diff --git a/pytorch_lightning/pt_overrides/override_data_parallel.py b/pytorch_lightning/pt_overrides/override_data_parallel.py
index e2590b5248..2366b882c0 100644
--- a/pytorch_lightning/pt_overrides/override_data_parallel.py
+++ b/pytorch_lightning/pt_overrides/override_data_parallel.py
@@ -1,6 +1,7 @@
 from torch.nn import DataParallel
 from torch.nn.parallel import DistributedDataParallel
 import itertools
+from itertools import chain
 
 import threading
 import torch
@@ -42,6 +43,29 @@ class LightningDataParallel(DataParallel):
     Override the forward call in lightning so it goes to training and validation step respectively
     """
 
+    def forward(self, *inputs, **kwargs):
+        if not self.device_ids:
+            return self.module(*inputs, **kwargs)
+
+        for t in chain(self.module.parameters(), self.module.buffers()):
+            if t.device != self.src_device_obj:
+                raise RuntimeError("module must have its parameters and buffers "
+                                   "on device {} (device_ids[0]) but found one of "
+                                   "them on device: {}".format(self.src_device_obj, t.device))
+
+        inputs, kwargs = self.scatter(inputs, kwargs, self.device_ids)
+        if len(self.device_ids) == 1:
+            # lightning
+            if self.module.training:
+                return self.module.training_step(*inputs[0], **kwargs[0])
+            else:
+                return self.module.validation_step(*inputs[0], **kwargs[0])
+
+        replicas = self.replicate(self.module, self.device_ids[:len(inputs)])
+        outputs = self.parallel_apply(replicas, inputs, kwargs)
+        return self.gather(outputs, self.output_device)
+
+
     def parallel_apply(self, replicas, inputs, kwargs):
         print('LDP')
         return parallel_apply(replicas, inputs, kwargs, self.device_ids[:len(replicas)])