added training router

pytorch_lightning/models/trainer.py

@@ -17,7 +17,7 @@ import tqdm
 
 from pytorch_lightning.root_module.memory import get_gpu_memory_map
 from pytorch_lightning.root_module.model_saving import TrainerIO
-from pytorch_lightning.pt_overrides.override_data_parallel import LightningDistributedDataParallel
+from pytorch_lightning.pt_overrides.override_data_parallel import LightningDistributedDataParallel, LightningDataParallel
 
 
 try:
@@ -152,6 +152,10 @@ class Trainer(TrainerIO):
             '''
             warnings.warn(msg)
 
+    @property
+    def data_parallel(self):
+        return self.use_dp or self.use_ddp
+
     def __determine_data_use_amount(self, train_percent_check, val_percent_check, test_percent_check, overfit_pct):
         """
         Use less data for debugging purposes
@@ -305,15 +309,15 @@ class Trainer(TrainerIO):
     # -----------------------------
     def fit(self, model):
 
-        # when using gpus, first thing we do is spawn a new process between each worker
-        #  multi-gpu and multi-nodes
-        if self.data_parallel:
+        # when using multi-node or DDP within a node start each module in a separate process
+        if self.use_ddp:
             self.experiment = self.experiment.get_meta_copy()
-            mp.spawn(self.dp_train, nprocs=len(self.data_parallel_device_ids), args=(model, ))
+            mp.spawn(self.ddp_train, nprocs=len(self.data_parallel_device_ids), args=(model, ))
 
-        # treat 1 gpu as a different case to avoid nccl bugs
-        elif self.data_parallel_device_ids is not None and len(self.data_parallel_device_ids) == 1:
-            self.single_gpu_train(model)
+        # 1 gpu or dp option triggers training using DP module
+        # easier to avoid NCCL issues
+        elif self.use_dp:
+            self.dp_train(model)
 
         else:
             # CHOOSE OPTIMIZER
@@ -330,14 +334,15 @@ class Trainer(TrainerIO):
 
             self.__run_pretrain_routine(model)
 
-    def single_gpu_train(self, model):
-        # torch.cuda.set_device(0)
-        model.cuda(0)
+    def dp_train(self, model):
 
         # CHOOSE OPTIMIZER
         # filter out the weights that were done on gpu so we can load on good old cpus
         self.optimizers = model.configure_optimizers()
 
+        # attach model to DP
+        model = LightningDataParallel(model, device_ids=self.data_parallel_device_ids)
+
         # run through amp wrapper
         if self.use_amp:
             # An example
@@ -348,7 +353,7 @@ class Trainer(TrainerIO):
 
         self.__run_pretrain_routine(model)
 
-    def dp_train(self, gpu_nb, model):
+    def ddp_train(self, gpu_nb, model):
         """
         Entry point into a DP thread
         :param gpu_nb: