157 lines
4.7 KiB
Python
157 lines
4.7 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 logging
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from typing import List, Tuple
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import torch
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try:
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from xgrammar import (
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CachedGrammarCompiler,
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CompiledGrammar,
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GrammarMatcher,
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TokenizerInfo,
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)
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import_error = None
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except ImportError as e:
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CachedGrammarCompiler = CompiledGrammar = GrammarMatcher = TokenizerInfo = (
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ImportError
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)
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import_error = e
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from sglang.srt.constrained.base_grammar_backend import (
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BaseGrammarBackend,
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BaseGrammarObject,
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)
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logger = logging.getLogger(__name__)
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MAX_ROLLBACK_TOKENS = 10
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class XGrammarGrammar(BaseGrammarObject):
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def __init__(
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self, matcher: GrammarMatcher, vocab_size: int, ctx: CompiledGrammar
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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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def accept_token(self, token: int):
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assert self.matcher.accept_token(token)
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def try_jump_forward(self, tokenizer) -> 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 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 self.matcher.allocate_token_bitmask(vocab_size, batch_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 apply_vocab_mask(logits: torch.Tensor, vocab_mask: torch.Tensor) -> None:
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GrammarMatcher.apply_token_bitmask_inplace(logits, vocab_mask)
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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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vocab_size=self.vocab_size,
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)
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return XGrammarGrammar(matcher, self.vocab_size, self.ctx)
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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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):
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super().__init__()
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if import_error:
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logger.warning(
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f"Ignore import error for the grammar backend: {import_error}"
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)
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self.grammar_cache = None
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return
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tokenizer_info = TokenizerInfo.from_huggingface(tokenizer)
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self.grammar_cache = CachedGrammarCompiler(tokenizer_info=tokenizer_info)
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self.vocab_size = vocab_size
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def init_value_impl(self, key: Tuple[str, str]) -> XGrammarGrammar:
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if import_error:
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raise import_error
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key_type, key_string = key
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if key_type == "json":
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try:
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ctx = self.grammar_cache.compile_json_schema_grammar(schema=key_string)
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except RuntimeError as e:
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logging.warning(
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f"Skip invalid json_schema: json_schema={key_string}, {e=}"
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)
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return None
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elif key_type == "regex":
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logger.warning(
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"regex hasn't been supported by xgrammar yet. This is skipped."
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)
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return None
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else:
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raise ValueError(f"Invalid key_type: {key_type}")
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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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vocab_size=self.vocab_size,
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)
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return XGrammarGrammar(matcher, self.vocab_size, ctx)
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def reset(self):
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if self.grammar_cache:
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self.grammar_cache.clear()
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