1057 lines
49 KiB
Python
1057 lines
49 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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import asyncio
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import json
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import time
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from collections.abc import AsyncGenerator, AsyncIterator
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from collections.abc import Sequence as GenericSequence
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from typing import Callable, Final, Optional, Union
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import jinja2
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from fastapi import Request
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from vllm.config import ModelConfig
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from vllm.engine.protocol import EngineClient
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from vllm.entrypoints.chat_utils import (ChatTemplateContentFormatOption,
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ConversationMessage)
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from vllm.entrypoints.logger import RequestLogger
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from vllm.entrypoints.openai.protocol import (
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ChatCompletionLogProb, ChatCompletionLogProbs,
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ChatCompletionLogProbsContent, ChatCompletionNamedToolChoiceParam,
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ChatCompletionRequest, ChatCompletionResponse,
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ChatCompletionResponseChoice, ChatCompletionResponseStreamChoice,
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ChatCompletionStreamResponse, ChatMessage, DeltaFunctionCall, DeltaMessage,
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DeltaToolCall, ErrorResponse, FunctionCall, PromptTokenUsageInfo,
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RequestResponseMetadata, ToolCall, UsageInfo)
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from vllm.entrypoints.openai.reasoning_parsers import (ReasoningParser,
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ReasoningParserManager)
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from vllm.entrypoints.openai.serving_engine import (OpenAIServing,
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clamp_prompt_logprobs)
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from vllm.entrypoints.openai.serving_models import OpenAIServingModels
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from vllm.entrypoints.openai.tool_parsers import ToolParser, ToolParserManager
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from vllm.entrypoints.openai.tool_parsers.mistral_tool_parser import (
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MistralToolCall)
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from vllm.logger import init_logger
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from vllm.outputs import CompletionOutput, RequestOutput
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from vllm.sampling_params import BeamSearchParams, SamplingParams
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from vllm.sequence import Logprob
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from vllm.transformers_utils.tokenizer import AnyTokenizer, MistralTokenizer
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from vllm.transformers_utils.tokenizers import (maybe_serialize_tool_calls,
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truncate_tool_call_ids)
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logger = init_logger(__name__)
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class OpenAIServingChat(OpenAIServing):
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def __init__(
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self,
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engine_client: EngineClient,
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model_config: ModelConfig,
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models: OpenAIServingModels,
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response_role: str,
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*,
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request_logger: Optional[RequestLogger],
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chat_template: Optional[str],
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chat_template_content_format: ChatTemplateContentFormatOption,
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return_tokens_as_token_ids: bool = False,
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enable_reasoning: bool = False,
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reasoning_parser: Optional[str] = None,
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enable_auto_tools: bool = False,
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tool_parser: Optional[str] = None,
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enable_prompt_tokens_details: bool = False,
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) -> None:
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super().__init__(engine_client=engine_client,
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model_config=model_config,
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models=models,
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request_logger=request_logger,
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return_tokens_as_token_ids=return_tokens_as_token_ids)
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self.response_role = response_role
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self.chat_template = chat_template
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self.chat_template_content_format: Final = chat_template_content_format
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# set up tool use
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self.enable_auto_tools: bool = enable_auto_tools
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if self.enable_auto_tools:
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logger.info(
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"\"auto\" tool choice has been enabled please note that while"
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" the parallel_tool_calls client option is preset for "
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"compatibility reasons, it will be ignored.")
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self.enable_reasoning: bool = enable_reasoning
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self.reasoning_parser: Optional[Callable[[AnyTokenizer],
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ReasoningParser]] = None
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if self.enable_reasoning:
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try:
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self.reasoning_parser = (
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ReasoningParserManager.get_reasoning_parser(
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reasoning_parser))
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except Exception as e:
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raise TypeError("Error: --enable-reasoning requires "
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f"reasoning_parser:'{reasoning_parser}' "
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"which has not been registered") from e
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self.tool_parser: Optional[Callable[[AnyTokenizer], ToolParser]] = None
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if self.enable_auto_tools:
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try:
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if (tool_parser == "pythonic" and
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model_config.model.startswith("meta-llama/Llama-3.2")):
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logger.warning(
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"Llama3.2 models may struggle to emit valid pythonic"
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" tool calls")
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self.tool_parser = ToolParserManager.get_tool_parser(
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tool_parser)
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except Exception as e:
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raise TypeError("Error: --enable-auto-tool-choice requires "
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f"tool_parser:'{tool_parser}' which has not "
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"been registered") from e
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self.enable_prompt_tokens_details = enable_prompt_tokens_details
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self.default_sampling_params = (
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self.model_config.get_diff_sampling_param())
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if self.default_sampling_params:
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source = self.model_config.generation_config
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source = "model" if source == "auto" else source
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logger.info("Using default chat sampling params from %s: %s",
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source, self.default_sampling_params)
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async def create_chat_completion(
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self,
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request: ChatCompletionRequest,
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raw_request: Optional[Request] = None,
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) -> Union[AsyncGenerator[str, None], ChatCompletionResponse,
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ErrorResponse]:
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"""
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Chat Completion API similar to OpenAI's API.
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See https://platform.openai.com/docs/api-reference/chat/create
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for the API specification. This API mimics the OpenAI
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Chat Completion API.
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"""
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error_check_ret = await self._check_model(request)
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if error_check_ret is not None:
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logger.error("Error with model %s", error_check_ret)
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return error_check_ret
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# If the engine is dead, raise the engine's DEAD_ERROR.
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# This is required for the streaming case, where we return a
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# success status before we actually start generating text :).
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if self.engine_client.errored:
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raise self.engine_client.dead_error
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try:
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(
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lora_request,
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prompt_adapter_request,
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) = self._maybe_get_adapters(request)
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model_name = self._get_model_name(request.model, lora_request)
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tokenizer = await self.engine_client.get_tokenizer(lora_request)
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tool_parser = self.tool_parser
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# validation for OpenAI tools
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# tool_choice = "required" is not supported
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if request.tool_choice == "required":
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return self.create_error_response(
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"tool_choice = \"required\" is not supported!")
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if isinstance(tokenizer, MistralTokenizer):
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# because of issues with pydantic we need to potentially
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# re-serialize the tool_calls field of the request
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# for more info: see comment in `maybe_serialize_tool_calls`
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maybe_serialize_tool_calls(request)
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truncate_tool_call_ids(request)
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if (request.tool_choice == "auto" and
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not (self.enable_auto_tools and tool_parser is not None)
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and not isinstance(tokenizer, MistralTokenizer)):
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# for hf tokenizers, "auto" tools requires
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# --enable-auto-tool-choice and --tool-call-parser
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return self.create_error_response(
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"\"auto\" tool choice requires "
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"--enable-auto-tool-choice and --tool-call-parser to be set"
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)
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tool_dicts = None if request.tools is None else [
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tool.model_dump() for tool in request.tools
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]
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(
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conversation,
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request_prompts,
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engine_prompts,
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) = await self._preprocess_chat(
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request,
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tokenizer,
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request.messages,
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chat_template=request.chat_template or self.chat_template,
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chat_template_content_format=self.chat_template_content_format,
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add_generation_prompt=request.add_generation_prompt,
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continue_final_message=request.continue_final_message,
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tool_dicts=tool_dicts,
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documents=request.documents,
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chat_template_kwargs=request.chat_template_kwargs,
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tool_parser=tool_parser,
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truncate_prompt_tokens=request.truncate_prompt_tokens,
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add_special_tokens=request.add_special_tokens,
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)
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except (ValueError, TypeError, RuntimeError,
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jinja2.TemplateError) as e:
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logger.exception("Error in preprocessing prompt inputs")
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return self.create_error_response(str(e))
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request_id = "chatcmpl-" \
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f"{self._base_request_id(raw_request, request.request_id)}"
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request_metadata = RequestResponseMetadata(request_id=request_id)
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if raw_request:
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raw_request.state.request_metadata = request_metadata
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# Schedule the request and get the result generator.
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generators: list[AsyncGenerator[RequestOutput, None]] = []
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try:
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for i, engine_prompt in enumerate(engine_prompts):
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sampling_params: Union[SamplingParams, BeamSearchParams]
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default_max_tokens = self.max_model_len - len(
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engine_prompt["prompt_token_ids"])
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if request.use_beam_search:
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sampling_params = request.to_beam_search_params(
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default_max_tokens, self.default_sampling_params)
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else:
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sampling_params = request.to_sampling_params(
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default_max_tokens,
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self.model_config.logits_processor_pattern,
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self.default_sampling_params)
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self._log_inputs(request_id,
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request_prompts[i],
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params=sampling_params,
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lora_request=lora_request,
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prompt_adapter_request=prompt_adapter_request)
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trace_headers = (None if raw_request is None else await
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self._get_trace_headers(raw_request.headers))
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if isinstance(sampling_params, BeamSearchParams):
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generator = self.engine_client.beam_search(
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prompt=engine_prompt,
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request_id=request_id,
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params=sampling_params,
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)
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else:
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generator = self.engine_client.generate(
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engine_prompt,
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sampling_params,
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request_id,
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lora_request=lora_request,
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trace_headers=trace_headers,
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prompt_adapter_request=prompt_adapter_request,
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priority=request.priority,
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)
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generators.append(generator)
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except ValueError as e:
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# TODO: Use a vllm-specific Validation Error
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return self.create_error_response(str(e))
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assert len(generators) == 1
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result_generator, = generators
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# Streaming response
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if request.stream:
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return self.chat_completion_stream_generator(
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request, result_generator, request_id, model_name,
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conversation, tokenizer, request_metadata)
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try:
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return await self.chat_completion_full_generator(
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request, result_generator, request_id, model_name,
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conversation, tokenizer, request_metadata)
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except ValueError as e:
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# TODO: Use a vllm-specific Validation Error
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return self.create_error_response(str(e))
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def get_chat_request_role(self, request: ChatCompletionRequest) -> str:
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if request.add_generation_prompt:
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return self.response_role
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return request.messages[-1]["role"]
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async def chat_completion_stream_generator(
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self,
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request: ChatCompletionRequest,
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result_generator: AsyncIterator[RequestOutput],
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request_id: str,
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model_name: str,
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conversation: list[ConversationMessage],
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tokenizer: AnyTokenizer,
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request_metadata: RequestResponseMetadata,
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) -> AsyncGenerator[str, None]:
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created_time = int(time.time())
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chunk_object_type: Final = "chat.completion.chunk"
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first_iteration = True
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# Send response for each token for each request.n (index)
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num_choices = 1 if request.n is None else request.n
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previous_num_tokens = [0] * num_choices
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finish_reason_sent = [False] * num_choices
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num_prompt_tokens = 0
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num_cached_tokens = None
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if isinstance(request.tool_choice, ChatCompletionNamedToolChoiceParam):
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tool_choice_function_name = request.tool_choice.function.name
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else:
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tool_choice_function_name = None
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# Determine whether tools are in use with "auto" tool choice
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tool_choice_auto = (
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not tool_choice_function_name
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and self._should_stream_with_auto_tool_parsing(request))
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should_stream_with_reasoning_parsing = (
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self._should_stream_with_reasoning_parsing(request))
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all_previous_token_ids: Optional[list[list[int]]]
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# Only one of these will be used, thus previous_texts and
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# all_previous_token_ids will not be used twice in the same iteration.
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if tool_choice_auto or should_stream_with_reasoning_parsing:
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# These are only required in "auto" tool choice case
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previous_texts = [""] * num_choices
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all_previous_token_ids = [[]] * num_choices
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# For reasoning parser and tool call all enabled
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added_content_delta_arr = [False] * num_choices
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reasoning_end_arr = [False] * num_choices
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else:
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previous_texts, all_previous_token_ids = None, None
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try:
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# There is no need to check if the reasoning_parser is None
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# because the should_stream_with_reasoning_parsing check
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# already ensures that the reasoning_parser is not None.
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# but the pre-commit hook requires it.
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if should_stream_with_reasoning_parsing and \
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self.reasoning_parser is not None:
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reasoning_parser = self.reasoning_parser(tokenizer)
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except RuntimeError as e:
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logger.exception("Error in reasoning parser creation.")
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data = self.create_streaming_error_response(str(e))
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yield f"data: {data}\n\n"
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yield "data: [DONE]\n\n"
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return
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# Prepare the tool parser if it's needed
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try:
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if tool_choice_auto and self.tool_parser:
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tool_parsers: list[Optional[ToolParser]] = [
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self.tool_parser(tokenizer)
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] * num_choices
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else:
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tool_parsers = [None] * num_choices
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except Exception as e:
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logger.exception("Error in tool parser creation.")
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data = self.create_streaming_error_response(str(e))
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yield f"data: {data}\n\n"
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yield "data: [DONE]\n\n"
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return
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stream_options = request.stream_options
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if stream_options:
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include_usage = stream_options.include_usage
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include_continuous_usage = include_usage and \
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stream_options.continuous_usage_stats
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else:
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include_usage, include_continuous_usage = False, False
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try:
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async for res in result_generator:
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if res.prompt_token_ids is not None:
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num_prompt_tokens = len(res.prompt_token_ids)
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if res.encoder_prompt_token_ids is not None:
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num_prompt_tokens += len(res.encoder_prompt_token_ids)
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# We need to do it here, because if there are exceptions in
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# the result_generator, it needs to be sent as the FIRST
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# response (by the try...catch).
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if first_iteration:
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num_cached_tokens = res.num_cached_tokens
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# Send first response for each request.n (index) with
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# the role
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role = self.get_chat_request_role(request)
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# NOTE num_choices defaults to 1 so this usually executes
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# once per request
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for i in range(num_choices):
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choice_data = ChatCompletionResponseStreamChoice(
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index=i,
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delta=DeltaMessage(
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role=role,
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content="",
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),
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logprobs=None,
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finish_reason=None)
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chunk = ChatCompletionStreamResponse(
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id=request_id,
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object=chunk_object_type,
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created=created_time,
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choices=[choice_data],
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model=model_name)
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|
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# if continuous usage stats are requested, add it
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if include_continuous_usage:
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chunk.usage = UsageInfo(
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prompt_tokens=num_prompt_tokens,
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completion_tokens=0,
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total_tokens=num_prompt_tokens)
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data = chunk.model_dump_json(exclude_unset=True)
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yield f"data: {data}\n\n"
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|
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# Send response to echo the input portion of the
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# last message
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if request.echo:
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last_msg_content: Union[str, list[dict[str, str]]] = ""
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if conversation and "content" in conversation[
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-1] and conversation[-1].get("role") == role:
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last_msg_content = conversation[-1]["content"] or ""
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|
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if last_msg_content:
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for i in range(num_choices):
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choice_data = (
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ChatCompletionResponseStreamChoice(
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index=i,
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delta=DeltaMessage(
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content=last_msg_content),
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logprobs=None,
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finish_reason=None))
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chunk = ChatCompletionStreamResponse(
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id=request_id,
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object=chunk_object_type,
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created=created_time,
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choices=[choice_data],
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model=model_name)
|
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if include_continuous_usage:
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chunk.usage = UsageInfo(
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prompt_tokens=num_prompt_tokens,
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completion_tokens=0,
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total_tokens=num_prompt_tokens)
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|
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data = chunk.model_dump_json(
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exclude_unset=True)
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yield f"data: {data}\n\n"
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first_iteration = False
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|
|
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for output in res.outputs:
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i = output.index
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tool_parser = tool_parsers[i]
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|
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if finish_reason_sent[i]:
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continue
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|
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if request.logprobs and request.top_logprobs is not None:
|
|
assert output.logprobs is not None, (
|
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"Did not output logprobs")
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|
logprobs = self._create_chat_logprobs(
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token_ids=output.token_ids,
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top_logprobs=output.logprobs,
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tokenizer=tokenizer,
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num_output_top_logprobs=request.top_logprobs,
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return_as_token_id=request.
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return_tokens_as_token_ids,
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)
|
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else:
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logprobs = None
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delta_text = output.text
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|
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if not delta_text and not output.token_ids and \
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not previous_num_tokens[i]:
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# Chunked prefill case, don't return empty chunks
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continue
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|
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delta_message: Optional[DeltaMessage]
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|
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# just update previous_texts and previous_token_ids
|
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if tool_choice_auto or should_stream_with_reasoning_parsing:
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assert previous_texts is not None
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assert all_previous_token_ids is not None
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previous_text = previous_texts[i]
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previous_token_ids = all_previous_token_ids[i]
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current_text = previous_text + delta_text
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current_token_ids = previous_token_ids + list(
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output.token_ids)
|
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|
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# handle streaming deltas for tools with named tool_choice
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if tool_choice_function_name:
|
|
if (self.enable_reasoning
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and not reasoning_parser.is_reasoning_end(
|
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previous_token_ids)):
|
|
assert reasoning_parser is not None
|
|
delta_message = (
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reasoning_parser.
|
|
extract_reasoning_content_streaming(
|
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previous_text,
|
|
current_text,
|
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delta_text,
|
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previous_token_ids,
|
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current_token_ids,
|
|
output.token_ids,
|
|
))
|
|
# When encountering think end id in delta_token_ids,
|
|
# process the `content`. Only keep 'content',
|
|
# remove 'reasoning_content'
|
|
if reasoning_parser.is_reasoning_end(
|
|
list(output.token_ids)):
|
|
if delta_message and delta_message.content:
|
|
# This need to be added to next `delta_text`
|
|
current_text = delta_message.content
|
|
delta_message.content = None
|
|
else:
|
|
current_text = ""
|
|
else:
|
|
# Just to add remaining `content`
|
|
if self.enable_reasoning:
|
|
delta_text = previous_text + delta_text
|
|
current_text = ""
|
|
|
|
delta_message = DeltaMessage(tool_calls=[
|
|
DeltaToolCall(function=DeltaFunctionCall(
|
|
name=tool_choice_function_name,
|
|
arguments=delta_text),
|
|
index=i)
|
|
])
|
|
|
|
# handle streaming deltas for tools with "auto" tool choice
|
|
# and reasoning parser
|
|
elif tool_choice_auto and self.enable_reasoning:
|
|
assert tool_parser is not None
|
|
assert reasoning_parser is not None
|
|
assert added_content_delta_arr is not None
|
|
assert reasoning_end_arr is not None
|
|
if not reasoning_end_arr[i]:
|
|
delta_message = (
|
|
reasoning_parser.
|
|
extract_reasoning_content_streaming(
|
|
previous_text,
|
|
current_text,
|
|
delta_text,
|
|
previous_token_ids,
|
|
current_token_ids,
|
|
output.token_ids,
|
|
))
|
|
|
|
# When encountering think end id in delta_token_ids,
|
|
# set reasoning status to end.
|
|
# Remove the text and token ids related
|
|
# to 'reasoning_content'.
|
|
if reasoning_parser.is_reasoning_end(
|
|
list(output.token_ids)):
|
|
reasoning_end_arr[i] = True
|
|
current_token_ids = \
|
|
reasoning_parser.extract_content_ids(
|
|
list(output.token_ids))
|
|
if delta_message and delta_message.content:
|
|
current_text = delta_message.content
|
|
delta_message.content = None
|
|
else:
|
|
current_text = ""
|
|
|
|
# handle tool calls only after reasoning is done,
|
|
else:
|
|
delta_token_ids = list(output.token_ids)
|
|
# First time to tool call,
|
|
# add the remaining text and token ids
|
|
# to delta from previous
|
|
if not added_content_delta_arr[i]:
|
|
added_content_delta_arr[i] = True
|
|
previous_text = ""
|
|
previous_token_ids = []
|
|
delta_text = current_text
|
|
delta_token_ids = current_token_ids
|
|
|
|
delta_message = (
|
|
tool_parser.extract_tool_calls_streaming(
|
|
previous_text=previous_text,
|
|
current_text=current_text,
|
|
delta_text=delta_text,
|
|
previous_token_ids=previous_token_ids,
|
|
current_token_ids=current_token_ids,
|
|
delta_token_ids=delta_token_ids,
|
|
request=request))
|
|
# when only tool calls
|
|
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))
|
|
# when only reasoning
|
|
elif self.enable_reasoning:
|
|
assert reasoning_parser is not None
|
|
delta_message = (reasoning_parser.
|
|
extract_reasoning_content_streaming(
|
|
previous_text,
|
|
current_text,
|
|
delta_text,
|
|
previous_token_ids,
|
|
current_token_ids,
|
|
output.token_ids,
|
|
))
|
|
# handle streaming just a content delta
|
|
else:
|
|
delta_message = DeltaMessage(content=delta_text)
|
|
|
|
# update the previous values for the next iteration
|
|
if tool_choice_auto or should_stream_with_reasoning_parsing:
|
|
assert previous_texts is not None
|
|
assert all_previous_token_ids is not None
|
|
previous_texts[i] = current_text
|
|
all_previous_token_ids[i] = current_token_ids
|
|
|
|
# set the previous values for the next iteration
|
|
previous_num_tokens[i] += len(output.token_ids)
|
|
|
|
# if the message delta is None (e.g. because it was a
|
|
# "control token" for tool calls or the parser otherwise
|
|
# wasn't ready to send a token, then
|
|
# get the next token without streaming a chunk
|
|
if delta_message is None:
|
|
continue
|
|
|
|
if output.finish_reason is None:
|
|
# Send token-by-token response for each request.n
|
|
choice_data = ChatCompletionResponseStreamChoice(
|
|
index=i,
|
|
delta=delta_message,
|
|
logprobs=logprobs,
|
|
finish_reason=None)
|
|
|
|
# if the model is finished generating
|
|
else:
|
|
# check to make sure we haven't "forgotten" to stream
|
|
# any tokens that were generated but previously
|
|
# matched by partial json parsing
|
|
# only happens if we are NOT using guided decoding
|
|
auto_tools_called = False
|
|
if tool_parser:
|
|
auto_tools_called = len(
|
|
tool_parser.prev_tool_call_arr) > 0
|
|
index = len(tool_parser.prev_tool_call_arr
|
|
) - 1 if auto_tools_called else 0
|
|
else:
|
|
index = 0
|
|
|
|
if self._should_check_for_unstreamed_tool_arg_tokens(
|
|
delta_message, output) and tool_parser:
|
|
latest_delta_len = 0
|
|
if ((isinstance(
|
|
delta_message.tool_calls[0].function,
|
|
DeltaFunctionCall)) and isinstance(
|
|
delta_message.tool_calls[0].function.
|
|
arguments, str)):
|
|
latest_delta_len = len(
|
|
delta_message.tool_calls[0].function.
|
|
arguments)
|
|
|
|
# get the expected call based on partial JSON
|
|
# parsing which "autocompletes" the JSON
|
|
expected_call = json.dumps(
|
|
tool_parser.prev_tool_call_arr[index].get(
|
|
"arguments", {}),
|
|
ensure_ascii=False)
|
|
|
|
# get what we've streamed so far for arguments
|
|
# for the current tool
|
|
actual_call = tool_parser.streamed_args_for_tool[
|
|
index]
|
|
if (latest_delta_len > 0):
|
|
actual_call = actual_call[:-latest_delta_len]
|
|
|
|
# check to see if there's anything left to stream
|
|
remaining_call = expected_call.replace(
|
|
actual_call, "", 1)
|
|
# set that as a delta message
|
|
delta_message = DeltaMessage(tool_calls=[
|
|
DeltaToolCall(index=index,
|
|
function=DeltaFunctionCall(
|
|
arguments=remaining_call).
|
|
model_dump(exclude_none=True))
|
|
])
|
|
|
|
# Send the finish response for each request.n only once
|
|
choice_data = ChatCompletionResponseStreamChoice(
|
|
index=i,
|
|
delta=delta_message,
|
|
logprobs=logprobs,
|
|
finish_reason=output.finish_reason
|
|
if not auto_tools_called else "tool_calls",
|
|
stop_reason=output.stop_reason)
|
|
|
|
finish_reason_sent[i] = True
|
|
|
|
chunk = ChatCompletionStreamResponse(
|
|
id=request_id,
|
|
object=chunk_object_type,
|
|
created=created_time,
|
|
choices=[choice_data],
|
|
model=model_name)
|
|
|
|
# handle usage stats if requested & if continuous
|
|
if include_continuous_usage:
|
|
completion_tokens = previous_num_tokens[i]
|
|
chunk.usage = UsageInfo(
|
|
prompt_tokens=num_prompt_tokens,
|
|
completion_tokens=completion_tokens,
|
|
total_tokens=num_prompt_tokens + completion_tokens,
|
|
)
|
|
|
|
data = chunk.model_dump_json(exclude_unset=True)
|
|
yield f"data: {data}\n\n"
|
|
|
|
# once the final token is handled, if stream_options.include_usage
|
|
# is sent, send the usage
|
|
if include_usage:
|
|
completion_tokens = sum(previous_num_tokens)
|
|
final_usage = UsageInfo(prompt_tokens=num_prompt_tokens,
|
|
completion_tokens=completion_tokens,
|
|
total_tokens=num_prompt_tokens +
|
|
completion_tokens)
|
|
if self.enable_prompt_tokens_details and num_cached_tokens:
|
|
final_usage.prompt_tokens_details = PromptTokenUsageInfo(
|
|
cached_tokens=num_cached_tokens)
|
|
|
|
final_usage_chunk = ChatCompletionStreamResponse(
|
|
id=request_id,
|
|
object=chunk_object_type,
|
|
created=created_time,
|
|
choices=[],
|
|
model=model_name,
|
|
usage=final_usage)
|
|
final_usage_data = (final_usage_chunk.model_dump_json(
|
|
exclude_unset=True, exclude_none=True))
|
|
yield f"data: {final_usage_data}\n\n"
|
|
|
|
# report to FastAPI middleware aggregate usage across all choices
|
|
num_completion_tokens = sum(previous_num_tokens)
|
|
request_metadata.final_usage_info = UsageInfo(
|
|
prompt_tokens=num_prompt_tokens,
|
|
completion_tokens=num_completion_tokens,
|
|
total_tokens=num_prompt_tokens + num_completion_tokens)
|
|
|
|
except Exception as e:
|
|
# TODO: Use a vllm-specific Validation Error
|
|
logger.exception("Error in chat completion stream generator.")
|
|
data = self.create_streaming_error_response(str(e))
|
|
yield f"data: {data}\n\n"
|
|
# Send the final done message after all response.n are finished
|
|
yield "data: [DONE]\n\n"
|
|
|
|
async def chat_completion_full_generator(
|
|
self,
|
|
request: ChatCompletionRequest,
|
|
result_generator: AsyncIterator[RequestOutput],
|
|
request_id: str,
|
|
model_name: str,
|
|
conversation: list[ConversationMessage],
|
|
tokenizer: AnyTokenizer,
|
|
request_metadata: RequestResponseMetadata,
|
|
) -> Union[ErrorResponse, ChatCompletionResponse]:
|
|
|
|
created_time = int(time.time())
|
|
final_res: Optional[RequestOutput] = None
|
|
|
|
try:
|
|
async for res in result_generator:
|
|
final_res = res
|
|
except asyncio.CancelledError:
|
|
return self.create_error_response("Client disconnected")
|
|
except ValueError as e:
|
|
# TODO: Use a vllm-specific Validation Error
|
|
return self.create_error_response(str(e))
|
|
|
|
assert final_res is not None
|
|
|
|
choices: list[ChatCompletionResponseChoice] = []
|
|
|
|
role = self.get_chat_request_role(request)
|
|
for output in final_res.outputs:
|
|
token_ids = output.token_ids
|
|
out_logprobs = output.logprobs
|
|
|
|
if request.logprobs and request.top_logprobs is not None:
|
|
assert out_logprobs is not None, "Did not output logprobs"
|
|
logprobs = self._create_chat_logprobs(
|
|
token_ids=token_ids,
|
|
top_logprobs=out_logprobs,
|
|
num_output_top_logprobs=request.top_logprobs,
|
|
tokenizer=tokenizer,
|
|
return_as_token_id=request.return_tokens_as_token_ids,
|
|
)
|
|
else:
|
|
logprobs = None
|
|
|
|
should_stream_with_reasoning_parsing = (
|
|
self._should_stream_with_reasoning_parsing(request))
|
|
|
|
# In the OpenAI API the finish_reason is "tools_called"
|
|
# if the tool choice is auto and the model produced a tool
|
|
# call. The same is not true for named function calls
|
|
auto_tools_called = False
|
|
|
|
if should_stream_with_reasoning_parsing and \
|
|
self.reasoning_parser is not None:
|
|
try:
|
|
reasoning_parser = self.reasoning_parser(tokenizer)
|
|
except RuntimeError as e:
|
|
logger.exception("Error in reasoning parser creation.")
|
|
return self.create_error_response(str(e))
|
|
# If the reasoning parser is enabled,
|
|
# tool calls are extracted exclusively from the content.
|
|
reasoning_content, content = (
|
|
reasoning_parser.extract_reasoning_content(
|
|
output.text, request=request))
|
|
else:
|
|
reasoning_content = None
|
|
content = output.text
|
|
|
|
# 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_content,
|
|
content=content)
|
|
|
|
# if the request uses tools and specified a tool choice
|
|
elif request.tool_choice and type(
|
|
request.tool_choice) is ChatCompletionNamedToolChoiceParam:
|
|
|
|
tool_call_class = MistralToolCall if isinstance(
|
|
tokenizer, MistralTokenizer) else ToolCall
|
|
message = ChatMessage(
|
|
role=role,
|
|
reasoning_content=reasoning_content,
|
|
content="",
|
|
tool_calls=[
|
|
tool_call_class(function=FunctionCall(
|
|
name=request.tool_choice.function.name,
|
|
arguments=content))
|
|
])
|
|
|
|
# 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_content,
|
|
content=content)
|
|
|
|
# handle when there are tools and tool choice is auto
|
|
elif request.tools and (
|
|
request.tool_choice == "auto"
|
|
or request.tool_choice is None) and self.enable_auto_tools \
|
|
and self.tool_parser:
|
|
|
|
try:
|
|
tool_parser = self.tool_parser(tokenizer)
|
|
except RuntimeError as e:
|
|
logger.exception("Error in tool parser creation.")
|
|
return self.create_error_response(str(e))
|
|
|
|
tool_call_info = tool_parser.extract_tool_calls(
|
|
content if content is not None else "", request=request)
|
|
# In the OpenAI API the finish_reason is "tools_called"
|
|
# if the tool choice is auto and the model produced a tool
|
|
# call. The same is not true for named function calls
|
|
auto_tools_called = tool_call_info.tools_called
|
|
if tool_call_info.tools_called:
|
|
message = ChatMessage(role=role,
|
|
reasoning_content=reasoning_content,
|
|
content=tool_call_info.content,
|
|
tool_calls=tool_call_info.tool_calls)
|
|
|
|
else:
|
|
# FOR NOW make it a chat message; we will have to detect
|
|
# the type to make it later.
|
|
message = ChatMessage(role=role,
|
|
reasoning_content=reasoning_content,
|
|
content=content)
|
|
|
|
# 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_content,
|
|
content=content)
|
|
|
|
choice_data = ChatCompletionResponseChoice(
|
|
index=output.index,
|
|
message=message,
|
|
logprobs=logprobs,
|
|
finish_reason="tool_calls" if auto_tools_called else
|
|
output.finish_reason if output.finish_reason else "stop",
|
|
stop_reason=output.stop_reason)
|
|
choices.append(choice_data)
|
|
|
|
if request.echo:
|
|
last_msg_content: Union[str, list[dict[str, str]]] = ""
|
|
if conversation and "content" in conversation[-1] and conversation[
|
|
-1].get("role") == role:
|
|
last_msg_content = conversation[-1]["content"] or ""
|
|
if isinstance(last_msg_content, list):
|
|
last_msg_content = "\n".join(msg['text']
|
|
for msg in last_msg_content)
|
|
|
|
for choice in choices:
|
|
full_message = last_msg_content + (choice.message.content
|
|
or "")
|
|
choice.message.content = full_message
|
|
|
|
assert final_res.prompt_token_ids is not None
|
|
num_prompt_tokens = len(final_res.prompt_token_ids)
|
|
if final_res.encoder_prompt_token_ids is not None:
|
|
num_prompt_tokens += len(final_res.encoder_prompt_token_ids)
|
|
num_generated_tokens = sum(
|
|
len(output.token_ids) for output in final_res.outputs)
|
|
usage = UsageInfo(prompt_tokens=num_prompt_tokens,
|
|
completion_tokens=num_generated_tokens,
|
|
total_tokens=num_prompt_tokens +
|
|
num_generated_tokens)
|
|
if self.enable_prompt_tokens_details and final_res.num_cached_tokens:
|
|
usage.prompt_tokens_details = PromptTokenUsageInfo(
|
|
cached_tokens=final_res.num_cached_tokens)
|
|
|
|
request_metadata.final_usage_info = usage
|
|
|
|
response = ChatCompletionResponse(
|
|
id=request_id,
|
|
created=created_time,
|
|
model=model_name,
|
|
choices=choices,
|
|
usage=usage,
|
|
prompt_logprobs=clamp_prompt_logprobs(final_res.prompt_logprobs),
|
|
)
|
|
|
|
return response
|
|
|
|
def _get_top_logprobs(
|
|
self, logprobs: dict[int, Logprob], top_logprobs: Optional[int],
|
|
tokenizer: AnyTokenizer,
|
|
should_return_as_token_id: bool) -> list[ChatCompletionLogProb]:
|
|
return [
|
|
ChatCompletionLogProb(token=(token := self._get_decoded_token(
|
|
p[1],
|
|
p[0],
|
|
tokenizer,
|
|
return_as_token_id=should_return_as_token_id)),
|
|
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,
|
|
return_as_token_id: Optional[bool] = None,
|
|
) -> ChatCompletionLogProbs:
|
|
"""Create OpenAI-style logprobs."""
|
|
logprobs_content: list[ChatCompletionLogProbsContent] = []
|
|
|
|
should_return_as_token_id = return_as_token_id if \
|
|
return_as_token_id is not None else self.return_tokens_as_token_ids
|
|
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 should_return_as_token_id:
|
|
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,
|
|
should_return_as_token_id,
|
|
),
|
|
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, should_return_as_token_id),
|
|
))
|
|
|
|
return ChatCompletionLogProbs(content=logprobs_content)
|
|
|
|
def _should_stream_with_auto_tool_parsing(self,
|
|
request: ChatCompletionRequest):
|
|
"""
|
|
Utility function to check if streamed tokens should go through the tool
|
|
call parser that was configured.
|
|
|
|
We only want to do this IF user-provided tools are set, a tool parser
|
|
is configured, "auto" tool choice is enabled, and the request's tool
|
|
choice field indicates that "auto" tool choice should be used.
|
|
"""
|
|
return (request.tools and self.tool_parser and self.enable_auto_tools
|
|
and request.tool_choice in ['auto', None])
|
|
|
|
def _should_stream_with_reasoning_parsing(self,
|
|
request: ChatCompletionRequest):
|
|
"""
|
|
Utility function to check if streamed tokens should go through the
|
|
reasoning parser that was configured.
|
|
|
|
We only want to do this IF reasoning is enabled and a reasoning
|
|
parser is configured.
|
|
"""
|
|
return self.enable_reasoning and self.reasoning_parser is not None
|
|
|
|
def _should_check_for_unstreamed_tool_arg_tokens(
|
|
self,
|
|
delta_message: Optional[DeltaMessage],
|
|
output: CompletionOutput,
|
|
) -> bool:
|
|
"""
|
|
Check to see if we should check for unstreamed tool arguments tokens.
|
|
This is only applicable when auto tool parsing is enabled, the delta
|
|
is a tool call with arguments.
|
|
"""
|
|
|
|
# yapf: disable
|
|
return bool(
|
|
# if there is a delta message that includes tool calls which
|
|
# include a function that has arguments
|
|
output.finish_reason is not None
|
|
and self.enable_auto_tools and self.tool_parser and delta_message
|
|
and delta_message.tool_calls and delta_message.tool_calls[0]
|
|
and delta_message.tool_calls[0].function
|
|
and delta_message.tool_calls[0].function.arguments is not None
|
|
)
|