115 lines
3.6 KiB
Python
115 lines
3.6 KiB
Python
"""
|
|
Copyright 2023-2024 SGLang Team
|
|
Licensed under the Apache License, Version 2.0 (the "License");
|
|
you may not use this file except in compliance with the License.
|
|
You may obtain a copy of the License at
|
|
|
|
http://www.apache.org/licenses/LICENSE-2.0
|
|
|
|
Unless required by applicable law or agreed to in writing, software
|
|
distributed under the License is distributed on an "AS IS" BASIS,
|
|
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
See the License for the specific language governing permissions and
|
|
limitations under the License.
|
|
"""
|
|
|
|
"""Constrained decoding with xgrammar backend."""
|
|
|
|
from typing import List, Tuple
|
|
|
|
import torch
|
|
from xgrammar import CachedGrammarCompiler, CompiledGrammar, GrammarMatcher
|
|
|
|
from sglang.srt.constrained.base_grammar_backend import (
|
|
BaseGrammarBackend,
|
|
BaseGrammarObject,
|
|
)
|
|
|
|
MAX_ROLLBACK_TOKENS = 10
|
|
|
|
|
|
class XGrammarGrammar(BaseGrammarObject):
|
|
|
|
def __init__(
|
|
self, matcher: GrammarMatcher, vocab_size: int, ctx: CompiledGrammar
|
|
) -> None:
|
|
self.matcher = matcher
|
|
self.vocab_size = vocab_size
|
|
self.ctx = ctx
|
|
|
|
def accept_token(self, token: int):
|
|
assert self.matcher.accept_token(token)
|
|
|
|
def try_jump_forward(self, tokenizer) -> Tuple[List[int], str]:
|
|
s = self.matcher.find_jump_forward_string()
|
|
if s:
|
|
return [], s
|
|
return None
|
|
|
|
def jump_forward_str_state(self, helper: Tuple[List[int], str]) -> Tuple[str, int]:
|
|
_, data = helper
|
|
return data, -1
|
|
|
|
def jump_and_retokenize(
|
|
self, old_output_ids: List[int], new_output_ids: List[int], next_state: int
|
|
):
|
|
k = 0
|
|
for i, old_id in enumerate(old_output_ids):
|
|
if old_id == new_output_ids[i]:
|
|
k = i + 1
|
|
else:
|
|
break
|
|
|
|
# rollback to the last token that is the same
|
|
if k < len(old_output_ids):
|
|
self.matcher.rollback(len(old_output_ids) - k)
|
|
|
|
for i in range(k, len(new_output_ids)):
|
|
assert self.matcher.accept_token(new_output_ids[i])
|
|
|
|
def fill_vocab_mask(self, vocab_mask: torch.Tensor):
|
|
# Note that this bitmask is a bitset, not bool
|
|
bitmask = self.matcher.get_next_token_bitmask()
|
|
# Mask the tokens that are not allowed
|
|
vocab_mask[
|
|
self.matcher.get_rejected_tokens_from_bitmask(bitmask, self.vocab_size)
|
|
] = 1
|
|
|
|
def copy(self):
|
|
matcher = GrammarMatcher(
|
|
self.ctx,
|
|
max_rollback_tokens=MAX_ROLLBACK_TOKENS,
|
|
mask_vocab_size=self.vocab_size,
|
|
)
|
|
return XGrammarGrammar(matcher, self.vocab_size, self.ctx)
|
|
|
|
|
|
class XGrammarGrammarBackend(BaseGrammarBackend):
|
|
def __init__(
|
|
self,
|
|
tokenizer,
|
|
vocab_size: int,
|
|
):
|
|
super().__init__()
|
|
self.grammar_cache = CachedGrammarCompiler(tokenizer_or_vocab=tokenizer)
|
|
self.vocab_size = vocab_size
|
|
|
|
def init_value_impl(self, key: Tuple[str, str]) -> XGrammarGrammar:
|
|
key_type, key_string = key
|
|
if key_type == "json":
|
|
ctx = self.grammar_cache.get_compiled_grammar_for_json_schema(key_string)
|
|
elif key_type == "regex":
|
|
raise ValueError("regex hasn't been supported by xgrammar yet")
|
|
else:
|
|
raise ValueError(f"Invalid key_type: {key_type}")
|
|
|
|
matcher = GrammarMatcher(
|
|
ctx,
|
|
max_rollback_tokens=MAX_ROLLBACK_TOKENS,
|
|
mask_vocab_size=self.vocab_size,
|
|
)
|
|
return XGrammarGrammar(matcher, self.vocab_size, ctx)
|
|
|
|
def reset(self):
|
|
self.grammar_cache.clear()
|