enable fast_dev_run test

Author: rohitgr7

pytorch_lightning/callbacks/batch_size_finder.py

@@ -63,7 +63,7 @@ def __init__(
 
     def scale_batch_size(self, trainer, pl_module):
         if trainer.fast_dev_run:
-            rank_zero_warn("Skiping batch size scaler since `fast_dev_run` is enabled.")
+            rank_zero_warn("Skipping batch size scaler since `fast_dev_run` is enabled.")
             return
 
         if not lightning_hasattr(pl_module, self.batch_arg_name):
@@ -209,7 +209,6 @@ def _run_binary_scaling(self, trainer, pl_module, new_size):
     def _try_loop_run(self, trainer):
         if trainer.state.fn == TrainerFn.FITTING:
             trainer.fit_loop.global_step = self._dumped_params["global_step"]
-            # trainer.fit_loop.current_epoch = self._dumped_params["current_epoch"]
             loop = trainer.fit_loop
         elif trainer.state.fn == TrainerFn.VALIDATING:
             loop = trainer.validate_loop
@@ -235,7 +234,6 @@ def _reset_dataloaders(trainer, pl_module):
 
     def _dump_params(self, trainer):
         self._dumped_params = {
-            # "current_epoch": trainer.current_epoch,
             "logger": trainer.logger,
             "callbacks": trainer.callbacks,
         }
@@ -280,7 +278,6 @@ def _restore_params(self, trainer):
         trainer.callbacks = self._dumped_params["callbacks"]
 
         if trainer.state.fn == TrainerFn.FITTING:
-            # trainer.fit_loop.current_epoch = self._dumped_params["current_epoch"]
             trainer.fit_loop.global_step = self._dumped_params["global_step"]
             loop = trainer.fit_loop
             loop.max_steps = self._dumped_params["max_steps"]
@@ -300,6 +297,9 @@ def _restore_params(self, trainer):
             loop.verbose = self._dumped_params["loop_verbose"]
 
     def pre_early_exit(self, trainer):
+        if trainer.fast_dev_run:
+            return
+
         if trainer.state.fn == TrainerFn.FITTING:
             trainer.should_stop = True
             self._dumped_params["num_training_batches"] = trainer.num_training_batches
@@ -315,6 +315,9 @@ def pre_early_exit(self, trainer):
             trainer.num_predict_batches = [0]
 
     def post_early_exit(self, trainer):
+        if trainer.fast_dev_run:
+            return
+
         if trainer.state.fn == TrainerFn.FITTING:
             trainer.num_training_batches = self._dumped_params["num_training_batches"]
             loop = trainer.fit_loop

pytorch_lightning/tuner/lr_finder.py

@@ -201,7 +201,7 @@ def lr_find(
 ) -> Optional[_LRFinder]:
     """See :meth:`~pytorch_lightning.tuner.tuning.Tuner.lr_find`"""
     if trainer.fast_dev_run:
-        rank_zero_warn("Skipping learning rate finder since fast_dev_run is enabled.")
+        rank_zero_warn("Skipping learning rate finder since `fast_dev_run` is enabled.")
         return
 
     # Determine lr attr

tests/trainer/flags/test_fast_dev_run.py

@@ -11,7 +11,7 @@
 
 
 @pytest.mark.parametrize("tuner_alg", ["batch size scaler", "learning rate finder"])
-@pytest.mark.skip(reason="Temperory skip")
+# @pytest.mark.skip(reason="Temperory skip")
 def test_skip_on_fast_dev_run_tuner(tmpdir, tuner_alg):
     """Test that tuner algorithms are skipped if fast dev run is enabled."""
 
@@ -24,7 +24,7 @@ def test_skip_on_fast_dev_run_tuner(tmpdir, tuner_alg):
         auto_lr_find=(tuner_alg == "learning rate finder"),
         fast_dev_run=True,
     )
-    expected_message = f"Skipping {tuner_alg} since fast_dev_run is enabled."
+    expected_message = f"Skipping {tuner_alg} since `fast_dev_run` is enabled."
     with pytest.warns(UserWarning, match=expected_message):
         trainer.tune(model)