[dependabot]

Updates the requirements on GPUArrays, JET and SUNRepresentations to permit the latest version.
Updates GPUArrays to 12.2.0

Release notes

Sourced from GPUArrays's releases.

v12.2.0

GPUArrays v12.2.0

Diff since v12.1.1

Merged pull requests:

• Compact sparse-vector broadcast output on the device (#795) (@​maleadt)
• Fix sparse mapreduce for operators other than + (#796) (@​maleadt)
• Don't launch a kernel to permute empty arrays (#798) (@​maleadt)
• Test assigning through multi-dimensional masks (#818) (@​maleadt)
• Reduce with operators not known to commute in element order (#822) (@​maleadt)
• Fix storage-level matrix-vector mul! with step-range vectors (#823) (@​maleadt)
• Let Base handle zero-source map! on GPU arrays (#824) (@​maleadt)
• Accumulate mean in floating point and divide afterwards (#825) (@​maleadt)
• Define copyto! for GPU Diagonals (#829) (@​christiangnrd)
• Make the Float16 statistics and ComplexF16 product tests robust to summation order (#834) (@​maleadt)
• Require support for arrays of isbits unions in the testsuite (#835) (@​maleadt)
• Render code blocks with DocumenterCodeBlocks (#838) (@​maleadt)
• Inline the range indexing kernel bodies and launch getindex linearly (#839) (@​unsound-mint)
• Define neutral elements for the @​fastmath reduction operators (#840) (@​unsound-mint)
• Follow Base when accumulating along a dimension beyond the array's (#841) (@​maleadt)
• Return the destination from map! when there is nothing to map (#842) (@​maleadt)
• Only test Julia 1.10 and 1.13 on pull requests (#844) (@​maleadt)

Closed issues:

• Concatenation (#92)
• Generic softmax (#96)
• Convolutions and Pooling (#102)
• Re-add convolution kernel (#117)
• TestSuite.compare does not handle Nothing (#272)
• mean can overflow on integer inputs (#819)
• Better error for map! misuse (#482)
• Vectorized getindex of wrapped array falls back to scalar indexing (#509)
• Test copy constructor (#557)
• Add support for rand(rng, n) (#578)
• Kronecker product between Diagonal and AbstractGPUArray (#585)
• Hermitian() wrapper missing a GPU compatible iterator (#586)
• Adding @​views yields ambiguity for certain assignment of mutable CuArray (#587)
• GPUArrays rand seeding fails when container is bigger than workgroup size (#643)
• Statistics.mean(Float32, arr, dims=1) throws KernelError (#611)
• kron doesn't support Diagonals (#668)
• Missing indexing on GPUSparseDeviceMatrixCSC (#730)
• JLArrays 0.4 takes ~4.5 s to load (was 0.5 s), spent activating its methods (#807)
• Storage-level matrix-vector mul! scalar-indexes step views (#816)
• @fastmath reductions are 2–5× slower: no neutral_element for Base.FastMath.max_fast and co. (#833)
• Range getindex and setindex! are up to 17× slower than copyto!: getindex_generated/setindex_generated are not inlined (#837)

Changelog

Sourced from GPUArrays's changelog.

GPUArrays.jl release history

This document lists the noteworthy changes in GPUArrays.jl releases that back-ends act on, newest first, aimed at the authors of back-end packages. For the complete list of merged pull requests, see the GitHub releases.

v12.1

GPUArrays defines Base.dataids and Base.mightalias for every AbstractGPUArray. A back-end that implements the new GPUArrays.memory_location hook (the coordinate of an array's allocation, normally its address, and its byte offset into it) gets precise alias detection: disjoint views of one buffer don't alias, empty arrays alias nothing on every Julia version, and SubArrays of two such arrays are compared without converting their parents to pointers. Arrays wrapped in a SubArray or other wrapper are still compared by allocation, which is conservative. Back-ends should implement the hook and delete their own dataids and mightalias methods, which would keep winning dispatch.

v12.0

Base's reductions, sorting, scans, findall, logical indexing and reverse are now implemented once for every AnyGPUArray, on top of AcceleratedKernels.jl (AK) 0.5. A back-end gets all of them by defining nothing beyond the array interface and a KernelAbstractions back-end, and they follow Base's semantics: stable sorting by default, init applied once, no neutral element needed, Base's result types, empty results and errors.

Breaking changes for back-ends:

• The GPUArrays.mapreducedim! hook is removed and GPUArrays no longer picks neutral elements. Every Base reduction goes to AK directly. Back-ends must delete their methods for the hook, together with the reduction kernels only it used.
• GPUArrays.default_rng and the deprecated RNG(state::AbstractGPUArray) and seed!(rng, ::Vector{UInt32}) methods are removed. Construct the RNG with RNG{AT}() and seed it with an integer.
• KernelAbstractions 0.9.43 or later and Adapt 4.7.3 or later are required.

What back-ends should delete:

Methods narrower than GPUArrays' keep winning dispatch, so a back-end that keeps its own findall, to_index/to_indices, _accumulate!/accumulate, sort!/sortperm!/ partialsort!, or reverse! bypasses GPUArrays' Base conformance, and some of those methods are ambiguous with GPUArrays' (e.g. findall(f, ::MyArray) against findall(::Fix2{typeof(in)}, ::AnyGPUArray)). Delete them.

A vendor sort or scan (e.g. one built on MPSGraph) can stay as a fast path, as long as it falls back to GPUArrays' method for the cases it doesn't handle and accepts AK's algorithm objects (alg=AK.MergeSort() etc.). There is no extension point for vendor reductions; GPUArrays._ak_mapreducedim! is internal.

... (truncated)

Commits

• 4c71d50 Bump version.
• d88ea8e Define copyto! for GPU Diagonals (#829)
• 111f0b9 Inline the range indexing kernel bodies and launch getindex linearly (#839)
• a74fe95 Define neutral elements for the @​fastmath reduction operators (#840)
• 33c7073 Require support for arrays of isbits unions in the testsuite (#835)
• 4eead0c Fix sparse mapreduce for operators other than + (#796)
• 9b3052c Don't launch a kernel to permute empty arrays (#798)
• 523f963 Reduce with operators not known to commute in element order (#822)
• da93fde Let Base handle zero-source map! on GPU arrays (#824)
• 685d1c1 Accumulate mean in floating point and divide afterwards (#825)
• Additional commits viewable in compare view

Updates JET to 0.13.1

Release notes

Sourced from JET's releases.

v0.13.1

JET v0.13.1

Diff since v0.13.0

Changelog

Sourced from JET's changelog.

[0.13.1]

Changed

• Improved the performance of the error analysis of JETAnalyzer, e.g. report_call and report_package, on Julia 1.14.

Fixed

• Fixed false positive reports on Julia 1.14 and later with code coverage enabled, e.g. under Pkg.test(; coverage=true), where JET could reuse the analysis results cached during its precompilation and fail to constant-fold calls:

  const SV = Core.svec(1, 2, 3)
  f() = sin(length(SV) == 3 ? 1.0 : "1")
  @test_call f() # failed, reporting no method for `sin(::String)`

[0.13.0]

Added

• Added support for Julia v1.14. JET now loads full functionality on Julia 1.14 instead of empty stubs.

• Added the allow_unsupported_julia preference, which loads full JET functionality on unsupported Julia versions, e.g. nightly builds, without the development features that JET_DEV_MODE also enables:

  LocalPreferences.toml

  [JET]
  allow_unsupported_julia = true # previously `JET_DEV_MODE = true` was needed

  JET emits a warning when it is loaded on an unsupported Julia version this way, since it may not work correctly there.

• JETInterface now exports has_problems, the predicate that call_test_ex and func_test use to decide whether an analysis result fails a test.

• Calls whose matching methods are ambiguous are now reported as AmbiguousMethodErrorReport instead of MethodErrorReport saying that no matching method is found. For a union-split call, the ambiguous signatures are reported separately from those without any matching method:

  k(::Int, ::Any) = 1
  k(::Any, ::Int) = 2
  m(x::Int) = k(x, x)               # reported: ambiguous `k(::Int, ::Int)`
  n(x::Union{Int,String}) = k(x, x) # reported: ambiguous `k(::Int, ::Int)`
                                    # and no method for `k(::String, ::String)`

... (truncated)

Commits

• See full diff in compare view

Updates SUNRepresentations to 0.5.0

Release notes

Sourced from SUNRepresentations's releases.

v0.5.0

SUNRepresentations v0.5.0

Diff since v0.4.1

This release makes the order of ⊗ deterministic and caches CGCs, F- and R-symbols with MemoizationKit. It also makes ⊗, Nsymbol and dim much faster. Finally, the CGCs are now computed in a more numerically stable manner, improving the precision of the resulting symbols.

Breaking changes

• s1 ⊗ s2 now returns the irreps in sorted (isless) order. That is the same order as values(SUNIrrep{N}). The old order depended on hash(::SUNIrrep), so it changes once: tensor data stored with the old fusion tree layout must be converted. (#58, fixes #54)
• The disk cache of Clebsch-Gordan coefficients now uses MemoizationKit's SQLite cache instead of JLD2 files. Set its location, and whether it is used, with MemoizationKit preferences. use_disk_cache, cgc_cache_dir and clear_disk_cache! are removed. precompute_disk_cache and cache_info remain. CGCs cached by earlier versions are not reused. (#59)
• SQLite replaces JLD2, Pidfile, Scratch and LRUCache as dependencies. (#59)

Performance

• ⊗ and Nsymbol use a pruned Littlewood-Richardson enumeration, which is much faster for large irreps. (#58)
• dim uses integer arithmetic instead of Rational, which makes it about 20× faster. It throws an error if the dimension does not fit in an Int. (#56)
• Lookups of cached F- and R-symbols no longer take a lock, so TensorKit no longer serializes on them across threads. CGCs on disk are stored in a compact format of their own, a third smaller and 7× faster to read than with Serialization. (#59)
• Static unbounded sizes in the SU(N) Manhattan indexing. (#60)

Fixed

• README usage example for an explicit SUNIrrep rank. (#55)

Merged pull requests:

• Fix README usage example for explicit SUNIrrep rank (#55) (@​leburgel)
• Compute dim in integer arithmetic (#56) (@​leburgel)
• Sort ⊗ output and speed up Nsymbol and ⊗ (#58) (@​lkdvos)
• Cache CGCs, F- and R-symbols and direct products with MemoizationKit (#59) (@​lkdvos)
• Static unbounded sizes in the SU(N) Manhattan indexing (#60) (@​leburgel)
• Numerically stable computation of lower-weight CGCs (#61) (@​dgrinko)
• Release v0.5.0 (#63) (@​lkdvos)
• Faster stable lower-weight CGC solve (#64) (@​lkdvos)
• Keep small highest-weight CGCs in gaugefix! (#66) (@​lkdvos)
• CI: only test macOS and Windows on Julia 1 on the default branch (#68) (@​lkdvos)

Closed issues:

• Order of ⊗ depends on hash(::SUNIrrep), which makes the data layout of SU(N) tensors depend on hash (#54)
• Severe numerical instabilities (#62)

Commits

• 6e78644 CI: only test macOS and Windows on Julia 1 on the default branch (#68)
• 4786dba Keep small highest-weight CGCs in gaugefix! (#66)
• 27cee1e Faster stable lower-weight CGC solve (#64)
• 973831a Numerically stable computation of lower-weight CGCs (#61)
• 2d62a70 Release v0.5.0 (#63)
• c40734f Cache CGCs, F- and R-symbols and direct products with MemoizationKit (#59)
• 0013387 Static unbounded sizes in the SU(N) Manhattan indexing (#60)
• f49fab2 Sort ⊗ output and speed up Nsymbol and ⊗ (#58)
• d5ba94c Compute dim in integer arithmetic (#56)
• 2e01f78 Fix README usage example for explicit SUNIrrep rank (#55)
• Additional commits viewable in compare view

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