637 lines
27 KiB
Python
637 lines
27 KiB
Python
import codecs
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import time
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from dataclasses import dataclass, field
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from functools import cached_property
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from typing import (AsyncGenerator, AsyncIterator, Awaitable, Dict, Iterable,
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List, Optional)
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from typing import Sequence as GenericSequence
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from typing import TypedDict, Union, cast, final
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from fastapi import Request
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from openai.types.chat import (ChatCompletionContentPartImageParam,
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ChatCompletionContentPartTextParam)
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from vllm.config import ModelConfig
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from vllm.engine.async_llm_engine import AsyncLLMEngine
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from vllm.entrypoints.openai.protocol import (
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ChatCompletionContentPartParam, ChatCompletionLogProb,
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ChatCompletionLogProbs, ChatCompletionLogProbsContent,
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ChatCompletionMessageParam, ChatCompletionNamedToolChoiceParam,
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ChatCompletionRequest, ChatCompletionResponse,
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ChatCompletionResponseChoice, ChatCompletionResponseStreamChoice,
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ChatCompletionStreamResponse, ChatMessage, DeltaMessage, ErrorResponse,
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FunctionCall, ToolCall, UsageInfo)
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from vllm.entrypoints.openai.serving_engine import (LoRAModulePath,
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OpenAIServing)
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from vllm.inputs import PromptInputs
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from vllm.logger import init_logger
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from vllm.model_executor.guided_decoding import (
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get_guided_decoding_logits_processor)
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from vllm.multimodal import MultiModalDataDict
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from vllm.multimodal.utils import async_get_and_parse_image
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from vllm.outputs import RequestOutput
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from vllm.sequence import Logprob
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from vllm.tracing import (contains_trace_headers, extract_trace_headers,
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log_tracing_disabled_warning)
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from vllm.utils import random_uuid
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logger = init_logger(__name__)
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@final # So that it should be compatible with Dict[str, str]
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class ConversationMessage(TypedDict):
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role: str
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content: str
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@dataclass(frozen=True)
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class ChatMessageParseResult:
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messages: List[ConversationMessage]
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mm_futures: List[Awaitable[MultiModalDataDict]] = field(
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default_factory=list)
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class OpenAIServingChat(OpenAIServing):
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def __init__(self,
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engine: AsyncLLMEngine,
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model_config: ModelConfig,
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served_model_names: List[str],
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response_role: str,
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lora_modules: Optional[List[LoRAModulePath]] = None,
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chat_template: Optional[str] = None):
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super().__init__(engine=engine,
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model_config=model_config,
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served_model_names=served_model_names,
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lora_modules=lora_modules)
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self.response_role = response_role
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self._load_chat_template(chat_template)
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def _load_chat_template(self, chat_template: Optional[str]):
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tokenizer = self.tokenizer
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if chat_template is not None:
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try:
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with open(chat_template, "r") as f:
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tokenizer.chat_template = f.read()
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except OSError as e:
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JINJA_CHARS = "{}\n"
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if not any(c in chat_template for c in JINJA_CHARS):
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msg = (f"The supplied chat template ({chat_template}) "
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f"looks like a file path, but it failed to be "
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f"opened. Reason: {e}")
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raise ValueError(msg) from e
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# If opening a file fails, set chat template to be args to
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# ensure we decode so our escape are interpreted correctly
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tokenizer.chat_template = codecs.decode(
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chat_template, "unicode_escape")
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logger.info("Using supplied chat template:\n%s",
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tokenizer.chat_template)
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elif tokenizer.chat_template is not None:
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logger.info("Using default chat template:\n%s",
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tokenizer.chat_template)
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else:
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logger.warning(
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"No chat template provided. Chat API will not work.")
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@cached_property
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def image_token_str(self) -> Optional[str]:
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# TODO: Let user specify how to insert image tokens into prompt
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# (similar to chat template)
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model_type = self.model_config.hf_config.model_type
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if model_type == "phi3_v":
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# Workaround since this token is not defined in the tokenizer
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return "<|image_1|>"
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if model_type in ("blip-2", "chatglm", "fuyu", "minicpmv",
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"paligemma"):
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# These models do not use image tokens in the prompt
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return None
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# The default behaviour assumes that the image token is
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# available to the tokenizer.
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# (Suitable for LLaVA, Idefics2, DeepSeek-VL)
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vlm_config = self.model_config.multimodal_config
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if vlm_config is None:
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raise ValueError(
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"'image_url' input is not supported as the loaded "
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"model is not multimodal.")
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image_token_id = vlm_config.image_token_id
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if vlm_config.image_token_id is None:
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raise ValueError(
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"'image_url' input is not supported as the loaded "
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"model does not specify an image token.")
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return self.tokenizer.decode(image_token_id)
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# TODO: Let user specify how to insert image tokens into prompt
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# (similar to chat template)
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def _get_full_image_text_prompt(self, image_token_str: str,
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text_prompt: str) -> str:
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"""Combine image and text prompts for vision language model"""
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# NOTE: For now we assume all model architectures use the same
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# image + text prompt format. This may change in the future.
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return f"{image_token_str}\n{text_prompt}"
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def _parse_chat_message_content_parts(
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self,
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role: str,
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parts: Iterable[ChatCompletionContentPartParam],
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) -> ChatMessageParseResult:
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texts: List[str] = []
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mm_futures: List[Awaitable[MultiModalDataDict]] = []
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for part in parts:
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part_type = part["type"]
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if part_type == "text":
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text = cast(ChatCompletionContentPartTextParam, part)["text"]
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texts.append(text)
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elif part_type == "image_url":
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if len(mm_futures) > 0:
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raise NotImplementedError(
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"Multiple 'image_url' input is currently not supported."
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)
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image_url = cast(ChatCompletionContentPartImageParam,
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part)["image_url"]
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if image_url.get("detail", "auto") != "auto":
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logger.warning(
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"'image_url.detail' is currently not supported and "
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"will be ignored.")
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image_future = async_get_and_parse_image(image_url["url"])
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mm_futures.append(image_future)
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else:
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raise NotImplementedError(f"Unknown part type: {part_type}")
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text_prompt = "\n".join(texts)
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if mm_futures:
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image_token_str = self.image_token_str
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if image_token_str is not None:
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if image_token_str in text_prompt:
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logger.warning(
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"Detected image token string in the text prompt. "
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"Skipping prompt formatting.")
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else:
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text_prompt = self._get_full_image_text_prompt(
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image_token_str=image_token_str,
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text_prompt=text_prompt,
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)
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messages = [ConversationMessage(role=role, content=text_prompt)]
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return ChatMessageParseResult(messages=messages, mm_futures=mm_futures)
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def _parse_chat_message_content(
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self,
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message: ChatCompletionMessageParam,
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) -> ChatMessageParseResult:
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role = message["role"]
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content = message.get("content")
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if content is None:
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return ChatMessageParseResult(messages=[], mm_futures=[])
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if isinstance(content, str):
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messages = [ConversationMessage(role=role, content=content)]
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return ChatMessageParseResult(messages=messages, mm_futures=[])
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return self._parse_chat_message_content_parts(role, content)
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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[ErrorResponse, AsyncGenerator[str, None],
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ChatCompletionResponse]:
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"""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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ChatCompletion API.
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NOTE: Currently we do not support the following feature:
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- function_call (Users should implement this by themselves)
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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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return error_check_ret
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try:
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conversation: List[ConversationMessage] = []
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mm_futures: List[Awaitable[MultiModalDataDict]] = []
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for msg in request.messages:
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chat_parsed_result = self._parse_chat_message_content(msg)
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conversation.extend(chat_parsed_result.messages)
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mm_futures.extend(chat_parsed_result.mm_futures)
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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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prompt = self.tokenizer.apply_chat_template(
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conversation=conversation,
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tokenize=False,
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add_generation_prompt=request.add_generation_prompt,
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tools=tool_dicts,
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documents=request.documents,
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chat_template=request.chat_template,
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**(request.chat_template_kwargs or {}),
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)
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except Exception as e:
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logger.error("Error in applying chat template from request: %s", e)
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return self.create_error_response(str(e))
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mm_data: Optional[MultiModalDataDict] = None
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try:
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if len(mm_futures):
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# since we support only single mm data currently
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assert len(
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mm_futures
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) == 1, "Multiple 'image_url' input is currently not supported."
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mm_data = await mm_futures[0]
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except Exception as e:
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logger.error("Error in loading multi-modal data: %s", e)
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return self.create_error_response(str(e))
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request_id = f"cmpl-{random_uuid()}"
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try:
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# Tokenize/detokenize depending on prompt format (string/token list)
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prompt_ids, prompt_text = self._validate_prompt_and_tokenize(
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request,
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prompt=prompt,
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add_special_tokens=request.add_special_tokens)
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sampling_params = request.to_sampling_params()
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lora_request = self._maybe_get_lora(request)
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decoding_config = await self.engine.get_decoding_config()
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guided_decoding_backend = request.guided_decoding_backend \
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or decoding_config.guided_decoding_backend
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guided_decode_logits_processor = (
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await get_guided_decoding_logits_processor(
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guided_decoding_backend, request, await
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self.engine.get_tokenizer()))
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if guided_decode_logits_processor:
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if sampling_params.logits_processors is None:
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sampling_params.logits_processors = []
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sampling_params.logits_processors.append(
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guided_decode_logits_processor)
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except ValueError as e:
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return self.create_error_response(str(e))
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inputs: PromptInputs = {
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"prompt": prompt_text,
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"prompt_token_ids": prompt_ids,
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}
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if mm_data:
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inputs["multi_modal_data"] = mm_data
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is_tracing_enabled = await self.engine.is_tracing_enabled()
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trace_headers = None
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if is_tracing_enabled and raw_request:
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trace_headers = extract_trace_headers(raw_request.headers)
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if not is_tracing_enabled and raw_request and contains_trace_headers(
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raw_request.headers):
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log_tracing_disabled_warning()
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result_generator = self.engine.generate(
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inputs,
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sampling_params,
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request_id,
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lora_request,
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trace_headers=trace_headers,
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)
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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, conversation)
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else:
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try:
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return await self.chat_completion_full_generator(
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request, raw_request, result_generator, request_id,
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conversation)
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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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else:
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return request.messages[-1]["role"]
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async def chat_completion_stream_generator(
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self, request: ChatCompletionRequest,
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result_generator: AsyncIterator[RequestOutput], request_id: str,
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conversation: List[ConversationMessage]
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) -> AsyncGenerator[str, None]:
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model_name = self.served_model_names[0]
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created_time = int(time.time())
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chunk_object_type = "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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assert request.n is not None
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previous_texts = [""] * request.n
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previous_num_tokens = [0] * request.n
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finish_reason_sent = [False] * request.n
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try:
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async for res in result_generator:
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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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# 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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for i in range(request.n):
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choice_data = ChatCompletionResponseStreamChoice(
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index=i,
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delta=DeltaMessage(role=role),
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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 (request.stream_options
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and request.stream_options.include_usage):
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chunk.usage = None
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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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# 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 = ""
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if conversation and conversation[-1].get(
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"content") and conversation[-1].get(
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"role") == role:
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last_msg_content = conversation[-1]["content"]
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if last_msg_content:
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for i in range(request.n):
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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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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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logprobs=None,
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model=model_name)
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if (request.stream_options and
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request.stream_options.include_usage):
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chunk.usage = None
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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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for output in res.outputs:
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i = output.index
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if finish_reason_sent[i]:
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continue
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delta_token_ids = output.token_ids[previous_num_tokens[i]:]
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out_logprobs = output.logprobs[
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previous_num_tokens[i]:] if output.logprobs else None
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if request.logprobs and request.top_logprobs is not None:
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assert out_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=delta_token_ids,
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top_logprobs=out_logprobs,
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num_output_top_logprobs=request.top_logprobs,
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)
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else:
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logprobs = None
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delta_text = output.text[len(previous_texts[i]):]
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previous_texts[i] = output.text
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previous_num_tokens[i] = len(output.token_ids)
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if request.tool_choice and type(
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request.tool_choice
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) is ChatCompletionNamedToolChoiceParam:
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delta_message = DeltaMessage(tool_calls=[
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ToolCall(function=FunctionCall(
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name=request.tool_choice.function.name,
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arguments=delta_text))
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])
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else:
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delta_message = DeltaMessage(content=delta_text)
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if output.finish_reason is None:
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# Send token-by-token response for each request.n
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choice_data = ChatCompletionResponseStreamChoice(
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index=i,
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delta=delta_message,
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logprobs=logprobs,
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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 (request.stream_options
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and request.stream_options.include_usage):
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chunk.usage = None
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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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else:
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# Send the finish response for each request.n only once
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prompt_tokens = len(res.prompt_token_ids)
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choice_data = ChatCompletionResponseStreamChoice(
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index=i,
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delta=delta_message,
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logprobs=logprobs,
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finish_reason=output.finish_reason,
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stop_reason=output.stop_reason)
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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 (request.stream_options
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and request.stream_options.include_usage):
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chunk.usage = None
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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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finish_reason_sent[i] = True
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if (request.stream_options
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and request.stream_options.include_usage):
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final_usage = UsageInfo(
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prompt_tokens=prompt_tokens,
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completion_tokens=previous_num_tokens[i],
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total_tokens=prompt_tokens + previous_num_tokens[i],
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)
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final_usage_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=[],
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model=model_name,
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usage=final_usage)
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final_usage_data = (final_usage_chunk.model_dump_json(
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exclude_unset=True, exclude_none=True))
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yield f"data: {final_usage_data}\n\n"
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except ValueError as e:
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# TODO: Use a vllm-specific Validation Error
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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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# Send the final done message after all response.n are finished
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|
yield "data: [DONE]\n\n"
|
|
|
|
async def chat_completion_full_generator(
|
|
self, request: ChatCompletionRequest, raw_request: Optional[Request],
|
|
result_generator: AsyncIterator[RequestOutput], request_id: str,
|
|
conversation: List[ConversationMessage]
|
|
) -> Union[ErrorResponse, ChatCompletionResponse]:
|
|
|
|
model_name = self.served_model_names[0]
|
|
created_time = int(time.time())
|
|
final_res: Optional[RequestOutput] = None
|
|
|
|
async for res in result_generator:
|
|
if raw_request is not None and await raw_request.is_disconnected():
|
|
# Abort the request if the client disconnects.
|
|
await self.engine.abort(request_id)
|
|
return self.create_error_response("Client disconnected")
|
|
final_res = res
|
|
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,
|
|
)
|
|
else:
|
|
logprobs = None
|
|
|
|
if request.tool_choice and type(
|
|
request.tool_choice) is ChatCompletionNamedToolChoiceParam:
|
|
message = ChatMessage(
|
|
role=role,
|
|
content="",
|
|
tool_calls=[
|
|
ToolCall(function=FunctionCall(
|
|
name=request.tool_choice.function.name,
|
|
arguments=output.text))
|
|
])
|
|
elif not request.tool_choice or request.tool_choice == "none":
|
|
message = ChatMessage(role=role, content=output.text)
|
|
|
|
choice_data = ChatCompletionResponseChoice(
|
|
index=output.index,
|
|
message=message,
|
|
logprobs=logprobs,
|
|
finish_reason=output.finish_reason,
|
|
stop_reason=output.stop_reason)
|
|
choices.append(choice_data)
|
|
|
|
if request.echo:
|
|
last_msg_content = ""
|
|
if conversation and conversation[-1].get(
|
|
"content") and conversation[-1].get("role") == role:
|
|
last_msg_content = conversation[-1]["content"]
|
|
|
|
for choice in choices:
|
|
full_message = last_msg_content + choice.message.content
|
|
choice.message.content = full_message
|
|
|
|
num_prompt_tokens = len(final_res.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,
|
|
)
|
|
response = ChatCompletionResponse(
|
|
id=request_id,
|
|
created=created_time,
|
|
model=model_name,
|
|
choices=choices,
|
|
usage=usage,
|
|
)
|
|
|
|
return response
|
|
|
|
def _get_top_logprobs(
|
|
self, logprobs: Dict[int, Logprob],
|
|
top_logprobs: Optional[int]) -> List[ChatCompletionLogProb]:
|
|
return [
|
|
ChatCompletionLogProb(
|
|
token=self._get_decoded_token(p[1], p[0]),
|
|
logprob=max(p[1].logprob, -9999.0),
|
|
bytes=list(
|
|
self._get_decoded_token(p[1],
|
|
p[0]).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]]],
|
|
num_output_top_logprobs: Optional[int] = None,
|
|
) -> ChatCompletionLogProbs:
|
|
"""Create OpenAI-style logprobs."""
|
|
|
|
logprobs_content = []
|
|
|
|
for i, token_id in enumerate(token_ids):
|
|
step_top_logprobs = top_logprobs[i]
|
|
if step_top_logprobs is None:
|
|
logprobs_content.append(
|
|
ChatCompletionLogProbsContent(
|
|
token=self.tokenizer.decode(token_id),
|
|
bytes=list(
|
|
self.tokenizer.decode(token_id).encode(
|
|
"utf-8", errors="replace"))))
|
|
else:
|
|
logprobs_content.append(
|
|
ChatCompletionLogProbsContent(
|
|
token=step_top_logprobs[token_id].decoded_token,
|
|
logprob=max(step_top_logprobs[token_id].logprob,
|
|
-9999.0),
|
|
bytes=list(
|
|
step_top_logprobs[token_id].decoded_token.encode(
|
|
"utf-8", errors="replace")),
|
|
top_logprobs=self._get_top_logprobs(
|
|
step_top_logprobs, num_output_top_logprobs)))
|
|
|
|
return ChatCompletionLogProbs(content=logprobs_content)
|