127 lines
6.4 KiB
Plaintext
127 lines
6.4 KiB
Plaintext
Metadata-Version: 2.1
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Name: opt-einsum
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Version: 3.3.0
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Summary: Optimizing numpys einsum function
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Home-page: https://github.com/dgasmith/opt_einsum
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Author: Daniel Smith
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Author-email: dgasmith@icloud.com
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License: MIT
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Platform: UNKNOWN
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Classifier: Development Status :: 5 - Production/Stable
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Classifier: Intended Audience :: Science/Research
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Classifier: Intended Audience :: Developers
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Classifier: License :: OSI Approved
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Classifier: Programming Language :: Python
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Classifier: Programming Language :: Python :: 3
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Classifier: Programming Language :: Python :: 3.5
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Classifier: Programming Language :: Python :: 3.6
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Classifier: Programming Language :: Python :: 3.7
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Classifier: Programming Language :: Python :: 3.8
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Requires-Python: >=3.5
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Description-Content-Type: text/markdown
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Requires-Dist: numpy (>=1.7)
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Provides-Extra: docs
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Requires-Dist: sphinx (==1.2.3) ; extra == 'docs'
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Requires-Dist: sphinxcontrib-napoleon ; extra == 'docs'
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Requires-Dist: sphinx-rtd-theme ; extra == 'docs'
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Requires-Dist: numpydoc ; extra == 'docs'
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Provides-Extra: tests
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Requires-Dist: pytest ; extra == 'tests'
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Requires-Dist: pytest-cov ; extra == 'tests'
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Requires-Dist: pytest-pep8 ; extra == 'tests'
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[](https://travis-ci.org/dgasmith/opt_einsum)
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[](https://codecov.io/gh/dgasmith/opt_einsum)
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[](https://anaconda.org/conda-forge/opt_einsum)
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[](https://pypi.org/project/opt-einsum/#description)
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[](https://pypistats.org/packages/opt-einsum)
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[](http://optimized-einsum.readthedocs.io/en/latest/?badge=latest)
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[](https://doi.org/10.21105/joss.00753)
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Optimized Einsum: A tensor contraction order optimizer
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======================================================
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Optimized einsum can significantly reduce the overall execution time of einsum-like expressions (e.g.,
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[`np.einsum`](https://docs.scipy.org/doc/numpy/reference/generated/numpy.einsum.html),
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[`dask.array.einsum`](https://docs.dask.org/en/latest/array-api.html#dask.array.einsum),
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[`pytorch.einsum`](https://pytorch.org/docs/stable/torch.html#torch.einsum),
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[`tensorflow.einsum`](https://www.tensorflow.org/api_docs/python/tf/einsum),
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)
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by optimizing the expression's contraction order and dispatching many
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operations to canonical BLAS, cuBLAS, or other specialized routines. Optimized
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einsum is agnostic to the backend and can handle NumPy, Dask, PyTorch,
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Tensorflow, CuPy, Sparse, Theano, JAX, and Autograd arrays as well as potentially
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any library which conforms to a standard API. See the
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[**documentation**](http://optimized-einsum.readthedocs.io) for more
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information.
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## Example usage
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The [`opt_einsum.contract`](https://optimized-einsum.readthedocs.io/en/latest/autosummary/opt_einsum.contract.html#opt-einsum-contract)
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function can often act as a drop-in replacement for `einsum`
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functions without futher changes to the code while providing superior performance.
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Here, a tensor contraction is preformed with and without optimization:
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```python
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import numpy as np
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from opt_einsum import contract
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N = 10
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C = np.random.rand(N, N)
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I = np.random.rand(N, N, N, N)
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%timeit np.einsum('pi,qj,ijkl,rk,sl->pqrs', C, C, I, C, C)
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1 loops, best of 3: 934 ms per loop
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%timeit contract('pi,qj,ijkl,rk,sl->pqrs', C, C, I, C, C)
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1000 loops, best of 3: 324 us per loop
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```
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In this particular example, we see a ~3000x performance improvement which is
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not uncommon when compared against unoptimized contractions. See the [backend
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examples](https://optimized-einsum.readthedocs.io/en/latest/backends.html)
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for more information on using other backends.
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## Features
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The algorithms found in this repository often power the `einsum` optimizations
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in many of the above projects. For example, the optimization of `np.einsum`
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has been passed upstream and most of the same features that can be found in
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this repository can be enabled with `np.einsum(..., optimize=True)`. However,
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this repository often has more up to date algorithms for complex contractions.
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The following capabilities are enabled by `opt_einsum`:
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* Inspect [detailed information](http://optimized-einsum.readthedocs.io/en/latest/path_finding.html) about the path chosen.
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* Perform contractions with [numerous backends](http://optimized-einsum.readthedocs.io/en/latest/backends.html), including on the GPU and with libraries such as [TensorFlow](https://www.tensorflow.org) and [PyTorch](https://pytorch.org).
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* Generate [reusable expressions](http://optimized-einsum.readthedocs.io/en/latest/reusing_paths.html), potentially with [constant tensors](http://optimized-einsum.readthedocs.io/en/latest/reusing_paths.html#specifying-constants), that can be compiled for greater performance.
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* Use an arbitrary number of indices to find contractions for [hundreds or even thousands of tensors](http://optimized-einsum.readthedocs.io/en/latest/ex_large_expr_with_greedy.html).
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* Share [intermediate computations](http://optimized-einsum.readthedocs.io/en/latest/sharing_intermediates.html) among multiple contractions.
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* Compute gradients of tensor contractions using [autograd](https://github.com/HIPS/autograd) or [jax](https://github.com/google/jax)
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Please see the [documentation](http://optimized-einsum.readthedocs.io/en/latest/?badge=latest) for more features!
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## Installation
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`opt_einsum` can either be installed via `pip install opt_einsum` or from conda `conda install opt_einsum -c conda-forge`. See the installation [documenation](http://optimized-einsum.readthedocs.io/en/latest/install.html) for further methods.
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## Citation
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If this code has benefited your research, please support us by citing:
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Daniel G. A. Smith and Johnnie Gray, opt_einsum - A Python package for optimizing contraction order for einsum-like expressions. *Journal of Open Source Software*, **2018**, 3(26), 753
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DOI: https://doi.org/10.21105/joss.00753
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## Contributing
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All contributions, bug reports, bug fixes, documentation improvements, enhancements, and ideas are welcome.
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A detailed overview on how to contribute can be found in the [contributing guide](https://github.com/dgasmith/opt_einsum/blob/master/.github/CONTRIBUTING.md).
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