Add back-compatibility for checkpoint io plugins in pl/plugins/io (#14519)

src/lightning_lite/plugins/io/torch_plugin.py

@@ -15,7 +15,6 @@ import logging
 import os
 from typing import Any, Callable, Dict, Optional
 
-import pytorch_lightning as pl
 from lightning_lite.plugins.io.checkpoint_plugin import CheckpointIO
 from lightning_lite.utilities.cloud_io import atomic_save, get_filesystem
 from lightning_lite.utilities.cloud_io import load as pl_load
@@ -53,9 +52,10 @@ class TorchCheckpointIO(CheckpointIO):
             # write the checkpoint dictionary on the file
             atomic_save(checkpoint, path)
         except AttributeError as err:
-            # todo (sean): is this try catch necessary still?
+            # todo: is this try catch necessary still?
             # https://github.com/Lightning-AI/lightning/pull/431
-            key = pl.LightningModule.CHECKPOINT_HYPER_PARAMS_KEY
+            # TODO(lite): Lite doesn't support hyperparameters in the checkpoint, so this should be refactored
+            key = "hyper_parameters"
             checkpoint.pop(key, None)
             rank_zero_warn(f"Warning, `{key}` dropped from checkpoint. An attribute is not picklable: {err}")
             atomic_save(checkpoint, path)

src/lightning_lite/plugins/io/xla_plugin.py

@@ -18,8 +18,8 @@ from lightning_utilities.core.apply_func import apply_to_collection
 
 from lightning_lite.plugins.io.torch_plugin import TorchCheckpointIO
 from lightning_lite.utilities.cloud_io import get_filesystem
+from lightning_lite.utilities.imports import _OMEGACONF_AVAILABLE, _TPU_AVAILABLE
 from lightning_lite.utilities.types import _PATH
-from pytorch_lightning.utilities import _OMEGACONF_AVAILABLE, _TPU_AVAILABLE
 
 if _TPU_AVAILABLE:
     import torch_xla.core.xla_model as xm

src/pytorch_lightning/plugins/io/checkpoint_plugin.py

@@ -0,0 +1,16 @@
+# Copyright The PyTorch Lightning team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# 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.
+
+# For backward-compatibility
+from lightning_lite.plugins.io.checkpoint_plugin import CheckpointIO  # noqa: F401

src/pytorch_lightning/plugins/io/torch_plugin.py

@@ -0,0 +1,16 @@
+# Copyright The PyTorch Lightning team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# 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.
+
+# For backward-compatibility
+from lightning_lite.plugins.io.torch_plugin import TorchCheckpointIO  # noqa: F401

src/pytorch_lightning/plugins/io/xla_plugin.py

@@ -0,0 +1,16 @@
+# Copyright The PyTorch Lightning team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# 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.
+
+# For backward-compatibility
+from lightning_lite.plugins.io.xla_plugin import XLACheckpointIO  # noqa: F401

tests/tests_pytorch/checkpointing/test_trainer_checkpoint.py

@@ -90,7 +90,7 @@ def test_trainer_save_checkpoint_storage_options(tmpdir):
     instance_path = tmpdir + "/path.ckpt"
     instance_storage_options = "my instance storage options"
 
-    with mock.patch("pytorch_lightning.plugins.io.torch_plugin.TorchCheckpointIO.save_checkpoint") as io_mock:
+    with mock.patch("lightning_lite.plugins.io.torch_plugin.TorchCheckpointIO.save_checkpoint") as io_mock:
         trainer.save_checkpoint(instance_path, storage_options=instance_storage_options)
         io_mock.assert_called_with(ANY, instance_path, storage_options=instance_storage_options)
         trainer.save_checkpoint(instance_path)

tests/tests_pytorch/deprecated_api/test_remove_1-10.py

@@ -19,13 +19,13 @@ import pytest
 import torch
 from torch.utils.data import DataLoader
 
-from lightning_lite.plugins.environments import LightningEnvironment
 from pytorch_lightning import Trainer
 from pytorch_lightning.core.mixins.device_dtype_mixin import DeviceDtypeModuleMixin
 from pytorch_lightning.demos.boring_classes import BoringModel, RandomDataset
 from pytorch_lightning.overrides import LightningDistributedModule, LightningParallelModule
 from pytorch_lightning.overrides.base import unwrap_lightning_module
 from pytorch_lightning.overrides.fairscale import LightningShardedDataParallel, unwrap_lightning_module_sharded
+from pytorch_lightning.plugins.environments import LightningEnvironment
 from pytorch_lightning.strategies.bagua import LightningBaguaModule
 from pytorch_lightning.strategies.deepspeed import LightningDeepSpeedModule
 from pytorch_lightning.strategies.ipu import LightningIPUModule