Smaller output vocabulary for GPT-2

I noticed that by default, GPT2LMHeadModel returns prediction scores of shape (batch_size, sequence_length, config.vocab_size) (docs link). Is there any way for me to limit the output vocabulary to only a subset of words?

I want to take the existing weights from GPT-2, but re-train a new top linear layer with a smaller vocabulary. I suppose I could mask the logits at the end, but then it feels like a waste of computational power to even predict them.

Note that this model has the weights of the encoder and the decoder tied, so if you want to use the existing weights, you probably want to just mask the indices of the tokens you don’t want to use in your predictions.
Otherwise you can try to replace the last layer, but you will need to adapt the code in modeling_gpt2.py to do this.