docs: class migration from sphinx to mkdocs (multimodal)

Signed-off-by: Zerohertz <[email protected]>

Author: Zerohertz

vllm/multimodal/__init__.py

@@ -8,11 +8,9 @@
 
 MULTIMODAL_REGISTRY = MultiModalRegistry()
 """
-The global {class}`~MultiModalRegistry` is used by model runners to
-dispatch data processing according to the target model.
-
-Info:
-    [mm-processing][]
+The global [`MultiModalRegistry`][vllm.multimodal.registry.MultiModalRegistry]
+is used by model runners to dispatch data processing according to the target
+model.
 """
 
 __all__ = [

vllm/multimodal/inputs.py

@@ -29,14 +29,14 @@
 
 HfImageItem: TypeAlias = Union["Image", np.ndarray, "torch.Tensor"]
 """
-A {class}`transformers.image_utils.ImageInput` representing a single image
+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 {class}`transformers.image_utils.VideoInput` representing a single video
+A `transformers.image_utils.VideoInput` representing a single video
 item, which can be passed to a HuggingFace `VideoProcessor`.
 """
 
@@ -48,7 +48,7 @@
 
 ImageItem: TypeAlias = Union[HfImageItem, "torch.Tensor"]
 """
-A {class}`transformers.image_utils.ImageInput` representing a single image
+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,
@@ -58,7 +58,7 @@
 
 VideoItem: TypeAlias = Union[HfVideoItem, "torch.Tensor"]
 """
-A {class}`transformers.image_utils.VideoInput` representing a single video
+A `transformers.image_utils.VideoInput` representing a single video
 item, which can be passed to a HuggingFace `VideoProcessor`.
 
 Alternatively, a 3-D tensor or batch of 2-D tensors,
@@ -108,7 +108,8 @@ class MultiModalDataBuiltins(TypedDict, total=False):
 """
 A dictionary containing an entry for each modality type to input.
 
-The built-in modalities are defined by {class}`MultiModalDataBuiltins`.
+The built-in modalities are defined by
+[`MultiModalDataBuiltins`][vllm.multimodal.inputs.MultiModalDataBuiltins].
 """
 
 
@@ -169,7 +170,8 @@ def __eq__(self, other: object) -> bool:
 
 
 def nested_tensors_equal(a: NestedTensors, b: NestedTensors) -> bool:
-    """Equality check between {data}`NestedTensors` objects."""
+    """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):
@@ -189,15 +191,15 @@ def nested_tensors_equal(a: NestedTensors, b: NestedTensors) -> bool:
 BatchedTensorInputs: TypeAlias = Mapping[str, NestedTensors]
 """
 A dictionary containing nested tensors which have been batched via
-{meth}`MultiModalKwargs.batch`.
+[`MultiModalKwargs.batch`][vllm.multimodal.inputs.MultiModalKwargs.batch].
 """
 
 
 @dataclass(frozen=True)
 class MultiModalFieldElem:
     """
     Represents a keyword argument corresponding to a multi-modal item
-    in {class}`MultiModalKwargs`.
+    in [`MultiModalKwargs`][vllm.multimodal.inputs.MultiModalKwargs].
     """
 
     modality: str
@@ -208,13 +210,15 @@ class MultiModalFieldElem:
 
     key: str
     """
-    The key of this field in {class}`MultiModalKwargs`,
+    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 {class}`MultiModalKwargs`,
+    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.
     """
 
@@ -237,7 +241,8 @@ def __eq__(self, other: object) -> bool:
 class BaseMultiModalField(ABC):
     """
     Defines how to interpret tensor data belonging to a keyword argument in
-    {class}`MultiModalKwargs` for multiple multi-modal items, and vice versa.
+    [`MultiModalKwargs`][vllm.multimodal.inputs.MultiModalKwargs] for multiple
+    multi-modal items, and vice versa.
     """
 
     def _field_factory(self, *, modality: str, key: str):
@@ -262,10 +267,12 @@ def build_elems(
         data: NestedTensors,
     ) -> Sequence[MultiModalFieldElem]:
         """
-        Construct {class}`MultiModalFieldElem` instances to represent
-        the provided data.
+        Construct
+        [`MultiModalFieldElem`][vllm.multimodal.inputs.MultiModalFieldElem]
+        instances to represent the provided data.
 
-        This is the inverse of {meth}`reduce_data`.
+        This is the inverse of
+        [`reduce_data`][vllm.multimodal.inputs.BaseMultiModalField.reduce_data].
         """
         raise NotImplementedError
 
@@ -275,9 +282,11 @@ def _reduce_data(self, batch: list[NestedTensors]) -> NestedTensors:
 
     def reduce_data(self, elems: list[MultiModalFieldElem]) -> NestedTensors:
         """
-        Merge the data from multiple instances of {class}`MultiModalFieldElem`.
+        Merge the data from multiple instances of
+        [`MultiModalFieldElem`][vllm.multimodal.inputs.MultiModalFieldElem].
 
-        This is the inverse of {meth}`build_elems`.
+        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:
@@ -290,7 +299,7 @@ def reduce_data(self, elems: list[MultiModalFieldElem]) -> NestedTensors:
 class MultiModalBatchedField(BaseMultiModalField):
     """
     Info:
-        [MultiModalFieldConfig.batched][]
+        [`MultiModalFieldConfig.batched`][vllm.multimodal.inputs.MultiModalFieldConfig.batched]
     """
 
     def build_elems(
@@ -320,8 +329,8 @@ def _reduce_data(self, batch: list[NestedTensors]) -> NestedTensors:
 class MultiModalFlatField(BaseMultiModalField):
     """
     Info:
-        [MultiModalFieldConfig.flat][]
-        [MultiModalFieldConfig.flat_from_sizes][]
+        [`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
@@ -362,7 +371,7 @@ def _expect_same_shape(tensor: torch.Tensor):
 class MultiModalSharedField(BaseMultiModalField):
     """
     Info:
-        [MultiModalFieldConfig.shared][]
+        [`MultiModalFieldConfig.shared`][vllm.multimodal.inputs.MultiModalFieldConfig.shared]
     """
     batch_size: int
 
@@ -508,7 +517,7 @@ def flat_from_sizes(modality: str,
         ```
 
         Info:
-            [MultiModalFieldConfig.flat][]
+            [`MultiModalFieldConfig.flat`][vllm.multimodal.inputs.MultiModalFieldConfig.flat]
         """
 
         if size_per_item.ndim != 1:
@@ -572,8 +581,10 @@ def build_elems(
 
 class MultiModalKwargsItem(UserDict[str, MultiModalFieldElem]):
     """
-    A collection of {class}`MultiModalFieldElem`
-    corresponding to a data item in {class}`MultiModalDataItems`.
+    A collection of
+    [`MultiModalFieldElem`][vllm.multimodal.inputs.MultiModalFieldElem]
+    corresponding to a data item in
+    [`MultiModalDataItems`][vllm.multimodal.parse.MultiModalDataItems].
     """
 
     @staticmethod
@@ -592,11 +603,13 @@ def modality(self) -> str:
 class MultiModalKwargs(UserDict[str, NestedTensors]):
     """
     A dictionary that represents the keyword arguments to
-    {meth}`~torch.nn.Module.forward`.
+    [`torch.nn.Module.forward`][].
 
     The metadata `items` enables us to obtain the keyword arguments
-    corresponding to each data item in {class}`MultiModalDataItems`, via
-    {meth}`get_item` and {meth}`get_items`.
+    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
@@ -635,7 +648,9 @@ def from_hf_inputs(
 
     @staticmethod
     def from_items(items: Sequence[MultiModalKwargsItem]):
-        """Construct a new {class}`MultiModalKwargs` from multiple items."""
+        """Construct a new
+        [`MultiModalKwargs`][vllm.multimodal.inputs.MultiModalKwargs]
+        from multiple items."""
         elems_by_key = defaultdict[str, list[MultiModalFieldElem]](list)
         for item in items:
             for key, elem in item.items():
@@ -800,7 +815,7 @@ def get_items(self, modality: str) -> Sequence[MultiModalKwargsItem]:
 class MultiModalInputs(TypedDict):
     """
     Represents the outputs of
-    {class}`vllm.multimodal.processing.BaseMultiModalProcessor`,
+    [`BaseMultiModalProcessor`][vllm.multimodal.processing.BaseMultiModalProcessor],
     ready to be passed to vLLM internals.
     """
 
@@ -836,7 +851,8 @@ class MultiModalInputs(TypedDict):
 
 class MultiModalEncDecInputs(MultiModalInputs):
     """
-    Represents the outputs of {class}`vllm.multimodal.EncDecMultiModalProcessor`
+    Represents the outputs of
+    [`EncDecMultiModalProcessor`][vllm.multimodal.processing.EncDecMultiModalProcessor]
     ready to be passed to vLLM internals.
     """
 

vllm/multimodal/parse.py

@@ -28,7 +28,8 @@
 
 class ModalityDataItems(ABC, Generic[_T, _I]):
     """
-    Represents data items for a modality in {class}`MultiModalDataItems`.
+    Represents data items for a modality in
+    [`MultiModalDataItems`][vllm.multimodal.parse.MultiModalDataItems].
     """
 
     def __init__(self, data: _T, modality: str) -> None:
@@ -251,15 +252,15 @@ def __init__(self, data: Union[torch.Tensor, list[torch.Tensor]]) -> None:
 
 class MultiModalDataItems(UserDict[str, ModalityDataItems[Any, Any]]):
     """
-    As {data}`~vllm.multimodal.inputs.MultiModalDataDict`, but normalized
-    such that each entry corresponds to a list.
+    As [`MultiModalDataDict`][vllm.multimodal.inputs.MultiModalDataDict], but
+    normalized such that each entry corresponds to a list.
     """
 
     def get_count(self, modality: str, *, strict: bool = True) -> int:
         """
         Get the number of data items belonging to a modality.
 
-        If `strict=False`, return `0` instead of raising {exc}`KeyError`
+        If `strict=False`, return `0` instead of raising [`KeyError`][]
         even if the modality is not found.
         """
         if modality not in self:
@@ -305,8 +306,8 @@ def get_items(
 
 class MultiModalDataParser:
     """
-    Parses {data}`~vllm.multimodal.inputs.MultiModalDataDict` into
-    {class}`MultiModalDataItems`.
+    Parses [`MultiModalDataDict`][vllm.multimodal.inputs.MultiModalDataDict]
+    into [`MultiModalDataItems`][vllm.multimodal.parse.MultiModalDataItems].
 
     Args:
         target_sr (float, optional): Enables automatic resampling of audio