Add unit tests for batched guided and non-guided requests

tests/v1/entrypoints/llm/test_struct_output_generate.py

@@ -11,9 +11,11 @@
 import jsonschema
 import pytest
 import regex as re
+import torch
 from pydantic import BaseModel
 
 from tests.reasoning.utils import run_reasoning_extraction
+from vllm.distributed import cleanup_dist_env_and_memory
 from vllm.entrypoints.llm import LLM
 from vllm.outputs import RequestOutput
 from vllm.platforms import current_platform
@@ -721,3 +723,83 @@ def generate_with_backend(backend):
     assert "a4" not in generated
     assert "a5" not in generated
     assert "a6" not in generated
+
+
+@pytest.mark.parametrize("guided_decoding_backend",
+                         ["guidance", "xgrammar", "outlines"])
+def test_structured_output_batched_with_non_guided_requests(
+    monkeypatch: pytest.MonkeyPatch,
+    sample_json_schema: dict[str, Any],
+    guided_decoding_backend: str,
+):
+    monkeypatch.setenv("VLLM_USE_V1", "1")
+
+    # Don't use eager execution on TPUs because we want to test for no
+    # recompilation at runtime
+    enforce_eager = bool(not current_platform.is_tpu())
+
+    llm = LLM(
+        model="meta-llama/Meta-Llama-3.1-8B-Instruct",
+        enforce_eager=enforce_eager,
+        max_model_len=1024,
+        guided_decoding_backend=guided_decoding_backend,
+        guided_decoding_disable_any_whitespace=(guided_decoding_backend
+                                                in {"xgrammar", "guidance"}),
+    )
+
+    guided_prompt = (
+        "Give an example JSON for an employee profile that fits this "
+        "schema. Make the response as short as possible. Schema: "
+        f"{sample_json_schema}")
+
+    non_guided_prompt = "The diameter of the Earth in kilometers is "
+
+    prompts = [guided_prompt, non_guided_prompt]
+    sampling_params = [
+        SamplingParams(
+            temperature=1.0,
+            max_tokens=400,
+            guided_decoding=GuidedDecodingParams(json=sample_json_schema)),
+        # No max tokens, temp=0 to assert on contents
+        SamplingParams(
+            seed=42,
+            temperature=0,
+            top_p=1.0,
+        ),
+    ]
+
+    outputs = llm.generate(prompts=prompts,
+                           sampling_params=sampling_params,
+                           use_tqdm=True)
+
+    assert outputs is not None
+
+    # Free memory as soon as possible as failed assertions
+    # will short circuit and not free up memory
+    del llm
+    torch.cuda.empty_cache()
+    cleanup_dist_env_and_memory()
+
+    for index, output in enumerate(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:\n{prompt!r}\nGenerated text:\n{generated_text!r}")
+
+        if index == 0:
+            # First prompt is guided, expect valid JSON
+            assert "\n" not in generated_text
+            output_json = json.loads(generated_text)
+            jsonschema.validate(instance=output_json,
+                                schema=sample_json_schema)
+        else:
+            # Second prompt is not guided, expect valid output
+            # Cannot assert on exact output, but we can expect it to be factual
+            assert "12,742" in generated_text
+
+            # non-guided requests should not return a valid JSON here
+            with pytest.raises(ValueError):
+                output_json = json.loads(generated_text)