GitHub - MurtyShikhar/Pushdown-Layers: Code for Pushdown Layers from our EMNLP 2023 paper (original) (raw)

Pushdown Transformer LMs

Code Repository for our EMNLP '23 paper: "Pushdown Layers: Encoding Recursive Structure in Transformer Language Models".

Overview of Pushdown-LMs.

Setup Environment

conda env create -f environment.yml
conda activate pushdown-lm

Quickstart (I just want to run inference on some sentences)

Download a pre-trained pushdown-LM (16 layers transformer with pushdown self-attention trained on BLLIP-LG):

# install huggingface-cli if you do not have it
pip install -U "huggingface_hub[cli]"

from huggingface_hub import hf_hub_download hf_hub_download(repo_id="smurty/pushdown-bllip", filename="model.pt", local_dir="bllip-lg"); hf_hub_download(repo_id="smurty/pushdown-bllip", filename="vocab.pkl", local_dir="bllip-lg");

We provide a script for running inference on a pushdown-LM in eval_utils/eval_pushdown_model.py. This script can be used to score sentences and obtain parses. Given a file data_utils/sample_sents.txt containing sentences you want to score and parse via a trained Pushdown-LM, simply run the following:

cd eval_utils
python eval_pushdown_model.py settings.model_dir=../bllip-lg settings.eval_mode=beam settings.dataset="../data_utils/sample_sents.txt" settings.shard_id=-1 settings.dir_name=sent_outputs

Get Datasets

Dyck

We provide the generated dyck data in data_utils/dyck_data/, produced using scripts in data_utils/dyck_helpers.py

BLLIP

To get the BLLIP-LG datasets we use for training our models, please follow the instructions here. Once you have BLLIP-LG store it inside data_utils and run:

cd data_utils
python process_bllip.py

This script tokenizes the dataset using the GPT2 tokenizer, and converts every parse tree into unlabeled binarized trees.

WikiTrees

WIP. Please send Shikhar Murty an email (can be found on the paper link) if you urgently need the WikiTrees dataset.

Training

Note: This repository uses WandB for logging. Make sure you set your WandB account before training.

Pushdown LMs on Dyck

To train 6 layer pushdown LMs on the Dyck dataset, run:

python train_transformers.py train.dataset=dyck train.use_stack_tape=True train.vec_dim=128 train.callback=True train.save_dir=dyck_model-with-stack-acc-mlp-stack-key-modulator +recursive_layer.compose_keys_and_stack_info=True train.max_steps=10000 train.eval_every=500

Pushdown LMs on BLLIP

To train 16 layer pushdown LMs on BLLIP-LG, run:

python train_transformers.py train.encoder_n_layers=16 train.dataset=bllip-lg-depth train.vec_dim=1024 train.n_heads=8 +train.dropout=0.1 +train.embedding_dropout=0.1 +train.output_dropout=0.1 train.lr=0.0001 train.use_stack_tape=True recursive_layer.attachment_decisions=True recursive_layer.rec_layers=[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15] recursive_layer.stack_pred_layer=16 +train.with_depth_info=True +recursive_layer.compose_keys_and_stack_info=True train.save_dir=bllip-lg-latest train.train_batch_size=12 train.accum_steps=10

To train a model without pushdown layers, but with syntactic supervision, run:

python train_transformers.py train.encoder_n_layers=16 train.dataset=bllip-lg-depth train.vec_dim=1024 train.n_heads=8 +train.dropout=0.1 +train.embedding_dropout=0.1 +train.output_dropout=0.1 train.lr=0.0001 train.save_dir= train.save_dir=path/to/save/dir train_batch_size=12 train.accum_steps=10

Inference / Evaluation

Evaluating Dyck Generalization

To run the depth / length generalization eval from the paper, run the script: eval_utils/eval_dyck.py. Here is one example

cd eval_utils
python eval_dyck.py model_name=/path/to/save/dir/ckpt.pickle eval_type=depth

Evals on Language Tasks

Parsing on PTB

See eval_utils/eval_ptb.py for getting model parses on PTB. Make sure to set the shard_ids and num_shards correctly, since beam search can be slow.

Note: Since PTB requires a license, we provide a small sample file. Replace this with the PTB test set (consisting of 2416 examples). Feel free to send an email to Shikhar Murty for questions about PTB pre-processing.

Preprocessed BLIMP (for evaluation)

We preprocess BLIMP using a GPT-2 tokenizer under data_utils/blimp.pkl. Use the eval_utils/eval_pushdown_layers.py script to obtain sentence log-likelihoods, which can then be aggregated to get the final BLIMP numbers.

python eval_pushdown_model.py settings.model_dir=../bllip-lg settings.eval_mode=beam settings.dataset=blimp settings.num_shards=-1

Note: To speed up eval, please use sharding

SyntaxGym (for surprisal evaluation)

We provide all SyntaxGym data inside sg_test_suites. Use the script eval_utils/eval_surprisal.py to perform evaluation on each of the test suites in SyntaxGym. This script computes surprisal values (using an incremental version of beam search), and computes the unique surprisal formula for each test suite.