[Chore] Rename Processor to InputProcessor

tests/entrypoints/openai/test_lora_resolvers.py

@@ -114,7 +114,7 @@ async def mock_generate(*args, **kwargs):
     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 @@ async def serving_responses_instance(self):
         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 @@ async def serving_responses_instance(self):
         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

@@ -7,8 +7,8 @@
 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 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
@@ -18,7 +18,7 @@
 # Mock processor for testing
 def _mk_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.
@@ -65,7 +65,7 @@ def __init__(self, gb: float):
         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):

vllm/engine/protocol.py

@@ -15,15 +15,15 @@
 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):
     """Protocol class for Clients to Engine"""
 
     vllm_config: VllmConfig
     model_config: ModelConfig
-    processor: Processor
+    input_processor: InputProcessor
     io_processor: IOProcessor | None
 
     @property

vllm/entrypoints/llm.py

@@ -347,7 +347,7 @@ def __init__(
         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 @@ def set_tokenizer(self, tokenizer: AnyTokenizer) -> None:
             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 @@ def _process_inputs(
             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 @@ def __init__(
         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 @@ def _get_reasoning_parser(
         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(
@@ -348,7 +348,7 @@ async def beam_search(
         length_penalty = params.length_penalty
         include_stop_str_in_output = params.include_stop_str_in_output
 
-        processor = self.processor
+        processor = self.input_processor
         tokenizer = processor.tokenizer
         if tokenizer is None:
             raise ValueError(
@@ -1214,7 +1214,7 @@ async def _process_inputs(
             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 @@ def __init__(
             )
         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/model_executor/models/nemotron_vl.py

@@ -34,7 +34,7 @@
 from vllm.multimodal.image import convert_image_mode
 from vllm.multimodal.processing import PromptUpdateDetails
 from vllm.sequence import IntermediateTensors
-from vllm.transformers_utils.processor import cached_image_processor_from_config
+from vllm.transformers_utils import cached_image_processor_from_config
 from vllm.transformers_utils.tokenizer import AnyTokenizer
 
 from .interfaces import (

vllm/v1/engine/async_llm.py

@@ -35,9 +35,9 @@
 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 +112,7 @@ def __init__(
         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,
@@ -297,7 +297,7 @@ async def add_request(
                 "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 +481,7 @@ def _run_output_handler(self):
         output_processor = self.output_processor
         log_stats = self.log_stats
         logger_manager = self.logger_manager
-        processor = self.processor
+        processor = self.input_processor
 
         async def output_handler():
             try:
@@ -699,11 +699,11 @@ async def encode(
 
     @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 +738,7 @@ async def stop_profile(self) -> None:
         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: