diff --git a/pytorch_lightning/pt_overrides/override_data_parallel.py b/pytorch_lightning/pt_overrides/override_data_parallel.py
index cf4f476e..78436a3d 100644
--- a/pytorch_lightning/pt_overrides/override_data_parallel.py
+++ b/pytorch_lightning/pt_overrides/override_data_parallel.py
@@ -1,5 +1,6 @@
 from torch.nn import DataParallel
 from torch.nn.parallel import DistributedDataParallel
+import itertools
 
 import threading
 import torch
@@ -7,6 +8,20 @@ from torch.cuda._utils import _get_device_index
 import pdb
 
 
+def _find_tensors(obj):
+    r"""
+    Recursively find all tensors contained in the specified object.
+    """
+    if isinstance(obj, torch.Tensor):
+        return [obj]
+    if isinstance(obj, (list, tuple)):
+        return itertools.chain(*map(_find_tensors, obj))
+    if isinstance(obj, dict):
+        return itertools.chain(*map(_find_tensors, obj.values()))
+    return []
+
+
+
 def get_a_var(obj):
     if isinstance(obj, torch.Tensor):
         return obj
@@ -30,6 +45,34 @@ class LightningDataParallel(DataParallel):
     def parallel_apply(self, replicas, inputs, kwargs):
         return parallel_apply(replicas, inputs, kwargs, self.device_ids[:len(replicas)])
 
+    def forward(self, *inputs, **kwargs):
+        print('in forward')
+        self._sync_params()
+        if self.device_ids:
+            inputs, kwargs = self.scatter(inputs, kwargs, self.device_ids)
+            if len(self.device_ids) == 1:
+                print('a')
+                output = self.module(*inputs[0], **kwargs[0])
+            else:
+                print('b')
+                outputs = self.parallel_apply(self._module_copies[:len(inputs)], inputs, kwargs)
+                output = self.gather(outputs, self.output_device)
+        else:
+            print('c')
+            output = self.module(*inputs, **kwargs)
+
+        if torch.is_grad_enabled():
+            # We'll return the output object verbatim since it is a freeform
+            # object. We need to find any tensors in this object, though,
+            # because we need to figure out which parameters were used during
+            # this forward pass, to ensure we short circuit reduction for any
+            # unused parameters. Only if `find_unused_parameters` is set.
+            if self.find_unused_parameters:
+                self.reducer.prepare_for_backward(list(_find_tensors(output)))
+            else:
+                self.reducer.prepare_for_backward([])
+        return output
+
 
 class LightningDistributedDataParallel(DistributedDataParallel):
     """
@@ -112,4 +155,4 @@ def parallel_apply(modules, inputs, kwargs_tup=None, devices=None):
         if isinstance(output, Exception):
             raise output
         outputs.append(output)
-    return outputs
\ No newline at end of file
+    return outputs