[Core] Remove legacy input mapper/processor from V0

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

Author: DarkLight1337

vllm/core/scheduler.py

@@ -1596,7 +1596,6 @@ def schedule(
                     multi_modal_placeholders=(
                         seq_group.multi_modal_placeholders
                         if scheduler_outputs.num_prefill_groups > 0 else None),
-                    mm_processor_kwargs=seq_group.mm_processor_kwargs,
                     prompt_adapter_request=seq_group.prompt_adapter_request,
                 )
             else:

vllm/engine/async_llm_engine.py

@@ -490,12 +490,11 @@ async def add_request_async(
             tokenizer = await self.get_tokenizer_async(lora_request)
             self._validate_token_prompt(prompt, tokenizer=tokenizer)
 
-        preprocessed_inputs = await self.input_preprocessor.preprocess_async(
+        processed_inputs = await self.input_preprocessor.preprocess_async(
             prompt,
             lora_request=lora_request,
             prompt_adapter_request=prompt_adapter_request,
         )
-        processed_inputs = self.input_processor(preprocessed_inputs)
 
         if isinstance(params, SamplingParams) and \
             params.guided_decoding is not None:

vllm/engine/llm_engine.py

@@ -29,8 +29,7 @@
 from vllm.entrypoints.openai.logits_processors import (
     get_logits_processors as get_openai_logits_processors)
 from vllm.executor.executor_base import ExecutorBase
-from vllm.inputs import (INPUT_REGISTRY, InputRegistry, ProcessorInputs,
-                         PromptType)
+from vllm.inputs import ProcessorInputs, PromptType
 from vllm.inputs.parse import is_token_prompt, split_enc_dec_inputs
 from vllm.inputs.preprocess import InputPreprocessor
 from vllm.logger import init_logger
@@ -212,7 +211,6 @@ def __init__(
         log_stats: bool,
         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:
@@ -273,11 +271,7 @@ def get_tokenizer_for_seq(sequence: Sequence) -> AnyTokenizer:
                                                     self.tokenizer,
                                                     mm_registry)
 
-        self.input_registry = input_registry
-        self.input_processor = input_registry.create_input_processor(
-            self.model_config)
-
-        self.model_executor = executor_class(vllm_config=vllm_config, )
+        self.model_executor = executor_class(vllm_config=vllm_config)
 
         if self.model_config.runner_type != "pooling":
             self._initialize_kv_caches()
@@ -776,12 +770,11 @@ def add_request(
                 prompt,
                 tokenizer=self.get_tokenizer(lora_request=lora_request))
 
-        preprocessed_inputs = self.input_preprocessor.preprocess(
+        processed_inputs = self.input_preprocessor.preprocess(
             prompt,
             lora_request=lora_request,
             prompt_adapter_request=prompt_adapter_request,
         )
-        processed_inputs = self.input_processor(preprocessed_inputs)
 
         self._add_processed_request(
             request_id=request_id,

vllm/inputs/__init__.py

@@ -2,12 +2,10 @@
 
 from .data import (DecoderOnlyInputs, EncoderDecoderInputs,
                    ExplicitEncoderDecoderPrompt, ProcessorInputs, PromptType,
-                   SingletonInputs, SingletonInputsAdapter, SingletonPrompt,
-                   TextPrompt, TokenInputs, TokensPrompt,
-                   build_explicit_enc_dec_prompt, to_enc_dec_tuple_list,
-                   token_inputs, zip_enc_dec_prompts)
-from .registry import (DummyData, InputContext, InputProcessingContext,
-                       InputRegistry)
+                   SingletonInputs, SingletonPrompt, TextPrompt, TokenInputs,
+                   TokensPrompt, build_explicit_enc_dec_prompt,
+                   to_enc_dec_tuple_list, token_inputs, zip_enc_dec_prompts)
+from .registry import InputContext, InputProcessingContext, InputRegistry
 
 INPUT_REGISTRY = InputRegistry()
 """
@@ -27,13 +25,11 @@
     "EncoderDecoderInputs",
     "ProcessorInputs",
     "SingletonInputs",
-    "SingletonInputsAdapter",
     "build_explicit_enc_dec_prompt",
     "to_enc_dec_tuple_list",
     "zip_enc_dec_prompts",
-    "INPUT_REGISTRY",
-    "DummyData",
+    "INPUT_REGISTRY",  # DEPRECATED
     "InputContext",
     "InputProcessingContext",
-    "InputRegistry",
+    "InputRegistry",  # DEPRECATED
 ]

vllm/inputs/data.py

@@ -1,17 +1,11 @@
 # SPDX-License-Identifier: Apache-2.0
-
 from collections.abc import Iterable
-from dataclasses import dataclass
-from functools import cached_property
 from typing import TYPE_CHECKING, Any, Generic, Literal, Optional, Union, cast
 
-import torch
-from typing_extensions import NotRequired, TypedDict, TypeVar, assert_never
+from typing_extensions import NotRequired, TypedDict, TypeVar
 
 if TYPE_CHECKING:
-    from vllm.multimodal import (MultiModalDataDict, MultiModalKwargs,
-                                 MultiModalPlaceholderDict)
-    from vllm.multimodal.inputs import MultiModalInputs
+    from vllm.multimodal.inputs import MultiModalDataDict, MultiModalInputs
 
 
 class TextPrompt(TypedDict):
@@ -153,22 +147,6 @@ class TokenInputs(TypedDict):
     if the model supports it.
     """
 
-    multi_modal_inputs: NotRequired["MultiModalKwargs"]
-    """
-    Optional multi-modal inputs to pass to the model,
-    if the model supports it.
-    """
-
-    multi_modal_placeholders: NotRequired["MultiModalPlaceholderDict"]
-    """
-    Placeholder ranges for the multi-modal data.
-    """
-
-    multi_modal_hashes: NotRequired[list[str]]
-    """
-    The hashes of the multi-modal data.
-    """
-
     mm_processor_kwargs: NotRequired[dict[str, Any]]
     """
     Optional multi-modal processor kwargs to be forwarded to the
@@ -183,9 +161,6 @@ def token_inputs(
     token_type_ids: Optional[list[int]] = None,
     prompt: Optional[str] = None,
     multi_modal_data: Optional["MultiModalDataDict"] = None,
-    multi_modal_inputs: Optional["MultiModalKwargs"] = None,
-    multi_modal_hashes: Optional[list[str]] = None,
-    multi_modal_placeholders: Optional["MultiModalPlaceholderDict"] = None,
     mm_processor_kwargs: Optional[dict[str, Any]] = None,
 ) -> TokenInputs:
     """Construct :class:`TokenInputs` from optional values."""
@@ -197,12 +172,6 @@ def token_inputs(
         inputs["token_type_ids"] = token_type_ids
     if multi_modal_data is not None:
         inputs["multi_modal_data"] = multi_modal_data
-    if multi_modal_inputs is not None:
-        inputs["multi_modal_inputs"] = multi_modal_inputs
-    if multi_modal_hashes is not None:
-        inputs["multi_modal_hashes"] = multi_modal_hashes
-    if multi_modal_placeholders is not None:
-        inputs["multi_modal_placeholders"] = multi_modal_placeholders
     if mm_processor_kwargs is not None:
         inputs["mm_processor_kwargs"] = mm_processor_kwargs
 
@@ -237,112 +206,6 @@ class EncoderDecoderInputs(TypedDict):
 :class:`vllm.sequence.Sequence`.
 """
 
-
-@dataclass
-class SingletonInputsAdapter:
-    """
-    Unified interface to access the components of :class:`SingletonInputs`.
-    """
-    inputs: SingletonInputs
-
-    @cached_property
-    def prompt(self) -> Optional[str]:
-        inputs = self.inputs
-
-        if inputs["type"] == "token" or inputs["type"] == "multimodal":
-            return inputs.get("prompt")
-
-        assert_never(inputs)  # type: ignore[arg-type]
-
-    @cached_property
-    def prompt_token_ids(self) -> list[int]:
-        inputs = self.inputs
-
-        if inputs["type"] == "token" or inputs["type"] == "multimodal":
-            return inputs.get("prompt_token_ids", [])
-
-        assert_never(inputs)  # type: ignore[arg-type]
-
-    @cached_property
-    def token_type_ids(self) -> list[int]:
-        inputs = self.inputs
-
-        if inputs["type"] == "token" or inputs["type"] == "multimodal":
-            return inputs.get("token_type_ids", [])
-
-        assert_never(inputs)  # type: ignore[arg-type]
-
-    @cached_property
-    def prompt_embeds(self) -> Optional[torch.Tensor]:
-        inputs = self.inputs
-
-        if inputs["type"] == "token" or inputs["type"] == "multimodal":
-            return None
-
-        assert_never(inputs)  # type: ignore[arg-type]
-
-    @cached_property
-    def multi_modal_data(self) -> "MultiModalDataDict":
-        inputs = self.inputs
-
-        if inputs["type"] == "token":
-            return inputs.get("multi_modal_data", {})
-
-        if inputs["type"] == "multimodal":
-            return inputs.get("mm_kwargs", {})
-
-        assert_never(inputs)  # type: ignore[arg-type]
-
-    @cached_property
-    def multi_modal_inputs(self) -> Union[dict, "MultiModalKwargs"]:
-        inputs = self.inputs
-
-        if inputs["type"] == "token":
-            return inputs.get("multi_modal_inputs", {})
-
-        if inputs["type"] == "multimodal":
-            return inputs.get("mm_kwargs", {})
-
-        assert_never(inputs)  # type: ignore[arg-type]
-
-    @cached_property
-    def multi_modal_hashes(self) -> list[str]:
-        inputs = self.inputs
-
-        if inputs["type"] == "token":
-            return inputs.get("multi_modal_hashes", [])
-
-        if inputs["type"] == "multimodal":
-            # only the case when we use MultiModalInputs
-            return inputs.get("mm_hashes", [])  # type: ignore[return-value]
-
-        assert_never(inputs)  # type: ignore[arg-type]
-
-    @cached_property
-    def multi_modal_placeholders(self) -> "MultiModalPlaceholderDict":
-        inputs = self.inputs
-
-        if inputs["type"] == "token":
-            return inputs.get("multi_modal_placeholders", {})
-
-        if inputs["type"] == "multimodal":
-            return inputs.get("mm_placeholders", {})
-
-        assert_never(inputs)  # type: ignore[arg-type]
-
-    @cached_property
-    def mm_processor_kwargs(self) -> dict[str, Any]:
-        inputs = self.inputs
-
-        if inputs["type"] == "token":
-            return inputs.get("mm_processor_kwargs", {})
-
-        if inputs["type"] == "multimodal":
-            return {}
-
-        assert_never(inputs)  # type: ignore[arg-type]
-
-
 ProcessorInputs = Union[DecoderOnlyInputs, EncoderDecoderInputs]
 """
 The inputs to :data:`vllm.inputs.InputProcessor`.

vllm/inputs/preprocess.py

@@ -223,28 +223,6 @@ async def _tokenize_prompt_async(
             lora_request=lora_request,
             add_special_tokens=add_special_tokens)
 
-    def _can_process_multimodal(self) -> bool:
-        model_config = self.model_config
-
-        if not model_config.is_multimodal_model:
-            raise ValueError("Your model does not support multi-modal inputs")
-
-        # Interim measure so we can handle models that have yet to be
-        # updated to use the new multi-modal processor
-        can_process_multimodal = self.mm_registry.has_processor(model_config)
-        if not can_process_multimodal:
-            from vllm.model_executor.models.registry import _VLLM_MODELS
-            if not any(arch in _VLLM_MODELS
-                       for arch in model_config.architectures):
-                logger.warning_once(
-                    "Your model uses the legacy input pipeline, which will be "
-                    "removed in an upcoming release. "
-                    "Please upgrade to the new multi-modal processing pipeline "
-                    "(https://docs.vllm.ai/en/latest/design/mm_processing.html)"
-                )
-
-        return can_process_multimodal
-
     def _process_multimodal(
         self,
         prompt: Union[str, list[int]],
@@ -258,8 +236,7 @@ def _process_multimodal(
         returning the corresponding token IDs and metadata.
         """
         # At the moment on model (PrithviGeoSpatialMAE) requires to be
-        # initialized without a tokenizer while using also multi-modal
-        # input.
+        # initialized without a tokenizer while using also multi-modal input
         if not self.tokenizer:
             tokenizer = object()  # Dummy
         else:
@@ -285,8 +262,7 @@ async def _process_multimodal_async(
     ) -> MultiModalInputs:
         """Async version of :meth:`_process_multimodal`."""
         # At the moment on model (PrithviGeoSpatialMAE) requires to be
-        # initialized without a tokenizer while using also multi-modal
-        # input.
+        # initialized without a tokenizer while using also multi-modal input
         if not self.tokenizer:
             tokenizer = object()  # Dummy
         else:
@@ -343,7 +319,7 @@ def _prompt_to_llm_inputs(
             multi_modal_data = tokens_content.get("multi_modal_data")
             mm_processor_kwargs = tokens_content.get("mm_processor_kwargs")
 
-            if multi_modal_data is not None and self._can_process_multimodal():
+            if multi_modal_data is not None:
                 return self._process_multimodal(
                     prompt_token_ids,
                     multi_modal_data,
@@ -366,7 +342,7 @@ def _prompt_to_llm_inputs(
             multi_modal_data = text_content.get("multi_modal_data")
             mm_processor_kwargs = text_content.get("mm_processor_kwargs")
 
-            if multi_modal_data is not None and self._can_process_multimodal():
+            if multi_modal_data is not None:
                 return self._process_multimodal(
                     prompt_text,
                     multi_modal_data,
@@ -417,7 +393,7 @@ async def _prompt_to_llm_inputs_async(
             multi_modal_data = tokens_content.get("multi_modal_data")
             mm_processor_kwargs = tokens_content.get("mm_processor_kwargs")
 
-            if multi_modal_data is not None and self._can_process_multimodal():
+            if multi_modal_data is not None:
                 return await self._process_multimodal_async(
                     prompt_token_ids,
                     multi_modal_data,
@@ -439,7 +415,7 @@ async def _prompt_to_llm_inputs_async(
             multi_modal_data = text_content.get("multi_modal_data")
             mm_processor_kwargs = text_content.get("mm_processor_kwargs")
 
-            if multi_modal_data is not None and self._can_process_multimodal():
+            if multi_modal_data is not None:
                 return await self._process_multimodal_async(
                     prompt_text,
                     multi_modal_data,
@@ -594,15 +570,13 @@ def _process_encoder_decoder_prompt(
                 decoder_inputs = self._prompt_to_llm_inputs(decoder_input)
             # For multimodal model, override decoder prompt from processor
             # with explicit decoder prompt.
-            if self.model_config.is_multimodal_model and (
-                    self._can_process_multimodal()):
+            if self.model_config.is_multimodal_model:
                 encoder_inputs, decoder_inputs = (
                     self._separate_enc_dec_inputs_from_mm_processor_outputs(
                         encoder_inputs, decoder_inputs))
         else:
             inputs = self._prompt_to_llm_inputs(prompt)
-            if self.model_config.is_multimodal_model and (
-                    self._can_process_multimodal()):
+            if self.model_config.is_multimodal_model:
                 # Encoder-Decoder Multimodal model
                 encoder_inputs, decoder_inputs = (
                     self._separate_enc_dec_inputs_from_mm_processor_outputs(
@@ -637,15 +611,13 @@ async def _process_encoder_decoder_prompt_async(
 
             # For multimodal model, override decoder prompt from processor
             # with explicit decoder prompt.
-            if self.model_config.is_multimodal_model and (
-                    self._can_process_multimodal()):
+            if self.model_config.is_multimodal_model:
                 encoder_inputs, decoder_inputs = (
                     self._separate_enc_dec_inputs_from_mm_processor_outputs(
                         encoder_inputs, decoder_inputs))
         else:
             inputs = await self._prompt_to_llm_inputs_async(prompt)
-            if self.model_config.is_multimodal_model and (
-                    self._can_process_multimodal()):
+            if self.model_config.is_multimodal_model:
                 # Encoder-Decoder Multimodal model
                 encoder_inputs, decoder_inputs = (
                     self._separate_enc_dec_inputs_from_mm_processor_outputs(