Official Python package for Deep Kernel Shaping (DKS) and Tailored Activation Transformations (TAT)

This Python package implements the activation function transformations and weight initializations used Deep Kernel Shaping (DKS) and Tailored Activation Transformations (TAT). DKS and TAT, which were introduced in the DKS paper and TAT paper, are methods constructing/transforming neural networks to make them much easier to train. For example, these methods can be used in conjunction with K-FAC to train deep vanilla deep convnets (without skip connections or normalization layers) as fast as standard ResNets of the same depth.

The package supports the JAX, PyTorch, and TensorFlow tensor programming frameworks.

Questions/comments about the code can be sent to dks-dev@google.com.

NOTE: we are not taking code contributions from Github at this time. All PRs from Github will be rejected. Instead, please email us if you find a bug.

Usage

For each of the supported tensor programming frameworks, there is a corresponding directory/subpackage which handles the activation function transformations and weight initializations. It's up to the user to import these and use them appropriately within their model code. Activation functions are transformed by the function get_transformed_activations() in module activation_transform of the appropriate subpackage. Weight sampling is done using functions inside of the module parameter_sampling_functions of said subpackage.

In addition to using these functions, the user is responsble for ensuring that their model meets the architectural requirements of DKS/TAT, and for converting any weighted sums in their model to "normalized sums" (which are weighted sums whoses non-trainable weights have a sum of squares equal to 1). This package doesn't currently include an implementation of Per-Location Normalization (PLN) data pre-processing. While not required for CIFAR or ImageNet, PLN could potentially be important for other datasets. See the section titled "Summary of our method" in the DKS paper for more details about the requirements and execution steps of DKS. To read about the additional requirements of TAT, such as the subset maximizing function, refer to Appendix B of the TAT paper.

Note that ReLUs are only partially supported by DKS, and unsupported by TAT, and their use is highly discouraged. Instead, one should use Leaky ReLUs, which are fully supported by DKS, and work especially well with TAT.

Note that in order to avoid having to import all of the tensor programming frameworks, the user is required to individually import whatever framework subpackage they want. e.g. import dks.jax. Meanwhile, import dks won't actually do anything.

Example

dks.examples.haiku.modified_resnet is a Haiku ResNet model which has been modified as described in the DKS/TAT papers, and includes support for both DKS and TAT. By default, it removes the normalization layers and skip connections found in standard ResNets, making it a "vanilla network". It can be used as an instructive example for how to build DKS/TAT models using this package. See the section titled "Application to various modified ResNets" from the DKS paper for more details.

Installation

This package can be installed directly from GitHub using pip with

pip install git+https://github.com/deepmind/dks.git

or

pip install -e git+https://github.com/deepmind/dks.git#egg=dks[<extras>]

or from PyPI with

pip install dks

or

pip install dks[<extras>]

Here <extras> is a common-separated list (with no spaces) of strings that can be passed to install extra dependencies for different tensor programming frameworks. Valid strings are jax, tf, and pytorch. So for example, to install dks with the extra requirements for JAX and PyTorch, one does

pip install dks[jax,pytorch]

Testing

To run tests in a Python virtual environment with specific pinned versions of all the dependencies one can do:

git clone https://github.com/deepmind/dks.git
cd dks
./test.sh

However, it is strongly recommended that you run the tests in the same Python environment (with the same package versions) as you plan to actually use dks. This can be accomplished by installing dks for all three tensors programming frameworks (e.g. with pip install dks[jax,pytorch,tf] or some other installation method), and then doing

pip install pytest-xdist
git clone https://github.com/deepmind/dks.git
cd dks
python -m pytest -n 16 tests

Disclaimer

This is not an official Google product.