Update dependency com_gitlab_libeigen_eigen to v3.4.1

[mend-for-github.amrom.workers.dev]

This PR contains the following updates:

Package	Type	Update	Change
com_gitlab_libeigen_eigen	http_archive	minor	3.3.8 -> 3.4.1

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Release Notes

libeigen/eigen (com_gitlab_libeigen_eigen)

v3.4.1

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Many bug fixes have been backported from the main branch.

A list of new issues addressed can be found via the 3.4.1 label on GitLab.

Check the git commit history for the full list of changes.

v3.4.0

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Notice: 3.4.x will be the last major release series of Eigen that will support c++03.

Breaking changes

• Using float or double for indexing matrices, vectors and arrays will now fail to compile
• Behavioral change: Transform::computeRotationScaling() and Transform::computeScalingRotation() are now more continuous across degeneracies (see !349).

New features

• Add c++11 initializer_list constructors to Matrix and Array [doc]
• Add STL-compatible iterators for dense expressions [doc].
• New versatile API for sub-matrices, slices, and indexed views [doc].
• Add C++11 template aliases for Matrix, Vector, and Array of common sizes, including generic Vector<Type,Size> and RowVector<Type,Size> aliases [doc].
• New support for bfloat16.

New backends

• Arm SVE: fixed-length Scalable Vector Extensions vectors for uint32_t and float are available.
• MIPS MSA:: MIPS SIMD Architecture (MSA)
• AMD ROCm/HIP: generic GPU backend that unifies support for NVIDIA/CUDA and AMD/HIP.
• Power 10 MMA: initial support for Power 10 matrix multiplication assist instructions for float32 and float64, real and complex.

Improvements

• Eigen now uses the c++11 alignas keyword for static alignment. Users targeting C++17 only and recent compilers (e.g., GCC>=7, clang>=5, MSVC>=19.12) will thus be able to completely forget about all issues related to static alignment, including EIGEN_MAKE_ALIGNED_OPERATOR_NEW.
• Various performance improvements for products and Eigen's GEBP and GEMV kernels have been implemented:
  • By using half and quater-packets the performance of matrix multiplications of small to medium sized matrices has been improved
  • Eigen's GEMM now falls back to GEMV if it detects that a matrix is a run-time vector
  • The performance of matrix products using Arm Neon has been drastically improved (up to 20%)
  • Performance of many special cases of matrix products has been improved
• Large speed up from blocked algorithm for transposeInPlace.
• Speed up misc. operations by propagating compile-time sizes (col/row-wise reverse, PartialPivLU, and others)
• Faster specialized SIMD kernels for small fixed-size inverse, LU decomposition, and determinant.
• Improved or added vectorization of partial or slice reductions along the outer-dimension, for instance: colmajor_mat.rowwise().mean().

Elementwise math functions

• Many functions are now implemented and vectorized in generic (backend-agnostic) form.
• Many improvements to correctness, accuracy, and compatibility with c++ standard library.
  • Much improved implementation of ldexp.
  • Misc. fixes for corner cases, NaN/Inf inputs and singular points of many functions.
  • New implementation of the Payne-Hanek for argument reduction algorithm for sin and cos with huge arguments.
  • New faithfully rounded algorithm for pow(x,y).
• Speedups from (new or improved) vectorized versions of pow, log, sin, cos, arg, pow, log2, complex sqrt, erf, expm1, logp1, logistic, rint, gamma and bessel functions, and more.
• Improved special function support (Bessel and gamma functions, ndtri, erfc, inverse hyperbolic functions and more)
• New elementwise functions for absolute_difference, rint.

Dense matrix decompositions and solvers

• All dense linear solvers (i.e., Cholesky, *LU, *QR, CompleteOrthogonalDecomposition, *SVD) now inherit SolverBase and thus support .transpose(), .adjoint() and .solve() APIs.
• SVD implementations now have an info() method for checking convergence.
• Most decompositions now fail quickly when invalid inputs are detected.
• Optimized the product of a HouseholderSequence with the identity, as well as the evaluation of a HouseholderSequence to a dense matrix using faster blocked product.
• Fixed aliasing issues with in-place small matrix inversions.
• Fixed several edge-cases with empty or zero inputs.

Sparse matrix support, decompositions and solvers

• Enabled assignment and addition with diagonal matrix expressions.
• Support added for SuiteSparse KLU routines via the KLUSupport module. SuiteSparse must be installed to use this module.
• SparseCholesky now works with row-major matrices.
• Various bug fixes and performance improvements.

Type support

• Improved support for half
  • Native support added for ARM __fp16, CUDA/HIP __half, and F16C conversion intrinsics.
  • Better vectorization support added across all backends.
• Improved bool support
  • Partial vectorization support added for boolean operations.
  • Significantly improved performance (x25) for logical operations with Matrix or Tensor of bool.
• Improved support for custom types
  • More custom types work out-of-the-box (see #​2201).

Backend-specific improvements

• Arm NEON
  • Now provides vectorization for uint64_t, int64_t, uint32_t, int16_t, uint16_t, int16_t, int8_t, and uint8_t
  • Emulates bfloat16 support when using Eigen::bfloat16
  • Supports emulated and native float16 when using Eigen::half
• SSE/AVX/AVX512
  • General performance improvements and bugfixes.
  • Enabled AVX512 instructions by default if available.
  • New std::complex, half, and bfloat16 vectorization support added.
  • Many missing packet functions added.
• Altivec/Power
  • General performance improvement and bugfixes.
  • Enhanced vectorization of real and complex scalars.
  • Changes to the gebp_kernel specific to Altivec, using VSX implementation of the MMA instructions that gain speed improvements up to 4x for matrix-matrix products.
  • Dynamic dispatch for GCC greater than 10 enabling selection of MMA or VSX instructions based on __builtin_cpu_supports.
• GPU (CUDA and HIP)
  • Several optimized math functions added, better support for std::complex.
  • Added option to disable CUDA entirely by defining EIGEN_NO_CUDA.
  • Many more functions can now be used in device code (e.g. comparisons, small matrix inversion).
• ZVector
  • Vectorized float and std::complex<float> support added.
  • Added z14 support.
• SYCL
  • Redesigned SYCL implementation for use with the [https://eigen.tuxfamily.org/dox/unsupported/eigen_tensors.html Tensor] module, which can be enabled by defining EIGEN_USE_SYCL.
  • New generic memory model introduced used by TensorDeviceSycl.
  • Better integration with OpenCL devices.
  • Added many math function specializations.

Miscellaneous API Changes

• New setConstant(...) methods for preserving one dimension of a matrix by passing in NoChange.
• Added setUnit(Index i) for vectors that sets the ''i'' th coefficient to one and all others to zero.
• Added transpose(), adjoint(), conjugate() methods to SelfAdjointView.
• Added shiftLeft<N>() and shiftRight<N>() coefficient-wise arithmetic shift functions to Arrays.
• Enabled adding and subtracting of diagonal expressions.
• Allow user-defined default cache sizes via defining EIGEN_DEFAULT_L1_CACHE_SIZE, ..., EIGEN_DEFAULT_L3_CACHE_SIZE.
• Added EIGEN_ALIGNOF(X) macro for determining alignment of a provided variable.
• Allow plugins for VectorwiseOp by defining a file EIGEN_VECTORWISEOP_PLUGIN (e.g. -DEIGEN_VECTORWISEOP_PLUGIN=my_vectorwise_op_plugins.h).
• Allow disabling of IO operations by defining EIGEN_NO_IO.

Improvement to NaN propagation

• Improvements to NaN correctness for elementwise functions.
• New NaNPropagation template argument to control whether NaNs are propagated or suppressed in elementwise min/max and corresponding reductions on Array, Matrix, and Tensor.

New low-latency non-blocking ThreadPool module

• Originally a part of the Tensor module, Eigen::ThreadPool is now separate and more portable, and forms the basis for multi-threading in TensorFlow, for example.

Changes to Tensor module

• Support for c++03 was officially dropped in Tensor module, since most of the code was written in c++11 anyway. This will prevent building the code for CUDA with older version of nvcc.
• Performance optimizations of Tensor contraction
  • Speed up "outer-product-like" operations by parallelizing over the contraction dimension, using thread_local buffers and recursive work splitting.
  • Improved threading heuristics.
  • Support for fusing element-wise operations into contraction during evaluation.
• Performance optimizations of other Tensor operator
  • Speedups from improved vectorization, block evaluation, and multi-threading for most operators.
  • Significant speedup to broadcasting.
  • Reduction of index computation overhead, e.g. using fast divisors in TensorGenerator, squeezing dimensions in TensorPadding.
• Complete rewrite of the block (tiling) evaluation framework for tensor expressions lead to significant speedups and reduced number of memory allocations.
• Added new API for asynchronous evaluation of tensor expressions.
• Misc. minor behavior changes & fixes:
  • Fix const correctness for TensorMap.
  • Modify tensor argmin/argmax to always return first occurrence.
  • More numerically stable tree reduction.
  • Improve randomness of the tensor random generator.
  • Update the padding computation for PADDING_SAME to be consistent with TensorFlow.
  • Support static dimensions (aka IndexList) in resizing/reshape/broadcast.
  • Improved accuracy of Tensor FFT.

Changes to sparse iterative solvers

• Added new IDRS iterative linear solver.

Other relevant changes

• Eigen now provides an option to test with an external BLAS library

See the announcement for more details.

v3.3.9

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Changes since 3.3.8:

• Commit 4e5385c: Introduce rendering Doxygen math formulas with MathJax and the option EIGEN_DOC_USE_MATHJAX to control this.
• #​1746: Removed implementation of standard copy-constructor and standard copy-assign-operator from PermutationMatrix and Transpositions to allow malloc-less std::move.
• #​2036: Make sure the find_standard_math_library_test_program compiles and doesn't optimize away functions we try to test for.
• #​2046: Rename test/array.cpp to test/array_cwise.cpp to fix an issue with the C++ standard library header "array"
• #​2040: Fix an issue in test/ctorleak that occured when disabling exceptions.
• #​2011: Remove error counting in OpenMP parallel section in Eigen's GEMM parallelizing logic.
• #​2012: Define coeff-wise binary array operators for base class to fix an issue when using Eigen with C++20
• Commit bfdd4a9: Fix an issue with Intel® MKL PARDISO support.

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