Description:
mimodulezoo 1.0.0
Machine Intelligence PyTorch Module Zoo
This package contains implementations standalone, commonly reusable PyTorch nn.Modules. To
install it run
pip install mi-module-zoo
Documentation of the library is found at https://microsoft.github.io/mi-module-zoo/.
This library is maintained by the Machine Intelligence group in Microsoft Research.
Modules
A list of the modules follows, for detailed documentation, please check the docstring
of each module.
mi_model_zoo.mlp.construct_mlp() A function that generates an nn.Sequential for a
multilinear perceptron.
mi_model_zoo.settransformer.SetTransformer The Set Transformer models.
mi_model_zoo.settransformer.ISAB An Inducing-point Self-Attention Block from the Set Transformer paper.
mi_model_zoo.RelationalMultiheadAttention The relational multi-head attention variants,
supporting both sparse and dense relationships,
including Shaw et. al. (2019), RAT-SQL,
and GREAT variants.
mi_model_zoo.relationaltransformerlayers.RelationalTransformerEncoderLayer A relational
transformer encoder layer that supports both dense and sparse relations among elements. Supports
ReZero and a variety of normalization modes.
mi_model_zoo.relationaltransformerlayers.RelationalTransformerDecoderLayer A relational
transformer decoder layer that supports both dense and sparse relations among encoded-decoded
and decoded-decoded elements. Supports ReZero and a variety of normalization modes.
Utilities
mi_model_zoo.utils.randomutils.set_seed() Set the seed across Python, NumPy, and PyTorch (CPU+CUDA).
mi_model_zoo.utils.activationutils.get_activation_fn() Get an activation function by name.
Developing
To develop in this repository, clone the repository, install pre-commit, and run
pre-commit install
Releasing to pip
To deploy a package to PyPI, create a release on GitHub with a git tag of the form vX.Y.Z.
A GitHub Action will automatically build and push the package to PyPI.
Contributing
This project welcomes contributions and suggestions. Most contributions require you to agree to a
Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us
the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.
When you submit a pull request, a CLA bot will automatically determine whether you need to provide
a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions
provided by the bot. You will only need to do this once across all repos using our CLA.
This project has adopted the Microsoft Open Source Code of Conduct.
For more information see the Code of Conduct FAQ or
contact opencode@microsoft.com with any additional questions or comments.
Trademarks
This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft
trademarks or logos is subject to and must follow
Microsoft's Trademark & Brand Guidelines.
Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship.
Any use of third-party trademarks or logos are subject to those third-party's policies.
License
For personal and professional use. You cannot resell or redistribute these repositories in their original state.
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