Update docs on arg train_dataloader in fit (#6076)

* add to docs

* update docs

* Apply suggestions from code review

* Update pytorch_lightning/core/hooks.py

Co-authored-by: Rohit Gupta <rohitgr1998@gmail.com>

* nested loaders

* Apply suggestions from code review

Co-authored-by: Adrian Wälchli <aedu.waelchli@gmail.com>

* shorten text length

* Update pytorch_lightning/core/hooks.py

Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com>
Co-authored-by: Rohit Gupta <rohitgr1998@gmail.com>
Co-authored-by: Adrian Wälchli <aedu.waelchli@gmail.com>

docs/source/advanced/multiple_loaders.rst

@@ -16,6 +16,8 @@ Lightning supports multiple dataloaders in a few ways.
 
 ----------
 
+.. _multiple-training-dataloaders:
+
 Multiple training dataloaders
 -----------------------------
 For training, the usual way to use multiple dataloaders is to create a ``DataLoader`` class
@@ -86,6 +88,27 @@ For more details please have a look at :attr:`~pytorch_lightning.trainer.trainer
 
             return loaders
 
+Furthermore, Lightning also supports that nested lists and dicts (or a combination) can
+be returned 
+
+.. testcode::
+
+    class LitModel(LightningModule):
+
+        def train_dataloader(self):
+
+            loader_a = torch.utils.data.DataLoader(range(8), batch_size=4)
+            loader_b = torch.utils.data.DataLoader(range(16), batch_size=4)
+            loader_c = torch.utils.data.DataLoader(range(32), batch_size=4)
+            loader_c = torch.utils.data.DataLoader(range(64), batch_size=4)
+
+            # pass loaders as a nested dict. This will create batches like this:
+            # {'loader_a_b': {'a': batch from loader a, 'b': batch from loader b},
+            #  'loader_c_d': {'c': batch from loader c, 'd': batch from loader d}}
+            loaders = {'loaders_a_b': {'a': loader_a, 'b': loader_b},
+                       'loaders_c_d': {'c': loader_c, 'd': loader_d}}
+            return loaders
+
 ----------
 
 Test/Val dataloaders

pytorch_lightning/core/hooks.py

@@ -383,12 +383,14 @@ def prepare_data(self):
             model.test_dataloader()
         """
 
-    def train_dataloader(self) -> DataLoader:
+    def train_dataloader(self) -> Any:
         """
-        Implement a PyTorch DataLoader for training.
+        Implement one or more PyTorch DataLoaders for training.
 
         Return:
-            Single PyTorch :class:`~torch.utils.data.DataLoader`.
+            Either a single PyTorch :class:`~torch.utils.data.DataLoader` or a collection of these
+            (list, dict, nested lists and dicts). In the case of multiple dataloaders, please see
+            this :ref:`page <multiple-training-dataloaders>`
 
         The dataloader you return will not be called every epoch unless you set
         :paramref:`~pytorch_lightning.trainer.Trainer.reload_dataloaders_every_epoch` to ``True``.
@@ -414,6 +416,7 @@ def train_dataloader(self) -> DataLoader:
 
         Example::
 
+            # single dataloader
             def train_dataloader(self):
                 transform = transforms.Compose([transforms.ToTensor(),
                                                 transforms.Normalize((0.5,), (1.0,))])
@@ -426,6 +429,32 @@ def train_dataloader(self):
                 )
                 return loader
 
+            # multiple dataloaders, return as list
+            def train_dataloader(self):
+                mnist = MNIST(...)
+                cifar = CIFAR(...)
+                mnist_loader = torch.utils.data.DataLoader(
+                    dataset=mnist, batch_size=self.batch_size, shuffle=True
+                )
+                cifar_loader = torch.utils.data.DataLoader(
+                    dataset=cifar, batch_size=self.batch_size, shuffle=True
+                )
+                # each batch will be a list of tensors: [batch_mnist, batch_cifar]
+                return [mnist_loader, cifar_loader]
+
+            # multiple dataloader, return as dict
+            def train_dataloader(self):
+                mnist = MNIST(...)
+                cifar = CIFAR(...)
+                mnist_loader = torch.utils.data.DataLoader(
+                    dataset=mnist, batch_size=self.batch_size, shuffle=True
+                )
+                cifar_loader = torch.utils.data.DataLoader(
+                    dataset=cifar, batch_size=self.batch_size, shuffle=True
+                )
+                # each batch will be a dict of tensors: {'mnist': batch_mnist, 'cifar': batch_cifar}
+                return {'mnist': mnist_loader, 'cifar': cifar_loader}
+
         """
         rank_zero_warn("`train_dataloader` must be implemented to be used with the Lightning Trainer")
 

pytorch_lightning/trainer/trainer.py

@@ -16,7 +16,7 @@
 import warnings
 from itertools import count
 from pathlib import Path
-from typing import Dict, Iterable, List, Optional, Union
+from typing import Any, Dict, Iterable, List, Optional, Union
 
 import torch
 from torch.utils.data import DataLoader
@@ -399,7 +399,7 @@ def setup_trainer(self, model: LightningModule):
     def fit(
         self,
         model: LightningModule,
-        train_dataloader: Optional[DataLoader] = None,
+        train_dataloader: Any = None,
         val_dataloaders: Optional[Union[DataLoader, List[DataLoader]]] = None,
         datamodule: Optional[LightningDataModule] = None,
     ):
@@ -411,8 +411,9 @@ def fit(
 
             model: Model to fit.
 
-            train_dataloader: A Pytorch DataLoader with training samples. If the model has
-                a predefined train_dataloader method this will be skipped.
+            train_dataloader: Either a single PyTorch DataLoader or a collection of these
+                (list, dict, nested lists and dicts). In the case of multiple dataloaders, please
+                see this :ref:`page <multiple-training-dataloaders>`
 
             val_dataloaders: Either a single Pytorch Dataloader or a list of them, specifying validation samples.
                 If the model has a predefined val_dataloaders method this will be skipped