[Doc] Guide for adding multi-modal plugins

docs/source/_templates/sections/header.html

@@ -5,6 +5,7 @@
     justify-content: center;
     align-items: center;
     font-size: 16px;
+    padding: 0 6px 0 6px;
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   .notification-bar p {
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docs/source/dev/multimodal/adding_multimodal_plugin.rst

@@ -0,0 +1,17 @@
+.. _adding_multimodal_plugin:
+
+Adding a Multimodal Plugin
+==========================
+
+This document teaches you how to add a new modality to vLLM.
+
+Each modality in vLLM is represented by a :class:`~vllm.multimodal.MultiModalPlugin` and registered to :data:`~vllm.multimodal.MULTIMODAL_REGISTRY`.
+For vLLM to recognize a new modality type, you have to create a new plugin and then pass it to :meth:`~vllm.multimodal.MultiModalRegistry.register_plugin`.
+
+The remainder of this document details how to define custom :class:`~vllm.multimodal.MultiModalPlugin` s.
+
+.. note::
+  This article is a work in progress.
+
+..
+  TODO: Add more instructions on how to add new plugins once embeddings is in.

docs/source/dev/multimodal/multimodal_index.rst

@@ -7,17 +7,21 @@ Multi-Modality
 
 vLLM provides experimental support for multi-modal models through the :mod:`vllm.multimodal` package.
 
-Multi-modal input can be passed alongside text and token prompts to :ref:`supported models <supported_vlms>`
+Multi-modal inputs can be passed alongside text and token prompts to :ref:`supported models <supported_vlms>`
 via the ``multi_modal_data`` field in :class:`vllm.inputs.PromptStrictInputs`.
 
-.. note::
-   ``multi_modal_data`` can accept keys and values beyond the builtin ones, as long as a customized plugin is registered through 
-   the :class:`~vllm.multimodal.MULTIMODAL_REGISTRY`.
+Currently, vLLM only has built-in support for image data. You can extend vLLM to process additional modalities
+by following :ref:`this guide <adding_multimodal_plugin>`.
 
-To implement a new multi-modal model in vLLM, please follow :ref:`this guide <enabling_multimodal_inputs>`.
+Looking to add your own multi-modal model? Please follow the instructions listed :ref:`here <enabling_multimodal_inputs>`.
 
-..
-  TODO: Add more instructions on how to add new plugins once embeddings is in.
+Guides
+++++++
+
+.. toctree::
+   :maxdepth: 1
+
+   adding_multimodal_plugin
 
 Module Contents
 +++++++++++++++
@@ -36,10 +40,14 @@ Registry
 Base Classes
 ------------
 
-.. autoclass:: vllm.multimodal.MultiModalDataDict
+.. autodata:: vllm.multimodal.BatchedTensors
+
+.. autoclass:: vllm.multimodal.MultiModalDataBuiltins
     :members:
     :show-inheritance:
 
+.. autodata:: vllm.multimodal.MultiModalDataDict
+
 .. autoclass:: vllm.multimodal.MultiModalInputs
     :members:
     :show-inheritance:

vllm/multimodal/__init__.py

@@ -1,5 +1,5 @@
-from .base import (BatchedTensors, MultiModalDataDict, MultiModalInputs,
-                   MultiModalPlugin)
+from .base import (BatchedTensors, MultiModalDataBuiltins, MultiModalDataDict,
+                   MultiModalInputs, MultiModalPlugin)
 from .registry import MultiModalRegistry
 
 MULTIMODAL_REGISTRY = MultiModalRegistry()
@@ -13,6 +13,7 @@
 
 __all__ = [
     "BatchedTensors",
+    "MultiModalDataBuiltins",
     "MultiModalDataDict",
     "MultiModalInputs",
     "MultiModalPlugin",

vllm/multimodal/base.py

@@ -43,9 +43,6 @@ def try_concat(
         *,
         device: torch.types.Device,
     ) -> BatchedTensors:
-        # Avoid initializing CUDA too early
-        import torch
-
         unbatched_shape = tensors[0].shape[1:]
 
         for tensor in tensors:
@@ -84,16 +81,21 @@ def batch(
 
 
 class MultiModalDataBuiltins(TypedDict, total=False):
+    """Modality types that are predefined by vLLM."""
+
     image: Image.Image
+    """The input image."""
 
 
 MultiModalDataDict = Union[MultiModalDataBuiltins, Dict[str, Any]]
 """
 A dictionary containing an item for each modality type to input.
 
-The data belonging to each modality is converted into keyword arguments 
-to the model by the corresponding mapper. By default, the mapper of 
-the corresponding plugin with the same modality key is applied.
+Note:
+    This dictionary also accepts modality keys defined outside
+    :class:`MultiModalDataBuiltins` as long as a customized plugin is registered
+    through the :class:`~vllm.multimodal.MULTIMODAL_REGISTRY`.
+    Read more on that :ref:`here <adding_multimodal_plugin>`.
 """
 
 MultiModalInputMapper = Callable[[InputContext, object], MultiModalInputs]
@@ -123,6 +125,9 @@ class MultiModalPlugin(ABC):
     process the same data differently). This registry is in turn used by
     :class:`~MultiModalRegistry` which acts at a higher level
     (i.e., the modality of the data).
+
+    See also:
+        :ref:`adding_multimodal_plugin`
     """
 
     def __init__(self) -> None:
@@ -183,8 +188,8 @@ def wrapper(model_cls: N) -> N:
     def map_input(self, model_config: ModelConfig,
                   data: object) -> MultiModalInputs:
         """
-        Apply an input mapper to a data passed
-        to the model, transforming the data into a dictionary of model inputs.
+        Transform the data into a dictionary of model inputs using the
+        input mapper registered for that model.
 
         The model is identified by ``model_config``.
 

vllm/multimodal/image.py

@@ -100,6 +100,7 @@ def repeat_and_pad_image_tokens(
 
 
 class ImagePlugin(MultiModalPlugin):
+    """Plugin for image data."""
 
     def get_data_key(self) -> str:
         return "image"

vllm/multimodal/registry.py

@@ -15,10 +15,8 @@
 
 class MultiModalRegistry:
     """
-    A registry to dispatch data processing
-    according to its modality and the target model.
-
-    The registry handles both external and internal data input.
+    A registry that dispatches data processing to the
+    :class:`~vllm.multimodal.MultiModalPlugin` for each modality.
     """
 
     DEFAULT_PLUGINS = (ImagePlugin(), )
@@ -30,6 +28,12 @@ def __init__(
         self._plugins = {p.get_data_key(): p for p in plugins}
 
     def register_plugin(self, plugin: MultiModalPlugin) -> None:
+        """
+        Register a multi-modal plugin so it can be recognized by vLLM.
+
+        See also:
+            :ref:`adding_multimodal_plugin`
+        """
         data_type_key = plugin.get_data_key()
 
         if data_type_key in self._plugins:
@@ -75,7 +79,11 @@ def map_input(self, model_config: ModelConfig,
                   data: MultiModalDataDict) -> MultiModalInputs:
         """
         Apply an input mapper to the data passed to the model.
-
+
+        The data belonging to each modality is passed to the corresponding
+        plugin which in turn converts the data into into keyword arguments
+        via the input mapper registered for that model.
+
         See :meth:`MultiModalPlugin.map_input` for more details.
         """
         merged_dict: Dict[str, torch.Tensor] = {}