Initial prototype for multi-modal processor

Signed-off-by: DarkLight1337 <[email protected]>

Author: DarkLight1337

docs/source/dev/multimodal/multimodal_index.rst

@@ -53,7 +53,7 @@ Base Classes
 
 .. autodata:: vllm.multimodal.MultiModalDataDict
 
-.. autoclass:: vllm.multimodal.MultiModalInputs
+.. autoclass:: vllm.multimodal.MultiModalKwargs
     :members:
     :show-inheritance:
 

docs/source/models/enabling_multimodal_inputs.rst

@@ -66,7 +66,7 @@ A default mapper is available for each modality in the core vLLM library. This i
 3. Register maximum number of multi-modal tokens
 ------------------------------------------------
 
-For each modality type that the model accepts as input, calculate the maximum possible number of tokens per data instance
+For each modality type that the model accepts as input, calculate the maximum possible number of tokens per data item
 and register it via :meth:`INPUT_REGISTRY.register_dummy_data <vllm.inputs.registry.InputRegistry.register_max_multimodal_tokens>`.
 
 .. code-block:: diff

tests/models/decoder_only/vision_language/mm_processor_kwargs/test_qwen.py

@@ -6,7 +6,7 @@
 from PIL.Image import Image
 
 from vllm.inputs import InputContext, token_inputs
-from vllm.multimodal.base import MultiModalInputs
+from vllm.multimodal import MultiModalKwargs
 from vllm.multimodal.utils import cached_get_tokenizer
 
 from .....conftest import IMAGE_ASSETS
@@ -96,7 +96,7 @@ def test_input_mapper_valid_mm_data(input_mapper_for_qwen,
     mapped_img_data = input_mapper_for_qwen(qwen_vl_context, img_data)
     # Ensure that we get the appropriately shaped pixel_values
     # for images and image embeddings, respectively.
-    assert isinstance(mapped_img_data, MultiModalInputs)
+    assert isinstance(mapped_img_data, MultiModalKwargs)
     assert "pixel_values" in mapped_img_data
     assert mapped_img_data["pixel_values"].shape == expected_shape
 

tests/multimodal/test_base.py

@@ -1,6 +1,6 @@
 import torch
 
-from vllm.multimodal.base import MultiModalInputs, NestedTensors
+from vllm.multimodal.base import MultiModalKwargs, NestedTensors
 
 
 def assert_nested_tensors_equal(expected: NestedTensors,
@@ -13,40 +13,40 @@ def assert_nested_tensors_equal(expected: NestedTensors,
             assert_nested_tensors_equal(expected_item, actual_item)
 
 
-def assert_multimodal_inputs_equal(expected: MultiModalInputs,
-                                   actual: MultiModalInputs):
+def assert_multimodal_inputs_equal(expected: MultiModalKwargs,
+                                   actual: MultiModalKwargs):
     assert set(expected.keys()) == set(actual.keys())
     for key in expected:
         assert_nested_tensors_equal(expected[key], actual[key])
 
 
 def test_multimodal_input_batch_single_tensor():
     t = torch.rand([1, 2])
-    result = MultiModalInputs.batch([{"image": t}])
+    result = MultiModalKwargs.batch([{"image": t}])
     assert_multimodal_inputs_equal(result, {"image": t.unsqueeze(0)})
 
 
 def test_multimodal_input_batch_multiple_tensors():
     a = torch.rand([1, 1, 2])
     b = torch.rand([1, 1, 2])
     c = torch.rand([1, 1, 2])
-    result = MultiModalInputs.batch([{"image": a}, {"image": b}, {"image": c}])
+    result = MultiModalKwargs.batch([{"image": a}, {"image": b}, {"image": c}])
     assert_multimodal_inputs_equal(result, {"image": torch.stack([a, b, c])})
 
 
 def test_multimodal_input_batch_multiple_heterogeneous_tensors():
     a = torch.rand([1, 2, 2])
     b = torch.rand([1, 3, 2])
     c = torch.rand([1, 4, 2])
-    result = MultiModalInputs.batch([{"image": a}, {"image": b}, {"image": c}])
+    result = MultiModalKwargs.batch([{"image": a}, {"image": b}, {"image": c}])
     assert_multimodal_inputs_equal(result, {"image": [a, b, c]})
 
 
 def test_multimodal_input_batch_nested_tensors():
     a = torch.rand([2, 3])
     b = torch.rand([2, 3])
     c = torch.rand([2, 3])
-    result = MultiModalInputs.batch([{
+    result = MultiModalKwargs.batch([{
         "image": [a]
     }, {
         "image": [b]
@@ -65,7 +65,7 @@ def test_multimodal_input_batch_heterogeneous_lists():
     a = torch.rand([1, 2, 3])
     b = torch.rand([1, 2, 3])
     c = torch.rand([1, 2, 3])
-    result = MultiModalInputs.batch([{"image": [a, b]}, {"image": [c]}])
+    result = MultiModalKwargs.batch([{"image": [a, b]}, {"image": [c]}])
     assert_multimodal_inputs_equal(
         result,
         {"image": [torch.stack([a, b]), c.unsqueeze(0)]})
@@ -76,7 +76,7 @@ def test_multimodal_input_batch_multiple_batchable_lists():
     b = torch.rand([1, 2, 3])
     c = torch.rand([1, 2, 3])
     d = torch.rand([1, 2, 3])
-    result = MultiModalInputs.batch([{"image": [a, b]}, {"image": [c, d]}])
+    result = MultiModalKwargs.batch([{"image": [a, b]}, {"image": [c, d]}])
     assert_multimodal_inputs_equal(
         result,
         {"image": torch.stack([torch.stack([a, b]),
@@ -88,8 +88,8 @@ def test_multimodal_input_batch_mixed_stacking_depths():
     b = torch.rand([1, 3, 3])
     c = torch.rand([1, 4, 3])
 
-    result = MultiModalInputs.batch([{"image": [a, b]}, {"image": [c]}])
+    result = MultiModalKwargs.batch([{"image": [a, b]}, {"image": [c]}])
     assert_multimodal_inputs_equal(result, {"image": [[a, b], c.unsqueeze(0)]})
 
-    result = MultiModalInputs.batch([{"image": [a]}, {"image": [b, c]}])
+    result = MultiModalKwargs.batch([{"image": [a]}, {"image": [b, c]}])
     assert_multimodal_inputs_equal(result, {"image": [a.unsqueeze(0), [b, c]]})

vllm/config.py

@@ -107,7 +107,7 @@ class ModelConfig:
             matches the model name exposed via the APIs. If multiple model
             names provided, the first name will be used. If not specified,
             the model name will be the same as `model`.
-        limit_mm_per_prompt: Maximum number of data instances per modality
+        limit_mm_per_prompt: Maximum number of data items per modality
             per prompt. Only applicable for multimodal models.
         override_neuron_config: Initialize non default neuron config or
             override default neuron config that are specific to Neuron devices,

vllm/engine/async_llm_engine.py

@@ -19,6 +19,7 @@
 from vllm.executor.gpu_executor import GPUExecutorAsync
 from vllm.executor.ray_utils import initialize_ray_cluster
 from vllm.inputs import PromptType
+from vllm.inputs.preprocess import InputPreprocessor
 from vllm.logger import init_logger
 from vllm.lora.request import LoRARequest
 from vllm.model_executor.guided_decoding import (
@@ -721,6 +722,9 @@ def _error_callback(self, exc: Exception) -> None:
         self.set_errored(exc)
         self._request_tracker.propagate_exception(exc)
 
+    async def get_input_preprocessor(self) -> InputPreprocessor:
+        return self.engine.input_preprocessor
+
     async def get_tokenizer(
         self,
         lora_request: Optional[LoRARequest] = None,

vllm/engine/llm_engine.py

@@ -39,6 +39,7 @@
 from vllm.model_executor.guided_decoding import (
     get_local_guided_decoding_logits_processor)
 from vllm.model_executor.layers.sampler import SamplerOutput
+from vllm.multimodal import MULTIMODAL_REGISTRY, MultiModalRegistry
 from vllm.outputs import (EmbeddingRequestOutput, RequestOutput,
                           RequestOutputFactory)
 from vllm.pooling_params import PoolingParams
@@ -226,6 +227,7 @@ def __init__(
         usage_context: UsageContext = UsageContext.ENGINE_CONTEXT,
         stat_loggers: Optional[Dict[str, StatLoggerBase]] = None,
         input_registry: InputRegistry = INPUT_REGISTRY,
+        mm_registry: MultiModalRegistry = MULTIMODAL_REGISTRY,
         use_cached_outputs: bool = False,
     ) -> None:
 
@@ -338,7 +340,8 @@ def get_tokenizer_for_seq(sequence: Sequence) -> AnyTokenizer:
             model_config)
 
         self.input_preprocessor = InputPreprocessor(model_config,
-                                                    self.tokenizer)
+                                                    self.tokenizer,
+                                                    mm_registry)
 
         self.input_registry = input_registry
         self.input_processor = input_registry.create_input_processor(

vllm/engine/multiprocessing/client.py

@@ -31,6 +31,7 @@
 # yapf: enable
 from vllm.envs import VLLM_RPC_TIMEOUT
 from vllm.inputs import PromptType
+from vllm.inputs.preprocess import InputPreprocessor
 from vllm.logger import init_logger
 from vllm.lora.request import LoRARequest
 from vllm.model_executor.layers.sampler import SamplerOutput
@@ -94,6 +95,8 @@ def __init__(self, ipc_path: str, engine_config: VllmConfig,
             parallel_config=engine_config.parallel_config,
             enable_lora=bool(engine_config.lora_config),
         )
+        self.input_preprocessor = InputPreprocessor(self.model_config,
+                                                    self.tokenizer)
 
         # Send RPCGenerateRequest to the MQLLMEngine.
         self.input_socket: Socket = self.context.socket(zmq.constants.PUSH)
@@ -345,6 +348,9 @@ async def _check_success(error_message: str, socket: Socket):
               or response != VLLM_RPC_SUCCESS_STR):
             raise ValueError(error_message)
 
+    async def get_input_preprocessor(self) -> InputPreprocessor:
+        return self.input_preprocessor
+
     async def get_tokenizer(self, lora_request: Optional[LoRARequest] = None):
         return await self.tokenizer.get_lora_tokenizer_async(lora_request)
 

vllm/engine/protocol.py

@@ -62,7 +62,6 @@ def generate(
     async def beam_search(
         self,
         prompt: PromptType,
-        model_config: ModelConfig,
         request_id: str,
         params: BeamSearchParams,
     ) -> AsyncGenerator[RequestOutput, None]:
@@ -74,13 +73,14 @@ async def beam_search(
         length_penalty = params.length_penalty
         include_stop_str_in_output = params.include_stop_str_in_output
 
-        tokenizer = await self.get_tokenizer()
-        input_preprocessor = InputPreprocessor(model_config, tokenizer)
+        preprocessor = await self.get_input_preprocessor()
+        tokenizer_group = preprocessor.get_tokenizer_group()
+        tokenizer = await tokenizer_group.get_lora_tokenizer_async()
 
         if is_explicit_encoder_decoder_prompt(prompt):
             raise NotImplementedError
         else:
-            processed_inputs = input_preprocessor._prompt_to_llm_inputs(
+            processed_inputs = preprocessor._prompt_to_llm_inputs(
                 prompt,
                 request_id=request_id,
             )
@@ -220,6 +220,7 @@ async def abort(self, request_id: str) -> None:
         Args:
             request_id: The unique id of the request.
         """
+        ...
 
     @abstractmethod
     async def get_model_config(self) -> ModelConfig:
@@ -228,8 +229,13 @@ async def get_model_config(self) -> ModelConfig:
 
     @abstractmethod
     async def get_decoding_config(self) -> DecodingConfig:
-        ...
         """Get the decoding configuration of the vLLM engine."""
+        ...
+
+    @abstractmethod
+    async def get_input_preprocessor(self) -> InputPreprocessor:
+        """Get the input processor of the vLLM engine."""
+        ...
 
     @abstractmethod
     async def get_tokenizer(

vllm/entrypoints/openai/serving_chat.py

@@ -187,7 +187,6 @@ async def create_chat_completion(
                 if isinstance(sampling_params, BeamSearchParams):
                     generator = self.engine_client.beam_search(
                         prompt=engine_prompt,
-                        model_config=self.model_config,
                         request_id=request_id,
                         params=sampling_params,
                     )