diff --git a/pytorch_lightning/core/lightning.py b/pytorch_lightning/core/lightning.py
index 7419e1746f..8a550e5df9 100644
--- a/pytorch_lightning/core/lightning.py
+++ b/pytorch_lightning/core/lightning.py
@@ -418,7 +418,8 @@ class LightningModule(ABC, GradInformation, ModelIO, ModelHooks):
         The outputs here are strictly for the progress bar.
          If you don't need to display anything, don't return anything.
          Any keys present in 'log', 'progress_bar' or the rest of the dictionary
-         are available for callbacks to access.
+         are available for callbacks to access. If you want to manually set current step, you can specify it with
+         'step' key in the 'log' Dict.
 
         Example
         -------
@@ -468,7 +469,7 @@ class LightningModule(ABC, GradInformation, ModelIO, ModelHooks):
                 # show val_loss and val_acc in progress bar but only log val_loss
                 results = {
                     'progress_bar': tqdm_dict,
-                    'log': {'val_loss': val_loss_mean.item()}
+                    'log': {'val_loss': val_loss_mean.item(), 'step': self.current_epoch}
                 }
                 return results
 
diff --git a/pytorch_lightning/trainer/logging.py b/pytorch_lightning/trainer/logging.py
index 2025bd511e..34b1c114b3 100644
--- a/pytorch_lightning/trainer/logging.py
+++ b/pytorch_lightning/trainer/logging.py
@@ -39,13 +39,12 @@ class TrainerLoggingMixin(ABC):
 
     def log_metrics(self, metrics, grad_norm_dic, step=None):
         """Logs the metric dict passed in.
-
-        :param metrics:
-        :param grad_norm_dic:
+        If `step` parameter is None and `step` key is presented is metrics,
+        uses metrics["step"] as a step
+        :param metrics (dict): Metric values
+        :param grad_norm_dic (dict): Gradient norms
+        :param step (int): Step for which metrics should be logged. Default value corresponds to `self.global_step`
         """
-        # added metrics by Lightning for convenience
-        metrics['epoch'] = self.current_epoch
-
         # add gpu memory
         if self.on_gpu and self.log_gpu_memory:
             mem_map = memory.get_memory_profile(self.log_gpu_memory)
@@ -57,7 +56,12 @@ class TrainerLoggingMixin(ABC):
         # turn all tensors to scalars
         scalar_metrics = self.metrics_to_scalars(metrics)
 
-        step = step if step is not None else self.global_step
+        if "step" in scalar_metrics and step is None:
+            step = scalar_metrics.pop("step")
+        else:
+            # added metrics by Lightning for convenience
+            metrics['epoch'] = self.current_epoch
+            step = step if step is not None else self.global_step
         # log actual metrics
         if self.proc_rank == 0 and self.logger is not None:
             self.logger.log_metrics(scalar_metrics, step=step)
diff --git a/tests/test_logging.py b/tests/test_logging.py
index c0166796ca..0d4104ef7a 100644
--- a/tests/test_logging.py
+++ b/tests/test_logging.py
@@ -376,3 +376,33 @@ def test_custom_logger(tmpdir):
     assert logger.hparams_logged == hparams
     assert logger.metrics_logged != {}
     assert logger.finalized_status == "success"
+
+
+def test_adding_step_key(tmpdir):
+    logged_step = 0
+
+    def _validation_end(outputs):
+        nonlocal logged_step
+        logged_step += 1
+        return {"log": {"step": logged_step, "val_acc": logged_step / 10}}
+
+    def _log_metrics_decorator(log_metrics_fn):
+        def decorated(metrics, step):
+            if "val_acc" in metrics:
+                assert step == logged_step
+            return log_metrics_fn(metrics, step)
+
+        return decorated
+
+    model, hparams = tutils.get_model()
+    model.validation_end = _validation_end
+    trainer_options = dict(
+        max_epochs=4,
+        default_save_path=tmpdir,
+        train_percent_check=0.001,
+        val_percent_check=0.01,
+        num_sanity_val_steps=0
+    )
+    trainer = Trainer(**trainer_options)
+    trainer.logger.log_metrics = _log_metrics_decorator(trainer.logger.log_metrics)
+    trainer.fit(model)