[Fix][Structured Output] using vocab_size to construct matcher (#14868)

Signed-off-by: Russell Bryant <rbryant@redhat.com>
Signed-off-by: Robert Shaw <114415538+robertgshaw2-redhat@users.noreply.github.com>
Signed-off-by: Aaron Pham <contact@aarnphm.xyz>
Co-authored-by: rshaw@neuralmagic.com <rshaw@neuralmagic.com>
Co-authored-by: Russell Bryant <rbryant@redhat.com>
Co-authored-by: Robert Shaw <114415538+robertgshaw2-redhat@users.noreply.github.com>

.buildkite/test-pipeline.yaml

@@ -200,6 +200,7 @@ steps:
     - pytest -v -s v1/core
     - pytest -v -s v1/entrypoints
     - pytest -v -s v1/engine
+    - pytest -v -s v1/entrypoints
     - pytest -v -s v1/sample
     - pytest -v -s v1/worker
     - pytest -v -s v1/structured_output

requirements/common.txt

@@ -20,7 +20,7 @@ tiktoken >= 0.6.0  # Required for DBRX tokenizer
 lm-format-enforcer >= 0.10.11, < 0.11
 outlines == 0.1.11
 lark == 1.2.2
-xgrammar == 0.1.15; platform_machine == "x86_64" or platform_machine == "aarch64"
+xgrammar == 0.1.16; platform_machine == "x86_64" or platform_machine == "aarch64"
 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

@@ -1,5 +1,6 @@
 # SPDX-License-Identifier: Apache-2.0
 
+import json
 import pickle
 
 import pytest
@@ -208,8 +209,6 @@ def test_guided_decoding_backend_options():
 
 
 def test_pickle_xgrammar_tokenizer_data():
-
-    # TODO: move to another test file for xgrammar
     try:
         import xgrammar as xgr
     except ImportError:
@@ -217,7 +216,11 @@ def test_pickle_xgrammar_tokenizer_data():
 
     from vllm.model_executor.guided_decoding.xgrammar_decoding import (
         TokenizerData)
-    tokenizer_data = TokenizerData(vocab_type=xgr.VocabType.RAW)
+    tokenizer_data = TokenizerData(
+        metadata=
+        '{"vocab_type":2,"vocab_size":151665,"add_prefix_space":false,"stop_token_ids":[151645]}',
+        encoded_vocab=['!', '"', '#', '$', '%'],
+    )
     pickled = pickle.dumps(tokenizer_data)
 
     assert pickled is not None
@@ -225,4 +228,5 @@ def test_pickle_xgrammar_tokenizer_data():
     depickled: TokenizerData = pickle.loads(pickled)
 
     assert depickled is not None
-    assert depickled.vocab_type == xgr.VocabType.RAW
+    assert json.loads(
+        depickled.metadata)['vocab_type'] == xgr.VocabType.BYTE_LEVEL.value

tests/v1/entrypoints/llm/test_struct_output_generate.py

@@ -18,9 +18,6 @@ MODELS_TO_TEST = [
     "Qwen/Qwen2.5-1.5B-Instruct", "mistralai/Ministral-8B-Instruct-2410"
 ]
 
-# Undo after https://github.com/vllm-project/vllm/pull/14868
-pytest.skip(allow_module_level=True)
-
 
 @pytest.mark.skip_global_cleanup
 @pytest.mark.parametrize("guided_decoding_backend",

vllm/model_executor/guided_decoding/__init__.py

@@ -9,7 +9,6 @@ from vllm.model_executor.guided_decoding.reasoner import get_reasoner
 from vllm.model_executor.guided_decoding.utils import (
     convert_lark_to_gbnf, grammar_is_likely_lark,
     has_lmf_unsupported_json_features, has_xgrammar_unsupported_json_features)
-from vllm.platforms import CpuArchEnum
 
 if TYPE_CHECKING:
     from transformers import PreTrainedTokenizer
@@ -53,19 +52,12 @@ def maybe_backend_fallback(
     if guided_params.backend_name == "xgrammar":
         from vllm.model_executor.guided_decoding.xgrammar_decoding import (
             xgr_installed)
-        # xgrammar only has x86 wheels for linux, fallback to outlines
-        from vllm.platforms import current_platform
-        if current_platform.get_cpu_architecture() is not CpuArchEnum.X86:
-            fallback_or_error(guided_params,
-                              "xgrammar is only supported on x86 CPUs.",
-                              "outlines")
 
         # xgrammar doesn't support regex, fallback to outlines
         if guided_params.regex is not None:
             fallback_or_error(
                 guided_params,
                 "xgrammar does not support regex guided decoding.", "outlines")
-
         # xgrammar doesn't support some JSON schema features
         elif (guided_params.json is not None
               and has_xgrammar_unsupported_json_features(guided_params.json)):

vllm/model_executor/guided_decoding/xgrammar_decoding.py

@@ -9,13 +9,11 @@ from dataclasses import dataclass, field
 from typing import TYPE_CHECKING, Any, List
 
 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
     xgr_installed = True
 except ImportError:
     xgr_installed = False
@@ -35,7 +33,6 @@ if TYPE_CHECKING:
 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,
@@ -52,18 +49,8 @@ def get_local_xgrammar_guided_decoding_logits_processor(
 @dataclass(frozen=True)
 class TokenizerData:
     """Immutable container for cached tokenizer data."""
+    metadata: str
     encoded_vocab: list[str] = field(default_factory=list)
-    stop_token_ids: list[int] | None = None
-    # These fields are mutually exclusive: `backend_str` is used to create a
-    # TokenizeInfo with `TokenizerInfo.from_huggingface` while `vocab_type` is
-    # used within the constructor of TokenizeInfo
-    backend_str: str | None = None
-    vocab_type: xgr.VocabType | None = None
-
-    def __post_init__(self):
-        # Check for mutual exclusive
-        assert not (self.backend_str and self.vocab_type), \
-            "backend_str and vocab_type are mutual exclusive"
 
 
 class TokenizerDataCache:
@@ -71,46 +58,52 @@ class TokenizerDataCache:
     _cache: dict[int, TokenizerData] = {}
 
     @classmethod
-    def get_tokenizer_data(cls,
+    def get_tokenizer_data(
-                           tokenizer: PreTrainedTokenizer) -> TokenizerData:
+        cls,
-        tokenizer_hash = hash(tokenizer)
+        tokenizer: PreTrainedTokenizer,
+        /,
+        *,
+        tokenizer_hash: int,
+        vocab_size: int,
+    ) -> TokenizerData:
 
         if tokenizer_hash not in cls._cache:
-            # Vendored from xgrammar logic since we cannot pickle the tokenizer
+            tokenizer_info = xgr.TokenizerInfo.from_huggingface(
-            # https://github.com/mlc-ai/xgrammar/blob/d77c0a0173ef14779c918e3be7966ba852f7910f/python/xgrammar/tokenizer_info.py#L98 # noqa: E501
+                tokenizer,
+                # NOTE: We will need to use lm_head's vocab_size
+                # to determine correct special_token_ids for this tokenizer.
+                # See https://github.com/mlc-ai/xgrammar/commit/70c959fb6d9cea75aae33c414763cd0602022d92  # noqa: E501
+                vocab_size=vocab_size,
+            )
+            metadata = json.loads(tokenizer_info.dump_metadata())
+
+            # Vendored from xgrammar logic to get encoded_vocab
+            # https://github.com/mlc-ai/xgrammar/blob/989222175c2a30fb7987d8bcce35bec1bf6817f2/python/xgrammar/tokenizer_info.py#L127 # noqa: E501
             try:
-                encoded_vocab = [
+                vocab_dict = tokenizer.get_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
+            # maintain tokenizer's indexing
-            backend_str = ""
+            encoded_vocab = [""] * tokenizer_info.vocab_size
-            vocab_type = xgr.VocabType.RAW
+            for token, idx in vocab_dict.items():
+                if idx < tokenizer_info.vocab_size:
+                    encoded_vocab[idx] = token
 
-            if stop_token_ids is None and hasattr(
+            if isinstance(tokenizer, MistralTokenizer):
-                    tokenizer,
-                    "eos_token_id") and tokenizer.eos_token_id is not None:
-                stop_token_ids = [tokenizer.eos_token_id]
-
-            if isinstance(tokenizer, PreTrainedTokenizerFast):
-                backend_str = tokenizer.backend_tokenizer.to_str()
-                vocab_type = None
-
-            elif isinstance(tokenizer, MistralTokenizer):
                 # REF: https://github.com/mlc-ai/xgrammar/blob/5e141f6ff1ca02bc31f9e512e68b61f2a8ae88e5/tests/python/test_tokenizer_info.py#L43 # noqa: E501
-                vocab_type = xgr.VocabType.BYTE_FALLBACK
+                metadata.update({
+                    "vocab_type": xgr.VocabType.BYTE_FALLBACK,
+                    "add_prefix_space": True
+                })
 
             cls._cache[tokenizer_hash] = TokenizerData(
                 encoded_vocab=encoded_vocab,
-                stop_token_ids=stop_token_ids,
+                metadata=json.dumps(metadata),
-                backend_str=backend_str,
+            )
-                vocab_type=vocab_type)
 
         return cls._cache[tokenizer_hash]
 
@@ -129,30 +122,15 @@ class GrammarCompilerCache:
         cache_key = str(config.tokenizer_hash)
 
         if cache_key not in cls._cache:
-            assert config.tokenizer_data is not None
-            assert config.tokenizer_data.encoded_vocab is not None
-
             config_data = config.tokenizer_data
 
             # In TokenizerDataCache.get_tokenizer_data, a serializable
             # tokenizer_data is created and cached. This data is used to build
             # a tokenizer_info and create an xgrammar compiler.
-            # - If tokenizer_data has backend_str set, use
+            tokenizer_info = xgr.TokenizerInfo.from_vocab_and_metadata(
-            # xgr_core.TokenizerInfo.from_huggingface (a C++ bind).
+                encoded_vocab=config_data.encoded_vocab,
-            # - Otherwise, use the default constructor with vocab_type.
+                metadata=config_data.metadata,
-            # - xgr_core.TokenizerInfo.from_huggingface !=
+            )
-            #   xgr.TokenizerInfo.from_huggingface.
-            if config_data.backend_str:
-                tokenizer_info = xgr.TokenizerInfo._create_from_handle(
-                    xgr_core.TokenizerInfo.from_huggingface(
-                        config_data.encoded_vocab, config_data.backend_str,
-                        config.vocab_size, config_data.stop_token_ids))
-            else:
-                tokenizer_info = xgr.TokenizerInfo(
-                    config_data.encoded_vocab,
-                    config_data.vocab_type,
-                    vocab_size=config.vocab_size,
-                    stop_token_ids=config_data.stop_token_ids)
             cls._cache[cache_key] = xgr.GrammarCompiler(
                 tokenizer_info, max_threads=config.max_threads)
 
@@ -163,13 +141,12 @@ class GrammarCompilerCache:
 class GrammarConfig:
     """Serializable configuration for grammar compilation"""
     tokenizer_hash: int
-    vocab_size: int
+    tokenizer_data: TokenizerData
     json_str: str | None = None
     grammar_str: str | None = None
     json_object: bool | None = None
     any_whitespace: bool = True
     max_threads: int = 8
-    tokenizer_data: TokenizerData | None = None
 
     @classmethod
     def from_guided_params(cls,
@@ -179,7 +156,11 @@ class GrammarConfig:
                            max_threads: int = 8) -> GrammarConfig:
 
         tokenizer_hash = hash(tokenizer)
-        tokenizer_data = TokenizerDataCache.get_tokenizer_data(tokenizer)
+        tokenizer_data = TokenizerDataCache.get_tokenizer_data(
+            tokenizer,
+            tokenizer_hash=tokenizer_hash,
+            vocab_size=model_config.hf_text_config.vocab_size,
+        )
 
         if guided_params.json:
             if not isinstance(guided_params.json, str):
@@ -218,7 +199,6 @@ class GrammarConfig:
                 raise ValueError(str(err)) from err
 
             return cls(json_str=json_str,
-                       vocab_size=model_config.hf_text_config.vocab_size,
                        tokenizer_hash=tokenizer_hash,
                        max_threads=max_threads,
                        tokenizer_data=tokenizer_data,
@@ -246,14 +226,12 @@ class GrammarConfig:
                 raise ValueError(str(err)) from err
 
             return cls(grammar_str=grammar_str,
-                       vocab_size=model_config.hf_text_config.vocab_size,
                        tokenizer_hash=tokenizer_hash,
                        max_threads=max_threads,
                        tokenizer_data=tokenizer_data)
         elif guided_params.json_object:
             return cls(
                 json_object=True,
-                vocab_size=model_config.hf_text_config.vocab_size,
                 tokenizer_hash=tokenizer_hash,
                 max_threads=max_threads,
                 tokenizer_data=tokenizer_data,
@@ -267,7 +245,6 @@ class GrammarConfig:
 
             return cls(
                 grammar_str=choice_str,
-                vocab_size=model_config.hf_text_config.vocab_size,
                 tokenizer_hash=tokenizer_hash,
                 max_threads=max_threads,
                 tokenizer_data=tokenizer_data,
@@ -291,6 +268,13 @@ class GrammarConfig:
         grammar = ('root ::= ' + ' | '.join(f'"{c}"' for c in escaped_choices))
         return grammar
 
+    @staticmethod
+    def tokenizer_info(tokenizer_data: TokenizerData) -> xgr.TokenizerInfo:
+        return xgr.TokenizerInfo.from_vocab_and_metadata(
+            encoded_vocab=tokenizer_data.encoded_vocab,
+            metadata=tokenizer_data.metadata,
+        )
+
 
 @dataclass
 class XGrammarLogitsProcessor:
@@ -299,11 +283,16 @@ class XGrammarLogitsProcessor:
     reasoner: Reasoner | None = None
 
     ctx: xgr.CompiledGrammar | None = None
+    tokenizer_info: xgr.TokenizerInfo = None  # type: ignore[assignment]
     token_bitmask: torch.Tensor = None  # type: ignore[assignment]
     matchers: list[xgr.GrammarMatcher] = field(default_factory=list)
     batch_size: int = field(default=1)
     prefilled: bool = field(default=False)
 
+    def __post_init__(self):
+        self.tokenizer_info = self.config.tokenizer_info(
+            self.config.tokenizer_data)
+
     def __getstate__(self) -> dict[str, Any]:
         return {'config': self.config, 'reasoner': self.reasoner}
 
@@ -311,6 +300,8 @@ class XGrammarLogitsProcessor:
         self.config = state['config']
         self.reasoner = state['reasoner']
 
+        self.tokenizer_info = GrammarConfig.tokenizer_info(
+            self.config.tokenizer_data)
         self.ctx = None
         self.matchers = []
         self.batch_size = 1
@@ -352,7 +343,7 @@ class XGrammarLogitsProcessor:
                 xgr.GrammarMatcher(self.ctx) for _ in range(self.batch_size)
             ]
             self.token_bitmask = xgr.allocate_token_bitmask(
-                self.batch_size, self.config.vocab_size)
+                self.batch_size, self.tokenizer_info.vocab_size)
 
         if not self.prefilled:
             # Have not sampled a token yet

vllm/v1/structured_output/__init__.py

@@ -40,7 +40,7 @@ class StructuredOutputManager:
         tokenizer_group.ping()
 
         tokenizer = tokenizer_group.get_lora_tokenizer(None)
-        self.vocab_size = len(tokenizer.get_vocab())
+        self.vocab_size = self.vllm_config.model_config.get_vocab_size()
         if isinstance(tokenizer, MistralTokenizer):
             # NOTE: ideally, xgrammar should handle this accordingly.
             # refer to https://github.com/mlc-ai/xgrammar/blob/d77c0a0173ef14779c918e3be7966ba852f7910f/python/xgrammar/tokenizer_info.py#L98