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We introduce Tangent Attention Fine-Tuning (TAFT), a method for fine-tuninglinearized transformers obtained by computing a First-order Taylor Expansionaround a pre-trained initialization. We show that the Jacobian-Vector Productresulting from linearization can be computed efficiently in a single forwardpass, reducing training and inference cost to the same order of magnitude asits original non-linear counterpart, while using the same number of parameters.Furthermore, we show that, when applied to various downstream visualclassification tasks, the resulting Tangent Transformer fine-tuned with TAFTcan perform comparably with fine-tuning the original non-linear network. SinceTangent Transformers are linear with respect to the new set of weights, and theresulting fine-tuning loss is convex, we show that TAFT enjoys severaladvantages compared to non-linear fine-tuning when it comes to modelcomposition, parallel training, machine unlearning, and differential privacy.

Tangent Transformers for Composition, Privacy and Removal