Changelog¶
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¶
- A error effecting 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¶
- Tensor contraction order optimizer.
opt_einsum.contract()as a drop-in replacement fornumpy.einsum().