[Core] Registry for processing model inputs (vllm-project#5214)

Co-authored-by: ywang96 <[email protected]>

docs/source/dev/input_processing/input_processing_pipeline.rst

@@ -0,0 +1,20 @@
+.. _input_processing_pipeline:
+
+Input Processing Pipeline
+=========================
+
+1. Input data is passed to :class:`~vllm.LLMEngine` (or :class:`~vllm.AsyncLLMEngine`).
+
+2. Tokenize the data if necessary.
+
+3. Process the inputs using :meth:`INPUT_REGISTRY.process_input <vllm.inputs.registry.InputRegistry.process_input>`.
+
+   - For example, add placeholder tokens to reserve KV cache for multi-modal embeddings.
+
+4. Send the processed inputs to :class:`~vllm.executor.executor_base.ExecutorBase`.
+
+5. Distribute the inputs via :class:`~vllm.worker.worker_base.WorkerBase` to :class:`~vllm.worker.model_runner_base.ModelRunnerBase`.
+
+6. If the data contains multi-modal data, convert it into keyword arguments using :meth:`MULTIMODAL_REGISTRY.map_input <vllm.multimodal.MultiModalRegistry.map_input>`.
+
+   - For example, convert a :class:`PIL.Image.Image` input to its pixel values for a vision language model.

docs/source/dev/input_processing/model_inputs_index.rst

@@ -0,0 +1,39 @@
+.. _input_processing:
+
+Input Processing
+================
+
+.. currentmodule:: vllm.inputs
+
+vLLM provides a mechanism for defining input processors for each model so that the inputs are processed
+in :class:`~vllm.LLMEngine` before they are passed to model executors. 
+
+Currently, this mechanism is only utilized in **multi-modal models** for preprocessing multi-modal input 
+data in addition to input prompt, but it can be extended to text-only language models when needed.
+
+Guides
+++++++
+
+.. toctree::
+   :maxdepth: 1
+
+   input_processing_pipeline
+
+Module Contents
++++++++++++++++
+
+LLM Engine Inputs
+-----------------
+
+.. autoclass:: vllm.inputs.LLMInputs
+    :members:
+    :show-inheritance:
+
+Registry
+--------
+
+.. autodata:: vllm.inputs.INPUT_REGISTRY
+
+.. automodule:: vllm.inputs.registry
+    :members:
+    :show-inheritance:

docs/source/dev/multimodal/multimodal_index.rst

@@ -12,10 +12,6 @@ By default, vLLM models do not support multi-modal inputs. To enable multi-modal
 you must decorate the model class with :meth:`MULTIMODAL_REGISTRY.register_dummy_data <MultiModalRegistry.register_dummy_data>`,
 as well as :meth:`MULTIMODAL_REGISTRY.register_input <MultiModalRegistry.register_input>` for each modality type to support.
 
-.. contents::
-   :local:
-   :backlinks: none
-
 Module Contents
 +++++++++++++++
 
@@ -24,9 +20,7 @@ Module Contents
 Registry
 --------
 
-.. data:: vllm.multimodal.MULTIMODAL_REGISTRY
-
-    The global :class:`MultiModalRegistry` which is used by model runners.
+.. autodata:: vllm.multimodal.MULTIMODAL_REGISTRY
 
 .. autoclass:: vllm.multimodal.MultiModalRegistry
     :members:

docs/source/index.rst

@@ -120,6 +120,7 @@ Documentation
    dev/offline_inference/offline_index
    dev/engine/engine_index
    dev/kernel/paged_attention
+   dev/input_processing/model_inputs_index
    dev/multimodal/multimodal_index
    dev/dockerfile/dockerfile
 

docs/source/models/adding_model.rst

@@ -37,7 +37,7 @@ For instance, vLLM's `OPT model <https://github.com/vllm-project/vllm/blob/main/
 2. Rewrite the :code:`forward` methods
 --------------------------------------
 
-Next, you need to rewrite the :code:`forward` methods of your model by following these steps:
+Next, you need to rewrite the :meth:`~torch.nn.Module.forward` method of your model by following these steps:
 
 1. Remove any unnecessary code, such as the code only used for training.
 2. Change the input parameters:
@@ -75,7 +75,7 @@ Next, you need to rewrite the :code:`forward` methods of your model by following
 
 If your model is too large to fit into a single GPU, you can use tensor parallelism to manage it.
 To do this, substitute your model's linear and embedding layers with their tensor-parallel versions.
-For the embedding layer, you can simply replace :code:`nn.Embedding` with :code:`VocabParallelEmbedding`. For the output LM head, you can use :code:`ParallelLMHead`.
+For the embedding layer, you can simply replace :class:`torch.nn.Embedding` with :code:`VocabParallelEmbedding`. For the output LM head, you can use :code:`ParallelLMHead`.
 When it comes to the linear layers, we provide the following options to parallelize them:
 
 * :code:`ReplicatedLinear`: Replicates the inputs and weights across multiple GPUs. No memory saving.

examples/phi3v_example.py

@@ -11,14 +11,15 @@ def run_phi3v():
     model_path = "microsoft/Phi-3-vision-128k-instruct"
 
     # Note: The model has 128k context length by default which may cause OOM
-    # If that's the case, override `max_model_len` with a smaller value via args
+    # In this example, we override max_model_len to 2048.
     llm = LLM(
         model=model_path,
         trust_remote_code=True,
         image_input_type="pixel_values",
         image_token_id=32044,
         image_input_shape="1,3,1008,1344",
         image_feature_size=1921,
+        max_model_len=2048,
     )
 
     image = Image.open("images/cherry_blossom.jpg")

tests/multimodal/test_processor.py → tests/multimodal/test_mapper.py

@@ -25,25 +25,24 @@ def test_clip_image_processor(image_assets, dtype):
         seed=0,
         dtype=dtype,
         revision=None,
-    )
-    vlm_config = VisionLanguageConfig(
-        image_input_type=VisionLanguageConfig.ImageInputType.PIXEL_VALUES,
-        image_token_id=32000,
-        image_input_shape=(1, 3, IMAGE_HEIGHT, IMAGE_WIDTH),
-        image_feature_size=576,
-        image_processor=MODEL_NAME,
-        image_processor_revision=None,
+        multimodal_config=VisionLanguageConfig(
+            image_input_type=VisionLanguageConfig.ImageInputType.PIXEL_VALUES,
+            image_token_id=32000,
+            image_input_shape=(1, 3, IMAGE_HEIGHT, IMAGE_WIDTH),
+            image_feature_size=576,
+            image_processor=MODEL_NAME,
+            image_processor_revision=None,
+        ),
     )
 
     for asset in image_assets:
         hf_result = hf_processor.preprocess(
             asset.pil_image,
             return_tensors="pt",
         ).to(dtype=_STR_DTYPE_TO_TORCH_DTYPE[dtype])
-        vllm_result = MULTIMODAL_REGISTRY.process_input(
+        vllm_result = MULTIMODAL_REGISTRY.map_input(
+            model_config,
             ImagePixelData(asset.pil_image),
-            model_config=model_config,
-            vlm_config=vlm_config,
         )
 
         assert hf_result.keys() == vllm_result.keys()
@@ -74,25 +73,24 @@ def test_llava_next_image_processor(image_assets, dtype):
         seed=0,
         dtype=dtype,
         revision=None,
-    )
-    vlm_config = VisionLanguageConfig(
-        image_input_type=VisionLanguageConfig.ImageInputType.PIXEL_VALUES,
-        image_token_id=64000,
-        image_input_shape=(1, 3, IMAGE_HEIGHT, IMAGE_WIDTH),
-        image_feature_size=2928,
-        image_processor=MODEL_NAME,
-        image_processor_revision=None,
+        multimodal_config=VisionLanguageConfig(
+            image_input_type=VisionLanguageConfig.ImageInputType.PIXEL_VALUES,
+            image_token_id=64000,
+            image_input_shape=(1, 3, IMAGE_HEIGHT, IMAGE_WIDTH),
+            image_feature_size=2928,
+            image_processor=MODEL_NAME,
+            image_processor_revision=None,
+        ),
     )
 
     for asset in image_assets:
         hf_result = hf_processor.preprocess(
             asset.pil_image,
             return_tensors="pt",
         ).to(dtype=_STR_DTYPE_TO_TORCH_DTYPE[dtype])
-        vllm_result = MULTIMODAL_REGISTRY.process_input(
+        vllm_result = MULTIMODAL_REGISTRY.map_input(
+            model_config,
             ImagePixelData(asset.pil_image),
-            model_config=model_config,
-            vlm_config=vlm_config,
         )
 
         assert hf_result.keys() == vllm_result.keys()
@@ -119,26 +117,23 @@ def test_image_pixel_types(image_assets, dtype):
         seed=0,
         dtype=dtype,
         revision=None,
-    )
-    vlm_config = VisionLanguageConfig(
-        image_input_type=VisionLanguageConfig.ImageInputType.PIXEL_VALUES,
-        image_token_id=32000,
-        image_input_shape=(1, 3, IMAGE_HEIGHT, IMAGE_WIDTH),
-        image_feature_size=576,
-        image_processor=MODEL_NAME,
-        image_processor_revision=None,
-    )
+        multimodal_config=VisionLanguageConfig(
+            image_input_type=VisionLanguageConfig.ImageInputType.PIXEL_VALUES,
+            image_token_id=32000,
+            image_input_shape=(1, 3, IMAGE_HEIGHT, IMAGE_WIDTH),
+            image_feature_size=576,
+            image_processor=MODEL_NAME,
+            image_processor_revision=None,
+        ))
 
     for asset in image_assets:
-        image_result = MULTIMODAL_REGISTRY.process_input(
+        image_result = MULTIMODAL_REGISTRY.map_input(
+            model_config,
             ImagePixelData(asset.pil_image),
-            model_config=model_config,
-            vlm_config=vlm_config,
         )
-        tensor_result = MULTIMODAL_REGISTRY.process_input(
+        tensor_result = MULTIMODAL_REGISTRY.map_input(
+            model_config,
             ImagePixelData(asset.pixel_values),
-            model_config=model_config,
-            vlm_config=vlm_config,
         )
 
         assert image_result.keys() == tensor_result.keys()

vllm/config.py

@@ -109,6 +109,7 @@ def __init__(
         disable_sliding_window: bool = False,
         skip_tokenizer_init: bool = False,
         served_model_name: Optional[Union[str, List[str]]] = None,
+        multimodal_config: Optional["VisionLanguageConfig"] = None,
     ) -> None:
         self.model = model
         self.tokenizer = tokenizer
@@ -159,6 +160,8 @@ def __init__(
             sliding_window_len=self.get_hf_config_sliding_window())
         self.served_model_name = get_served_model_name(model,
                                                        served_model_name)
+        self.multimodal_config = multimodal_config
+
         if not self.skip_tokenizer_init:
             self._verify_tokenizer_mode()
         self._verify_embedding_mode()

vllm/engine/arg_utils.py

@@ -659,6 +659,36 @@ def create_engine_config(self, ) -> EngineConfig:
             raise ValueError(
                 "BitsAndBytes load format and QLoRA adapter only support "
                 f"'bitsandbytes' quantization, but got {self.quantization}")
+        if self.image_input_type:
+            if (not self.image_token_id or not self.image_input_shape
+                    or not self.image_feature_size):
+                raise ValueError(
+                    'Specify `image_token_id`, `image_input_shape` and '
+                    '`image_feature_size` together with `image_input_type`.')
+
+            if self.image_processor is None:
+                self.image_processor = self.model
+            if self.disable_image_processor:
+                if self.image_processor != self.model:
+                    warnings.warn(
+                        "You've specified an image processor "
+                        f"({self.image_processor}) but also disabled "
+                        "it via `--disable-image-processor`.",
+                        stacklevel=2)
+
+                self.image_processor = None
+
+            vision_language_config = VisionLanguageConfig(
+                image_input_type=VisionLanguageConfig.
+                get_image_input_enum_type(self.image_input_type),
+                image_token_id=self.image_token_id,
+                image_input_shape=str_to_int_tuple(self.image_input_shape),
+                image_feature_size=self.image_feature_size,
+                image_processor=self.image_processor,
+                image_processor_revision=self.image_processor_revision,
+            )
+        else:
+            vision_language_config = None
 
         device_config = DeviceConfig(device=self.device)
         model_config = ModelConfig(
@@ -682,7 +712,8 @@ def create_engine_config(self, ) -> EngineConfig:
             max_logprobs=self.max_logprobs,
             disable_sliding_window=self.disable_sliding_window,
             skip_tokenizer_init=self.skip_tokenizer_init,
-            served_model_name=self.served_model_name)
+            served_model_name=self.served_model_name,
+            multimodal_config=vision_language_config)
         cache_config = CacheConfig(
             block_size=self.block_size,
             gpu_memory_utilization=self.gpu_memory_utilization,

vllm/engine/llm_engine.py

@@ -20,7 +20,7 @@
 from vllm.engine.output_processor.util import create_output_by_sequence_group
 from vllm.executor.executor_base import ExecutorBase
 from vllm.executor.ray_utils import initialize_ray_cluster
-from vllm.inputs import LLMInputs, PromptInputs
+from vllm.inputs import INPUT_REGISTRY, LLMInputs, PromptInputs
 from vllm.logger import init_logger
 from vllm.lora.request import LoRARequest
 from vllm.outputs import (EmbeddingRequestOutput, RequestOutput,
@@ -230,6 +230,9 @@ def __init__(
         self.generation_config_fields = _load_generation_config_dict(
             model_config)
 
+        self.input_processor = INPUT_REGISTRY.create_input_processor(
+            self.model_config)
+
         self.model_executor = executor_class(
             model_config=model_config,
             cache_config=cache_config,

vllm/inputs/__init__.py

@@ -0,0 +1,19 @@
+from .data import (LLMInputs, ParsedText, ParsedTokens, PromptInputs,
+                   PromptStrictInputs, TextPrompt, TextTokensPrompt,
+                   TokensPrompt, parse_and_batch_prompt)
+from .registry import InputContext, InputRegistry
+
+INPUT_REGISTRY = InputRegistry()
+"""
+The global :class:`~InputRegistry` which is used by :class:`~vllm.LLMEngine`
+to dispatch data processing according to the target model.
+
+See also:
+    :ref:`input_processing_pipeline`
+"""
+
+__all__ = [
+    "ParsedText", "ParsedTokens", "parse_and_batch_prompt", "TextPrompt",
+    "TokensPrompt", "TextTokensPrompt", "PromptStrictInputs", "PromptInputs",
+    "LLMInputs", "INPUT_REGISTRY", "InputContext", "InputRegistry"
+]

vllm/inputs.py → vllm/inputs/data.py

@@ -101,8 +101,7 @@ class TextTokensPrompt(TypedDict):
     """The prompt text."""
 
     prompt_token_ids: List[int]
-    """The token IDs of the prompt. If None, we use the
-    tokenizer to convert the prompts to token IDs."""
+    """The token IDs of the prompt."""
 
     multi_modal_data: NotRequired["MultiModalData"]
     """
@@ -125,6 +124,21 @@ class TextTokensPrompt(TypedDict):
 
 
 class LLMInputs(TypedDict):
+    """
+    The inputs in :class:`~vllm.LLMEngine` before they are
+    passed to the model executor.
+    """
+
     prompt_token_ids: List[int]
+    """The token IDs of the prompt."""
+
     prompt: NotRequired[Optional[str]]
+    """
+    The original prompt text corresponding to the token IDs, if available.
+    """
+
     multi_modal_data: NotRequired[Optional["MultiModalData"]]
+    """
+    Optional multi-modal data to pass to the model,
+    if the model supports it.
+    """