Fix val_check_interval with fast_dev_run (#5540)

* fix val_check_interval with fast_dev_run

* chlog

Author: rohitgr7

CHANGELOG.md

@@ -27,6 +27,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Fixed `reinit_scheduler_properties` with correct optimizer ([#5519](https://github.com/PyTorchLightning/pytorch-lightning/pull/5519))
 
 
+- Fixed `val_check_interval` with `fast_dev_run` ([#5540](https://github.com/PyTorchLightning/pytorch-lightning/pull/5540))
+
+
 ## [1.1.4] - 2021-01-12
 
 ### Added

pytorch_lightning/trainer/connectors/debugging_connector.py

@@ -59,7 +59,7 @@ def on_init_start(
             self.trainer.max_steps = fast_dev_run
             self.trainer.num_sanity_val_steps = 0
             self.trainer.max_epochs = 1
-            self.trainer.val_check_interval = 1.0
+            val_check_interval = 1.0
             self.trainer.check_val_every_n_epoch = 1
             self.trainer.logger = DummyLogger()
 

tests/trainer/flags/test_fast_dev_run.py

@@ -36,30 +36,59 @@ def test_callbacks_and_logger_not_called_with_fastdevrun(tmpdir, fast_dev_run):
     class FastDevRunModel(BoringModel):
         def __init__(self):
             super().__init__()
-            self.training_step_called = False
-            self.validation_step_called = False
-            self.test_step_called = False
+            self.training_step_call_count = 0
+            self.training_epoch_end_call_count = 0
+            self.validation_step_call_count = 0
+            self.validation_epoch_end_call_count = 0
+            self.test_step_call_count = 0
 
         def training_step(self, batch, batch_idx):
             self.log('some_metric', torch.tensor(7.))
             self.logger.experiment.dummy_log('some_distribution', torch.randn(7) + batch_idx)
-            self.training_step_called = True
+            self.training_step_call_count += 1
             return super().training_step(batch, batch_idx)
 
+        def training_epoch_end(self, outputs):
+            self.training_epoch_end_call_count += 1
+            super().training_epoch_end(outputs)
+
         def validation_step(self, batch, batch_idx):
-            self.validation_step_called = True
+            self.validation_step_call_count += 1
             return super().validation_step(batch, batch_idx)
 
+        def validation_epoch_end(self, outputs):
+            self.validation_epoch_end_call_count += 1
+            super().validation_epoch_end(outputs)
+
+        def test_step(self, batch, batch_idx):
+            self.test_step_call_count += 1
+            return super().test_step(batch, batch_idx)
+
     checkpoint_callback = ModelCheckpoint()
     early_stopping_callback = EarlyStopping()
     trainer_config = dict(
         fast_dev_run=fast_dev_run,
+        val_check_interval=2,
         logger=True,
         log_every_n_steps=1,
         callbacks=[checkpoint_callback, early_stopping_callback],
     )
 
-    def _make_fast_dev_run_assertions(trainer):
+    def _make_fast_dev_run_assertions(trainer, model):
+        # check the call count for train/val/test step/epoch
+        assert model.training_step_call_count == fast_dev_run
+        assert model.training_epoch_end_call_count == 1
+        assert model.validation_step_call_count == 0 if model.validation_step is None else fast_dev_run
+        assert model.validation_epoch_end_call_count == 0 if model.validation_step is None else 1
+        assert model.test_step_call_count == fast_dev_run
+
+        # check trainer arguments
+        assert trainer.max_steps == fast_dev_run
+        assert trainer.num_sanity_val_steps == 0
+        assert trainer.max_epochs == 1
+        assert trainer.val_check_interval == 1.0
+        assert trainer.check_val_every_n_epoch == 1
+
         # there should be no logger with fast_dev_run
         assert isinstance(trainer.logger, DummyLogger)
         assert len(trainer.dev_debugger.logged_metrics) == fast_dev_run
@@ -76,13 +105,10 @@ def _make_fast_dev_run_assertions(trainer):
     train_val_step_model = FastDevRunModel()
     trainer = Trainer(**trainer_config)
     results = trainer.fit(train_val_step_model)
-    assert results
+    trainer.test(ckpt_path=None)
 
-    # make sure both training_step and validation_step were called
-    assert train_val_step_model.training_step_called
-    assert train_val_step_model.validation_step_called
-
-    _make_fast_dev_run_assertions(trainer)
+    assert results
+    _make_fast_dev_run_assertions(trainer, train_val_step_model)
 
     # -----------------------
     # also called once with no val step
@@ -92,10 +118,7 @@ def _make_fast_dev_run_assertions(trainer):
 
     trainer = Trainer(**trainer_config)
     results = trainer.fit(train_step_only_model)
-    assert results
+    trainer.test(ckpt_path=None)
 
-    # make sure only training_step was called
-    assert train_step_only_model.training_step_called
-    assert not train_step_only_model.validation_step_called
-
-    _make_fast_dev_run_assertions(trainer)
+    assert results
+    _make_fast_dev_run_assertions(trainer, train_step_only_model)