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vllm.pooling_params

PoolingParams

Bases: Struct

API parameters for pooling models. This

Attributes:

Name Type Description
dimensions Optional[int]

Reduce the dimensions of embeddings if model support matryoshka representation.
Source code in vllm/pooling_params.py 
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class PoolingParams(
        msgspec.Struct,
        omit_defaults=True,  # type: ignore[call-arg]
        array_like=True):  # type: ignore[call-arg]
    """API parameters for pooling models. This 

    Attributes:
        dimensions: Reduce the dimensions of embeddings
                    if model support matryoshka representation.
    """

    dimensions: Optional[int] = None

    use_cross_encoder: bool = False
    """Internal use only."""

    logits_processing_needs_token_ids: bool = False
    """Internal use only."""

    output_kind: RequestOutputKind = RequestOutputKind.FINAL_ONLY

    def clone(self) -> "PoolingParams":
        """Returns a deep copy of the PoolingParams instance."""
        return PoolingParams(
            dimensions=self.dimensions,
            use_cross_encoder=self.use_cross_encoder,
            logits_processing_needs_token_ids=self.
            logits_processing_needs_token_ids,
        )

    def verify(self, model_config: "ModelConfig") -> None:
        if self.dimensions is not None:
            if not model_config.is_matryoshka:
                raise ValueError(
                    f'Model "{model_config.served_model_name}" does not '
                    f'support matryoshka representation, '
                    f'changing output dimensions will lead to poor results.')

            mds = model_config.matryoshka_dimensions
            if mds is not None:
                if self.dimensions not in mds:
                    raise ValueError(
                        f'Model "{model_config.served_model_name}" '
                        f'only supports {str(mds)} matryoshka dimensions, '
                        f'use other output dimensions will '
                        f'lead to poor results.')
            elif self.dimensions < 1:
                raise ValueError("Dimensions must be greater than 0")

    def __repr__(self) -> str:
        return (
            f"PoolingParams("
            f"dimensions={self.dimensions}, "
            f"use_cross_encoder={self.use_cross_encoder}, "
            f"logits_processing_needs_token_ids={self.logits_processing_needs_token_ids})"
        )

    def __post_init__(self) -> None:
        assert self.output_kind == RequestOutputKind.FINAL_ONLY,\
            "For pooling output_kind has to be FINAL_ONLY"

dimensions class-attribute instance-attribute 

dimensions: Optional[int] = None

logits_processing_needs_token_ids class-attribute instance-attribute 

logits_processing_needs_token_ids: bool = False

Internal use only.

output_kind class-attribute instance-attribute 

output_kind: RequestOutputKind = FINAL_ONLY

use_cross_encoder class-attribute instance-attribute 

use_cross_encoder: bool = False

Internal use only.

__post_init__ 

__post_init__() -> None
Source code in vllm/pooling_params.py 
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def __post_init__(self) -> None:
    assert self.output_kind == RequestOutputKind.FINAL_ONLY,\
        "For pooling output_kind has to be FINAL_ONLY"

__repr__ 

__repr__() -> str
Source code in vllm/pooling_params.py 
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def __repr__(self) -> str:
    return (
        f"PoolingParams("
        f"dimensions={self.dimensions}, "
        f"use_cross_encoder={self.use_cross_encoder}, "
        f"logits_processing_needs_token_ids={self.logits_processing_needs_token_ids})"
    )

clone 

clone() -> PoolingParams

Returns a deep copy of the PoolingParams instance.

Source code in vllm/pooling_params.py 
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def clone(self) -> "PoolingParams":
    """Returns a deep copy of the PoolingParams instance."""
    return PoolingParams(
        dimensions=self.dimensions,
        use_cross_encoder=self.use_cross_encoder,
        logits_processing_needs_token_ids=self.
        logits_processing_needs_token_ids,
    )

verify 

verify(model_config: ModelConfig) -> None
Source code in vllm/pooling_params.py 
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def verify(self, model_config: "ModelConfig") -> None:
    if self.dimensions is not None:
        if not model_config.is_matryoshka:
            raise ValueError(
                f'Model "{model_config.served_model_name}" does not '
                f'support matryoshka representation, '
                f'changing output dimensions will lead to poor results.')

        mds = model_config.matryoshka_dimensions
        if mds is not None:
            if self.dimensions not in mds:
                raise ValueError(
                    f'Model "{model_config.served_model_name}" '
                    f'only supports {str(mds)} matryoshka dimensions, '
                    f'use other output dimensions will '
                    f'lead to poor results.')
        elif self.dimensions < 1:
            raise ValueError("Dimensions must be greater than 0")