Add reasoning parser mechanism + qwen3 parser + bugfixes

qwen3_6_scripts/api_server.py

@@ -0,0 +1,595 @@
+import asyncio
+import importlib
+import inspect
+import multiprocessing
+import os
+import regex as re
+import signal
+import socket
+import tempfile
+from argparse import Namespace
+from contextlib import asynccontextmanager
+from functools import partial
+from http import HTTPStatus
+from typing import AsyncIterator, Set
+
+import uvloop
+from fastapi import APIRouter, FastAPI, Request
+from fastapi.exceptions import RequestValidationError
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.responses import JSONResponse, Response, StreamingResponse
+from starlette.datastructures import State
+from starlette.routing import Mount
+from typing_extensions import assert_never
+
+import vllm.envs as envs
+from vllm.config import ModelConfig
+from vllm.engine.arg_utils import AsyncEngineArgs
+from vllm.engine.async_llm_engine import AsyncLLMEngine
+from vllm.engine.multiprocessing.client import MQLLMEngineClient
+from vllm.engine.multiprocessing.engine import run_mp_engine
+from vllm.engine.protocol import EngineClient
+from vllm.entrypoints.launcher import serve_http
+from vllm.entrypoints.logger import RequestLogger
+from vllm.entrypoints.openai.cli_args import (make_arg_parser,
+                                              validate_parsed_serve_args)
+# yapf conflicts with isort for this block
+# yapf: disable
+from vllm.entrypoints.openai.protocol import (ChatCompletionRequest,
+                                              ChatCompletionResponse,
+                                              CompletionRequest,
+                                              CompletionResponse,
+                                              DetokenizeRequest,
+                                              DetokenizeResponse,
+                                              EmbeddingRequest,
+                                              EmbeddingResponse, ErrorResponse,
+                                              LoadLoraAdapterRequest,
+                                              TokenizeRequest,
+                                              TokenizeResponse,
+                                              UnloadLoraAdapterRequest)
+# yapf: enable
+from vllm.entrypoints.openai.serving_chat import OpenAIServingChat
+from vllm.entrypoints.openai.serving_completion import OpenAIServingCompletion
+from vllm.entrypoints.openai.serving_embedding import OpenAIServingEmbedding
+from vllm.entrypoints.openai.serving_engine import BaseModelPath
+from vllm.entrypoints.openai.serving_tokenization import (
+    OpenAIServingTokenization)
+from vllm.entrypoints.openai.tool_parsers import ToolParserManager
+from vllm.reasoning import ReasoningParserManager
+from vllm.logger import init_logger
+from vllm.usage.usage_lib import UsageContext
+from vllm.utils import FlexibleArgumentParser, get_open_zmq_ipc_path
+from vllm.version import __version__ as VLLM_VERSION
+
+TIMEOUT_KEEP_ALIVE = 5  # seconds
+
+prometheus_multiproc_dir: tempfile.TemporaryDirectory
+
+# Cannot use __name__ (https://github.com/vllm-project/vllm/pull/4765)
+logger = init_logger('vllm.entrypoints.openai.api_server')
+
+_running_tasks: Set[asyncio.Task] = set()
+
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    try:
+        if app.state.log_stats:
+            engine_client: EngineClient = app.state.engine_client
+
+            async def _force_log():
+                while True:
+                    await asyncio.sleep(10.)
+                    await engine_client.do_log_stats()
+
+            task = asyncio.create_task(_force_log())
+            _running_tasks.add(task)
+            task.add_done_callback(_running_tasks.remove)
+        else:
+            task = None
+        try:
+            yield
+        finally:
+            if task is not None:
+                task.cancel()
+    finally:
+        # Ensure app state including engine ref is gc'd
+        del app.state
+
+
+@asynccontextmanager
+async def build_async_engine_client(
+        args: Namespace) -> AsyncIterator[EngineClient]:
+
+    # Context manager to handle engine_client lifecycle
+    # Ensures everything is shutdown and cleaned up on error/exit
+    engine_args = AsyncEngineArgs.from_cli_args(args)
+
+    async with build_async_engine_client_from_engine_args(
+            engine_args, args.disable_frontend_multiprocessing) as engine:
+        yield engine
+
+
+@asynccontextmanager
+async def build_async_engine_client_from_engine_args(
+    engine_args: AsyncEngineArgs,
+    disable_frontend_multiprocessing: bool = False,
+) -> AsyncIterator[EngineClient]:
+    """
+    Create EngineClient, either:
+        - in-process using the AsyncLLMEngine Directly
+        - multiprocess using AsyncLLMEngine RPC
+
+    Returns the Client or None if the creation failed.
+    """
+
+    # Fall back
+    # TODO: fill out feature matrix.
+    if (MQLLMEngineClient.is_unsupported_config(engine_args)
+            or disable_frontend_multiprocessing):
+        engine_config = engine_args.create_engine_config()
+        uses_ray = getattr(AsyncLLMEngine._get_executor_cls(engine_config),
+                           "uses_ray", False)
+
+        build_engine = partial(AsyncLLMEngine.from_engine_args,
+                               engine_args=engine_args,
+                               engine_config=engine_config,
+                               usage_context=UsageContext.OPENAI_API_SERVER)
+        if uses_ray:
+            # Must run in main thread with ray for its signal handlers to work
+            engine_client = build_engine()
+        else:
+            engine_client = await asyncio.get_running_loop().run_in_executor(
+                None, build_engine)
+
+        yield engine_client
+        return
+
+    # Otherwise, use the multiprocessing AsyncLLMEngine.
+    else:
+        if "PROMETHEUS_MULTIPROC_DIR" not in os.environ:
+            # Make TemporaryDirectory for prometheus multiprocessing
+            # Note: global TemporaryDirectory will be automatically
+            #   cleaned up upon exit.
+            global prometheus_multiproc_dir
+            prometheus_multiproc_dir = tempfile.TemporaryDirectory()
+            os.environ[
+                "PROMETHEUS_MULTIPROC_DIR"] = prometheus_multiproc_dir.name
+        else:
+            logger.warning(
+                "Found PROMETHEUS_MULTIPROC_DIR was set by user. "
+                "This directory must be wiped between vLLM runs or "
+                "you will find inaccurate metrics. Unset the variable "
+                "and vLLM will properly handle cleanup.")
+
+        # Select random path for IPC.
+        ipc_path = get_open_zmq_ipc_path()
+        logger.info("Multiprocessing frontend to use %s for IPC Path.",
+                    ipc_path)
+
+        # Start RPCServer in separate process (holds the LLMEngine).
+        # the current process might have CUDA context,
+        # so we need to spawn a new process
+        context = multiprocessing.get_context("spawn")
+
+        engine_process = context.Process(target=run_mp_engine,
+                                         args=(engine_args,
+                                               UsageContext.OPENAI_API_SERVER,
+                                               ipc_path))
+        engine_process.start()
+        logger.info("Started engine process with PID %d", engine_process.pid)
+
+        # Build RPCClient, which conforms to EngineClient Protocol.
+        # NOTE: Actually, this is not true yet. We still need to support
+        # embedding models via RPC (see TODO above)
+        engine_config = engine_args.create_engine_config()
+        mp_engine_client = MQLLMEngineClient(ipc_path, engine_config)
+
+        try:
+            while True:
+                try:
+                    await mp_engine_client.setup()
+                    break
+                except TimeoutError:
+                    if not engine_process.is_alive():
+                        raise RuntimeError(
+                            "Engine process failed to start") from None
+
+            yield mp_engine_client  # type: ignore[misc]
+        finally:
+            # Ensure rpc server process was terminated
+            engine_process.terminate()
+
+            # Close all open connections to the backend
+            mp_engine_client.close()
+
+            # Wait for engine process to join
+            engine_process.join(4)
+            if engine_process.exitcode is None:
+                # Kill if taking longer than 5 seconds to stop
+                engine_process.kill()
+
+            # Lazy import for prometheus multiprocessing.
+            # We need to set PROMETHEUS_MULTIPROC_DIR environment variable
+            # before prometheus_client is imported.
+            # See https://prometheus.github.io/client_python/multiprocess/
+            from prometheus_client import multiprocess
+            multiprocess.mark_process_dead(engine_process.pid)
+
+
+router = APIRouter()
+
+
+def mount_metrics(app: FastAPI):
+    # Lazy import for prometheus multiprocessing.
+    # We need to set PROMETHEUS_MULTIPROC_DIR environment variable
+    # before prometheus_client is imported.
+    # See https://prometheus.github.io/client_python/multiprocess/
+    from prometheus_client import (CollectorRegistry, make_asgi_app,
+                                   multiprocess)
+
+    prometheus_multiproc_dir_path = os.getenv("PROMETHEUS_MULTIPROC_DIR", None)
+    if prometheus_multiproc_dir_path is not None:
+        logger.info("vLLM to use %s as PROMETHEUS_MULTIPROC_DIR",
+                    prometheus_multiproc_dir_path)
+        registry = CollectorRegistry()
+        multiprocess.MultiProcessCollector(registry)
+
+        # Add prometheus asgi middleware to route /metrics requests
+        metrics_route = Mount("/metrics", make_asgi_app(registry=registry))
+    else:
+        # Add prometheus asgi middleware to route /metrics requests
+        metrics_route = Mount("/metrics", make_asgi_app())
+
+    # Workaround for 307 Redirect for /metrics
+    metrics_route.path_regex = re.compile("^/metrics(?P<path>.*)$")
+    app.routes.append(metrics_route)
+
+
+def chat(request: Request) -> OpenAIServingChat:
+    return request.app.state.openai_serving_chat
+
+
+def completion(request: Request) -> OpenAIServingCompletion:
+    return request.app.state.openai_serving_completion
+
+
+def tokenization(request: Request) -> OpenAIServingTokenization:
+    return request.app.state.openai_serving_tokenization
+
+
+def embedding(request: Request) -> OpenAIServingEmbedding:
+    return request.app.state.openai_serving_embedding
+
+
+def engine_client(request: Request) -> EngineClient:
+    return request.app.state.engine_client
+
+
+@router.get("/health")
+async def health(raw_request: Request) -> Response:
+    """Health check."""
+    await engine_client(raw_request).check_health()
+    return Response(status_code=200)
+
+
+@router.post("/tokenize")
+async def tokenize(request: TokenizeRequest, raw_request: Request):
+    generator = await tokenization(raw_request).create_tokenize(request)
+    if isinstance(generator, ErrorResponse):
+        return JSONResponse(content=generator.model_dump(),
+                            status_code=generator.code)
+    elif isinstance(generator, TokenizeResponse):
+        return JSONResponse(content=generator.model_dump())
+
+    assert_never(generator)
+
+
+@router.post("/detokenize")
+async def detokenize(request: DetokenizeRequest, raw_request: Request):
+    generator = await tokenization(raw_request).create_detokenize(request)
+    if isinstance(generator, ErrorResponse):
+        return JSONResponse(content=generator.model_dump(),
+                            status_code=generator.code)
+    elif isinstance(generator, DetokenizeResponse):
+        return JSONResponse(content=generator.model_dump())
+
+    assert_never(generator)
+
+
+@router.get("/v1/models")
+async def show_available_models(raw_request: Request):
+    models = await completion(raw_request).show_available_models()
+    return JSONResponse(content=models.model_dump())
+
+
+@router.get("/version")
+async def show_version():
+    ver = {"version": VLLM_VERSION}
+    return JSONResponse(content=ver)
+
+
+@router.post("/v1/chat/completions")
+async def create_chat_completion(request: ChatCompletionRequest,
+                                 raw_request: Request):
+
+    generator = await chat(raw_request).create_chat_completion(
+        request, raw_request)
+
+    if isinstance(generator, ErrorResponse):
+        return JSONResponse(content=generator.model_dump(),
+                            status_code=generator.code)
+
+    elif isinstance(generator, ChatCompletionResponse):
+        return JSONResponse(content=generator.model_dump())
+
+    return StreamingResponse(content=generator, media_type="text/event-stream")
+
+
+@router.post("/v1/completions")
+async def create_completion(request: CompletionRequest, raw_request: Request):
+    generator = await completion(raw_request).create_completion(
+        request, raw_request)
+    if isinstance(generator, ErrorResponse):
+        return JSONResponse(content=generator.model_dump(),
+                            status_code=generator.code)
+    elif isinstance(generator, CompletionResponse):
+        return JSONResponse(content=generator.model_dump())
+
+    return StreamingResponse(content=generator, media_type="text/event-stream")
+
+
+@router.post("/v1/embeddings")
+async def create_embedding(request: EmbeddingRequest, raw_request: Request):
+    generator = await embedding(raw_request).create_embedding(
+        request, raw_request)
+    if isinstance(generator, ErrorResponse):
+        return JSONResponse(content=generator.model_dump(),
+                            status_code=generator.code)
+    elif isinstance(generator, EmbeddingResponse):
+        return JSONResponse(content=generator.model_dump())
+
+    assert_never(generator)
+
+
+if envs.VLLM_TORCH_PROFILER_DIR:
+    logger.warning(
+        "Torch Profiler is enabled in the API server. This should ONLY be "
+        "used for local development!")
+
+    @router.post("/start_profile")
+    async def start_profile(raw_request: Request):
+        logger.info("Starting profiler...")
+        await engine_client(raw_request).start_profile()
+        logger.info("Profiler started.")
+        return Response(status_code=200)
+
+    @router.post("/stop_profile")
+    async def stop_profile(raw_request: Request):
+        logger.info("Stopping profiler...")
+        await engine_client(raw_request).stop_profile()
+        logger.info("Profiler stopped.")
+        return Response(status_code=200)
+
+
+if envs.VLLM_ALLOW_RUNTIME_LORA_UPDATING:
+    logger.warning(
+        "Lora dynamic loading & unloading is enabled in the API server. "
+        "This should ONLY be used for local development!")
+
+    @router.post("/v1/load_lora_adapter")
+    async def load_lora_adapter(request: LoadLoraAdapterRequest,
+                                raw_request: Request):
+        response = await chat(raw_request).load_lora_adapter(request)
+        if isinstance(response, ErrorResponse):
+            return JSONResponse(content=response.model_dump(),
+                                status_code=response.code)
+
+        response = await completion(raw_request).load_lora_adapter(request)
+        if isinstance(response, ErrorResponse):
+            return JSONResponse(content=response.model_dump(),
+                                status_code=response.code)
+
+        return Response(status_code=200, content=response)
+
+    @router.post("/v1/unload_lora_adapter")
+    async def unload_lora_adapter(request: UnloadLoraAdapterRequest,
+                                  raw_request: Request):
+        response = await chat(raw_request).unload_lora_adapter(request)
+        if isinstance(response, ErrorResponse):
+            return JSONResponse(content=response.model_dump(),
+                                status_code=response.code)
+
+        response = await completion(raw_request).unload_lora_adapter(request)
+        if isinstance(response, ErrorResponse):
+            return JSONResponse(content=response.model_dump(),
+                                status_code=response.code)
+
+        return Response(status_code=200, content=response)
+
+
+def build_app(args: Namespace) -> FastAPI:
+    if args.disable_fastapi_docs:
+        app = FastAPI(openapi_url=None,
+                      docs_url=None,
+                      redoc_url=None,
+                      lifespan=lifespan)
+    else:
+        app = FastAPI(lifespan=lifespan)
+    app.include_router(router)
+    app.root_path = args.root_path
+
+    mount_metrics(app)
+
+    app.add_middleware(
+        CORSMiddleware,
+        allow_origins=args.allowed_origins,
+        allow_credentials=args.allow_credentials,
+        allow_methods=args.allowed_methods,
+        allow_headers=args.allowed_headers,
+    )
+
+    @app.exception_handler(RequestValidationError)
+    async def validation_exception_handler(_, exc):
+        chat = app.state.openai_serving_chat
+        err = chat.create_error_response(message=str(exc))
+        return JSONResponse(err.model_dump(),
+                            status_code=HTTPStatus.BAD_REQUEST)
+
+    if token := envs.VLLM_API_KEY or args.api_key:
+
+        @app.middleware("http")
+        async def authentication(request: Request, call_next):
+            root_path = "" if args.root_path is None else args.root_path
+            if request.method == "OPTIONS":
+                return await call_next(request)
+            if not request.url.path.startswith(f"{root_path}/v1"):
+                return await call_next(request)
+            if request.headers.get("Authorization") != "Bearer " + token:
+                return JSONResponse(content={"error": "Unauthorized"},
+                                    status_code=401)
+            return await call_next(request)
+
+    for middleware in args.middleware:
+        module_path, object_name = middleware.rsplit(".", 1)
+        imported = getattr(importlib.import_module(module_path), object_name)
+        if inspect.isclass(imported):
+            app.add_middleware(imported)
+        elif inspect.iscoroutinefunction(imported):
+            app.middleware("http")(imported)
+        else:
+            raise ValueError(f"Invalid middleware {middleware}. "
+                             f"Must be a function or a class.")
+
+    return app
+
+
+def init_app_state(
+    engine_client: EngineClient,
+    model_config: ModelConfig,
+    state: State,
+    args: Namespace,
+) -> None:
+    if args.served_model_name is not None:
+        served_model_names = args.served_model_name
+    else:
+        served_model_names = [args.model]
+
+    if args.disable_log_requests:
+        request_logger = None
+    else:
+        request_logger = RequestLogger(max_log_len=args.max_log_len)
+
+    base_model_paths = [
+        BaseModelPath(name=name, model_path=args.model)
+        for name in served_model_names
+    ]
+
+    state.engine_client = engine_client
+    state.log_stats = not args.disable_log_stats
+
+    state.openai_serving_chat = OpenAIServingChat(
+        engine_client,
+        model_config,
+        base_model_paths,
+        args.response_role,
+        lora_modules=args.lora_modules,
+        prompt_adapters=args.prompt_adapters,
+        request_logger=request_logger,
+        chat_template=args.chat_template,
+        return_tokens_as_token_ids=args.return_tokens_as_token_ids,
+        enable_auto_tools=args.enable_auto_tool_choice,
+        tool_parser=args.tool_call_parser,
+        reasoning_parser=getattr(args, 'reasoning_parser', None))
+    state.openai_serving_completion = OpenAIServingCompletion(
+        engine_client,
+        model_config,
+        base_model_paths,
+        lora_modules=args.lora_modules,
+        prompt_adapters=args.prompt_adapters,
+        request_logger=request_logger,
+        return_tokens_as_token_ids=args.return_tokens_as_token_ids,
+    )
+    state.openai_serving_embedding = OpenAIServingEmbedding(
+        engine_client,
+        model_config,
+        base_model_paths,
+        request_logger=request_logger,
+    )
+    state.openai_serving_tokenization = OpenAIServingTokenization(
+        engine_client,
+        model_config,
+        base_model_paths,
+        lora_modules=args.lora_modules,
+        request_logger=request_logger,
+        chat_template=args.chat_template,
+    )
+
+
+async def run_server(args, **uvicorn_kwargs) -> None:
+    logger.info("vLLM API server version %s", VLLM_VERSION)
+    logger.info("args: %s", args)
+
+    if args.tool_parser_plugin and len(args.tool_parser_plugin) > 3:
+        ToolParserManager.import_tool_parser(args.tool_parser_plugin)
+
+    valide_tool_parses = ToolParserManager.tool_parsers.keys()
+    if args.enable_auto_tool_choice \
+        and args.tool_call_parser not in valide_tool_parses:
+        raise KeyError(f"invalid tool call parser: {args.tool_call_parser} "
+                       f"(chose from {{ {','.join(valide_tool_parses)} }})")
+
+    reasoning_parser = getattr(args, 'reasoning_parser', None)
+    if reasoning_parser:
+        valid_reasoning = ReasoningParserManager.list_registered()
+        if reasoning_parser not in valid_reasoning:
+            raise KeyError(
+                f"invalid reasoning parser: {reasoning_parser} "
+                f"(chose from {{ {','.join(valid_reasoning)} }})")
+
+    # workaround to make sure that we bind the port before the engine is set up.
+    # This avoids race conditions with ray.
+    # see https://github.com/vllm-project/vllm/issues/8204
+    sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
+    sock.bind(("", args.port))
+
+    def signal_handler(*_) -> None:
+        # Interrupt server on sigterm while initializing
+        raise KeyboardInterrupt("terminated")
+
+    signal.signal(signal.SIGTERM, signal_handler)
+
+    async with build_async_engine_client(args) as engine_client:
+        app = build_app(args)
+
+        model_config = await engine_client.get_model_config()
+        init_app_state(engine_client, model_config, app.state, args)
+
+        shutdown_task = await serve_http(
+            app,
+            host=args.host,
+            port=args.port,
+            log_level=args.uvicorn_log_level,
+            timeout_keep_alive=TIMEOUT_KEEP_ALIVE,
+            ssl_keyfile=args.ssl_keyfile,
+            ssl_certfile=args.ssl_certfile,
+            ssl_ca_certs=args.ssl_ca_certs,
+            ssl_cert_reqs=args.ssl_cert_reqs,
+            fd=sock.fileno(),
+            **uvicorn_kwargs,
+        )
+
+    # NB: Await server shutdown only after the backend context is exited
+    await shutdown_task
+
+
+if __name__ == "__main__":
+    # NOTE(simon):
+    # This section should be in sync with vllm/scripts.py for CLI entrypoints.
+    parser = FlexibleArgumentParser(
+        description="vLLM OpenAI-Compatible RESTful API server.")
+    parser = make_arg_parser(parser)
+    args = parser.parse_args()
+    validate_parsed_serve_args(args)
+
+    uvloop.run(run_server(args))

qwen3_6_scripts/chat_utils.py

@@ -0,0 +1,598 @@
+import asyncio
+import codecs
+import json
+from abc import ABC, abstractmethod
+from collections import defaultdict
+from functools import lru_cache, partial
+from pathlib import Path
+from typing import (Any, Awaitable, Dict, Generic, Iterable, List, Literal,
+                    Mapping, Optional, Tuple, TypeVar, Union, cast)
+
+# yapf conflicts with isort for this block
+# yapf: disable
+from openai.types.chat import (ChatCompletionAssistantMessageParam,
+                               ChatCompletionContentPartImageParam)
+from openai.types.chat import (
+    ChatCompletionContentPartParam as OpenAIChatCompletionContentPartParam)
+from openai.types.chat import (ChatCompletionContentPartRefusalParam,
+                               ChatCompletionContentPartTextParam)
+from openai.types.chat import (
+    ChatCompletionMessageParam as OpenAIChatCompletionMessageParam)
+from openai.types.chat import (ChatCompletionMessageToolCallParam,
+                               ChatCompletionToolMessageParam)
+# yapf: enable
+# pydantic needs the TypedDict from typing_extensions
+from pydantic import ConfigDict
+from transformers import PreTrainedTokenizer, PreTrainedTokenizerFast
+from typing_extensions import Required, TypeAlias, TypedDict
+
+from vllm.config import ModelConfig
+from vllm.logger import init_logger
+from vllm.multimodal import MultiModalDataDict
+from vllm.multimodal.utils import (async_get_and_parse_audio,
+                                   async_get_and_parse_image,
+                                   get_and_parse_audio, get_and_parse_image)
+from vllm.transformers_utils.tokenizer import AnyTokenizer, MistralTokenizer
+
+logger = init_logger(__name__)
+
+
+class AudioURL(TypedDict, total=False):
+    url: Required[str]
+    """
+    Either a URL of the audio or a data URL with base64 encoded audio data.
+    """
+
+
+class ChatCompletionContentPartAudioParam(TypedDict, total=False):
+    audio_url: Required[AudioURL]
+
+    type: Required[Literal["audio_url"]]
+    """The type of the content part."""
+
+
+class CustomChatCompletionContentPartParam(TypedDict, total=False):
+    __pydantic_config__ = ConfigDict(extra="allow")  # type: ignore
+
+    type: Required[str]
+    """The type of the content part."""
+
+
+ChatCompletionContentPartParam: TypeAlias = Union[
+    OpenAIChatCompletionContentPartParam, ChatCompletionContentPartAudioParam,
+    ChatCompletionContentPartRefusalParam,
+    CustomChatCompletionContentPartParam]
+
+
+class CustomChatCompletionMessageParam(TypedDict, total=False):
+    """Enables custom roles in the Chat Completion API."""
+    role: Required[str]
+    """The role of the message's author."""
+
+    content: Union[str, List[ChatCompletionContentPartParam]]
+    """The contents of the message."""
+
+    name: str
+    """An optional name for the participant.
+
+    Provides the model information to differentiate between participants of the
+    same role.
+    """
+
+    tool_call_id: Optional[str]
+    """Tool call that this message is responding to."""
+
+    tool_calls: Optional[Iterable[ChatCompletionMessageToolCallParam]]
+    """The tool calls generated by the model, such as function calls."""
+
+    reasoning_content: Optional[str]
+    """Reasoning / thinking content for assistant messages (vLLM extension).
+    When present in a previous assistant turn, it is rendered as
+    <think>...</think> before the main content so the model sees its own
+    chain-of-thought in subsequent turns."""
+
+
+ChatCompletionMessageParam = Union[OpenAIChatCompletionMessageParam,
+                                   CustomChatCompletionMessageParam]
+
+
+# TODO: Make fields ReadOnly once mypy supports it
+class ConversationMessage(TypedDict, total=False):
+    role: Required[str]
+    """The role of the message's author."""
+
+    content: Optional[str]
+    """The contents of the message"""
+
+    tool_call_id: Optional[str]
+    """Tool call that this message is responding to."""
+
+    name: Optional[str]
+    """The name of the function to call"""
+
+    tool_calls: Optional[Iterable[ChatCompletionMessageToolCallParam]]
+    """The tool calls generated by the model, such as function calls."""
+
+
+ModalityStr = Literal["image", "audio", "video"]
+_T = TypeVar("_T")
+
+
+class BaseMultiModalItemTracker(ABC, Generic[_T]):
+    """
+    Tracks multi-modal items in a given request and ensures that the number
+    of multi-modal items in a given request does not exceed the configured
+    maximum per prompt.
+    """
+
+    def __init__(self, model_config: ModelConfig, tokenizer: AnyTokenizer):
+        super().__init__()
+
+        self._model_config = model_config
+        self._tokenizer = tokenizer
+        self._allowed_items = (model_config.multimodal_config.limit_per_prompt
+                               if model_config.multimodal_config else {})
+        self._consumed_items = {k: 0 for k in self._allowed_items}
+
+        self._items: List[_T] = []
+
+    @staticmethod
+    @lru_cache(maxsize=None)
+    def _cached_token_str(tokenizer: AnyTokenizer, token_index: int) -> str:
+        return tokenizer.decode(token_index)
+
+    def _placeholder_str(self, modality: ModalityStr,
+                         current_count: int) -> Optional[str]:
+        # TODO: Let user specify how to insert image tokens into prompt
+        # (similar to chat template)
+        hf_config = self._model_config.hf_config
+        model_type = hf_config.model_type
+
+        if modality == "image":
+            if model_type == "phi3_v":
+                # Workaround since this token is not defined in the tokenizer
+                return f"<|image_{current_count}|>"
+            if model_type == "minicpmv":
+                return "(<image>./</image>)"
+            if model_type in ("blip-2", "chatglm", "fuyu", "paligemma",
+                              "pixtral"):
+                # These models do not use image tokens in the prompt
+                return None
+            if model_type == "qwen":
+                return f"Picture {current_count}: <img></img>"
+            if model_type.startswith("llava"):
+                return self._cached_token_str(self._tokenizer,
+                                              hf_config.image_token_index)
+            if model_type in ("chameleon", "internvl_chat", "NVLM_D"):
+                return "<image>"
+            if model_type == "mllama":
+                return "<|image|>"
+            if model_type in ("qwen2_vl","qwen2_5_vl"):
+                return "<|vision_start|><|image_pad|><|vision_end|>"
+            if model_type == "molmo":
+                return ""
+
+            raise TypeError(f"Unknown model type: {model_type}")
+        elif modality == "audio":
+            if model_type == "ultravox":
+                return "<|reserved_special_token_0|>"
+            raise TypeError(f"Unknown model type: {model_type}")
+        elif modality == "video":
+            if model_type in ("qwen2_vl","qwen2_5_vl"):
+                return "<|vision_start|><|video_pad|><|vision_end|>"
+            raise TypeError(f"Unknown model type: {model_type}")
+        else:
+            raise TypeError(f"Unknown modality: {modality}")
+
+    @staticmethod
+    def _combine(items: List[MultiModalDataDict]) -> MultiModalDataDict:
+        mm_lists: Mapping[str, List[object]] = defaultdict(list)
+
+        # Merge all the multi-modal items
+        for single_mm_data in items:
+            for mm_key, mm_item in single_mm_data.items():
+                if isinstance(mm_item, list):
+                    mm_lists[mm_key].extend(mm_item)
+                else:
+                    mm_lists[mm_key].append(mm_item)
+
+        # Unpack any single item lists for models that don't expect multiple.
+        return {
+            mm_key: mm_list[0] if len(mm_list) == 1 else mm_list
+            for mm_key, mm_list in mm_lists.items()
+        }
+
+    def add(self, modality: ModalityStr, item: _T) -> Optional[str]:
+        """
+        Add a multi-modal item to the current prompt and returns the
+        placeholder string to use, if any.
+        """
+        allowed_count = self._allowed_items.get(modality, 1)
+        current_count = self._consumed_items.get(modality, 0) + 1
+        if current_count > allowed_count:
+            raise ValueError(
+                f"At most {allowed_count} {modality}(s) may be provided in "
+                "one request.")
+
+        self._consumed_items[modality] = current_count
+        self._items.append(item)
+
+        return self._placeholder_str(modality, current_count)
+
+    @abstractmethod
+    def create_parser(self) -> "BaseMultiModalContentParser":
+        raise NotImplementedError
+
+
+class MultiModalItemTracker(BaseMultiModalItemTracker[MultiModalDataDict]):
+
+    def all_mm_data(self) -> Optional[MultiModalDataDict]:
+        return self._combine(self._items) if self._items else None
+
+    def create_parser(self) -> "BaseMultiModalContentParser":
+        return MultiModalContentParser(self)
+
+
+class AsyncMultiModalItemTracker(
+        BaseMultiModalItemTracker[Awaitable[MultiModalDataDict]]):
+
+    async def all_mm_data(self) -> Optional[MultiModalDataDict]:
+        if self._items:
+            items = await asyncio.gather(*self._items)
+            return self._combine(items)
+
+        return None
+
+    def create_parser(self) -> "BaseMultiModalContentParser":
+        return AsyncMultiModalContentParser(self)
+
+
+class BaseMultiModalContentParser(ABC):
+
+    def __init__(self) -> None:
+        super().__init__()
+
+        # multimodal placeholder_string : count
+        self._placeholder_counts: Dict[str, int] = defaultdict(lambda: 0)
+
+    def _add_placeholder(self, placeholder: Optional[str]):
+        if placeholder:
+            self._placeholder_counts[placeholder] += 1
+
+    def mm_placeholder_counts(self) -> Dict[str, int]:
+        return dict(self._placeholder_counts)
+
+    @abstractmethod
+    def parse_image(self, image_url: str) -> None:
+        raise NotImplementedError
+
+    @abstractmethod
+    def parse_audio(self, audio_url: str) -> None:
+        raise NotImplementedError
+
+
+class MultiModalContentParser(BaseMultiModalContentParser):
+
+    def __init__(self, tracker: MultiModalItemTracker) -> None:
+        super().__init__()
+
+        self._tracker = tracker
+
+    def parse_image(self, image_url: str) -> None:
+        image = get_and_parse_image(image_url)
+
+        placeholder = self._tracker.add("image", image)
+        self._add_placeholder(placeholder)
+
+    def parse_audio(self, audio_url: str) -> None:
+        audio = get_and_parse_audio(audio_url)
+
+        placeholder = self._tracker.add("audio", audio)
+        self._add_placeholder(placeholder)
+
+
+class AsyncMultiModalContentParser(BaseMultiModalContentParser):
+
+    def __init__(self, tracker: AsyncMultiModalItemTracker) -> None:
+        super().__init__()
+
+        self._tracker = tracker
+
+    def parse_image(self, image_url: str) -> None:
+        image_coro = async_get_and_parse_image(image_url)
+
+        placeholder = self._tracker.add("image", image_coro)
+        self._add_placeholder(placeholder)
+
+    def parse_audio(self, audio_url: str) -> None:
+        audio_coro = async_get_and_parse_audio(audio_url)
+
+        placeholder = self._tracker.add("audio", audio_coro)
+        self._add_placeholder(placeholder)
+
+
+def validate_chat_template(chat_template: Optional[Union[Path, str]]):
+    """Raises if the provided chat template appears invalid."""
+    if chat_template is None:
+        return
+
+    elif isinstance(chat_template, Path) and not chat_template.exists():
+        raise FileNotFoundError(
+            "the supplied chat template path doesn't exist")
+
+    elif isinstance(chat_template, str):
+        JINJA_CHARS = "{}\n"
+        if not any(c in chat_template
+                   for c in JINJA_CHARS) and not Path(chat_template).exists():
+            raise ValueError(
+                f"The supplied chat template string ({chat_template}) "
+                f"appears path-like, but doesn't exist!")
+
+    else:
+        raise TypeError(
+            f"{type(chat_template)} is not a valid chat template type")
+
+
+def load_chat_template(
+        chat_template: Optional[Union[Path, str]]) -> Optional[str]:
+    if chat_template is None:
+        return None
+    try:
+        with open(chat_template, "r") as f:
+            resolved_chat_template = f.read()
+    except OSError as e:
+        if isinstance(chat_template, Path):
+            raise
+
+        JINJA_CHARS = "{}\n"
+        if not any(c in chat_template for c in JINJA_CHARS):
+            msg = (f"The supplied chat template ({chat_template}) "
+                   f"looks like a file path, but it failed to be "
+                   f"opened. Reason: {e}")
+            raise ValueError(msg) from e
+
+        # If opening a file fails, set chat template to be args to
+        # ensure we decode so our escape are interpreted correctly
+        resolved_chat_template = codecs.decode(chat_template, "unicode_escape")
+
+    logger.info("Using supplied chat template:\n%s", resolved_chat_template)
+    return resolved_chat_template
+
+
+# TODO: Let user specify how to insert multimodal tokens into prompt
+# (similar to chat template)
+def _get_full_multimodal_text_prompt(placeholder_counts: Dict[str, int],
+                                     text_prompt: str) -> str:
+    """Combine multimodal prompts for a multimodal language model."""
+
+    # Look through the text prompt to check for missing placeholders
+    missing_placeholders: List[str] = []
+    for placeholder in placeholder_counts:
+
+        # For any existing placeholder in the text prompt, we leave it as is
+        placeholder_counts[placeholder] -= text_prompt.count(placeholder)
+
+        if placeholder_counts[placeholder] < 0:
+            raise ValueError(
+                f"Found more '{placeholder}' placeholders in input prompt than "
+                "actual multimodal data items.")
+
+        missing_placeholders.extend([placeholder] *
+                                    placeholder_counts[placeholder])
+
+    # NOTE: For now we always add missing placeholders at the front of
+    # the prompt. This may change to be customizable in the future.
+    return "\n".join(missing_placeholders + [text_prompt])
+
+
+# No need to validate using Pydantic again
+_TextParser = partial(cast, ChatCompletionContentPartTextParam)
+_ImageParser = partial(cast, ChatCompletionContentPartImageParam)
+_AudioParser = partial(cast, ChatCompletionContentPartAudioParam)
+_RefusalParser = partial(cast, ChatCompletionContentPartRefusalParam)
+MODEL_KEEP_MULTI_MODAL_CONTENT = {'mllama'}
+
+
+def _parse_chat_message_content_parts(
+    role: str,
+    parts: Iterable[ChatCompletionContentPartParam],
+    mm_tracker: BaseMultiModalItemTracker,
+) -> List[ConversationMessage]:
+    texts: List[str] = []
+
+    mm_parser = mm_tracker.create_parser()
+    keep_multimodal_content = \
+        mm_tracker._model_config.hf_config.model_type in \
+            MODEL_KEEP_MULTI_MODAL_CONTENT
+
+    has_image = False
+    for part in parts:
+        part_type = part["type"]
+        if part_type == "text":
+            text = _TextParser(part)["text"]
+            texts.append(text)
+        elif part_type == "image_url":
+            image_url = _ImageParser(part)["image_url"]
+
+            if image_url.get("detail", "auto") != "auto":
+                logger.warning(
+                    "'image_url.detail' is currently not supported and "
+                    "will be ignored.")
+
+            mm_parser.parse_image(image_url["url"])
+            has_image = True
+        elif part_type == "audio_url":
+            audio_url = _AudioParser(part)["audio_url"]
+
+            mm_parser.parse_audio(audio_url["url"])
+        elif part_type == "refusal":
+            text = _RefusalParser(part)["refusal"]
+            texts.append(text)
+        else:
+            raise NotImplementedError(f"Unknown part type: {part_type}")
+
+    text_prompt = "\n".join(texts)
+    if keep_multimodal_content:
+        text_prompt = "\n".join(texts)
+        role_content = [{'type': 'text', 'text': text_prompt}]
+
+        if has_image:
+            role_content = [{'type': 'image'}] + role_content
+        return [ConversationMessage(role=role,
+                                    content=role_content)]  # type: ignore
+    else:
+        mm_placeholder_counts = mm_parser.mm_placeholder_counts()
+        if mm_placeholder_counts:
+            text_prompt = _get_full_multimodal_text_prompt(
+                mm_placeholder_counts, text_prompt)
+        return [ConversationMessage(role=role, content=text_prompt)]
+
+
+# No need to validate using Pydantic again
+_AssistantParser = partial(cast, ChatCompletionAssistantMessageParam)
+_ToolParser = partial(cast, ChatCompletionToolMessageParam)
+
+
+def _parse_chat_message_content(
+    message: ChatCompletionMessageParam,
+    mm_tracker: BaseMultiModalItemTracker,
+) -> List[ConversationMessage]:
+    role = message["role"]
+    content = message.get("content")
+
+    if content is None:
+        content = []
+    elif isinstance(content, str):
+        content = [
+            ChatCompletionContentPartTextParam(type="text", text=content)
+        ]
+
+    result = _parse_chat_message_content_parts(
+        role,
+        content,  # type: ignore
+        mm_tracker,
+    )
+
+    for result_msg in result:
+        if role == 'assistant':
+            parsed_msg = _AssistantParser(message)
+
+            if "tool_calls" in parsed_msg:
+                result_msg["tool_calls"] = list(parsed_msg["tool_calls"])
+
+            # Prepend reasoning_content as <think>...</think> so the model
+            # sees its own chain-of-thought in multi-turn conversations.
+            reasoning = message.get("reasoning_content")  # type: ignore[arg-type]
+            if reasoning and isinstance(reasoning, str):
+                existing = result_msg.get("content") or ""
+                result_msg["content"] = (
+                    f"<think>{reasoning}</think>\n\n{existing}"
+                    if existing else f"<think>{reasoning}</think>"
+                )
+
+        elif role == "tool":
+            parsed_msg = _ToolParser(message)
+            if "tool_call_id" in parsed_msg:
+                result_msg["tool_call_id"] = parsed_msg["tool_call_id"]
+
+        if "name" in message and isinstance(message["name"], str):
+            result_msg["name"] = message["name"]
+
+    return result
+
+
+def _postprocess_messages(messages: List[ConversationMessage]) -> None:
+    # per the Transformers docs & maintainers, tool call arguments in
+    # assistant-role messages with tool_calls need to be dicts not JSON str -
+    # this is how tool-use chat templates will expect them moving forwards
+    # so, for messages that have tool_calls, parse the string (which we get
+    # from openAI format) to dict
+    for message in messages:
+        if (message["role"] == "assistant" and "tool_calls" in message
+                and isinstance(message["tool_calls"], list)):
+
+            for item in message["tool_calls"]:
+                item["function"]["arguments"] = json.loads(
+                    item["function"]["arguments"])
+
+
+def parse_chat_messages(
+    messages: List[ChatCompletionMessageParam],
+    model_config: ModelConfig,
+    tokenizer: AnyTokenizer,
+) -> Tuple[List[ConversationMessage], Optional[MultiModalDataDict]]:
+    conversation: List[ConversationMessage] = []
+    mm_tracker = MultiModalItemTracker(model_config, tokenizer)
+
+    for msg in messages:
+        sub_messages = _parse_chat_message_content(msg, mm_tracker)
+
+        conversation.extend(sub_messages)
+
+    _postprocess_messages(conversation)
+
+    return conversation, mm_tracker.all_mm_data()
+
+
+def parse_chat_messages_futures(
+    messages: List[ChatCompletionMessageParam],
+    model_config: ModelConfig,
+    tokenizer: AnyTokenizer,
+) -> Tuple[List[ConversationMessage], Awaitable[Optional[MultiModalDataDict]]]:
+    conversation: List[ConversationMessage] = []
+    mm_tracker = AsyncMultiModalItemTracker(model_config, tokenizer)
+
+    for msg in messages:
+        sub_messages = _parse_chat_message_content(msg, mm_tracker)
+
+        conversation.extend(sub_messages)
+
+    _postprocess_messages(conversation)
+
+    return conversation, mm_tracker.all_mm_data()
+
+
+def apply_hf_chat_template(
+    tokenizer: Union[PreTrainedTokenizer, PreTrainedTokenizerFast],
+    conversation: List[ConversationMessage],
+    chat_template: Optional[str],
+    *,
+    tokenize: bool = False,  # Different from HF's default
+    **kwargs: Any,
+) -> str:
+    if chat_template is None and tokenizer.chat_template is None:
+        raise ValueError(
+            "As of transformers v4.44, default chat template is no longer "
+            "allowed, so you must provide a chat template if the tokenizer "
+            "does not define one.")
+
+    return tokenizer.apply_chat_template(
+        conversation=conversation,  # type: ignore[arg-type]
+        chat_template=chat_template,
+        tokenize=tokenize,
+        **kwargs,
+    )
+
+
+def apply_mistral_chat_template(
+    tokenizer: MistralTokenizer,
+    messages: List[ChatCompletionMessageParam],
+    chat_template: Optional[str] = None,
+    **kwargs: Any,
+) -> List[int]:
+    if chat_template is not None:
+        logger.warning(
+            "'chat_template' cannot be overridden for mistral tokenizer.")
+    if "add_generation_prompt" in kwargs:
+        logger.warning(
+            "'add_generation_prompt' is not supported for mistral tokenizer, "
+            "so it will be ignored.")
+    if "continue_final_message" in kwargs:
+        logger.warning(
+            "'continue_final_message' is not supported for mistral tokenizer, "
+            "so it will be ignored.")
+
+    return tokenizer.apply_chat_template(
+        messages=messages,
+        **kwargs,
+    )

qwen3_6_scripts/cli_args.py

@@ -0,0 +1,261 @@
+"""
+This file contains the command line arguments for the vLLM's
+OpenAI-compatible server. It is kept in a separate file for documentation
+purposes.
+"""
+
+import argparse
+import json
+import ssl
+from typing import List, Optional, Sequence, Union
+
+from vllm.engine.arg_utils import AsyncEngineArgs, nullable_str
+from vllm.entrypoints.chat_utils import validate_chat_template
+from vllm.entrypoints.openai.serving_engine import (LoRAModulePath,
+                                                    PromptAdapterPath)
+from vllm.entrypoints.openai.tool_parsers import ToolParserManager
+from vllm.utils import FlexibleArgumentParser
+
+
+class LoRAParserAction(argparse.Action):
+
+    def __call__(
+        self,
+        parser: argparse.ArgumentParser,
+        namespace: argparse.Namespace,
+        values: Optional[Union[str, Sequence[str]]],
+        option_string: Optional[str] = None,
+    ):
+        if values is None:
+            values = []
+        if isinstance(values, str):
+            raise TypeError("Expected values to be a list")
+
+        lora_list: List[LoRAModulePath] = []
+        for item in values:
+            if item in [None, '']:  # Skip if item is None or empty string
+                continue
+            if '=' in item and ',' not in item:  # Old format: name=path
+                name, path = item.split('=')
+                lora_list.append(LoRAModulePath(name, path))
+            else:  # Assume JSON format
+                try:
+                    lora_dict = json.loads(item)
+                    lora = LoRAModulePath(**lora_dict)
+                    lora_list.append(lora)
+                except json.JSONDecodeError:
+                    parser.error(
+                        f"Invalid JSON format for --lora-modules: {item}")
+                except TypeError as e:
+                    parser.error(
+                        f"Invalid fields for --lora-modules: {item} - {str(e)}"
+                    )
+        setattr(namespace, self.dest, lora_list)
+
+
+class PromptAdapterParserAction(argparse.Action):
+
+    def __call__(
+        self,
+        parser: argparse.ArgumentParser,
+        namespace: argparse.Namespace,
+        values: Optional[Union[str, Sequence[str]]],
+        option_string: Optional[str] = None,
+    ):
+        if values is None:
+            values = []
+        if isinstance(values, str):
+            raise TypeError("Expected values to be a list")
+
+        adapter_list: List[PromptAdapterPath] = []
+        for item in values:
+            name, path = item.split('=')
+            adapter_list.append(PromptAdapterPath(name, path))
+        setattr(namespace, self.dest, adapter_list)
+
+
+def make_arg_parser(parser: FlexibleArgumentParser) -> FlexibleArgumentParser:
+    parser.add_argument("--host",
+                        type=nullable_str,
+                        default=None,
+                        help="host name")
+    parser.add_argument("--port", type=int, default=8000, help="port number")
+    parser.add_argument(
+        "--uvicorn-log-level",
+        type=str,
+        default="info",
+        choices=['debug', 'info', 'warning', 'error', 'critical', 'trace'],
+        help="log level for uvicorn")
+    parser.add_argument("--allow-credentials",
+                        action="store_true",
+                        help="allow credentials")
+    parser.add_argument("--allowed-origins",
+                        type=json.loads,
+                        default=["*"],
+                        help="allowed origins")
+    parser.add_argument("--allowed-methods",
+                        type=json.loads,
+                        default=["*"],
+                        help="allowed methods")
+    parser.add_argument("--allowed-headers",
+                        type=json.loads,
+                        default=["*"],
+                        help="allowed headers")
+    parser.add_argument("--api-key",
+                        type=nullable_str,
+                        default=None,
+                        help="If provided, the server will require this key "
+                        "to be presented in the header.")
+    parser.add_argument(
+        "--lora-modules",
+        type=nullable_str,
+        default=None,
+        nargs='+',
+        action=LoRAParserAction,
+        help="LoRA module configurations in either 'name=path' format"
+        "or JSON format. "
+        "Example (old format): 'name=path' "
+        "Example (new format): "
+        "'{\"name\": \"name\", \"local_path\": \"path\", "
+        "\"base_model_name\": \"id\"}'")
+    parser.add_argument(
+        "--prompt-adapters",
+        type=nullable_str,
+        default=None,
+        nargs='+',
+        action=PromptAdapterParserAction,
+        help="Prompt adapter configurations in the format name=path. "
+        "Multiple adapters can be specified.")
+    parser.add_argument("--chat-template",
+                        type=nullable_str,
+                        default=None,
+                        help="The file path to the chat template, "
+                        "or the template in single-line form "
+                        "for the specified model")
+    parser.add_argument("--response-role",
+                        type=nullable_str,
+                        default="assistant",
+                        help="The role name to return if "
+                        "`request.add_generation_prompt=true`.")
+    parser.add_argument("--ssl-keyfile",
+                        type=nullable_str,
+                        default=None,
+                        help="The file path to the SSL key file")
+    parser.add_argument("--ssl-certfile",
+                        type=nullable_str,
+                        default=None,
+                        help="The file path to the SSL cert file")
+    parser.add_argument("--ssl-ca-certs",
+                        type=nullable_str,
+                        default=None,
+                        help="The CA certificates file")
+    parser.add_argument(
+        "--ssl-cert-reqs",
+        type=int,
+        default=int(ssl.CERT_NONE),
+        help="Whether client certificate is required (see stdlib ssl module's)"
+    )
+    parser.add_argument(
+        "--root-path",
+        type=nullable_str,
+        default=None,
+        help="FastAPI root_path when app is behind a path based routing proxy")
+    parser.add_argument(
+        "--middleware",
+        type=nullable_str,
+        action="append",
+        default=[],
+        help="Additional ASGI middleware to apply to the app. "
+        "We accept multiple --middleware arguments. "
+        "The value should be an import path. "
+        "If a function is provided, vLLM will add it to the server "
+        "using @app.middleware('http'). "
+        "If a class is provided, vLLM will add it to the server "
+        "using app.add_middleware(). ")
+    parser.add_argument(
+        "--return-tokens-as-token-ids",
+        action="store_true",
+        help="When --max-logprobs is specified, represents single tokens as "
+        "strings of the form 'token_id:{token_id}' so that tokens that "
+        "are not JSON-encodable can be identified.")
+    parser.add_argument(
+        "--disable-frontend-multiprocessing",
+        action="store_true",
+        help="If specified, will run the OpenAI frontend server in the same "
+        "process as the model serving engine.")
+
+    parser.add_argument(
+        "--enable-auto-tool-choice",
+        action="store_true",
+        default=False,
+        help=
+        "Enable auto tool choice for supported models. Use --tool-call-parser"
+        "to specify which parser to use")
+
+    valid_tool_parsers = ToolParserManager.tool_parsers.keys()
+    parser.add_argument(
+        "--tool-call-parser",
+        type=str,
+        metavar="{" + ",".join(valid_tool_parsers) + "} or name registered in "
+        "--tool-parser-plugin",
+        default=None,
+        help=
+        "Select the tool call parser depending on the model that you're using."
+        " This is used to parse the model-generated tool call into OpenAI API "
+        "format. Required for --enable-auto-tool-choice.")
+
+    parser.add_argument(
+        "--tool-parser-plugin",
+        type=str,
+        default="",
+        help=
+        "Special the tool parser plugin write to parse the model-generated tool"
+        " into OpenAI API format, the name register in this plugin can be used "
+        "in --tool-call-parser.")
+
+    parser.add_argument(
+        "--reasoning-parser",
+        type=str,
+        default=None,
+        help=
+        "Select the reasoning parser to split <think>...</think> content into "
+        "reasoning_content vs content in the response. "
+        "Supported: qwen3")
+
+    parser = AsyncEngineArgs.add_cli_args(parser)
+
+    parser.add_argument('--max-log-len',
+                        type=int,
+                        default=None,
+                        help='Max number of prompt characters or prompt '
+                        'ID numbers being printed in log.'
+                        '\n\nDefault: Unlimited')
+
+    parser.add_argument(
+        "--disable-fastapi-docs",
+        action='store_true',
+        default=False,
+        help="Disable FastAPI's OpenAPI schema, Swagger UI, and ReDoc endpoint"
+    )
+
+    return parser
+
+
+def validate_parsed_serve_args(args: argparse.Namespace):
+    """Quick checks for model serve args that raise prior to loading."""
+    if hasattr(args, "subparser") and args.subparser != "serve":
+        return
+
+    # Ensure that the chat template is valid; raises if it likely isn't
+    validate_chat_template(args.chat_template)
+
+    # Enable auto tool needs a tool call parser to be valid
+    if args.enable_auto_tool_choice and not args.tool_call_parser:
+        raise TypeError("Error: --enable-auto-tool-choice requires "
+                        "--tool-call-parser")
+
+
+def create_parser_for_docs() -> FlexibleArgumentParser:
+    parser_for_docs = FlexibleArgumentParser(
+        prog="-m vllm.entrypoints.openai.api_server")
+    return make_arg_parser(parser_for_docs)

qwen3_6_scripts/patch_ops.sh

@@ -58,3 +58,14 @@ python3 ./patch_xformers_sdpa_seq.py
 # Use at server start: --tool-call-parser qwen3_coder --enable-auto-tool-choice
 cp ./qwen3coder_tool_parser.py /usr/local/corex/lib/python3/dist-packages/vllm/entrypoints/openai/tool_parsers/
 python3 ./patch_vllm_tool_parser.py
+
+# --- reasoning parser: Qwen3 <think>...</think> split ------------------------
+# Adds --reasoning-parser qwen3 support.
+# Routes thinking tokens to reasoning_content, rest to content in the delta.
+# Works together with --tool-call-parser qwen3_coder (think → tool call flow).
+cp -r ./reasoning /usr/local/corex/lib/python3/dist-packages/vllm/
+cp ./protocol.py /usr/local/corex/lib/python3/dist-packages/vllm/entrypoints/openai/protocol.py
+cp ./cli_args.py /usr/local/corex/lib/python3/dist-packages/vllm/entrypoints/openai/cli_args.py
+cp ./serving_chat.py /usr/local/corex/lib/python3/dist-packages/vllm/entrypoints/openai/serving_chat.py
+cp ./api_server.py /usr/local/corex/lib/python3/dist-packages/vllm/entrypoints/openai/api_server.py
+cp ./chat_utils.py /usr/local/corex/lib/python3/dist-packages/vllm/entrypoints/chat_utils.py

qwen3_6_scripts/protocol.py

@@ -0,0 +1,995 @@
+# Adapted from
+# https://github.com/lm-sys/FastChat/blob/168ccc29d3f7edc50823016105c024fe2282732a/fastchat/protocol/openai_api_protocol.py
+import time
+from argparse import Namespace
+from typing import Any, Dict, List, Literal, Optional, Union
+
+import torch
+from openai.types.chat import ChatCompletionContentPartParam
+from pydantic import BaseModel, ConfigDict, Field, model_validator
+from typing_extensions import Annotated, Required, TypedDict
+
+from vllm.entrypoints.chat_utils import ChatCompletionMessageParam
+from vllm.pooling_params import PoolingParams
+from vllm.sampling_params import (BeamSearchParams, GuidedDecodingParams,
+                                  RequestOutputKind, SamplingParams)
+from vllm.sequence import Logprob
+from vllm.utils import random_uuid
+
+# torch is mocked during docs generation,
+# so we have to provide the values as literals
+_MOCK_LONG_INFO = Namespace(min=-9223372036854775808, max=9223372036854775807)
+_LONG_INFO: Union["torch.iinfo", Namespace]
+
+try:
+    from sphinx.ext.autodoc.mock import _MockModule
+
+    if isinstance(torch, _MockModule):
+        _LONG_INFO = _MOCK_LONG_INFO
+    else:
+        _LONG_INFO = torch.iinfo(torch.long)
+except ModuleNotFoundError:
+    _LONG_INFO = torch.iinfo(torch.long)
+
+assert _LONG_INFO.min == _MOCK_LONG_INFO.min
+assert _LONG_INFO.max == _MOCK_LONG_INFO.max
+
+
+class CustomChatCompletionMessageParam(TypedDict, total=False):
+    """Enables custom roles in the Chat Completion API."""
+    role: Required[str]
+    """The role of the message's author."""
+
+    content: Union[str, List[ChatCompletionContentPartParam]]
+    """The contents of the message."""
+
+    name: str
+    """An optional name for the participant.
+
+    Provides the model information to differentiate between participants of the
+    same role.
+    """
+
+    tool_call_id: Optional[str]
+
+    tool_calls: Optional[List[dict]]
+
+
+class OpenAIBaseModel(BaseModel):
+    # OpenAI API does not allow extra fields
+    model_config = ConfigDict(extra="forbid")
+
+
+class ErrorResponse(OpenAIBaseModel):
+    object: str = "error"
+    message: str
+    type: str
+    param: Optional[str] = None
+    code: int
+
+
+class ModelPermission(OpenAIBaseModel):
+    id: str = Field(default_factory=lambda: f"modelperm-{random_uuid()}")
+    object: str = "model_permission"
+    created: int = Field(default_factory=lambda: int(time.time()))
+    allow_create_engine: bool = False
+    allow_sampling: bool = True
+    allow_logprobs: bool = True
+    allow_search_indices: bool = False
+    allow_view: bool = True
+    allow_fine_tuning: bool = False
+    organization: str = "*"
+    group: Optional[str] = None
+    is_blocking: bool = False
+
+
+class ModelCard(OpenAIBaseModel):
+    id: str
+    object: str = "model"
+    created: int = Field(default_factory=lambda: int(time.time()))
+    owned_by: str = "vllm"
+    root: Optional[str] = None
+    parent: Optional[str] = None
+    max_model_len: Optional[int] = None
+    permission: List[ModelPermission] = Field(default_factory=list)
+
+
+class ModelList(OpenAIBaseModel):
+    object: str = "list"
+    data: List[ModelCard] = Field(default_factory=list)
+
+
+class UsageInfo(OpenAIBaseModel):
+    prompt_tokens: int = 0
+    total_tokens: int = 0
+    completion_tokens: Optional[int] = 0
+    reasoning_tokens: Optional[int] = 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] = True
+
+
+class FunctionDefinition(OpenAIBaseModel):
+    name: str
+    description: Optional[str] = None
+    parameters: Optional[Dict[str, Any]] = None
+
+
+class ChatCompletionToolsParam(OpenAIBaseModel):
+    type: Literal["function"] = "function"
+    function: FunctionDefinition
+
+
+class ChatCompletionNamedFunction(OpenAIBaseModel):
+    name: str
+
+
+class ChatCompletionNamedToolChoiceParam(OpenAIBaseModel):
+    function: ChatCompletionNamedFunction
+    type: Literal["function"] = "function"
+
+
+class ChatCompletionRequest(OpenAIBaseModel):
+    # Ordered by official OpenAI API documentation
+    # https://platform.openai.com/docs/api-reference/chat/create
+    messages: List[ChatCompletionMessageParam]
+    model: str
+    frequency_penalty: Optional[float] = 0.0
+    logit_bias: Optional[Dict[str, float]] = None
+    logprobs: Optional[bool] = False
+    top_logprobs: Optional[int] = 0
+    max_tokens: Optional[int] = None
+    n: Optional[int] = 1
+    presence_penalty: Optional[float] = 0.0
+    response_format: Optional[ResponseFormat] = None
+    seed: Optional[int] = Field(None, ge=_LONG_INFO.min, le=_LONG_INFO.max)
+    stop: Optional[Union[str, List[str]]] = Field(default_factory=list)
+    stream: Optional[bool] = False
+    stream_options: Optional[StreamOptions] = None
+    temperature: Optional[float] = 0.7
+    top_p: Optional[float] = 1.0
+    tools: Optional[List[ChatCompletionToolsParam]] = None
+    tool_choice: Optional[Union[Literal["none"], Literal["auto"],
+                                ChatCompletionNamedToolChoiceParam]] = "none"
+
+    # NOTE this will be ignored by VLLM -- the model determines the behavior
+    parallel_tool_calls: Optional[bool] = False
+    user: Optional[str] = None
+
+    # doc: begin-chat-completion-sampling-params
+    best_of: Optional[int] = None
+    use_beam_search: bool = False
+    top_k: int = -1
+    min_p: float = 0.0
+    repetition_penalty: float = 1.0
+    length_penalty: float = 1.0
+    stop_token_ids: Optional[List[int]] = Field(default_factory=list)
+    include_stop_str_in_output: bool = False
+    ignore_eos: bool = False
+    min_tokens: int = 0
+    skip_special_tokens: bool = True
+    spaces_between_special_tokens: bool = True
+    truncate_prompt_tokens: Optional[Annotated[int, Field(ge=1)]] = None
+    prompt_logprobs: Optional[int] = None
+    # doc: end-chat-completion-sampling-params
+
+    # doc: begin-chat-completion-extra-params
+    echo: bool = Field(
+        default=False,
+        description=(
+            "If true, the new message will be prepended with the last message "
+            "if they belong to the same role."),
+    )
+    add_generation_prompt: bool = Field(
+        default=True,
+        description=
+        ("If true, the generation prompt will be added to the chat template. "
+         "This is a parameter used by chat template in tokenizer config of the "
+         "model."),
+    )
+    continue_final_message: bool = Field(
+        default=False,
+        description=
+        ("If this is set, the chat will be formatted so that the final "
+         "message in the chat is open-ended, without any EOS tokens. The "
+         "model will continue this message rather than starting a new one. "
+         "This allows you to \"prefill\" part of the model's response for it. "
+         "Cannot be used at the same time as `add_generation_prompt`."),
+    )
+    add_special_tokens: bool = Field(
+        default=False,
+        description=(
+            "If true, special tokens (e.g. BOS) will be added to the prompt "
+            "on top of what is added by the chat template. "
+            "For most models, the chat template takes care of adding the "
+            "special tokens so this should be set to false (as is the "
+            "default)."),
+    )
+    documents: Optional[List[Dict[str, str]]] = Field(
+        default=None,
+        description=
+        ("A list of dicts representing documents that will be accessible to "
+         "the model if it is performing RAG (retrieval-augmented generation)."
+         " If the template does not support RAG, this argument will have no "
+         "effect. We recommend that each document should be a dict containing "
+         "\"title\" and \"text\" keys."),
+    )
+    chat_template: Optional[str] = Field(
+        default=None,
+        description=(
+            "A Jinja template to use for this conversion. "
+            "As of transformers v4.44, default chat template is no longer "
+            "allowed, so you must provide a chat template if the tokenizer "
+            "does not define one."),
+    )
+    chat_template_kwargs: Optional[Dict[str, Any]] = Field(
+        default=None,
+        description=("Additional kwargs to pass to the template renderer. "
+                     "Will be accessible by the chat template."),
+    )
+    guided_json: Optional[Union[str, dict, BaseModel]] = Field(
+        default=None,
+        description=("If specified, the output will follow the JSON schema."),
+    )
+    guided_regex: Optional[str] = Field(
+        default=None,
+        description=(
+            "If specified, the output will follow the regex pattern."),
+    )
+    guided_choice: Optional[List[str]] = Field(
+        default=None,
+        description=(
+            "If specified, the output will be exactly one of the choices."),
+    )
+    guided_grammar: Optional[str] = Field(
+        default=None,
+        description=(
+            "If specified, the output will follow the context free grammar."),
+    )
+    guided_decoding_backend: Optional[str] = Field(
+        default=None,
+        description=(
+            "If specified, will override the default guided decoding backend "
+            "of the server for this specific request. If set, must be either "
+            "'outlines' / 'lm-format-enforcer'"))
+    guided_whitespace_pattern: Optional[str] = Field(
+        default=None,
+        description=(
+            "If specified, will override the default whitespace pattern "
+            "for guided json decoding."))
+    priority: int = Field(
+        default=0,
+        description=(
+            "The priority of the request (lower means earlier handling; "
+            "default: 0). Any priority other than 0 will raise an error "
+            "if the served model does not use priority scheduling."))
+
+    # doc: end-chat-completion-extra-params
+
+    def to_beam_search_params(self,
+                              default_max_tokens: int) -> BeamSearchParams:
+        max_tokens = self.max_tokens
+        if max_tokens is None:
+            max_tokens = default_max_tokens
+
+        n = self.n if self.n is not None else 1
+        temperature = self.temperature if self.temperature is not None else 0.0
+
+        return BeamSearchParams(
+            beam_width=n,
+            max_tokens=max_tokens,
+            ignore_eos=self.ignore_eos,
+            temperature=temperature,
+            length_penalty=self.length_penalty,
+        )
+
+    def to_sampling_params(self, default_max_tokens: int) -> SamplingParams:
+        max_tokens = self.max_tokens
+        if max_tokens is None:
+            max_tokens = default_max_tokens
+
+        prompt_logprobs = self.prompt_logprobs
+        if prompt_logprobs is None and self.echo:
+            prompt_logprobs = self.top_logprobs
+
+        guided_json_object = None
+        if (self.response_format is not None
+                and self.response_format.type == "json_object"):
+            guided_json_object = True
+
+        guided_decoding = GuidedDecodingParams.from_optional(
+            json=self._get_guided_json_from_tool() or self.guided_json,
+            regex=self.guided_regex,
+            choice=self.guided_choice,
+            grammar=self.guided_grammar,
+            json_object=guided_json_object,
+            backend=self.guided_decoding_backend,
+            whitespace_pattern=self.guided_whitespace_pattern)
+
+        return SamplingParams.from_optional(
+            n=self.n,
+            best_of=self.best_of,
+            presence_penalty=self.presence_penalty,
+            frequency_penalty=self.frequency_penalty,
+            repetition_penalty=self.repetition_penalty,
+            temperature=self.temperature,
+            top_p=self.top_p,
+            top_k=self.top_k,
+            min_p=self.min_p,
+            seed=self.seed,
+            stop=self.stop,
+            stop_token_ids=self.stop_token_ids,
+            logprobs=self.top_logprobs if self.logprobs else None,
+            prompt_logprobs=prompt_logprobs,
+            ignore_eos=self.ignore_eos,
+            max_tokens=max_tokens,
+            min_tokens=self.min_tokens,
+            skip_special_tokens=self.skip_special_tokens,
+            spaces_between_special_tokens=self.spaces_between_special_tokens,
+            include_stop_str_in_output=self.include_stop_str_in_output,
+            truncate_prompt_tokens=self.truncate_prompt_tokens,
+            output_kind=RequestOutputKind.DELTA if self.stream \
+                else RequestOutputKind.FINAL_ONLY,
+            guided_decoding=guided_decoding,
+            logit_bias=self.logit_bias)
+
+    def _get_guided_json_from_tool(
+            self) -> Optional[Union[str, dict, BaseModel]]:
+        # user has chosen to not use any tool
+        if self.tool_choice == "none" or self.tools is None:
+            return None
+
+        # user has chosen to use a named tool
+        if type(self.tool_choice) is ChatCompletionNamedToolChoiceParam:
+            tool_name = self.tool_choice.function.name
+            tools = {tool.function.name: tool.function for tool in self.tools}
+            if tool_name not in tools:
+                raise ValueError(
+                    f"Tool '{tool_name}' has not been passed in `tools`.")
+            tool = tools[tool_name]
+            return tool.parameters
+
+        return None
+
+    @model_validator(mode="before")
+    @classmethod
+    def validate_stream_options(cls, data):
+        if data.get("stream_options") and not data.get("stream"):
+            raise ValueError(
+                "Stream options can only be defined when `stream=True`.")
+
+        return data
+
+    @model_validator(mode="before")
+    @classmethod
+    def check_logprobs(cls, data):
+        if (prompt_logprobs := data.get("prompt_logprobs")) is not None:
+            if data.get("stream") and prompt_logprobs > 0:
+                raise ValueError(
+                    "`prompt_logprobs` are not available when `stream=True`.")
+
+            if prompt_logprobs < 0:
+                raise ValueError("`prompt_logprobs` must be a positive value.")
+
+        if (top_logprobs := data.get("top_logprobs")) is not None:
+            if top_logprobs < 0:
+                raise ValueError("`top_logprobs` must be a positive value.")
+
+            if not data.get("logprobs"):
+                raise ValueError(
+                    "when using `top_logprobs`, `logprobs` must be set to true."
+                )
+
+        return data
+
+    @model_validator(mode="before")
+    @classmethod
+    def check_guided_decoding_count(cls, data):
+        if isinstance(data, ValueError):
+            raise data
+
+        guide_count = sum([
+            "guided_json" in data and data["guided_json"] is not None,
+            "guided_regex" in data and data["guided_regex"] is not None,
+            "guided_choice" in data and data["guided_choice"] is not None
+        ])
+        # you can only use one kind of guided decoding
+        if guide_count > 1:
+            raise ValueError(
+                "You can only use one kind of guided decoding "
+                "('guided_json', 'guided_regex' or 'guided_choice').")
+        # you can only either use guided decoding or tools, not both
+        if guide_count > 1 and data.get("tool_choice",
+                                        "none") not in ("none", "auto"):
+            raise ValueError(
+                "You can only either use guided decoding or tools, not both.")
+        return data
+
+    @model_validator(mode="before")
+    @classmethod
+    def check_tool_usage(cls, data):
+
+        # if "tool_choice" is not specified but tools are provided,
+        # default to "auto" tool_choice
+        if "tool_choice" not in data and data.get("tools"):
+            data["tool_choice"] = "auto"
+
+        # if "tool_choice" is specified -- validation
+        if "tool_choice" in data:
+
+            # ensure that if "tool choice" is specified, tools are present
+            if "tools" not in data or data["tools"] is None:
+                raise ValueError(
+                    "When using `tool_choice`, `tools` must be set.")
+
+            # make sure that tool choice is either a named tool
+            # OR that it's set to "auto"
+            if data["tool_choice"] != "auto" and not isinstance(
+                    data["tool_choice"], dict):
+                raise ValueError(
+                    "`tool_choice` must either be a named tool or \"auto\". "
+                    "`tool_choice=\"none\" is not supported.")
+
+            # ensure that if "tool_choice" is specified as an object,
+            # it matches a valid tool
+            if isinstance(data["tool_choice"], dict):
+                valid_tool = False
+                specified_function = data["tool_choice"]["function"]
+                if not specified_function:
+                    raise ValueError(
+                        "Incorrectly formatted `tool_choice`. Should be like "
+                        "`{\"type\": \"function\","
+                        " \"function\": {\"name\": \"my_function\"}}`")
+                specified_function_name = specified_function["name"]
+                if not specified_function_name:
+                    raise ValueError(
+                        "Incorrectly formatted `tool_choice`. Should be like "
+                        "`{\"type\": \"function\", "
+                        "\"function\": {\"name\": \"my_function\"}}`")
+                for tool in data["tools"]:
+                    if tool["function"]["name"] == specified_function_name:
+                        valid_tool = True
+                        break
+                if not valid_tool:
+                    raise ValueError(
+                        "The tool specified in `tool_choice` does not match any"
+                        " of the specified `tools`")
+        return data
+
+    @model_validator(mode="before")
+    @classmethod
+    def check_generation_prompt(cls, data):
+        if data.get("continue_final_message") and data.get(
+                "add_generation_prompt"):
+            raise ValueError("Cannot set both `continue_final_message` and "
+                             "`add_generation_prompt` to True.")
+        return data
+
+
+class CompletionRequest(OpenAIBaseModel):
+    # Ordered by official OpenAI API documentation
+    # https://platform.openai.com/docs/api-reference/completions/create
+    model: str
+    prompt: Union[List[int], List[List[int]], str, List[str]]
+    best_of: Optional[int] = None
+    echo: Optional[bool] = False
+    frequency_penalty: Optional[float] = 0.0
+    logit_bias: Optional[Dict[str, float]] = None
+    logprobs: Optional[int] = None
+    max_tokens: Optional[int] = 16
+    n: int = 1
+    presence_penalty: Optional[float] = 0.0
+    seed: Optional[int] = Field(None, ge=_LONG_INFO.min, le=_LONG_INFO.max)
+    stop: Optional[Union[str, List[str]]] = Field(default_factory=list)
+    stream: Optional[bool] = False
+    stream_options: Optional[StreamOptions] = None
+    suffix: Optional[str] = None
+    temperature: Optional[float] = 1.0
+    top_p: Optional[float] = 1.0
+    user: Optional[str] = None
+
+    # doc: begin-completion-sampling-params
+    use_beam_search: bool = False
+    top_k: int = -1
+    min_p: float = 0.0
+    repetition_penalty: float = 1.0
+    length_penalty: float = 1.0
+    stop_token_ids: Optional[List[int]] = Field(default_factory=list)
+    include_stop_str_in_output: bool = False
+    ignore_eos: bool = False
+    min_tokens: int = 0
+    skip_special_tokens: bool = True
+    spaces_between_special_tokens: bool = True
+    truncate_prompt_tokens: Optional[Annotated[int, Field(ge=1)]] = None
+    allowed_token_ids: Optional[List[int]] = None
+    prompt_logprobs: Optional[int] = None
+    # doc: end-completion-sampling-params
+
+    # doc: begin-completion-extra-params
+    add_special_tokens: bool = Field(
+        default=True,
+        description=(
+            "If true (the default), special tokens (e.g. BOS) will be added to "
+            "the prompt."),
+    )
+    response_format: Optional[ResponseFormat] = Field(
+        default=None,
+        description=
+        ("Similar to chat completion, this parameter specifies the format of "
+         "output. Only {'type': 'json_object'} or {'type': 'text' } is "
+         "supported."),
+    )
+    guided_json: Optional[Union[str, dict, BaseModel]] = Field(
+        default=None,
+        description="If specified, the output will follow the JSON schema.",
+    )
+    guided_regex: Optional[str] = Field(
+        default=None,
+        description=(
+            "If specified, the output will follow the regex pattern."),
+    )
+    guided_choice: Optional[List[str]] = Field(
+        default=None,
+        description=(
+            "If specified, the output will be exactly one of the choices."),
+    )
+    guided_grammar: Optional[str] = Field(
+        default=None,
+        description=(
+            "If specified, the output will follow the context free grammar."),
+    )
+    guided_decoding_backend: Optional[str] = Field(
+        default=None,
+        description=(
+            "If specified, will override the default guided decoding backend "
+            "of the server for this specific request. If set, must be one of "
+            "'outlines' / 'lm-format-enforcer'"))
+    guided_whitespace_pattern: Optional[str] = Field(
+        default=None,
+        description=(
+            "If specified, will override the default whitespace pattern "
+            "for guided json decoding."))
+    priority: int = Field(
+        default=0,
+        description=(
+            "The priority of the request (lower means earlier handling; "
+            "default: 0). Any priority other than 0 will raise an error "
+            "if the served model does not use priority scheduling."))
+
+    # doc: end-completion-extra-params
+
+    def to_beam_search_params(self,
+                              default_max_tokens: int) -> BeamSearchParams:
+        max_tokens = self.max_tokens
+        if max_tokens is None:
+            max_tokens = default_max_tokens
+
+        n = self.n if self.n is not None else 1
+        temperature = self.temperature if self.temperature is not None else 0.0
+
+        return BeamSearchParams(
+            beam_width=n,
+            max_tokens=max_tokens,
+            ignore_eos=self.ignore_eos,
+            temperature=temperature,
+            length_penalty=self.length_penalty,
+        )
+
+    def to_sampling_params(self, default_max_tokens: int) -> SamplingParams:
+        max_tokens = self.max_tokens
+        if max_tokens is None:
+            max_tokens = default_max_tokens
+
+        prompt_logprobs = self.prompt_logprobs
+        if prompt_logprobs is None and self.echo:
+            prompt_logprobs = self.logprobs
+
+        echo_without_generation = self.echo and self.max_tokens == 0
+
+        guided_json_object = None
+        if (self.response_format is not None
+                and self.response_format.type == "json_object"):
+            guided_json_object = True
+
+        guided_decoding = GuidedDecodingParams.from_optional(
+            json=self.guided_json,
+            regex=self.guided_regex,
+            choice=self.guided_choice,
+            grammar=self.guided_grammar,
+            json_object=guided_json_object,
+            backend=self.guided_decoding_backend,
+            whitespace_pattern=self.guided_whitespace_pattern)
+
+        return SamplingParams.from_optional(
+            n=self.n,
+            best_of=self.best_of,
+            presence_penalty=self.presence_penalty,
+            frequency_penalty=self.frequency_penalty,
+            repetition_penalty=self.repetition_penalty,
+            temperature=self.temperature,
+            top_p=self.top_p,
+            top_k=self.top_k,
+            min_p=self.min_p,
+            seed=self.seed,
+            stop=self.stop,
+            stop_token_ids=self.stop_token_ids,
+            logprobs=self.logprobs,
+            ignore_eos=self.ignore_eos,
+            max_tokens=max_tokens if not echo_without_generation else 1,
+            min_tokens=self.min_tokens,
+            prompt_logprobs=prompt_logprobs,
+            skip_special_tokens=self.skip_special_tokens,
+            spaces_between_special_tokens=self.spaces_between_special_tokens,
+            include_stop_str_in_output=self.include_stop_str_in_output,
+            truncate_prompt_tokens=self.truncate_prompt_tokens,
+            output_kind=RequestOutputKind.DELTA if self.stream \
+                else RequestOutputKind.FINAL_ONLY,
+            guided_decoding=guided_decoding,
+            logit_bias=self.logit_bias,
+            allowed_token_ids=self.allowed_token_ids)
+
+    @model_validator(mode="before")
+    @classmethod
+    def check_guided_decoding_count(cls, data):
+        guide_count = sum([
+            "guided_json" in data and data["guided_json"] is not None,
+            "guided_regex" in data and data["guided_regex"] is not None,
+            "guided_choice" in data and data["guided_choice"] is not None
+        ])
+        if guide_count > 1:
+            raise ValueError(
+                "You can only use one kind of guided decoding "
+                "('guided_json', 'guided_regex' or 'guided_choice').")
+        return data
+
+    @model_validator(mode="before")
+    @classmethod
+    def check_logprobs(cls, data):
+        if (prompt_logprobs := data.get("prompt_logprobs")) is not None:
+            if data.get("stream") and prompt_logprobs > 0:
+                raise ValueError(
+                    "`prompt_logprobs` are not available when `stream=True`.")
+
+            if prompt_logprobs < 0:
+                raise ValueError("`prompt_logprobs` must be a positive value.")
+
+        if (logprobs := data.get("logprobs")) is not None and logprobs < 0:
+            raise ValueError("`logprobs` must be a positive value.")
+
+        return data
+
+    @model_validator(mode="before")
+    @classmethod
+    def validate_stream_options(cls, data):
+        if data.get("stream_options") and not data.get("stream"):
+            raise ValueError(
+                "Stream options can only be defined when `stream=True`.")
+
+        return data
+
+
+class EmbeddingRequest(OpenAIBaseModel):
+    # Ordered by official OpenAI API documentation
+    # https://platform.openai.com/docs/api-reference/embeddings
+    model: str
+    input: Union[List[int], List[List[int]], str, List[str]]
+    encoding_format: Literal["float", "base64"] = "float"
+    dimensions: Optional[int] = None
+    user: Optional[str] = None
+    truncate_prompt_tokens: Optional[Annotated[int, Field(ge=1)]] = None
+
+    # doc: begin-embedding-pooling-params
+    additional_data: Optional[Any] = None
+
+    # doc: end-embedding-pooling-params
+
+    # doc: begin-embedding-extra-params
+    priority: int = Field(
+        default=0,
+        description=(
+            "The priority of the request (lower means earlier handling; "
+            "default: 0). Any priority other than 0 will raise an error "
+            "if the served model does not use priority scheduling."))
+
+    # doc: end-embedding-extra-params
+
+    def to_pooling_params(self):
+        return PoolingParams(additional_data=self.additional_data)
+
+
+class CompletionLogProbs(OpenAIBaseModel):
+    text_offset: List[int] = Field(default_factory=list)
+    token_logprobs: List[Optional[float]] = Field(default_factory=list)
+    tokens: List[str] = Field(default_factory=list)
+    top_logprobs: List[Optional[Dict[str,
+                                     float]]] = Field(default_factory=list)
+
+
+class CompletionResponseChoice(OpenAIBaseModel):
+    index: int
+    text: str
+    logprobs: Optional[CompletionLogProbs] = None
+    finish_reason: Optional[str] = None
+    stop_reason: Optional[Union[int, str]] = Field(
+        default=None,
+        description=(
+            "The stop string or token id that caused the completion "
+            "to stop, None if the completion finished for some other reason "
+            "including encountering the EOS token"),
+    )
+    prompt_logprobs: Optional[List[Optional[Dict[int, Logprob]]]] = None
+
+
+class CompletionResponse(OpenAIBaseModel):
+    id: str = Field(default_factory=lambda: f"cmpl-{random_uuid()}")
+    object: str = "text_completion"
+    created: int = Field(default_factory=lambda: int(time.time()))
+    model: str
+    choices: List[CompletionResponseChoice]
+    usage: UsageInfo
+
+
+class CompletionResponseStreamChoice(OpenAIBaseModel):
+    index: int
+    text: str
+    logprobs: Optional[CompletionLogProbs] = None
+    finish_reason: Optional[str] = None
+    stop_reason: Optional[Union[int, str]] = Field(
+        default=None,
+        description=(
+            "The stop string or token id that caused the completion "
+            "to stop, None if the completion finished for some other reason "
+            "including encountering the EOS token"),
+    )
+
+
+class CompletionStreamResponse(OpenAIBaseModel):
+    id: str = Field(default_factory=lambda: f"cmpl-{random_uuid()}")
+    object: str = "text_completion"
+    created: int = Field(default_factory=lambda: int(time.time()))
+    model: str
+    choices: List[CompletionResponseStreamChoice]
+    usage: Optional[UsageInfo] = Field(default=None)
+
+
+class EmbeddingResponseData(OpenAIBaseModel):
+    index: int
+    object: str = "embedding"
+    embedding: Union[List[float], str]
+
+
+class EmbeddingResponse(OpenAIBaseModel):
+    id: str = Field(default_factory=lambda: f"cmpl-{random_uuid()}")
+    object: str = "list"
+    created: int = Field(default_factory=lambda: int(time.time()))
+    model: str
+    data: List[EmbeddingResponseData]
+    usage: UsageInfo
+
+
+class FunctionCall(OpenAIBaseModel):
+    name: str
+    arguments: str
+
+
+class ToolCall(OpenAIBaseModel):
+    id: str = Field(default_factory=lambda: f"chatcmpl-tool-{random_uuid()}")
+    type: Literal["function"] = "function"
+    function: FunctionCall
+
+
+class DeltaFunctionCall(BaseModel):
+    name: Optional[str] = None
+    arguments: Optional[str] = None
+
+
+# a tool call delta where everything is optional
+class DeltaToolCall(OpenAIBaseModel):
+    id: str = Field(default_factory=lambda: f"chatcmpl-tool-{random_uuid()}")
+    type: Literal["function"] = "function"
+    index: int
+    function: Optional[DeltaFunctionCall] = None
+
+
+class ExtractedToolCallInformation(BaseModel):
+    # indicate if tools were called
+    tools_called: bool
+
+    # extracted tool calls
+    tool_calls: List[ToolCall]
+
+    # content - per OpenAI spec, content AND tool calls can be returned rarely
+    # But some models will do this intentionally
+    content: Optional[str] = None
+
+
+class ChatMessage(OpenAIBaseModel):
+    role: str
+    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
+    reasoning_content: Optional[str] = None
+    content: Optional[str] = None
+    tool_calls: List[DeltaToolCall] = Field(default_factory=list)
+
+
+class ChatCompletionResponseStreamChoice(OpenAIBaseModel):
+    index: int
+    delta: DeltaMessage
+    logprobs: Optional[ChatCompletionLogProbs] = None
+    finish_reason: Optional[str] = None
+    stop_reason: Optional[Union[int, str]] = None
+
+
+class ChatCompletionStreamResponse(OpenAIBaseModel):
+    id: str = Field(default_factory=lambda: f"chatcmpl-{random_uuid()}")
+    object: Literal["chat.completion.chunk"] = "chat.completion.chunk"
+    created: int = Field(default_factory=lambda: int(time.time()))
+    model: str
+    choices: List[ChatCompletionResponseStreamChoice]
+    usage: Optional[UsageInfo] = Field(default=None)
+
+
+class BatchRequestInput(OpenAIBaseModel):
+    """
+    The per-line object of the batch input file.
+
+    NOTE: Currently only the `/v1/chat/completions` endpoint is supported.
+    """
+
+    # A developer-provided per-request id that will be used to match outputs to
+    # inputs. Must be unique for each request in a batch.
+    custom_id: str
+
+    # The HTTP method to be used for the request. Currently only POST is
+    # supported.
+    method: str
+
+    # The OpenAI API relative URL to be used for the request. Currently
+    # /v1/chat/completions is supported.
+    url: str
+
+    # The parameters of the request.
+    body: Union[ChatCompletionRequest, EmbeddingRequest]
+
+
+class BatchResponseData(OpenAIBaseModel):
+    # HTTP status code of the response.
+    status_code: int = 200
+
+    # An unique identifier for the API request.
+    request_id: str
+
+    # The body of the response.
+    body: Optional[Union[ChatCompletionResponse, EmbeddingResponse]] = None
+
+
+class BatchRequestOutput(OpenAIBaseModel):
+    """
+    The per-line object of the batch output and error files
+    """
+
+    id: str
+
+    # A developer-provided per-request id that will be used to match outputs to
+    # inputs.
+    custom_id: str
+
+    response: Optional[BatchResponseData]
+
+    # For requests that failed with a non-HTTP error, this will contain more
+    # information on the cause of the failure.
+    error: Optional[Any]
+
+
+class TokenizeCompletionRequest(OpenAIBaseModel):
+    model: str
+    prompt: str
+
+    add_special_tokens: bool = Field(default=True)
+
+
+class TokenizeChatRequest(OpenAIBaseModel):
+    model: str
+    messages: List[ChatCompletionMessageParam]
+
+    add_generation_prompt: bool = Field(default=True)
+    continue_final_message: bool = Field(default=False)
+    add_special_tokens: bool = Field(default=False)
+
+    @model_validator(mode="before")
+    @classmethod
+    def check_generation_prompt(cls, data):
+        if data.get("continue_final_message") and data.get(
+                "add_generation_prompt"):
+            raise ValueError("Cannot set both `continue_final_message` and "
+                             "`add_generation_prompt` to True.")
+        return data
+
+
+TokenizeRequest = Union[TokenizeCompletionRequest, TokenizeChatRequest]
+
+
+class TokenizeResponse(OpenAIBaseModel):
+    count: int
+    max_model_len: int
+    tokens: List[int]
+
+
+class DetokenizeRequest(OpenAIBaseModel):
+    model: str
+    tokens: List[int]
+
+
+class DetokenizeResponse(OpenAIBaseModel):
+    prompt: str
+
+
+class LoadLoraAdapterRequest(BaseModel):
+    lora_name: str
+    lora_path: str
+
+
+class UnloadLoraAdapterRequest(BaseModel):
+    lora_name: str
+    lora_int_id: Optional[int] = Field(default=None)

qwen3_6_scripts/reasoning/__init__.py

@@ -0,0 +1,16 @@
+"""
+Reasoning parser module for vLLM 0.6.3 (BI-V100 / Qwen3.6-27B adaptation).
+
+Usage: --reasoning-parser qwen3
+"""
+
+from vllm.reasoning.abs_reasoning_parsers import ReasoningParser, ReasoningParserManager
+
+__all__ = ["ReasoningParser", "ReasoningParserManager"]
+
+# Lazy-register Qwen3 parser; imported on first get_reasoning_parser("qwen3").
+ReasoningParserManager.register_lazy(
+    "qwen3",
+    "vllm.reasoning.qwen3_reasoning_parser",
+    "Qwen3ReasoningParser",
+)

qwen3_6_scripts/reasoning/abs_reasoning_parsers.py

@@ -0,0 +1,243 @@
+"""
+Abstract reasoning parser base classes for vLLM 0.6.3.
+Adapted from vllm-original/vllm/reasoning/abs_reasoning_parsers.py:
+  - Removed vllm.entrypoints.mcp, vllm.utils.collection_utils, import_utils
+  - DeltaMessage from vllm 0.6.3 protocol path
+  - TokenizerLike -> AnyTokenizer
+  - ReasoningParserManager: simplified eager + lazy registration
+"""
+
+import importlib
+from abc import abstractmethod
+from collections.abc import Iterable, Sequence
+from functools import cached_property
+from typing import Any, Optional, TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from vllm.entrypoints.openai.protocol import DeltaMessage
+    from vllm.transformers_utils.tokenizer import AnyTokenizer
+else:
+    DeltaMessage = Any
+    AnyTokenizer = Any
+
+
+class ReasoningParser:
+    """Abstract base for all reasoning parsers."""
+
+    def __init__(self, tokenizer: "AnyTokenizer", *args, **kwargs):
+        self.model_tokenizer = tokenizer
+
+    @cached_property
+    def vocab(self) -> dict:
+        return self.model_tokenizer.get_vocab()
+
+    @abstractmethod
+    def is_reasoning_end(self, input_ids: Sequence[int]) -> bool:
+        """Return True once the reasoning block has closed in input_ids."""
+
+    def is_reasoning_end_streaming(
+        self, input_ids: Sequence[int], delta_ids: Iterable[int]
+    ) -> bool:
+        return self.is_reasoning_end(input_ids)
+
+    @abstractmethod
+    def extract_content_ids(self, input_ids: list) -> list:
+        """Return token ids that belong to the content (post-reasoning) part."""
+
+    def count_reasoning_tokens(self, token_ids: Sequence[int]) -> int:
+        return 0
+
+    @abstractmethod
+    def extract_reasoning(
+        self, model_output: str, request: Any
+    ) -> "tuple[Optional[str], Optional[str]]":
+        """
+        Split a complete model output into (reasoning_text, content_text).
+        Either part may be None.
+        """
+
+    @abstractmethod
+    def extract_reasoning_streaming(
+        self,
+        previous_text: str,
+        current_text: str,
+        delta_text: str,
+        previous_token_ids: Sequence[int],
+        current_token_ids: Sequence[int],
+        delta_token_ids: Sequence[int],
+    ) -> Optional["DeltaMessage"]:
+        """
+        Extract reasoning from a streaming delta.
+        Returns a DeltaMessage with reasoning_content and/or content set,
+        or None if this delta should be suppressed (control token).
+        """
+
+
+class BaseThinkingReasoningParser(ReasoningParser):
+    """
+    Base for parsers that use <start_token>...</end_token> delimiters.
+    Subclasses define start_token / end_token properties.
+    """
+
+    @property
+    @abstractmethod
+    def start_token(self) -> str:
+        raise NotImplementedError
+
+    @property
+    @abstractmethod
+    def end_token(self) -> str:
+        raise NotImplementedError
+
+    def __init__(self, tokenizer: "AnyTokenizer", *args, **kwargs):
+        super().__init__(tokenizer, *args, **kwargs)
+
+        if not self.model_tokenizer:
+            raise ValueError("Tokenizer must be passed to ReasoningParser.")
+        if not self.start_token or not self.end_token:
+            raise ValueError("start_token and end_token must be defined.")
+
+        self.start_token_id: Optional[int] = self.vocab.get(self.start_token)
+        self.end_token_id: Optional[int] = self.vocab.get(self.end_token)
+        if self.start_token_id is None or self.end_token_id is None:
+            raise RuntimeError(
+                f"{self.__class__.__name__}: could not find think tokens "
+                f"'{self.start_token}'/'{self.end_token}' in tokenizer vocab."
+            )
+
+    def is_reasoning_end(self, input_ids: Sequence[int]) -> bool:
+        for token_id in reversed(input_ids):
+            if token_id == self.start_token_id:
+                return False
+            if token_id == self.end_token_id:
+                return True
+        return False
+
+    def is_reasoning_end_streaming(
+        self, input_ids: Sequence[int], delta_ids: Iterable[int]
+    ) -> bool:
+        return self.end_token_id in delta_ids
+
+    def extract_content_ids(self, input_ids: list) -> list:
+        if self.end_token_id not in input_ids[:-1]:
+            return []
+        return input_ids[input_ids.index(self.end_token_id) + 1:]
+
+    def count_reasoning_tokens(self, token_ids: Sequence[int]) -> int:
+        count = 0
+        depth = 0
+        for tid in token_ids:
+            if tid == self.start_token_id:
+                depth += 1
+            elif tid == self.end_token_id:
+                if depth > 0:
+                    depth -= 1
+            elif depth > 0:
+                count += 1
+        return count
+
+    def extract_reasoning(
+        self, model_output: str, request: Any
+    ) -> "tuple[Optional[str], Optional[str]]":
+        # Strip <think> if the model generated it (old-style template).
+        parts = model_output.partition(self.start_token)
+        model_output = parts[2] if parts[1] else parts[0]
+
+        if self.end_token not in model_output:
+            return model_output, None
+        reasoning, _, content = model_output.partition(self.end_token)
+        return reasoning, content or None
+
+    def extract_reasoning_streaming(
+        self,
+        previous_text: str,
+        current_text: str,
+        delta_text: str,
+        previous_token_ids: Sequence[int],
+        current_token_ids: Sequence[int],
+        delta_token_ids: Sequence[int],
+    ) -> Optional["DeltaMessage"]:
+        from vllm.entrypoints.openai.protocol import DeltaMessage as _DeltaMessage
+
+        # Suppress lone control tokens.
+        if len(delta_token_ids) == 1 and delta_token_ids[0] in (
+            self.start_token_id, self.end_token_id
+        ):
+            return None
+
+        start_in_prev = self.start_token_id in previous_token_ids
+        start_in_delta = self.start_token_id in delta_token_ids
+        end_in_prev = self.end_token_id in previous_token_ids
+        end_in_delta = self.end_token_id in delta_token_ids
+
+        if start_in_prev:
+            if end_in_delta:
+                end_idx = delta_text.find(self.end_token)
+                reasoning = delta_text[:end_idx] if end_idx >= 0 else ""
+                content = delta_text[end_idx + len(self.end_token):] if end_idx >= 0 else None
+                return _DeltaMessage(
+                    reasoning_content=reasoning or None,
+                    content=content or None,
+                )
+            elif end_in_prev:
+                return _DeltaMessage(content=delta_text)
+            else:
+                return _DeltaMessage(reasoning_content=delta_text)
+
+        elif start_in_delta:
+            if end_in_delta:
+                start_idx = delta_text.find(self.start_token)
+                end_idx = delta_text.find(self.end_token)
+                reasoning = delta_text[start_idx + len(self.start_token):end_idx]
+                content = delta_text[end_idx + len(self.end_token):]
+                return _DeltaMessage(
+                    reasoning_content=reasoning or None,
+                    content=content or None,
+                )
+            else:
+                return _DeltaMessage(reasoning_content=delta_text)
+
+        else:
+            return _DeltaMessage(content=delta_text)
+
+
+class ReasoningParserManager:
+    """
+    Registry for ReasoningParser implementations.
+    Supports eager and lazy registration.
+    """
+
+    _parsers: dict = {}           # name -> class (eager)
+    _lazy: dict = {}              # name -> (module_path, class_name)
+
+    @classmethod
+    def register_module(cls, name: str, parser_cls: type) -> None:
+        """Eagerly register a ReasoningParser class."""
+        if not issubclass(parser_cls, ReasoningParser):
+            raise TypeError(f"{parser_cls} is not a ReasoningParser subclass.")
+        cls._parsers[name] = parser_cls
+
+    @classmethod
+    def register_lazy(cls, name: str, module_path: str, class_name: str) -> None:
+        """Register a parser for deferred import."""
+        cls._lazy[name] = (module_path, class_name)
+
+    @classmethod
+    def get_reasoning_parser(cls, name: str) -> type:
+        if name in cls._parsers:
+            return cls._parsers[name]
+        if name in cls._lazy:
+            module_path, class_name = cls._lazy[name]
+            mod = importlib.import_module(module_path)
+            parser_cls = getattr(mod, class_name)
+            cls._parsers[name] = parser_cls
+            return parser_cls
+        registered = sorted(set(cls._parsers) | set(cls._lazy))
+        raise KeyError(
+            f"Reasoning parser '{name}' not found. "
+            f"Available: {registered}"
+        )
+
+    @classmethod
+    def list_registered(cls) -> list:
+        return sorted(set(cls._parsers) | set(cls._lazy))

qwen3_6_scripts/reasoning/qwen3_reasoning_parser.py

@@ -0,0 +1,108 @@
+"""
+Reasoning parser for Qwen3 / Qwen3.5 / Qwen3.6 model family.
+Adapted from vllm-original/vllm/reasoning/qwen3_reasoning_parser.py.
+
+The model uses <think>...</think> to wrap chain-of-thought output.
+For Qwen3.5+ the chat template injects <think> into the prompt, so only
+</think> appears in the generated tokens; older templates generate <think>
+themselves.  Both styles are handled.
+"""
+
+from typing import Optional, Sequence, Any
+
+from vllm.reasoning.abs_reasoning_parsers import (
+    BaseThinkingReasoningParser,
+    ReasoningParserManager,
+)
+
+
+class Qwen3ReasoningParser(BaseThinkingReasoningParser):
+
+    def __init__(self, tokenizer: Any, *args, **kwargs):
+        super().__init__(tokenizer, *args, **kwargs)
+        chat_kwargs = kwargs.get("chat_template_kwargs", {}) or {}
+        self.thinking_enabled = chat_kwargs.get("enable_thinking", True)
+
+    @property
+    def start_token(self) -> str:
+        return "<think>"
+
+    @property
+    def end_token(self) -> str:
+        return "</think>"
+
+    def extract_reasoning(
+        self, model_output: str, request: Any
+    ) -> "tuple[Optional[str], Optional[str]]":
+        # Strip <think> if the model generated it (old template / edge case).
+        parts = model_output.partition(self.start_token)
+        model_output = parts[2] if parts[1] else parts[0]
+
+        if self.end_token not in model_output:
+            if not self.thinking_enabled:
+                return None, model_output
+            # Thinking enabled but output truncated before </think>.
+            return model_output, None
+
+        reasoning, _, content = model_output.partition(self.end_token)
+        return reasoning, content or None
+
+    def count_reasoning_tokens(self, token_ids: Sequence[int]) -> int:
+        token_ids = list(token_ids)
+        if self.start_token_id in token_ids:
+            # Old-style template: model generates <think> itself.
+            # Use depth-counting from the base class.
+            return super().count_reasoning_tokens(token_ids)
+        elif self.end_token_id in token_ids:
+            # New-style template (Qwen3.5+): <think> is injected into the
+            # prompt, so output starts already inside the thinking block.
+            # Every token before </think> is a reasoning token.
+            return token_ids.index(self.end_token_id)
+        else:
+            # No </think> in output: either truncated (all reasoning)
+            # or thinking disabled (none).
+            return len(token_ids) if self.thinking_enabled else 0
+
+    def extract_reasoning_streaming(
+        self,
+        previous_text: str,
+        current_text: str,
+        delta_text: str,
+        previous_token_ids: Sequence[int],
+        current_token_ids: Sequence[int],
+        delta_token_ids: Sequence[int],
+    ):
+        from vllm.entrypoints.openai.protocol import DeltaMessage
+
+        if not self.thinking_enabled:
+            return DeltaMessage(content=delta_text) if delta_text else None
+
+        # Strip <think> from delta if the model generates it itself.
+        if self.start_token_id in delta_token_ids:
+            start_idx = delta_text.find(self.start_token)
+            if start_idx >= 0:
+                delta_text = delta_text[start_idx + len(self.start_token):]
+
+        if self.end_token_id in delta_token_ids:
+            end_idx = delta_text.find(self.end_token)
+            if end_idx >= 0:
+                reasoning = delta_text[:end_idx]
+                content = delta_text[end_idx + len(self.end_token):]
+                if not reasoning and not content:
+                    return None
+                return DeltaMessage(
+                    reasoning_content=reasoning or None,
+                    content=content or None,
+                )
+            return None
+
+        if not delta_text:
+            return None
+        elif self.end_token_id in previous_token_ids:
+            return DeltaMessage(content=delta_text)
+        else:
+            return DeltaMessage(reasoning_content=delta_text)
+
+
+# Register immediately when this module is imported.
+ReasoningParserManager.register_module("qwen3", Qwen3ReasoningParser)

qwen3_6_scripts/serving_chat.py

@@ -0,0 +1,994 @@
+import asyncio
+import json
+import time
+from typing import (AsyncGenerator, AsyncIterator, Callable, Dict, Final, List,
+                    Optional)
+from typing import Sequence as GenericSequence
+from typing import Union
+
+from fastapi import Request
+
+from vllm.config import ModelConfig
+from vllm.engine.async_llm_engine import AsyncLLMEngine
+from vllm.engine.multiprocessing.client import MQLLMEngineClient
+from vllm.engine.protocol import EngineClient
+from vllm.entrypoints.chat_utils import (ConversationMessage,
+                                         apply_hf_chat_template,
+                                         apply_mistral_chat_template,
+                                         load_chat_template,
+                                         parse_chat_messages_futures)
+from vllm.entrypoints.logger import RequestLogger
+from vllm.entrypoints.openai.protocol import (
+    ChatCompletionLogProb, ChatCompletionLogProbs,
+    ChatCompletionLogProbsContent, ChatCompletionNamedToolChoiceParam,
+    ChatCompletionRequest, ChatCompletionResponse,
+    ChatCompletionResponseChoice, ChatCompletionResponseStreamChoice,
+    ChatCompletionStreamResponse, ChatMessage, DeltaFunctionCall, DeltaMessage,
+    DeltaToolCall, ErrorResponse, FunctionCall, RequestResponseMetadata,
+    ToolCall, UsageInfo)
+from vllm.entrypoints.openai.serving_engine import (BaseModelPath,
+                                                    LoRAModulePath,
+                                                    OpenAIServing,
+                                                    PromptAdapterPath,
+                                                    TextTokensPrompt)
+from vllm.entrypoints.openai.tool_parsers import ToolParser, ToolParserManager
+from vllm.inputs import TokensPrompt
+from vllm.logger import init_logger
+from vllm.outputs import CompletionOutput, RequestOutput
+from vllm.sampling_params import BeamSearchParams, SamplingParams
+from vllm.sequence import Logprob
+from vllm.tracing import (contains_trace_headers, extract_trace_headers,
+                          log_tracing_disabled_warning)
+from vllm.transformers_utils.tokenizer import AnyTokenizer, MistralTokenizer
+from vllm.utils import iterate_with_cancellation, random_uuid
+
+logger = init_logger(__name__)
+
+
+class OpenAIServingChat(OpenAIServing):
+
+    def __init__(self,
+                 engine_client: EngineClient,
+                 model_config: ModelConfig,
+                 base_model_paths: List[BaseModelPath],
+                 response_role: str,
+                 *,
+                 lora_modules: Optional[List[LoRAModulePath]],
+                 prompt_adapters: Optional[List[PromptAdapterPath]],
+                 request_logger: Optional[RequestLogger],
+                 chat_template: Optional[str],
+                 return_tokens_as_token_ids: bool = False,
+                 enable_auto_tools: bool = False,
+                 tool_parser: Optional[str] = None,
+                 reasoning_parser: Optional[str] = None):
+        super().__init__(engine_client=engine_client,
+                         model_config=model_config,
+                         base_model_paths=base_model_paths,
+                         lora_modules=lora_modules,
+                         prompt_adapters=prompt_adapters,
+                         request_logger=request_logger,
+                         return_tokens_as_token_ids=return_tokens_as_token_ids)
+
+        self.response_role = response_role
+        self.use_tool_use_model_template = False
+        self.chat_template = load_chat_template(chat_template)
+
+        # set up tool use
+        self.enable_auto_tools: bool = enable_auto_tools
+        if self.enable_auto_tools:
+            logger.info(
+                "\"auto\" tool choice has been enabled please note that while"
+                " the parallel_tool_calls client option is preset for "
+                "compatibility reasons, it will be ignored.")
+
+        self.tool_parser: Optional[Callable[[AnyTokenizer], ToolParser]] = None
+        if self.enable_auto_tools:
+            try:
+                self.tool_parser = ToolParserManager.get_tool_parser(
+                    tool_parser)
+            except Exception as e:
+                raise TypeError("Error: --enable-auto-tool-choice requires "
+                                f"tool_parser:'{tool_parser}' which has not "
+                                "been registered") from e
+
+        # set up reasoning parser
+        self.reasoning_parser_cls = None
+        if reasoning_parser:
+            try:
+                from vllm.reasoning import ReasoningParserManager
+                self.reasoning_parser_cls = \
+                    ReasoningParserManager.get_reasoning_parser(reasoning_parser)
+                logger.info("Reasoning parser '%s' enabled.", reasoning_parser)
+            except Exception as e:
+                raise TypeError(
+                    f"Error: --reasoning-parser '{reasoning_parser}' could not "
+                    "be loaded. Make sure vllm/reasoning/ is installed."
+                ) from e
+
+    async def create_chat_completion(
+        self,
+        request: ChatCompletionRequest,
+        raw_request: Optional[Request] = None,
+    ) -> Union[AsyncGenerator[str, None], ChatCompletionResponse,
+               ErrorResponse]:
+        """Completion API similar to OpenAI's API.
+
+        See https://platform.openai.com/docs/api-reference/chat/create
+        for the API specification. This API mimics the OpenAI
+        ChatCompletion API.
+
+        """
+        error_check_ret = await self._check_model(request)
+        if error_check_ret is not None:
+            logger.error("Error with model %s", error_check_ret)
+            return error_check_ret
+
+        # If the engine is dead, raise the engine's DEAD_ERROR.
+        # This is required for the streaming case, where we return a
+        # success status before we actually start generating text :).
+        if self.engine_client.errored:
+            raise self.engine_client.dead_error
+
+        try:
+            (
+                lora_request,
+                prompt_adapter_request,
+            ) = self._maybe_get_adapters(request)
+
+            model_config = self.model_config
+            tokenizer = await self.engine_client.get_tokenizer(lora_request)
+
+            conversation, mm_data_future = parse_chat_messages_futures(
+                request.messages, model_config, tokenizer)
+
+            tool_dicts = None if request.tools is None else [
+                tool.model_dump() for tool in request.tools
+            ]
+
+            prompt: Union[str, List[int]]
+            is_mistral_tokenizer = isinstance(tokenizer, MistralTokenizer)
+            if is_mistral_tokenizer:
+                prompt = apply_mistral_chat_template(
+                    tokenizer,
+                    messages=request.messages,
+                    chat_template=request.chat_template or self.chat_template,
+                    add_generation_prompt=request.add_generation_prompt,
+                    continue_final_message=request.continue_final_message,
+                    tools=tool_dicts,
+                    documents=request.documents,
+                    **(request.chat_template_kwargs or {}),
+                )
+            else:
+                prompt = apply_hf_chat_template(
+                    tokenizer,
+                    conversation=conversation,
+                    chat_template=request.chat_template or self.chat_template,
+                    add_generation_prompt=request.add_generation_prompt,
+                    continue_final_message=request.continue_final_message,
+                    tools=tool_dicts,
+                    documents=request.documents,
+                    **(request.chat_template_kwargs or {}),
+                )
+        except Exception as e:
+            logger.exception("Error in applying chat template from request")
+            return self.create_error_response(str(e))
+
+        try:
+            mm_data = await mm_data_future
+        except Exception as e:
+            logger.exception("Error in loading multi-modal data")
+            return self.create_error_response(str(e))
+
+        # validation for OpenAI tools
+        # tool_choice = "required" is not supported
+        if request.tool_choice == "required":
+            return self.create_error_response(
+                "tool_choice = \"required\" is not supported!")
+
+        if not is_mistral_tokenizer and request.tool_choice == "auto" and not (
+                self.enable_auto_tools and self.tool_parser is not None):
+            # for hf tokenizers, "auto" tools requires
+            # --enable-auto-tool-choice and --tool-call-parser
+            return self.create_error_response(
+                "\"auto\" tool choice requires "
+                "--enable-auto-tool-choice and --tool-call-parser to be set")
+
+        request_id = f"chat-{random_uuid()}"
+
+        request_metadata = RequestResponseMetadata(request_id=request_id)
+        if raw_request:
+            raw_request.state.request_metadata = request_metadata
+
+        try:
+            if self.enable_auto_tools and self.tool_parser:
+                request = self.tool_parser(tokenizer).adjust_request(
+                    request=request)
+
+            if isinstance(prompt, str):
+                prompt_inputs = self._tokenize_prompt_input(
+                    request,
+                    tokenizer,
+                    prompt,
+                    truncate_prompt_tokens=request.truncate_prompt_tokens,
+                    add_special_tokens=request.add_special_tokens,
+                )
+            else:
+                assert isinstance(prompt, list) and isinstance(
+                    prompt[0], int
+                ), "Prompt has to be either a string or a list of token ids"
+                prompt_inputs = TextTokensPrompt(
+                    prompt=tokenizer.decode(prompt), prompt_token_ids=prompt)
+
+            assert prompt_inputs is not None
+
+            sampling_params: Union[SamplingParams, BeamSearchParams]
+            default_max_tokens = self.max_model_len - len(
+                prompt_inputs["prompt_token_ids"])
+            if request.use_beam_search:
+                sampling_params = request.to_beam_search_params(
+                    default_max_tokens)
+            else:
+                sampling_params = request.to_sampling_params(
+                    default_max_tokens)
+
+            self._log_inputs(request_id,
+                             prompt_inputs,
+                             params=sampling_params,
+                             lora_request=lora_request,
+                             prompt_adapter_request=prompt_adapter_request)
+
+            engine_inputs = TokensPrompt(
+                prompt_token_ids=prompt_inputs["prompt_token_ids"])
+            if mm_data is not None:
+                engine_inputs["multi_modal_data"] = mm_data
+
+            is_tracing_enabled = (await
+                                  self.engine_client.is_tracing_enabled())
+            trace_headers = None
+            if is_tracing_enabled and raw_request:
+                trace_headers = extract_trace_headers(raw_request.headers)
+            if (not is_tracing_enabled and raw_request
+                    and contains_trace_headers(raw_request.headers)):
+                log_tracing_disabled_warning()
+
+            if isinstance(sampling_params, BeamSearchParams):
+                assert isinstance(self.engine_client,
+                                    (AsyncLLMEngine,
+                                    MQLLMEngineClient)), \
+                    "Beam search is only supported with" \
+                    "AsyncLLMEngine and MQLLMEngineClient."
+                result_generator = self.engine_client.beam_search(
+                    engine_inputs['prompt_token_ids'],
+                    request_id,
+                    sampling_params,
+                )
+            else:
+                result_generator = self.engine_client.generate(
+                    engine_inputs,
+                    sampling_params,
+                    request_id,
+                    lora_request=lora_request,
+                    trace_headers=trace_headers,
+                    prompt_adapter_request=prompt_adapter_request,
+                    priority=request.priority,
+                )
+        except ValueError as e:
+            # TODO: Use a vllm-specific Validation Error
+            return self.create_error_response(str(e))
+
+        if raw_request:
+            result_generator = iterate_with_cancellation(
+                result_generator, raw_request.is_disconnected)
+
+        # Streaming response
+        if request.stream:
+            return self.chat_completion_stream_generator(
+                request, result_generator, request_id, conversation, tokenizer,
+                request_metadata)
+
+        try:
+            return await self.chat_completion_full_generator(
+                request, result_generator, request_id, conversation, tokenizer,
+                request_metadata)
+        except ValueError as e:
+            # TODO: Use a vllm-specific Validation Error
+            return self.create_error_response(str(e))
+
+    def get_chat_request_role(self, request: ChatCompletionRequest) -> str:
+        if request.add_generation_prompt:
+            return self.response_role
+        return request.messages[-1]["role"]
+
+    async def chat_completion_stream_generator(
+        self,
+        request: ChatCompletionRequest,
+        result_generator: AsyncIterator[RequestOutput],
+        request_id: str,
+        conversation: List[ConversationMessage],
+        tokenizer: AnyTokenizer,
+        request_metadata: RequestResponseMetadata,
+    ) -> AsyncGenerator[str, None]:
+        model_name = self.base_model_paths[0].name
+        created_time = int(time.time())
+        chunk_object_type: Final = "chat.completion.chunk"
+        first_iteration = True
+
+        # Send response for each token for each request.n (index)
+        num_choices = 1 if request.n is None else request.n
+        previous_num_tokens = [0] * num_choices
+        finish_reason_sent = [False] * num_choices
+        num_prompt_tokens = 0
+
+        if isinstance(request.tool_choice, ChatCompletionNamedToolChoiceParam):
+            tool_choice_function_name = request.tool_choice.function.name
+        else:
+            tool_choice_function_name = None
+
+        # Determine whether tools are in use with "auto" tool choice
+        tool_choice_auto = (
+            not tool_choice_function_name
+            and self._should_stream_with_auto_tool_parsing(request))
+
+        use_reasoning = self.reasoning_parser_cls is not None
+
+        all_previous_token_ids: Optional[List[List[int]]]
+        # previous_texts / all_previous_token_ids are needed for both tool
+        # parsing and reasoning parsing (both require full-history context).
+        if tool_choice_auto or use_reasoning:
+            previous_texts = [""] * num_choices
+            all_previous_token_ids = [[]] * num_choices
+        else:
+            previous_texts, all_previous_token_ids = None, None
+
+        # Prepare the tool parser if it's needed
+        try:
+            if tool_choice_auto and self.tool_parser:
+                tool_parsers: List[Optional[ToolParser]] = [
+                    self.tool_parser(tokenizer)
+                ] * num_choices
+            else:
+                tool_parsers = [None] * num_choices
+        except RuntimeError as e:
+            logger.error("Error in tool parser creation: %s", e)
+            data = self.create_streaming_error_response(str(e))
+            yield f"data: {data}\n\n"
+            yield "data: [DONE]\n\n"
+            return
+
+        # Prepare reasoning parsers (one instance per choice for state isolation)
+        reasoning_parsers: List[Optional[object]] = [None] * num_choices
+        reasoning_end_arr: List[bool] = [False] * num_choices
+        reasoning_token_counts: List[int] = [0] * num_choices
+        if use_reasoning:
+            try:
+                reasoning_parsers = [
+                    self.reasoning_parser_cls(
+                        tokenizer,
+                        chat_template_kwargs=request.chat_template_kwargs)
+                    for _ in range(num_choices)
+                ]
+                # If thinking is disabled per-request, mark reasoning as
+                # already ended so the tool-auto branch is reachable.
+                for idx, rp in enumerate(reasoning_parsers):
+                    if hasattr(rp, 'thinking_enabled') and not rp.thinking_enabled:
+                        reasoning_end_arr[idx] = True
+            except RuntimeError as e:
+                logger.error("Error in reasoning parser creation: %s", e)
+                data = self.create_streaming_error_response(str(e))
+                yield f"data: {data}\n\n"
+                yield "data: [DONE]\n\n"
+                return
+
+        try:
+            async for res in result_generator:
+                if res.prompt_token_ids is not None:
+                    num_prompt_tokens = len(res.prompt_token_ids)
+                    if res.encoder_prompt_token_ids is not None:
+                        num_prompt_tokens += len(res.encoder_prompt_token_ids)
+
+                # We need to do it here, because if there are exceptions in
+                # the result_generator, it needs to be sent as the FIRST
+                # response (by the try...catch).
+                if first_iteration:
+                    # Send first response for each request.n (index) with
+                    # the role
+                    role = self.get_chat_request_role(request)
+
+                    # NOTE num_choices defaults to 1 so this usually executes
+                    # once per request
+                    for i in range(num_choices):
+                        tool_parser = tool_parsers[i]
+                        choice_data = ChatCompletionResponseStreamChoice(
+                            index=i,
+                            delta=DeltaMessage(
+                                role=role,
+                                content="",
+                            ),
+                            logprobs=None,
+                            finish_reason=None)
+                        chunk = ChatCompletionStreamResponse(
+                            id=request_id,
+                            object=chunk_object_type,
+                            created=created_time,
+                            choices=[choice_data],
+                            model=model_name)
+
+                        # if usage should be included
+                        if (request.stream_options
+                                and request.stream_options.include_usage):
+                            # if continuous usage stats are requested, add it
+                            if request.stream_options.continuous_usage_stats:
+                                usage = UsageInfo(
+                                    prompt_tokens=num_prompt_tokens,
+                                    completion_tokens=0,
+                                    total_tokens=num_prompt_tokens)
+                                chunk.usage = usage
+                            # otherwise don't
+                            else:
+                                chunk.usage = None
+
+                        data = chunk.model_dump_json(exclude_unset=True)
+                        yield f"data: {data}\n\n"
+
+                    # Send response to echo the input portion of the
+                    # last message
+                    if request.echo or request.continue_final_message:
+                        last_msg_content: str = ""
+                        if conversation and "content" in conversation[
+                                -1] and conversation[-1].get("role") == role:
+                            last_msg_content = conversation[-1]["content"] or ""
+
+                        if last_msg_content:
+                            for i in range(num_choices):
+                                choice_data = (
+                                    ChatCompletionResponseStreamChoice(
+                                        index=i,
+                                        delta=DeltaMessage(
+                                            content=last_msg_content),
+                                        logprobs=None,
+                                        finish_reason=None))
+                                chunk = ChatCompletionStreamResponse(
+                                    id=request_id,
+                                    object=chunk_object_type,
+                                    created=created_time,
+                                    choices=[choice_data],
+                                    model=model_name)
+                                if (request.stream_options and
+                                        request.stream_options.include_usage):
+                                    if (request.stream_options.
+                                            continuous_usage_stats):
+                                        usage = UsageInfo(
+                                            prompt_tokens=num_prompt_tokens,
+                                            completion_tokens=0,
+                                            total_tokens=num_prompt_tokens)
+                                        chunk.usage = usage
+                                    else:
+                                        chunk.usage = None
+
+                                data = chunk.model_dump_json(
+                                    exclude_unset=True)
+                                yield f"data: {data}\n\n"
+                    first_iteration = False
+
+                for output in res.outputs:
+                    i = output.index
+                    tool_parser = tool_parsers[i]
+
+                    if finish_reason_sent[i]:
+                        continue
+
+                    if request.logprobs and request.top_logprobs is not None:
+                        assert output.logprobs is not None, (
+                            "Did not output logprobs")
+                        logprobs = self._create_chat_logprobs(
+                            token_ids=output.token_ids,
+                            top_logprobs=output.logprobs,
+                            tokenizer=tokenizer,
+                            num_output_top_logprobs=request.top_logprobs,
+                        )
+                    else:
+                        logprobs = None
+
+                    delta_text = output.text
+                    delta_message: Optional[DeltaMessage]
+
+                    # Maintain text/token history when either reasoning or
+                    # auto-tool parsing is active.
+                    assert previous_texts is not None or not (
+                        tool_choice_auto or use_reasoning)
+                    if 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)
+                        previous_texts[i] = current_text
+                        all_previous_token_ids[i] = current_token_ids
+                    else:
+                        previous_text = ""
+                        previous_token_ids = []
+                        current_text = delta_text
+                        current_token_ids = list(output.token_ids)
+
+                    # 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 reasoning: route through reasoning parser while
+                    # </think> has not yet been seen.
+                    elif use_reasoning and not reasoning_end_arr[i]:
+                        r_parser = reasoning_parsers[i]
+                        delta_message = r_parser.extract_reasoning_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,
+                        )
+                        # Mark reasoning as ended when end token appears.
+                        if r_parser.end_token_id in current_token_ids:
+                            reasoning_end_arr[i] = True
+
+                    # handle streaming deltas for tools with "auto" tool choice
+                    # (only reached after reasoning block, if any, has ended)
+                    elif tool_choice_auto:
+                        assert tool_parser is not None
+                        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))
+
+                    # handle streaming just a content delta
+                    else:
+                        delta_message = DeltaMessage(content=delta_text)
+
+                    # set the previous values for the next iteration
+                    previous_num_tokens[i] += len(output.token_ids)
+
+                    # if the message delta is None (e.g. because it was a
+                    # "control token" for tool calls or the parser otherwise
+                    # wasn't ready to send a token, then
+                    #   get the next token without streaming a chunk
+                    if delta_message is None:
+                        continue
+
+                    if output.finish_reason is None:
+                        # Send token-by-token response for each request.n
+
+                        choice_data = ChatCompletionResponseStreamChoice(
+                            index=i,
+                            delta=delta_message,
+                            logprobs=logprobs,
+                            finish_reason=None)
+                        chunk = ChatCompletionStreamResponse(
+                            id=request_id,
+                            object=chunk_object_type,
+                            created=created_time,
+                            choices=[choice_data],
+                            model=model_name)
+
+                        # handle usage stats if requested & if continuous
+                        if (request.stream_options
+                                and request.stream_options.include_usage):
+                            if request.stream_options.continuous_usage_stats:
+                                completion_tokens = len(output.token_ids)
+                                usage = UsageInfo(
+                                    prompt_tokens=num_prompt_tokens,
+                                    completion_tokens=completion_tokens,
+                                    total_tokens=num_prompt_tokens +
+                                    completion_tokens,
+                                )
+                                chunk.usage = usage
+                            else:
+                                chunk.usage = None
+
+                        data = chunk.model_dump_json(exclude_unset=True)
+                        yield f"data: {data}\n\n"
+
+                    # if the model is finished generating
+                    else:
+                        # check to make sure we haven't "forgotten" to stream
+                        #   any tokens that were generated but previously
+                        #   matched by partial json parsing
+                        # only happens if we are NOT using guided decoding
+                        auto_tools_called = False
+                        if tool_parser:
+                            auto_tools_called = len(
+                                tool_parser.prev_tool_call_arr) > 0
+                            index = len(tool_parser.prev_tool_call_arr
+                                        ) - 1 if auto_tools_called else 0
+                        else:
+                            index = 0
+
+                        if self._should_check_for_unstreamed_tool_arg_tokens(
+                                delta_message, output) and tool_parser:
+                            # get the expected call based on partial JSON
+                            # parsing which "autocompletes" the JSON
+                            expected_call = json.dumps(
+                                tool_parser.prev_tool_call_arr[index].get(
+                                    "arguments", {}))
+
+                            # get what we've streamed so far for arguments
+                            # for the current tool
+                            actual_call = tool_parser.streamed_args_for_tool[
+                                index]
+
+                            # check to see if there's anything left to stream
+                            remaining_call = expected_call.replace(
+                                actual_call, "", 1)
+
+                            # set that as a delta message
+                            delta_message = DeltaMessage(tool_calls=[
+                                DeltaToolCall(index=index,
+                                              function=DeltaFunctionCall(
+                                                  arguments=remaining_call).
+                                              model_dump(exclude_none=True))
+                            ])
+
+                        # Count reasoning tokens for this choice at finish time.
+                        if use_reasoning and all_previous_token_ids is not None:
+                            r_parser = reasoning_parsers[i]
+                            reasoning_token_counts[i] = \
+                                r_parser.count_reasoning_tokens(
+                                    all_previous_token_ids[i])
+
+                        # Send the finish response for each request.n only once
+                        choice_data = ChatCompletionResponseStreamChoice(
+                            index=i,
+                            delta=delta_message,
+                            logprobs=logprobs,
+                            finish_reason=output.finish_reason
+                            if not auto_tools_called else "tool_calls",
+                            stop_reason=output.stop_reason)
+                        chunk = ChatCompletionStreamResponse(
+                            id=request_id,
+                            object=chunk_object_type,
+                            created=created_time,
+                            choices=[choice_data],
+                            model=model_name)
+                        if (request.stream_options
+                                and request.stream_options.include_usage):
+                            if request.stream_options.continuous_usage_stats:
+                                completion_tokens = len(output.token_ids)
+                                usage = UsageInfo(
+                                    prompt_tokens=num_prompt_tokens,
+                                    completion_tokens=completion_tokens,
+                                    total_tokens=num_prompt_tokens +
+                                    completion_tokens,
+                                )
+                                chunk.usage = usage
+                            else:
+                                chunk.usage = None
+                        data = chunk.model_dump_json(exclude_unset=True)
+                        yield f"data: {data}\n\n"
+                        finish_reason_sent[i] = True
+
+            # once the final token is handled, if stream_options.include_usage
+            # is sent, send the usage
+            if (request.stream_options
+                    and request.stream_options.include_usage):
+                completion_tokens = previous_num_tokens[i]
+                total_reasoning = sum(reasoning_token_counts) if use_reasoning else None
+                final_usage = UsageInfo(
+                    prompt_tokens=num_prompt_tokens,
+                    completion_tokens=completion_tokens,
+                    total_tokens=num_prompt_tokens + completion_tokens,
+                    reasoning_tokens=total_reasoning,
+                )
+
+                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)
+            total_reasoning = sum(reasoning_token_counts) if use_reasoning else None
+            request_metadata.final_usage_info = UsageInfo(
+                prompt_tokens=num_prompt_tokens,
+                completion_tokens=num_completion_tokens,
+                total_tokens=num_prompt_tokens + num_completion_tokens,
+                reasoning_tokens=total_reasoning)
+
+        except ValueError as e:
+            # TODO: Use a vllm-specific Validation Error
+            logger.error("error in chat completion stream generator: %s", e)
+            data = self.create_streaming_error_response(str(e))
+            yield f"data: {data}\n\n"
+        # Send the final done message after all response.n are finished
+        yield "data: [DONE]\n\n"
+
+    async def chat_completion_full_generator(
+        self,
+        request: ChatCompletionRequest,
+        result_generator: AsyncIterator[RequestOutput],
+        request_id: str,
+        conversation: List[ConversationMessage],
+        tokenizer: AnyTokenizer,
+        request_metadata: RequestResponseMetadata,
+    ) -> Union[ErrorResponse, ChatCompletionResponse]:
+
+        model_name = self.base_model_paths[0].name
+        created_time = int(time.time())
+        final_res: Optional[RequestOutput] = None
+
+        try:
+            async for res in result_generator:
+                final_res = res
+        except asyncio.CancelledError:
+            return self.create_error_response("Client disconnected")
+
+        assert final_res is not None
+
+        choices: List[ChatCompletionResponseChoice] = []
+
+        role = self.get_chat_request_role(request)
+        for output in final_res.outputs:
+            token_ids = output.token_ids
+            out_logprobs = output.logprobs
+
+            if request.logprobs and request.top_logprobs is not None:
+                assert out_logprobs is not None, "Did not output logprobs"
+                logprobs = self._create_chat_logprobs(
+                    token_ids=token_ids,
+                    top_logprobs=out_logprobs,
+                    num_output_top_logprobs=request.top_logprobs,
+                    tokenizer=tokenizer,
+                )
+            else:
+                logprobs = None
+
+            # In the OpenAI API the finish_reason is "tools_called"
+            # if the tool choice is auto and the model produced a tool
+            # call. The same is not true for named function calls
+            auto_tools_called = False
+
+            # Extract reasoning content if parser is configured.
+            # output_text is what remains after stripping <think>...</think>.
+            reasoning_text: Optional[str] = None
+            output_text: str = output.text
+            if self.reasoning_parser_cls:
+                r_parser = self.reasoning_parser_cls(
+                    tokenizer,
+                    chat_template_kwargs=request.chat_template_kwargs)
+                reasoning_text, extracted = r_parser.extract_reasoning(
+                    output.text, request)
+                output_text = extracted or ""
+
+            # if auto tools are not enabled, and a named tool choice using
+            #   outlines is not being used
+            if (not self.enable_auto_tools
+                    or not self.tool_parser) and not isinstance(
+                        request.tool_choice,
+                        ChatCompletionNamedToolChoiceParam):
+                message = ChatMessage(role=role,
+                                      reasoning_content=reasoning_text,
+                                      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,
+                    reasoning_content=reasoning_text,
+                    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,
+                                      reasoning_content=reasoning_text,
+                                      content=output_text)
+
+            # handle when there are tools and tool choice is auto
+            elif request.tools and (
+                    request.tool_choice == "auto"
+                    or request.tool_choice is None) and self.enable_auto_tools \
+                    and self.tool_parser:
+
+                try:
+                    tool_parser = self.tool_parser(tokenizer)
+                except RuntimeError as e:
+                    logger.error("Error in tool parser creation: %s", e)
+                    return self.create_error_response(str(e))
+
+                # Parse tool calls from the post-reasoning content.
+                tool_call_info = tool_parser.extract_tool_calls(
+                    output_text, request=request)
+                auto_tools_called = tool_call_info.tools_called
+                if tool_call_info.tools_called:
+                    message = ChatMessage(
+                        role=role,
+                        reasoning_content=reasoning_text,
+                        content=tool_call_info.content,
+                        tool_calls=tool_call_info.tool_calls)
+                else:
+                    message = ChatMessage(role=role,
+                                          reasoning_content=reasoning_text,
+                                          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,
+                                      reasoning_content=reasoning_text,
+                                      content=output_text)
+
+            choice_data = ChatCompletionResponseChoice(
+                index=output.index,
+                message=message,
+                logprobs=logprobs,
+                finish_reason="tool_calls" if auto_tools_called else
+                output.finish_reason if output.finish_reason else "stop",
+                stop_reason=output.stop_reason)
+            choices.append(choice_data)
+
+        if request.echo or request.continue_final_message:
+            last_msg_content = ""
+            if conversation and "content" in conversation[-1] and conversation[
+                    -1].get("role") == role:
+                last_msg_content = conversation[-1]["content"] or ""
+
+            for choice in choices:
+                full_message = last_msg_content + (choice.message.content
+                                                   or "")
+                choice.message.content = full_message
+
+        assert final_res.prompt_token_ids is not None
+        num_prompt_tokens = len(final_res.prompt_token_ids)
+        if final_res.encoder_prompt_token_ids is not None:
+            num_prompt_tokens += len(final_res.encoder_prompt_token_ids)
+        num_generated_tokens = sum(
+            len(output.token_ids) for output in final_res.outputs)
+        total_reasoning_tokens: Optional[int] = None
+        if self.reasoning_parser_cls:
+            rp = self.reasoning_parser_cls(
+                tokenizer,
+                chat_template_kwargs=request.chat_template_kwargs)
+            total_reasoning_tokens = sum(
+                rp.count_reasoning_tokens(list(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,
+            reasoning_tokens=total_reasoning_tokens,
+        )
+
+        request_metadata.final_usage_info = usage
+
+        response = ChatCompletionResponse(
+            id=request_id,
+            created=created_time,
+            model=model_name,
+            choices=choices,
+            usage=usage,
+            prompt_logprobs=final_res.prompt_logprobs,
+        )
+
+        return response
+
+    def _get_top_logprobs(
+            self, logprobs: Dict[int, Logprob], top_logprobs: Optional[int],
+            tokenizer: AnyTokenizer) -> List[ChatCompletionLogProb]:
+        return [
+            ChatCompletionLogProb(token=(token := self._get_decoded_token(
+                p[1],
+                p[0],
+                tokenizer,
+                return_as_token_id=self.return_tokens_as_token_ids)),
+                                  logprob=max(p[1].logprob, -9999.0),
+                                  bytes=list(
+                                      token.encode("utf-8", errors="replace")))
+            for i, p in enumerate(logprobs.items())
+            if top_logprobs and i < top_logprobs
+        ]
+
+    def _create_chat_logprobs(
+        self,
+        token_ids: GenericSequence[int],
+        top_logprobs: GenericSequence[Optional[Dict[int, Logprob]]],
+        tokenizer: AnyTokenizer,
+        num_output_top_logprobs: Optional[int] = None,
+    ) -> ChatCompletionLogProbs:
+        """Create OpenAI-style logprobs."""
+        logprobs_content: List[ChatCompletionLogProbsContent] = []
+
+        for i, token_id in enumerate(token_ids):
+            step_top_logprobs = top_logprobs[i]
+            if step_top_logprobs is None:
+                token = tokenizer.decode(token_id)
+                if self.return_tokens_as_token_ids:
+                    token = f"token_id:{token_id}"
+
+                logprobs_content.append(
+                    ChatCompletionLogProbsContent(
+                        token=token,
+                        bytes=list(token.encode("utf-8", errors="replace")),
+                    ))
+            else:
+                step_token = step_top_logprobs[token_id]
+                step_decoded = step_token.decoded_token
+
+                logprobs_content.append(
+                    ChatCompletionLogProbsContent(
+                        token=self._get_decoded_token(
+                            step_token,
+                            token_id,
+                            tokenizer,
+                            self.return_tokens_as_token_ids,
+                        ),
+                        logprob=max(step_token.logprob, -9999.0),
+                        bytes=None if step_decoded is None else list(
+                            step_decoded.encode("utf-8", errors="replace")),
+                        top_logprobs=self._get_top_logprobs(
+                            step_top_logprobs,
+                            num_output_top_logprobs,
+                            tokenizer,
+                        ),
+                    ))
+
+        return ChatCompletionLogProbs(content=logprobs_content)
+
+    def _should_stream_with_auto_tool_parsing(self,
+                                              request: ChatCompletionRequest):
+        """
+        Utility function to check if streamed tokens should go through the tool
+        call parser that was configured.
+
+        We only want to do this IF user-provided tools are set, a tool parser
+        is configured, "auto" tool choice is enabled, and the request's tool
+        choice field indicates that "auto" tool choice should be used.
+        """
+        return (request.tools and self.tool_parser and self.enable_auto_tools
+                and request.tool_choice in ['auto', None])
+
+    def _should_check_for_unstreamed_tool_arg_tokens(
+        self,
+        delta_message: Optional[DeltaMessage],
+        output: CompletionOutput,
+    ) -> bool:
+        """
+        Check to see if we should check for unstreamed tool arguments tokens.
+        This is only applicable when auto tool parsing is enabled, the delta
+        is a tool call with arguments.
+        """
+
+        # yapf: disable
+        return bool(
+            # if there is a delta message that includes tool calls which
+            # include a function that has arguments
+            output.finish_reason is not None
+            and self.enable_auto_tools and self.tool_parser and delta_message
+            and delta_message.tool_calls and delta_message.tool_calls[0]
+            and delta_message.tool_calls[0].function
+            and delta_message.tool_calls[0].function.arguments is not None
+        )