[Doc][4/N] Reorganize API Reference (#11843)

Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>

.buildkite/test-pipeline.yaml

@@ -38,7 +38,7 @@ steps:
   - pip install -r requirements-docs.txt
   - SPHINXOPTS=\"-W\" make html
   # Check API reference (if it fails, you may have missing mock imports)
-  - grep \"sig sig-object py\" build/html/dev/sampling_params.html
+  - grep \"sig sig-object py\" build/html/api/params.html
 
 - label: Async Engine, Inputs, Utils, Worker Test # 24min
   fast_check: true

Dockerfile

@@ -2,8 +2,8 @@
 # to run the OpenAI compatible server.
 
 # Please update any changes made here to
-# docs/source/dev/dockerfile/dockerfile.md and
-# docs/source/assets/dev/dockerfile-stages-dependency.png
+# docs/source/contributing/dockerfile/dockerfile.md and
+# docs/source/assets/contributing/dockerfile-stages-dependency.png
 
 ARG CUDA_VERSION=12.4.1
 #################### BASE BUILD IMAGE ####################

docs/source/api/multimodal/index.md

@@ -11,18 +11,8 @@ vLLM provides experimental support for multi-modal models through the {mod}`vllm
 Multi-modal inputs can be passed alongside text and token prompts to [supported models](#supported-mm-models)
 via the `multi_modal_data` field in {class}`vllm.inputs.PromptType`.
 
-Currently, vLLM only has built-in support for image data. You can extend vLLM to process additional modalities
-by following [this guide](#adding-multimodal-plugin).
-
 Looking to add your own multi-modal model? Please follow the instructions listed [here](#enabling-multimodal-inputs).
 
-## Guides
-
-```{toctree}
-:maxdepth: 1
-
-adding_multimodal_plugin
-```
 
 ## Module Contents
 

docs/source/api/params.md

@@ -0,0 +1,22 @@
+# Optional Parameters
+
+Optional parameters for vLLM APIs.
+
+(sampling-params)=
+
+## Sampling Parameters
+
+```{eval-rst}
+.. autoclass:: vllm.SamplingParams
+    :members:
+```
+
+(pooling-params)=
+
+## Pooling Parameters
+
+```{eval-rst}
+.. autoclass:: vllm.PoolingParams
+    :members:
+```
+

docs/source/contributing/dockerfile/dockerfile.md

@@ -17,7 +17,7 @@ The edges of the build graph represent:
 
 - `RUN --mount=(.\*)from=...` dependencies (with a dotted line and an empty diamond arrow head)
 
-  > ```{figure} ../../assets/dev/dockerfile-stages-dependency.png
+  > ```{figure} /assets/contributing/dockerfile-stages-dependency.png
   > :align: center
   > :alt: query
   > :width: 100%

docs/source/design/arch_overview.md

@@ -53,7 +53,7 @@ for output in outputs:
 ```
 
 More API details can be found in the {doc}`Offline Inference
-</dev/offline_inference/offline_index>` section of the API docs.
+</api/offline_inference/index>` section of the API docs.
 
 The code for the `LLM` class can be found in <gh-file:vllm/entrypoints/llm.py>.
 

docs/source/design/multimodal/adding_multimodal_plugin.md

@@ -1,16 +0,0 @@
-(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/pooling_params.md

@@ -1,6 +0,0 @@
-# Pooling Parameters
-
-```{eval-rst}
-.. autoclass:: vllm.PoolingParams
-    :members:
-```

docs/source/dev/sampling_params.md

@@ -1,6 +0,0 @@
-# Sampling Parameters
-
-```{eval-rst}
-.. autoclass:: vllm.SamplingParams
-    :members:
-```

docs/source/getting_started/quickstart.md

@@ -42,7 +42,7 @@ The first line of this example imports the classes {class}`~vllm.LLM` and {class
 from vllm import LLM, SamplingParams
 ```
 
-The next section defines a list of input prompts and sampling parameters for text generation. The [sampling temperature](https://arxiv.org/html/2402.05201v1) is set to `0.8` and the [nucleus sampling probability](https://en.wikipedia.org/wiki/Top-p_sampling) is set to `0.95`. You can find more information about the sampling parameters [here](https://docs.vllm.ai/en/stable/dev/sampling_params.html).
+The next section defines a list of input prompts and sampling parameters for text generation. The [sampling temperature](https://arxiv.org/html/2402.05201v1) is set to `0.8` and the [nucleus sampling probability](https://en.wikipedia.org/wiki/Top-p_sampling) is set to `0.95`. You can find more information about the sampling parameters [here](#sampling-params).
 
 ```python
 prompts = [

docs/source/index.md

@@ -137,10 +137,10 @@ community/sponsors
 :caption: API Reference
 :maxdepth: 2
 
-dev/sampling_params
-dev/pooling_params
-dev/offline_inference/offline_index
-dev/engine/engine_index
+api/offline_inference/index
+api/engine/index
+api/multimodal/index
+api/params
 ```
 
 % Design Documents: Details about vLLM internals
@@ -154,7 +154,6 @@ design/huggingface_integration
 design/plugin_system
 design/kernel/paged_attention
 design/input_processing/model_inputs_index
-design/multimodal/multimodal_index
 design/automatic_prefix_caching
 design/multiprocessing
 ```

docs/source/serving/offline_inference.md

@@ -23,7 +23,7 @@ The available APIs depend on the type of model that is being run:
 Please refer to the above pages for more details about each API.
 
 ```{seealso}
-[API Reference](/dev/offline_inference/offline_index)
+[API Reference](/api/offline_inference/index)
 ```
 
 ## Configuration Options

docs/source/serving/openai_compatible_server.md

@@ -195,7 +195,7 @@ Code example: <gh-file:examples/online_serving/openai_completion_client.py>
 
 #### Extra parameters
 
-The following [sampling parameters (click through to see documentation)](../dev/sampling_params.md) are supported.
+The following [sampling parameters](#sampling-params) are supported.
 
 ```{literalinclude} ../../../vllm/entrypoints/openai/protocol.py
 :language: python
@@ -226,7 +226,7 @@ Code example: <gh-file:examples/online_serving/openai_chat_completion_client.py>
 
 #### Extra parameters
 
-The following [sampling parameters (click through to see documentation)](../dev/sampling_params.md) are supported.
+The following [sampling parameters](#sampling-params) are supported.
 
 ```{literalinclude} ../../../vllm/entrypoints/openai/protocol.py
 :language: python
@@ -259,7 +259,7 @@ Code example: <gh-file:examples/online_serving/openai_embedding_client.py>
 
 #### Extra parameters
 
-The following [pooling parameters (click through to see documentation)](../dev/pooling_params.md) are supported.
+The following [pooling parameters](#pooling-params) are supported.
 
 ```{literalinclude} ../../../vllm/entrypoints/openai/protocol.py
 :language: python
@@ -447,7 +447,7 @@ Response:
 
 #### Extra parameters
 
-The following [pooling parameters (click through to see documentation)](../dev/pooling_params.md) are supported.
+The following [pooling parameters](#pooling-params) are supported.
 
 ```{literalinclude} ../../../vllm/entrypoints/openai/protocol.py
 :language: python

vllm/multimodal/base.py

@@ -49,9 +49,6 @@ 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:

vllm/multimodal/inputs.py

@@ -99,12 +99,6 @@ class MultiModalDataBuiltins(TypedDict, total=False):
 MultiModalDataDict: TypeAlias = Mapping[str, ModalityData[Any]]
 """
 A dictionary containing an entry for each modality type to input.
-
-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>`.
 """
 
 

vllm/multimodal/registry.py

@@ -125,9 +125,6 @@ class MultiModalRegistry:
     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()
 

vllm/pooling_params.py

@@ -7,7 +7,7 @@ class PoolingParams(
         msgspec.Struct,
         omit_defaults=True,  # type: ignore[call-arg]
         array_like=True):  # type: ignore[call-arg]
-    """Pooling parameters for embeddings API.
+    """API parameters for pooling models. This is currently a placeholder.
 
     Attributes:
         additional_data: Any additional data needed for pooling.