[Refactor]Reduce duplicate code in serving_chat (#26627)

Signed-off-by: chaunceyjiang <chaunceyjiang@gmail.com>

vllm/entrypoints/openai/serving_chat.py

@@ -6,7 +6,7 @@ import json
 import time
 from collections.abc import AsyncGenerator, AsyncIterator
 from collections.abc import Sequence as GenericSequence
-from typing import Callable, Final, Optional, Union
+from typing import Final, Optional, Union
 
 import jinja2
 import partial_json_parser
@@ -56,14 +56,13 @@ from vllm.entrypoints.openai.protocol import (
 )
 from vllm.entrypoints.openai.serving_engine import OpenAIServing, clamp_prompt_logprobs
 from vllm.entrypoints.openai.serving_models import OpenAIServingModels
-from vllm.entrypoints.openai.tool_parsers import ToolParser, ToolParserManager
+from vllm.entrypoints.openai.tool_parsers import ToolParser
 from vllm.entrypoints.openai.tool_parsers.mistral_tool_parser import MistralToolCall
 from vllm.entrypoints.utils import get_max_tokens
 from vllm.inputs.data import TokensPrompt as EngineTokensPrompt
 from vllm.logger import init_logger
 from vllm.logprobs import Logprob
 from vllm.outputs import CompletionOutput, RequestOutput
-from vllm.reasoning import ReasoningParser, ReasoningParserManager
 from vllm.sampling_params import BeamSearchParams, SamplingParams
 from vllm.transformers_utils.tokenizer import AnyTokenizer, MistralTokenizer
 from vllm.transformers_utils.tokenizers import (
@@ -112,42 +111,15 @@ class OpenAIServingChat(OpenAIServing):
         self.trust_request_chat_template = trust_request_chat_template
         self.enable_log_outputs = enable_log_outputs
 
+        # set up reasoning parser
+        self.reasoning_parser = self._get_reasoning_parser(
+            reasoning_parser_name=reasoning_parser
+        )
         # set up tool use
         self.enable_auto_tools: bool = enable_auto_tools
-        if self.enable_auto_tools:
+        self.tool_parser = self._get_tool_parser(
-            logger.info(
+            tool_parser_name=tool_parser, enable_auto_tools=enable_auto_tools
-                '"auto" tool choice has been enabled please note that while'
-                " the parallel_tool_calls client option is preset for "
-                "compatibility reasons, it will be ignored."
-            )
-
-        self.reasoning_parser: Optional[Callable[[AnyTokenizer], ReasoningParser]] = (
-            None
         )
-        if reasoning_parser:
-            try:
-                self.reasoning_parser = ReasoningParserManager.get_reasoning_parser(
-                    reasoning_parser
-                )
-                assert self.reasoning_parser is not None
-            except Exception as e:
-                raise TypeError(f"{reasoning_parser=} has not been registered") from e
-        self.tool_parser: Optional[Callable[[AnyTokenizer], ToolParser]] = None
-        if self.enable_auto_tools:
-            try:
-                if tool_parser == "pythonic" and self.model_config.model.startswith(
-                    "meta-llama/Llama-3.2"
-                ):
-                    logger.warning(
-                        "Llama3.2 models may struggle to emit valid pythonic tool calls"
-                    )
-                self.tool_parser = ToolParserManager.get_tool_parser(tool_parser)
-            except Exception as e:
-                raise TypeError(
-                    "Error: --enable-auto-tool-choice requires "
-                    f"tool_parser:'{tool_parser}' which has not "
-                    "been registered"
-                ) from e
         self.exclude_tools_when_tool_choice_none = exclude_tools_when_tool_choice_none
 
         self.enable_prompt_tokens_details = enable_prompt_tokens_details

vllm/entrypoints/openai/serving_engine.py

@@ -63,7 +63,7 @@ from vllm.entrypoints.openai.protocol import (
     TranslationRequest,
 )
 from vllm.entrypoints.openai.serving_models import OpenAIServingModels
-from vllm.entrypoints.openai.tool_parsers import ToolParser
+from vllm.entrypoints.openai.tool_parsers import ToolParser, ToolParserManager
 from vllm.entrypoints.renderer import BaseRenderer, CompletionRenderer, RenderConfig
 from vllm.entrypoints.utils import _validate_truncation_size
 from vllm.inputs.data import PromptType
@@ -82,6 +82,7 @@ from vllm.multimodal import (  # noqa: F401 - Required to resolve Pydantic error
 )
 from vllm.outputs import CompletionOutput, PoolingRequestOutput, RequestOutput
 from vllm.pooling_params import PoolingParams
+from vllm.reasoning import ReasoningParser, ReasoningParserManager
 from vllm.sampling_params import BeamSearchParams, SamplingParams
 from vllm.tracing import (
     contains_trace_headers,
@@ -274,6 +275,50 @@ class OpenAIServing:
         self.model_config = self.models.model_config
         self.max_model_len = self.model_config.max_model_len
 
+    def _get_tool_parser(
+        self, tool_parser_name: Optional[str] = None, enable_auto_tools: bool = False
+    ) -> Optional[Callable[[AnyTokenizer], ToolParser]]:
+        """Get the tool parser based on the name."""
+        parser = None
+        if not enable_auto_tools or tool_parser_name is None:
+            return parser
+        logger.info(
+            '"auto" tool choice has been enabled please note that while'
+            " the parallel_tool_calls client option is preset for "
+            "compatibility reasons, it will be ignored."
+        )
+
+        try:
+            if tool_parser_name == "pythonic" and self.model_config.model.startswith(
+                "meta-llama/Llama-3.2"
+            ):
+                logger.warning(
+                    "Llama3.2 models may struggle to emit valid pythonic tool calls"
+                )
+            parser = ToolParserManager.get_tool_parser(tool_parser_name)
+        except Exception as e:
+            raise TypeError(
+                "Error: --enable-auto-tool-choice requires "
+                f"tool_parser:'{tool_parser_name}' which has not "
+                "been registered"
+            ) from e
+        return parser
+
+    def _get_reasoning_parser(
+        self,
+        reasoning_parser_name: str,
+    ) -> Optional[Callable[[AnyTokenizer], ReasoningParser]]:
+        """Get the reasoning parser based on the name."""
+        parser = None
+        if not reasoning_parser_name:
+            return None
+        try:
+            parser = ReasoningParserManager.get_reasoning_parser(reasoning_parser_name)
+            assert parser is not None
+        except Exception as e:
+            raise TypeError(f"{reasoning_parser_name=} has not been registered") from e
+        return parser
+
     async def reset_mm_cache(self) -> None:
         self.processor.clear_mm_cache()
         await self.engine_client.reset_mm_cache()

vllm/entrypoints/openai/serving_responses.py

@@ -96,7 +96,6 @@ 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.reasoning import ReasoningParser, ReasoningParserManager
 from vllm.sampling_params import SamplingParams
 from vllm.transformers_utils.tokenizer import AnyTokenizer
 from vllm.utils import random_uuid
@@ -136,18 +135,9 @@ class OpenAIServingResponses(OpenAIServing):
         self.chat_template_content_format: Final = chat_template_content_format
         self.enable_log_outputs = enable_log_outputs
 
-        self.reasoning_parser: Optional[Callable[[AnyTokenizer], ReasoningParser]] = (
+        self.reasoning_parser = self._get_reasoning_parser(
-            None
+            reasoning_parser_name=reasoning_parser
         )
-        if reasoning_parser:
-            try:
-                self.reasoning_parser = ReasoningParserManager.get_reasoning_parser(
-                    reasoning_parser
-                )
-                assert self.reasoning_parser is not None
-            except Exception as e:
-                raise TypeError(f"{reasoning_parser=} has not been registered") from e
-
         self.enable_prompt_tokens_details = enable_prompt_tokens_details
         self.enable_force_include_usage = enable_force_include_usage
         self.default_sampling_params = self.model_config.get_diff_sampling_param()