BeyondML (formerly MANN)

BeyondML is a Python package which enables creating sparse multitask artificial neural networks (MANNs) compatible with TensorFlow and PyTorch. This package contains custom layers and utilities to facilitate the training and optimization of models using the Reduction of Sub-Network Neuroplasticity (RSN2) training procedure developed by AI Squared, Inc.

Installation

This package is available through PyPi and can be installed via the following command:

pip install beyondml

To install the current version directly from GitHub without cloning, run the following command:

pip install git+https://github.com/Beyond-ML-Labs/BeyondML.git

Alternatively, you can install the package by cloning the repository from GitHub using the following commands:

# clone the repository and cd into it
git clone https://github.com/Beyond-ML-Labs/BeyondML
cd BeyondML

# install the package
pip install .

Mac M1 Users

For those with a Mac with the M1 processor, this package can be installed, but the standard version of TensorFlow is not compatible with the M1 SOC. In order to install a compatible version of TensorFlow, please install the Miniforge conda environment, which utilizes the conda-forge channel only. Once you are using Miniforge, using conda to install TensorFlow in that environment should install the correct version. After installing TensorFlow, the command pip install beyondml will install the BeyondML package.

Contributing

For those who are interested in contributing to this project, we first thank you for your interest! Please refer to the CONTRIBUTING.md file in this repository for information about best practices for how to contribute.

Vulnerability reporting

In the event you notice a vulnerability within this project, please open a GitHub Issue detailing the vulnerability to report it. In the event you would like to keep the report private, please email [email protected].

Capabilities

To view current capabilities within the BeyondML package, we welcome you to check the BeyondML documentation.

Feature Roadmap

Lists of features slated for this project will be added here.

Changes

Below are a list of additional features, bug fixes, and other changes made for each version.

MANN

The below version numbers and logged changes refer to the MANN package.

Version 0.2.2

• Small documentation changes
• Added quantize_model function
• Added build_transformer_block and build_token_position_embedding_block functions for transformer functionality
• Removed unnecessary imports breaking imports in minimal environments

Version 0.2.3

• Per-task pruning
  • Functionality for this feature is implemented, but usage is expected to be incomplete. Note that task gradients have to be passed retrieved and passed to the function directly (helper function available), and that the model has to initially be compiled using a compatible loss function (recommended 'mse') to identify gradients.
  • It has been found that this functionality is currently only supported for models with the following layers:
    • MaskedConv2D
    • MaskedDense
    • MultiMaskedDense
  • Note also that this functionality does not support cases where layers of an individual model are other TensorFlow models, but supporting this functionality is on the roadmap.
• Iterative training using per-task pruning
  • Functionality for this feature is implemented, but there are known bugs when trying to apply this methodology to models with the MultiMaskedConv2D layer present

Version 0.3.0

• Support for PyTorch layers
• Support for additional custom objects in the quantize_model function
• Added tests to the package functionality
• Added auto-generated documentation

BeyondML

The below version numbers and changes refer to the BeyondML package

Version 0.1.0

• Refactored existing MANN repository to rename to BeyondML

Version 0.1.1

• Added the SparseDense, SparseConv, SparseMultiDense, and SparseMultiConv layers to beyondml.tflow.layers, giving users the functionality to utilize sparse tensors during inference

Version 0.1.2

• Added the MaskedMultiHeadAttention, MaskedTransformerEncoderLayer, and MaskedTransformerDecoderLayer layers to beyondml.pt.layers to add pruning to the transformer architecture
• Added MaskedConv3D, MultiMaskedConv3D, MultiConv3D, MultiMaxPool3D, SparseConv3D, and SparseMultiConv3D layers to beyondml.tflow.layers
• Added MaskedConv3D, MultiMaskedConv3D, MultiConv3D, MultiMaxPool3D, SparseConv3D, SparseMultiConv3D, and MultiMaxPool2D layers to beyondml.pt.layers

Version 0.1.3

• Added beyondml.pt compatibility with more native PyTorch functionality for using models on different devices and datatypes
• Added train_model function to beyondml.tflow.utils
• Added MultitaskNormalization layer to beyondml.tflow.layers and beyondml.pt.layers

Version 0.1.4

• Updated documentation to use Sphinx

Version 0.1.5

• Updated requirements to use newer version of TensorFlow
• Fixed errors with changes to types of input_shape in TensorFlow Keras layers
• Fixed errors resulting from model/configuration changes with TensorFlow

Version 0.1.6

• Fixed issues with converting between masked and unmasked models in TensorFlow

Version 0.1.7

• Updated Pytorch implementation of Transformer-based architectures