Changelog

3.2.0 / 2020-03-01

Small fixes for the dp path and support for a new mars backend.

New Features

  • (GH#109) Adds mars backend support.

Enhancements

  • (GH#110) New auto-hq and 'random-greedy-128' paths.

  • (GH#119) Fixes several edge cases in the dp path.

Bug fixes

  • (GH#127) Fixes an issue where Python 3.6 features are required while Python 3.5 is opt_einsum’s stated minimum version.

3.1.0 / 2019-09-30

Adds a new dynamic programming algorithm to the suite of paths.

New Features

3.0.0 / 2019-08-10

This release moves opt_einsum to be backend agnostic while adding support additional backends such as Jax and Autograd. Support for Python 2.7 has been dropped and Python 3.5 will become the new minimum version, a Python deprecation policy equivalent to NumPy’s has been adopted.

New Features

  • (GH#78) A new random-optimizer has been implemented which uses Boltzmann weighting to explore alternative near-minimum paths using greedy-like schemes. This provides a fairly large path performance enhancements with a linear path time overhead.

  • (GH#78) A new PathOptimizer class has been implemented to provide a framework for building new optimizers. An example is that now custom cost functions can now be provided in the greedy formalism for building custom optimizers without a large amount of additional code.

  • (GH#81) The backend="auto" keyword has been implemented for contract allowing automatic detection of the correct backend to use based off provided tensors in the contraction.

  • (GH#88) Autograd and Jax support have been implemented.

  • (GH#96) Deprecates Python 2 functionality and devops improvements.

Enhancements

  • (GH#84) The contract_path function can now accept shape tuples rather than full tensors.

  • (GH#84) The contract_path automated path algorithm decision technology has been refactored to a standalone function.

2.3.0 / 2018-12-01

This release primarily focuses on expanding the suite of available path technologies to provide better optimization characistics for 4-20 tensors while decreasing the time to find paths for 50-200+ tensors. See Path Overview for more information.

New Features

  • (GH#60) A new greedy implementation has been added which is up to two orders of magnitude faster for 200 tensors.

  • (GH#73) Adds a new branch path that uses greedy ideas to prune the optimal exploration space to provide a better path than greedy at sub optimal cost.

  • (GH#73) Adds a new auto keyword to the opt_einsum.contract() path option. This keyword automatically chooses the best path technology that takes under 1ms to execute.

Enhancements

  • (GH#61) The opt_einsum.contract() path keyword has been changed to optimize to more closely match NumPy. path will be deprecated in the future.

  • (GH#61) The opt_einsum.contract_path() now returns a opt_einsum.contract.PathInfo() object that can be queried for the scaling, flops, and intermediates of the path. The print representation of this object is identical to before.

  • (GH#61) The default memory_limit is now unlimited by default based on community feedback.

  • (GH#66) The Torch backend will now use tensordot when using a version of Torch which includes this functionality.

  • (GH#68) Indices can now be any hashable object when provided in the “Interleaved Input” syntax.

  • (GH#74) Allows the default transpose operation to be overridden to take advantage of more advanced tensor transpose libraries.

  • (GH#73) The optimal path is now significantly faster.

  • (GH#81) A documentation pass for v3.0.

Bug fixes

2.2.0 / 2018-07-29

New Features

  • (GH#48) Intermediates can now be shared between contractions, see here for more details.

  • (GH#53) Intermediate caching is thread safe.

Enhancements

  • (GH#48) Expressions are now mapped to non-unicode index set so that unicode input is support for all backends.

  • (GH#54) General documenation update.

Bug fixes

  • (GH#41) PyTorch indices are mapped back to a small a-z subset valid for PyTorch’s einsum implementation.

2.1.3 / 2018-8-23

Bug fixes

  • Fixes unicode issue for large numbers of tensors in Python 2.7.

  • Fixes unicode install bug in README.md.

2.1.2 / 2018-8-16

Bug fixes

  • Ensures versioneer.py is in MANIFEST.in for a clean pip install.

2.1.1 / 2018-8-15

Bug fixes

  • Corrected Markdown display on PyPi.

2.1.0 / 2018-8-15

opt_einsum continues to improve its support for additional backends beyond NumPy with PyTorch.

We have also published the opt_einsum package in the Journal of Open Source Software. If you use this package in your work, please consider citing us!

New features

  • PyTorch backend support

  • Tensorflow eager-mode execution backend support

Enhancements

  • Intermediate tensordot-like expressions are now ordered to avoid transposes.

  • CI now uses conda backend to better support GPU and tensor libraries.

  • Now accepts arbitrary unicode indices rather than a subset.

  • New auto path option which switches between optimal and greedy at four tensors.

Bug fixes

  • Fixed issue where broadcast indices were incorrectly locked out of tensordot-like evaluations even after their dimension was broadcast.

2.0.1 / 2018-6-28

New Features

  • Allows unlimited Unicode indices.

  • Adds a Journal of Open-Source Software paper.

  • Minor documentation improvements.

2.0.0 / 2018-5-17

opt_einsum is a powerful tensor contraction order optimizer for NumPy and related ecosystems.

New Features

  • Expressions can be precompiled so that the expression optimization need not happen multiple times.

  • The greedy order optimization algorithm has been tuned to be able to handle hundreds of tensors in several seconds.

  • Input indices can now be unicode so that expressions can have many thousands of indices.

  • GPU and distributed computing backends have been added such as Dask, TensorFlow, CUPy, Theano, and Sparse.

Bug Fixes

  • An error affecting cases where opt_einsum mistook broadcasting operations for matrix multiply has been fixed.

  • Most error messages are now more expressive.

1.0.0 / 2016-10-14

Einsum is a very powerful function for contracting tensors of arbitrary dimension and index. However, it is only optimized to contract two terms at a time resulting in non-optimal scaling for contractions with many terms. Opt_einsum aims to fix this by optimizing the contraction order which can lead to arbitrarily large speed ups at the cost of additional intermediate tensors.

Opt_einsum is also implemented into the np.einsum function as of NumPy v1.12.

New Features