zk_dtypes

CI

zk_dtypes is a stand-alone implementation of several NumPy dtype extensions used in Zero Knowledge libraries inspired by ml_dtypes, including:

  • Narrow integer:

    • int2
    • int4
    • uint2
    • uint4

  • Prime Field:

    • babybear
    • babybear_mont
    • goldilocks
    • goldilocks_mont
    • koalabear
    • koalabear_mont
    • mersenne31

  • Extension Field:

    • babybearx4
    • babybearx4_mont
    • goldilocksx3
    • goldilocksx3_mont
    • koalabearx4
    • koalabearx4_mont
    • mersenne31x2

  • Binary field:

    • binary_field_t0
    • binary_field_t1
    • binary_field_t2
    • binary_field_t3
    • binary_field_t4
    • binary_field_t5
    • binary_field_t6
    • binary_field_t7

  • Elliptic curve:

    • bn254_sf
    • bn254_sf_mont
    • bn254_g1_affine
    • bn254_g1_affine_mont
    • bn254_g1_jacobian
    • bn254_g1_jacobian_mont
    • bn254_g1_xyzz
    • bn254_g1_xyzz_mont
    • bn254_g2_affine
    • bn254_g2_affine_mont
    • bn254_g2_jacobian
    • bn254_g2_jacobian_mont
    • bn254_g2_xyzz
    • bn254_g2_xyzz_mont

Prerequisite

  1. Follow the bazel installation guide.

Build instructions

  1. Clone the zk_dtypes repo

    git clone https://github.com/fractalyze/zk_dtypes

  2. Build zk_dtypes

    bazel build //...

  3. Test zk_dtypes

    bazel test //...

Installation

The zk_dtypes package is tested with Python versions 3.11-3.13, and can be installed with the following command:

pip install zk_dtypes

To test your installation, you can run the following:

pip install absl-py pytest
pytest zk_dtypes/tests

Installation from source

To build and install the package from source, run:

pip install .

Installation from prebuilt binary

Use USE_BAZEL_OUTPUT=1 for a faster installation that uses pre-built Bazel artifacts. This is the recommended path for development.

  • On Linux / macOS

    # Build the shared library (.so)
bazel build //zk_dtypes:_zk_dtypes_ext.so

# Install using the Bazel output
USE_BAZEL_OUTPUT=1 pip install .

  • On Windows

    # Build the Python extension (.pyd)
bazel build //zk_dtypes:_zk_dtypes_ext.pyd

# Install using the Bazel output (PowerShell syntax)
$env:USE_BAZEL_OUTPUT=1; pip install .

Example Usage

>>> from zk_dtypes import babybear_mont
>>> import numpy as np
>>> a = np.array([-1, -3, 2**30, 7], dtype=babybear_mont)
>>> b = np.array([5, 2, 4, 10], dtype=babybear_mont)
>>> a + b
array([4, 2013265920, 1073741828, 17], dtype=babybear_mont)

Importing zk_dtypes also registers the data types with numpy, so that they may be referred to by their string name:

>>> np.dtype('babybear_mont')
dtype(babybear_mont)

See examples/zk_dtypes_examples.ipynb for more examples.

Benchmarks

Benchmarks are disabled by default (though CI verifies their validity). To execute a specific benchmark (e.g., field_mul_benchmark), run the following command manually:

bazel run -c opt //zk_dtypes:field_mul_benchmark

License

The zk_dtypes source code is a modified derivative of the ml_dtypes project and inherits the original Apache 2.0 License (see LICENSE). All subsequent modifications comply with and are released under the same license.

Pre-compiled Wheels Dependencies

Note that pre-compiled wheels utilize the following dependencies:

Project links

Data verified by PyPI on Jul 9, 2026
Data provided by the project maintainers, verified at the time the release was uploaded to PyPI.

Key dates

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  • Released:
Newer release available (0.0.17)

1 maintainer

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Avatar for fractalyze-contact from gravatar.com fractalyze-contact

Credits

PyPI data
Data sourced directly from PyPI's database.

Author: zk_dtypes authors

GitHub Statistics

Data verified by PyPI on Jul 9, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License expression

Apache-2.0
View SPDX License List

Requires

Python >=3.11

Provides Extra

dev

Classifiers

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