new way of passing dataloaders (#759)

* new way of passing dataloaders

* fixed docs

* fixed codestyle to follow flake8

* allow val/test be list of dataloaders and smarter checking

* added test

* fix flake error

* fix linking to new test model

* split into multiple test

* fix naming and typo

* minor documentation changes

* remove random file

* Update trainer.py

* Update trainer.py

* Update trainer.py

* Update trainer.py

* Update trainer.py

* Update trainer.py

* better error/warning message

* final adjustments

* update CHANGELOG.md

Co-authored-by: William Falcon <waf2107@columbia.edu>

CHANGELOG.md

@@ -11,6 +11,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Added a tool for profiling training runs ([#782](https://github.com/PyTorchLightning/pytorch-lightning/pull/782))
 - Improved flexibility for naming of TensorBoard logs, can now set `version` to a `str` to just save to that directory, and use `name=''` to prevent experiment-name directory ([#804](https://github.com/PyTorchLightning/pytorch-lightning/pull/804))
 - Added option to specify `step` key when logging metrics ([#808](https://github.com/PyTorchLightning/pytorch-lightning/pull/808))
+- Added `train_dataloader`, `val_dataloader` and `test_dataloader` arguments to `Trainer.fit()`, for alternative data parsing ([#759]([https://github.com/PyTorchLightning/pytorch-lightning/pull/759]))
 ### Changed
 - 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))

pytorch_lightning/core/decorators.py

@@ -10,7 +10,7 @@ def data_loader(fn):
     """
     wraps(fn)
     attr_name = '_lazy_' + fn.__name__
-
+    @wraps(fn)
     def _get_data_loader(self):
         try:
             value = getattr(self, attr_name)

pytorch_lightning/core/lightning.py

@@ -869,7 +869,6 @@ class LightningModule(ABC, GradInformation, ModelIO, ModelHooks):
         return splits
 
     @data_loader
-    @abstractmethod
     def train_dataloader(self):
         """Implement a PyTorch DataLoader
 
@@ -895,8 +894,8 @@ class LightningModule(ABC, GradInformation, ModelIO, ModelHooks):
                 )
                 return loader
 
-
         """
+        return None
 
     @data_loader
     def tng_dataloader(self):  # todo: remove in v0.8.0

pytorch_lightning/trainer/trainer.py

@@ -18,7 +18,7 @@ from pytorch_lightning.trainer.distrib_parts import (
     parse_gpu_ids,
     determine_root_gpu_device
 )
-
+from pytorch_lightning.core.lightning import LightningModule
 from pytorch_lightning.trainer.evaluation_loop import TrainerEvaluationLoopMixin
 from pytorch_lightning.trainer.logging import TrainerLoggingMixin
 from pytorch_lightning.trainer.model_hooks import TrainerModelHooksMixin
@@ -840,17 +840,56 @@ class Trainer(TrainerIOMixin,
     # -----------------------------
     # MODEL TRAINING
     # -----------------------------
-    def fit(self, model):
+    def fit(self, model, train_dataloader=None, val_dataloader=None, test_dataloader=None):
         r"""
         Runs the full optimization routine.
 
+        Args:
+            model (LightningModule): Model to fit.
+
+            train_dataloader (:class:`.torch.utils.data.DataLoader`): A Pytorch
+                DataLoader with training samples. If the model has
+                a predefined train_dataloader method this will be skipped.
+
+            val_dataloader (:class:`.torch.utils.data.DataLoader`): Either a single
+                Pytorch Dataloader or a list of them, specifying validation samples.
+                If the model has a predefined val_dataloader method this will be skipped
+
+            test_dataloader (:class:`.torch.utils.data.DataLoader`): Either a single
+                Pytorch Dataloader or a list of them, specifying validation samples.
+                If the model has a predefined val_dataloader method this will be skipped
+
         Example::
 
+            # Option 1,
+            # Define the train_dataloader(), test_dataloader() and val_dataloader() fxs
+            # in the lightningModule
+            # RECOMMENDED FOR MOST RESEARCH AND APPLICATIONS TO MAINTAIN READABILITY
             trainer = Trainer()
             model = LightningModule()
+            trainer.fit(model)
+
+            # Option 2
+            # in production cases we might want to pass different datasets to the same model
+            # Recommended for PRODUCTION SYSTEMS
+            train, val, test = DataLoader(...), DataLoader(...), DataLoader(...)
+            trainer = Trainer()
+            model = LightningModule()
+            trainer.fit(model, train_dataloader=train,
+                        val_dataloader=val, test_dataloader=test)
+
+            # Option 1 & 2 can be mixed, for example the training set can be
+            # defined as part of the model, and validation/test can then be
+            # feed to .fit()
 
-            trainer.fit()
         """
+
+        # Update the dataloader attributes of the model with the ones supplied here,
+        # if they are not already defined in model
+        _set_dataloader(model, train_dataloader, 'train_dataloader')
+        _set_dataloader(model, val_dataloader, 'val_dataloader')
+        _set_dataloader(model, test_dataloader, 'test_dataloader')
+
         # when using multi-node or DDP within a node start each module in a separate process
         if self.use_ddp2:
             task = int(os.environ['SLURM_LOCALID'])
@@ -1048,3 +1087,49 @@ class Trainer(TrainerIOMixin,
             self.fit(model)
         else:
             self.run_evaluation(test=True)
+
+
+def _set_dataloader(model, dataloader, attribute):
+    r'''
+    Check dataloaders passed to .fit() method if they are pytorch DataLoader
+    objects and whether or not we should overright the corresponding dataloader
+    in the model
+
+    Args:
+        model (LightningModule): The model to check
+
+        dataloader: If a pytorch dataloader (or a list of pytorch dataloaders)
+            is passed, it will be incorporate into the model as model.attribute.
+            If attribute alreay exist it will warn the userpass. If not a
+            dataloader will throw an error
+
+        attribute (str): The attribute to save the dataloader under
+
+    '''
+    # Check if attribute comes directly from base class or
+    # derived in user subclass
+    if LightningModule.__qualname__ in getattr(model, attribute).__qualname__:
+        # Val and test should be list of dataloaders
+        dataloader = dataloader if attribute == 'train_dataloader' or \
+            (attribute != 'train_dataloader' and isinstance(dataloader, list)) else [dataloader]
+
+        # Check we are given valid dataloaders
+        is_dataloader = isinstance(dataloader, torch.utils.data.DataLoader)
+        is_dataloader_list = isinstance(dataloader, list)
+        if is_dataloader_list:
+            valid_loaders = all(isinstance(d, torch.utils.data.DataLoader) for d in dataloader)
+        if is_dataloader or is_dataloader_list and valid_loaders:
+
+            # Overwrite abstract methods
+            dl = lambda: dataloader
+            dl.__name__ = attribute
+            setattr(model, attribute, dl)
+
+        elif dataloader and dataloader != [None]:
+            raise ValueError(f'`{attribute}` needs to be an instance of '
+                             '`torch.utils.data.DataLoader` or a list of '
+                             'DataLoaders, instead got %r`' % dataloader)
+
+    elif dataloader:  # if default (None) is passed, do not warn the user
+        warnings.warn(f'Model has predefined `{attribute}`,'
+                      f' will skip `{attribute}={dataloader}` passed to fit method.')

tests/models/__init__.py

@@ -2,7 +2,7 @@
 
 import torch
 
-from .base import LightningTestModelBase
+from .base import LightningTestModelBase, LightningTestModelBaseWithoutDataloader
 from .mixins import (
     LightningValidationStepMixin,
     LightningValidationMixin,

tests/models/base.py

@@ -36,7 +36,7 @@ class TestingMNIST(MNIST):
         self.targets = self.targets[:num_samples]
 
 
-class LightningTestModelBase(LightningModule):
+class TestModelBase(LightningModule):
     """
     Base LightningModule for testing. Implements only the required
     interface
@@ -48,7 +48,7 @@ class LightningTestModelBase(LightningModule):
         :param hparams:
         """
         # init superclass
-        super(LightningTestModelBase, self).__init__()
+        super(TestModelBase, self).__init__()
         self.hparams = hparams
 
         self.batch_size = hparams.batch_size
@@ -178,10 +178,6 @@ class LightningTestModelBase(LightningModule):
 
         return loader
 
-    @data_loader
-    def train_dataloader(self):
-        return self._dataloader(train=True)
-
     @staticmethod
     def add_model_specific_args(parent_parser, root_dir):  # pragma: no cover
         """
@@ -218,3 +214,15 @@ class LightningTestModelBase(LightningModule):
                         options=[32, 64, 128, 256], tunable=False,
                         help='batch size will be divided over all gpus being used across all nodes')
         return parser
+
+
+class LightningTestModelBase(TestModelBase):
+    """ with pre-defined train dataloader """
+    @data_loader
+    def train_dataloader(self):
+        return self._dataloader(train=True)
+
+
+class LightningTestModelBaseWithoutDataloader(TestModelBase):
+    """ without pre-defined train dataloader """
+    pass

tests/test_trainer.py

@@ -13,6 +13,7 @@ from pytorch_lightning.callbacks import (
 from tests.models import (
     LightningTestModel,
     LightningTestModelBase,
+    LightningTestModelBaseWithoutDataloader,
     LightningValidationStepMixin,
     LightningValidationMultipleDataloadersMixin,
     LightningTestMultipleDataloadersMixin,
@@ -449,6 +450,165 @@ def test_multiple_test_dataloader(tmpdir):
     trainer.test()
 
 
+def test_train_dataloaders_passed_to_fit(tmpdir):
+    """ Verify that train dataloader can be passed to fit """
+    tutils.reset_seed()
+
+    class CurrentTestModel(
+        LightningTestModelBaseWithoutDataloader
+    ):
+        pass
+
+    hparams = tutils.get_hparams()
+
+    # logger file to get meta
+    trainer_options = dict(
+        default_save_path=tmpdir,
+        max_epochs=1,
+        val_percent_check=0.1,
+        train_percent_check=0.2
+    )
+
+    # only train passed to fit
+    model = CurrentTestModel(hparams)
+    trainer = Trainer(**trainer_options)
+    fit_options = dict(train_dataloader=model._dataloader(train=True))
+    results = trainer.fit(model, **fit_options)
+
+
+def test_train_val_dataloaders_passed_to_fit(tmpdir):
+    """ Verify that train & val dataloader can be passed to fit """
+    tutils.reset_seed()
+
+    class CurrentTestModel(
+        LightningTestModelBaseWithoutDataloader
+    ):
+        pass
+
+    hparams = tutils.get_hparams()
+
+    # logger file to get meta
+    trainer_options = dict(
+        default_save_path=tmpdir,
+        max_epochs=1,
+        val_percent_check=0.1,
+        train_percent_check=0.2
+    )
+
+    # train, val passed to fit
+    model = CurrentTestModel(hparams)
+    trainer = Trainer(**trainer_options)
+    fit_options = dict(train_dataloader=model._dataloader(train=True),
+                       val_dataloader=model._dataloader(train=False))
+    results = trainer.fit(model, **fit_options)
+    assert len(trainer.get_val_dataloaders()) == 1, \
+        f'`val_dataloaders` not initiated properly, got {trainer.get_val_dataloaders()}'
+
+
+def test_all_dataloaders_passed_to_fit(tmpdir):
+    """ Verify train, val & test dataloader can be passed to fit """
+    tutils.reset_seed()
+
+    class CurrentTestModel(
+        LightningTestModelBaseWithoutDataloader
+    ):
+        pass
+
+    hparams = tutils.get_hparams()
+
+    # logger file to get meta
+    trainer_options = dict(
+        default_save_path=tmpdir,
+        max_epochs=1,
+        val_percent_check=0.1,
+        train_percent_check=0.2
+    )
+
+    # train, val and test passed to fit
+    model = CurrentTestModel(hparams)
+    trainer = Trainer(**trainer_options)
+    fit_options = dict(train_dataloader=model._dataloader(train=True),
+                       val_dataloader=model._dataloader(train=False),
+                       test_dataloader=model._dataloader(train=False))
+    results = trainer.fit(model, **fit_options)
+
+    assert len(trainer.get_val_dataloaders()) == 1, \
+        f'`val_dataloaders` not initiated properly, got {trainer.get_val_dataloaders()}'
+    assert len(trainer.get_test_dataloaders()) == 1, \
+        f'`test_dataloaders` not initiated properly, got {trainer.get_test_dataloaders()}'
+
+
+def test_multiple_dataloaders_passed_to_fit(tmpdir):
+    """ Verify that multiple val & test dataloaders can be passed to fit """
+    tutils.reset_seed()
+
+    class CurrentTestModel(
+        LightningTestModelBaseWithoutDataloader
+    ):
+        pass
+
+    hparams = tutils.get_hparams()
+
+    # logger file to get meta
+    trainer_options = dict(
+        default_save_path=tmpdir,
+        max_epochs=1,
+        val_percent_check=0.1,
+        train_percent_check=0.2
+    )
+
+    # train, multiple val and multiple test passed to fit
+    model = CurrentTestModel(hparams)
+    trainer = Trainer(**trainer_options)
+    fit_options = dict(train_dataloader=model._dataloader(train=True),
+                       val_dataloader=[model._dataloader(train=False),
+                                       model._dataloader(train=False)],
+                       test_dataloader=[model._dataloader(train=False),
+                                        model._dataloader(train=False)])
+    results = trainer.fit(model, **fit_options)
+
+    assert len(trainer.get_val_dataloaders()) == 2, \
+        f'Multiple `val_dataloaders` not initiated properly, got {trainer.get_val_dataloaders()}'
+    assert len(trainer.get_test_dataloaders()) == 2, \
+        f'Multiple `test_dataloaders` not initiated properly, got {trainer.get_test_dataloaders()}'
+
+
+def test_mixing_of_dataloader_options(tmpdir):
+    """Verify that dataloaders can be passed to fit"""
+    tutils.reset_seed()
+
+    class CurrentTestModel(
+        LightningTestModelBase
+    ):
+        pass
+
+    hparams = tutils.get_hparams()
+    model = CurrentTestModel(hparams)
+
+    # logger file to get meta
+    trainer_options = dict(
+        default_save_path=tmpdir,
+        max_epochs=1,
+        val_percent_check=0.1,
+        train_percent_check=0.2
+    )
+
+    # fit model
+    trainer = Trainer(**trainer_options)
+    fit_options = dict(val_dataloader=model._dataloader(train=False))
+    results = trainer.fit(model, **fit_options)
+
+    # fit model
+    trainer = Trainer(**trainer_options)
+    fit_options = dict(val_dataloader=model._dataloader(train=False),
+                       test_dataloader=model._dataloader(train=False))
+    results = trainer.fit(model, **fit_options)
+    assert len(trainer.get_val_dataloaders()) == 1, \
+        f'`val_dataloaders` not initiated properly, got {trainer.get_val_dataloaders()}'
+    assert len(trainer.get_test_dataloaders()) == 1, \
+        f'`test_dataloaders` not initiated properly, got {trainer.get_test_dataloaders()}'
+
+
 def _init_steps_model():
     """private method for initializing a model with 5% train epochs"""
     tutils.reset_seed()
@@ -533,5 +693,6 @@ def test_trainer_min_steps_and_epochs(tmpdir):
     assert trainer.global_step >= math.floor(num_train_samples * 1.5) and \
         trainer.current_epoch > 0, "Model did not train for at least min_steps"
 
+
 # if __name__ == '__main__':
 #     pytest.main([__file__])