vllm.multimodal.cache

class BaseMultiModalCache(ABC, Generic[_I, _O]):
    """
    Abstract base class to read/write multi-modal items from cache.

    The idea of multi-modal caching is based on having a client and server
    where the client executes in the frontend process (=P0) and
    the server in the core process (=P1). The data flow is as follows:

    ```
                  is_cached() x N    get_and_update()
    P0: From API -----------------> -----------------> To P1

                 get_and_update()
    P1: From P0 -----------------> To model
    ```

    `is_cached()` can be called any number of times in P0. However,
    `get_and_update()` must be called in P0 and P1 one after another
    so that their cache eviction order remains the same.

    This ensures that the keys in P0 and P1 caches are mirrored,
    allowing us to determine whether a key is cached in P1 by looking
    up the P0 cache, without having to communicate with P1.
    """

    @abstractmethod
    def get_and_update_item(
        self,
        mm_item: _I,
        mm_hash: str,
    ) -> _O:
        """
        Possibly update a multi-modal item based on whether it is
        in the underlying cache.

        This update is done out-of-place and updates the cache eviction order.

        Args:
            mm_item: The multi-modal item to update.
            mm_hash: The hash of `mm_item`.

        Returns:
            The update multi-modal item.
        """
        raise NotImplementedError

    def get_and_update(
        self,
        mm_items: Sequence[_I],
        mm_hashes: list[str],
    ) -> list[_O]:
        """
        Possibly update a sequence of multi-modal items based on whether they
        are in the underlying cache.

        This update is done out-of-place and updates the cache eviction order.

        Args:
            mm_items: The multi-modal items to update.
            mm_hashes: The hash of each item in `mm_items`.

        Returns:
            A new list of updated multi-modal items.
        """
        assert len(mm_items) == len(mm_hashes)

        return [
            self.get_and_update_item(mm_item, mm_hash)
            for mm_item, mm_hash in zip(mm_items, mm_hashes)
        ]

    @abstractmethod
    def clear_cache(self) -> None:
        """Clear the underlying cache."""
        raise NotImplementedError

class BaseMultiModalProcessorCache(
        BaseMultiModalCache[MultiModalProcessorCacheInItem,
                            MultiModalProcessorCacheOutItem]):
    """The required interface for caches on P0."""

    @abstractmethod
    def is_cached_item(self, mm_hash: str) -> bool:
        """
        Check whether a multi-modal item is
        in the underlying cache.

        This **DOES NOT** update the cache eviction order.

        Args:
            mm_hash: The hash of the item to check.

        Returns:
            `True` if the item is cached, otherwise `False`.
        """
        raise NotImplementedError

    def is_cached(self, mm_hashes: list[str]) -> list[bool]:
        """
        Check whether a sequence of multi-modal items are
        in the underlying cache.

        This **DOES NOT** update the cache eviction order.

        Args:
            mm_hashes: The hash of each item to check.

        Returns:
            For each item, `True` if the item is cached, otherwise `False`.
        """
        return [self.is_cached_item(mm_hash) for mm_hash in mm_hashes]

class BaseMultiModalReceiverCache(
        BaseMultiModalCache[Optional[MultiModalKwargsItem],
                            MultiModalKwargsItem]):
    """The required interface for caches on P1."""

    def get_and_update_features(
        self,
        mm_features: list["MultiModalFeatureSpec"],
    ) -> list["MultiModalFeatureSpec"]:
        """Update multimodal features with cached encoder outputs."""
        for feature in mm_features:
            feature.data = self.get_and_update_item(feature.data,
                                                    feature.identifier)
        return mm_features

class MultiModalCache:

    @classmethod
    def get_leaf_size(
        cls,
        leaf: object,
        *,
        debug: bool = False,
    ) -> int:
        if isinstance(leaf, MultiModalProcessorCacheItem):
            return cls.get_leaf_size(leaf.item)
        if isinstance(leaf, MultiModalProcessorCacheItemMetadata):
            return leaf.item_size

        # These are not subclasses of dict
        if isinstance(leaf, MultiModalKwargsItems):
            return cls.get_item_size(leaf.data)  # type: ignore
        if isinstance(leaf, MultiModalKwargsItem):
            return cls.get_item_size(leaf.data)  # type: ignore
        if isinstance(leaf, MultiModalKwargs):
            return cls.get_item_size(leaf.data)  # type: ignore

        if isinstance(leaf, MultiModalFieldElem):
            return cls.get_item_size(leaf.data)  # type: ignore

        # sys.getsizeof doesn't work for tensors
        if isinstance(leaf, torch.Tensor):
            return leaf.nbytes

        return sys.getsizeof(leaf)

    @classmethod
    def get_item_size(
        cls,
        value: MultiModalCacheValue,
        *,
        debug: bool = False,
    ) -> int:
        size = json_reduce_leaves(
            lambda a, b: a + b,
            json_map_leaves(lambda x: cls.get_leaf_size(x, debug=debug),
                            value),
        )

        if debug:
            leaf_count = json_count_leaves(value)
            logger.debug(
                "Calculated size of %s to be %.2f GiB (%d leaves)",
                type(value),
                size / GiB_bytes,
                leaf_count,
            )

        return size

    @classmethod
    def get_item_complexity(cls, value: MultiModalCacheValue) -> int:
        """
        Get the number of leaf elements in a multi-modal cache value.

        This provides a measure of structural complexity that can be useful
        for debugging cache performance and understanding data patterns.

        Args:
            value: The multi-modal cache value to analyze.

        Returns:
            The number of leaf elements in the nested structure.
        """
        return json_count_leaves(value)

    @classmethod
    def get_lru_cache(
        cls,
        capacity_gb: float,
        value_type: type[_V],
        *,
        debug: bool = False,
    ) -> LRUCache[str, _V]:
        return LRUCache(
            GiB_bytes * capacity_gb,
            getsizeof=lambda x: cls.get_item_size(x, debug=debug),
        )

class MultiModalProcessorCacheItem:
    """
    The data to store inside `MultiModalProcessorOnlyCache`.

    Args:
        item: The processed tensor data corresponding to a multi-modal item.
        prompt_updates: The prompt updates corresponding to `item`.
    """

    def __init__(
        self,
        item: MultiModalKwargsItem,
        prompt_updates: Sequence["ResolvedPromptUpdate"],
    ) -> None:
        super().__init__()

        self.item = item
        self.prompt_updates = prompt_updates

class MultiModalProcessorCacheItemMetadata:
    """
    The metadata to store inside `MultiModalProcessorSenderCache`.

    Args:
        item: The processed tensor data corresponding to a multi-modal item.
            Since P1 already stores the tensor data, we only store its size
            metadata in P0 to reduce memory usage. The size metadata is still
            needed to keep the same cache eviction policy as P0.
        prompt_updates: The prompt updates corresponding to `item`.
            This needs to stay on P0 because for some models, they are
            dependent on the processed tensor data (cached on P1).
    """

    def __init__(
        self,
        item: MultiModalKwargsItem,
        prompt_updates: Sequence["ResolvedPromptUpdate"],
    ) -> None:
        super().__init__()

        self.item_size = MultiModalCache.get_item_size(item)
        self.prompt_updates = prompt_updates

class MultiModalProcessorOnlyCache(BaseMultiModalProcessorCache):
    """
    The cache which is used on P0 when IPC caching is disabled.

    How to update each item:

    - If the item is in the cache, replace the input with the cached item.
    - If the item is not in the cache, store that item (which includes
      tensor data and metadata) into the cache, and return the input.
    """

    def __init__(self, model_config: "ModelConfig") -> None:
        super().__init__()

        mm_config = model_config.get_multimodal_config()

        self._cache = MultiModalCache.get_lru_cache(
            mm_config.mm_processor_cache_gb,
            MultiModalProcessorCacheItem,
        )

    @override
    def is_cached_item(self, mm_hash: str) -> bool:
        return mm_hash in self._cache

    @override
    def get_and_update_item(
        self,
        mm_item: MultiModalProcessorCacheInItem,
        mm_hash: str,
    ) -> MultiModalProcessorCacheOutItem:
        if (cached_item := self._cache.get(mm_hash)) is not None:
            return cached_item.item, cached_item.prompt_updates

        assert mm_item is not None, f"Expected a cached item for {mm_hash=}"

        self._cache[mm_hash] = MultiModalProcessorCacheItem(*mm_item)

        return mm_item

    @override
    def clear_cache(self) -> None:
        self._cache.clear()

class MultiModalProcessorSenderCache(BaseMultiModalProcessorCache):
    """
    The cache which is used on P0 when IPC caching is enabled.

    How to update each item:

    - If the item is already in the cache, clear the input to avoid
      unnecessary IPC.

    - If the item is not in the cache, store the metadata of that item so
      that the eviction policy remains the same as the cache on P1,
      and return the input.
      By only storing the metadata, we avoid keeping the data itself in
      memory inside P0.
    """

    def __init__(self, model_config: "ModelConfig") -> None:
        super().__init__()

        mm_config = model_config.get_multimodal_config()

        self._cache = MultiModalCache.get_lru_cache(
            mm_config.mm_processor_cache_gb,
            MultiModalProcessorCacheItemMetadata,
        )

    @override
    def is_cached_item(self, mm_hash: str) -> bool:
        return mm_hash in self._cache

    @override
    def get_and_update_item(
        self,
        mm_item: MultiModalProcessorCacheInItem,
        mm_hash: str,
    ) -> MultiModalProcessorCacheOutItem:
        if (cached_item := self._cache.get(mm_hash)) is not None:
            return None, cached_item.prompt_updates

        assert mm_item is not None, f"Expected a cached item for {mm_hash=}"

        self._cache[mm_hash] = MultiModalProcessorCacheItemMetadata(*mm_item)

        return mm_item

    @override
    def clear_cache(self) -> None:
        self._cache.clear()

class MultiModalReceiverCache(BaseMultiModalReceiverCache):
    """
    The cache which is used on P1 when IPC caching is enabled.

    How to update each item:

    - If the item is in the cache, replace the input with the cached item.
    - If the item is not in the cache, store that item (which includes tensor
      data) into the cache, and return the input.
    """

    def __init__(self, model_config: "ModelConfig") -> None:
        super().__init__()

        mm_config = model_config.get_multimodal_config()

        self._cache = MultiModalCache.get_lru_cache(
            mm_config.mm_processor_cache_gb,
            MultiModalKwargsItem,
        )

    @override
    def get_and_update_item(
        self,
        mm_item: Optional[MultiModalKwargsItem],
        mm_hash: str,
    ) -> MultiModalKwargsItem:
        if (cached_item := self._cache.get(mm_hash)) is not None:
            return cached_item

        assert mm_item is not None, f"Expected a cached item for {mm_hash=}"

        self._cache[mm_hash] = mm_item
        return mm_item

    @override
    def clear_cache(self) -> None:
        self._cache.clear()