271 lines
8.9 KiB
Python
271 lines
8.9 KiB
Python
# Copyright 2023-2024 SGLang Team
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""The baseclass of a backend for grammar-guided constrained decoding."""
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import logging
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import time
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from concurrent.futures import Future, ThreadPoolExecutor
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from dataclasses import dataclass, field
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from typing import Dict, List, Optional, Tuple
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import torch
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from sglang.srt.server_args import ServerArgs
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logger = logging.getLogger(__name__)
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@dataclass
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class GrammarStats:
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compilation_time: Optional[float] = None
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schema_count: Optional[int] = None
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ebnf_size: Optional[int] = None
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is_cache_hit: bool = False
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is_grammar_aborted: bool = False
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tree_traversal_time: List[float] = field(default_factory=list)
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dispatch_type: Optional[str] = None
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num_timeout: int = 0
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class BaseGrammarObject:
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def __init__(self):
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self._finished = False
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self.grammar_stats = None
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self.current_token = None
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def maybe_init_reasoning(self, reasoning: bool):
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pass
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def accept_token(self, token: int) -> None:
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"""
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Accept a token in the grammar.
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"""
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raise NotImplementedError()
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def rollback(self, k: int):
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raise NotImplementedError()
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def is_terminated(self):
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return False
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def allocate_vocab_mask(
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self, vocab_size: int, batch_size: int, device
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) -> torch.Tensor:
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raise NotImplementedError()
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def fill_vocab_mask(self, vocab_mask: torch.Tensor, idx: int) -> None:
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raise NotImplementedError()
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@staticmethod
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def move_vocab_mask(vocab_mask: torch.Tensor, device) -> torch.Tensor:
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raise NotImplementedError()
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@staticmethod
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def apply_vocab_mask(logits: torch.Tensor, vocab_mask: torch.Tensor) -> None:
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raise NotImplementedError()
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def copy(self) -> "BaseGrammarObject":
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return self
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@property
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def finished(self):
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return self._finished
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@finished.setter
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def finished(self, finished):
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self._finished = finished
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def try_jump_forward(self, tokenizer) -> Optional[Tuple[List[int], str]]:
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"""
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Try to jump forward in the grammar.
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Returns:
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A jump forward helper which may be used in `jump_forward_str_state`.
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None if the jump forward is not possible.
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"""
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raise NotImplementedError()
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def jump_forward_str_state(self, helper: Tuple[List[int], str]) -> Tuple[str, int]:
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"""
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Jump forward for the grammar.
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Returns:
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A tuple of the jump forward string and the next state of the grammar
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(which can be used in `jump_and_retokenize` if needed).
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"""
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raise NotImplementedError()
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def jump_and_retokenize(
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self, old_output_ids: List[int], new_output_ids: List[int], next_state: int
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) -> None:
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"""
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Jump forward occurs, and update the grammar state if needed.
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"""
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raise NotImplementedError()
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class InvalidGrammarObject(BaseGrammarObject):
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"""Represents a grammar that failed to compile, carrying the original error message."""
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def __init__(self, error_message: str = "Unknown grammar error"):
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super().__init__()
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self.error_message = error_message
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def __repr__(self):
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return f"InvalidGrammarObject(error_message={self.error_message!r})"
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class BaseGrammarBackend:
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def __init__(self):
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self.executor = ThreadPoolExecutor()
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self.cache: Dict[Tuple[str, str], BaseGrammarObject] = {}
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def _not_supported(self, key_type: str, key_string: str) -> BaseGrammarObject:
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logger.warning(f"Skip unsupported {key_type=}, {key_string=}")
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return InvalidGrammarObject()
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def dispatch_fallback(self, key_type: str, key_string: str) -> BaseGrammarObject:
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"""
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This function should not be reached in any case.
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"""
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raise ValueError(f"Invalid key_type: {key_type}={key_string}")
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def dispatch_json(self, key_string: str) -> BaseGrammarObject:
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return self._not_supported("json", key_string)
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def dispatch_regex(self, key_string: str) -> BaseGrammarObject:
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return self._not_supported("regex", key_string)
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def dispatch_ebnf(self, key_string: str) -> BaseGrammarObject:
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return self._not_supported("ebnf", key_string)
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def dispatch_structural_tag(self, key_string: str) -> BaseGrammarObject:
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return self._not_supported("structural_tag", key_string)
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def _init_value_dispatch(
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self, key: Tuple[str, str], require_reasoning: bool
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) -> BaseGrammarObject:
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s = time.perf_counter()
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key_type, key_string = key
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if key_type == "json":
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grammar = self.dispatch_json(key_string)
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elif key_type == "regex":
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grammar = self.dispatch_regex(key_string)
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elif key_type == "ebnf":
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grammar = self.dispatch_ebnf(key_string)
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elif key_type == "structural_tag":
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grammar = self.dispatch_structural_tag(key_string)
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else:
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grammar = self.dispatch_fallback(key_type, key_string)
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if grammar is not None and grammar.grammar_stats is not None:
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grammar.grammar_stats.compilation_time = time.perf_counter() - s
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return grammar
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def get_cached_or_future_value(
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self, key: Tuple[str, str], require_reasoning: bool
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) -> Tuple[BaseGrammarObject | Future[BaseGrammarObject], bool]:
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value = self.cache.get(key)
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if value:
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copied_value = value.copy()
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copied_value.maybe_init_reasoning(require_reasoning)
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return copied_value, True
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value = self.executor.submit(self._init_value_dispatch, key, require_reasoning)
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return value, False
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def set_cache(self, key: Tuple[str, str], value: BaseGrammarObject):
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self.cache[key] = value
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def reset(self):
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self.cache.clear()
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GRAMMAR_BACKEND_REGISTRY = {}
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def register_grammar_backend(name, init_func):
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GRAMMAR_BACKEND_REGISTRY[name] = init_func
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def create_grammar_backend(
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server_args: ServerArgs,
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tokenizer,
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vocab_size: int,
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eos_token_ids: Optional[set] = None,
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) -> Optional[BaseGrammarBackend]:
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name = server_args.grammar_backend
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# Custom grammar backend has the highest priority
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if name in GRAMMAR_BACKEND_REGISTRY:
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return GRAMMAR_BACKEND_REGISTRY[name](
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server_args, tokenizer, vocab_size, eos_token_ids
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)
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# Default grammar backends
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if name == "outlines":
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from sglang.srt.constrained.outlines_backend import OutlinesGrammarBackend
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grammar_backend = OutlinesGrammarBackend(
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tokenizer,
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whitespace_pattern=server_args.constrained_json_whitespace_pattern,
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)
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elif name == "xgrammar":
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from sglang.srt.constrained.xgrammar_backend import (
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TokenizerNotSupportedError,
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XGrammarGrammarBackend,
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)
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# Convert Set[int] to List[int] if needed
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eos_list = list(eos_token_ids) if eos_token_ids else None
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try:
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grammar_backend = XGrammarGrammarBackend(
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tokenizer,
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vocab_size=vocab_size,
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model_eos_token_ids=eos_list,
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any_whitespace=not server_args.constrained_json_disable_any_whitespace,
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)
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except TokenizerNotSupportedError as e:
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logger.warning(
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f"Grammar backend disabled because tokenizer is not supported by XGrammar: {e}. "
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"Falling back to grammar_backend='none'. "
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"Structured outputs (JSON schema, regex, EBNF) will not be available."
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)
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server_args.grammar_backend = "none"
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return None
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elif name == "llguidance":
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from sglang.srt.constrained.llguidance_backend import GuidanceBackend
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grammar_backend = GuidanceBackend(
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tokenizer=tokenizer,
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any_whitespace=not server_args.constrained_json_disable_any_whitespace,
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whitespace_pattern=server_args.constrained_json_whitespace_pattern,
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)
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elif name == "none":
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return None
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else:
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raise ValueError(f"Invalid grammar backend: {name}")
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if server_args.reasoning_parser and hasattr(tokenizer, "think_end_id"):
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from sglang.srt.constrained.reasoner_grammar_backend import (
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ReasonerGrammarBackend,
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)
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grammar_backend = ReasonerGrammarBackend(
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grammar_backend, tokenizer.think_end_id
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)
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return grammar_backend
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