⚡🩹 Fix lightning training without validation

src/pykeen/contrib/lightning.py

@@ -45,7 +45,7 @@
 from pykeen.sampling import NegativeSampler
 from pykeen.training import LCWATrainingLoop, SLCWATrainingLoop
 from pykeen.triples.triples_factory import CoreTriplesFactory
-from pykeen.typing import InductiveMode
+from pykeen.typing import InductiveMode, OneOrSequence
 
 __all__ = [
     "LitModule",
@@ -150,13 +150,15 @@ def _dataloader(self, triples_factory: CoreTriplesFactory, shuffle: bool = False
         """Create a data loader."""
         raise NotImplementedError
 
-    def train_dataloader(self):
+    def train_dataloader(self) -> torch.utils.data.DataLoader:
         """Create the training data loader."""
         return self._dataloader(triples_factory=self.dataset.training, shuffle=True)
 
-    def val_dataloader(self):
+    def val_dataloader(self) -> OneOrSequence[torch.utils.data.DataLoader]:
         """Create the validation data loader."""
         # TODO: In sLCWA, we still want to calculate validation *metrics* in LCWA
+        if self.dataset.validation is None:
+            return []
         return self._dataloader(triples_factory=self.dataset.validation, shuffle=False)
 
     def configure_optimizers(self):

tests/test_lightning.py

@@ -4,15 +4,19 @@
 
 import pytest
 
+from pykeen import models
+from pykeen.datasets import EagerDataset, get_dataset
+from pykeen.typing import TRAINING
+from tests.utils import needs_packages
+
 try:
-    from pykeen.contrib.lightning import lit_module_resolver
+    from pykeen.contrib.lightning import lit_module_resolver, lit_pipeline
 
     LIT_MODULES = lit_module_resolver.lookup_dict.keys()
 except ImportError:
     LIT_MODULES = []
-from pykeen import models
-from pykeen.datasets import get_dataset
-from pykeen.typing import TRAINING
+    lit_pipeline = None
+
 
 EMBEDDING_DIM = 8
 # TODO: this could be shared with the model tests
@@ -61,12 +65,11 @@
 
 
 # test combinations of models with training loops
+@needs_packages("pytorch_lightning")
 @pytest.mark.skipif(True, reason="instability related to https://github.com/Lightning-AI/lightning/pull/14117")
 @pytest.mark.parametrize(("model", "model_kwargs", "training_loop"), TEST_CONFIGURATIONS)
 def test_lit_training(model, model_kwargs, training_loop):
     """Test training models with PyTorch Lightning."""
-    from pykeen.contrib.lightning import lit_pipeline
-
     # some models require inverse relations
     create_inverse_triples = model is not models.RGCN
     dataset = get_dataset(dataset="nations", dataset_kwargs=dict(create_inverse_triples=create_inverse_triples))
@@ -103,3 +106,27 @@ def test_lit_training(model, model_kwargs, training_loop):
             max_epochs=2,
         ),
     )
+
+
+@needs_packages("pytorch_lightning")
+def test_lit_pipeline_with_dataset_without_validation():
+    """Test training on a dataset without validation triples."""
+    dataset = get_dataset(dataset="nations")
+    dataset = EagerDataset(training=dataset.training, testing=dataset.testing, metadata=dataset.metadata)
+    lit_pipeline(
+        training_loop="slcwa",
+        training_loop_kwargs=dict(
+            model="transe",
+            dataset=dataset,
+        ),
+        trainer_kwargs=dict(
+            # automatically choose accelerator
+            accelerator="auto",
+            # defaults to TensorBoard; explicitly disabled here
+            logger=False,
+            # disable checkpointing
+            enable_checkpointing=False,
+            # fast run
+            max_epochs=2,
+        ),
+    )