Trainable Tokens: Support for Weight Tying #2399
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February 26, 2025 17:21
Notably we are removing the duplication filter of `named_modules` when searching for the (tied) target modules since tied weights are by definition duplicates.
It's now possible to let the adapter decide which is the input embedding layer based on the output of `model.get_input_embeddings()`. If that fails, the default is still `embed_tokens`.
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This is a follow-up PR of #2376 to add support for weight-tying. Do not merge before the other is not merged.
What is this
Some models, such as gpt2, tie the weights between the LM head and the input embeddings for various reasons. If we use the trainable tokens adapter, we're changing the result of the
forward()of the input embeddings but we do not change the weights (unless wemerge()). This means that the changes are not reflected in the tied weights, such as the LM head, leading to wrong results when training.How it is solved
The current approach is searching for tied layers and putting
TrainableTokensLayeradapters on them as well but initialized to use the parameters from the embedding layer'sTrainableTokensLayer. This is done via thetied_adapterargument ofTrailableTokensLayer.__init__().What needs to be done
TrainableTokensadapter