Add possibility for custom naming when using multiple dataloaders

CHANGELOG.md

@@ -15,6 +15,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Added `checkpoint` parameter to callback's `on_save_checkpoint` hook ([#6072](https://github.com/PyTorchLightning/pytorch-lightning/pull/6072))
 
 
+- Added arg to `self.log` that enables users to give custom names when dealing with multiple dataloaders ([#6274](https://github.com/PyTorchLightning/pytorch-lightning/pull/6274))
+
+
 ### Changed
 
 - Changed the order of `backward`, `step`, `zero_grad` to `zero_grad`, `backward`, `step` ([#6147](https://github.com/PyTorchLightning/pytorch-lightning/pull/6147))

pytorch_lightning/core/lightning.py

@@ -225,6 +225,7 @@ def log(
         sync_dist: bool = False,
         sync_dist_op: Union[Any, str] = 'mean',
         sync_dist_group: Optional[Any] = None,
+        add_dataloader_idx: bool = True,
     ):
         """
         Log a key, value
@@ -259,7 +260,10 @@ def log(
             enable_graph: if True, will not auto detach the graph
             sync_dist: if True, reduces the metric across GPUs/TPUs
             sync_dist_op: the op to sync across GPUs/TPUs
-            sync_dist_group: the ddp group
+            sync_dist_group: the ddp group to sync across
+            add_dataloader_idx: if True, appends the index of the current dataloader to
+                the name (when using multiple). If False, user needs to give unique names for
+                each dataloader to not mix values
         """
         if self._results is not None:
             # in any epoch end can't log step metrics (only epoch metric)
@@ -291,6 +295,9 @@ def log(
 
             training_type_plugin = self.trainer.training_type_plugin
 
+            # Determine if dataloader index should be added
+            dataloader_idx = self._current_dataloader_idx if add_dataloader_idx else None
+
             self._results.log(
                 name,
                 value,
@@ -306,7 +313,7 @@ def log(
                 sync_dist_op,
                 sync_dist_group,
                 training_type_plugin.reduce,
-                self._current_dataloader_idx,
+                dataloader_idx,
                 self.device,
             )
 
@@ -324,6 +331,7 @@ def log_dict(
         sync_dist: bool = False,
         sync_dist_op: Union[Any, str] = 'mean',
         sync_dist_group: Optional[Any] = None,
+        add_dataloader_idx: bool = True,
     ):
         """
         Log a dictonary of values at once
@@ -345,7 +353,10 @@ def log_dict(
             enable_graph: if True, will not auto detach the graph
             sync_dist: if True, reduces the metric across GPUs/TPUs
             sync_dist_op: the op to sync across GPUs/TPUs
-            sync_dist_group: the ddp group:
+            sync_dist_group: the ddp group sync across
+            add_dataloader_idx: if True, appends the index of the current dataloader to
+                the name (when using multiple). If False, user needs to give unique names for
+                each dataloader to not mix values
         """
         for k, v in dictionary.items():
             self.log(
@@ -362,6 +373,7 @@ def log_dict(
                 sync_dist_op=sync_dist_op,
                 tbptt_pad_token=tbptt_pad_token,
                 tbptt_reduce_fx=tbptt_reduce_fx,
+                add_dataloader_idx=add_dataloader_idx
             )
 
     def write_prediction(

tests/trainer/logging_/test_logger_connector.py

@@ -471,3 +471,41 @@ def training_step(self, *args, **kwargs):
     )
     with pytest.warns(UserWarning, match="The progress bar already tracks a metric with the .* 'loss'"):
         trainer.fit(model)
+
+
+@pytest.mark.parametrize("add_dataloader_idx", [False, True])
+def test_auto_add_dataloader_idx(tmpdir, add_dataloader_idx):
+    """ test that auto_add_dataloader_idx argument works """
+
+    class TestModel(BoringModel):
+        def val_dataloader(self):
+            dl = super().val_dataloader()
+            return [dl, dl]
+
+        def validation_step(self, *args, **kwargs):
+            output = super().validation_step(*args[:-1], **kwargs)
+            if add_dataloader_idx:
+                name = "val_loss"
+            else:
+                name = f"val_loss_custom_naming_{args[-1]}"
+
+            self.log(name, output["x"], add_dataloader_idx=add_dataloader_idx)
+            return output
+
+    model = TestModel()
+    model.validation_epoch_end = None
+
+    trainer = Trainer(
+        default_root_dir=tmpdir,
+        max_steps=5
+    )
+    trainer.fit(model)
+    logged = trainer.logged_metrics
+
+    # Check that the correct keys exist
+    if add_dataloader_idx:
+        assert 'val_loss/dataloader_idx_0' in logged
+        assert 'val_loss/dataloader_idx_1' in logged
+    else:
+        assert 'val_loss_custom_naming_0' in logged
+        assert 'val_loss_custom_naming_1' in logged