[Renderer] Move Processor out of LLMEngine (#26165)

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

vllm/entrypoints/llm.py

@@ -37,6 +37,7 @@ from vllm.entrypoints.utils import (_validate_truncation_size,
                                     log_non_default_args)
 from vllm.inputs import (DataPrompt, PromptType, SingletonPrompt, TextPrompt,
                          TokensPrompt)
+from vllm.inputs.parse import get_prompt_components
 from vllm.logger import init_logger
 from vllm.lora.request import LoRARequest
 from vllm.model_executor.layers.quantization import QuantizationMethods
@@ -49,10 +50,13 @@ from vllm.sampling_params import (BeamSearchParams, RequestOutputKind,
                                   SamplingParams)
 from vllm.tasks import PoolingTask
 from vllm.transformers_utils.tokenizer import (AnyTokenizer, MistralTokenizer,
-                                               get_cached_tokenizer)
+                                               get_cached_tokenizer,
+                                               init_tokenizer_from_configs)
 from vllm.usage.usage_lib import UsageContext
 from vllm.utils import Counter, Device, as_iter, is_list_of
+from vllm.v1.engine import EngineCoreRequest
 from vllm.v1.engine.llm_engine import LLMEngine
+from vllm.v1.engine.processor import Processor
 from vllm.v1.sample.logits_processor import LogitsProcessor
 
 if TYPE_CHECKING:
@@ -312,6 +316,10 @@ class LLM:
         self.io_processor = get_io_processor(self.llm_engine.vllm_config,
                                              io_processor_plugin)
 
+    @property
+    def model_config(self):
+        return self.llm_engine.model_config
+
     def get_tokenizer(self) -> AnyTokenizer:
         return self.llm_engine.get_tokenizer()
 
@@ -324,6 +332,16 @@ class LLM:
         else:
             self.llm_engine.tokenizer = get_cached_tokenizer(tokenizer)
 
+    def _get_processor(self) -> Processor:
+        if not hasattr(self, "_processor"):
+            vllm_config = self.llm_engine.vllm_config
+            if self.model_config.skip_tokenizer_init:
+                tokenizer = None
+            else:
+                tokenizer = init_tokenizer_from_configs(self.model_config)
+            self._processor = Processor(vllm_config, tokenizer)
+        return self._processor
+
     def get_default_sampling_params(self) -> SamplingParams:
         if self.default_sampling_params is None:
             self.default_sampling_params = (
@@ -1497,8 +1515,6 @@ class LLM:
             tqdm_func = use_tqdm if callable(use_tqdm) else tqdm
             it = tqdm_func(it, desc="Adding requests")
 
-        model_config = self.llm_engine.model_config
-
         for i, prompt in enumerate(it):
 
             if isinstance(prompt, dict):
@@ -1506,17 +1522,9 @@ class LLM:
                     prompt.get("multi_modal_data"),
                     prompt.get("multi_modal_uuids"))
 
-            param = params[i] if isinstance(params, Sequence) else params
-
-            tokenization_kwargs: dict[str, Any] = {}
-            _validate_truncation_size(model_config.max_model_len,
-                                      param.truncate_prompt_tokens,
-                                      tokenization_kwargs)
-
             self._add_request(
                 prompt,
                 params[i] if isinstance(params, Sequence) else params,
-                tokenization_kwargs=tokenization_kwargs,
                 lora_request=lora_request[i] if isinstance(
                     lora_request, Sequence) else lora_request,
                 priority=priority[i] if priority else 0,
@@ -1557,22 +1565,58 @@ class LLM:
                     raise ValueError(f"Multi-modal data for {modality} is None"
                                      f" but UUID is not provided")
 
+    def _process_inputs(
+        self,
+        request_id: str,
+        engine_prompt: PromptType,
+        params: Union[SamplingParams, PoolingParams],
+        *,
+        lora_request: Optional[LoRARequest],
+        priority: int,
+    ) -> tuple[EngineCoreRequest, dict[str, Any]]:
+        """Use the Processor to process inputs for LLMEngine."""
+        tokenization_kwargs: dict[str, Any] = {}
+        _validate_truncation_size(self.model_config.max_model_len,
+                                  params.truncate_prompt_tokens,
+                                  tokenization_kwargs)
+
+        processor = self._get_processor()
+        engine_request = processor.process_inputs(
+            request_id,
+            engine_prompt,
+            params,
+            lora_request=lora_request,
+            tokenization_kwargs=tokenization_kwargs,
+            priority=priority,
+        )
+        return engine_request, tokenization_kwargs
+
     def _add_request(
         self,
         prompt: PromptType,
         params: Union[SamplingParams, PoolingParams],
-        tokenization_kwargs: Optional[dict[str, Any]] = None,
         lora_request: Optional[LoRARequest] = None,
         priority: int = 0,
     ) -> None:
+        prompt_text, _, _ = get_prompt_components(prompt)
         request_id = str(next(self.request_counter))
-        self.llm_engine.add_request(
+
+        engine_request, tokenization_kwargs = self._process_inputs(
             request_id,
             prompt,
             params,
             lora_request=lora_request,
+            priority=priority,
+        )
+
+        self.llm_engine.add_request(
+            request_id,
+            engine_request,
+            params,
+            lora_request=lora_request,
             tokenization_kwargs=tokenization_kwargs,
             priority=priority,
+            prompt_text=prompt_text,
         )
 
     def _run_engine(

vllm/entrypoints/openai/serving_engine.py

@@ -7,8 +7,7 @@ import traceback
 from collections.abc import AsyncGenerator, Iterable, Mapping, Sequence
 from concurrent.futures import ThreadPoolExecutor
 from http import HTTPStatus
-from typing import (Any, Callable, ClassVar, Generic, NamedTuple, Optional,
-                    TypeVar, Union)
+from typing import Any, Callable, ClassVar, Generic, Optional, TypeVar, Union
 
 import torch
 from fastapi import Request
@@ -69,6 +68,7 @@ from vllm.entrypoints.renderer import (BaseRenderer, CompletionRenderer,
 # yapf: enable
 from vllm.inputs.data import PromptType
 from vllm.inputs.data import TokensPrompt as EngineTokensPrompt
+from vllm.inputs.parse import PromptComponents, get_prompt_components
 from vllm.logger import init_logger
 from vllm.logprobs import Logprob, PromptLogprobs
 from vllm.lora.request import LoRARequest
@@ -140,12 +140,6 @@ def is_embeds_prompt(prompt: RequestPrompt) -> TypeIs[EmbedsPrompt]:
             and "prompt_embeds" in prompt)
 
 
-class PromptComponents(NamedTuple):
-    text: Optional[str] = None
-    token_ids: Optional[list[int]] = None
-    embeds: Optional[torch.Tensor] = None
-
-
 RequestT = TypeVar("RequestT", bound=AnyRequest)
 
 
@@ -876,25 +870,23 @@ class OpenAIServing:
         self,
         request_id: str,
         engine_prompt: PromptType,
-        sampling_params: SamplingParams,
+        params: Union[SamplingParams, PoolingParams],
         *,
         lora_request: Optional[LoRARequest],
         trace_headers: Optional[Mapping[str, str]],
         priority: int,
     ) -> tuple[EngineCoreRequest, dict[str, Any]]:
-        """
-        using the Processor to process inputs for AsyncLLM
-        """
+        """Use the Processor to process inputs for AsyncLLM."""
         tokenization_kwargs: dict[str, Any] = {}
         _validate_truncation_size(self.max_model_len,
-                                  sampling_params.truncate_prompt_tokens,
+                                  params.truncate_prompt_tokens,
                                   tokenization_kwargs)
 
         processor = await self._get_processor()
         engine_request = processor.process_inputs(
             request_id,
             engine_prompt,
-            sampling_params,
+            params,
             lora_request=lora_request,
             tokenization_kwargs=tokenization_kwargs,
             trace_headers=trace_headers,
@@ -973,25 +965,12 @@ class OpenAIServing:
 
     def _get_prompt_components(
         self,
-        inputs: Union[RequestPrompt, PromptType],
+        prompt: Union[RequestPrompt, PromptType],
     ) -> PromptComponents:
-        if isinstance(inputs, str):
-            return PromptComponents(text=inputs)
-        if isinstance(inputs, list):
-            return PromptComponents(token_ids=inputs)
-        if isinstance(inputs, dict):
-            return PromptComponents(
-                text=inputs.get("prompt"),  # type: ignore[arg-type]
-                token_ids=inputs.get(
-                    "prompt_token_ids"),  # type: ignore[arg-type]
-                embeds=inputs.get("prompt_embeds"),
-            )
+        if isinstance(prompt, list):
+            return PromptComponents(token_ids=prompt)
 
-        return PromptComponents(
-            text=getattr(inputs, "prompt", None),
-            token_ids=getattr(inputs, "prompt_token_ids", None),
-            embeds=getattr(inputs, "prompt_embeds", None),
-        )
+        return get_prompt_components(prompt)  # type: ignore[arg-type]
 
     def _log_inputs(
         self,

vllm/inputs/parse.py

 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 from collections.abc import Sequence
-from typing import Literal, Optional, TypedDict, Union, cast, overload
+from typing import (TYPE_CHECKING, Literal, NamedTuple, Optional, TypedDict,
+                    Union, cast, overload)
 
 from typing_extensions import TypeIs
 
@@ -11,6 +12,9 @@ from .data import (EmbedsPrompt, ExplicitEncoderDecoderPrompt, ProcessorInputs,
                    PromptType, SingletonInputs, SingletonPrompt, TextPrompt,
                    TokensPrompt)
 
+if TYPE_CHECKING:
+    import torch
+
 
 class ParsedText(TypedDict):
     content: str
@@ -149,3 +153,23 @@ def split_enc_dec_inputs(
         )
 
     return None, inputs
+
+
+class PromptComponents(NamedTuple):
+    text: Optional[str] = None
+    token_ids: Optional[list[int]] = None
+    embeds: Optional["torch.Tensor"] = None
+
+
+def get_prompt_components(prompt: PromptType) -> PromptComponents:
+    if isinstance(prompt, str):
+        return PromptComponents(text=prompt)
+
+    if (encoder_prompt := prompt.get("encoder_prompt")):
+        return get_prompt_components(encoder_prompt)  # type: ignore[arg-type]
+
+    return PromptComponents(
+        text=prompt.get("prompt"),  # type: ignore[arg-type]
+        token_ids=prompt.get("prompt_token_ids"),  # type: ignore[arg-type]
+        embeds=prompt.get("prompt_embeds"),
+    )

vllm/v1/engine/llm_engine.py

@@ -27,6 +27,7 @@ from vllm.transformers_utils.tokenizer import (AnyTokenizer,
                                                init_tokenizer_from_configs)
 from vllm.usage.usage_lib import UsageContext
 from vllm.utils import Device
+from vllm.v1.engine import EngineCoreRequest
 from vllm.v1.engine.core_client import EngineCoreClient
 from vllm.v1.engine.output_processor import OutputProcessor
 from vllm.v1.engine.parallel_sampling import ParentRequest
@@ -213,13 +214,14 @@ class LLMEngine:
     def add_request(
         self,
         request_id: str,
-        prompt: PromptType,
+        prompt: Union[EngineCoreRequest, PromptType],
         params: Union[SamplingParams, PoolingParams],
         arrival_time: Optional[float] = None,
         lora_request: Optional[LoRARequest] = None,
         tokenization_kwargs: Optional[dict[str, Any]] = None,
         trace_headers: Optional[Mapping[str, str]] = None,
         priority: int = 0,
+        prompt_text: Optional[str] = None,
     ) -> None:
         # Validate the request_id type.
         if not isinstance(request_id, str):
@@ -227,12 +229,18 @@ class LLMEngine:
                 f"request_id must be a string, got {type(request_id)}")
 
         # Process raw inputs into the request.
-        request = self.processor.process_inputs(request_id, prompt, params,
-                                                arrival_time, lora_request,
-                                                tokenization_kwargs,
-                                                trace_headers, priority)
-        prompt_text = prompt if isinstance(prompt,
-                                           str) else prompt.get("prompt")
+        if isinstance(prompt, EngineCoreRequest):
+            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_id, prompt, params,
+                                                    arrival_time, lora_request,
+                                                    tokenization_kwargs,
+                                                    trace_headers, priority)
+            prompt_text = (prompt if isinstance(prompt, str) else
+                           prompt.get("prompt"))
 
         n = params.n if isinstance(params, SamplingParams) else 1