Markéta Machová
5a50f2cbc6
- Update to version 0.6.2
* Fix final, output contractions being mistakenly marked as not tensordot-able.
* When `implementation="autoray"` don't require a backend to have both
`einsum` and `tensordot`, instead fallback to `cotengra`'s own.
- from version 0.6.1
* The number of workers initialized (for non-distributed pools) is now set to,
in order of preference, 1. the environment variable `COTENGRA_NUM_WORKERS`,
2. the environment variable `OMP_NUM_THREADS`, or 3. `os.cpu_count()`.
* Add [RandomGreedyOptimizer](cotengra.pathfinders.path_basic.RandomGreedyOptimizer)
which is a lightweight and performant randomized greedy optimizer, eschewing both
hyper parameter tuning and full contraction tree construction, making it suitable
for very large contractions (10,000s of tensors+).
* Add [optimize_random_greedy_track_flops](cotengra.pathfinders.path_basic.optimize_\
random_greedy_track_flops) which runs N trials of (random) greedy path optimization,
whilst computing the FLOP count simultaneously. This or its accelerated rust counterpart
in `cotengrust` is the driver for the above optimizer.
* Add `parallel="threads"` backend, and make it the default for `RandomGreedyOptimizer`
when `cotengrust` is present, since its version of `optimize_random_greedy_track_flops`
releases the GIL.
* Significantly improve both the speed and memory usage of [`SliceFinder`](cotengra.slicer.SliceFinder)
* Alias `tree.total_cost()` to `tree.combo_cost()`
- from version 0.6.0
* All input node legs and pre-processing steps are now calculated lazily,
allowing slicing of indices including those 'simplified' away {issue}`31`.
* Make [`tree.peak_size`](cotengra.ContractionTree.peak_size) more accurate,
by taking max assuming left, right and parent intermediate tensors are all
present at the same time.
* Add simulated annealing tree refinement (in `path_simulated_annealing.py`),
based on "Multi-Tensor Contraction for XEB Verification of
Quantum Circuits" by Gleb Kalachev, Pavel Panteleev, Man-Hong Yung
OBS-URL: https://build.opensuse.org/request/show/1177286
OBS-URL: https://build.opensuse.org/package/show/devel:languages:python:numeric/python-cotengra?expand=0&rev=5
92 lines
3.2 KiB
RPMSpec
92 lines
3.2 KiB
RPMSpec
#
|
|
# spec file for package python-cotengra
|
|
#
|
|
# Copyright (c) 2024 SUSE LLC
|
|
#
|
|
# All modifications and additions to the file contributed by third parties
|
|
# remain the property of their copyright owners, unless otherwise agreed
|
|
# upon. The license for this file, and modifications and additions to the
|
|
# file, is the same license as for the pristine package itself (unless the
|
|
# license for the pristine package is not an Open Source License, in which
|
|
# case the license is the MIT License). An "Open Source License" is a
|
|
# license that conforms to the Open Source Definition (Version 1.9)
|
|
# published by the Open Source Initiative.
|
|
|
|
# Please submit bugfixes or comments via https://bugs.opensuse.org/
|
|
#
|
|
|
|
|
|
Name: python-cotengra
|
|
Version: 0.6.2
|
|
Release: 0
|
|
Summary: Hyper optimized contraction trees for large tensor networks and einsums
|
|
License: Apache-2.0
|
|
URL: https://github.com/jcmgray/cotengra
|
|
Source: https://files.pythonhosted.org/packages/source/c/cotengra/cotengra-%{version}.tar.gz
|
|
BuildRequires: %{python_module pip}
|
|
BuildRequires: %{python_module setuptools >= 45}
|
|
BuildRequires: %{python_module setuptools_scm >= 6.2}
|
|
BuildRequires: %{python_module wheel}
|
|
BuildRequires: fdupes
|
|
BuildRequires: python-rpm-macros
|
|
Requires: python-autoray
|
|
Recommends: python-cotengrust
|
|
Recommends: python-cytoolz
|
|
Recommends: python-kahypar
|
|
Recommends: python-networkx
|
|
Recommends: python-numpy
|
|
Recommends: python-opt-einsum
|
|
Recommends: python-optuna
|
|
Recommends: python-ray
|
|
# SECTION test requirements
|
|
BuildRequires: %{python_module altair}
|
|
BuildRequires: %{python_module autoray}
|
|
BuildRequires: %{python_module dask}
|
|
BuildRequires: %{python_module matplotlib}
|
|
BuildRequires: %{python_module networkx}
|
|
BuildRequires: %{python_module numpy}
|
|
BuildRequires: %{python_module opt-einsum}
|
|
BuildRequires: %{python_module pytest-xdist}
|
|
BuildRequires: %{python_module pytest}
|
|
BuildRequires: %{python_module seaborn}
|
|
BuildRequires: %{python_module tqdm}
|
|
# /SECTION
|
|
BuildArch: noarch
|
|
%python_subpackages
|
|
|
|
%description
|
|
A python library for contracting tensor networks or einsum expressions involving large numbers of tensors.
|
|
Some of the key feautures of cotengra include:
|
|
* drop-in einsum replacement
|
|
* an explicit contraction tree object that can be flexibly built, modified and visualized
|
|
* a 'hyper optimizer' that samples trees while tuning the generating meta-paremeters
|
|
* dynamic slicing for massive memory savings and parallelism
|
|
* support for hyper edge tensor networks and thus arbitrary einsum equations
|
|
* paths that can be supplied to numpy.einsum, opt_einsum, quimb among others
|
|
* performing contractions with tensors from many libraries via cotengra, even if they don't provide einsum
|
|
or tensordot but do have (batch) matrix multiplication
|
|
|
|
%prep
|
|
%autosetup -p1 -n cotengra-%{version}
|
|
sed -i '/addopts/d' pyproject.toml
|
|
rm cotengra/.gitattributes
|
|
# Note: ignore the rust files for now gh#jcmgray/cotengra#30
|
|
|
|
%build
|
|
%pyproject_wheel
|
|
|
|
%install
|
|
%pyproject_install
|
|
%python_expand %fdupes %{buildroot}%{$python_sitelib}
|
|
|
|
%check
|
|
%pytest -n auto
|
|
|
|
%files %{python_files}
|
|
%doc README.md
|
|
%license LICENSE.md
|
|
%{python_sitelib}/cotengra
|
|
%{python_sitelib}/cotengra-%{version}.dist-info
|
|
|
|
%changelog
|