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🏄 🤙 Fix gradient error in CompGCN buffering #573

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merged 3 commits into from
Aug 19, 2021
Merged

🏄 🤙 Fix gradient error in CompGCN buffering #573

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a88e191
Invalidate buffered representations when returning from evaluation mode
mberr Aug 19, 2021
0337503
Add model test case for pipeline with early stopping
mberr Aug 19, 2021
4e12ac4
Fix early stopping pipeline test
mberr Aug 19, 2021
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8 changes: 8 additions & 0 deletions src/pykeen/nn/emb.py
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Original file line number Diff line number Diff line change
Expand Up @@ -935,6 +935,14 @@ def post_parameter_update(self) -> None: # noqa: D102
# invalidate enriched embeddings
self.enriched_representations = None

def train(self, mode: bool = True): # noqa: D102
# when changing from evaluation to training mode, the buffered representations have been computed without
# gradient tracking. hence, we need to invalidate them.
# note: this occurs in practice when continuing training after evaluation.
if mode and not self.training:
self.enriched_representations = None
return super().train(mode=mode)

def forward(
self,
) -> Tuple[torch.FloatTensor, torch.FloatTensor]:
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21 changes: 21 additions & 0 deletions tests/cases.py
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Expand Up @@ -37,6 +37,7 @@
from pykeen.nn.emb import RepresentationModule
from pykeen.nn.modules import FunctionalInteraction, Interaction, LiteralInteraction
from pykeen.optimizers import optimizer_resolver
from pykeen.pipeline import pipeline
from pykeen.regularizers import LpRegularizer, Regularizer
from pykeen.trackers import ResultTracker
from pykeen.training import LCWATrainingLoop, SLCWATrainingLoop, TrainingLoop
Expand Down Expand Up @@ -1057,6 +1058,26 @@ def test_cli_training_nations(self):
"""Test running the pipeline on almost all models with only training data."""
self._help_test_cli(['-t', NATIONS_TRAIN_PATH] + self._cli_extras)

@pytest.mark.slow
def test_pipeline_nations_early_stopper(self):
"""Test running the pipeline with early stopping."""
model_kwargs = dict(self.instance_kwargs)
# triples factory is added by the pipeline
model_kwargs.pop("triples_factory")
pipeline(
model=self.cls,
model_kwargs=model_kwargs,
dataset="nations",
dataset_kwargs=dict(create_inverse_triples=self.create_inverse_triples),
stopper="early",
training_loop_kwargs=self.training_loop_kwargs,
stopper_kwargs=dict(frequency=1),
training_kwargs=dict(
batch_size=self.train_batch_size,
num_epochs=self.train_num_epochs,
),
)

@pytest.mark.slow
def test_cli_training_kinships(self):
"""Test running the pipeline on almost all models with only training data."""
Expand Down