[Frontend] Delegate preprocessing to `OpenAIServingRender` (#36483)

Signed-off-by: Sage Ahrac <sagiahrak@gmail.com>

tests/entrypoints/openai/test_chat_error.py

@@ -13,6 +13,7 @@ from vllm.entrypoints.openai.chat_completion.serving import OpenAIServingChat
 from vllm.entrypoints.openai.engine.protocol import GenerationError
 from vllm.entrypoints.openai.models.protocol import BaseModelPath
 from vllm.entrypoints.openai.models.serving import OpenAIServingModels
+from vllm.entrypoints.serve.render.serving import OpenAIServingRender
 from vllm.outputs import CompletionOutput, RequestOutput
 from vllm.renderers.hf import HfRenderer
 from vllm.tokenizers.registry import tokenizer_args_from_config
@@ -84,10 +85,20 @@ def _build_serving_chat(engine: AsyncLLM) -> OpenAIServingChat:
         engine_client=engine,
         base_model_paths=BASE_MODEL_PATHS,
     )
+    serving_render = OpenAIServingRender(
+        model_config=engine.model_config,
+        renderer=engine.renderer,
+        io_processor=engine.io_processor,
+        model_registry=models.registry,
+        request_logger=None,
+        chat_template=None,
+        chat_template_content_format="auto",
+    )
     serving_chat = OpenAIServingChat(
         engine,
         models,
         response_role="assistant",
+        openai_serving_render=serving_render,
         request_logger=None,
         chat_template=None,
         chat_template_content_format="auto",
@@ -100,7 +111,9 @@ def _build_serving_chat(engine: AsyncLLM) -> OpenAIServingChat:
             [{"prompt_token_ids": [1, 2, 3]}],
         )
 
-    serving_chat._preprocess_chat = AsyncMock(side_effect=_fake_preprocess_chat)
+    serving_chat.openai_serving_render._preprocess_chat = AsyncMock(
+        side_effect=_fake_preprocess_chat
+    )
     return serving_chat
 
 

tests/entrypoints/openai/test_completion_error.py

@@ -13,6 +13,7 @@ from vllm.entrypoints.openai.completion.serving import OpenAIServingCompletion
 from vllm.entrypoints.openai.engine.protocol import GenerationError
 from vllm.entrypoints.openai.models.protocol import BaseModelPath
 from vllm.entrypoints.openai.models.serving import OpenAIServingModels
+from vllm.entrypoints.serve.render.serving import OpenAIServingRender
 from vllm.outputs import CompletionOutput, RequestOutput
 from vllm.renderers.hf import HfRenderer
 from vllm.tokenizers.registry import tokenizer_args_from_config
@@ -74,9 +75,19 @@ def _build_serving_completion(engine: AsyncLLM) -> OpenAIServingCompletion:
         engine_client=engine,
         base_model_paths=BASE_MODEL_PATHS,
     )
+    serving_render = OpenAIServingRender(
+        model_config=engine.model_config,
+        renderer=engine.renderer,
+        io_processor=engine.io_processor,
+        model_registry=models.registry,
+        request_logger=None,
+        chat_template=None,
+        chat_template_content_format="auto",
+    )
     return OpenAIServingCompletion(
         engine,
         models,
+        openai_serving_render=serving_render,
         request_logger=None,
     )
 

tests/entrypoints/openai/test_lora_resolvers.py

@@ -14,6 +14,7 @@ from vllm.entrypoints.openai.completion.serving import OpenAIServingCompletion
 from vllm.entrypoints.openai.engine.protocol import ErrorResponse
 from vllm.entrypoints.openai.models.protocol import BaseModelPath
 from vllm.entrypoints.openai.models.serving import OpenAIServingModels
+from vllm.entrypoints.serve.render.serving import OpenAIServingRender
 from vllm.lora.request import LoRARequest
 from vllm.lora.resolver import LoRAResolver, LoRAResolverRegistry
 from vllm.renderers.hf import HfRenderer
@@ -145,8 +146,17 @@ def mock_serving_setup():
         base_model_paths=BASE_MODEL_PATHS,
     )
 
+    serving_render = OpenAIServingRender(
+        model_config=mock_engine.model_config,
+        renderer=mock_engine.renderer,
+        io_processor=mock_engine.io_processor,
+        model_registry=models.registry,
+        request_logger=None,
+        chat_template=None,
+        chat_template_content_format="auto",
+    )
     serving_completion = OpenAIServingCompletion(
-        mock_engine, models, request_logger=None
+        mock_engine, models, openai_serving_render=serving_render, request_logger=None
     )
 
     return mock_engine, serving_completion

tests/entrypoints/openai/test_serving_chat.py

@@ -21,8 +21,13 @@ from vllm.entrypoints.openai.engine.protocol import (
     ErrorResponse,
     RequestResponseMetadata,
 )
-from vllm.entrypoints.openai.models.serving import BaseModelPath, OpenAIServingModels
+from vllm.entrypoints.openai.models.serving import (
+    BaseModelPath,
+    OpenAIModelRegistry,
+    OpenAIServingModels,
+)
 from vllm.entrypoints.openai.parser.harmony_utils import get_encoding
+from vllm.entrypoints.serve.render.serving import OpenAIServingRender
 from vllm.exceptions import VLLMValidationError
 from vllm.inputs import TokensPrompt
 from vllm.outputs import CompletionOutput, RequestOutput
@@ -557,15 +562,32 @@ def _build_renderer(model_config: MockModelConfig):
     )
 
 
+def _build_serving_render(
+    engine, model_registry: OpenAIModelRegistry
+) -> OpenAIServingRender:
+    return OpenAIServingRender(
+        model_config=engine.model_config,
+        renderer=engine.renderer,
+        io_processor=engine.io_processor,
+        model_registry=model_registry,
+        request_logger=None,
+        chat_template=CHAT_TEMPLATE,
+        chat_template_content_format="auto",
+    )
+
+
 def _build_serving_chat(engine: AsyncLLM) -> OpenAIServingChat:
     models = OpenAIServingModels(
         engine_client=engine,
         base_model_paths=BASE_MODEL_PATHS,
     )
+    openai_serving_render = _build_serving_render(engine, models.registry)
+
     serving_chat = OpenAIServingChat(
         engine,
         models,
         response_role="assistant",
+        openai_serving_render=openai_serving_render,
         chat_template=CHAT_TEMPLATE,
         chat_template_content_format="auto",
         request_logger=None,
@@ -586,10 +608,13 @@ async def _async_serving_chat_init():
     engine = MockEngine()
 
     models = OpenAIServingModels(engine, BASE_MODEL_PATHS)
+    openai_serving_render = _build_serving_render(engine, models.registry)
+
     serving_completion = OpenAIServingChat(
         engine,
         models,
         response_role="assistant",
+        openai_serving_render=openai_serving_render,
         chat_template=CHAT_TEMPLATE,
         chat_template_content_format="auto",
         request_logger=None,
@@ -1182,7 +1207,9 @@ class TestServingChatWithHarmony:
 
         # Test the Harmony messages for the first turn's input
         req = ChatCompletionRequest(model=MODEL_NAME, messages=messages)
-        input_messages, _ = serving_chat._make_request_with_harmony(req)
+        input_messages, _ = (
+            serving_chat.openai_serving_render._make_request_with_harmony(req)
+        )
         verify_harmony_messages(
             input_messages,
             [
@@ -1209,7 +1236,9 @@ class TestServingChatWithHarmony:
 
         # Test the Harmony messages for the second turn's input
         req_2 = ChatCompletionRequest(model=MODEL_NAME, messages=messages)
-        input_messages_2, _ = serving_chat._make_request_with_harmony(req_2)
+        input_messages_2, _ = (
+            serving_chat.openai_serving_render._make_request_with_harmony(req_2)
+        )
         verify_harmony_messages(
             input_messages_2,
             [
@@ -1230,7 +1259,9 @@ class TestServingChatWithHarmony:
 
         # Test the Harmony messages for the first turn's input
         req = ChatCompletionRequest(model=MODEL_NAME, messages=messages, tools=tools)
-        input_messages, _ = serving_chat._make_request_with_harmony(req)
+        input_messages, _ = (
+            serving_chat.openai_serving_render._make_request_with_harmony(req)
+        )
         verify_harmony_messages(
             input_messages,
             [
@@ -1274,7 +1305,9 @@ class TestServingChatWithHarmony:
 
         # Test the Harmony messages for the second turn's input
         req_2 = ChatCompletionRequest(model=MODEL_NAME, messages=messages, tools=tools)
-        input_messages_2, _ = serving_chat._make_request_with_harmony(req_2)
+        input_messages_2, _ = (
+            serving_chat.openai_serving_render._make_request_with_harmony(req_2)
+        )
         verify_harmony_messages(
             input_messages_2,
             [
@@ -1311,7 +1344,9 @@ class TestServingChatWithHarmony:
 
         # Test the Harmony messages for the first turn's input
         req = ChatCompletionRequest(model=MODEL_NAME, messages=messages, tools=tools)
-        input_messages, _ = serving_chat._make_request_with_harmony(req)
+        input_messages, _ = (
+            serving_chat.openai_serving_render._make_request_with_harmony(req)
+        )
         verify_harmony_messages(
             input_messages,
             [
@@ -1355,7 +1390,9 @@ class TestServingChatWithHarmony:
 
         # Test the Harmony messages for the second turn's input
         req_2 = ChatCompletionRequest(model=MODEL_NAME, messages=messages, tools=tools)
-        input_messages_2, _ = serving_chat._make_request_with_harmony(req_2)
+        input_messages_2, _ = (
+            serving_chat.openai_serving_render._make_request_with_harmony(req_2)
+        )
         verify_harmony_messages(
             input_messages_2,
             [
@@ -1392,7 +1429,9 @@ class TestServingChatWithHarmony:
 
         # Test the Harmony messages for the first turn's input
         req = ChatCompletionRequest(model=MODEL_NAME, messages=messages, tools=tools)
-        input_messages, _ = serving_chat._make_request_with_harmony(req)
+        input_messages, _ = (
+            serving_chat.openai_serving_render._make_request_with_harmony(req)
+        )
         verify_harmony_messages(
             input_messages,
             [
@@ -1436,7 +1475,9 @@ class TestServingChatWithHarmony:
 
         # Test the Harmony messages for the second turn's input
         req_2 = ChatCompletionRequest(model=MODEL_NAME, messages=messages, tools=tools)
-        input_messages_2, _ = serving_chat._make_request_with_harmony(req_2)
+        input_messages_2, _ = (
+            serving_chat.openai_serving_render._make_request_with_harmony(req_2)
+        )
         verify_harmony_messages(
             input_messages_2,
             [
@@ -1486,7 +1527,9 @@ class TestServingChatWithHarmony:
 
         # Test the Harmony messages for the third turn's input
         req_3 = ChatCompletionRequest(model=MODEL_NAME, messages=messages, tools=tools)
-        input_messages_3, _ = serving_chat._make_request_with_harmony(req_3)
+        input_messages_3, _ = (
+            serving_chat.openai_serving_render._make_request_with_harmony(req_3)
+        )
         verify_harmony_messages(
             input_messages_3,
             [
@@ -1549,7 +1592,9 @@ class TestServingChatWithHarmony:
 
         # Test the Harmony messages for the fourth turn's input
         req_4 = ChatCompletionRequest(model=MODEL_NAME, messages=messages, tools=tools)
-        input_messages_4, _ = serving_chat._make_request_with_harmony(req_4)
+        input_messages_4, _ = (
+            serving_chat.openai_serving_render._make_request_with_harmony(req_4)
+        )
         verify_harmony_messages(
             input_messages_4,
             [
@@ -1598,7 +1643,9 @@ class TestServingChatWithHarmony:
             },
         ]
         req = ChatCompletionRequest(model=MODEL_NAME, messages=messages)
-        input_messages, _ = serving_chat._make_request_with_harmony(req)
+        input_messages, _ = (
+            serving_chat.openai_serving_render._make_request_with_harmony(req)
+        )
 
         verify_harmony_messages(
             input_messages,
@@ -1629,7 +1676,9 @@ class TestServingChatWithHarmony:
             },
         ]
         req = ChatCompletionRequest(model=MODEL_NAME, messages=messages)
-        input_messages, _ = serving_chat._make_request_with_harmony(req)
+        input_messages, _ = (
+            serving_chat.openai_serving_render._make_request_with_harmony(req)
+        )
 
         verify_harmony_messages(
             input_messages,
@@ -1658,7 +1707,9 @@ class TestServingChatWithHarmony:
             },
         ]
         req = ChatCompletionRequest(model=MODEL_NAME, messages=messages)
-        input_messages, _ = serving_chat._make_request_with_harmony(req)
+        input_messages, _ = (
+            serving_chat.openai_serving_render._make_request_with_harmony(req)
+        )
 
         verify_harmony_messages(
             input_messages,
@@ -1689,11 +1740,14 @@ async def test_tool_choice_validation_without_parser():
         engine_client=mock_engine,
         base_model_paths=BASE_MODEL_PATHS,
     )
+    openai_serving_render = _build_serving_render(mock_engine, models.registry)
+
     # Create serving_chat without tool_parser (enable_auto_tools=False)
     serving_chat = OpenAIServingChat(
         mock_engine,
         models,
         response_role="assistant",
+        openai_serving_render=openai_serving_render,
         chat_template=CHAT_TEMPLATE,
         chat_template_content_format="auto",
         request_logger=None,

tests/v1/engine/test_async_llm.py

@@ -508,11 +508,25 @@ async def test_header_dp_rank_argument():
             base_model_paths=BASE_MODEL_PATHS,
         )
 
+        # Create render serving instance (required by OpenAIServingChat)
+        from vllm.entrypoints.serve.render.serving import OpenAIServingRender
+
+        serving_render = OpenAIServingRender(
+            model_config=engine.model_config,
+            renderer=engine.renderer,
+            io_processor=engine.io_processor,
+            model_registry=models.registry,
+            request_logger=None,
+            chat_template=None,
+            chat_template_content_format="auto",
+        )
+
         # Create serving chat instance
         serving_chat = OpenAIServingChat(
             engine_client=engine,
             models=models,
             response_role="assistant",
+            openai_serving_render=serving_render,
             chat_template=None,
             chat_template_content_format="auto",
             request_logger=None,

vllm/entrypoints/anthropic/serving.py

@@ -10,7 +10,7 @@ import logging
 import time
 import uuid
 from collections.abc import AsyncGenerator
-from typing import Any
+from typing import TYPE_CHECKING, Any
 
 from fastapi import Request
 
@@ -43,6 +43,9 @@ from vllm.entrypoints.openai.engine.protocol import (
 )
 from vllm.entrypoints.openai.models.serving import OpenAIServingModels
 
+if TYPE_CHECKING:
+    from vllm.entrypoints.serve.render.serving import OpenAIServingRender
+
 logger = logging.getLogger(__name__)
 
 
@@ -59,6 +62,7 @@ class AnthropicServingMessages(OpenAIServingChat):
         models: OpenAIServingModels,
         response_role: str,
         *,
+        openai_serving_render: "OpenAIServingRender",
         request_logger: RequestLogger | None,
         chat_template: str | None,
         chat_template_content_format: ChatTemplateContentFormatOption,
@@ -73,6 +77,7 @@ class AnthropicServingMessages(OpenAIServingChat):
             engine_client=engine_client,
             models=models,
             response_role=response_role,
+            openai_serving_render=openai_serving_render,
             request_logger=request_logger,
             chat_template=chat_template,
             chat_template_content_format=chat_template_content_format,

vllm/entrypoints/openai/chat_completion/serving.py

@@ -6,12 +6,11 @@ import json
 import time
 from collections.abc import AsyncGenerator, AsyncIterator
 from collections.abc import Sequence as GenericSequence
-from typing import Any, Final
+from typing import TYPE_CHECKING, Any, Final
 
 import partial_json_parser
 import regex as re
 from fastapi import Request
-from openai_harmony import Message as OpenAIMessage
 from partial_json_parser.core.options import Allow
 
 from vllm.engine.protocol import EngineClient
@@ -56,17 +55,13 @@ from vllm.entrypoints.openai.engine.serving import (
 )
 from vllm.entrypoints.openai.models.serving import OpenAIServingModels
 from vllm.entrypoints.openai.parser.harmony_utils import (
-    get_developer_message,
     get_stop_tokens_for_assistant_actions,
     get_streamable_parser_for_assistant,
-    get_system_message,
-    parse_chat_inputs_to_harmony_messages,
     parse_chat_output,
-    render_for_completion,
 )
 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 ProcessorInputs, TokensPrompt
+from vllm.inputs.data import ProcessorInputs
 from vllm.logger import init_logger
 from vllm.logprobs import Logprob
 from vllm.outputs import CompletionOutput, RequestOutput
@@ -80,7 +75,9 @@ from vllm.tool_parsers.mistral_tool_parser import MistralToolCall
 from vllm.tool_parsers.utils import partial_json_loads
 from vllm.utils.collection_utils import as_list
 from vllm.utils.mistral import is_mistral_tokenizer
-from vllm.utils.mistral import mt as _mt
+
+if TYPE_CHECKING:
+    from vllm.entrypoints.serve.render.serving import OpenAIServingRender
 
 logger = init_logger(__name__)
 
@@ -92,6 +89,7 @@ class OpenAIServingChat(OpenAIServing):
         models: OpenAIServingModels,
         response_role: str,
         *,
+        openai_serving_render: "OpenAIServingRender",
         request_logger: RequestLogger | None,
         chat_template: str | None,
         chat_template_content_format: ChatTemplateContentFormatOption,
@@ -114,6 +112,7 @@ class OpenAIServingChat(OpenAIServing):
             return_tokens_as_token_ids=return_tokens_as_token_ids,
         )
 
+        self.openai_serving_render = openai_serving_render
         self.response_role = response_role
         self.chat_template = chat_template
         self.chat_template_content_format: Final = chat_template_content_format
@@ -186,7 +185,10 @@ class OpenAIServingChat(OpenAIServing):
         request: ChatCompletionRequest,
     ) -> tuple[list[ConversationMessage], list[ProcessorInputs]] | ErrorResponse:
         """
-        render chat request by validating and preprocessing inputs.
+        Validate the model and preprocess a chat completion request.
+
+        Delegates preprocessing logic to OpenAIServingRender, adding the
+        engine-aware checks (LoRA model validation, engine health).
 
         Returns:
             A tuple of (conversation, engine_prompts) on success,
@@ -203,78 +205,7 @@ class OpenAIServingChat(OpenAIServing):
         if self.engine_client.errored:
             raise self.engine_client.dead_error
 
-        tokenizer = self.renderer.tokenizer
-
-        tool_parser = self.tool_parser
-
-        if is_mistral_tokenizer(tokenizer):
-            # because of issues with pydantic we need to potentially
-            # re-serialize the tool_calls field of the request
-            # for more info: see comment in `maybe_serialize_tool_calls`
-            _mt.maybe_serialize_tool_calls(request)  # type: ignore[arg-type]
-            _mt.truncate_tool_call_ids(request)  # type: ignore[arg-type]
-            _mt.validate_request_params(request)
-
-        # Check if tool parsing is unavailable (common condition)
-        tool_parsing_unavailable = (
-            tool_parser is None
-            and not is_mistral_tokenizer(tokenizer)
-            and not self.use_harmony
-        )
-
-        # Validate tool_choice when tool parsing is required but unavailable
-        if tool_parsing_unavailable and request.tool_choice not in (
-            None,
-            "none",
-        ):
-            if request.tool_choice == "auto" and not self.enable_auto_tools:
-                # for hf tokenizers, "auto" tools requires
-                # --enable-auto-tool-choice and --tool-call-parser
-                return self.create_error_response(
-                    '"auto" tool choice requires '
-                    "--enable-auto-tool-choice and --tool-call-parser to be set"
-                )
-            elif request.tool_choice != "auto":
-                # "required" or named tool requires tool parser
-                return self.create_error_response(
-                    f'tool_choice="{request.tool_choice}" requires '
-                    "--tool-call-parser to be set"
-                )
-
-        if request.tools is None or (
-            request.tool_choice == "none" and self.exclude_tools_when_tool_choice_none
-        ):
-            tool_dicts = None
-        else:
-            tool_dicts = [tool.model_dump() for tool in request.tools]
-
-        if not self.use_harmony:
-            # Common case.
-            error_check_ret = self._validate_chat_template(
-                request_chat_template=request.chat_template,
-                chat_template_kwargs=request.chat_template_kwargs,
-                trust_request_chat_template=self.trust_request_chat_template,
-            )
-            if error_check_ret is not None:
-                return error_check_ret
-
-            conversation, engine_prompts = await self._preprocess_chat(
-                request,
-                request.messages,
-                default_template=self.chat_template,
-                default_template_content_format=self.chat_template_content_format,
-                default_template_kwargs=self.default_chat_template_kwargs,
-                tool_dicts=tool_dicts,
-                tool_parser=tool_parser,
-            )
-        else:
-            # For GPT-OSS.
-            should_include_tools = tool_dicts is not None
-            conversation, engine_prompts = self._make_request_with_harmony(
-                request, should_include_tools
-            )
-
-        return conversation, engine_prompts
+        return await self.openai_serving_render.render_chat(request)
 
     async def create_chat_completion(
         self,
@@ -1875,50 +1806,3 @@ class OpenAIServingChat(OpenAIServing):
                 )
             ]
         )
-
-    def _make_request_with_harmony(
-        self,
-        request: ChatCompletionRequest,
-        should_include_tools: bool = True,
-    ):
-        messages: list[OpenAIMessage] = []
-
-        # because of issues with pydantic we need to potentially
-        # re-serialize the tool_calls field of the request
-        # for more info: see comment in `maybe_serialize_tool_calls`
-        _mt.maybe_serialize_tool_calls(request)  # type: ignore[arg-type]
-
-        # Add system message.
-        # NOTE: In Chat Completion API, browsing is enabled by default
-        # if the model supports it. TODO: Support browsing.
-        assert not self.supports_browsing
-        assert not self.supports_code_interpreter
-        if (reasoning_effort := request.reasoning_effort) == "none":
-            raise ValueError(f"Harmony does not support {reasoning_effort=}")
-        sys_msg = get_system_message(
-            reasoning_effort=reasoning_effort,
-            browser_description=None,
-            python_description=None,
-            with_custom_tools=should_include_tools,
-        )
-        messages.append(sys_msg)
-
-        # Add developer message.
-        if request.tools:
-            dev_msg = get_developer_message(
-                tools=request.tools if should_include_tools else None  # type: ignore[arg-type]
-            )
-            messages.append(dev_msg)
-
-        # Add user message.
-        messages.extend(parse_chat_inputs_to_harmony_messages(request.messages))
-
-        # Render prompt token ids.
-        prompt_token_ids = render_for_completion(messages)
-        engine_prompt = TokensPrompt(prompt_token_ids=prompt_token_ids)
-
-        # Add cache_salt if provided in the request
-        if request.cache_salt is not None:
-            engine_prompt["cache_salt"] = request.cache_salt
-
-        return messages, [engine_prompt]

vllm/entrypoints/openai/completion/serving.py

@@ -5,7 +5,7 @@ import asyncio
 import time
 from collections.abc import AsyncGenerator, AsyncIterator
 from collections.abc import Sequence as GenericSequence
-from typing import cast
+from typing import TYPE_CHECKING, cast
 
 from fastapi import Request
 
@@ -42,6 +42,9 @@ from vllm.tokenizers import TokenizerLike
 from vllm.utils.async_utils import merge_async_iterators
 from vllm.utils.collection_utils import as_list
 
+if TYPE_CHECKING:
+    from vllm.entrypoints.serve.render.serving import OpenAIServingRender
+
 logger = init_logger(__name__)
 
 
@@ -51,6 +54,7 @@ class OpenAIServingCompletion(OpenAIServing):
         engine_client: EngineClient,
         models: OpenAIServingModels,
         *,
+        openai_serving_render: "OpenAIServingRender",
         request_logger: RequestLogger | None,
         return_tokens_as_token_ids: bool = False,
         enable_prompt_tokens_details: bool = False,
@@ -63,6 +67,7 @@ class OpenAIServingCompletion(OpenAIServing):
             return_tokens_as_token_ids=return_tokens_as_token_ids,
         )
 
+        self.openai_serving_render = openai_serving_render
         self.enable_prompt_tokens_details = enable_prompt_tokens_details
         self.enable_force_include_usage = enable_force_include_usage
 
@@ -79,7 +84,10 @@ class OpenAIServingCompletion(OpenAIServing):
         request: CompletionRequest,
     ) -> list[ProcessorInputs] | ErrorResponse:
         """
-        render completion request by validating and preprocessing inputs.
+        Validate the model and preprocess a completion request.
+
+        Delegates preprocessing logic to OpenAIServingRender, adding the
+        engine-aware checks (LoRA model validation, engine health).
 
         Returns:
             A list of engine_prompts on success,
@@ -95,25 +103,7 @@ class OpenAIServingCompletion(OpenAIServing):
         if self.engine_client.errored:
             raise self.engine_client.dead_error
 
-        # Return error for unsupported features.
-        if request.suffix is not None:
-            return self.create_error_response("suffix is not currently supported")
-
-        if request.echo and request.prompt_embeds is not None:
-            return self.create_error_response("Echo is unsupported with prompt embeds.")
-
-        if request.prompt_logprobs is not None and request.prompt_embeds is not None:
-            return self.create_error_response(
-                "prompt_logprobs is not compatible with prompt embeds."
-            )
-
-        engine_prompts = await self._preprocess_completion(
-            request,
-            prompt_input=request.prompt,
-            prompt_embeds=request.prompt_embeds,
-        )
-
-        return engine_prompts
+        return await self.openai_serving_render.render_completion(request)
 
     async def create_completion(
         self,

vllm/entrypoints/openai/generate/api_router.py

@@ -72,6 +72,29 @@ async def init_generate_state(
         tool_server = None
     resolved_chat_template = load_chat_template(args.chat_template)
 
+    # Render endpoints are always backed by OpenAIServingRender so that
+    # /v1/chat/completions/render and /v1/completions/render work on both
+    # generate-mode and render-only servers.
+    # It is created first so that OpenAIServingChat and OpenAIServingCompletion
+    # can delegate their preprocessing logic to it.
+    from vllm.entrypoints.serve.render.serving import OpenAIServingRender
+
+    state.openai_serving_render = OpenAIServingRender(
+        model_config=engine_client.model_config,
+        renderer=engine_client.renderer,
+        io_processor=engine_client.io_processor,
+        model_registry=state.openai_serving_models.registry,
+        request_logger=request_logger,
+        chat_template=resolved_chat_template,
+        chat_template_content_format=args.chat_template_content_format,
+        trust_request_chat_template=args.trust_request_chat_template,
+        enable_auto_tools=args.enable_auto_tool_choice,
+        exclude_tools_when_tool_choice_none=args.exclude_tools_when_tool_choice_none,
+        tool_parser=args.tool_call_parser,
+        default_chat_template_kwargs=args.default_chat_template_kwargs,
+        log_error_stack=args.log_error_stack,
+    )
+
     state.openai_serving_responses = (
         OpenAIServingResponses(
             engine_client,
@@ -96,6 +119,7 @@ async def init_generate_state(
             engine_client,
             state.openai_serving_models,
             args.response_role,
+            openai_serving_render=state.openai_serving_render,
             request_logger=request_logger,
             chat_template=resolved_chat_template,
             chat_template_content_format=args.chat_template_content_format,
@@ -120,6 +144,7 @@ async def init_generate_state(
         OpenAIServingCompletion(
             engine_client,
             state.openai_serving_models,
+            openai_serving_render=state.openai_serving_render,
             request_logger=request_logger,
             return_tokens_as_token_ids=args.return_tokens_as_token_ids,
             enable_prompt_tokens_details=args.enable_prompt_tokens_details,
@@ -133,6 +158,7 @@ async def init_generate_state(
             engine_client,
             state.openai_serving_models,
             args.response_role,
+            openai_serving_render=state.openai_serving_render,
             request_logger=request_logger,
             chat_template=resolved_chat_template,
             chat_template_content_format=args.chat_template_content_format,
@@ -159,24 +185,3 @@ async def init_generate_state(
         if "generate" in supported_tasks
         else None
     )
-
-    # Render endpoints are always backed by OpenAIServingRender so that
-    # /v1/chat/completions/render and /v1/completions/render work on both
-    # generate-mode and render-only servers.
-    from vllm.entrypoints.serve.render.serving import OpenAIServingRender
-
-    state.openai_serving_render = OpenAIServingRender(
-        model_config=engine_client.model_config,
-        renderer=engine_client.renderer,
-        io_processor=engine_client.io_processor,
-        model_registry=state.openai_serving_models.registry,
-        request_logger=request_logger,
-        chat_template=resolved_chat_template,
-        chat_template_content_format=args.chat_template_content_format,
-        trust_request_chat_template=args.trust_request_chat_template,
-        enable_auto_tools=args.enable_auto_tool_choice,
-        exclude_tools_when_tool_choice_none=args.exclude_tools_when_tool_choice_none,
-        tool_parser=args.tool_call_parser,
-        default_chat_template_kwargs=args.default_chat_template_kwargs,
-        log_error_stack=args.log_error_stack,
-    )

vllm/entrypoints/serve/render/serving.py

@@ -87,15 +87,26 @@ class OpenAIServingRender:
         self,
         request: ChatCompletionRequest,
     ) -> tuple[list[ConversationMessage], list[ProcessorInputs]] | ErrorResponse:
-        """Copied from OpenAIServingChat.render_chat_request.
+        """Validate the model and preprocess a chat completion request.
 
-        Differences: engine_client.errored check removed (no engine client).
+        This is the authoritative implementation used directly by the
+        GPU-less render server and delegated to by OpenAIServingChat.
         """
         error_check_ret = await self._check_model(request)
         if error_check_ret is not None:
             logger.error("Error with model %s", error_check_ret)
             return error_check_ret
+        return await self.render_chat(request)
 
+    async def render_chat(
+        self,
+        request: ChatCompletionRequest,
+    ) -> tuple[list[ConversationMessage], list[ProcessorInputs]] | ErrorResponse:
+        """Core preprocessing logic for chat requests (no model/engine check).
+
+        Called directly by render_chat_request and delegated to by
+        OpenAIServingChat.render_chat_request after its engine-aware checks.
+        """
         tokenizer = self.renderer.tokenizer
 
         tool_parser = self.tool_parser
@@ -173,14 +184,25 @@ class OpenAIServingRender:
         self,
         request: CompletionRequest,
     ) -> list[ProcessorInputs] | ErrorResponse:
-        """Copied from OpenAIServingCompletion.render_completion_request.
+        """Validate the model and preprocess a completion request.
 
-        Differences: engine_client.errored check removed (no engine client).
+        This is the authoritative implementation used directly by the
+        GPU-less render server and delegated to by OpenAIServingCompletion.
         """
         error_check_ret = await self._check_model(request)
         if error_check_ret is not None:
             return error_check_ret
+        return await self.render_completion(request)
 
+    async def render_completion(
+        self,
+        request: CompletionRequest,
+    ) -> list[ProcessorInputs] | ErrorResponse:
+        """Core preprocessing logic for completion requests (no model/engine check).
+
+        Called directly by render_completion_request and delegated to by
+        OpenAIServingCompletion.render_completion_request after its engine-aware checks.
+        """
         # Return error for unsupported features.
         if request.suffix is not None:
             return self.create_error_response("suffix is not currently supported")
@@ -206,7 +228,7 @@ class OpenAIServingRender:
         request: ChatCompletionRequest,
         should_include_tools: bool = True,
     ):
-        """Copied from OpenAIServingChat._make_request_with_harmony."""
+        """Build Harmony (GPT-OSS) messages and engine prompt from a chat request."""
         messages: list[OpenAIMessage] = []
 
         # because of issues with pydantic we need to potentially
@@ -219,11 +241,10 @@ class OpenAIServingRender:
         # if the model supports it. TODO: Support browsing.
         assert not self.supports_browsing
         assert not self.supports_code_interpreter
-        assert request.reasoning_effort != "none", (
-            "Harmony does not support reasoning_effort='none'"
-        )
+        if (reasoning_effort := request.reasoning_effort) == "none":
+            raise ValueError(f"Harmony does not support {reasoning_effort=}")
         sys_msg = get_system_message(
-            reasoning_effort=request.reasoning_effort,
+            reasoning_effort=reasoning_effort,
             browser_description=None,
             python_description=None,
             with_custom_tools=should_include_tools,