[🐛BUG] Freezed Pre-trained Embeddings

[giuspillo]

I have correctly loaded my pre-trained embeddings by using a dummy model, and by default they are frezeed, meaning that any gradient operation won't affect them.

But this also means the models cannot be trained, as you can see here:

Traceback (most recent call last):
  File "dummy_model.py", line 47, in <module>
    best_valid_score, best_valid_result = trainer.fit(train_data, valid_data)  File "/progs/recbole/recbole_env/lib/python3.8/site-packages/recbole/trainer/trainer.py", line 439, in fit
    train_loss = self._train_epoch(  File "/progs/recbole/recbole_env/lib/python3.8/site-packages/recbole/trainer/trainer.py", line 261, in _train_epoch
    scaler.scale(loss + sync_loss).backward()  File "/progs/recbole/recbole_env/lib/python3.8/site-packages/torch/_tensor.py", line 492, in backward
    torch.autograd.backward(  File "/progs/recbole/recbole_env/lib/python3.8/site-packages/torch/autograd/__init__.py", line 251, in backward
    Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
RuntimeError: element 0 of tensors does not require grad and does not have a grad_fn

Of course, if I put freeze to False, it works.
The question is: is there a way to keep the embedding freezed during the training?