[V1] Structured Outputs + Thinking compatibility (#16577)

Signed-off-by: Aaron Pham <contact@aarnphm.xyz>
Co-authored-by: Russell Bryant <rbryant@redhat.com>

docs/source/features/reasoning_outputs.md

@@ -141,10 +141,10 @@ Remember to check whether the `reasoning_content` exists in the response before
 The reasoning content is also available in the structured output. The structured output engine like `xgrammar` will use the reasoning content to generate structured output. It is only supported in v0 engine now.
 
 ```bash
-VLLM_USE_V1=0 vllm serve deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B --reasoning-parser deepseek_r1
+vllm serve deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B --reasoning-parser deepseek_r1
 ```
 
-Please note that the `VLLM_USE_V1` environment variable must be set to `0` to use the v0 engine.
+The following is an example client:
 
 ```python
 from openai import OpenAI

tests/v1/entrypoints/llm/test_struct_output_generate.py

@@ -1,3 +1,4 @@
+# ruff: noqa: E501
 # SPDX-License-Identifier: Apache-2.0
 
 from __future__ import annotations
@@ -5,17 +6,22 @@ from __future__ import annotations
 import json
 import re
 from enum import Enum
-from typing import Any
+from typing import TYPE_CHECKING, Any
 
 import jsonschema
 import pytest
 from pydantic import BaseModel
 
+from tests.reasoning.utils import run_reasoning_extraction
 from vllm.entrypoints.llm import LLM
 from vllm.outputs import RequestOutput
 from vllm.platforms import current_platform
+from vllm.reasoning.abs_reasoning_parsers import ReasoningParserManager
 from vllm.sampling_params import GuidedDecodingParams, SamplingParams
 
+if TYPE_CHECKING:
+    from vllm.config import TokenizerMode
+
 NGRAM_SPEC_CONFIG = {
     "model": "[ngram]",
     "num_speculative_tokens": 5,
@@ -444,7 +450,7 @@ def test_structured_output(
 
     prompt = """
 You have access to the following function to retrieve the weather in a city:
-         
+
     {
         "name": "get_weather",
         "parameters": {
@@ -455,7 +461,7 @@ You have access to the following function to retrieve the weather in a city:
             }
         }
     }
-         
+
 If a you choose to call a function ONLY reply in the following format:
 <{start_tag}={function_name}>{parameters}{end_tag}
 where
@@ -476,7 +482,7 @@ Reminder:
 - Always add your sources when using search results to answer the user query
 
 You are a helpful assistant.
-         
+
 Given the previous instructions, what is the weather in New York City? \
 Make the response as short as possible.
 """
@@ -514,6 +520,88 @@ Make the response as short as possible.
                         f"{generated_text!r}\nError: {str(e)}")
 
 
+@pytest.mark.skip_global_cleanup
+@pytest.mark.parametrize(
+    "model_name, guided_decoding_backend, tokenizer_mode, reasoning_parser, speculative_config",  # noqa: E501
+    [
+        ("deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B", "xgrammar", "auto",
+         "deepseek_r1", NGRAM_SPEC_CONFIG),
+        ("Qwen/Qwen3-1.7B", "xgrammar", "auto", "deepseek_r1", None),
+    ],
+)
+def test_structured_output_with_reasoning_matrices(
+    monkeypatch: pytest.MonkeyPatch,
+    guided_decoding_backend: str,
+    tokenizer_mode: TokenizerMode,
+    reasoning_parser: str,
+    model_name: str,
+    speculative_config: dict[str, Any] | None,
+):
+    monkeypatch.setenv("VLLM_USE_V1", "1")
+
+    if current_platform.is_tpu() and speculative_config:
+        pytest.skip("TPU does not support speculative decoding")
+
+    # Use a single LLM instance for several scenarios to
+    # speed up the test suite.
+    llm = LLM(
+        model=model_name,
+        # Don't use eager execution on TPUs because we want to test for no
+        # recompilation at runtime
+        enforce_eager=bool(not current_platform.is_tpu()),
+        max_model_len=1024,
+        max_num_seqs=16,
+        guided_decoding_backend=guided_decoding_backend,
+        guided_decoding_disable_any_whitespace=True,
+        tokenizer_mode=tokenizer_mode,
+        reasoning_parser=reasoning_parser,
+        speculative_config=speculative_config,
+    )
+    tokenizer = llm.get_tokenizer(None)
+    reasoner = ReasoningParserManager.get_reasoning_parser(reasoning_parser)(
+        tokenizer=tokenizer)
+
+    reasoning_prompt = "Solve the following math problem step-by-step, then provide the final answer as JSON object with a single key 'result'. Make sure to correct your reasoning if there are any issue should it arise.\nProblem: What is 5 * 8 + 2?"  # noqa: E501
+    reasoning_schema = {
+        "type": "object",
+        "properties": {
+            "result": {
+                "type": "integer"
+            }
+        },
+        "required": ["result"],
+        "additionalProperties": False
+    }
+    if "Qwen3" in model_name:
+        reasoning_prompt += "<think>\n"
+
+    sampling_params = SamplingParams(
+        temperature=0.1,
+        max_tokens=8192,
+        guided_decoding=GuidedDecodingParams(json=reasoning_schema),
+    )
+    outputs = llm.generate(
+        [reasoning_prompt],
+        sampling_params=sampling_params,
+        use_tqdm=True,
+    )
+
+    assert outputs is not None
+    output = outputs[0]
+    assert output is not None and isinstance(output, RequestOutput)
+    prompt = output.prompt
+    generated_text = output.outputs[0].text
+    reasoning_content, content = run_reasoning_extraction(
+        reasoner, [generated_text])
+    print(
+        f"Prompt: {prompt!r}\nReasoning: {reasoning_content!r}\nContent: {content!r}"
+    )
+
+    assert content is not None and reasoning_content is not None
+    output_json = json.loads(content)
+    jsonschema.validate(instance=output_json, schema=reasoning_schema)
+
+
 @pytest.mark.skip_global_cleanup
 @pytest.mark.parametrize("model_name, tokenizer_mode",
                          PARAMS_MODELS_TOKENIZER_MODE)

vllm/config.py

@@ -2332,7 +2332,7 @@ class SpeculativeConfig:
     `TypicalAcceptanceSampler`."""
 
     speculative_token_tree: Optional[str] = None
-    """Specifies the tree structure for speculative token generation. 
+    """Specifies the tree structure for speculative token generation.
     """
     # required configuration params passed from engine
     target_model_config: ModelConfig = field(default=None,
@@ -4024,7 +4024,7 @@ class VllmConfig:
     """LoRA configuration."""
     speculative_config: Optional[SpeculativeConfig] = None
     """Speculative decoding configuration."""
-    decoding_config: Optional[DecodingConfig] = None
+    decoding_config: DecodingConfig = field(default_factory=DecodingConfig)
     """Decoding configuration."""
     observability_config: Optional[ObservabilityConfig] = None
     """Observability configuration."""

vllm/reasoning/abs_reasoning_parsers.py

@@ -1,5 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 
+from __future__ import annotations
+
 import os
 from abc import abstractmethod
 from collections.abc import Sequence
@@ -33,7 +35,7 @@ class ReasoningParser:
         return self.model_tokenizer.get_vocab()
 
     @abstractmethod
-    def is_reasoning_end(self, input_ids: list[int]) -> bool:
+    def is_reasoning_end(self, input_ids: Sequence[int]) -> bool:
         """
         Check if the reasoning content ends in the input_ids.
 
@@ -106,7 +108,7 @@ class ReasoningParserManager:
     reasoning_parsers: dict[str, type] = {}
 
     @classmethod
-    def get_reasoning_parser(cls, name) -> type:
+    def get_reasoning_parser(cls, name: str | None) -> type[ReasoningParser]:
         """
         Get reasoning parser by name which is registered by `register_module`.
 

vllm/v1/core/sched/scheduler.py

@@ -758,7 +758,8 @@ class Scheduler(SchedulerInterface):
                 # the outer lists can be of length > 1.
                 new_logprobs = logprobs.slice(req_index, req_index + 1)
 
-            if new_token_ids and request.use_structured_output:
+            if new_token_ids and self.structured_output_manager.should_advance(
+                    request):
                 # NOTE: structured_output_request
                 # should not be None if use_structured_output, we have
                 # check above, so safe to ignore type warning
@@ -767,11 +768,10 @@ class Scheduler(SchedulerInterface):
 
             # Add newly generated spec token ids to the request.
             if spec_token_ids is not None:
-                if request.use_structured_output:
+                if self.structured_output_manager.should_advance(request):
                     metadata = request.structured_output_request
-                    assert metadata is not None and metadata.grammar is not None
                     # Needs to happen after new_token_ids are accepted.
-                    request.spec_token_ids = metadata.grammar.validate_tokens(
+                    request.spec_token_ids = metadata.grammar.validate_tokens(  # type: ignore[union-attr]
                         spec_token_ids[req_index])
                 else:
                     request.spec_token_ids = spec_token_ids[req_index]

vllm/v1/structured_output/__init__.py

@@ -7,16 +7,23 @@ from typing import TYPE_CHECKING, Optional
 
 from vllm.config import VllmConfig
 from vllm.logger import init_logger
+from vllm.reasoning import ReasoningParserManager
+from vllm.transformers_utils.tokenizer_group import init_tokenizer_from_configs
+from vllm.utils import LazyLoader
 from vllm.v1.structured_output.backend_guidance import GuidanceBackend
 from vllm.v1.structured_output.backend_types import (StructuredOutputBackend,
                                                      StructuredOutputGrammar)
+from vllm.v1.structured_output.backend_xgrammar import XgrammarBackend
 
 if TYPE_CHECKING:
     import numpy as np
     import numpy.typing as npt
     import torch
 
+    from vllm.reasoning import ReasoningParser
     from vllm.v1.request import Request
+else:
+    torch = LazyLoader("torch", globals(), "torch")
 
 logger = init_logger(__name__)
 
@@ -26,9 +33,11 @@ class StructuredOutputManager:
 
     def __init__(self, vllm_config: VllmConfig):
         self.backend: Optional[StructuredOutputBackend] = None
+        self.reasoner: Optional[ReasoningParser] = None
         self.vllm_config = vllm_config
 
         self._grammar_bitmask: Optional[torch.Tensor] = None
+        self._full_mask = torch.tensor(-1, dtype=torch.int32)
 
         # The default max_workers if not specified is the number of CPUs * 5,
         # which is way too high since these tasks are CPU-bound, not I/O bound.
@@ -36,24 +45,43 @@ class StructuredOutputManager:
         # compilation, so we set it to half the number of CPUs.
         max_workers = max(1, (multiprocessing.cpu_count() + 1) // 2)
         self.executor = ThreadPoolExecutor(max_workers=max_workers)
+        self.tokenizer = init_tokenizer_from_configs(
+            model_config=self.vllm_config.model_config,
+            scheduler_config=self.vllm_config.scheduler_config,
+            lora_config=self.vllm_config.lora_config,
+        ).get_lora_tokenizer(None)
+        reasoning_backend = vllm_config.decoding_config.reasoning_backend
+        if reasoning_backend:
+            reasoner_cls = ReasoningParserManager.get_reasoning_parser(
+                reasoning_backend)
+            self.reasoner = reasoner_cls(tokenizer=self.tokenizer)
 
     def grammar_init(self, request: Request) -> None:
         if request.structured_output_request is None:
             return
 
+        if TYPE_CHECKING:
+            assert request.sampling_params.guided_decoding is not None
+
         # Initialize the backend the first time it is needed.
         #
         # NOTE: We only support a single backend. We do NOT support different
         # backends on a per-request basis in V1 (for now, anyway...).
         if self.backend is None:
             backend = request.sampling_params.guided_decoding.backend
+            vocab_size = self.vllm_config.model_config.get_vocab_size()
             if backend == "xgrammar":
-                from vllm.v1.structured_output.backend_xgrammar import (
+                self.backend = XgrammarBackend(
-                    XgrammarBackend)
+                    self.vllm_config,
-
+                    tokenizer=self.tokenizer,
-                self.backend = XgrammarBackend(self.vllm_config)
+                    vocab_size=vocab_size,
+                )
             elif backend == "guidance":
-                self.backend = GuidanceBackend(self.vllm_config)
+                self.backend = GuidanceBackend(
+                    self.vllm_config,
+                    tokenizer=self.tokenizer,
+                    vocab_size=vocab_size,
+                )
             else:
                 raise ValueError(
                     f"Unsupported structured output backend: {backend}")
@@ -87,14 +115,14 @@ class StructuredOutputManager:
         if not structured_output_request_ids:
             return None
 
+        max_num_spec_tokens = 0
+        if self.vllm_config.speculative_config is not None:
+            max_num_spec_tokens = \
+                self.vllm_config.speculative_config.num_speculative_tokens
+
         if self._grammar_bitmask is None:
             assert self.backend is not None
             max_batch_size = self.vllm_config.scheduler_config.max_num_seqs
-            if self.vllm_config.speculative_config is not None:
-                max_num_spec_tokens = self.vllm_config.\
-                    speculative_config.num_speculative_tokens
-            else:
-                max_num_spec_tokens = 0
 
             # Allocate a bitmask for each token needing to be checked:
             # one for each speculative position, and one more for the
@@ -103,6 +131,7 @@ class StructuredOutputManager:
                 self.backend.allocate_token_bitmask(
                     max_batch_size * (1 + max_num_spec_tokens))
 
+        bitmask_tensor = self._grammar_bitmask
         # Generate a batched bitmask for all structured output requests.
         # When speculative decoding is enabled, we need to include multiple
         # masks for each request, one for each possible bonus token position.
@@ -110,16 +139,30 @@ class StructuredOutputManager:
         cumulative_index = 0
         ordered_seq = sorted(structured_output_request_ids.items(),
                              key=lambda x: x[1])
+
+        # Note that for thinking support, we will need to
+        # reset the relevant part of the bitmask for consequent
+        # request here.
+        bitmask_tensor[:(len(ordered_seq) * (1 + max_num_spec_tokens))].fill_(
+            self._full_mask)
+
         # NOTE: This outer loop can likely be parallelized to improve
         # performance of bitmask generation for large batches.
         for req_id, _ in ordered_seq:
             request = requests[req_id].structured_output_request
-            assert request is not None and request.grammar is not None
+            if TYPE_CHECKING:
+                assert request is not None
+                assert request.grammar is not None
+
+            apply_bitmask = (
+                request.reasoning_ended if self.reasoner is not None else True
+            )  # noqa: E501
+
             state_advancements = 0
             req_tokens = scheduled_spec_decode_tokens.get(req_id, []) + [None]
             for i, token in enumerate(req_tokens):
-                if not request.grammar.is_terminated():
+                if apply_bitmask and not request.grammar.is_terminated():
-                    request.grammar.fill_bitmask(self._grammar_bitmask,
+                    request.grammar.fill_bitmask(bitmask_tensor,
                                                  cumulative_index)
                     if token is not None:
                         # In order to generate the correct bitmask for each
@@ -132,15 +175,41 @@ class StructuredOutputManager:
             if state_advancements > 0:
                 request.grammar.rollback(state_advancements)
 
-        bitmask_tensor = self._grammar_bitmask
+        if cumulative_index < bitmask_tensor.shape[0]:
-        if cumulative_index < self._grammar_bitmask.shape[0]:
+            bitmask_tensor = bitmask_tensor[:cumulative_index]
-            bitmask_tensor = self._grammar_bitmask[:cumulative_index]
 
         # After finishing with the xgrammar operations, we convert to
         # np.ndarray, because that is much more efficient for serialization
         # and deserialization when sending this to the GPU workers.
         return bitmask_tensor.numpy()
 
+    def should_advance(self, request: Request) -> bool:
+        if not request.use_structured_output:
+            return False
+
+        # To determine whether we can advance the FSM.
+        # Supports thinking usage where we skip the reasoning components.
+        if TYPE_CHECKING:
+            assert request.structured_output_request is not None
+            assert request.structured_output_request.grammar is not None
+        # by default, we should always advance
+        # for cases that doesn't uses thinking mode.
+        if self.reasoner is not None:
+            structured_req = request.structured_output_request
+
+            if structured_req.reasoning_ended:
+                return True
+
+            # Check if reasoning ends in *this* step
+            if self.reasoner.is_reasoning_end(request.all_token_ids):
+                # Reasoning just ended, so we shouldn't advanced til
+                # next pass
+                structured_req.reasoning_ended = True
+
+            return False
+        else:
+            return True
+
     def clear_backend(self) -> None:
         if self.backend is not None:
             self.backend.destroy()

vllm/v1/structured_output/backend_guidance.py

@@ -1,5 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 
+from __future__ import annotations
+
 import copy
 import json
 import os
@@ -8,10 +10,8 @@ from typing import TYPE_CHECKING, Any, Optional, Union
 
 import torch
 
-from vllm.config import VllmConfig
 from vllm.logger import init_logger
 from vllm.sampling_params import SamplingParams
-from vllm.transformers_utils.tokenizer_group import init_tokenizer_from_configs
 from vllm.utils import LazyLoader
 from vllm.v1.structured_output.backend_types import (StructuredOutputBackend,
                                                      StructuredOutputGrammar,
@@ -54,25 +54,17 @@ def process_for_additional_properties(
     return guide_json_obj
 
 
+@dataclass
 class GuidanceBackend(StructuredOutputBackend):
 
-    def __init__(self, vllm_config: VllmConfig):
+    def __post_init__(self):
-        self.vllm_config = vllm_config
-        tokenizer_group = init_tokenizer_from_configs(
-            model_config=vllm_config.model_config,
-            scheduler_config=vllm_config.scheduler_config,
-            lora_config=vllm_config.lora_config)  # type: ignore[arg-type]
-        self.vllm_config = vllm_config
-        self.vocab_size = vllm_config.model_config.get_vocab_size()
-
         self.disable_any_whitespace = \
-            vllm_config.decoding_config.disable_any_whitespace
+            self.vllm_config.decoding_config.disable_any_whitespace
         self.disable_additional_properties = \
-            vllm_config.decoding_config.disable_additional_properties
+            self.vllm_config.decoding_config.disable_additional_properties
 
-        tokenizer = tokenizer_group.get_lora_tokenizer(None)
         self.ll_tokenizer = llguidance_hf.from_tokenizer(
-            tokenizer, self.vocab_size)
+            self.tokenizer, self.vocab_size)
 
     def compile_grammar(self, request_type: StructuredOutputOptions,
                         grammar_spec: str) -> StructuredOutputGrammar:

vllm/v1/structured_output/backend_types.py

@@ -1,9 +1,17 @@
 # SPDX-License-Identifier: Apache-2.0
 
+from __future__ import annotations
+
 import enum
 from abc import ABC, abstractmethod
+from dataclasses import dataclass
+from typing import TYPE_CHECKING
 
-import torch
+if TYPE_CHECKING:
+    import torch
+
+    from vllm.config import VllmConfig
+    from vllm.transformers_utils.tokenizer import AnyTokenizer
 
 
 class StructuredOutputOptions(enum.Enum):
@@ -85,9 +93,14 @@ class StructuredOutputGrammar(ABC):
         """
 
 
+@dataclass
 class StructuredOutputBackend(ABC):
     """Engine-level backend for structured output requests."""
 
+    vllm_config: VllmConfig
+    tokenizer: AnyTokenizer
+    vocab_size: int
+
     @abstractmethod
     def compile_grammar(self, request_type: StructuredOutputOptions,
                         grammar_spec: str) -> StructuredOutputGrammar:
@@ -104,7 +117,7 @@ class StructuredOutputBackend(ABC):
         """
 
     @abstractmethod
-    def allocate_token_bitmask(self, max_num_seqs: int):
+    def allocate_token_bitmask(self, max_num_seqs: int) -> torch.Tensor:
         """
         Allocates a token bitmask for the specified maximum number of sequences.
 

vllm/v1/structured_output/backend_xgrammar.py

@@ -1,5 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 
+from __future__ import annotations
+
 import json
 from dataclasses import dataclass, field
 from typing import TYPE_CHECKING, Any
@@ -7,10 +9,8 @@ from typing import TYPE_CHECKING, Any
 import torch
 
 import vllm.envs
-from vllm.config import VllmConfig
 from vllm.logger import init_logger
 from vllm.sampling_params import SamplingParams
-from vllm.transformers_utils.tokenizer_group import init_tokenizer_from_configs
 from vllm.transformers_utils.tokenizers.mistral import MistralTokenizer
 from vllm.utils import LazyLoader
 from vllm.v1.structured_output.backend_types import (StructuredOutputBackend,
@@ -28,61 +28,49 @@ else:
 logger = init_logger(__name__)
 
 
+@dataclass
 class XgrammarBackend(StructuredOutputBackend):
 
-    def __init__(self, vllm_config: VllmConfig):
+    def __post_init__(self):
-        self.vllm_config = vllm_config
-        tokenizer_group = init_tokenizer_from_configs(
-            model_config=vllm_config.model_config,
-            scheduler_config=vllm_config.scheduler_config,
-            lora_config=vllm_config.lora_config)  # type: ignore[arg-type]
-
         self.disable_any_whitespace = \
-            vllm_config.decoding_config.disable_any_whitespace
+            self.vllm_config.decoding_config.disable_any_whitespace
 
-        self.num_speculative_tokens = 0
+        if isinstance(self.tokenizer, MistralTokenizer):
-        if self.vllm_config.speculative_config is not None:
-            self.num_speculative_tokens = \
-                self.vllm_config.speculative_config.num_speculative_tokens
-
-        tokenizer = tokenizer_group.get_lora_tokenizer(None)
-        self.vocab_size = vllm_config.model_config.get_vocab_size()
-        if isinstance(tokenizer, MistralTokenizer):
             # NOTE: ideally, xgrammar should handle this accordingly.
             # refer to https://github.com/mlc-ai/xgrammar/blob/d77c0a0173ef14779c918e3be7966ba852f7910f/python/xgrammar/tokenizer_info.py#L98
             try:
-                if tokenizer.is_tekken:
+                if self.tokenizer.is_tekken:
-                    encoded_vocab = tokenizer._vocab
+                    encoded_vocab = self.tokenizer._vocab
                 else:
                     encoded_vocab = [
                         token for token, _ in sorted(
-                            tokenizer.get_vocab().items(),
+                            self.tokenizer.get_vocab().items(),
                             key=lambda x: x[1],
                         )
                     ]
                 stop_token_ids = None
-                if hasattr(
+                if (hasattr(
-                        tokenizer,
+                        self.tokenizer,
                         "eos_token_id",
-                ) and tokenizer.eos_token_id is not None:
+                ) and self.tokenizer.eos_token_id is not None):
-                    stop_token_ids = [tokenizer.eos_token_id]
+                    stop_token_ids = [self.tokenizer.eos_token_id]
             except AttributeError as e:
                 raise ValueError(
                     f"Cannot get the vocabulary of the tokenizer "
-                    f"{type(tokenizer)}. The tokenizer should have a "
+                    f"{type(self.tokenizer)}. The tokenizer should have a "
                     "get_vocab method.") from e
             tokenizer_info = xgr.TokenizerInfo(  # type: ignore
                 encoded_vocab=encoded_vocab,
                 # NOTE: https://github.com/mlc-ai/xgrammar/blob/5e141f6ff1ca02bc31f9e512e68b61f2a8ae88e5/tests/python/test_tokenizer_info.py#L43 # noqa: E501
                 vocab_type=xgr.VocabType.RAW
-                if tokenizer.is_tekken else xgr.VocabType.BYTE_FALLBACK,
+                if self.tokenizer.is_tekken else xgr.VocabType.BYTE_FALLBACK,
                 vocab_size=self.vocab_size,
                 stop_token_ids=stop_token_ids,
                 add_prefix_space=True,
             )
         else:
             tokenizer_info = xgr.TokenizerInfo.from_huggingface(
-                tokenizer,
+                self.tokenizer,
                 vocab_size=self.vocab_size,
             )
         self.compiler = xgr.GrammarCompiler(
@@ -92,6 +80,11 @@ class XgrammarBackend(StructuredOutputBackend):
             cache_limit_bytes=vllm.envs.VLLM_XGRAMMAR_CACHE_MB * 1024 * 1024,
         )
 
+        self.num_speculative_tokens = 0
+        if self.vllm_config.speculative_config is not None:
+            self.num_speculative_tokens = \
+                self.vllm_config.speculative_config.num_speculative_tokens
+
     def compile_grammar(self, request_type: StructuredOutputOptions,
                         grammar_spec: str) -> StructuredOutputGrammar:
         if request_type == StructuredOutputOptions.JSON:

vllm/v1/structured_output/request.py

@@ -20,6 +20,7 @@ class StructuredOutputRequest:
     sampling_params: SamplingParams
     _grammar: Optional[Union[Future[StructuredOutputGrammar],
                              StructuredOutputGrammar]] = None
+    reasoning_ended: bool = False
 
     def _check_grammar_completion(self) -> bool:
         # NOTE: We have to lazy import to gate circular imports