[BugFix] Avoid unnecessary Ray import warnings

Currently the logs are polluted with multiple Ray import warnings even in cases where Ray is not being used. There should be an error only in the case that Ray is configured and/or required but not available, and in that case it should be fatal.

In our own builds/tests this was also interfering with the subprocess-based custom all-reduce p2p check recently introduced in vllm-project#5669.

vllm/config.py

@@ -655,11 +655,13 @@ def __init__(
 
             from vllm.executor import ray_utils
             backend = "mp"
-            ray_found = ray_utils.ray is not None
+            ray_found = ray_utils.ray_is_available()
             if cuda_device_count_stateless() < self.world_size:
                 if not ray_found:
                     raise ValueError("Unable to load Ray which is "
-                                     "required for multi-node inference")
+                                     "required for multi-node inference, "
+                                     "please install Ray with `pip install "
+                                     "ray`.") from ray_utils.ray_import_err
                 backend = "ray"
             elif ray_found:
                 if self.placement_group:
@@ -691,6 +693,9 @@ def _verify_args(self) -> None:
             raise ValueError(
                 "Unrecognized distributed executor backend. Supported values "
                 "are 'ray' or 'mp'.")
+        if self.distributed_executor_backend == "ray":
+            from vllm.executor import ray_utils
+            ray_utils.assert_ray_available()
         if not self.disable_custom_all_reduce and self.world_size > 1:
             if is_hip():
                 self.disable_custom_all_reduce = True

vllm/engine/async_llm_engine.py

@@ -378,6 +378,11 @@ def from_engine_args(
         """Creates an async LLM engine from the engine arguments."""
         # Create the engine configs.
         engine_config = engine_args.create_engine_config()
+
+        if engine_args.engine_use_ray:
+            from vllm.executor import ray_utils
+            ray_utils.assert_ray_available()
+
         distributed_executor_backend = (
             engine_config.parallel_config.distributed_executor_backend)
 

vllm/executor/ray_utils.py

@@ -42,14 +42,26 @@ def execute_model_compiled_dag_remote(self, ignored):
             output = pickle.dumps(output)
             return output
 
+    ray_import_err = None
+
 except ImportError as e:
-    logger.warning(
-        "Failed to import Ray with %r. For multi-node inference, "
-        "please install Ray with `pip install ray`.", e)
     ray = None  # type: ignore
+    ray_import_err = e
     RayWorkerWrapper = None  # type: ignore
 
 
+def ray_is_available() -> bool:
+    """Returns True if Ray is available."""
+    return ray is not None
+
+
+def assert_ray_available():
+    """Raise an exception if Ray is not available."""
+    if ray is None:
+        raise ValueError("Failed to import Ray, please install Ray with "
+                         "`pip install ray`.") from ray_import_err
+
+
 def initialize_ray_cluster(
     parallel_config: ParallelConfig,
     ray_address: Optional[str] = None,
@@ -65,10 +77,7 @@ def initialize_ray_cluster(
         ray_address: The address of the Ray cluster. If None, uses
             the default Ray cluster address.
     """
-    if ray is None:
-        raise ImportError(
-            "Ray is not installed. Please install Ray to use multi-node "
-            "serving.")
+    assert_ray_available()
 
     # Connect to a ray cluster.
     if is_hip() or is_xpu():