processor.py 57.2 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,
    Protocol,
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    TypeAlias,
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    cast,
)
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import regex as re
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import torch
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from typing_extensions import TypeVar, assert_never
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from vllm.logger import init_logger
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from vllm.tokenizers import TokenizerLike
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from vllm.utils.collection_utils import flatten_2d_lists, full_groupby
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from ..inputs import (
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    MultiModalEncDecInputs,
    MultiModalFieldConfig,
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    MultiModalHashes,
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    MultiModalInputs,
    MultiModalKwargsItem,
    MultiModalKwargsItems,
    MultiModalKwargsOptionalItems,
    PlaceholderRange,
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    mm_enc_dec_inputs,
    mm_inputs,
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)
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from ..parse import (
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    DictEmbeddingItems,
    EmbeddingItems,
    MultiModalDataItems,
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    MultiModalUUIDItems,
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)
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from .context import BaseProcessingInfo, TimingContext
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from .dummy_inputs import BaseDummyInputsBuilder
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from .inputs import ProcessorInputs
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if TYPE_CHECKING:
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    from transformers.feature_extraction_utils import BatchFeature

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    from ..cache import BaseMultiModalProcessorCache
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else:
    BatchFeature = object

    BaseMultiModalProcessorCache = object
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logger = init_logger(__name__)
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_S = TypeVar("_S", str, list[int])
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PromptSeq: TypeAlias = str | list[int]
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"""A token sequence (list of token IDs) or text."""
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@lru_cache(maxsize=2048)
def _cached_encode(
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    tokenizer: TokenizerLike,
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    text: str,
    *,
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    add_special_tokens: bool = True,
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) -> list[int]:
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    return tokenizer.encode(text, add_special_tokens=add_special_tokens)
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@lru_cache(maxsize=2048)
def _cached_decode(
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    tokenizer: TokenizerLike,
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    token_ids: tuple[int, ...],
    *,
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    skip_special_tokens: bool = False,
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) -> str:
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    return tokenizer.decode(list(token_ids), skip_special_tokens=skip_special_tokens)
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def _seq2text(
    tokenizer: TokenizerLike | None,
    seq: PromptSeq,
    *,
    use_cache: bool = True,
) -> str:
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    if isinstance(seq, str):
        return seq

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    if tokenizer is None:
        raise ValueError("You cannot decode tokens when `skip_tokenizer_init=True`")

    if not use_cache:
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        return tokenizer.decode(seq)
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    return _cached_decode(tokenizer, tuple(seq))


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def _seq2tokens(
    tokenizer: TokenizerLike | None,
    seq: PromptSeq,
    *,
    use_cache: bool = True,
) -> list[int]:
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    if isinstance(seq, str):
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        if tokenizer is None:
            raise ValueError("You cannot encode text when `skip_tokenizer_init=True`")

        if not use_cache:
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            return tokenizer.encode(seq, add_special_tokens=False)
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        return _cached_encode(tokenizer, seq, add_special_tokens=False)

    return seq


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class _GetMatchIndex(Protocol):
    def __call__(
        self,
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        tokenizer: TokenizerLike | None,
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        prompt: PromptSeq,
        start_idx: int = 0,
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    ) -> int | None: ...
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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(
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            tokenizer: TokenizerLike | None,
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            prompt: PromptSeq,
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            start_idx: int = 0,
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        ) -> int | None:
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            if start_idx != 0:
                return None

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            prefix = seq

            if isinstance(prompt, str):
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                # Make both `str`
                prefix = _seq2text(tokenizer, prefix, use_cache=False)
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            else:
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                # Make both `list[int]`
                prefix = _seq2tokens(tokenizer, prefix, use_cache=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: TypeAlias = PromptSeq | PromptIndex
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"""
The token sequence or text to update.
"""

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PromptUpdateTarget: TypeAlias = Callable[[int], UpdateTarget] | UpdateTarget
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"""
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: Callable[[TokenizerLike | None, PromptSeq], torch.Tensor] | None = 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.embed_multimodal`][vllm.model_executor.models.interfaces.SupportsMultiModal.embed_multimodal].
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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: TokenizerLike | None, full: PromptSeq) -> torch.Tensor:
            embed_token_ids = _seq2tokens(tokenizer, embed_text, use_cache=False)
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            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: TokenizerLike | None, full: PromptSeq) -> torch.Tensor:
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            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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    @staticmethod
    def select_token_ids(
        seq: _S,
        embed_token_ids: list[int],
    ) -> "PromptUpdateDetails[_S]":
        def is_embed(tokenizer: TokenizerLike | None, full: PromptSeq) -> torch.Tensor:
            token_ids = _seq2tokens(tokenizer, full)

            return torch.isin(
                torch.tensor(token_ids),
                torch.tensor(embed_token_ids),
            )

        return PromptUpdateDetails(full=seq, is_embed=is_embed)

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PromptUpdateInfo: TypeAlias = 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: TypeAlias = Callable[[int], PromptUpdateInfo] | PromptUpdateInfo
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"""
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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:
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    ```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,
    )
    ```

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    Insert these tokens after a prefix `Images:`:
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    ```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
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    equal to the feature size of the vision encoder:

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

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

    ```python
    PromptReplacement(
        modality="image",
        target="<image>",
        replacement=PromptUpdateDetails(
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            full="".join(
                [
                    "<image_bos>",
                    "<image>" * image_feature_size,
                    "<image_eos>",
                ]
            ),
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            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(
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            full=(
                [image_bos_id] + [image_token_id] * image_feature_size + [image_eos_id]
            ),
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            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):
    @property
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    def modality(self) -> str: ...
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_M = TypeVar("_M", bound=_HasModalityAttr | _HasModalityProp)
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def full_groupby_modality(values: Iterable[_M]) -> ItemsView[str, list[_M]]:
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    """
    Convenience function to apply
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    [`full_groupby`][vllm.utils.collection_utils.full_groupby]
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    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],
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        tokenizer: TokenizerLike | None,
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        *,
        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, target_token_ids, start_idx=start_idx):
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            yield PromptTargetMatch(match.start_idx, match.end_idx)
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    def iter_text_matches(
        self,
        prompt: str,
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        tokenizer: TokenizerLike | None,
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        *,
        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)

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

    def iter_matches(
        self,
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        prompt: list[int] | str,
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        tokenizer: TokenizerLike | None,
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        *,
        start_idx: int = 0,
    ) -> Generator[PromptTargetMatch]:
        """Yield each instance of `self.target` found in `prompt`."""
        if isinstance(prompt, str):
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            return self.iter_text_matches(prompt, tokenizer, start_idx=start_idx)
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        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: torch.Tensor | None
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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",
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    tokenizer: TokenizerLike | None,
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703
    *,
    prev_end_idx: int = 0,
    current_result: "MultiModalPromptUpdatesApplyResult",
704
705
) -> tuple[UpdateMode | None, list[_MatchToApply]]:
    mode: UpdateMode | None = None
706
707
708
709
710
711
712
713
714
715
716
717
    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(
718
719
720
                    prompt,
                    tokenizer,
                    start_idx=prev_end_idx,
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
                ):
                    # 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
750
751


752
753
754
755
756
757
758
759
760
761
def _all_items_found(
    mm_item_counts: dict[str, int],
    mm_found_counts: dict[str, int],
) -> bool:
    return all(
        item_idx >= mm_item_counts[modality]
        for modality, item_idx in mm_found_counts.items()
    )


762
def _apply_matches(
763
    prompt: _S,
764
    mm_prompt_updates: "MultiModalPromptUpdates",
765
    tokenizer: TokenizerLike | None,
766
) -> tuple[list[_S], "MultiModalPromptUpdatesApplyResult"]:
767
    mm_item_counts = {m: len(items) for m, items in mm_prompt_updates.items()}
768

769
    out_seqs = list[str | list[int]]()
770
    out_result: MultiModalPromptUpdatesApplyResult = {
771
        m: [None] * len(items) for m, items in mm_prompt_updates.items()
772
    }
773

774
    # Early exit if no items to find
775
776
777
778
779
780
    mm_found_counts = {
        m: sum(r is not None for r in res) for m, res in out_result.items()
    }
    if _all_items_found(mm_item_counts, mm_found_counts):
        return [prompt], out_result

781
782
    prev_end_idx = 0
    while True:
783
784
785
786
787
788
789
        mode, matches_to_apply = _find_matches(
            prompt,
            mm_prompt_updates,
            tokenizer,
            prev_end_idx=prev_end_idx,
            current_result=out_result,
        )
790

791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
        if mode is None:
            break  # No more matches to find

        for (modality, item_idx), (match, update_idx) in matches_to_apply:
            matched_update = mm_prompt_updates[modality][item_idx][update_idx]
            matched_content = matched_update.content.full

            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)

            out_seqs.append(prompt[prev_end_idx:end_idx_to_insert])
            out_seqs.append(
                _seq2text(tokenizer, matched_content)
                if isinstance(prompt, str)
                else _seq2tokens(tokenizer, matched_content)
            )
            out_result[modality][item_idx] = update_idx

            # Exclude overlapping matches
            prev_end_idx = match.end_idx

        # Early exit if all items found
        mm_found_counts = {
            m: sum(r is not None for r in res) for m, res in out_result.items()
        }
        if _all_items_found(mm_item_counts, mm_found_counts):
            break
822
823
824

    out_seqs.append(prompt[prev_end_idx:])

825
    return cast(list[_S], out_seqs), out_result
826
827


828
def apply_token_matches(
829
    prompt: list[int],
830
    mm_prompt_updates: "MultiModalPromptUpdates",
831
    tokenizer: TokenizerLike | None,
832
833
834
) -> tuple[list[int], "MultiModalPromptUpdatesApplyResult"]:
    """
    Apply the updates in `mm_prompt_updates` to `prompt`.
835

836
837
838
839
    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.
    """
840
    token_id_seqs, result = _apply_matches(prompt, mm_prompt_updates, tokenizer)
841

842
    return flatten_2d_lists(token_id_seqs), result
843
844


845
def apply_text_matches(
846
    prompt: str,
847
    mm_prompt_updates: "MultiModalPromptUpdates",
848
    tokenizer: TokenizerLike | None,
849
850
851
) -> tuple[str, "MultiModalPromptUpdatesApplyResult"]:
    """
    Apply the updates in `mm_prompt_updates` to `prompt`.
852

853
854
855
856
857
    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)
858

859
    return "".join(texts), result
860
861


862
def _iter_placeholders(
863
    prompt: list[int],
864
    mm_prompt_updates: "MultiModalPromptUpdates",
865
    tokenizer: TokenizerLike | None,
866
) -> Iterable[PlaceholderFeaturesInfo]:
867
    """
868
    Yield each set of placeholder tokens found in `prompt`.
869
870
871

    Matches are exclusive even when multiple modalities share
    the same placeholder tokens. In that case, the modality that
872
    appears earlier in `mm_prompt_updates` takes priority.
873

874
875
    Note that empty matches are ignored.
    """
876
    mm_item_counts = {m: len(items) for m, items in mm_prompt_updates.items()}
877
    item_idx_by_modality = {modality: 0 for modality in mm_prompt_updates}
878

879
880
    if _all_items_found(mm_item_counts, item_idx_by_modality):
        return
881

882
    prompt_len = len(prompt)
883
    start_idx = 0
884

885
886
887
    while start_idx < prompt_len:
        found = False

888
        for modality, modality_updates in mm_prompt_updates.items():
889
890
            item_idx = item_idx_by_modality[modality]
            if item_idx >= mm_item_counts.get(modality, 0):
891
                continue
892

893
894
            for update in modality_updates[item_idx]:
                content = update.content
895
                content_tokens_full = _seq2tokens(tokenizer, content.full)
896
897
                content_len_full = len(content_tokens_full)
                end_idx_full = start_idx + content_len_full
898

899
                if content_len_full == 0 or end_idx_full > prompt_len:
900
901
                    continue

902
                if prompt[start_idx:end_idx_full] == content_tokens_full:
903
904
                    content_is_embed = content.is_embed
                    if content_is_embed is not None:
905
                        content_is_embed = content_is_embed(tokenizer, content.full)
906
907
908
909
910
911
912
913

                    yield PlaceholderFeaturesInfo(
                        modality=modality,
                        item_idx=item_idx,
                        start_idx=start_idx,
                        tokens=content_tokens_full,
                        is_embed=content_is_embed,
                    )
914

915
                    # Exclude overlapping matches
916
                    start_idx = end_idx_full
917
918
919
                    item_idx_by_modality[modality] += 1
                    found = True
                    break
920

921
            if found:
922
923
924
                if _all_items_found(mm_item_counts, item_idx_by_modality):
                    return

925
                break  # Go back to the outer while loop
926
927
928

        if not found:
            start_idx += 1
929
930


931
932
def find_mm_placeholders(
    prompt: list[int],
933
    mm_prompt_updates: "MultiModalPromptUpdates",
934
    tokenizer: TokenizerLike | None,
935
) -> Mapping[str, list[PlaceholderFeaturesInfo]]:
936
    it = _iter_placeholders(prompt, mm_prompt_updates, tokenizer)
937
938
939
    return dict(full_groupby_modality(it))


940
941
942
MultiModalIsCached = dict[str, list[bool]]
"""
A collection of the `is_cached` flag for each item, with a similar structure as
943
[`MultiModalKwargsItems`][vllm.multimodal.inputs.MultiModalKwargsItems].
944
945
"""

946
MultiModalPromptUpdates = Mapping[str, list[Sequence[ResolvedPromptUpdate]]]
947
948
949
950
951
"""
A collection of prompt updates with a similar structure as
[`MultiModalKwargsItems`][vllm.multimodal.inputs.MultiModalKwargsItems].
"""

952
MultiModalPromptUpdatesApplyResult = Mapping[str, list[int | None]]
953
954
955
956
957
958
959
"""
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.
"""

960
961
_I = TypeVar("_I", bound=BaseProcessingInfo)

962
963

class MultiModalProcessingInfo(NamedTuple):
964
    kwargs: MultiModalKwargsOptionalItems
965
    hashes: MultiModalHashes
966
967
    prompt_updates: MultiModalPromptUpdates

968
969

class BaseMultiModalProcessor(ABC, Generic[_I]):
970
    """
971
    Abstract base class to process multi-modal inputs to be used in vLLM.
972

973
    Not to be confused with `transformers.ProcessorMixin`.
974
975
    """

976
977
978
979
980
    def __init__(
        self,
        info: _I,
        dummy_inputs: "BaseDummyInputsBuilder[_I]",
        *,
981
        cache: BaseMultiModalProcessorCache | None = None,
982
    ) -> None:
983
984
        super().__init__()

985
986
        self.info = info
        self.dummy_inputs = dummy_inputs
987
        self.cache = cache
988

989
        # TODO: Remove in v0.18
990
        if hasattr(self, "_get_data_parser"):
991
992
993
994
            raise ValueError(
                "BaseMultiModalProcessor._get_data_parser has been "
                "moved to `BaseProcessingInfo.build_data_parser` in v0.16. "
                "You should override `BaseProcessingInfo.build_data_parser` instead."
995
996
            )

997
        self.data_parser = self.info.get_data_parser()
998

999
    def __call__(
1000
        self,
1001
        prompt: str,
1002
        mm_items: MultiModalDataItems,
1003
1004
        mm_uuid_items: MultiModalUUIDItems | None = None,
        hf_processor_mm_kwargs: Mapping[str, object] | None = None,
1005
    ) -> MultiModalInputs:
1006
        processor_inputs = ProcessorInputs(
1007
1008
1009
            prompt,
            mm_items,
            mm_uuid_items,
1010
            hf_processor_mm_kwargs=hf_processor_mm_kwargs or {},
1011
        )
1012

1013
1014
        return self.apply(processor_inputs, TimingContext(enabled=False))

1015
1016
1017
    @abstractmethod
    def _get_mm_fields_config(
        self,
1018
        hf_inputs: BatchFeature,
1019
1020
1021
1022
1023
        hf_processor_mm_kwargs: Mapping[str, object],
    ) -> Mapping[str, MultiModalFieldConfig]:
        """Given the HF-processed data, output the metadata of each field."""
        raise NotImplementedError

1024
    @abstractmethod
1025
    def _get_prompt_updates(
1026
        self,
1027
        mm_items: MultiModalDataItems,
1028
        hf_processor_mm_kwargs: Mapping[str, object],
1029
        out_mm_kwargs: MultiModalKwargsItems,
1030
    ) -> Sequence[PromptUpdate]:
1031
1032
        """
        Given the original multi-modal items for this modality
1033
        and HF-processed data, output the updates to perform.
1034

1035
1036
1037
1038
1039
1040
        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
1041
1042
        in order to construct
        [`PlaceholderRange`][vllm.multimodal.inputs.PlaceholderRange]
1043
        for each multi-modal item.
1044
1045
        """
        raise NotImplementedError
1046

1047
1048
1049
1050
1051
1052
    def _bind_and_group_updates(
        self,
        prompt_updates: Sequence[PromptUpdate],
        mm_item_counts: Mapping[str, int],
    ) -> MultiModalPromptUpdates:
        return {
1053
1054
1055
1056
            modality: [
                [update.resolve(item_idx) for update in updates]
                for item_idx in range(mm_item_counts.get(modality, 0))
            ]
1057
1058
1059
1060
1061
1062
1063
1064
1065
1066
1067
1068
1069
1070
1071
1072
1073
1074
1075
1076
1077
1078
1079
1080
1081
1082
1083
1084
1085
1086
1087
1088
1089
1090
1091
1092
1093
            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

1094
    def _find_mm_placeholders(
1095
1096
        self,
        new_token_ids: list[int],
1097
        mm_prompt_updates: MultiModalPromptUpdates,
1098
    ) -> Mapping[str, list[PlaceholderFeaturesInfo]]:
1099
1100
        tokenizer = self.info.get_tokenizer()

1101
        return find_mm_placeholders(new_token_ids, mm_prompt_updates, tokenizer)
1102

1103
    def _get_hf_mm_data(
1104
        self,
1105
        mm_items: MultiModalDataItems,
1106
    ) -> tuple[Mapping[str, object], Mapping[str, object]]:
1107
        """Extract processor and passthrough data from multi-modal items."""
1108
1109
        processor_data = dict[str, object]()
        passthrough_data = dict[str, object]()
1110

1111
1112
1113
        for items in mm_items.values():
            processor_data.update(items.get_processor_data())
            passthrough_data.update(items.get_passthrough_data())
1114

1115
1116
        return processor_data, passthrough_data

1117
1118
1119
    def _call_hf_processor(
        self,
        prompt: str,
1120
1121
1122
1123
        # 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],
1124
        tok_kwargs: Mapping[str, object],
1125
    ) -> BatchFeature:
1126
1127
1128
1129
        """
        Call the HF processor on the prompt text and
        associated multi-modal data.
        """
1130
1131
1132
1133
1134
        return self.info.ctx.call_hf_processor(
            self.info.get_hf_processor(**mm_kwargs),
            dict(text=prompt, **mm_data),
            dict(**mm_kwargs, **tok_kwargs),
        )
1135

1136
    def _hf_processor_applies_updates(
1137
1138
1139
1140
        self,
        prompt_text: str,
        mm_items: MultiModalDataItems,
        hf_processor_mm_kwargs: Mapping[str, object],
1141
        tokenization_kwargs: Mapping[str, object],
1142
1143
    ) -> bool:
        """
1144
        Return whether the HF processor applies prompt updates.
1145

1146
1147
        For most HF processors, this should be `True` when multi-modal
        data items are passed, but `False` when multi-modal embeddings
1148
1149
1150
1151
        are passed.
        """
        return not any(
            isinstance(items, (EmbeddingItems, DictEmbeddingItems))
1152
1153
            for items in mm_items.values()
        )
1154

1155
    def _apply_hf_processor_text_mm(
1156
        self,
1157
        prompt_text: str,
1158
        mm_items: MultiModalDataItems,
1159
        hf_processor_mm_kwargs: Mapping[str, object],
1160
        tokenization_kwargs: Mapping[str, object],
1161
    ) -> tuple[list[int], BatchFeature, bool]:
1162
        """
1163
1164
        Apply the HF processor on the prompt text and multi-modal data
        together.
1165

1166
        In addition, return whether prompt updates have been applied.
1167
        """
1168
1169
1170
1171
        valid_mm_items = mm_items.select(
            {k for k, c in mm_items.get_all_counts().items() if c > 0}
        )
        processor_data, passthrough_data = self._get_hf_mm_data(valid_mm_items)
1172
1173
1174
1175
1176

        processed_data = self._call_hf_processor(
            prompt=prompt_text,
            mm_data=processor_data,
            mm_kwargs=hf_processor_mm_kwargs,
1177
            tok_kwargs=tokenization_kwargs,
1178
1179
        )
        processed_data.update(passthrough_data)
1180

1181
        (prompt_ids,) = processed_data.pop("input_ids").tolist()
1182

1183
        is_update_applied = self._hf_processor_applies_updates(
1184
1185
1186
            prompt_text=prompt_text,
            mm_items=mm_items,
            hf_processor_mm_kwargs=hf_processor_mm_kwargs,
1187
            tokenization_kwargs=tokenization_kwargs,
1188
1189
        )

1190
        return prompt_ids, processed_data, is_update_applied
1191

1192
    def _apply_hf_processor_text_only(
1193
1194
1195
1196
        self,
        prompt_text: str,
        tokenization_kwargs: Mapping[str, object],
    ) -> list[int]:
1197
        """
1198
        Apply the HF processor on the prompt text only.
1199

1200
1201
1202
        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.
1203
        """
1204
        prompt_ids, _, _ = self._apply_hf_processor_text_mm(
1205
1206
1207
            prompt_text=prompt_text,
            mm_items=MultiModalDataItems({}),
            hf_processor_mm_kwargs={},
1208
            tokenization_kwargs=tokenization_kwargs,
1209
1210
        )

1211
1212
1213
1214
1215
1216
1217
1218
1219
1220
1221
1222
        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
1223
1224
1225
        with the output of
        [`_apply_hf_processor_text_only`][vllm.multimodal.processing.BaseMultiModalProcessor._apply_hf_processor_text_only]
        on the
1226
1227
1228
1229
1230
1231
1232
1233
        corresponding text.
        """
        return prompt_tokens

    def _apply_hf_processor_mm_only(
        self,
        mm_items: MultiModalDataItems,
        hf_processor_mm_kwargs: Mapping[str, object],
1234
        tokenization_kwargs: Mapping[str, object],
1235
    ) -> BatchFeature:
1236
1237
1238
1239
1240
        """
        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
1241
        [`DummyInputsBuilder`][vllm.multimodal.processing.BaseDummyInputsBuilder]
1242
        to go along with the multi-modal data.
1243
1244
1245
        """
        mm_counts = mm_items.get_all_counts()

1246
        _, mm_processed_data, _ = self._apply_hf_processor_text_mm(
1247
            prompt_text=self.dummy_inputs.get_dummy_text(mm_counts),
1248
1249
            mm_items=mm_items,
            hf_processor_mm_kwargs=hf_processor_mm_kwargs,
1250
            tokenization_kwargs=tokenization_kwargs,
1251
1252
        )

1253
        return mm_processed_data
1254
1255
1256

    def _apply_hf_processor_main(
        self,
1257
        prompt: str | list[int],
1258
1259
        mm_items: MultiModalDataItems,
        hf_processor_mm_kwargs: Mapping[str, object],
1260
        tokenization_kwargs: Mapping[str, object],
1261
        *,
1262
        enable_hf_prompt_update: bool,
1263
    ) -> tuple[list[int], BatchFeature, bool]:
1264
1265
1266
        """
        Apply the HF processor on the prompt text and multi-modal data.

1267
        In addition, return whether prompt updates have been applied
1268
        (for most HF processors, this should be `True`).
1269

1270
        Note:
1271
            If `enable_hf_prompt_update=False`, we use HF processor
1272
            to perform prompt updates if available; HF processor requires
1273
            that the prompt corresponds to multi-modal items.
1274
1275
        """
        if isinstance(prompt, str):
1276
            if enable_hf_prompt_update:
1277
1278
1279
1280
                return self._apply_hf_processor_text_mm(
                    prompt_text=prompt,
                    mm_items=mm_items,
                    hf_processor_mm_kwargs=hf_processor_mm_kwargs,
1281
                    tokenization_kwargs=tokenization_kwargs,
1282
1283
                )

1284
            prompt_ids = self._apply_hf_processor_text_only(prompt, tokenization_kwargs)
1285
1286
1287
        else:
            prompt_ids = self._apply_hf_processor_tokens_only(prompt)

1288
        mm_processed_data = self._apply_hf_processor_mm_only(
1289
            mm_items=mm_items,
1290
            hf_processor_mm_kwargs=hf_processor_mm_kwargs,
1291
            tokenization_kwargs=tokenization_kwargs,
1292
1293
        )

1294
        return prompt_ids, mm_processed_data, False
1295

1296
1297
    def _get_cache_missing_items(
        self,
1298
        cache: BaseMultiModalProcessorCache,
1299
1300
        mm_data_items: MultiModalDataItems,
        mm_hashes: MultiModalHashes,
1301
    ) -> tuple[MultiModalIsCached, MultiModalDataItems]:
1302
        mm_is_cached = {
1303
            modality: cache.is_cached(hashes) for modality, hashes in mm_hashes.items()
1304
1305
1306
1307
        }

        mm_missing_idxs = {
            modality: [
1308
1309
                idx
                for idx, item_is_cached in enumerate(items_is_cached)
1310
1311
1312
1313
                if not item_is_cached
            ]
            for modality, items_is_cached in mm_is_cached.items()
        }
1314

1315
1316
1317
1318
1319
1320
1321
1322
        mm_missing_data = {}
        for modality, idxs in mm_missing_idxs.items():
            missing_modality_data = []
            for idx in idxs:
                data = mm_data_items[modality][idx]
                if data is None:
                    raise ValueError(
                        f"Cache miss for {modality} at index {idx} "
1323
1324
                        f"but data is not provided."
                    )
1325
1326
1327
                else:
                    missing_modality_data.append(data)
            mm_missing_data[modality] = missing_modality_data
1328

1329
        mm_missing_items = self.info.parse_mm_data(mm_missing_data, validate=False)
1330
1331

        return mm_is_cached, mm_missing_items
1332
1333
1334
1335
1336
1337
1338
1339
1340
1341
1342
1343

    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)

1344
1345
    def _merge_mm_kwargs(
        self,
1346
        cache: BaseMultiModalProcessorCache,
1347
        mm_hashes: MultiModalHashes,
1348
        mm_is_cached: MultiModalIsCached,
1349
        mm_missing_kwargs: MultiModalKwargsItems,
1350
1351
        mm_missing_prompt_updates: MultiModalPromptUpdates,
    ) -> tuple[MultiModalKwargsOptionalItems, MultiModalPromptUpdates]:
1352
1353
1354
1355
1356
        # Need to touch all mm hashes before update to avoid hash in updated
        # list evict during update
        for hashes in mm_hashes.values():
            for item_hash in hashes:
                cache.touch_sender_cache_item(item_hash)
1357

1358
        mm_missing_next_idx = defaultdict[str, int](lambda: 0)
1359

1360
        merged_kwargs = defaultdict[str, list[MultiModalKwargsItem | None]](list)
1361
1362
1363
        merged_prompt_updates = defaultdict[str, list[Sequence[ResolvedPromptUpdate]]](
            list
        )
1364
1365
        for modality, hashes in mm_hashes.items():
            missing_kwargs = mm_missing_kwargs.get(modality, [])
1366
            missing_prompt_updates = mm_missing_prompt_updates.get(modality, [])
1367
1368
1369
1370

            for item_idx, item_hash in enumerate(hashes):
                if not mm_is_cached[modality][item_idx]:
                    missing_next_idx = mm_missing_next_idx[modality]
1371
1372
                    missing_kwargs_item = missing_kwargs[missing_next_idx]
                    missing_updates_item = missing_prompt_updates[missing_next_idx]
1373

1374
                    mm_missing_next_idx[modality] += 1
1375

1376
                    item = missing_kwargs_item, missing_updates_item
1377
                else:
1378
1379
1380
1381
1382
                    item = None

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

                merged_kwargs[modality].append(kwargs)
1383
1384
1385
1386
1387
1388
                merged_prompt_updates[modality].append(
                    [
                        self._recompute_cached_prompt_update(update, item_idx)
                        for update in updates
                    ]
                )
1389

1390
1391
        mm_kwargs = MultiModalKwargsItems(merged_kwargs)
        mm_prompt_updates = dict(merged_prompt_updates)
1392

1393
        return mm_kwargs, mm_prompt_updates
1394
1395
1396

    def _apply_hf_processor(
        self,
1397
1398
        inputs: ProcessorInputs,
        timing_ctx: TimingContext,
1399
    ) -> tuple[list[int], MultiModalProcessingInfo, bool]:
1400
1401
1402
1403
1404
1405
1406
1407
1408
1409
1410
1411
        with timing_ctx.record("apply_hf_processor"):
            (
                prompt_ids,
                mm_processed_data,
                is_update_applied,
            ) = self._apply_hf_processor_main(
                prompt=inputs.prompt,
                mm_items=inputs.mm_data_items,
                hf_processor_mm_kwargs=inputs.hf_processor_mm_kwargs,
                tokenization_kwargs=inputs.tokenization_kwargs,
                enable_hf_prompt_update=True,
            )
1412

1413
        mm_kwargs = MultiModalKwargsItems.from_hf_inputs(
1414
            mm_processed_data,
1415
1416
1417
            self._get_mm_fields_config(
                mm_processed_data, inputs.hf_processor_mm_kwargs
            ),
1418
1419
        )

1420
        # Use overrides if provided; fallback to data-dependent hashing.
1421
1422
        with timing_ctx.record("get_mm_hashes"):
            mm_hashes = inputs.get_mm_hashes(self.info.model_id)
1423

1424
        mm_prompt_updates = self._get_mm_prompt_updates(
1425
1426
            inputs.mm_data_items,
            inputs.hf_processor_mm_kwargs,
1427
1428
1429
1430
1431
1432
1433
1434
1435
1436
            mm_kwargs,
        )

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

        return prompt_ids, mm_info, is_update_applied
1437

1438
1439
    def _cached_apply_hf_processor(
        self,
1440
1441
        inputs: ProcessorInputs,
        timing_ctx: TimingContext,
1442
    ) -> tuple[list[int], MultiModalProcessingInfo, bool]:
1443
1444
1445
1446
1447
1448
        """
        Apply the HF processor on the full prompt text,
        caching the results and reusing cached results.
        """
        cache = self.cache

1449
        _, passthrough_data = self._get_hf_mm_data(inputs.mm_data_items)
1450
        if cache is None or passthrough_data:
1451
            return self._apply_hf_processor(inputs, timing_ctx)
1452

1453
1454
        with timing_ctx.record("get_mm_hashes"):
            mm_hashes = inputs.get_mm_hashes(self.info.model_id)
1455

1456
        with timing_ctx.record("get_cache_missing_items"):
1457
1458
            mm_is_cached, mm_missing_data_items = self._get_cache_missing_items(
                cache=cache,
1459
                mm_data_items=inputs.mm_data_items,
1460
1461
                mm_hashes=mm_hashes,
            )
1462

1463
        # NOTE: `prompt` does not correspond to `mm_missing_data_items`,
1464
        # so we can't apply prompt updates until the new multimodal
1465
        # items are combined with the cached multimodal items
1466
1467
1468
1469
1470
1471
1472
1473
1474
1475
1476
1477
        with timing_ctx.record("apply_hf_processor"):
            (
                prompt_ids,
                mm_missing_processed_data,
                is_update_applied,
            ) = self._apply_hf_processor_main(
                prompt=inputs.prompt,
                mm_items=mm_missing_data_items,
                hf_processor_mm_kwargs=inputs.hf_processor_mm_kwargs,
                tokenization_kwargs=inputs.tokenization_kwargs,
                enable_hf_prompt_update=False,
            )
1478

1479
        mm_missing_kwargs = MultiModalKwargsItems.from_hf_inputs(
1480
            mm_missing_processed_data,
1481
            self._get_mm_fields_config(
1482
                mm_missing_processed_data, inputs.hf_processor_mm_kwargs
1483
            ),
1484
1485
        )

1486
1487
        mm_missing_prompt_updates = self._get_mm_prompt_updates(
            mm_missing_data_items,
1488
            inputs.hf_processor_mm_kwargs,
1489
            mm_missing_kwargs,
1490
        )
1491

1492
        with timing_ctx.record("merge_mm_kwargs"):
1493
1494
1495
1496
1497
1498
1499
            mm_kwargs, mm_prompt_updates = self._merge_mm_kwargs(
                cache,
                mm_hashes=mm_hashes,
                mm_is_cached=mm_is_cached,
                mm_missing_kwargs=mm_missing_kwargs,
                mm_missing_prompt_updates=mm_missing_prompt_updates,
            )
1500
1501
1502

        mm_info = MultiModalProcessingInfo(
            kwargs=mm_kwargs,
1503
            hashes=mm_hashes,
1504
1505
            prompt_updates=mm_prompt_updates,
        )
1506

1507
        return prompt_ids, mm_info, is_update_applied
1508

1509
1510
1511
    def _apply_token_matches(
        self,
        prompt: list[int],
1512
1513
1514
1515
        mm_prompt_updates: MultiModalPromptUpdates,
    ) -> tuple[list[int], MultiModalPromptUpdatesApplyResult]:
        tokenizer = self.info.get_tokenizer()
        return apply_token_matches(prompt, mm_prompt_updates, tokenizer)
1516
1517
1518
1519

    def _apply_text_matches(
        self,
        prompt: str,
1520
1521
1522
1523
        mm_prompt_updates: MultiModalPromptUpdates,
    ) -> tuple[str, MultiModalPromptUpdatesApplyResult]:
        tokenizer = self.info.get_tokenizer()
        return apply_text_matches(prompt, mm_prompt_updates, tokenizer)
1524

1525
    def _apply_prompt_updates(
1526
1527
        self,
        token_ids: list[int],
1528
        mm_prompt_updates: MultiModalPromptUpdates,
1529
    ) -> tuple[list[int], Mapping[str, list[PlaceholderFeaturesInfo]]]:
1530
        """Apply multi-modal prompt updates to token IDs."""
1531
        tokenizer = self.info.get_tokenizer()
1532

1533
1534
1535
1536
        new_token_ids, match_result = self._apply_token_matches(
            token_ids,
            mm_prompt_updates,
        )
1537
1538
1539
1540
1541
1542
1543
1544
1545

        # 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
1546
1547
        # of the search text in the prompt, we instead perform string-based
        # updates on the decoded token IDs, then encode them back.
1548
        if not all(
1549
1550
1551
            all(update_idx is not None for update_idx in update_idxs)
            for update_idxs in match_result.values()
        ):
1552
            new_text, match_result = self._apply_text_matches(
1553
                _seq2text(tokenizer, token_ids, use_cache=False),
1554
                mm_prompt_updates,
1555
1556
            )

1557
            new_token_ids = _seq2tokens(tokenizer, new_text, use_cache=False)
1558

1559
        matched_updates = defaultdict[str, list[Sequence[ResolvedPromptUpdate]]](list)
1560
1561
1562
1563
        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 "
1564
1565
                    f"mm_items[{modality!r}][{item_idx}]"
                )
1566
1567

                matched_updates[modality].append(
1568
1569
                    [mm_prompt_updates[modality][item_idx][update_idx]]
                )
1570
1571

        placeholders = self._find_mm_placeholders(
1572
1573
            new_token_ids,
            dict(matched_updates),
1574
        )
1575

1576
        return new_token_ids, placeholders
1577

1578
1579
    def _validate_mm_kwargs(
        self,
1580
        mm_kwargs: MultiModalKwargsOptionalItems,
1581
1582
1583
        mm_item_counts: Mapping[str, int],
    ) -> None:
        for modality, item_count in mm_item_counts.items():
1584
            items = mm_kwargs.get(modality, [])
1585
1586
1587
1588
1589
1590
1591
1592
1593

            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 "
1594
1595
                    "`_call_hf_processor` and `_get_mm_fields_config`)."
                )
1596

1597
    def _validate_mm_updates(
1598
        self,
1599
        mm_updates: MultiModalPromptUpdates,
1600
        mm_item_counts: Mapping[str, int],
1601
    ) -> None:
1602
        for modality, item_count in mm_item_counts.items():
1603
            placeholders = mm_updates.get(modality, [])
1604

1605
            if len(placeholders) != item_count:
1606
                raise RuntimeError(
1607
                    f"Expected there to be {item_count} prompt updates "
1608
                    f"corresponding to {item_count} {modality} items, but "
1609
                    f"instead found {len(placeholders)} prompt updates! "
1610
1611
1612
                    "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 "
1613
1614
                    "sure you have applied it before calling `LLM.generate`."
                )
1615

1616
1617
1618
1619
1620
1621
1622
1623
1624
1625
1626
1627
1628
1629
    def _validate_mm_placeholders(
        self,
        mm_placeholders: Mapping[str, list[PlaceholderFeaturesInfo]],
        mm_item_counts: Mapping[str, int],
    ) -> None:
        for modality, item_count in mm_item_counts.items():
            placeholders = mm_placeholders.get(modality, [])

            if len(placeholders) != item_count:
                raise RuntimeError(
                    f"Expected there to be {item_count} prompt placeholders "
                    f"corresponding to {item_count} {modality} items, but "
                    f"instead found {len(placeholders)} prompt placeholders! "
                    "Make sure the implementation of `_call_hf_processor` and "
1630
1631
                    "`_get_mm_fields_config` are consistent with each other."
                )
1632

1633
1634
1635
1636
    def _maybe_apply_prompt_updates(
        self,
        mm_items: MultiModalDataItems,
        prompt_ids: list[int],
1637
        mm_kwargs: MultiModalKwargsOptionalItems,
1638
        mm_prompt_updates: MultiModalPromptUpdates,
1639
        is_update_applied: bool,
1640
    ) -> tuple[list[int], Mapping[str, list[PlaceholderFeaturesInfo]]]:
1641
        mm_item_counts = mm_items.get_all_counts()
1642
        self._validate_mm_kwargs(mm_kwargs, mm_item_counts)
1643
        self._validate_mm_updates(mm_prompt_updates, mm_item_counts)
1644

1645
        if is_update_applied:
1646
1647
            mm_placeholders = self._find_mm_placeholders(
                prompt_ids,
1648
                mm_prompt_updates,
1649
            )
1650
            self._validate_mm_placeholders(mm_placeholders, mm_item_counts)
1651
        else:
1652
            prompt_ids, mm_placeholders = self._apply_prompt_updates(
1653
                prompt_ids,
1654
                mm_prompt_updates,
1655
            )
1656
            self._validate_mm_placeholders(mm_placeholders, mm_item_counts)
1657

1658
        return prompt_ids, mm_placeholders
1659
1660
1661

    def apply(
        self,
1662
1663
        inputs: ProcessorInputs,
        timing_ctx: TimingContext,
1664
1665
1666
1667
1668
1669
1670
1671
1672
1673
1674
1675
1676
1677
1678
1679
    ) -> 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.
        """
        (
            prompt_ids,
1680
            mm_info,
1681
            is_update_applied,
1682
        ) = self._cached_apply_hf_processor(inputs, timing_ctx)
1683

1684
        # NOTE: tokenization_kwargs are not required to init processor
1685
        with timing_ctx.record("apply_prompt_updates"):
1686
            prompt_ids, mm_placeholders = self._maybe_apply_prompt_updates(
1687
                mm_items=inputs.mm_data_items,
1688
1689
1690
1691
1692
                prompt_ids=prompt_ids,
                mm_kwargs=mm_info.kwargs,
                mm_prompt_updates=mm_info.prompt_updates,
                is_update_applied=is_update_applied,
            )
1693

1694
1695
1696
1697
        mm_placeholder_ranges = {
            modality: [item.to_range() for item in placeholders]
            for modality, placeholders in mm_placeholders.items()
        }
1698

1699
        return mm_inputs(
1700
            prompt_token_ids=prompt_ids,
1701
1702
            mm_kwargs=mm_info.kwargs,
            mm_hashes=mm_info.hashes,
1703
            mm_placeholders=mm_placeholder_ranges,
1704
        )
1705
1706
1707
1708
1709
1710


class EncDecMultiModalProcessor(BaseMultiModalProcessor[_I]):
    @abstractmethod
    def create_encoder_prompt(
        self,
1711
        prompt: str | list[int],
1712
        mm_items: MultiModalDataItems,
1713
    ) -> str | list[int]:
1714
        """
1715
        Create input prompt for the encoder. HF processor will be applied on
1716
1717
        this prompt during profiling and generation.
        """
1718
1719
        raise NotImplementedError

1720
1721
    def create_decoder_prompt(
        self,
1722
        prompt: str | list[int],
1723
        mm_items: MultiModalDataItems,
1724
    ) -> str | list[int]:
1725
1726
1727
        """Create input prompt for the decoder."""
        return prompt

1728
    def _get_enc_dec_inputs(
1729
        self,
1730
        prompt: str | list[int],
1731
        mm_items: MultiModalDataItems,
1732
1733
        encoder_inputs: MultiModalInputs,
    ):
1734
        tokenizer = self.info.get_tokenizer()
1735
        decoder_prompt_raw = self.create_decoder_prompt(prompt, mm_items)
1736
        if isinstance(decoder_prompt_raw, str):
1737
            decoder_prompt_text = decoder_prompt_raw
1738
1739
            decoder_prompt_ids = tokenizer.encode(
                decoder_prompt_raw, add_special_tokens=False
1740
            )
1741
        else:
1742
            decoder_prompt_text = None
1743
            decoder_prompt_ids = decoder_prompt_raw
1744

1745
1746
1747
        return mm_enc_dec_inputs(
            encoder_inputs,
            decoder_prompt_ids,
1748
            decoder_prompt=decoder_prompt_text,
1749
        )
1750
1751
1752

    def apply(
        self,
1753
1754
        inputs: ProcessorInputs,
        timing_ctx: TimingContext,
1755
1756
1757
1758
1759
1760
1761
1762
    ) -> 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.
        """
1763
1764
1765
1766
1767
        encoder_prompt = self.create_encoder_prompt(
            inputs.prompt,
            inputs.mm_data_items,
        )
        encoder_processor_inputs = ProcessorInputs(
1768
            encoder_prompt,
1769
1770
1771
1772
            inputs.mm_data_items,
            inputs.mm_uuid_items,
            hf_processor_mm_kwargs=inputs.hf_processor_mm_kwargs,
            tokenization_kwargs=inputs.tokenization_kwargs,
1773
1774
        )

1775
1776
        encoder_inputs = super().apply(encoder_processor_inputs, timing_ctx)

1777
        return self._get_enc_dec_inputs(
1778
1779
            prompt=inputs.prompt,
            mm_items=inputs.mm_data_items,
1780
1781
            encoder_inputs=encoder_inputs,
        )