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mberr avatar mberr commented on June 26, 2024 1

Hi @LuisaWerner ,

I think the problem is fixed by calling model.post_parameter_update() after each optimizer step.

Background: RGCN's forward call involves

  • a message passing step over the full training graph to obtain enriched entity representations
  • an evaluation of an interaction function on top of these enriched representations

Since the message passing part can become a computational bottleneck in evaluation, RGCN supports to cache the enriched representations, i.e., perform the message passing only once and then re-use the already calculated vectors for the next evaluation batch. This cache gets invalidated whenever the parameters change, i.e., after each parameter update. This is done by post_parameter_update.

from pykeen.

LuisaWerner avatar LuisaWerner commented on June 26, 2024

Thanks! This solved my problem :-)

I still get the message in the printouts but this doesn't seem to affect the model.

Layers RGCNLayer(
  (fwd): BasesDecomposition(
    (relation_representations): LowRankRepresentation(
      (bases): Embedding(
        (_embeddings): Embedding(4, 250000)
      )
    )
  )
  (bwd): BasesDecomposition(
    (relation_representations): LowRankRepresentation(
      (bases): Embedding(
        (_embeddings): Embedding(4, 250000)
      )
    )
  )
  (self_loop): Linear(in_features=500, out_features=500, bias=True)
  (dropout): Dropout(p=0.2, inplace=False)
) has parameters, but no reset_parameters.
Layers RGCNLayer(
  (fwd): BasesDecomposition(
    (relation_representations): LowRankRepresentation(
      (bases): Embedding(
        (_embeddings): Embedding(4, 250000)
      )
    )
  )
  (bwd): BasesDecomposition(
    (relation_representations): LowRankRepresentation(
      (bases): Embedding(
        (_embeddings): Embedding(4, 250000)
      )
    )
  )
  (self_loop): Linear(in_features=500, out_features=500, bias=True)
  (dropout): Dropout(p=0.2, inplace=False)
) has parameters, but no reset_parameters.

from pykeen.

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