[Bugfix][V0] XGrammar structured output supports Enum (#15878)

Signed-off-by: Leon Seidel <leon.seidel@fau.de>

Author: leon-seidel

tests/entrypoints/llm/test_guided_generate.py

@@ -3,9 +3,11 @@
 import json
 import re
 import weakref
+from enum import Enum
 
 import jsonschema
 import pytest
+from pydantic import BaseModel
 
 from vllm.distributed import cleanup_dist_env_and_memory
 from vllm.entrypoints.llm import LLM
@@ -330,3 +332,44 @@ 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)
+
+
+class CarType(str, Enum):
+    sedan = "sedan"
+    suv = "SUV"
+    truck = "Truck"
+    coupe = "Coupe"
+
+
+class CarDescription(BaseModel):
+    brand: str
+    model: str
+    car_type: CarType
+
+
+@pytest.mark.skip_global_cleanup
+@pytest.mark.parametrize("guided_decoding_backend", GUIDED_DECODING_BACKENDS)
+def test_guided_json_completion_with_enum(llm, guided_decoding_backend: str):
+    json_schema = CarDescription.model_json_schema()
+    sampling_params = SamplingParams(temperature=1.0,
+                                     max_tokens=1000,
+                                     guided_decoding=GuidedDecodingParams(
+                                         json=json_schema,
+                                         backend=guided_decoding_backend))
+    outputs = llm.generate(
+        prompts="Generate a JSON with the brand, model and car_type of"
+        "the most iconic car from the 90's",
+        sampling_params=sampling_params,
+        use_tqdm=True)
+
+    assert outputs is not None
+    for output in outputs:
+        assert output is not None
+        assert isinstance(output, RequestOutput)
+        prompt = output.prompt
+
+        generated_text = output.outputs[0].text
+        assert generated_text is not None
+        print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
+        output_json = json.loads(generated_text)
+        jsonschema.validate(instance=output_json, schema=json_schema)

vllm/model_executor/guided_decoding/utils.py

@@ -14,10 +14,6 @@ def check_object(obj: dict) -> bool:
         if "pattern" in obj:
             return True
 
-        # Check for enum restrictions
-        if "enum" in obj:
-            return True
-
         # Check for numeric ranges
         if obj.get("type") in ("integer", "number") and any(
                 key in obj for key in [