Remove deprecated callback hooks

docs/source-pytorch/extensions/callbacks.rst

@@ -154,12 +154,6 @@ state_key
 Hooks
 =====
 
-on_configure_sharded_model
-^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-.. automethod:: pytorch_lightning.callbacks.Callback.on_configure_sharded_model
-    :noindex:
-
 setup
 ^^^^^
 
@@ -256,9 +250,6 @@ on_predict_epoch_end
 .. automethod:: pytorch_lightning.callbacks.Callback.on_predict_epoch_end
     :noindex:
 
-.. automethod:: pytorch_lightning.callbacks.Callback.on_epoch_end
-    :noindex:
-
 on_validation_batch_start
 ^^^^^^^^^^^^^^^^^^^^^^^^^
 

src/pytorch_lightning/CHANGELOG.md

@@ -255,6 +255,16 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Removed the deprecated way to set the distributed backend via the environment variable `PL_TORCH_DISTRIBUTED_BACKEND`, in favor of setting the `process_group_backend` in the strategy constructor ([#14693](https://github.com/Lightning-AI/lightning/pull/14693))
 
 
+- Removed deprecated callback hooks ([#14834](https://github.com/Lightning-AI/lightning/pull/14834))
+  * `Callback.on_configure_sharded_model` in favor of `Callback.setup`
+  * `Callback.on_before_accelerator_backend_setup` in favor of `Callback.setup`
+  * `Callback.on_batch_start` in favor of `Callback.on_train_batch_start`
+  * `Callback.on_batch_end` in favor of `Callback.on_train_batch_end`
+  * `Callback.on_epoch_start` in favor of `Callback.on_{train,validation,test}_epoch_start`
+  * `Callback.on_epoch_end` in favor of `Callback.on_{train,validation,test}_epoch_end`
+  * `Callback.on_pretrain_routine_{start,end}` in favor of `Callback.on_fit_start`
+
+
 - Removed the deprecated device attributes `Trainer.{devices,gpus,num_gpus,ipus,tpu_cores}` in favor of the accelerator-agnostic `Trainer.num_devices` ([#14829](https://github.com/Lightning-AI/lightning/pull/14829))
 
 

src/pytorch_lightning/callbacks/callback.py

@@ -56,22 +56,6 @@ def _generate_state_key(self, **kwargs: Any) -> str:
         """
         return f"{self.__class__.__qualname__}{repr(kwargs)}"
 
-    def on_configure_sharded_model(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
-        r"""
-        .. deprecated:: v1.6
-            This callback hook was deprecated in v1.6 and will be removed in v1.8. Use `setup()` instead.
-
-        Called before configure sharded model.
-        """
-
-    def on_before_accelerator_backend_setup(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
-        r"""
-        .. deprecated:: v1.6
-            This callback hook was deprecated in v1.6 and will be removed in v1.8. Use ``setup()`` instead.
-
-        Called before accelerator is being setup.
-        """
-
     def setup(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", stage: str) -> None:
         """Called when fit, validate, test, predict, or tune begins."""
 
@@ -130,42 +114,6 @@ def on_predict_epoch_start(self, trainer: "pl.Trainer", pl_module: "pl.Lightning
     def on_predict_epoch_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", outputs: List[Any]) -> None:
         """Called when the predict epoch ends."""
 
-    def on_epoch_start(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
-        r"""
-        .. deprecated:: v1.6
-            This callback hook was deprecated in v1.6 and will be removed in v1.8. Use
-            ``on_<train/validation/test>_epoch_start`` instead.
-
-        Called when either of train/val/test epoch begins.
-        """
-
-    def on_epoch_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
-        r"""
-        .. deprecated:: v1.6
-            This callback hook was deprecated in v1.6 and will be removed in v1.8. Use
-            ``on_<train/validation/test>_epoch_end`` instead.
-
-        Called when either of train/val/test epoch ends.
-        """
-
-    def on_batch_start(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
-        r"""
-        .. deprecated:: v1.6
-            This callback hook was deprecated in v1.6 and will be removed in v1.8. Use
-            ``on_train_batch_start`` instead.
-
-        Called when the training batch begins.
-        """
-
-    def on_batch_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
-        r"""
-        .. deprecated:: v1.6
-            This callback hook was deprecated in v1.6 and will be removed in v1.8. Use
-            ``on_train_batch_end`` instead.
-
-        Called when the training batch ends.
-        """
-
     def on_validation_batch_start(
         self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", batch: Any, batch_idx: int, dataloader_idx: int
     ) -> None:
@@ -220,24 +168,6 @@ def on_train_start(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule")
     def on_train_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
         """Called when the train ends."""
 
-    def on_pretrain_routine_start(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
-        r"""
-        .. deprecated:: v1.6
-
-            This callback hook was deprecated in v1.6 and will be removed in v1.8. Use ``on_fit_start`` instead.
-
-        Called when the pretrain routine begins.
-        """
-
-    def on_pretrain_routine_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
-        r"""
-        .. deprecated:: v1.6
-
-            This callback hook was deprecated in v1.6 and will be removed in v1.8. Use ``on_fit_start`` instead.
-
-        Called when the pretrain routine ends.
-        """
-
     def on_validation_start(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
         """Called when the validation loop begins."""
 

src/pytorch_lightning/callbacks/lambda_function.py

@@ -40,9 +40,7 @@ class LambdaCallback(Callback):
 
     def __init__(
         self,
-        on_before_accelerator_backend_setup: Optional[Callable] = None,
         setup: Optional[Callable] = None,
-        on_configure_sharded_model: Optional[Callable] = None,
         teardown: Optional[Callable] = None,
         on_fit_start: Optional[Callable] = None,
         on_fit_end: Optional[Callable] = None,
@@ -56,18 +54,12 @@ def __init__(
         on_validation_epoch_end: Optional[Callable] = None,
         on_test_epoch_start: Optional[Callable] = None,
         on_test_epoch_end: Optional[Callable] = None,
-        on_epoch_start: Optional[Callable] = None,
-        on_epoch_end: Optional[Callable] = None,
-        on_batch_start: Optional[Callable] = None,
         on_validation_batch_start: Optional[Callable] = None,
         on_validation_batch_end: Optional[Callable] = None,
         on_test_batch_start: Optional[Callable] = None,
         on_test_batch_end: Optional[Callable] = None,
-        on_batch_end: Optional[Callable] = None,
         on_train_start: Optional[Callable] = None,
         on_train_end: Optional[Callable] = None,
-        on_pretrain_routine_start: Optional[Callable] = None,
-        on_pretrain_routine_end: Optional[Callable] = None,
         on_validation_start: Optional[Callable] = None,
         on_validation_end: Optional[Callable] = None,
         on_test_start: Optional[Callable] = None,

src/pytorch_lightning/callbacks/model_checkpoint.py

@@ -39,7 +39,7 @@
 from lightning_lite.utilities.types import _PATH
 from pytorch_lightning.callbacks import Checkpoint
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
-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.types import STEP_OUTPUT
 
 log = logging.getLogger(__name__)
@@ -351,19 +351,12 @@ def load_state_dict(self, state_dict: Dict[str, Any]) -> None:
 
         self.best_model_path = state_dict["best_model_path"]
 
-    def save_checkpoint(self, trainer: "pl.Trainer") -> None:  # pragma: no-cover
-        """Performs the main logic around saving a checkpoint.
-
-        This method runs on all ranks. It is the responsibility of `trainer.save_checkpoint` to correctly handle the
-        behaviour in distributed training, i.e., saving only on rank 0 for data parallel use cases.
-        """
-        rank_zero_deprecation(
-            f"`{self.__class__.__name__}.save_checkpoint()` was deprecated in v1.6 and will be removed in v1.8."
-            " Instead, you can use `trainer.save_checkpoint()` to manually save a checkpoint."
+    def save_checkpoint(self, trainer: "pl.Trainer") -> None:
+        raise NotImplementedError(
+            f"`{self.__class__.__name__}.save_checkpoint()` was deprecated in v1.6 and is no longer supported"
+            f" as of 1.8. Please use `trainer.save_checkpoint()` to manually save a checkpoint. This method will be"
+            f" removed completely in v2.0."
         )
-        monitor_candidates = self._monitor_candidates(trainer)
-        self._save_topk_checkpoint(trainer, monitor_candidates)
-        self._save_last_checkpoint(trainer, monitor_candidates)
 
     def _save_topk_checkpoint(self, trainer: "pl.Trainer", monitor_candidates: Dict[str, Tensor]) -> None:
         if self.save_top_k == 0:

src/pytorch_lightning/core/hooks.py

@@ -63,32 +63,6 @@ def on_predict_start(self) -> None:
     def on_predict_end(self) -> None:
         """Called at the end of predicting."""
 
-    def on_pretrain_routine_start(self) -> None:
-        """Called at the beginning of the pretrain routine (between fit and train start).
-
-        - fit
-        - pretrain_routine start
-        - pretrain_routine end
-        - training_start
-
-        .. deprecated:: v1.6
-            :meth:`on_pretrain_routine_start` has been deprecated in v1.6 and will be removed in v1.8.
-            Use ``on_fit_start`` instead.
-        """
-
-    def on_pretrain_routine_end(self) -> None:
-        """Called at the end of the pretrain routine (between fit and train start).
-
-        - fit
-        - pretrain_routine start
-        - pretrain_routine end
-        - training_start
-
-        .. deprecated:: v1.6
-            :meth:`on_pretrain_routine_end` has been deprecated in v1.6 and will be removed in v1.8.
-            Use ``on_fit_start`` instead.
-        """
-
     def on_train_batch_start(self, batch: Any, batch_idx: int) -> Optional[int]:
         """Called in the training loop before anything happens for that batch.
 
@@ -189,22 +163,6 @@ def on_predict_model_eval(self) -> None:
         """Sets the model to eval during the predict loop."""
         self.trainer.model.eval()
 
-    def on_epoch_start(self) -> None:
-        """Called when either of train/val/test epoch begins.
-
-        .. deprecated:: v1.6
-            :meth:`on_epoch_start` has been deprecated in v1.6 and will be removed in v1.8.
-            Use ``on_<train/validation/test>_epoch_start`` instead.
-        """
-
-    def on_epoch_end(self) -> None:
-        """Called when either of train/val/test epoch ends.
-
-        .. deprecated:: v1.6
-            :meth:`on_epoch_end` has been deprecated in v1.6 and will be removed in v1.8.
-            Use ``on_<train/validation/test>_epoch_end`` instead.
-        """
-
     def on_train_epoch_start(self) -> None:
         """Called in the training loop at the very beginning of the epoch."""
 

src/pytorch_lightning/loops/dataloader/evaluation_loop.py

@@ -267,10 +267,8 @@ def _on_evaluation_end(self, *args: Any, **kwargs: Any) -> None:
         self.trainer._logger_connector.reset_results()
 
     def _on_evaluation_epoch_start(self, *args: Any, **kwargs: Any) -> None:
-        """Runs ``on_epoch_start`` and ``on_{validation/test}_epoch_start`` hooks."""
+        """Runs the ``on_{validation/test}_epoch_start`` hooks."""
         self.trainer._logger_connector.on_epoch_start()
-        self.trainer._call_callback_hooks("on_epoch_start", *args, **kwargs)
-        self.trainer._call_lightning_module_hook("on_epoch_start", *args, **kwargs)
 
         hook_name = "on_test_epoch_start" if self.trainer.testing else "on_validation_epoch_start"
         self.trainer._call_callback_hooks(hook_name, *args, **kwargs)
@@ -295,8 +293,6 @@ def _on_evaluation_epoch_end(self) -> None:
         self.trainer._call_callback_hooks(hook_name)
         self.trainer._call_lightning_module_hook(hook_name)
 
-        self.trainer._call_callback_hooks("on_epoch_end")
-        self.trainer._call_lightning_module_hook("on_epoch_end")
         self.trainer._logger_connector.on_epoch_end()
 
     @staticmethod

src/pytorch_lightning/loops/epoch/training_epoch_loop.py

@@ -200,9 +200,6 @@ def advance(self, data_fetcher: AbstractDataFetcher) -> None:  # type: ignore[ov
             self._warning_cache.warn("train_dataloader yielded None. If this was on purpose, ignore this warning...")
             batch_output = []
         else:
-            # hook
-            self.trainer._call_callback_hooks("on_batch_start")
-
             # hook
             self.trainer._call_callback_hooks("on_train_batch_start", batch, batch_idx)
             response = self.trainer._call_lightning_module_hook("on_train_batch_start", batch, batch_idx)
@@ -232,7 +229,6 @@ def advance(self, data_fetcher: AbstractDataFetcher) -> None:  # type: ignore[ov
 
         self.trainer._call_callback_hooks("on_train_batch_end", batch_end_outputs, batch, batch_idx)
         self.trainer._call_lightning_module_hook("on_train_batch_end", batch_end_outputs, batch, batch_idx)
-        self.trainer._call_callback_hooks("on_batch_end")
         self.trainer._logger_connector.on_batch_end()
 
         self.batch_progress.increment_completed()

src/pytorch_lightning/loops/fit_loop.py

@@ -219,8 +219,7 @@ def on_run_start(self) -> None:  # type: ignore[override]
         self.trainer._call_strategy_hook("on_train_start")
 
     def on_advance_start(self) -> None:  # type: ignore[override]
-        """Prepares the dataloader for training and calls the hooks ``on_epoch_start`` and
-        ``on_train_epoch_start``"""
+        """Prepares the dataloader for training and calls the hook ``on_train_epoch_start``"""
         model = self.trainer.lightning_module
 
         # reset train dataloader
@@ -246,9 +245,6 @@ def on_advance_start(self) -> None:  # type: ignore[override]
 
         self.trainer._logger_connector.on_epoch_start()
 
-        self.trainer._call_callback_hooks("on_epoch_start")
-        self.trainer._call_lightning_module_hook("on_epoch_start")
-
         self.trainer._call_callback_hooks("on_train_epoch_start")
         self.trainer._call_lightning_module_hook("on_train_epoch_start")
 
@@ -299,9 +295,6 @@ def on_advance_end(self) -> None:
         self.trainer._call_callback_hooks("on_train_epoch_end")
         self.trainer._call_lightning_module_hook("on_train_epoch_end")
 
-        self.trainer._call_callback_hooks("on_epoch_end")
-        self.trainer._call_lightning_module_hook("on_epoch_end")
-
         self.trainer._logger_connector.on_epoch_end()
 
         if self.epoch_loop._num_ready_batches_reached():