chore: import upstream snapshot with attribution

doc/source/llm/doc_code/serve/qwen/llm_config_example.yaml

@@ -0,0 +1,23 @@
+# config.yaml
+applications:
+- args:
+    llm_configs:
+        - model_loading_config:
+            model_id: qwen-0.5b
+            model_source: Qwen/Qwen2.5-0.5B-Instruct
+          accelerator_type: A10G
+          deployment_config:
+            autoscaling_config:
+                min_replicas: 1
+                max_replicas: 2
+        - model_loading_config:
+            model_id: qwen-1.5b
+            model_source: Qwen/Qwen2.5-1.5B-Instruct
+          accelerator_type: A10G
+          deployment_config:
+            autoscaling_config:
+                min_replicas: 1
+                max_replicas: 2
+  import_path: ray.serve.llm:build_openai_app
+  name: llm_app
+  route_prefix: "/" 

doc/source/llm/doc_code/serve/qwen/llm_yaml_config_example.py

@@ -0,0 +1,53 @@
+"""
+This file serves as a documentation example and CI test for YAML config deployment.
+
+Structure:
+1. Monkeypatch setup: Ensures serve.run is non-blocking and removes accelerator requirements for CI testing.
+2. Load YAML config and convert to Python using build_openai_app
+3. Test validation (deployment status polling + cleanup)
+"""
+
+import time
+import os
+import yaml
+from ray import serve
+from ray.serve.schema import ApplicationStatus
+from ray.serve._private.constants import SERVE_DEFAULT_APP_NAME
+from ray.serve import llm
+
+
+config_path = os.path.join(os.path.dirname(__file__), "llm_config_example.yaml")
+with open(config_path, "r") as f:
+    config_dict = yaml.safe_load(f)
+
+llm_configs = config_dict["applications"][0]["args"]["llm_configs"]
+for config in llm_configs:
+    config.pop("accelerator_type", None)
+    # Disable compile cache to avoid cache corruption in CI
+    if "runtime_env" not in config:
+        config["runtime_env"] = {}
+    if "env_vars" not in config["runtime_env"]:
+        config["runtime_env"]["env_vars"] = {}
+    config["runtime_env"]["env_vars"]["VLLM_DISABLE_COMPILE_CACHE"] = "1"
+
+app = llm.build_openai_app({"llm_configs": llm_configs})
+serve.run(app, blocking=False)
+
+status = ApplicationStatus.NOT_STARTED
+timeout_seconds = 180
+start_time = time.time()
+
+while (
+    status != ApplicationStatus.RUNNING and time.time() - start_time < timeout_seconds
+):
+    status = serve.status().applications[SERVE_DEFAULT_APP_NAME].status
+
+    if status in [ApplicationStatus.DEPLOY_FAILED, ApplicationStatus.UNHEALTHY]:
+        raise AssertionError(f"Deployment failed with status: {status}")
+
+    time.sleep(1)
+
+if status != ApplicationStatus.RUNNING:
+    raise AssertionError(
+        f"Deployment failed to reach RUNNING status within {timeout_seconds}s. Current status: {status}"
+    )

doc/source/llm/doc_code/serve/qwen/qwen_example.py

@@ -0,0 +1,89 @@
+"""
+This file serves as a documentation example and CI test.
+
+Structure:
+1. Monkeypatch setup: Ensures serve.run is non-blocking and removes accelerator requirements for CI testing.
+2. Docs example (between __qwen_example_start/end__): Embedded in Sphinx docs via literalinclude.
+3. Test validation (deployment status polling + cleanup)
+"""
+
+import time
+from ray import serve
+from ray.serve.schema import ApplicationStatus
+from ray.serve._private.constants import SERVE_DEFAULT_APP_NAME
+from ray.serve import llm
+
+_original_serve_run = serve.run
+_original_build_openai_app = llm.build_openai_app
+
+
+def _non_blocking_serve_run(app, **kwargs):
+    """Forces blocking=False for testing"""
+    kwargs["blocking"] = False
+    return _original_serve_run(app, **kwargs)
+
+
+def _testing_build_openai_app(llm_serving_args):
+    """Removes accelerator requirements for testing"""
+    for config in llm_serving_args["llm_configs"]:
+        config.accelerator_type = None
+        # Disable compile cache to avoid cache corruption in CI
+        if not config.runtime_env:
+            config.runtime_env = {}
+        if "env_vars" not in config.runtime_env:
+            config.runtime_env["env_vars"] = {}
+        config.runtime_env["env_vars"]["VLLM_DISABLE_COMPILE_CACHE"] = "1"
+
+    return _original_build_openai_app(llm_serving_args)
+
+
+serve.run = _non_blocking_serve_run
+llm.build_openai_app = _testing_build_openai_app
+
+# __qwen_example_start__
+from ray import serve
+from ray.serve.llm import LLMConfig, build_openai_app
+
+llm_config = LLMConfig(
+    model_loading_config={
+        "model_id": "qwen-0.5b",
+        "model_source": "Qwen/Qwen2.5-0.5B-Instruct",
+    },
+    deployment_config={
+        "autoscaling_config": {
+            "min_replicas": 1,
+            "max_replicas": 2,
+        }
+    },
+    # Pass the desired accelerator type (e.g. A10G, L4, etc.)
+    accelerator_type="A10G",
+    # You can customize the engine arguments (e.g. vLLM engine kwargs)
+    engine_kwargs={
+        "tensor_parallel_size": 2,
+    },
+)
+
+app = build_openai_app({"llm_configs": [llm_config]})
+serve.run(app, blocking=True)
+# __qwen_example_end__
+
+status = ApplicationStatus.NOT_STARTED
+timeout_seconds = 180
+start_time = time.time()
+
+while (
+    status != ApplicationStatus.RUNNING and time.time() - start_time < timeout_seconds
+):
+    status = serve.status().applications[SERVE_DEFAULT_APP_NAME].status
+
+    if status in [ApplicationStatus.DEPLOY_FAILED, ApplicationStatus.UNHEALTHY]:
+        raise AssertionError(f"Deployment failed with status: {status}")
+
+    time.sleep(1)
+
+if status != ApplicationStatus.RUNNING:
+    raise AssertionError(
+        f"Deployment failed to reach RUNNING status within {timeout_seconds}s. Current status: {status}"
+    )
+
+serve.shutdown()