Return bool from snapshot restore_from_latest

Summary:
As title, this indicates to callers if the states were restored or not. This allows callers to do logic like:

```
restored = TorchSnapshotSaver.restore_from_latest(...)

# no prior checkpoints, so initialize weights for the first attempt
if not restored:
    <initialization logic>
```

Reviewed By: daniellepintz

Differential Revision: D48207346

fbshipit-source-id: 6ea20dced7ee433aa9ee27f52998e3855a9f217e

Author: ananthsub

tests/framework/callbacks/test_torchsnapshot_saver.py

@@ -21,7 +21,11 @@
 
 from torchtnt.framework._test_utils import DummyTrainUnit, generate_random_dataloader
 from torchtnt.framework.auto_unit import AutoUnit
-from torchtnt.framework.callbacks import Lambda, TorchSnapshotSaver
+from torchtnt.framework.callbacks.lambda_callback import Lambda
+from torchtnt.framework.callbacks.torchsnapshot_saver import (
+    _get_latest_checkpoint_path,
+    TorchSnapshotSaver,
+)
 from torchtnt.framework.state import State
 from torchtnt.framework.train import train
 from torchtnt.utils.distributed import get_global_rank, PGWrapper
@@ -59,7 +63,6 @@ def test_save_every_n_train_steps(self) -> None:
             snapshot = TorchSnapshotSaver(
                 temp_dir,
                 save_every_n_train_steps=save_every_n_train_steps,
-                replicated=["**"],
             )
             # Artificially increase the step duration, otherwise torchsnapshot
             # doesn't have the time to save all snapshots and will skip some.
@@ -91,7 +94,6 @@ def test_save_every_n_train_epochs(self) -> None:
             snapshot = TorchSnapshotSaver(
                 temp_dir,
                 save_every_n_epochs=save_every_n_train_epochs,
-                replicated=["**"],
             )
             train(my_unit, dataloader, max_epochs=max_epochs, callbacks=[snapshot])
             self.assertTrue(
@@ -124,7 +126,6 @@ def test_save_restore(self) -> None:
             snapshot_cb = TorchSnapshotSaver(
                 temp_dir,
                 save_every_n_train_steps=save_every_n_train_steps,
-                replicated=["**"],
             )
             train(my_unit, dataloader, max_epochs=max_epochs, callbacks=[snapshot_cb])
 
@@ -152,7 +153,6 @@ def test_save_restore_dataloader_state(self) -> None:
             snapshot_cb = TorchSnapshotSaver(
                 temp_dir,
                 save_every_n_train_steps=save_every_n_train_steps,
-                replicated=["**"],
             )
             train(
                 my_unit,
@@ -204,18 +204,18 @@ def test_restore_from_latest(self) -> None:
             snapshot_cb = TorchSnapshotSaver(
                 temp_dir,
                 save_every_n_train_steps=save_every_n_train_steps,
-                replicated=["**"],
             )
             train(my_unit, dataloader, max_epochs=max_epochs, callbacks=[snapshot_cb])
 
             with mock.patch(
                 "torchtnt.framework.callbacks.torchsnapshot_saver.TorchSnapshotSaver.restore"
             ) as mock_restore:
-                snapshot_cb.restore_from_latest(temp_dir, my_unit)
+                restored = snapshot_cb.restore_from_latest(temp_dir, my_unit)
                 self.assertIn(
                     temp_dir + f"/epoch_{max_epochs}_step_{expected_steps_per_epoch}",
                     mock_restore.call_args.args,
                 )
+                self.assertTrue(restored)
 
     def test_restore_from_latest_empty_dir(self) -> None:
         input_dim = 2
@@ -226,17 +226,17 @@ def test_restore_from_latest_empty_dir(self) -> None:
             snapshot_cb = TorchSnapshotSaver(
                 temp_dir,
                 save_every_n_train_steps=save_every_n_train_steps,
-                replicated=["**"],
             )
 
             with self.assertLogs(level="WARNING") as log:
-                snapshot_cb.restore_from_latest(temp_dir, my_unit)
+                restored = snapshot_cb.restore_from_latest(temp_dir, my_unit)
                 self.assertEqual(
                     log.output,
                     [
                         f"WARNING:torchtnt.framework.callbacks.torchsnapshot_saver:Input dirpath doesn't contain any subdirectories: {temp_dir}"
                     ],
                 )
+                self.assertFalse(restored)
 
     def test_save_restore_no_train_progress(self) -> None:
         input_dim = 2
@@ -264,7 +264,6 @@ def test_save_restore_no_train_progress(self) -> None:
             snapshot_cb = TorchSnapshotSaver(
                 temp_dir,
                 save_every_n_train_steps=save_every_n_train_steps,
-                replicated=["**"],
             )
             train(my_unit, dataloader, max_epochs=max_epochs, callbacks=[snapshot_cb])
 
@@ -293,7 +292,6 @@ def test_save_on_train_end(self) -> None:
             self.assertFalse(os.path.exists(os.path.join(temp_dir, expected_path)))
             snapshot_cb = TorchSnapshotSaver(
                 temp_dir,
-                replicated=["**"],
             )
             train(my_unit, dataloader, max_epochs=max_epochs, callbacks=[snapshot_cb])
 
@@ -355,6 +353,7 @@ def _save_restore_fsdp() -> None:
         snapshot_cb = TorchSnapshotSaver(
             temp_dir,
             save_every_n_epochs=save_every_n_epochs,
+            replicated=["**"],
         )
         temp_dir = snapshot_cb.dirpath
         train(my_unit, dataloader, max_epochs=max_epochs, callbacks=[snapshot_cb])
@@ -396,14 +395,12 @@ def test_saver_invalid_args(self) -> None:
 
     def test_latest_checkpoint_path(self) -> None:
         with tempfile.TemporaryDirectory() as temp_dir:
-            self.assertIsNone(TorchSnapshotSaver.get_latest_checkpoint_path(temp_dir))
+            self.assertIsNone(_get_latest_checkpoint_path(temp_dir))
 
         with tempfile.TemporaryDirectory() as temp_dir:
             latest_path = os.path.join(temp_dir, "epoch_0_step_0")
             os.mkdir(latest_path)
-            self.assertEqual(
-                TorchSnapshotSaver.get_latest_checkpoint_path(temp_dir), latest_path
-            )
+            self.assertEqual(_get_latest_checkpoint_path(temp_dir), latest_path)
 
         with tempfile.TemporaryDirectory() as temp_dir:
             path_1 = os.path.join(temp_dir, "epoch_0_step_0")
@@ -414,9 +411,7 @@ def test_latest_checkpoint_path(self) -> None:
             os.mkdir(path_3)
             path_4 = os.path.join(temp_dir, "epoch_700")
             os.mkdir(path_4)
-            self.assertEqual(
-                TorchSnapshotSaver.get_latest_checkpoint_path(temp_dir), path_3
-            )
+            self.assertEqual(_get_latest_checkpoint_path(temp_dir), path_3)
 
     @unittest.skipUnless(
         torch.distributed.is_available(), reason="Torch distributed is needed to run"
@@ -436,7 +431,7 @@ def _latest_checkpoint_path_distributed() -> None:
             temp_dir = tempfile.mkdtemp()
         else:
             temp_dir = ""
-        tc.assertIsNone(TorchSnapshotSaver.get_latest_checkpoint_path(temp_dir))
+        tc.assertIsNone(_get_latest_checkpoint_path(temp_dir))
         if is_rank0:
             shutil.rmtree(temp_dir)  # delete temp directory
 
@@ -458,9 +453,7 @@ def _latest_checkpoint_path_distributed() -> None:
         path_container = [path_3] if is_rank0 else [None]
         pg.broadcast_object_list(path_container, 0)
         expected_path = path_container[0]
-        tc.assertEqual(
-            TorchSnapshotSaver.get_latest_checkpoint_path(temp_dir), expected_path
-        )
+        tc.assertEqual(_get_latest_checkpoint_path(temp_dir), expected_path)
 
         if is_rank0:
             shutil.rmtree(temp_dir)  # delete temp directory

torchtnt/framework/callbacks/torchsnapshot_saver.py

@@ -226,7 +226,6 @@ def restore(
             restore_train_progress: Whether to restore the training progress state.
             restore_eval_progress: Whether to restore the evaluation progress state.
             storage_options: Additional keyword options for the storage plugin to use, to be passed to `torchsnapshot.Snapshot <https://pytorch.org/torchsnapshot/stable/api_reference.html#torchsnapshot.Snapshot>`_. See each storage plugin's documentation for customizations.
-
         """
 
         _validate_snapshot_available()
@@ -268,7 +267,7 @@ def restore_from_latest(
         restore_train_progress: bool = True,
         restore_eval_progress: bool = True,
         storage_options: Optional[Dict[str, Any]] = None,
-    ) -> None:
+    ) -> bool:
         """
         Given a parent directory where checkpoints are saved, restore the snapshot state from the latest checkpoint in the directory.
 
@@ -282,10 +281,13 @@ def restore_from_latest(
             restore_train_progress: Whether to restore the training progress state.
             restore_eval_progress: Whether to restore the evaluation progress state.
             storage_options: Additional keyword options for the storage plugin to use, to be passed to `torchsnapshot.Snapshot <https://pytorch.org/torchsnapshot/stable/api_reference.html#torchsnapshot.Snapshot>`_. See each storage plugin's documentation for customizations.
+
+        Returns:
+            True if the latest snapshot directory was found and successfully restored, otherwise False.
         """
-        path = TorchSnapshotSaver.get_latest_checkpoint_path(dirpath)
+        path = _get_latest_checkpoint_path(dirpath)
         if path is None:
-            return
+            return False
         TorchSnapshotSaver.restore(
             path,
             unit,
@@ -294,27 +296,28 @@ def restore_from_latest(
             restore_eval_progress=restore_eval_progress,
             storage_options=storage_options,
         )
+        return True
 
-    @staticmethod
-    def get_latest_checkpoint_path(dirpath: str) -> Optional[str]:
-        """Given a parent directory where checkpoints are saved, return the latest checkpoint subdirectory."""
 
-        ret = None
-        rank = get_global_rank()
-        # Do all filesystem reads from rank 0 only
-        if rank == 0:
-            ret = _latest_checkpoint_path(dirpath)
+def _get_latest_checkpoint_path(dirpath: str) -> Optional[str]:
+    """Given a parent directory where checkpoints are saved, return the latest checkpoint subdirectory."""
 
-        # If not running in a distributed setting, return as is
-        if not (dist.is_available() and dist.is_initialized()):
-            return ret
-
-        # Otherwise, broadcast result from rank 0 to all ranks
-        pg = PGWrapper(dist.group.WORLD)
-        path_container = [ret] if rank == 0 else [None]
-        pg.broadcast_object_list(path_container, 0)
-        val = path_container[0]
-        return val
+    ret = None
+    rank = get_global_rank()
+    # Do all filesystem reads from rank 0 only
+    if rank == 0:
+        ret = _latest_checkpoint_path(dirpath)
+
+    # If not running in a distributed setting, return as is
+    if not (dist.is_available() and dist.is_initialized()):
+        return ret
+
+    # Otherwise, broadcast result from rank 0 to all ranks
+    pg = PGWrapper(dist.group.WORLD)
+    path_container = [ret] if rank == 0 else [None]
+    pg.broadcast_object_list(path_container, 0)
+    val = path_container[0]
+    return val
 
 
 def _latest_checkpoint_path(dirpath: str) -> Optional[str]: