145 lines
4.0 KiB
Python
145 lines
4.0 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 time
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from typing import Any, TypeAlias
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from pydantic import BaseModel, Field
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from vllm import PoolingParams
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from vllm.config import ModelConfig
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from vllm.entrypoints.openai.engine.protocol import OpenAIBaseModel, UsageInfo
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from vllm.entrypoints.pooling.base.protocol import (
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ClassifyRequestMixin,
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PoolingBasicRequestMixin,
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)
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from vllm.entrypoints.pooling.score.utils import (
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ScoreContentPartParam,
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ScoreMultiModalParam,
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)
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from vllm.renderers import TokenizeParams
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from vllm.utils import random_uuid
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class ScoreRequestMixin(PoolingBasicRequestMixin, ClassifyRequestMixin):
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# --8<-- [start:score-extra-params]
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mm_processor_kwargs: dict[str, Any] | None = Field(
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default=None,
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description=("Additional kwargs to pass to the HF processor."),
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)
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# --8<-- [end:score-extra-params]
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def build_tok_params(self, model_config: ModelConfig) -> TokenizeParams:
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encoder_config = model_config.encoder_config or {}
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return TokenizeParams(
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max_total_tokens=model_config.max_model_len,
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max_output_tokens=0,
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truncate_prompt_tokens=self.truncate_prompt_tokens,
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do_lower_case=encoder_config.get("do_lower_case", False),
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max_total_tokens_param="max_model_len",
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)
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def to_pooling_params(self):
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return PoolingParams(
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truncate_prompt_tokens=self.truncate_prompt_tokens,
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use_activation=self.use_activation,
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)
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class ScoreDataRequest(ScoreRequestMixin):
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data_1: list[str] | str | ScoreMultiModalParam
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data_2: list[str] | str | ScoreMultiModalParam
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class ScoreQueriesDocumentsRequest(ScoreRequestMixin):
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queries: list[str] | str | ScoreMultiModalParam
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documents: list[str] | str | ScoreMultiModalParam
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@property
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def data_1(self):
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return self.queries
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@property
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def data_2(self):
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return self.documents
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class ScoreTextRequest(ScoreRequestMixin):
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text_1: list[str] | str | ScoreMultiModalParam
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text_2: list[str] | str | ScoreMultiModalParam
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@property
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def data_1(self):
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return self.text_1
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@property
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def data_2(self):
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return self.text_2
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ScoreRequest: TypeAlias = (
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ScoreQueriesDocumentsRequest | ScoreDataRequest | ScoreTextRequest
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)
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class RerankRequest(PoolingBasicRequestMixin, ClassifyRequestMixin):
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query: str | ScoreMultiModalParam
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documents: list[str] | ScoreMultiModalParam
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top_n: int = Field(default_factory=lambda: 0)
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# --8<-- [start:rerank-extra-params]
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mm_processor_kwargs: dict[str, Any] | None = Field(
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default=None,
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description=("Additional kwargs to pass to the HF processor."),
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)
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# --8<-- [end:rerank-extra-params]
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def build_tok_params(self, model_config: ModelConfig) -> TokenizeParams:
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encoder_config = model_config.encoder_config or {}
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return TokenizeParams(
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max_total_tokens=model_config.max_model_len,
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max_output_tokens=0,
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truncate_prompt_tokens=self.truncate_prompt_tokens,
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do_lower_case=encoder_config.get("do_lower_case", False),
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max_total_tokens_param="max_model_len",
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)
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class RerankDocument(BaseModel):
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text: str | None = None
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multi_modal: ScoreContentPartParam | None = None
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class RerankResult(BaseModel):
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index: int
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document: RerankDocument
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relevance_score: float
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class RerankUsage(BaseModel):
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prompt_tokens: int
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total_tokens: int
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class RerankResponse(OpenAIBaseModel):
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id: str
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model: str
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usage: RerankUsage
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results: list[RerankResult]
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class ScoreResponseData(OpenAIBaseModel):
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index: int
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object: str = "score"
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score: float
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class ScoreResponse(OpenAIBaseModel):
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id: str = Field(default_factory=lambda: f"embd-{random_uuid()}")
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object: str = "list"
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created: int = Field(default_factory=lambda: int(time.time()))
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model: str
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data: list[ScoreResponseData]
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usage: UsageInfo
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