240 lines
8.4 KiB
Python
240 lines
8.4 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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"""Constrained decoding with xgrammar backend."""
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import json
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import logging
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from typing import List, Optional, Tuple, Union
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import torch
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from xgrammar import (
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CompiledGrammar,
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GrammarCompiler,
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GrammarMatcher,
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StructuralTagItem,
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TokenizerInfo,
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allocate_token_bitmask,
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)
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from sglang.srt.constrained.base_grammar_backend import (
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INVALID_GRAMMAR_OBJ,
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BaseGrammarBackend,
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BaseGrammarObject,
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)
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from sglang.srt.utils import is_hip
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_is_hip = is_hip()
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if _is_hip:
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from sgl_kernel import apply_token_bitmask_inplace_cuda
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else:
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from sglang.srt.constrained.triton_ops.bitmask_ops import (
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apply_token_bitmask_inplace_triton,
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)
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logger = logging.getLogger(__name__)
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MAX_ROLLBACK_TOKENS = 200
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class XGrammarGrammar(BaseGrammarObject):
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def __init__(
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self,
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matcher: GrammarMatcher,
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vocab_size: int,
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ctx: CompiledGrammar,
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override_stop_tokens: Optional[Union[List[int], int]],
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key_string: Optional[str] = None, # TODO (sk): for debugging, remove later
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) -> None:
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self.matcher = matcher
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self.vocab_size = vocab_size
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self.ctx = ctx
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self.override_stop_tokens = override_stop_tokens
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self.finished = False
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self.accepted_tokens = []
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self.key_string = key_string
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def accept_token(self, token: int):
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if not self.is_terminated():
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accepted = self.matcher.accept_token(token)
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if not accepted:
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# log for debugging
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raise ValueError(
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f"Tokens not accepted: {token}\n"
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f"Accepted tokens: {self.accepted_tokens}\n"
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f"Key string: {self.key_string}"
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)
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else:
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self.accepted_tokens.append(token)
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def rollback(self, k: int):
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self.matcher.rollback(k)
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self.accepted_tokens = self.accepted_tokens[:-k]
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def is_terminated(self):
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return self.matcher.is_terminated()
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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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return allocate_token_bitmask(batch_size, vocab_size)
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def fill_vocab_mask(self, vocab_mask: torch.Tensor, idx: int) -> None:
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self.matcher.fill_next_token_bitmask(vocab_mask, idx)
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@staticmethod
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def move_vocab_mask(vocab_mask: torch.Tensor, device) -> torch.Tensor:
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return vocab_mask.to(device, non_blocking=True)
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def apply_vocab_mask(self, logits: torch.Tensor, vocab_mask: torch.Tensor) -> None:
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if logits.device.type == "cuda":
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if _is_hip:
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apply_token_bitmask_inplace_cuda(logits, vocab_mask)
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else:
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apply_token_bitmask_inplace_triton(logits, vocab_mask)
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elif logits.device.type == "cpu" and self.apply_vocab_mask_cpu:
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self.apply_vocab_mask_cpu(logits, vocab_mask)
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else:
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raise RuntimeError(f"Unsupported device: {logits.device.type}")
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def copy(self):
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matcher = GrammarMatcher(
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self.ctx,
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max_rollback_tokens=MAX_ROLLBACK_TOKENS,
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override_stop_tokens=self.override_stop_tokens,
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)
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return XGrammarGrammar(
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matcher,
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self.vocab_size,
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self.ctx,
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self.override_stop_tokens,
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self.key_string,
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)
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def try_jump_forward(self, tokenizer) -> Optional[Tuple[List[int], str]]:
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s = self.matcher.find_jump_forward_string()
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if s:
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return [], s
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return None
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def jump_forward_str_state(self, helper: Tuple[List[int], str]) -> Tuple[str, int]:
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_, data = helper
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return data, -1
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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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):
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k = 0
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for i, old_id in enumerate(old_output_ids):
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if old_id == new_output_ids[i]:
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k = i + 1
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else:
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break
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# rollback to the last token that is the same
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if k < len(old_output_ids):
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self.matcher.rollback(len(old_output_ids) - k)
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for i in range(k, len(new_output_ids)):
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assert self.matcher.accept_token(new_output_ids[i])
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def __repr__(self):
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return f"XGrammarGrammar({self.key_string=}, {self.accepted_tokens=})"
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class XGrammarGrammarBackend(BaseGrammarBackend):
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def __init__(
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self,
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tokenizer,
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vocab_size: int,
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model_eos_token_ids: Optional[List[int]] = None,
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):
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super().__init__()
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if hasattr(tokenizer, "init_xgrammar"):
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# For special tokenizer
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tokenizer_info, override_stop_tokens = tokenizer.init_xgrammar()
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else:
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# Create TokenizerInfo with model's EOS tokens as the authoritative stop tokens
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# This ensures consistency between what the model considers EOS and what XGrammar uses
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tokenizer_info = TokenizerInfo.from_huggingface(
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tokenizer, vocab_size=vocab_size, stop_token_ids=model_eos_token_ids
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)
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override_stop_tokens = None
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self.grammar_compiler = GrammarCompiler(tokenizer_info=tokenizer_info)
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self.vocab_size = vocab_size
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self.override_stop_tokens = override_stop_tokens
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def _from_context(self, ctx: CompiledGrammar, key_string: str) -> XGrammarGrammar:
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matcher = GrammarMatcher(
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ctx,
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max_rollback_tokens=MAX_ROLLBACK_TOKENS,
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override_stop_tokens=self.override_stop_tokens,
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)
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return XGrammarGrammar(
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matcher, self.vocab_size, ctx, self.override_stop_tokens, key_string
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)
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def dispatch_json(self, key_string: str) -> Optional[XGrammarGrammar]:
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try:
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if key_string == "$$ANY$$":
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# Note: This builtin JSON grammar includes *all* valid JSON (including, for example, arrays at the root)
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ctx = self.grammar_compiler.compile_builtin_json_grammar()
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else:
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ctx = self.grammar_compiler.compile_json_schema(schema=key_string)
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except (RuntimeError, json.decoder.JSONDecodeError) as e:
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logging.error(f"Hit invalid json_schema: {key_string=}, {e=}")
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return INVALID_GRAMMAR_OBJ
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return self._from_context(ctx, key_string)
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def dispatch_ebnf(self, key_string: str) -> Optional[XGrammarGrammar]:
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try:
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ctx = self.grammar_compiler.compile_grammar(key_string)
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except RuntimeError as e:
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logging.error(f"Hit invalid ebnf: {key_string=}, {e=}")
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return INVALID_GRAMMAR_OBJ
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return self._from_context(ctx, key_string)
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def dispatch_regex(self, key_string: str) -> Optional[XGrammarGrammar]:
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try:
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ctx = self.grammar_compiler.compile_regex(key_string)
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except RuntimeError as e:
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logging.error(f"Hit invalid regex: {key_string=}, {e=}")
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return INVALID_GRAMMAR_OBJ
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return self._from_context(ctx, key_string)
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def dispatch_structural_tag(self, key_string: str) -> Optional[XGrammarGrammar]:
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try:
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structural_tag = json.loads(key_string)
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tags = [
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StructuralTagItem(
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begin=structure["begin"],
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schema=json.dumps(structure["schema"]),
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end=structure["end"],
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)
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for structure in structural_tag["structures"]
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]
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ctx = self.grammar_compiler.compile_structural_tag(
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tags, structural_tag["triggers"]
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)
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except (RuntimeError, json.decoder.JSONDecodeError) as e:
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logging.error(f"Hit invalid structural_tag: {key_string=}, {e=}")
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return INVALID_GRAMMAR_OBJ
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return self._from_context(ctx, key_string)
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def reset(self):
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self.grammar_compiler.clear_cache()
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