diff --git a/tests/trainer/logging/test_train_loop_logging_1_0.py b/tests/trainer/logging/test_train_loop_logging_1_0.py
index a852c4efe2..848e6e74a5 100644
--- a/tests/trainer/logging/test_train_loop_logging_1_0.py
+++ b/tests/trainer/logging/test_train_loop_logging_1_0.py
@@ -275,3 +275,80 @@ def test__training_step__log_max_reduce_fx(tmpdir, batches, fx, result):
     # make sure types are correct
     assert trainer.logged_metrics['foo'] == result
     assert trainer.logged_metrics['bar'] == result
+
+
+def test_tbptt_log(tmpdir):
+    """
+    Tests that only training_step can be used
+    """
+    truncated_bptt_steps = 2
+    sequence_size = 30
+    batch_size = 30
+
+    x_seq = torch.rand(batch_size, sequence_size, 1)
+    y_seq_list = torch.rand(batch_size, sequence_size, 1).tolist()
+
+    class MockSeq2SeqDataset(torch.utils.data.Dataset):
+        def __getitem__(self, i):
+            return x_seq, y_seq_list
+
+        def __len__(self):
+            return 1
+
+    class TestModel(BoringModel):
+        def __init__(self):
+            super().__init__()
+            self.test_hidden = None
+            self.layer = torch.nn.Linear(2, 2)
+
+        def training_step(self, batch, batch_idx, hiddens):
+            try:
+                assert hiddens == self.test_hidden, "Hidden state not persistent between tbptt steps"
+            except Exception as e:
+                print(e)
+
+            self.test_hidden = torch.rand(1)
+
+            x_tensor, y_list = batch
+            assert x_tensor.shape[1] == truncated_bptt_steps, "tbptt split Tensor failed"
+
+            y_tensor = torch.tensor(y_list, dtype=x_tensor.dtype)
+            assert y_tensor.shape[1] == truncated_bptt_steps, "tbptt split list failed"
+
+            pred = self(x_tensor.view(batch_size, truncated_bptt_steps))
+            loss_val = torch.nn.functional.mse_loss(
+                pred, y_tensor.view(batch_size, truncated_bptt_steps))
+
+            self.log('a', loss_val, on_epoch=True)
+
+            return {'loss': loss_val, 'hiddens': self.test_hidden}
+
+        def on_train_epoch_start(self) -> None:
+            self.test_hidden = None
+
+        def train_dataloader(self):
+            return torch.utils.data.DataLoader(
+                dataset=MockSeq2SeqDataset(),
+                batch_size=batch_size,
+                shuffle=False,
+                sampler=None,
+            )
+
+    model = TestModel()
+    model.training_epoch_end = None
+    model.example_input_array = torch.randn(5, truncated_bptt_steps)
+
+    trainer = Trainer(
+        default_root_dir=tmpdir,
+        limit_train_batches=10,
+        limit_val_batches=0,
+        truncated_bptt_steps=truncated_bptt_steps,
+        max_epochs=2,
+        row_log_interval=2,
+        weights_summary=None,
+    )
+    trainer.fit(model)
+
+    generated = set(trainer.logged_metrics.keys())
+    expected = {'a', 'step_a', 'epoch_a', 'epoch'}
+    assert generated == expected