Fix global step update when the epoch is skipped (#7677)

* Fix global step update when the epoch is skipped

* Update CHANGELOG

* Move test

CHANGELOG.md

@@ -17,6 +17,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Fixed broadcasting in multi-node, multi-gpu DDP using torch 1.7 ([#7592](https://github.com/PyTorchLightning/pytorch-lightning/pull/7592))
 - Fixed dataloaders are not reset when tuning the model ([#7566](https://github.com/PyTorchLightning/pytorch-lightning/pull/7566))
 - Fixed print errors in `ProgressBar` when `trainer.fit` is not called ([#7674](https://github.com/PyTorchLightning/pytorch-lightning/pull/7674))
+- Fixed global step update when the epoch is skipped ([#7677](https://github.com/PyTorchLightning/pytorch-lightning/pull/7677))
 
 ## [1.3.2] - 2021-05-18
 

pytorch_lightning/trainer/training_loop.py

@@ -574,9 +574,8 @@ def run_training_epoch(self):
             self.trainer.run_evaluation(on_epoch=True)
             self.trainer.training = True
 
-        # increment the global step once
-        # progress global step according to grads progress
-        self.increment_accumulated_grad_global_step()
+        if batch_output.signal != -1:
+            self.increment_accumulated_grad_global_step()
 
     def on_train_epoch_end(self, epoch_output: List[List[List[Result]]]) -> None:
         # inform logger the batch loop has finished

tests/models/test_hooks.py

@@ -225,26 +225,6 @@ def train_dataloader(self):
     trainer.fit(model)
 
 
-@pytest.mark.parametrize('max_epochs,batch_idx_', [(2, 5), (3, 8), (4, 12)])
-def test_on_train_batch_start_hook(max_epochs, batch_idx_):
-
-    class CurrentModel(BoringModel):
-
-        def on_train_batch_start(self, batch, batch_idx, dataloader_idx):
-            if batch_idx == batch_idx_:
-                return -1
-
-    model = CurrentModel()
-    trainer = Trainer(max_epochs=max_epochs)
-    trainer.fit(model)
-    if batch_idx_ > len(model.val_dataloader()) - 1:
-        assert trainer.batch_idx == len(model.val_dataloader()) - 1
-        assert trainer.global_step == len(model.val_dataloader()) * max_epochs
-    else:
-        assert trainer.batch_idx == batch_idx_
-        assert trainer.global_step == (batch_idx_ + 1) * max_epochs
-
-
 def test_trainer_model_hook_system(tmpdir):
     """Test the LightningModule hook system."""
 

tests/trainer/loops/test_training_loop.py

@@ -11,6 +11,7 @@
 # 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.
+import pytest
 import torch
 
 from pytorch_lightning import seed_everything, Trainer
@@ -201,3 +202,23 @@ def run_training(**trainer_kwargs):
         num_sanity_val_steps=2,
     )
     assert torch.allclose(sequence0, sequence1)
+
+
+@pytest.mark.parametrize(['max_epochs', 'batch_idx_'], [(2, 5), (3, 8), (4, 12)])
+def test_on_train_batch_start_return_minus_one(max_epochs, batch_idx_):
+
+    class CurrentModel(BoringModel):
+
+        def on_train_batch_start(self, batch, batch_idx, dataloader_idx):
+            if batch_idx == batch_idx_:
+                return -1
+
+    model = CurrentModel()
+    trainer = Trainer(max_epochs=max_epochs, limit_train_batches=10)
+    trainer.fit(model)
+    if batch_idx_ > trainer.num_training_batches - 1:
+        assert trainer.batch_idx == trainer.num_training_batches - 1
+        assert trainer.global_step == trainer.num_training_batches * max_epochs
+    else:
+        assert trainer.batch_idx == batch_idx_
+        assert trainer.global_step == batch_idx_ * max_epochs