Support for multiple val_dataloaders (#97)

* Added support for multiple validation dataloaders

* Fix typo in README.md

* Update trainer.py

* Add support for multiple dataloaders

* Rename dataloader_index to dataloader_i

* Added warning to check val_dataloaders

Added a warning to ensure that all val_dataloaders were DistributedSamplers if ddp is enabled

* Updated DistributedSampler warning

* Fixed typo

* Added multiple val_dataloaders

* Multiple val_dataloader test

* Update lightning_module_template.py

Added dataloader_i to validation_step parameters

* Update trainer.py

* Reverted template changes

* Create multi_val_module.py

* Update no_val_end_module.py

* New MultiValModel

* Rename MultiValModel to MultiValTestModel

* Revert to LightningTestModel

* Update test_models.py

* Update trainer.py

* Update test_models.py

* multiple val_dataloaders in test template

* Fixed flake8 warnings

* Update trainer.py

* Fix flake errors

* Fixed Flake8 errors

* Update lm_test_module.py

keep this test model with a single dataset for val

* Update trainer.py

* Update trainer.py

* Update trainer.py

* Update trainer.py

* Update trainer.py

* Update test_models.py

* Update trainer.py

* Update trainer.py

* Update trainer.py

* Update trainer.py

* Update trainer.py

* Update trainer.py

* Update RequiredTrainerInterface.md

* Update RequiredTrainerInterface.md

* Update test_models.py

* Update trainer.py

dont need the else clause, val_dataloader is either a list or none because of get_dataloaders()

* Update trainer.py

fixed flake errors

* Update trainer.py

pytorch_lightning/testing/lm_test_module.py

@@ -109,7 +109,7 @@ class LightningTestModel(LightningModule):
         if self.trainer.batch_nb % 2 == 0:
             return loss_val
 
-    def validation_step(self, data_batch, batch_i):
+    def validation_step(self, data_batch, batch_i, dataloader_i):
         """
         Lightning calls this inside the validation loop
         :param data_batch:
@@ -151,6 +151,12 @@ class LightningTestModel(LightningModule):
                 'test_dic': {'val_loss_a': loss_val}
             })
             return output
+        if batch_i % 5 == 0:
+            output = OrderedDict({
+                f'val_loss_{dataloader_i}': loss_val,
+                f'val_acc_{dataloader_i}': val_acc,
+            })
+            return output
 
     def validation_end(self, outputs):
         """

pytorch_lightning/testing/no_val_end_module.py

@@ -109,7 +109,7 @@ class NoValEndTestModel(LightningModule):
         if self.trainer.batch_nb % 2 == 0:
             return loss_val
 
-    def validation_step(self, data_batch, batch_i):
+    def validation_step(self, data_batch, batch_i, dataloader_i):
         """
         Lightning calls this inside the validation loop
         :param data_batch: