Improve DataLoader performance when batch_size=None (#423)

pina/data/data_module.py

@@ -8,6 +8,19 @@ from torch.utils.data import DataLoader, BatchSampler, SequentialSampler, \
 from torch.utils.data.distributed import DistributedSampler
 from .dataset import PinaDatasetFactory
 
+class DummyDataloader:
+    def __init__(self, dataset, device):
+        self.dataset = dataset.get_all_data()
+
+    def __iter__(self):
+        return self
+
+    def __len__(self):
+        return 1
+
+    def __next__(self):
+        return self.dataset
+
 class Collator:
     def __init__(self, max_conditions_lengths, ):
         self.max_conditions_lengths = max_conditions_lengths
@@ -232,40 +245,41 @@ class PinaDataModule(LightningDataModule):
         """
         Create the validation dataloader
         """
-
-        batch_size = self.batch_size if self.batch_size is not None else len(
-            self.val_dataset)
-
+        # Use custom batching (good if batch size is large)
+        if self.batch_size is not None:
             # Use default batching in torch DataLoader (good is batch size is small)
             if self.automatic_batching:
                 collate = Collator(self.find_max_conditions_lengths('val'))
-            return DataLoader(self.val_dataset, batch_size,
+                return DataLoader(self.val_dataset, self.batch_size,
                           collate_fn=collate)
             collate = Collator(None)
-        # Use custom batching (good if batch size is large)
-        sampler = PinaBatchSampler(self.val_dataset, batch_size, shuffle=False)
+            sampler = PinaBatchSampler(self.val_dataset, self.batch_size, shuffle=False)
             return DataLoader(self.val_dataset, sampler=sampler,
                           collate_fn=collate)
+        dataloader = DummyDataloader(self.train_dataset, self.trainer.strategy.root_device)
+        dataloader.dataset = self.transfer_batch_to_device(dataloader.dataset, self.trainer.strategy.root_device, 0)
+        self.transfer_batch_to_device = self.dummy_transfer_to_device
 
     def train_dataloader(self):
         """
         Create the training dataloader
         """
+        # Use custom batching (good if batch size is large)
+        if self.batch_size is not None:
             # Use default batching in torch DataLoader (good is batch size is small)
-        batch_size = self.batch_size if self.batch_size is not None else len(
-            self.train_dataset)
-
             if self.automatic_batching:
                 collate = Collator(self.find_max_conditions_lengths('train'))
-            return DataLoader(self.train_dataset, batch_size,
+                return DataLoader(self.train_dataset, self.batch_size,
                                   collate_fn=collate)
             collate = Collator(None)
-        # Use custom batching (good if batch size is large)
-
-        sampler = PinaBatchSampler(self.train_dataset, batch_size,
+            sampler = PinaBatchSampler(self.train_dataset, self.batch_size,
                                            shuffle=False)
             return DataLoader(self.train_dataset, sampler=sampler,
                               collate_fn=collate)
+        dataloader = DummyDataloader(self.train_dataset, self.trainer.strategy.root_device)
+        dataloader.dataset = self.transfer_batch_to_device(dataloader.dataset, self.trainer.strategy.root_device, 0)
+        self.transfer_batch_to_device = self.dummy_transfer_to_device
+        return dataloader
 
     def test_dataloader(self):
         """
@@ -279,6 +293,9 @@ class PinaDataModule(LightningDataModule):
         """
         raise NotImplementedError("Predict dataloader not implemented")
 
+    def dummy_transfer_to_device(self, batch, device, dataloader_idx):
+        return batch
+
     def transfer_batch_to_device(self, batch, device, dataloader_idx):
         """
         Transfer the batch to the device. This method is called in the

pina/data/dataset.py

@@ -75,6 +75,10 @@ class PinaTensorDataset(PinaDataset):
                                          for k, v in data.items()}
         return to_return_dict
 
+    def get_all_data(self):
+        index = [i for i in range(len(self))]
+        return self._getitem_list(index)
+
     def __getitem__(self, idx):
         return self._getitem_func(idx)