Out of memory after some epochs, for some models about pykeen HOT 7 CLOSED

+1. I have the same issue when I used the forward() function and loss() function seperately in the trainer.

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Hi @ferzcam (and @renzhonglu11 ),

this likely comes DistMult using a regularizer by default, cf.

To fix it, either

• configure the model without regularizer, e.g., for DistMult pass regularizer=None, or
• if you want to make use of the regularizer, make sure to call collect_regularization_term on the models and include this into your loss terms

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@mberr your solution works. Thanks a lot!😁

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You can see with

from pykeen.datasets import get_dataset
from pykeen.models import DistMult

dataset = get_dataset(dataset="nations")
model = DistMult(triples_factory=dataset.training)
print(model)

how the resulting structure looks like:

DistMult(
  (loss): MarginRankingLoss(
    (margin_activation): ReLU()
  )
  (interaction): DistMultInteraction()
  (entity_representations): ModuleList(
    (0): Embedding(
      (_embeddings): Embedding(14, 50)
    )
  )
  (relation_representations): ModuleList(
    (0): Embedding(
      (regularizer): LpRegularizer()
      (_embeddings): Embedding(55, 50)
    )
  )
  (weight_regularizers): ModuleList()
)

Notice how the relation_representations as a regularizer. compare this to

from pykeen.datasets import get_dataset
from pykeen.models import DistMult

dataset = get_dataset(dataset="nations")
model = DistMult(triples_factory=dataset.training, regularizer=None)
print(model)

resulting in

DistMult(
  ...
  (relation_representations): ModuleList(
    (0): Embedding(
      (_embeddings): Embedding(55, 50)
    )
  )
  ...
)

The regularization term of the relation embedding is updated here

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Nice. Now it makes more sense. And I also found out where Pykeen calls the collect_regularization_term(). Thanks for your explanation.

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Thanks a lot. But I am still quite not sure if it is really the reason. I took a look at Pykeen's source code. It seems Pykeen calculates the loss just with a function in the trainer. (

In my KGE Model

class PykeenKGE:
    def training_step(self, batch):
        x_batch, y_batch = batch
        yhat_batch = self.forward(x_batch)
        loss_batch = self.loss(yhat_batch, y_batch)
        return loss_batch + self.model.collect_regularization_term()

In Trainer:

 batch_loss = self.model.training_step(batch)

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Hi @mberr. Thanks for the explanation. I was able to make it work now!

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