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@@ -0,0 +1 @@ +/opt_einsum-3.3.0.tar.gz diff --git a/python-opt-einsum.spec b/python-opt-einsum.spec new file mode 100644 index 0000000..2fefe9b --- /dev/null +++ b/python-opt-einsum.spec @@ -0,0 +1,245 @@ +%global _empty_manifest_terminate_build 0 +Name: python-opt-einsum +Version: 3.3.0 +Release: 1 +Summary: Optimizing numpys einsum function +License: MIT +URL: https://github.com/dgasmith/opt_einsum +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/7d/bf/9257e53a0e7715bc1127e15063e831f076723c6cd60985333a1c18878fb8/opt_einsum-3.3.0.tar.gz +BuildArch: noarch + +Requires: python3-numpy +Requires: python3-sphinx +Requires: python3-sphinxcontrib-napoleon +Requires: python3-sphinx-rtd-theme +Requires: python3-numpydoc +Requires: python3-pytest +Requires: python3-pytest-cov +Requires: python3-pytest-pep8 + +%description +Optimized einsum can significantly reduce the overall execution time of einsum-like expressions (e.g., +[`np.einsum`](https://docs.scipy.org/doc/numpy/reference/generated/numpy.einsum.html), +[`dask.array.einsum`](https://docs.dask.org/en/latest/array-api.html#dask.array.einsum), +[`pytorch.einsum`](https://pytorch.org/docs/stable/torch.html#torch.einsum), +[`tensorflow.einsum`](https://www.tensorflow.org/api_docs/python/tf/einsum), +) +by optimizing the expression's contraction order and dispatching many +operations to canonical BLAS, cuBLAS, or other specialized routines. Optimized +einsum is agnostic to the backend and can handle NumPy, Dask, PyTorch, +Tensorflow, CuPy, Sparse, Theano, JAX, and Autograd arrays as well as potentially +any library which conforms to a standard API. See the +[**documentation**](http://optimized-einsum.readthedocs.io) for more +information. +## Example usage +The [`opt_einsum.contract`](https://optimized-einsum.readthedocs.io/en/latest/autosummary/opt_einsum.contract.html#opt-einsum-contract) +function can often act as a drop-in replacement for `einsum` +functions without futher changes to the code while providing superior performance. +Here, a tensor contraction is preformed with and without optimization: +```python +import numpy as np +from opt_einsum import contract +N = 10 +C = np.random.rand(N, N) +I = np.random.rand(N, N, N, N) +%timeit np.einsum('pi,qj,ijkl,rk,sl->pqrs', C, C, I, C, C) +1 loops, best of 3: 934 ms per loop +%timeit contract('pi,qj,ijkl,rk,sl->pqrs', C, C, I, C, C) +1000 loops, best of 3: 324 us per loop +``` +In this particular example, we see a ~3000x performance improvement which is +not uncommon when compared against unoptimized contractions. See the [backend +examples](https://optimized-einsum.readthedocs.io/en/latest/backends.html) +for more information on using other backends. +## Features +The algorithms found in this repository often power the `einsum` optimizations +in many of the above projects. For example, the optimization of `np.einsum` +has been passed upstream and most of the same features that can be found in +this repository can be enabled with `np.einsum(..., optimize=True)`. However, +this repository often has more up to date algorithms for complex contractions. +The following capabilities are enabled by `opt_einsum`: +* Inspect [detailed information](http://optimized-einsum.readthedocs.io/en/latest/path_finding.html) about the path chosen. +* 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). +* 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. +* 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). +* Share [intermediate computations](http://optimized-einsum.readthedocs.io/en/latest/sharing_intermediates.html) among multiple contractions. +* Compute gradients of tensor contractions using [autograd](https://github.com/HIPS/autograd) or [jax](https://github.com/google/jax) +Please see the [documentation](http://optimized-einsum.readthedocs.io/en/latest/?badge=latest) for more features! +## Installation +`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. +## Citation +If this code has benefited your research, please support us by citing: +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 +DOI: https://doi.org/10.21105/joss.00753 +## Contributing +All contributions, bug reports, bug fixes, documentation improvements, enhancements, and ideas are welcome. +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). + +%package -n python3-opt-einsum +Summary: Optimizing numpys einsum function +Provides: python-opt-einsum +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-opt-einsum +Optimized einsum can significantly reduce the overall execution time of einsum-like expressions (e.g., +[`np.einsum`](https://docs.scipy.org/doc/numpy/reference/generated/numpy.einsum.html), +[`dask.array.einsum`](https://docs.dask.org/en/latest/array-api.html#dask.array.einsum), +[`pytorch.einsum`](https://pytorch.org/docs/stable/torch.html#torch.einsum), +[`tensorflow.einsum`](https://www.tensorflow.org/api_docs/python/tf/einsum), +) +by optimizing the expression's contraction order and dispatching many +operations to canonical BLAS, cuBLAS, or other specialized routines. Optimized +einsum is agnostic to the backend and can handle NumPy, Dask, PyTorch, +Tensorflow, CuPy, Sparse, Theano, JAX, and Autograd arrays as well as potentially +any library which conforms to a standard API. See the +[**documentation**](http://optimized-einsum.readthedocs.io) for more +information. +## Example usage +The [`opt_einsum.contract`](https://optimized-einsum.readthedocs.io/en/latest/autosummary/opt_einsum.contract.html#opt-einsum-contract) +function can often act as a drop-in replacement for `einsum` +functions without futher changes to the code while providing superior performance. +Here, a tensor contraction is preformed with and without optimization: +```python +import numpy as np +from opt_einsum import contract +N = 10 +C = np.random.rand(N, N) +I = np.random.rand(N, N, N, N) +%timeit np.einsum('pi,qj,ijkl,rk,sl->pqrs', C, C, I, C, C) +1 loops, best of 3: 934 ms per loop +%timeit contract('pi,qj,ijkl,rk,sl->pqrs', C, C, I, C, C) +1000 loops, best of 3: 324 us per loop +``` +In this particular example, we see a ~3000x performance improvement which is +not uncommon when compared against unoptimized contractions. See the [backend +examples](https://optimized-einsum.readthedocs.io/en/latest/backends.html) +for more information on using other backends. +## Features +The algorithms found in this repository often power the `einsum` optimizations +in many of the above projects. For example, the optimization of `np.einsum` +has been passed upstream and most of the same features that can be found in +this repository can be enabled with `np.einsum(..., optimize=True)`. However, +this repository often has more up to date algorithms for complex contractions. +The following capabilities are enabled by `opt_einsum`: +* Inspect [detailed information](http://optimized-einsum.readthedocs.io/en/latest/path_finding.html) about the path chosen. +* 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). +* 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. +* 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). +* Share [intermediate computations](http://optimized-einsum.readthedocs.io/en/latest/sharing_intermediates.html) among multiple contractions. +* Compute gradients of tensor contractions using [autograd](https://github.com/HIPS/autograd) or [jax](https://github.com/google/jax) +Please see the [documentation](http://optimized-einsum.readthedocs.io/en/latest/?badge=latest) for more features! +## Installation +`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. +## Citation +If this code has benefited your research, please support us by citing: +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 +DOI: https://doi.org/10.21105/joss.00753 +## Contributing +All contributions, bug reports, bug fixes, documentation improvements, enhancements, and ideas are welcome. +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). + +%package help +Summary: Development documents and examples for opt-einsum +Provides: python3-opt-einsum-doc +%description help +Optimized einsum can significantly reduce the overall execution time of einsum-like expressions (e.g., +[`np.einsum`](https://docs.scipy.org/doc/numpy/reference/generated/numpy.einsum.html), +[`dask.array.einsum`](https://docs.dask.org/en/latest/array-api.html#dask.array.einsum), +[`pytorch.einsum`](https://pytorch.org/docs/stable/torch.html#torch.einsum), +[`tensorflow.einsum`](https://www.tensorflow.org/api_docs/python/tf/einsum), +) +by optimizing the expression's contraction order and dispatching many +operations to canonical BLAS, cuBLAS, or other specialized routines. Optimized +einsum is agnostic to the backend and can handle NumPy, Dask, PyTorch, +Tensorflow, CuPy, Sparse, Theano, JAX, and Autograd arrays as well as potentially +any library which conforms to a standard API. See the +[**documentation**](http://optimized-einsum.readthedocs.io) for more +information. +## Example usage +The [`opt_einsum.contract`](https://optimized-einsum.readthedocs.io/en/latest/autosummary/opt_einsum.contract.html#opt-einsum-contract) +function can often act as a drop-in replacement for `einsum` +functions without futher changes to the code while providing superior performance. +Here, a tensor contraction is preformed with and without optimization: +```python +import numpy as np +from opt_einsum import contract +N = 10 +C = np.random.rand(N, N) +I = np.random.rand(N, N, N, N) +%timeit np.einsum('pi,qj,ijkl,rk,sl->pqrs', C, C, I, C, C) +1 loops, best of 3: 934 ms per loop +%timeit contract('pi,qj,ijkl,rk,sl->pqrs', C, C, I, C, C) +1000 loops, best of 3: 324 us per loop +``` +In this particular example, we see a ~3000x performance improvement which is +not uncommon when compared against unoptimized contractions. See the [backend +examples](https://optimized-einsum.readthedocs.io/en/latest/backends.html) +for more information on using other backends. +## Features +The algorithms found in this repository often power the `einsum` optimizations +in many of the above projects. For example, the optimization of `np.einsum` +has been passed upstream and most of the same features that can be found in +this repository can be enabled with `np.einsum(..., optimize=True)`. However, +this repository often has more up to date algorithms for complex contractions. +The following capabilities are enabled by `opt_einsum`: +* Inspect [detailed information](http://optimized-einsum.readthedocs.io/en/latest/path_finding.html) about the path chosen. +* 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). +* 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. +* 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). +* Share [intermediate computations](http://optimized-einsum.readthedocs.io/en/latest/sharing_intermediates.html) among multiple contractions. +* Compute gradients of tensor contractions using [autograd](https://github.com/HIPS/autograd) or [jax](https://github.com/google/jax) +Please see the [documentation](http://optimized-einsum.readthedocs.io/en/latest/?badge=latest) for more features! +## Installation +`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. +## Citation +If this code has benefited your research, please support us by citing: +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 +DOI: https://doi.org/10.21105/joss.00753 +## Contributing +All contributions, bug reports, bug fixes, documentation improvements, enhancements, and ideas are welcome. +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). + +%prep +%autosetup -n opt-einsum-3.3.0 + +%build +%py3_build + +%install +%py3_install +install -d -m755 %{buildroot}/%{_pkgdocdir} +if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi +if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi +if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi +if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi +pushd %{buildroot} +if [ -d usr/lib ]; then + find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/lib64 ]; then + find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/bin ]; then + find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/sbin ]; then + find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst +fi +touch doclist.lst +if [ -d usr/share/man ]; then + find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst +fi +popd +mv %{buildroot}/filelist.lst . +mv %{buildroot}/doclist.lst . + +%files -n python3-opt-einsum -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Mon Apr 10 2023 Python_Bot <Python_Bot@openeuler.org> - 3.3.0-1 +- Package Spec generated @@ -0,0 +1 @@ +acf0a3997aab84b4e9a854296cc34971 opt_einsum-3.3.0.tar.gz |