The counterpart of "strict" flag for loading the optimizer state dict

If we load a saved checkpoint to a model, but the parameters in the model do not exactly correspond to the checkpoint’s parameters, we can use a strict=False flag to allow for this behavior. However, in the case of optimizer I don’t see such a flag, and and loading the save optimizer state_dict results in an error. How can I load the optimizer state_dict when the parameters are different?

model.load_state_dict(checkpoint['model'], strict=False)

ValueError: loaded state dict contains a parameter group that doesn't match the size of optimizer's group
1 Like

I have the same problem. How can we load the optimizer states when additional parameters are added to the models?
There is an attempt to do this (Add "strict" flag to ignore missing parameters in Optimizer.load_state_dict · Issue #34660 · pytorch/pytorch · GitHub) , however it its still not implemented, anyone has some updates on this?