reset val dataloader for binsearch (#9975)

CHANGELOG.md

@@ -546,6 +546,8 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 - Fixed issue with non-init dataclass fields in `apply_to_collection` ([#9963](https://github.com/PyTorchLightning/pytorch-lightning/issues/9963))
 
+- Reset `val_dataloader` in `tuner/batch_size_scaling` for binsearch ([#9975](https://github.com/PyTorchLightning/pytorch-lightning/pull/9975))
+
 
 ## [1.4.9] - 2021-09-30
 

pytorch_lightning/tuner/batch_size_scaling.py

@@ -205,6 +205,7 @@ def _run_binsearch_scaling(
             if changed:
                 # Force the train dataloader to reset as the batch size has changed
                 trainer.reset_train_dataloader(model)
+                trainer.reset_val_dataloader(model)
             else:
                 break
 

tests/tuner/test_scale_batch_size.py

@@ -274,10 +274,11 @@ def __init__(self):
         trainer.tuner.scale_batch_size(model, mode="ThisModeDoesNotExist")
 
 
-def test_dataloader_reset_with_scale_batch_size(tmpdir):
+@pytest.mark.parametrize("scale_method", ["power", "binsearch"])
+def test_dataloader_reset_with_scale_batch_size(tmpdir, scale_method):
     """Test that train and val dataloaders are reset at every update in scale batch size."""
     model = BatchSizeModel(batch_size=16)
-    scale_batch_size_kwargs = {"max_trials": 5, "init_val": 4}
+    scale_batch_size_kwargs = {"max_trials": 5, "init_val": 4, "mode": scale_method}
 
     trainer = Trainer(max_epochs=2, auto_scale_batch_size=True)
     new_batch_size = trainer.tune(model, scale_batch_size_kwargs=scale_batch_size_kwargs)["scale_batch_size"]