[Renderer] Move InputPreprocessor into Renderer (2/2) (#34560)

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

tests/entrypoints/llm/test_chat.py

@@ -195,18 +195,15 @@ def test_chat_batch_failure_cleanup(llm_for_failure_test):
     valid_msg = [{"role": "user", "content": "Hello"}]
     long_text = "This is a very long text to test the error " * 50
     invalid_msg = [{"role": "user", "content": long_text}]
-    batch_1 = [
-        valid_msg,
-        valid_msg,
-        invalid_msg,
-    ]
-    batch_2 = [
-        valid_msg,
-        valid_msg,
-    ]
+
+    batch_1 = [valid_msg, valid_msg, invalid_msg]
+    batch_2 = [valid_msg, valid_msg]
     sampling_params = SamplingParams(temperature=0, max_tokens=10)
+
     with pytest.raises(ValueError, match="context length is only"):
         llm.chat(batch_1, sampling_params=sampling_params)
+    assert llm.llm_engine.get_num_unfinished_requests() == 0
+
     outputs_2 = llm.chat(batch_2, sampling_params=sampling_params)
     assert len(outputs_2) == len(batch_2)
     assert llm.llm_engine.get_num_unfinished_requests() == 0

tests/models/multimodal/processing/test_common.py

@@ -489,8 +489,9 @@ def _assert_inputs_equal(
     if ignore_mm_keys is None:
         ignore_mm_keys = set()
 
-    a_rest = {k: v for k, v in a.items() if k != "mm_kwargs"}
-    b_rest = {k: v for k, v in b.items() if k != "mm_kwargs"}
+    ignore_prompt_keys = ("prompt", "mm_kwargs")
+    a_rest = {k: v for k, v in a.items() if k not in ignore_prompt_keys}
+    b_rest = {k: v for k, v in b.items() if k not in ignore_prompt_keys}
 
     assert a_rest == b_rest, msg
 

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

@@ -6,18 +6,17 @@ import pytest
 from vllm.assets.image import ImageAsset
 from vllm.assets.video import VideoAsset
 from vllm.config import CacheConfig, ModelConfig, VllmConfig
-from vllm.multimodal import MultiModalUUIDDict
-from vllm.sampling_params import SamplingParams
-from vllm.v1.engine.input_processor import InputProcessor
+from vllm.renderers.hf import HfRenderer
+from vllm.tokenizers.registry import tokenizer_args_from_config
 
 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
 
 
-def _build_input_processor(
+def _build_renderer(
     *, mm_cache_gb: float = 4.0, enable_prefix_caching: bool = True
-) -> InputProcessor:
+) -> HfRenderer:
     model_config = ModelConfig(
         model="Qwen/Qwen2.5-VL-3B-Instruct",
         max_model_len=128,
@@ -29,47 +28,45 @@ def _build_input_processor(
         cache_config=CacheConfig(enable_prefix_caching=enable_prefix_caching),
     )
 
-    return InputProcessor(vllm_config)
+    _, tokenizer_name, _, kwargs = tokenizer_args_from_config(model_config)
+
+    return HfRenderer.from_config(
+        vllm_config,
+        tokenizer_kwargs={**kwargs, "tokenizer_name": tokenizer_name},
+    )
 
 
 def test_multi_modal_uuids_length_mismatch_raises():
-    input_processor = _build_input_processor()
+    renderer = _build_renderer()
 
-    prompt = {
-        "prompt": "USER: <image>\nDescribe\nASSISTANT:",
-        "multi_modal_data": {"image": [cherry_pil_image, stop_pil_image]},
-        # Mismatch: 2 items but only 1 uuid provided
-        "multi_modal_uuids": {"image": ["hash_cherry"]},
-    }
+    mm_data = {"image": [cherry_pil_image, stop_pil_image]}
+
+    # Mismatch: 2 items but only 1 uuid provided
+    mm_uuids = {"image": ["hash_cherry"]}
+
+    mm_processor = renderer.get_mm_processor()
+    mm_items = mm_processor.info.parse_mm_data(mm_data)
 
     with pytest.raises(ValueError, match="must have same length as"):
-        input_processor.process_inputs(
-            request_id="req-1",
-            prompt=prompt,  # type: ignore[arg-type]
-            params=SamplingParams(),
-        )
+        renderer._process_mm_uuids(mm_data, mm_items, mm_uuids, "req-1")
 
 
 def test_multi_modal_uuids_missing_modality_raises():
-    input_processor = _build_input_processor()
-
-    prompt = {
-        "prompt": "USER: <image><video>\nDescribe\nASSISTANT:",
-        # Two modalities provided in data
-        "multi_modal_data": {
-            "image": [cherry_pil_image],
-            "video": None,
-        },
-        # Only image uuids provided; video missing should raise
-        "multi_modal_uuids": {"image": ["hash_cherry"]},
+    renderer = _build_renderer()
+
+    mm_data = {
+        "image": [cherry_pil_image],
+        "video": None,
     }
 
+    # Only image uuids provided; video missing should raise
+    mm_uuids = {"image": ["hash_cherry"]}
+
+    mm_processor = renderer.get_mm_processor()
+    mm_items = mm_processor.info.parse_mm_data(mm_data)
+
     with pytest.raises(ValueError, match="is empty but .* is missing"):
-        input_processor.process_inputs(
-            request_id="req-2",
-            prompt=prompt,  # type: ignore[arg-type]
-            params=SamplingParams(),
-        )
+        renderer._process_mm_uuids(mm_data, mm_items, mm_uuids, "req-2")
 
 
 @pytest.mark.parametrize(
@@ -83,92 +80,86 @@ def test_multi_modal_uuids_missing_modality_raises():
 def test_multi_modal_uuids_accepts_none_and_passes_through(
     monkeypatch, mm_cache_gb: float, enable_prefix_caching: bool
 ):
-    input_processor = _build_input_processor(
+    renderer = _build_renderer(
         mm_cache_gb=mm_cache_gb,
         enable_prefix_caching=enable_prefix_caching,
     )
 
-    # Capture the overrides passed to InputPreprocessor.preprocess
-    captured: dict[str, object] = {}
-
-    def fake_preprocess(
-        prompt, *, tokenization_kwargs=None, lora_request=None, mm_uuids=None
-    ):
-        captured["mm_uuids"] = mm_uuids
-        # Minimal processed inputs for decoder-only flow
-        return {"type": "token", "prompt_token_ids": [1]}
-
-    # Monkeypatch only the bound preprocess method on this instance
-    monkeypatch.setattr(
-        input_processor.input_preprocessor, "preprocess", fake_preprocess, raising=True
-    )
+    mm_data = {
+        "image": [cherry_pil_image, stop_pil_image],
+        "video": baby_reading_np_ndarrays,
+    }
 
     # Use a consistent two-image scenario across all configurations
     mm_uuids = {"image": [None, "hash_stop"], "video": None}
-    prompt = {
-        "prompt": "USER: <image><image>\nTwo images\nASSISTANT:",
-        "multi_modal_data": {
-            "image": [cherry_pil_image, stop_pil_image],
-            "video": baby_reading_np_ndarrays,
-        },
-        "multi_modal_uuids": mm_uuids,
-    }
 
-    input_processor.process_inputs(
-        request_id="req-3",
-        prompt=prompt,  # type: ignore[arg-type]
-        params=SamplingParams(),
+    mm_processor = renderer.get_mm_processor()
+    mm_items = mm_processor.info.parse_mm_data(mm_data)
+    processed_mm_uuids = renderer._process_mm_uuids(
+        mm_data, mm_items, mm_uuids, "req-3"
     )
 
-    assert captured["mm_uuids"] == mm_uuids
+    assert processed_mm_uuids == mm_uuids
 
 
-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.
-    input_processor = _build_input_processor(
-        mm_cache_gb=0.0, enable_prefix_caching=False
+@pytest.mark.parametrize(
+    "mm_cache_gb, enable_prefix_caching",
+    [
+        (4.0, True),  # default behavior
+        (4.0, False),  # prefix caching disabled
+        (0.0, True),  # processor cache disabled
+    ],
+)
+def test_multi_modal_uuids_accepts_empty(
+    monkeypatch, mm_cache_gb: float, enable_prefix_caching: bool
+):
+    renderer = _build_renderer(
+        mm_cache_gb=mm_cache_gb,
+        enable_prefix_caching=enable_prefix_caching,
     )
 
-    captured: dict[str, MultiModalUUIDDict] = {}
+    # While None means cached multi-modal input requiring UUIDs
+    # an empty list means no multi-modal input
+    mm_data = {"image": [], "video": []}  # type: ignore[var-annotated]
+    mm_uuids = {"image": [], "video": None}  # type: ignore[var-annotated]
 
-    def fake_preprocess(
-        prompt, *, tokenization_kwargs=None, lora_request=None, mm_uuids=None
-    ):
-        captured["mm_uuids"] = mm_uuids
-        return {"type": "token", "prompt_token_ids": [1]}
-
-    monkeypatch.setattr(
-        input_processor.input_preprocessor, "preprocess", fake_preprocess, raising=True
+    mm_processor = renderer.get_mm_processor()
+    mm_items = mm_processor.info.parse_mm_data(mm_data)
+    processed_mm_uuids = renderer._process_mm_uuids(
+        mm_data, mm_items, mm_uuids, "req-4"
     )
 
+    assert processed_mm_uuids == mm_uuids
+
+
+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.
+    renderer = _build_renderer(mm_cache_gb=0.0, enable_prefix_caching=False)
+
     request_id = "req-42"
-    mm_uuids = {"image": ["hash_cherry", "hash_stop"], "video": ["hash_video"]}
-    prompt = {
-        "prompt": "USER: <image><image><video>\nDescribe\nASSISTANT:",
-        "multi_modal_data": {
-            "image": [cherry_pil_image, stop_pil_image],
-            "video": [baby_reading_np_ndarrays],
-        },
-        "multi_modal_uuids": mm_uuids,
+    mm_data = {
+        "image": [cherry_pil_image, stop_pil_image],
+        "video": baby_reading_np_ndarrays,
     }
+    mm_uuids = {"image": ["hash_cherry", "hash_stop"], "video": ["hash_video"]}
 
-    input_processor.process_inputs(
-        request_id=request_id,
-        prompt=prompt,  # type: ignore[arg-type]
-        params=SamplingParams(),
+    mm_processor = renderer.get_mm_processor()
+    mm_items = mm_processor.info.parse_mm_data(mm_data)
+    processed_mm_uuids = renderer._process_mm_uuids(
+        mm_data, mm_items, mm_uuids, request_id
     )
 
     # Expect request-id-based overrides are passed through
     assert set(mm_uuids.keys()) == {"image", "video"}
     assert len(mm_uuids["image"]) == 2
     assert len(mm_uuids["video"]) == 1
-    assert captured["mm_uuids"]["image"][0].startswith(
+    assert processed_mm_uuids["image"][0].startswith(
         f"{request_id}-image-"
-    ) and captured["mm_uuids"]["image"][0].endswith("-0")
-    assert captured["mm_uuids"]["image"][1].startswith(
+    ) and processed_mm_uuids["image"][0].endswith("-0")
+    assert processed_mm_uuids["image"][1].startswith(
         f"{request_id}-image-"
-    ) and captured["mm_uuids"]["image"][1].endswith("-1")
-    assert captured["mm_uuids"]["video"][0].startswith(
+    ) and processed_mm_uuids["image"][1].endswith("-1")
+    assert processed_mm_uuids["video"][0].startswith(
         f"{request_id}-video-"
-    ) and captured["mm_uuids"]["video"][0].endswith("-0")
+    ) and processed_mm_uuids["video"][0].endswith("-0")

tests/samplers/test_beam_search.py

@@ -20,7 +20,6 @@ MM_BEAM_WIDTHS = [2]
 MODELS = ["TinyLlama/TinyLlama-1.1B-Chat-v1.0"]
 
 
-@pytest.mark.skip_v1  # V1 engine does not yet support beam search
 @pytest.mark.parametrize("model", MODELS)
 @pytest.mark.parametrize("dtype", ["half"])
 @pytest.mark.parametrize("max_tokens", MAX_TOKENS)
@@ -62,7 +61,6 @@ def test_beam_search_single_input(
             )
 
 
-@pytest.mark.skip_v1  # V1 engine does not yet support beam search
 @pytest.mark.parametrize("model", MODELS)
 @pytest.mark.parametrize("dtype", ["half"])
 @pytest.mark.parametrize("max_tokens", MAX_TOKENS)

vllm/beam_search.py

@@ -2,13 +2,11 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 from dataclasses import dataclass
-from typing import TYPE_CHECKING, Any
 
+from vllm.inputs import TokenInputs, token_inputs
 from vllm.logprobs import Logprob
 from vllm.lora.request import LoRARequest
-
-if TYPE_CHECKING:
-    from vllm.multimodal import MultiModalDataDict
+from vllm.multimodal.inputs import MultiModalInputs, mm_inputs
 
 
 @dataclass
@@ -19,6 +17,8 @@ class BeamSearchSequence:
     about to be returned to the user.
     """
 
+    orig_prompt: TokenInputs | MultiModalInputs
+
     # The tokens include the prompt.
     tokens: list[int]
     logprobs: list[dict[int, Logprob]]
@@ -27,8 +27,28 @@ class BeamSearchSequence:
     text: str | None = None
     finish_reason: str | None = None
     stop_reason: int | str | None = None
-    multi_modal_data: "MultiModalDataDict | None" = None
-    mm_processor_kwargs: dict[str, Any] | None = None
+
+    def get_prompt(self):
+        prompt = self.orig_prompt
+
+        prompt_text = prompt.get("prompt")
+        cache_salt = prompt.get("cache_salt")
+
+        if prompt["type"] == "token":
+            return token_inputs(
+                self.tokens,
+                prompt=prompt_text,
+                cache_salt=cache_salt,
+            )
+
+        return mm_inputs(
+            prompt_token_ids=self.tokens,
+            mm_kwargs=prompt["mm_kwargs"],
+            mm_hashes=prompt["mm_hashes"],
+            mm_placeholders=prompt["mm_placeholders"],
+            prompt=prompt_text,
+            cache_salt=cache_salt,
+        )
 
 
 @dataclass
@@ -44,14 +64,15 @@ class BeamSearchOutput:
 class BeamSearchInstance:
     def __init__(
         self,
-        prompt_tokens: list[int],
+        prompt: TokenInputs | MultiModalInputs,
         lora_request: LoRARequest | None = None,
         logprobs: list[dict[int, Logprob]] | None = None,
         **kwargs,
     ):
         self.beams: list[BeamSearchSequence] = [
             BeamSearchSequence(
-                tokens=prompt_tokens,
+                orig_prompt=prompt,
+                tokens=prompt["prompt_token_ids"],
                 logprobs=[] if logprobs is None else list(logprobs),
                 lora_request=lora_request,
                 **kwargs,

vllm/engine/protocol.py

@@ -11,13 +11,12 @@ from vllm.distributed.weight_transfer.base import (
     WeightTransferInitRequest,
     WeightTransferUpdateRequest,
 )
-from vllm.inputs.data import PromptType
+from vllm.inputs.data import ProcessorInputs, PromptType
 from vllm.lora.request import LoRARequest
 from vllm.outputs import PoolingRequestOutput, RequestOutput
 from vllm.plugins.io_processors import IOProcessor
 from vllm.pooling_params import PoolingParams
 from vllm.renderers import BaseRenderer
-from vllm.renderers.inputs import DictPrompt, TokPrompt
 from vllm.sampling_params import SamplingParams
 from vllm.tasks import SupportedTask
 from vllm.v1.engine import EngineCoreRequest
@@ -35,7 +34,7 @@ class StreamingInput:
     where inputs are provided via an async generator.
     """
 
-    prompt: PromptType
+    prompt: ProcessorInputs
     sampling_params: SamplingParams | None = None
 
 
@@ -69,8 +68,7 @@ class EngineClient(ABC):
         self,
         prompt: EngineCoreRequest
         | PromptType
-        | DictPrompt
-        | TokPrompt
+        | ProcessorInputs
         | AsyncGenerator[StreamingInput, None],
         sampling_params: SamplingParams,
         request_id: str,
@@ -81,6 +79,7 @@ class EngineClient(ABC):
         trace_headers: Mapping[str, str] | None = None,
         priority: int = 0,
         data_parallel_rank: int | None = None,
+        reasoning_ended: bool | None = None,
     ) -> AsyncGenerator[RequestOutput, None]:
         """Generate outputs for a request."""
         ...
@@ -88,13 +87,14 @@ class EngineClient(ABC):
     @abstractmethod
     def encode(
         self,
-        prompt: PromptType | DictPrompt | TokPrompt,
+        prompt: PromptType | ProcessorInputs,
         pooling_params: PoolingParams,
         request_id: str,
         lora_request: LoRARequest | None = None,
         trace_headers: Mapping[str, str] | None = None,
         priority: int = 0,
         tokenization_kwargs: dict[str, Any] | None = None,
+        reasoning_ended: bool | None = None,
     ) -> AsyncGenerator[PoolingRequestOutput, None]:
         """Generate outputs for a request from a pooling model."""
         ...

vllm/entrypoints/openai/chat_completion/serving.py

@@ -67,13 +67,12 @@ from vllm.entrypoints.openai.parser.harmony_utils import (
 )
 from vllm.entrypoints.openai.utils import maybe_filter_parallel_tool_calls
 from vllm.entrypoints.utils import get_max_tokens, should_include_usage
-from vllm.inputs.data import TokensPrompt
+from vllm.inputs.data import ProcessorInputs, TokensPrompt
 from vllm.logger import init_logger
 from vllm.logprobs import Logprob
 from vllm.outputs import CompletionOutput, RequestOutput
 from vllm.parser import ParserManager
 from vllm.reasoning import ReasoningParser
-from vllm.renderers.inputs import TokPrompt
 from vllm.sampling_params import BeamSearchParams, SamplingParams
 from vllm.tokenizers import TokenizerLike
 from vllm.tokenizers.mistral import (
@@ -221,7 +220,7 @@ class OpenAIServingChat(OpenAIServing):
     async def render_chat_request(
         self,
         request: ChatCompletionRequest,
-    ) -> tuple[list[ConversationMessage], list[TokPrompt]] | ErrorResponse:
+    ) -> tuple[list[ConversationMessage], list[ProcessorInputs]] | ErrorResponse:
         """
         render chat request by validating and preprocessing inputs.
 
@@ -380,7 +379,9 @@ class OpenAIServingChat(OpenAIServing):
         generators: list[AsyncGenerator[RequestOutput, None]] = []
         try:
             for i, engine_prompt in enumerate(engine_prompts):
-                prompt_text = self._extract_prompt_text(engine_prompt)
+                prompt_token_ids = self._extract_prompt_components(
+                    engine_prompt
+                ).token_ids
 
                 # If we are creating sub requests for multiple prompts, ensure that they
                 # have unique request ids.
@@ -431,35 +432,21 @@ class OpenAIServingChat(OpenAIServing):
                         trace_headers=trace_headers,
                     )
                 else:
-                    tok_params = request.build_tok_params(self.model_config)
-                    tokenization_kwargs = tok_params.get_encode_kwargs()
-
-                    engine_request = self.input_processor.process_inputs(
-                        sub_request_id,
-                        engine_prompt,
-                        sampling_params,
-                        lora_request=lora_request,
-                        tokenization_kwargs=tokenization_kwargs,
-                        trace_headers=trace_headers,
-                        priority=request.priority,
-                        data_parallel_rank=data_parallel_rank,
+                    reasoning_ended = (
+                        reasoning_parser.is_reasoning_end(prompt_token_ids or [])
+                        if reasoning_parser
+                        else None
                     )
-                    reasoning_ended = None
-                    if reasoning_parser:
-                        reasoning_ended = reasoning_parser.is_reasoning_end(
-                            engine_request.prompt_token_ids or []  # type: ignore[attr-defined]
-                        )
-                        engine_request.reasoning_ended = reasoning_ended
+
                     generator = self.engine_client.generate(
-                        engine_request,
+                        engine_prompt,
                         sampling_params,
                         sub_request_id,
                         lora_request=lora_request,
                         trace_headers=trace_headers,
                         priority=request.priority,
-                        prompt_text=prompt_text,
-                        tokenization_kwargs=tokenization_kwargs,
                         data_parallel_rank=data_parallel_rank,
+                        reasoning_ended=reasoning_ended,
                     )
 
                 generators.append(generator)

vllm/entrypoints/openai/completion/serving.py

@@ -34,10 +34,10 @@ from vllm.entrypoints.openai.engine.serving import (
 from vllm.entrypoints.openai.models.serving import OpenAIServingModels
 from vllm.entrypoints.utils import get_max_tokens, should_include_usage
 from vllm.exceptions import VLLMValidationError
+from vllm.inputs.data import ProcessorInputs
 from vllm.logger import init_logger
 from vllm.logprobs import Logprob
 from vllm.outputs import RequestOutput
-from vllm.renderers.inputs import TokPrompt
 from vllm.sampling_params import BeamSearchParams, SamplingParams
 from vllm.tokenizers import TokenizerLike
 from vllm.utils.async_utils import merge_async_iterators
@@ -80,7 +80,7 @@ class OpenAIServingCompletion(OpenAIServing):
     async def render_completion_request(
         self,
         request: CompletionRequest,
-    ) -> list[TokPrompt] | ErrorResponse:
+    ) -> list[ProcessorInputs] | ErrorResponse:
         """
         render completion request by validating and preprocessing inputs.
 
@@ -163,8 +163,6 @@ class OpenAIServingCompletion(OpenAIServing):
         generators: list[AsyncGenerator[RequestOutput, None]] = []
         try:
             for i, engine_prompt in enumerate(engine_prompts):
-                prompt_text = self._extract_prompt_text(engine_prompt)
-
                 max_tokens = get_max_tokens(
                     max_model_len,
                     request.max_tokens,
@@ -208,29 +206,13 @@ class OpenAIServingCompletion(OpenAIServing):
                         trace_headers=trace_headers,
                     )
                 else:
-                    tok_params = request.build_tok_params(self.model_config)
-                    tokenization_kwargs = tok_params.get_encode_kwargs()
-
-                    engine_request = self.input_processor.process_inputs(
-                        request_id_item,
-                        engine_prompt,
-                        sampling_params,
-                        lora_request=lora_request,
-                        tokenization_kwargs=tokenization_kwargs,
-                        trace_headers=trace_headers,
-                        priority=request.priority,
-                        data_parallel_rank=data_parallel_rank,
-                    )
-
                     generator = self.engine_client.generate(
-                        engine_request,
+                        engine_prompt,
                         sampling_params,
                         request_id_item,
                         lora_request=lora_request,
                         trace_headers=trace_headers,
                         priority=request.priority,
-                        prompt_text=prompt_text,
-                        tokenization_kwargs=tokenization_kwargs,
                         data_parallel_rank=data_parallel_rank,
                     )
 
@@ -312,7 +294,7 @@ class OpenAIServingCompletion(OpenAIServing):
     async def completion_stream_generator(
         self,
         request: CompletionRequest,
-        engine_prompts: list[TokPrompt],
+        engine_prompts: list[ProcessorInputs],
         result_generator: AsyncIterator[tuple[int, RequestOutput]],
         request_id: str,
         created_time: int,

vllm/entrypoints/openai/engine/serving.py

@@ -96,15 +96,19 @@ from vllm.entrypoints.serve.tokenize.protocol import (
 )
 from vllm.entrypoints.utils import get_max_tokens, sanitize_message
 from vllm.exceptions import VLLMValidationError
-from vllm.inputs.data import PromptType, SingletonPrompt, TokensPrompt
+from vllm.inputs.data import (
+    ProcessorInputs,
+    PromptType,
+    SingletonPrompt,
+    TokensPrompt,
+    token_inputs,
+)
 from vllm.logger import init_logger
 from vllm.logprobs import Logprob, PromptLogprobs
 from vllm.lora.request import LoRARequest
-from vllm.multimodal import MultiModalDataDict
 from vllm.outputs import CompletionOutput, PoolingRequestOutput, RequestOutput
 from vllm.pooling_params import PoolingParams
 from vllm.renderers import ChatParams, TokenizeParams, merge_kwargs
-from vllm.renderers.inputs import TokPrompt
 from vllm.renderers.inputs.preprocess import (
     extract_prompt_components,
     extract_prompt_len,
@@ -206,7 +210,7 @@ class ServeContext(Generic[RequestT]):
     request_id: str
     created_time: int = field(default_factory=lambda: int(time.time()))
     lora_request: LoRARequest | None = None
-    engine_prompts: list[TokPrompt] | None = None
+    engine_prompts: list[ProcessorInputs] | None = None
 
     result_generator: AsyncGenerator[tuple[int, PoolingRequestOutput], None] | None = (
         None
@@ -249,7 +253,7 @@ class OpenAIServing:
 
     async def beam_search(
         self,
-        prompt: TokPrompt,
+        prompt: ProcessorInputs,
         request_id: str,
         params: BeamSearchParams,
         lora_request: LoRARequest | None = None,
@@ -262,86 +266,53 @@ class OpenAIServing:
         length_penalty = params.length_penalty
         include_stop_str_in_output = params.include_stop_str_in_output
 
-        input_processor = self.input_processor
-        tokenizer = input_processor.tokenizer
-        if tokenizer is None:
-            raise VLLMValidationError(
-                "You cannot use beam search when `skip_tokenizer_init=True`",
-                parameter="skip_tokenizer_init",
-                value=True,
-            )
-
-        eos_token_id: int = tokenizer.eos_token_id  # type: ignore
-
-        if isinstance(prompt, dict) and "encoder_prompt" in prompt:
-            raise NotImplementedError("Encoder-decoder prompt not supported")
-
-        prompt_text: str | None = prompt.get("prompt")  # type: ignore
-        prompt_token_ids: list[int] = prompt.get("prompt_token_ids", [])  # type: ignore
-        multi_modal_data: MultiModalDataDict | None = prompt.get("multi_modal_data")  # type: ignore
-
-        mm_processor_kwargs: dict[str, Any] | None = None
+        tokenizer = self.renderer.get_tokenizer()
+        eos_token_id = tokenizer.eos_token_id
+        sort_beams_key = create_sort_beams_key_function(eos_token_id, length_penalty)
 
-        # This is a workaround to fix multimodal beam search; this is a
-        # bandaid fix for 2 small problems:
-        # 1. Multi_modal_data on the processed_inputs currently resolves to
-        #    `None`.
-        # 2. preprocessing above expands the multimodal placeholders. However,
-        #    this happens again in generation, so the double expansion causes
-        #    a mismatch.
-        # TODO - would be ideal to handle this more gracefully.
+        if prompt["type"] == "embeds":
+            raise NotImplementedError("Embedding prompt not supported for beam search")
+        if prompt["type"] == "enc_dec":
+            raise NotImplementedError(
+                "Encoder-decoder prompt not supported for beam search"
+            )
 
+        prompt_text = prompt.get("prompt")
+        prompt_token_ids = prompt["prompt_token_ids"]
         tokenized_length = len(prompt_token_ids)
 
-        sort_beams_key = create_sort_beams_key_function(eos_token_id, length_penalty)
-
         logprobs_num = 2 * beam_width
-        beam_search_params = SamplingParams(
+        sampling_params = SamplingParams(
             logprobs=logprobs_num,
             max_tokens=1,
             temperature=temperature,
         )
         all_beams = [
             BeamSearchSequence(
+                orig_prompt=prompt,
                 tokens=prompt_token_ids,
                 cum_logprob=0,
                 logprobs=[],
-                multi_modal_data=multi_modal_data,
-                mm_processor_kwargs=mm_processor_kwargs,
                 lora_request=lora_request,
             )
         ]
         completed = []
 
         for _ in range(max_tokens):
-            prompts_batch, lora_req_batch = zip(
-                *[
-                    (
-                        TokensPrompt(
-                            prompt_token_ids=beam.tokens,
-                            multi_modal_data=beam.multi_modal_data,
-                            mm_processor_kwargs=beam.mm_processor_kwargs,
-                        ),
-                        beam.lora_request,
-                    )
-                    for beam in all_beams
-                ]
-            )
-
             tasks = []
             request_id_batch = f"{request_id}-{random_uuid()}"
 
-            for i, (individual_prompt, lora_req) in enumerate(
-                zip(prompts_batch, lora_req_batch)
-            ):
+            for i, beam in enumerate(all_beams):
+                prompt_item = beam.get_prompt()
+                lora_request_item = beam.lora_request
                 request_id_item = f"{request_id_batch}-beam-{i}"
                 task = asyncio.create_task(
                     collect_from_async_generator(
                         self.engine_client.generate(
-                            individual_prompt,
-                            beam_search_params,
+                            prompt_item,
+                            sampling_params,
                             request_id_item,
-                            lora_request=lora_req,
+                            lora_request=lora_request_item,
                             trace_headers=trace_headers,
                         )
                     )
@@ -406,6 +377,7 @@ class OpenAIServing:
                     logprobs_entry = result.outputs[0].logprobs[0]
                     completed.append(
                         BeamSearchSequence(
+                            orig_prompt=prompt,
                             tokens=current_beam.tokens + [eos_token_id]
                             if include_stop_str_in_output
                             else current_beam.tokens,
@@ -433,12 +405,11 @@ class OpenAIServing:
                 logprobs_entry = result.outputs[0].logprobs[0]
                 new_beams.append(
                     BeamSearchSequence(
+                        orig_prompt=prompt,
                         tokens=current_beam.tokens + [token_id],
                         logprobs=current_beam.logprobs + [logprobs_entry],
                         lora_request=current_beam.lora_request,
                         cum_logprob=float(all_beams_logprob[idx]),
-                        multi_modal_data=current_beam.multi_modal_data,
-                        mm_processor_kwargs=current_beam.mm_processor_kwargs,
                     )
                 )
 
@@ -958,7 +929,7 @@ class OpenAIServing:
         request: RendererRequest,
         prompt_input: str | list[str] | list[int] | list[list[int]] | None,
         prompt_embeds: bytes | list[bytes] | None,
-    ) -> list[TokPrompt]:
+    ) -> list[ProcessorInputs]:
         prompts = list[SingletonPrompt | bytes]()
         if prompt_embeds is not None:  # embeds take higher priority
             prompts.extend(prompt_to_seq(prompt_embeds))
@@ -971,7 +942,7 @@ class OpenAIServing:
         self,
         request: RendererRequest,
         prompts: Sequence[PromptType | bytes],
-    ) -> list[TokPrompt]:
+    ) -> list[ProcessorInputs]:
         renderer = self.renderer
         model_config = self.model_config
 
@@ -1004,7 +975,7 @@ class OpenAIServing:
         default_template_kwargs: dict[str, Any] | None,
         tool_dicts: list[dict[str, Any]] | None = None,
         tool_parser: Callable[[TokenizerLike], ToolParser] | None = None,
-    ) -> tuple[list[ConversationMessage], list[TokPrompt]]:
+    ) -> tuple[list[ConversationMessage], list[ProcessorInputs]]:
         from vllm.tokenizers.mistral import MistralTokenizer
 
         renderer = self.renderer
@@ -1052,13 +1023,13 @@ class OpenAIServing:
 
         return conversation, [engine_prompt]
 
-    def _extract_prompt_components(self, prompt: object):
+    def _extract_prompt_components(self, prompt: PromptType | ProcessorInputs):
         return extract_prompt_components(self.model_config, prompt)
 
-    def _extract_prompt_text(self, prompt: object):
+    def _extract_prompt_text(self, prompt: ProcessorInputs):
         return self._extract_prompt_components(prompt).text
 
-    def _extract_prompt_len(self, prompt: object):
+    def _extract_prompt_len(self, prompt: ProcessorInputs):
         return extract_prompt_len(self.model_config, prompt)
 
     async def _render_next_turn(
@@ -1088,16 +1059,14 @@ class OpenAIServing:
     async def _generate_with_builtin_tools(
         self,
         request_id: str,
-        engine_prompt: TokPrompt,
+        engine_prompt: ProcessorInputs,
         sampling_params: SamplingParams,
-        tok_params: TokenizeParams,
         context: ConversationContext,
         lora_request: LoRARequest | None = None,
         priority: int = 0,
         trace_headers: Mapping[str, str] | None = None,
     ):
         max_model_len = self.model_config.max_model_len
-        prompt_text = self._extract_prompt_text(engine_prompt)
 
         orig_priority = priority
         sub_request = 0
@@ -1112,26 +1081,13 @@ class OpenAIServing:
                 lora_request=lora_request,
             )
 
-            tokenization_kwargs = tok_params.get_encode_kwargs()
-            engine_request = self.input_processor.process_inputs(
-                sub_request_id,
-                engine_prompt,
-                sampling_params,
-                lora_request=lora_request,
-                tokenization_kwargs=tokenization_kwargs,
-                trace_headers=trace_headers,
-                priority=priority,
-            )
-
             generator = self.engine_client.generate(
-                engine_request,
+                engine_prompt,
                 sampling_params,
                 sub_request_id,
                 lora_request=lora_request,
                 trace_headers=trace_headers,
                 priority=priority,
-                prompt_text=prompt_text,
-                tokenization_kwargs=tokenization_kwargs,
             )
 
             async for res in generator:
@@ -1154,11 +1110,11 @@ class OpenAIServing:
             # Render the next prompt token ids and update sampling_params.
             if isinstance(context, (HarmonyContext, StreamingHarmonyContext)):
                 token_ids = context.render_for_completion()
-                engine_prompt = TokensPrompt(prompt_token_ids=token_ids)
+                engine_prompt = token_inputs(token_ids)
 
                 sampling_params.max_tokens = max_model_len - len(token_ids)
             elif isinstance(context, ParsableContext):
-                engine_prompts = await self._render_next_turn(
+                (engine_prompt,) = await self._render_next_turn(
                     context.request,
                     context.parser.response_messages,
                     context.tool_dicts,
@@ -1166,8 +1122,6 @@ class OpenAIServing:
                     context.chat_template,
                     context.chat_template_content_format,
                 )
-                engine_prompt = engine_prompts[0]
-                prompt_text = self._extract_prompt_text(engine_prompt)
 
                 sampling_params.max_tokens = get_max_tokens(
                     max_model_len,
@@ -1184,7 +1138,7 @@ class OpenAIServing:
     def _log_inputs(
         self,
         request_id: str,
-        inputs: PromptType | TokPrompt,
+        inputs: PromptType | ProcessorInputs,
         params: SamplingParams | PoolingParams | BeamSearchParams | None,
         lora_request: LoRARequest | None,
     ) -> None:

vllm/entrypoints/openai/realtime/serving.py

@@ -15,6 +15,7 @@ from vllm.entrypoints.openai.models.serving import OpenAIServingModels
 from vllm.inputs.data import PromptType
 from vllm.logger import init_logger
 from vllm.model_executor.models.interfaces import SupportsRealtime
+from vllm.renderers.inputs.preprocess import parse_model_prompt
 
 logger = init_logger(__name__)
 
@@ -70,15 +71,20 @@ class OpenAIServingRealtime(OpenAIServing):
         Yields:
             StreamingInput objects containing audio prompts for the engine
         """
+        model_config = self.model_config
+        renderer = self.renderer
 
         # mypy is being stupid
         # TODO(Patrick) - fix this
         stream_input_iter = cast(
             AsyncGenerator[PromptType, None],
             self.model_cls.buffer_realtime_audio(
-                audio_stream, input_stream, self.model_config
+                audio_stream, input_stream, model_config
             ),
         )
 
         async for prompt in stream_input_iter:
-            yield StreamingInput(prompt=prompt)
+            parsed_prompt = parse_model_prompt(model_config, prompt)
+            (engine_prompt,) = await renderer.render_cmpl_async([parsed_prompt])
+
+            yield StreamingInput(prompt=engine_prompt)

vllm/entrypoints/openai/responses/context.py

@@ -9,7 +9,7 @@ from abc import ABC, abstractmethod
 from collections.abc import Callable
 from contextlib import AsyncExitStack
 from dataclasses import replace
-from typing import TYPE_CHECKING, Union
+from typing import TYPE_CHECKING, Final, Union
 
 from openai.types.responses.response_function_tool_call_output_item import (
     ResponseFunctionToolCallOutputItem,
@@ -304,7 +304,7 @@ class ParsableContext(ConversationContext):
 
         self.tool_dicts = construct_tool_dicts(request.tools, request.tool_choice)
         self.chat_template = chat_template
-        self.chat_template_content_format = chat_template_content_format
+        self.chat_template_content_format: Final = chat_template_content_format
 
         self.input_messages: list[ResponseRawMessageAndToken] = []
         self.output_messages: list[ResponseRawMessageAndToken] = []

vllm/entrypoints/openai/responses/serving.py

@@ -116,13 +116,12 @@ from vllm.entrypoints.openai.responses.utils import (
 )
 from vllm.entrypoints.utils import get_max_tokens
 from vllm.exceptions import VLLMValidationError
-from vllm.inputs.data import TokensPrompt
+from vllm.inputs.data import ProcessorInputs, token_inputs
 from vllm.logger import init_logger
 from vllm.logprobs import Logprob as SampleLogprob
 from vllm.logprobs import SampleLogprobs
 from vllm.outputs import CompletionOutput
 from vllm.parser import ParserManager
-from vllm.renderers.inputs import TokPrompt
 from vllm.sampling_params import SamplingParams, StructuredOutputsParams
 from vllm.tokenizers import TokenizerLike
 from vllm.utils import random_uuid
@@ -298,7 +297,7 @@ class OpenAIServingResponses(OpenAIServing):
 
     def _validate_generator_input(
         self,
-        engine_prompt: TokPrompt,
+        engine_prompt: ProcessorInputs,
     ) -> ErrorResponse | None:
         """Add validations to the input to the generator here."""
         prompt_len = self._extract_prompt_len(engine_prompt)
@@ -458,7 +457,6 @@ class OpenAIServingResponses(OpenAIServing):
                 sampling_params = request.to_sampling_params(
                     default_max_tokens, self.default_sampling_params
                 )
-                tok_params = request.build_tok_params(self.model_config)
 
                 trace_headers = (
                     None
@@ -512,7 +510,6 @@ class OpenAIServingResponses(OpenAIServing):
                     request_id=request.request_id,
                     engine_prompt=engine_prompt,
                     sampling_params=sampling_params,
-                    tok_params=tok_params,
                     context=context,
                     lora_request=lora_request,
                     priority=request.priority,
@@ -647,7 +644,7 @@ class OpenAIServingResponses(OpenAIServing):
 
         messages = self._construct_input_messages_with_harmony(request, prev_response)
         prompt_token_ids = render_for_completion(messages)
-        engine_prompt = TokensPrompt(prompt_token_ids=prompt_token_ids)
+        engine_prompt = token_inputs(prompt_token_ids)
 
         # Add cache_salt if provided in the request
         if request.cache_salt is not None:

vllm/entrypoints/openai/speech_to_text/speech_to_text.py

@@ -36,14 +36,15 @@ from vllm.entrypoints.openai.speech_to_text.protocol import (
     TranslationSegment,
     TranslationStreamResponse,
 )
+from vllm.entrypoints.utils import get_max_tokens
 from vllm.exceptions import VLLMValidationError
-from vllm.inputs.data import PromptType
+from vllm.inputs import ProcessorInputs
 from vllm.logger import init_logger
 from vllm.logprobs import FlatLogprobs, Logprob
 from vllm.model_executor.models import SupportsTranscription, supports_transcription
 from vllm.outputs import RequestOutput
-from vllm.renderers.inputs import EncoderDecoderDictPrompt
-from vllm.renderers.inputs.preprocess import parse_enc_dec_prompt
+from vllm.renderers.inputs import DictPrompt, EncoderDecoderDictPrompt
+from vllm.renderers.inputs.preprocess import parse_enc_dec_prompt, parse_model_prompt
 from vllm.tokenizers import get_tokenizer
 from vllm.utils.import_utils import PlaceholderModule
 
@@ -202,8 +203,6 @@ class OpenAISpeechToText(OpenAIServing):
             return
 
         try:
-            from vllm.sampling_params import SamplingParams
-
             warmup_start = time.perf_counter()
             logger.info("Warming up multimodal input processor...")
 
@@ -221,21 +220,11 @@ class OpenAISpeechToText(OpenAIServing):
                 request_prompt="",
                 to_language=None,
             )
-
-            # Create minimal sampling params
-            dummy_params = SamplingParams(
-                max_tokens=1,
-                temperature=0.0,
-                skip_clone=True,  # Internal warmup, safe to skip clone
-            )
+            parsed_prompt = parse_model_prompt(self.model_config, dummy_prompt)
 
             # Process the dummy input through the input processor
             # This will trigger all the multimodal processing initialization
-            _ = self.input_processor.process_inputs(
-                request_id="warmup",
-                prompt=dummy_prompt,
-                params=dummy_params,
-            )
+            _ = self.renderer.render_cmpl([parsed_prompt])
 
             warmup_elapsed = time.perf_counter() - warmup_start
             logger.info("Input processor warmup completed in %.2fs", warmup_elapsed)
@@ -257,7 +246,7 @@ class OpenAISpeechToText(OpenAIServing):
         self,
         request: SpeechToTextRequest,
         audio_data: bytes,
-    ) -> tuple[list[PromptType], float]:
+    ) -> tuple[list[ProcessorInputs], float]:
         # Validate request
         language = self.model_cls.validate_language(request.language)
         # Skip to_language validation to avoid extra logging for Whisper.
@@ -285,7 +274,7 @@ class OpenAISpeechToText(OpenAIServing):
             and duration > self.asr_config.max_audio_clip_s
         )
         chunks = [y] if not do_split_audio else self._split_audio(y, int(sr))
-        prompts = []
+        parsed_prompts: list[DictPrompt] = []
         for chunk in chunks:
             # The model has control over the construction, as long as it
             # returns a valid PromptType.
@@ -298,12 +287,19 @@ class OpenAISpeechToText(OpenAIServing):
                 request_prompt=request.prompt,
                 to_language=to_language,
             )
+
+            parsed_prompt: DictPrompt
             if request.response_format == "verbose_json":
-                prompt = self._preprocess_verbose_prompt(parse_enc_dec_prompt(prompt))
+                parsed_prompt = parse_enc_dec_prompt(prompt)
+                parsed_prompt = self._preprocess_verbose_prompt(parsed_prompt)
+            else:
+                parsed_prompt = parse_model_prompt(self.model_config, prompt)
+
+            parsed_prompts.append(parsed_prompt)
 
-            prompts.append(prompt)
+        engine_prompts = await self.renderer.render_cmpl_async(parsed_prompts)
 
-        return prompts, duration
+        return engine_prompts, duration
 
     def _preprocess_verbose_prompt(self, prompt: EncoderDecoderDictPrompt):
         dec_prompt = prompt["decoder_prompt"]
@@ -436,7 +432,7 @@ class OpenAISpeechToText(OpenAIServing):
         try:
             lora_request = self._maybe_get_adapters(request)
 
-            prompts, duration_s = await self._preprocess_speech_to_text(
+            engine_prompts, duration_s = await self._preprocess_speech_to_text(
                 request=request,
                 audio_data=audio_data,
             )
@@ -445,57 +441,54 @@ class OpenAISpeechToText(OpenAIServing):
             logger.exception("Error in preprocessing prompt inputs")
             return self.create_error_response(e)
 
+        # Schedule the request and get the result generator.
+        max_model_len = self.model_config.max_model_len
         list_result_generator: list[AsyncGenerator[RequestOutput, None]] | None = None
         try:
             # Unlike most decoder-only models, whisper generation length is not
             # constrained by the size of the input audio, which is mapped to a
             # fixed-size log-mel-spectogram. Still, allow for fewer tokens to be
             # generated by respecting the extra completion tokens arg.
-            if request.max_completion_tokens is None:
-                default_max_tokens = self.model_config.max_model_len
-            else:
-                default_max_tokens = min(
-                    self.model_config.max_model_len, request.max_completion_tokens
-                )
+            max_tokens = get_max_tokens(
+                max_model_len,
+                request.max_completion_tokens,
+                0,
+                self.default_sampling_params,
+            )
+
             sampling_params = request.to_sampling_params(
-                default_max_tokens, self.default_sampling_params
+                max_tokens,
+                self.default_sampling_params,
             )
             if request.response_format == "verbose_json":
                 sampling_params.logprobs = 1
 
-            self._log_inputs(
-                request_id,
-                # It will not display special tokens like <|startoftranscript|>
-                request.prompt,
-                params=sampling_params,
-                lora_request=lora_request,
-            )
-
-            trace_headers = (
-                None
-                if raw_request is None
-                else await self._get_trace_headers(raw_request.headers)
-            )
-
             list_result_generator = []
-            for i, prompt in enumerate(prompts):
+            for i, engine_prompt in enumerate(engine_prompts):
                 request_id_item = f"{request_id}_{i}"
-                engine_request = self.input_processor.process_inputs(
+
+                self._log_inputs(
                     request_id_item,
-                    prompt,
+                    engine_prompt,
+                    params=sampling_params,
+                    lora_request=lora_request,
+                )
+
+                trace_headers = (
+                    None
+                    if raw_request is None
+                    else await self._get_trace_headers(raw_request.headers)
+                )
+
+                generator = self.engine_client.generate(
+                    engine_prompt,
                     sampling_params,
+                    request_id_item,
                     lora_request=lora_request,
                     trace_headers=trace_headers,
-                    priority=0,
-                )
-                list_result_generator.append(
-                    self.engine_client.generate(
-                        engine_request,
-                        sampling_params,
-                        request_id_item,
-                        lora_request=lora_request,
-                    )
                 )
+
+                list_result_generator.append(generator)
         except ValueError as e:
             return self.create_error_response(e)
 

vllm/entrypoints/pooling/embed/serving.py

@@ -28,11 +28,10 @@ from vllm.entrypoints.pooling.utils import (
     encode_pooling_output_base64,
     encode_pooling_output_float,
 )
-from vllm.inputs.data import TokensPrompt
+from vllm.inputs.data import ProcessorInputs, TokensPrompt, token_inputs
 from vllm.logger import init_logger
 from vllm.outputs import PoolingOutput, PoolingRequestOutput
 from vllm.pooling_params import PoolingParams
-from vllm.renderers.inputs import TokPrompt
 from vllm.utils.async_utils import merge_async_iterators
 from vllm.utils.collection_utils import chunk_list
 from vllm.utils.serial_utils import EmbedDType, Endianness
@@ -256,7 +255,7 @@ class OpenAIServingEmbedding(OpenAIServing):
             chunk_request_id = f"{ctx.request_id}-prompt-{prompt_idx}-chunk-{chunk_idx}"
 
             # Create engine prompt for this chunk
-            chunk_engine_prompt = TokensPrompt(prompt_token_ids=chunk_tokens)
+            chunk_engine_prompt = token_inputs(chunk_tokens)
 
             # Log the chunk
             self._log_inputs(
@@ -266,16 +265,12 @@ class OpenAIServingEmbedding(OpenAIServing):
                 lora_request=ctx.lora_request,
             )
 
-            tok_params = ctx.request.build_tok_params(self.model_config)
-            tokenization_kwargs = tok_params.get_encode_kwargs()
-
             # Create generator for this chunk and wrap it to return indices
             original_generator = self.engine_client.encode(
                 chunk_engine_prompt,
                 pooling_params,
                 chunk_request_id,
                 lora_request=ctx.lora_request,
-                tokenization_kwargs=tokenization_kwargs,
                 trace_headers=trace_headers,
                 priority=ctx.request.priority,
             )
@@ -362,7 +357,7 @@ class OpenAIServingEmbedding(OpenAIServing):
     async def _create_single_prompt_generator(
         self,
         ctx: EmbeddingServeContext,
-        engine_prompt: TokPrompt,
+        engine_prompt: ProcessorInputs,
         pooling_params: PoolingParams,
         trace_headers: Mapping[str, str] | None,
         prompt_index: int,
@@ -377,16 +372,12 @@ class OpenAIServingEmbedding(OpenAIServing):
             lora_request=ctx.lora_request,
         )
 
-        tok_params = ctx.request.build_tok_params(self.model_config)
-        tokenization_kwargs = tok_params.get_encode_kwargs()
-
         # Return the original generator without wrapping
         return self.engine_client.encode(
             engine_prompt,
             pooling_params,
             request_id_item,
             lora_request=ctx.lora_request,
-            tokenization_kwargs=tokenization_kwargs,
             trace_headers=trace_headers,
             priority=ctx.request.priority,
         )

vllm/entrypoints/pooling/pooling/serving.py

@@ -33,10 +33,9 @@ from vllm.entrypoints.pooling.utils import (
     encode_pooling_output_base64,
     encode_pooling_output_float,
 )
-from vllm.inputs import PromptType
+from vllm.inputs import ProcessorInputs
 from vllm.logger import init_logger
 from vllm.outputs import PoolingRequestOutput
-from vllm.renderers.inputs import TokPrompt
 from vllm.renderers.inputs.preprocess import prompt_to_seq
 from vllm.utils.async_utils import merge_async_iterators
 from vllm.utils.serial_utils import EmbedDType, EncodingFormat, Endianness
@@ -93,7 +92,7 @@ class OpenAIServingPooling(OpenAIServing):
                     "dimensions is currently not supported"
                 )
 
-            engine_prompts: Sequence[PromptType | TokPrompt]
+            engine_prompts: Sequence[ProcessorInputs]
             if use_io_processor := isinstance(request, IOProcessorRequest):
                 if self.io_processor is None:
                     raise ValueError(
@@ -152,9 +151,6 @@ class OpenAIServingPooling(OpenAIServing):
             else:
                 pooling_params = request.to_pooling_params()  # type: ignore
 
-            tok_params = request.build_tok_params(self.model_config)
-            tokenization_kwargs = tok_params.get_encode_kwargs()
-
             for i, engine_prompt in enumerate(engine_prompts):
                 request_id_item = f"{request_id}-{i}"
 
@@ -176,7 +172,6 @@ class OpenAIServingPooling(OpenAIServing):
                     pooling_params,
                     request_id_item,
                     lora_request=lora_request,
-                    tokenization_kwargs=tokenization_kwargs,
                     trace_headers=trace_headers,
                     priority=request.priority,
                 )

vllm/entrypoints/pooling/score/serving.py

@@ -35,7 +35,7 @@ from vllm.entrypoints.pooling.score.utils import (
     get_score_prompt,
     validate_score_input,
 )
-from vllm.inputs.data import TokensPrompt
+from vllm.inputs.data import ProcessorInputs, TokensPrompt, token_inputs
 from vllm.logger import init_logger
 from vllm.lora.request import LoRARequest
 from vllm.outputs import PoolingRequestOutput, ScoringRequestOutput
@@ -108,12 +108,15 @@ class ServingScores(OpenAIServing):
             *(encode_async(t, **tokenization_kwargs) for t in input_texts)
         )
 
-        engine_prompts: list[TokensPrompt] = []
+        engine_prompts: list[ProcessorInputs] = []
         for tok_result, input_text in zip(tokenized_prompts, input_texts):
             text_token_prompt = self._validate_input(request, tok_result, input_text)
 
             engine_prompts.append(
-                TokensPrompt(prompt_token_ids=text_token_prompt["prompt_token_ids"])
+                token_inputs(
+                    text_token_prompt["prompt_token_ids"],
+                    prompt=input_text,
+                )
             )
 
         # Schedule the request and get the result generator.
@@ -125,7 +128,7 @@ class ServingScores(OpenAIServing):
 
             self._log_inputs(
                 request_id_item,
-                input_texts[i],
+                engine_prompt,
                 params=pooling_params,
                 lora_request=lora_request,
             )
@@ -207,12 +210,15 @@ class ServingScores(OpenAIServing):
             *(encode_async(t, **tokenization_kwargs) for t in input_texts)
         )
 
-        engine_prompts: list[TokensPrompt] = []
+        engine_prompts: list[ProcessorInputs] = []
         for tok_result, input_text in zip(tokenized_prompts, input_texts):
             text_token_prompt = self._validate_input(request, tok_result, input_text)
 
             engine_prompts.append(
-                TokensPrompt(prompt_token_ids=text_token_prompt["prompt_token_ids"])
+                token_inputs(
+                    text_token_prompt["prompt_token_ids"],
+                    prompt=input_text,
+                )
             )
 
         # Schedule the request and get the result generator.
@@ -225,7 +231,7 @@ class ServingScores(OpenAIServing):
 
             self._log_inputs(
                 request_id_item,
-                input_texts[i],
+                engine_prompt,
                 params=pooling_params,
                 lora_request=lora_request,
             )

vllm/entrypoints/serve/disagg/serving.py

@@ -29,7 +29,6 @@ from vllm.entrypoints.serve.disagg.protocol import (
     GenerateResponse,
     GenerateResponseChoice,
 )
-from vllm.inputs.data import TokensPrompt
 from vllm.logger import init_logger
 from vllm.logprobs import Logprob
 from vllm.outputs import RequestOutput
@@ -116,7 +115,7 @@ class ServingTokens(OpenAIServing):
 
             self._log_inputs(
                 request_id,
-                TokensPrompt(prompt_token_ids=request.token_ids),
+                engine_prompt,
                 params=sampling_params,
                 lora_request=lora_request,
             )
@@ -127,27 +126,13 @@ class ServingTokens(OpenAIServing):
                 else await self._get_trace_headers(raw_request.headers)
             )
 
-            tok_params = request.build_tok_params(self.model_config)
-            tokenization_kwargs = tok_params.get_encode_kwargs()
-
-            engine_request = self.input_processor.process_inputs(
-                request_id,
-                engine_prompt,
-                sampling_params,
-                lora_request=lora_request,
-                tokenization_kwargs=tokenization_kwargs,
-                trace_headers=trace_headers,
-                priority=request.priority,
-            )
-
             result_generator = self.engine_client.generate(
-                engine_request,
+                engine_prompt,
                 sampling_params,
                 request_id,
                 lora_request=lora_request,
                 trace_headers=trace_headers,
                 priority=request.priority,
-                tokenization_kwargs=tokenization_kwargs,
             )
 
         except ValueError as e:

vllm/entrypoints/serve/tokenize/serving.py

@@ -20,7 +20,7 @@ from vllm.entrypoints.serve.tokenize.protocol import (
     TokenizeResponse,
     TokenizerInfoResponse,
 )
-from vllm.inputs import TokensPrompt
+from vllm.inputs import TokensPrompt, token_inputs
 from vllm.logger import init_logger
 from vllm.tokenizers import TokenizerLike
 
@@ -135,7 +135,7 @@ class OpenAIServingTokenization(OpenAIServing):
 
         self._log_inputs(
             request_id,
-            TokensPrompt(prompt_token_ids=request.tokens),
+            token_inputs(request.tokens),
             params=None,
             lora_request=lora_request,
         )

vllm/inputs/data.py

@@ -187,6 +187,9 @@ class _InputOptions(TypedDict):
     Additional options available to all input types.
     """
 
+    arrival_time: NotRequired[float]
+    """The time when the input was received (before rendering)."""
+
     cache_salt: NotRequired[str]
     """Optional cache salt to be used for prefix caching."""
 
@@ -300,6 +303,9 @@ class EncoderDecoderInputs(TypedDict):
     decoder_prompt: DecoderInputs
     """The inputs for the decoder portion."""
 
+    arrival_time: NotRequired[float]
+    """The time when the input was received (before rendering)."""
+
 
 ProcessorInputs: TypeAlias = DecoderOnlyInputs | EncoderDecoderInputs
 """