Remove deprecated prepare_data_per_node in Trainer

CHANGELOG.md

@@ -63,7 +63,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Removed the deprecated `progress_bar_refresh_rate` argument from the `Trainer` constructor ([#12514](https://github.com/PyTorchLightning/pytorch-lightning/pull/12514))
 
 
--
+- Remove deprecated `prepare_data_per_node` flag from `Trainer` ([#12536](https://github.com/PyTorchLightning/pytorch-lightning/pull/12536))
 
 
 -

docs/source/common/trainer.rst

@@ -1171,39 +1171,6 @@ To define your own behavior, subclass the relevant class and pass it in. Here's
 
     trainer = Trainer(plugins=[MyCluster()], ...)
 
-
-prepare_data_per_node
-^^^^^^^^^^^^^^^^^^^^^
-.. warning:: ``prepare_data_per_node`` has been deprecated in v1.5 and will be removed in v1.7.
-    Please set its value inside ``LightningDataModule`` and/or ``LightningModule`` directly described
-    in the following code:
-
-    .. testcode::
-
-        class LitDataModule(LightningDataModule):
-            def __init__(self):
-                super().__init__()
-                self.prepare_data_per_node = True
-
-.. raw:: html
-
-    <video width="50%" max-width="400px" controls
-    poster="https://pl-bolts-doc-images.s3.us-east-2.amazonaws.com/pl_docs/trainer_flags/thumb/prepare_data_per_node.jpg"
-    src="https://pl-bolts-doc-images.s3.us-east-2.amazonaws.com/pl_docs/trainer_flags/prepare_data_per_node.mp4"></video>
-
-|
-
-If set to ``True`` will call ``prepare_data()`` on LOCAL_RANK=0 for every node.
-If set to ``False`` will only call from NODE_RANK=0, LOCAL_RANK=0.
-
-.. testcode::
-
-    # default
-    Trainer(prepare_data_per_node=True)
-
-    # use only NODE_RANK=0, LOCAL_RANK=0
-    Trainer(prepare_data_per_node=False)
-
 precision
 ^^^^^^^^^
 

docs/source/starter/core_guide.rst

@@ -596,8 +596,7 @@ will cause all sorts of issues.
 To solve this problem, make sure your download code is in the ``prepare_data`` method in the DataModule.
 In this method we do all the preparation we need to do once (instead of on every GPU).
 
-``prepare_data`` can be called in two ways, once per node or only on the root node
-(``Trainer(prepare_data_per_node=False)``).
+``prepare_data`` can be called in two ways, once per node or only on the root node.
 
 .. code-block:: python
 

pytorch_lightning/core/hooks.py

@@ -361,21 +361,27 @@ def prepare_data(self):
                 self.split = data_split
                 self.some_state = some_other_state()
 
-        In DDP ``prepare_data`` can be called in two ways (using Trainer(prepare_data_per_node)):
+        In DDP ``prepare_data`` can be called in two ways
+        (using :ref:`prepare_data_per_node<common/lightning_module:prepare_data_per_node>`)
 
         1. Once per node. This is the default and is only called on LOCAL_RANK=0.
         2. Once in total. Only called on GLOBAL_RANK=0.
 
-        See :ref:`prepare_data_per_node<common/lightning_module:prepare_data_per_node>`.
-
         Example::
 
             # DEFAULT
             # called once per node on LOCAL_RANK=0 of that node
-            Trainer(prepare_data_per_node=True)
+            class LitDataModule(LightningDataModule):
+                def __init__(self):
+                    super().__init__()
+                    self.prepare_data_per_node = True
+
 
             # call on GLOBAL_RANK=0 (great for shared file systems)
-            Trainer(prepare_data_per_node=False)
+            class LitDataModule(LightningDataModule):
+                def __init__(self):
+                    super().__init__()
+                    self.prepare_data_per_node = False
 
         This is called before requesting the dataloaders:
 
@@ -387,6 +393,7 @@ def prepare_data(self):
             model.train_dataloader()
             model.val_dataloader()
             model.test_dataloader()
+            model.predict_dataloader()
         """
 
     def setup(self, stage: Optional[str] = None) -> None:

pytorch_lightning/trainer/connectors/data_connector.py

@@ -39,7 +39,7 @@
 from pytorch_lightning.utilities.exceptions import 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_warn
+from pytorch_lightning.utilities.rank_zero import rank_zero_warn
 from pytorch_lightning.utilities.types import EVAL_DATALOADERS, TRAIN_DATALOADERS
 from pytorch_lightning.utilities.warnings import PossibleUserWarning, WarningCache
 
@@ -73,18 +73,9 @@ def on_trainer_init(
         self,
         check_val_every_n_epoch: int,
         reload_dataloaders_every_n_epochs: int,
-        prepare_data_per_node: Optional[bool] = None,
     ) -> None:
         self.trainer.datamodule = None
 
-        if prepare_data_per_node is not None:
-            rank_zero_deprecation(
-                "Setting `prepare_data_per_node` with the trainer flag is deprecated in v1.5.0 and will be removed in"
-                " v1.7.0. Please set `prepare_data_per_node` in `LightningDataModule` and/or `LightningModule`"
-                " directly instead."
-            )
-        self.trainer.prepare_data_per_node = prepare_data_per_node
-
         if not isinstance(check_val_every_n_epoch, int):
             raise MisconfigurationException(
                 f"check_val_every_n_epoch should be an integer. Found {check_val_every_n_epoch}"
@@ -112,28 +103,12 @@ def prepare_data(self) -> None:
         # check for prepare_data_per_node & datamodule lifecycle properties before calling datamodule.prepare_data
         if datamodule is not None:
             dm_prepare_data_per_node = datamodule.prepare_data_per_node
-            dm_eq_prepare_data = datamodule.prepare_data_per_node == self.trainer.prepare_data_per_node
-            if self.trainer.prepare_data_per_node is not None and not dm_eq_prepare_data:
-                raise MisconfigurationException(
-                    "Inconsistent settings found for `prepare_data_per_node`."
-                    f" Value was set with both `Trainer(prepare_data_per_node={self.trainer.prepare_data_per_node}.)`"
-                    f" and `DataModule.prepare_data_per_node={datamodule.prepare_data_per_node}`."
-                    " Move `prepare_data_per_node` setting to DataModule property."
-                )
             if (dm_prepare_data_per_node and local_rank_zero) or (not dm_prepare_data_per_node and global_rank_zero):
                 self.trainer.datamodule.prepare_data()
         # handle lightning module prepare data:
         # check for prepare_data_per_node before calling lightning_module.prepare_data
         if lightning_module is not None:
             lm_prepare_data_per_node = lightning_module.prepare_data_per_node
-            lm_eq_prepare_data = lightning_module.prepare_data_per_node == self.trainer.prepare_data_per_node
-            if (self.trainer.prepare_data_per_node is not None) and not lm_eq_prepare_data:
-                raise MisconfigurationException(
-                    "Inconsistent settings found for `prepare_data_per_node`."
-                    f" Value was set with both `Trainer(prepare_data_per_node={self.trainer.prepare_data_per_node}.)`"
-                    f" and `LightningModule.prepare_data_per_node={lightning_module.prepare_data_per_node}`."
-                    " Move `prepare_data_per_node` setting to LightningModule property."
-                )
             if (lm_prepare_data_per_node and local_rank_zero) or (not lm_prepare_data_per_node and global_rank_zero):
                 self.trainer._call_lightning_module_hook("prepare_data")
                 self.trainer._is_data_prepared = True

pytorch_lightning/trainer/trainer.py

@@ -181,7 +181,6 @@ def __init__(
         replace_sampler_ddp: bool = True,
         detect_anomaly: bool = False,
         auto_scale_batch_size: Union[str, bool] = False,
-        prepare_data_per_node: Optional[bool] = None,
         plugins: Optional[Union[PLUGIN_INPUT, List[PLUGIN_INPUT]]] = None,
         amp_backend: str = "native",
         amp_level: Optional[str] = None,
@@ -314,14 +313,6 @@ def __init__(
             log_every_n_steps: How often to log within steps.
                 Default: ``50``.
 
-            prepare_data_per_node: If True, each LOCAL_RANK=0 will call prepare data.
-                Otherwise only NODE_RANK=0, LOCAL_RANK=0 will prepare data
-
-                .. deprecated:: v1.5
-                    Deprecated in v1.5.0 and will be removed in v1.7.0
-                    Please set ``prepare_data_per_node`` in ``LightningDataModule`` and/or
-                    ``LightningModule`` directly instead.
-
             process_position: Orders the progress bar when running multiple models on same machine.
 
                 .. deprecated:: v1.5
@@ -542,7 +533,6 @@ def __init__(
         self._data_connector.on_trainer_init(
             check_val_every_n_epoch,
             reload_dataloaders_every_n_epochs,
-            prepare_data_per_node,
         )
 
         if terminate_on_nan is not None:

tests/core/test_datamodules.py

@@ -487,14 +487,3 @@ class BoringDataModule2(LightningDataModule):
     assert hasattr(BoringDataModule2, "__repr__")
     assert BoringDataModule2(batch_size=32).prepare_data() is None
     assert BoringDataModule2(batch_size=32) == BoringDataModule2(batch_size=32)
-
-
-def test_inconsistent_prepare_data_per_node(tmpdir):
-    with pytest.raises(MisconfigurationException, match="Inconsistent settings found for `prepare_data_per_node`."):
-        model = BoringModel()
-        dm = BoringDataModule()
-        with pytest.deprecated_call(match="prepare_data_per_node` with the trainer flag is deprecated"):
-            trainer = Trainer(prepare_data_per_node=False)
-        trainer.model = model
-        trainer.datamodule = dm
-        trainer._data_connector.prepare_data()

tests/deprecated_api/test_remove_1-7.py

@@ -125,11 +125,6 @@ def get_progress_bar_dict(self):
         _ = trainer.progress_bar_dict
 
 
-def test_v1_7_0_trainer_prepare_data_per_node(tmpdir):
-    with pytest.deprecated_call(match="Setting `prepare_data_per_node` with the trainer flag is deprecated in v1.5.0"):
-        _ = Trainer(prepare_data_per_node=False)
-
-
 @pytest.mark.parametrize("terminate_on_nan", [True, False])
 def test_v1_7_0_trainer_terminate_on_nan(tmpdir, terminate_on_nan):
     with pytest.deprecated_call(