[V0][V1][Core] Add outlines integration for V1, and update V0 integration. (#15975)

Signed-off-by: Nathan Hoos <thwackyy.y@gmail.com>

requirements/common.txt

@@ -21,7 +21,9 @@ prometheus-fastapi-instrumentator >= 7.0.0
 tiktoken >= 0.6.0  # Required for DBRX tokenizer
 lm-format-enforcer >= 0.10.11, < 0.11
 llguidance >= 0.7.11, < 0.8.0; platform_machine == "x86_64" or platform_machine == "arm64" or platform_machine == "aarch64"
-outlines == 0.1.11
+outlines_core == 0.2.10
+# required for outlines backend disk cache
+diskcache == 5.6.3
 lark == 1.2.2
 xgrammar == 0.1.19; platform_machine == "x86_64" or platform_machine == "aarch64" or platform_machine == "arm64"
 typing_extensions >= 4.10

tests/entrypoints/llm/test_guided_generate.py

@@ -16,14 +16,18 @@ from vllm.outputs import RequestOutput
 from vllm.sampling_params import GuidedDecodingParams, SamplingParams
 
 MODEL_NAME = "Qwen/Qwen2.5-1.5B-Instruct"
-GUIDED_DECODING_BACKENDS = [
+
+# Separate backends which support grammars vs ones
+# which only support regex based constraints in tests.
+GRAMMAR_DECODING_BACKENDS = [
     # (backend, disable_any_whitespace),
-    ("outlines", False),
     ("lm-format-enforcer", False),
     ("xgrammar", True),
     ("guidance", True),
 ]
 
+ALL_DECODING_BACKENDS = ([("outlines", False)] + GRAMMAR_DECODING_BACKENDS)
+
 
 @pytest.fixture(scope="module")
 def llm():
@@ -39,7 +43,7 @@ def llm():
 
 @pytest.mark.skip_global_cleanup
 @pytest.mark.parametrize("guided_decoding_backend,disable_any_whitespace",
-                         GUIDED_DECODING_BACKENDS)
+                         ALL_DECODING_BACKENDS)
 def test_guided_regex(sample_regex, llm, guided_decoding_backend: str,
                       disable_any_whitespace: bool):
     sampling_params = SamplingParams(
@@ -49,6 +53,7 @@ def test_guided_regex(sample_regex, llm, guided_decoding_backend: str,
             regex=sample_regex,
             backend=guided_decoding_backend,
             disable_any_whitespace=disable_any_whitespace))
+
     outputs = llm.generate(prompts=[
         f"Give an example IPv4 address with this regex: {sample_regex}"
     ] * 2,
@@ -69,7 +74,7 @@ def test_guided_regex(sample_regex, llm, guided_decoding_backend: str,
 
 @pytest.mark.skip_global_cleanup
 @pytest.mark.parametrize("guided_decoding_backend,disable_any_whitespace",
-                         GUIDED_DECODING_BACKENDS)
+                         ALL_DECODING_BACKENDS)
 def test_guided_json_completion(sample_json_schema, llm,
                                 guided_decoding_backend: str,
                                 disable_any_whitespace: bool):
@@ -103,7 +108,7 @@ def test_guided_json_completion(sample_json_schema, llm,
 
 @pytest.mark.skip_global_cleanup
 @pytest.mark.parametrize("guided_decoding_backend,disable_any_whitespace",
-                         GUIDED_DECODING_BACKENDS)
+                         ALL_DECODING_BACKENDS)
 def test_guided_complex_json_completion(sample_complex_json_schema, llm,
                                         guided_decoding_backend: str,
                                         disable_any_whitespace: bool):
@@ -138,7 +143,7 @@ def test_guided_complex_json_completion(sample_complex_json_schema, llm,
 
 @pytest.mark.skip_global_cleanup
 @pytest.mark.parametrize("guided_decoding_backend,disable_any_whitespace",
-                         GUIDED_DECODING_BACKENDS)
+                         ALL_DECODING_BACKENDS)
 def test_guided_definition_json_completion(sample_definition_json_schema, llm,
                                            guided_decoding_backend: str,
                                            disable_any_whitespace: bool):
@@ -173,7 +178,7 @@ def test_guided_definition_json_completion(sample_definition_json_schema, llm,
 
 @pytest.mark.skip_global_cleanup
 @pytest.mark.parametrize("guided_decoding_backend,disable_any_whitespace",
-                         GUIDED_DECODING_BACKENDS)
+                         ALL_DECODING_BACKENDS)
 def test_guided_enum_json_completion(sample_enum_json_schema, llm,
                                      guided_decoding_backend: str,
                                      disable_any_whitespace: bool):
@@ -218,7 +223,7 @@ def test_guided_enum_json_completion(sample_enum_json_schema, llm,
 
 @pytest.mark.skip_global_cleanup
 @pytest.mark.parametrize("guided_decoding_backend,disable_any_whitespace",
-                         GUIDED_DECODING_BACKENDS)
+                         ALL_DECODING_BACKENDS)
 def test_guided_choice_completion(sample_guided_choice, llm,
                                   guided_decoding_backend: str,
                                   disable_any_whitespace: bool):
@@ -248,7 +253,7 @@ def test_guided_choice_completion(sample_guided_choice, llm,
 
 @pytest.mark.skip_global_cleanup
 @pytest.mark.parametrize("guided_decoding_backend,disable_any_whitespace",
-                         GUIDED_DECODING_BACKENDS)
+                         GRAMMAR_DECODING_BACKENDS)
 def test_guided_grammar(sample_sql_statements, llm,
                         guided_decoding_backend: str,
                         disable_any_whitespace: bool):
@@ -344,7 +349,7 @@ def test_disable_guided_decoding_fallback(sample_regex, llm):
 
 @pytest.mark.skip_global_cleanup
 @pytest.mark.parametrize("guided_decoding_backend,disable_any_whitespace",
-                         GUIDED_DECODING_BACKENDS)
+                         GRAMMAR_DECODING_BACKENDS)
 def test_guided_json_object(llm, guided_decoding_backend: str,
                             disable_any_whitespace: bool):
     sampling_params = SamplingParams(
@@ -377,7 +382,9 @@ def test_guided_json_object(llm, guided_decoding_backend: str,
 
             # Parse to verify it is valid JSON
             parsed_json = json.loads(generated_text)
-            assert isinstance(parsed_json, dict)
+            # A list is not what was intended, but is still valid
+            # json.
+            assert isinstance(parsed_json, (dict, list))
 
 
 class CarType(str, Enum):
@@ -395,7 +402,7 @@ class CarDescription(BaseModel):
 
 @pytest.mark.skip_global_cleanup
 @pytest.mark.parametrize("guided_decoding_backend,disable_any_whitespace",
-                         GUIDED_DECODING_BACKENDS)
+                         ALL_DECODING_BACKENDS)
 def test_guided_json_completion_with_enum(llm, guided_decoding_backend: str,
                                           disable_any_whitespace: bool):
     json_schema = CarDescription.model_json_schema()
@@ -427,7 +434,7 @@ def test_guided_json_completion_with_enum(llm, guided_decoding_backend: str,
 
 @pytest.mark.skip_global_cleanup
 @pytest.mark.parametrize("guided_decoding_backend,disable_any_whitespace",
-                         GUIDED_DECODING_BACKENDS)
+                         ALL_DECODING_BACKENDS)
 def test_guided_number_range_json_completion(llm, guided_decoding_backend: str,
                                              disable_any_whitespace: bool):
     sample_output_schema = {

tests/model_executor/test_guided_processors.py

@@ -46,20 +46,15 @@ def test_guided_logits_processors(zephyr_7B_tokenzer, sample_regex,
                                   whitespace_pattern=None,
                                   reasoner=None)
 
-    token_ids = zephyr_7B_tokenzer.encode(
-        f"Give an example IPv4 address with this regex: {sample_regex}")
     tensor = torch.rand(32000)
     original_tensor = torch.clone(tensor)
-    regex_LP(token_ids, tensor)
+    tensor = regex_LP([], tensor)
     assert tensor.shape == original_tensor.shape
     assert not torch.allclose(tensor, original_tensor)
 
-    token_ids = zephyr_7B_tokenzer.encode(
-        f"Give an employee profile that fits this schema: {sample_json_schema}"
-    )
     tensor = torch.rand(32000)
     original_tensor = torch.clone(tensor)
-    json_LP(token_ids, tensor)
+    tensor = json_LP([], tensor)
     assert tensor.shape == original_tensor.shape
     assert not torch.allclose(tensor, original_tensor)
 
@@ -81,8 +76,6 @@ async def test_guided_logits_processor_black_box(backend: str, is_local: bool,
         seed=0,
         dtype="bfloat16",
     )
-    token_ids = zephyr_7B_tokenzer.encode(
-        f"Give an example IPv4 address with this regex: {sample_regex}")
     regex_request = GuidedDecodingParams(regex=sample_regex, backend=backend)
 
     regex_lp = get_local_guided_decoding_logits_processor(
@@ -92,13 +85,11 @@ async def test_guided_logits_processor_black_box(backend: str, is_local: bool,
     assert regex_lp is not None
     tensor = torch.rand(32000)
     original_tensor = torch.clone(tensor)
-    tensor = regex_lp(token_ids, tensor)
+    # allowed tokens at state 0
+    tensor = regex_lp([], tensor)
     assert tensor.shape == original_tensor.shape
     assert not torch.allclose(tensor, original_tensor)
 
-    token_ids = zephyr_7B_tokenzer.encode(
-        f"Give an employee profile that fits this schema: {sample_json_schema}"
-    )
     json_request = GuidedDecodingParams(json=sample_json_schema,
                                         backend=backend)
     json_lp = await get_guided_decoding_logits_processor(
@@ -106,7 +97,7 @@ async def test_guided_logits_processor_black_box(backend: str, is_local: bool,
     assert json_lp is not None
     tensor = torch.rand(32000)
     original_tensor = torch.clone(tensor)
-    tensor = json_lp(token_ids, tensor)
+    tensor = json_lp([], tensor)
     assert tensor.shape == original_tensor.shape
     assert not torch.allclose(tensor, original_tensor)
 
@@ -130,7 +121,6 @@ async def test_guided_logits_processor_with_reasoning(
         dtype="bfloat16",
     )
     token_ids = deepseek_r1_qwen_tokenizer.encode(
-        f"Give an example IPv4 address with this regex: {sample_regex}."
         "<think>here is the thinking process")
     regex_request = GuidedDecodingParams(regex=sample_regex, backend=backend)
 
@@ -141,14 +131,13 @@ async def test_guided_logits_processor_with_reasoning(
                     regex_request, deepseek_r1_qwen_tokenizer, config,
                     reasoning_backend)
     assert regex_lp is not None
-    tensor = torch.rand(32000)
+    tensor = torch.rand(151664)
     original_tensor = torch.clone(tensor)
     tensor = regex_lp(token_ids, tensor)
     assert tensor.shape == original_tensor.shape
     assert torch.allclose(tensor, original_tensor)
 
     token_ids = deepseek_r1_qwen_tokenizer.encode(
-        f"Give an employee profile that fits this schema: {sample_json_schema}."
         "<think>here is the thinking process")
     json_request = GuidedDecodingParams(json=sample_json_schema,
                                         backend=backend)
@@ -158,7 +147,7 @@ async def test_guided_logits_processor_with_reasoning(
         await get_guided_decoding_logits_processor(
             json_request, deepseek_r1_qwen_tokenizer, config, reasoning_backend)
     assert json_lp is not None
-    tensor = torch.rand(32000)
+    tensor = torch.rand(151664)
     original_tensor = torch.clone(tensor)
     tensor = json_lp(token_ids, tensor)
     assert tensor.shape == original_tensor.shape
@@ -166,8 +155,7 @@ async def test_guided_logits_processor_with_reasoning(
 
     # Thinking is over, so the tensor should change.
     token_ids = deepseek_r1_qwen_tokenizer.encode(
-        f"Give an employee profile that fits this schema: {sample_json_schema}."
-        "<think>here is the thinking process</think> Then")
+        "<think>here is the thinking process</think>")
     json_request = GuidedDecodingParams(json=sample_json_schema,
                                         backend=backend)
     json_lp = get_local_guided_decoding_logits_processor(
@@ -176,7 +164,7 @@ async def test_guided_logits_processor_with_reasoning(
         await get_guided_decoding_logits_processor(
             json_request, deepseek_r1_qwen_tokenizer, config, reasoning_backend)
     assert json_lp is not None
-    tensor = torch.rand(32000)
+    tensor = torch.rand(151664)
     original_tensor = torch.clone(tensor)
     tensor = json_lp(token_ids, tensor)
     assert tensor.shape == original_tensor.shape

tests/tool_use/test_tool_choice_required.py

@@ -72,7 +72,7 @@ def _compile_and_check(tools: list[ChatCompletionToolsParam], sample_output,
     assert isinstance(schema, dict)
 
     # use build_regex_from_schema used in JSONLogitsProcessor to create Guide
-    from outlines_core.fsm.json_schema import build_regex_from_schema
+    from outlines_core.json_schema import build_regex_from_schema
     regex = build_regex_from_schema(json.dumps(schema))
     compiled = re.compile(regex)
     matches = compiled.fullmatch(json.dumps(sample_output)) is not None

tests/v1/entrypoints/llm/test_struct_output_generate.py

@@ -41,6 +41,10 @@ PARAMS_MODELS_BACKENDS_TOKENIZER_MODE = [
     ("mistralai/Ministral-8B-Instruct-2410", "guidance", "auto", None),
     ("mistralai/Ministral-8B-Instruct-2410", "xgrammar", "mistral", None),
     ("Qwen/Qwen2.5-1.5B-Instruct", "xgrammar", "auto", None),
+    ("mistralai/Ministral-8B-Instruct-2410", "outlines", "auto", None),
+    ("mistralai/Ministral-8B-Instruct-2410", "outlines", "mistral", None),
+    ("mistralai/Ministral-8B-Instruct-2410", "outlines", "auto",
+     NGRAM_SPEC_CONFIG),
     #FIXME: This test is flaky on CI thus disabled
     #("Qwen/Qwen2.5-1.5B-Instruct", "guidance", "auto"),
     ("mistralai/Ministral-8B-Instruct-2410", "guidance", "auto",
@@ -106,11 +110,13 @@ def test_structured_output(
     enforce_eager = bool(not current_platform.is_tpu())
     # Use a single LLM instance for several scenarios to
     # speed up the test suite.
-    llm = LLM(model=model_name,
+    llm = LLM(
+        model=model_name,
         enforce_eager=enforce_eager,
         max_model_len=1024,
         guided_decoding_backend=guided_decoding_backend,
-              guided_decoding_disable_any_whitespace=True,
+        guided_decoding_disable_any_whitespace=(guided_decoding_backend
+                                                in {"xgrammar", "guidance"}),
         tokenizer_mode=tokenizer_mode,
         speculative_config=speculative_config)
 
@@ -146,14 +152,15 @@ def test_structured_output(
     #
     # Test 2: Generate JSON object without a schema
     #
+    if guided_decoding_backend != "outlines":
         sampling_params = SamplingParams(
             temperature=1.0,
             max_tokens=4096,
             n=2,
             guided_decoding=GuidedDecodingParams(json_object=True))
 
-    outputs = llm.generate(
-        prompts=("Generate a JSON object with curly braces for a person with "
+        outputs = llm.generate(prompts=(
+            "Generate a JSON object with curly braces for a person with "
             "name and age fields for John Smith who is 31 years old. "
             "Make the response as short as possible."),
                                sampling_params=sampling_params,
@@ -210,6 +217,7 @@ def test_structured_output(
             parsed_json = json.loads(generated_text)
             assert isinstance(parsed_json, dict)
 
+    if guided_decoding_backend != "outlines":
         #
         # Test 4: Generate SQL statement using EBNF grammar
         #
@@ -293,8 +301,8 @@ def test_structured_output(
             guided_decoding=GuidedDecodingParams(grammar="not a grammar"))
         with pytest.raises(ValueError, match="Failed to convert the grammar "):
             llm.generate(
-            prompts=(
-                "Generate a sql statement that selects col_1 from "
+                prompts=
+                ("Generate a sql statement that selects col_1 from "
                  "table_1 where it is equal to 1. Make the response as short "
                  "as possible."),
                 sampling_params=sampling_params,
@@ -421,6 +429,7 @@ def test_structured_output(
         output_json = json.loads(generated_text)
         jsonschema.validate(instance=output_json, schema=json_schema)
 
+    if guided_decoding_backend != "outlines":
         #
         # Test 11: Generate structured output using structural_tag format
         #

vllm/config.py

@@ -3580,7 +3580,8 @@ def get_served_model_name(model: str,
 
 GuidedDecodingBackendV0 = Literal["auto", "outlines", "lm-format-enforcer",
                                   "xgrammar", "guidance"]
-GuidedDecodingBackendV1 = Literal["auto", "xgrammar", "guidance"]
+
+GuidedDecodingBackendV1 = Literal["auto", "xgrammar", "guidance", "outlines"]
 GuidedDecodingBackend = Literal[GuidedDecodingBackendV0,
                                 GuidedDecodingBackendV1]
 

vllm/envs.py

@@ -117,6 +117,7 @@ if TYPE_CHECKING:
     VLLM_RANDOMIZE_DP_DUMMY_INPUTS: bool = False
     VLLM_MARLIN_USE_ATOMIC_ADD: bool = False
     VLLM_V0_USE_OUTLINES_CACHE: bool = False
+    VLLM_V1_USE_OUTLINES_CACHE: bool = False
     VLLM_TPU_BUCKET_PADDING_GAP: int = 0
     VLLM_TPU_MOST_MODEL_LEN: Optional[int] = None
     VLLM_USE_DEEP_GEMM: bool = False
@@ -847,6 +848,12 @@ environment_variables: dict[str, Callable[[], Any]] = {
     "VLLM_V0_USE_OUTLINES_CACHE":
     lambda: os.environ.get("VLLM_V0_USE_OUTLINES_CACHE", "0") == "1",
 
+    # Whether to turn on the outlines cache for V1
+    # This cache is unbounded and on disk, so it's not safe to use in
+    # an environment with potentially malicious users.
+    "VLLM_V1_USE_OUTLINES_CACHE":
+    lambda: os.environ.get("VLLM_V1_USE_OUTLINES_CACHE", "0") == "1",
+
     # Gap between padding buckets for the forward pass. So we have
     # 8, we will run forward pass with [16, 24, 32, ...].
     "VLLM_TPU_BUCKET_PADDING_GAP":

vllm/model_executor/guided_decoding/__init__.py

@@ -79,20 +79,33 @@ def maybe_backend_fallback(
                 fallback_or_error(
                     guided_params,
                     "xgrammar does not support Lark grammars and the "
-                    "grammar failed to convert to GBNF.", "outlines")
+                    "grammar failed to convert to GBNF.", "guidance")
 
         # If the xgrammar module cannot be imported successfully,
         # we should still allow users to use guided decoding with a fallback.
         elif not xgr_installed:
             fallback_or_error(
                 guided_params,
-                "xgrammar module cannot be imported successfully.", "outlines")
+                "xgrammar module cannot be imported successfully.", "guidance")
 
-    if (guided_params.backend == "outlines"
-            and guided_params.json_object is not None):
+    if guided_params.backend == "outlines":
+        if guided_params.json_object is not None:
             # outlines doesn't support json_object, fallback to guidance
             fallback_or_error(guided_params,
-                          "outlines does not support json_object.", "guidance")
+                              "outlines does not support json_object.",
+                              "guidance")
+        elif guided_params.grammar is not None:
+            # outlines grammar support has been removed, fallback to guidance
+            # if it is a lark-based grammar and xgrammar otherwise
+            if grammar_is_likely_lark(guided_params.grammar):
+                fallback_or_error(guided_params,
+                                  "outlines no longer supports grammars.",
+                                  "guidance")
+            else:
+                # The grammar is likely already GBNF format.
+                fallback_or_error(guided_params,
+                                  "outlines no longer supports grammars.",
+                                  "xgrammar")
 
     return guided_params
 
@@ -111,7 +124,6 @@ async def get_guided_decoding_logits_processor(
 
     guided_params = maybe_backend_fallback(guided_params)
 
-    # CFG grammar not supported by LMFE, so we use outlines instead
     if guided_params.backend == 'outlines':
         # NOTE: lazy import outlines to avoid https://github.com/vllm-project/vllm/issues/4193
         from vllm.model_executor.guided_decoding.outlines_decoding import (  # noqa
@@ -152,7 +164,6 @@ def get_local_guided_decoding_logits_processor(
             reasoning_backend)
         reasoner = reasoner_class(tokenizer)
 
-    # CFG grammar not supported by LMFE, so we use outlines instead
     if guided_params.backend == 'outlines':
         # NOTE: lazy import outlines to avoid https://github.com/vllm-project/vllm/issues/4193
         from vllm.model_executor.guided_decoding.outlines_decoding import (  # noqa

vllm/model_executor/guided_decoding/outlines_decoding.py

@@ -12,7 +12,7 @@ from regex import escape as regex_escape
 from transformers import PreTrainedTokenizerBase
 
 from vllm.model_executor.guided_decoding.outlines_logits_processors import (
-    CFGLogitsProcessor, JSONLogitsProcessor, RegexLogitsProcessor)
+    JSONLogitsProcessor, RegexLogitsProcessor)
 from vllm.reasoning import ReasoningParser
 from vllm.sampling_params import GuidedDecodingParams
 
@@ -21,36 +21,8 @@ class GuidedDecodingMode(Enum):
     JSON = "json"
     REGEX = "regex"
     CHOICE = "choice"
-    GRAMMAR = "grammar"
 
 
-# https://github.com/outlines-dev/outlines/blob/main/outlines/grammars/json.lark
-# the main difference is that we changed the start: value to
-# start: object | array, so we are denying scalar values as the root of the
-# JSON. Starting with scalars as the root seems to cause llama to generate
-# without stop.
-JSON_GRAMMAR = r"""
-?start: object | array
-
-?value: object
-| array
-| UNESCAPED_STRING
-| SIGNED_NUMBER      -> number
-| "true"             -> true
-| "false"            -> false
-| "null"             -> null
-
-array  : "[" [value ("," value)*] "]"
-object : "{" [pair ("," pair)*] "}"
-pair   : UNESCAPED_STRING ":" value
-
-%import common.UNESCAPED_STRING
-%import common.SIGNED_NUMBER
-%import common.WS
-
-%ignore WS
-"""
-
 global_thread_pool = None  # used for generating logits processor fsm
 
 # It's not yet clear that using more provides a benefit, and it could
@@ -60,16 +32,12 @@ _MAX_THREADPOOL_WORKERS = 16
 
 
 async def get_outlines_guided_decoding_logits_processor(
-    guided_params: GuidedDecodingParams,
-    tokenizer: PreTrainedTokenizerBase,
-    reasoner: Optional[ReasoningParser],
-) -> Union[JSONLogitsProcessor, RegexLogitsProcessor, CFGLogitsProcessor,
-           None]:
+    guided_params: GuidedDecodingParams, tokenizer: PreTrainedTokenizerBase,
+    reasoner: Optional[ReasoningParser]
+) -> Union[JSONLogitsProcessor, RegexLogitsProcessor, None]:
     """
     Given an OpenAI-compatible request, check for guided decoding parameters
     and get the necessary logits processor for the given guide.
-    We cache logit processors by (guide, tokenizer), and on cache hit
-    we make a shallow copy to reuse the same underlying FSM.
     """
     global global_thread_pool
     guide, mode = _get_guide_and_mode(guided_params)
@@ -83,7 +51,6 @@ async def get_outlines_guided_decoding_logits_processor(
         global_thread_pool = concurrent.futures.ThreadPoolExecutor(
             max_workers=max_workers)
     loop = asyncio.get_running_loop()
-
     return await loop.run_in_executor(global_thread_pool,
                                       _get_logits_processor, guide, tokenizer,
                                       mode, guided_params.whitespace_pattern,
@@ -91,16 +58,12 @@ async def get_outlines_guided_decoding_logits_processor(
 
 
 def get_local_outlines_guided_decoding_logits_processor(
-    guided_params: GuidedDecodingParams,
-    tokenizer: PreTrainedTokenizerBase,
-    reasoner: Optional[ReasoningParser],
-) -> Union[JSONLogitsProcessor, RegexLogitsProcessor, CFGLogitsProcessor,
-           None]:
+    guided_params: GuidedDecodingParams, tokenizer: PreTrainedTokenizerBase,
+    reasoner: Optional[ReasoningParser]
+) -> Union[JSONLogitsProcessor, RegexLogitsProcessor, None]:
     """
     Given an OpenAI-compatible request, check for guided decoding parameters
     and get the necessary logits processor for the given guide.
-    We cache logit processors by (guide, tokenizer), and on cache hit
-    we make a shallow copy to reuse the same underlying FSM.
     """
     guide, mode = _get_guide_and_mode(guided_params)
     if not guide or not mode:
@@ -130,9 +93,10 @@ def _get_guide_and_mode(
         choices_regex = "(" + "|".join(choices) + ")"
         return choices_regex, GuidedDecodingMode.CHOICE
     elif guided_params.grammar:
-        return guided_params.grammar, GuidedDecodingMode.GRAMMAR
-    elif guided_params.json_object:
-        return JSON_GRAMMAR, GuidedDecodingMode.GRAMMAR
+        raise ValueError(
+            "The `outlines` guided decoding backend no longer supports grammar "
+            "guided generation. Please use either the `xgrammar` or `guidance` "
+            "backend")
     else:
         return None, None
 
@@ -143,13 +107,11 @@ def _get_logits_processor(
     mode: GuidedDecodingMode,
     whitespace_pattern: Union[str, None],
     reasoner: Optional[ReasoningParser],
-) -> Union[JSONLogitsProcessor, RegexLogitsProcessor, CFGLogitsProcessor]:
+) -> Union[JSONLogitsProcessor, RegexLogitsProcessor]:
     if mode == GuidedDecodingMode.JSON:
         return JSONLogitsProcessor(guide, tokenizer, whitespace_pattern,
                                    reasoner)
     elif mode == GuidedDecodingMode.REGEX or mode == GuidedDecodingMode.CHOICE:
         return RegexLogitsProcessor(guide, tokenizer, reasoner)
-    elif mode == GuidedDecodingMode.GRAMMAR:
-        return CFGLogitsProcessor(guide, tokenizer, reasoner)
     else:
         raise ValueError(f"Unknown guided decoding mode {mode}")

vllm/model_executor/guided_decoding/outlines_logits_processors.py

 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# SPDX-FileCopyrightText: Copyright 2024-present the Outlines developers
+from __future__ import annotations
 
-# Copyright 2024- the Outlines developers
-# This file is adapted from
-# https://github.com/outlines-dev/outlines/blob/main/outlines/serve/vllm.py
-#
-# 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.
 import copy
+import hashlib
+import importlib.metadata
 import json
-from collections import defaultdict
-from functools import lru_cache
-from typing import Callable, Optional, Union
+import os
+from typing import Optional, Union
 
-import numpy as np
+import regex as re
 import torch
-from outlines import grammars
-from outlines.caching import cache, disable_cache
-from outlines.fsm.guide import (CFGGuide, CFGState, Generate, Guide,
-                                RegexGuide, Write)
-from outlines.fsm.parsing import PartialLark
-from outlines_core.fsm.json_schema import build_regex_from_schema
+from cachetools import LRUCache
+from diskcache import Cache
+from outlines_core import Guide, Index, Vocabulary
+from outlines_core.json_schema import build_regex_from_schema
+from outlines_core.kernels.torch import (_apply_token_bitmask_inplace_kernel,
+                                         allocate_token_bitmask)
 from pydantic import BaseModel
 from transformers import PreTrainedTokenizerBase
+from transformers.file_utils import SPIECE_UNDERLINE
+from transformers.models.gpt2.tokenization_gpt2 import bytes_to_unicode
 
 import vllm.envs as envs
 from vllm.logger import init_logger
-from vllm.platforms import current_platform
 from vllm.reasoning import ReasoningParser
+from vllm.transformers_utils.tokenizer import AnyTokenizer
 
 logger = init_logger(__name__)
 
-if envs.VLLM_V0_USE_OUTLINES_CACHE:
-    logger.warning("Enabling outlines cache. This is an unbounded on-disk "
-                   "cache. It may consume a lot of disk space and should "
-                   "not be used with untrusted clients.")
-else:
-    disable_cache()
+CACHE = None
 
 
 class BaseLogitsProcessor:
 
-    def __init__(self, guide: Guide, reasoner: Optional[ReasoningParser]):
+    def __init__(self, guide: Guide, eos_token_id: int,
+                 reasoner: Optional[ReasoningParser]) -> None:
         self._guide: Guide = guide
+        self._eos_token_id: int = eos_token_id
         self._reasoner: Optional[ReasoningParser] = reasoner
-        # CFGState is used for the FSM state for CFGGuide
-        self._fsm_state: defaultdict[int, Union[int,
-                                                CFGState]] = defaultdict(int)
-
-    def clone(self) -> "BaseLogitsProcessor":
-        cloned = copy.copy(self)
-        cloned._guide = self._guide.copy()
-        cloned._fsm_state = copy.deepcopy(self._fsm_state)
-        return cloned
+        self._mask: Optional[torch.Tensor] = None
 
     def __call__(self, input_ids: list[int],
                  scores: torch.Tensor) -> torch.Tensor:
-        """Use the FSM to bias the logits before sampling the next token."""
+        if self._mask is None:
+            self._mask = allocate_token_bitmask(scores.size(-1))
 
         # Skip the structured logits processing if reasoning is not finished.
         # reasoner is not None only when `--reasoning-parser` is set.
-        if self._reasoner is not None:
-            if not self._reasoner.is_reasoning_end(input_ids):
+        if self._reasoner is not None and not self._reasoner.is_reasoning_end(
+                input_ids):
             return scores
-            else:
+
         # Remove the reasoning tokens from the input_ids
         # We need this because our implementation relies on the
-                # hash of the input_ids to store the FSM state.
-                input_ids = self._reasoner.extract_content_ids(input_ids)
-
-        seq_id = hash(tuple(input_ids))
-
-        if len(input_ids) > 0:
-            last_token = input_ids[-1]
-            last_seq_id = hash(tuple(input_ids[:-1]))
-            self._fsm_state[seq_id] = self._guide.get_next_state(
-                state=self._fsm_state[last_seq_id], token_id=last_token)
-        else:
-            # Note: this is a hack.
-            # Lark pickling does not work properly (silent failure),
-            # which breaks the RPC (which uses python pickleing).
-            # We need to find a better solution.
-            # On the first time this is called, we simply re-create
-            # the Lark object.
-            if isinstance(self._guide, CFGGuide):
-                self._guide.parser = PartialLark(
-                    self._guide.cfg_string,
-                    parser="lalr",
-                    import_paths=[grammars.GRAMMAR_PATH],
+        # input_ids sequence to store the FSM state.
+        input_ids = (self._reasoner.extract_content_ids(input_ids)
+                     if self._reasoner is not None else input_ids)
+
+        # Vllm V0 engine has a weird bug where we have to repeat
+        # the eos token id twice for generation to stop, or at least
+        # that is what we have to do from here in any case.
+        # This is a patch until a better solution can be pushed
+        # to outlines_core
+        if input_ids and input_ids[-1] != self._eos_token_id:
+            self._guide.advance(token_id=input_ids[-1], return_tokens=False)
+
+        self._guide.write_mask_into(
+            data_ptr=self._mask.data_ptr(),
+            numel=self._mask.numel(),
+            element_size=self._mask.element_size(),
         )
-                self._fsm_state[seq_id] = CFGState(
-                    parser_state=self._guide.parser.parse(""), prev_token=None)
 
-        instruction = self._guide.get_next_instruction(
-            state=self._fsm_state[seq_id])
+        # Any allowed tokens beyond the length of the scores will
+        # be ignored by the kernel, taking care of the issue with
+        # models such as Llama 3.2 Vision with an `<|image|>` token
+        # with id 128256, but scores.shape == torch.Size([128256])
+        _apply_token_bitmask_inplace_kernel(
+            logits=scores.unsqueeze(dim=0),
+            # mask must be on same device
+            mask=self._mask.to(scores.device, non_blocking=True))
+        self._mask.to("cpu", non_blocking=True)
 
-        if type(instruction) == Generate:  # noqa: E721
-            allowed_tokens = instruction.tokens
-        elif type(instruction) == Write:  # noqa: E721
-            # TODO: support fast forward tokens
-            allowed_tokens = [instruction.tokens[0]]
-        else:
-            raise TypeError(
-                f"Unsupported instruction type {type(instruction)}")
-
-        mask = torch.full((scores.shape[-1], ),
-                          -torch.inf,
-                          device=scores.device)
-        # The tokenizer may support more token ids than the model can generate,
-        # eg. Llama 3.2 Vision models have an `<|image|>` token with id 128256
-        # but scores.shape == torch.Size([128256])
-        # Using NumPy is faster for filtering token ids
-        allowed_tokens = np.array(allowed_tokens, dtype=np.int64)
-        allowed_tokens = torch.tensor(allowed_tokens, device=scores.device)
-        allowed_tokens = allowed_tokens.masked_select(
-            allowed_tokens < scores.shape[-1])
-        mask.index_fill_(0, allowed_tokens, 0)
-        if current_platform.is_hpu():
-            # Workaround for HPU bug where add_() raise RuntimeError:
-            # synNodeCreateWithId failed for node: strided_insert
-            # with synStatus 1 [Invalid argument], hopefully it will
-            # be fixed in the future releases of the HPU runtime.
-            scores = scores.add(mask)
-        else:
-            scores.add_(mask)
         return scores
 
+    def clone(self) -> BaseLogitsProcessor:
+        guide = copy.deepcopy(self._guide)
+        guide.reset()
+        return BaseLogitsProcessor(guide=guide,
+                                   eos_token_id=self._eos_token_id,
+                                   reasoner=self._reasoner)
+
 
 class RegexLogitsProcessor(BaseLogitsProcessor):
 
     @classmethod
-    @cache()
     def _get_guide(cls, regex_string: str,
                    tokenizer: PreTrainedTokenizerBase) -> Guide:
-        tokenizer = _adapt_tokenizer(tokenizer)
-        return RegexGuide.from_regex(regex_string, tokenizer)
+        global CACHE
+        if CACHE is None:
+            CACHE = get_cache()
+        vocabulary = get_vocabulary(tokenizer)  # type: ignore[arg-type]
+        cache_key = f"{vocabulary._hash}_{regex_string}"
+        if CACHE is not None and cache_key in CACHE:
+            return Guide(CACHE[cache_key])
 
-    def __init__(
-        self,
-        regex_string: str,
-        tokenizer: PreTrainedTokenizerBase,
-        reasoner: Optional[ReasoningParser],
-    ):
-        """Compile the FSM that drives the regex-structured generation.
+        index = Index(regex_string, vocabulary.inner)
 
-        Parameters
-        ----------
-        regex_string
-            A string that represents a regular expression
-        tokenizer
-            The model's tokenizer
+        if CACHE is not None:
+            CACHE[cache_key] = index
 
-        """
+        return Guide(index)
+
+    def __init__(self, regex_string: str, tokenizer: PreTrainedTokenizerBase,
+                 reasoner: Optional[ReasoningParser]) -> None:
         super().__init__(
-            RegexLogitsProcessor._get_guide(regex_string, tokenizer), reasoner)
+            guide=RegexLogitsProcessor._get_guide(regex_string, tokenizer),
+            eos_token_id=tokenizer.eos_token_id,  # type: ignore
+            reasoner=reasoner)
 
 
 class JSONLogitsProcessor(RegexLogitsProcessor):
@@ -170,22 +126,8 @@ class JSONLogitsProcessor(RegexLogitsProcessor):
     def __init__(self, schema: Union[str, dict, BaseModel],
                  tokenizer: PreTrainedTokenizerBase,
                  whitespace_pattern: Union[str, None],
-                 reasoner: Optional[ReasoningParser]):
-        """Compile the FSM that drives the JSON-guided generation.
-
-        Parameters
-        ----------
-        schema
-            A JSON schema that encodes the structure we want the model to
-            generate
-        tokenizer
-            The model's tokenizer
-        whitespace_pattern
-            Pattern to use for JSON syntactic whitespace (doesn't impact
-            string literals)
-            Example: allow only a single space or newline with
-            `whitespace_pattern=r"[\n ]?"`
-        """
+                 reasoner: Optional[ReasoningParser]) -> None:
+
         if isinstance(schema, type(BaseModel)):
             schema_str = json.dumps(schema.model_json_schema())
         elif isinstance(schema, dict):
@@ -197,63 +139,42 @@ class JSONLogitsProcessor(RegexLogitsProcessor):
                 f"Cannot parse schema {schema}. The schema must be either "
                 f"a Pydantic object, a dictionary or a string that contains "
                 f"the JSON Schema specification")
+
         regex_string = build_regex_from_schema(schema_str, whitespace_pattern)
         super().__init__(regex_string, tokenizer, reasoner)
 
 
-class CFGLogitsProcessor(BaseLogitsProcessor):
-
-    @classmethod
-    @cache()
-    def _get_guide(cls, cfg: str, tokenizer: PreTrainedTokenizerBase) -> Guide:
-        tokenizer = _adapt_tokenizer(tokenizer)
-        return CFGGuide(cfg, tokenizer)
-
-    def __init__(self, cfg: str, tokenizer: PreTrainedTokenizerBase,
-                 reasoner: Optional[ReasoningParser]):
-        """Compile the FSM that drives the context free grammar generation.
-
-        Parameters
-        ----------
-        cfg
-            A string that represents a context-free grammar
-        tokenizer
-            The model's tokenizer
-
+class OutlinesVocabulary:
+    """
+    Wrapper class for `outlines_core.Vocabulary`,
+    which allows us to store a hash with the vocabulary
     """
-        super().__init__(CFGLogitsProcessor._get_guide(cfg, tokenizer),
-                         reasoner)
-        self._guide = self._guide.copy()
-
-    def clone(self) -> "CFGLogitsProcessor":
-        cloned = copy.copy(self)
-        cloned._fsm_state = copy.deepcopy(self._fsm_state)
-        cloned._guide = self._guide.copy()
-        return cloned
 
+    def __init__(self, vocabulary: Vocabulary) -> None:
+        # Actual vocabulary object
+        self.inner = vocabulary
+        # Have to do abs(hash()) because python hashes can
+        # be negative, and we are using hash as a cache key.
+        hex_str = hashlib.sha256(
+            vocabulary.__repr__().encode('utf-8')).hexdigest()
+        hash_int = int(hex_str, 16)
+        self._hash = hash_int
 
-@lru_cache(maxsize=32)
-def _adapt_tokenizer(tokenizer: PreTrainedTokenizerBase):
-    """Adapt vLLM's tokenizer to use to compile the FSM.
 
-    The API of Outlines tokenizers is slightly different to that of
-    `transformers`. The decoder of outlines, returns a list whereas
-    the decode of vLLM returns an str. To sync the vLLM decoder with
-    outlines internal api, the decoder should be adapted. In addition
-    we need to handle the missing spaces to Llama's tokenizer to be
-    able to compile FSMs for this model.
+re_llama_byte_token = re.compile(r"^<0x[0-9A-F]{2}>$")
+re_replacement_seq = re.compile(r"^.{0,6}�+.{0,6}$")
 
-    """
-    if getattr(tokenizer, "_outlines_adapted", False):
-        return tokenizer
 
-    tokenizer = copy.deepcopy(tokenizer)
+def _reduced_vocabulary(tokenizer: AnyTokenizer,
+                        eos_token_id: int) -> dict[bytes, list[int]]:
+    """Create a map from vocabulary tokens to lists of equivalent token ids.
     
-    tokenizer.vocabulary = tokenizer.get_vocab()
-    tokenizer.special_tokens = set(tokenizer.all_special_tokens)
+    Returns:
+        A Dict of token string -> equivalent token ids
+    """
+    unicode_to_bytes = {v: k for k, v in bytes_to_unicode().items()}
 
     def convert_token_to_string(token: str) -> str:
-        from transformers.file_utils import SPIECE_UNDERLINE
 
         string = tokenizer.convert_tokens_to_string([token])
 
@@ -264,21 +185,123 @@ def _adapt_tokenizer(tokenizer: PreTrainedTokenizerBase):
 
         return string
 
-    def change_decoder(
-        decoder: Callable[[list[int]],
-                          str]) -> Callable[[list[int]], list[str]]:
-        """Sync vLLM's decoder with the outlines by returning list."""
+    vocabulary: dict[bytes, list[int]] = {}
+    empty_token_ids: list[int] = []
+    for token, token_idx in tokenizer.get_vocab().items():
+        if token in tokenizer.all_special_tokens:  # type: ignore
+            continue
+
+        token_str = convert_token_to_string(token)
+        if token_str:
+            if isinstance(token, (bytes, bytearray)):
+                # For BPE tokenizers where tokens are stored as bytes.
+
+                # safe to ignore since token_str is of type (bytearray, bytes)
+                # by this point.
+                token_bytes = bytes(token_str)  # type: ignore[arg-type]
+
+            elif "\ufffd" in token_str and not re_replacement_seq.match(
+                    token_str):
+                # Handle tokens with invalid UTF-8 sequences.
+                if re_llama_byte_token.match(token):
+                    # Llama-like tokenizers use <0xXX> for incomplete sequences.
+                    token_bytes = bytes([int(token[3:5], 16)])
+                else:
+                    # GPT2 tokenizers: map each byte back using unicode_to_bytes
+                    byte_vals = [unicode_to_bytes.get(c) for c in token]
+                    if None in byte_vals:
+                        raise RuntimeError(
+                            f"Cannot convert token `{token}`"
+                            f" ({token_idx}) to bytes: {token_str}")
+                    # safe to ignore, since if None in byte_vals,
+                    # an error is thrown.
+                    token_bytes = bytes(byte_vals)  # type: ignore[arg-type]
+            else:
+                token_bytes = token_str.encode('utf-8')
+
+            if token_idx != eos_token_id:
+                vocabulary.setdefault(token_bytes, []).append(token_idx)
+        else:
+            empty_token_ids.append(token_idx)
+
+    return vocabulary
+
 
-        def new_decoder(inp_tokens: list[int]) -> list[str]:
-            if (isinstance(inp_tokens, list) and len(inp_tokens) == 1
-                    and isinstance(inp_tokens[0], list)):
-                inp_tokens = inp_tokens[0]
-            return [decoder(inp_tokens)]
+def get_vocabulary(tokenizer: AnyTokenizer) -> Vocabulary:
+    """Get the `Vocabulary` object for a given tokenizer.
+    """
+    if hasattr(tokenizer, "_outlines_vocabulary"):
+        return tokenizer._outlines_vocabulary  # type: ignore
+
+    try:
+        if hasattr(
+                tokenizer,
+                "eos_token_id",
+        ) and tokenizer.eos_token_id is not None:
+            eos_token_id = tokenizer.eos_token_id
+        else:
+            raise ValueError(
+                f"Error during guided decoding setup: Tokenizer"
+                f" ({type(tokenizer)}) has no `eos_token_id` property, "
+                "but `eos_token_id` is required for guided decoding"
+                " to work properly.")
+
+        reduced_vocab = _reduced_vocabulary(
+            tokenizer,
+            eos_token_id  #type: ignore
+        )
+        vocabulary = OutlinesVocabulary(Vocabulary(eos_token_id,
+                                                   reduced_vocab))
+        tokenizer._outlines_vocabulary = vocabulary  # type: ignore
+
+        return vocabulary
+    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
+
+
+def get_cache_path() -> str:
+    """Get the context object that contains previously-computed return values"""
+    outlines_cache_dir = os.getenv("OUTLINES_CACHE_DIR")
+    xdg_cache_home = os.getenv("XDG_CACHE_HOME")
+    home_dir = os.path.expanduser("~")
+
+    if outlines_cache_dir:
+        # OUTLINES_CACHE_DIR takes precedence
+        return outlines_cache_dir
+    elif xdg_cache_home:
+        return os.path.join(xdg_cache_home, ".cache", "outlines")
+    # If homedir is "/", we may be inside a container, and thus writing to
+    # root would be problematic, so we fallback to using a tempfile.
+    # Also validate the path exists, since os.path.expanduser does
+    # not garuntee existence.
+    elif os.path.isdir(home_dir) and home_dir != "/":
+        # Default Unix fallback: ~/.cache/outlines
+        return os.path.join(home_dir, ".cache", "outlines")
+    else:
+        import tempfile
 
-        return new_decoder
+        # home_dir may be / inside a docker container without existing user
+        tempdir = tempfile.gettempdir()
+        return os.path.join(tempdir, ".cache", "outlines")
 
-    tokenizer.convert_token_to_string = convert_token_to_string
-    tokenizer.decode = change_decoder(tokenizer.decode)
-    setattr(tokenizer, "_outlines_adapted", True)  # noqa: B010
 
-    return tokenizer
+def get_cache():
+    """Get the Cache instance to be used for index caching"""
+
+    cache_dir = get_cache_path()
+    if envs.VLLM_V0_USE_OUTLINES_CACHE:
+        logger.warning("Enabling outlines cache. This is an unbounded on-disk "
+                       "cache. It may consume a lot of disk space and should "
+                       "not be used with untrusted clients.")
+        cache = Cache(cache_dir, eviction_policy="none", cull_limit=0)
+        outlines_version = importlib.metadata.version("outlines_core")
+
+        cached_version = cache.get('__version__', None)
+        if cached_version != outlines_version:
+            cache.clear()
+        cache.set('__version__', outlines_version)
+        return cache
+    else:
+        return LRUCache(maxsize=128)

vllm/v1/engine/processor.py

@@ -23,6 +23,8 @@ from vllm.v1.engine import EngineCoreRequest
 from vllm.v1.engine.mm_input_cache import MirroredProcessingCache
 from vllm.v1.structured_output.backend_guidance import (
     validate_guidance_grammar)
+from vllm.v1.structured_output.backend_outlines import (
+    validate_structured_output_request_outlines)
 from vllm.v1.structured_output.backend_xgrammar import (
     validate_xgrammar_grammar)
 
@@ -193,6 +195,9 @@ class Processor:
             # https://github.com/guidance-ai/llguidance/blob/main/docs/syntax.md#special-tokens
             # Without tokenizer these are disallowed in grammars.
             validate_guidance_grammar(params, tokenizer=None)
+        elif engine_level_backend == "outlines":
+            # outlines backend
+            validate_structured_output_request_outlines(params)
         else:
             # NOTE: engine_level_backend must be "auto" here, because we have
             # checked supported_backends above.

vllm/v1/structured_output/__init__.py

@@ -88,6 +88,15 @@ class StructuredOutputManager:
                     tokenizer=self.tokenizer,
                     vocab_size=vocab_size,
                 )
+            elif backend == "outlines":
+                from vllm.v1.structured_output.backend_outlines import (
+                    OutlinesBackend)
+
+                self.backend = OutlinesBackend(
+                    self.vllm_config,
+                    tokenizer=self.tokenizer,
+                    vocab_size=vocab_size,
+                )
             else:
                 raise ValueError(
                     f"Unsupported structured output backend: {backend}")

vllm/v1/structured_output/backend_outlines.py

+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright 2025-present the Outlines developers
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from __future__ import annotations
+
+import ast
+import importlib
+import json
+import sys
+from dataclasses import dataclass, field
+from typing import TYPE_CHECKING
+
+import torch
+from regex import escape as regex_escape
+
+from vllm.model_executor.guided_decoding.outlines_logits_processors import (
+    OutlinesVocabulary, get_cache, get_vocabulary)
+from vllm.sampling_params import SamplingParams
+from vllm.utils import LazyLoader
+from vllm.v1.structured_output.backend_types import (StructuredOutputBackend,
+                                                     StructuredOutputGrammar,
+                                                     StructuredOutputOptions)
+
+if TYPE_CHECKING:
+    import outlines_core as oc
+    import outlines_core.json_schema as json_schema
+else:
+    oc = LazyLoader("oc", globals(), "outlines_core")
+    json_schema = LazyLoader("json_schema", globals(),
+                             "outlines_core.json_schema")
+
+# Python 3.11+ sre_parse and sre_constants
+# are deprecated, so we must import them from re
+if sys.version_info >= (3, 11):
+    # Hack to get around pre-commit regex module rule
+    # because going through re is the only way to get sre_parse
+    # and sre_constants in Python 3.11+
+    _re = importlib.import_module("re")
+    sre_parse = _re._parser
+    sre_constants = _re._constants
+else:
+    import sre_constants
+    import sre_parse
+
+
+@dataclass
+class OutlinesBackend(StructuredOutputBackend):
+
+    def __post_init__(self):
+        self.vocabulary = get_vocabulary(self.tokenizer)
+        self.cache = get_cache()
+
+    def _compile_index(self, regex_string: str,
+                       vocabulary: OutlinesVocabulary) -> oc.Index:
+        cache_key = f"{vocabulary._hash}_{regex_string}"
+        if cache_key in self.cache:
+            return self.cache[cache_key]
+
+        index = oc.Index(regex_string, vocabulary.inner)
+        self.cache[cache_key] = index
+
+        return index
+
+    def compile_grammar(self, request_type: StructuredOutputOptions,
+                        grammar_spec: str) -> StructuredOutputGrammar:
+        if request_type == StructuredOutputOptions.JSON:
+            regex = json_schema.build_regex_from_schema(grammar_spec)
+        elif request_type == StructuredOutputOptions.REGEX:
+            regex = grammar_spec
+        elif request_type == StructuredOutputOptions.CHOICE:
+            choices = ast.literal_eval(grammar_spec)
+            choices = [regex_escape(c) for c in choices]
+            regex = "(" + "|".join(choices) + ")"
+        else:
+            raise ValueError(
+                f"Invalid request type for Outlines backend ({request_type!s})"
+            )
+        index = self._compile_index(regex, self.vocabulary)
+        max_rollback_tokens = (
+            self.vllm_config.speculative_config.num_speculative_tokens
+            if self.vllm_config.speculative_config is not None else 0)
+        return OutlinesGrammar(vocab_size=self.vocab_size,
+                               guide=oc.Guide(
+                                   index, max_rollback=max_rollback_tokens))
+
+    def allocate_token_bitmask(self, max_num_seqs: int) -> torch.Tensor:
+        return torch.full(
+            (max_num_seqs, (self.vocab_size + 31) // 32),
+            -1,
+            dtype=torch.int32,
+            pin_memory=torch.cuda.is_available(),
+        )
+
+    def destroy(self):
+        pass
+
+
+@dataclass
+class OutlinesGrammar(StructuredOutputGrammar):
+
+    vocab_size: int
+    guide: oc.Guide = field(hash=False)
+    num_processed_tokens: int = field(default_factory=lambda: 0,
+                                      repr=False,
+                                      hash=False,
+                                      init=False)
+
+    # outlines_core signals done on DFA accept; vLLM expects done after EOS.
+    # We delay the finished flag by one step so EOS can still be emitted.
+    _prev_finished: bool = field(default=False,
+                                 init=False,
+                                 repr=False,
+                                 hash=False)
+
+    def accept_tokens(self, request_id: str, tokens: list[int]) -> bool:
+        """Accepts a list of tokens and advances the FSM.
+
+        Returns True if the FSM was advanced successfully.
+        Returns False if the FSM failed to advance.
+        """
+        if self.guide.accepts_tokens(tokens):
+            # Advance cannot fail because we checked Guide.accepts_tokens()
+            for t in tokens:
+                self.guide.advance(t)
+                self.num_processed_tokens += 1
+            return True
+        return False
+
+    def rollback(self, num_tokens: int) -> None:
+        self.guide.rollback_state(num_tokens)
+        self.num_processed_tokens -= num_tokens
+
+    def validate_tokens(self, tokens: list[int]) -> list[int]:
+        accepted: list[int] = []
+        for tok in tokens:
+            accepted.append(tok)
+            if not self.guide.accepts_tokens(accepted):
+                accepted.pop()
+                break
+        return accepted
+
+    def fill_bitmask(self, bitmask: torch.Tensor, idx: int) -> None:
+        mask = bitmask[idx]
+        self.guide.write_mask_into(mask.data_ptr(), mask.numel(),
+                                   mask.element_size())
+
+    def is_terminated(self) -> bool:
+        curr = self.guide.is_finished()
+        prev = self._prev_finished
+        self._prev_finished = curr
+        return prev
+
+    def reset(self):
+        self.num_processed_tokens = 0
+        self._prev_finished = False
+        self.guide.reset()
+
+
+def validate_structured_output_request_outlines(params: SamplingParams):
+    if params.guided_decoding is None:
+        return
+
+    gd_params = params.guided_decoding
+
+    if gd_params.regex:
+        validate_regex_is_buildable(gd_params.regex)
+    elif gd_params.json:
+        if isinstance(gd_params.json, str):
+            try:
+                # make sure schema is valid json
+                json.loads(gd_params.json)
+                schema = gd_params.json
+            except json.JSONDecodeError as e:
+                raise ValueError("Invalid JSON grammar specification.") from e
+        else:
+            try:
+                schema = json.dumps(gd_params.json)
+            except Exception as e:
+                raise ValueError(
+                    f"Error serializing guided decoding jsonschema: {e}"
+                ) from e
+        pattern = json_schema.build_regex_from_schema(schema)
+        validate_regex_is_buildable(pattern)
+    elif gd_params.choice:
+        choices = [regex_escape(str(choice)) for choice in gd_params.choice]
+        regex = "(" + "|".join(choices) + ")"
+        validate_regex_is_buildable(regex)
+    elif gd_params.grammar:
+        raise ValueError("Outlines guided decoding backend "
+                         "does not support grammar specifications")
+
+
+def _prefix_needs_context(parsed) -> bool:
+    """Return True if there's a look-around/anchor before any consumer."""
+
+    def subpattern_consumes(parsed) -> bool:
+        """Return True if subpattern can consume at least one character."""
+        tokens = parsed.data if hasattr(parsed, 'data') else parsed
+        for ttype, tval in tokens:
+            # literal, character class, or dot always consumes
+            if ttype in (sre_parse.LITERAL, sre_parse.IN, sre_parse.ANY):
+                return True
+            # quantified subpattern: check inner pattern
+            elif ttype == sre_parse.MAX_REPEAT:
+                _, mx, sub = tval
+                if mx != 0 and subpattern_consumes(sub):
+                    return True
+            # alternation: if any branch consumes, the whole does
+            elif ttype == sre_parse.BRANCH:
+                _, branches = tval
+                if any(subpattern_consumes(br) for br in branches):
+                    return True
+            # grouped subpattern: recurse into its contents
+            elif ttype == sre_parse.SUBPATTERN and subpattern_consumes(
+                    tval[3]):
+                return True
+        # No consumers, return False
+        return False
+
+    tokens = parsed.data if hasattr(parsed, 'data') else parsed
+    for ttype, tval in tokens:
+        # Direct anchors or look-around
+        if ttype == sre_parse.AT or ttype in (sre_constants.ASSERT,
+                                              sre_constants.ASSERT_NOT):
+            return True
+
+        # Nested subpattern: check
+        if ttype == sre_parse.SUBPATTERN:
+            # tval: (group, add_flags, del_flags, subpattern)
+            if _prefix_needs_context(tval[3]):
+                return True
+            if subpattern_consumes(tval[3]):
+                return False
+
+        # if any branch has a prefix anchor => True,
+        # else if at least one branch consumes => prefix ends => False
+        elif ttype == sre_parse.BRANCH:
+            saw_consumer = False
+            for br in tval[1]:
+                if _prefix_needs_context(br):
+                    return True
+                if subpattern_consumes(br):
+                    saw_consumer = True
+            if saw_consumer:
+                return False
+
+        # Immediate consumer tokens
+        elif ttype in (sre_parse.LITERAL, sre_parse.IN, sre_parse.ANY):
+            return False
+
+        # if subpattern has anchor => True, if it can consume => stop
+        elif ttype == sre_parse.MAX_REPEAT:
+            if _prefix_needs_context(tval[2]):
+                return True
+            if subpattern_consumes(tval[2]):
+                return False
+
+    return False
+
+
+def _check_unsupported(parsed) -> None:
+    """Check for regex features unsupported by regex-automata"""
+    tokens = parsed.data if hasattr(parsed, 'data') else parsed
+    for ttype, tval in tokens:
+
+        # backreference
+        if ttype in (sre_parse.GROUPREF, sre_parse.GROUPREF_EXISTS):
+            raise ValueError("Backreferences are unsupported.")
+
+        # look-around assertion
+        elif ttype in (sre_constants.ASSERT, sre_constants.ASSERT_NOT):
+            raise ValueError("Look-Around assertion are unsupported.")
+
+        # unicode word boundaries
+        elif ttype == sre_parse.AT:
+            if tval in (sre_constants.AT_BOUNDARY,
+                        sre_constants.AT_NON_BOUNDARY):
+                raise ValueError("Unicode word boundaries are unsupported.")
+
+        elif ttype == sre_parse.BRANCH:
+            # tval is (None, branches)
+            for branch in tval[1]:
+                _check_unsupported(branch)
+
+        # tval is (min, max, subpattern)
+        elif ttype == sre_parse.MAX_REPEAT:
+            _check_unsupported(tval[2])
+
+
+def validate_regex_is_buildable(pattern: str) -> None:
+    """
+    Validates that the input regex is not using unsupported features
+    of the `regex-automata` crate (outlines_core regex engine) and has a
+    universal start state.
+    definition of universal start state used can be found at:
+    https://docs.rs/regex-automata/latest/regex_automata/dfa/trait.Automaton.html#method.universal_start_state
+    """
+    try:
+        parsed = sre_parse.parse(pattern)
+
+    except sre_constants.error as e:
+        raise ValueError(f"Error parsing regex: {e}") from e
+
+    try:
+        _check_unsupported(parsed)
+    except ValueError as e:
+        raise ValueError(
+            f"Regex uses unsupported feature for guided decoding: {e}. "
+            "Only basic matching constructs are supported—lookarounds, "
+            "backreferences, and unicode boundaries are not.") from e
+
+    if _prefix_needs_context(parsed):
+        raise ValueError(
+            "Regex does not have a anchored universal start state"
+            "This means that the Regex uses anchors (^) or look-arounds "
+            "in a way which requires context before any token is matched."
+            "Guided decoding needs regexes that can match without needing "
+            "that context. Try rewriting the pattern without using these "
+            f"constructs. Pattern:\n{pattern}")