434 lines
16 KiB
Python
434 lines
16 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import asyncio
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import time
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from collections.abc import AsyncGenerator, Mapping
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from typing import Any, Optional, 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 EngineClient
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from vllm.entrypoints.logger import RequestLogger
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from vllm.entrypoints.openai.protocol import (ErrorResponse, RerankDocument,
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RerankRequest, RerankResponse,
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RerankResult, RerankUsage,
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ScoreRequest, ScoreResponse,
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ScoreResponseData, UsageInfo)
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from vllm.entrypoints.openai.serving_engine import OpenAIServing
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from vllm.entrypoints.openai.serving_models import OpenAIServingModels
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from vllm.entrypoints.score_utils import (_cosine_similarity,
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_validate_score_input_lens)
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from vllm.entrypoints.utils import _validate_truncation_size
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from vllm.inputs.data import TokensPrompt
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from vllm.logger import init_logger
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from vllm.lora.request import LoRARequest
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from vllm.outputs import PoolingRequestOutput, ScoringRequestOutput
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from vllm.prompt_adapter.request import PromptAdapterRequest
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from vllm.transformers_utils.tokenizer import (AnyTokenizer, MistralTokenizer,
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PreTrainedTokenizer,
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PreTrainedTokenizerFast)
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from vllm.utils import make_async, merge_async_iterators
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logger = init_logger(__name__)
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class ServingScores(OpenAIServing):
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def __init__(
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self,
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engine_client: EngineClient,
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model_config: ModelConfig,
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models: OpenAIServingModels,
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*,
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request_logger: Optional[RequestLogger],
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) -> None:
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super().__init__(engine_client=engine_client,
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model_config=model_config,
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models=models,
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request_logger=request_logger)
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async def _embedding_score(
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self,
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tokenizer: Union[PreTrainedTokenizer, PreTrainedTokenizerFast],
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texts_1: list[str],
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texts_2: list[str],
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request: Union[RerankRequest, ScoreRequest],
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request_id=str,
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tokenization_kwargs: Optional[dict[str, Any]] = None,
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lora_request: Optional[Union[LoRARequest, None]] = None,
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prompt_adapter_request: Optional[Union[PromptAdapterRequest,
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None]] = None,
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trace_headers: Optional[Mapping[str, str]] = None,
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) -> list[PoolingRequestOutput]:
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input_texts = texts_1 + texts_2
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engine_prompts: list[TokensPrompt] = []
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tokenize_async = make_async(tokenizer.__call__,
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executor=self._tokenizer_executor)
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tokenization_kwargs = tokenization_kwargs or {}
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tokenized_prompts = await asyncio.gather(
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*(tokenize_async(t, **tokenization_kwargs) for t in input_texts))
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for tok_result, input_text in zip(tokenized_prompts, input_texts):
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text_token_prompt = \
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self._validate_input(
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request,
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tok_result["input_ids"],
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input_text)
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engine_prompts.append(
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TokensPrompt(
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prompt_token_ids=text_token_prompt["prompt_token_ids"]))
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# Schedule the request and get the result generator.
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generators: list[AsyncGenerator[PoolingRequestOutput, None]] = []
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pooling_params = request.to_pooling_params()
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for i, engine_prompt in enumerate(engine_prompts):
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request_id_item = f"{request_id}-{i}"
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self._log_inputs(request_id_item,
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input_texts[i],
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params=pooling_params,
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lora_request=lora_request,
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prompt_adapter_request=prompt_adapter_request)
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generators.append(
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self.engine_client.encode(
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engine_prompt,
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pooling_params,
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request_id_item,
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lora_request=lora_request,
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trace_headers=trace_headers,
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priority=request.priority,
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))
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result_generator = merge_async_iterators(*generators)
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# Non-streaming response
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final_res_batch: list[PoolingRequestOutput] = []
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embeddings: list[Optional[PoolingRequestOutput]] =\
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[None] * len(engine_prompts)
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async for i, res in result_generator:
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embeddings[i] = res
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emb_texts_1: list[PoolingRequestOutput] = []
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emb_texts_2: list[PoolingRequestOutput] = []
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for i in range(0, len(texts_1)):
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assert (emb := embeddings[i]) is not None
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emb_texts_1.append(emb)
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for i in range(len(texts_1), len(embeddings)):
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assert (emb := embeddings[i]) is not None
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emb_texts_2.append(emb)
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if len(emb_texts_1) == 1:
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emb_texts_1 = emb_texts_1 * len(emb_texts_2)
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final_res_batch = _cosine_similarity(tokenizer=tokenizer,
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embed_1=emb_texts_1,
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embed_2=emb_texts_2)
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return final_res_batch
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async def _cross_encoding_score(
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self,
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tokenizer: Union[AnyTokenizer],
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texts_1: list[str],
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texts_2: list[str],
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request: Union[RerankRequest, ScoreRequest],
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request_id=str,
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tokenization_kwargs: Optional[dict[str, Any]] = None,
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lora_request: Optional[Union[LoRARequest, None]] = None,
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prompt_adapter_request: Optional[Union[PromptAdapterRequest,
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None]] = None,
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trace_headers: Optional[Mapping[str, str]] = None,
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) -> list[PoolingRequestOutput]:
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request_prompts: list[str] = []
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engine_prompts: list[TokensPrompt] = []
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if len(texts_1) == 1:
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texts_1 = texts_1 * len(texts_2)
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input_pairs = [(t1, t2) for t1, t2 in zip(texts_1, texts_2)]
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if isinstance(tokenizer, MistralTokenizer):
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raise ValueError(
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"MistralTokenizer not supported for cross-encoding")
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tokenize_async = make_async(tokenizer.__call__,
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executor=self._tokenizer_executor)
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tokenization_kwargs = tokenization_kwargs or {}
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tokenized_prompts = await asyncio.gather(
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*(tokenize_async(text=t1, text_pair=t2, **tokenization_kwargs)
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for t1, t2 in input_pairs))
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for prompt_inputs, (t1, t2) in zip(tokenized_prompts, input_pairs):
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request_prompt = f"{t1}{tokenizer.sep_token}{t2}"
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input_ids = prompt_inputs["input_ids"]
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text_token_prompt = \
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self._validate_input(request, input_ids, request_prompt)
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engine_prompt = TokensPrompt(
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prompt_token_ids=text_token_prompt["prompt_token_ids"],
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token_type_ids=prompt_inputs.get("token_type_ids"))
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request_prompts.append(request_prompt)
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engine_prompts.append(engine_prompt)
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# Schedule the request and get the result generator.
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generators: list[AsyncGenerator[PoolingRequestOutput, None]] = []
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pooling_params = request.to_pooling_params()
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for i, engine_prompt in enumerate(engine_prompts):
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request_id_item = f"{request_id}-{i}"
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self._log_inputs(request_id_item,
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request_prompts[i],
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params=pooling_params,
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lora_request=lora_request,
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prompt_adapter_request=prompt_adapter_request)
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generator = self.engine_client.encode(
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engine_prompt,
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pooling_params,
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request_id_item,
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lora_request=lora_request,
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trace_headers=trace_headers,
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priority=request.priority,
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)
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generators.append(generator)
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result_generator = merge_async_iterators(*generators)
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# Non-streaming response
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final_res_batch: list[
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Optional[PoolingRequestOutput]] = [None] * len(engine_prompts)
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async for i, res in result_generator:
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final_res_batch[i] = res
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return [out for out in final_res_batch if out is not None]
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async def _run_scoring(
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self,
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texts_1: Union[str, list[str]],
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texts_2: Union[str, list[str]],
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request: Union[ScoreRequest, RerankRequest],
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request_id: str,
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raw_request: Optional[Request] = None,
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truncate_prompt_tokens: Optional[int] = None,
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) -> list[PoolingRequestOutput]:
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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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if prompt_adapter_request is not None:
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raise NotImplementedError("Prompt adapter is not supported "
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"for scoring models")
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tokenizer = await self.engine_client.get_tokenizer(lora_request)
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tokenization_kwargs: dict[str, Any] = {}
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_validate_truncation_size(self.max_model_len, truncate_prompt_tokens,
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tokenization_kwargs)
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trace_headers = (None if raw_request is None else await
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self._get_trace_headers(raw_request.headers))
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if isinstance(texts_1, str):
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texts_1 = [texts_1]
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if isinstance(texts_2, str):
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texts_2 = [texts_2]
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_validate_score_input_lens(texts_1, texts_2)
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if self.model_config.is_cross_encoder:
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return await self._cross_encoding_score(
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tokenizer=tokenizer,
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texts_1=texts_1,
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texts_2=texts_2,
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request=request,
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request_id=request_id,
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tokenization_kwargs=tokenization_kwargs,
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lora_request=lora_request,
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prompt_adapter_request=prompt_adapter_request,
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trace_headers=trace_headers)
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else:
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return await self._embedding_score(
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tokenizer=tokenizer,
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texts_1=texts_1,
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texts_2=texts_2,
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request=request,
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request_id=request_id,
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tokenization_kwargs=tokenization_kwargs,
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lora_request=lora_request,
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prompt_adapter_request=prompt_adapter_request,
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trace_headers=trace_headers)
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async def create_score(
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self,
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request: ScoreRequest,
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raw_request: Optional[Request] = None,
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) -> Union[ScoreResponse, ErrorResponse]:
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"""
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Score API similar to Sentence Transformers cross encoder
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See https://sbert.net/docs/package_reference/cross_encoder
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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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request_id = f"score-{self._base_request_id(raw_request)}"
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created_time = int(time.time())
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try:
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final_res_batch = await self._run_scoring(
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request.text_1,
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request.text_2,
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request,
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request_id,
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raw_request,
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request.truncate_prompt_tokens,
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)
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return self.request_output_to_score_response(
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final_res_batch,
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request_id,
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created_time,
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self._get_model_name(request.model),
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)
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except asyncio.CancelledError:
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return self.create_error_response("Client disconnected")
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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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async def do_rerank(
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self,
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request: RerankRequest,
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raw_request: Optional[Request] = None
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) -> Union[RerankResponse, ErrorResponse]:
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"""
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Rerank API based on JinaAI's rerank API; implements the same
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API interface. Designed for compatibility with off-the-shelf
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tooling, since this is a common standard for reranking APIs
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See example client implementations at
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https://github.com/infiniflow/ragflow/blob/main/rag/llm/rerank_model.py
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numerous clients use this standard.
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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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request_id = f"rerank-{self._base_request_id(raw_request)}"
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documents = request.documents
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top_n = request.top_n if request.top_n > 0 else len(documents)
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try:
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final_res_batch = await self._run_scoring(
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request.query,
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documents,
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request,
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request_id,
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raw_request,
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request.truncate_prompt_tokens,
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)
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return self.request_output_to_rerank_response(
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final_res_batch,
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request_id,
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self._get_model_name(request.model),
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documents,
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top_n,
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)
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except asyncio.CancelledError:
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return self.create_error_response("Client disconnected")
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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 request_output_to_score_response(
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self,
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final_res_batch: list[PoolingRequestOutput],
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request_id: str,
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created_time: int,
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model_name: str,
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) -> ScoreResponse:
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items: list[ScoreResponseData] = []
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num_prompt_tokens = 0
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for idx, final_res in enumerate(final_res_batch):
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classify_res = ScoringRequestOutput.from_base(final_res)
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item = ScoreResponseData(
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index=idx,
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score=classify_res.outputs.score,
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)
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prompt_token_ids = final_res.prompt_token_ids
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items.append(item)
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num_prompt_tokens += len(prompt_token_ids)
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usage = UsageInfo(
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prompt_tokens=num_prompt_tokens,
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total_tokens=num_prompt_tokens,
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)
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return ScoreResponse(
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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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data=items,
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usage=usage,
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)
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def request_output_to_rerank_response(
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self, final_res_batch: list[PoolingRequestOutput], request_id: str,
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model_name: str, documents: list[str],
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top_n: int) -> RerankResponse:
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"""
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Convert the output of do_rank to a RerankResponse
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"""
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results: list[RerankResult] = []
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num_prompt_tokens = 0
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for idx, final_res in enumerate(final_res_batch):
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classify_res = ScoringRequestOutput.from_base(final_res)
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result = RerankResult(
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index=idx,
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document=RerankDocument(text=documents[idx]),
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relevance_score=classify_res.outputs.score,
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)
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results.append(result)
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prompt_token_ids = final_res.prompt_token_ids
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num_prompt_tokens += len(prompt_token_ids)
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# sort by relevance, then return the top n if set
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results.sort(key=lambda x: x.relevance_score, reverse=True)
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if top_n < len(documents):
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results = results[:top_n]
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return RerankResponse(
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id=request_id,
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model=model_name,
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results=results,
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usage=RerankUsage(total_tokens=num_prompt_tokens))
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