remove obsolete self._device in Trainer

pytorch_lightning/core/lightning.py

@@ -53,10 +53,6 @@ def __init__(self, *args, **kwargs):
         self.logger = None
         self.example_input_array = None
 
-        #: True if your model is currently running on GPUs.
-        #: Useful to set flags around the LightningModule for different CPU vs GPU behavior.
-        self.on_gpu = False
-
         #: True if using dp
         self.use_dp = False
 
@@ -72,10 +68,19 @@ def __init__(self, *args, **kwargs):
         self.hparams = None
 
         #: Current dtype
-        self._dtype = torch.FloatTensor
+        self._dtype = torch.float
+
         #: device reference
         self._device = torch.device('cpu')
 
+    @property
+    def on_gpu(self):
+        """
+        True if your model is currently running on GPUs.
+        Useful to set flags around the LightningModule for different CPU vs GPU behavior.
+        """
+        return self.device.type == 'cuda'
+
     def print(self, *args, **kwargs) -> None:
         r"""
         Prints only from process 0. Use this in any distributed mode to log only once.

pytorch_lightning/trainer/distrib_data_parallel.py

@@ -360,7 +360,6 @@ def ddp_train(self, process_idx, model):
         # copy model to each gpu
         if self.on_gpu:
             self.root_gpu = process_idx
-            self._device = torch.device('cuda', self.root_gpu)
             torch.cuda.set_device(self.root_gpu)
             model.cuda(self.root_gpu)
 

pytorch_lightning/trainer/distrib_parts.py

@@ -422,7 +422,6 @@ def copy_trainer_model_properties(self, model):
 
         for m in [model, ref_model]:
             m.trainer = self
-            m.on_gpu = self.on_gpu
             m.use_dp = self.use_dp
             m.use_ddp2 = self.use_ddp2
             m.use_ddp = self.use_ddp
@@ -432,7 +431,6 @@ def copy_trainer_model_properties(self, model):
             m.use_tpu = self.use_tpu
             m.tpu_local_core_rank = self.tpu_local_core_rank
             m.tpu_global_core_rank = self.tpu_global_core_rank
-            m._device = self._device
 
     def transfer_batch_to_tpu(self, batch):
         return self.__transfer_data_to_device(batch, device='tpu')
@@ -488,7 +486,6 @@ def __transfer_data_to_device(self, batch, device, gpu_id=None):
 
     def single_gpu_train(self, model):
         model.cuda(self.root_gpu)
-        self._device = torch.device('cuda', self.root_gpu)
 
         # CHOOSE OPTIMIZER
         # allow for lr schedulers as well
@@ -505,7 +502,6 @@ def single_gpu_train(self, model):
     def tpu_train(self, tpu_core_idx, model):
         # put model on tpu
         model.to(xm.xla_device())
-        self._device = xm.xla_device()
 
         # get the appropriate tpu ranks
         self.tpu_local_core_rank = xm.get_local_ordinal()
@@ -545,7 +541,6 @@ def dp_train(self, model):
         self.optimizers, self.lr_schedulers, self.optimizer_frequencies = self.init_optimizers(model)
 
         model.cuda(self.root_gpu)
-        self._device = torch.device('cuda', self.root_gpu)
 
         # hack forward to do autocast for the user
         model_autocast_original_forward = model.forward
@@ -585,7 +580,6 @@ def horovod_train(self, model):
             assert self.root_gpu == hvd.local_rank()
             torch.cuda.set_device(self.root_gpu)
             model.cuda(self.root_gpu)
-            self._device = torch.device('cuda', self.root_gpu)
 
         # avoid duplicating progress bar
         if hvd.rank() != 0 and self.progress_bar_callback is not None:

pytorch_lightning/trainer/trainer.py

@@ -473,7 +473,6 @@ def __init__(
         # distributed backend choice
         self.distributed_backend = distributed_backend
         self.set_distributed_mode(distributed_backend)
-        self._device = torch.device('cpu')
 
         # override dist backend when using tpus
         if self.on_tpu: