Supermasks in Superposition

Mitchell Wortsman^*, Vivek Ramanujan^*, Rosanne Liu, Aniruddha Kembhavi, Mohammad Rastegari, Jason Yosinski, Ali Farhadi

Blog post: https://mitchellnw.github.io/blog/2020/supsup

This is not the official Repo! Please go to https://github.com/RAIVNLab/supsup.

Simple one-file notebook example

We have added mnist.ipynb as a self-contained example of the GG and GNs scenarios. It runs well without GPUs!

General directory structure

• data/ contains all of our dataset declarations. Each dataset object has a train_loader, val_loader, and an update_task method, all used elsewhere in our code. The update_task method takes an integer and changes the train_loader and val_loader variables to the appropriate enumerated task.
• models/ contains model declarations in associated files (resnet, ). If would you like to build your own, use the {builder.conv1x1, builder.conv3x3 etc.} methods so that your model uses the appropriate convolution type declared by the --conv-type flag. See an existing model file for details.
  • small.py contains small models used in GNu and NNs experiments.
  • gemresnet.py contains a smaller version of ResNet-20 used in the GG SplitCIFAR100 experiments.
  • resnet.py contains the standard ResNet architecures for our GG SplitImageNet experiments.
  • modules.py contains layers used in our experiments. In particular, we use FastMultitaskMaskConv for GNu and NNs experiments and MultitaskMaskConv for GG experiments.
• trainers/ contains model trainers for different scenarios.
  • default.py is a simple classification setup, used for GG, GNu, and GNs
  • nns.py is used for the NNs scenario
  • reinit.py is used for the Transfer experiment in Figure 3 (right).
• experiments/ contains code for running experiments, grouped into GG/, GNu/, and NNs/ in accordance with our paper's hierarchy.

Environment set-up

We include requirements file in requirements.txt. Make a new virtual environment in your favorite environment manager (conda, virtualenv) and run pip install -r requirements.txt.

GG Experiments

Directory Structure

The experiments/GG/splitcifar100/ folder contains the following experiment scripts:

1. experiments/GG/splitcifar100/rn18-batche-randw.py -> Corresponds to BatchE (GG) - Rand W in Figure 3 (right)
2. experiments/GG/splitcifar100/rn18-separate-heads.py -> Corresponds to Separate Heads in Figure 3 (right)
3. experiments/GG/splitcifar100/rn18-separate-heads-randw.py -> Corresponds to Separate Heads - Rand W in Figure 3 (right)
4. experiments/GG/splitcifar100/rn18-supsup.py -> Corresponds to SupSup (our method) in Figure 3 (right)
5. experiments/GG/splitcifar100/rn18-supsup-transfer.py -> Corresponds to SupSup Transfer (our method with transfer) in Figure 3 (right)
6. experiments/GG/splitcifar100/rn18-upperbound.py -> Corresponds to Upper Bound in Figure 3 (right)

The splitimagenet/ folder contains one experiment script:

1. experiments/GG/splitimagenet/rn50-supsup.py -> Corresponds to all 3 runs of Sup Sup in Figure 3 (left)

The actual settings for these experiments (e.g. hyperparameters) are stored in experiments/GG/splitcifar100/configs and experiments/GG/splitimagenet/configs

How to run an experiment

Go to the root directory of this code repository and invoke one of the scripts from above with --gpu-sets, --seeds, and --data flags, e.g.

python experiments/GG/splitcifar100/rn18-supsup.py --gpu-sets="0|1|2|3" --data=/path/to/dataset/parent --seeds 1

The --data flag is the path to the folder which contains the required dataset, in this case CIFAR100 or ImageNet, which we then split into tasks. CIFAR100 will be automatically downloaded if it's not in --data, ImageNet will not. --seeds says how many seeds (from 0 to --seeds - 1 to evaluate on. For all of our reported SplitCIFAR100 experiments we use 5. Our reported experiments for SplitImageNet are with 1 seed (with fixed ImageNet split). The default number of seeds in this repo is 1.

Since we are in the GG scenario, these models can be trained on each task individually. As such these scripts are built to take advantage of parallelism. The --gpu-sets command takes comma-separated sets of GPUs separated by |. For example, --gpu-sets="0|1|2|3" means that each experiment will be run individually on a GPU with ID in [0, 1, 2, 3]. If you want to use multiple GPUs per experiment, say for ResNet-50 on SplitImagenet, you can specify this by using comma-separated lists. For example, --gpu-sets="0,1|2,3" means that each task will be trained invidually (in parallel) either on GPUs {0, 1} or {2, 3}. Specifying a lone gpu, --gpu-sets=0, means that experiments will be run sequentially on GPU 0.

Where are the results stored?

Results are automatically stored after each run in the runs/<experiment-name> folder, where <experiment-name> is the name of the script file (sans .py extension). The actual numbers corresponding to our plot are stored in runs/<experiment-name>/results.csv where each row has a self-explanatory Name column describing what the result is.

GNu/NNs Experiments

The experiments/GNu/MNISTPerm contains the MNISTPerm experiments.

E.g. experiments/GNu/MNISTPerm/LeNet-250-tasks and experiments/GNu/MNISTPerm/FC-250-tasks correspond to Figure 4 (left) and (right) respectively and experiments/GNu/MNISTPerm/LeNet-2500-tasks correspond to the GNu experiments in Figure 5.

The experiments/GNu/MNISTRotate contains the MNISTRotate experiments in Figure 6, and the experiments/GNu/SplitMNIST contains the HopSupSup experiment.

The experiments/NNs contains the NNs experiments, which appear on Figure 5.

For example, an experiment can be run with

python experiments/GNu/MNISTPerm/LeNet-2500-tasks/supsup_h.py

where args.data should point to a directory containing the dataset and checkpoints/results will be logged at args.log_dir. These can be changed in the python file. The ablations can be reproduced by e.g. changing output size.