processing.py 63.3 KB
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# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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from abc import ABC, abstractmethod
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from collections import defaultdict
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from collections.abc import (Callable, Generator, ItemsView, Iterable, Mapping,
                             Sequence)
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from dataclasses import dataclass, field, replace
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from enum import Enum
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from functools import lru_cache
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from typing import (TYPE_CHECKING, Generic, NamedTuple, Optional, Protocol,
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                    TypeVar, Union, cast)
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import regex as re
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import torch
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from typing_extensions import assert_never
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from vllm.inputs import InputProcessingContext
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from vllm.logger import init_logger
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from vllm.transformers_utils.tokenizer import (AnyTokenizer, decode_tokens,
                                               encode_tokens)
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from vllm.utils import flatten_2d_lists, full_groupby
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from .hasher import MultiModalHasher
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from .inputs import (MultiModalDataDict, MultiModalEncDecInputs,
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                     MultiModalFieldConfig, MultiModalInputs,
                     MultiModalKwargsItem, MultiModalKwargsItems,
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                     MultiModalKwargsOptionalItems, MultiModalUUIDDict,
                     PlaceholderRange)
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from .parse import (DictEmbeddingItems, EmbeddingItems, MultiModalDataItems,
                    MultiModalDataParser)
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if TYPE_CHECKING:
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    from transformers.configuration_utils import PretrainedConfig
    from transformers.feature_extraction_utils import BatchFeature
    from transformers.processing_utils import ProcessorMixin

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    from .cache import BaseMultiModalProcessorCache
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    from .profiling import BaseDummyInputsBuilder
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logger = init_logger(__name__)
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_S = TypeVar("_S", str, list[int])
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PromptSeq = Union[str, list[int]]
"""A token sequence (list of token IDs) or text."""
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@lru_cache(maxsize=2048)
def _cached_encode(
    tokenizer: AnyTokenizer,
    text: str,
    *,
    add_special_tokens: Optional[bool] = None,
) -> list[int]:
    return encode_tokens(tokenizer,
                         text,
                         add_special_tokens=add_special_tokens)


@lru_cache(maxsize=2048)
def _cached_decode(
    tokenizer: AnyTokenizer,
    token_ids: tuple[int, ...],
    *,
    skip_special_tokens: Optional[bool] = None,
) -> str:
    return decode_tokens(tokenizer,
                         list(token_ids),
                         skip_special_tokens=skip_special_tokens)


def _seq2text(tokenizer: AnyTokenizer, seq: PromptSeq) -> str:
    if isinstance(seq, str):
        return seq

    return _cached_decode(tokenizer, tuple(seq))


def _seq2tokens(tokenizer: AnyTokenizer, seq: PromptSeq) -> list[int]:
    if isinstance(seq, str):
        return _cached_encode(tokenizer, seq, add_special_tokens=False)

    return seq


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class _GetMatchIndex(Protocol):

    def __call__(
        self,
        tokenizer: AnyTokenizer,
        prompt: PromptSeq,
        start_idx: int = 0,
    ) -> Optional[int]:
        ...


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@dataclass
class PromptIndex:
    """Resolves to an index in the prompt."""
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    get_match_index: _GetMatchIndex
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class PromptIndexTargets:

    @staticmethod
    def start() -> PromptIndex:
        """
        Resolves to the start of the prompt (before the first token).

        This results in a match even if the prompt is empty.
        """
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        return PromptIndex(lambda tokenizer, prompt, start_idx=0: 0)
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    @staticmethod
    def prefix(seq: PromptSeq) -> PromptIndex:
        """
        Resolves to a location in the prompt after the given prefix.
        """

        def get_match_index(
            tokenizer: AnyTokenizer,
            prompt: PromptSeq,
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            start_idx: int = 0,
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        ) -> Optional[int]:
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            if start_idx != 0:
                return None

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            prefix = seq

            if isinstance(prompt, str):
                if not isinstance(prefix, str):
                    # Make both `str`
                    prefix = decode_tokens(tokenizer, prefix)
            else:
                if isinstance(prefix, str):
                    # Make both `list[int]`
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                    prefix = encode_tokens(tokenizer,
                                           prefix,
                                           add_special_tokens=False)
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            match_idx = len(prefix)
            return match_idx if prompt[:match_idx] == prefix else None

        return PromptIndex(get_match_index)

    @staticmethod
    def end() -> PromptIndex:
        """
        Resolves to the end of the prompt (after the last token).

        This results in a match even if the prompt is empty.
        """
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        return PromptIndex(lambda tokenizer, prompt, start_idx=0: len(prompt))
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UpdateTarget = Union[PromptSeq, PromptIndex]
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"""
The token sequence or text to update.
"""

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PromptUpdateTarget = Union[Callable[[int], UpdateTarget], UpdateTarget]
"""
Given the index of the processed item within
[`modality`][vllm.multimodal.processing.PromptUpdate.modality],
output the corresponding token sequence (or text).

For convenience, you can directly pass in the token sequence (or text)
instead of a function if it does not depend on the input.
"""

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@dataclass
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class PromptUpdateDetails(Generic[_S]):
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    """Details about the token sequence or text that are part of the update."""
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    full: _S
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    """The full content."""
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    is_embed: Optional[Callable[[AnyTokenizer, PromptSeq],
                                torch.Tensor]] = None
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    """
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    Given [`full`][vllm.multimodal.processing.PromptUpdateDetails.full],
    return a boolean mask of shape `(len(full),)` indicating which positions
    of `full` to assign embeddings to.
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    `None` (default) means to assign embeddings to all positions of `full`.

    The embeddings are obtained by calling
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    [`SupportsMultiModal.get_multimodal_embeddings`][vllm.model_executor.models.interfaces.SupportsMultiModal.get_multimodal_embeddings].
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    """

    @staticmethod
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    def from_seq(seq: _S) -> "PromptUpdateDetails[_S]":
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        return PromptUpdateDetails(full=seq)

    @staticmethod
    def select_text(
        seq: _S,
        embed_text: str,
    ) -> "PromptUpdateDetails[_S]":

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        def is_embed(tokenizer: AnyTokenizer, full: PromptSeq) -> torch.Tensor:
            embed_token_ids = encode_tokens(tokenizer, embed_text)
            token_ids = _seq2tokens(tokenizer, full)
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            return torch.isin(
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                torch.tensor(token_ids),
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                torch.tensor(embed_token_ids),
            )

        return PromptUpdateDetails(full=seq, is_embed=is_embed)

    @staticmethod
    def select_token_id(
        seq: _S,
        embed_token_id: int,
    ) -> "PromptUpdateDetails[_S]":
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        def is_embed(tokenizer: AnyTokenizer, full: PromptSeq) -> torch.Tensor:
            token_ids = _seq2tokens(tokenizer, full)

            return torch.tensor(token_ids) == embed_token_id

        return PromptUpdateDetails(full=seq, is_embed=is_embed)
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PromptUpdateInfo = Union[PromptSeq, PromptUpdateDetails]
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"""
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The token sequence or text that are part of the update.
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If only part of the content corresponds to feature placeholders, you can
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use [`PromptUpdateDetails`][vllm.multimodal.processing.PromptUpdateDetails] to
specify which part.
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"""
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PromptUpdateContent = Union[Callable[[int], PromptUpdateInfo],
                            PromptUpdateInfo]
"""
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Given the index of the processed item within
[`modality`][vllm.multimodal.processing.PromptUpdate.modality],
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output the corresponding token sequence (or text).

For convenience, you can directly pass in the token sequence (or text)
instead of a function if it does not depend on the input.
"""


class UpdateMode(str, Enum):
    INSERT = "insert"
    REPLACE = "replace"


@dataclass
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class PromptUpdate(ABC):
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    """
    Defines how to update a prompt with placeholder tokens.
    """

    modality: str
    """The modality for which the update is made."""

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    target: PromptUpdateTarget
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    """The token sequence (or text) to update."""

    @property
    @abstractmethod
    def content(self) -> PromptUpdateContent:
        """The placeholder tokens that are part of the update."""
        raise NotImplementedError

    @property
    @abstractmethod
    def mode(self) -> UpdateMode:
        """Defines how to update the prompt."""
        raise NotImplementedError

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    def _resolve_target(self, item_idx: int) -> UpdateTarget:
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        target = self.target
        if callable(target):
            target = target(item_idx)

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        return target
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    def _resolve_content(self, item_idx: int) -> PromptUpdateDetails:
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        content = self.content
        if callable(content):
            content = content(item_idx)

        if not isinstance(content, PromptUpdateDetails):
            content = PromptUpdateDetails.from_seq(content)

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        return content
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    def resolve(self, item_idx: int) -> "ResolvedPromptUpdate":
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        """
        Given the index of the processed item within
        [`modality`][vllm.multimodal.processing.PromptUpdate.modality],
        output a copy of this object with its lazy attributes resolved.
        """
        return ResolvedPromptUpdate(
            modality=self.modality,
            item_idx=item_idx,
            mode=self.mode,
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            target=self._resolve_target(item_idx),
            content=self._resolve_content(item_idx),
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        )

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@dataclass
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class PromptInsertion(PromptUpdate):
    """
    Defines how to insert placeholder tokens into a prompt.

    Example:

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    For each image, insert a number of ``<image>`` feature placeholders
    equal to the feature size of the vision encoder after the ``<s>`` token:

    ```python
    PromptInsertion(
        modality="image",
        target="<s>",
        insertion="<image>" * image_feature_size,
    )
    ```

    Insert these tokens at the start of the prompt:

    ```python
    PromptInsertion(
        modality="image",
        target=PromptIndexTargets.start(),
        insertion="<image>" * image_feature_size,
    )
    ```

    Insert these tokens after a prefix ``Images:``:

    ```python
    PromptInsertion(
        modality="image",
        target=PromptIndexTargets.prefix("Images:"),
        insertion="<image>" * image_feature_size,
    )
    ```

    Insert these tokens at the end of the prompt:

    ```python
    PromptInsertion(
        modality="image",
        target=PromptIndexTargets.end(),
        insertion="<image>" * image_feature_size,
    )
    ```
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    """

    insertion: PromptUpdateContent = field(repr=False)
    """
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    Given the index of the processed item within
    [`modality`][vllm.multimodal.processing.PromptUpdate.modality],
    output the token sequence (or text) to insert right after
    [`target`][vllm.multimodal.processing.PromptUpdate.target].
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    For convenience, you can directly pass in the token sequence (or text)
    instead of a function if it does not depend on the input.
    """

    @property
    def content(self) -> PromptUpdateContent:
        return self.insertion

    @property
    def mode(self) -> UpdateMode:
        return UpdateMode.INSERT


@dataclass
class PromptReplacement(PromptUpdate):
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    """
    Defines how to replace portions of an input prompt with placeholder tokens.
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    Example:

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    For each image, replace one ``<image>`` input placeholder in the prompt
    with a number of ``<image>`` feature placeholders
    equal to the feature size of the vision encoder:

    ```python
    PromptReplacement(
        modality="image",
        target="<image>",
        replacement="<image>" * image_feature_size,
    )
    ```

    As above, but further pad the feature placeholders with ``<image_bos>``
    and `<image_eos>``, which are not supposed to be passed to the vision
    encoder:

    ```python
    PromptReplacement(
        modality="image",
        target="<image>",
        replacement=PromptUpdateDetails(
            full="".join([
                "<image_bos>",
                "<image>" * image_feature_size,
                "<image_eos>",
            ]),
            features="<image>" * image_feature_size,
        ),
    )
    ```

    To avoid unnecessary tokenization during prompt replacement,
    we recommended passing token sequences instead of text:

    ```python
    PromptReplacement(
        modality="image",
        target=[image_token_id],
        replacement=PromptUpdateDetails(
            full=([image_bos_id] + [image_token_id] * image_feature_size
                    + [image_eos_id]),
            features=[image_token_id] * image_feature_size,
        ),
    )
    ```
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    """

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    replacement: PromptUpdateContent = field(repr=False)
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    """
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    Given the index of the processed item within
    [`modality`][vllm.multimodal.processing.PromptUpdate.modality],
    output the token sequence (or text) to replace
    [`target`][vllm.multimodal.processing.PromptUpdate.target].
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    For convenience, you can directly pass in the token sequence (or text)
    instead of a function if it does not depend on the input.
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    """

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    @property
    def content(self) -> PromptUpdateContent:
        return self.replacement

    @property
    def mode(self) -> UpdateMode:
        return UpdateMode.REPLACE
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class _HasModalityAttr(Protocol):
    modality: str

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class _HasModalityProp(Protocol):
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    @property
    def modality(self) -> str:
        ...


_M = TypeVar("_M", bound=Union[_HasModalityAttr, _HasModalityProp])


def full_groupby_modality(values: Iterable[_M]) -> ItemsView[str, list[_M]]:
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    """Convenience function to apply [`full_groupby`][vllm.utils.full_groupby]
    based on modality."""
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    return full_groupby(values, key=lambda x: x.modality)


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class PromptTargetMatch(NamedTuple):
    start_idx: int
    end_idx: int


@dataclass(frozen=True)
class ResolvedPromptUpdate:
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    """
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    A [`PromptUpdate`][vllm.multimodal.processing.PromptUpdate] with its
    lazy attributes resolved, apart from those related to tokenization.
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    """
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    modality: str
    """The modality for which the update is made."""
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    item_idx: int
    """The index within `modality` of the item this update pertains to."""
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    mode: UpdateMode
    """Defines how to update the prompt."""
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    target: UpdateTarget
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    """The token sequence (or text) to update."""
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    content: PromptUpdateDetails = field(repr=False)
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    """The placeholder tokens that are part of the update."""
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    def iter_token_matches(
        self,
        prompt: list[int],
        tokenizer: AnyTokenizer,
        *,
        start_idx: int = 0,
    ) -> Generator[PromptTargetMatch]:
        """Yield each instance of `self.target` found in `prompt`."""
        target = self.target
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        if isinstance(target, PromptIndex):
            match_idx = target.get_match_index(tokenizer, prompt, start_idx)
            if match_idx is not None:
                yield PromptTargetMatch(match_idx, match_idx)
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            return
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        target_token_ids = _seq2tokens(tokenizer, target)

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        for match in iter_token_matches(prompt,
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                                        target_token_ids,
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                                        start_idx=start_idx):
            yield PromptTargetMatch(match.start_idx, match.end_idx)
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    def iter_text_matches(
        self,
        prompt: str,
        tokenizer: AnyTokenizer,
        *,
        start_idx: int = 0,
    ) -> Generator[PromptTargetMatch]:
        """Yield each instance of `self.target` found in `prompt`."""
        target = self.target
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        if isinstance(target, PromptIndex):
            match_idx = target.get_match_index(tokenizer, prompt, start_idx)
            if match_idx is not None:
                yield PromptTargetMatch(match_idx, match_idx)
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            return
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        target_text = _seq2text(tokenizer, target)

        for match in re.finditer(re.escape(target_text), prompt,
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                                 pos=start_idx):
            yield PromptTargetMatch(match.start(), match.end())

    def iter_matches(
        self,
        prompt: Union[list[int], str],
        tokenizer: AnyTokenizer,
        *,
        start_idx: int = 0,
    ) -> Generator[PromptTargetMatch]:
        """Yield each instance of `self.target` found in `prompt`."""
        if isinstance(prompt, str):
            return self.iter_text_matches(prompt,
                                          tokenizer,
                                          start_idx=start_idx)

        return self.iter_token_matches(prompt, tokenizer, start_idx=start_idx)
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    def with_target(self, target: UpdateTarget):
        return replace(self, target=target)

    def with_content(self, content: PromptUpdateInfo):
        if not isinstance(content, PromptUpdateDetails):
            content = PromptUpdateDetails.from_seq(content)

        return replace(self, content=content)

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class _TokenMatch(NamedTuple):
    start_idx: int
    end_idx: int
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def iter_token_matches(
    token_ids: list[int],
    match_ids: list[int],
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    *,
    start_idx: int = 0,
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) -> Generator[_TokenMatch]:
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    """
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    Yield each occurrence of `match_ids` in `token_ids`.
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    Note that empty matches are ignored.
    """
    prompt_len = len(token_ids)
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    match_len = len(match_ids)
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    if match_len == 0:
        return
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    while start_idx < prompt_len - match_len + 1:
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        end_idx = start_idx + match_len
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        if token_ids[start_idx:end_idx] == match_ids:
            yield _TokenMatch(start_idx=start_idx, end_idx=end_idx)
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            # Exclude overlapping matches
            start_idx = end_idx
        else:
            start_idx += 1
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def replace_token_matches(
    token_ids: list[int],
    match_ids: list[int],
    new_ids: list[int],
) -> list[int]:
    """
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    Replace each occurrence of `match_ids` in `token_ids`
    with `new_ids`.
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    Note that empty matches are ignored.
    """
    out_seqs = list[list[int]]()
    prev_end_idx = 0

    for match in iter_token_matches(token_ids, match_ids):
        start_idx = match.start_idx
        end_idx = match.end_idx

        out_seqs.append(token_ids[prev_end_idx:start_idx])
        out_seqs.append(new_ids)
        prev_end_idx = end_idx

    out_seqs.append(token_ids[prev_end_idx:])

    return flatten_2d_lists(out_seqs)


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@dataclass
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class PlaceholderFeaturesInfo:
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    modality: str
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    item_idx: int
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    start_idx: int
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    tokens: list[int]
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    is_embed: Optional[torch.Tensor]
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    @property
    def length(self) -> int:
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        return len(self.tokens)
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    def to_range(self) -> PlaceholderRange:
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        # TODO: Is it worth it to optimize this by stripping the
        # leading and ending positions where `is_embed=False`?
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        return PlaceholderRange(
            offset=self.start_idx,
            length=self.length,
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            is_embed=self.is_embed,
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        )
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_MatchToApply = tuple[tuple[str, int], tuple[PromptTargetMatch, int]]
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def _find_matches(
    prompt: _S,
    mm_prompt_updates: "MultiModalPromptUpdates",
    tokenizer: AnyTokenizer,
    *,
    prev_end_idx: int = 0,
    current_result: "MultiModalPromptUpdatesApplyResult",
) -> tuple[Optional[UpdateMode], list[_MatchToApply]]:
    mode: Optional[UpdateMode] = None
    mm_matches = dict[tuple[str, int], tuple[PromptTargetMatch, int]]()

    for modality, modality_updates in mm_prompt_updates.items():
        for item_idx, item_updates in enumerate(modality_updates):
            if current_result[modality][item_idx] is not None:
                continue  # Updates have already been applied for this item

            for update_idx, update in enumerate(item_updates):
                if (modality, item_idx) in mm_matches:
                    break  # Already found a match for this item

                for match in update.iter_matches(
                        prompt,
                        tokenizer,
                        start_idx=prev_end_idx,
                ):
                    # All matches should share the same mode
                    if mode is None:
                        mode = update.mode
                    elif mode != update.mode:
                        continue

                    mm_matches[(modality, item_idx)] = match, update_idx
                    break  # Get only the first valid match per item

    # Prioritize earlier matches
    matches_to_apply = sorted(mm_matches.items(), key=lambda item: item[1][0])

    # To avoid conflicts, only replace one non-empty item at a time
    if mode == UpdateMode.REPLACE:
        matches_to_apply_ = list[_MatchToApply]()
        has_non_empty_matches = False

        for item in matches_to_apply:
            _, (match, _) = item
            if match.start_idx == match.end_idx:
                matches_to_apply_.append(item)
            elif not has_non_empty_matches:
                has_non_empty_matches = True
                matches_to_apply_.append(item)

        matches_to_apply = matches_to_apply_

    return mode, matches_to_apply
710
711


712
def _apply_matches(
713
    prompt: _S,
714
715
716
717
718
    mm_prompt_updates: "MultiModalPromptUpdates",
    tokenizer: AnyTokenizer,
) -> tuple[list[_S], "MultiModalPromptUpdatesApplyResult"]:
    prompt_len = len(prompt)

719
    out_seqs = list[Union[str, list[int]]]()
720
721
722
723
    out_result: MultiModalPromptUpdatesApplyResult = {
        m: [None] * len(items)
        for m, items in mm_prompt_updates.items()
    }
724

725
726
727
    start_idx = prev_end_idx = 0
    while start_idx < max(prompt_len, 1):  # Allow inserts into empty prompt
        found = False
728

729
730
731
732
733
734
735
        mode, matches_to_apply = _find_matches(
            prompt,
            mm_prompt_updates,
            tokenizer,
            prev_end_idx=prev_end_idx,
            current_result=out_result,
        )
736

737
738
739
        if mode is not None:
            for (modality, item_idx), (match, update_idx) in matches_to_apply:
                found = True
740

741
742
                matched_update = mm_prompt_updates[modality][item_idx][
                    update_idx]
743
                matched_content = matched_update.content.full
744

745
746
747
748
749
750
                if mode == UpdateMode.INSERT:
                    end_idx_to_insert = match.end_idx
                elif mode == UpdateMode.REPLACE:
                    end_idx_to_insert = match.start_idx
                else:
                    assert_never(mode)
751

752
                out_seqs.append(prompt[prev_end_idx:end_idx_to_insert])
753
754
755
756
                out_seqs.append(
                    _seq2text(tokenizer, matched_content
                              ) if isinstance(prompt, str) else _seq2tokens(
                                  tokenizer, matched_content))
757
                out_result[modality][item_idx] = update_idx
758

759
760
761
762
763
                # Exclude overlapping matches
                start_idx = prev_end_idx = match.end_idx

        if not found:
            start_idx += 1
764
765
766

    out_seqs.append(prompt[prev_end_idx:])

767
    return cast(list[_S], out_seqs), out_result
768
769


770
def apply_token_matches(
771
    prompt: list[int],
772
773
774
775
776
    mm_prompt_updates: "MultiModalPromptUpdates",
    tokenizer: AnyTokenizer,
) -> tuple[list[int], "MultiModalPromptUpdatesApplyResult"]:
    """
    Apply the updates in `mm_prompt_updates` to `prompt`.
777

778
779
780
781
782
783
    Matches are exclusive even when multiple modalities share
    the same placeholder tokens. In that case, the modality that
    appears earlier in `mm_prompt_updates` takes priority.
    """
    token_id_seqs, result = _apply_matches(prompt, mm_prompt_updates,
                                           tokenizer)
784

785
    return flatten_2d_lists(token_id_seqs), result
786
787


788
def apply_text_matches(
789
    prompt: str,
790
791
792
793
794
    mm_prompt_updates: "MultiModalPromptUpdates",
    tokenizer: AnyTokenizer,
) -> tuple[str, "MultiModalPromptUpdatesApplyResult"]:
    """
    Apply the updates in `mm_prompt_updates` to `prompt`.
795

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797
798
799
800
    Matches are exclusive even when multiple modalities share
    the same placeholder tokens. In that case, the modality that
    appears earlier in `mm_prompt_updates` takes priority.
    """
    texts, result = _apply_matches(prompt, mm_prompt_updates, tokenizer)
801

802
    return "".join(texts), result
803
804


805
def _iter_placeholders(
806
    prompt: list[int],
807
    mm_prompt_updates: "MultiModalPromptUpdates",
808
    tokenizer: AnyTokenizer,
809
) -> Iterable[PlaceholderFeaturesInfo]:
810
    """
811
    Yield each set of placeholder tokens found in `prompt`.
812
813
814

    Matches are exclusive even when multiple modalities share
    the same placeholder tokens. In that case, the modality that
815
    appears earlier in `mm_prompt_updates` takes priority.
816

817
818
    Note that empty matches are ignored.
    """
819
    prompt_len = len(prompt)
820
821
    mm_item_counts = {m: len(items) for m, items in mm_prompt_updates.items()}

822
    item_idx_by_modality = defaultdict[str, int](lambda: 0)
823
824
825
826
827

    start_idx = 0
    while start_idx < prompt_len:
        found = False

828
        for modality, modality_updates in mm_prompt_updates.items():
829
830
            item_idx = item_idx_by_modality[modality]
            if item_idx >= mm_item_counts.get(modality, 0):
831
                continue
832

833
834
            for update in modality_updates[item_idx]:
                content = update.content
835
                content_tokens_full = _seq2tokens(tokenizer, content.full)
836
837
                content_len_full = len(content_tokens_full)
                end_idx_full = start_idx + content_len_full
838

839
                if content_len_full == 0 or end_idx_full > prompt_len:
840
841
                    continue

842
                if prompt[start_idx:end_idx_full] == content_tokens_full:
843
844
                    content_is_embed = content.is_embed
                    if content_is_embed is not None:
845
846
                        content_is_embed = content_is_embed(
                            tokenizer, content.full)
847
848
849
850
851
852
853
854

                    yield PlaceholderFeaturesInfo(
                        modality=modality,
                        item_idx=item_idx,
                        start_idx=start_idx,
                        tokens=content_tokens_full,
                        is_embed=content_is_embed,
                    )
855

856
                    # Exclude overlapping matches
857
                    start_idx = end_idx_full
858
859
860
                    item_idx_by_modality[modality] += 1
                    found = True
                    break
861

862
863
            if found:
                break  # Go back to the outer while loop
864
865
866

        if not found:
            start_idx += 1
867
868


869
870
def find_mm_placeholders(
    prompt: list[int],
871
    mm_prompt_updates: "MultiModalPromptUpdates",
872
    tokenizer: AnyTokenizer,
873
) -> Mapping[str, list[PlaceholderFeaturesInfo]]:
874
    it = _iter_placeholders(prompt, mm_prompt_updates, tokenizer)
875
876
877
    return dict(full_groupby_modality(it))


878
class BaseProcessingInfo:
879
    """Base class to provide the information necessary for data processing."""
880

881
882
    def __init__(self, ctx: InputProcessingContext) -> None:
        super().__init__()
883

884
885
886
887
888
889
890
        self.ctx = ctx

    @property
    def model_id(self) -> str:
        return self.ctx.model_config.model

    def get_tokenizer(self) -> AnyTokenizer:
891
892
        return self.ctx.tokenizer

893
    def get_hf_config(self) -> "PretrainedConfig":
894
895
        return self.ctx.get_hf_config()

896
    def get_hf_processor(self, **kwargs: object) -> "ProcessorMixin":
897
898
899
900
901
902
        """
        Subclasses can override this method to handle
        specific kwargs from model config or user inputs.
        """
        return self.ctx.get_hf_processor(**kwargs)

903
904
905
906
907
908
909
910
911
912
913
914
    @abstractmethod
    def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
        """
        Return the maximum supported number of items for each modality.

        A value of `None` means unlimited number of items.

        Omitting a modality from the returned dictionary means that
        it is not supported at all.
        """
        raise NotImplementedError

915
916
917
918
919
920
921
922
923
924
925
926
927
928
    def get_allowed_mm_limits(self) -> Mapping[str, int]:
        """Return the maximum allowed number of items for each modality."""
        supported_mm_limits = self.get_supported_mm_limits()
        mm_config = self.ctx.get_mm_config()

        allowed_limits = dict[str, int]()
        for modality, supported_limit in supported_mm_limits.items():
            user_limit = mm_config.get_limit_per_prompt(modality)

            allowed_limits[modality] = (user_limit if supported_limit is None
                                        else min(user_limit, supported_limit))

        return allowed_limits

929
930
931
932
933
934
935
936
937
938
939
940
    def get_mm_max_tokens_per_item(
        self,
        seq_len: int,
        mm_counts: Mapping[str, int],
    ) -> Optional[Mapping[str, int]]:
        """
        Return the maximum number of tokens per item of for each modality.
        
        When `None` (the default) is returned, vLLM will generate dummy inputs
        (images/videos) at maximum possible sizes and process them to determine
        the maximum token count per modality.

941
942
943
944
945
        This approach works but can be very slow for certain models (e.g.,
        Qwen2.5-VL), leading to very long startup time. For better performance,
        each model can override this method to return pre-computed maximum token
        counts, avoiding the need for dummy input generation and processing.

946
947
948
949
950
951
        Note:
            The maximum number of tokens per item of each modality returned 
            from this function should respect the model's maximum sequence
            length and the maximum number of items of each modality allowed,
            and agree with dummy inputs (images/videos) at maximum possible
            sizes.
952
953
954
        """
        return None

955
956

_I = TypeVar("_I", bound=BaseProcessingInfo)
957

958
959
MultiModalHashes = dict[str, list[str]]
"""
960
A collection of hashes with a similar structure as
961
[`MultiModalKwargsItems`][vllm.multimodal.inputs.MultiModalKwargsItems].
962
963
"""

964
MultiModalPromptUpdates = Mapping[str, list[Sequence[ResolvedPromptUpdate]]]
965
966
967
968
969
"""
A collection of prompt updates with a similar structure as
[`MultiModalKwargsItems`][vllm.multimodal.inputs.MultiModalKwargsItems].
"""

970
971
972
973
974
975
976
977
MultiModalPromptUpdatesApplyResult = Mapping[str, list[Optional[int]]]
"""
For an item `MultiModalPromptUpdates[k][i]`,
`MultiModalPromptUpdatesApplyResult[k][i]` represents the index of the
`ResolvedPromptUpdate` instance that has been applied, or `None` if none of the
`ResolvedPromptUpdate` instances have been applied.
"""

978
979

class MultiModalProcessingInfo(NamedTuple):
980
    kwargs: MultiModalKwargsOptionalItems
981
    hashes: MultiModalHashes
982
983
    prompt_updates: MultiModalPromptUpdates

984
985

class BaseMultiModalProcessor(ABC, Generic[_I]):
986
    """
987
    Abstract base class to process multi-modal inputs to be used in vLLM.
988

989
    Not to be confused with `transformers.ProcessorMixin`.
990
991
    """

992
993
994
995
996
997
998
    def __init__(
        self,
        info: _I,
        dummy_inputs: "BaseDummyInputsBuilder[_I]",
        *,
        cache: Optional["BaseMultiModalProcessorCache"] = None,
    ) -> None:
999
1000
        super().__init__()

1001
1002
        self.info = info
        self.dummy_inputs = dummy_inputs
1003
        self.cache = cache
1004

1005
1006
        self.data_parser = self._get_data_parser()

1007
1008
1009
1010
1011
1012
1013
1014
1015
1016
1017
1018
        # Avoid unnecessary recomputation
        self._supported_mm_limits = self.info.get_supported_mm_limits()
        self._allowed_mm_limits = self.info.get_allowed_mm_limits()

    @property
    def supported_mm_limits(self):
        return self._supported_mm_limits

    @property
    def allowed_mm_limits(self):
        return self._allowed_mm_limits

1019
    def __call__(
1020
        self,
1021
1022
        prompt: str,
        mm_data: MultiModalDataDict,
1023
        hf_processor_mm_kwargs: Mapping[str, object],
1024
        *,
1025
        mm_uuids: Optional[MultiModalUUIDDict] = None,
1026
    ) -> MultiModalInputs:
1027
1028
1029
        return self.apply(prompt,
                          mm_data,
                          hf_processor_mm_kwargs,
1030
                          mm_uuids=mm_uuids)
1031

1032
1033
    def _get_data_parser(self) -> MultiModalDataParser:
        """
1034
        Construct a parser to preprocess multi-modal data items
1035
1036
        before passing them to
        [`_get_hf_mm_data`][vllm.multimodal.processing.BaseMultiModalProcessor._get_hf_mm_data].
1037
1038

        You can support additional modalities by creating a subclass
1039
1040
        of [`MultiModalDataParser`][vllm.multimodal.parse.MultiModalDataParser]
        that has additional subparsers.
1041
1042
1043
        """
        return MultiModalDataParser()

1044
1045
1046
1047
1048
1049
1050
1051
1052
1053
1054
1055
1056
1057
1058
1059
1060
1061
1062
1063
1064
1065
    def validate_num_items(
        self,
        modality: str,
        num_items: int,
    ) -> None:
        supported_limit = self.supported_mm_limits.get(modality, 0)
        allowed_limit = self.allowed_mm_limits.get(modality, 0)

        if supported_limit is None:
            supported_limit = allowed_limit

        limit = min(supported_limit, allowed_limit)

        if num_items > limit:
            msg = (f"At most {limit} {modality}(s) may be provided in "
                   "one prompt.")

            if num_items <= supported_limit:
                msg += " Set `--limit-mm-per-prompt` to increase this limit."

            raise ValueError(msg)

1066
    def _to_mm_items(
1067
1068
1069
        self,
        mm_data: MultiModalDataDict,
    ) -> MultiModalDataItems:
1070
        """
1071
1072
1073
1074
1075
        Normalize
        [`MultiModalDataDict`][vllm.multimodal.inputs.MultiModalDataDict]
        to [`MultiModalDataItems`][vllm.multimodal.parse.MultiModalDataItems]
        before passing them to
        [`_get_hf_mm_data`][vllm.multimodal.processing.BaseMultiModalProcessor._get_hf_mm_data].
1076
        """
1077
        mm_items = self.data_parser.parse_mm_data(mm_data)
1078
1079

        for modality, items in mm_items.items():
1080
            self.validate_num_items(modality, len(items))
1081
1082

        return mm_items
1083

1084
1085
1086
    @abstractmethod
    def _get_mm_fields_config(
        self,
1087
        hf_inputs: "BatchFeature",
1088
1089
1090
1091
1092
        hf_processor_mm_kwargs: Mapping[str, object],
    ) -> Mapping[str, MultiModalFieldConfig]:
        """Given the HF-processed data, output the metadata of each field."""
        raise NotImplementedError

1093
    @abstractmethod
1094
    def _get_prompt_updates(
1095
        self,
1096
        mm_items: MultiModalDataItems,
1097
        hf_processor_mm_kwargs: Mapping[str, object],
1098
        out_mm_kwargs: MultiModalKwargsItems,
1099
    ) -> Sequence[PromptUpdate]:
1100
1101
        """
        Given the original multi-modal items for this modality
1102
        and HF-processed data, output the updates to perform.
1103

1104
1105
1106
1107
1108
1109
        The information returned by this method is used to update token inputs
        which bypass the HF processor. It is also used to update the output of
        HF processor if the HF process does not apply prompt updates to text
        inputs.

        Moreover, this information is critical to determine the token positions
1110
1111
        in order to construct
        [`PlaceholderRange`][vllm.multimodal.inputs.PlaceholderRange]
1112
        for each multi-modal item.
1113
1114
        """
        raise NotImplementedError
1115

1116
1117
1118
1119
1120
1121
    def _bind_and_group_updates(
        self,
        prompt_updates: Sequence[PromptUpdate],
        mm_item_counts: Mapping[str, int],
    ) -> MultiModalPromptUpdates:
        return {
1122
1123
            modality: [[update.resolve(item_idx) for update in updates]
                       for item_idx in range(mm_item_counts.get(modality, 0))]
1124
1125
1126
1127
1128
1129
1130
1131
1132
1133
1134
1135
1136
1137
1138
1139
1140
1141
1142
1143
1144
1145
1146
1147
1148
1149
1150
1151
1152
1153
1154
1155
1156
1157
1158
1159
1160
            for modality, updates in full_groupby_modality(prompt_updates)
        }

    def _get_mm_prompt_updates(
        self,
        mm_items: MultiModalDataItems,
        hf_processor_mm_kwargs: Mapping[str, object],
        out_mm_kwargs: MultiModalKwargsItems,
    ) -> MultiModalPromptUpdates:
        unbound_prompt_updates = self._get_prompt_updates(
            mm_items=mm_items,
            hf_processor_mm_kwargs=hf_processor_mm_kwargs,
            out_mm_kwargs=out_mm_kwargs,
        )

        mm_prompt_updates = self._bind_and_group_updates(
            unbound_prompt_updates,
            mm_items.get_all_counts(),
        )

        for modality, prompt_updates in mm_prompt_updates.items():
            for item_idx, item_prompt_updates in enumerate(prompt_updates):
                if len(item_prompt_updates) > 1:
                    logger.warning_once(
                        "Detected %d prompt updates for `mm_items[%r][%s]`. "
                        "Multiple prompt updates per item is now "
                        "deprecated and may be removed in v0.13. "
                        "Instead, please specify dynamic update targets "
                        "in the same prompt update definition by passing "
                        "a function to `PromptUpdate.target`.",
                        len(prompt_updates),
                        modality,
                        item_idx,
                    )

        return mm_prompt_updates

1161
    def _find_mm_placeholders(
1162
1163
        self,
        new_token_ids: list[int],
1164
        mm_prompt_updates: MultiModalPromptUpdates,
1165
    ) -> Mapping[str, list[PlaceholderFeaturesInfo]]:
1166
1167
1168
1169
        tokenizer = self.info.get_tokenizer()

        return find_mm_placeholders(new_token_ids, mm_prompt_updates,
                                    tokenizer)
1170

1171
    def _get_hf_mm_data(
1172
        self,
1173
        mm_items: MultiModalDataItems,
1174
1175
1176
    ) -> tuple[Mapping[str, object], Mapping[str, object]]:
        processor_data = dict[str, object]()
        passthrough_data = dict[str, object]()
1177

1178
1179
1180
        for items in mm_items.values():
            processor_data.update(items.get_processor_data())
            passthrough_data.update(items.get_passthrough_data())
1181

1182
1183
        return processor_data, passthrough_data

1184
1185
1186
    def _call_hf_processor(
        self,
        prompt: str,
1187
1188
1189
1190
        # Not to be confused with `mm_data` in `self.apply`.
        # This refers to the data to be passed to HF processor.
        mm_data: Mapping[str, object],
        mm_kwargs: Mapping[str, object],
1191
        tok_kwargs: Mapping[str, object],
1192
    ) -> "BatchFeature":
1193
1194
1195
1196
        """
        Call the HF processor on the prompt text and
        associated multi-modal data.
        """
1197
1198
        return self.info.ctx.call_hf_processor(
            self.info.get_hf_processor(**mm_kwargs),
1199
            dict(text=prompt, **mm_data),
1200
            dict(**mm_kwargs, **tok_kwargs),
1201
1202
        )

1203
    def _hf_processor_applies_updates(
1204
1205
1206
1207
        self,
        prompt_text: str,
        mm_items: MultiModalDataItems,
        hf_processor_mm_kwargs: Mapping[str, object],
1208
        tokenization_kwargs: Mapping[str, object],
1209
1210
    ) -> bool:
        """
1211
        Return whether the HF processor applies prompt updates.
1212

1213
1214
        For most HF processors, this should be `True` when multi-modal
        data items are passed, but `False` when multi-modal embeddings
1215
1216
1217
1218
1219
1220
        are passed.
        """
        return not any(
            isinstance(items, (EmbeddingItems, DictEmbeddingItems))
            for items in mm_items.values())

1221
    def _apply_hf_processor_text_mm(
1222
        self,
1223
        prompt_text: str,
1224
        mm_items: MultiModalDataItems,
1225
        hf_processor_mm_kwargs: Mapping[str, object],
1226
        tokenization_kwargs: Mapping[str, object],
1227
    ) -> tuple[list[int], "BatchFeature", bool]:
1228
        """
1229
1230
        Apply the HF processor on the prompt text and multi-modal data
        together.
1231

1232
        In addition, return whether prompt updates have been applied.
1233
1234
1235
1236
1237
1238
1239
        """
        processor_data, passthrough_data = self._get_hf_mm_data(mm_items)

        processed_data = self._call_hf_processor(
            prompt=prompt_text,
            mm_data=processor_data,
            mm_kwargs=hf_processor_mm_kwargs,
1240
            tok_kwargs=tokenization_kwargs,
1241
1242
        )
        processed_data.update(passthrough_data)
1243

1244
        prompt_ids, = processed_data.pop("input_ids").tolist()
1245

1246
        is_update_applied = self._hf_processor_applies_updates(
1247
1248
1249
            prompt_text=prompt_text,
            mm_items=mm_items,
            hf_processor_mm_kwargs=hf_processor_mm_kwargs,
1250
            tokenization_kwargs=tokenization_kwargs,
1251
1252
        )

1253
        return prompt_ids, processed_data, is_update_applied
1254

1255
    def _apply_hf_processor_text_only(
1256
1257
1258
1259
        self,
        prompt_text: str,
        tokenization_kwargs: Mapping[str, object],
    ) -> list[int]:
1260
        """
1261
        Apply the HF processor on the prompt text only.
1262

1263
1264
1265
        Since HF processor requires that text and multi-modal items
        correspond to each other, we create dummy multi-modal items
        to go along with the text.
1266
        """
1267
        prompt_ids, _, _ = self._apply_hf_processor_text_mm(
1268
1269
1270
            prompt_text=prompt_text,
            mm_items=MultiModalDataItems({}),
            hf_processor_mm_kwargs={},
1271
            tokenization_kwargs=tokenization_kwargs,
1272
1273
        )

1274
1275
1276
1277
1278
1279
1280
1281
1282
1283
1284
1285
        return prompt_ids

    def _apply_hf_processor_tokens_only(
        self,
        prompt_tokens: list[int],
    ) -> list[int]:
        """
        Apply the HF processor on the prompt tokens only.

        Most HF processors accept prompt text but not prompt tokens.
        If the HF processor adds or removes tokens that are not related to
        multi-modal data, you should override this method so it is consistent
1286
1287
1288
        with the output of
        [`_apply_hf_processor_text_only`][vllm.multimodal.processing.BaseMultiModalProcessor._apply_hf_processor_text_only]
        on the
1289
1290
1291
1292
1293
1294
1295
1296
        corresponding text.
        """
        return prompt_tokens

    def _apply_hf_processor_mm_only(
        self,
        mm_items: MultiModalDataItems,
        hf_processor_mm_kwargs: Mapping[str, object],
1297
        tokenization_kwargs: Mapping[str, object],
1298
    ) -> "BatchFeature":
1299
1300
1301
1302
1303
        """
        Apply the HF processor on the multi-modal data only.

        Since HF processor requires that text and multi-modal items
        correspond to each other, we generate dummy text using
1304
1305
        [`DummyInputsBuilder`][vllm.multimodal.profiling.BaseDummyInputsBuilder]
        to go along with the multi-modal data.
1306
1307
1308
        """
        mm_counts = mm_items.get_all_counts()

1309
        _, mm_processed_data, _ = self._apply_hf_processor_text_mm(
1310
            prompt_text=self.dummy_inputs.get_dummy_text(mm_counts),
1311
1312
            mm_items=mm_items,
            hf_processor_mm_kwargs=hf_processor_mm_kwargs,
1313
            tokenization_kwargs=tokenization_kwargs,
1314
1315
        )

1316
        return mm_processed_data
1317
1318
1319
1320
1321
1322

    def _apply_hf_processor_main(
        self,
        prompt: Union[str, list[int]],
        mm_items: MultiModalDataItems,
        hf_processor_mm_kwargs: Mapping[str, object],
1323
        tokenization_kwargs: Mapping[str, object],
1324
        *,
1325
        enable_hf_prompt_update: bool,
1326
    ) -> tuple[list[int], "BatchFeature", bool]:
1327
1328
1329
        """
        Apply the HF processor on the prompt text and multi-modal data.

1330
        In addition, return whether prompt updates have been applied
1331
        (for most HF processors, this should be `True`).
1332

1333
        Note:
1334
            If `enable_hf_prompt_update=False`, we use HF processor
1335
            to perform prompt updates if available; HF processor requires
1336
            that the prompt corresponds to multi-modal items.
1337
1338
        """
        if isinstance(prompt, str):
1339
            if enable_hf_prompt_update:
1340
1341
1342
1343
                return self._apply_hf_processor_text_mm(
                    prompt_text=prompt,
                    mm_items=mm_items,
                    hf_processor_mm_kwargs=hf_processor_mm_kwargs,
1344
                    tokenization_kwargs=tokenization_kwargs,
1345
1346
                )

1347
1348
            prompt_ids = self._apply_hf_processor_text_only(
                prompt, tokenization_kwargs)
1349
1350
1351
        else:
            prompt_ids = self._apply_hf_processor_tokens_only(prompt)

1352
        mm_processed_data = self._apply_hf_processor_mm_only(
1353
            mm_items=mm_items,
1354
            hf_processor_mm_kwargs=hf_processor_mm_kwargs,
1355
            tokenization_kwargs=tokenization_kwargs,
1356
1357
        )

1358
        return prompt_ids, mm_processed_data, False
1359

1360
    def _hash_mm_items(
1361
1362
1363
1364
        self,
        mm_items: MultiModalDataItems,
        hf_processor_mm_kwargs: Mapping[str, object],
        tokenization_kwargs: Mapping[str, object],
1365
        *,
1366
        mm_uuids: Optional[MultiModalUUIDDict] = None,
1367
    ) -> MultiModalHashes:
1368
1369
        """Create MM hashes to be returned (only used in V1).

1370

1371
1372
1373
        Note: When overrides are provided via callers of `apply`,
        `_hash_mm_items` will be bypassed and the overrides will be used.
        """
1374
1375
        model_id = self.info.model_id

1376
        hashes: MultiModalHashes = {}
1377
        mm_uuids = mm_uuids or {}
1378
1379

        for modality, items in mm_items.items():
1380
1381
1382
1383
            if modality in mm_uuids:
                mm_uuids_per_modality = mm_uuids[modality]
                if isinstance(mm_uuids_per_modality, str):
                    mm_uuids_per_modality = [mm_uuids_per_modality]
1384
1385
1386
1387

                # For None entries, compute a hash; otherwise, use provided ID.
                computed: list[str] = []
                for i, item in enumerate(items):
1388
                    item_uuid = mm_uuids_per_modality[i]
1389

1390
                    # NOTE: Even if a item_uuid is provided, we still compute a
1391
1392
1393
                    # hash if `hf_processor_mm_kwargs` or `tokenization_kwargs`
                    # are provided. This is because the processed multimodal
                    # inputs can be different depending on the processor kwargs.
1394
                    if item_uuid is None or \
1395
1396
1397
1398
1399
                        hf_processor_mm_kwargs or \
                        tokenization_kwargs:

                        # NOTE: use provided hash string to hash with kwargs
                        # if available for better performance.
1400
                        item = item_uuid if item_uuid is not None else item
1401
1402
1403
1404
1405
1406
1407
                        computed.append(
                            MultiModalHasher.hash_kwargs(
                                model_id=model_id,
                                **{modality: item},
                                **hf_processor_mm_kwargs,
                                **tokenization_kwargs))
                    else:
1408
                        computed.append(item_uuid)
1409
1410
1411
1412
1413
1414
1415
1416
1417
1418
1419
                hashes[modality] = computed
            else:
                hashes[modality] = [
                    MultiModalHasher.hash_kwargs(model_id=model_id,
                                                 **{modality: item},
                                                 **hf_processor_mm_kwargs,
                                                 **tokenization_kwargs)
                    for item in items
                ]

        return hashes
1420

1421
1422
1423
1424
1425
1426
1427
1428
1429
1430
1431
1432
1433
1434
1435
1436
1437
1438
1439
1440
1441
1442
1443
1444
1445
1446
1447
1448
1449
1450
1451
1452
1453
1454
1455
1456
    def _get_cache_missing_items(
        self,
        cache: "BaseMultiModalProcessorCache",
        mm_data_items: MultiModalDataItems,
        mm_hashes: MultiModalHashes,
    ) -> MultiModalDataItems:
        mm_is_cached = {
            modality: cache.is_cached(hashes)
            for modality, hashes in mm_hashes.items()
        }

        mm_missing_idxs = {
            modality: [
                idx for idx, item_is_cached in enumerate(items_is_cached)
                if not item_is_cached
            ]
            for modality, items_is_cached in mm_is_cached.items()
        }
        mm_missing_data = {
            modality: [mm_data_items[modality][idx] for idx in idxs]
            for modality, idxs in mm_missing_idxs.items()
        }

        return self._to_mm_items(mm_missing_data)

    def _recompute_cached_prompt_update(
        self,
        cached_update: ResolvedPromptUpdate,
        new_item_idx: int,
    ) -> ResolvedPromptUpdate:
        """
        Override this if other attributes of `ResolvedPromptUpdate`
        also need to be recomputed after retrieving from the cache.
        """
        return replace(cached_update, item_idx=new_item_idx)

1457
1458
    def _merge_mm_kwargs(
        self,
1459
1460
        cache: "BaseMultiModalProcessorCache",
        mm_hashes: MultiModalHashes,
1461
        mm_missing_kwargs: MultiModalKwargsItems,
1462
1463
1464
1465
1466
1467
1468
1469
1470
        mm_missing_prompt_updates: MultiModalPromptUpdates,
    ) -> tuple[MultiModalKwargsOptionalItems, MultiModalPromptUpdates]:
        # Need to calculate this at the beginning to avoid skipping cache logic
        # for subsequently repeated items in the same modality
        mm_is_cached = {
            modality: cache.is_cached(hashes)
            for modality, hashes in mm_hashes.items()
        }

1471
        mm_missing_next_idx = defaultdict[str, int](lambda: 0)
1472

1473
1474
1475
1476
1477
1478
1479
1480
1481
1482
1483
1484
1485
1486
1487
1488
        merged_kwargs = defaultdict[str,
                                    list[Optional[MultiModalKwargsItem]]](list)
        merged_prompt_updates = defaultdict[
            str, list[Sequence[ResolvedPromptUpdate]]](list)
        for modality, hashes in mm_hashes.items():
            missing_kwargs = mm_missing_kwargs.get(modality, [])
            missing_prompt_updates = mm_missing_prompt_updates.get(
                modality, [])

            for item_idx, item_hash in enumerate(hashes):
                kwargs: Optional[MultiModalKwargsItem]
                if not mm_is_cached[modality][item_idx]:
                    missing_next_idx = mm_missing_next_idx[modality]
                    kwargs = missing_kwargs[missing_next_idx]
                    updates = missing_prompt_updates[missing_next_idx]

1489
                    mm_missing_next_idx[modality] += 1
1490
1491

                    item = kwargs, updates
1492
                else:
1493
1494
1495
1496
1497
1498
1499
1500
1501
                    item = None

                kwargs, updates = cache.get_and_update_item(item, item_hash)

                merged_kwargs[modality].append(kwargs)
                merged_prompt_updates[modality].append([
                    self._recompute_cached_prompt_update(update, item_idx)
                    for update in updates
                ])
1502

1503
1504
        mm_kwargs = MultiModalKwargsItems(merged_kwargs)
        mm_prompt_updates = dict(merged_prompt_updates)
1505

1506
        return mm_kwargs, mm_prompt_updates
1507
1508
1509
1510
1511
1512

    def _apply_hf_processor(
        self,
        prompt: Union[str, list[int]],
        mm_data_items: MultiModalDataItems,
        hf_processor_mm_kwargs: Mapping[str, object],
1513
        tokenization_kwargs: Mapping[str, object],
1514
        *,
1515
        mm_uuids: Optional[MultiModalUUIDDict] = None,
1516
    ) -> tuple[list[int], MultiModalProcessingInfo, bool]:
1517
1518
        (
            prompt_ids,
1519
            mm_processed_data,
1520
1521
1522
1523
1524
            is_update_applied,
        ) = self._apply_hf_processor_main(
            prompt=prompt,
            mm_items=mm_data_items,
            hf_processor_mm_kwargs=hf_processor_mm_kwargs,
1525
            tokenization_kwargs=tokenization_kwargs,
1526
1527
1528
            enable_hf_prompt_update=True,
        )

1529
        mm_kwargs = MultiModalKwargsItems.from_hf_inputs(
1530
1531
1532
1533
1534
            mm_processed_data,
            self._get_mm_fields_config(mm_processed_data,
                                       hf_processor_mm_kwargs),
        )

1535
        # Use overrides if provided; fallback to data-dependent hashing.
1536
1537
1538
        mm_hashes = self._hash_mm_items(mm_data_items,
                                        hf_processor_mm_kwargs,
                                        tokenization_kwargs,
1539
                                        mm_uuids=mm_uuids)
1540

1541
        mm_prompt_updates = self._get_mm_prompt_updates(
1542
1543
1544
1545
1546
1547
1548
1549
1550
1551
1552
1553
            mm_data_items,
            hf_processor_mm_kwargs,
            mm_kwargs,
        )

        mm_info = MultiModalProcessingInfo(
            kwargs=mm_kwargs,
            hashes=mm_hashes,
            prompt_updates=mm_prompt_updates,
        )

        return prompt_ids, mm_info, is_update_applied
1554

1555
1556
    def _cached_apply_hf_processor(
        self,
1557
        prompt: Union[str, list[int]],
1558
1559
        mm_data_items: MultiModalDataItems,
        hf_processor_mm_kwargs: Mapping[str, object],
1560
        tokenization_kwargs: Mapping[str, object],
1561
        *,
1562
        mm_uuids: Optional[MultiModalUUIDDict] = None,
1563
    ) -> tuple[list[int], MultiModalProcessingInfo, bool]:
1564
1565
1566
1567
1568
1569
        """
        Apply the HF processor on the full prompt text,
        caching the results and reusing cached results.
        """
        cache = self.cache

1570
1571
        _, passthrough_data = self._get_hf_mm_data(mm_data_items)
        if cache is None or passthrough_data:
1572
            return self._apply_hf_processor(
1573
                prompt=prompt,
1574
                mm_data_items=mm_data_items,
1575
                hf_processor_mm_kwargs=hf_processor_mm_kwargs,
1576
                tokenization_kwargs=tokenization_kwargs,
1577
                mm_uuids=mm_uuids,
1578
1579
            )

1580
1581
1582
        mm_hashes = self._hash_mm_items(mm_data_items,
                                        hf_processor_mm_kwargs,
                                        tokenization_kwargs,
1583
                                        mm_uuids=mm_uuids)
1584
1585

        mm_missing_data_items = self._get_cache_missing_items(
1586
1587
            cache=cache,
            mm_data_items=mm_data_items,
1588
            mm_hashes=mm_hashes,
1589
        )
1590

1591
        # NOTE: `prompt` does not correspond to `mm_missing_data_items`,
1592
        # so we can't apply prompt updates until the new multimodal
1593
1594
1595
        # items are combined with the cached multimodal items
        (
            prompt_ids,
1596
            mm_missing_processed_data,
1597
            is_update_applied,
1598
        ) = self._apply_hf_processor_main(
1599
            prompt=prompt,
1600
            mm_items=mm_missing_data_items,
1601
            hf_processor_mm_kwargs=hf_processor_mm_kwargs,
1602
            tokenization_kwargs=tokenization_kwargs,
1603
            enable_hf_prompt_update=False,
1604
1605
        )

1606
        mm_missing_kwargs = MultiModalKwargsItems.from_hf_inputs(
1607
1608
1609
1610
1611
            mm_missing_processed_data,
            self._get_mm_fields_config(mm_missing_processed_data,
                                       hf_processor_mm_kwargs),
        )

1612
1613
1614
1615
        mm_missing_prompt_updates = self._get_mm_prompt_updates(
            mm_missing_data_items,
            hf_processor_mm_kwargs,
            mm_missing_kwargs,
1616
        )
1617

1618
1619
1620
1621
1622
        mm_kwargs, mm_prompt_updates = self._merge_mm_kwargs(
            cache,
            mm_hashes=mm_hashes,
            mm_missing_kwargs=mm_missing_kwargs,
            mm_missing_prompt_updates=mm_missing_prompt_updates,
1623
1624
1625
1626
        )

        mm_info = MultiModalProcessingInfo(
            kwargs=mm_kwargs,
1627
            hashes=mm_hashes,
1628
1629
            prompt_updates=mm_prompt_updates,
        )
1630

1631
        return prompt_ids, mm_info, is_update_applied
1632

1633
1634
1635
    def _apply_token_matches(
        self,
        prompt: list[int],
1636
1637
1638
1639
        mm_prompt_updates: MultiModalPromptUpdates,
    ) -> tuple[list[int], MultiModalPromptUpdatesApplyResult]:
        tokenizer = self.info.get_tokenizer()
        return apply_token_matches(prompt, mm_prompt_updates, tokenizer)
1640
1641
1642
1643

    def _apply_text_matches(
        self,
        prompt: str,
1644
1645
1646
1647
        mm_prompt_updates: MultiModalPromptUpdates,
    ) -> tuple[str, MultiModalPromptUpdatesApplyResult]:
        tokenizer = self.info.get_tokenizer()
        return apply_text_matches(prompt, mm_prompt_updates, tokenizer)
1648

1649
    def _apply_prompt_updates(
1650
1651
        self,
        token_ids: list[int],
1652
        mm_prompt_updates: MultiModalPromptUpdates,
1653
    ) -> tuple[list[int], str, Mapping[str, list[PlaceholderFeaturesInfo]]]:
1654
        tokenizer = self.info.get_tokenizer()
1655

1656
1657
1658
1659
        new_token_ids, match_result = self._apply_token_matches(
            token_ids,
            mm_prompt_updates,
        )
1660
1661
1662
1663
1664
1665
1666
1667
1668

        # If the search text does not represent a special token,
        # it may have different token IDs in the prompt, because
        # the tokens may go across the boundaries of the search text.
        # ----
        # e.g. when searching for "foo" in "food", if "food" itself makes
        # up a token, then the token ID of "foo" will not appear at all
        # ----
        # Since it is inefficient to search for all possible tokenizations
1669
1670
        # of the search text in the prompt, we instead perform string-based
        # updates on the decoded token IDs, then encode them back.
1671
        if all(
1672
1673
1674
1675
1676
1677
1678
                all(update_idx is not None for update_idx in update_idxs)
                for update_idxs in match_result.values()):
            new_text = decode_tokens(tokenizer, new_token_ids)
        else:
            new_text, match_result = self._apply_text_matches(
                decode_tokens(tokenizer, token_ids),
                mm_prompt_updates,
1679
1680
            )

1681
1682
1683
1684
            new_token_ids = encode_tokens(
                tokenizer,
                new_text,
                add_special_tokens=False,
1685
1686
            )

1687
1688
1689
1690
1691
1692
1693
1694
1695
1696
        matched_updates = defaultdict[
            str, list[Sequence[ResolvedPromptUpdate]]](list)
        for modality, update_idxs in match_result.items():
            for item_idx, update_idx in enumerate(update_idxs):
                assert update_idx is not None, (
                    "Failed to apply prompt replacement for "
                    f"mm_items[{modality!r}][{item_idx}]")

                matched_updates[modality].append(
                    [mm_prompt_updates[modality][item_idx][update_idx]])
1697
1698

        placeholders = self._find_mm_placeholders(
1699
1700
            new_token_ids,
            dict(matched_updates),
1701
        )
1702

1703
        return new_token_ids, new_text, placeholders
1704

1705
1706
    def _validate_mm_kwargs(
        self,
1707
        mm_kwargs: MultiModalKwargsOptionalItems,
1708
1709
1710
        mm_item_counts: Mapping[str, int],
    ) -> None:
        for modality, item_count in mm_item_counts.items():
1711
            items = mm_kwargs.get(modality, [])
1712
1713
1714
1715
1716
1717
1718
1719
1720
1721
1722
1723
1724

            if len(items) != item_count:
                raise RuntimeError(
                    f"Expected there to be {item_count} {modality} items in "
                    f"keyword arguments corresponding to {item_count} "
                    f"{modality} data items, but only found {len(items)}! "
                    "There is likely a problem with your "
                    "implementation of merged multi-modal processor for this "
                    "model (usually arising from an inconsistency between "
                    "`_call_hf_processor` and `_get_mm_fields_config`).")

    def _validate_mm_placeholders(
        self,
1725
        mm_placeholders: Mapping[str, list[PlaceholderFeaturesInfo]],
1726
        mm_item_counts: Mapping[str, int],
1727
    ) -> None:
1728
1729
1730
        for modality, item_count in mm_item_counts.items():
            placeholders = mm_placeholders.get(modality, [])

1731
            if len(placeholders) != item_count:
1732
1733
1734
                # NOTE: If you are a model developer, this can also arise from
                # an inconsistency between `_call_hf_processor` and
                # `_get_mm_fields_config` implementations
1735
                raise RuntimeError(
1736
                    f"Expected there to be {item_count} prompt updates "
1737
                    f"corresponding to {item_count} {modality} items, but "
1738
                    f"instead found {len(placeholders)} prompt updates! "
1739
1740
1741
1742
                    "This is likely because you forgot to include input "
                    "placeholder tokens (e.g., `<image>`, `<|image_pad|>`) "
                    "in the prompt. If the model has a chat template, make "
                    "sure you have applied it before calling `LLM.generate`.")
1743

1744
1745
1746
1747
    def _maybe_apply_prompt_updates(
        self,
        mm_items: MultiModalDataItems,
        prompt_ids: list[int],
1748
        mm_kwargs: MultiModalKwargsOptionalItems,
1749
        mm_prompt_updates: MultiModalPromptUpdates,
1750
1751
        is_update_applied: bool,
    ) -> tuple[list[int], str, Mapping[str, list[PlaceholderFeaturesInfo]]]:
1752
        mm_item_counts = mm_items.get_all_counts()
1753
1754
        self._validate_mm_kwargs(mm_kwargs, mm_item_counts)

1755
        if is_update_applied:
1756
1757
            mm_placeholders = self._find_mm_placeholders(
                prompt_ids,
1758
                mm_prompt_updates,
1759
            )
1760
            self._validate_mm_placeholders(mm_placeholders, mm_item_counts)
1761

1762
            tokenizer = self.info.get_tokenizer()
1763
            prompt = decode_tokens(tokenizer, prompt_ids)
1764
1765
1766
        else:
            (
                prompt_ids,
1767
                prompt,
1768
                mm_placeholders,
1769
            ) = self._apply_prompt_updates(
1770
                prompt_ids,
1771
                mm_prompt_updates,
1772
            )
1773
            self._validate_mm_placeholders(mm_placeholders, mm_item_counts)
1774

1775
1776
1777
1778
1779
1780
1781
        return prompt_ids, prompt, mm_placeholders

    def apply(
        self,
        prompt: Union[str, list[int]],
        mm_data: MultiModalDataDict,
        hf_processor_mm_kwargs: Mapping[str, object],
1782
        tokenization_kwargs: Optional[Mapping[str, object]] = None,
1783
        *,
1784
        mm_uuids: Optional[MultiModalUUIDDict] = None,
1785
1786
1787
1788
1789
1790
1791
1792
1793
1794
1795
1796
1797
1798
1799
1800
    ) -> MultiModalInputs:
        """
        Process multi-modal inputs to be used in vLLM.

        The main steps are:

        1. Apply HF Processor on prompt text and multi-modal data together,
           outputting token IDs and processed tensors.
        2. Find and update sequences in the token IDs with placeholder tokens.
           The number of placeholder tokens equals the feature size of the
           multi-modal data outputted by the multi-modal encoder.
        3. Extract information about the placeholder tokens from the
           processed token IDs.
        """
        mm_items = self._to_mm_items(mm_data)

1801
1802
1803
        if tokenization_kwargs is None:
            tokenization_kwargs = {}

1804
1805
        (
            prompt_ids,
1806
            mm_info,
1807
1808
1809
1810
1811
            is_update_applied,
        ) = self._cached_apply_hf_processor(
            prompt,
            mm_items,
            hf_processor_mm_kwargs,
1812
            tokenization_kwargs=tokenization_kwargs,
1813
            mm_uuids=mm_uuids,
1814
1815
        )

1816
        # NOTE: tokenization_kwargs are not required to init processor
1817
1818
1819
        prompt_ids, prompt, mm_placeholders = self._maybe_apply_prompt_updates(
            mm_items=mm_items,
            prompt_ids=prompt_ids,
1820
1821
            mm_kwargs=mm_info.kwargs,
            mm_prompt_updates=mm_info.prompt_updates,
1822
1823
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            is_update_applied=is_update_applied,
        )

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        mm_placeholder_ranges = {
            modality: [item.to_range() for item in placeholders]
            for modality, placeholders in mm_placeholders.items()
        }
1829

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        return MultiModalInputs(
1831
            type="multimodal",
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            prompt=prompt,
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            prompt_token_ids=prompt_ids,
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            mm_kwargs=mm_info.kwargs,
            mm_hashes=mm_info.hashes,
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            mm_placeholders=mm_placeholder_ranges,
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        )
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class EncDecMultiModalProcessor(BaseMultiModalProcessor[_I]):

    @abstractmethod
    def create_encoder_prompt(
        self,
        prompt: Union[str, list[int]],
        mm_data: MultiModalDataDict,
    ) -> Union[str, list[int]]:
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        """
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        Create input prompt for the encoder. HF processor will be applied on
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        this prompt during profiling and generation.
        """
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        raise NotImplementedError

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    @property
    def pad_dummy_encoder_prompt(self) -> bool:
        return False

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    def create_decoder_prompt(
        self,
        prompt: Union[str, list[int]],
        mm_data: MultiModalDataDict,
    ) -> Union[str, list[int]]:
        """Create input prompt for the decoder."""
        return prompt

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    def _get_enc_dec_inputs(
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        self,
        prompt: Union[str, list[int]],
        mm_data: MultiModalDataDict,
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        encoder_inputs: MultiModalInputs,
    ):
1872
        tokenizer = self.info.get_tokenizer()
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        decoder_prompt = self.create_decoder_prompt(prompt, mm_data)
        if isinstance(decoder_prompt, str):
1875
            decoder_prompt_ids = encode_tokens(tokenizer,
1876
                                               decoder_prompt,
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                                               add_special_tokens=False)
        else:
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            decoder_prompt_ids = decoder_prompt
            decoder_prompt = decode_tokens(tokenizer, decoder_prompt)
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        mm_inputs = MultiModalEncDecInputs(
            encoder_prompt=encoder_inputs["prompt"],
            encoder_prompt_token_ids=encoder_inputs["prompt_token_ids"],
            **encoder_inputs)
        mm_inputs.update({
            "prompt": decoder_prompt,
            "prompt_token_ids": decoder_prompt_ids
        })
        return mm_inputs
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    def apply(
        self,
        prompt: Union[str, list[int]],
        mm_data: MultiModalDataDict,
        hf_processor_mm_kwargs: Mapping[str, object],
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        tokenization_kwargs: Optional[Mapping[str, object]] = None,
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        *,
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        mm_uuids: Optional[MultiModalUUIDDict] = None,
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    ) -> MultiModalEncDecInputs:
        """
        Process multi-modal inputs to be used in vLLM.
        The main processing steps are modified to fit encoder-decoder model:
        1. Create encoder prompt from input prompt text.
        2. Apply the HF processor on encoder prompt.
        3. Copy the input prompt text as decoder prompt inputs.
        """
        encoder_prompt = self.create_encoder_prompt(prompt, mm_data)
        encoder_inputs = super().apply(
            encoder_prompt,
            mm_data,
            hf_processor_mm_kwargs,
1913
            tokenization_kwargs,
1914
            mm_uuids=mm_uuids,
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        )

        return self._get_enc_dec_inputs(
            prompt=prompt,
            mm_data=mm_data,
            encoder_inputs=encoder_inputs,
        )