[Core] add xgrammar as guided generation provider

requirements-common.txt

@@ -19,6 +19,7 @@ prometheus-fastapi-instrumentator >= 7.0.0
 tiktoken >= 0.6.0  # Required for DBRX tokenizer
 lm-format-enforcer >= 0.10.9, < 0.11
 outlines >= 0.0.43, < 0.1
+xgrammar
 typing_extensions >= 4.10
 filelock >= 3.16.1 # need to contain https://github.com/tox-dev/filelock/pull/317
 partial-json-parser # used for parsing partial JSON outputs

tests/model_executor/test_guided_processors.py

@@ -36,7 +36,8 @@ def test_guided_logits_processors(sample_regex, sample_json_schema):
 
 
 @pytest.mark.asyncio
-@pytest.mark.parametrize("backend", ["outlines", "lm-format-enforcer"])
+@pytest.mark.parametrize("backend",
+                         ["outlines", "lm-format-enforcer", "xgrammar"])
 async def test_guided_logits_processor_black_box(backend: str, sample_regex,
                                                  sample_json_schema):
     tokenizer = AutoTokenizer.from_pretrained('HuggingFaceH4/zephyr-7b-beta')

vllm/config.py

@@ -1999,7 +1999,7 @@ class DecodingConfig:
     guided_decoding_backend: str = 'outlines'
 
     def __post_init__(self):
-        valid_guided_backends = ['outlines', 'lm-format-enforcer']
+        valid_guided_backends = ['outlines', 'lm-format-enforcer', 'xgrammar']
         backend = self.guided_decoding_backend
         if backend not in valid_guided_backends:
             raise ValueError(f"Invalid guided_decoding_backend '{backend},"

vllm/engine/arg_utils.py

@@ -362,7 +362,7 @@ def add_cli_args(parser: FlexibleArgumentParser) -> FlexibleArgumentParser:
             '--guided-decoding-backend',
             type=str,
             default='outlines',
-            choices=['outlines', 'lm-format-enforcer'],
+            choices=['outlines', 'lm-format-enforcer', 'xgrammar'],
             help='Which engine will be used for guided decoding'
             ' (JSON schema / regex etc) by default. Currently support '
             'https://github.com/outlines-dev/outlines and '

vllm/model_executor/guided_decoding/__init__.py

@@ -19,6 +19,11 @@ async def get_guided_decoding_logits_processor(
             get_local_lm_format_enforcer_guided_decoding_logits_processor)
         return get_local_lm_format_enforcer_guided_decoding_logits_processor(
             guided_params, tokenizer)
+    if guided_params.backend == 'xgrammar':
+        from vllm.model_executor.guided_decoding.xgrammar_decoding import (  # noqa
+            get_local_xgrammar_guided_decoding_logits_processor)
+        return get_local_xgrammar_guided_decoding_logits_processor(
+            guided_params, tokenizer)
 
     raise ValueError(
         f"Unknown guided decoding backend '{guided_params.backend}'. "
@@ -40,6 +45,11 @@ def get_local_guided_decoding_logits_processor(
             get_local_lm_format_enforcer_guided_decoding_logits_processor)
         return get_local_lm_format_enforcer_guided_decoding_logits_processor(
             guided_params, tokenizer)
+    if guided_params.backend == "xgrammar":
+        from vllm.model_executor.guided_decoding.xgrammar_decoding import (  # noqa
+            get_local_xgrammar_guided_decoding_logits_processor)
+        return get_local_xgrammar_guided_decoding_logits_processor(
+            guided_params, tokenizer)
 
     raise ValueError(
         f"Unknown guided decoding backend '{guided_params.backend}'. "

vllm/model_executor/guided_decoding/xgrammar_decoding.py

@@ -0,0 +1,241 @@
+# noqa: UP007
+from __future__ import annotations
+
+import json
+import time
+from dataclasses import dataclass, field
+from typing import TYPE_CHECKING, Any, NamedTuple
+
+import torch
+from transformers import PreTrainedTokenizerFast
+
+from vllm.logger import init_logger
+
+try:
+    import xgrammar as xgr
+    from xgrammar.base import _core as xgr_core
+except ImportError:
+    pass
+
+if TYPE_CHECKING:
+    from transformers import PreTrainedTokenizer
+
+    from vllm.sampling_params import GuidedDecodingParams
+
+logger = init_logger(__name__)
+
+
+# TODO: passing batch size to max threads here
+def get_local_xgrammar_guided_decoding_logits_processor(
+        guided_params: GuidedDecodingParams,
+        tokenizer: PreTrainedTokenizer,
+        max_threads: int = 8):
+    config = GrammarConfig.from_guided_params(guided_params=guided_params,
+                                              tokenizer=tokenizer,
+                                              max_threads=max_threads)
+    return XGrammarLogitsProcessor(config)
+
+
+class TokenizerData(NamedTuple):
+    """Immutable container for cached tokenizer data."""
+    encoded_vocab: list[str]
+    stop_token_ids: list[int] | None
+    backend_str: str
+
+
+class TokenizerDataCache:
+    """Cache manager for tokenizer data to avoid repeated processing."""
+    _cache: dict[int, TokenizerData] = {}
+
+    @classmethod
+    def get_tokenizer_data(cls,
+                           tokenizer: PreTrainedTokenizer) -> TokenizerData:
+        tokenizer_hash = hash(tokenizer)
+
+        if tokenizer_hash not in cls._cache:
+            # Vendored from xgrammar logic since we cannot pickle the tokenizer
+            # https://github.com/mlc-ai/xgrammar/blob/d77c0a0173ef14779c918e3be7966ba852f7910f/python/xgrammar/tokenizer_info.py#L98 # noqa: E501
+            try:
+                encoded_vocab = [
+                    token for token, _ in sorted(tokenizer.get_vocab().items(),
+                                                 key=lambda x: x[1])
+                ]
+            except AttributeError as e:
+                raise ValueError(
+                    f"Cannot get the vocabulary of the tokenizer "
+                    f"{type(tokenizer)}. The tokenizer should have a "
+                    "get_vocab method.") from e
+
+            stop_token_ids = None
+            backend_str = xgr.VocabType.RAW
+            if isinstance(tokenizer, PreTrainedTokenizerFast):
+                backend_str = tokenizer.backend_tokenizer.to_str()
+                if stop_token_ids is None and hasattr(
+                        tokenizer,
+                        "eos_token_id") and tokenizer.eos_token_id is not None:
+                    stop_token_ids = [tokenizer.eos_token_id]
+
+            cls._cache[tokenizer_hash] = TokenizerData(
+                encoded_vocab=encoded_vocab,
+                stop_token_ids=stop_token_ids,
+                backend_str=backend_str)
+
+        return cls._cache[tokenizer_hash]
+
+
+class GrammarCompilerCache:
+    """
+    Cache for GrammarCompiler instances based on tokenizer.
+
+    This cache reduces the overhead of creating new compiler instances when
+    using the same tokenizer configuration.
+    """
+    _cache: dict[str, xgr.GrammarCompiler] = {}
+
+    @classmethod
+    def get_compiler(cls, config: GrammarConfig) -> xgr.GrammarCompiler:
+        cache_key = str(config.tokenizer_hash)
+
+        if cache_key not in cls._cache:
+            assert config.encoded_vocab is not None
+            tokenizer_info = xgr.TokenizerInfo._create_from_handle(
+                xgr_core.TokenizerInfo.from_huggingface(
+                    config.encoded_vocab, config.backend_str,
+                    config.vocab_size, config.stop_token_ids))
+            cls._cache[cache_key] = xgr.GrammarCompiler(
+                tokenizer_info, max_threads=config.max_threads)
+
+        return cls._cache[cache_key]
+
+
+@dataclass
+class GrammarConfig:
+    """Serializable configuration for grammar compilation"""
+    tokenizer_hash: int
+    vocab_size: int
+    json_str: str | None = None
+    grammar_str: str | None = None
+    max_threads: int = 8
+    # Only populated if tokenizer_hash not in cache
+    encoded_vocab: list[str] | None = None
+    stop_token_ids: list[int] | None = None
+    backend_str: str | None = None
+
+    @classmethod
+    def from_guided_params(cls,
+                           guided_params: GuidedDecodingParams,
+                           tokenizer: PreTrainedTokenizer,
+                           max_threads: int = 8) -> GrammarConfig:
+
+        tokenizer_hash = hash(tokenizer)
+        # Only get tokenizer data if not already cached
+        if tokenizer_hash in TokenizerDataCache._cache:
+            encoded_vocab = None
+            stop_token_ids = None
+            backend_str = None
+        else:
+            tokenizer_data = TokenizerDataCache.get_tokenizer_data(tokenizer)
+            encoded_vocab = tokenizer_data.encoded_vocab
+            stop_token_ids = tokenizer_data.stop_token_ids
+            backend_str = tokenizer_data.backend_str
+
+        if guided_params.json:
+            if not isinstance(guided_params.json, str):
+                json_str = json.dumps(guided_params.json)
+            else:
+                json_str = guided_params.json
+            return cls(json_str=json_str,
+                       vocab_size=len(tokenizer.get_vocab().keys()),
+                       encoded_vocab=encoded_vocab,
+                       stop_token_ids=stop_token_ids,
+                       backend_str=backend_str,
+                       tokenizer_hash=tokenizer_hash,
+                       max_threads=max_threads)
+        elif guided_params.grammar:
+            return cls(grammar_str=guided_params.grammar,
+                       vocab_size=len(tokenizer.get_vocab().keys()),
+                       encoded_vocab=encoded_vocab,
+                       stop_token_ids=stop_token_ids,
+                       backend_str=backend_str,
+                       tokenizer_hash=tokenizer_hash,
+                       max_threads=max_threads)
+        else:
+            raise ValueError(
+                "Currently only support JSON and EBNF grammar mode for xgrammar"
+            )
+
+
+@dataclass
+class XGrammarLogitsProcessor:
+    """Wrapper class to support pickle protocol"""
+    config: GrammarConfig
+
+    ctx: xgr.CompiledGrammar | None = None
+    matchers: list[xgr.GrammarMatcher] = field(default_factory=list)
+    batch_size: int = 1
+    token_bitmask: torch.Tensor = None
+    prefilled: bool = False
+
+    def __getstate__(self) -> dict[str, Any]:
+        return {'config': self.config}
+
+    def __setstate__(self, state: dict[str, Any]):
+        self.config = state['config']
+
+        self.ctx = None
+        self.matchers = []
+        self.batch_size = 1
+        self.token_bitmask = None
+        self.prefilled = False
+
+    def _ensure_ctx(self):
+        """Lazily initialize the processor in the worker process"""
+        if self.ctx is None:
+            start_time = time.time()
+            compiler = GrammarCompilerCache.get_compiler(self.config)
+            if self.config.json_str is not None:
+                self.ctx = compiler.compile_json_schema(self.config.json_str)
+            else:
+                self.ctx = compiler.compile_grammar(self.config.grammar_str)
+            compile_time = time.time() - start_time
+            if compile_time > 0.1:
+                log_str = ("Xgrammar compiled grammar/schema "
+                           f"in {compile_time:.2f} seconds")
+                logger.info(log_str)
+
+    def __call__(self, input_ids: list[int],
+                 scores: torch.Tensor) -> torch.Tensor:
+        if self.ctx is None:
+            self._ensure_ctx()
+
+        if len(self.matchers) == 0:
+            self.matchers = [
+                xgr.GrammarMatcher(self.ctx) for _ in range(self.batch_size)
+            ]
+            self.token_bitmask = xgr.allocate_token_bitmask(
+                self.batch_size, self.config.vocab_size)
+
+        if not self.prefilled:
+            # Have not sampled a token yet
+            self.prefilled = True
+        else:
+            for i, matcher in enumerate(self.matchers):
+                if not matcher.is_terminated():
+                    sampled_token = input_ids[-1]
+                    assert self.matchers[i].accept_token(sampled_token)
+
+        for i, matcher in enumerate(self.matchers):
+            if not matcher.is_terminated():
+                matcher.fill_next_token_bitmask(self.token_bitmask, i)
+
+        # token_bitmask is a CPU tensor for use with accept_token and
+        # fill_next_token_bitmask so we move it to the device of scores
+        device_type = scores.device.type
+        if device_type != "cuda":
+            scores = scores.to("cpu")
+        xgr.apply_token_bitmask_inplace(scores,
+                                        self.token_bitmask.to(scores.device))
+        if device_type != "cuda":
+            scores = scores.to(device_type)
+
+        return scores