577 lines
25 KiB
Python
577 lines
25 KiB
Python
import asyncio
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import time
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from typing import AsyncGenerator, AsyncIterator, Dict, Final, List, Optional
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from typing import Sequence as GenericSequence
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from typing import Union
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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 AsyncEngineClient
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from vllm.entrypoints.chat_utils import (ConversationMessage,
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apply_chat_template,
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load_chat_template,
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parse_chat_messages_futures)
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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, 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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PromptAdapterPath,
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TextTokensPrompt)
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from vllm.inputs import TokensPrompt
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from vllm.logger import init_logger
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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.transformers_utils.tokenizer import AnyTokenizer
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from vllm.utils import iterate_with_cancellation, random_uuid
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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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async_engine_client: AsyncEngineClient,
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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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*,
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lora_modules: Optional[List[LoRAModulePath]],
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prompt_adapters: Optional[List[PromptAdapterPath]],
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request_logger: Optional[RequestLogger],
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chat_template: Optional[str],
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return_tokens_as_token_ids: bool = False,
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):
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super().__init__(async_engine_client=async_engine_client,
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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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prompt_adapters=prompt_adapters,
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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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# If this is None we use the tokenizer's default chat template
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self.chat_template = load_chat_template(chat_template)
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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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"""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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(
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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_config = self.model_config
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tokenizer = await self.async_engine_client.get_tokenizer(
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lora_request)
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conversation, mm_data_future = parse_chat_messages_futures(
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request.messages, model_config, tokenizer)
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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 = apply_chat_template(
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tokenizer,
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conversation=conversation,
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chat_template=request.chat_template or self.chat_template,
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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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**(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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try:
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mm_data = await mm_data_future
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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"chat-{random_uuid()}"
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try:
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guided_decode_logits_processor = (
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await self._guided_decode_logits_processor(request, tokenizer))
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if isinstance(prompt, str):
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prompt_inputs = self._tokenize_prompt_input(
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request,
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tokenizer,
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prompt,
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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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else:
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assert isinstance(prompt, list) and isinstance(
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prompt[0], int
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), "Prompt has to be either a string or a list of token ids"
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prompt_inputs = TextTokensPrompt(
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prompt=tokenizer.decode(prompt), prompt_token_ids=prompt)
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assert prompt_inputs is not None
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sampling_params = request.to_sampling_params(
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tokenizer,
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guided_decode_logits_processor,
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default_max_tokens=self.max_model_len -
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len(prompt_inputs["prompt_token_ids"]))
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self._log_inputs(request_id,
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prompt_inputs,
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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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engine_inputs = TokensPrompt(
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prompt_token_ids=prompt_inputs["prompt_token_ids"])
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if mm_data is not None:
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engine_inputs["multi_modal_data"] = mm_data
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is_tracing_enabled = (
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await self.async_engine_client.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
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and contains_trace_headers(raw_request.headers)):
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log_tracing_disabled_warning()
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result_generator = self.async_engine_client.generate(
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engine_inputs,
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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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)
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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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if raw_request:
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result_generator = iterate_with_cancellation(
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result_generator, raw_request.is_disconnected)
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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, tokenizer)
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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, conversation, tokenizer)
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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,
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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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conversation: List[ConversationMessage],
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tokenizer: AnyTokenizer,
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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: 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_texts = [""] * num_choices
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previous_num_tokens = [0] * num_choices
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finish_reason_sent = [False] * num_choices
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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(num_choices):
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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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if (request.stream_options.continuous_usage_stats):
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prompt_tokens = len(res.prompt_token_ids)
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usage = UsageInfo(prompt_tokens=prompt_tokens,
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completion_tokens=0,
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total_tokens=prompt_tokens)
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chunk.usage = usage
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else:
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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(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 (request.stream_options and
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request.stream_options.include_usage):
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if (request.stream_options.
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continuous_usage_stats):
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prompt_tokens = len(
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res.prompt_token_ids)
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usage = UsageInfo(
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prompt_tokens=prompt_tokens,
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completion_tokens=0,
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total_tokens=prompt_tokens)
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chunk.usage = usage
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else:
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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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tokenizer=tokenizer,
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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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if (request.stream_options.continuous_usage_stats):
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prompt_tokens = len(res.prompt_token_ids)
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completion_tokens = len(output.token_ids)
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usage = UsageInfo(
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prompt_tokens=prompt_tokens,
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completion_tokens=completion_tokens,
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total_tokens=prompt_tokens +
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completion_tokens,
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)
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chunk.usage = usage
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else:
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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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if (request.stream_options.continuous_usage_stats):
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prompt_tokens = len(res.prompt_token_ids)
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completion_tokens = len(output.token_ids)
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usage = UsageInfo(
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prompt_tokens=prompt_tokens,
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completion_tokens=completion_tokens,
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total_tokens=prompt_tokens +
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completion_tokens,
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)
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chunk.usage = usage
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else:
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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"
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async def chat_completion_full_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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conversation: List[ConversationMessage],
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tokenizer: AnyTokenizer,
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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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try:
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async for res in result_generator:
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final_res = res
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except asyncio.CancelledError:
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return self.create_error_response("Client disconnected")
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assert final_res is not None
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choices: List[ChatCompletionResponseChoice] = []
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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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out_logprobs = output.logprobs
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if request.logprobs and request.top_logprobs is not None:
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assert out_logprobs is not None, "Did not output logprobs"
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logprobs = self._create_chat_logprobs(
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token_ids=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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tokenizer=tokenizer,
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)
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else:
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logprobs = None
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if request.tool_choice and type(
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request.tool_choice) is ChatCompletionNamedToolChoiceParam:
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message = ChatMessage(
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role=role,
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content="",
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tool_calls=[
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ToolCall(function=FunctionCall(
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name=request.tool_choice.function.name,
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arguments=output.text))
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])
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elif not request.tool_choice or request.tool_choice == "none":
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message = ChatMessage(role=role, content=output.text)
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choice_data = ChatCompletionResponseChoice(
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index=output.index,
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message=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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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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prompt_logprobs=final_res.prompt_logprobs,
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)
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return response
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def _get_top_logprobs(
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self, logprobs: Dict[int, Logprob], top_logprobs: Optional[int],
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tokenizer: AnyTokenizer) -> List[ChatCompletionLogProb]:
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return [
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ChatCompletionLogProb(token=(token := self._get_decoded_token(
|
|
p[1],
|
|
p[0],
|
|
tokenizer,
|
|
return_as_token_id=self.return_tokens_as_token_ids)),
|
|
logprob=max(p[1].logprob, -9999.0),
|
|
bytes=list(
|
|
token.encode("utf-8", errors="replace")))
|
|
for i, p in enumerate(logprobs.items())
|
|
if top_logprobs and i < top_logprobs
|
|
]
|
|
|
|
def _create_chat_logprobs(
|
|
self,
|
|
token_ids: GenericSequence[int],
|
|
top_logprobs: GenericSequence[Optional[Dict[int, Logprob]]],
|
|
tokenizer: AnyTokenizer,
|
|
num_output_top_logprobs: Optional[int] = None,
|
|
) -> ChatCompletionLogProbs:
|
|
"""Create OpenAI-style logprobs."""
|
|
logprobs_content: List[ChatCompletionLogProbsContent] = []
|
|
|
|
for i, token_id in enumerate(token_ids):
|
|
step_top_logprobs = top_logprobs[i]
|
|
if step_top_logprobs is None:
|
|
token = tokenizer.decode(token_id)
|
|
if self.return_tokens_as_token_ids:
|
|
token = f"token_id:{token_id}"
|
|
|
|
logprobs_content.append(
|
|
ChatCompletionLogProbsContent(
|
|
token=token,
|
|
bytes=list(token.encode("utf-8", errors="replace")),
|
|
))
|
|
else:
|
|
step_token = step_top_logprobs[token_id]
|
|
step_decoded = step_token.decoded_token
|
|
|
|
logprobs_content.append(
|
|
ChatCompletionLogProbsContent(
|
|
token=self._get_decoded_token(
|
|
step_token,
|
|
token_id,
|
|
tokenizer,
|
|
self.return_tokens_as_token_ids,
|
|
),
|
|
logprob=max(step_token.logprob, -9999.0),
|
|
bytes=None if step_decoded is None else list(
|
|
step_decoded.encode("utf-8", errors="replace")),
|
|
top_logprobs=self._get_top_logprobs(
|
|
step_top_logprobs,
|
|
num_output_top_logprobs,
|
|
tokenizer,
|
|
),
|
|
))
|
|
|
|
return ChatCompletionLogProbs(content=logprobs_content)
|