linear-shortcut

This repository contains some of the matrices as described in

• Alexander Yom Din, Taelin Karidi, Leshem Choshen, Mor Geva. 2023. Jump to Conclusions: Short-Cutting Transformers With Linear Transformations. (arXiv:2303.09435)

please cite the paper as:

@article{din2023jump,
      title={Jump to Conclusions: Short-Cutting Transformers With Linear Transformations},
      author={Yom Din, Alexander and Karidi, Taelin and Choshen, Leshem and Geva, Mor},
      journal={arXiv preprint arXiv:2303.09435},
      year={2023},
}

For example, the file in gpt2-medium/wikipedia/6_9.pickle contains the matrix trained, on the wikipedia dataset, to transform 6th-layer hidden representations of tokens into 9th-layer hidden representations, for the Huggingface transformers gpt2-medium model. One loads and multiplies as follows:

import pickle
import torch

with open(file_name, 'rb') as f:
    mat = pickle.load(f)

assert(isinstance(mat, torch.Tensor))
assert(len(mat.shape) == 2)
assert(mat.shape[0] == mat.shape[1])

v = torch.rand(mat.shape[1])

w = mat @ v

assert(w.shape == v.shape)

Some more information is in https://github.com/sashayd/mat.