class StructuredOutputManager:
"""Engine-level manager for structured output requests."""
def __init__(self, vllm_config: VllmConfig):
self.backend: StructuredOutputBackend | None = None
# We only store the class of the reasoner in the manager.
# The parser instance is request-scoped because some reasoning parsers
# depend on per-request chat-template kwargs.
self.reasoner_cls: type[ReasoningParser] | None = None
self.vllm_config = vllm_config
# When in external_launcher mode, async grammar compilation causes deadlocks
# due to external_launcher mode having a scheduler for each TP rank.
# Async grammar compilation causes the
# WAITING_FOR_STRUCTURED_OUTPUT_GRAMMAR → WAITING transition to
# happen at different times on different TP ranks,
# breaking the determinism assumption that external_launcher relies on.
self._use_async_grammar_compilation = (
vllm_config.parallel_config.distributed_executor_backend
!= "external_launcher"
)
self._grammar_bitmask: torch.Tensor | None = None
self._full_mask = torch.tensor(-1, dtype=torch.int32)
max_batch_size = self.vllm_config.scheduler_config.max_num_seqs
self.fill_bitmask_parallel_threshold = 128
if self.fill_bitmask_parallel_threshold < max_batch_size:
self.fill_bitmask_parallel_batch_size = 16
# Use:
# - at least 1 CPU
# - at most half the number of CPUs or 8, whichever is less
max_workers = max(1, min(multiprocessing.cpu_count() // 2, 8))
self.executor_for_fillmask = ThreadPoolExecutor(max_workers=max_workers)
if not self.vllm_config.model_config.skip_tokenizer_init:
# 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. We also know we would never dominate CPU usage
# with just grammar 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 = cached_tokenizer_from_config(
model_config=self.vllm_config.model_config
)
reasoning_parser_plugin = (
self.vllm_config.structured_outputs_config.reasoning_parser_plugin
)
if reasoning_parser_plugin and len(reasoning_parser_plugin) > 3:
ReasoningParserManager.import_reasoning_parser(reasoning_parser_plugin)
reasoning_parser = (
self.vllm_config.structured_outputs_config.reasoning_parser
)
if reasoning_parser:
self.reasoner_cls = ReasoningParserManager.get_reasoning_parser(
reasoning_parser
)
self.enable_in_reasoning = (
self.vllm_config.structured_outputs_config.enable_in_reasoning
)
def _get_reasoner(self, request: "Request") -> "ReasoningParser | None":
structured_req = request.structured_output_request
if structured_req is None or self.reasoner_cls is None:
return None
if structured_req.reasoner is None:
# Lazily build the request-local parser so the structured-output
# gate observes the same template kwargs used by the frontend.
parser_kwargs = structured_req.reasoning_parser_kwargs or {}
structured_req.reasoner = self.reasoner_cls(
tokenizer=self.tokenizer,
**parser_kwargs,
)
return structured_req.reasoner
def grammar_init(self, request: "Request") -> None:
if request.structured_output_request is None:
return
if TYPE_CHECKING:
assert (
request.sampling_params is not None
and request.sampling_params.structured_outputs 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...).
# _backend is set in Processor._validate_structured_output
if self.backend is None:
assert request.sampling_params is not None
backend = request.sampling_params.structured_outputs._backend
vocab_size = self.vllm_config.model_config.get_vocab_size()
if backend == "xgrammar":
self.backend = XgrammarBackend(
self.vllm_config,
tokenizer=self.tokenizer,
vocab_size=vocab_size,
)
elif backend == "guidance":
self.backend = GuidanceBackend(
self.vllm_config,
tokenizer=self.tokenizer,
vocab_size=vocab_size,
)
elif backend == "outlines":
from vllm.v1.structured_output.backend_outlines import OutlinesBackend
self.backend = OutlinesBackend(
self.vllm_config,
tokenizer=self.tokenizer,
vocab_size=vocab_size,
)
elif backend == "lm-format-enforcer":
from vllm.v1.structured_output.backend_lm_format_enforcer import ( # noqa: E501
LMFormatEnforcerBackend,
)
self.backend = LMFormatEnforcerBackend(
self.vllm_config,
tokenizer=self.tokenizer,
vocab_size=vocab_size,
)
else:
raise ValueError(f"Unsupported structured output backend: {backend}")
if self._use_async_grammar_compilation:
grammar = self.executor.submit(self._create_grammar, request)
else:
grammar = self._create_grammar(request) # type: ignore[assignment]
request.structured_output_request.grammar = grammar # type: ignore[assignment]
def _create_grammar(self, request: "Request") -> StructuredOutputGrammar:
key = request.structured_output_request.structured_output_key # type: ignore[union-attr]
# Note that the request was validated in the engine core client,
# so at this point we know it is a supported type of request.
#
# TODO: we still need to handle xgrammar compilation failures,
# though it should be unlikely as we test that up front as well.
request_type, grammar_spec = key
assert self.backend is not None
return self.backend.compile_grammar(request_type, grammar_spec)
def _fill_bitmasks(
self, batch: Iterable[tuple[StructuredOutputGrammar, int, bool]]
) -> None:
assert self._grammar_bitmask is not None
for grammar, index, apply_bitmask in batch:
if apply_bitmask and not grammar.is_terminated():
grammar.fill_bitmask(self._grammar_bitmask, index)
else:
# Note that for thinking support, we will need to
# reset the relevant part of the bitmask for consequent
# requests here.
self._grammar_bitmask[index].fill_(self._full_mask)
def _async_submit_fill_bitmask(
self, batch: list[tuple[StructuredOutputGrammar, int, bool]]
) -> Future:
return self.executor_for_fillmask.submit(self._fill_bitmasks, batch)
def grammar_bitmask(
self,
requests: dict[str, "Request"],
structured_output_request_ids: list[str],
scheduled_spec_decode_tokens: dict[str, list[int]],
) -> "npt.NDArray[np.int32] | None":
# Prepare the structured output bitmask for this batch.
if not structured_output_request_ids:
return None
# Covers both speculative decoding and diffusion LLMs (canvas_length).
max_num_spec_tokens = self.vllm_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
# Allocate a bitmask for each token needing to be checked:
# one for each speculative position, and one more for the
# bonus token / non-speculative token.
self._grammar_bitmask = self.backend.allocate_token_bitmask(
max_batch_size * (1 + max_num_spec_tokens)
)
# 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.
# These are stored inline in the tensor and unpacked by the gpu runner.
cumulative_index = 0
# Optimized parallel filling of bitmasks for
# non-spec, large-batch-size cases
if (
len(structured_output_request_ids) > self.fill_bitmask_parallel_threshold
and max_num_spec_tokens == 0
):
promises = []
batch = []
for req_id in structured_output_request_ids:
request = requests[req_id]
structured_output_request = request.structured_output_request
if TYPE_CHECKING:
assert structured_output_request is not None
assert structured_output_request.grammar is not None
grammar = structured_output_request.grammar
apply_bitmask = self.should_fill_bitmask(request)
batch.append((grammar, cumulative_index, apply_bitmask))
if len(batch) == self.fill_bitmask_parallel_batch_size:
promises.append(self._async_submit_fill_bitmask(batch))
batch = []
cumulative_index += 1
if batch:
promises.append(self._async_submit_fill_bitmask(batch))
# Wait for all bitmask filling tasks to complete.
for promise in promises:
promise.result()
else:
# Fallback to serial filling of bitmasks for small-batch-size cases
for req_id in structured_output_request_ids:
request = requests[req_id]
structured_output_request = request.structured_output_request
if TYPE_CHECKING:
assert structured_output_request is not None
assert structured_output_request.grammar is not None
grammar = structured_output_request.grammar
apply_bitmask = self.should_fill_bitmask(request)
reasoner = self._get_reasoner(request)
detect_reasoning_end = (
not apply_bitmask
and reasoner is not None
and not self.enable_in_reasoning
)
simulated_buf: list[int] | None = None
history_len = 0
state_advancements = 0
post_reasoning_end_in_window = False
req_tokens = scheduled_spec_decode_tokens.get(req_id, ())
for i, token in enumerate(req_tokens):
self._fill_bitmasks(((grammar, cumulative_index, apply_bitmask),))
advance_grammar = apply_bitmask
if token == -1:
apply_bitmask = False
advance_grammar = False
elif (
detect_reasoning_end
and reasoner is not None
and not apply_bitmask
):
if simulated_buf is None:
history = list(request.all_token_ids)
history_len = len(history)
simulated_buf = history + list(req_tokens)
simulated = simulated_buf[: history_len + i + 1]
if reasoner.is_reasoning_end_streaming(simulated, [token]):
# Reasoning ended mid-window. Constrain the rest
# of the window via bitmask. Skip grammar advance
# through the marker (it is reasoning content);
# try to advance through subsequent drafts so the
# next bitmask row reflects the post-advance state,
# but tolerate rejection since those drafts predate
# the bitmask and are not guaranteed valid.
apply_bitmask = True
advance_grammar = False
post_reasoning_end_in_window = True
if advance_grammar and not grammar.is_terminated():
accepted = grammar.accept_tokens(req_id, [token])
if accepted:
state_advancements += 1
elif not post_reasoning_end_in_window:
raise AssertionError(
(token, req_id, scheduled_spec_decode_tokens)
)
cumulative_index += 1
# Diffusion LLMs don't sample a bonus token after the
# scheduled positions, so skip its bitmask in that case.
if not (self.vllm_config.model_config.is_diffusion and req_tokens):
# bonus_apply must be True when the bonus-row position
# should be grammar-constrained. Two triggers:
# - should_fill_bitmask(request): reasoning was already
# over at step start (or no reasoner /
# enable_in_reasoning).
# - apply_bitmask: reasoning ended mid-window in this
# call and was flipped True after the marker;
# should_fill_bitmask still returns False here because
# reasoning_ended is only persisted later by
# should_advance.
bonus_apply = self.should_fill_bitmask(request) or apply_bitmask
self._fill_bitmasks(((grammar, cumulative_index, bonus_apply),))
cumulative_index += 1
if state_advancements > 0:
grammar.rollback(state_advancements)
bitmask_tensor = self._grammar_bitmask
if cumulative_index < bitmask_tensor.shape[0]:
bitmask_tensor = bitmask_tensor[: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_fill_bitmask(self, request: "Request") -> bool:
# NOTE (Hanchen) if enable_in_reasoning is True, it means that
# the model needs to be constrained in reasoning. So we should always
# enable the bitmask filling.
reasoner = self._get_reasoner(request)
if reasoner is not None:
if self.enable_in_reasoning:
return True
assert request.structured_output_request is not None
if request.structured_output_request.reasoning_ended is None:
# This should be removed here, but since `openai_gptoss`
# is an independent code path, it is kept for now.
# After unifying the `openai_gptoss` and non-`openai_gptoss` styles,
# it can be removed.
request.structured_output_request.reasoning_ended = (
reasoner.is_reasoning_end(request.prompt_token_ids or [])
)
return request.structured_output_request.reasoning_ended
return True
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 don't use thinking mode.
reasoner = self._get_reasoner(request)
if reasoner is None:
return True
# if the model needs structured in reasoning, we should advance
if self.enable_in_reasoning:
return True
structured_req = request.structured_output_request
if structured_req.reasoning_ended:
return True
# Check if reasoning ends in *this* step
delta_from = request.num_computed_tokens - request.num_output_placeholders
all_token_ids = request.all_token_ids
start = (
delta_from if delta_from >= 0 else max(len(all_token_ids) + delta_from, 0)
)
if reasoner.is_reasoning_end_streaming(
all_token_ids, itertools.islice(all_token_ids, start, None)
):
structured_req.reasoning_ended = True
# Reasoning just ended this step. Defer FSM advance until the next
# pass (see reasoning_ended check above) for JSON/regex/choice/grammar:
# advancing on the closing boundary token can accept tokens that still
# belong to the reasoning stream. Structural tags are the only safe
# same-step exception: they model phased output (e.g. thinking tag ->
# answer tag), and speculative decoding must run grammar.validate_tokens
# on draft tokens produced immediately after that transition.
if (
self.vllm_config.speculative_config is not None
and structured_req.structured_output_key[0]
== StructuredOutputOptions.STRUCTURAL_TAG
):
# The scheduler will advance the grammar with this step's
# tokens right away, but the step still contains reasoning
# content up to and including the end marker. Record where
# it ends so trim_reasoning_for_advance() can drop it.
structured_req.reasoning_end_token_index = (
self._find_reasoning_end_index(reasoner, all_token_ids, start)
)
return True
return False
@staticmethod
def _find_reasoning_end_index(
reasoner: "ReasoningParser", all_token_ids: Sequence[int], start: int
) -> int:
"""Locates the last reasoning token within ``all_token_ids[start:]``.
Returns:
The absolute index of the token at which
``is_reasoning_end_streaming`` first fires. Falls back to the
final index when no single token triggers the detection (e.g.
a multi-token marker only recognized on the full delta), which
conservatively treats the whole step as reasoning content.
"""
prefix = list(itertools.islice(all_token_ids, start))
for idx in range(start, len(all_token_ids)):
token = all_token_ids[idx]
prefix.append(token)
if reasoner.is_reasoning_end_streaming(prefix, [token]):
return idx
return len(all_token_ids) - 1
def trim_reasoning_for_advance(
self, request: "Request", new_token_ids: list[int]
) -> list[int]:
"""Drops reasoning content from tokens about to advance the grammar.
When reasoning ends mid-step (see should_advance), the step's output
still contains reasoning tokens up to and including the end marker.
Those are not grammar content: feeding them to accept_tokens makes
the grammar reject the marker and kills the request (#44006).
Returns:
The suffix of ``new_token_ids`` that follows the reasoning-end
marker. Steps fully after the boundary are returned unchanged.
"""
structured_req = request.structured_output_request
if structured_req is None:
return new_token_ids
end_idx = structured_req.reasoning_end_token_index
if end_idx is None:
return new_token_ids
first_idx = len(request.all_token_ids) - len(new_token_ids)
num_reasoning = end_idx + 1 - first_idx
if num_reasoning <= 0:
return new_token_ids
return new_token_ids[num_reasoning:]
def clear_backend(self) -> None:
if self.backend is not None:
self.backend.destroy()