Loading a Model from an Old Version of PyKEEN¶
If your model was trained on a different version of PyKEEN, you might
have difficulty loading the model using
This could be due to one or both of the following:
The model class structure might have changed.
The model weight names might have changed.
Note that PyKEEN currently cannot support model migration. Please attempt the following steps to load the model.
If the model class structure has changed¶
You will likely see an exception like this one:
No module named ...
In this case, try to instantiate the model class directly and only load the state dict from the model file.
Save the model’s
state_dictusing the version of PyKEEN used for training:
import torch from pykeen.pipeline import pipeline result = pipeline(dataset="Nations", model="RotatE") torch.save(result.model.state_dict(), "v1.7.0/model.state_dict.pt")
Load the model using the version of PyKEEN you want to use. First instantiate the model, then load the state dict:
import torch from pykeen.datasets import get_dataset from pykeen.models import RotatE dataset = get_dataset(dataset="Nations") model = RotatE(triples_factory=dataset.training) state_dict = torch.load("v1.7.0/model.state_dict.pt") model.load_state_dict(state_dict)
If the model weight names have changed¶
You will likely see an exception similar to this one:
RuntimeError: Error(s) in loading state_dict for RotatE: Missing key(s) in state_dict: "entity_representations.0._embeddings.weight", "relation_representations.0._embeddings.weight". Unexpected key(s) in state_dict: "regularizer.weight", "regularizer.regularization_term", "entity_embeddings._embeddings.weight", "relation_embeddings._embeddings.weight".
In this case, you need to inspect the state-dict dictionaries in the different version, and try to match the keys. Then modify the state dict accordingly before loading it. For example:
import torch from pykeen.datasets import get_dataset from pykeen.models import RotatE dataset = get_dataset(dataset="Nations") model = RotatE(triples_factory=dataset.training) state_dict = torch.load("v1.7.0/model.state_dict.pt") # these are some example changes in weight names for RotatE between two different pykeen versions for old_name, new_name in [ ( "entity_embeddings._embeddings.weight", "entity_representations.0._embeddings.weight", ), ( "relation_embeddings._embeddings.weight", "relation_representations.0._embeddings.weight", ), ]: state_dict[new_name] = state_dict.pop(old_name) # in this example, the new model does not have a regularizer, so we need to delete corresponding data for name in ["regularizer.weight", "regularizer.regularization_term"]: state_dict.pop(name) model.load_state_dict(state_dict)
Even if the state dict can be loaded, there is still a risk that the the weights are used differently. This can lead to a difference in model behavior. To be sure that the model is still functioning the same way, you should also check some model predictions and inspect how the model definition has changed.