If your model was trained on a different version of PyKEEN, you might have difficulty loading the model using torch.load('trained_model.pkl').

This could be due to one or both of the following:

1. The model class structure might have changed.
2. The model weight names might have changed.

Note that PyKEEN currently cannot support model migration. Please attempt the following steps to load the model.

You will likely see an exception like this one: ModuleNotFoundError: No module named ...

In this case, try to instantiate the model class directly and only load the state dict from the model file.

1. Save the model's state_dict using 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")

2. 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)

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)