pooler.py

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from abc import ABC, abstractmethod
from dataclasses import dataclass
from enum import IntEnum
from typing import Callable, Literal, Optional, TypeVar, Union

import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import PretrainedConfig
from typing_extensions import assert_never

from vllm.config import ModelConfig, PoolerConfig
from vllm.model_executor.pooling_metadata import (  # noqa: E501
    PoolingMetadata as V0PoolingMetadata)
from vllm.model_executor.pooling_metadata import PoolingTensors
from vllm.pooling_params import PoolingParams
from vllm.sequence import PoolerOutput, PoolingSequenceGroupOutput
from vllm.utils import resolve_obj_by_qualname
from vllm.v1.pool.metadata import PoolingMetadata as V1PoolingMetadata

PoolingMetadata = Union[V0PoolingMetadata, V1PoolingMetadata]
PoolingTask = Literal["encode", "embed", "classify", "score"]


class PoolingType(IntEnum):
    """Enumeration for different types of pooling methods."""
    LAST = 0
    ALL = 1
    CLS = 2
    STEP = 3
    MEAN = 4


@dataclass(frozen=True)
class ResolvedPoolingConfig:
    pooling_type: PoolingType

    normalize: bool
    softmax: bool
    step_tag_id: Optional[int]
    returned_token_ids: Optional[list[int]]

    @classmethod
    def from_config_with_defaults(
        cls,
        pooler_config: PoolerConfig,
        pooling_type: PoolingType,
        normalize: bool,
        softmax: bool,
        step_tag_id: Optional[int] = None,
        returned_token_ids: Optional[list[int]] = None,
    ) -> "ResolvedPoolingConfig":
        return cls(
            pooling_type=PoolingType[pooler_config.pooling_type]
            if pooler_config.pooling_type is not None else pooling_type,
            normalize=pooler_config.normalize
            if pooler_config.normalize is not None else normalize,
            softmax=pooler_config.softmax
            if pooler_config.softmax is not None else softmax,
            step_tag_id=pooler_config.step_tag_id
            if pooler_config.step_tag_id is not None else step_tag_id,
            returned_token_ids=pooler_config.returned_token_ids
            if pooler_config.returned_token_ids is not None else
            returned_token_ids,
        )


class Pooler(nn.Module, ABC):
    """The interface required for all poolers used in pooling models in vLLM."""

    @staticmethod
    def from_config_with_defaults(
        pooler_config: PoolerConfig,
        pooling_type: PoolingType,
        normalize: bool,
        softmax: bool,
        step_tag_id: Optional[int] = None,
        returned_token_ids: Optional[list[int]] = None,
    ) -> "Pooler":
        resolved_config = ResolvedPoolingConfig.from_config_with_defaults(
            pooler_config=pooler_config,
            pooling_type=pooling_type,
            normalize=normalize,
            softmax=softmax,
            step_tag_id=step_tag_id,
            returned_token_ids=returned_token_ids,
        )

        if pooling_type == PoolingType.STEP:
            return StepPooler.from_config(resolved_config)

        return SimplePooler.from_config(resolved_config)

    def get_pooling_params(self, task: PoolingTask) -> Optional[PoolingParams]:
        """
        Construct the pooling parameters to use for a task,
        or `None` if the task is not supported.
        """
        return None

    @abstractmethod
    def forward(
        self,
        hidden_states: Union[list[torch.Tensor], torch.Tensor],
        pooling_metadata: PoolingMetadata,
    ) -> PoolerOutput:
        raise NotImplementedError


def get_prompt_lens(
    hidden_states: Union[torch.Tensor, list[torch.Tensor]],
    pooling_metadata: PoolingMetadata,
) -> torch.Tensor:
    if isinstance(pooling_metadata, V1PoolingMetadata):
        return pooling_metadata.prompt_lens

    assert isinstance(hidden_states, torch.Tensor)
    return PoolingTensors.from_pooling_metadata(
        pooling_metadata, hidden_states.device).prompt_lens


def get_classification_activation_function(config: PretrainedConfig):
    return PoolerClassify()


def get_cross_encoder_activation_function(config: PretrainedConfig):
    function_name: Optional[str] = None
    if (hasattr(config, "sentence_transformers")
            and "activation_fn" in config.sentence_transformers):
        function_name = config.sentence_transformers["activation_fn"]
    elif (hasattr(config, "sbert_ce_default_activation_function")
          and config.sbert_ce_default_activation_function is not None):
        function_name = config.sbert_ce_default_activation_function

    if function_name is not None:
        assert function_name.startswith("torch.nn.modules."), (
            "Loading of activation functions is restricted to "
            "torch.nn.modules for security reasons")
        fn = resolve_obj_by_qualname(function_name)()
        return PoolerActivation.wraps(fn)

    return PoolerScore()


def build_output(all_data: torch.Tensor) -> PoolerOutput:
    all_outputs = [PoolingSequenceGroupOutput(data) for data in all_data]
    return PoolerOutput(outputs=all_outputs)


class PoolingMethod(nn.Module, ABC):

    @staticmethod
    def from_pooling_type(pooling_type: PoolingType) -> "PoolingMethod":
        if pooling_type == PoolingType.LAST:
            return LastPool()
        if pooling_type == PoolingType.ALL:
            return AllPool()
        if pooling_type == PoolingType.CLS:
            return CLSPool()
        if pooling_type == PoolingType.MEAN:
            return MeanPool()

        raise NotImplementedError(f"Unsupported method: {pooling_type}")

    @abstractmethod
    def get_pooling_params(self, task: PoolingTask) -> Optional[PoolingParams]:
        raise NotImplementedError

    @abstractmethod
    def forward_one(
        self,
        hidden_states: torch.Tensor,
        prompt_len: Optional[torch.Tensor] = None,
    ) -> torch.Tensor:
        """
        Note:
            `prompt_len=None` means `prompt_len=len(hidden_states)`.
        """
        raise NotImplementedError

    @abstractmethod
    def forward_all(
        self,
        hidden_states: torch.Tensor,
        prompt_lens: torch.Tensor,
    ) -> Union[list[torch.Tensor], torch.Tensor]:
        raise NotImplementedError

    def forward(
        self,
        hidden_states: Union[torch.Tensor, list[torch.Tensor]],
        pooling_metadata: PoolingMetadata,
    ) -> Union[list[torch.Tensor], torch.Tensor]:
        prompt_lens = get_prompt_lens(hidden_states, pooling_metadata)

        if isinstance(hidden_states, list):
            return [
                self.forward_one(h, prompt_len)
                for h, prompt_len in zip(hidden_states, prompt_lens)
            ]

        return self.forward_all(hidden_states, prompt_lens)


class CLSPool(PoolingMethod):

    def get_pooling_params(self, task: PoolingTask) -> Optional[PoolingParams]:
        # The equalities are split up to keep mypy happy
        if (task == "encode" or task == "embed" or task == "classify"
                or task == "score"):
            return PoolingParams()

        assert_never(task)

    def forward_one(
        self,
        hidden_states: torch.Tensor,
        prompt_len: Optional[torch.Tensor] = None,
    ) -> torch.Tensor:
        assert prompt_len is None or prompt_len == hidden_states.shape[0], \
            "partial prefill not supported with CLS pooling"

        return hidden_states[0]

    def forward_all(
        self,
        hidden_states: torch.Tensor,
        prompt_lens: torch.Tensor,
    ) -> Union[list[torch.Tensor], torch.Tensor]:
        first_token_flat_indices = torch.zeros_like(prompt_lens)
        first_token_flat_indices[1:] += torch.cumsum(prompt_lens, dim=0)[:-1]
        return hidden_states[first_token_flat_indices]


class LastPool(PoolingMethod):

    def get_pooling_params(self, task: PoolingTask) -> Optional[PoolingParams]:
        # The equalities are split up to keep mypy happy
        if (task == "encode" or task == "embed" or task == "classify"
                or task == "score"):
            return PoolingParams()

        assert_never(task)

    def forward_one(
        self,
        hidden_states: torch.Tensor,
        prompt_len: Optional[torch.Tensor] = None,
    ) -> torch.Tensor:
        return hidden_states[-1]

    def forward_all(
        self,
        hidden_states: torch.Tensor,
        prompt_lens: torch.Tensor,
    ) -> Union[list[torch.Tensor], torch.Tensor]:
        last_token_flat_indices = torch.cumsum(prompt_lens, dim=0) - 1
        return hidden_states[last_token_flat_indices]


class AllPool(PoolingMethod):

    def get_pooling_params(self, task: PoolingTask) -> Optional[PoolingParams]:
        if task == "encode":
            return PoolingParams()

        # The equalities are split up to keep mypy happy
        if task == "embed" or task == "classify" or task == "score":
            return None

        assert_never(task)

    def forward_one(
        self,
        hidden_states: torch.Tensor,
        prompt_len: Optional[torch.Tensor] = None,
    ) -> torch.Tensor:
        assert prompt_len is None or prompt_len == hidden_states.shape[0], \
            "partial prefill not supported with ALL pooling"

        return hidden_states

    def forward_all(
        self,
        hidden_states: torch.Tensor,
        prompt_lens: torch.Tensor,
    ) -> Union[list[torch.Tensor], torch.Tensor]:
        offset = 0
        pooled_data = list[torch.Tensor]()

        for prompt_len in prompt_lens:
            pooled_data.append(hidden_states[offset:offset + prompt_len])
            offset += prompt_len

        return pooled_data


class MeanPool(PoolingMethod):

    def get_pooling_params(self, task: PoolingTask) -> Optional[PoolingParams]:
        # The equalities are split up to keep mypy happy
        if (task == "encode" or task == "embed" or task == "classify"
                or task == "score"):
            return PoolingParams()

        assert_never(task)

    def forward_one(
        self,
        hidden_states: torch.Tensor,
        prompt_len: Optional[torch.Tensor] = None,
    ) -> torch.Tensor:
        assert prompt_len is None or prompt_len == hidden_states.shape[0], \
            "partial prefill not supported with MEAN pooling"

        return hidden_states.mean(dim=0, dtype=torch.float32)

    def forward_all(
        self,
        hidden_states: torch.Tensor,
        prompt_lens: torch.Tensor,
    ) -> Union[list[torch.Tensor], torch.Tensor]:
        # Use float32 for torch.cumsum in MeanPool,
        # otherwise precision will be lost significantly.
        cumsum = torch.cumsum(hidden_states, dim=0, dtype=torch.float32)

        start_indices = torch.cat([
            torch.tensor([0], device=hidden_states.device),
            torch.cumsum(prompt_lens[:-1], dim=0)
        ])
        end_indices = torch.cumsum(prompt_lens, dim=0)
        return (cumsum[end_indices - 1] - cumsum[start_indices] +
                hidden_states[start_indices]) / prompt_lens.unsqueeze(1)


_T = TypeVar("_T", torch.Tensor, list[torch.Tensor])


class BasePoolerActivation(nn.Module, ABC):

    @abstractmethod
    def forward(self, pooled_data: _T) -> _T:
        # shape:
        # classify (& score) -> (batch_size, num_classes)
        # embed -> (batch_size, embedding_dim) or list(embedding_dim)
        #          (batch_size, dimensions) or list(dimensions) if using MRL
        raise NotImplementedError


class PoolerActivation(BasePoolerActivation):

    @staticmethod
    def wraps(module: nn.Module):
        if isinstance(module, nn.Identity):
            return PoolerIdentity()
        if isinstance(module, (nn.Sigmoid, nn.Softmax)):
            return PoolerClassify()

        return LambdaPoolerActivation(module)

    @abstractmethod
    def forward_chunk(self, pooled_data: torch.Tensor) -> torch.Tensor:
        raise NotImplementedError

    def forward(self, pooled_data: _T) -> _T:
        if isinstance(pooled_data, list):
            return [self.forward_chunk(data) for data in pooled_data]

        return self.forward_chunk(pooled_data)


class PoolerIdentity(PoolerActivation):

    def forward_chunk(self, pooled_data: torch.Tensor) -> torch.Tensor:
        return pooled_data


class PoolerNormalize(PoolerActivation):

    def forward_chunk(self, pooled_data: torch.Tensor) -> torch.Tensor:
        x = F.normalize(pooled_data.float(), p=2, dim=-1)
        return x.to(pooled_data.dtype)


class PoolerClassify(PoolerActivation):

    def forward_chunk(self, pooled_data: torch.Tensor) -> torch.Tensor:
        num_labels = pooled_data.shape[-1]
        if num_labels < 2:
            return F.sigmoid(pooled_data.float()).to(pooled_data.dtype)

        return F.softmax(pooled_data.float(), dim=-1).to(pooled_data.dtype)


class PoolerScore(PoolerActivation):

    def forward_chunk(self, pooled_data: torch.Tensor) -> torch.Tensor:
        num_labels = pooled_data.shape[-1]
        if num_labels < 2:
            return F.sigmoid(pooled_data.float()).to(pooled_data.dtype)

        return pooled_data


class LambdaPoolerActivation(PoolerActivation):

    def __init__(self, fn: Callable[[torch.Tensor], torch.Tensor]):
        super().__init__()

        self.fn = fn

    def forward_chunk(self, pooled_data: torch.Tensor) -> torch.Tensor:
        return self.fn(pooled_data)


class PoolerHead(nn.Module):

    @classmethod
    def from_config(cls, pooler_config: ResolvedPoolingConfig) -> "PoolerHead":
        if pooler_config.normalize and pooler_config.softmax:
            raise ValueError("`normalize=True` and `softmax=True` should not "
                             "be set together")

        activation: PoolerActivation
        if pooler_config.normalize:
            activation = PoolerNormalize()
        elif pooler_config.softmax:
            activation = PoolerClassify()
        else:
            activation = PoolerIdentity()

        return cls(activation)

    def __init__(self, activation: PoolerActivation) -> None:
        super().__init__()

        self.activation = activation

    def forward(self, pooled_data: Union[list[torch.Tensor], torch.Tensor],
                pooling_metadata: PoolingMetadata):

        # Using float32 in PoolerHead
        if isinstance(pooled_data, list):
            for i in range(len(pooled_data)):
                pooled_data[i] = pooled_data[i].to(torch.float32)
        else:
            pooled_data = pooled_data.to(torch.float32)

        # for matryoshka representation
        if isinstance(pooling_metadata, V0PoolingMetadata):
            dimensions_list = [
                pooling_param.dimensions
                for _, pooling_param in pooling_metadata.seq_groups
            ]
        else:
            assert isinstance(pooled_data, list)
            dimensions_list = [
                pooling_param.dimensions
                for pooling_param in pooling_metadata.pooling_params
            ]
        if any(d is not None for d in dimensions_list):
            # change the output dimension
            assert len(pooled_data) == len(dimensions_list)
            if len(set(dimensions_list)) == 1 and not isinstance(
                    pooled_data, list):
                # if all dimensions are the same
                d = dimensions_list[0]
                pooled_data = pooled_data[..., :d]
            else:
                pooled_data = [
                    vecs if d is None else vecs[..., :d]
                    for vecs, d in zip(pooled_data, dimensions_list)
                ]

        return self.activation(pooled_data)


class SimplePooler(Pooler):
    """A layer that pools specific information from hidden states.

    This layer does the following:
    1. Extracts specific tokens or aggregates data based on pooling method.
    2. Normalizes output if specified.
    3. Returns structured results as `PoolerOutput`.
    """

    @classmethod
    def from_config_with_defaults(  # type: ignore[override]
        cls,
        pooler_config: PoolerConfig,
        pooling_type: PoolingType,
        normalize: bool,
        softmax: bool,
    ) -> "SimplePooler":
        resolved_config = ResolvedPoolingConfig.from_config_with_defaults(
            pooler_config=pooler_config,
            pooling_type=pooling_type,
            normalize=normalize,
            softmax=softmax,
        )
        assert resolved_config.pooling_type != PoolingType.STEP

        return cls.from_config(resolved_config)

    @classmethod
    def from_config(
        cls,
        pooler_config: ResolvedPoolingConfig,
    ) -> "SimplePooler":
        pooling = PoolingMethod.from_pooling_type(pooler_config.pooling_type)
        head = PoolerHead.from_config(pooler_config)

        return cls(pooling, head)

    def __init__(self, pooling: PoolingMethod, head: PoolerHead) -> None:
        super().__init__()

        self.pooling = pooling
        self.head = head

    def get_pooling_params(self, task: PoolingTask) -> Optional[PoolingParams]:
        return self.pooling.get_pooling_params(task)

    def forward(
        self,
        hidden_states: Union[torch.Tensor, list[torch.Tensor]],
        pooling_metadata: PoolingMetadata,
    ) -> PoolerOutput:
        pooled_data = self.pooling(hidden_states, pooling_metadata)
        pooled_data = self.head(pooled_data, pooling_metadata)
        return build_output(pooled_data)


class StepPooler(Pooler):

    @classmethod
    def from_config(cls, pooler_config: ResolvedPoolingConfig) -> "StepPooler":
        assert pooler_config.pooling_type == PoolingType.STEP

        return cls(
            PoolerHead.from_config(pooler_config),
            step_tag_id=pooler_config.step_tag_id,
            returned_token_ids=pooler_config.returned_token_ids,
        )

    def __init__(
        self,
        head: PoolerHead,
        *,
        step_tag_id: Optional[int] = None,
        returned_token_ids: Optional[list[int]] = None,
    ) -> None:
        super().__init__()

        self.pooling = AllPool()
        self.head = head
        self.step_tag_id = step_tag_id
        self.returned_token_ids = returned_token_ids

    def get_prompt_token_ids(
        self,
        pooling_metadata: PoolingMetadata,
    ) -> list[torch.Tensor]:
        if isinstance(pooling_metadata, V1PoolingMetadata):
            return [
                pooling_metadata.prompt_token_ids[i, :num]
                for i, num in enumerate(pooling_metadata.prompt_lens)
            ]
        return [
            torch.tensor(seq_data_i.prompt_token_ids)
            for seq_data_i in pooling_metadata.seq_data.values()
        ]

    def extract_states(
        self,
        hidden_states: Union[torch.Tensor, list[torch.Tensor]],
        pooling_metadata: PoolingMetadata,
    ) -> Union[list[torch.Tensor], torch.Tensor]:
        pooled_data_lst = self.pooling(hidden_states, pooling_metadata)
        prompt_token_ids = self.get_prompt_token_ids(pooling_metadata)

        pooled_data = list[torch.Tensor]()
        returned_token_ids = self.returned_token_ids
        step_tag_id = self.step_tag_id

        for data, token_id in zip(pooled_data_lst, prompt_token_ids):
            if returned_token_ids is not None and len(returned_token_ids) > 0:
                data = data[:, returned_token_ids]

            if step_tag_id is not None:
                data = data[token_id == step_tag_id]
            pooled_data.append(data)

        return pooled_data

    def get_pooling_params(self, task: PoolingTask) -> Optional[PoolingParams]:
        if task == "encode":
            return PoolingParams(logits_processing_needs_token_ids=True)

        # The equalities are split up to keep mypy happy
        if task == "embed" or task == "classify" or task == "score":
            return None

        assert_never(task)

    def forward(
        self,
        hidden_states: Union[torch.Tensor, list[torch.Tensor]],
        pooling_metadata: PoolingMetadata,
    ) -> PoolerOutput:
        pooled_data = self.extract_states(hidden_states, pooling_metadata)
        pooled_data = self.head(pooled_data, pooling_metadata)
        return build_output(pooled_data)


PoolingFn = Callable[
    [Union[torch.Tensor, list[torch.Tensor]], PoolingMetadata],
    Union[torch.Tensor, list[torch.Tensor]]]
ClassifierFn = Callable[[torch.Tensor], torch.Tensor]


class ClassifierPooler(nn.Module):
    """A pooling layer for classification tasks.

    This layer does the following:
    1. Applies a classification layer to the hidden states.
    2. Optionally applies a pooler layer.
    3. Applies an activation function to the output. In the case of
       classification models it is either sigmoid or softmax. In the
       case of scoring models, the same behavior is configuration
       dependent, as in the sentence-transformers library.
    """

    def __init__(
        self,
        config: ModelConfig,
        pooling: PoolingFn,
        classifier: ClassifierFn,
        act_fn: Optional[PoolerActivation] = None,
    ) -> None:
        super().__init__()

        self.pooling = pooling
        self.classifier = classifier

        self.classification_act_fn = get_classification_activation_function(
            config.hf_config) if act_fn is None else act_fn
        self.cross_encoder_act_fn = get_cross_encoder_activation_function(
            config.hf_config) if act_fn is None else act_fn

    def _get_act_fn(self, use_cross_encoder: bool):
        return (self.cross_encoder_act_fn
                if use_cross_encoder else self.classification_act_fn)

    def get_pooling_params(self, task: PoolingTask) -> Optional[PoolingParams]:
        if task == "encode":
            return PoolingParams()
        if task == "embed":
            return None
        if task == "classify":
            return PoolingParams()
        if task == "score":
            return PoolingParams(use_cross_encoder=True)

        assert_never(task)

    def forward(
        self,
        hidden_states: Union[torch.Tensor, list[torch.Tensor]],
        pooling_metadata: PoolingMetadata,
    ) -> PoolerOutput:
        """Pools sentence pair scores from the hidden_states."""
        pooled_data = self.pooling(hidden_states, pooling_metadata)

        # apply classifier once on the full batch if possible
        if isinstance(pooled_data, torch.Tensor):
            pooled_output = self.classifier(pooled_data)
        elif len({data.shape for data in pooled_data}) <= 1:
            pooled_output = self.classifier(torch.stack(pooled_data))
        else:
            pooled_output = [self.classifier(data) for data in pooled_data]

        if isinstance(pooling_metadata, V0PoolingMetadata):
            use_cross_encoder_list = [
                pooling_param.use_cross_encoder
                for _, pooling_param in pooling_metadata.seq_groups
            ]
        else:
            use_cross_encoder_list = [
                pooling_param.use_cross_encoder
                for pooling_param in pooling_metadata.pooling_params
            ]

        # shape of scores: (batch_size, num_labels)
        if all(use_cross_encoder == use_cross_encoder_list[0]
               for use_cross_encoder in use_cross_encoder_list):
            act_fn = self._get_act_fn(use_cross_encoder_list[0])
            scores = act_fn(pooled_output)
        else:
            scores = torch.stack([
                self._get_act_fn(use_cross_encoder)(vecs)
                for use_cross_encoder, vecs in zip(use_cross_encoder_list,
                                                   pooled_output)
            ])

        return build_output(scores)