Fix the condition for calling update_learning_rates (#7032)

Co-authored-by: Adrian Wälchli <aedu.waelchli@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

CHANGELOG.md

@@ -344,6 +344,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
     * Remove hardcoding of local rank in accelerator connector ([#6878](https://github.com/PyTorchLightning/pytorch-lightning/pull/6878))
 
 
+- Fixed incorrect number of calls to LR scheduler when `check_val_every_n_epoch > 1` ([#7032](https://github.com/PyTorchLightning/pytorch-lightning/pull/7032))
+
+
 ## [1.2.7] - 2021-04-06
 
 ### Fixed

pytorch_lightning/trainer/training_loop.py

@@ -474,7 +474,6 @@ def run_training_epoch(self):
 
         train_dataloader = self.trainer.data_connector.get_profiled_train_dataloader(train_dataloader)
         dataloader_idx = 0
-        val_loop_called = False
 
         batch_idx = None
         is_last_batch = None
@@ -516,7 +515,6 @@ def run_training_epoch(self):
                 self.trainer.validating = True
                 self.trainer.run_evaluation()
                 self.trainer.training = True
-                val_loop_called = True
 
             # -----------------------------------------
             # SAVE LOGGERS (ie: Tensorboard, etc...)
@@ -565,7 +563,7 @@ def run_training_epoch(self):
         should_train_only = self.trainer.disable_validation or should_skip_eval
 
         # update epoch level lr_schedulers if no val loop outside train loop is triggered
-        if (val_loop_called and not should_check_val) or should_train_only:
+        if not should_check_val or should_train_only:
             self.trainer.optimizer_connector.update_learning_rates(interval='epoch')
 
         if should_train_only:

tests/trainer/optimization/test_optimizers.py

@@ -11,6 +11,8 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+from unittest import mock
+
 import pytest
 import torch
 from torch import optim
@@ -577,21 +579,21 @@ def configure_optimizers(self):
         trainer.fit(model)
 
 
-class TestModel(BoringModel):
+@RunIf(min_gpus=2, special=True)
+def test_optimizer_state_on_device(tmpdir):
+    """ Test that optimizers that create state initially at instantiation still end up with the state on the GPU. """
 
-    def configure_optimizers(self):
-        # Adagrad creates state tensors immediately, model is not yet on GPU.
-        return optim.Adagrad(self.parameters())
+    class TestModel(BoringModel):
 
-    def on_train_start(self, *args, **kwargs):
-        opt = self.optimizers()
-        _, state = next(iter(opt.state.items()))
-        assert state["sum"].device == torch.device("cuda", self.local_rank) == self.device
+        def configure_optimizers(self):
+            # Adagrad creates state tensors immediately, model is not yet on GPU.
+            return optim.Adagrad(self.parameters())
 
+        def on_train_start(self, *args, **kwargs):
+            opt = self.optimizers()
+            _, state = next(iter(opt.state.items()))
+            assert state["sum"].device == torch.device("cuda", self.local_rank) == self.device
 
-@RunIf(min_gpus=2, special=True)
-def test_optimizer_state_on_device(tmpdir):
-    """ Test that optimizers that create state initially at instantiation still end up with the state on the GPU. """
     model = TestModel()
     trainer = Trainer(
         default_root_dir=tmpdir,
@@ -600,3 +602,21 @@ def test_optimizer_state_on_device(tmpdir):
         fast_dev_run=True,
     )
     trainer.fit(model)
+
+
+@pytest.mark.parametrize("check_val_every_n_epoch", [1, 2])
+@mock.patch("torch.optim.lr_scheduler.StepLR.step")
+def test_lr_scheduler_epoch_step_frequency(mocked_sched, check_val_every_n_epoch, tmpdir):
+    epochs = 4
+    expected_steps = epochs + 1  # every LRScheduler gets called once at init
+
+    model = BoringModel()
+    trainer = Trainer(
+        default_root_dir=tmpdir,
+        limit_train_batches=2,
+        limit_val_batches=2,
+        check_val_every_n_epoch=check_val_every_n_epoch,
+        max_epochs=epochs,
+    )
+    trainer.fit(model)
+    assert mocked_sched.call_count == expected_steps