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Robustness Verification for Transformers

In this work, we propose and develop the first algorithm for verifying the robustness of Transformers, under Lp-norm embedding perturbation.

Cite this work:

Zhouxing Shi, Huan Zhang, Kai-Wei Chang, Minlie Huang, Cho-Jui Hsieh. Robustness Verification for Transformers. ICLR 2020.

New work: We have developed a stronger algorithm, auto_LiRPA, which can be used for robustness verification on general computational graphs and general perturbation specifications. See our latest paper and code.

Prerequisites

We used Python 3. To install the required python libraries with pip:

pip intall -r requirements.txt

Also, please download data files.

Train models

We first train models with different configurations on Yelp and SST-2 datasets:

./train_yelp.sh
./train_sst.sh

You may manually distribute the training runs in the scripts to different devices for efficiency.

To evaluate the clean accuracy of the models:

python eval_accuracy.py

And the results will be saved to res_acc.json.

Run verification

To run verification for a model:

python main.py --verify \
            --dir DIR \
            --data DATA \
            --method METHOD \
            --p P \
            --perturbed_words PERTURBED_WORDS \
            --samples SAMPLES \
            --max_verify_length MAX_VERIFY_LENGTH \
            --log LOG \
            --res RES

where the arguments are:

  • dir: directory that stores a trained model to be verified
  • data: dataset, either yelp or sst.
  • method: verification method, selected from baf (backward & forward), backward, forward, ibp, and discrete
  • p: Lp norm; any value greater or equal to 10 will be regarded as infinity
  • max_verify_length: maximum length of sentences during verification
  • perturbed_words: number of perturbed words
  • samples: number of samples for verification
  • log: path of the log file to be output
  • res: path of the result file to be output

Most of the arguments have default values and may be regarded as optional. Please refer to Parser.py for details.

Simple example

On Yelp dataset, to verify a model stored at ./model using the backward & forward method, under one-word L2-norm perturbation setting:

python main.py --verify\
			--dir model --data yelp --method baf --p 2 --perturbed_words 1

With the default arguments, this is also equivalent to

python main.py --verify --dir model --data yelp

Reproduce experiments

We have a tool run_bounds.py for running experiments in batch:

python run_bounds.py \
        --data DATA \
        --model MODEL_1 MODEL_2 ... MODEL_N \
        --p P_1 P_2 ... P_M \
        --method METHOD_1 METHOD_2 ... METHOD_K \
        --perturbed_words PERTURBED_WORDS \
        --samples SAMPLES \
        --max_verify_length MAX_VERIFY_LENGTH \
        --samples SAMPLES \
        --suffix SUFFIX

where SUFFIX is used for the naming of log and result files.

run_bounds.sh contains all the commands for verification needed to reproduce our experiments, and you may manually run them on different devices respectively. Results will be saved to JSON files.