Remove deprecated on_init_start_end

docs/source-pytorch/extensions/callbacks.rst

@@ -171,18 +171,6 @@ teardown
 .. automethod:: pytorch_lightning.callbacks.Callback.teardown
     :noindex:
 
-on_init_start
-^^^^^^^^^^^^^
-
-.. automethod:: pytorch_lightning.callbacks.Callback.on_init_start
-    :noindex:
-
-on_init_end
-^^^^^^^^^^^
-
-.. automethod:: pytorch_lightning.callbacks.Callback.on_init_end
-    :noindex:
-
 on_fit_start
 ^^^^^^^^^^^^
 

src/pytorch_lightning/CHANGELOG.md

@@ -248,6 +248,8 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 - Removed the deprecated `Trainer.use_amp` and `LightningModule.use_amp` attributes ([#14832](https://github.com/Lightning-AI/lightning/pull/14832))
 
+- Removed the deprecated callback hooks `Callback.on_init_start` and `Callback.on_init_end` ([#14867](https://github.com/Lightning-AI/lightning/pull/14867))
+
 
 - Removed the deprecated `Trainer.run_stage` in favor of `Trainer.{fit,validate,test,predict}` ([#14870](https://github.com/Lightning-AI/lightning/pull/14870))
 

src/pytorch_lightning/callbacks/callback.py

@@ -78,22 +78,6 @@ def setup(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", stage: s
     def teardown(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", stage: str) -> None:
         """Called when fit, validate, test, predict, or tune ends."""
 
-    def on_init_start(self, trainer: "pl.Trainer") -> None:
-        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.
-        """
-
-    def on_init_end(self, trainer: "pl.Trainer") -> None:
-        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.
-        """
-
     def on_fit_start(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
         """Called when fit begins."""
 

src/pytorch_lightning/callbacks/lambda_function.py

@@ -44,8 +44,6 @@ def __init__(
         setup: Optional[Callable] = None,
         on_configure_sharded_model: Optional[Callable] = None,
         teardown: Optional[Callable] = None,
-        on_init_start: Optional[Callable] = None,
-        on_init_end: Optional[Callable] = None,
         on_fit_start: Optional[Callable] = None,
         on_fit_end: Optional[Callable] = None,
         on_sanity_check_start: Optional[Callable] = None,

src/pytorch_lightning/core/module.py

@@ -1180,10 +1180,6 @@ def configure_callbacks(self):
                 early_stop = EarlyStopping(monitor="val_acc", mode="max")
                 checkpoint = ModelCheckpoint(monitor="val_loss")
                 return [early_stop, checkpoint]
-
-        Note:
-            Certain callback methods like :meth:`~pytorch_lightning.callbacks.base.Callback.on_init_start`
-            will never be invoked on the new callbacks returned here.
         """
         return []
 

src/pytorch_lightning/trainer/configuration_validator.py

@@ -210,13 +210,6 @@ def _check_on_pretrain_routine(model: "pl.LightningModule") -> None:
 
 def _check_deprecated_callback_hooks(trainer: "pl.Trainer") -> None:
     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.")
-
         if is_overridden(method_name="on_configure_sharded_model", instance=callback):
             rank_zero_deprecation(
                 "The `on_configure_sharded_model` callback hook was deprecated in"

src/pytorch_lightning/trainer/connectors/logger_connector/fx_validator.py

@@ -57,8 +57,6 @@ class _LogOptions(TypedDict):
         "optimizer_zero_grad": _LogOptions(
             allowed_on_step=(False, True), allowed_on_epoch=(False, True), default_on_step=True, default_on_epoch=False
         ),
-        "on_init_start": None,
-        "on_init_end": None,
         "on_fit_start": None,
         "on_fit_end": None,
         "on_sanity_check_start": None,

src/pytorch_lightning/trainer/trainer.py

@@ -453,9 +453,6 @@ def __init__(
             accumulate_grad_batches,
         )
 
-        # hook
-        self._call_callback_hooks("on_init_start")
-
         # init data flags
         self.check_val_every_n_epoch: Optional[int]
         self._data_connector.on_trainer_init(
@@ -523,9 +520,6 @@ def __init__(
             num_sanity_val_steps,
         )
 
-        # Callback system
-        self._call_callback_hooks("on_init_end")
-
     def _setup_on_init(self) -> None:
         setup._log_device_info(self)
 
@@ -1333,15 +1327,6 @@ def _call_callback_hooks(
         **kwargs: Any,
     ) -> None:
         log.debug(f"{self.__class__.__name__}: calling callback hook: {hook_name}")
-        # 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
-            for callback in self.callbacks:
-                fn = getattr(callback, hook_name)
-                if callable(fn):
-                    fn(self, *args, **kwargs)
-            return
 
         pl_module = self.lightning_module
         if pl_module:

tests/tests_pytorch/callbacks/test_callbacks.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 from pathlib import Path
 from re import escape
-from unittest.mock import call, Mock
+from unittest.mock import Mock
 
 import pytest
 
@@ -39,13 +39,8 @@ def configure_callbacks(self):
     )
 
     def assert_expected_calls(_trainer, model_callback, trainer_callback):
-        # some methods in callbacks configured through model won't get called
-        uncalled_methods = [call.on_init_start(_trainer), call.on_init_end(_trainer)]
-        for uncalled in uncalled_methods:
-            assert uncalled not in model_callback.method_calls
-
         # assert that the rest of calls are the same as for trainer callbacks
-        expected_calls = [m for m in trainer_callback.method_calls if m not in uncalled_methods]
+        expected_calls = [m for m in trainer_callback.method_calls if m]
         assert expected_calls
         assert model_callback.method_calls == expected_calls
 

tests/tests_pytorch/callbacks/test_lambda_function.py

@@ -49,7 +49,7 @@ def call(hook, *_, **__):
         callbacks=[LambdaCallback(**hooks_args)],
     )
     with pytest.deprecated_call(
-        match="`on_init_start` callback hook was deprecated in v1.6 and will be removed in v1.8."
+        match="`on_configure_sharded_model` callback hook was deprecated in v1.6 and will be removed in v1.8"
     ):
         trainer.fit(model)
     ckpt_path = trainer.checkpoint_callback.best_model_path
@@ -65,15 +65,15 @@ def call(hook, *_, **__):
         callbacks=[LambdaCallback(**hooks_args)],
     )
     with pytest.deprecated_call(
-        match="`on_init_start` callback hook was deprecated in v1.6 and will be removed in v1.8."
+        match="`on_configure_sharded_model` callback hook was deprecated in v1.6 and will be removed in v1.8"
     ):
         trainer.fit(model, ckpt_path=ckpt_path)
     with pytest.deprecated_call(
-        match="`on_init_start` callback hook was deprecated in v1.6 and will be removed in v1.8."
+        match="`on_configure_sharded_model` callback hook was deprecated in v1.6 and will be removed in v1.8"
     ):
         trainer.test(model)
     with pytest.deprecated_call(
-        match="`on_init_start` callback hook was deprecated in v1.6 and will be removed in v1.8."
+        match="`on_configure_sharded_model` callback hook was deprecated in v1.6 and will be removed in v1.8"
     ):
         trainer.predict(model)
 

tests/tests_pytorch/deprecated_api/test_remove_1-8.py

@@ -21,34 +21,6 @@
 from pytorch_lightning.demos.boring_classes import BoringModel
 
 
-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_v_1_8_0_deprecated_device_stats_monitor_prefix_metric_keys():
     from pytorch_lightning.callbacks.device_stats_monitor import prefix_metric_keys
 

tests/tests_pytorch/models/test_hooks.py

@@ -504,10 +504,6 @@ def training_step(self, batch, batch_idx):
         track_grad_norm=1,
         **kwargs,
     )
-    assert called == [
-        dict(name="Callback.on_init_start", args=(trainer,)),
-        dict(name="Callback.on_init_end", args=(trainer,)),
-    ]
     trainer.fit(model)
     saved_ckpt = {
         "callbacks": ANY,
@@ -523,8 +519,6 @@ def training_step(self, batch, batch_idx):
         saved_ckpt[trainer.precision_plugin.__class__.__qualname__] = ANY
     device = torch.device("cuda:0" if "accelerator" in kwargs and kwargs["accelerator"] == "gpu" else "cpu")
     expected = [
-        dict(name="Callback.on_init_start", args=(trainer,)),
-        dict(name="Callback.on_init_end", args=(trainer,)),
         dict(name="configure_callbacks"),
         dict(name="prepare_data"),
         dict(name="Callback.on_before_accelerator_backend_setup", args=(trainer, model)),
@@ -621,10 +615,6 @@ def test_trainer_model_hook_system_fit_no_val_and_resume_max_epochs(tmpdir):
         callbacks=[callback],
         track_grad_norm=1,
     )
-    assert called == [
-        dict(name="Callback.on_init_start", args=(trainer,)),
-        dict(name="Callback.on_init_end", args=(trainer,)),
-    ]
 
     # resume from checkpoint with HookedModel
     model = HookedModel(called)
@@ -641,8 +631,6 @@ def test_trainer_model_hook_system_fit_no_val_and_resume_max_epochs(tmpdir):
     }
     saved_ckpt = {**loaded_ckpt, "global_step": 4, "epoch": 1}
     expected = [
-        dict(name="Callback.on_init_start", args=(trainer,)),
-        dict(name="Callback.on_init_end", args=(trainer,)),
         dict(name="configure_callbacks"),
         dict(name="prepare_data"),
         dict(name="Callback.on_before_accelerator_backend_setup", args=(trainer, model)),
@@ -718,10 +706,6 @@ def test_trainer_model_hook_system_fit_no_val_and_resume_max_steps(tmpdir):
         callbacks=[callback],
         track_grad_norm=1,
     )
-    assert called == [
-        dict(name="Callback.on_init_start", args=(trainer,)),
-        dict(name="Callback.on_init_end", args=(trainer,)),
-    ]
 
     trainer.fit(model, ckpt_path=best_model_path)
     loaded_ckpt = {
@@ -736,8 +720,6 @@ def test_trainer_model_hook_system_fit_no_val_and_resume_max_steps(tmpdir):
     }
     saved_ckpt = {**loaded_ckpt, "global_step": steps_after_reload}
     expected = [
-        dict(name="Callback.on_init_start", args=(trainer,)),
-        dict(name="Callback.on_init_end", args=(trainer,)),
         dict(name="configure_callbacks"),
         dict(name="prepare_data"),
         dict(name="Callback.on_before_accelerator_backend_setup", args=(trainer, model)),
@@ -799,10 +781,6 @@ def test_trainer_model_hook_system_eval(tmpdir, batches, verb, noun, dataloader,
         enable_model_summary=False,
         callbacks=[callback],
     )
-    assert called == [
-        dict(name="Callback.on_init_start", args=(trainer,)),
-        dict(name="Callback.on_init_end", args=(trainer,)),
-    ]
     fn = getattr(trainer, verb)
     fn(model, verbose=False)
     hooks = [
@@ -819,8 +797,6 @@ def test_trainer_model_hook_system_eval(tmpdir, batches, verb, noun, dataloader,
         dict(name=f"on_{noun}_model_train"),
     ]
     expected = [
-        dict(name="Callback.on_init_start", args=(trainer,)),
-        dict(name="Callback.on_init_end", args=(trainer,)),
         dict(name="configure_callbacks"),
         dict(name="prepare_data"),
         dict(name="Callback.on_before_accelerator_backend_setup", args=(trainer, model)),
@@ -843,14 +819,8 @@ def test_trainer_model_hook_system_predict(tmpdir):
     trainer = Trainer(
         default_root_dir=tmpdir, limit_predict_batches=batches, enable_progress_bar=False, callbacks=[callback]
     )
-    assert called == [
-        dict(name="Callback.on_init_start", args=(trainer,)),
-        dict(name="Callback.on_init_end", args=(trainer,)),
-    ]
     trainer.predict(model)
     expected = [
-        dict(name="Callback.on_init_start", args=(trainer,)),
-        dict(name="Callback.on_init_end", args=(trainer,)),
         dict(name="configure_callbacks"),
         dict(name="prepare_data"),
         dict(name="Callback.on_before_accelerator_backend_setup", args=(trainer, model)),

tests/tests_pytorch/trainer/logging_/test_logger_connector.py

@@ -46,8 +46,6 @@ def test_fx_validator():
         "on_fit_end",
         "on_configure_sharded_model",
         "on_fit_start",
-        "on_init_end",
-        "on_init_start",
         "on_exception",
         "on_load_checkpoint",
         "load_state_dict",
@@ -90,8 +88,6 @@ def test_fx_validator():
         "on_fit_end",
         "on_fit_start",
         "on_configure_sharded_model",
-        "on_init_end",
-        "on_init_start",
         "on_exception",
         "on_load_checkpoint",
         "load_state_dict",
@@ -164,10 +160,6 @@ def call(hook, trainer=None, model=None, *_, **__):
                 return
 
             lightning_module = trainer.lightning_module or model
-            if lightning_module is None:
-                # `on_init_{start,end}` do not have the `LightningModule` available
-                assert hook in ("on_init_start", "on_init_end")
-                return
 
             if hook in not_supported:
                 with pytest.raises(MisconfigurationException, match=not_supported[hook]):