Clean up dataloader logic

CHANGELOG.md

@@ -8,6 +8,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 ### Added
 
+- Added automatic sampler setup. Depending on DDP or TPU, lightning configures the sampler correctly (user needs to do nothing)  ([#926](https://github.com/PyTorchLightning/pytorch-lightning/pull/926))
+- Added `reload_dataloaders_every_epoch=False` flag for trainer. Some users require reloading data every epoch  ([#926](https://github.com/PyTorchLightning/pytorch-lightning/pull/926))
+- Added `progress_bar_refresh_rate=50` flag for trainer. Throttle refresh rate on notebooks  ([#926](https://github.com/PyTorchLightning/pytorch-lightning/pull/926))
 - Updated governance docs
 - Added a check to ensure that the metric used for early stopping exists before training commences ([#542](https://github.com/PyTorchLightning/pytorch-lightning/pull/542))
 - Added `optimizer_idx` argument to `backward` hook ([#733](https://github.com/PyTorchLightning/pytorch-lightning/pull/733))
@@ -22,6 +25,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 ### Changed
 
+- Removed `@data_loader` decorator  ([#926](https://github.com/PyTorchLightning/pytorch-lightning/pull/926))
 - Changed default TQDM to use `tqdm.auto` for prettier outputs in IPython notebooks ([#752](https://github.com/PyTorchLightning/pytorch-lightning/pull/752))
 - Changed `pytorch_lightning.logging` to `pytorch_lightning.loggers` ([#767](https://github.com/PyTorchLightning/pytorch-lightning/pull/767))
 - Moved the default `tqdm_dict` definition from Trainer to `LightningModule`, so it can be overridden by the user ([#749](https://github.com/PyTorchLightning/pytorch-lightning/pull/749))

docs/source/hooks.rst

@@ -8,9 +8,9 @@ Training set-up
 - init_optimizers
 - configure_apex
 - configure_ddp
-- get_train_dataloader
-- get_test_dataloaders
-- get_val_dataloaders
+- train_dataloader
+- test_dataloaders
+- val_dataloaders
 - summarize
 - restore_weights
 

pl_examples/basic_examples/lightning_module_template.py

@@ -192,37 +192,35 @@ def __dataloader(self, train):
         transform = transforms.Compose([transforms.ToTensor(),
                                         transforms.Normalize((0.5,), (1.0,))])
         dataset = MNIST(root=self.hparams.data_root, train=train,
-                        transform=transform, download=True)
+                        transform=transform, download=False)
 
         # when using multi-node (ddp) we need to add the  datasampler
-        train_sampler = None
         batch_size = self.hparams.batch_size
 
-        if self.use_ddp:
-            train_sampler = DistributedSampler(dataset)
-
-        should_shuffle = train_sampler is None
         loader = DataLoader(
             dataset=dataset,
             batch_size=batch_size,
-            shuffle=should_shuffle,
-            sampler=train_sampler,
             num_workers=0
         )
 
         return loader
 
-    @pl.data_loader
+    def prepare_data(self):
+        transform = transforms.Compose([transforms.ToTensor(),
+                                        transforms.Normalize((0.5,), (1.0,))])
+        dataset = MNIST(root=self.hparams.data_root, train=True,
+                        transform=transform, download=True)
+        dataset = MNIST(root=self.hparams.data_root, train=False,
+                        transform=transform, download=True)
+
     def train_dataloader(self):
         log.info('Training data loader called.')
         return self.__dataloader(train=True)
 
-    @pl.data_loader
     def val_dataloader(self):
         log.info('Validation data loader called.')
         return self.__dataloader(train=False)
 
-    @pl.data_loader
     def test_dataloader(self):
         log.info('Test data loader called.')
         return self.__dataloader(train=False)

pytorch_lightning/core/decorators.py

@@ -1,5 +1,6 @@
 import traceback
 from functools import wraps
+import warnings
 
 
 def data_loader(fn):
@@ -8,27 +9,9 @@ def data_loader(fn):
     :param fn:
     :return:
     """
-    wraps(fn)
-    attr_name = '_lazy_' + fn.__name__
-    @wraps(fn)
-    def _get_data_loader(self):
-        try:
-            value = getattr(self, attr_name)
-        except AttributeError:
-            try:
-                value = fn(self)  # Lazy evaluation, done only once.
-                if (
-                        value is not None and
-                        not isinstance(value, list) and
-                        fn.__name__ in ['test_dataloader', 'val_dataloader']
-                ):
-                    value = [value]
-            except AttributeError as e:
-                # Guard against AttributeError suppression. (Issue #142)
-                traceback.print_exc()
-                error = f'{fn.__name__}: An AttributeError was encountered: ' + str(e)
-                raise RuntimeError(error) from e
-            setattr(self, attr_name, value)  # Memoize evaluation.
-        return value
+    w = 'data_loader decorator deprecated in 0.6.1. Will remove 0.8.0'
+    warnings.warn(w)
 
-    return _get_data_loader
+    def inner_fx(self):
+        return fn(self)
+    return inner_fx

pytorch_lightning/core/lightning.py

@@ -16,6 +16,13 @@
 from pytorch_lightning.core.memory import ModelSummary
 from pytorch_lightning.overrides.data_parallel import LightningDistributedDataParallel
 
+try:
+    import torch_xla.core.xla_model as xm
+    XLA_AVAILABLE = True
+
+except ImportError:
+    XLA_AVAILABLE = False
+
 
 class LightningModule(ABC, GradInformation, ModelIO, ModelHooks):
 
@@ -798,7 +805,9 @@ def optimizer_step(self, current_epoch, batch_idx, optimizer, optimizer_idx, sec
                 optimizer.zero_grad()
 
         """
-        if isinstance(optimizer, torch.optim.LBFGS):
+        if self.trainer.use_tpu and XLA_AVAILABLE:
+            xm.optimizer_step(optimizer)
+        elif isinstance(optimizer, torch.optim.LBFGS):
             optimizer.step(second_order_closure)
         else:
             optimizer.step()
@@ -868,7 +877,33 @@ def tbptt_split_batch(self, batch, split_size):
 
         return splits
 
-    @data_loader
+    def prepare_data(self):
+        """Use this to download and prepare data.
+        In distributed (GPU, TPU), this will only be called once
+
+        :return: PyTorch DataLoader
+
+        This is called before requesting the dataloaders
+
+        .. code-block:: python
+
+            model.prepare_data()
+            model.train_dataloader()
+            model.val_dataloader()
+            model.test_dataloader()
+
+        Example
+        -------
+
+        .. code-block:: python
+
+            def prepare_data(self):
+                download_imagenet()
+                clean_imagenet()
+                cache_imagenet()
+        """
+        return None
+
     def train_dataloader(self):
         """Implement a PyTorch DataLoader
 
@@ -908,7 +943,6 @@ def tng_dataloader(self):  # todo: remove in v0.8.0
                       " and this method will be removed in v0.8.0", DeprecationWarning)
         return output
 
-    @data_loader
     def test_dataloader(self):
         r"""
 
@@ -942,7 +976,6 @@ def test_dataloader(self):
         """
         return None
 
-    @data_loader
     def val_dataloader(self):
         r"""