[Train] Fix lightning trainer devices setting

python/ray/train/lightning/_lightning_utils.py

@@ -21,12 +21,21 @@
 LIGHTNING_REPORT_STAGE_KEY = "_report_on"
 
 
+def get_worker_root_device():
+    """Get the first torch device of the current worker if there are multiple."""
+    devices = ray.train.torch.get_device()
+    if isinstance(devices, list):
+        return devices[0]
+    else:
+        return devices
+
+
 class RayDDPStrategy(DDPStrategy):
     """Subclass of DDPStrategy to ensure compatibility with Ray orchestration."""
 
     @property
     def root_device(self) -> torch.device:
-        return ray.train.torch.get_device()
+        return get_worker_root_device()
 
     @property
     def distributed_sampler_kwargs(self) -> Dict[str, Any]:

python/ray/train/lightning/lightning_trainer.py

@@ -1,5 +1,4 @@
 import os
-import ray
 from inspect import isclass
 from typing import Any, Dict, Optional, Type
 import pytorch_lightning as pl
@@ -26,6 +25,7 @@
     RayEnvironment,
     RayDataModule,
     RayModelCheckpoint,
+    get_worker_root_device,
 )
 
 
@@ -503,7 +503,7 @@ def _lightning_train_loop_per_worker(config):
 
     # Setup trainer's parallel devices
     if trainer_config.get("accelerator", None) == "gpu":
-        current_device = ray.train.torch.get_device()
+        current_device = get_worker_root_device()
         trainer_config["devices"] = [current_device.index]
 
     # Setup ray cluster environment info