wehub-resource-sync
478 lines
22 KiB
Python
478 lines
22 KiB
Python
# SPDX-License-Identifier: Apache-2.0
|
|
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
|
|
import itertools
|
|
import multiprocessing
|
|
from collections.abc import Iterable, Sequence
|
|
from concurrent.futures import Future, ThreadPoolExecutor
|
|
from typing import TYPE_CHECKING
|
|
|
|
from vllm.config import VllmConfig
|
|
from vllm.logger import init_logger
|
|
from vllm.reasoning import ReasoningParserManager
|
|
from vllm.tokenizers import cached_tokenizer_from_config
|
|
from vllm.utils.import_utils import LazyLoader
|
|
from vllm.v1.structured_output.backend_guidance import GuidanceBackend
|
|
from vllm.v1.structured_output.backend_types import (
|
|
StructuredOutputBackend,
|
|
StructuredOutputGrammar,
|
|
StructuredOutputOptions,
|
|
)
|
|
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: 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()
|