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FastIF: Scalable Influence Functions for Efficient Model Interpretation and Debugging

Link to the Paper

Requirements

Please see requirements.txt and Dockerfile for detailed dependencies. The major ones include

• python 3.6 or later (for type annotations and f-string)
• pytorch==1.5.1
• transformers==3.0.2

Setup

Docker Setup

To build the docker image, run the following script.

DOCKER_BUILDKIT=1 docker build \
    -t ${TAG} \
    -f Dockerfile .

Data Setup

1. Download the data following the examples from here and here.
2. Mount the data into /export/home/Data/Glue and /export/home/Data/HANS inside the image.

Experiments

1. To train the base models, please use scripts/run_MNLI.sh and scripts/run_HANS.sh.
2. To build FAISS indices, please see the function create_FAISS_index in experiments/hans.py.
3. Modify the paths in experiments/constants.py based on your setup.
4. To run the experiments, please follow the instructions in run_experiments.py where we have provided most of the default configurations/hyper-parameters.

Code Structure

experiments/

• This directory contains code that are used to conduct experiments.
• However, the entry-point for experiments is run_experiments.py.

influence_utils/

This directory contains the core components of the influence functions. Most of the codes are designed to be independent of the experiments so could be adapted for others downstream needs. Two of the most important ones are:

• influence_utils/nn_influence_utils.py contains the code for influence functions.
• influence_utils/parallel.py contains the code for the parallel variant. Note that when running the parallel variant, make sure to turn off wandb (see here for details) as the current codebase does not work well with wandb turned on.