[train] New persistence mode cleanup: Train internals

python/ray/train/_internal/backend_executor.py

@@ -8,18 +8,17 @@
 import ray._private.ray_constants as ray_constants
 from ray.data import Dataset
 from ray._private.ray_constants import env_integer
-from ray.air.config import CheckpointConfig
 from ray.exceptions import RayActorError
 from ray.train import DataConfig
 from ray.air.checkpoint import Checkpoint
 from ray.train._internal.session import (
-    TrainingResult,
+    _TrainingResult,
     TrialInfo,
     get_session,
     init_session,
     shutdown_session,
 )
-from ray.train._internal.storage import _use_storage_context, StorageContext
+from ray.train._internal.storage import StorageContext
 from ray.train._internal.utils import check_for_failure
 from ray.train._internal.worker_group import WorkerGroup
 from ray.train.backend import BackendConfig
@@ -28,7 +27,6 @@
     ENABLE_SHARE_CUDA_VISIBLE_DEVICES_ENV,
     TRAIN_ENABLE_WORKER_SPREAD_ENV,
     TRAIN_PLACEMENT_GROUP_TIMEOUT_S_ENV,
-    DISABLE_LAZY_CHECKPOINTING_ENV,
     ENABLE_SHARE_NEURON_CORES_ACCELERATOR_ENV,
 )
 from ray.util.placement_group import get_current_placement_group, remove_placement_group
@@ -96,7 +94,6 @@ def __init__(
         num_gpus_per_worker: float = 0,
         additional_resources_per_worker: Optional[Dict[str, float]] = None,
         max_retries: int = 3,
-        checkpoint_config: Optional[CheckpointConfig] = None,
     ):
         self._backend_config = backend_config
         self._backend = backend_config.backend_cls()
@@ -117,12 +114,6 @@ def __init__(
         self.worker_group = InactiveWorkerGroup()
         self.dataset_shards = None
 
-        self._checkpoint_keep_all_ranks = (
-            checkpoint_config and checkpoint_config._checkpoint_keep_all_ranks
-        )
-        self._checkpoint_upload_from_workers = (
-            checkpoint_config and checkpoint_config._checkpoint_upload_from_workers
-        )
         self._resource_configs = [
             ResourceConfig(
                 ray_constants.NEURON_CORES,
@@ -456,7 +447,6 @@ def start_training(
         use_detailed_autofilled_metrics = env_integer(
             ENABLE_DETAILED_AUTOFILLED_METRICS_ENV, 0
         )
-        use_lazy_checkpointing = not env_integer(DISABLE_LAZY_CHECKPOINTING_ENV, 0)
 
         # First initialize the session.
         def initialize_session(
@@ -470,8 +460,6 @@ def initialize_session(
             checkpoint,
             dataset_shard,
             metadata,
-            checkpoint_keep_all_ranks,
-            checkpoint_upload_from_workers,
             storage,
         ):
             try:
@@ -487,9 +475,6 @@ def initialize_session(
                     metadata=metadata,
                     checkpoint=checkpoint,
                     detailed_autofilled_metrics=use_detailed_autofilled_metrics,
-                    enable_lazy_checkpointing=use_lazy_checkpointing,
-                    checkpoint_keep_all_ranks=checkpoint_keep_all_ranks,
-                    checkpoint_upload_from_workers=(checkpoint_upload_from_workers),
                     storage=storage,
                 )
             except ValueError:
@@ -532,10 +517,6 @@ def initialize_session(
                     dataset_shard=self.dataset_shards[index],
                     metadata=metadata,
                     checkpoint=checkpoint,
-                    checkpoint_keep_all_ranks=self._checkpoint_keep_all_ranks,
-                    checkpoint_upload_from_workers=(
-                        self._checkpoint_upload_from_workers
-                    ),
                     storage=storage,
                 )
             )
@@ -554,15 +535,15 @@ def train_async():
 
         self.worker_group.execute_async(train_async)
 
-    def get_next_results(self) -> Optional[List[TrainingResult]]:
-        """Fetches the next ``TrainingResult`` from each worker.
+    def get_next_results(self) -> Optional[List[_TrainingResult]]:
+        """Fetches the next ``_TrainingResult`` from each worker.
 
-        Each ``TrainingResult`` is expected to correspond to the same step from
-        each worker (e.g. the same call to ``session.report()``).
+        Each ``_TrainingResult`` is expected to correspond to the same step from
+        each worker (e.g. the same call to ``train.report()``).
 
         Returns:
-            A list of ``TrainingResult``s with the same
-            ``TrainingResultType``, or ``None`` if there are no more results.
+            A list of ``_TrainingResult``s or ``None`` if there are no more results
+            since the training function has exited on all workers.
         """
 
         def get_next():
@@ -598,29 +579,8 @@ def get_next():
                 # Return None if all results are None.
                 return None
 
-        if not _use_storage_context():
-            first_result = results[0]
-            result_type = first_result.type
-            if any(r.type != result_type for r in results):
-                raise RuntimeError(
-                    "Some workers returned results with "
-                    "different types. Make sure that "
-                    "`session.report()` are called the "
-                    "same number of times on all workers."
-                )
-
         return results
 
-    def _set_legacy_checkpoint_uri(self, uri: str):
-        """Tell remote sessions where to upload the chekcpoint."""
-
-        def set_uri():
-            session = _get_session("_set_legacy_checkpoint_uri")
-            session._set_legacy_checkpoint_uri(uri)
-
-        futures = self.worker_group.execute_async(set_uri)
-        self.get_with_failure_handling(futures)
-
     def pause_reporting(self):
         """Disable workers from enqueuing results from ``session.report()``.