# SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project from __future__ import annotations import multiprocessing from concurrent.futures import Future, ThreadPoolExecutor 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__) class StructuredOutputManager: """Engine-level manager for structured output requests.""" 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) 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 = 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 = \ self.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 is not None and \ 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: assert request.sampling_params is not None backend = request.sampling_params.guided_decoding.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, ) else: raise ValueError( f"Unsupported structured output backend: {backend}") grammar = self.executor.submit(self._async_create_grammar, request) request.structured_output_request.grammar = grammar # type: ignore[assignment] def _async_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: list[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: dict[str, int], scheduled_spec_decode_tokens: dict[str, list[int]], ) -> Optional[npt.NDArray[np.int32]]: # Prepare the structured output bitmask for this batch. 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 # 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 ordered_seq = sorted(structured_output_request_ids.items(), key=lambda x: x[1]) # Optimized parallel filling of bitmasks for # non-spec, large-batch-size cases if len(ordered_seq) > self.fill_bitmask_parallel_threshold and \ max_num_spec_tokens == 0: promises = [] batch = [] for req_id, _ in ordered_seq: 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 apply_bitmask = self.should_fill_bitmask(request) batch.append((structured_output_request.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 ordered_seq: 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 apply_bitmask = self.should_fill_bitmask(request) state_advancements = 0 req_tokens = scheduled_spec_decode_tokens.get(req_id, []) for i, token in enumerate(req_tokens + [None]): self._fill_bitmasks([(structured_output_request.grammar, cumulative_index, apply_bitmask)]) if apply_bitmask and token is not None and \ not structured_output_request.grammar.is_terminated(): assert structured_output_request.grammar.accept_tokens( req_id, [token]) state_advancements += 1 cumulative_index += 1 if state_advancements > 0: structured_output_request.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: if self.reasoner is not None: assert request.structured_output_request is not None if request.structured_output_request.reasoning_ended is None: request.structured_output_request.reasoning_ended = \ self.reasoner.is_reasoning_end(request.prompt_token_ids) 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 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()