Fix: passing wrong strings for scheduler interval doesn't throw an error (#5923)

* Raise if scheduler interval not 'step' or 'epoch'

* Add test for unknown 'interval' value in scheduler

* Use BoringModel instead of EvalModelTemplate

Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com>

* Fix import order

* Apply yapf in test_datamodules

* Add missing imports to test_datamodules

* Fix too long comment

* Update pytorch_lightning/trainer/optimizers.py

* Fix unused imports and exception message

* Fix failing test

Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com>
Co-authored-by: Carlos Mocholí <carlossmocholi@gmail.com>

pytorch_lightning/trainer/optimizers.py

@@ -117,6 +117,12 @@ class TrainerOptimizersMixin(ABC):
                     raise MisconfigurationException(
                         'The lr scheduler dict must have the key "scheduler" with its item being an lr scheduler'
                     )
+                if 'interval' in scheduler and scheduler['interval'] not in ('step', 'epoch'):
+                    raise MisconfigurationException(
+                        f'The "interval" key in lr scheduler dict must be "step" or "epoch"'
+                        f' but is "{scheduler["interval"]}"'
+                    )
+
                 scheduler['reduce_on_plateau'] = isinstance(
                     scheduler['scheduler'], optim.lr_scheduler.ReduceLROnPlateau
                 )

tests/trainer/optimization/test_optimizers.py

@@ -459,6 +459,24 @@ def test_unknown_configure_optimizers_raises(tmpdir):
         trainer.fit(model)
 
 
+def test_lr_scheduler_with_unknown_interval_raises(tmpdir):
+    """
+    Test exception when lr_scheduler dict has unknown interval param value
+    """
+    model = BoringModel()
+    optimizer = torch.optim.Adam(model.parameters())
+    model.configure_optimizers = lambda: {
+        'optimizer': optimizer,
+        'lr_scheduler': {
+            'scheduler': torch.optim.lr_scheduler.StepLR(optimizer, 1),
+            'interval': "incorrect_unknown_value"
+        },
+    }
+    trainer = Trainer(default_root_dir=tmpdir, fast_dev_run=True)
+    with pytest.raises(MisconfigurationException, match=r'The "interval" key in lr scheduler dict must be'):
+        trainer.fit(model)
+
+
 def test_lr_scheduler_with_extra_keys_warns(tmpdir):
     """
     Test warning when lr_scheduler dict has extra keys