[CI]Add guided decoding test (#422)

### What this PR does / why we need it?
After extensive testing, we are happy to say that guided_decoding is
fully supported by npu, in this pr, we add guided_decoding integrated
with our test, mainly does the following things:
1. test v0 supported backends including ` "outlines",
"lm-format-enforcer","xgrammar"`
2. test v1 supported backends including ` "guidance", "xgrammar"`

---------

Signed-off-by: wangli <wangli858794774@gmail.com>

Author: Potabk

requirements-dev.txt

@@ -4,3 +4,4 @@ pytest >= 6.0
 pytest-asyncio
 lm-eval
 ray
+types-jsonschema

tests/singlecard/test_guided_decoding.py

@@ -0,0 +1,175 @@
+#
+# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
+# This file is a part of the vllm-ascend project.
+# Adapted from vllm/tests/entrypoints/llm/test_guided_generate.py
+# Copyright 2023 The vLLM team.
+#
+# 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 json
+import os
+import re
+
+import jsonschema
+import pytest
+from vllm.outputs import RequestOutput
+from vllm.sampling_params import GuidedDecodingParams, SamplingParams
+
+from tests.conftest import VllmRunner
+
+os.environ["PYTORCH_NPU_ALLOC_CONF"] = "max_split_size_mb:256"
+MODEL_NAME = "Qwen/Qwen2.5-1.5B-Instruct"
+GuidedDecodingBackendV0 = [
+    "outlines",
+    "lm-format-enforcer",
+    "xgrammar",
+]
+GuidedDecodingBackendV1 = ["xgrammar", "guidance:disable-any-whitespace"]
+GuidedDecodingBackend = list(
+    set(GuidedDecodingBackendV0 + GuidedDecodingBackendV1))
+
+
+@pytest.fixture(scope="module")
+def sample_regex():
+    return (r"((25[0-5]|(2[0-4]|1\d|[1-9]|)\d)\.){3}"
+            r"(25[0-5]|(2[0-4]|1\d|[1-9]|)\d)")
+
+
+@pytest.fixture(scope="module")
+def sample_json_schema():
+    return {
+        "type": "object",
+        "properties": {
+            "name": {
+                "type": "string"
+            },
+            "age": {
+                "type": "integer"
+            },
+            "skills": {
+                "type": "array",
+                "items": {
+                    "type": "string",
+                    "maxLength": 10
+                },
+                "minItems": 3
+            },
+            "work_history": {
+                "type": "array",
+                "items": {
+                    "type": "object",
+                    "properties": {
+                        "company": {
+                            "type": "string"
+                        },
+                        "duration": {
+                            "type": "number"
+                        },
+                        "position": {
+                            "type": "string"
+                        }
+                    },
+                    "required": ["company", "position"]
+                }
+            }
+        },
+        "required": ["name", "age", "skills", "work_history"]
+    }
+
+
+@pytest.mark.parametrize("guided_decoding_backend", GuidedDecodingBackend)
+def test_guided_json_completion(guided_decoding_backend: str,
+                                sample_json_schema):
+    if guided_decoding_backend == "xgrammar":
+        # xgrammar does not support json schema, will fall back to outlines, skip it
+        pytest.skip(
+            f"{guided_decoding_backend} will fall back to outlines, skip it")
+    if guided_decoding_backend not in GuidedDecodingBackendV0 and os.getenv(
+            "VLLM_USE_V1") == "0":
+        # guidance does not support on v0, skip it
+        pytest.skip(
+            f"{guided_decoding_backend} does not support on v0, skip it")
+    if guided_decoding_backend not in GuidedDecodingBackendV1 and os.getenv(
+            "VLLM_USE_V1") == "1":
+        pytest.skip(f"{guided_decoding_backend} does not support v1, skip it")
+
+    sampling_params = SamplingParams(
+        temperature=1.0,
+        max_tokens=1000,
+        guided_decoding=GuidedDecodingParams(json=sample_json_schema))
+    with VllmRunner(
+            MODEL_NAME,
+            seed=0,
+            dtype="auto",
+            guided_decoding_backend=guided_decoding_backend,
+    ) as vllm_model:
+        prompts = [
+            f"Give an example JSON for an employee profile "
+            f"that fits this schema: {sample_json_schema}"
+        ] * 2
+        inputs = vllm_model.get_inputs(prompts)
+        outputs = vllm_model.model.generate(inputs,
+                                            sampling_params=sampling_params)
+
+        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=sample_json_schema)
+
+
+@pytest.mark.parametrize("guided_decoding_backend", GuidedDecodingBackend)
+def test_guided_regex(guided_decoding_backend: str, sample_regex):
+    if guided_decoding_backend not in GuidedDecodingBackendV0 and os.getenv(
+            "VLLM_USE_V1") == "0":
+        # guidance does not support on v0, skip it
+        pytest.skip(
+            f"{guided_decoding_backend} does not support on v0, skip it")
+    if guided_decoding_backend not in GuidedDecodingBackendV1 and os.getenv(
+            "VLLM_USE_V1") == "1":
+        pytest.skip(f"{guided_decoding_backend} does not support v1, skip it")
+
+    sampling_params = SamplingParams(temperature=0.8,
+                                     top_p=0.95,
+                                     guided_decoding=GuidedDecodingParams(
+                                         regex=sample_regex, ))
+    with VllmRunner(
+            MODEL_NAME,
+            seed=0,
+            dtype="auto",
+            guided_decoding_backend=guided_decoding_backend,
+    ) as vllm_model:
+        prompts = [
+            f"Give an example IPv4 address with this regex: {sample_regex}"
+        ] * 2
+        inputs = vllm_model.get_inputs(prompts)
+        outputs = vllm_model.model.generate(inputs,
+                                            sampling_params=sampling_params)
+        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
+            print(generated_text)
+            assert generated_text is not None
+            assert re.fullmatch(".*", generated_text) is not None
+            print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")