Deprecate tuning enum and trainer properties

src/pytorch_lightning/CHANGELOG.md

@@ -184,6 +184,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Deprecated duplicate `SaveConfigCallback` parameters in `LightningCLI.__init__`: `save_config_kwargs`, `save_config_overwrite` and `save_config_multifile`. New `save_config_kwargs` parameter should be used instead ([#14998](https://github.com/Lightning-AI/lightning/pull/14998)
 
 
+- Deprecated `TrainerFn.TUNING`, `RunningStage.TUNING` and `trainer.tuning` property ([#15100](https://github.com/Lightning-AI/lightning/pull/15100)
+
+
 ### Removed
 
 - Removed the deprecated `Trainer.training_type_plugin` property in favor of `Trainer.strategy` ([#14011](https://github.com/Lightning-AI/lightning/pull/14011))

src/pytorch_lightning/strategies/deepspeed.py

@@ -605,7 +605,7 @@ def setup_optimizers(self, trainer: "pl.Trainer") -> None:
         Args:
             trainer: the Trainer, these optimizers should be connected to
         """
-        if trainer.state.fn not in (TrainerFn.FITTING, TrainerFn.TUNING):
+        if trainer.state.fn != TrainerFn.FITTING:
             return
         # Skip initializing optimizers here as DeepSpeed handles optimizers via config.
         # User may have specified config options instead in configure_optimizers, but this is handled

src/pytorch_lightning/strategies/ipu.py

@@ -130,7 +130,7 @@ def setup(self, trainer: "pl.Trainer") -> None:
         # Separate models are instantiated for different stages, but they share the same weights on host.
         # When validation/test models are run, weights are synced first.
         trainer_fn = self.lightning_module.trainer.state.fn
-        if trainer_fn in (TrainerFn.FITTING, TrainerFn.TUNING):
+        if trainer_fn == TrainerFn.FITTING:
             # Create model for training and validation which will run on fit
             training_opts = self.training_opts
             inference_opts = self.inference_opts

src/pytorch_lightning/strategies/strategy.py

@@ -136,7 +136,7 @@ def setup_optimizers(self, trainer: "pl.Trainer") -> None:
         Args:
             trainer: the Trainer, these optimizers should be connected to
         """
-        if trainer.state.fn not in (TrainerFn.FITTING, TrainerFn.TUNING):
+        if trainer.state.fn != TrainerFn.FITTING:
             return
         assert self.lightning_module is not None
         self.optimizers, self.lr_scheduler_configs, self.optimizer_frequencies = _init_optimizers_and_lr_schedulers(

src/pytorch_lightning/trainer/configuration_validator.py

@@ -37,7 +37,7 @@ def verify_loop_configurations(trainer: "pl.Trainer") -> None:
 
     if trainer.state.fn is None:
         raise ValueError("Unexpected: Trainer state fn must be set before validating loop configuration.")
-    if trainer.state.fn in (TrainerFn.FITTING, TrainerFn.TUNING):
+    if trainer.state.fn == TrainerFn.FITTING:
         __verify_train_val_loop_configuration(trainer, model)
         __verify_manual_optimization_support(trainer, model)
         __check_training_step_requires_dataloader_iter(model)

src/pytorch_lightning/trainer/connectors/checkpoint_connector.py

@@ -368,7 +368,7 @@ def restore_loops(self) -> None:
         assert self.trainer.state.fn is not None
         state_dict = self._loaded_checkpoint.get("loops")
         if state_dict is not None:
-            if self.trainer.state.fn in (TrainerFn.FITTING, TrainerFn.TUNING):
+            if self.trainer.state.fn == TrainerFn.FITTING:
                 fit_loop.load_state_dict(state_dict["fit_loop"])
             elif self.trainer.state.fn == TrainerFn.VALIDATING:
                 self.trainer.validate_loop.load_state_dict(state_dict["validate_loop"])

src/pytorch_lightning/trainer/states.py

@@ -12,12 +12,40 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 from dataclasses import dataclass, field
-from typing import Optional
+from enum import Enum, EnumMeta
+from typing import Any, Optional
+
+from lightning_utilities.core.rank_zero import rank_zero_deprecation
 
 from pytorch_lightning.utilities import LightningEnum
 from pytorch_lightning.utilities.enums import _FaultTolerantMode
 
 
+class _DeprecatedEnumMeta(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: _DeprecatedEnumMeta = 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):
     """Enum for the status of the :class:`~pytorch_lightning.trainer.trainer.Trainer`"""
 
@@ -31,7 +59,7 @@ def stopped(self) -> bool:
         return self in (self.FINISHED, self.INTERRUPTED)
 
 
-class TrainerFn(LightningEnum):
+class TrainerFn(LightningEnum, metaclass=_DeprecatedEnumMeta):
     """
     Enum for the user-facing functions of the :class:`~pytorch_lightning.trainer.trainer.Trainer`
     such as :meth:`~pytorch_lightning.trainer.trainer.Trainer.fit` and
@@ -44,16 +72,14 @@ class TrainerFn(LightningEnum):
     PREDICTING = "predict"
     TUNING = "tune"
 
-    @property
-    def _setup_fn(self) -> "TrainerFn":
-        """``FITTING`` is used instead of ``TUNING`` as there are no "tune" dataloaders.
+    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 v1.10.0."
+            )
 
-        This is used for the ``setup()`` and ``teardown()`` hooks
-        """
-        return TrainerFn.FITTING if self == TrainerFn.TUNING else self
 
-
-class RunningStage(LightningEnum):
+class RunningStage(LightningEnum, metaclass=_DeprecatedEnumMeta):
     """Enum for the current running stage.
 
     This stage complements :class:`TrainerFn` by specifying the current running stage for each function.
@@ -79,12 +105,18 @@ def evaluating(self) -> bool:
 
     @property
     def dataloader_prefix(self) -> Optional[str]:
-        if self in (self.SANITY_CHECKING, self.TUNING):
+        if self == self.SANITY_CHECKING:
             return None
         if self == self.VALIDATING:
             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 v1.10.0."
+            )
+
 
 @dataclass
 class TrainerState:

src/pytorch_lightning/trainer/trainer.py

@@ -955,7 +955,7 @@ def _restore_modules_and_callbacks(self, checkpoint_path: Optional[_PATH] = None
     def _run(
         self, model: "pl.LightningModule", ckpt_path: Optional[str] = None
     ) -> Optional[Union[_EVALUATE_OUTPUT, _PREDICT_OUTPUT]]:
-        if self.state.fn in (TrainerFn.FITTING, TrainerFn.TUNING):
+        if self.state.fn == TrainerFn.FITTING:
             min_epochs, max_epochs = _parse_loop_limits(
                 self.min_steps, self.max_steps, self.min_epochs, self.max_epochs, self
             )
@@ -1225,7 +1225,7 @@ def _run_sanity_check(self) -> None:
 
     def _call_setup_hook(self) -> None:
         assert self.state.fn is not None
-        fn = self.state.fn._setup_fn
+        fn = self.state.fn
 
         self.strategy.barrier("pre_setup")
 
@@ -1248,7 +1248,7 @@ def _call_configure_sharded_model(self) -> None:
 
     def _call_teardown_hook(self) -> None:
         assert self.state.fn is not None
-        fn = self.state.fn._setup_fn
+        fn = self.state.fn
 
         if self.datamodule is not None:
             self._call_lightning_datamodule_hook("teardown", stage=fn)
@@ -1441,7 +1441,7 @@ def __setup_profiler(self) -> None:
         assert self.state.fn is not None
         local_rank = self.local_rank if self.world_size > 1 else None
         self.profiler._lightning_module = proxy(self.lightning_module)
-        self.profiler.setup(stage=self.state.fn._setup_fn, local_rank=local_rank, log_dir=self.log_dir)
+        self.profiler.setup(stage=self.state.fn, local_rank=local_rank, log_dir=self.log_dir)
 
     """
     Data loading methods
@@ -1957,10 +1957,15 @@ def predicting(self, val: bool) -> None:
 
     @property
     def tuning(self) -> bool:
+        rank_zero_deprecation("`trainer.tuning` property has been deprecated in v1.8.0 and will be removed in v1.10.0.")
         return self.state.stage == RunningStage.TUNING
 
     @tuning.setter
     def tuning(self, val: bool) -> None:
+        rank_zero_deprecation(
+            "Setting `trainer.tuning` property has been deprecated in v1.8.0 and will be removed in v1.10.0."
+        )
+
         if val:
             self.state.stage = RunningStage.TUNING
         elif self.tuning:
@@ -2089,7 +2094,7 @@ def predict_loop(self, loop: PredictionLoop) -> None:
 
     @property
     def _evaluation_loop(self) -> EvaluationLoop:
-        if self.state.fn in (TrainerFn.FITTING, TrainerFn.TUNING):
+        if self.state.fn == TrainerFn.FITTING:
             return self.fit_loop.epoch_loop.val_loop
         if self.state.fn == TrainerFn.VALIDATING:
             return self.validate_loop

src/pytorch_lightning/tuner/tuning.py

@@ -20,7 +20,7 @@
 from pytorch_lightning.callbacks.callback import Callback
 from pytorch_lightning.callbacks.lr_finder import LearningRateFinder
 from pytorch_lightning.core.datamodule import LightningDataModule
-from pytorch_lightning.trainer.states import TrainerFn, TrainerStatus
+from pytorch_lightning.trainer.states import TrainerStatus
 from pytorch_lightning.tuner.lr_finder import _LRFinder
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
 from pytorch_lightning.utilities.types import EVAL_DATALOADERS, TRAIN_DATALOADERS
@@ -77,9 +77,7 @@ def _tune(
 
         # Run learning rate finder:
         if self.trainer.auto_lr_find:
-            self.trainer.state.fn = TrainerFn.TUNING
             self.trainer.state.status = TrainerStatus.RUNNING
-            self.tuning = True
 
             # TODO: Remove this once LRFinder is converted to a Callback
             # if a datamodule comes in as the second arg, then fix it for the user
@@ -112,7 +110,6 @@ def _run(self, *args: Any, **kwargs: Any) -> None:
         self.trainer.state.status = TrainerStatus.RUNNING  # last `_run` call might have set it to `FINISHED`
         self.trainer.training = True
         self.trainer._run(*args, **kwargs)
-        self.trainer.tuning = True
 
     def scale_batch_size(
         self,
@@ -170,10 +167,6 @@ def scale_batch_size(
                 - ``model.hparams``
                 - ``trainer.datamodule`` (the datamodule passed to the tune method)
         """
-        # TODO: Remove TrainerFn.TUNING since we are now calling fit/validate/test/predict methods directly
-        self.trainer.state.fn = TrainerFn.TUNING
-        self.tuning = True
-
         _check_tuner_configuration(self.trainer, train_dataloaders, val_dataloaders, dataloaders, method)
 
         batch_size_finder: Callback = BatchSizeFinder(
@@ -254,9 +247,6 @@ def lr_find(
                 If learning rate/lr in ``model`` or ``model.hparams`` isn't overridden when ``auto_lr_find=True``,
                 or if you are using more than one optimizer.
         """
-        self.trainer.state.fn = TrainerFn.TUNING
-        self.tuning = True
-
         if method != "fit":
             raise MisconfigurationException("method='fit' is an invalid configuration to run lr finder.")
 

tests/tests_pytorch/deprecated_api/test_remove_1-10.py

@@ -35,6 +35,7 @@
 from pytorch_lightning.strategies.bagua import LightningBaguaModule
 from pytorch_lightning.strategies.deepspeed import LightningDeepSpeedModule
 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,
@@ -297,3 +298,29 @@ def test_lite_convert_deprecated_tpus_argument(tpu_available):
 def test_lightningCLI_save_config_init_params_deprecation_warning(name, value):
     with mock.patch("sys.argv", ["any.py"]), pytest.deprecated_call(match=f".*{name!r} init parameter is deprecated.*"):
         LightningCLI(BoringModel, run=False, **{name: value})
+
+
+def test_tuning_enum():
+    with pytest.deprecated_call(
+        match="`TrainerFn.TUNING` has been deprecated in v1.8.0 and will be removed in v1.10.0."
+    ):
+        TrainerFn.TUNING
+
+    with pytest.deprecated_call(
+        match="`RunningStage.TUNING` has been deprecated in v1.8.0 and will be removed in v1.10.0."
+    ):
+        RunningStage.TUNING
+
+
+def test_tuning_trainer_property():
+    trainer = Trainer()
+
+    with pytest.deprecated_call(
+        match="`trainer.tuning` property has been deprecated in v1.8.0 and will be removed in v1.10.0."
+    ):
+        trainer.tuning
+
+    with pytest.deprecated_call(
+        match="Setting `trainer.tuning` property has been deprecated in v1.8.0 and will be removed in v1.10.0."
+    ):
+        trainer.tuning = True

tests/tests_pytorch/helpers/datamodules.py

@@ -20,7 +20,7 @@
 from pytorch_lightning.core.datamodule import LightningDataModule
 from tests_pytorch.helpers.datasets import MNIST, SklearnDataset, TrialMNIST
 
-_SKLEARN_AVAILABLE = RequirementCache("sklearn")
+_SKLEARN_AVAILABLE = RequirementCache("scikit-learn")
 
 
 class MNISTDataModule(LightningDataModule):

tests/tests_pytorch/models/test_restore.py

@@ -183,39 +183,28 @@ def _check_model_state_dict(self):
                 for actual, expected in zip(self.state_dict(), state_dict["state_dict"])
             )
 
-        def _test_on_val_test_predict_tune_start(self):
+        def _test_on_val_test_predict_start(self):
             assert self.trainer.current_epoch == state_dict["epoch"]
             assert self.trainer.global_step == state_dict["global_step"]
             assert self._check_model_state_dict()
 
-            # no optimizes and schedulers are loaded otherwise
-            if self.trainer.state.fn != TrainerFn.TUNING:
-                return
-
-            assert not self._check_optimizers()
-            assert not self._check_schedulers()
-
         def on_train_start(self):
-            if self.trainer.state.fn == TrainerFn.TUNING:
-                self._test_on_val_test_predict_tune_start()
-            else:
-                assert self.trainer.current_epoch == state_dict["epoch"] + 1
-                assert self.trainer.global_step == state_dict["global_step"]
-                assert self._check_model_state_dict()
-                assert self._check_optimizers()
-                assert self._check_schedulers()
+            assert self.trainer.current_epoch == state_dict["epoch"] + 1
+            assert self.trainer.global_step == state_dict["global_step"]
+            assert self._check_model_state_dict()
+            assert self._check_optimizers()
+            assert self._check_schedulers()
 
         def on_validation_start(self):
             if self.trainer.state.fn == TrainerFn.VALIDATING:
-                self._test_on_val_test_predict_tune_start()
+                self._test_on_val_test_predict_start()
 
         def on_test_start(self):
-            self._test_on_val_test_predict_tune_start()
+            self._test_on_val_test_predict_start()
 
     for fn in ("fit", "validate", "test", "predict"):
         model = CustomClassifModel()
         dm = ClassifDataModule()
-        trainer_args["auto_scale_batch_size"] = (fn == "tune",)
         trainer = Trainer(**trainer_args)
         trainer_fn = getattr(trainer, fn)
         trainer_fn(model, datamodule=dm, ckpt_path=resume_ckpt)

tests/tests_pytorch/strategies/test_ddp_strategy.py

@@ -155,9 +155,7 @@ def test_ddp_configure_ddp():
 
 
 @RunIf(min_cuda_gpus=1)
-@pytest.mark.parametrize(
-    "trainer_fn", (TrainerFn.VALIDATING, TrainerFn.TUNING, TrainerFn.TESTING, TrainerFn.PREDICTING)
-)
+@pytest.mark.parametrize("trainer_fn", (TrainerFn.VALIDATING, TrainerFn.TESTING, TrainerFn.PREDICTING))
 def test_ddp_dont_configure_sync_batchnorm(trainer_fn):
     model = BoringModelGPU()
     model.layer = torch.nn.BatchNorm1d(10)

tests/tests_pytorch/trainer/connectors/test_checkpoint_connector.py

@@ -124,21 +124,19 @@ def test_loops_restore(tmpdir):
     trainer.strategy.connect(model)
 
     for fn in TrainerFn:
-        if fn != TrainerFn.TUNING:
-            trainer_fn = getattr(trainer, f"{fn}_loop")
-            trainer_fn.load_state_dict = mock.Mock()
+        trainer_fn = getattr(trainer, f"{fn}_loop")
+        trainer_fn.load_state_dict = mock.Mock()
 
     for fn in TrainerFn:
-        if fn != TrainerFn.TUNING:
-            trainer.state.fn = fn
-            trainer._checkpoint_connector.resume_start(ckpt_path)
-            trainer._checkpoint_connector.restore_loops()
+        trainer.state.fn = fn
+        trainer._checkpoint_connector.resume_start(ckpt_path)
+        trainer._checkpoint_connector.restore_loops()
 
-            trainer_loop = getattr(trainer, f"{fn}_loop")
-            trainer_loop.load_state_dict.assert_called()
-            trainer_loop.load_state_dict.reset_mock()
+        trainer_loop = getattr(trainer, f"{fn}_loop")
+        trainer_loop.load_state_dict.assert_called()
+        trainer_loop.load_state_dict.reset_mock()
 
         for fn2 in TrainerFn:
-            if fn2 not in (fn, TrainerFn.TUNING):
+            if fn2 != fn:
                 trainer_loop2 = getattr(trainer, f"{fn2}_loop")
                 trainer_loop2.load_state_dict.assert_not_called()