[Chore] Rename `Processor` to `InputProcessor` (#29682)

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

tests/entrypoints/openai/test_lora_resolvers.py

@@ -114,7 +114,7 @@ def mock_serving_setup():
     mock_engine.add_lora.reset_mock()
 
     mock_engine.model_config = MockModelConfig()
-    mock_engine.processor = MagicMock()
+    mock_engine.input_processor = MagicMock()
     mock_engine.io_processor = MagicMock()
 
     models = OpenAIServingModels(

tests/entrypoints/openai/test_serving_chat.py

@@ -429,7 +429,7 @@ async def test_serving_chat_returns_correct_model_name():
     mock_engine.get_tokenizer.return_value = get_tokenizer(MODEL_NAME)
     mock_engine.errored = False
     mock_engine.model_config = MockModelConfig()
-    mock_engine.processor = MagicMock()
+    mock_engine.input_processor = MagicMock()
     mock_engine.io_processor = MagicMock()
 
     serving_chat = _build_serving_chat(mock_engine)
@@ -459,7 +459,7 @@ async def test_serving_chat_should_set_correct_max_tokens():
     mock_engine.get_tokenizer.return_value = get_tokenizer(MODEL_NAME)
     mock_engine.errored = False
     mock_engine.model_config = MockModelConfig()
-    mock_engine.processor = MagicMock()
+    mock_engine.input_processor = MagicMock()
     mock_engine.io_processor = MagicMock()
 
     serving_chat = _build_serving_chat(mock_engine)
@@ -492,7 +492,7 @@ async def test_serving_chat_should_set_correct_max_tokens():
     mock_engine.get_tokenizer.return_value = get_tokenizer(MODEL_NAME)
     mock_engine.errored = False
     mock_engine.model_config = mock_model_config
-    mock_engine.processor = MagicMock()
+    mock_engine.input_processor = MagicMock()
     mock_engine.io_processor = MagicMock()
 
     # Initialize the serving chat
@@ -537,7 +537,7 @@ async def test_serving_chat_should_set_correct_max_tokens():
     mock_engine.get_tokenizer.return_value = get_tokenizer(MODEL_NAME)
     mock_engine.errored = False
     mock_engine.model_config = mock_model_config
-    mock_engine.processor = MagicMock()
+    mock_engine.input_processor = MagicMock()
     mock_engine.io_processor = MagicMock()
 
     # Initialize the serving chat
@@ -583,7 +583,7 @@ async def test_serving_chat_could_load_correct_generation_config():
     mock_engine.get_tokenizer.return_value = get_tokenizer(MODEL_NAME)
     mock_engine.errored = False
     mock_engine.model_config = mock_model_config
-    mock_engine.processor = MagicMock()
+    mock_engine.input_processor = MagicMock()
     mock_engine.io_processor = MagicMock()
 
     # Initialize the serving chat
@@ -629,7 +629,7 @@ async def test_serving_chat_did_set_correct_cache_salt(model_type):
     mock_engine.get_tokenizer.return_value = get_tokenizer(MODEL_NAME)
     mock_engine.errored = False
     mock_engine.model_config = mock_model_config
-    mock_engine.processor = MagicMock()
+    mock_engine.input_processor = MagicMock()
     mock_engine.io_processor = MagicMock()
 
     serving_chat = _build_serving_chat(mock_engine)
@@ -662,7 +662,7 @@ async def test_serving_chat_data_parallel_rank_extraction():
     mock_engine.get_tokenizer.return_value = get_tokenizer(MODEL_NAME)
     mock_engine.errored = False
     mock_engine.model_config = MockModelConfig()
-    mock_engine.processor = MagicMock()
+    mock_engine.input_processor = MagicMock()
     mock_engine.io_processor = MagicMock()
 
     # Mock the generate method to return an async generator

tests/entrypoints/openai/test_serving_engine.py

@@ -23,7 +23,7 @@ def serving() -> OpenAIServing:
     model_config.max_model_len = 32768
     models = Mock(spec=OpenAIServingModels)
     models.model_config = model_config
-    models.processor = Mock()
+    models.input_processor = Mock()
     models.io_processor = Mock()
 
     serving = OpenAIServing(

tests/entrypoints/openai/test_serving_models.py

@@ -30,7 +30,7 @@ async def _async_serving_models_init() -> OpenAIServingModels:
     mock_model_config = MagicMock(spec=ModelConfig)
     mock_model_config.max_model_len = 2048
     mock_engine_client.model_config = mock_model_config
-    mock_engine_client.processor = MagicMock()
+    mock_engine_client.input_processor = MagicMock()
     mock_engine_client.io_processor = MagicMock()
 
     serving_models = OpenAIServingModels(

tests/entrypoints/openai/test_serving_responses.py

@@ -127,7 +127,7 @@ class TestInitializeToolSessions:
         model_config.get_diff_sampling_param.return_value = {}
         engine_client.model_config = model_config
 
-        engine_client.processor = MagicMock()
+        engine_client.input_processor = MagicMock()
         engine_client.io_processor = MagicMock()
 
         models = MagicMock()
@@ -213,7 +213,7 @@ class TestValidateGeneratorInput:
         model_config.get_diff_sampling_param.return_value = {}
         engine_client.model_config = model_config
 
-        engine_client.processor = MagicMock()
+        engine_client.input_processor = MagicMock()
         engine_client.io_processor = MagicMock()
 
         models = MagicMock()

tests/v1/engine/test_processor_multi_modal_uuids.py → tests/v1/engine/test_process_multi_modal_uuids.py

@@ -7,18 +7,17 @@ from vllm.assets.image import ImageAsset
 from vllm.assets.video import VideoAsset
 from vllm.config import CacheConfig, DeviceConfig, ModelConfig, VllmConfig
 from vllm.sampling_params import SamplingParams
-from vllm.v1.engine import processor as processor_mod
-from vllm.v1.engine.processor import Processor
+from vllm.v1.engine import input_processor as input_processor_mod
+from vllm.v1.engine.input_processor import InputProcessor
 
 cherry_pil_image = ImageAsset("cherry_blossom").pil_image
 stop_pil_image = ImageAsset("stop_sign").pil_image
 baby_reading_np_ndarrays = VideoAsset("baby_reading").np_ndarrays
 
 
-# Mock processor for testing
-def _mk_processor(
+def _mock_input_processor(
     monkeypatch, *, mm_cache_gb: float = 4.0, enable_prefix_caching: bool = True
-) -> Processor:
+) -> InputProcessor:
     """
     Create a Processor instance with minimal configuration suitable for unit
     tests without accessing external resources.
@@ -36,7 +35,7 @@ def _mk_processor(
         raising=True,
     )
     monkeypatch.setattr(
-        processor_mod,
+        input_processor_mod,
         "processor_cache_from_config",
         lambda vllm_config, mm_registry: None,
         raising=True,
@@ -65,11 +64,11 @@ def _mk_processor(
         device_config=DeviceConfig(device="cpu"),
     )
 
-    return Processor(vllm_config, tokenizer=None)
+    return InputProcessor(vllm_config, tokenizer=None)
 
 
 def test_multi_modal_uuids_length_mismatch_raises(monkeypatch):
-    processor = _mk_processor(monkeypatch)
+    input_processor = _mock_input_processor(monkeypatch)
 
     prompt = {
         "prompt": "USER: <image>\nDescribe\nASSISTANT:",
@@ -79,7 +78,7 @@ def test_multi_modal_uuids_length_mismatch_raises(monkeypatch):
     }
 
     with pytest.raises(ValueError, match="must have same length as data"):
-        processor.process_inputs(
+        input_processor.process_inputs(
             request_id="req-1",
             prompt=prompt,  # type: ignore[arg-type]
             params=SamplingParams(),
@@ -87,7 +86,7 @@ def test_multi_modal_uuids_length_mismatch_raises(monkeypatch):
 
 
 def test_multi_modal_uuids_missing_modality_raises(monkeypatch):
-    processor = _mk_processor(monkeypatch)
+    input_processor = _mock_input_processor(monkeypatch)
 
     prompt = {
         "prompt": "USER: <image><video>\nDescribe\nASSISTANT:",
@@ -101,7 +100,7 @@ def test_multi_modal_uuids_missing_modality_raises(monkeypatch):
     }
 
     with pytest.raises(ValueError, match="must be provided if multi_modal_data"):
-        processor.process_inputs(
+        input_processor.process_inputs(
             request_id="req-2",
             prompt=prompt,  # type: ignore[arg-type]
             params=SamplingParams(),
@@ -119,7 +118,7 @@ def test_multi_modal_uuids_missing_modality_raises(monkeypatch):
 def test_multi_modal_uuids_accepts_none_and_passes_through(
     monkeypatch, mm_cache_gb: float, enable_prefix_caching: bool
 ):
-    processor = _mk_processor(
+    input_processor = _mock_input_processor(
         monkeypatch,
         mm_cache_gb=mm_cache_gb,
         enable_prefix_caching=enable_prefix_caching,
@@ -137,7 +136,7 @@ def test_multi_modal_uuids_accepts_none_and_passes_through(
 
     # Monkeypatch only the bound preprocess method on this instance
     monkeypatch.setattr(
-        processor.input_preprocessor, "preprocess", fake_preprocess, raising=True
+        input_processor.input_preprocessor, "preprocess", fake_preprocess, raising=True
     )
 
     # Use a consistent two-image scenario across all configurations
@@ -151,7 +150,7 @@ def test_multi_modal_uuids_accepts_none_and_passes_through(
         "multi_modal_uuids": mm_uuids,
     }
 
-    processor.process_inputs(
+    input_processor.process_inputs(
         request_id="req-3",
         prompt=prompt,  # type: ignore[arg-type]
         params=SamplingParams(),
@@ -163,7 +162,9 @@ def test_multi_modal_uuids_accepts_none_and_passes_through(
 def test_multi_modal_uuids_ignored_when_caching_disabled(monkeypatch):
     # When both processor cache is 0 and prefix caching disabled, the
     # processor builds overrides from request id instead of using user UUIDs.
-    processor = _mk_processor(monkeypatch, mm_cache_gb=0.0, enable_prefix_caching=False)
+    input_processor = _mock_input_processor(
+        monkeypatch, mm_cache_gb=0.0, enable_prefix_caching=False
+    )
 
     captured: dict[str, object] = {}
 
@@ -174,7 +175,7 @@ def test_multi_modal_uuids_ignored_when_caching_disabled(monkeypatch):
         return {"type": "token", "prompt_token_ids": [1]}
 
     monkeypatch.setattr(
-        processor.input_preprocessor, "preprocess", fake_preprocess, raising=True
+        input_processor.input_preprocessor, "preprocess", fake_preprocess, raising=True
     )
 
     request_id = "req-42"
@@ -188,7 +189,7 @@ def test_multi_modal_uuids_ignored_when_caching_disabled(monkeypatch):
         "multi_modal_uuids": mm_uuids,
     }
 
-    processor.process_inputs(
+    input_processor.process_inputs(
         request_id=request_id,
         prompt=prompt,  # type: ignore[arg-type]
         params=SamplingParams(),

vllm/engine/protocol.py

@@ -15,7 +15,7 @@ from vllm.sampling_params import SamplingParams
 from vllm.tasks import SupportedTask
 from vllm.transformers_utils.tokenizer import AnyTokenizer
 from vllm.v1.engine import EngineCoreRequest
-from vllm.v1.engine.processor import Processor
+from vllm.v1.engine.input_processor import InputProcessor
 
 
 class EngineClient(ABC):
@@ -23,7 +23,7 @@ class EngineClient(ABC):
 
     vllm_config: VllmConfig
     model_config: ModelConfig
-    processor: Processor
+    input_processor: InputProcessor
     io_processor: IOProcessor | None
 
     @property

vllm/entrypoints/llm.py

@@ -347,7 +347,7 @@ class LLM:
         self.supported_tasks = supported_tasks
 
         self.model_config = self.llm_engine.model_config
-        self.processor = self.llm_engine.processor
+        self.input_processor = self.llm_engine.input_processor
         self.io_processor = self.llm_engine.io_processor
 
     def get_tokenizer(self) -> AnyTokenizer:
@@ -364,7 +364,7 @@ class LLM:
             self.llm_engine.tokenizer = get_cached_tokenizer(tokenizer)
 
     def reset_mm_cache(self) -> None:
-        self.processor.clear_mm_cache()
+        self.input_processor.clear_mm_cache()
         self.llm_engine.reset_mm_cache()
 
     def get_default_sampling_params(self) -> SamplingParams:
@@ -1674,7 +1674,7 @@ class LLM:
             tokenization_kwargs,
         )
 
-        engine_request = self.processor.process_inputs(
+        engine_request = self.input_processor.process_inputs(
             request_id,
             engine_prompt,
             params,

vllm/entrypoints/openai/serving_engine.py

@@ -284,7 +284,7 @@ class OpenAIServing:
         self._async_tokenizer_pool: dict[AnyTokenizer, AsyncMicrobatchTokenizer] = {}
         self.log_error_stack = log_error_stack
 
-        self.processor = self.models.processor
+        self.input_processor = self.models.input_processor
         self.io_processor = self.models.io_processor
         self.model_config = self.models.model_config
         self.max_model_len = self.model_config.max_model_len
@@ -330,7 +330,7 @@ class OpenAIServing:
         return parser
 
     async def reset_mm_cache(self) -> None:
-        self.processor.clear_mm_cache()
+        self.input_processor.clear_mm_cache()
         await self.engine_client.reset_mm_cache()
 
     async def beam_search(
@@ -348,8 +348,8 @@ class OpenAIServing:
         length_penalty = params.length_penalty
         include_stop_str_in_output = params.include_stop_str_in_output
 
-        processor = self.processor
-        tokenizer = processor.tokenizer
+        input_processor = self.input_processor
+        tokenizer = input_processor.tokenizer
         if tokenizer is None:
             raise ValueError(
                 "You cannot use beam search when `skip_tokenizer_init` is True"
@@ -1214,7 +1214,7 @@ class OpenAIServing:
             self.max_model_len, params.truncate_prompt_tokens, tokenization_kwargs
         )
 
-        engine_request = self.processor.process_inputs(
+        engine_request = self.input_processor.process_inputs(
             request_id,
             engine_prompt,
             params,

vllm/entrypoints/openai/serving_models.py

@@ -69,7 +69,7 @@ class OpenAIServingModels:
             )
         self.lora_resolver_lock: dict[str, Lock] = defaultdict(Lock)
 
-        self.processor = self.engine_client.processor
+        self.input_processor = self.engine_client.input_processor
         self.io_processor = self.engine_client.io_processor
         self.model_config = self.engine_client.model_config
         self.max_model_len = self.model_config.max_model_len

vllm/v1/engine/async_llm.py

@@ -10,6 +10,7 @@ from typing import Any, cast
 
 import numpy as np
 import torch
+from typing_extensions import deprecated
 
 import vllm.envs as envs
 from vllm.config import VllmConfig
@@ -35,9 +36,9 @@ from vllm.utils.math_utils import cdiv
 from vllm.v1.engine import EngineCoreRequest
 from vllm.v1.engine.core_client import EngineCoreClient
 from vllm.v1.engine.exceptions import EngineDeadError, EngineGenerateError
+from vllm.v1.engine.input_processor import InputProcessor
 from vllm.v1.engine.output_processor import OutputProcessor, RequestOutputCollector
 from vllm.v1.engine.parallel_sampling import ParentRequest
-from vllm.v1.engine.processor import Processor
 from vllm.v1.executor import Executor
 from vllm.v1.metrics.loggers import (
     StatLoggerFactory,
@@ -112,7 +113,7 @@ class AsyncLLM(EngineClient):
         else:
             tokenizer = init_tokenizer_from_configs(self.model_config)
 
-        self.processor = Processor(self.vllm_config, tokenizer)
+        self.input_processor = InputProcessor(self.vllm_config, tokenizer)
         self.io_processor = get_io_processor(
             self.vllm_config,
             self.model_config.io_processor_plugin,
@@ -193,6 +194,14 @@ class AsyncLLM(EngineClient):
         else:
             self.profiler = None
 
+    @property
+    @deprecated(
+        "`AsyncLLM.processor` has been renamed to `AsyncLLM.input_processor`. "
+        "The old name will be removed in v0.13."
+    )
+    def processor(self):
+        return self.input_processor
+
     @classmethod
     def from_vllm_config(
         cls,
@@ -293,11 +302,7 @@ class AsyncLLM(EngineClient):
             request = prompt
         else:
             assert prompt_text is None
-            logger.warning_once(
-                "Processor has been moved under OpenAIServing and will "
-                "be removed from AsyncLLM in v0.13."
-            )
-            request = self.processor.process_inputs(
+            request = self.input_processor.process_inputs(
                 request_id,
                 prompt,
                 params,
@@ -481,7 +486,7 @@ class AsyncLLM(EngineClient):
         output_processor = self.output_processor
         log_stats = self.log_stats
         logger_manager = self.logger_manager
-        processor = self.processor
+        input_processor = self.input_processor
 
         async def output_handler():
             try:
@@ -532,7 +537,7 @@ class AsyncLLM(EngineClient):
                             engine_idx=outputs.engine_index,
                             scheduler_stats=outputs.scheduler_stats,
                             iteration_stats=iteration_stats,
-                            mm_cache_stats=processor.stat_mm_cache(),
+                            mm_cache_stats=input_processor.stat_mm_cache(),
                         )
             except Exception as e:
                 logger.exception("AsyncLLM output_handler failed.")
@@ -699,11 +704,11 @@ class AsyncLLM(EngineClient):
 
     @property
     def tokenizer(self) -> AnyTokenizer | None:
-        return self.processor.tokenizer
+        return self.input_processor.tokenizer
 
     @tokenizer.setter
     def tokenizer(self, tokenizer: AnyTokenizer | None) -> None:
-        self.processor.tokenizer = tokenizer
+        self.input_processor.tokenizer = tokenizer
 
     async def get_tokenizer(self) -> AnyTokenizer:
         if self.tokenizer is None:
@@ -738,7 +743,7 @@ class AsyncLLM(EngineClient):
         await asyncio.gather(*coros)
 
     async def reset_mm_cache(self) -> None:
-        self.processor.clear_mm_cache()
+        self.input_processor.clear_mm_cache()
         await self.engine_core.reset_mm_cache_async()
 
     async def reset_prefix_cache(self) -> None:

vllm/v1/engine/input_processor.py

+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import time
+from collections.abc import Mapping
+from typing import Any, Literal, cast
+
+from vllm.config import VllmConfig
+from vllm.inputs import ProcessorInputs, PromptType, SingletonInputs
+from vllm.inputs.parse import split_enc_dec_inputs
+from vllm.inputs.preprocess import InputPreprocessor
+from vllm.logger import init_logger
+from vllm.lora.request import LoRARequest
+from vllm.multimodal import MULTIMODAL_REGISTRY, MultiModalRegistry
+from vllm.multimodal.cache import processor_cache_from_config
+from vllm.multimodal.inputs import MultiModalFeatureSpec, MultiModalUUIDDict
+from vllm.multimodal.parse import MultiModalDataParser
+from vllm.multimodal.processing import EncDecMultiModalProcessor
+from vllm.multimodal.utils import argsort_mm_positions
+from vllm.pooling_params import PoolingParams
+from vllm.sampling_params import SamplingParams
+from vllm.transformers_utils.tokenizer import AnyTokenizer
+from vllm.transformers_utils.tokenizers.mistral import MistralTokenizer
+from vllm.utils import length_from_prompt_token_ids_or_embeds
+from vllm.v1.engine import EngineCoreRequest
+from vllm.v1.metrics.stats import MultiModalCacheStats
+from vllm.v1.structured_output.backend_guidance import validate_guidance_grammar
+from vllm.v1.structured_output.backend_lm_format_enforcer import (
+    validate_structured_output_request_lm_format_enforcer,
+)
+from vllm.v1.structured_output.backend_outlines import (
+    validate_structured_output_request_outlines,
+)
+from vllm.v1.structured_output.backend_xgrammar import validate_xgrammar_grammar
+
+logger = init_logger(__name__)
+
+
+class InputProcessor:
+    def __init__(
+        self,
+        vllm_config: VllmConfig,
+        tokenizer: AnyTokenizer | None,
+        mm_registry: MultiModalRegistry = MULTIMODAL_REGISTRY,
+    ) -> None:
+        self.vllm_config = vllm_config
+        self.model_config = vllm_config.model_config
+        self.cache_config = vllm_config.cache_config
+        self.lora_config = vllm_config.lora_config
+        self.structured_outputs_config = vllm_config.structured_outputs_config
+
+        self.generation_config_fields = self.model_config.try_get_generation_config()
+
+        self.mm_registry = mm_registry
+        self.mm_processor_cache = processor_cache_from_config(vllm_config, mm_registry)
+
+        self.input_preprocessor = InputPreprocessor(
+            self.model_config,
+            tokenizer,
+            mm_registry,
+            mm_processor_cache=self.mm_processor_cache,
+        )
+
+    @property
+    def tokenizer(self) -> AnyTokenizer | None:
+        return self.input_preprocessor.tokenizer
+
+    @tokenizer.setter
+    def tokenizer(self, tokenizer: AnyTokenizer | None) -> None:
+        self.input_preprocessor.tokenizer = tokenizer
+
+    def _validate_logprobs(
+        self,
+        params: SamplingParams,
+    ) -> None:
+        max_logprobs = self.model_config.max_logprobs
+        if max_logprobs == -1:
+            max_logprobs = self.model_config.get_vocab_size()
+
+        # Validate sample logprobs.
+        if params.logprobs:
+            num_logprobs = params.logprobs
+            if num_logprobs == -1:
+                num_logprobs = self.model_config.get_vocab_size()
+            if num_logprobs > max_logprobs:
+                raise ValueError(
+                    f"Requested sample logprobs of {num_logprobs}, "
+                    f"which is greater than max allowed: {max_logprobs}"
+                )
+
+        # Validate prompt logprobs.
+        if params.prompt_logprobs:
+            num_prompt_logprobs = params.prompt_logprobs
+            if num_prompt_logprobs == -1:
+                num_prompt_logprobs = self.model_config.get_vocab_size()
+            if num_prompt_logprobs > max_logprobs:
+                raise ValueError(
+                    f"Requested prompt logprobs of {num_prompt_logprobs}, "
+                    f"which is greater than max allowed: {max_logprobs}"
+                )
+
+    def _validate_sampling_params(
+        self,
+        params: SamplingParams,
+    ) -> None:
+        self._validate_structured_output(params)
+        self._validate_logit_bias(params)
+
+        if params.allowed_token_ids is None:
+            return
+        if not params.allowed_token_ids:
+            raise ValueError("allowed_token_ids is not None and empty!")
+        if self.tokenizer is None:
+            # When skip_tokenizer_init=True, we can't validate token IDs
+            # Skip validation and let the model handle invalid tokens
+            return
+        vocab_size = len(self.tokenizer)
+        if not all(0 <= tid < vocab_size for tid in params.allowed_token_ids):
+            raise ValueError("allowed_token_ids contains out-of-vocab token id!")
+
+    def _validate_logit_bias(
+        self,
+        params: SamplingParams,
+    ) -> None:
+        """Validate logit_bias token IDs are within vocabulary range."""
+        if not params.logit_bias:
+            return
+
+        vocab_size = self.model_config.get_vocab_size()
+        invalid_token_ids = []
+
+        for token_id in params.logit_bias:
+            if token_id < 0 or token_id >= vocab_size:
+                invalid_token_ids.append(token_id)
+
+        if invalid_token_ids:
+            raise ValueError(
+                f"token_id(s) {invalid_token_ids} in logit_bias contain "
+                f"out-of-vocab token ids. Vocabulary size: {vocab_size}"
+            )
+
+    def _validate_supported_sampling_params(
+        self,
+        params: SamplingParams,
+    ) -> None:
+        # Logits processors not supported.
+        if params.logits_processors:
+            raise ValueError(
+                "vLLM V1 does not support per request user provided logits processors."
+            )
+        # Async scheduling + spec decode currently incompatible with some
+        # sampling parameters.
+        if (
+            self.vllm_config.speculative_config is not None
+            and self.vllm_config.scheduler_config.async_scheduling
+            and (
+                params.frequency_penalty != 0.0
+                or params.presence_penalty != 0.0
+                or params.repetition_penalty != 1.0
+                or params.bad_words_token_ids
+                or params.structured_outputs
+            )
+        ):
+            raise ValueError(
+                "async scheduling with spec decoding doesn't yet support "
+                "penalties, bad words or structured outputs in sampling parameters."
+            )
+
+    def _validate_params(
+        self,
+        params: SamplingParams | PoolingParams,
+    ):
+        """
+        Validate supported SamplingParam.
+        Should raise ValueError if unsupported for API Server.
+        """
+
+        if isinstance(params, PoolingParams):
+            return
+
+        self._validate_logprobs(params)
+        self._validate_sampling_params(params)
+        self._validate_supported_sampling_params(params)
+
+    def _validate_multi_modal_uuids(self, prompt: PromptType) -> None:
+        """
+        Validate that user-provided multi_modal_uuids align with
+        multi_modal_data in the incoming request prompt(s).
+        Only checks lengths; `None` entries are allowed and will be
+        auto-hashed downstream.
+        """
+
+        def _validate_single_prompt(single_prompt: dict | str) -> None:
+            if not isinstance(single_prompt, dict):
+                return
+            mm_data = single_prompt.get("multi_modal_data")
+            mm_uuids = single_prompt.get("multi_modal_uuids")
+            if not mm_data or not mm_uuids:
+                return
+
+            for modality, items in mm_data.items():
+                if modality in mm_uuids:
+                    data_len = len(items) if isinstance(items, list) else 1
+                    uuid_len = (
+                        len(mm_uuids[modality])
+                        if isinstance(mm_uuids[modality], list)
+                        else 1
+                    )
+                    if uuid_len != data_len:
+                        raise ValueError(
+                            f"multi_modal_uuids for modality '{modality}' "
+                            "must have same length as data: got "
+                            f"{uuid_len} uuids vs "
+                            f"{data_len} items."
+                        )
+                else:
+                    raise ValueError(
+                        f"multi_modal_uuids for modality '{modality}' must "
+                        "be provided if multi_modal_data is provided."
+                    )
+
+        # Handle explicit encoder/decoder prompts or singleton prompt
+        if isinstance(prompt, dict) and "encoder_prompt" in prompt:
+            enc = prompt.get("encoder_prompt")
+            dec = prompt.get("decoder_prompt")
+            if enc is not None:
+                _validate_single_prompt(cast(dict | str, enc))
+            if dec is not None:
+                _validate_single_prompt(cast(dict | str, dec))
+        else:
+            _validate_single_prompt(prompt)  # type: ignore[arg-type]
+
+    def _validate_lora(self, lora_request: LoRARequest | None) -> None:
+        if lora_request is None:
+            return
+
+        # LoRA request passed in while LoRA is not enabled
+        if not self.lora_config:
+            raise ValueError(
+                f"Got lora_request {lora_request} but LoRA is not enabled!"
+            )
+
+        if self.tokenizer is not None:
+            logger.warning_once(
+                "vLLM has deprecated support for supporting different "
+                "tokenizers for different LoRAs. By default, vLLM uses base "
+                "model's tokenizer. If you are using a LoRA "
+                "with its own tokenizer, consider specifying `--tokenizer "
+                "[lora_path]` to use the LoRA tokenizer."
+            )
+
+    def _validate_structured_output(self, params: SamplingParams) -> None:
+        if not params.structured_outputs or not self.structured_outputs_config:
+            return
+
+        if self.model_config.skip_tokenizer_init and params.structured_outputs:
+            raise ValueError(
+                "Structured outputs requires a tokenizer so it can't be used with 'skip_tokenizer_init'"  # noqa: E501
+            )
+
+        backend = self.structured_outputs_config.backend
+        if _backend := params.structured_outputs._backend:
+            # Request-level backend selection is not supported.
+            # The values may differ if `params` is reused and was set
+            # to a specific backend based on `auto` behavior in a previous
+            # request. We remember that it was set as a result of `auto`
+            # using the `_backend_was_auto` field set in the params.
+            if backend != _backend and not (
+                backend == "auto" and params.structured_outputs._backend_was_auto
+            ):
+                raise ValueError(
+                    "Request-level structured output backend selection is not "
+                    f"supported. The request specified '{_backend}', but vLLM "
+                    f"was initialised with '{backend}'. This error can be "
+                    "resolved by removing '_backend' from the request."
+                )
+        else:
+            params.structured_outputs._backend = backend
+
+        # Request content validation
+        if (
+            isinstance(params.structured_outputs.choice, list)
+            and not params.structured_outputs.choice
+        ):
+            # It is invalid for choice to be an empty list
+            raise ValueError(
+                f"Choice '{params.structured_outputs.choice}' cannot be an empty list"  # noqa: E501
+            )
+        # Reject empty string grammar early to avoid engine-side crashes
+        if (
+            isinstance(params.structured_outputs.grammar, str)
+            and params.structured_outputs.grammar.strip() == ""
+        ):
+            raise ValueError("structured_outputs.grammar cannot be an empty string")
+
+        if backend.startswith("xgrammar"):
+            # xgrammar with no fallback
+            validate_xgrammar_grammar(params)
+        elif backend.startswith("guidance"):
+            # TODO: ideally we would have the LLTokenizer here as Lark syntax
+            # allows <|special_token|> and similar, see
+            # https://github.com/guidance-ai/llguidance/blob/main/docs/syntax.md#special-tokens
+            # Without tokenizer these are disallowed in grammars.
+            if isinstance(self.tokenizer, MistralTokenizer):
+                raise ValueError(
+                    "Mistral tokenizer is not supported for the 'guidance' "
+                    "structured output backend. Please use ['xgrammar', 'outlines'] "
+                    "backends or tokenizer_mode='hf' instead."
+                )
+            validate_guidance_grammar(params, tokenizer=None)
+        elif backend == "outlines":
+            # outlines backend
+            validate_structured_output_request_outlines(params)
+        elif backend == "lm-format-enforcer":
+            # lm format enforcer backend
+            if isinstance(self.tokenizer, MistralTokenizer):
+                raise ValueError(
+                    "Mistral tokenizer is not supported for the 'lm-format-enforcer' "
+                    "structured output backend. Please use ['xgrammar', 'outlines'] "
+                    "backends or tokenizer_mode='hf' instead."
+                )
+            validate_structured_output_request_lm_format_enforcer(params)
+        else:
+            # NOTE: backend must be "auto" here, because we have
+            # checked supported_backends above.
+            # In this mode, we set opinionated defaults based on what we think
+            # will satisfy the most use cases without having to worry about
+            # this setting. We include fallback behavior here, but not with any
+            # other setting where a specific backend was specified.
+            try:
+                validate_xgrammar_grammar(params)
+                params.structured_outputs._backend = "xgrammar"
+            except ValueError:
+                # The request either failed validation
+                # or includes some jsonschema feature(s) that
+                # are not supported in xgrammar.
+                if isinstance(self.tokenizer, MistralTokenizer):
+                    # Fall back to outlines if the tokenizer is Mistral
+                    validate_structured_output_request_outlines(params)
+                    params.structured_outputs._backend = "outlines"
+                else:
+                    # Fall back to guidance by default.
+                    validate_guidance_grammar(params, tokenizer=None)
+                    params.structured_outputs._backend = "guidance"
+            # Remember that this backend was set automatically
+            params.structured_outputs._backend_was_auto = True
+
+    def _maybe_build_mm_uuids(
+        self,
+        request_id: str,
+        prompt: PromptType,
+    ) -> MultiModalUUIDDict | None:
+        """Build per-item multimodal hash overrides when enabled. In this case,
+        multimodal data items are identified by their request id, modality and
+        index rather than their content.
+
+        Returns a dictionary of modality -> list[str] of overrides, or None if
+        disabled or no multimodal data is present.
+        """
+
+        def _extract_mm_data(p: PromptType):
+            if isinstance(p, dict) and "encoder_prompt" in p:
+                enc = p.get("encoder_prompt")
+                if isinstance(enc, dict):
+                    return enc.get("multi_modal_data")
+                return None
+            if isinstance(p, dict):
+                return p.get("multi_modal_data")
+            return None
+
+        mm_data = _extract_mm_data(prompt)
+        if not mm_data:
+            return None
+
+        mm_uuids: dict[str, list[str | None] | str] = {}
+        for modality, data in mm_data.items():
+            # Hash each item for embedding inputs.
+            n = (
+                len(data)
+                if isinstance(data, list) or MultiModalDataParser.is_embeddings(data)
+                else 1
+            )
+            mm_uuids[modality] = [f"{request_id}-{modality}-{i}" for i in range(n)]
+        return mm_uuids
+
+    def process_inputs(
+        self,
+        request_id: str,
+        prompt: PromptType,
+        params: SamplingParams | PoolingParams,
+        arrival_time: float | None = None,
+        lora_request: LoRARequest | None = None,
+        tokenization_kwargs: dict[str, Any] | None = None,
+        trace_headers: Mapping[str, str] | None = None,
+        priority: int = 0,
+        data_parallel_rank: int | None = None,
+    ) -> EngineCoreRequest:
+        self._validate_lora(lora_request)
+        self._validate_params(params)
+
+        data_parallel_size = self.vllm_config.parallel_config.data_parallel_size
+        if data_parallel_rank is not None and not (
+            0 <= data_parallel_rank < data_parallel_size
+        ):
+            raise ValueError(
+                f"data_parallel_rank {data_parallel_rank} "
+                f"is out of range [0, {data_parallel_size})."
+            )
+
+        if arrival_time is None:
+            arrival_time = time.time()
+
+        # Optionally generate multimodal hash overrides to avoid hashing
+        # multimodal data items by their content as their identifiers.
+
+        # NOTE: when users explicitly turn off BOTH prefix caching and input
+        # processing caching, no multimodal features or embeddings will be
+        # reused across requests, therefore identifying multimodal data items
+        # by their content is no longer necessary, and we create uuids with
+        # request id-modality-index as multimodal hash overrides.
+        if (
+            self.model_config.multimodal_config
+            and self.model_config.multimodal_config.mm_processor_cache_gb == 0
+            and not self.cache_config.enable_prefix_caching
+        ):
+            mm_uuids = self._maybe_build_mm_uuids(request_id, prompt)
+        else:
+            # Otherwise, use user-provided uuids as multimodal hash overrides
+            # if provided.
+            self._validate_multi_modal_uuids(prompt)
+            if isinstance(prompt, dict):
+                mm_uuids = cast(
+                    MultiModalUUIDDict | None, prompt.get("multi_modal_uuids")
+                )
+            else:
+                mm_uuids = None
+
+        # Process inputs, which includes:
+        # 1. Tokenize text prompt, with LoRA request if one exists.
+        # 2. For multimodal models with a merged preprocessor, preprocess
+        #   multimodal data and expand prompt token ids accordingly.
+        processed_inputs: ProcessorInputs = self.input_preprocessor.preprocess(
+            prompt,
+            tokenization_kwargs=tokenization_kwargs,
+            mm_uuids=mm_uuids,
+        )
+        from vllm.platforms import current_platform
+
+        current_platform.validate_request(
+            prompt=prompt,
+            params=params,
+            processed_inputs=processed_inputs,
+        )
+
+        eos_token_id = self.input_preprocessor.get_eos_token_id()
+
+        encoder_inputs, decoder_inputs = split_enc_dec_inputs(processed_inputs)
+        self._validate_model_inputs(encoder_inputs, decoder_inputs)
+
+        # Mypy can be conservative for TypedDict unions; normalize access.
+        if decoder_inputs["type"] == "embeds":
+            prompt_token_ids = None
+            prompt_embeds = decoder_inputs["prompt_embeds"]
+        else:
+            prompt_token_ids = decoder_inputs["prompt_token_ids"]
+            prompt_embeds = None
+
+        sampling_params = None
+        pooling_params = None
+        if isinstance(params, SamplingParams):
+            # TODO: can we avoid cloning here in multiproc case?
+            sampling_params = params.clone()
+            # If unset max tokens, then generate up to the max_model_len.
+            if sampling_params.max_tokens is None:
+                seq_len = length_from_prompt_token_ids_or_embeds(
+                    prompt_token_ids, prompt_embeds
+                )
+                sampling_params.max_tokens = self.model_config.max_model_len - seq_len
+            sampling_params.update_from_generation_config(
+                self.generation_config_fields, eos_token_id
+            )
+            if self.tokenizer is not None:
+                sampling_params.update_from_tokenizer(self.tokenizer)
+        else:
+            pooling_params = params.clone()
+
+        # Multimodal related.
+        mm_features: list[MultiModalFeatureSpec] | None = None
+
+        if decoder_inputs["type"] == "multimodal":
+            decoder_mm_inputs = decoder_inputs["mm_kwargs"]
+            decoder_mm_positions = decoder_inputs["mm_placeholders"]
+            decoder_mm_hashes = decoder_inputs["mm_hashes"]
+
+            # Merge and flatten multimodal placeholders, hashes and inputs
+            # from dictionaries to lists, and sort them by each item's position
+            # in the input sequence.
+            sorted_mm_idxs = argsort_mm_positions(decoder_mm_positions)
+
+            mm_features = []
+            for modality, idx in sorted_mm_idxs:
+                mm_features.append(
+                    MultiModalFeatureSpec(
+                        data=decoder_mm_inputs[modality][idx],
+                        modality=modality,
+                        identifier=decoder_mm_hashes[modality][idx],
+                        mm_position=decoder_mm_positions[modality][idx],
+                    )
+                )
+
+        return EngineCoreRequest(
+            request_id=request_id,
+            prompt_token_ids=prompt_token_ids,
+            prompt_embeds=prompt_embeds,
+            mm_features=mm_features,
+            sampling_params=sampling_params,
+            pooling_params=pooling_params,
+            eos_token_id=eos_token_id,
+            arrival_time=arrival_time,
+            lora_request=lora_request,
+            cache_salt=decoder_inputs.get("cache_salt"),
+            priority=priority,
+            data_parallel_rank=data_parallel_rank,
+            trace_headers=trace_headers,
+        )
+
+    def _validate_model_inputs(
+        self, encoder_inputs: SingletonInputs | None, decoder_inputs: SingletonInputs
+    ):
+        if encoder_inputs is not None:
+            self._validate_model_input(encoder_inputs, prompt_type="encoder")
+
+        self._validate_model_input(decoder_inputs, prompt_type="decoder")
+
+    def _validate_model_input(
+        self,
+        prompt_inputs: SingletonInputs,
+        *,
+        prompt_type: Literal["encoder", "decoder"],
+    ):
+        model_config = self.model_config
+
+        prompt_ids = (
+            None
+            if prompt_inputs["type"] == "embeds"
+            else prompt_inputs["prompt_token_ids"]
+        )
+        prompt_embeds = (
+            prompt_inputs["prompt_embeds"]
+            if prompt_inputs["type"] == "embeds"
+            else None
+        )
+        prompt_len = length_from_prompt_token_ids_or_embeds(prompt_ids, prompt_embeds)
+        if not prompt_ids:
+            if prompt_type == "encoder" and model_config.is_multimodal_model:
+                pass  # Mllama may have empty encoder inputs for text-only data
+            elif prompt_inputs["type"] == "embeds":
+                pass  # Prompt embeds should not have prompt_ids.
+            else:
+                raise ValueError(f"The {prompt_type} prompt cannot be empty")
+
+        tokenizer = self.tokenizer
+        if tokenizer is not None:
+            max_input_id = max(prompt_ids or [], default=0)
+
+            # NOTE: tokenizer.max_token_id is the tokenizer’s vocab size while
+            # self.model_config.get_vocab_size() is the model’s vocab size.
+            # For Qwen3 models, the language model has extra tokens that do
+            # not exist in the tokenizer, and vice versa for multimodal
+            # placeholder tokens in some multimodal models.
+            # See https://github.com/QwenLM/Qwen3/issues/29#issuecomment-1933720399 # noqa: E501
+            # and https://github.com/vllm-project/vllm/pull/22471#discussion_r2312251421 # noqa: E501
+
+            # Here we take the max of the two to determine if a token id is
+            # truly out-of-vocabulary.
+            if max_input_id > max(
+                tokenizer.max_token_id, self.model_config.get_vocab_size() - 1
+            ):
+                raise ValueError(f"Token id {max_input_id} is out of vocabulary")
+
+        max_prompt_len = self.model_config.max_model_len
+        if prompt_len > max_prompt_len:
+            if prompt_type == "encoder" and model_config.is_multimodal_model:
+                mm_registry = self.input_preprocessor.mm_registry
+                mm_processor = mm_registry.create_processor(
+                    model_config,
+                    tokenizer=tokenizer,
+                )
+                assert isinstance(mm_processor, EncDecMultiModalProcessor)
+
+                if mm_processor.pad_dummy_encoder_prompt:
+                    return  # Skip encoder length check for Whisper
+
+            if model_config.is_multimodal_model:
+                suggestion = (
+                    "Make sure that `max_model_len` is no smaller than the "
+                    "number of text tokens plus multimodal tokens. For image "
+                    "inputs, the number of image tokens depends on the number "
+                    "of images, and possibly their aspect ratios as well."
+                )
+            else:
+                suggestion = (
+                    "Make sure that `max_model_len` is no smaller than the "
+                    "number of text tokens."
+                )
+
+            raise ValueError(
+                f"The {prompt_type} prompt (length {prompt_len}) is "
+                f"longer than the maximum model length of {max_prompt_len}. "
+                f"{suggestion}"
+            )
+
+            # TODO: Find out how many placeholder tokens are there so we can
+            # check that chunked prefill does not truncate them
+            # max_batch_len = self.scheduler_config.max_num_batched_tokens
+
+        if (
+            prompt_len == max_prompt_len
+            and prompt_type == "decoder"
+            and not model_config.is_multimodal_model
+            and self.model_config.runner_type != "pooling"
+        ):
+            suggestion = (
+                "Make sure that `max_model_len` is no smaller than the "
+                "number of text tokens (prompt + requested output tokens)."
+            )
+            raise ValueError(
+                f"The {prompt_type} prompt (length {prompt_len}) plus the number of "
+                f"requested output tokens (at least 1) is longer than the maximum "
+                f"model length of {max_prompt_len}. {suggestion}"
+            )
+
+    def stat_mm_cache(self) -> MultiModalCacheStats | None:
+        return self.input_preprocessor.stat_mm_cache()
+
+    def clear_mm_cache(self) -> None:
+        self.input_preprocessor.clear_mm_cache()

vllm/v1/engine/llm_engine.py

@@ -7,7 +7,7 @@ from copy import copy
 from typing import Any, cast
 
 import torch.nn as nn
-from typing_extensions import TypeVar
+from typing_extensions import TypeVar, deprecated
 
 import vllm.envs as envs
 from vllm.config import ParallelConfig, VllmConfig
@@ -28,9 +28,9 @@ from vllm.transformers_utils.tokenizer import AnyTokenizer, init_tokenizer_from_
 from vllm.usage.usage_lib import UsageContext
 from vllm.v1.engine import EngineCoreRequest
 from vllm.v1.engine.core_client import EngineCoreClient
+from vllm.v1.engine.input_processor import InputProcessor
 from vllm.v1.engine.output_processor import OutputProcessor
 from vllm.v1.engine.parallel_sampling import ParentRequest
-from vllm.v1.engine.processor import Processor
 from vllm.v1.executor import Executor
 from vllm.v1.metrics.loggers import StatLoggerFactory, StatLoggerManager
 from vllm.v1.metrics.reader import Metric, get_metrics_snapshot
@@ -88,7 +88,7 @@ class LLMEngine:
         else:
             tokenizer = init_tokenizer_from_configs(self.model_config)
 
-        self.processor = Processor(self.vllm_config, tokenizer)
+        self.input_processor = InputProcessor(self.vllm_config, tokenizer)
         self.io_processor = get_io_processor(
             self.vllm_config,
             self.model_config.io_processor_plugin,
@@ -135,6 +135,14 @@ class LLMEngine:
         # Don't keep the dummy data in memory
         self.reset_mm_cache()
 
+    @property
+    @deprecated(
+        "`LLMEngine.processor` has been renamed to `LLMEngine.input_processor`. "
+        "The old name will be removed in v0.13."
+    )
+    def processor(self):
+        return self.input_processor
+
     @classmethod
     def from_vllm_config(
         cls,
@@ -231,11 +239,7 @@ class LLMEngine:
             request = prompt
         else:
             assert prompt_text is None
-            logger.warning_once(
-                "Processor has been moved under LLM and will "
-                "be removed from LLMEngine in v0.13."
-            )
-            request = self.processor.process_inputs(
+            request = self.input_processor.process_inputs(
                 request_id,
                 prompt,
                 params,
@@ -307,7 +311,7 @@ class LLMEngine:
                 self.logger_manager.record(
                     scheduler_stats=outputs.scheduler_stats,
                     iteration_stats=iteration_stats,
-                    mm_cache_stats=self.processor.stat_mm_cache(),
+                    mm_cache_stats=self.input_processor.stat_mm_cache(),
                 )
                 self.do_log_stats_with_interval()
 
@@ -320,7 +324,7 @@ class LLMEngine:
         self.engine_core.profile(False)
 
     def reset_mm_cache(self):
-        self.processor.clear_mm_cache()
+        self.input_processor.clear_mm_cache()
         self.engine_core.reset_mm_cache()
 
     def reset_prefix_cache(self):
@@ -347,11 +351,11 @@ class LLMEngine:
 
     @property
     def tokenizer(self) -> AnyTokenizer | None:
-        return self.processor.tokenizer
+        return self.input_processor.tokenizer
 
     @tokenizer.setter
     def tokenizer(self, tokenizer: AnyTokenizer | None) -> None:
-        self.processor.tokenizer = tokenizer
+        self.input_processor.tokenizer = tokenizer
 
     def get_tokenizer(self) -> AnyTokenizer:
         if self.tokenizer is None:

vllm/v1/engine/processor.py

 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import warnings
 
-import time
-from collections.abc import Mapping
-from typing import Any, Literal, cast
 
-from vllm.config import VllmConfig
-from vllm.inputs import ProcessorInputs, PromptType, SingletonInputs
-from vllm.inputs.parse import split_enc_dec_inputs
-from vllm.inputs.preprocess import InputPreprocessor
-from vllm.logger import init_logger
-from vllm.lora.request import LoRARequest
-from vllm.multimodal import MULTIMODAL_REGISTRY, MultiModalRegistry
-from vllm.multimodal.cache import processor_cache_from_config
-from vllm.multimodal.inputs import MultiModalFeatureSpec, MultiModalUUIDDict
-from vllm.multimodal.parse import MultiModalDataParser
-from vllm.multimodal.processing import EncDecMultiModalProcessor
-from vllm.multimodal.utils import argsort_mm_positions
-from vllm.pooling_params import PoolingParams
-from vllm.sampling_params import SamplingParams
-from vllm.transformers_utils.tokenizer import AnyTokenizer
-from vllm.transformers_utils.tokenizers.mistral import MistralTokenizer
-from vllm.utils import length_from_prompt_token_ids_or_embeds
-from vllm.v1.engine import EngineCoreRequest
-from vllm.v1.metrics.stats import MultiModalCacheStats
-from vllm.v1.structured_output.backend_guidance import validate_guidance_grammar
-from vllm.v1.structured_output.backend_lm_format_enforcer import (
-    validate_structured_output_request_lm_format_enforcer,
-)
-from vllm.v1.structured_output.backend_outlines import (
-    validate_structured_output_request_outlines,
-)
-from vllm.v1.structured_output.backend_xgrammar import validate_xgrammar_grammar
+def __getattr__(name: str):
+    if name == "Processor":
+        from .input_processor import InputProcessor
 
-logger = init_logger(__name__)
-
-
-class Processor:
-    def __init__(
-        self,
-        vllm_config: VllmConfig,
-        tokenizer: AnyTokenizer | None,
-        mm_registry: MultiModalRegistry = MULTIMODAL_REGISTRY,
-    ) -> None:
-        self.vllm_config = vllm_config
-        self.model_config = vllm_config.model_config
-        self.cache_config = vllm_config.cache_config
-        self.lora_config = vllm_config.lora_config
-        self.structured_outputs_config = vllm_config.structured_outputs_config
-
-        self.generation_config_fields = self.model_config.try_get_generation_config()
-
-        self.mm_registry = mm_registry
-        self.mm_processor_cache = processor_cache_from_config(vllm_config, mm_registry)
-
-        self.input_preprocessor = InputPreprocessor(
-            self.model_config,
-            tokenizer,
-            mm_registry,
-            mm_processor_cache=self.mm_processor_cache,
+        warnings.warn(
+            "`vllm.v1.engine.processor.Processor` has been moved to "
+            "`vllm.v1.engine.input_processor.InputProcessor`. "
+            "The old name will be removed in v0.13.",
+            DeprecationWarning,
+            stacklevel=2,
         )
 
-    @property
-    def tokenizer(self) -> AnyTokenizer | None:
-        return self.input_preprocessor.tokenizer
-
-    @tokenizer.setter
-    def tokenizer(self, tokenizer: AnyTokenizer | None) -> None:
-        self.input_preprocessor.tokenizer = tokenizer
-
-    def _validate_logprobs(
-        self,
-        params: SamplingParams,
-    ) -> None:
-        max_logprobs = self.model_config.max_logprobs
-        if max_logprobs == -1:
-            max_logprobs = self.model_config.get_vocab_size()
-
-        # Validate sample logprobs.
-        if params.logprobs:
-            num_logprobs = params.logprobs
-            if num_logprobs == -1:
-                num_logprobs = self.model_config.get_vocab_size()
-            if num_logprobs > max_logprobs:
-                raise ValueError(
-                    f"Requested sample logprobs of {num_logprobs}, "
-                    f"which is greater than max allowed: {max_logprobs}"
-                )
-
-        # Validate prompt logprobs.
-        if params.prompt_logprobs:
-            num_prompt_logprobs = params.prompt_logprobs
-            if num_prompt_logprobs == -1:
-                num_prompt_logprobs = self.model_config.get_vocab_size()
-            if num_prompt_logprobs > max_logprobs:
-                raise ValueError(
-                    f"Requested prompt logprobs of {num_prompt_logprobs}, "
-                    f"which is greater than max allowed: {max_logprobs}"
-                )
-
-    def _validate_sampling_params(
-        self,
-        params: SamplingParams,
-    ) -> None:
-        self._validate_structured_output(params)
-        self._validate_logit_bias(params)
-
-        if params.allowed_token_ids is None:
-            return
-        if not params.allowed_token_ids:
-            raise ValueError("allowed_token_ids is not None and empty!")
-        if self.tokenizer is None:
-            # When skip_tokenizer_init=True, we can't validate token IDs
-            # Skip validation and let the model handle invalid tokens
-            return
-        vocab_size = len(self.tokenizer)
-        if not all(0 <= tid < vocab_size for tid in params.allowed_token_ids):
-            raise ValueError("allowed_token_ids contains out-of-vocab token id!")
-
-    def _validate_logit_bias(
-        self,
-        params: SamplingParams,
-    ) -> None:
-        """Validate logit_bias token IDs are within vocabulary range."""
-        if not params.logit_bias:
-            return
-
-        vocab_size = self.model_config.get_vocab_size()
-        invalid_token_ids = []
-
-        for token_id in params.logit_bias:
-            if token_id < 0 or token_id >= vocab_size:
-                invalid_token_ids.append(token_id)
-
-        if invalid_token_ids:
-            raise ValueError(
-                f"token_id(s) {invalid_token_ids} in logit_bias contain "
-                f"out-of-vocab token ids. Vocabulary size: {vocab_size}"
-            )
-
-    def _validate_supported_sampling_params(
-        self,
-        params: SamplingParams,
-    ) -> None:
-        # Logits processors not supported.
-        if params.logits_processors:
-            raise ValueError(
-                "vLLM V1 does not support per request user provided logits processors."
-            )
-        # Async scheduling + spec decode currently incompatible with some
-        # sampling parameters.
-        if (
-            self.vllm_config.speculative_config is not None
-            and self.vllm_config.scheduler_config.async_scheduling
-            and (
-                params.frequency_penalty != 0.0
-                or params.presence_penalty != 0.0
-                or params.repetition_penalty != 1.0
-                or params.bad_words_token_ids
-                or params.structured_outputs
-            )
-        ):
-            raise ValueError(
-                "async scheduling with spec decoding doesn't yet support "
-                "penalties, bad words or structured outputs in sampling parameters."
-            )
-
-    def _validate_params(
-        self,
-        params: SamplingParams | PoolingParams,
-    ):
-        """
-        Validate supported SamplingParam.
-        Should raise ValueError if unsupported for API Server.
-        """
-
-        if isinstance(params, PoolingParams):
-            return
-
-        self._validate_logprobs(params)
-        self._validate_sampling_params(params)
-        self._validate_supported_sampling_params(params)
-
-    def _validate_multi_modal_uuids(self, prompt: PromptType) -> None:
-        """
-        Validate that user-provided multi_modal_uuids align with
-        multi_modal_data in the incoming request prompt(s).
-        Only checks lengths; `None` entries are allowed and will be
-        auto-hashed downstream.
-        """
-
-        def _validate_single_prompt(single_prompt: dict | str) -> None:
-            if not isinstance(single_prompt, dict):
-                return
-            mm_data = single_prompt.get("multi_modal_data")
-            mm_uuids = single_prompt.get("multi_modal_uuids")
-            if not mm_data or not mm_uuids:
-                return
-
-            for modality, items in mm_data.items():
-                if modality in mm_uuids:
-                    data_len = len(items) if isinstance(items, list) else 1
-                    uuid_len = (
-                        len(mm_uuids[modality])
-                        if isinstance(mm_uuids[modality], list)
-                        else 1
-                    )
-                    if uuid_len != data_len:
-                        raise ValueError(
-                            f"multi_modal_uuids for modality '{modality}' "
-                            "must have same length as data: got "
-                            f"{uuid_len} uuids vs "
-                            f"{data_len} items."
-                        )
-                else:
-                    raise ValueError(
-                        f"multi_modal_uuids for modality '{modality}' must "
-                        "be provided if multi_modal_data is provided."
-                    )
-
-        # Handle explicit encoder/decoder prompts or singleton prompt
-        if isinstance(prompt, dict) and "encoder_prompt" in prompt:
-            enc = prompt.get("encoder_prompt")
-            dec = prompt.get("decoder_prompt")
-            if enc is not None:
-                _validate_single_prompt(cast(dict | str, enc))
-            if dec is not None:
-                _validate_single_prompt(cast(dict | str, dec))
-        else:
-            _validate_single_prompt(prompt)  # type: ignore[arg-type]
-
-    def _validate_lora(self, lora_request: LoRARequest | None) -> None:
-        if lora_request is None:
-            return
-
-        # LoRA request passed in while LoRA is not enabled
-        if not self.lora_config:
-            raise ValueError(
-                f"Got lora_request {lora_request} but LoRA is not enabled!"
-            )
-
-        if self.tokenizer is not None:
-            logger.warning_once(
-                "vLLM has deprecated support for supporting different "
-                "tokenizers for different LoRAs. By default, vLLM uses base "
-                "model's tokenizer. If you are using a LoRA "
-                "with its own tokenizer, consider specifying `--tokenizer "
-                "[lora_path]` to use the LoRA tokenizer."
-            )
-
-    def _validate_structured_output(self, params: SamplingParams) -> None:
-        if not params.structured_outputs or not self.structured_outputs_config:
-            return
-
-        if self.model_config.skip_tokenizer_init and params.structured_outputs:
-            raise ValueError(
-                "Structured outputs requires a tokenizer so it can't be used with 'skip_tokenizer_init'"  # noqa: E501
-            )
-
-        backend = self.structured_outputs_config.backend
-        if _backend := params.structured_outputs._backend:
-            # Request-level backend selection is not supported.
-            # The values may differ if `params` is reused and was set
-            # to a specific backend based on `auto` behavior in a previous
-            # request. We remember that it was set as a result of `auto`
-            # using the `_backend_was_auto` field set in the params.
-            if backend != _backend and not (
-                backend == "auto" and params.structured_outputs._backend_was_auto
-            ):
-                raise ValueError(
-                    "Request-level structured output backend selection is not "
-                    f"supported. The request specified '{_backend}', but vLLM "
-                    f"was initialised with '{backend}'. This error can be "
-                    "resolved by removing '_backend' from the request."
-                )
-        else:
-            params.structured_outputs._backend = backend
-
-        # Request content validation
-        if (
-            isinstance(params.structured_outputs.choice, list)
-            and not params.structured_outputs.choice
-        ):
-            # It is invalid for choice to be an empty list
-            raise ValueError(
-                f"Choice '{params.structured_outputs.choice}' cannot be an empty list"  # noqa: E501
-            )
-        # Reject empty string grammar early to avoid engine-side crashes
-        if (
-            isinstance(params.structured_outputs.grammar, str)
-            and params.structured_outputs.grammar.strip() == ""
-        ):
-            raise ValueError("structured_outputs.grammar cannot be an empty string")
-
-        if backend.startswith("xgrammar"):
-            # xgrammar with no fallback
-            validate_xgrammar_grammar(params)
-        elif backend.startswith("guidance"):
-            # TODO: ideally we would have the LLTokenizer here as Lark syntax
-            # allows <|special_token|> and similar, see
-            # https://github.com/guidance-ai/llguidance/blob/main/docs/syntax.md#special-tokens
-            # Without tokenizer these are disallowed in grammars.
-            if isinstance(self.tokenizer, MistralTokenizer):
-                raise ValueError(
-                    "Mistral tokenizer is not supported for the 'guidance' "
-                    "structured output backend. Please use ['xgrammar', 'outlines'] "
-                    "backends or tokenizer_mode='hf' instead."
-                )
-            validate_guidance_grammar(params, tokenizer=None)
-        elif backend == "outlines":
-            # outlines backend
-            validate_structured_output_request_outlines(params)
-        elif backend == "lm-format-enforcer":
-            # lm format enforcer backend
-            if isinstance(self.tokenizer, MistralTokenizer):
-                raise ValueError(
-                    "Mistral tokenizer is not supported for the 'lm-format-enforcer' "
-                    "structured output backend. Please use ['xgrammar', 'outlines'] "
-                    "backends or tokenizer_mode='hf' instead."
-                )
-            validate_structured_output_request_lm_format_enforcer(params)
-        else:
-            # NOTE: backend must be "auto" here, because we have
-            # checked supported_backends above.
-            # In this mode, we set opinionated defaults based on what we think
-            # will satisfy the most use cases without having to worry about
-            # this setting. We include fallback behavior here, but not with any
-            # other setting where a specific backend was specified.
-            try:
-                validate_xgrammar_grammar(params)
-                params.structured_outputs._backend = "xgrammar"
-            except ValueError:
-                # The request either failed validation
-                # or includes some jsonschema feature(s) that
-                # are not supported in xgrammar.
-                if isinstance(self.tokenizer, MistralTokenizer):
-                    # Fall back to outlines if the tokenizer is Mistral
-                    validate_structured_output_request_outlines(params)
-                    params.structured_outputs._backend = "outlines"
-                else:
-                    # Fall back to guidance by default.
-                    validate_guidance_grammar(params, tokenizer=None)
-                    params.structured_outputs._backend = "guidance"
-            # Remember that this backend was set automatically
-            params.structured_outputs._backend_was_auto = True
-
-    def _maybe_build_mm_uuids(
-        self,
-        request_id: str,
-        prompt: PromptType,
-    ) -> MultiModalUUIDDict | None:
-        """Build per-item multimodal hash overrides when enabled. In this case,
-        multimodal data items are identified by their request id, modality and
-        index rather than their content.
-
-        Returns a dictionary of modality -> list[str] of overrides, or None if
-        disabled or no multimodal data is present.
-        """
-
-        def _extract_mm_data(p: PromptType):
-            if isinstance(p, dict) and "encoder_prompt" in p:
-                enc = p.get("encoder_prompt")
-                if isinstance(enc, dict):
-                    return enc.get("multi_modal_data")
-                return None
-            if isinstance(p, dict):
-                return p.get("multi_modal_data")
-            return None
-
-        mm_data = _extract_mm_data(prompt)
-        if not mm_data:
-            return None
-
-        mm_uuids: dict[str, list[str | None] | str] = {}
-        for modality, data in mm_data.items():
-            # Hash each item for embedding inputs.
-            n = (
-                len(data)
-                if isinstance(data, list) or MultiModalDataParser.is_embeddings(data)
-                else 1
-            )
-            mm_uuids[modality] = [f"{request_id}-{modality}-{i}" for i in range(n)]
-        return mm_uuids
-
-    def process_inputs(
-        self,
-        request_id: str,
-        prompt: PromptType,
-        params: SamplingParams | PoolingParams,
-        arrival_time: float | None = None,
-        lora_request: LoRARequest | None = None,
-        tokenization_kwargs: dict[str, Any] | None = None,
-        trace_headers: Mapping[str, str] | None = None,
-        priority: int = 0,
-        data_parallel_rank: int | None = None,
-    ) -> EngineCoreRequest:
-        self._validate_lora(lora_request)
-        self._validate_params(params)
-
-        data_parallel_size = self.vllm_config.parallel_config.data_parallel_size
-        if data_parallel_rank is not None and not (
-            0 <= data_parallel_rank < data_parallel_size
-        ):
-            raise ValueError(
-                f"data_parallel_rank {data_parallel_rank} "
-                f"is out of range [0, {data_parallel_size})."
-            )
-
-        if arrival_time is None:
-            arrival_time = time.time()
-
-        # Optionally generate multimodal hash overrides to avoid hashing
-        # multimodal data items by their content as their identifiers.
-
-        # NOTE: when users explicitly turn off BOTH prefix caching and input
-        # processing caching, no multimodal features or embeddings will be
-        # reused across requests, therefore identifying multimodal data items
-        # by their content is no longer necessary, and we create uuids with
-        # request id-modality-index as multimodal hash overrides.
-        if (
-            self.model_config.multimodal_config
-            and self.model_config.multimodal_config.mm_processor_cache_gb == 0
-            and not self.cache_config.enable_prefix_caching
-        ):
-            mm_uuids = self._maybe_build_mm_uuids(request_id, prompt)
-        else:
-            # Otherwise, use user-provided uuids as multimodal hash overrides
-            # if provided.
-            self._validate_multi_modal_uuids(prompt)
-            if isinstance(prompt, dict):
-                mm_uuids = cast(
-                    MultiModalUUIDDict | None, prompt.get("multi_modal_uuids")
-                )
-            else:
-                mm_uuids = None
-
-        # Process inputs, which includes:
-        # 1. Tokenize text prompt, with LoRA request if one exists.
-        # 2. For multimodal models with a merged preprocessor, preprocess
-        #   multimodal data and expand prompt token ids accordingly.
-        processed_inputs: ProcessorInputs = self.input_preprocessor.preprocess(
-            prompt,
-            tokenization_kwargs=tokenization_kwargs,
-            mm_uuids=mm_uuids,
-        )
-        from vllm.platforms import current_platform
-
-        current_platform.validate_request(
-            prompt=prompt,
-            params=params,
-            processed_inputs=processed_inputs,
-        )
-
-        eos_token_id = self.input_preprocessor.get_eos_token_id()
-
-        encoder_inputs, decoder_inputs = split_enc_dec_inputs(processed_inputs)
-        self._validate_model_inputs(encoder_inputs, decoder_inputs)
-
-        # Mypy can be conservative for TypedDict unions; normalize access.
-        if decoder_inputs["type"] == "embeds":
-            prompt_token_ids = None
-            prompt_embeds = decoder_inputs["prompt_embeds"]
-        else:
-            prompt_token_ids = decoder_inputs["prompt_token_ids"]
-            prompt_embeds = None
-
-        sampling_params = None
-        pooling_params = None
-        if isinstance(params, SamplingParams):
-            # TODO: can we avoid cloning here in multiproc case?
-            sampling_params = params.clone()
-            # If unset max tokens, then generate up to the max_model_len.
-            if sampling_params.max_tokens is None:
-                seq_len = length_from_prompt_token_ids_or_embeds(
-                    prompt_token_ids, prompt_embeds
-                )
-                sampling_params.max_tokens = self.model_config.max_model_len - seq_len
-            sampling_params.update_from_generation_config(
-                self.generation_config_fields, eos_token_id
-            )
-            if self.tokenizer is not None:
-                sampling_params.update_from_tokenizer(self.tokenizer)
-        else:
-            pooling_params = params.clone()
-
-        # Multimodal related.
-        mm_features: list[MultiModalFeatureSpec] | None = None
-
-        if decoder_inputs["type"] == "multimodal":
-            decoder_mm_inputs = decoder_inputs["mm_kwargs"]
-            decoder_mm_positions = decoder_inputs["mm_placeholders"]
-            decoder_mm_hashes = decoder_inputs["mm_hashes"]
-
-            # Merge and flatten multimodal placeholders, hashes and inputs
-            # from dictionaries to lists, and sort them by each item's position
-            # in the input sequence.
-            sorted_mm_idxs = argsort_mm_positions(decoder_mm_positions)
-
-            mm_features = []
-            for modality, idx in sorted_mm_idxs:
-                mm_features.append(
-                    MultiModalFeatureSpec(
-                        data=decoder_mm_inputs[modality][idx],
-                        modality=modality,
-                        identifier=decoder_mm_hashes[modality][idx],
-                        mm_position=decoder_mm_positions[modality][idx],
-                    )
-                )
-
-        return EngineCoreRequest(
-            request_id=request_id,
-            prompt_token_ids=prompt_token_ids,
-            prompt_embeds=prompt_embeds,
-            mm_features=mm_features,
-            sampling_params=sampling_params,
-            pooling_params=pooling_params,
-            eos_token_id=eos_token_id,
-            arrival_time=arrival_time,
-            lora_request=lora_request,
-            cache_salt=decoder_inputs.get("cache_salt"),
-            priority=priority,
-            data_parallel_rank=data_parallel_rank,
-            trace_headers=trace_headers,
-        )
-
-    def _validate_model_inputs(
-        self, encoder_inputs: SingletonInputs | None, decoder_inputs: SingletonInputs
-    ):
-        if encoder_inputs is not None:
-            self._validate_model_input(encoder_inputs, prompt_type="encoder")
-
-        self._validate_model_input(decoder_inputs, prompt_type="decoder")
-
-    def _validate_model_input(
-        self,
-        prompt_inputs: SingletonInputs,
-        *,
-        prompt_type: Literal["encoder", "decoder"],
-    ):
-        model_config = self.model_config
-
-        prompt_ids = (
-            None
-            if prompt_inputs["type"] == "embeds"
-            else prompt_inputs["prompt_token_ids"]
-        )
-        prompt_embeds = (
-            prompt_inputs["prompt_embeds"]
-            if prompt_inputs["type"] == "embeds"
-            else None
-        )
-        prompt_len = length_from_prompt_token_ids_or_embeds(prompt_ids, prompt_embeds)
-        if not prompt_ids:
-            if prompt_type == "encoder" and model_config.is_multimodal_model:
-                pass  # Mllama may have empty encoder inputs for text-only data
-            elif prompt_inputs["type"] == "embeds":
-                pass  # Prompt embeds should not have prompt_ids.
-            else:
-                raise ValueError(f"The {prompt_type} prompt cannot be empty")
-
-        tokenizer = self.tokenizer
-        if tokenizer is not None:
-            max_input_id = max(prompt_ids or [], default=0)
-
-            # NOTE: tokenizer.max_token_id is the tokenizer’s vocab size while
-            # self.model_config.get_vocab_size() is the model’s vocab size.
-            # For Qwen3 models, the language model has extra tokens that do
-            # not exist in the tokenizer, and vice versa for multimodal
-            # placeholder tokens in some multimodal models.
-            # See https://github.com/QwenLM/Qwen3/issues/29#issuecomment-1933720399 # noqa: E501
-            # and https://github.com/vllm-project/vllm/pull/22471#discussion_r2312251421 # noqa: E501
-
-            # Here we take the max of the two to determine if a token id is
-            # truly out-of-vocabulary.
-            if max_input_id > max(
-                tokenizer.max_token_id, self.model_config.get_vocab_size() - 1
-            ):
-                raise ValueError(f"Token id {max_input_id} is out of vocabulary")
-
-        max_prompt_len = self.model_config.max_model_len
-        if prompt_len > max_prompt_len:
-            if prompt_type == "encoder" and model_config.is_multimodal_model:
-                mm_registry = self.input_preprocessor.mm_registry
-                mm_processor = mm_registry.create_processor(
-                    model_config,
-                    tokenizer=tokenizer,
-                )
-                assert isinstance(mm_processor, EncDecMultiModalProcessor)
-
-                if mm_processor.pad_dummy_encoder_prompt:
-                    return  # Skip encoder length check for Whisper
-
-            if model_config.is_multimodal_model:
-                suggestion = (
-                    "Make sure that `max_model_len` is no smaller than the "
-                    "number of text tokens plus multimodal tokens. For image "
-                    "inputs, the number of image tokens depends on the number "
-                    "of images, and possibly their aspect ratios as well."
-                )
-            else:
-                suggestion = (
-                    "Make sure that `max_model_len` is no smaller than the "
-                    "number of text tokens."
-                )
-
-            raise ValueError(
-                f"The {prompt_type} prompt (length {prompt_len}) is "
-                f"longer than the maximum model length of {max_prompt_len}. "
-                f"{suggestion}"
-            )
-
-            # TODO: Find out how many placeholder tokens are there so we can
-            # check that chunked prefill does not truncate them
-            # max_batch_len = self.scheduler_config.max_num_batched_tokens
-
-        if (
-            prompt_len == max_prompt_len
-            and prompt_type == "decoder"
-            and not model_config.is_multimodal_model
-            and self.model_config.runner_type != "pooling"
-        ):
-            suggestion = (
-                "Make sure that `max_model_len` is no smaller than the "
-                "number of text tokens (prompt + requested output tokens)."
-            )
-            raise ValueError(
-                f"The {prompt_type} prompt (length {prompt_len}) plus the number of "
-                f"requested output tokens (at least 1) is longer than the maximum "
-                f"model length of {max_prompt_len}. {suggestion}"
-            )
-
-    def stat_mm_cache(self) -> MultiModalCacheStats | None:
-        return self.input_preprocessor.stat_mm_cache()
+        return InputProcessor
 
-    def clear_mm_cache(self) -> None:
-        self.input_preprocessor.clear_mm_cache()
+    raise AttributeError(f"module {__name__!r} has no attribute {name!r}")