[Refactor][Frontend] Keep all logic about reasoning into one class (#14428)

Signed-off-by: Ce Gao <cegao@tensorchord.ai>

tests/entrypoints/openai/reasoning_parsers/test_deepseekr1_reasoning_parser.py → tests/reasoning/test_deepseekr1_reasoning_parser.py

@@ -3,74 +3,92 @@
 import pytest
 from transformers import AutoTokenizer
 
-from tests.entrypoints.openai.reasoning_parsers.utils import (
-    run_reasoning_extraction)
-from vllm.entrypoints.openai.reasoning_parsers import (ReasoningParser,
-                                                       ReasoningParserManager)
+from tests.reasoning.utils import run_reasoning_extraction
+from vllm.reasoning import ReasoningParser, ReasoningParserManager
 
 parser_name = "deepseek_r1"
 start_token = "<think>"
 end_token = "</think>"
 
+REASONING_MODEL_NAME = "deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B"
+
+
+@pytest.fixture(scope="module")
+def deepseek_r1_qwen_tokenizer():
+    return AutoTokenizer.from_pretrained(REASONING_MODEL_NAME)
+
+
 SIMPLE_REASONING = {
     "output": "This is a reasoning section</think>This is the rest",
     "reasoning_content": "This is a reasoning section",
     "content": "This is the rest",
+    "is_reasoning_end": True,
 }
 COMPLETE_REASONING = {
     "output": "This is a reasoning section</think>",
     "reasoning_content": "This is a reasoning section",
     "content": None,
+    "is_reasoning_end": True,
 }
 NO_CONTENT = {
     "output": "This is content",
     "reasoning_content": "This is content",
     "content": None,
+    "is_reasoning_end": False,
 }
 NO_REASONING_STREAMING = {
     "output": "This is a reasoning section",
     "reasoning_content": "This is a reasoning section",
     "content": None,
+    "is_reasoning_end": False,
 }
 MULTIPLE_LINES = {
     "output": "This\nThat</think>This is the rest\nThat",
     "reasoning_content": "This\nThat",
     "content": "This is the rest\nThat",
+    "is_reasoning_end": True,
 }
 SHORTEST_REASONING_NO_STREAMING = {
     "output": "</think>This is the rest",
     "reasoning_content": "",
     "content": "This is the rest",
+    "is_reasoning_end": True,
 }
 SHORTEST_REASONING = {
     "output": "</think>This is the rest",
     "reasoning_content": None,
     "content": "This is the rest",
+    "is_reasoning_end": True,
 }
 REASONING_WITH_THINK = {
     "output": "<think>This is a reasoning section</think>This is the rest",
     "reasoning_content": "This is a reasoning section",
     "content": "This is the rest",
+    "is_reasoning_end": True,
 }
 COMPLETE_REASONING_WITH_THINK = {
     "output": "<think>This is a reasoning section</think>",
     "reasoning_content": "This is a reasoning section",
     "content": None,
+    "is_reasoning_end": True,
 }
 MULTIPLE_LINES_WITH_THINK = {
     "output": "<think>This\nThat</think>This is the rest\nThat",
     "reasoning_content": "This\nThat",
     "content": "This is the rest\nThat",
+    "is_reasoning_end": True,
 }
 SHORTEST_REASONING_NO_STREAMING_WITH_THINK = {
     "output": "</think>This is the rest",
     "reasoning_content": "",
     "content": "This is the rest",
+    "is_reasoning_end": True,
 }
 SHORTEST_REASONING_WITH_THINK = {
     "output": "</think>This is the rest",
     "reasoning_content": None,
     "content": "This is the rest",
+    "is_reasoning_end": True,
 }
 
 TEST_CASES = [
@@ -166,23 +184,21 @@ TEST_CASES = [
     ),
 ]
 
-# Global tokenizer initialization to avoid repeated loading
-tokenizer = AutoTokenizer.from_pretrained("facebook/opt-125m")
-tokenizer.add_tokens([start_token, end_token])
-
 
 @pytest.mark.parametrize("streaming, param_dict", TEST_CASES)
 def test_reasoning(
     streaming: bool,
     param_dict: dict,
+    deepseek_r1_qwen_tokenizer,
 ):
-    output = tokenizer.tokenize(param_dict["output"])
+    output = deepseek_r1_qwen_tokenizer.tokenize(param_dict["output"])
     # decode everything to tokens
     output_tokens: list[str] = [
-        tokenizer.convert_tokens_to_string([token]) for token in output
+        deepseek_r1_qwen_tokenizer.convert_tokens_to_string([token])
+        for token in output
     ]
     parser: ReasoningParser = ReasoningParserManager.get_reasoning_parser(
-        parser_name)(tokenizer)
+        parser_name)(deepseek_r1_qwen_tokenizer)
 
     reasoning, content = run_reasoning_extraction(parser,
                                                   output_tokens,
@@ -190,3 +206,17 @@ def test_reasoning(
 
     assert reasoning == param_dict["reasoning_content"]
     assert content == param_dict["content"]
+
+    # Test is_reasoning_end
+    output_ids = deepseek_r1_qwen_tokenizer.convert_tokens_to_ids(output)
+    is_reasoning_end = parser.is_reasoning_end(output_ids)
+    assert is_reasoning_end == param_dict["is_reasoning_end"]
+
+    # Test extract_content
+    if param_dict["content"] is not None:
+        content = parser.extract_content_ids(output_ids)
+        assert content == deepseek_r1_qwen_tokenizer.convert_tokens_to_ids(
+            deepseek_r1_qwen_tokenizer.tokenize(param_dict["content"]))
+    else:
+        content = parser.extract_content_ids(output)
+        assert content == []

tests/entrypoints/openai/reasoning_parsers/test_granite_reasoning_parser.py → tests/reasoning/test_granite_reasoning_parser.py

@@ -2,10 +2,8 @@
 import pytest
 from transformers import AutoTokenizer
 
-from tests.entrypoints.openai.reasoning_parsers.utils import (
-    DeltaMessage, run_reasoning_extraction)
-from vllm.entrypoints.openai.reasoning_parsers import (ReasoningParser,
-                                                       ReasoningParserManager)
+from tests.reasoning.utils import DeltaMessage, run_reasoning_extraction
+from vllm.reasoning import ReasoningParser, ReasoningParserManager
 
 parser_name = "granite"
 START_REASONING = "Here is my thought process:"

tests/entrypoints/openai/reasoning_parsers/utils.py → tests/reasoning/utils.py

@@ -4,7 +4,7 @@ from typing import Optional, Union
 
 from vllm.entrypoints.openai.protocol import (ChatCompletionRequest,
                                               DeltaMessage)
-from vllm.entrypoints.openai.reasoning_parsers import ReasoningParser
+from vllm.reasoning import ReasoningParser
 
 
 class StreamingReasoningReconstructor:

vllm/engine/arg_utils.py

@@ -23,6 +23,7 @@ from vllm.executor.executor_base import ExecutorBase
 from vllm.logger import init_logger
 from vllm.model_executor.layers.quantization import QUANTIZATION_METHODS
 from vllm.plugins import load_general_plugins
+from vllm.reasoning import ReasoningParserManager
 from vllm.test_utils import MODEL_WEIGHTS_S3_BUCKET, MODELS_ON_S3
 from vllm.transformers_utils.utils import check_gguf_file
 from vllm.usage.usage_lib import UsageContext
@@ -1119,7 +1120,7 @@ class EngineArgs:
         parser.add_argument(
             "--reasoning-parser",
             type=str,
-            choices=["deepseek_r1", "granite"],
+            choices=list(ReasoningParserManager.reasoning_parsers),
             default=None,
             help=
             "Select the reasoning parser depending on the model that you're "

vllm/engine/llm_engine.py

@@ -2080,8 +2080,9 @@ class LLMEngine:
             guided_decoding.backend = guided_decoding.backend or \
                 self.decoding_config.guided_decoding_backend
 
-            logger.debug("Reasoning backend: %s",
-                         self.decoding_config.reasoning_backend)
+            if self.decoding_config.reasoning_backend is not None:
+                logger.debug("Building with reasoning backend %s",
+                             self.decoding_config.reasoning_backend)
 
             processor = get_local_guided_decoding_logits_processor(
                 guided_params=guided_decoding,

vllm/entrypoints/openai/api_server.py

@@ -68,7 +68,6 @@ from vllm.entrypoints.openai.protocol import (ChatCompletionRequest,
                                               TranscriptionRequest,
                                               TranscriptionResponse,
                                               UnloadLoRAAdapterRequest)
-from vllm.entrypoints.openai.reasoning_parsers import ReasoningParserManager
 # yapf: enable
 from vllm.entrypoints.openai.serving_chat import OpenAIServingChat
 from vllm.entrypoints.openai.serving_completion import OpenAIServingCompletion
@@ -85,6 +84,7 @@ from vllm.entrypoints.openai.serving_transcription import (
 from vllm.entrypoints.openai.tool_parsers import ToolParserManager
 from vllm.entrypoints.utils import load_aware_call, with_cancellation
 from vllm.logger import init_logger
+from vllm.reasoning import ReasoningParserManager
 from vllm.transformers_utils.config import (
     maybe_register_config_serialize_by_value)
 from vllm.transformers_utils.tokenizer import MistralTokenizer

vllm/entrypoints/openai/serving_chat.py

@@ -23,8 +23,6 @@ from vllm.entrypoints.openai.protocol import (
     ChatCompletionStreamResponse, ChatMessage, DeltaFunctionCall, DeltaMessage,
     DeltaToolCall, ErrorResponse, FunctionCall, PromptTokenUsageInfo,
     RequestResponseMetadata, ToolCall, UsageInfo)
-from vllm.entrypoints.openai.reasoning_parsers import (ReasoningParser,
-                                                       ReasoningParserManager)
 from vllm.entrypoints.openai.serving_engine import (OpenAIServing,
                                                     clamp_prompt_logprobs)
 from vllm.entrypoints.openai.serving_models import OpenAIServingModels
@@ -33,6 +31,7 @@ from vllm.entrypoints.openai.tool_parsers.mistral_tool_parser import (
     MistralToolCall)
 from vllm.logger import init_logger
 from vllm.outputs import CompletionOutput, RequestOutput
+from vllm.reasoning import ReasoningParser, ReasoningParserManager
 from vllm.sampling_params import BeamSearchParams, SamplingParams
 from vllm.sequence import Logprob
 from vllm.transformers_utils.tokenizer import AnyTokenizer, MistralTokenizer

vllm/model_executor/guided_decoding/__init__.py

@@ -5,10 +5,10 @@ from __future__ import annotations
 from typing import TYPE_CHECKING
 
 from vllm.logger import init_logger
-from vllm.model_executor.guided_decoding.reasoner import get_reasoner
 from vllm.model_executor.guided_decoding.utils import (
     convert_lark_to_gbnf, grammar_is_likely_lark,
     has_lmf_unsupported_json_features, has_xgrammar_unsupported_json_features)
+from vllm.reasoning import ReasoningParserManager
 
 if TYPE_CHECKING:
     from transformers import PreTrainedTokenizer
@@ -107,7 +107,11 @@ async def get_guided_decoding_logits_processor(
         model_config: ModelConfig,
         reasoning_backend: str | None = None) -> LogitsProcessor | None:
 
-    reasoner = get_reasoner(tokenizer, reasoning_backend)
+    reasoner = None
+    if reasoning_backend is not None:
+        reasoner_class = ReasoningParserManager.get_reasoning_parser(
+            reasoning_backend)
+        reasoner = reasoner_class(tokenizer)
 
     guided_params = maybe_backend_fallback(guided_params)
 
@@ -146,8 +150,11 @@ def get_local_guided_decoding_logits_processor(
         reasoning_backend: str | None = None) -> LogitsProcessor | None:
     guided_params = maybe_backend_fallback(guided_params)
 
-    # Get the reasoner if needed, it will be None if reasoning_
-    reasoner = get_reasoner(tokenizer, reasoning_backend)
+    reasoner = None
+    if reasoning_backend is not None:
+        reasoner_class = ReasoningParserManager.get_reasoning_parser(
+            reasoning_backend)
+        reasoner = reasoner_class(tokenizer)
 
     # CFG grammar not supported by LMFE, so we use outlines instead
     if guided_params.backend_name == 'outlines':

vllm/model_executor/guided_decoding/outlines_decoding.py

@@ -12,7 +12,7 @@ from transformers import PreTrainedTokenizerBase
 
 from vllm.model_executor.guided_decoding.outlines_logits_processors import (
     CFGLogitsProcessor, JSONLogitsProcessor, RegexLogitsProcessor)
-from vllm.model_executor.guided_decoding.reasoner import Reasoner
+from vllm.reasoning import ReasoningParser
 from vllm.sampling_params import GuidedDecodingParams
 
 
@@ -61,7 +61,7 @@ _MAX_THREADPOOL_WORKERS = 16
 async def get_outlines_guided_decoding_logits_processor(
     guided_params: GuidedDecodingParams,
     tokenizer: PreTrainedTokenizerBase,
-    reasoner: Optional[Reasoner],
+    reasoner: Optional[ReasoningParser],
 ) -> Union[JSONLogitsProcessor, RegexLogitsProcessor, CFGLogitsProcessor,
            None]:
     """
@@ -92,7 +92,7 @@ async def get_outlines_guided_decoding_logits_processor(
 def get_local_outlines_guided_decoding_logits_processor(
     guided_params: GuidedDecodingParams,
     tokenizer: PreTrainedTokenizerBase,
-    reasoner: Optional[Reasoner],
+    reasoner: Optional[ReasoningParser],
 ) -> Union[JSONLogitsProcessor, RegexLogitsProcessor, CFGLogitsProcessor,
            None]:
     """
@@ -141,7 +141,7 @@ def _get_logits_processor(
     tokenizer: PreTrainedTokenizerBase,
     mode: GuidedDecodingMode,
     whitespace_pattern: Union[str, None],
-    reasoner: Optional[Reasoner],
+    reasoner: Optional[ReasoningParser],
 ) -> Union[JSONLogitsProcessor, RegexLogitsProcessor, CFGLogitsProcessor]:
     if mode == GuidedDecodingMode.JSON:
         return JSONLogitsProcessor(guide, tokenizer, whitespace_pattern,

vllm/model_executor/guided_decoding/outlines_logits_processors.py

@@ -34,8 +34,8 @@ from transformers import PreTrainedTokenizerBase
 
 import vllm.envs as envs
 from vllm.logger import init_logger
-from vllm.model_executor.guided_decoding.reasoner import Reasoner
 from vllm.platforms import current_platform
+from vllm.reasoning import ReasoningParser
 
 logger = init_logger(__name__)
 
@@ -49,9 +49,9 @@ else:
 
 class BaseLogitsProcessor:
 
-    def __init__(self, guide: Guide, reasoner: Optional[Reasoner]):
+    def __init__(self, guide: Guide, reasoner: Optional[ReasoningParser]):
         self._guide: Guide = guide
-        self._reasoner: Optional[Reasoner] = reasoner
+        self._reasoner: Optional[ReasoningParser] = reasoner
         # CFGState is used for the FSM state for CFGGuide
         self._fsm_state: DefaultDict[int, Union[int,
                                                 CFGState]] = defaultdict(int)
@@ -69,7 +69,7 @@ class BaseLogitsProcessor:
                 # Remove the reasoning tokens from the input_ids
                 # We need this because our implementation relies on the
                 # hash of the input_ids to store the FSM state.
-                input_ids = self._reasoner.extract_content(input_ids)
+                input_ids = self._reasoner.extract_content_ids(input_ids)
 
         seq_id = hash(tuple(input_ids))
 
@@ -142,7 +142,7 @@ class RegexLogitsProcessor(BaseLogitsProcessor):
         self,
         regex_string: str,
         tokenizer: PreTrainedTokenizerBase,
-        reasoner: Optional[Reasoner],
+        reasoner: Optional[ReasoningParser],
     ):
         """Compile the FSM that drives the regex-structured generation.
 
@@ -163,7 +163,7 @@ class JSONLogitsProcessor(RegexLogitsProcessor):
     def __init__(self, schema: Union[str, Dict, BaseModel],
                  tokenizer: PreTrainedTokenizerBase,
                  whitespace_pattern: Union[str, None],
-                 reasoner: Optional[Reasoner]):
+                 reasoner: Optional[ReasoningParser]):
         """Compile the FSM that drives the JSON-guided generation.
 
         Parameters
@@ -203,7 +203,7 @@ class CFGLogitsProcessor(BaseLogitsProcessor):
         return CFGGuide(cfg, tokenizer)
 
     def __init__(self, cfg: str, tokenizer: PreTrainedTokenizerBase,
-                 reasoner: Optional[Reasoner]):
+                 reasoner: Optional[ReasoningParser]):
         """Compile the FSM that drives the context free grammar generation.
 
         Parameters

vllm/model_executor/guided_decoding/reasoner/deepseek_reasoner.py

-# SPDX-License-Identifier: Apache-2.0
-from dataclasses import dataclass
-
-from transformers import PreTrainedTokenizer
-
-from vllm.model_executor.guided_decoding.reasoner.reasoner import Reasoner
-
-
-@dataclass
-class DeepSeekReasoner(Reasoner):
-    """
-    Reasoner for DeepSeek R series models.
-    """
-    start_token_id: int
-    end_token_id: int
-
-    start_token: str = "<think>"
-    end_token: str = "</think>"
-
-    @classmethod
-    def from_tokenizer(cls, tokenizer: PreTrainedTokenizer) -> Reasoner:
-        return cls(start_token_id=tokenizer.encode(
-            "<think>", add_special_tokens=False)[0],
-                   end_token_id=tokenizer.encode("</think>",
-                                                 add_special_tokens=False)[0])
-
-    def is_reasoning_end(self, input_ids: list[int]) -> bool:
-        return self.end_token_id in input_ids
-
-    def extract_content(self, input_ids: list[int]) -> list[int]:
-        """
-        Extract the content after the end tokens
-        """
-        if self.end_token_id not in input_ids or \
-            input_ids.index(self.end_token_id) + 1 == len(input_ids):
-            return []
-        else:
-            return input_ids[input_ids.index(self.end_token_id) + 1:]

vllm/model_executor/guided_decoding/reasoner/reasoner.py

-# SPDX-License-Identifier: Apache-2.0
-from __future__ import annotations
-
-from abc import ABC, abstractmethod
-from dataclasses import dataclass
-
-from transformers import PreTrainedTokenizer
-
-
-@dataclass
-class Reasoner(ABC):
-
-    @abstractmethod
-    def from_tokenizer(cls, tokenizer: PreTrainedTokenizer) -> Reasoner:
-        pass
-
-    @abstractmethod
-    def is_reasoning_end(self, input_ids: list[int]) -> bool:
-        pass
-
-    @abstractmethod
-    def extract_content(self, input_ids: list[int]) -> list[int]:
-        pass

vllm/model_executor/guided_decoding/xgrammar_decoding.py

@@ -27,7 +27,7 @@ if TYPE_CHECKING:
     from transformers import PreTrainedTokenizer
 
     from vllm.config import ModelConfig
-    from vllm.model_executor.guided_decoding.reasoner import Reasoner
+    from vllm.reasoning import ReasoningParser
     from vllm.sampling_params import GuidedDecodingParams
 
 logger = init_logger(__name__)
@@ -37,7 +37,7 @@ def get_local_xgrammar_guided_decoding_logits_processor(
         guided_params: GuidedDecodingParams,
         tokenizer: PreTrainedTokenizer,
         model_config: ModelConfig,
-        reasoner: Reasoner | None,
+        reasoner: ReasoningParser | None,
         max_threads: int = 8):
     config = GrammarConfig.from_guided_params(guided_params=guided_params,
                                               model_config=model_config,
@@ -280,7 +280,7 @@ class GrammarConfig:
 class XGrammarLogitsProcessor:
     """Wrapper class to support pickle protocol"""
     config: GrammarConfig
-    reasoner: Reasoner | None = None
+    reasoner: ReasoningParser | None = None
 
     ctx: xgr.CompiledGrammar | None = None
     tokenizer_info: xgr.TokenizerInfo = None  # type: ignore[assignment]

vllm/entrypoints/openai/reasoning_parsers/abs_reasoning_parsers.py → vllm/reasoning/abs_reasoning_parsers.py

@@ -17,7 +17,7 @@ logger = init_logger(__name__)
 
 class ReasoningParser:
     """
-    Abstract reasoning parser class that should not be used directly. 
+    Abstract reasoning parser class that should not be used directly.
     Provided and methods should be used in derived classes.
 
     It is used to extract reasoning content from the model output.
@@ -32,6 +32,36 @@ class ReasoningParser:
         # whereas all tokenizers have .get_vocab()
         return self.model_tokenizer.get_vocab()
 
+    @abstractmethod
+    def is_reasoning_end(self, input_ids: list[int]) -> bool:
+        """
+        Check if the reasoning content ends in the input_ids.
+
+        It is used in structured engines like `xgrammar` to check if the
+        reasoning content ends in the model output.
+
+        Parameters:
+        input_ids: list[int]
+            The input_ids of the model output.
+
+        Returns:
+        bool
+            True if the reasoning content ends in the input_ids.
+        """
+
+    @abstractmethod
+    def extract_content_ids(self, input_ids: list[int]) -> list[int]:
+        """
+        Extract content token ids from the input_ids.
+        Parameters:
+        input_ids: list[int]
+            The input_ids of the model output.
+        Returns:
+        list[int]
+            The extracted content from the input_ids.
+        """
+
+    @abstractmethod
     def extract_reasoning_content(
             self, model_output: str, request: ChatCompletionRequest
     ) -> tuple[Optional[str], Optional[str]]:
@@ -53,10 +83,7 @@ class ReasoningParser:
             A tuple containing the reasoning content and the content.
         """
 
-        raise NotImplementedError(
-            "AbstractReasoningParser.extract_reasoning_calls "
-            "has not been implemented!")
-
+    @abstractmethod
     def extract_reasoning_content_streaming(
         self,
         previous_text: str,
@@ -73,43 +100,6 @@ class ReasoningParser:
         the current tokens/diffs, but also the information about what has
         previously been parsed and extracted (see constructor)
         """
-        raise NotImplementedError(
-            "AbstractReasoningParser.extract_reasoning_content_streaming "
-            "has not been implemented!")
-
-    # TODO: need to rebase by PR #14428
-    @abstractmethod
-    def is_reasoning_end(self, input_ids: list[int]) -> bool:
-        """
-        Check if the reasoning content ends in the input_ids.
-        Parameters:
-        input_ids: list[int]
-            The input_ids of the model output.
-        Returns:
-        bool
-            True if the reasoning content ends in the input_ids.
-        """
-
-        raise NotImplementedError(
-            "AbstractReasoningParser.is_reasoning_end has"
-            "not been implemented!")
-
-    # TODO: need to rebase by PR #14428
-    @abstractmethod
-    def extract_content_ids(self, input_ids: list[int]) -> list[int]:
-        """
-        Extract content token ids from the input_ids.
-        Parameters:
-        input_ids: list[int]
-            The input_ids of the model output.
-        Returns:
-        list[int]
-            The extracted content from the input_ids.
-        """
-
-        raise NotImplementedError(
-            "AbstractReasoningParser.extract_content_ids has"
-            " not been implemented!")
 
 
 class ReasoningParserManager:
@@ -125,14 +115,16 @@ class ReasoningParserManager:
         if name in cls.reasoning_parsers:
             return cls.reasoning_parsers[name]
 
-        raise KeyError(f"reasoning helper: '{name}' not found in "
-                       "reasoning_parsers")
+        raise KeyError(
+            f"reasoning helper: '{name}' not found in reasoning_parsers")
 
     @classmethod
-    def _register_module(cls,
-                         module: type,
-                         module_name: Optional[Union[str, list[str]]] = None,
-                         force: bool = True) -> None:
+    def _register_module(
+        cls,
+        module: type,
+        module_name: Optional[Union[str, list[str]]] = None,
+        force: bool = True,
+    ) -> None:
         if not issubclass(module, ReasoningParser):
             raise TypeError("module must be subclass of ReasoningParser, "
                             f"but got {type(module)}")
@@ -149,13 +141,14 @@ class ReasoningParserManager:
 
     @classmethod
     def register_module(
-            cls,
-            name: Optional[Union[str, list[str]]] = None,
-            force: bool = True,
-            module: Union[type, None] = None) -> Union[type, Callable]:
+        cls,
+        name: Optional[Union[str, list[str]]] = None,
+        force: bool = True,
+        module: Union[type, None] = None,
+    ) -> Union[type, Callable]:
         """
         Register module with the given name or name list. it can be used as a
-        decoder(with module as None) or normal function(with module as not 
+        decoder(with module as None) or normal function(with module as not
         None).
         """
         if not isinstance(force, bool):
@@ -183,7 +176,7 @@ class ReasoningParserManager:
     @classmethod
     def import_reasoning_parser(cls, plugin_path: str) -> None:
         """
-        Import a user-defined reasoning parser by the path 
+        Import a user-defined reasoning parser by the path
         of the reasoning parser define file.
         """
         module_name = os.path.splitext(os.path.basename(plugin_path))[0]

vllm/entrypoints/openai/reasoning_parsers/deepseek_r1_reasoning_parser.py → vllm/reasoning/deepseek_r1_reasoning_parser.py

@@ -8,9 +8,8 @@ from transformers import PreTrainedTokenizerBase
 
 from vllm.entrypoints.openai.protocol import (ChatCompletionRequest,
                                               DeltaMessage)
-from vllm.entrypoints.openai.reasoning_parsers.abs_reasoning_parsers import (
-    ReasoningParser, ReasoningParserManager)
 from vllm.logger import init_logger
+from vllm.reasoning import ReasoningParser, ReasoningParserManager
 
 logger = init_logger(__name__)
 
@@ -20,43 +19,45 @@ class DeepSeekR1ReasoningParser(ReasoningParser):
     """
     Reasoning parser for DeepSeek R1 model.
 
-    The DeepSeek R1 model uses <think>...</think> tokens to denote reasoning 
+    The DeepSeek R1 model uses <think>...</think> tokens to denote reasoning
     text. This parser extracts the reasoning content from the model output.
     """
 
+    start_token_id: int
+    end_token_id: int
+
+    start_token: str = "<think>"
+    end_token: str = "</think>"
+
     def __init__(self, tokenizer: PreTrainedTokenizerBase):
         super().__init__(tokenizer)
-        self.think_start_token = "<think>"
-        self.think_end_token = "</think>"
 
         self.reasoning_regex = re.compile(
-            rf"{self.think_start_token}(.*?){self.think_end_token}", re.DOTALL)
+            rf"{self.start_token}(.*?){self.end_token}", re.DOTALL)
 
         if not self.model_tokenizer:
             raise ValueError(
                 "The model tokenizer must be passed to the ReasoningParser "
                 "constructor during construction.")
 
-        self.think_start_token_id = self.vocab.get(self.think_start_token)
-        self.think_end_token_id = self.vocab.get(self.think_end_token)
-        if (self.think_start_token_id is None
-                or self.think_end_token_id is None):
+        self.start_token_id = self.vocab.get(self.start_token)
+        self.end_token_id = self.vocab.get(self.end_token)
+        if self.start_token_id is None or self.end_token_id is None:
             raise RuntimeError(
                 "DeepSeek R1 reasoning parser could not locate think start/end "
                 "tokens in the tokenizer!")
 
-    # TODO: need to rebase by PR #14428
     def is_reasoning_end(self, input_ids: list[int]) -> bool:
-        return self.think_end_token_id in input_ids
+        return self.end_token_id in input_ids
 
     def extract_content_ids(self, input_ids: list[int]) -> list[int]:
         """
         Extract the content after the end tokens
         """
-        if self.think_end_token_id not in input_ids[:-1]:
+        if self.end_token_id not in input_ids[:-1]:
             return []
         else:
-            return input_ids[input_ids.index(self.think_end_token_id) + 1:]
+            return input_ids[input_ids.index(self.end_token_id) + 1:]
 
     def extract_reasoning_content_streaming(
         self,
@@ -77,22 +78,24 @@ class DeepSeekR1ReasoningParser(ReasoningParser):
         """
         # Skip single special tokens
         if len(delta_token_ids) == 1 and (delta_token_ids[0] in [
-                self.think_start_token_id, self.think_end_token_id
+                self.start_token_id, self.end_token_id
         ]):
             return None
 
         # Check if <think> is present in previous or delta.
         # Keep compatibility with models that don't generate <think> tokens.
-        if self.think_start_token_id in previous_token_ids:
-            if self.think_end_token_id in delta_token_ids:
+        if self.start_token_id in previous_token_ids:
+            if self.end_token_id in delta_token_ids:
                 # <think> in previous, </think> in delta,
                 # extract reasoning content
-                end_index = delta_text.find(self.think_end_token)
+                end_index = delta_text.find(self.end_token)
                 reasoning_content = delta_text[:end_index]
-                content = delta_text[end_index + len(self.think_end_token):]
-                return DeltaMessage(reasoning_content=reasoning_content,
-                                    content=content if content else None)
-            elif self.think_end_token_id in previous_token_ids:
+                content = delta_text[end_index + len(self.end_token):]
+                return DeltaMessage(
+                    reasoning_content=reasoning_content,
+                    content=content if content else None,
+                )
+            elif self.end_token_id in previous_token_ids:
                 # <think> in previous, </think> in previous,
                 # reasoning content continues
                 return DeltaMessage(content=delta_text)
@@ -100,17 +103,18 @@ class DeepSeekR1ReasoningParser(ReasoningParser):
                 # <think> in previous, no </think> in previous or delta,
                 # reasoning content continues
                 return DeltaMessage(reasoning_content=delta_text)
-        elif self.think_start_token_id in delta_token_ids:
-            if self.think_end_token_id in delta_token_ids:
+        elif self.start_token_id in delta_token_ids:
+            if self.end_token_id in delta_token_ids:
                 # <think> in delta, </think> in delta, extract reasoning content
-                start_index = delta_text.find(self.think_start_token)
-                end_index = delta_text.find(self.think_end_token)
+                start_index = delta_text.find(self.start_token)
+                end_index = delta_text.find(self.end_token)
                 reasoning_content = delta_text[start_index +
-                                               len(self.think_start_token
-                                                   ):end_index]
-                content = delta_text[end_index + len(self.think_end_token):]
-                return DeltaMessage(reasoning_content=reasoning_content,
-                                    content=content if content else None)
+                                               len(self.start_token):end_index]
+                content = delta_text[end_index + len(self.end_token):]
+                return DeltaMessage(
+                    reasoning_content=reasoning_content,
+                    content=content if content else None,
+                )
             else:
                 # <think> in delta, no </think> in delta,
                 # reasoning content continues
@@ -119,15 +123,17 @@ class DeepSeekR1ReasoningParser(ReasoningParser):
             # No <think> in previous or delta, also need to check for </think>.
             # Because the model may have generated </think> without <think>
             # Ref https://huggingface.co/deepseek-ai/DeepSeek-R1/commit/8a58a132790c9935686eb97f042afa8013451c9f
-            if self.think_end_token_id in delta_token_ids:
+            if self.end_token_id in delta_token_ids:
                 # </think> in delta with more tokens,
                 # extract reasoning content and content
-                end_index = delta_text.find(self.think_end_token)
+                end_index = delta_text.find(self.end_token)
                 reasoning_content = delta_text[:end_index]
-                content = delta_text[end_index + len(self.think_end_token):]
-                return DeltaMessage(reasoning_content=reasoning_content,
-                                    content=content if content else None)
-            elif self.think_end_token_id in previous_token_ids:
+                content = delta_text[end_index + len(self.end_token):]
+                return DeltaMessage(
+                    reasoning_content=reasoning_content,
+                    content=content if content else None,
+                )
+            elif self.end_token_id in previous_token_ids:
                 # </think> in previous, thinking content ends
                 return DeltaMessage(content=delta_text)
             else:
@@ -137,22 +143,20 @@ class DeepSeekR1ReasoningParser(ReasoningParser):
     def extract_reasoning_content(
             self, model_output: str, request: ChatCompletionRequest
     ) -> tuple[Optional[str], Optional[str]]:
-
         # DeepSeek R1 doesn't generate <think> now.
         # Thus we assume the reasoning content is always at the start.
         # Ref https://huggingface.co/deepseek-ai/DeepSeek-R1/commit/8a58a132790c9935686eb97f042afa8013451c9f
-        if self.think_end_token not in model_output:
+        if self.end_token not in model_output:
             return model_output, None
         else:
             # Add a start token if it's missing to keep compatibility.
-            if self.think_start_token not in model_output:
-                model_output = f"{self.think_start_token}{model_output}"
+            if self.start_token not in model_output:
+                model_output = f"{self.start_token}{model_output}"
             # Use a regex to find the reasoning content
             reasoning_content = self.reasoning_regex.findall(model_output)[0]
 
             end_index = len(
-                f"{self.think_start_token}{reasoning_content}{self.think_end_token}"
-            )
+                f"{self.start_token}{reasoning_content}{self.end_token}")
             final_output = model_output[end_index:]
 
             if len(final_output) == 0:

vllm/entrypoints/openai/reasoning_parsers/granite_reasoning_parser.py → vllm/reasoning/granite_reasoning_parser.py

@@ -8,9 +8,8 @@ from transformers import PreTrainedTokenizerBase
 
 from vllm.entrypoints.openai.protocol import (ChatCompletionRequest,
                                               DeltaMessage)
-from vllm.entrypoints.openai.reasoning_parsers.abs_reasoning_parsers import (
-    ReasoningParser, ReasoningParserManager)
 from vllm.logger import init_logger
+from vllm.reasoning import ReasoningParser, ReasoningParserManager
 
 logger = init_logger(__name__)