSAGE_CONTENT_MM_PART_TYPES and not content:
+        logger.warning(
+            "Skipping multimodal part (type: '%s')"
+            "with empty / unparsable content.", part_type)
+        return None
+
+    if part_type in ("text", "refusal"):
+        str_content = cast(str, content)
+        if wrap_dicts:
+            return {'type': 'text', 'text': str_content}
+        else:
+            return str_content
+
+    if part_type == "image_url":
+        str_content = cast(str, content)
+        mm_parser.parse_image(str_content)
+        return {'type': 'image'} if wrap_dicts else None
+
+    if part_type == "audio_url":
+        str_content = cast(str, content)
+        mm_parser.parse_audio(str_content)
+        return {'type': 'audio'} if wrap_dicts else None
+
+    if part_type == "input_audio":
+        dict_content = cast(InputAudio, content)
+        mm_parser.parse_input_audio(dict_content)
+        return {'type': 'audio'} if wrap_dicts else None
+
+    if part_type == "video_url":
+        str_content = cast(str, content)
+        mm_parser.parse_video(str_content)
+        return {'type': 'video'} if wrap_dicts else None
+
+    raise NotImplementedError(f"Unknown part type: {part_type}")
+
+
+# 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,
+    content_format: _ChatTemplateContentFormat,
+) -> 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,
+        wrap_dicts=(content_format == "openai"),
+    )
+
+    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,
+    content_format: _ChatTemplateContentFormat,
+) -> 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,
+            content_format,
+        )
+
+        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,
+    content_format: _ChatTemplateContentFormat,
+) -> 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,
+            content_format,
+        )
+
+        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_once(
+            "'chat_template' cannot be overridden for mistral tokenizer.")
+    if "add_generation_prompt" in kwargs:
+        logger.warning_once(
+            "'add_generation_prompt' is not supported for mistral tokenizer, "
+            "so it will be ignored.")
+    if "continue_final_message" in kwargs:
+        logger.warning_once(
+            "'continue_final_message' is not supported for mistral tokenizer, "
+            "so it will be ignored.")
+
+    return tokenizer.apply_chat_template(
+        messages=messages,
+        **kwargs,
+    )

.venv/lib/python3.11/site-packages/vllm/entrypoints/launcher.py

@@ -0,0 +1,105 @@
+# SPDX-License-Identifier: Apache-2.0
+
+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)

.venv/lib/python3.11/site-packages/vllm/entrypoints/llm.py

@@ -0,0 +1,1414 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import itertools
+import warnings
+from contextlib import contextmanager
+from typing import (Any, Callable, ClassVar, Dict, List, Optional, Sequence,
+                    Tuple, Type, Union, cast, overload)
+
+import cloudpickle
+import torch
+import torch.nn as nn
+from tqdm import tqdm
+from typing_extensions import TypeVar, deprecated
+
+from vllm import envs
+from vllm.beam_search import (BeamSearchInstance, BeamSearchOutput,
+                              BeamSearchSequence, get_beam_search_score)
+from vllm.config import CompilationConfig
+from vllm.engine.arg_utils import (EngineArgs, HfOverrides, PoolerConfig,
+                                   TaskOption)
+from vllm.engine.llm_engine import LLMEngine
+from vllm.entrypoints.chat_utils import (ChatCompletionMessageParam,
+                                         ChatTemplateContentFormatOption,
+                                         apply_hf_chat_template,
+                                         apply_mistral_chat_template,
+                                         parse_chat_messages,
+                                         resolve_chat_template_content_format)
+from vllm.inputs import PromptType, SingletonPrompt, TextPrompt, TokensPrompt
+from vllm.inputs.parse import is_token_prompt, 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 (ClassificationRequestOutput, EmbeddingRequestOutput,
+                          PoolingRequestOutput, RequestOutput,
+                          ScoringRequestOutput)
+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_args, deprecate_kwargs, is_list_of
+
+logger = init_logger(__name__)
+
+_R = TypeVar("_R", default=Any)
+
+
+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.
+        allowed_local_media_path: Allowing API requests to read local images
+            or videos from directories specified by the server file system.
+            This is a security risk. Should only be enabled in trusted
+            environments.
+        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_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 :class:`~vllm.config.ParallelConfig`
+        disable_async_output_proc: Disable async output processing.
+            This may result in lower performance.
+        hf_overrides: If a dictionary, contains arguments to be forwarded to the
+            HuggingFace config. If a callable, it is called to update the
+            HuggingFace config.
+        compilation_config: Either an integer or a dictionary. If it is an
+            integer, it is used as the level of compilation optimization. If it
+            is a dictionary, it can specify the full compilation configuration.
+        **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] = True
+    """A flag to toggle whether to deprecate the legacy generate/encode API."""
+
+    DEPRECATE_INIT_POSARGS: ClassVar[bool] = True
+    """
+    A flag to toggle whether to deprecate positional arguments in
+    :meth:`LLM.__init__`.
+    """
+
+    @classmethod
+    @contextmanager
+    def deprecate_legacy_api(cls):
+        cls.DEPRECATE_LEGACY = True
+
+        yield
+
+        cls.DEPRECATE_LEGACY = False
+
+    @deprecate_args(
+        start_index=2,  # Ignore self and model
+        is_deprecated=lambda: LLM.DEPRECATE_INIT_POSARGS,
+        additional_message=(
+            "All positional arguments other than `model` will be "
+            "replaced with keyword arguments in an upcoming version."),
+    )
+    def __init__(
+        self,
+        model: str,
+        tokenizer: Optional[str] = None,
+        tokenizer_mode: str = "auto",
+        skip_tokenizer_init: bool = False,
+        trust_remote_code: bool = False,
+        allowed_local_media_path: str = "",
+        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_seq_len_to_capture: int = 8192,
+        disable_custom_all_reduce: bool = False,
+        disable_async_output_proc: bool = False,
+        hf_overrides: Optional[HfOverrides] = None,
+        mm_processor_kwargs: Optional[Dict[str, Any]] = None,
+        # After positional args are removed, move this right below `model`
+        task: TaskOption = "auto",
+        override_pooler_config: Optional[PoolerConfig] = None,
+        compilation_config: Optional[Union[int, 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
+
+        if "worker_cls" in kwargs:
+            worker_cls = kwargs["worker_cls"]
+            # if the worker_cls is not qualified string name,
+            # we serialize it using cloudpickle to avoid pickling issues
+            if isinstance(worker_cls, type):
+                kwargs["worker_cls"] = cloudpickle.dumps(worker_cls)
+
+        if compilation_config is not None:
+            if isinstance(compilation_config, (int, dict)):
+                compilation_config_instance = CompilationConfig.from_cli(
+                    str(compilation_config))
+            else:
+                compilation_config_instance = compilation_config
+        else:
+            compilation_config_instance = None
+
+        engine_args = EngineArgs(
+            model=model,
+            task=task,
+            tokenizer=tokenizer,
+            tokenizer_mode=tokenizer_mode,
+            skip_tokenizer_init=skip_tokenizer_init,
+            trust_remote_code=trust_remote_code,
+            allowed_local_media_path=allowed_local_media_path,
+            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_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,
+            hf_overrides=hf_overrides,
+            mm_processor_kwargs=mm_processor_kwargs,
+            override_pooler_config=override_pooler_config,
+            compilation_config=compilation_config_instance,
+            **kwargs,
+        )
+        # Logic to switch between engines is done at runtime instead of import
+        # to avoid import order issues
+        self.engine_class = self.get_engine_class()
+        self.llm_engine = self.engine_class.from_engine_args(
+            engine_args, usage_context=UsageContext.LLM_CLASS)
+
+        self.request_counter = Counter()
+
+    @staticmethod
+    def get_engine_class() -> Type[LLMEngine]:
+        if envs.VLLM_USE_V1:
+            # Lazy import: the v1 package isn't distributed
+            from vllm.v1.engine.llm_engine import LLMEngine as V1LLMEngine
+            return V1LLMEngine  # type: ignore
+        return LLMEngine
+
+    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)
+
+    def get_default_sampling_params(self) -> SamplingParams:
+        diff_sampling_param = (
+            self.llm_engine.model_config.get_diff_sampling_param())
+        if diff_sampling_param:
+            return SamplingParams.from_optional(**diff_sampling_param)
+        return SamplingParams()
+
+    @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,
+        prompt_adapter_request: Optional[PromptAdapterRequest] = None,
+        guided_options_request: Optional[Union[LLMGuidedOptions,
+                                               GuidedDecodingRequest]] = None,
+    ) -> List[RequestOutput]:
+        ...
+
+    @overload  # LEGACY: single (prompt + optional token ids)
+    @deprecated("'prompt_token_ids' will become part of 'prompts'")
+    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,
+        prompt_adapter_request: Optional[PromptAdapterRequest] = None,
+        guided_options_request: Optional[Union[LLMGuidedOptions,
+                                               GuidedDecodingRequest]] = None,
+    ) -> List[RequestOutput]:
+        ...
+
+    @overload  # LEGACY: multi (prompt + optional token ids)
+    @deprecated("'prompt_token_ids' will become part of 'prompts'")
+    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,
+        prompt_adapter_request: Optional[PromptAdapterRequest] = None,
+        guided_options_request: Optional[Union[LLMGuidedOptions,
+                                               GuidedDecodingRequest]] = None,
+    ) -> List[RequestOutput]:
+        ...
+
+    @overload  # LEGACY: single (token ids + optional prompt)
+    @deprecated("'prompt_token_ids' will become part of 'prompts'")
+    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,
+        prompt_adapter_request: Optional[PromptAdapterRequest] = None,
+        guided_options_request: Optional[Union[LLMGuidedOptions,
+                                               GuidedDecodingRequest]] = None,
+    ) -> List[RequestOutput]:
+        ...
+
+    @overload  # LEGACY: multi (token ids + optional prompt)
+    @deprecated("'prompt_token_ids' will become part of 'prompts'")
+    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,
+        prompt_adapter_request: Optional[PromptAdapterRequest] = None,
+        guided_options_request: Optional[Union[LLMGuidedOptions,
+                                               GuidedDecodingRequest]] = None,
+    ) -> List[RequestOutput]:
+        ...
+
+    @overload  # LEGACY: single or multi token ids [pos-only]
+    @deprecated("'prompt_token_ids' will become part of 'prompts'")
+    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,
+        prompt_adapter_request: Optional[PromptAdapterRequest] = None,
+        guided_options_request: Optional[Union[LLMGuidedOptions,
+                                               GuidedDecodingRequest]] = 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.
+        """
+        runner_type = self.llm_engine.model_config.runner_type
+        if runner_type != "generate":
+            messages = [
+                "LLM.generate() is only supported for (conditional) generation "
+                "models (XForCausalLM, XForConditionalGeneration).",
+            ]
+
+            supported_runner_types = self.llm_engine.model_config \
+                .supported_runner_types
+            if "generate" in supported_runner_types:
+                messages.append(
+                    "Your model supports the 'generate' runner, but is "
+                    f"currently initialized for the '{runner_type}' runner. "
+                    "Please initialize vLLM using `--task generate`.")
+
+            raise ValueError(" ".join(messages))
+
+        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 = self.get_default_sampling_params()
+
+        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 self.engine_class.validate_outputs(outputs, RequestOutput)
+
+    def collective_rpc(self,
+                       method: Union[str, Callable[..., _R]],
+                       timeout: Optional[float] = None,
+                       args: Tuple = (),
+                       kwargs: Optional[Dict[str, Any]] = None) -> List[_R]:
+        """
+        Execute an RPC call on all workers.
+
+        Args:
+            method: Name of the worker method to execute, or a callable that
+                is serialized and sent to all workers to execute.
+
+                If the method is a callable, it should accept an additional
+                `self` argument, in addition to the arguments passed in `args`
+                and `kwargs`. The `self` argument will be the worker object.
+            timeout: Maximum time in seconds to wait for execution. Raises a
+                :exc:`TimeoutError` on timeout. `None` means wait indefinitely.
+            args: Positional arguments to pass to the worker method.
+            kwargs: Keyword arguments to pass to the worker method.
+
+        Returns:
+            A list containing the results from each worker.
+        
+        Note:
+            It is recommended to use this API to only pass control messages,
+            and set up data-plane communication to pass data.
+        """
+        executor = self.llm_engine.model_executor
+        return executor.collective_rpc(method, timeout, args, kwargs)
+
+    def apply_model(self, func: Callable[[nn.Module], _R]) -> list[_R]:
+        """
+        Run a function directly on the model inside each worker,
+        returning the result for each of them.
+        """
+        executor = self.llm_engine.model_executor
+        return executor.apply_model(func)
+
+    def beam_search(
+        self,
+        prompts: List[Union[TokensPrompt, TextPrompt]],
+        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:
+            if is_token_prompt(prompt):
+                prompt_tokens = prompt["prompt_token_ids"]
+            else:
+                prompt_tokens = tokenizer.encode(prompt["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],
+                                logprobs=current_beam.logprobs + [logprobs],
+                                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,
+        chat_template_content_format: ChatTemplateContentFormatOption = "auto",
+        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.
+            chat_template_content_format: The format to render message content.
+
+              - "string" will render the content as a string.
+                Example: ``"Who are you?"``
+              - "openai" will render the content as a list of dictionaries,
+                similar to OpenAI schema.
+                Example: ``[{"type": "text", "text": "Who are you?"}]``
+
+            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)
+            ]
+
+        tokenizer = self.get_tokenizer()
+        model_config = self.llm_engine.get_model_config()
+        resolved_content_format = resolve_chat_template_content_format(
+            chat_template,
+            chat_template_content_format,
+            tokenizer,
+        )
+
+        prompts: List[Union[TokensPrompt, TextPrompt]] = []
+
+        for msgs in list_of_messages:
+            # 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,
+                content_format=resolved_content_format,
+            )
+
+            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
+    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,
+        prompt_adapter_request: Optional[PromptAdapterRequest] = None,
+    ) -> List[PoolingRequestOutput]:
+        ...
+
+    @overload  # LEGACY: single (prompt + optional token ids)
+    @deprecated("'prompt_token_ids' will become part of 'prompts'")
+    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,
+        prompt_adapter_request: Optional[PromptAdapterRequest] = None,
+    ) -> List[PoolingRequestOutput]:
+        ...
+
+    @overload  # LEGACY: multi (prompt + optional token ids)
+    @deprecated("'prompt_token_ids' will become part of 'prompts'")
+    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,
+        prompt_adapter_request: Optional[PromptAdapterRequest] = None,
+    ) -> List[PoolingRequestOutput]:
+        ...
+
+    @overload  # LEGACY: single (token ids + optional prompt)
+    @deprecated("'prompt_token_ids' will become part of 'prompts'")
+    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,
+        prompt_adapter_request: Optional[PromptAdapterRequest] = None,
+    ) -> List[PoolingRequestOutput]:
+        ...
+
+    @overload  # LEGACY: multi (token ids + optional prompt)
+    @deprecated("'prompt_token_ids' will become part of 'prompts'")
+    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,
+        prompt_adapter_request: Optional[PromptAdapterRequest] = None,
+    ) -> List[PoolingRequestOutput]:
+        ...
+
+    @overload  # LEGACY: single or multi token ids [pos-only]
+    @deprecated("'prompt_token_ids' will become part of 'prompts'")
+    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,
+        prompt_adapter_request: Optional[PromptAdapterRequest] = None,
+    ) -> List[PoolingRequestOutput]:
+        ...
+
+    @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[PoolingRequestOutput]:
+        """Apply pooling to the hidden states corresponding to 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 ``PoolingRequestOutput`` objects containing the
+            pooled hidden states 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.
+        """
+        runner_type = self.llm_engine.model_config.runner_type
+        if runner_type != "pooling":
+            messages = ["LLM.encode() is only supported for pooling models."]
+
+            supported_runner_types = self.llm_engine.model_config \
+                .supported_runner_types
+            if "pooling" in supported_runner_types:
+                messages.append(
+                    "Your model supports the 'pooling' runner, but is "
+                    f"currently initialized for the '{runner_type}' runner. "
+                    "Please initialize vLLM using `--task embed`, "
+                    "`--task classify`, `--task score` etc.")
+
+            raise ValueError(" ".join(messages))
+
+        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 self.engine_class.validate_outputs(outputs,
+                                                  PoolingRequestOutput)
+
+    def embed(
+        self,
+        prompts: Union[PromptType, Sequence[PromptType]],
+        /,
+        *,
+        use_tqdm: bool = True,
+        lora_request: Optional[Union[List[LoRARequest], LoRARequest]] = None,
+        prompt_adapter_request: Optional[PromptAdapterRequest] = None,
+    ) -> List[EmbeddingRequestOutput]:
+        """
+        Generate an embedding vector for each prompt.
+
+        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.
+            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
+            embedding vectors in the same order as the input prompts.
+        """
+        if self.llm_engine.model_config.task != "embed":
+            raise ValueError(
+                "Embedding API is only enabled for `--task embed`")
+
+        items = self.encode(prompts,
+                            use_tqdm=use_tqdm,
+                            lora_request=lora_request,
+                            prompt_adapter_request=prompt_adapter_request)
+
+        return [EmbeddingRequestOutput.from_base(item) for item in items]
+
+    def classify(
+        self,
+        prompts: Union[PromptType, Sequence[PromptType]],
+        /,
+        *,
+        use_tqdm: bool = True,
+        lora_request: Optional[Union[List[LoRARequest], LoRARequest]] = None,
+        prompt_adapter_request: Optional[PromptAdapterRequest] = None,
+    ) -> List[ClassificationRequestOutput]:
+        """
+        Generate class logits for each prompt.
+
+        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.
+            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 ``ClassificationRequestOutput`` objects containing the
+            embedding vectors in the same order as the input prompts.
+        """
+        if self.llm_engine.model_config.task != "classify":
+            raise ValueError(
+                "Classification API is only enabled for `--task classify`")
+
+        items = self.encode(prompts,
+                            use_tqdm=use_tqdm,
+                            lora_request=lora_request,
+                            prompt_adapter_request=prompt_adapter_request)
+
+        return [ClassificationRequestOutput.from_base(item) for item in items]
+
+    def _embedding_score(
+        self,
+        tokenizer: AnyTokenizer,
+        text_1: List[Union[str, TextPrompt, TokensPrompt]],
+        text_2: List[Union[str, TextPrompt, TokensPrompt]],
+        truncate_prompt_tokens: Optional[int] = None,
+        use_tqdm: bool = True,
+        lora_request: Optional[Union[List[LoRARequest], LoRARequest]] = None,
+        prompt_adapter_request: Optional[PromptAdapterRequest] = None,
+    ) -> List[ScoringRequestOutput]:
+
+        encoded_output = self.encode(
+            text_1 + text_2,
+            use_tqdm=use_tqdm,
+            lora_request=lora_request,
+            prompt_adapter_request=prompt_adapter_request)
+        encoded_output_1 = encoded_output[0:len(text_1)]
+        encoded_output_2 = encoded_output[len(text_1):]
+
+        if len(encoded_output_1) == 1:
+            encoded_output_1 = encoded_output_1 * len(encoded_output_2)
+
+        output_pairs = [(t1, t2)
+                        for t1, t2 in zip(encoded_output_1, encoded_output_2)]
+
+        scores = []
+        scorer = torch.nn.CosineSimilarity(0)
+
+        for embed_1, embed_2 in output_pairs:
+            pair_score = scorer(embed_1.outputs.data, embed_2.outputs.data)
+
+            if (pad_token_id := getattr(tokenizer, "pad_token_id",
+                                        None)) is not None:
+                tokens = embed_1.prompt_token_ids + [
+                    pad_token_id
+                ] + embed_2.prompt_token_ids
+            else:
+                tokens = embed_1.prompt_token_ids + embed_2.prompt_token_ids
+
+            scores.append(
+                PoolingRequestOutput(
+                    request_id=f"{embed_1.request_id}_{embed_2.request_id}",
+                    outputs=pair_score,
+                    prompt_token_ids=tokens,
+                    finished=True))
+
+        items = self.engine_class.validate_outputs(scores,
+                                                   PoolingRequestOutput)
+        return [ScoringRequestOutput.from_base(item) for item in items]
+
+    def _cross_encoding_score(
+        self,
+        tokenizer: Union[AnyTokenizer],
+        text_1: List[Union[str, TextPrompt, TokensPrompt]],
+        text_2: List[Union[str, TextPrompt, TokensPrompt]],
+        truncate_prompt_tokens: Optional[int] = None,
+        use_tqdm: bool = True,
+        lora_request: Optional[Union[List[LoRARequest], LoRARequest]] = None,
+        prompt_adapter_request: Optional[PromptAdapterRequest] = None,
+    ) -> List[ScoringRequestOutput]:
+
+        if isinstance(tokenizer, MistralTokenizer):
+            raise ValueError(
+                "Score API is only enabled for `--task embed or score`")
+
+        if len(text_1) == 1:
+            text_1 = text_1 * len(text_2)
+
+        input_pairs = [(t1, t2) for t1, t2 in zip(text_1, text_2)]
+
+        pooling_params = PoolingParams()
+
+        tokenization_kwargs: Dict[str, Any] = {}
+        if truncate_prompt_tokens is not None:
+            tokenization_kwargs["truncation"] = True
+            tokenization_kwargs["max_length"] = truncate_prompt_tokens
+
+        parsed_prompts = []
+
+        for q, t in input_pairs:
+            prompt_inputs = tokenizer(text=q,
+                                      text_pair=t,
+                                      **tokenization_kwargs)
+            engine_prompt = TokensPrompt(
+                prompt_token_ids=prompt_inputs["input_ids"],
+                token_type_ids=prompt_inputs.get("token_type_ids"))
+            parsed_prompts.append(engine_prompt)
+
+        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)
+        items = self.engine_class.validate_outputs(outputs,
+                                                   PoolingRequestOutput)
+
+        return [ScoringRequestOutput.from_base(item) for item in items]
+
+    def score(
+        self,
+        text_1: Union[SingletonPrompt, Sequence[SingletonPrompt]],
+        text_2: Union[SingletonPrompt, Sequence[SingletonPrompt]],
+        /,
+        *,
+        truncate_prompt_tokens: Optional[int] = None,
+        use_tqdm: bool = True,
+        lora_request: Optional[Union[List[LoRARequest], LoRARequest]] = None,
+        prompt_adapter_request: Optional[PromptAdapterRequest] = None,
+    ) -> List[ScoringRequestOutput]:
+        """Generate similarity scores for all pairs ``<text,text_pair>``.
+
+        The inputs can be ``1 -> 1``, ``1 -> N`` or ``N -> N``.
+        In the ``1 - N`` case the ``text_1`` sentence will be replicated ``N``
+        times to pair with the ``text_2`` sentences.
+        The input pairs are used to build a list of prompts for the
+        cross encoder model. This class automatically batches the prompts,
+        considering the memory constraint. For the best performance, put all
+        of your texts into a single list and pass it to this method.
+
+        Args:
+            text_1: can be a single prompt or a list of prompts, in which
+                case it has to have the same length as the ``text_2`` list
+            text_2: The texts to pair with the query to form the input
+                to the LLM. See :class:`~vllm.inputs.PromptType` for
+                more details about the format of each prompts.
+            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 ``ScoringRequestOutput`` objects containing the
+            generated scores in the same order as the input prompts.
+        """
+        runner_type = self.llm_engine.model_config.runner_type
+        if runner_type != "pooling":
+            messages = ["LLM.score() is only supported for pooling models."]
+
+            supported_runner_types = self.llm_engine.model_config \
+                .supported_runner_types
+            if "pooling" in supported_runner_types:
+                messages.append(
+                    "Your model supports the 'pooling' runner, but is "
+                    f"currently initialized for the '{runner_type}' runner. "
+                    "Please initialize vLLM using `--task embed`, "
+                    "`--task classify`, `--task score` etc.")
+
+            raise ValueError(" ".join(messages))
+
+        if self.llm_engine.model_config.task not in ("embed", "score"):
+            raise ValueError(
+                "Score API is only enabled for `--task embed or --task score`")
+
+        # the tokenizer for models such as
+        # "cross-encoder/ms-marco-MiniLM-L-6-v2" doesn't support passing
+        # lists of tokens to the `text` and `text_pair` kwargs
+        tokenizer = self.llm_engine.get_tokenizer()
+
+        def ensure_str(prompt: SingletonPrompt):
+            if isinstance(prompt, dict):
+                if "multi_modal_data" in prompt:
+                    raise ValueError("Multi-modal prompt is not "
+                                     "supported for scoring")
+                elif "prompt_token_ids" in prompt:
+                    prompt = tokenizer.decode(
+                        cast(TokensPrompt, prompt)["prompt_token_ids"])
+                elif "prompt" in prompt:
+                    prompt = cast(TextPrompt, prompt)["prompt"]
+            assert type(prompt) is str
+            return prompt
+
+        if isinstance(text_1, (str, dict)):
+            # Convert a single prompt to a list.
+            text_1 = [text_1]
+        text_1 = [ensure_str(t) for t in text_1]
+
+        if isinstance(text_2, (str, dict)):
+            # Convert a single prompt to a list.
+            text_2 = [text_2]
+        text_2 = [ensure_str(t) for t in text_2]
+
+        if len(text_1) > 1 and len(text_1) != len(text_2):
+            raise ValueError("Input lengths must be either 1:1, 1:N or N:N")
+        if len(text_1) == 0:
+            raise ValueError("At least one text element must be given")
+        if len(text_2) == 0:
+            raise ValueError("At least one text_pair element must be given")
+
+        if self.llm_engine.model_config.is_cross_encoder:
+            return self._cross_encoding_score(tokenizer, text_1, text_2,
+                                              truncate_prompt_tokens, use_tqdm,
+                                              lora_request,
+                                              prompt_adapter_request)
+        else:
+            return self._embedding_score(tokenizer, text_1, text_2,
+                                         truncate_prompt_tokens, use_tqdm,
+                                         lora_request, prompt_adapter_request)
+
+    def start_profile(self) -> None:
+        self.llm_engine.start_profile()
+
+    def stop_profile(self) -> None:
+        self.llm_engine.stop_profile()
+
+    def reset_prefix_cache(self) -> bool:
+        return self.llm_engine.reset_prefix_cache()
+
+    def sleep(self, level: int = 1):
+        """
+        Put the engine to sleep. The engine should not process any requests.
+        The caller should guarantee that no requests are being processed
+        during the sleep period, before `wake_up` is called.
+
+        :param level: The sleep level. Level 1 sleep will offload the model 
+            weights and discard the kv cache. The content of kv cache is 
+            forgotten. Level 1 sleep is good for sleeping and waking up the 
+            engine to run the same model again. The model weights are backed 
+            up in CPU memory. Please make sure there's enough CPU memory to 
+            store the model weights. Level 2 sleep will discard both the model 
+            weights and the kv cache. The content of both the model weights 
+            and kv cache is forgotten. Level 2 sleep is good for sleeping and 
+            waking up the engine to run a different model or update the model, 
+            where previous model weights are not needed. It reduces CPU memory 
+            pressure.
+        """
+        self.reset_prefix_cache()
+        self.llm_engine.sleep(level=level)
+
+    def wake_up(self):
+        """
+        Wake up the engine from sleep mode. See the :meth:`sleep` method
+        for more details."""
+        self.llm_engine.wake_up()
+
+    # 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, PoolingRequestOutput]]:
+        # 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, PoolingRequestOutput]] = []
+        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))

.venv/lib/python3.11/site-packages/vllm/entrypoints/logger.py

@@ -0,0 +1,44 @@
+# SPDX-License-Identifier: Apache-2.0
+
+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)

.venv/lib/python3.11/site-packages/vllm/entrypoints/openai/api_server.py

@@ -0,0 +1,911 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import asyncio
+import atexit
+import gc
+import importlib
+import inspect
+import multiprocessing
+import os
+import re
+import signal
+import socket
+import sys
+import tempfile
+import uuid
+from argparse import Namespace
+from contextlib import asynccontextmanager
+from functools import partial
+from http import HTTPStatus
+from typing import AsyncIterator, Dict, Optional, Set, Tuple, Union
+
+import uvloop
+from fastapi import APIRouter, FastAPI, HTTPException, 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  # type: ignore
+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.chat_utils import load_chat_template
+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,
+                                              EmbeddingChatRequest,
+                                              EmbeddingCompletionRequest,
+                                              EmbeddingRequest,
+                                              EmbeddingResponse,
+                                              EmbeddingResponseData,
+                                              ErrorResponse,
+                                              LoadLoraAdapterRequest,
+                                              PoolingChatRequest,
+                                              PoolingCompletionRequest,
+                                              PoolingRequest, PoolingResponse,
+                                              RerankRequest, RerankResponse,
+                                              ScoreRequest, ScoreResponse,
+                                              TokenizeRequest,
+                                              TokenizeResponse,
+                                              UnloadLoraAdapterRequest)
+from vllm.entrypoints.openai.reasoning_parsers import ReasoningParserManager
+# 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 OpenAIServing
+from vllm.entrypoints.openai.serving_models import (BaseModelPath,
+                                                    OpenAIServingModels)
+from vllm.entrypoints.openai.serving_pooling import OpenAIServingPooling
+from vllm.entrypoints.openai.serving_rerank import JinaAIServingRerank
+from vllm.entrypoints.openai.serving_score import OpenAIServingScores
+from vllm.entrypoints.openai.serving_tokenization import (
+    OpenAIServingTokenization)
+from vllm.entrypoints.openai.tool_parsers import ToolParserManager
+from vllm.entrypoints.utils import with_cancellation
+from vllm.logger import init_logger
+from vllm.usage.usage_lib import UsageContext
+from vllm.utils import (FlexibleArgumentParser, get_open_zmq_ipc_path,
+                        is_valid_ipv6_address, set_ulimit)
+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
+
+        # Mark the startup heap as static so that it's ignored by GC.
+        # Reduces pause times of oldest generation collections.
+        gc.collect()
+        gc.freeze()
+        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.
+    """
+
+    # AsyncLLMEngine.
+    if (MQLLMEngineClient.is_unsupported_config(engine_args)
+            or envs.VLLM_USE_V1 or disable_frontend_multiprocessing):
+
+        engine_client: Optional[EngineClient] = None
+        try:
+            engine_client = AsyncLLMEngine.from_engine_args(
+                engine_args=engine_args,
+                usage_context=UsageContext.OPENAI_API_SERVER)
+            yield engine_client
+        finally:
+            if engine_client and hasattr(engine_client, "shutdown"):
+                engine_client.shutdown()
+
+    # MQLLMEngine.
+    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.debug("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")
+
+        # The Process can raise an exception during startup, which may
+        # not actually result in an exitcode being reported. As a result
+        # we use a shared variable to communicate the information.
+        engine_alive = multiprocessing.Value('b', True, lock=False)
+        engine_process = context.Process(target=run_mp_engine,
+                                         args=(engine_args,
+                                               UsageContext.OPENAI_API_SERVER,
+                                               ipc_path, engine_alive))
+        engine_process.start()
+        engine_pid = engine_process.pid
+        assert engine_pid is not None, "Engine process failed to start."
+        logger.info("Started engine process with PID %d", engine_pid)
+
+        def _cleanup_ipc_path():
+            socket_path = ipc_path.replace("ipc://", "")
+            if os.path.exists(socket_path):
+                os.remove(socket_path)
+
+        # Ensure we clean up the local IPC socket file on exit.
+        atexit.register(_cleanup_ipc_path)
+
+        # Build RPCClient, which conforms to EngineClient Protocol.
+        engine_config = engine_args.create_engine_config()
+        build_client = partial(MQLLMEngineClient, ipc_path, engine_config,
+                               engine_pid)
+        mq_engine_client = await asyncio.get_running_loop().run_in_executor(
+            None, build_client)
+        try:
+            while True:
+                try:
+                    await mq_engine_client.setup()
+                    break
+                except TimeoutError:
+                    if (not engine_process.is_alive()
+                            or not engine_alive.value):
+                        raise RuntimeError(
+                            "Engine process failed to start. See stack "
+                            "trace for the root cause.") from None
+
+            yield mq_engine_client  # type: ignore[misc]
+        finally:
+            # Ensure rpc server process was terminated
+            engine_process.terminate()
+
+            # Close all open connections to the backend
+            mq_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.debug("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 base(request: Request) -> OpenAIServing:
+    # Reuse the existing instance
+    return tokenization(request)
+
+
+def models(request: Request) -> OpenAIServingModels:
+    return request.app.state.openai_serving_models
+
+
+def chat(request: Request) -> Optional[OpenAIServingChat]:
+    return request.app.state.openai_serving_chat
+
+
+def completion(request: Request) -> Optional[OpenAIServingCompletion]:
+    return request.app.state.openai_serving_completion
+
+
+def pooling(request: Request) -> Optional[OpenAIServingPooling]:
+    return request.app.state.openai_serving_pooling
+
+
+def embedding(request: Request) -> Optional[OpenAIServingEmbedding]:
+    return request.app.state.openai_serving_embedding
+
+
+def score(request: Request) -> Optional[OpenAIServingScores]:
+    return request.app.state.openai_serving_scores
+
+
+def rerank(request: Request) -> Optional[JinaAIServingRerank]:
+    return request.app.state.jinaai_serving_reranking
+
+
+def tokenization(request: Request) -> OpenAIServingTokenization:
+    return request.app.state.openai_serving_tokenization
+
+
+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.api_route("/ping", methods=["GET", "POST"])
+async def ping(raw_request: Request) -> Response:
+    """Ping check. Endpoint required for SageMaker"""
+    return await health(raw_request)
+
+
+@router.post("/tokenize")
+@with_cancellation
+async def tokenize(request: TokenizeRequest, raw_request: Request):
+    handler = tokenization(raw_request)
+
+    generator = await handler.create_tokenize(request, raw_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")
+@with_cancellation
+async def detokenize(request: DetokenizeRequest, raw_request: Request):
+    handler = tokenization(raw_request)
+
+    generator = await handler.create_detokenize(request, raw_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):
+    handler = models(raw_request)
+
+    models_ = await handler.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")
+@with_cancellation
+async def create_chat_completion(request: ChatCompletionRequest,
+                                 raw_request: Request):
+    handler = chat(raw_request)
+    if handler is None:
+        return base(raw_request).create_error_response(
+            message="The model does not support Chat Completions API")
+
+    generator = await handler.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")
+@with_cancellation
+async def create_completion(request: CompletionRequest, raw_request: Request):
+    handler = completion(raw_request)
+    if handler is None:
+        return base(raw_request).create_error_response(
+            message="The model does not support Completions API")
+
+    generator = await handler.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")
+@with_cancellation
+async def create_embedding(request: EmbeddingRequest, raw_request: Request):
+    handler = embedding(raw_request)
+    if handler is None:
+        fallback_handler = pooling(raw_request)
+        if fallback_handler is None:
+            return base(raw_request).create_error_response(
+                message="The model does not support Embeddings API")
+
+        logger.warning(
+            "Embeddings API will become exclusive to embedding models "
+            "in a future release. To return the hidden states directly, "
+            "use the Pooling API (`/pooling`) instead.")
+
+        res = await fallback_handler.create_pooling(request, raw_request)
+
+        generator: Union[ErrorResponse, EmbeddingResponse]
+        if isinstance(res, PoolingResponse):
+            generator = EmbeddingResponse(
+                id=res.id,
+                object=res.object,
+                created=res.created,
+                model=res.model,
+                data=[
+                    EmbeddingResponseData(
+                        index=d.index,
+                        embedding=d.data,  # type: ignore
+                    ) for d in res.data
+                ],
+                usage=res.usage,
+            )
+        else:
+            generator = res
+    else:
+        generator = await handler.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)
+
+
+@router.post("/pooling")
+@with_cancellation
+async def create_pooling(request: PoolingRequest, raw_request: Request):
+    handler = pooling(raw_request)
+    if handler is None:
+        return base(raw_request).create_error_response(
+            message="The model does not support Pooling API")
+
+    generator = await handler.create_pooling(request, raw_request)
+    if isinstance(generator, ErrorResponse):
+        return JSONResponse(content=generator.model_dump(),
+                            status_code=generator.code)
+    elif isinstance(generator, PoolingResponse):
+        return JSONResponse(content=generator.model_dump())
+
+    assert_never(generator)
+
+
+@router.post("/score")
+@with_cancellation
+async def create_score(request: ScoreRequest, raw_request: Request):
+    handler = score(raw_request)
+    if handler is None:
+        return base(raw_request).create_error_response(
+            message="The model does not support Score API")
+
+    generator = await handler.create_score(request, raw_request)
+    if isinstance(generator, ErrorResponse):
+        return JSONResponse(content=generator.model_dump(),
+                            status_code=generator.code)
+    elif isinstance(generator, ScoreResponse):
+        return JSONResponse(content=generator.model_dump())
+
+    assert_never(generator)
+
+
+@router.post("/v1/score")
+@with_cancellation
+async def create_score_v1(request: ScoreRequest, raw_request: Request):
+    logger.warning(
+        "To indicate that Score API is not part of standard OpenAI API, we "
+        "have moved it to `/score`. Please update your client accordingly.")
+
+    return await create_score(request, raw_request)
+
+
+@router.post("/rerank")
+@with_cancellation
+async def do_rerank(request: RerankRequest, raw_request: Request):
+    handler = rerank(raw_request)
+    if handler is None:
+        return base(raw_request).create_error_response(
+            message="The model does not support Rerank (Score) API")
+    generator = await handler.do_rerank(request, raw_request)
+    if isinstance(generator, ErrorResponse):
+        return JSONResponse(content=generator.model_dump(),
+                            status_code=generator.code)
+    elif isinstance(generator, RerankResponse):
+        return JSONResponse(content=generator.model_dump())
+
+    assert_never(generator)
+
+
+@router.post("/v1/rerank")
+@with_cancellation
+async def do_rerank_v1(request: RerankRequest, raw_request: Request):
+    logger.warning_once(
+        "To indicate that the rerank API is not part of the standard OpenAI"
+        " API, we have located it at `/rerank`. Please update your client"
+        "accordingly. (Note: Conforms to JinaAI rerank API)")
+
+    return await do_rerank(request, raw_request)
+
+
+@router.post("/v2/rerank")
+@with_cancellation
+async def do_rerank_v2(request: RerankRequest, raw_request: Request):
+    return await do_rerank(request, raw_request)
+
+
+TASK_HANDLERS: Dict[str, Dict[str, tuple]] = {
+    "generate": {
+        "messages": (ChatCompletionRequest, create_chat_completion),
+        "default": (CompletionRequest, create_completion),
+    },
+    "embed": {
+        "messages": (EmbeddingChatRequest, create_embedding),
+        "default": (EmbeddingCompletionRequest, create_embedding),
+    },
+    "score": {
+        "default": (RerankRequest, do_rerank)
+    },
+    "rerank": {
+        "default": (RerankRequest, do_rerank)
+    },
+    "reward": {
+        "messages": (PoolingChatRequest, create_pooling),
+        "default": (PoolingCompletionRequest, create_pooling),
+    },
+    "classify": {
+        "messages": (PoolingChatRequest, create_pooling),
+        "default": (PoolingCompletionRequest, create_pooling),
+    },
+}
+
+if envs.VLLM_SERVER_DEV_MODE:
+
+    @router.post("/reset_prefix_cache")
+    async def reset_prefix_cache(raw_request: Request):
+        """
+        Reset the prefix cache. Note that we currently do not check if the
+        prefix cache is successfully reset in the API server.
+        """
+        logger.info("Resetting prefix cache...")
+        await engine_client(raw_request).reset_prefix_cache()
+        return Response(status_code=200)
+
+
+@router.post("/invocations")
+async def invocations(raw_request: Request):
+    """
+    For SageMaker, routes requests to other handlers based on model `task`.
+    """
+    body = await raw_request.json()
+    task = raw_request.app.state.task
+
+    if task not in TASK_HANDLERS:
+        raise HTTPException(
+            status_code=400,
+            detail=f"Unsupported task: '{task}' for '/invocations'. "
+            f"Expected one of {set(TASK_HANDLERS.keys())}")
+
+    handler_config = TASK_HANDLERS[task]
+    if "messages" in body:
+        request_model, handler = handler_config["messages"]
+    else:
+        request_model, handler = handler_config["default"]
+
+    # this is required since we lose the FastAPI automatic casting
+    request = request_model.model_validate(body)
+    return await handler(request, raw_request)
+
+
+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):
+        handler = models(raw_request)
+        response = await handler.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):
+        handler = models(raw_request)
+        response = await handler.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):
+        err = ErrorResponse(message=str(exc),
+                            type="BadRequestError",
+                            code=HTTPStatus.BAD_REQUEST)
+        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):
+            if request.method == "OPTIONS":
+                return await call_next(request)
+            url_path = request.url.path
+            if app.root_path and url_path.startswith(app.root_path):
+                url_path = url_path[len(app.root_path):]
+            if not url_path.startswith("/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)
+
+    if args.enable_request_id_headers:
+        logger.warning(
+            "CAUTION: Enabling X-Request-Id headers in the API Server. "
+            "This can harm performance at high QPS.")
+
+        @app.middleware("http")
+        async def add_request_id(request: Request, call_next):
+            request_id = request.headers.get(
+                "X-Request-Id") or uuid.uuid4().hex
+            response = await call_next(request)
+            response.headers["X-Request-Id"] = request_id
+            return response
+
+    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)  # type: ignore[arg-type]
+        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
+
+
+async 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
+
+    resolved_chat_template = load_chat_template(args.chat_template)
+    logger.info("Using supplied chat template:\n%s", resolved_chat_template)
+
+    state.openai_serving_models = OpenAIServingModels(
+        engine_client=engine_client,
+        model_config=model_config,
+        base_model_paths=base_model_paths,
+        lora_modules=args.lora_modules,
+        prompt_adapters=args.prompt_adapters,
+    )
+    await state.openai_serving_models.init_static_loras()
+    state.openai_serving_chat = OpenAIServingChat(
+        engine_client,
+        model_config,
+        state.openai_serving_models,
+        args.response_role,
+        request_logger=request_logger,
+        chat_template=resolved_chat_template,
+        chat_template_content_format=args.chat_template_content_format,
+        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,
+        enable_reasoning=args.enable_reasoning,
+        reasoning_parser=args.reasoning_parser,
+        enable_prompt_tokens_details=args.enable_prompt_tokens_details,
+    ) if model_config.runner_type == "generate" else None
+    state.openai_serving_completion = OpenAIServingCompletion(
+        engine_client,
+        model_config,
+        state.openai_serving_models,
+        request_logger=request_logger,
+        return_tokens_as_token_ids=args.return_tokens_as_token_ids,
+    ) if model_config.runner_type == "generate" else None
+    state.openai_serving_pooling = OpenAIServingPooling(
+        engine_client,
+        model_config,
+        state.openai_serving_models,
+        request_logger=request_logger,
+        chat_template=resolved_chat_template,
+        chat_template_content_format=args.chat_template_content_format,
+    ) if model_config.runner_type == "pooling" else None
+    state.openai_serving_embedding = OpenAIServingEmbedding(
+        engine_client,
+        model_config,
+        state.openai_serving_models,
+        request_logger=request_logger,
+        chat_template=resolved_chat_template,
+        chat_template_content_format=args.chat_template_content_format,
+    ) if model_config.task == "embed" else None
+    state.openai_serving_scores = OpenAIServingScores(
+        engine_client,
+        model_config,
+        state.openai_serving_models,
+        request_logger=request_logger
+    ) if model_config.task == "score" else None
+    state.jinaai_serving_reranking = JinaAIServingRerank(
+        engine_client,
+        model_config,
+        state.openai_serving_models,
+        request_logger=request_logger
+    ) if model_config.task == "score" else None
+    state.openai_serving_tokenization = OpenAIServingTokenization(
+        engine_client,
+        model_config,
+        state.openai_serving_models,
+        request_logger=request_logger,
+        chat_template=resolved_chat_template,
+        chat_template_content_format=args.chat_template_content_format,
+    )
+    state.task = model_config.task
+
+
+def create_server_socket(addr: Tuple[str, int]) -> socket.socket:
+    family = socket.AF_INET
+    if is_valid_ipv6_address(addr[0]):
+        family = socket.AF_INET6
+
+    sock = socket.socket(family=family, type=socket.SOCK_STREAM)
+    sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
+    sock.bind(addr)
+
+    return sock
+
+
+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)
+
+    valid_tool_parses = ToolParserManager.tool_parsers.keys()
+    if args.enable_auto_tool_choice \
+        and args.tool_call_parser not in valid_tool_parses:
+        raise KeyError(f"invalid tool call parser: {args.tool_call_parser} "
+                       f"(chose from {{ {','.join(valid_tool_parses)} }})")
+
+    valid_reasoning_parses = ReasoningParserManager.reasoning_parsers.keys()
+    if args.enable_reasoning \
+        and args.reasoning_parser not in valid_reasoning_parses:
+        raise KeyError(
+            f"invalid reasoning parser: {args.reasoning_parser} "
+            f"(chose from {{ {','.join(valid_reasoning_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_addr = (args.host or "", args.port)
+    sock = create_server_socket(sock_addr)
+
+    # workaround to avoid footguns where uvicorn drops requests with too
+    # many concurrent requests active
+    set_ulimit()
+
+    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()
+        await 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,
+            # Workaround to work on macOS
+            fd=sock.fileno() if sys.platform.startswith("darwin") else None,
+            **uvicorn_kwargs,
+        )
+
+    # NB: Await server shutdown only after the backend context is exited
+    await shutdown_task
+
+    sock.close()
+
+
+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))

.venv/lib/python3.11/site-packages/vllm/entrypoints/openai/cli_args.py

@@ -0,0 +1,305 @@
+# SPDX-License-Identifier: Apache-2.0
+"""
+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, get_args
+
+from vllm.engine.arg_utils import AsyncEngineArgs, nullable_str
+from vllm.entrypoints.chat_utils import (ChatTemplateContentFormatOption,
+                                         validate_chat_template)
+from vllm.entrypoints.openai.reasoning_parsers import ReasoningParserManager
+from vllm.entrypoints.openai.serving_models 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\", \"path\": \"lora_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(
+        '--chat-template-content-format',
+        type=str,
+        default="auto",
+        choices=get_args(ChatTemplateContentFormatOption),
+        help='The format to render message content within a chat template.'
+        '\n\n'
+        '* "string" will render the content as a string. '
+        'Example: ``"Hello World"``\n'
+        '* "openai" will render the content as a list of dictionaries, '
+        'similar to OpenAI schema. '
+        'Example: ``[{"type": "text", "text": "Hello world!"}]``')
+    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-request-id-headers",
+        action="store_true",
+        help="If specified, API server will add X-Request-Id header to "
+        "responses. Caution: this hurts performance at high QPS.")
+    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.")
+    parser.add_argument(
+        "--enable-reasoning",
+        action="store_true",
+        default=False,
+        help="Whether to enable reasoning_content for the model. "
+        "If enabled, the model will be able to generate reasoning content.")
+
+    valid_reasoning_parsers = ReasoningParserManager.reasoning_parsers.keys()
+    parser.add_argument(
+        "--reasoning-parser",
+        type=str,
+        metavar="{" + ",".join(valid_reasoning_parsers) + "}",
+        default=None,
+        help=
+        "Select the reasoning parser depending on the model that you're using."
+        " This is used to parse the reasoning content into OpenAI API "
+        "format. Required for ``--enable-reasoning``.")
+
+    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."
+    )
+    parser.add_argument(
+        "--enable-prompt-tokens-details",
+        action='store_true',
+        default=False,
+        help="If set to True, enable prompt_tokens_details in usage.")
+
+    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")
+
+    # Enable reasoning needs a reasoning parser to be valid
+    if args.enable_reasoning and not args.reasoning_parser:
+        raise TypeError("Error: --enable-reasoning requires "
+                        "--reasoning-parser")
+
+    # Ref https://api-docs.deepseek.com/guides/reasoning_model
+    # tool call and reasoning cannot be enabled at the same time.
+    if args.enable_auto_tool_choice and args.enable_reasoning:
+        raise TypeError(
+            "Error: --enable-auto-tool-choice and "
+            "--enable-reasoning cannot be enabled at the same time")
+
+
+def create_parser_for_docs() -> FlexibleArgumentParser:
+    parser_for_docs = FlexibleArgumentParser(
+        prog="-m vllm.entrypoints.openai.api_server")
+    return make_arg_parser(parser_for_docs)

.venv/lib/python3.11/site-packages/vllm/entrypoints/openai/logits_processors.py

@@ -0,0 +1,88 @@
+# SPDX-License-Identifier: Apache-2.0
+
+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 >= len(tokenizer):
+                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), len(tokenizer)))
+
+    return logits_processors

.venv/lib/python3.11/site-packages/vllm/entrypoints/openai/protocol.py

@@ -0,0 +1,1428 @@
+# SPDX-License-Identifier: Apache-2.0
+
+# Adapted from
+# https://github.com/lm-sys/FastChat/blob/168ccc29d3f7edc50823016105c024fe2282732a/fastchat/protocol/openai_api_protocol.py
+import re
+import time
+from argparse import Namespace
+from typing import Any, ClassVar, Dict, List, Literal, Optional, Set, Union
+
+import torch
+from pydantic import (BaseModel, ConfigDict, Field, TypeAdapter,
+                      ValidationInfo, field_validator, model_validator)
+from typing_extensions import Annotated
+
+from vllm.entrypoints.chat_utils import ChatCompletionMessageParam
+from vllm.logger import init_logger
+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, resolve_obj_by_qualname
+
+logger = init_logger(__name__)
+
+# 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 OpenAIBaseModel(BaseModel):
+    # OpenAI API does allow extra fields
+    model_config = ConfigDict(extra="allow")
+
+    # Cache class field names
+    field_names: ClassVar[Optional[Set[str]]] = None
+
+    @model_validator(mode="wrap")
+    @classmethod
+    def __log_extra_fields__(cls, data, handler):
+        result = handler(data)
+        if not isinstance(data, dict):
+            return result
+        field_names = cls.field_names
+        if field_names is None:
+            # Get all class field names and their potential aliases
+            field_names = set()
+            for field_name, field in cls.model_fields.items():
+                field_names.add(field_name)
+                if alias := getattr(field, 'alias', None):
+                    field_names.add(alias)
+            cls.field_names = field_names
+
+        # Compare against both field names and aliases
+        if any(k not in field_names for k in data):
+            logger.warning(
+                "The following fields were present in the request "
+                "but ignored: %s",
+                data.keys() - field_names)
+        return result
+
+
+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 PromptTokenUsageInfo(OpenAIBaseModel):
+    cached_tokens: Optional[int] = None
+
+
+class UsageInfo(OpenAIBaseModel):
+    prompt_tokens: int = 0
+    total_tokens: int = 0
+    completion_tokens: Optional[int] = 0
+    prompt_tokens_details: Optional[PromptTokenUsageInfo] = None
+
+
+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] = False
+
+
+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 LogitsProcessorConstructor(BaseModel):
+    qualname: str
+    args: Optional[List[Any]] = None
+    kwargs: Optional[Dict[str, Any]] = None
+
+
+LogitsProcessors = List[Union[str, LogitsProcessorConstructor]]
+
+
+def get_logits_processors(processors: Optional[LogitsProcessors],
+                          pattern: Optional[str]) -> Optional[List[Any]]:
+    if processors and pattern:
+        logits_processors = []
+        for processor in processors:
+            qualname = processor if isinstance(processor,
+                                               str) else processor.qualname
+            if not re.match(pattern, qualname):
+                raise ValueError(
+                    f"Logits processor '{qualname}' is not allowed by this "
+                    "server. See --logits-processor-pattern engine argument "
+                    "for more information.")
+            try:
+                logits_processor = resolve_obj_by_qualname(qualname)
+            except Exception as e:
+                raise ValueError(
+                    f"Logits processor '{qualname}' could not be resolved: {e}"
+                ) from e
+            if isinstance(processor, LogitsProcessorConstructor):
+                logits_processor = logits_processor(*processor.args or [],
+                                                    **processor.kwargs or {})
+            logits_processors.append(logits_processor)
+        return logits_processors
+    elif processors:
+        raise ValueError(
+            "The `logits_processors` argument is not supported by this "
+            "server. See --logits-processor-pattern engine argugment "
+            "for more information.")
+    return None
+
+
+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
+    # TODO(#9845): remove max_tokens when field is removed from OpenAI API
+    max_tokens: Optional[int] = Field(
+        default=None,
+        deprecated=
+        'max_tokens is deprecated in favor of the max_completion_tokens field')
+    max_completion_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] = None
+    top_p: Optional[float] = None
+    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: Optional[int] = None
+    min_p: Optional[float] = None
+    repetition_penalty: Optional[float] = None
+    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."))
+    request_id: str = Field(
+        default_factory=lambda: f"{random_uuid()}",
+        description=(
+            "The request_id related to this request. If the caller does "
+            "not set it, a random_uuid will be generated. This id is used "
+            "through out the inference process and return in response."))
+    logits_processors: Optional[LogitsProcessors] = Field(
+        default=None,
+        description=(
+            "A list of either qualified names of logits processors, or "
+            "constructor objects, to apply when sampling. A constructor is "
+            "a JSON object with a required 'qualname' field specifying the "
+            "qualified name of the processor class/factory, and optional "
+            "'args' and 'kwargs' fields containing positional and keyword "
+            "arguments. For example: {'qualname': "
+            "'my_module.MyLogitsProcessor', 'args': [1, 2], 'kwargs': "
+            "{'param': 'value'}}."))
+
+    # doc: end-chat-completion-extra-params
+
+    # Default sampling parameters for chat completion requests
+    _DEFAULT_SAMPLING_PARAMS: dict = {
+        "repetition_penalty": 1.0,
+        "temperature": 1.0,
+        "top_p": 1.0,
+        "top_k": -1,
+        "min_p": 0.0,
+    }
+
+    def to_beam_search_params(
+            self,
+            default_max_tokens: int,
+            default_sampling_params: Optional[dict] = None
+    ) -> BeamSearchParams:
+        # TODO(#9845): remove max_tokens when field is removed from OpenAI API
+        max_tokens = self.max_completion_tokens or self.max_tokens
+
+        if default_sampling_params is None:
+            default_sampling_params = {}
+        n = self.n if self.n is not None else 1
+
+        # Use minimum of context window, user request & server limit.
+        max_tokens = min(
+            val for val in (default_max_tokens, max_tokens,
+                            default_sampling_params.get("max_tokens", None))
+            if val is not None)
+
+        if (temperature := self.temperature) is None:
+            temperature = default_sampling_params.get(
+                "temperature", self._DEFAULT_SAMPLING_PARAMS["temperature"])
+
+        return BeamSearchParams(
+            beam_width=n,
+            max_tokens=max_tokens,
+            ignore_eos=self.ignore_eos,
+            temperature=temperature,
+            length_penalty=self.length_penalty,
+            include_stop_str_in_output=self.include_stop_str_in_output)
+
+    def to_sampling_params(
+            self,
+            default_max_tokens: int,
+            logits_processor_pattern: Optional[str],
+            default_sampling_params: Optional[dict] = None) -> SamplingParams:
+        # TODO(#9845): remove max_tokens when field is removed from OpenAI API
+        max_tokens = self.max_completion_tokens or self.max_tokens
+
+        if default_sampling_params is None:
+            default_sampling_params = {}
+
+        # Use minimum of context window, user request & server limit.
+        max_tokens = min(
+            val for val in (default_max_tokens, max_tokens,
+                            default_sampling_params.get("max_tokens", None))
+            if val is not None)
+
+        # Default parameters
+        if (repetition_penalty := self.repetition_penalty) is None:
+            repetition_penalty = default_sampling_params.get(
+                "repetition_penalty",
+                self._DEFAULT_SAMPLING_PARAMS["repetition_penalty"],
+            )
+        if (temperature := self.temperature) is None:
+            temperature = default_sampling_params.get(
+                "temperature", self._DEFAULT_SAMPLING_PARAMS["temperature"])
+        if (top_p := self.top_p) is None:
+            top_p = default_sampling_params.get(
+                "top_p", self._DEFAULT_SAMPLING_PARAMS["top_p"])
+        if (top_k := self.top_k) is None:
+            top_k = default_sampling_params.get(
+                "top_k", self._DEFAULT_SAMPLING_PARAMS["top_k"])
+        if (min_p := self.min_p) is None:
+            min_p = default_sampling_params.get(
+                "min_p", self._DEFAULT_SAMPLING_PARAMS["min_p"])
+
+        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:
+            if self.response_format.type == "json_object":
+                guided_json_object = True
+            elif self.response_format.type == "json_schema":
+                json_schema = self.response_format.json_schema
+                assert json_schema is not None
+                self.guided_json = json_schema.json_schema
+                if self.guided_decoding_backend is None:
+                    self.guided_decoding_backend = "xgrammar"
+
+        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=repetition_penalty,
+            temperature=temperature,
+            top_p=top_p,
+            top_k=top_k,
+            min_p=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,
+            logits_processors=get_logits_processors(self.logits_processors,
+                                                    logits_processor_pattern),
+            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 "none" -- ignore tools if present
+        if "tool_choice" in data and data["tool_choice"] == "none":
+            # ensure that no tools are present
+            data.pop("tools", None)
+            return data
+
+        # 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, \"auto\", "
+                    "or \"none\".")
+
+            # 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"].get("function")
+                if not specified_function:
+                    raise ValueError(
+                        "Expected field `function` in `tool_choice`."
+                        " Correct usage: `{\"type\": \"function\","
+                        " \"function\": {\"name\": \"my_function\"}}`")
+                specified_function_name = specified_function.get("name")
+                if not specified_function_name:
+                    raise ValueError(
+                        "Expected field `name` in `function` in `tool_choice`."
+                        "Correct usage: `{\"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] = None
+    top_p: Optional[float] = None
+    user: Optional[str] = None
+
+    # doc: begin-completion-sampling-params
+    use_beam_search: bool = False
+    top_k: Optional[int] = None
+    min_p: Optional[float] = None
+    repetition_penalty: Optional[float] = None
+    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'}, {'type': 'json_schema'} 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."))
+    logits_processors: Optional[LogitsProcessors] = Field(
+        default=None,
+        description=(
+            "A list of either qualified names of logits processors, or "
+            "constructor objects, to apply when sampling. A constructor is "
+            "a JSON object with a required 'qualname' field specifying the "
+            "qualified name of the processor class/factory, and optional "
+            "'args' and 'kwargs' fields containing positional and keyword "
+            "arguments. For example: {'qualname': "
+            "'my_module.MyLogitsProcessor', 'args': [1, 2], 'kwargs': "
+            "{'param': 'value'}}."))
+
+    # doc: end-completion-extra-params
+
+    # Default sampling parameters for completion requests
+    _DEFAULT_SAMPLING_PARAMS: dict = {
+        "repetition_penalty": 1.0,
+        "temperature": 1.0,
+        "top_p": 1.0,
+        "top_k": -1,
+        "min_p": 0.0,
+    }
+
+    def to_beam_search_params(
+            self,
+            default_max_tokens: int,
+            default_sampling_params: Optional[dict] = None
+    ) -> BeamSearchParams:
+        max_tokens = self.max_tokens
+
+        if default_sampling_params is None:
+            default_sampling_params = {}
+        n = self.n if self.n is not None else 1
+
+        # Use minimum of context window, user request & server limit.
+        max_tokens = min(
+            val for val in (default_max_tokens, max_tokens,
+                            default_sampling_params.get("max_tokens", None))
+            if val is not None)
+
+        if (temperature := self.temperature) is None:
+            temperature = default_sampling_params.get("temperature", 1.0)
+
+        return BeamSearchParams(
+            beam_width=n,
+            max_tokens=max_tokens,
+            ignore_eos=self.ignore_eos,
+            temperature=temperature,
+            length_penalty=self.length_penalty,
+            include_stop_str_in_output=self.include_stop_str_in_output)
+
+    def to_sampling_params(
+            self,
+            default_max_tokens: int,
+            logits_processor_pattern: Optional[str],
+            default_sampling_params: Optional[dict] = None) -> SamplingParams:
+        max_tokens = self.max_tokens
+
+        if default_sampling_params is None:
+            default_sampling_params = {}
+
+        # Use minimum of context window, user request & server limit.
+        max_tokens = min(
+            val for val in (default_max_tokens, max_tokens,
+                            default_sampling_params.get("max_tokens", None))
+            if val is not None)
+
+        # Default parameters
+        if (repetition_penalty := self.repetition_penalty) is None:
+            repetition_penalty = default_sampling_params.get(
+                "repetition_penalty",
+                self._DEFAULT_SAMPLING_PARAMS["repetition_penalty"],
+            )
+        if (temperature := self.temperature) is None:
+            temperature = default_sampling_params.get(
+                "temperature", self._DEFAULT_SAMPLING_PARAMS["temperature"])
+        if (top_p := self.top_p) is None:
+            top_p = default_sampling_params.get(
+                "top_p", self._DEFAULT_SAMPLING_PARAMS["top_p"])
+        if (top_k := self.top_k) is None:
+            top_k = default_sampling_params.get(
+                "top_k", self._DEFAULT_SAMPLING_PARAMS["top_k"])
+        if (min_p := self.min_p) is None:
+            min_p = default_sampling_params.get(
+                "min_p", self._DEFAULT_SAMPLING_PARAMS["min_p"])
+
+        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=repetition_penalty,
+            temperature=temperature,
+            top_p=top_p,
+            top_k=top_k,
+            min_p=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,
+            logits_processors=get_logits_processors(self.logits_processors,
+                                                    logits_processor_pattern),
+            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 EmbeddingCompletionRequest(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
+    add_special_tokens: bool = Field(
+        default=True,
+        description=(
+            "If true (the default), special tokens (e.g. BOS) will be added to "
+            "the prompt."),
+    )
+    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 EmbeddingChatRequest(OpenAIBaseModel):
+    model: str
+    messages: List[ChatCompletionMessageParam]
+
+    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-chat-embedding-pooling-params
+    additional_data: Optional[Any] = None
+    # doc: end-chat-embedding-pooling-params
+
+    # doc: begin-chat-embedding-extra-params
+    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)."),
+    )
+    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."),
+    )
+    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-embedding-extra-params
+
+    @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
+
+    def to_pooling_params(self):
+        return PoolingParams(additional_data=self.additional_data)
+
+
+EmbeddingRequest = Union[EmbeddingCompletionRequest, EmbeddingChatRequest]
+
+PoolingCompletionRequest = EmbeddingCompletionRequest
+PoolingChatRequest = EmbeddingChatRequest
+PoolingRequest = Union[PoolingCompletionRequest, PoolingChatRequest]
+
+
+class ScoreRequest(OpenAIBaseModel):
+    model: str
+    text_1: Union[List[str], str]
+    text_2: Union[List[str], str]
+    truncate_prompt_tokens: Optional[Annotated[int, Field(ge=1)]] = None
+
+    # doc: begin-score-pooling-params
+    additional_data: Optional[Any] = None
+    # doc: end-score-pooling-params
+
+    # doc: begin-score-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-score-extra-params
+
+    def to_pooling_params(self):
+        return PoolingParams(additional_data=self.additional_data)
+
+
+class RerankRequest(OpenAIBaseModel):
+    model: str
+    query: str
+    documents: List[str]
+    top_n: int = Field(default_factory=lambda: 0)
+    truncate_prompt_tokens: Optional[Annotated[int, Field(ge=1)]] = None
+
+    # doc: begin-rerank-pooling-params
+    additional_data: Optional[Any] = None
+    # doc: end-rerank-pooling-params
+
+    # doc: begin-rerank-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-rerank-extra-params
+
+    def to_pooling_params(self):
+        return PoolingParams(additional_data=self.additional_data)
+
+
+class RerankDocument(BaseModel):
+    text: str
+
+
+class RerankResult(BaseModel):
+    index: int
+    document: RerankDocument
+    relevance_score: float
+
+
+class RerankUsage(BaseModel):
+    total_tokens: int
+
+
+class RerankResponse(OpenAIBaseModel):
+    id: str
+    model: str
+    usage: RerankUsage
+    results: List[RerankResult]
+
+
+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"embd-{random_uuid()}")
+    object: str = "list"
+    created: int = Field(default_factory=lambda: int(time.time()))
+    model: str
+    data: List[EmbeddingResponseData]
+    usage: UsageInfo
+
+
+class PoolingResponseData(OpenAIBaseModel):
+    index: int
+    object: str = "pooling"
+    data: Union[List[List[float]], List[float], str]
+
+
+class PoolingResponse(OpenAIBaseModel):
+    id: str = Field(default_factory=lambda: f"pool-{random_uuid()}")
+    object: str = "list"
+    created: int = Field(default_factory=lambda: int(time.time()))
+    model: str
+    data: List[PoolingResponseData]
+    usage: UsageInfo
+
+
+class ScoreResponseData(OpenAIBaseModel):
+    index: int
+    object: str = "score"
+    score: float
+
+
+class ScoreResponse(OpenAIBaseModel):
+    id: str = Field(default_factory=lambda: f"embd-{random_uuid()}")
+    object: str = "list"
+    created: int = Field(default_factory=lambda: int(time.time()))
+    model: str
+    data: List[ScoreResponseData]
+    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
+    reasoning_content: Optional[str] = None
+    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
+    reasoning_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, ScoreRequest]
+
+    @field_validator('body', mode='plain')
+    @classmethod
+    def check_type_for_url(cls, value: Any, info: ValidationInfo):
+        # Use url to disambiguate models
+        url = info.data['url']
+        if url == "/v1/chat/completions":
+            return ChatCompletionRequest.model_validate(value)
+        if url == "/v1/embeddings":
+            return TypeAdapter(EmbeddingRequest).validate_python(value)
+        if url == "/v1/score":
+            return ScoreRequest.model_validate(value)
+        return TypeAdapter(Union[ChatCompletionRequest, EmbeddingRequest,
+                                 ScoreRequest]).validate_python(value)
+
+
+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,
+                         ScoreResponse]] = 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,
+        description=(
+            "If true (the default), special tokens (e.g. BOS) will be added to "
+            "the prompt."),
+    )
+
+
+class TokenizeChatRequest(OpenAIBaseModel):
+    model: str
+    messages: List[ChatCompletionMessageParam]
+
+    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)."),
+    )
+    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."),
+    )
+
+    @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)

.venv/lib/python3.11/site-packages/vllm/entrypoints/openai/run_batch.py

@@ -0,0 +1,342 @@
+# SPDX-License-Identifier: Apache-2.0
+
+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,
+                                              ScoreResponse)
+# yapf: enable
+from vllm.entrypoints.openai.serving_chat import OpenAIServingChat
+from vllm.entrypoints.openai.serving_embedding import OpenAIServingEmbedding
+from vllm.entrypoints.openai.serving_models import (BaseModelPath,
+                                                    OpenAIServingModels)
+from vllm.entrypoints.openai.serving_score import OpenAIServingScores
+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).",
+    )
+    parser.add_argument(
+        "--enable-prompt-tokens-details",
+        action='store_true',
+        default=False,
+        help="If set to True, enable prompt_tokens_details in usage.")
+
+    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, 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, ScoreResponse)):
+        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_models = OpenAIServingModels(
+        engine_client=engine,
+        model_config=model_config,
+        base_model_paths=base_model_paths,
+        lora_modules=None,
+        prompt_adapters=None,
+    )
+    openai_serving_chat = OpenAIServingChat(
+        engine,
+        model_config,
+        openai_serving_models,
+        args.response_role,
+        request_logger=request_logger,
+        chat_template=None,
+        chat_template_content_format="auto",
+        enable_prompt_tokens_details=args.enable_prompt_tokens_details,
+    ) if model_config.runner_type == "generate" else None
+    openai_serving_embedding = OpenAIServingEmbedding(
+        engine,
+        model_config,
+        openai_serving_models,
+        request_logger=request_logger,
+        chat_template=None,
+        chat_template_content_format="auto",
+    ) if model_config.task == "embed" else None
+    openai_serving_scores = (OpenAIServingScores(
+        engine,
+        model_config,
+        openai_serving_models,
+        request_logger=request_logger,
+    ) if model_config.task == "score" else None)
+
+    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":
+            handler_fn = (None if openai_serving_chat is None else
+                          openai_serving_chat.create_chat_completion)
+            if handler_fn is None:
+                response_futures.append(
+                    make_async_error_request_output(
+                        request,
+                        error_msg=
+                        "The model does not support Chat Completions API",
+                    ))
+                continue
+
+            response_futures.append(run_request(handler_fn, request, tracker))
+            tracker.submitted()
+        elif request.url == "/v1/embeddings":
+            handler_fn = (None if openai_serving_embedding is None else
+                          openai_serving_embedding.create_embedding)
+            if handler_fn is None:
+                response_futures.append(
+                    make_async_error_request_output(
+                        request,
+                        error_msg="The model does not support Embeddings API",
+                    ))
+                continue
+
+            response_futures.append(run_request(handler_fn, request, tracker))
+            tracker.submitted()
+        elif request.url == "/v1/score":
+            handler_fn = (None if openai_serving_scores is None else
+                          openai_serving_scores.create_score)
+            if handler_fn is None:
+                response_futures.append(
+                    make_async_error_request_output(
+                        request,
+                        error_msg="The model does not support Scores API",
+                    ))
+                continue
+
+            response_futures.append(run_request(handler_fn, request, tracker))
+            tracker.submitted()
+        else:
+            response_futures.append(
+                make_async_error_request_output(
+                    request,
+                    error_msg=
+                    "Only /v1/chat/completions, /v1/embeddings, and /v1/score "
+                    "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))

.venv/lib/python3.11/site-packages/vllm/entrypoints/openai/serving_chat.py

@@ -0,0 +1,955 @@
+# SPDX-License-Identifier: Apache-2.0
+
+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.protocol import EngineClient
+from vllm.entrypoints.chat_utils import (ChatTemplateContentFormatOption,
+                                         ConversationMessage)
+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, PromptTokenUsageInfo,
+    RequestResponseMetadata, ToolCall, UsageInfo)
+from vllm.entrypoints.openai.reasoning_parsers import (ReasoningParser,
+                                                       ReasoningParserManager)
+from vllm.entrypoints.openai.serving_engine import OpenAIServing
+from vllm.entrypoints.openai.serving_models import OpenAIServingModels
+from vllm.entrypoints.openai.tool_parsers import ToolParser, ToolParserManager
+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.transformers_utils.tokenizer import AnyTokenizer, MistralTokenizer
+from vllm.transformers_utils.tokenizers import maybe_serialize_tool_calls
+
+logger = init_logger(__name__)
+
+
+class OpenAIServingChat(OpenAIServing):
+
+    def __init__(
+        self,
+        engine_client: EngineClient,
+        model_config: ModelConfig,
+        models: OpenAIServingModels,
+        response_role: str,
+        *,
+        request_logger: Optional[RequestLogger],
+        chat_template: Optional[str],
+        chat_template_content_format: ChatTemplateContentFormatOption,
+        return_tokens_as_token_ids: bool = False,
+        enable_reasoning: bool = False,
+        reasoning_parser: Optional[str] = None,
+        enable_auto_tools: bool = False,
+        tool_parser: Optional[str] = None,
+        enable_prompt_tokens_details: bool = False,
+    ) -> None:
+        super().__init__(engine_client=engine_client,
+                         model_config=model_config,
+                         models=models,
+                         request_logger=request_logger,
+                         return_tokens_as_token_ids=return_tokens_as_token_ids)
+
+        self.response_role = response_role
+        self.chat_template = chat_template
+        self.chat_template_content_format: Final = chat_template_content_format
+
+        # 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.enable_reasoning: bool = enable_reasoning
+        self.reasoning_parser: Optional[Callable[[AnyTokenizer],
+                                                 ReasoningParser]] = None
+        if self.enable_reasoning:
+            try:
+                self.reasoning_parser = (
+                    ReasoningParserManager.get_reasoning_parser(
+                        reasoning_parser))
+            except Exception as e:
+                raise TypeError("Error: --enable-reasoning requires "
+                                f"reasoning_parser:'{reasoning_parser}' "
+                                "which has not been registered") from e
+        self.tool_parser: Optional[Callable[[AnyTokenizer], ToolParser]] = None
+        if self.enable_auto_tools:
+            try:
+                if (tool_parser == "pythonic" and
+                        model_config.model.startswith("meta-llama/Llama-3.2")):
+                    logger.warning(
+                        "Llama3.2 models may struggle to emit valid pythonic"
+                        " tool calls")
+                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
+
+        self.enable_prompt_tokens_details = enable_prompt_tokens_details
+        diff_sampling_param = self.model_config.get_diff_sampling_param()
+        if diff_sampling_param:
+            logger.info("Overwriting default chat sampling param with: %s",
+                        diff_sampling_param)
+
+    async def create_chat_completion(
+        self,
+        request: ChatCompletionRequest,
+        raw_request: Optional[Request] = None,
+    ) -> Union[AsyncGenerator[str, None], ChatCompletionResponse,
+               ErrorResponse]:
+        """
+        Chat 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
+        Chat Completion 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_name = self.models.model_name(lora_request)
+
+            tokenizer = await self.engine_client.get_tokenizer(lora_request)
+
+            tool_parser = self.tool_parser
+
+            # 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!")
+
+            # because of issues with pydantic we need to potentially
+            # re-serialize the tool_calls field of the request
+            # for more info: see comment in `maybe_serialize_tool_calls`
+            if isinstance(tokenizer, MistralTokenizer):
+                maybe_serialize_tool_calls(request)
+
+            if (request.tool_choice == "auto" and
+                    not (self.enable_auto_tools and tool_parser is not None)
+                    and not isinstance(tokenizer, MistralTokenizer)):
+                # 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"
+                )
+
+            tool_dicts = None if request.tools is None else [
+                tool.model_dump() for tool in request.tools
+            ]
+
+            (
+                conversation,
+                request_prompts,
+                engine_prompts,
+            ) = await self._preprocess_chat(
+                request,
+                tokenizer,
+                request.messages,
+                chat_template=request.chat_template or self.chat_template,
+                chat_template_content_format=self.chat_template_content_format,
+                add_generation_prompt=request.add_generation_prompt,
+                continue_final_message=request.continue_final_message,
+                tool_dicts=tool_dicts,
+                documents=request.documents,
+                chat_template_kwargs=request.chat_template_kwargs,
+                tool_parser=tool_parser,
+                truncate_prompt_tokens=request.truncate_prompt_tokens,
+                add_special_tokens=request.add_special_tokens,
+            )
+        except ValueError as e:
+            logger.exception("Error in preprocessing prompt inputs")
+            return self.create_error_response(str(e))
+
+        request_id = "chatcmpl-" \
+                     f"{self._base_request_id(raw_request, request.request_id)}"
+
+        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:
+            for i, engine_prompt in enumerate(engine_prompts):
+                sampling_params: Union[SamplingParams, BeamSearchParams]
+                default_max_tokens = self.max_model_len - len(
+                    engine_prompt["prompt_token_ids"])
+                # Build default sampling params
+                default_sampling_params = (
+                    self.model_config.get_diff_sampling_param())
+                if request.use_beam_search:
+                    sampling_params = request.to_beam_search_params(
+                        default_max_tokens, default_sampling_params)
+                else:
+                    sampling_params = request.to_sampling_params(
+                        default_max_tokens,
+                        self.model_config.logits_processor_pattern,
+                        default_sampling_params)
+
+                self._log_inputs(request_id,
+                                 request_prompts[i],
+                                 params=sampling_params,
+                                 lora_request=lora_request,
+                                 prompt_adapter_request=prompt_adapter_request)
+
+                trace_headers = (None if raw_request is None else await
+                                 self._get_trace_headers(raw_request.headers))
+
+                if isinstance(sampling_params, BeamSearchParams):
+                    generator = self.engine_client.beam_search(
+                        prompt=engine_prompt,
+                        request_id=request_id,
+                        params=sampling_params,
+                    )
+                else:
+                    generator = self.engine_client.generate(
+                        engine_prompt,
+                        sampling_params,
+                        request_id,
+                        lora_request=lora_request,
+                        trace_headers=trace_headers,
+                        prompt_adapter_request=prompt_adapter_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))
+
+        assert len(generators) == 1
+        result_generator, = generators
+
+        # Streaming response
+        if request.stream:
+            return self.chat_completion_stream_generator(
+                request, result_generator, request_id, model_name,
+                conversation, tokenizer, request_metadata)
+
+        try:
+            return await self.chat_completion_full_generator(
+                request, result_generator, request_id, model_name,
+                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,
+        model_name: str,
+        conversation: List[ConversationMessage],
+        tokenizer: AnyTokenizer,
+        request_metadata: RequestResponseMetadata,
+    ) -> AsyncGenerator[str, None]:
+        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
+        num_cached_tokens = None
+
+        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))
+
+        should_stream_with_reasoning_parsing = (
+            self._should_stream_with_reasoning_parsing(request))
+
+        all_previous_token_ids: Optional[List[List[int]]]
+
+        # Only one of these will be used, thus previous_texts and
+        # all_previous_token_ids will not be used twice in the same iteration.
+        if tool_choice_auto or should_stream_with_reasoning_parsing:
+            # 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
+
+        try:
+            # There is no need to check if the reasoning_parser is None
+            # because the should_stream_with_reasoning_parsing check
+            # already ensures that the reasoning_parser is not None.
+            # but the pre-commit hook requires it.
+            if should_stream_with_reasoning_parsing and \
+                self.reasoning_parser is not None:
+                reasoning_parser = self.reasoning_parser(tokenizer)
+        except RuntimeError as e:
+            logger.exception("Error in reasoning parser creation.")
+            data = self.create_streaming_error_response(str(e))
+            yield f"data: {data}\n\n"
+            yield "data: [DONE]\n\n"
+            return
+
+        # 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 Exception as e:
+            logger.exception("Error in tool parser creation.")
+            data = self.create_streaming_error_response(str(e))
+            yield f"data: {data}\n\n"
+            yield "data: [DONE]\n\n"
+            return
+
+        stream_options = request.stream_options
+        if stream_options:
+            include_usage = stream_options.include_usage
+            include_continuous_usage = include_usage and \
+                                       stream_options.continuous_usage_stats
+        else:
+            include_usage, include_continuous_usage = False, False
+
+        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:
+                    num_cached_tokens = res.num_cached_tokens
+                    # 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):
+                        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 continuous usage stats are requested, add it
+                        if include_continuous_usage:
+                            chunk.usage = UsageInfo(
+                                prompt_tokens=num_prompt_tokens,
+                                completion_tokens=0,
+                                total_tokens=num_prompt_tokens)
+
+                        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:
+                        last_msg_content: Union[str, List[Dict[str, 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 include_continuous_usage:
+                                    chunk.usage = UsageInfo(
+                                        prompt_tokens=num_prompt_tokens,
+                                        completion_tokens=0,
+                                        total_tokens=num_prompt_tokens)
+
+                                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
+
+                    if not delta_text and not output.token_ids and \
+                        not previous_num_tokens[i]:
+                        # Chunked prefill case, don't return empty chunks
+                        continue
+
+                    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
+                    # reasoning_content cannot be enabled with tool_choice.
+                    # If it is, the tool_choice will be used instead.
+                    elif self.enable_reasoning:
+                        # handle reasoning_content delta
+                        assert reasoning_parser is not None
+                        assert previous_texts is not None
+                        assert all_previous_token_ids is not None
+                        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 = (reasoning_parser.
+                                         extract_reasoning_content_streaming(
+                                             previous_text,
+                                             current_text,
+                                             delta_text,
+                                             previous_token_ids,
+                                             current_token_ids,
+                                             output.token_ids,
+                                         ))
+
+                        # 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)
+
+                    # 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:
+                            latest_delta_len = 0
+                            if ((isinstance(
+                                    delta_message.tool_calls[0].function,
+                                    DeltaFunctionCall)) and isinstance(
+                                        delta_message.tool_calls[0].function.
+                                        arguments, str)):
+                                latest_delta_len = len(
+                                    delta_message.tool_calls[0].function.
+                                    arguments)
+
+                            # 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", {}),
+                                ensure_ascii=False)
+
+                            # get what we've streamed so far for arguments
+                            # for the current tool
+                            actual_call = tool_parser.streamed_args_for_tool[
+                                index]
+                            if (latest_delta_len > 0):
+                                actual_call = actual_call[:-latest_delta_len]
+
+                            # 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)
+
+                        finish_reason_sent[i] = True
+
+                    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 include_continuous_usage:
+                        completion_tokens = previous_num_tokens[i]
+                        chunk.usage = UsageInfo(
+                            prompt_tokens=num_prompt_tokens,
+                            completion_tokens=completion_tokens,
+                            total_tokens=num_prompt_tokens + completion_tokens,
+                        )
+
+                    data = chunk.model_dump_json(exclude_unset=True)
+                    yield f"data: {data}\n\n"
+
+            # once the final token is handled, if stream_options.include_usage
+            # is sent, send the usage
+            if include_usage:
+                completion_tokens = sum(previous_num_tokens)
+                final_usage = UsageInfo(prompt_tokens=num_prompt_tokens,
+                                        completion_tokens=completion_tokens,
+                                        total_tokens=num_prompt_tokens +
+                                        completion_tokens)
+                if self.enable_prompt_tokens_details and num_cached_tokens:
+                    final_usage.prompt_tokens_details = PromptTokenUsageInfo(
+                        cached_tokens=num_cached_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 Exception as e:
+            # TODO: Use a vllm-specific Validation Error
+            logger.exception("Error in chat completion stream generator.")
+            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,
+        model_name: str,
+        conversation: List[ConversationMessage],
+        tokenizer: AnyTokenizer,
+        request_metadata: RequestResponseMetadata,
+    ) -> Union[ErrorResponse, ChatCompletionResponse]:
+
+        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")
+        except ValueError as e:
+            # TODO: Use a vllm-specific Validation Error
+            return self.create_error_response(str(e))
+
+        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
+
+            should_stream_with_reasoning_parsing = (
+                self._should_stream_with_reasoning_parsing(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 = False
+
+            if should_stream_with_reasoning_parsing and \
+                self.reasoning_parser is not None:
+                try:
+                    reasoning_parser = self.reasoning_parser(tokenizer)
+                except RuntimeError as e:
+                    logger.exception("Error in reasoning parser creation.")
+                    return self.create_error_response(str(e))
+
+                reasoning_content, content = (
+                    reasoning_parser.extract_reasoning_content(
+                        output.text, request=request))
+
+                if reasoning_content:
+                    message = ChatMessage(role=role,
+                                          content=content,
+                                          reasoning_content=reasoning_content)
+                else:
+                    message = ChatMessage(role=role, content=output.text)
+
+            # if auto tools are not enabled, and a named tool choice using
+            #   outlines is not being used
+            elif (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.exception("Error in tool parser creation.")
+                    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:
+            last_msg_content: Union[str, List[Dict[str, str]]] = ""
+            if conversation and "content" in conversation[-1] and conversation[
+                    -1].get("role") == role:
+                last_msg_content = conversation[-1]["content"] or ""
+            if isinstance(last_msg_content, list):
+                last_msg_content = "\n".join(msg['text']
+                                             for msg in last_msg_content)
+
+            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)
+        if self.enable_prompt_tokens_details and final_res.num_cached_tokens:
+            usage.prompt_tokens_details = PromptTokenUsageInfo(
+                cached_tokens=final_res.num_cached_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_stream_with_reasoning_parsing(self,
+                                              request: ChatCompletionRequest):
+        """
+            Utility function to check if streamed tokens should go through the
+            reasoning parser that was configured.
+    
+            We only want to do this IF reasoning is enabled and a reasoning 
+            parser is configured.
+            """
+        return self.enable_reasoning and self.reasoning_parser is not 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
+        )

.venv/lib/python3.11/site-packages/vllm/entrypoints/openai/serving_completion.py

@@ -0,0 +1,547 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import asyncio
+import time
+from typing import AsyncGenerator, AsyncIterator, 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.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 OpenAIServing
+from vllm.entrypoints.openai.serving_models import OpenAIServingModels
+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.transformers_utils.tokenizer import AnyTokenizer
+from vllm.utils import merge_async_iterators
+
+logger = init_logger(__name__)
+
+
+class OpenAIServingCompletion(OpenAIServing):
+
+    def __init__(
+        self,
+        engine_client: EngineClient,
+        model_config: ModelConfig,
+        models: OpenAIServingModels,
+        *,
+        request_logger: Optional[RequestLogger],
+        return_tokens_as_token_ids: bool = False,
+    ):
+        super().__init__(engine_client=engine_client,
+                         model_config=model_config,
+                         models=models,
+                         request_logger=request_logger,
+                         return_tokens_as_token_ids=return_tokens_as_token_ids)
+        diff_sampling_param = self.model_config.get_diff_sampling_param()
+        if diff_sampling_param:
+            logger.info(
+                "Overwriting default completion sampling param with: %s",
+                diff_sampling_param)
+
+    async def create_completion(
+        self,
+        request: CompletionRequest,
+        raw_request: Optional[Request] = None,
+    ) -> 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")
+
+        request_id = f"cmpl-{self._base_request_id(raw_request)}"
+        created_time = int(time.time())
+
+        request_metadata = RequestResponseMetadata(request_id=request_id)
+        if raw_request:
+            raw_request.state.request_metadata = request_metadata
+
+        try:
+            (
+                lora_request,
+                prompt_adapter_request,
+            ) = self._maybe_get_adapters(request)
+
+            tokenizer = await self.engine_client.get_tokenizer(lora_request)
+
+            request_prompts, engine_prompts = await self._preprocess_completion(
+                request,
+                tokenizer,
+                request.prompt,
+                truncate_prompt_tokens=request.truncate_prompt_tokens,
+                add_special_tokens=request.add_special_tokens,
+            )
+        except ValueError as e:
+            logger.exception("Error in preprocessing prompt inputs")
+            return self.create_error_response(str(e))
+
+        # Schedule the request and get the result generator.
+        generators: List[AsyncGenerator[RequestOutput, None]] = []
+        try:
+            for i, engine_prompt in enumerate(engine_prompts):
+                sampling_params: Union[SamplingParams, BeamSearchParams]
+                default_max_tokens = self.max_model_len - len(
+                    engine_prompt["prompt_token_ids"])
+                # Build default sampling params
+                default_sampling_params = (
+                    self.model_config.get_diff_sampling_param())
+                if request.use_beam_search:
+                    sampling_params = request.to_beam_search_params(
+                        default_max_tokens, default_sampling_params)
+                else:
+                    sampling_params = request.to_sampling_params(
+                        default_max_tokens,
+                        self.model_config.logits_processor_pattern,
+                        default_sampling_params)
+
+                request_id_item = f"{request_id}-{i}"
+
+                self._log_inputs(request_id_item,
+                                 request_prompts[i],
+                                 params=sampling_params,
+                                 lora_request=lora_request,
+                                 prompt_adapter_request=prompt_adapter_request)
+
+                trace_headers = (None if raw_request is None else await
+                                 self._get_trace_headers(raw_request.headers))
+
+                if isinstance(sampling_params, BeamSearchParams):
+                    generator = self.engine_client.beam_search(
+                        prompt=engine_prompt,
+                        request_id=request_id,
+                        params=sampling_params,
+                    )
+                else:
+                    generator = self.engine_client.generate(
+                        engine_prompt,
+                        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)
+
+        model_name = self.models.model_name(lora_request)
+        num_prompts = len(engine_prompts)
+
+        # 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=num_prompts,
+                tokenizer=tokenizer,
+                request_metadata=request_metadata)
+
+        # Non-streaming response
+        final_res_batch: List[Optional[RequestOutput]] = [None] * num_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 = request_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
+
+        stream_options = request.stream_options
+        if stream_options:
+            include_usage = stream_options.include_usage
+            include_continuous_usage = include_usage and \
+                                       stream_options.continuous_usage_stats
+        else:
+            include_usage, include_continuous_usage = False, False
+
+        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
+
+                    assert request.max_tokens is not None
+                    if request.echo and not has_echoed[i]:
+                        assert prompt_token_ids is not None
+                        assert prompt_text is not None
+                        if request.max_tokens == 0:
+                            # only return the prompt
+                            delta_text = prompt_text
+                            delta_token_ids = prompt_token_ids
+                            out_logprobs = prompt_logprobs
+                        else:
+                            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 not delta_text and not delta_token_ids \
+                            and not previous_num_tokens[i]:
+                            # Chunked prefill case, don't return empty chunks
+                            continue
+
+                    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 include_continuous_usage:
+                        prompt_tokens = num_prompt_tokens[prompt_idx]
+                        completion_tokens = previous_num_tokens[i]
+                        chunk.usage = UsageInfo(
+                            prompt_tokens=prompt_tokens,
+                            completion_tokens=completion_tokens,
+                            total_tokens=prompt_tokens + completion_tokens,
+                        )
+
+                    response_json = chunk.model_dump_json(exclude_unset=False)
+                    yield f"data: {response_json}\n\n"
+
+            total_prompt_tokens = sum(num_prompt_tokens)
+            total_completion_tokens = sum(previous_num_tokens)
+            final_usage_info = UsageInfo(
+                prompt_tokens=total_prompt_tokens,
+                completion_tokens=total_completion_tokens,
+                total_tokens=total_prompt_tokens + total_completion_tokens)
+
+            if include_usage:
+                final_usage_chunk = CompletionStreamResponse(
+                    id=request_id,
+                    created=created_time,
+                    model=model_name,
+                    choices=[],
+                    usage=final_usage_info,
+                )
+                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
+            request_metadata.final_usage_info = final_usage_info
+
+        except Exception 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
+            if prompt_logprobs:
+                for logprob_dict in prompt_logprobs:
+                    if logprob_dict:
+                        for logprob_values in logprob_dict.values():
+                            if logprob_values.logprob == float('-inf'):
+                                logprob_values.logprob = -9999.0
+            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:
+                    assert prompt_text is not None
+                    if request.max_tokens == 0:
+                        token_ids = prompt_token_ids
+                        out_logprobs = prompt_logprobs
+                        output_text = prompt_text
+                    else:
+                        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,
+        )

.venv/lib/python3.11/site-packages/vllm/entrypoints/openai/serving_embedding.py

@@ -0,0 +1,242 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import asyncio
+import base64
+import time
+from typing import AsyncGenerator, Final, 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.chat_utils import ChatTemplateContentFormatOption
+from vllm.entrypoints.logger import RequestLogger
+from vllm.entrypoints.openai.protocol import (EmbeddingChatRequest,
+                                              EmbeddingRequest,
+                                              EmbeddingResponse,
+                                              EmbeddingResponseData,
+                                              ErrorResponse, UsageInfo)
+from vllm.entrypoints.openai.serving_engine import OpenAIServing
+from vllm.entrypoints.openai.serving_models import OpenAIServingModels
+from vllm.logger import init_logger
+from vllm.outputs import (EmbeddingOutput, EmbeddingRequestOutput,
+                          PoolingRequestOutput)
+from vllm.utils import merge_async_iterators
+
+logger = init_logger(__name__)
+
+
+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)
+
+
+class OpenAIServingEmbedding(OpenAIServing):
+
+    def __init__(
+        self,
+        engine_client: EngineClient,
+        model_config: ModelConfig,
+        models: OpenAIServingModels,
+        *,
+        request_logger: Optional[RequestLogger],
+        chat_template: Optional[str],
+        chat_template_content_format: ChatTemplateContentFormatOption,
+    ) -> None:
+        super().__init__(engine_client=engine_client,
+                         model_config=model_config,
+                         models=models,
+                         request_logger=request_logger)
+
+        self.chat_template = chat_template
+        self.chat_template_content_format: Final = chat_template_content_format
+
+    async def create_embedding(
+        self,
+        request: EmbeddingRequest,
+        raw_request: Optional[Request] = None,
+    ) -> Union[EmbeddingResponse, ErrorResponse]:
+        """
+        Embedding 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.
+        """
+        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-{self._base_request_id(raw_request)}"
+        created_time = int(time.time())
+
+        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.")
+
+        try:
+            (
+                lora_request,
+                prompt_adapter_request,
+            ) = self._maybe_get_adapters(request)
+
+            tokenizer = await self.engine_client.get_tokenizer(lora_request)
+
+            if prompt_adapter_request is not None:
+                raise NotImplementedError("Prompt adapter is not supported "
+                                          "for embedding models")
+
+            if isinstance(request, EmbeddingChatRequest):
+                (
+                    _,
+                    request_prompts,
+                    engine_prompts,
+                ) = await self._preprocess_chat(
+                    request,
+                    tokenizer,
+                    request.messages,
+                    chat_template=request.chat_template or self.chat_template,
+                    chat_template_content_format=self.
+                    chat_template_content_format,
+                    # In embedding requests, we are not generating tokens,
+                    # so there is no need to append extra tokens to the input
+                    add_generation_prompt=False,
+                    continue_final_message=False,
+                    truncate_prompt_tokens=truncate_prompt_tokens,
+                    add_special_tokens=request.add_special_tokens,
+                )
+            else:
+                (request_prompts,
+                 engine_prompts) = await self._preprocess_completion(
+                     request,
+                     tokenizer,
+                     request.input,
+                     truncate_prompt_tokens=truncate_prompt_tokens,
+                     add_special_tokens=request.add_special_tokens,
+                 )
+        except ValueError as e:
+            logger.exception("Error in preprocessing prompt inputs")
+            return self.create_error_response(str(e))
+
+        # Schedule the request and get the result generator.
+        generators: List[AsyncGenerator[PoolingRequestOutput, None]] = []
+        try:
+            pooling_params = request.to_pooling_params()
+
+            for i, engine_prompt in enumerate(engine_prompts):
+                request_id_item = f"{request_id}-{i}"
+
+                self._log_inputs(request_id_item,
+                                 request_prompts[i],
+                                 params=pooling_params,
+                                 lora_request=lora_request,
+                                 prompt_adapter_request=prompt_adapter_request)
+
+                trace_headers = (None if raw_request is None else await
+                                 self._get_trace_headers(raw_request.headers))
+
+                generator = self.engine_client.encode(
+                    engine_prompt,
+                    pooling_params,
+                    request_id_item,
+                    lora_request=lora_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)
+
+        num_prompts = len(engine_prompts)
+
+        # Non-streaming response
+        final_res_batch: List[Optional[PoolingRequestOutput]]
+        final_res_batch = [None] * num_prompts
+        try:
+            async for i, res in result_generator:
+                final_res_batch[i] = res
+
+            assert all(final_res is not None for final_res in final_res_batch)
+
+            final_res_batch_checked = cast(List[PoolingRequestOutput],
+                                           final_res_batch)
+
+            response = self.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 request_output_to_embedding_response(
+        self,
+        final_res_batch: List[PoolingRequestOutput],
+        request_id: str,
+        created_time: int,
+        model_name: str,
+        encoding_format: Literal["float", "base64"],
+    ) -> EmbeddingResponse:
+        items: List[EmbeddingResponseData] = []
+        num_prompt_tokens = 0
+
+        for idx, final_res in enumerate(final_res_batch):
+            embedding_res = EmbeddingRequestOutput.from_base(final_res)
+
+            item = EmbeddingResponseData(
+                index=idx,
+                embedding=_get_embedding(embedding_res.outputs,
+                                         encoding_format),
+            )
+            prompt_token_ids = final_res.prompt_token_ids
+
+            items.append(item)
+            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=items,
+            usage=usage,
+        )

.venv/lib/python3.11/site-packages/vllm/entrypoints/openai/serving_engine.py

@@ -0,0 +1,524 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import json
+from concurrent.futures.thread import ThreadPoolExecutor
+from http import HTTPStatus
+from typing import (Any, Callable, Dict, Iterable, Iterator, List, Mapping,
+                    Optional, Sequence, Tuple, TypedDict, Union)
+
+from fastapi import Request
+from pydantic import Field
+from starlette.datastructures import Headers
+from typing_extensions import Annotated
+
+from vllm.config import ModelConfig
+from vllm.engine.protocol import EngineClient
+# yapf conflicts with isort for this block
+# yapf: disable
+from vllm.entrypoints.chat_utils import (ChatCompletionMessageParam,
+                                         ChatTemplateContentFormatOption,
+                                         ConversationMessage,
+                                         apply_hf_chat_template,
+                                         apply_mistral_chat_template,
+                                         parse_chat_messages_futures,
+                                         resolve_chat_template_content_format)
+from vllm.entrypoints.logger import RequestLogger
+from vllm.entrypoints.openai.protocol import (ChatCompletionRequest,
+                                              CompletionRequest,
+                                              DetokenizeRequest,
+                                              EmbeddingChatRequest,
+                                              EmbeddingCompletionRequest,
+                                              ErrorResponse, RerankRequest,
+                                              ScoreRequest,
+                                              TokenizeChatRequest,
+                                              TokenizeCompletionRequest)
+from vllm.entrypoints.openai.serving_models import OpenAIServingModels
+from vllm.entrypoints.openai.tool_parsers import ToolParser
+# yapf: enable
+from vllm.inputs import TokensPrompt
+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.tracing import (contains_trace_headers, extract_trace_headers,
+                          log_tracing_disabled_warning)
+from vllm.transformers_utils.tokenizer import AnyTokenizer, MistralTokenizer
+from vllm.utils import is_list_of, make_async, random_uuid
+
+logger = init_logger(__name__)
+
+CompletionLikeRequest = Union[CompletionRequest, DetokenizeRequest,
+                              EmbeddingCompletionRequest, ScoreRequest,
+                              TokenizeCompletionRequest]
+
+ChatLikeRequest = Union[ChatCompletionRequest, EmbeddingChatRequest,
+                        TokenizeChatRequest]
+
+AnyRequest = Union[CompletionLikeRequest, ChatLikeRequest]
+
+
+class TextTokensPrompt(TypedDict):
+    prompt: str
+    prompt_token_ids: List[int]
+
+
+RequestPrompt = Union[List[int], str, TextTokensPrompt]
+
+
+class OpenAIServing:
+
+    def __init__(
+        self,
+        engine_client: EngineClient,
+        model_config: ModelConfig,
+        models: OpenAIServingModels,
+        *,
+        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.models = models
+
+        self.request_logger = request_logger
+        self.return_tokens_as_token_ids = return_tokens_as_token_ids
+
+        self._tokenizer_executor = ThreadPoolExecutor(max_workers=1)
+
+        self._tokenize_prompt_input_async = make_async(
+            self._tokenize_prompt_input, executor=self._tokenizer_executor)
+        self._tokenize_prompt_input_or_inputs_async = make_async(
+            self._tokenize_prompt_input_or_inputs,
+            executor=self._tokenizer_executor)
+
+    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.models.lora_requests
+        ]:
+            return None
+        if request.model in [
+                prompt_adapter.prompt_adapter_name
+                for prompt_adapter in self.models.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.models.lora_requests:
+            if request.model == lora.lora_name:
+                return lora, None
+        for prompt_adapter in self.models.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 (self.model_config.encoder_config is not None
+                and self.model_config.encoder_config.get(
+                    "do_lower_case", False)):
+            prompt = prompt.lower()
+
+        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 and ScoreRequest doesn't have max_tokens
+        if isinstance(request,
+                      (EmbeddingChatRequest, EmbeddingCompletionRequest,
+                       ScoreRequest, RerankRequest)):
+
+            operation = "score" if isinstance(request, ScoreRequest) \
+                else "embedding generation"
+            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 {operation}. "
+                    f"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)
+
+        # chat completion endpoint supports max_completion_tokens
+        if isinstance(request, ChatCompletionRequest):
+            # TODO(#9845): remove max_tokens when field dropped from OpenAI API
+            max_tokens = request.max_completion_tokens or request.max_tokens
+        else:
+            max_tokens = request.max_tokens
+        if 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 + 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"{max_tokens + token_num} tokens "
+                f"({token_num} in the messages, "
+                f"{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,
+    ) -> List[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.
+        """
+        # 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
+        return [
+            self._normalize_prompt_text_to_input(
+                request,
+                tokenizer,
+                prompt=prompt_input["content"],
+                truncate_prompt_tokens=truncate_prompt_tokens,
+                add_special_tokens=add_special_tokens)
+            if prompt_input["is_tokens"] is False else
+            self._normalize_prompt_tokens_to_input(
+                request,
+                tokenizer,
+                prompt_ids=prompt_input["content"],
+                truncate_prompt_tokens=truncate_prompt_tokens)
+            for prompt_input in parse_and_batch_prompt(input_or_inputs)
+        ]
+
+    async def _preprocess_completion(
+        self,
+        request: CompletionLikeRequest,
+        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,
+    ) -> Tuple[List[TextTokensPrompt], List[TokensPrompt]]:
+        request_prompts = await self._tokenize_prompt_input_or_inputs_async(
+            request,
+            tokenizer,
+            input_or_inputs,
+            truncate_prompt_tokens=truncate_prompt_tokens,
+            add_special_tokens=add_special_tokens,
+        )
+
+        engine_prompts = [
+            TokensPrompt(prompt_token_ids=request_prompt["prompt_token_ids"])
+            for request_prompt in request_prompts
+        ]
+
+        return request_prompts, engine_prompts
+
+    async def _preprocess_chat(
+        self,
+        request: ChatLikeRequest,
+        tokenizer: AnyTokenizer,
+        messages: List[ChatCompletionMessageParam],
+        chat_template: Optional[str],
+        chat_template_content_format: ChatTemplateContentFormatOption,
+        add_generation_prompt: bool = True,
+        continue_final_message: bool = False,
+        tool_dicts: Optional[List[Dict[str, Any]]] = None,
+        documents: Optional[List[Dict[str, str]]] = None,
+        chat_template_kwargs: Optional[Dict[str, Any]] = None,
+        tool_parser: Optional[Callable[[AnyTokenizer], ToolParser]] = None,
+        truncate_prompt_tokens: Optional[Annotated[int, Field(ge=1)]] = None,
+        add_special_tokens: bool = False,
+    ) -> Tuple[List[ConversationMessage], Sequence[RequestPrompt],
+               List[TokensPrompt]]:
+        resolved_content_format = resolve_chat_template_content_format(
+            chat_template,
+            chat_template_content_format,
+            tokenizer,
+        )
+        conversation, mm_data_future = parse_chat_messages_futures(
+            messages,
+            self.model_config,
+            tokenizer,
+            content_format=resolved_content_format,
+        )
+
+        _chat_template_kwargs: Dict[str, Any] = dict(
+            chat_template=chat_template,
+            add_generation_prompt=add_generation_prompt,
+            continue_final_message=continue_final_message,
+            tools=tool_dicts,
+            documents=documents,
+        )
+        _chat_template_kwargs.update(chat_template_kwargs or {})
+
+        request_prompt: Union[str, List[int]]
+        is_mistral_tokenizer = isinstance(tokenizer, MistralTokenizer)
+        if is_mistral_tokenizer:
+            request_prompt = apply_mistral_chat_template(
+                tokenizer,
+                messages=messages,
+                **_chat_template_kwargs,
+            )
+        else:
+            request_prompt = apply_hf_chat_template(
+                tokenizer,
+                conversation=conversation,
+                **_chat_template_kwargs,
+            )
+
+        mm_data = await mm_data_future
+
+        # tool parsing is done only if a tool_parser has been set and if
+        # tool_choice is not "none" (if tool_choice is "none" but a tool_parser
+        # is set, we want to prevent parsing a tool_call hallucinated by the LLM
+        should_parse_tools = tool_parser is not None and (hasattr(
+            request, "tool_choice") and request.tool_choice != "none")
+
+        if should_parse_tools:
+            if not isinstance(request, ChatCompletionRequest):
+                msg = "Tool usage is only supported for Chat Completions API"
+                raise NotImplementedError(msg)
+
+            request = tool_parser(tokenizer).adjust_request(  # type: ignore
+                request=request)
+
+        if isinstance(request_prompt, str):
+            prompt_inputs = await self._tokenize_prompt_input_async(
+                request,
+                tokenizer,
+                request_prompt,
+                truncate_prompt_tokens=truncate_prompt_tokens,
+                add_special_tokens=add_special_tokens,
+            )
+        else:
+            # For MistralTokenizer
+            assert is_list_of(request_prompt, int), (
+                "Prompt has to be either a string or a list of token ids")
+            prompt_inputs = TextTokensPrompt(
+                prompt=tokenizer.decode(request_prompt),
+                prompt_token_ids=request_prompt)
+
+        engine_prompt = TokensPrompt(
+            prompt_token_ids=prompt_inputs["prompt_token_ids"])
+        if mm_data is not None:
+            engine_prompt["multi_modal_data"] = mm_data
+
+        return conversation, [request_prompt], [engine_prompt]
+
+    def _log_inputs(
+        self,
+        request_id: str,
+        inputs: RequestPrompt,
+        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,
+        )
+
+    async def _get_trace_headers(
+        self,
+        headers: Headers,
+    ) -> Optional[Mapping[str, str]]:
+        is_tracing_enabled = await self.engine_client.is_tracing_enabled()
+
+        if is_tracing_enabled:
+            return extract_trace_headers(headers)
+
+        if contains_trace_headers(headers):
+            log_tracing_disabled_warning()
+
+        return None
+
+    @staticmethod
+    def _base_request_id(raw_request: Optional[Request],
+                         default: Optional[str] = None) -> Optional[str]:
+        """Pulls the request id to use from a header, if provided"""
+        default = default or random_uuid()
+        if raw_request is None:
+            return default
+
+        return raw_request.headers.get("X-Request-Id", default)
+
+    @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)
+
+    def _is_model_supported(self, model_name):
+        return self.models.is_base_model(model_name)

.venv/lib/python3.11/site-packages/vllm/entrypoints/openai/serving_models.py

@@ -0,0 +1,244 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import json
+import pathlib
+from dataclasses import dataclass
+from http import HTTPStatus
+from typing import List, Optional, Union
+
+from vllm.config import ModelConfig
+from vllm.engine.protocol import EngineClient
+from vllm.entrypoints.openai.protocol import (ErrorResponse,
+                                              LoadLoraAdapterRequest,
+                                              ModelCard, ModelList,
+                                              ModelPermission,
+                                              UnloadLoraAdapterRequest)
+from vllm.logger import init_logger
+from vllm.lora.request import LoRARequest
+from vllm.prompt_adapter.request import PromptAdapterRequest
+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
+
+
+class OpenAIServingModels:
+    """Shared instance to hold data about the loaded base model(s) and adapters.
+
+    Handles the routes:
+    - /v1/models
+    - /v1/load_lora_adapter
+    - /v1/unload_lora_adapter
+    """
+
+    def __init__(
+        self,
+        engine_client: EngineClient,
+        model_config: ModelConfig,
+        base_model_paths: List[BaseModelPath],
+        *,
+        lora_modules: Optional[List[LoRAModulePath]] = None,
+        prompt_adapters: Optional[List[PromptAdapterPath]] = None,
+    ):
+        super().__init__()
+
+        self.base_model_paths = base_model_paths
+        self.max_model_len = model_config.max_model_len
+        self.engine_client = engine_client
+
+        self.static_lora_modules = lora_modules
+        self.lora_requests: List[LoRARequest] = []
+        self.lora_id_counter = AtomicCounter(0)
+
+        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))
+
+    async def init_static_loras(self):
+        """Loads all static LoRA modules.
+        Raises if any fail to load"""
+        if self.static_lora_modules is None:
+            return
+        for lora in self.static_lora_modules:
+            load_request = LoadLoraAdapterRequest(lora_path=lora.path,
+                                                  lora_name=lora.name)
+            load_result = await self.load_lora_adapter(
+                request=load_request, base_model_name=lora.base_model_name)
+            if isinstance(load_result, ErrorResponse):
+                raise ValueError(load_result.message)
+
+    def is_base_model(self, model_name):
+        return any(model.name == model_name for model in self.base_model_paths)
+
+    def model_name(self, lora_request: Optional[LoRARequest] = None) -> str:
+        """Returns the appropriate model name depending on the availability
+        and support of the LoRA or base model.
+        Parameters:
+        - lora: LoRARequest that contain a base_model_name.
+        Returns:
+        - str: The name of the base model or the first available model path.
+        """
+        if lora_request is not None:
+            return lora_request.lora_name
+        return self.base_model_paths[0].name
+
+    async def show_available_models(self) -> ModelList:
+        """Show available models. This includes the base model and all 
+        adapters"""
+        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)
+
+    async def load_lora_adapter(
+            self,
+            request: LoadLoraAdapterRequest,
+            base_model_name: Optional[str] = None
+    ) -> 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)
+        lora_request = LoRARequest(lora_name=lora_name,
+                                   lora_int_id=unique_id,
+                                   lora_path=lora_path)
+        if base_model_name is not None and self.is_base_model(base_model_name):
+            lora_request.base_model_name = base_model_name
+
+        # Validate that the adapter can be loaded into the engine
+        # This will also pre-load it for incoming requests
+        try:
+            await self.engine_client.add_lora(lora_request)
+        except BaseException as e:
+            error_type = "BadRequestError"
+            status_code = HTTPStatus.BAD_REQUEST
+            if isinstance(e, ValueError) and "No adapter found" in str(e):
+                error_type = "NotFoundError"
+                status_code = HTTPStatus.NOT_FOUND
+
+            return create_error_response(message=str(e),
+                                         err_type=error_type,
+                                         status_code=status_code)
+
+        self.lora_requests.append(lora_request)
+        logger.info("Loaded new LoRA adapter: name '%s', path '%s'", lora_name,
+                    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
+        ]
+        logger.info("Removed LoRA adapter: name '%s'", lora_name)
+        return f"Success: LoRA adapter '{lora_name}' removed successfully."
+
+    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 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 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 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 create_error_response(
+                message=
+                f"The lora adapter '{request.lora_name}' cannot be found.",
+                err_type="NotFoundError",
+                status_code=HTTPStatus.NOT_FOUND)
+
+        return None
+
+
+def create_error_response(
+        message: str,
+        err_type: str = "BadRequestError",
+        status_code: HTTPStatus = HTTPStatus.BAD_REQUEST) -> ErrorResponse:
+    return ErrorResponse(message=message,
+                         type=err_type,
+                         code=status_code.value)

.venv/lib/python3.11/site-packages/vllm/entrypoints/openai/serving_pooling.py

@@ -0,0 +1,235 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import asyncio
+import base64
+import time
+from typing import AsyncGenerator, Final, 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.chat_utils import ChatTemplateContentFormatOption
+from vllm.entrypoints.logger import RequestLogger
+from vllm.entrypoints.openai.protocol import (ErrorResponse,
+                                              PoolingChatRequest,
+                                              PoolingRequest, PoolingResponse,
+                                              PoolingResponseData, UsageInfo)
+from vllm.entrypoints.openai.serving_engine import OpenAIServing
+from vllm.entrypoints.openai.serving_models import OpenAIServingModels
+from vllm.logger import init_logger
+from vllm.outputs import PoolingOutput, PoolingRequestOutput
+from vllm.utils import merge_async_iterators
+
+logger = init_logger(__name__)
+
+
+def _get_data(
+    output: PoolingOutput,
+    encoding_format: Literal["float", "base64"],
+) -> Union[List[float], str]:
+    if encoding_format == "float":
+        return output.data.tolist()
+    elif encoding_format == "base64":
+        # Force to use float32 for base64 encoding
+        # to match the OpenAI python client behavior
+        pooling_bytes = np.array(output.data, dtype="float32").tobytes()
+        return base64.b64encode(pooling_bytes).decode("utf-8")
+
+    assert_never(encoding_format)
+
+
+class OpenAIServingPooling(OpenAIServing):
+
+    def __init__(
+        self,
+        engine_client: EngineClient,
+        model_config: ModelConfig,
+        models: OpenAIServingModels,
+        *,
+        request_logger: Optional[RequestLogger],
+        chat_template: Optional[str],
+        chat_template_content_format: ChatTemplateContentFormatOption,
+    ) -> None:
+        super().__init__(engine_client=engine_client,
+                         model_config=model_config,
+                         models=models,
+                         request_logger=request_logger)
+
+        self.chat_template = chat_template
+        self.chat_template_content_format: Final = chat_template_content_format
+
+    async def create_pooling(
+        self,
+        request: PoolingRequest,
+        raw_request: Optional[Request] = None,
+    ) -> Union[PoolingResponse, ErrorResponse]:
+        """
+        See https://platform.openai.com/docs/api-reference/embeddings/create
+        for the API specification. This API mimics the OpenAI Embedding API.
+        """
+        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"pool-{self._base_request_id(raw_request)}"
+        created_time = int(time.time())
+
+        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.")
+
+        try:
+            (
+                lora_request,
+                prompt_adapter_request,
+            ) = self._maybe_get_adapters(request)
+
+            tokenizer = await self.engine_client.get_tokenizer(lora_request)
+
+            if prompt_adapter_request is not None:
+                raise NotImplementedError("Prompt adapter is not supported "
+                                          "for pooling models")
+
+            if isinstance(request, PoolingChatRequest):
+                (
+                    _,
+                    request_prompts,
+                    engine_prompts,
+                ) = await self._preprocess_chat(
+                    request,
+                    tokenizer,
+                    request.messages,
+                    chat_template=request.chat_template or self.chat_template,
+                    chat_template_content_format=self.
+                    chat_template_content_format,
+                    # In pooling requests, we are not generating tokens,
+                    # so there is no need to append extra tokens to the input
+                    add_generation_prompt=False,
+                    continue_final_message=False,
+                    truncate_prompt_tokens=truncate_prompt_tokens,
+                    add_special_tokens=request.add_special_tokens,
+                )
+            else:
+                (request_prompts,
+                 engine_prompts) = await self._preprocess_completion(
+                     request,
+                     tokenizer,
+                     request.input,
+                     truncate_prompt_tokens=truncate_prompt_tokens,
+                     add_special_tokens=request.add_special_tokens,
+                 )
+        except ValueError as e:
+            logger.exception("Error in preprocessing prompt inputs")
+            return self.create_error_response(str(e))
+
+        # Schedule the request and get the result generator.
+        generators: List[AsyncGenerator[PoolingRequestOutput, None]] = []
+        try:
+            pooling_params = request.to_pooling_params()
+
+            for i, engine_prompt in enumerate(engine_prompts):
+                request_id_item = f"{request_id}-{i}"
+
+                self._log_inputs(request_id_item,
+                                 request_prompts[i],
+                                 params=pooling_params,
+                                 lora_request=lora_request,
+                                 prompt_adapter_request=prompt_adapter_request)
+
+                trace_headers = (None if raw_request is None else await
+                                 self._get_trace_headers(raw_request.headers))
+
+                generator = self.engine_client.encode(
+                    engine_prompt,
+                    pooling_params,
+                    request_id_item,
+                    lora_request=lora_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)
+
+        num_prompts = len(engine_prompts)
+
+        # Non-streaming response
+        final_res_batch: List[Optional[PoolingRequestOutput]]
+        final_res_batch = [None] * num_prompts
+        try:
+            async for i, res in result_generator:
+                final_res_batch[i] = res
+
+            assert all(final_res is not None for final_res in final_res_batch)
+
+            final_res_batch_checked = cast(List[PoolingRequestOutput],
+                                           final_res_batch)
+
+            response = self.request_output_to_pooling_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 request_output_to_pooling_response(
+        self,
+        final_res_batch: List[PoolingRequestOutput],
+        request_id: str,
+        created_time: int,
+        model_name: str,
+        encoding_format: Literal["float", "base64"],
+    ) -> PoolingResponse:
+        items: List[PoolingResponseData] = []
+        num_prompt_tokens = 0
+
+        for idx, final_res in enumerate(final_res_batch):
+            item = PoolingResponseData(
+                index=idx,
+                data=_get_data(final_res.outputs, encoding_format),
+            )
+            prompt_token_ids = final_res.prompt_token_ids
+
+            items.append(item)
+            num_prompt_tokens += len(prompt_token_ids)
+
+        usage = UsageInfo(
+            prompt_tokens=num_prompt_tokens,
+            total_tokens=num_prompt_tokens,
+        )
+
+        return PoolingResponse(
+            id=request_id,
+            created=created_time,
+            model=model_name,
+            data=items,
+            usage=usage,
+        )

.venv/lib/python3.11/site-packages/vllm/entrypoints/openai/serving_rerank.py

@@ -0,0 +1,208 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import asyncio
+from typing import Any, AsyncGenerator, Dict, List, Optional, Union, cast
+
+from fastapi import Request
+
+from vllm.config import ModelConfig
+from vllm.engine.protocol import EngineClient
+from vllm.entrypoints.logger import RequestLogger
+from vllm.entrypoints.openai.protocol import (ErrorResponse, RerankDocument,
+                                              RerankRequest, RerankResponse,
+                                              RerankResult, RerankUsage)
+from vllm.entrypoints.openai.serving_engine import OpenAIServing
+from vllm.entrypoints.openai.serving_models import OpenAIServingModels
+from vllm.inputs.data import TokensPrompt
+from vllm.logger import init_logger
+from vllm.outputs import PoolingRequestOutput, ScoringRequestOutput
+from vllm.transformers_utils.tokenizers.mistral import MistralTokenizer
+from vllm.utils import make_async, merge_async_iterators
+
+logger = init_logger(__name__)
+
+
+class JinaAIServingRerank(OpenAIServing):
+
+    def __init__(
+        self,
+        engine_client: EngineClient,
+        model_config: ModelConfig,
+        models: OpenAIServingModels,
+        *,
+        request_logger: Optional[RequestLogger],
+    ) -> None:
+        super().__init__(engine_client=engine_client,
+                         model_config=model_config,
+                         models=models,
+                         request_logger=request_logger)
+
+    async def do_rerank(
+        self,
+        request: RerankRequest,
+        raw_request: Optional[Request] = None
+    ) -> Union[RerankResponse, ErrorResponse]:
+        """
+        Rerank API based on JinaAI's rerank API; implements the same
+        API interface. Designed for compatibility with off-the-shelf
+        tooling, since this is a common standard for reranking APIs
+
+        See example client implementations at
+        https://github.com/infiniflow/ragflow/blob/main/rag/llm/rerank_model.py
+        numerous clients use this standard.
+        """
+        error_check_ret = await self._check_model(request)
+        if error_check_ret is not None:
+            return error_check_ret
+
+        model_name = request.model
+        request_id = f"rerank-{self._base_request_id(raw_request)}"
+        truncate_prompt_tokens = request.truncate_prompt_tokens
+        query = request.query
+        documents = request.documents
+        request_prompts = []
+        engine_prompts = []
+        top_n = request.top_n if request.top_n > 0 else len(documents)
+
+        try:
+            (
+                lora_request,
+                prompt_adapter_request,
+            ) = self._maybe_get_adapters(request)
+
+            tokenizer = await self.engine_client.get_tokenizer(lora_request)
+
+            if prompt_adapter_request is not None:
+                raise NotImplementedError("Prompt adapter is not supported "
+                                          "for scoring models")
+
+            if isinstance(tokenizer, MistralTokenizer):
+                raise ValueError(
+                    "MistralTokenizer not supported for cross-encoding")
+
+            if not self.model_config.is_cross_encoder:
+                raise ValueError("Model is not cross encoder.")
+
+            if truncate_prompt_tokens is not None and \
+                    truncate_prompt_tokens > self.max_model_len:
+                raise ValueError(
+                    f"truncate_prompt_tokens value ({truncate_prompt_tokens}) "
+                    f"is greater than max_model_len ({self.max_model_len})."
+                    f" Please, select a smaller truncation size.")
+            for doc in documents:
+                request_prompt = f"{query}{tokenizer.sep_token}{doc}"
+                tokenization_kwargs: Dict[str, Any] = {}
+                if truncate_prompt_tokens is not None:
+                    tokenization_kwargs["truncation"] = True
+                    tokenization_kwargs["max_length"] = truncate_prompt_tokens
+
+                tokenize_async = make_async(tokenizer.__call__,
+                                            executor=self._tokenizer_executor)
+                prompt_inputs = await tokenize_async(text=query,
+                                                     text_pair=doc,
+                                                     **tokenization_kwargs)
+
+                input_ids = prompt_inputs["input_ids"]
+                text_token_prompt = \
+                    self._validate_input(request, input_ids, request_prompt)
+                engine_prompt = TokensPrompt(
+                    prompt_token_ids=text_token_prompt["prompt_token_ids"],
+                    token_type_ids=prompt_inputs.get("token_type_ids"))
+
+                request_prompts.append(request_prompt)
+                engine_prompts.append(engine_prompt)
+
+        except ValueError as e:
+            logger.exception("Error in preprocessing prompt inputs")
+            return self.create_error_response(str(e))
+
+        # Schedule the request and get the result generator.
+        generators: List[AsyncGenerator[PoolingRequestOutput, None]] = []
+
+        try:
+            pooling_params = request.to_pooling_params()
+
+            for i, engine_prompt in enumerate(engine_prompts):
+                request_id_item = f"{request_id}-{i}"
+
+                self._log_inputs(request_id_item,
+                                 request_prompts[i],
+                                 params=pooling_params,
+                                 lora_request=lora_request,
+                                 prompt_adapter_request=prompt_adapter_request)
+
+                trace_headers = (None if raw_request is None else await
+                                 self._get_trace_headers(raw_request.headers))
+
+                generator = self.engine_client.encode(
+                    engine_prompt,
+                    pooling_params,
+                    request_id_item,
+                    lora_request=lora_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)
+
+        num_prompts = len(engine_prompts)
+
+        # Non-streaming response
+        final_res_batch: List[Optional[PoolingRequestOutput]]
+        final_res_batch = [None] * num_prompts
+
+        try:
+            async for i, res in result_generator:
+                final_res_batch[i] = res
+
+            assert all(final_res is not None for final_res in final_res_batch)
+
+            final_res_batch_checked = cast(List[PoolingRequestOutput],
+                                           final_res_batch)
+
+            response = self.request_output_to_rerank_response(
+                final_res_batch_checked, request_id, model_name, documents,
+                top_n)
+        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 request_output_to_rerank_response(
+            self, final_res_batch: List[PoolingRequestOutput], request_id: str,
+            model_name: str, documents: List[str],
+            top_n: int) -> RerankResponse:
+        """
+        Convert the output of do_rank to a RerankResponse
+        """
+        results: List[RerankResult] = []
+        num_prompt_tokens = 0
+        for idx, final_res in enumerate(final_res_batch):
+            classify_res = ScoringRequestOutput.from_base(final_res)
+
+            result = RerankResult(
+                index=idx,
+                document=RerankDocument(text=documents[idx]),
+                relevance_score=classify_res.outputs.score,
+            )
+            results.append(result)
+            prompt_token_ids = final_res.prompt_token_ids
+            num_prompt_tokens += len(prompt_token_ids)
+
+        # sort by relevance, then return the top n if set
+        results.sort(key=lambda x: x.relevance_score, reverse=True)
+        if top_n < len(documents):
+            results = results[:top_n]
+
+        return RerankResponse(
+            id=request_id,
+            model=model_name,
+            results=results,
+            usage=RerankUsage(total_tokens=num_prompt_tokens))

.venv/lib/python3.11/site-packages/vllm/entrypoints/openai/serving_score.py

@@ -0,0 +1,238 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import asyncio
+import time
+from typing import Any, AsyncGenerator, Dict, List, Optional, Union, cast
+
+from fastapi import Request
+
+from vllm.config import ModelConfig
+from vllm.engine.protocol import EngineClient
+from vllm.entrypoints.logger import RequestLogger
+from vllm.entrypoints.openai.protocol import (ErrorResponse, ScoreRequest,
+                                              ScoreResponse, ScoreResponseData,
+                                              UsageInfo)
+from vllm.entrypoints.openai.serving_engine import OpenAIServing
+from vllm.entrypoints.openai.serving_models import OpenAIServingModels
+from vllm.inputs.data import TokensPrompt
+from vllm.logger import init_logger
+from vllm.outputs import PoolingRequestOutput, ScoringRequestOutput
+from vllm.transformers_utils.tokenizers.mistral import MistralTokenizer
+from vllm.utils import make_async, merge_async_iterators
+
+logger = init_logger(__name__)
+
+
+def make_pairs(text_1: Union[List[str], str], text_2: Union[List[str],
+                                                            str]) -> List:
+    if isinstance(text_1, (str, dict)):
+        # Convert a single prompt to a list.
+        text_1 = [text_1]
+    text_1 = [t for t in text_1]
+
+    if isinstance(text_2, (str, dict)):
+        # Convert a single prompt to a list.
+        text_2 = [text_2]
+    text_2 = [t for t in text_2]
+    if len(text_1) > 1 and len(text_1) != len(text_2):
+        raise ValueError("Input lengths must be either 1:1, 1:N or N:N")
+    if len(text_1) == 0:
+        raise ValueError("At least one text element must be given")
+    if len(text_2) == 0:
+        raise ValueError("At least one text_pair element must be given")
+
+    if len(text_1) == 1:
+        text_1 = text_1 * len(text_2)
+
+    return [(t1, t2) for t1, t2 in zip(text_1, text_2)]
+
+
+class OpenAIServingScores(OpenAIServing):
+
+    def __init__(
+        self,
+        engine_client: EngineClient,
+        model_config: ModelConfig,
+        models: OpenAIServingModels,
+        *,
+        request_logger: Optional[RequestLogger],
+    ) -> None:
+        super().__init__(engine_client=engine_client,
+                         model_config=model_config,
+                         models=models,
+                         request_logger=request_logger)
+
+    async def create_score(
+        self,
+        request: ScoreRequest,
+        raw_request: Optional[Request] = None,
+    ) -> Union[ScoreResponse, ErrorResponse]:
+        """
+        Score API similar to Sentence Transformers cross encoder
+
+        See https://sbert.net/docs/package_reference/cross_encoder
+        """
+        error_check_ret = await self._check_model(request)
+        if error_check_ret is not None:
+            return error_check_ret
+
+        model_name = request.model
+        request_id = f"score-{self._base_request_id(raw_request)}"
+        created_time = int(time.time())
+        truncate_prompt_tokens = request.truncate_prompt_tokens
+
+        request_prompts = []
+        engine_prompts = []
+
+        try:
+            (
+                lora_request,
+                prompt_adapter_request,
+            ) = self._maybe_get_adapters(request)
+
+            tokenizer = await self.engine_client.get_tokenizer(lora_request)
+
+            if prompt_adapter_request is not None:
+                raise NotImplementedError("Prompt adapter is not supported "
+                                          "for scoring models")
+
+            if isinstance(tokenizer, MistralTokenizer):
+                raise ValueError(
+                    "MistralTokenizer not supported for cross-encoding")
+
+            if not self.model_config.is_cross_encoder:
+                raise ValueError("Model is not cross encoder.")
+
+            if truncate_prompt_tokens is not None and \
+                truncate_prompt_tokens > self.max_model_len:
+                raise ValueError(
+                    f"truncate_prompt_tokens value ({truncate_prompt_tokens}) "
+                    f"is greater than max_model_len ({self.max_model_len})."
+                    f" Please, select a smaller truncation size.")
+
+            input_pairs = make_pairs(request.text_1, request.text_2)
+            for q, t in input_pairs:
+                request_prompt = f"{q}{tokenizer.sep_token}{t}"
+
+                tokenization_kwargs: Dict[str, Any] = {}
+                if truncate_prompt_tokens is not None:
+                    tokenization_kwargs["truncation"] = True
+                    tokenization_kwargs["max_length"] = truncate_prompt_tokens
+
+                tokenize_async = make_async(tokenizer.__call__,
+                                            executor=self._tokenizer_executor)
+                prompt_inputs = await tokenize_async(text=q,
+                                                     text_pair=t,
+                                                     **tokenization_kwargs)
+
+                input_ids = prompt_inputs["input_ids"]
+                text_token_prompt = \
+                    self._validate_input(request, input_ids, request_prompt)
+                engine_prompt = TokensPrompt(
+                    prompt_token_ids=text_token_prompt["prompt_token_ids"],
+                    token_type_ids=prompt_inputs.get("token_type_ids"))
+
+                request_prompts.append(request_prompt)
+                engine_prompts.append(engine_prompt)
+
+        except ValueError as e:
+            logger.exception("Error in preprocessing prompt inputs")
+            return self.create_error_response(str(e))
+
+        # Schedule the request and get the result generator.
+        generators: List[AsyncGenerator[PoolingRequestOutput, None]] = []
+
+        try:
+            pooling_params = request.to_pooling_params()
+
+            for i, engine_prompt in enumerate(engine_prompts):
+                request_id_item = f"{request_id}-{i}"
+
+                self._log_inputs(request_id_item,
+                                 request_prompts[i],
+                                 params=pooling_params,
+                                 lora_request=lora_request,
+                                 prompt_adapter_request=prompt_adapter_request)
+
+                trace_headers = (None if raw_request is None else await
+                                 self._get_trace_headers(raw_request.headers))
+
+                generator = self.engine_client.encode(
+                    engine_prompt,
+                    pooling_params,
+                    request_id_item,
+                    lora_request=lora_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)
+
+        num_prompts = len(engine_prompts)
+
+        # Non-streaming response
+        final_res_batch: List[Optional[PoolingRequestOutput]]
+        final_res_batch = [None] * num_prompts
+
+        try:
+            async for i, res in result_generator:
+                final_res_batch[i] = res
+
+            assert all(final_res is not None for final_res in final_res_batch)
+
+            final_res_batch_checked = cast(List[PoolingRequestOutput],
+                                           final_res_batch)
+
+            response = self.request_output_to_score_response(
+                final_res_batch_checked,
+                request_id,
+                created_time,
+                model_name,
+            )
+        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 request_output_to_score_response(
+        self,
+        final_res_batch: List[PoolingRequestOutput],
+        request_id: str,
+        created_time: int,
+        model_name: str,
+    ) -> ScoreResponse:
+        items: List[ScoreResponseData] = []
+        num_prompt_tokens = 0
+
+        for idx, final_res in enumerate(final_res_batch):
+            classify_res = ScoringRequestOutput.from_base(final_res)
+
+            item = ScoreResponseData(
+                index=idx,
+                score=classify_res.outputs.score,
+            )
+            prompt_token_ids = final_res.prompt_token_ids
+
+            items.append(item)
+            num_prompt_tokens += len(prompt_token_ids)
+
+        usage = UsageInfo(
+            prompt_tokens=num_prompt_tokens,
+            total_tokens=num_prompt_tokens,
+        )
+
+        return ScoreResponse(
+            id=request_id,
+            created=created_time,
+            model=model_name,
+            data=items,
+            usage=usage,
+        )

.venv/lib/python3.11/site-packages/vllm/entrypoints/openai/serving_tokenization.py

@@ -0,0 +1,146 @@
+# SPDX-License-Identifier: Apache-2.0
+
+from typing import Final, List, Optional, Union
+
+from fastapi import Request
+
+from vllm.config import ModelConfig
+from vllm.engine.protocol import EngineClient
+from vllm.entrypoints.chat_utils import ChatTemplateContentFormatOption
+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 OpenAIServing
+from vllm.entrypoints.openai.serving_models import OpenAIServingModels
+from vllm.logger import init_logger
+
+logger = init_logger(__name__)
+
+
+class OpenAIServingTokenization(OpenAIServing):
+
+    def __init__(
+        self,
+        engine_client: EngineClient,
+        model_config: ModelConfig,
+        models: OpenAIServingModels,
+        *,
+        request_logger: Optional[RequestLogger],
+        chat_template: Optional[str],
+        chat_template_content_format: ChatTemplateContentFormatOption,
+    ) -> None:
+        super().__init__(engine_client=engine_client,
+                         model_config=model_config,
+                         models=models,
+                         request_logger=request_logger)
+
+        self.chat_template = chat_template
+        self.chat_template_content_format: Final = chat_template_content_format
+
+    async def create_tokenize(
+        self,
+        request: TokenizeRequest,
+        raw_request: Request,
+    ) -> 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-{self._base_request_id(raw_request)}"
+
+        try:
+            (
+                lora_request,
+                prompt_adapter_request,
+            ) = self._maybe_get_adapters(request)
+
+            tokenizer = await self.engine_client.get_tokenizer(lora_request)
+
+            if isinstance(request, TokenizeChatRequest):
+                (
+                    _,
+                    request_prompts,
+                    engine_prompts,
+                ) = await self._preprocess_chat(
+                    request,
+                    tokenizer,
+                    request.messages,
+                    chat_template=request.chat_template or self.chat_template,
+                    chat_template_content_format=self.
+                    chat_template_content_format,
+                    add_generation_prompt=request.add_generation_prompt,
+                    continue_final_message=request.continue_final_message,
+                    chat_template_kwargs=request.chat_template_kwargs,
+                    add_special_tokens=request.add_special_tokens,
+                )
+            else:
+                (request_prompts,
+                 engine_prompts) = await self._preprocess_completion(
+                     request,
+                     tokenizer,
+                     request.prompt,
+                     add_special_tokens=request.add_special_tokens,
+                 )
+        except ValueError as e:
+            logger.exception("Error in preprocessing prompt inputs")
+            return self.create_error_response(str(e))
+
+        input_ids: List[int] = []
+        for i, engine_prompt in enumerate(engine_prompts):
+            self._log_inputs(request_id,
+                             request_prompts[i],
+                             params=None,
+                             lora_request=lora_request,
+                             prompt_adapter_request=prompt_adapter_request)
+
+            # Silently ignore prompt adapter since it does not affect
+            # tokenization (Unlike in Embeddings API where an error is raised)
+
+            input_ids.extend(engine_prompt["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,
+        raw_request: Request,
+    ) -> 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-{self._base_request_id(raw_request)}"
+
+        (
+            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)
+
+        # Silently ignore prompt adapter since it does not affect tokenization
+        # (Unlike in Embeddings API where an error is raised)
+
+        prompt_input = await self._tokenize_prompt_input_async(
+            request,
+            tokenizer,
+            request.tokens,
+        )
+        input_text = prompt_input["prompt"]
+
+        return DetokenizeResponse(prompt=input_text)

.venv/lib/python3.11/site-packages/vllm/entrypoints/utils.py

@@ -0,0 +1,59 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import asyncio
+import functools
+
+from fastapi import Request
+
+
+async def listen_for_disconnect(request: Request) -> None:
+    """Returns if a disconnect message is received"""
+    while True:
+        message = await request.receive()
+        if message["type"] == "http.disconnect":
+            break
+
+
+def with_cancellation(handler_func):
+    """Decorator that allows a route handler to be cancelled by client
+    disconnections.
+    
+    This does _not_ use request.is_disconnected, which does not work with
+    middleware. Instead this follows the pattern from 
+    starlette.StreamingResponse, which simultaneously awaits on two tasks- one
+    to wait for an http disconnect message, and the other to do the work that we
+    want done. When the first task finishes, the other is cancelled.
+
+    A core assumption of this method is that the body of the request has already
+    been read. This is a safe assumption to make for fastapi handlers that have
+    already parsed the body of the request into a pydantic model for us.
+    This decorator is unsafe to use elsewhere, as it will consume and throw away
+    all incoming messages for the request while it looks for a disconnect
+    message.
+
+    In the case where a `StreamingResponse` is returned by the handler, this
+    wrapper will stop listening for disconnects and instead the response object
+    will start listening for disconnects.
+    """
+
+    # Functools.wraps is required for this wrapper to appear to fastapi as a
+    # normal route handler, with the correct request type hinting.
+    @functools.wraps(handler_func)
+    async def wrapper(*args, **kwargs):
+
+        # The request is either the second positional arg or `raw_request`
+        request = args[1] if len(args) > 1 else kwargs["raw_request"]
+
+        handler_task = asyncio.create_task(handler_func(*args, **kwargs))
+        cancellation_task = asyncio.create_task(listen_for_disconnect(request))
+
+        done, pending = await asyncio.wait([handler_task, cancellation_task],
+                                           return_when=asyncio.FIRST_COMPLETED)
+        for task in pending:
+            task.cancel()
+
+        if handler_task in done:
+            return handler_task.result()
+        return None
+
+    return wrapper

.venv/lib/python3.11/site-packages/vllm/multimodal/audio.py

@@ -0,0 +1,77 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import base64
+from io import BytesIO
+from pathlib import Path
+
+import numpy as np
+import numpy.typing as npt
+
+from vllm.inputs.registry import InputContext
+from vllm.utils import PlaceholderModule
+
+from .base import MediaIO, MultiModalPlugin
+from .inputs import AudioItem, ModalityData, MultiModalKwargs
+
+try:
+    import librosa
+except ImportError:
+    librosa = PlaceholderModule("librosa")  # type: ignore[assignment]
+
+try:
+    import soundfile
+except ImportError:
+    soundfile = PlaceholderModule("soundfile")  # type: ignore[assignment]
+
+
+class AudioPlugin(MultiModalPlugin):
+    """Plugin for audio data."""
+
+    def get_data_key(self) -> str:
+        return "audio"
+
+    def _default_input_mapper(
+        self,
+        ctx: InputContext,
+        data: ModalityData[AudioItem],
+        **mm_processor_kwargs,
+    ) -> MultiModalKwargs:
+        raise NotImplementedError("There is no default audio input mapper")
+
+    def _default_max_multimodal_tokens(self, ctx: InputContext) -> int:
+        raise NotImplementedError(
+            "There is no default maximum multimodal tokens")
+
+
+def resample_audio(
+    audio: npt.NDArray[np.floating],
+    *,
+    orig_sr: float,
+    target_sr: float,
+) -> npt.NDArray[np.floating]:
+    return librosa.resample(audio, orig_sr=orig_sr, target_sr=target_sr)
+
+
+class AudioMediaIO(MediaIO[tuple[npt.NDArray, float]]):
+
+    def load_bytes(self, data: bytes) -> tuple[npt.NDArray, float]:
+        return librosa.load(BytesIO(data), sr=None)
+
+    def load_base64(
+        self,
+        media_type: str,
+        data: str,
+    ) -> tuple[npt.NDArray, float]:
+        return self.load_bytes(base64.b64decode(data))
+
+    def load_file(self, filepath: Path) -> tuple[npt.NDArray, float]:
+        return librosa.load(filepath, sr=None)
+
+    def encode_base64(self, media: tuple[npt.NDArray, float]) -> str:
+        audio, sr = media
+
+        with BytesIO() as buffer:
+            soundfile.write(buffer, audio, sr, format="WAV")
+            data = buffer.getvalue()
+
+        return base64.b64encode(data).decode('utf-8')

.venv/lib/python3.11/site-packages/vllm/multimodal/base.py

@@ -0,0 +1,463 @@
+# SPDX-License-Identifier: Apache-2.0
+
+from abc import ABC, abstractmethod
+from collections import defaultdict
+from pathlib import Path
+from typing import (TYPE_CHECKING, Any, Callable, Generic, NamedTuple,
+                    Optional, Sequence, Tuple, Type, TypeVar, Union)
+
+from torch import nn
+
+from vllm.inputs import InputContext
+from vllm.logger import init_logger
+from vllm.utils import (ClassRegistry, get_allowed_kwarg_only_overrides,
+                        resolve_mm_processor_kwargs)
+
+if TYPE_CHECKING:
+    from vllm.config import ModelConfig
+    from vllm.sequence import SequenceGroupMetadata
+
+from .inputs import (ModalityData, MultiModalDataDict, MultiModalKwargs,
+                     PlaceholderRange)
+
+logger = init_logger(__name__)
+
+MultiModalInputMapper = Callable[[InputContext, ModalityData[object]],
+                                 MultiModalKwargs]
+"""
+Return a dictionary to be passed as keyword arguments to
+:meth:`~torch.nn.Module.forward`. This is similar in concept to tokenizers
+and processors in HuggingFace Transformers.
+
+If the data is not supported, throw :exc:`TypeError`.
+"""
+
+MultiModalTokensCalc = Union[int, Callable[[InputContext], int]]
+"""
+Calculate the maximum number of multimodal tokens input to the language
+model. This does not include tokens that correspond to the input text.
+"""
+
+_T = TypeVar("_T")
+N = TypeVar("N", bound=Type[nn.Module])
+
+
+class MultiModalPlugin(ABC):
+    """
+    Base class that defines data processing logic for a specific modality.
+
+    In particular, we adopt a registry pattern to dispatch data processing
+    according to the model being used (considering that different models may
+    process the same data differently). This registry is in turn used by
+    :class:`~MultiModalRegistry` which acts at a higher level
+    (i.e., the modality of the data).
+    """
+
+    def __init__(self) -> None:
+        self._input_mappers = ClassRegistry[nn.Module, MultiModalInputMapper]()
+        self._max_mm_tokens = ClassRegistry[nn.Module, MultiModalTokensCalc]()
+
+    @abstractmethod
+    def get_data_key(self) -> str:
+        """
+        Get the data key corresponding to the modality.
+        """
+        raise NotImplementedError
+
+    @abstractmethod
+    def _default_input_mapper(
+        self,
+        ctx: InputContext,
+        data: ModalityData[Any],
+        **mm_processor_kwargs,
+    ) -> MultiModalKwargs:
+        """
+        Return a dictionary to be passed as keyword arguments to
+        :meth:`~torch.nn.Module.forward`. This is similar in concept to
+        tokenizers and processors in HuggingFace Transformers.
+
+        If the data is not supported, throw :exc:`TypeError`.
+        """
+        raise NotImplementedError
+
+    def register_input_mapper(
+        self,
+        mapper: Optional[MultiModalInputMapper] = None,
+    ):
+        """
+        Register an input mapper to a model class.
+
+        When the model receives input data that matches the modality served by
+        this plugin (see :meth:`get_data_key`), the provided function is
+        invoked to transform the data into a dictionary of model inputs.
+
+        If `None` is provided, then the default input mapper is used instead.
+        """
+
+        def wrapper(model_cls: N) -> N:
+            if self._input_mappers.contains(model_cls, strict=True):
+                logger.warning(
+                    "Model class %s already has an input mapper "
+                    "registered to %s. It is overwritten by the new one.",
+                    model_cls,
+                    self,
+                )
+
+            self._input_mappers[model_cls] = (mapper
+                                              or self._default_input_mapper)
+
+            return model_cls
+
+        return wrapper
+
+    def map_input(
+        self,
+        model_config: "ModelConfig",
+        data: ModalityData[Any],
+        mm_processor_kwargs: Optional[dict[str, Any]],
+    ) -> MultiModalKwargs:
+        """
+        Transform the data into a dictionary of model inputs using the
+        input mapper registered for that model.
+
+        The model is identified by ``model_config``.
+
+        Raises:
+            TypeError: If the data type is not supported.
+        """
+
+        # Avoid circular import
+        from vllm.model_executor.model_loader import get_model_architecture
+
+        model_cls, _ = get_model_architecture(model_config)
+
+        mapper = self._input_mappers.get(model_cls)
+
+        if mapper is None:
+            raise KeyError(f"No input mapper in {self} is registered for "
+                           f"model class {model_cls.__name__}.")
+
+        if mm_processor_kwargs is None:
+            mm_processor_kwargs = {}
+
+        # In the case of the default mapper, we have to get resource
+        # processor through its HuggingFace autoclass; since this goes
+        # through **kwargs, we can't inspect it the same way, so we allow
+        # drop mm_processor_kwargs based on signature inspection
+        # if we're using the default mapper.
+        #
+        # This should be safe in general due to the sanitation, since the
+        # transformers resource should filter unused kwargs anyway.
+        uses_default_mapper = mapper == self._default_input_mapper
+        mm_processor_kwargs = resolve_mm_processor_kwargs(
+            model_config.mm_processor_kwargs,
+            mm_processor_kwargs,
+            callable=mapper,
+            allow_var_kwargs=uses_default_mapper,
+        )
+        return mapper(InputContext(model_config), data, **mm_processor_kwargs)
+
+    @abstractmethod
+    def _default_max_multimodal_tokens(self, ctx: InputContext) -> int:
+        """
+        Calculate the maximum number of tokens, corresponding to a single
+        instance of multimodal data, that are passed to the language model.
+        """
+        raise NotImplementedError
+
+    def _validate_max_multimodal_tokens(self, max_mm_tokens: int):
+        if max_mm_tokens < 1:
+            raise ValueError("You should set the number of tokens to a "
+                             f"positive integer. Found: {max_mm_tokens}")
+
+    def register_max_multimodal_tokens(
+        self,
+        max_mm_tokens: Optional[MultiModalTokensCalc] = None,
+    ):
+        """
+        Register the maximum number of tokens, corresponding to a single
+        instance of multimodal data, that are passed to the language model
+        for a model class.
+
+        If `None` is provided, then the default calculation is used instead.
+        """
+
+        def wrapper(model_cls: N) -> N:
+            if self._max_mm_tokens.contains(model_cls, strict=True):
+                logger.warning(
+                    "Model class %s already calculates maximum number of "
+                    "tokens in %s. It is overwritten by the new one.",
+                    model_cls,
+                    self,
+                )
+
+            if isinstance(max_mm_tokens, int):
+                self._validate_max_multimodal_tokens(max_mm_tokens)
+
+            self._max_mm_tokens[model_cls] = (
+                max_mm_tokens or self._default_max_multimodal_tokens)
+
+            return model_cls
+
+        return wrapper
+
+    def get_max_multimodal_tokens(self, model_config: "ModelConfig") -> int:
+        """
+        Get the maximum number of multi-modal tokens
+        for profiling the memory usage of a model.
+
+        If this registry is not applicable to the model, `0` is returned.
+
+        The model is identified by ``model_config``.
+        """
+        # Avoid circular import
+        from vllm.model_executor.model_loader import get_model_architecture
+        from vllm.model_executor.models import supports_multimodal
+
+        model_cls, _ = get_model_architecture(model_config)
+
+        if not supports_multimodal(model_cls):
+            return 0
+
+        max_mm_tokens = self._max_mm_tokens.get(model_cls)
+        if max_mm_tokens is None:
+            return 0
+
+        if callable(max_mm_tokens):
+            mm_processor_kwargs = get_allowed_kwarg_only_overrides(
+                max_mm_tokens, overrides=model_config.mm_processor_kwargs)
+            max_mm_tokens = max_mm_tokens(InputContext(model_config),
+                                          **mm_processor_kwargs)
+
+        self._validate_max_multimodal_tokens(max_mm_tokens)
+
+        return max_mm_tokens
+
+
+class MultiModalPlaceholderMap:
+    """
+    Relates multi-modal embeddings to their corresponding placeholders.
+    """
+
+    class IndexMap(NamedTuple):
+        src: list[int]
+        dest: list[int]
+
+    src_ranges: list[range]
+    """
+    The indices of the multi-modal embeddings that will replace the
+    corresponding placeholder embeddings pointed to by ``dest_ranges``.
+    """
+
+    src_len: int
+    """
+    The total number of flattened multi-modal embeddings.
+    """
+
+    dest_ranges: list[range]
+    """
+    The indices of the placeholder embeddings that will be replaced by the
+    multimodal embeddings.
+    """
+
+    dest_len: int
+    """
+    The total number of embeddings in the destination tensor.
+    """
+
+    def __init__(self):
+        self.src_ranges = []
+        self.src_len = 0
+        self.dest_ranges = []
+        self.dest_len = 0
+
+    @classmethod
+    def from_seq_group(
+        cls, seq_group: "SequenceGroupMetadata", positions: range
+    ) -> Tuple[Optional[MultiModalDataDict], dict[str,
+                                                  "MultiModalPlaceholderMap"]]:
+        """
+        Returns the multi-modal items that intersect with the portion of a
+        prompt (``seq_group``) represented by ``positions``, as well as a
+        ``MultiModalPlaceholderMap`` that relates the multi-modal embedding
+        vectors to their corresponding placeholders.
+
+        Examples:
+
+        .. code-block::
+
+            Prompt:    |AAAA BBBB What's in these images?|
+            Positions: |.................................|
+
+                images      = [A, B]
+                src_ranges  = [(0, 4), (4, 8)]
+                dest_ranges = [(0, 4), (5, 9)]
+
+            Prompt:    |AAAA BBBB What's in these images?|
+            Positions: |  .....                          |
+
+                images      = [A, B]
+                src_ranges  = [(2, 4), (4, 6)]
+                dest_ranges = [(0, 2), (3, 5)]
+
+            Prompt:    |AAAA BBBB What's in these images?|
+            Positions: |     .........                   |
+
+                images      = [B]
+                src_ranges  = [(0, 4)]
+                dest_ranges = [(0, 4)]
+
+            Prompt:    |AAAA BBBB What's in these images?|
+            Positions: |          .......................|
+
+                images      = []
+                src_ranges  = []
+                dest_ranges = []
+        """
+        seq_mm_data = seq_group.multi_modal_data
+        seq_mm_placeholders = seq_group.multi_modal_placeholders
+
+        if not seq_mm_data or not seq_mm_placeholders:
+            return seq_mm_data, {}
+
+        # For merged processor, we directly use mm_kwargs as mm_data
+        if isinstance(seq_mm_data, MultiModalKwargs):
+            placeholder_maps = dict[str, MultiModalPlaceholderMap]()
+
+            for modality, placeholders in seq_mm_placeholders.items():
+                placeholder_map = MultiModalPlaceholderMap()
+
+                if positions:
+                    placeholder_map.append_items_from_seq_group(
+                        positions,
+                        # Dummy, since we don't care about intersecting items
+                        [None] * len(placeholders),
+                        placeholders,
+                    )
+
+                placeholder_maps[modality] = placeholder_map
+
+            return seq_mm_data, placeholder_maps
+
+        mm_data = {**seq_mm_data}
+        placeholder_maps = defaultdict[str, MultiModalPlaceholderMap](
+            MultiModalPlaceholderMap)
+
+        for modality, placeholders in seq_mm_placeholders.items():
+            mm_items = mm_data.pop(modality)
+            if not isinstance(mm_items, list):
+                mm_items = [mm_items]
+
+            if positions:
+                intersecting_items = placeholder_maps[modality] \
+                    .append_items_from_seq_group(
+                        positions,
+                        mm_items,
+                        placeholders,
+                    )
+
+                if intersecting_items:
+                    mm_data[modality] = intersecting_items
+
+        return mm_data, placeholder_maps
+
+    def append_items_from_seq_group(
+        self,
+        positions: range,
+        multi_modal_items: list[_T],
+        multi_modal_placeholders: Sequence[PlaceholderRange],
+    ) -> list[_T]:
+        """
+        Adds the multi-modal items that intersect ```positions`` to this
+        placeholder map and returns the intersecting items.
+        """
+        intersecting_items = []
+
+        if len(multi_modal_items) != len(multi_modal_placeholders):
+            raise ValueError(
+                "Multi-modal placeholders and items must have the same length."
+            )
+        for placeholder_dict, mm_item in zip(multi_modal_placeholders,
+                                             multi_modal_items):
+            placeholder = range(
+                placeholder_dict["offset"],
+                placeholder_dict["offset"] + placeholder_dict["length"],
+            )
+            intersection = range(
+                max(positions.start, placeholder.start),
+                min(positions.stop, placeholder.stop),
+            )
+
+            if not intersection:
+                # Skip this multi-modal item.
+                continue
+
+            token_embedding_range = range(
+                intersection.start - positions.start,
+                intersection.stop - positions.start,
+            )
+
+            multimodal_embedding_range = range(
+                intersection.start - placeholder.start + self.src_len,
+                intersection.stop - placeholder.start + self.src_len,
+            )
+
+            intersecting_items.append(mm_item)
+            self.dest_ranges.append(token_embedding_range)
+            self.src_ranges.append(multimodal_embedding_range)
+            self.src_len += len(placeholder)
+
+        self.dest_len += len(positions)
+        return intersecting_items
+
+    def extend(self, other: "MultiModalPlaceholderMap"):
+        """
+        Adds the placeholders from another ``MultiModalPlaceholderMap`` to this
+        instance based on the source and destination tensors being
+        concatenated.
+        """
+
+        self.src_ranges.extend(
+            range(self.src_len + r.start, self.src_len + r.stop)
+            for r in other.src_ranges)
+        self.src_len += other.src_len
+        self.dest_ranges.extend(
+            range(self.dest_len + r.start, self.dest_len + r.stop)
+            for r in other.dest_ranges)
+        self.dest_len += other.dest_len
+
+    def index_map(self) -> "IndexMap":
+        """
+        Finalizes the placeholder map into lists of indices that can be used to
+        index the source and destination tensors.
+        """
+
+        src_indices = [i for r in self.src_ranges for i in r]
+        dest_indices = [i for r in self.dest_ranges for i in r]
+
+        if len(src_indices) != len(dest_indices):
+            raise ValueError(
+                f"The number of source ({len(src_indices)}) and destination "
+                f"indices ({len(dest_indices)}) must be the same.")
+
+        return MultiModalPlaceholderMap.IndexMap(src=src_indices,
+                                                 dest=dest_indices)
+
+
+class MediaIO(ABC, Generic[_T]):
+
+    @abstractmethod
+    def load_bytes(self, data: bytes) -> _T:
+        raise NotImplementedError
+
+    @abstractmethod
+    def load_base64(self, media_type: str, data: str) -> _T:
+        """
+        List of media types:
+        https://www.iana.org/assignments/media-types/media-types.xhtml
+        """
+        raise NotImplementedError
+
+    @abstractmethod
+    def load_file(self, filepath: Path) -> _T:
+        raise NotImplementedError