Remove the deprecated tuning property and enums (#16379)

src/pytorch_lightning/CHANGELOG.md

@@ -87,6 +87,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 - Removed the automatic addition of a moving average of the `training_step` loss in the progress bar. Use `self.log("loss", ..., prog_bar=True)` instead. ([#16192](https://github.com/Lightning-AI/lightning/issues/16192))
 
+- Tuner removal
+  * Removed the deprecated `trainer.tuning` property ([#16379](https://github.com/Lightning-AI/lightning/pull/16379))
+  * Removed the deprecated `TrainerFn.TUNING` and `RunningStage.TUNING` enums ([#16379](https://github.com/Lightning-AI/lightning/pull/16379))
 
 ### Fixed
 

src/pytorch_lightning/callbacks/timer.py

@@ -95,8 +95,8 @@ class Timer(Callback):
         self._duration = duration.total_seconds() if duration is not None else None
         self._interval = interval
         self._verbose = verbose
-        self._start_time: Dict[RunningStage, Optional[float]] = {stage: None for stage in RunningStage._without_tune()}
-        self._end_time: Dict[RunningStage, Optional[float]] = {stage: None for stage in RunningStage._without_tune()}
+        self._start_time: Dict[RunningStage, Optional[float]] = {stage: None for stage in RunningStage}
+        self._end_time: Dict[RunningStage, Optional[float]] = {stage: None for stage in RunningStage}
         self._offset = 0
 
     def start_time(self, stage: str = RunningStage.TRAINING) -> Optional[float]:
@@ -161,7 +161,7 @@ class Timer(Callback):
         self._check_time_remaining(trainer)
 
     def state_dict(self) -> Dict[str, Any]:
-        return {"time_elapsed": {stage.value: self.time_elapsed(stage) for stage in RunningStage._without_tune()}}
+        return {"time_elapsed": {stage.value: self.time_elapsed(stage) for stage in RunningStage}}
 
     def load_state_dict(self, state_dict: Dict[str, Any]) -> None:
         time_elapsed = state_dict.get("time_elapsed", {})

src/pytorch_lightning/trainer/states.py

@@ -12,37 +12,10 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 from dataclasses import dataclass, field
-from enum import Enum, EnumMeta
-from typing import Any, List, Optional
+from typing import Optional
 
 from pytorch_lightning.utilities import LightningEnum
 from pytorch_lightning.utilities.enums import _FaultTolerantMode
-from pytorch_lightning.utilities.rank_zero import rank_zero_deprecation
-
-
-class _DeprecationManagingEnumMeta(EnumMeta):
-    """Enum that calls `deprecate()` whenever a member is accessed.
-
-    Adapted from: https://stackoverflow.com/a/62309159/208880
-    """
-
-    def __getattribute__(cls, name: str) -> Any:
-        obj = super().__getattribute__(name)
-        # ignore __dunder__ names -- prevents potential recursion errors
-        if not (name.startswith("__") and name.endswith("__")) and isinstance(obj, Enum):
-            obj.deprecate()
-        return obj
-
-    def __getitem__(cls, name: str) -> Any:
-        member: _DeprecationManagingEnumMeta = super().__getitem__(name)
-        member.deprecate()
-        return member
-
-    def __call__(cls, *args: Any, **kwargs: Any) -> Any:
-        obj = super().__call__(*args, **kwargs)
-        if isinstance(obj, Enum):
-            obj.deprecate()
-        return obj
 
 
 class TrainerStatus(LightningEnum):
@@ -58,7 +31,7 @@ class TrainerStatus(LightningEnum):
         return self in (self.FINISHED, self.INTERRUPTED)
 
 
-class TrainerFn(LightningEnum, metaclass=_DeprecationManagingEnumMeta):
+class TrainerFn(LightningEnum):
     """
     Enum for the user-facing functions of the :class:`~pytorch_lightning.trainer.trainer.Trainer`
     such as :meth:`~pytorch_lightning.trainer.trainer.Trainer.fit` and
@@ -69,21 +42,9 @@ class TrainerFn(LightningEnum, metaclass=_DeprecationManagingEnumMeta):
     VALIDATING = "validate"
     TESTING = "test"
     PREDICTING = "predict"
-    TUNING = "tune"
-
-    def deprecate(self) -> None:
-        if self == self.TUNING:
-            rank_zero_deprecation(
-                f"`TrainerFn.{self.name}` has been deprecated in v1.8.0 and will be removed in v2.0.0."
-            )
-
-    @classmethod
-    def _without_tune(cls) -> List["TrainerFn"]:
-        fns = [fn for fn in cls if fn != "tune"]
-        return fns
 
 
-class RunningStage(LightningEnum, metaclass=_DeprecationManagingEnumMeta):
+class RunningStage(LightningEnum):
     """Enum for the current running stage.
 
     This stage complements :class:`TrainerFn` by specifying the current running stage for each function.
@@ -93,7 +54,6 @@ class RunningStage(LightningEnum, metaclass=_DeprecationManagingEnumMeta):
         - ``TrainerFn.VALIDATING`` - ``RunningStage.VALIDATING``
         - ``TrainerFn.TESTING`` - ``RunningStage.TESTING``
         - ``TrainerFn.PREDICTING`` - ``RunningStage.PREDICTING``
-        - ``TrainerFn.TUNING`` - ``RunningStage.{TUNING,SANITY_CHECKING,TRAINING,VALIDATING}``
     """
 
     TRAINING = "train"
@@ -101,7 +61,6 @@ class RunningStage(LightningEnum, metaclass=_DeprecationManagingEnumMeta):
     VALIDATING = "validate"
     TESTING = "test"
     PREDICTING = "predict"
-    TUNING = "tune"
 
     @property
     def evaluating(self) -> bool:
@@ -115,17 +74,6 @@ class RunningStage(LightningEnum, metaclass=_DeprecationManagingEnumMeta):
             return "val"
         return self.value
 
-    def deprecate(self) -> None:
-        if self == self.TUNING:
-            rank_zero_deprecation(
-                f"`RunningStage.{self.name}` has been deprecated in v1.8.0 and will be removed in v2.0.0."
-            )
-
-    @classmethod
-    def _without_tune(cls) -> List["RunningStage"]:
-        fns = [fn for fn in cls if fn != "tune"]
-        return fns
-
 
 @dataclass
 class TrainerState:

src/pytorch_lightning/trainer/trainer.py

@@ -96,7 +96,7 @@ from pytorch_lightning.utilities.data import has_len_all_ranks
 from pytorch_lightning.utilities.exceptions import ExitGracefullyException, MisconfigurationException
 from pytorch_lightning.utilities.imports import _fault_tolerant_training
 from pytorch_lightning.utilities.model_helpers import is_overridden
-from pytorch_lightning.utilities.rank_zero import rank_zero_deprecation, rank_zero_info, rank_zero_warn
+from pytorch_lightning.utilities.rank_zero import rank_zero_info, rank_zero_warn
 from pytorch_lightning.utilities.seed import isolate_rng
 from pytorch_lightning.utilities.types import (
     _EVALUATE_OUTPUT,
@@ -1891,20 +1891,6 @@ class Trainer:
         elif self.predicting:
             self.state.stage = None
 
-    @property
-    def tuning(self) -> bool:
-        rank_zero_deprecation("`Trainer.tuning` has been deprecated in v1.8.0 and will be removed in v2.0.0.")
-        return self.state.stage == RunningStage.TUNING
-
-    @tuning.setter
-    def tuning(self, val: bool) -> None:
-        rank_zero_deprecation("Setting `Trainer.tuning` has been deprecated in v1.8.0 and will be removed in v2.0.0.")
-
-        if val:
-            self.state.stage = RunningStage.TUNING
-        elif self.tuning:
-            self.state.stage = None
-
     @property
     def validating(self) -> bool:
         return self.state.stage == RunningStage.VALIDATING

tests/tests_pytorch/deprecated_api/test_remove_2-0.py

@@ -20,7 +20,6 @@ import torch
 from lightning_utilities.test.warning import no_warning_call
 from torch.utils.data import DataLoader
 
-from pytorch_lightning import Trainer
 from pytorch_lightning.accelerators.cpu import CPUAccelerator
 from pytorch_lightning.core.mixins.device_dtype_mixin import DeviceDtypeModuleMixin
 from pytorch_lightning.demos.boring_classes import BoringModel, RandomDataset
@@ -30,7 +29,6 @@ from pytorch_lightning.overrides.fairscale import LightningShardedDataParallel,
 from pytorch_lightning.plugins.environments import LightningEnvironment
 from pytorch_lightning.strategies.bagua import LightningBaguaModule
 from pytorch_lightning.strategies.utils import on_colab_kaggle
-from pytorch_lightning.trainer.states import RunningStage, TrainerFn
 from pytorch_lightning.utilities.apply_func import (
     apply_to_collection,
     apply_to_collections,
@@ -271,30 +269,6 @@ def test_v1_10_deprecated_accelerator_setup_environment_method():
         CPUAccelerator().setup_environment(torch.device("cpu"))
 
 
-def test_tuning_enum():
-    with pytest.deprecated_call(
-        match="`TrainerFn.TUNING` has been deprecated in v1.8.0 and will be removed in v2.0.0."
-    ):
-        TrainerFn.TUNING
-
-    with pytest.deprecated_call(
-        match="`RunningStage.TUNING` has been deprecated in v1.8.0 and will be removed in v2.0.0."
-    ):
-        RunningStage.TUNING
-
-
-def test_tuning_trainer_property():
-    trainer = Trainer()
-
-    with pytest.deprecated_call(match="`Trainer.tuning` has been deprecated in v1.8.0 and will be removed in v2.0.0."):
-        trainer.tuning
-
-    with pytest.deprecated_call(
-        match="Setting `Trainer.tuning` has been deprecated in v1.8.0 and will be removed in v2.0.0."
-    ):
-        trainer.tuning = True
-
-
 def test_v1_8_1_deprecated_rank_zero_only():
     from pytorch_lightning.utilities.distributed import rank_zero_only
 

tests/tests_pytorch/trainer/connectors/test_checkpoint_connector.py

@@ -150,8 +150,7 @@ def test_loops_restore(tmpdir):
     trainer = Trainer(**trainer_args)
     trainer.strategy.connect(model)
 
-    trainer_fns = [fn for fn in TrainerFn._without_tune()]
-
+    trainer_fns = list(TrainerFn)
     for fn in trainer_fns:
         trainer_fn = getattr(trainer, f"{fn}_loop")
         trainer_fn.load_state_dict = mock.Mock()