torch-tract 1.0.0

TrAct: Training Activations

TrAct - Training Activations

Official implementation for our NeurIPS 2024 paper TrAct: Making First-layer Pre-Activations Trainable. In this work, we provide TrAct, a method for more effective and efficient training of the first layer that accelerates training by between 25% and 300%. Herein, rather than using the training dynamics of training weight, we provide a closed form solution for training activations by indirectly updating weights, leading to faster and better training.

Video @ YouTube.

💻 Installation

TrAct can be installed via pip from PyPI with

pip install torch_tract

Alternatively, it is sufficient to copy the src/tract.py into your existing project.

👩‍💻 Usage

The tract.py file contains the TrAct wrapper, which replaces torch.nn.Linear and torch.nn.Conv2d modules by TrActLinear and TrActConv2d modules, respectively.

After initialization, simply wrap your first layer in a TrAct(layer, l=l), wherein l / λ is the only hyperparameter. The default for this is l=0.1 and performs very well. Please refer to the paper for additional information on the hyperparameter.

In the script, TrAct is implemented via:

# regular initialization of model
model = resnet18(num_classes=num_classes)
# apply TrAct to first layer
model.conv1 = TrAct(model.conv1, l=args.l)

Using the same change, it can also be applied to, e.g., ResNet training on ImageNet. Analogously, it can be applied for other codebases and experiments, e.g.:

# For CIFAR ViT: https://github.com/omihub777/ViT-CIFAR/
model.emb = TrAct(model.emb, l=args.l)
# For DeiT: https://github.com/facebookresearch/deit/
model.patch_embed.proj = TrAct(model.patch_embed.proj, l=args.l)

To reproduce the first seed of Figure 1, run:

python train_cifar.py --method normal        --n_epochs 100 --lr 0.08 --optim sgd_w_momentum_cosine --seed 0
python train_cifar.py --method normal        --n_epochs 200 --lr 0.08 --optim sgd_w_momentum_cosine --seed 0
python train_cifar.py --method normal        --n_epochs 400 --lr 0.08 --optim sgd_w_momentum_cosine --seed 0
python train_cifar.py --method normal        --n_epochs 800 --lr 0.08 --optim sgd_w_momentum_cosine --seed 0
python train_cifar.py --method tract --l 0.1 --n_epochs 100 --lr 0.08 --optim sgd_w_momentum_cosine --seed 0
python train_cifar.py --method tract --l 0.1 --n_epochs 200 --lr 0.08 --optim sgd_w_momentum_cosine --seed 0
python train_cifar.py --method tract --l 0.1 --n_epochs 400 --lr 0.08 --optim sgd_w_momentum_cosine --seed 0
python train_cifar.py --method tract --l 0.1 --n_epochs 800 --lr 0.08 --optim sgd_w_momentum_cosine --seed 0

python train_cifar.py --method normal        --n_epochs 100 --lr 0.010 --optim adam_cosine --seed 0
python train_cifar.py --method normal        --n_epochs 200 --lr 0.010 --optim adam_cosine --seed 0
python train_cifar.py --method normal        --n_epochs 400 --lr 0.001 --optim adam_cosine --seed 0
python train_cifar.py --method normal        --n_epochs 800 --lr 0.001 --optim adam_cosine --seed 0
python train_cifar.py --method tract --l 0.1 --n_epochs 100 --lr 0.010 --optim adam_cosine --seed 0
python train_cifar.py --method tract --l 0.1 --n_epochs 200 --lr 0.010 --optim adam_cosine --seed 0
python train_cifar.py --method tract --l 0.1 --n_epochs 400 --lr 0.001 --optim adam_cosine --seed 0
python train_cifar.py --method tract --l 0.1 --n_epochs 800 --lr 0.001 --optim adam_cosine --seed 0

📖 Citing

@inproceedings{petersen2024tract,
  title={TrAct: Making First-layer Pre-Activations Trainable},
  author={Petersen, Felix and Borgelt, Christian and Ermon, Stefano},
  booktitle={Conference on Neural Information Processing Systems (NeurIPS)},
  year={2024}
}

License

TrAct is released under the MIT license. See LICENSE for additional details about it.