207 lines
8.2 KiB
Python
207 lines
8.2 KiB
Python
import asyncio
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from typing import Any, AsyncGenerator, Dict, List, Optional, Union, cast
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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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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.inputs.data import TokensPrompt
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from vllm.logger import init_logger
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from vllm.outputs import PoolingRequestOutput, ScoringRequestOutput
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from vllm.transformers_utils.tokenizers.mistral import MistralTokenizer
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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 JinaAIServingRerank(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 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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model_name = request.model
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request_id = f"rerank-{self._base_request_id(raw_request)}"
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truncate_prompt_tokens = request.truncate_prompt_tokens
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query = request.query
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documents = request.documents
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request_prompts = []
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engine_prompts = []
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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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(
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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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tokenizer = await self.engine_client.get_tokenizer(lora_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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if isinstance(tokenizer, MistralTokenizer):
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raise ValueError(
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"MistralTokenizer not supported for cross-encoding")
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if not self.model_config.is_cross_encoder:
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raise ValueError("Model is not cross encoder.")
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if truncate_prompt_tokens is not None and \
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truncate_prompt_tokens > self.max_model_len:
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raise ValueError(
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f"truncate_prompt_tokens value ({truncate_prompt_tokens}) "
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f"is greater than max_model_len ({self.max_model_len})."
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f" Please, select a smaller truncation size.")
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for doc in documents:
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request_prompt = f"{query}{tokenizer.sep_token}{doc}"
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tokenization_kwargs: Dict[str, Any] = {}
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if truncate_prompt_tokens is not None:
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tokenization_kwargs["truncation"] = True
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tokenization_kwargs["max_length"] = truncate_prompt_tokens
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tokenize_async = make_async(tokenizer.__call__,
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executor=self._tokenizer_executor)
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prompt_inputs = await tokenize_async(text=query,
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text_pair=doc,
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**tokenization_kwargs)
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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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except ValueError as e:
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logger.exception("Error in preprocessing prompt inputs")
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return self.create_error_response(str(e))
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# Schedule the request and get the result generator.
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generators: List[AsyncGenerator[PoolingRequestOutput, None]] = []
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try:
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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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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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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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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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result_generator = merge_async_iterators(*generators)
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num_prompts = len(engine_prompts)
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# Non-streaming response
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final_res_batch: List[Optional[PoolingRequestOutput]]
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final_res_batch = [None] * num_prompts
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try:
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async for i, res in result_generator:
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final_res_batch[i] = res
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assert all(final_res is not None for final_res in final_res_batch)
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final_res_batch_checked = cast(List[PoolingRequestOutput],
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final_res_batch)
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response = self.request_output_to_rerank_response(
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final_res_batch_checked, request_id, model_name, documents,
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top_n)
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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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return response
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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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