Finish dataloader integration (#24201)

src/transformers/trainer.py

@@ -176,7 +176,6 @@ if is_datasets_available():
 if is_torch_tpu_available(check_device=False):
     import torch_xla.core.xla_model as xm
     import torch_xla.debug.metrics as met
-    import torch_xla.distributed.parallel_loader as pl
 
 if is_fairscale_available():
     dep_version_check("fairscale")
@@ -1762,10 +1761,6 @@ class Trainer:
 
         total_batched_samples = 0
         for epoch in range(epochs_trained, num_train_epochs):
-            if is_torch_tpu_available():
-                parallel_loader = pl.ParallelLoader(train_dataloader, [args.device]).per_device_loader(args.device)
-                epoch_iterator = parallel_loader
-            else:
             epoch_iterator = train_dataloader
 
             # Reset the past mems state at the beginning of each epoch if necessary.
@@ -3088,9 +3083,6 @@ class Trainer:
         # Do this before wrapping.
         eval_dataset = getattr(dataloader, "dataset", None)
 
-        if is_torch_tpu_available():
-            dataloader = pl.ParallelLoader(dataloader, [args.device]).per_device_loader(args.device)
-
         if args.past_index >= 0:
             self._past = None
 
@@ -3694,9 +3686,6 @@ class Trainer:
 
         model.eval()
 
-        if is_torch_tpu_available():
-            dataloader = pl.ParallelLoader(dataloader, [args.device]).per_device_loader(args.device)
-
         if args.past_index >= 0:
             self._past = None