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Jump to Conclusions: Short-Cutting Transformers With Linear Transformations

This is the repository for the code used in the paper:

• 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},
}

Running the code

To produce plots for gpt2 and Wikipedia sentences, run the following, in the written order:

• get_wikipedia_sentences.py

  (produces ./experiment/sentences/wikipedia_20K-sentences.pickle, containing 20K sentences from Wikipedia)

• add_tokenization.py

  (produces ./experiment/gpt2/wikipedia_tokenized_train.pickle containing the tokenizations and random token positions for the first 9000 sentences from the file produced by the previous script, and ./experiment/gpt2/wikipedia_tokenized_val.pickle containing the tokenizations and random token positions for the next 3000 sentences)

• add_linreg.py

  (produces ./linreg/gpt2/wikipedia/i_j.pickle where $0 leq i < j leq 12$, containing the matrix $A_{j,i}$ (as a torch.Tensor) used for skipping from layer $i$ to layer $j$)

• add_plot_r2.py

  (produces ./experiment/gpt2/wikipedia_r2_scores.pickle containing the $r^2$ scores for $texttt{mat}$ and $texttt{id}$, and also produces ./experiments/gpt2/plots/wikipedia/r2_scores_12.pdf containing the heatmap plots for these $r^2$ scores)

• add_linreg_submodules.py

  (produces ./linreg/gpt2/wikipedia/pi_a_b.pickle where $0 leq i < 12$ and $0 leq a < 6$ and $b = a + 1$; these contain matrices (as a torch.Tensor) used to linearly approximate the output of a sub-module in transformer block $i+1$ given its input. $b=1$ corresponds to the first layer normalization, $b=2$ corresponds to attention, $b=3$ corresponds to the first residual connection, $b=4$ correspodns to the second layer normalization, $b=5$ corresponds to the feed-forward network layer and $b=6$ corresponds to the second residual connection)

• add_results.py

  (produces ./experiment/gpt2/wikipedia_results.pickle containing (for each validation set sample) the top 10 tokens, as well as the model's surprisal of the top 1 token, according to the five mappings of the paper, at each layer; and also containing the top 10 tokens and number of layers processed when early-exiting and using the mappings $texttt{mat}$ and $texttt{id}$ (for various values of $lambda$))

• plot_results.py

  (produces some plots in ./experiment/gpt2/plots/wikipedia/ based on the results in the previous file's output)

To produce plots for bert-base-uncased and Wikipedia sentences, run the following, in the written order:

• get_wikipedia_sentences.py

  (the same as for gpt2 above, no need to re-run)

• bert_add_reps.py

  (produces ./experiment/bert-base-uncased_mask/wikipedia_train.pickle containing the tokenizations, random token positions and representations of the masked random token at all layers for the first 9000 sentences from the file produced by the previous script, and ./experiment/bert-base-uncased_mask/wikipedia_val.pickle containing the tokenizations, random token positions and representations of the masked random token at all layers for the next 3000 sentences)

• bert_add_linreg.py

  (produces ./linreg/bert-base-uncased_mask/wikipedia/i_j.pickle where $0 leq i < j leq 12$, containing the matrix $A_{j,i}$ (as a torch.Tensor) used for skipping from layer $i$ to layer $j$)

• bert_add_plot_r2.py

  (produces ./experiment/bert-base-uncased_mask/wikipedia_r2_scores.pickle containing the $r^2$ scores for $texttt{mat}$ and $texttt{id}$, and also produces ./experiments/bert-base-uncased_mask/plots/wikipedia/r2_scores_12.pdf containing the heatmap plots for these $r^2$ scores)

• bert_add_results.py

  (produces ./experiment/bert-base-uncased_mask/wikipedia_results.pickle containing (for each validation set sample) the top 10 tokens, as well as the model's surprisal of the top 1 token, according to $texttt{mat}$ and $texttt{id}$, at each layer; and also containing the top 10 tokens and number of layers processed when early-exiting and using the mappings $texttt{mat}$ and $texttt{id}$ (for various values of $lambda$))

• plot_results.py (change model_folder_name='bert-base-uncased_mask' and plot_parts = False)

  (produces some plots in ./experiment/bert-base-uncased_mask/plots/wikipedia/ based on the results in the previous file's output)

We also produced plots for gpt2-medium, gpt2-large, gpt2-xl, bert-large-uncased. To do that, one should modify, in a relatively stratight-forward way, the variables at the head of each script in the sequence.

Requirements

The code was ran with Python 3.10.4 and the following package versions:

torch.__version__ = 1.13.1+cu117
transformers.__version__ = 4.20.1
sklearn.__version__ = 1.2.0
pickle.format_version = 4.0
datasets.__version__ = 2.5.2  # used only to fetch Wikipedia sentences
spacy.__version__ = 3.5.0  # used only to fetch Wikipedia sentences

Trained Matrices

Some of the trained matrices can be found at https://huggingface.co/sashay/linear-shortcut.