For a full introduction, including Open Problems in vision mechanistic interpretability, see the original Less Wrong post here.
ViT Prisma is an open-source mechanistic interpretability library for vision and multimodal models. Currently, the library supports ViTs and CLIP. This library was created by Sonia Joseph. ViT Prisma is largely based on TransformerLens by Neel Nanda.
Contributors: Praneet Suresh, Yash Vadi, Rob Graham [and more coming soon]
We welcome new contributors. Check out our contributing guidelines here and our open Issues.
Installing with pip:
pip install vit_prisma
To install as an editable repo from source:
git clone https://github.com/soniajoseph/ViT-Prisma
cd ViT-Prisma
pip install -e .
Check out our guide.
Check out our tutorial notebooks for using the repo.
- Main ViT Demo - Overview of main mechanistic interpretability technique on a ViT, including direct logit attribution, attention head visualization, and activation patching. The activation patching switches the net's prediction from tabby cat to Border collie with a minimum ablation.
- Emoji Logit Lens - Deeper dive into layer- and patch-level predictions with interactive plots.
- Interactive Attention Head Tour - Deeper dive into the various types of attention heads a ViT contains with interactive JavaScript.
For a full demo of Prisma's features, including the visualizations below with interactivity, check out the demo notebooks above.
Prisma contains training code to train your own custom ViTs. Training small ViTs can be very useful when isolating specific behaviors in the model.
For training your own models, check out our guide.
This model was trained by Praneet Suresh. All models include training checkpoints, in case you want to analyze training dynamics.
This larger patch size ViT has inspectable attention heads; else the patch size 16 attention heads are too large to easily render in JavaScript.
| Size | NumLayers | Attention+MLP | AttentionOnly | Model Link |
|---|---|---|---|---|
| tiny | 3 | 0.22 | 0.42 | N/A | Attention+MLP |
The detailed training logs and metrics can be found here. These models were trained by Yash Vadi.
Table of Results
Accuracy [ <Acc> | <Top5 Acc> ]
| Size | NumLayers | Attention+MLP | AttentionOnly | Model Link |
|---|---|---|---|---|
| tiny | 1 | 0.16 | 0.33 | 0.11 | 0.25 | AttentionOnly, Attention+MLP |
| base | 2 | 0.23 | 0.44 | 0.16 | 0.34 | AttentionOnly, Attention+MLP |
| small | 3 | 0.28 | 0.51 | 0.17 | 0.35 | AttentionOnly, Attention+MLP |
| medium | 4 | 0.33 | 0.56 | 0.17 | 0.36 | AttentionOnly, Attention+MLP |
Original dataset is here.
Full results and training setup are here. These models were trained by Yash Vadi.
Table of Results
| Size | NumLayers | Attention+MLP | AttentionOnly | Model Link |
|---|---|---|---|---|
| tiny | 1 | 0.535 | 0.459 | AttentionOnly, Attention+MLP |
| base | 2 | 0.996 | 0.685 | AttentionOnly, Attention+MLP |
| small | 3 | 1.000 | 0.774 | AttentionOnly, Attention+MLP |
| medium | 4 | 1.000 | 0.991 | AttentionOnly, Attention+MLP |
Upload your trained models to Huggingface. Follow the Huggingface guidelines and also create a model card. Document as much of the training process as possible including links to loss and accuracy curves on weights and biases, dataset (and order of training data), hyperparameters, optimizer, learning rate schedule, hardware, and other details that may be relevant.
Include frequent checkpoints throughout training, which will help other researchers understand training dynamics.
Please cite this repository when used in papers or research projects.
@misc{joseph2023vit,
author = {Sonia Joseph},
title = {ViT Prisma: A Mechanistic Interpretability Library for Vision Transformers},
year = {2023},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/soniajoseph/vit-prisma}}
}
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