# SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project from abc import ABC, abstractmethod from collections import UserDict, defaultdict from collections.abc import Mapping, Sequence from dataclasses import dataclass from functools import partial from itertools import accumulate from typing import (TYPE_CHECKING, Any, Literal, Optional, TypedDict, TypeVar, Union, cast, final) import numpy as np from typing_extensions import NotRequired, TypeAlias from vllm.utils import LazyLoader, full_groupby, is_list_of from vllm.utils.jsontree import JSONTree, json_map_leaves if TYPE_CHECKING: import torch import torch.types from PIL.Image import Image from transformers.feature_extraction_utils import BatchFeature from .hasher import MultiModalHashDict else: torch = LazyLoader("torch", globals(), "torch") _T = TypeVar("_T") HfImageItem: TypeAlias = Union["Image", np.ndarray, "torch.Tensor"] """ A `transformers.image_utils.ImageInput` representing a single image item, which can be passed to a HuggingFace `ImageProcessor`. """ HfVideoItem: TypeAlias = Union[list["Image"], np.ndarray, "torch.Tensor", list[np.ndarray], list["torch.Tensor"]] """ A `transformers.image_utils.VideoInput` representing a single video item, which can be passed to a HuggingFace `VideoProcessor`. """ HfAudioItem: TypeAlias = Union[list[float], np.ndarray, "torch.Tensor"] """ Represents a single audio item, which can be passed to a HuggingFace `AudioProcessor`. """ ImageItem: TypeAlias = Union[HfImageItem, "torch.Tensor"] """ A `transformers.image_utils.ImageInput` representing a single image item, which can be passed to a HuggingFace `ImageProcessor`. Alternatively, a 3-D tensor or batch of 2-D tensors, which are treated as image embeddings; these are directly passed to the model without HF processing. """ VideoItem: TypeAlias = Union[HfVideoItem, "torch.Tensor", tuple[HfVideoItem, dict[str, Any]]] """ A `transformers.video_utils.VideoInput` representing a single video item. This can be passed to a HuggingFace `VideoProcessor` with `transformers.video_utils.VideoMetadata`. Alternatively, a 3-D tensor or batch of 2-D tensors, which are treated as video embeddings; these are directly passed to the model without HF processing. """ AudioItem: TypeAlias = Union[HfAudioItem, tuple[np.ndarray, float], "torch.Tensor"] """ Represents a single audio item, which can be passed to a HuggingFace `AudioProcessor`. Alternatively, a tuple `(audio, sampling_rate)`, where the sampling rate is different from that expected by the model; these are resampled to the model's sampling rate before being processed by HF. Alternatively, a 3-D tensor or batch of 2-D tensors, which are treated as audio embeddings; these are directly passed to the model without HF processing. """ ModalityData: TypeAlias = Union[_T, list[_T]] """ Either a single data item, or a list of data items. The number of data items allowed per modality is restricted by `--limit-mm-per-prompt`. """ @final class MultiModalDataBuiltins(TypedDict, total=False): """Type annotations for modality types predefined by vLLM.""" image: ModalityData[ImageItem] """The input image(s).""" video: ModalityData[VideoItem] """The input video(s).""" audio: ModalityData[AudioItem] """The input audio(s).""" MultiModalDataDict: TypeAlias = Mapping[str, ModalityData[Any]] """ A dictionary containing an entry for each modality type to input. The built-in modalities are defined by [`MultiModalDataBuiltins`][vllm.multimodal.inputs.MultiModalDataBuiltins]. """ @dataclass(frozen=True) class PlaceholderRange: """ Placeholder location information for multi-modal data. Example: Prompt: `AAAA BBBB What is in these images?` Images A and B will have: ``` A: PlaceholderRange(offset=0, length=4) B: PlaceholderRange(offset=5, length=4) ``` """ offset: int """The start index of the placeholder in the prompt.""" length: int """The length of the placeholder.""" is_embed: Optional["torch.Tensor"] = None """ A boolean mask of shape `(length,)` indicating which positions between `offset` and `offset + length` to assign embeddings to. """ def get_num_embeds(self) -> int: if self.is_embed is None: return self.length return int(self.is_embed.sum().item()) def __eq__(self, other: object) -> bool: if not isinstance(other, self.__class__): return False if not (self.offset, self.length) == (other.offset, other.length): return False if self.is_embed is None: return other.is_embed is None if other.is_embed is None: return self.is_embed is None return nested_tensors_equal(self.is_embed, other.is_embed) NestedTensors: TypeAlias = Union[list["NestedTensors"], list["torch.Tensor"], "torch.Tensor", tuple["torch.Tensor", ...]] """ Uses a list instead of a tensor if the dimensions of each element do not match. """ def nested_tensors_equal(a: NestedTensors, b: NestedTensors) -> bool: """Equality check between [`NestedTensors`][vllm.multimodal.inputs.NestedTensors] objects.""" if isinstance(a, torch.Tensor): return isinstance(b, torch.Tensor) and torch.equal(a, b) elif isinstance(b, torch.Tensor): return isinstance(a, torch.Tensor) and torch.equal(b, a) if isinstance(a, list): return (isinstance(b, list) and all(nested_tensors_equal(a_, b_) for a_, b_ in zip(a, b))) if isinstance(b, list): return (isinstance(a, list) and all(nested_tensors_equal(b_, a_) for b_, a_ in zip(b, a))) # Both a and b are scalars return a == b BatchedTensorInputs: TypeAlias = Mapping[str, NestedTensors] """ A dictionary containing nested tensors which have been batched via [`MultiModalKwargs.batch`][vllm.multimodal.inputs.MultiModalKwargs.batch]. """ @dataclass class MultiModalFieldElem: """ Represents a keyword argument corresponding to a multi-modal item in [`MultiModalKwargs`][vllm.multimodal.inputs.MultiModalKwargs]. """ modality: str """ The modality of the corresponding multi-modal item. Each multi-modal item can consist of multiple keyword arguments. """ key: str """ The key of this field in [`MultiModalKwargs`][vllm.multimodal.inputs.MultiModalKwargs], i.e. the name of the keyword argument to be passed to the model. """ data: NestedTensors """ The tensor data of this field in [`MultiModalKwargs`][vllm.multimodal.inputs.MultiModalKwargs], i.e. the value of the keyword argument to be passed to the model. It may be set to `None` if it is determined that the item is cached in `EngineCore`. """ field: "BaseMultiModalField" """ Defines how to combine the tensor data of this field with others in order to batch multi-modal items together for model inference. """ def __eq__(self, other: object) -> bool: if not isinstance(other, self.__class__): return False if self.data is None: data_equal = other.data is None elif other.data is None: data_equal = self.data is None else: data_equal = nested_tensors_equal(self.data, other.data) return ((self.modality, self.key) == (other.modality, other.key) and data_equal and type(self.field) == type(other.field)) # noqa: E721 @dataclass(frozen=True) class BaseMultiModalField(ABC): """ Defines how to interpret tensor data belonging to a keyword argument in [`MultiModalKwargs`][vllm.multimodal.inputs.MultiModalKwargs] for multiple multi-modal items, and vice versa. """ def _field_factory(self, *, modality: str, key: str): f = partial( MultiModalFieldElem, modality=modality, key=key, field=self, ) # Allow passing data as positional argument def factory(data: NestedTensors) -> MultiModalFieldElem: return f(data=data) return factory @abstractmethod def build_elems( self, modality: str, key: str, data: NestedTensors, ) -> Sequence[MultiModalFieldElem]: """ Construct [`MultiModalFieldElem`][vllm.multimodal.inputs.MultiModalFieldElem] instances to represent the provided data. This is the inverse of [`reduce_data`][vllm.multimodal.inputs.BaseMultiModalField.reduce_data]. """ raise NotImplementedError @abstractmethod def _reduce_data( self, batch: list[NestedTensors], *, pin_memory: bool, ) -> NestedTensors: raise NotImplementedError def reduce_data( self, elems: list[MultiModalFieldElem], *, pin_memory: bool = False, ) -> NestedTensors: """ Merge the data from multiple instances of [`MultiModalFieldElem`][vllm.multimodal.inputs.MultiModalFieldElem]. This is the inverse of [`build_elems`][vllm.multimodal.inputs.BaseMultiModalField.build_elems]. """ field_types = [type(item.field) for item in elems] if len(set(field_types)) > 1: raise ValueError(f"Cannot merge different {field_types=}") batch = [elem.data for elem in elems] return self._reduce_data(batch, pin_memory=pin_memory) @dataclass(frozen=True) class MultiModalBatchedField(BaseMultiModalField): """ Info: [`MultiModalFieldConfig.batched`][vllm.multimodal.inputs.MultiModalFieldConfig.batched] """ def build_elems( self, modality: str, key: str, data: NestedTensors, ) -> Sequence[MultiModalFieldElem]: field_factory = self._field_factory(modality=modality, key=key) return [field_factory(item) for item in data] def _reduce_data( self, batch: list[NestedTensors], *, pin_memory: bool, ) -> NestedTensors: if len(batch) > 0 and is_list_of(batch, torch.Tensor, check="all"): if len(batch) == 1: # An optimization when `batch` contains only one tensor: # - produce exactly same result as `torch.stack(batch)` # - will achieve zero-copy if the tensor is contiguous return batch[0].unsqueeze(0).contiguous() first_shape = batch[0].shape if all(elem.shape == first_shape for elem in batch): out = torch.empty((len(batch), *batch[0].shape), dtype=batch[0].dtype, device=batch[0].device, pin_memory=pin_memory) return torch.stack(batch, out=out) return batch @dataclass(frozen=True) class MultiModalFlatField(BaseMultiModalField): """ Info: [`MultiModalFieldConfig.flat`][vllm.multimodal.inputs.MultiModalFieldConfig.flat] [`MultiModalFieldConfig.flat_from_sizes`][vllm.multimodal.inputs.MultiModalFieldConfig.flat_from_sizes] """ slices: Union[Sequence[slice], Sequence[Sequence[slice]]] dim: int = 0 def build_elems( self, modality: str, key: str, data: NestedTensors, ) -> Sequence[MultiModalFieldElem]: field_factory = self._field_factory(modality=modality, key=key) if not is_list_of(self.slices, slice, check="all"): assert isinstance(data, torch.Tensor), \ "torch.Tensor is required for multiple slices" return [field_factory(data[cast(slice, s)]) for s in self.slices] def _reduce_data( self, batch: list[NestedTensors], *, pin_memory: bool, ) -> NestedTensors: if len(batch) > 0 and is_list_of(batch, torch.Tensor, check="all"): if len(batch) == 1: # An optimization when `batch` contains only one tensor: # - produce exactly same result as `torch.concat(batch)` # - will achieve zero-copy if the tensor is contiguous return batch[0].contiguous() dim = self.dim + (self.dim < 0) * len(batch[0].shape) def _shape_before_after(tensor: torch.Tensor): return tensor.shape[:dim], tensor.shape[dim + 1:] first_shape = _shape_before_after(batch[0]) if all(_shape_before_after(elem) == first_shape for elem in batch): shape_before, shape_after = first_shape shape_concat = sum(item.shape[dim] for item in batch) out = torch.empty((*shape_before, shape_concat, *shape_after), dtype=batch[0].dtype, device=batch[0].device, pin_memory=pin_memory) return torch.concat(batch, dim=self.dim, out=out) assert self.dim == 0, "dim == 0 is required for nested list" return [e for elem in batch for e in elem] @dataclass(frozen=True) class MultiModalSharedField(BaseMultiModalField): """ Info: [`MultiModalFieldConfig.shared`][vllm.multimodal.inputs.MultiModalFieldConfig.shared] """ batch_size: int def build_elems( self, modality: str, key: str, data: NestedTensors, ) -> Sequence[MultiModalFieldElem]: field_factory = self._field_factory(modality=modality, key=key) return [field_factory(data)] * self.batch_size def _reduce_data( self, batch: list[NestedTensors], *, pin_memory: bool, ) -> NestedTensors: return batch[0] class MultiModalFieldConfig: @staticmethod def batched(modality: str): """ Defines a field where an element in the batch is obtained by indexing into the first dimension of the underlying data. Args: modality: The modality of the multi-modal item that uses this keyword argument. Example: ``` Input: Data: [[AAAA] [BBBB] [CCCC]] Output: Element 1: [AAAA] Element 2: [BBBB] Element 3: [CCCC] ``` """ return MultiModalFieldConfig( field=MultiModalBatchedField(), modality=modality, ) @staticmethod def flat(modality: str, slices: Union[Sequence[slice], Sequence[Sequence[slice]]], dim: int = 0): """ Defines a field where an element in the batch is obtained by slicing along the first dimension of the underlying data. Args: modality: The modality of the multi-modal item that uses this keyword argument. slices: For each multi-modal item, a slice (dim=0) or a tuple of slices (dim>0) that is used to extract the data corresponding to it. dim: The dimension to extract data, default to 0. Example: ``` Given: slices: [slice(0, 3), slice(3, 7), slice(7, 9)] Input: Data: [AAABBBBCC] Output: Element 1: [AAA] Element 2: [BBBB] Element 3: [CC] ``` ``` Given: slices: [ (slice(None), slice(0, 3)), (slice(None), slice(3, 7)), (slice(None), slice(7, 9))] dim: 1 Input: Data: [[A],[A],[A],[B],[B],[B],[B],[C],[C]] Output: Element 1: [[A],[A],[A]] Element 2: [[B],[B],[B],[B]] Element 3: [[C],[C]] ``` """ return MultiModalFieldConfig( field=MultiModalFlatField(slices=slices, dim=dim), modality=modality, ) @staticmethod def flat_from_sizes(modality: str, size_per_item: "torch.Tensor", dim: int = 0): """ Defines a field where an element in the batch is obtained by slicing along the first dimension of the underlying data. Args: modality: The modality of the multi-modal item that uses this keyword argument. slices: For each multi-modal item, the size of the slice that is used to extract the data corresponding to it. dim: The dimension to slice, default to 0. Example: ``` Given: size_per_item: [3, 4, 2] Input: Data: [AAABBBBCC] Output: Element 1: [AAA] Element 2: [BBBB] Element 3: [CC] ``` ``` Given: slices: [3, 4, 2] dim: 1 Input: Data: [[A],[A],[A],[B],[B],[B],[B],[C],[C]] Output: Element 1: [[A],[A],[A]] Element 2: [[B],[B],[B],[B]] Element 3: [[C],[C]] ``` Info: [`MultiModalFieldConfig.flat`][vllm.multimodal.inputs.MultiModalFieldConfig.flat] """ if size_per_item.ndim != 1: raise ValueError("size_per_item should be a 1-D tensor, " f"but found shape: {size_per_item.shape}") slice_idxs = [0, *accumulate(size_per_item)] slices = [(slice(None, None, None), ) * dim + (slice(slice_idxs[i], slice_idxs[i + 1]), ) for i in range(len(size_per_item))] return MultiModalFieldConfig.flat(modality, slices, dim=dim) @staticmethod def shared(modality: str, batch_size: int): """ Defines a field where an element in the batch is obtained by taking the entirety of the underlying data. This means that the data is the same for each element in the batch. Args: modality: The modality of the multi-modal item that uses this keyword argument. batch_size: The number of multi-modal items which share this data. Example: ``` Given: batch_size: 4 Input: Data: [XYZ] Output: Element 1: [XYZ] Element 2: [XYZ] Element 3: [XYZ] Element 4: [XYZ] ``` """ return MultiModalFieldConfig( field=MultiModalSharedField(batch_size), modality=modality, ) def __init__(self, field: BaseMultiModalField, modality: str) -> None: super().__init__() self.field = field self.modality = modality def build_elems( self, key: str, batch: NestedTensors, ) -> Sequence[MultiModalFieldElem]: return self.field.build_elems(self.modality, key, batch) class MultiModalKwargsItem(UserDict[str, MultiModalFieldElem]): """ A collection of [`MultiModalFieldElem`][vllm.multimodal.inputs.MultiModalFieldElem] corresponding to a data item in [`MultiModalDataItems`][vllm.multimodal.parse.MultiModalDataItems]. """ @staticmethod def dummy(modality: str): """Convenience class for testing.""" mm_elem = MultiModalFieldElem( modality=modality, key="dummy", data=torch.empty(1), field=MultiModalSharedField(1), ) return MultiModalKwargsItem.from_elems([mm_elem]) @staticmethod def from_elems(elems: Sequence[MultiModalFieldElem]): return MultiModalKwargsItem({elem.key: elem for elem in elems}) def __init__(self, data: Mapping[str, MultiModalFieldElem] = {}) -> None: super().__init__(data) modalities = {elem.modality for elem in self.data.values()} assert len(modalities) == 1, f"Found different modalities={modalities}" self._modality = next(iter(modalities)) @property def modality(self) -> str: return self._modality def get_data(self) -> Mapping[str, NestedTensors]: return {key: elem.data for key, elem in self.items()} class MultiModalKwargs: """ A dictionary that represents the keyword arguments to [`torch.nn.Module.forward`][]. The metadata `items` enables us to obtain the keyword arguments corresponding to each data item in [`MultiModalDataItems`][vllm.multimodal.parse.MultiModalDataItems], via [`get_item`][vllm.multimodal.inputs.MultiModalKwargs.get_item] and [`get_items`][vllm.multimodal.inputs.MultiModalKwargs.get_items]. """ @staticmethod def from_hf_inputs( hf_inputs: "BatchFeature", config_by_key: Mapping[str, MultiModalFieldConfig], ): # NOTE: This skips fields in `hf_inputs` that are not in `config_by_key` # We assume that those fields are not used in vLLM elems_by_key = dict[str, Sequence[MultiModalFieldElem]]() keys_by_modality = defaultdict[str, set[str]](set) for key, config in config_by_key.items(): batch = hf_inputs.get(key) if batch is not None: elems = config.build_elems(key, batch) if len(elems) > 0: elems_by_key[key] = elems keys_by_modality[config.modality].add(key) items = list[MultiModalKwargsItem]() for modality, keys in keys_by_modality.items(): elems_in_modality = {k: elems_by_key[k] for k in keys} batch_sizes = {k: len(v) for k, v in elems_in_modality.items()} if len(set(batch_sizes.values())) > 1: raise ValueError( f"Cannot merge different batch sizes for {modality=}! " f"Found: {batch_sizes=}") batch_size = next(iter(batch_sizes.values())) for item_idx in range(batch_size): elems = [v[item_idx] for v in elems_in_modality.values()] items.append(MultiModalKwargsItem.from_elems(elems)) return MultiModalKwargs(items) def __init__(self, items: Sequence[MultiModalKwargsItem] = ()) -> None: super().__init__() items_by_modality = full_groupby(items, key=lambda x: x.modality) self._items_by_modality = dict(items_by_modality) self._data: Optional[Mapping[str, NestedTensors]] = None @property def modalities(self): return self._items_by_modality.keys() @staticmethod def _try_stack(nested_tensors: NestedTensors, pin_memory: bool = False) -> NestedTensors: """ Stack the inner dimensions that have the same shape in a nested list of tensors. Thus, a dimension represented by a list means that the inner dimensions are different for each element along that dimension. """ if isinstance(nested_tensors, torch.Tensor): return nested_tensors # TODO: Remove these once all models have been migrated if isinstance(nested_tensors, np.ndarray): return torch.from_numpy(nested_tensors) if isinstance(nested_tensors, (int, float)): return torch.tensor(nested_tensors) stacked = [ MultiModalKwargs._try_stack(t, pin_memory) for t in nested_tensors ] if not is_list_of(stacked, torch.Tensor, check="all"): # Only tensors (not lists) can be stacked. return stacked tensors_ = cast(list[torch.Tensor], stacked) if len(tensors_) == 1: # An optimization when `tensors_` contains only one tensor: # - produce exactly same result as `torch.stack(tensors_)` # - will achieve zero-copy if the tensor is contiguous return tensors_[0].unsqueeze(0).contiguous() if any(t.shape != tensors_[0].shape for t in tensors_): # The tensors have incompatible shapes and can't be stacked. return tensors_ outputs = torch.empty(len(tensors_), *tensors_[0].shape, dtype=tensors_[0].dtype, device=tensors_[0].device, pin_memory=pin_memory) return torch.stack(tensors_, out=outputs) @staticmethod def batch(inputs_list: list["MultiModalKwargs"], pin_memory: bool = False) -> BatchedTensorInputs: """ Batch multiple inputs together into a dictionary. The resulting dictionary has the same keys as the inputs. If the corresponding value from each input is a tensor and they all share the same shape, the output value is a single batched tensor; otherwise, the output value is a list containing the original value from each input. """ if len(inputs_list) == 0: return {} # We need to consider the case where each item in the batch # contains different modalities (i.e. different keys). item_lists = defaultdict[str, list[NestedTensors]](list) for inputs in inputs_list: for k, v in inputs.items(): item_lists[k].append(v) return { k: MultiModalKwargs._try_stack(item_list, pin_memory) for k, item_list in item_lists.items() } @staticmethod def as_kwargs( batched_inputs: BatchedTensorInputs, *, device: torch.types.Device, ) -> BatchedTensorInputs: json_inputs = cast(JSONTree[torch.Tensor], batched_inputs) json_mapped = json_map_leaves( lambda x: x.to(device=device, non_blocking=True), json_inputs, ) return cast(BatchedTensorInputs, json_mapped) def keys(self): return self.get_data().keys() def values(self): return self.get_data().values() def items(self): return self.get_data().items() def get(self, key: str, /, default=None): return self.get_data().get(key, default) def pop(self, key: str, *args, **kwargs): data = dict(self.get_data()) res = data.pop(key, *args, **kwargs) for items in self._items_by_modality.values(): for item in items: item.pop(key, *args, **kwargs) self._data = None return res def __iter__(self): return iter(self.get_data()) def __getitem__(self, key: str): return self.get_data()[key] def __eq__(self, other: object) -> bool: if not isinstance(other, self.__class__): return False return self._items_by_modality == other._items_by_modality def _validate_modality(self, method_name: str, modality: str) -> None: if not self._items_by_modality: raise RuntimeError( f"`{method_name}` is not supported when " "MultiModalKwargs is not initialized with `items`") if modality not in self._items_by_modality: available_modalities = set(self._items_by_modality.keys()) raise KeyError(f"Modality {modality!r} not found. " f"Available modalities: {available_modalities}") def get_item_count(self, modality: str) -> int: """Get the number of items belonging to a modality.""" self._validate_modality("get_item_count", modality) return len(self._items_by_modality[modality]) def get_item(self, modality: str, item_index: int) -> MultiModalKwargsItem: """ Get the keyword arguments corresponding to an item identified by its modality and index. """ self._validate_modality("get_item", modality) return self._items_by_modality[modality][item_index] def get_items(self, modality: str) -> Sequence[MultiModalKwargsItem]: """ Get the keyword arguments corresponding to each item belonging to a modality. """ self._validate_modality("get_items", modality) return self._items_by_modality[modality] def get_data(self, *, pin_memory: bool = False) -> Mapping[str, NestedTensors]: if self._data is not None: return self._data elems_by_key = defaultdict[str, list[MultiModalFieldElem]](list) for items in self._items_by_modality.values(): for item in items: for key, elem in item.items(): elems_by_key[key].append(elem) data = { key: elems[0].field.reduce_data(elems, pin_memory=pin_memory) for key, elems in elems_by_key.items() if len(elems) > 0 } self._data = data return data MultiModalPlaceholderDict: TypeAlias = Mapping[str, Sequence[PlaceholderRange]] """ A dictionary containing placeholder ranges for each modality. """ class MultiModalInputs(TypedDict): """ Represents the outputs of [`BaseMultiModalProcessor`][vllm.multimodal.processing.BaseMultiModalProcessor], ready to be passed to vLLM internals. """ type: Literal["multimodal"] """The type of inputs.""" prompt: str """The processed prompt text.""" prompt_token_ids: list[int] """The processed token IDs which includes placeholder tokens.""" token_type_ids: NotRequired[list[int]] """The token type IDs of the prompt.""" mm_kwargs: MultiModalKwargs """Keyword arguments to be directly passed to the model after batching.""" mm_hashes: Optional["MultiModalHashDict"] """The hashes of the multi-modal data.""" mm_placeholders: "MultiModalPlaceholderDict" """ For each modality, information about the placeholder tokens in `prompt_token_ids`. """ cache_salt: NotRequired[str] """ Optional cache salt to be used for prefix caching. """ class MultiModalEncDecInputs(MultiModalInputs): """ Represents the outputs of [`EncDecMultiModalProcessor`][vllm.multimodal.processing.EncDecMultiModalProcessor] ready to be passed to vLLM internals. """ encoder_prompt: str """The processed encoder prompt text.""" encoder_prompt_token_ids: list[int] """The processed token IDs of the encoder prompt.""" encoder_token_type_ids: NotRequired[list[int]] """The token type IDs of the encoder prompt."""