[Core][V0] Add guidance backend for structured output (vllm-project#14589)

Signed-off-by: Russell Bryant <[email protected]>
Co-authored-by: Loc Huynh <[email protected]>
Co-authored-by: Michal Moskal <[email protected]>
Co-authored-by: Aaron Pham <[email protected]>

Author: 4 people

benchmarks/benchmark_serving_structured_output.py

@@ -999,11 +999,12 @@ def main(args: argparse.Namespace):
                         type=float,
                         default=1.0,
                         help="Ratio of Structured Outputs requests")
-    parser.add_argument("--structured-output-backend",
-                        type=str,
-                        choices=["outlines", "lm-format-enforcer", "xgrammar"],
-                        default="xgrammar",
-                        help="Backend to use for structured outputs")
+    parser.add_argument(
+        "--structured-output-backend",
+        type=str,
+        choices=["outlines", "lm-format-enforcer", "xgrammar", "guidance"],
+        default="xgrammar",
+        help="Backend to use for structured outputs")
 
     args = parser.parse_args()
     main(args)

requirements/common.txt

@@ -18,6 +18,7 @@ pillow  # Required for image processing
 prometheus-fastapi-instrumentator >= 7.0.0
 tiktoken >= 0.6.0  # Required for DBRX tokenizer
 lm-format-enforcer >= 0.10.11, < 0.11
+llguidance >= 0.7.2, < 0.8.0; platform_machine == "x86_64" or platform_machine == "arm64" or platform_machine == "aarch64"
 outlines == 0.1.11
 lark == 1.2.2
 xgrammar == 0.1.16; platform_machine == "x86_64" or platform_machine == "aarch64"

tests/entrypoints/llm/test_guided_generate.py

@@ -14,7 +14,9 @@
 from vllm.sampling_params import GuidedDecodingParams, SamplingParams
 
 MODEL_NAME = "Qwen/Qwen2.5-1.5B-Instruct"
-GUIDED_DECODING_BACKENDS = ["outlines", "lm-format-enforcer", "xgrammar"]
+GUIDED_DECODING_BACKENDS = [
+    "outlines", "lm-format-enforcer", "xgrammar", "guidance"
+]
 
 
 @pytest.fixture(scope="module")

tests/model_executor/test_guided_processors.py

@@ -16,7 +16,9 @@
 from vllm.sampling_params import GuidedDecodingParams
 
 MODEL_NAME = 'HuggingFaceH4/zephyr-7b-beta'
-GUIDED_DECODING_BACKENDS = ["outlines", "lm-format-enforcer", "xgrammar"]
+GUIDED_DECODING_BACKENDS = [
+    "outlines", "lm-format-enforcer", "xgrammar", "guidance"
+]
 GUIDED_DECODING_BACKENDS_WITH_REASONING_SUPPORT = ["outlines", "xgrammar"]
 REASONING_MODEL_NAME = "deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B"
 

vllm/config.py

@@ -2785,7 +2785,9 @@ def compute_hash(self) -> str:
         return hash_str
 
     def __post_init__(self):
-        valid_guided_backends = ['outlines', 'lm-format-enforcer', 'xgrammar']
+        valid_guided_backends = [
+            'outlines', 'lm-format-enforcer', 'xgrammar', 'guidance'
+        ]
 
         backend = GuidedDecodingParams(
             backend=self.guided_decoding_backend).backend_name

vllm/model_executor/guided_decoding/__init__.py

@@ -79,6 +79,12 @@ def fallback_or_error(guided_params: GuidedDecodingParams, message: str,
                     "xgrammar does not support Lark grammars and the "
                     "grammar failed to convert to GBNF.", "outlines")
 
+        elif guided_params.json_object:
+            # https://github.com/mlc-ai/xgrammar/issues/256
+            fallback_or_error(guided_params,
+                              "xgrammar does not support json_object.",
+                              "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:
@@ -88,9 +94,9 @@ def fallback_or_error(guided_params: GuidedDecodingParams, message: str,
 
     if (guided_params.backend_name == "outlines"
             and guided_params.json_object is not None):
-        # outlines doesn't support json_object, fallback to xgrammar
+        # outlines doesn't support json_object, fallback to guidance
         fallback_or_error(guided_params,
-                          "outlines does not support json_object.", "xgrammar")
+                          "outlines does not support json_object.", "guidance")
 
     return guided_params
 
@@ -122,10 +128,15 @@ async def get_guided_decoding_logits_processor(
             get_local_xgrammar_guided_decoding_logits_processor)
         return get_local_xgrammar_guided_decoding_logits_processor(
             guided_params, tokenizer, model_config, reasoner)
-
+    if guided_params.backend_name == 'guidance':
+        from vllm.model_executor.guided_decoding.guidance_decoding import (
+            get_local_guidance_guided_decoding_logits_processor)
+        return get_local_guidance_guided_decoding_logits_processor(
+            guided_params, tokenizer)
     raise ValueError(
         f"Unknown guided decoding backend '{guided_params.backend}'. "
-        "Must be one of 'outlines, 'lm-format-enforcer', 'xgrammar'")
+        "Must be one of 'outlines, 'lm-format-enforcer', 'xgrammar', 'guidance'"
+    )
 
 
 def get_local_guided_decoding_logits_processor(
@@ -155,7 +166,13 @@ def get_local_guided_decoding_logits_processor(
             get_local_xgrammar_guided_decoding_logits_processor)
         return get_local_xgrammar_guided_decoding_logits_processor(
             guided_params, tokenizer, model_config, reasoner)
+    if guided_params.backend_name == 'guidance':
+        from vllm.model_executor.guided_decoding.guidance_decoding import (
+            get_local_guidance_guided_decoding_logits_processor)
+        return get_local_guidance_guided_decoding_logits_processor(
+            guided_params, tokenizer)
 
     raise ValueError(
         f"Unknown guided decoding backend '{guided_params.backend}'. "
-        "Must be one of 'outlines, 'lm-format-enforcer', 'xgrammar'")
+        "Must be one of 'outlines, 'lm-format-enforcer', 'xgrammar', 'guidance'"
+    )

vllm/model_executor/guided_decoding/guidance_decoding.py

@@ -0,0 +1,44 @@
+# SPDX-License-Identifier: Apache-2.0
+from re import escape as regex_escape
+
+import llguidance
+from transformers import PreTrainedTokenizerBase
+
+from vllm.model_executor.guided_decoding.guidance_logits_processors import (
+    GuidanceLogitsProcessor)
+from vllm.sampling_params import GuidedDecodingParams
+
+
+def get_local_guidance_guided_decoding_logits_processor(
+        guided_params: GuidedDecodingParams,
+        tokenizer: PreTrainedTokenizerBase) -> GuidanceLogitsProcessor:
+    """
+    Given an OpenAI-compatible request, check for guided decoding parameters
+    and get the necessary logits processor for the given guide.
+    """
+
+    grm = ""
+    if guided_params.json:
+        grm = llguidance.LLMatcher.grammar_from_json_schema(
+            guided_params.json,
+            overrides={"whitespace_pattern": guided_params.whitespace_pattern})
+    elif guided_params.json_object:
+        grm = llguidance.LLMatcher.grammar_from_json_schema(
+            '{"type": "object"}',
+            overrides={"whitespace_pattern": guided_params.whitespace_pattern})
+    elif guided_params.regex:
+        grm = llguidance.grammar_from("regex", guided_params.regex)
+    elif guided_params.choice:
+        # choice just uses regex
+        choices = (regex_escape(str(choice))
+                   for choice in guided_params.choice)
+        choices_regex = "(" + "|".join(choices) + ")"
+        grm = llguidance.grammar_from("regex", choices_regex)
+    elif guided_params.grammar:
+        # this supports Lark and GBNF
+        grm = llguidance.grammar_from("grammar", guided_params.grammar)
+
+    if grm:
+        return GuidanceLogitsProcessor(grm, tokenizer)
+
+    raise ValueError("Unknown guided decoding mode")

vllm/model_executor/guided_decoding/guidance_logits_processors.py

@@ -0,0 +1,85 @@
+# SPDX-License-Identifier: Apache-2.0
+import os
+from typing import Any, List
+
+import llguidance
+import llguidance.hf
+import llguidance.torch
+import torch
+from transformers import PreTrainedTokenizerBase
+
+from vllm.logger import init_logger
+
+logger = init_logger(__name__)
+
+
+class GuidanceLogitsProcessor:
+    """Base Guidance Logits Processor"""
+
+    cached_tokenizers: dict[str, Any] = {}
+
+    def __init__(
+        self,
+        grammar: str,
+        tokenizer: PreTrainedTokenizerBase,
+    ) -> None:
+        """Base Guidance Logits Processor
+
+        Args:
+            grammar (str)
+                grammar to guide the generation
+            tokenizer (PreTrainedTokenizerBase)
+                model's tokenizer
+        """
+        self.grammar = grammar
+        self.tokenizer = tokenizer
+        self.tokenizer_name = tokenizer.name_or_path
+        self.new_sampling = False
+        self.initialized = False
+
+    def _initialize(self):
+        if self.initialized:
+            return
+
+        ll_tokenizer = self.cached_tokenizers.get(self.tokenizer.name_or_path,
+                                                  None)
+        if ll_tokenizer is None:
+            ll_tokenizer = llguidance.hf.from_tokenizer(self.tokenizer, None)
+            self.cached_tokenizers[self.tokenizer.name_or_path] = ll_tokenizer
+
+        self.ll_tokenizer = ll_tokenizer
+        self.ll_matcher = llguidance.LLMatcher(
+            self.ll_tokenizer,
+            self.grammar,
+            log_level=int(os.environ.get("LLGUIDANCE_LOG_LEVEL", "1")),
+        )
+
+        # create reusable bitmask
+        self.bitmask = llguidance.torch.allocate_token_bitmask(
+            1, self.ll_tokenizer.vocab_size)
+
+        self.initialized = True
+
+    def __call__(
+        self,
+        input_ids: List[int],
+        scores: torch.Tensor,
+    ) -> torch.Tensor:
+        # we initialize the guidance model here
+        # to avoid pickling ll_tokenizer and ll_interpreter
+        self._initialize()
+
+        if self.new_sampling and len(input_ids) > 0:
+            self.ll_matcher.consume_token(input_ids[-1])
+            err = self.ll_matcher.get_error()
+            if err:
+                logger.warning("Error in LLMatcher: %s", err)
+
+        llguidance.torch.fill_next_token_bitmask(self.ll_matcher, self.bitmask,
+                                                 0)
+        llguidance.torch.apply_token_bitmask_inplace(
+            scores, self.bitmask.to(scores.device))
+
+        self.new_sampling = True
+
+        return scores