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| Name: python310-cotengra | Distribution: openSUSE Tumbleweed |
| Version: 0.6.2 | Vendor: openSUSE |
| Release: 1.1 | Build date: Tue May 28 09:54:10 2024 |
| Group: Unspecified | Build host: reproducible |
| Size: 1482384 | Source RPM: python-cotengra-0.6.2-1.1.src.rpm |
| Packager: https://bugs.opensuse.org | |
| Url: https://github.com/jcmgray/cotengra | |
| Summary: Hyper optimized contraction trees for large tensor networks and einsums | |
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
Apache-2.0
* Tue May 28 2024 John Paul Adrian Glaubitz <adrian.glaubitz@suse.com>
- 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
(arXiv:2108.05665), and the "treesa" implementation in
OMEinsumContractionOrders.jl by Jin-Guo Liu and Pan Zhang. This can be
accessed most easily by supplying
`opt = HyperOptimizer(simulated_annealing_opts={})`.
* Add [`ContractionTree.plot_flat`](cotengra.plot.plot_tree_flat): a new method
for plotting the contraction tree as a flat diagram showing all indices on
every intermediate (without requiring any graph layouts), which is useful for
visualizing and understanding small contractions.
* [`HyperGraph.plot`](cotengra.plot.plot_hypergraph): support showing hyper
outer indices, multi-edges, and automatic unique coloring of nodes and
indices (to match `plot_flat`).
* Add [`ContractionTree.plot_circuit](cotengra.plot.plot_tree_circuit) for
plotting the contraction tree as a circuit diagram, which is fast and useful
for visualizing the traversal ordering for larger trees.
* Add [`ContractionTree.restore_ind`](cotengra.ContractionTree.restore_ind)
for 'unslicing' or 'unprojecting' previously removed indices.
* [`ContractionTree.from_path`](cotengra.ContractionTree.from_path): add option
`complete` to automatically complete the tree given an incomplete path
(usually disconnected subgraphs - {issue}`29`).
* Add [`ContractionTree.get_incomplete_nodes`](cotengra.ContractionTree.get_incomplete_nodes)
for finding all uncontracted childless-parentless node groups.
* Add [`ContractionTree.autocomplete`](cotengra.ContractionTree.autocomplete)
for automatically completing a contraction tree, using above method.
* [`tree.plot_flat`](cotengra.plot.plot_tree_flat): show any preprocessing
steps and optionally list sliced indices
* Add [get_rng](cotengra.utils.get_rng) as a single entry point for getting or
propagating a random number generator, to help determinism.
* Set ``autojit="auto"`` for contractions, which by default turns on jit for
`backend="jax"` only.
* Add [`tree.describe`](cotengra.ContractionTree.describe) for a various levels
of information about a tree, e.g. `tree.describe("full")` and
`tree.describe("concise")`.
* Add [ctg.GreedyOptimizer](cotengra.pathfinders.path_basic.GreedyOptimizer)
and [ctg.OptimalOptimizer](cotengra.pathfinders.path_basic.OptimalOptimizer)
to the top namespace.
* Add [ContractionTree.benchmark](cotengra.ContractionTree.benchmark) for
for automatically assessing hardware performance vs theoretical cost.
* Contraction trees now have a `get_default_objective` method to return the
objective function they were optimized with, for simpler further refinement
or scoring, where it is now picked up automatically.
* Change the default 'sub' optimizer on divisive partition building algorithms
to be `'greedy'` rather than `'auto'`. This might make individual trials
slightly worse but makes each cheaper, see discussion: ({issue}`27`).
- Drop patches for issues fixed upstream
* fix-check.patch
* Thu Mar 21 2024 Guillaume GARDET <guillaume.gardet@opensuse.org>
- Fix test for aarch64 with:
* fix-check.patch
* Tue Feb 27 2024 Ben Greiner <code@bnavigator.de>
- Initial specfile for v0.5.6 required by quimb
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