Refined FSDP saving logic and error messaging when path exists (#18884)

src/lightning/fabric/CHANGELOG.md

@@ -35,6 +35,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Fixed false-positive warnings about method calls on the Fabric-wrapped module ([#18819](https://github.com/Lightning-AI/lightning/pull/18819))
 
 
+- Refined the FSDP saving logic and error messaging when path exists ([#18884](https://github.com/Lightning-AI/lightning/pull/18884))
+
+
 ## [2.1.0] - 2023-10-11
 
 ### Added

src/lightning/fabric/strategies/fsdp.py

@@ -11,7 +11,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-import os
+import shutil
 from contextlib import ExitStack
 from datetime import timedelta
 from functools import partial
@@ -432,8 +432,8 @@ def save_checkpoint(
 
         # broadcast the path from rank 0 to ensure all the states are saved in a common path
         path = Path(self.broadcast(path))
-        if path.is_dir() and os.listdir(path):
-            raise FileExistsError(f"The checkpoint directory already exists and is not empty: {path}")
+        if path.is_dir() and self._state_dict_type == "full" and not _is_sharded_checkpoint(path):
+            raise IsADirectoryError(f"The checkpoint path exists and is a directory: {path}")
 
         from torch.distributed.checkpoint import FileSystemWriter, save_state_dict
         from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
@@ -454,7 +454,10 @@ def save_checkpoint(
         module = modules[0]
 
         if self._state_dict_type == "sharded":
+            if path.is_file():
+                path.unlink()
             path.mkdir(parents=True, exist_ok=True)
+
             state_dict_ctx = _get_sharded_state_dict_context(module)
 
             # replace the modules and optimizer objects in the state with their local state dict
@@ -483,6 +486,9 @@ def save_checkpoint(
                 torch.save(metadata, path / _METADATA_FILENAME)
 
         elif self._state_dict_type == "full":
+            if _is_sharded_checkpoint(path):
+                shutil.rmtree(path)
+
             state_dict_ctx = _get_full_state_dict_context(module, world_size=self.world_size)
             full_state: Dict[str, Any] = {}
             with state_dict_ctx:

src/lightning/pytorch/CHANGELOG.md

@@ -39,6 +39,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Fixed an issue saving the `last.ckpt` file when using `ModelCheckpoint` on a remote filesystem and no logger is used ([#18867](https://github.com/Lightning-AI/lightning/issues/18867))
 
 
+- Refined the FSDP saving logic and error messaging when path exists ([#18884](https://github.com/Lightning-AI/lightning/pull/18884))
+
+
 ## [2.1.0] - 2023-10-11
 
 ### Added

src/lightning/pytorch/strategies/fsdp.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import logging
-import os
+import shutil
 from contextlib import contextmanager, nullcontext
 from datetime import timedelta
 from pathlib import Path
@@ -522,12 +522,14 @@ def save_checkpoint(
             )
 
         path = Path(self.broadcast(filepath))
-        if path.is_dir() and os.listdir(path):
-            raise FileExistsError(f"The checkpoint directory already exists and is not empty: {path}")
+        if path.is_dir() and self._state_dict_type == "full" and not _is_sharded_checkpoint(path):
+            raise IsADirectoryError(f"The checkpoint path exists and is a directory: {path}")
 
         if self._state_dict_type == "sharded":
             from torch.distributed.checkpoint import FileSystemWriter, save_state_dict
 
+            if path.is_file():
+                path.unlink()
             path.mkdir(parents=True, exist_ok=True)
 
             converted_state = {"model": checkpoint.pop("state_dict")}
@@ -542,6 +544,8 @@ def save_checkpoint(
             if self.global_rank == 0:
                 torch.save(checkpoint, path / _METADATA_FILENAME)
         elif self._state_dict_type == "full":
+            if _is_sharded_checkpoint(path):
+                shutil.rmtree(path)
             return super().save_checkpoint(checkpoint=checkpoint, filepath=path)
         else:
             raise ValueError(f"Unknown state_dict_type: {self._state_dict_type}")

tests/tests_fabric/strategies/test_fsdp.py

@@ -27,6 +27,7 @@
     _FSDPBackwardSyncControl,
     _get_full_state_dict_context,
     _has_meta_device_parameters,
+    _is_sharded_checkpoint,
 )
 from lightning.fabric.utilities.imports import _TORCH_GREATER_EQUAL_2_1
 from torch.distributed.fsdp.fully_sharded_data_parallel import CPUOffload, FullyShardedDataParallel, MixedPrecision
@@ -275,15 +276,61 @@ def test_fsdp_save_checkpoint_storage_options(tmp_path):
 
 
 @RunIf(min_torch="2.0.0")
+@mock.patch("torch.distributed.checkpoint.save_state_dict", return_value=MagicMock())
 @mock.patch("lightning.fabric.strategies.fsdp.FSDPStrategy.broadcast", lambda _, x: x)
-def test_fsdp_save_checkpoint_folder_exists(tmp_path):
-    path = tmp_path / "exists"
+@mock.patch("lightning.fabric.strategies.fsdp._get_full_state_dict_context", return_value=MagicMock())
+@mock.patch("lightning.fabric.strategies.fsdp._get_sharded_state_dict_context", return_value=MagicMock())
+@mock.patch("lightning.fabric.strategies.fsdp.torch.save", return_value=Mock())
+@mock.patch("lightning.fabric.strategies.fsdp.shutil", return_value=MagicMock())
+def test_fsdp_save_checkpoint_path_exists(shutil_mock, torch_save_mock, __, ___, ____, tmp_path):
+    strategy = FSDPStrategy(state_dict_type="full")
+
+    # state_dict_type='full', path exists, path is not a sharded checkpoint: error
+    path = tmp_path / "not-empty"
     path.mkdir()
     (path / "file").touch()
-    strategy = FSDPStrategy()
-    with pytest.raises(FileExistsError, match="exists and is not empty"):
+    assert not _is_sharded_checkpoint(path)
+    with pytest.raises(IsADirectoryError, match="exists and is a directory"):
         strategy.save_checkpoint(path=path, state=Mock())
 
+    # state_dict_type='full', path exists, path is a sharded checkpoint: no error (overwrite)
+    path = tmp_path / "sharded-checkpoint"
+    path.mkdir()
+    (path / "meta.pt").touch()
+    assert _is_sharded_checkpoint(path)
+    model = Mock(spec=FullyShardedDataParallel)
+    model.modules.return_value = [model]
+    strategy.save_checkpoint(path=path, state={"model": model})
+    shutil_mock.rmtree.assert_called_once_with(path)
+
+    # state_dict_type='full', path exists, path is a file: no error (overwrite)
+    path = tmp_path / "file.pt"
+    path.touch()
+    model = Mock(spec=FullyShardedDataParallel)
+    model.modules.return_value = [model]
+    torch_save_mock.reset_mock()
+    strategy.save_checkpoint(path=path, state={"model": model})
+    torch_save_mock.assert_called_once()
+
+    strategy = FSDPStrategy(state_dict_type="sharded")
+
+    # state_dict_type='sharded', path exists, path is a folder: no error (overwrite)
+    path = tmp_path / "not-empty-2"
+    path.mkdir()
+    (path / "file").touch()
+    model = Mock(spec=FullyShardedDataParallel)
+    model.modules.return_value = [model]
+    strategy.save_checkpoint(path=path, state={"model": model})
+    assert (path / "file").exists()
+
+    # state_dict_type='sharded', path exists, path is a file: no error (overwrite)
+    path = tmp_path / "file-2.pt"
+    path.touch()
+    model = Mock(spec=FullyShardedDataParallel)
+    model.modules.return_value = [model]
+    strategy.save_checkpoint(path=path, state={"model": model})
+    assert path.is_dir()
+
 
 @RunIf(min_torch="2.0.0")
 @mock.patch("lightning.fabric.strategies.fsdp.FSDPStrategy.broadcast", lambda _, x: x)

tests/tests_pytorch/strategies/test_fsdp.py

@@ -13,6 +13,7 @@
 import torch
 import torch.nn as nn
 from lightning.fabric.plugins.environments import LightningEnvironment
+from lightning.fabric.strategies.fsdp import _is_sharded_checkpoint
 from lightning.fabric.utilities.imports import (
     _TORCH_GREATER_EQUAL_2_0,
     _TORCH_GREATER_EQUAL_2_1,
@@ -760,14 +761,61 @@ def test_save_checkpoint_storage_options(tmp_path):
         strategy.save_checkpoint(filepath=tmp_path, checkpoint=Mock(), storage_options=Mock())
 
 
+@RunIf(min_torch="2.0.0")
+@mock.patch("torch.distributed.checkpoint.save_state_dict", return_value=MagicMock())
 @mock.patch("lightning.pytorch.strategies.fsdp.FSDPStrategy.broadcast", lambda _, x: x)
-def test_save_checkpoint_folder_exists(tmp_path):
-    path = tmp_path / "exists"
+@mock.patch("lightning.pytorch.strategies.fsdp._get_full_state_dict_context", return_value=MagicMock())
+@mock.patch("lightning.pytorch.strategies.fsdp._get_sharded_state_dict_context", return_value=MagicMock())
+@mock.patch("lightning.fabric.plugins.io.torch_io._atomic_save", return_value=Mock())
+@mock.patch("lightning.pytorch.strategies.fsdp.shutil", return_value=MagicMock())
+def test_fsdp_save_checkpoint_path_exists(shutil_mock, torch_save_mock, __, ___, ____, tmp_path):
+    strategy = FSDPStrategy(state_dict_type="full")
+
+    # state_dict_type='full', path exists, path is not a sharded checkpoint: error
+    path = tmp_path / "not-empty"
     path.mkdir()
     (path / "file").touch()
-    strategy = FSDPStrategy()
-    with pytest.raises(FileExistsError, match="exists and is not empty"):
-        strategy.save_checkpoint(filepath=tmp_path, checkpoint=Mock())
+    assert not _is_sharded_checkpoint(path)
+    with pytest.raises(IsADirectoryError, match="exists and is a directory"):
+        strategy.save_checkpoint(Mock(), filepath=path)
+
+    # state_dict_type='full', path exists, path is a sharded checkpoint: no error (overwrite)
+    path = tmp_path / "sharded-checkpoint"
+    path.mkdir()
+    (path / "meta.pt").touch()
+    assert _is_sharded_checkpoint(path)
+    model = Mock(spec=FullyShardedDataParallel)
+    model.modules.return_value = [model]
+    strategy.save_checkpoint(Mock(), filepath=path)
+    shutil_mock.rmtree.assert_called_once_with(path)
+
+    # state_dict_type='full', path exists, path is a file: no error (overwrite)
+    path = tmp_path / "file.pt"
+    path.touch()
+    model = Mock(spec=FullyShardedDataParallel)
+    model.modules.return_value = [model]
+    torch_save_mock.reset_mock()
+    strategy.save_checkpoint(Mock(), filepath=path)
+    torch_save_mock.assert_called_once()
+
+    strategy = FSDPStrategy(state_dict_type="sharded")
+
+    # state_dict_type='sharded', path exists, path is a folder: no error (overwrite)
+    path = tmp_path / "not-empty-2"
+    path.mkdir()
+    (path / "file").touch()
+    model = Mock(spec=FullyShardedDataParallel)
+    model.modules.return_value = [model]
+    strategy.save_checkpoint({"state_dict": {}, "optimizer_states": {"": {}}}, filepath=path)
+    assert (path / "file").exists()
+
+    # state_dict_type='sharded', path exists, path is a file: no error (overwrite)
+    path = tmp_path / "file-2.pt"
+    path.touch()
+    model = Mock(spec=FullyShardedDataParallel)
+    model.modules.return_value = [model]
+    strategy.save_checkpoint({"state_dict": {}, "optimizer_states": {"": {}}}, filepath=path)
+    assert path.is_dir()
 
 
 @mock.patch("lightning.pytorch.strategies.fsdp.FSDPStrategy.broadcast", lambda _, x: x)