ModelDiff: A Framework for Comparing Learning Algorithms

This repository provides an API for ModelDiff, a framework for fine-grained comparisons of supervised learning algorithms:

ModelDiff: A Framework for Comparing Learning Algorithms
Harshay Shah*, Sung Min Park*, Andrew Ilyas*, Aleksander Madry
Paper: https://arxiv.org/abs/2211.12491
Blog post: http://gradientscience.org/modeldiff/

@inproceedings{shah2023modeldiff,
  title={Modeldiff: A framework for comparing learning algorithms},
  author={Shah, Harshay and Park, Sung Min and Ilyas, Andrew and Madry, Aleksander},
  booktitle={International Conference on Machine Learning},
  pages={30646--30688},
  year={2023},
  organization={PMLR}
}

Overview

The figure above summarizes ModelDiff, our algorithm comparison framework.

• First, our method computes example-level data attributions (e.g., using datamodels or TRAK) for both learning algorithms (part A). In part B, we identify directions (in training set space) that are specific to each algorithm using residual datamodels.
• Then, we run PCA on the residual datamodels (part C) to find a set of distinguishing training directions---weighted combinations of training examples that disparately impact predictions of models trained with different algorithms. Each distinguishing direction surfaces a distinguishing subpopulation, from which we infer a testable distinguishing transformation (part D) that significantly impacts predictions of models trained with one algorithm but not the other.

In our paper, we apply ModelDiff to three case studies that compare models trained with/without standard data augmentation, with/without ImageNet pre-training, and with different SGD hyperparameters. As shown below, in all three cases, our framework allows us to pinpoint fine-grained differences between two learning algorithms:

Example Usage

from modeldiff import ModelDiff

# architecture and checkpoints of models trained with algorithms A and B
modelA = ...
modelB = ...

ckptsA = [...] 
ckptsB = [...] 

# dataloaders corresponding to train and validation set 
train_loader = ...
val_loader = ... 

# init ModelDiff 
md = ModelDiff(modelA, modelB, ckptsA, ckptsB, train_loader)

# Top-k distinguishing directions that are "important" for algorithm A but not algorithm B
diff_ab = md.get_A_minus_B(val_loader, num_pca_comps=2)

# Top-k distinguishing directions that are "important" for algorithm B but not algorithm A
diff_ba = md.get_B_minus_A(val_loader, num_pca_comps=2)

Check out our notebooks for end-to-end examples of using ModelDiff to analyze how standard design choices in ML pipelines---data augmentation, ImageNet pre-training, and SGD hyperparameters---alter model predictions.

Getting started

1. To use the API, simply

   pip install modeldiff
   

   If you want to also install the dependencies to run all analysis notebooks, run

   pip install modeldiff[notebooks]
   

2. For an example usage, check out notebooks/api_example.ipynb. In there, we (a) compute TRAK scores (from scratch) for two learning algorithms and (b) then run ModelDiff to compare these algorithms (this is all achieved with one line of code using modeldiff!).

3. Check out our notebooks for end-to-end ModelDiff examples; each notebook corresponds to a case study in our paper. For each case study, we provide scripts in counterfactuals/ to test the effect of the distinguishing transformationss (inferred via ModelDiff) on the predictions of trained using different learning algorithms.

If you want to compute data attribution scores from scratch with a method different from TRAK (e.g. datamodels), you can pre-compute those yourself and use the .get_A_minus_B_from_scores() and .get_B_minus_A_from_scores() methods:

# init ModelDiff  
md = ModelDiff()

# load data attribution scores corresponding to algorithms A and B 
scoresA = ...
scoresB = ...

# Get distinguishing directions that are important for one algorithm but not the other
diff_ab = md.get_A_minus_B_from_scores(scoresA, scoresB, num_pca_comps=2)
diff_ba = md.get_A_minus_B_from_scores(scoresB, scoresA, num_pca_comps=2)

Maintainers

• Harshay Shah
• Sung Min Park
• Andrew Ilyas
• Kristian Georgiev