[WIP] Stricter argument hygiene for passing datamodules to trainer.fit or t…

CHANGELOG.md

@@ -196,6 +196,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 ### Deprecated
 
 
+- Deprecated passing datamodule as an unnamed argument to `Trainer.fit` and `Trainer.tune` in favor of explicitly specifying `Trainer.fit/tune(model, datamodule=dm)` ([#7239](https://github.com/PyTorchLightning/pytorch-lightning/pull/7329/))
+
+
 - Deprecated `LightningModule.grad_norm` in favor of `pytorch_lightning.utilities.grads.grad_norm` ([#7292](https://github.com/PyTorchLightning/pytorch-lightning/pull/7292))
 
 

pytorch_lightning/trainer/trainer.py

@@ -59,8 +59,9 @@
 from pytorch_lightning.trainer.training_tricks import TrainerTrainingTricksMixin
 from pytorch_lightning.tuner.lr_finder import _LRFinder
 from pytorch_lightning.tuner.tuning import Tuner
-from pytorch_lightning.utilities import DeviceType, parsing, rank_zero_warn
+from pytorch_lightning.utilities import DeviceType, parsing
 from pytorch_lightning.utilities.debugging import InternalDebugger
+from pytorch_lightning.utilities.distributed import rank_zero_deprecation, rank_zero_warn
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
 from pytorch_lightning.utilities.memory import recursive_detach
 from pytorch_lightning.utilities.model_helpers import is_overridden
@@ -440,7 +441,13 @@ def fit(
  self.training = True
 
  # if a datamodule comes in as the second arg, then fix it for the user
+ # deprecated in v1.3 and will be removed in v1.5
  if isinstance(train_dataloader, LightningDataModule):
+ rank_zero_deprecation(
+ "Passing the datamodule without using named arguments is deprecated in v1.3"
+ " and will be removed in v1.5."
+ " Pass the datamodule explicitly to trainer.fit(..., datamodule=dm)"
+ )
  datamodule = train_dataloader
  train_dataloader = None
  # If you supply a datamodule you can't supply train_dataloader or val_dataloaders
@@ -664,9 +671,16 @@ def tune(
  self.tuning = True
 
  # if a datamodule comes in as the second arg, then fix it for the user
+ # deprecated in v1.3 and will be removed in v1.5
  if isinstance(train_dataloader, LightningDataModule):
+ rank_zero_deprecation(
+ "Passing the datamodule without using named arguments is deprecated in v1.3"
+ " and will be removed in v1.5."
+ " Pass the datamodule explicitly to trainer.fit(..., datamodule=dm)"
+ )
  datamodule = train_dataloader
  train_dataloader = None
+
  # If you supply a datamodule you can't supply train_dataloader or val_dataloaders
  if (train_dataloader is not None or val_dataloaders is not None) and datamodule is not None:
  raise MisconfigurationException(

tests/deprecated_api/test_remove_1-5.py

@@ -27,7 +27,7 @@
 from pytorch_lightning.profiler import AdvancedProfiler, BaseProfiler, PyTorchProfiler, SimpleProfiler
 from pytorch_lightning.trainer.callback_hook import warning_cache as callback_warning_cache
 from tests.deprecated_api import no_deprecated_call
-from tests.helpers import BoringModel, BoringDataModule
+from tests.helpers import BoringDataModule, BoringModel
 from tests.helpers.utils import no_warning_call
 
 
@@ -382,6 +382,21 @@ def test_v1_5_0_lighting_module_grad_norm(tmpdir):
  model.grad_norm(2)
 
 
+def test_v1_5_0_datamodule_named_argument(tmpdir):
+ model = BoringModel()
+ dm = BoringDataModule()
+ trainer = Trainer(
+ default_root_dir=tmpdir,
+ max_epochs=1,
+ checkpoint_callback=False,
+ logger=False,
+ )
+ with pytest.deprecated_call(match="is deprecated in v1.3 and will be removed in v1.5"):
+ trainer.fit(model, dm)
+ with pytest.deprecated_call(match="is deprecated in v1.3 and will be removed in v1.5"):
+ trainer.tune(model, dm)
+
+
 def test_v1_5_0_datamodule_setter():
  model = BoringModel()
  datamodule = BoringDataModule()