[Hot Fix] Ensure process_dataloader is called when tpu_cores > 1 to use Parallel DataLoader

CHANGELOG.md

@@ -269,6 +269,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Fixed passing wrong strings for scheduler interval doesn't throw an error ([#5923](https://github.com/PyTorchLightning/pytorch-lightning/pull/5923))
 
 
+- Fixed missing `process_dataloder` call for `TPUSpawn` ([#6015](https://github.com/PyTorchLightning/pytorch-lightning/pull/6015))
+
+
 ## [1.1.8] - 2021-02-08
 
 ### Fixed

pytorch_lightning/accelerators/accelerator.py

@@ -11,11 +11,10 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-from typing import Any, Callable, Iterable, Optional, TYPE_CHECKING, Union
+from typing import Any, Callable, Optional, TYPE_CHECKING, Union
 
 import torch
 from torch.optim import Optimizer
-from torch.utils.data import DataLoader
 
 from pytorch_lightning.core import LightningModule
 from pytorch_lightning.plugins.precision import (
@@ -226,14 +225,6 @@ def predict(self, args):
         args[0] = batch
         return self.training_type_plugin.predict(*args)
 
-    def process_dataloader(self, dataloader: Union[Iterable, DataLoader]) -> Union[Iterable, DataLoader]:
-        """Wraps the dataloader if necessary
-
-        Args:
-            dataloader: iterable. Ideally of type: :class:`torch.utils.data.DataLoader`
-        """
-        return dataloader
-
     def backward(
         self,
         closure_loss: torch.Tensor,

pytorch_lightning/plugins/training_type/training_type_plugin.py

@@ -11,13 +11,13 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-import os
 from abc import ABC, abstractmethod
-from typing import Any, Optional, TYPE_CHECKING, Union
+from typing import Any, Iterable, Optional, TYPE_CHECKING, Union
 
 import torch
 from torch.nn import Module
 from torch.optim import Optimizer
+from torch.utils.data import DataLoader
 
 from pytorch_lightning.core.lightning import LightningModule
 from pytorch_lightning.overrides.base import unwrap_lightning_module
@@ -144,3 +144,11 @@ def test_step_end(self, output):
 
     def on_save(self, checkpoint: dict) -> dict:
         return checkpoint
+
+    def process_dataloader(self, dataloader: Union[Iterable, DataLoader]) -> Union[Iterable, DataLoader]:
+        """Wraps the dataloader if necessary
+
+        Args:
+            dataloader: iterable. Ideally of type: :class:`torch.utils.data.DataLoader`
+        """
+        return dataloader

pytorch_lightning/trainer/trainer.py

@@ -672,7 +672,7 @@ def run_evaluation(self, max_batches=None, on_epoch=False):
         for dataloader_idx, dataloader in enumerate(dataloaders):
             # bookkeeping
             dl_outputs = []
-            dataloader = self.accelerator_backend.process_dataloader(dataloader)
+            dataloader = self.training_type_plugin.process_dataloader(dataloader)
             dl_max_batches = self.evaluation_loop.max_batches[dataloader_idx]
 
             for batch_idx, batch in enumerate(dataloader):

pytorch_lightning/trainer/training_loop.py

@@ -502,7 +502,7 @@ def tbptt_split_batch(self, batch):
 
     def run_training_epoch(self):
         # modify dataloader if needed (ddp, etc...)
-        train_dataloader = self.trainer.accelerator_backend.process_dataloader(self.trainer.train_dataloader)
+        train_dataloader = self.trainer.training_type_plugin.process_dataloader(self.trainer.train_dataloader)
 
         # track epoch output
         epoch_output = [[] for _ in range(self.num_optimizers)]