fix tuner.scale_batch_size not finding batch size attribute when using datamodule

CHANGELOG.md

@@ -107,6 +107,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Fixed DP reduction with collection ([#6324](https://github.com/PyTorchLightning/pytorch-lightning/pull/6324))
 
 
+- Fixed an issue with `Tuner.scale_batch_size` not finding the batch size attribute in the datamodule ([#5968](https://github.com/PyTorchLightning/pytorch-lightning/pull/5968))
+
+
 ## [1.2.1] - 2021-02-23
 
 ### Fixed

pytorch_lightning/trainer/training_loop.py

@@ -106,7 +106,7 @@ def on_train_start(self):
         # provide rank to profiler
         self.trainer.profile_connector.on_train_start(self.trainer)
 
-    def setup_fit(self, model, train_dataloader, val_dataloaders, datamodule):
+    def setup_fit(self, model, train_dataloader=None, val_dataloaders=None, datamodule=None):
         # clean hparams
         if hasattr(model, "hparams"):
             parsing.clean_namespace(model.hparams)

pytorch_lightning/tuner/tuning.py

@@ -32,13 +32,20 @@ def on_trainer_init(self, auto_lr_find, auto_scale_batch_size):
         self.trainer.auto_lr_find = auto_lr_find
         self.trainer.auto_scale_batch_size = auto_scale_batch_size
 
-    def tune(self, model, train_dataloader, val_dataloaders, datamodule):
+    def setup_trainer(
+        self,
+        model: LightningModule,
+        train_dataloader: Optional[DataLoader] = None,
+        val_dataloaders: Optional[Union[DataLoader, List[DataLoader]]] = None,
+        datamodule: LightningDataModule = None,
+    ):
+        self.trainer.model_connector.copy_trainer_model_properties(model)
         # setup data, etc...
         self.trainer.train_loop.setup_fit(model, train_dataloader, val_dataloaders, datamodule)
-
         # hook
         self.trainer.data_connector.prepare_data(model)
 
+    def tune(self, model, train_dataloader, val_dataloaders, datamodule):
         # Run auto batch size scaling
         if self.trainer.auto_scale_batch_size:
             if isinstance(self.trainer.auto_scale_batch_size, bool):
@@ -101,6 +108,7 @@ def scale_batch_size(
                 or datamodule.
 
         """
+        self.setup_trainer(model, **fit_kwargs)
         return scale_batch_size(
             self.trainer,
             model,
@@ -125,6 +133,7 @@ def lr_find(
         datamodule: Optional[LightningDataModule] = None,
         update_attr: bool = False,
     ):
+        self.setup_trainer(model, train_dataloader, val_dataloaders, datamodule)
         return lr_find(
             self.trainer,
             model,

tests/tuner/test_scale_batch_size.py

@@ -0,0 +1,52 @@
+import pytest
+from torch.utils.data import DataLoader
+
+from pytorch_lightning import Trainer
+from pytorch_lightning.tuner.tuning import Tuner
+from tests.helpers import BoringDataModule, BoringModel
+
+
+class BatchSizeDataModule(BoringDataModule):
+
+    def __init__(self, batch_size=None):
+        super().__init__()
+        if batch_size is not None:
+            self.batch_size = batch_size
+
+    def train_dataloader(self):
+        return DataLoader(self.random_train, batch_size=getattr(self, "batch_size", 1))
+
+
+class BatchSizeModel(BoringModel):
+
+    def __init__(self, batch_size=None):
+        super().__init__()
+        if batch_size is not None:
+            self.batch_size = batch_size
+
+
+@pytest.mark.parametrize(
+    "model,datamodule", [
+        (BatchSizeModel(2), None),
+        (BatchSizeModel(2), BatchSizeDataModule(2)),
+        (BatchSizeModel(2), BatchSizeDataModule(None)),
+        (BatchSizeModel(None), BatchSizeDataModule(2)),
+    ]
+)
+def test_scale_batch_size_method_with_model_or_datamodule(tmpdir, model, datamodule):
+    """ Test the tuner method `Tuner.scale_batch_size` with a datamodule. """
+    trainer = Trainer(
+        default_root_dir=tmpdir,
+        limit_train_batches=1,
+        limit_val_batches=0,
+        max_epochs=1,
+    )
+    tuner = Tuner(trainer)
+    new_batch_size = tuner.scale_batch_size(
+        model=model, mode="binsearch", init_val=4, max_trials=2, datamodule=datamodule
+    )
+    assert new_batch_size == 16
+    if hasattr(model, "batch_size"):
+        assert model.batch_size == 16
+    if datamodule is not None and hasattr(datamodule, "batch_size"):
+        assert datamodule.batch_size == 16