[Frontend] Add backend-specific options for guided decoding (vllm-pro…

…ject#13505)

Signed-off-by: Joe Runde <[email protected]>

docs/source/features/structured_outputs.md

@@ -16,7 +16,7 @@ The following parameters are supported, which must be added as extra parameters:
 - `guided_json`: the output will follow the JSON schema.
 - `guided_grammar`: the output will follow the context free grammar.
 - `guided_whitespace_pattern`: used to override the default whitespace pattern for guided json decoding.
-- `guided_decoding_backend`: used to select the guided decoding backend to use.
+- `guided_decoding_backend`: used to select the guided decoding backend to use. Additional backend-specific options can be supplied in a comma separated list following a colon after the backend name. For example `"xgrammar:no-fallback"` will not allow vLLM to fallback to a different backend on error.
 
 You can see the complete list of supported parameters on the [OpenAI-Compatible Server](#openai-compatible-server)page.
 

examples/online_serving/openai_chat_completion_structured_outputs.py

@@ -2,7 +2,7 @@
 
 from enum import Enum
 
-from openai import OpenAI
+from openai import BadRequestError, OpenAI
 from pydantic import BaseModel
 
 client = OpenAI(
@@ -94,3 +94,26 @@ class CarDescription(BaseModel):
     extra_body={"guided_grammar": simplified_sql_grammar},
 )
 print(completion.choices[0].message.content)
+
+# Extra backend options
+prompt = ("Generate an email address for Alan Turing, who works in Enigma."
+          "End in .com and new line. Example result:"
+          "[email protected]\n")
+
+try:
+    # The no-fallback option forces vLLM to use xgrammar, so when it fails
+    # you get a 400 with the reason why
+    completion = client.chat.completions.create(
+        model="Qwen/Qwen2.5-3B-Instruct",
+        messages=[{
+            "role": "user",
+            "content": prompt,
+        }],
+        extra_body={
+            "guided_regex": "\w+@\w+\.com\n",
+            "stop": ["\n"],
+            "guided_decoding_backend": "xgrammar:no-fallback"
+        },
+    )
+except BadRequestError as e:
+    print("This error is expected:", e)

tests/entrypoints/llm/test_guided_generate.py

@@ -280,6 +280,22 @@ def test_validation_against_both_guided_decoding_options(sample_regex, llm):
                      guided_options_request=dict(guided_regex=sample_regex))
 
 
+@pytest.mark.skip_global_cleanup
+def test_disable_guided_decoding_fallback(sample_regex, llm):
+    sampling_params = SamplingParams(temperature=0.8,
+                                     top_p=0.95,
+                                     guided_decoding=GuidedDecodingParams(
+                                         regex=sample_regex,
+                                         backend="xgrammar:no-fallback"))
+
+    with pytest.raises(
+            ValueError,
+            match="xgrammar does not support regex guided decoding"):
+        llm.generate(prompts="This should fail",
+                     sampling_params=sampling_params,
+                     use_tqdm=True)
+
+
 @pytest.mark.skip_global_cleanup
 @pytest.mark.parametrize("guided_decoding_backend", GUIDED_DECODING_BACKENDS)
 def test_guided_json_object(llm, guided_decoding_backend: str):

tests/model_executor/test_guided_processors.py

@@ -109,6 +109,16 @@ def test_multiple_guided_options_not_allowed(sample_json_schema, sample_regex):
         GuidedDecodingParams(json=sample_json_schema, grammar="test grammar")
 
 
+def test_guided_decoding_backend_options():
+    """Test backend-specific options"""
+    params = GuidedDecodingParams(
+        backend="xgrammar:option-1,option-2,option-3")
+    assert params.backend_options() == ["option-1", "option-2", "option-3"]
+
+    no_fallback = GuidedDecodingParams(backend="xgrammar:option-1,no-fallback")
+    assert no_fallback.no_fallback()
+
+
 def test_pickle_xgrammar_tokenizer_data():
 
     # TODO: move to another test file for xgrammar

vllm/config.py

@@ -25,6 +25,7 @@
                                                      get_quantization_config)
 from vllm.model_executor.models import ModelRegistry
 from vllm.platforms import CpuArchEnum
+from vllm.sampling_params import GuidedDecodingParams
 from vllm.tracing import is_otel_available, otel_import_error_traceback
 from vllm.transformers_utils.config import (
     ConfigFormat, get_config, get_hf_image_processor_config,
@@ -2631,7 +2632,9 @@ def compute_hash(self) -> str:
 
     def __post_init__(self):
         valid_guided_backends = ['outlines', 'lm-format-enforcer', 'xgrammar']
-        backend = self.guided_decoding_backend
+
+        backend = GuidedDecodingParams(
+            backend=self.guided_decoding_backend).backend_name
         if backend not in valid_guided_backends:
             raise ValueError(f"Invalid guided_decoding_backend '{backend},"
                              f"must be one of {valid_guided_backends}")

vllm/engine/arg_utils.py

@@ -372,14 +372,17 @@ def add_cli_args(parser: FlexibleArgumentParser) -> FlexibleArgumentParser:
             '--guided-decoding-backend',
             type=str,
             default='xgrammar',
-            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, '
             'https://github.com/mlc-ai/xgrammar, and '
             'https://github.com/noamgat/lm-format-enforcer.'
             ' Can be overridden per request via guided_decoding_backend'
-            ' parameter.')
+            ' parameter.\n'
+            'Backend-sepcific options can be supplied in a comma-separated '
+            'list following a colon after the backend name. Valid backends and '
+            'all available options are: [xgrammar:no-fallback, '
+            'outlines:no-fallback, lm-format-enforcer:no-fallback]')
         parser.add_argument(
             '--logits-processor-pattern',
             type=nullable_str,

vllm/model_executor/guided_decoding/__init__.py

@@ -22,47 +22,56 @@
 
 def maybe_backend_fallback(
         guided_params: GuidedDecodingParams) -> GuidedDecodingParams:
+
+    def fallback_or_error(guided_params: GuidedDecodingParams, message: str,
+                          fallback: str) -> None:
+        """Change the backend to the specified fallback with a warning log, 
+        or raise a ValueError if the `no-fallback` option is specified."""
+        if guided_params.no_fallback():
+            raise ValueError(message)
+
+        logger.warning("%s Falling back to use %s instead.", message, fallback)
+        guided_params.backend = fallback
+
     # lm-format-enforce doesn't support grammar, fallback to xgrammar
-    if guided_params.backend == "lm-format-enforcer":
+    if guided_params.backend_name == "lm-format-enforcer":
         if guided_params.grammar is not None:
-            logger.warning(
-                "lm-format-enforcer does not support grammar guided decoding. "
-                "Falling back to use xgrammar instead.")
-            guided_params.backend = "xgrammar"
+            fallback_or_error(
+                guided_params,
+                "lm-format-enforcer does not support grammar guided decoding.",
+                "xgrammar")
 
         # lm-format-enforcer doesn't support some JSON schema features
         elif (guided_params.json is not None
               and has_lmf_unsupported_json_features(guided_params.json)):
-            logger.warning(
+            fallback_or_error(
+                guided_params,
                 "lm-format-enforcer does not support advanced JSON schema "
-                "features like patterns or numeric ranges. "
-                "Falling back to use outlines instead.")
-            guided_params.backend = "outlines"
+                "features like patterns or numeric ranges.", "outlines")
 
-    if guided_params.backend == "xgrammar":
+    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:
-            logger.warning("xgrammar is only supported on x86 CPUs. "
-                           "Falling back to use outlines instead.")
-            guided_params.backend = "outlines"
+            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:
-            logger.warning("xgrammar does not support regex guided decoding. "
-                           "Falling back to use outlines instead.")
-            guided_params.backend = "outlines"
+            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)):
-            logger.warning(
+            fallback_or_error(
+                guided_params,
                 "xgrammar does not support advanced JSON schema features like "
-                "patterns or numeric ranges. "
-                "Falling back to use outlines instead.")
-            guided_params.backend = "outlines"
+                "enums, patterns or numeric ranges.", "outlines")
 
         # xgrammar only supports GBNF grammars, so we must convert Lark.
         # We must check if the grammar is likely Lark and if that
@@ -72,25 +81,23 @@ def maybe_backend_fallback(
             try:
                 convert_lark_to_gbnf(guided_params.grammar)
             except Exception:
-                logger.warning(
+                fallback_or_error(
+                    guided_params,
                     "xgrammar does not support Lark grammars and the "
-                    "grammar failed to convert to GBNF. "
-                    "Falling back to use outlines instead.")
-                guided_params.backend = "outlines"
+                    "grammar failed to convert to GBNF.", "outlines")
 
         # If the xgrammar module cannot be imported successfully,
         # we should still allow users to use guided decoding with a fallback.
         elif not xgr_installed:
-            logger.warning("xgrammar module cannot be imported successfully. "
-                           "Falling back to use outlines instead.")
-            guided_params.backend = "outlines"
+            fallback_or_error(
+                guided_params,
+                "xgrammar module cannot be imported successfully.", "outlines")
 
-    if (guided_params.backend == "outlines"
+    if (guided_params.backend_name == "outlines"
             and guided_params.json_object is not None):
         # outlines doesn't support json_object, fallback to xgrammar
-        logger.warning("outlines does not support json_object. "
-                       "Falling back to use xgrammar instead.")
-        guided_params.backend = "xgrammar"
+        fallback_or_error(guided_params,
+                          "outlines does not support json_object.", "xgrammar")
 
     return guided_params
 
@@ -100,18 +107,18 @@ async def get_guided_decoding_logits_processor(
         model_config: ModelConfig) -> LogitsProcessor | None:
     guided_params = maybe_backend_fallback(guided_params)
     # CFG grammar not supported by LMFE, so we use outlines instead
-    if guided_params.backend == 'outlines':
+    if guided_params.backend_name == '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
             get_outlines_guided_decoding_logits_processor)
         return await get_outlines_guided_decoding_logits_processor(
             guided_params, tokenizer)
-    if guided_params.backend == 'lm-format-enforcer':
+    if guided_params.backend_name == 'lm-format-enforcer':
         from vllm.model_executor.guided_decoding.lm_format_enforcer_decoding import (  # noqa
             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':
+    if guided_params.backend_name == '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(
@@ -127,18 +134,18 @@ def get_local_guided_decoding_logits_processor(
         model_config: ModelConfig) -> LogitsProcessor | None:
     guided_params = maybe_backend_fallback(guided_params)
     # CFG grammar not supported by LMFE, so we use outlines instead
-    if guided_params.backend == 'outlines':
+    if guided_params.backend_name == '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
             get_local_outlines_guided_decoding_logits_processor)
         return get_local_outlines_guided_decoding_logits_processor(
             guided_params, tokenizer)
-    if guided_params.backend == 'lm-format-enforcer':
+    if guided_params.backend_name == 'lm-format-enforcer':
         from vllm.model_executor.guided_decoding.lm_format_enforcer_decoding import (  # noqa
             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':
+    if guided_params.backend_name == '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(

vllm/sampling_params.py

@@ -64,6 +64,25 @@ def from_optional(
             whitespace_pattern=whitespace_pattern,
         )
 
+    @property
+    def backend_name(self) -> str:
+        """Return the backend name without any options.
+        
+        For example if the backend is "xgrammar:no-fallback", returns "xgrammar"
+        """
+        return (self.backend or "").split(":")[0]
+
+    def backend_options(self) -> List[str]:
+        """Return the backend options as a list of strings."""
+        if not self.backend or ":" not in self.backend:
+            return []
+        return self.backend.split(":")[1].split(",")
+
+    def no_fallback(self) -> bool:
+        """Returns True if the "no-fallback" option is supplied for the guided
+        decoding backend"""
+        return "no-fallback" in self.backend_options()
+
     def __post_init__(self):
         """Validate that some fields are mutually exclusive."""
         guide_count = sum([