417 lines
17 KiB
Python
417 lines
17 KiB
Python
import codecs
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import time
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from dataclasses import dataclass
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from typing import (AsyncGenerator, AsyncIterator, Iterable, List, Optional,
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TypedDict, Union, cast, final)
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from fastapi import Request
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from openai.types.chat import 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, ChatCompletionMessageParam,
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ChatCompletionRequest, ChatCompletionResponse,
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ChatCompletionResponseChoice, ChatCompletionResponseStreamChoice,
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ChatCompletionStreamResponse, ChatMessage, DeltaMessage, ErrorResponse,
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UsageInfo)
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from vllm.entrypoints.openai.serving_engine import (LoRAModulePath,
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OpenAIServing)
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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.outputs import RequestOutput
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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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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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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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for _, part in enumerate(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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else:
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raise NotImplementedError(f"Unknown part type: {part_type}")
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messages = [ConversationMessage(role=role, content="\n".join(texts))]
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return ChatMessageParseResult(messages=messages)
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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=[])
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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)
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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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for msg in request.messages:
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parsed_msg = self._parse_chat_message_content(msg)
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conversation.extend(parsed_msg.messages)
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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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)
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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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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, prompt=prompt, add_special_tokens=False)
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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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result_generator = self.engine.generate(
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{
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"prompt": prompt_text,
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"prompt_token_ids": prompt_ids
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},
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sampling_params,
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request_id,
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lora_request,
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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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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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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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top_logprobs = output.logprobs[
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previous_num_tokens[i]:] if output.logprobs else None
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if request.logprobs:
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logprobs = self._create_logprobs(
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token_ids=delta_token_ids,
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top_logprobs=top_logprobs,
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num_output_top_logprobs=request.logprobs,
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initial_text_offset=len(previous_texts[i]),
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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 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=DeltaMessage(content=delta_text),
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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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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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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 +
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previous_num_tokens[i],
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)
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choice_data = ChatCompletionResponseStreamChoice(
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index=i,
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delta=DeltaMessage(content=delta_text),
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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 final_usage is not None:
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chunk.usage = final_usage
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data = chunk.model_dump_json(exclude_unset=True,
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exclude_none=True)
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yield f"data: {data}\n\n"
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finish_reason_sent[i] = True
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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"
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async def chat_completion_full_generator(
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self, request: ChatCompletionRequest, raw_request: Optional[Request],
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result_generator: AsyncIterator[RequestOutput], request_id: str,
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conversation: List[ConversationMessage]
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) -> Union[ErrorResponse, ChatCompletionResponse]:
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model_name = self.served_model_names[0]
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created_time = int(time.time())
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final_res: Optional[RequestOutput] = None
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async for res in result_generator:
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if raw_request is not None and await raw_request.is_disconnected():
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# Abort the request if the client disconnects.
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await self.engine.abort(request_id)
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return self.create_error_response("Client disconnected")
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final_res = res
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assert final_res is not None
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choices = []
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role = self.get_chat_request_role(request)
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for output in final_res.outputs:
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token_ids = output.token_ids
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top_logprobs = output.logprobs
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if request.logprobs:
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logprobs = self._create_logprobs(
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token_ids=token_ids,
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top_logprobs=top_logprobs,
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num_output_top_logprobs=request.logprobs,
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)
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else:
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logprobs = None
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choice_data = ChatCompletionResponseChoice(
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index=output.index,
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message=ChatMessage(role=role, content=output.text),
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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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)
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choices.append(choice_data)
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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("role") == role:
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last_msg_content = conversation[-1]["content"]
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for choice in choices:
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full_message = last_msg_content + choice.message.content
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choice.message.content = full_message
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num_prompt_tokens = len(final_res.prompt_token_ids)
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num_generated_tokens = sum(
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len(output.token_ids) for output in final_res.outputs)
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usage = UsageInfo(
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prompt_tokens=num_prompt_tokens,
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completion_tokens=num_generated_tokens,
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total_tokens=num_prompt_tokens + num_generated_tokens,
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)
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response = ChatCompletionResponse(
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id=request_id,
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created=created_time,
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model=model_name,
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choices=choices,
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usage=usage,
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)
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return response
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