Reset current_fx properties on lightning module in teardown

pytorch_lightning/core/lightning.py

@@ -92,19 +92,19 @@ def __init__(self, *args: Any, **kwargs: Any) -> None:
         self._device_type = None
 
         #: True if using amp
-        self.use_amp = False
+        self.use_amp: bool = False
 
         #: The precision used
-        self.precision = 32
+        self.precision: int = 32
 
         # optionally can be set by user
         self._example_input_array = None
         self._datamodule = None
         self._results: Optional[Result] = None
-        self._current_fx_name = ''
-        self._running_manual_backward = False
-        self._current_hook_fx_name = None
-        self._current_dataloader_idx = None
+        self._current_fx_name: str = ''
+        self._running_manual_backward: bool = False
+        self._current_hook_fx_name: Optional[str] = None
+        self._current_dataloader_idx: Optional[int] = None
         self._automatic_optimization: bool = True
         self._param_requires_grad_state = dict()
 

pytorch_lightning/trainer/trainer.py

@@ -1151,6 +1151,10 @@ def call_teardown_hook(self, model: LightningModule) -> None:
         self.teardown(stage=state)
         model.teardown(stage=state)
 
+        model._current_fx_name = ""
+        model._current_hook_fx_name = None
+        model._current_dataloader_idx = None
+
     def _reset_result_and_set_hook_fx_name(self, hook_name: str) -> bool:
         # on_before_zero_grad is called within training_step
         if "batch_start" in hook_name or hook_name in ("on_before_zero_grad", "on_after_backward"):

tests/trainer/test_trainer.py

@@ -2047,3 +2047,33 @@ def test_fit_test_synchronization(tmpdir):
     trainer.fit(model)
     assert os.path.exists(checkpoint.best_model_path), f'Could not find checkpoint at rank {trainer.global_rank}'
     trainer.test()
+
+
+def test_module_current_fx_attributes_reset(tmpdir):
+    """ Ensure that lightning module's attributes related to current hook fx are reset at the end of execution. """
+    model = BoringModel()
+    model.validation_step = None
+    model.training_epoch_end = None
+    trainer = Trainer(
+        default_root_dir=tmpdir,
+        max_epochs=1,
+        checkpoint_callback=False,
+        logger=False,
+        limit_val_batches=0,
+    )
+    trainer.fit(model)
+    assert model._current_fx_name == "", f"_current_fx_name not reset after fit: {model._current_fx_name}"
+    assert (
+        model._current_hook_fx_name is None
+    ), f"_current_hook_fx_name not reset after fit: {model._current_hook_fx_name}"
+    assert (
+        model._current_dataloader_idx is None
+    ), f"_current_dataloader_idx not reset after fit: {model._current_dataloader_idx}"
+    trainer.test(model)
+    assert model._current_fx_name == "", f"_current_fx_name not reset after test: {model._current_fx_name}"
+    assert (
+        model._current_hook_fx_name is None
+    ), f"_current_hook_fx_name not reset after test: {model._current_hook_fx_name}"
+    assert (
+        model._current_dataloader_idx is None
+    ), f"_current_dataloader_idx not reset after test: {model._current_dataloader_idx}"