Deprecate callback hooks on_init_start and on_init_end

CHANGELOG.md

@@ -116,6 +116,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Deprecated the access to the attribute `IndexBatchSamplerWrapper.batch_indices` in favor of `IndexBatchSamplerWrapper.seen_batch_indices` ([#10870](https://github.com/PyTorchLightning/pytorch-lightning/pull/10870))
 
 
+- Deprecated `on_init_start` and `on_init_end` callback hooks ([#10940](https://github.com/PyTorchLightning/pytorch-lightning/pull/10940))
+
+
 - Deprecated `Trainer.call_hook` in favor of `Trainer._call_callback_hooks`, `Trainer._call_lightning_module_hook`, `Trainer._call_ttp_hook`, and `Trainer._call_accelerator_hook` ([#10979](https://github.com/PyTorchLightning/pytorch-lightning/pull/10979))
 
 

docs/source/extensions/callbacks.rst

@@ -46,23 +46,15 @@ Example:
 
 
     class MyPrintingCallback(Callback):
-        def on_init_start(self, trainer):
-            print("Starting to initialize the trainer!")
-
-        def on_init_end(self, trainer):
-            print("trainer is initialized now")
+        def on_train_start(self, trainer, pl_module):
+            print("Training is starting")
 
         def on_train_end(self, trainer, pl_module):
-            print("do something when training ends")
+            print("Training is ending")
 
 
     trainer = Trainer(callbacks=[MyPrintingCallback()])
 
-.. testoutput::
-
-    Starting to initialize the trainer!
-    trainer is initialized now
-
 We successfully extended functionality without polluting our super clean
 :doc:`lightning module <../common/lightning_module>` research code.
 

docs/source/starter/introduction_guide.rst

@@ -956,27 +956,18 @@ for hooks that you might care about
 
 
     class MyPrintingCallback(Callback):
-        def on_init_start(self, trainer):
-            print("Starting to init trainer!")
-
-        def on_init_end(self, trainer):
-            print("Trainer is init now")
+        def on_train_start(self, trainer, pl_module):
+            print("Training is starting")
 
         def on_train_end(self, trainer, pl_module):
-            print("do something when training ends")
+            print("Training is ending")
 
 And pass the callbacks into the trainer
 
 .. testcode::
 
     trainer = Trainer(callbacks=[MyPrintingCallback()])
 
-.. testoutput::
-    :hide:
-
-    Starting to init trainer!
-    Trainer is init now
-
 .. tip::
     See full list of 12+ hooks in the :doc:`callbacks <../extensions/callbacks>`.
 

pytorch_lightning/callbacks/base.py

@@ -73,11 +73,21 @@ def teardown(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", stage
         pass
 
     def on_init_start(self, trainer: "pl.Trainer") -> None:
-        """Called when the trainer initialization begins, model has not yet been set."""
+        r"""
+        .. deprecated:: v1.6
+            This callback hook was deprecated in v1.6 and will be removed in v1.8.
+
+        Called when the trainer initialization begins, model has not yet been set.
+        """
         pass
 
     def on_init_end(self, trainer: "pl.Trainer") -> None:
-        """Called when the trainer initialization ends, model has not yet been set."""
+        r"""
+        .. deprecated:: v1.6
+            This callback hook was deprecated in v1.6 and will be removed in v1.8.
+
+        Called when the trainer initialization ends, model has not yet been set.
+        """
         pass
 
     def on_fit_start(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:

pytorch_lightning/callbacks/early_stopping.py

@@ -125,7 +125,7 @@ def __init__(
     def state_key(self) -> str:
         return self._generate_state_key(monitor=self.monitor, mode=self.mode)
 
-    def on_init_end(self, trainer: "pl.Trainer") -> None:
+    def setup(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", stage: Optional[str] = None) -> None:
         if self._check_on_train_epoch_end is None:
             # if the user runs validation multiple times per training epoch or multiple training epochs without
             # validation, then we run after validation instead of on train epoch end

pytorch_lightning/callbacks/model_checkpoint.py

@@ -248,14 +248,13 @@ def state_key(self) -> str:
             save_on_train_epoch_end=self._save_on_train_epoch_end,
         )
 
-    def on_init_end(self, trainer: "pl.Trainer") -> None:
+    def on_pretrain_routine_start(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
+        """When pretrain routine starts we build the ckpt dir on the fly."""
         if self._save_on_train_epoch_end is None:
             # if the user runs validation multiple times per training epoch or multiple training epochs without
             # validation, then we run after validation instead of on train epoch end
             self._save_on_train_epoch_end = trainer.val_check_interval == 1.0 and trainer.check_val_every_n_epoch == 1
 
-    def on_pretrain_routine_start(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
-        """When pretrain routine starts we build the ckpt dir on the fly."""
         self.__resolve_ckpt_dir(trainer)
         if trainer.is_global_zero:
             self.__warn_if_dir_not_empty(self.dirpath)

pytorch_lightning/callbacks/progress/base.py

@@ -11,7 +11,7 @@
 # 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 typing import Dict, Union
+from typing import Dict, Optional, Union
 
 import pytorch_lightning as pl
 from pytorch_lightning.callbacks import Callback
@@ -152,7 +152,7 @@ def print(self, *args, **kwargs):
         """You should provide a way to print without breaking the progress bar."""
         print(*args, **kwargs)
 
-    def on_init_end(self, trainer):
+    def setup(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", stage: Optional[str] = None) -> None:
         self._trainer = trainer
 
     def on_train_start(self, trainer, pl_module):

pytorch_lightning/trainer/configuration_validator.py

@@ -43,14 +43,16 @@ def verify_loop_configurations(trainer: "pl.Trainer", model: "pl.LightningModule
 
     __verify_dp_batch_transfer_support(trainer, model)
     _check_add_get_queue(model)
-    # TODO(@daniellepintz): Delete _check_progress_bar in v1.7
+    # TODO: Delete _check_progress_bar in v1.7
     _check_progress_bar(model)
     # TODO: Delete _check_on_post_move_to_device in v1.7
     _check_on_post_move_to_device(model)
     # TODO: Delete _check_on_keyboard_interrupt in v1.7
     _check_on_keyboard_interrupt(trainer)
     # TODO: Remove this in v1.7 (deprecation: #9816)
     _check_dl_idx_in_on_train_batch_hooks(trainer, model)
+    # TODO: Remove this in v1.8
+    _check_on_init_start_end(trainer)
 
 
 def __verify_train_val_loop_configuration(trainer: "pl.Trainer", model: "pl.LightningModule") -> None:
@@ -290,3 +292,14 @@ def _check_dl_idx_in_on_train_batch_hooks(trainer: "pl.Trainer", model: "pl.Ligh
                     f"Base `Callback.{hook}` hook signature has changed in v1.5."
                     " The `dataloader_idx` argument will be removed in v1.7."
                 )
+
+
+def _check_on_init_start_end(trainer: "pl.Trainer") -> None:
+    """Checks if on_init_start/end are overridden and sends a deprecation warning."""
+    for callback in trainer.callbacks:
+        if is_overridden(method_name="on_init_start", instance=callback):
+            rank_zero_deprecation(
+                "The `on_init_start` callback hook was deprecated in v1.6 and will be removed in v1.8."
+            )
+        if is_overridden(method_name="on_init_end", instance=callback):
+            rank_zero_deprecation("The `on_init_end` callback hook was deprecated in v1.6 and will be removed in v1.8.")

pytorch_lightning/trainer/trainer.py

@@ -1537,6 +1537,7 @@ def _call_callback_hooks(
         *args: Any,
         **kwargs: Any,
     ) -> None:
+        # TODO: remove if block in v1.8
         if hook_name in ("on_init_start", "on_init_end"):
             # these `Callback` hooks are the only ones that do not take a lightning module.
             # we also don't profile bc profiler hasn't been set yet
@@ -1551,7 +1552,7 @@ def _call_callback_hooks(
             prev_fx_name = pl_module._current_fx_name
             pl_module._current_fx_name = hook_name
 
-        # TODO: remove if statement in v1.7
+        # TODO: remove if block in v1.7
         if hook_name in ("on_train_batch_start", "on_train_batch_end"):
             fn = getattr(self, hook_name)
             if callable(fn):

tests/callbacks/test_tqdm_progress_bar.py

@@ -81,9 +81,6 @@ def test_tqdm_progress_bar_totals(tmpdir):
 
     trainer = Trainer(default_root_dir=tmpdir, max_epochs=1)
     bar = trainer.progress_bar_callback
-    assert float("inf") == bar.total_train_batches
-    assert 0 == bar.total_val_batches
-    assert 0 == bar.total_test_batches
 
     trainer.fit(model)
 
@@ -584,7 +581,7 @@ def test_tqdm_progress_bar_main_bar_resume():
     trainer.num_val_batches = [3]
     trainer.fit_loop.epoch_loop.batch_progress.current.completed = 3
 
-    bar.on_init_end(trainer)
+    bar.setup(trainer, model)
     bar.on_train_start(trainer, model)
     bar.on_train_epoch_start(trainer, model)
 

tests/deprecated_api/test_remove_1-8.py

@@ -16,10 +16,11 @@
 import pytest
 import torch
 
-from pytorch_lightning import Trainer
+from pytorch_lightning import Callback, Trainer
 from pytorch_lightning.utilities.apply_func import move_data_to_device
 from pytorch_lightning.utilities.enums import DeviceType, DistributedType
 from pytorch_lightning.utilities.imports import _TORCHTEXT_LEGACY
+from tests.helpers.boring_model import BoringModel
 from tests.helpers.torchtext_utils import get_dummy_torchtext_data_iterator
 
 
@@ -44,6 +45,34 @@ def test_v1_8_0_deprecated_torchtext_batch():
         _ = move_data_to_device(batch=batch, device=torch.device("cpu"))
 
 
+def test_v1_8_0_on_init_start_end(tmpdir):
+    class TestCallback(Callback):
+        def on_init_start(self, trainer):
+            print("Starting to init trainer!")
+
+        def on_init_end(self, trainer):
+            print("Trainer is init now")
+
+    model = BoringModel()
+
+    trainer = Trainer(
+        callbacks=[TestCallback()],
+        max_epochs=1,
+        fast_dev_run=True,
+        enable_progress_bar=False,
+        logger=False,
+        default_root_dir=tmpdir,
+    )
+    with pytest.deprecated_call(
+        match="The `on_init_start` callback hook was deprecated in v1.6 and will be removed in v1.8"
+    ):
+        trainer.fit(model)
+    with pytest.deprecated_call(
+        match="The `on_init_end` callback hook was deprecated in v1.6 and will be removed in v1.8"
+    ):
+        trainer.validate(model)
+
+
 def test_v1_8_0_deprecated_call_hook():
     trainer = Trainer(
         max_epochs=1,