chore: import upstream snapshot with attribution

python/ray/train/v2/api/exceptions.py

@@ -0,0 +1,49 @@
+from typing import Dict
+
+from ray.train.v2._internal.exceptions import RayTrainError
+from ray.util.annotations import PublicAPI
+
+
+@PublicAPI(stability="alpha")
+class TrainingFailedError(RayTrainError):
+    """Exception raised when training fails from a `trainer.fit()` call.
+    This is either :class:`ray.train.WorkerGroupError` or :class:`ray.train.ControllerError`.
+    """
+
+
+@PublicAPI(stability="alpha")
+class WorkerGroupError(TrainingFailedError):
+    """Exception raised from the worker group during training.
+
+    Args:
+        error_message: A human-readable error message describing the training worker failures.
+        worker_failures: A mapping from worker rank to the exception that
+            occurred on that worker during training.
+    """
+
+    def __init__(self, error_message: str, worker_failures: Dict[int, Exception]):
+        super().__init__("Training failed due to worker errors:\n" + error_message)
+        self._error_message = error_message
+        self.worker_failures = worker_failures
+
+    def __reduce__(self):
+        return (self.__class__, (self._error_message, self.worker_failures))
+
+
+@PublicAPI(stability="alpha")
+class ControllerError(TrainingFailedError):
+    """Exception raised when training fails due to a controller error.
+
+    Args:
+        controller_failure: The exception that occurred on the controller.
+    """
+
+    def __init__(self, controller_failure: Exception):
+        super().__init__(
+            "Training failed due to controller error:\n" + str(controller_failure)
+        )
+        self.controller_failure = controller_failure
+        self.with_traceback(controller_failure.__traceback__)
+
+    def __reduce__(self):
+        return (self.__class__, (self.controller_failure,))