Add documentation on multi test dataloaders

docs/source-pytorch/common/evaluation_intermediate.rst

@@ -134,6 +134,121 @@ you can also pass in an :doc:`datamodules <../data/datamodule>` that have overri
     # test (pass in datamodule)
     trainer.test(datamodule=dm)
 
+
+Test with Multiple DataLoaders
+==============================
+
+When you need to evaluate your model on multiple test datasets simultaneously (e.g., different domains, conditions, or
+evaluation scenarios), PyTorch Lightning supports multiple test dataloaders out of the box.
+
+To use multiple test dataloaders, simply return a list of dataloaders from your ``test_dataloader()`` method:
+
+.. code-block:: python
+
+    class LitModel(L.LightningModule):
+        def test_dataloader(self):
+            return [
+                DataLoader(clean_test_dataset, batch_size=32),
+                DataLoader(noisy_test_dataset, batch_size=32),
+                DataLoader(adversarial_test_dataset, batch_size=32),
+            ]
+
+When using multiple test dataloaders, your ``test_step`` method **must** include a ``dataloader_idx`` parameter:
+
+.. code-block:: python
+
+    def test_step(self, batch, batch_idx, dataloader_idx: int = 0):
+        x, y = batch
+        y_hat = self(x)
+        loss = F.cross_entropy(y_hat, y)
+
+        # Use dataloader_idx to handle different test scenarios
+        return {'test_loss': loss}
+
+Logging Metrics Per Dataloader
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Lightning provides automatic support for logging metrics per dataloader:
+
+.. code-block:: python
+
+    def test_step(self, batch, batch_idx, dataloader_idx: int = 0):
+        x, y = batch
+        y_hat = self(x)
+        loss = F.cross_entropy(y_hat, y)
+        acc = (y_hat.argmax(dim=1) == y).float().mean()
+
+        # Lightning automatically adds "/dataloader_idx_X" suffix
+        self.log('test_loss', loss, add_dataloader_idx=True)
+        self.log('test_acc', acc, add_dataloader_idx=True)
+
+        return loss
+
+This will create metrics like ``test_loss/dataloader_idx_0``, ``test_loss/dataloader_idx_1``, etc.
+
+For more meaningful metric names, you can use custom naming where you need to make sure that individual names are
+unique across dataloaders.
+
+.. code-block:: python
+
+    def test_step(self, batch, batch_idx, dataloader_idx: int = 0):
+        # Define meaningful names for each dataloader
+        dataloader_names = {0: "clean", 1: "noisy", 2: "adversarial"}
+        dataset_name = dataloader_names.get(dataloader_idx, f"dataset_{dataloader_idx}")
+
+        # Log with custom names
+        self.log(f'test_loss_{dataset_name}', loss, add_dataloader_idx=False)
+        self.log(f'test_acc_{dataset_name}', acc, add_dataloader_idx=False)
+
+Processing Entire Datasets Per Dataloader
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+To perform calculations on the entire test dataset for each dataloader (e.g., computing overall metrics, creating
+visualizations), accumulate results during ``test_step`` and process them in ``on_test_epoch_end``:
+
+.. code-block:: python
+
+    class LitModel(L.LightningModule):
+        def __init__(self):
+            super().__init__()
+            # Store outputs per dataloader
+            self.test_outputs = {}
+
+        def test_step(self, batch, batch_idx, dataloader_idx: int = 0):
+            x, y = batch
+            y_hat = self(x)
+            loss = F.cross_entropy(y_hat, y)
+
+            # Initialize and store results
+            if dataloader_idx not in self.test_outputs:
+                self.test_outputs[dataloader_idx] = {'predictions': [], 'targets': []}
+            self.test_outputs[dataloader_idx]['predictions'].append(y_hat)
+            self.test_outputs[dataloader_idx]['targets'].append(y)
+            return loss
+
+        def on_test_epoch_end(self):
+            for dataloader_idx, outputs in self.test_outputs.items():
+                # Concatenate all predictions and targets for this dataloader
+                all_predictions = torch.cat(outputs['predictions'], dim=0)
+                all_targets = torch.cat(outputs['targets'], dim=0)
+
+                # Calculate metrics on the entire dataset, log and create visualizations
+                overall_accuracy = (all_predictions.argmax(dim=1) == all_targets).float().mean()
+                self.log(f'test_overall_acc_dataloader_{dataloader_idx}', overall_accuracy)
+                self._save_results(all_predictions, all_targets, dataloader_idx)
+
+            self.test_outputs.clear()
+
+.. note::
+    When using multiple test dataloaders, ``trainer.test()`` returns a list of results, one for each dataloader:
+
+    .. code-block:: python
+
+        results = trainer.test(model)
+        print(f"Results from {len(results)} test dataloaders:")
+        for i, result in enumerate(results):
+            print(f"Dataloader {i}: {result}")
+
 ----------
 
 **********