Transformer Lobotomy
Clone this repository and run
cd coms6998-project
pip install .
It is recommended to create and install into a virtual environment by cloning the repository, creating the virtual environment, and then installing.
cd coms6998-project
python3 -m venv .venv # Create a virtual environment named .venv
source .venv/bin/activate
pip install .
To run a scoring script, simply run
cd candbproj/experiments
python gpt2_trained.py
This will score the trained GPT2 model 10 times, each with a different random seed. To run a different number of times, each with a different random seed, do
python gpt2_trained.py -n <n>
Each script will attempt to load results from the coms6998-project/results directory if they exist. Otherwise, results
will be written to the coms6998-project/results directory once all results are completed, if the directory exists.
Results files are python pickle files and have the extension .pkl
Runs can take a long time. Scoring a model one time takes approximately 4 minutes on Intel i7 9700K CPU (3.60 GHz). Individual scripts vary from 10 to 150 scores computed. Instead, results files can be accessed on our Google Drive (A LionMail account is required to view).
To produce the plots found in the report, first results files must exist in coms6998-project/results. These can either
be generated by running all scoring scripts again (see Running) or they can be accessed on our
Google Drive (A LionMail
account is required to view). The result files are not committed to github due to their size.
# ensure you have the appropriate files in the results directory
$ ls coms6998-project/results
gpt2_char_tokenizer_result.pkl
gpt2_coarse_pos_ner_pos_result.pkl
gpt2_coarse_pos_result.pkl
gpt2_disable_attn_proj_lobotomy_result.pkl
gpt2_disable_key_proj_lobotomy_result.pkl
gpt2_disable_layer_norm_lobotomy_result.pkl
gpt2_disable_mlp_lobotomy_result.pkl
gpt2_disable_query_proj_lobotomy_result.pkl
gpt2_disable_value_proj_lobotomy_result.pkl
gpt2_fine_pos_ner_pos_result.pkl
gpt2_fine_pos_result.pkl
gpt2_ipa_char_tokenizer_result.pkl
gpt2_mfcc_embeddings_result.pkl
gpt2_n_inner_down_lobotomy_result.pkl
gpt2_n_inner_same_lobotomy_result.pkl
gpt2_n_inner_up_lobotomy_result.pkl
gpt2_no_pos_embed_lobotomy_result.pkl
gpt2_no_res_conns_lobotomy_result.pkl
gpt2_no_scaled_attn_lobotomy_result.pkl
gpt2_random_init_result.pkl
gpt2_trained_result.pkl
gpt2_varied_attn_heads_result.pkl
gpt2_varied_attn_pdrop_dropout_result.pkl
gpt2_varied_embd_pdrop_dropout_result.pkl
gpt2_varied_embeddings_result.pkl
gpt2_varied_embeddings_result_wrong.pkl
gpt2_varied_resid_pdrop_dropout_result.pkl
README.md
Once results files exist, you can run the appropriate script to produce a plot.
cd candproj/analyze/
python gpt2_trained_and_untrained_analysis.py
Plots that were used in the final report are available in candbproj/analyze/images/