enginex-vllm-bi100-qwen36/qwen3_6_scripts/serving_chat.py

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,
    PromptTokensDetails, 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))

        # n > max_num_seqs deadlock guard: scheduler uses break (not continue)
        # when can_schedule(num_new_seqs=n) fails, so an n that exceeds
        # max_num_seqs permanently blocks the entire waiting queue with no error.
        _sched_cfg = await self.engine_client.get_scheduler_config()
        _max_seqs = _sched_cfg.max_num_seqs
        if request.n is not None and request.n > _max_seqs:
            return self.create_error_response(
                f"n={request.n} exceeds max_num_seqs={_max_seqs}. "
                f"Use n<={_max_seqs} or omit n.")

        # 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
        num_cached_tokens: Optional[int] = None

        if isinstance(request.tool_choice, ChatCompletionNamedToolChoiceParam):
            tool_choice_function_name = request.tool_choice.function.name
        else:
            tool_choice_function_name = None

        # Determine whether tools are in use with "auto" tool choice
        tool_choice_auto = (
            not tool_choice_function_name
            and self._should_stream_with_auto_tool_parsing(request))

        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)
                if (num_cached_tokens is None
                        and res.metrics is not None
                        and res.metrics.num_cached_tokens is not None):
                    num_cached_tokens = res.metrics.num_cached_tokens

                # 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.
                    # However, if this is the finish token we must NOT skip —
                    # the finish block updates reasoning_token_counts, sets
                    # finish_reason_sent, and flushes the final usage chunk.
                    if delta_message is None:
                        if output.finish_reason is None:
                            continue
                        delta_message = DeltaMessage()

                    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,
                    prompt_tokens_details=(
                        PromptTokensDetails(cached_tokens=num_cached_tokens)
                        if num_cached_tokens is not None else None),
                )

                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 asyncio.CancelledError:
            # Client disconnected; abort the engine request so GPU is freed.
            await self.engine_client.abort(request_id)
            return
        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:
            await self.engine_client.abort(request_id)
            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)
        num_cached_tokens = (final_res.metrics.num_cached_tokens
                             if final_res.metrics is not None else None)
        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,
            prompt_tokens_details=(
                PromptTokensDetails(cached_tokens=num_cached_tokens)
                if num_cached_tokens is not None else None),
        )

        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
        )