Rename `_SupportsStateDict` --> `_Stateful` Protocol (#11469)

CHANGELOG.md

@@ -16,7 +16,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 
 - Fault Tolerant Manual
-    * Add `_SupportsStateDict` protocol to detect if classes are stateful ([#10646](https://github.com/PyTorchLightning/pytorch-lightning/pull/10646))
+    * Add `_Stateful` protocol to detect if classes are stateful ([#10646](https://github.com/PyTorchLightning/pytorch-lightning/pull/10646))
     * Add `_FaultTolerantMode` enum used to track different supported fault tolerant modes ([#10645](https://github.com/PyTorchLightning/pytorch-lightning/pull/10645))
     * Add a `_rotate_worker_indices` utility to reload the state according the latest worker ([#10647](https://github.com/PyTorchLightning/pytorch-lightning/pull/10647))
     * Add stateful workers ([#10674](https://github.com/PyTorchLightning/pytorch-lightning/pull/10674))

pytorch_lightning/core/optimizer.py

@@ -24,12 +24,7 @@ import pytorch_lightning as pl
 from pytorch_lightning.utilities import rank_zero_warn
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
 from pytorch_lightning.utilities.model_helpers import is_overridden
-from pytorch_lightning.utilities.types import (
-    _SupportsStateDict,
-    LRSchedulerConfig,
-    LRSchedulerTypeTuple,
-    ReduceLROnPlateau,
-)
+from pytorch_lightning.utilities.types import _Stateful, LRSchedulerConfig, LRSchedulerTypeTuple, ReduceLROnPlateau
 
 
 def do_nothing_closure() -> None:
@@ -338,7 +333,7 @@ def _configure_schedulers_manual_opt(schedulers: list) -> List[LRSchedulerConfig
 def _validate_scheduler_api(lr_scheduler_configs: List[LRSchedulerConfig], model: "pl.LightningModule") -> None:
     for config in lr_scheduler_configs:
         scheduler = config.scheduler
-        if not isinstance(scheduler, _SupportsStateDict):
+        if not isinstance(scheduler, _Stateful):
             raise TypeError(
                 f"The provided lr scheduler `{scheduler.__class__.__name__}` is invalid."
                 " It should have `state_dict` and `load_state_dict` methods defined."

pytorch_lightning/utilities/auto_restart.py

@@ -42,7 +42,7 @@ from pytorch_lightning.utilities.apply_func import apply_to_collection
 from pytorch_lightning.utilities.distributed import _collect_states_on_rank_zero
 from pytorch_lightning.utilities.enums import _FaultTolerantMode, AutoRestartBatchKeys
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
-from pytorch_lightning.utilities.types import _SupportsStateDict
+from pytorch_lightning.utilities.types import _Stateful
 
 
 class FastForwardSampler(Sampler):
@@ -581,17 +581,17 @@ def _reload_dataloader_state_dict_manual(dataloader: DataLoader, state_dict: Dic
     sampler_state = state_dict["state"][latest_worker_id].get("sampler_state", None)
     if sampler_state:
         # `sampler_state` keys contain all the DataLoader attribute names
-        # which matched `_SupportsStateDict` API interface while collecting the `state_dict`.
+        # which matched `_Stateful` API interface while collecting the `state_dict`.
         for dataloader_attr_name in sampler_state:
             obj = getattr(dataloader, dataloader_attr_name)
-            if not isinstance(obj, _SupportsStateDict):
+            if not isinstance(obj, _Stateful):
                 raise MisconfigurationException(
                     f"The DataLoader attribute {dataloader_attr_name}:{obj} should have a `load_state_dict` method."
                 )
 
             obj.load_state_dict(sampler_state[dataloader_attr_name])
 
-    if not isinstance(dataloader.dataset, _SupportsStateDict):
+    if not isinstance(dataloader.dataset, _Stateful):
         return
 
     dataset_state = {
@@ -645,9 +645,7 @@ class _StatefulDataLoaderIter:
     def _store_sampler_state(self) -> None:
         """This function is used to extract the sampler states if any."""
         sampler_state = {
-            k: v.state_dict()
-            for k, v in self._loader.__dict__.items()
-            if isinstance(v, _SupportsStateDict) and k != "dataset"
+            k: v.state_dict() for k, v in self._loader.__dict__.items() if isinstance(v, _Stateful) and k != "dataset"
         }
         self.__accumulate_state(sampler_state)
 

pytorch_lightning/utilities/types.py

@@ -49,8 +49,8 @@ _DEVICE = Union[torch.device, str, int]
 
 
 @runtime_checkable
-class _SupportsStateDict(Protocol):
-    """This class is used to detect if an object is stateful using `isinstance(obj, _SupportsStateDict)`."""
+class _Stateful(Protocol):
+    """This class is used to detect if an object is stateful using `isinstance(obj, _Stateful)`."""
 
     def state_dict(self) -> Dict[str, Any]:
         ...
@@ -62,7 +62,7 @@ class _SupportsStateDict(Protocol):
 # Inferred from `torch.optim.lr_scheduler.pyi`
 # Missing attributes were added to improve typing
 @runtime_checkable
-class _LRScheduler(_SupportsStateDict, Protocol):
+class _LRScheduler(_Stateful, Protocol):
     optimizer: Optimizer
 
     def __init__(self, optimizer: Optimizer, *args: Any, **kwargs: Any) -> None:
@@ -72,7 +72,7 @@ class _LRScheduler(_SupportsStateDict, Protocol):
 # Inferred from `torch.optim.lr_scheduler.pyi`
 # Missing attributes were added to improve typing
 @runtime_checkable
-class ReduceLROnPlateau(_SupportsStateDict, Protocol):
+class ReduceLROnPlateau(_Stateful, Protocol):
     in_cooldown: bool
     optimizer: Optimizer
 

tests/utilities/test_auto_restart.py

@@ -59,7 +59,6 @@ from pytorch_lightning.utilities.enums import _FaultTolerantMode, AutoRestartBat
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
 from pytorch_lightning.utilities.fetching import DataFetcher
 from pytorch_lightning.utilities.imports import _fault_tolerant_training
-from pytorch_lightning.utilities.types import _SupportsStateDict
 from tests.helpers.boring_model import BoringModel, RandomDataset
 from tests.helpers.runif import RunIf
 
@@ -1296,29 +1295,6 @@ def test_rotate_worker_indices():
         _rotate_worker_indices(state_dict, 2, 3)
 
 
-def test_supports_state_dict_protocol():
-    class StatefulClass:
-        def state_dict(self):
-            pass
-
-        def load_state_dict(self, state_dict):
-            pass
-
-    assert isinstance(StatefulClass(), _SupportsStateDict)
-
-    class NotStatefulClass:
-        def state_dict(self):
-            pass
-
-    assert not isinstance(NotStatefulClass(), _SupportsStateDict)
-
-    class NotStateful2Class:
-        def load_state_dict(self, state_dict):
-            pass
-
-    assert not isinstance(NotStateful2Class(), _SupportsStateDict)
-
-
 def test_fault_tolerant_mode_enum():
     with mock.patch.dict(os.environ, {"PL_FAULT_TOLERANT_TRAINING": "0"}):
         assert _FaultTolerantMode.DISABLED == _FaultTolerantMode.detect_current_mode()

tests/utilities/test_types.py

@@ -0,0 +1,37 @@
+# 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.
+from pytorch_lightning.utilities.types import _Stateful
+
+
+def test_stateful_protocol():
+    class StatefulClass:
+        def state_dict(self):
+            pass
+
+        def load_state_dict(self, state_dict):
+            pass
+
+    assert isinstance(StatefulClass(), _Stateful)
+
+    class NotStatefulClass:
+        def state_dict(self):
+            pass
+
+    assert not isinstance(NotStatefulClass(), _Stateful)
+
+    class NotStateful2Class:
+        def load_state_dict(self, state_dict):
+            pass
+
+    assert not isinstance(NotStateful2Class(), _Stateful)