Increment the total batch idx before the accumulation early exit

CHANGELOG.md

@@ -127,6 +127,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Fixed global step update when the epoch is skipped ([#7677](https://github.com/PyTorchLightning/pytorch-lightning/pull/7677))
 
 
+- Fixed training loop total batch counter when accumulate grad batches was enabled ([#7692](https://github.com/PyTorchLightning/pytorch-lightning/pull/7692))
+
+
 - Fixed broadcasting in multi-node, multi-gpu DDP using torch 1.7 ([#7592](https://github.com/PyTorchLightning/pytorch-lightning/pull/7592))
 
 

pytorch_lightning/trainer/training_loop.py

@@ -529,6 +529,8 @@ def run_training_epoch(self):
             self.update_train_loop_lr_schedulers(monitor_metrics=monitor_metrics)
             self.trainer.checkpoint_connector.has_trained = True
 
+            self.total_batch_idx += 1
+
             # max steps reached, end training
             if (
                 self.max_steps is not None and self.max_steps <= self.global_step + 1
@@ -542,8 +544,6 @@ def run_training_epoch(self):
             if self.trainer.should_stop:
                 break
 
-            self.total_batch_idx += 1
-
             # stop epoch if we limited the number of training batches
             if self._num_training_batches_reached(is_last_batch):
                 break

tests/tuner/test_lr_finder.py

@@ -197,31 +197,24 @@ def test_datamodule_parameter(tmpdir):
 
 
 def test_accumulation_and_early_stopping(tmpdir):
-    """ Test that early stopping of learning rate finder works, and that
-        accumulation also works for this feature """
+    """ Test that early stopping of learning rate finder works, and that accumulation also works for this feature """
 
-    hparams = EvalModelTemplate.get_default_hparams()
-    model = EvalModelTemplate(**hparams)
+    class TestModel(BoringModel):
 
-    before_lr = hparams.get('learning_rate')
-    # logger file to get meta
+        def __init__(self):
+            super().__init__()
+            self.lr = 1e-3
+
+    model = TestModel()
     trainer = Trainer(
         default_root_dir=tmpdir,
         accumulate_grad_batches=2,
     )
-
     lrfinder = trainer.tuner.lr_find(model, early_stop_threshold=None)
-    after_lr = lrfinder.suggestion()
 
-    expected_num_lrs = 100
-    expected_batch_idx = 200 - 1
-
-    assert before_lr != after_lr, \
-        'Learning rate was not altered after running learning rate finder'
-    assert len(lrfinder.results['lr']) == expected_num_lrs, \
-        'Early stopping for learning rate finder did not work'
-    assert lrfinder._total_batch_idx == expected_batch_idx, \
-        'Accumulation parameter did not work'
+    assert lrfinder.suggestion() != 1e-3
+    assert len(lrfinder.results['lr']) == 100
+    assert lrfinder._total_batch_idx == 200
 
 
 def test_suggestion_parameters_work(tmpdir):