Add BDCSVD_LAPACKE binding
This commit is contained in:
Melven Roehrig-Zoellner
Rasmus Munk Larsen
parent
03c9b4738c
commit
273f803846
@ -36,14 +36,17 @@
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#include "src/SVD/SVDBase.h"
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#include "src/SVD/JacobiSVD.h"
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#include "src/SVD/BDCSVD.h"
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#if defined(EIGEN_USE_LAPACKE) && !defined(EIGEN_USE_LAPACKE_STRICT)
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#ifdef EIGEN_USE_LAPACKE
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#ifdef EIGEN_USE_MKL
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#include "mkl_lapacke.h"
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#else
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#include "src/misc/lapacke.h"
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#endif
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#ifndef EIGEN_USE_LAPACKE_STRICT
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#include "src/SVD/JacobiSVD_LAPACKE.h"
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#endif
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#include "src/SVD/BDCSVD_LAPACKE.h"
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#endif
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#include "src/Core/util/ReenableStupidWarnings.h"
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@ -236,7 +236,6 @@ public:
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}
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private:
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void allocate(Index rows, Index cols, unsigned int computationOptions);
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BDCSVD& compute_impl(const MatrixType& matrix, unsigned int computationOptions);
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void divide(Index firstCol, Index lastCol, Index firstRowW, Index firstColW, Index shift);
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void computeSVDofM(Index firstCol, Index n, MatrixXr& U, VectorType& singVals, MatrixXr& V);
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@ -254,6 +253,7 @@ private:
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void computeBaseCase(SVDType& svd, Index n, Index firstCol, Index firstRowW, Index firstColW, Index shift);
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protected:
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void allocate(Index rows, Index cols, unsigned int computationOptions);
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MatrixXr m_naiveU, m_naiveV;
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MatrixXr m_computed;
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Index m_nRec;
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163
Eigen/src/SVD/BDCSVD_LAPACKE.h
Normal file
163
Eigen/src/SVD/BDCSVD_LAPACKE.h
Normal file
@ -0,0 +1,163 @@
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// This file is part of Eigen, a lightweight C++ template library
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// for linear algebra.
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//
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// Copyright (C) 2022 Melven Roehrig-Zoellner <Melven.Roehrig-Zoellner@DLR.de>
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// Copyright (c) 2011, Intel Corporation. All rights reserved.
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//
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// This file is based on the JacobiSVD_LAPACKE.h originally from Intel -
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// see license notice below:
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/*
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Redistribution and use in source and binary forms, with or without modification,
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are permitted provided that the following conditions are met:
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* Redistributions of source code must retain the above copyright notice, this
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list of conditions and the following disclaimer.
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* Redistributions in binary form must reproduce the above copyright notice,
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this list of conditions and the following disclaimer in the documentation
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and/or other materials provided with the distribution.
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* Neither the name of Intel Corporation nor the names of its contributors may
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be used to endorse or promote products derived from this software without
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specific prior written permission.
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THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
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ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
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WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
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DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
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ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
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(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
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LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
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ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
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(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
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SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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********************************************************************************
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* Content : Eigen bindings to LAPACKe
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* Singular Value Decomposition - SVD (divide and conquer variant)
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********************************************************************************
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*/
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#ifndef EIGEN_BDCSVD_LAPACKE_H
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#define EIGEN_BDCSVD_LAPACKE_H
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namespace Eigen {
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namespace internal {
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namespace lapacke_helpers {
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/** \internal Specialization for the data types supported by LAPACKe */
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// defining a derived class to allow access to protected members
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template <typename MatrixType_, int Options>
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class BDCSVD_LAPACKE : public BDCSVD<MatrixType_, Options> {
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typedef BDCSVD<MatrixType_, Options> SVD;
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typedef typename SVD::MatrixType MatrixType;
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typedef typename SVD::Scalar Scalar;
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typedef typename SVD::RealScalar RealScalar;
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public:
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// construct this by moving from a parent object
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BDCSVD_LAPACKE(SVD&& svd) : SVD(std::move(svd)) {}
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void compute_impl_lapacke(const MatrixType& matrix, unsigned int computationOptions) {
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SVD::allocate(matrix.rows(), matrix.cols(), computationOptions);
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SVD::m_nonzeroSingularValues = SVD::m_diagSize;
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// prepare arguments to ?gesdd
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const lapack_int matrix_order = lapack_storage_of(matrix);
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const char jobz = (SVD::m_computeFullU || SVD::m_computeFullV) ? 'A' : (SVD::m_computeThinU || SVD::m_computeThinV) ? 'S' : 'N';
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const lapack_int u_cols = (jobz == 'A') ? to_lapack(SVD::m_rows) : (jobz == 'S') ? to_lapack(SVD::m_diagSize) : 1;
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const lapack_int vt_rows = (jobz == 'A') ? to_lapack(SVD::m_cols) : (jobz == 'S') ? to_lapack(SVD::m_diagSize) : 1;
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lapack_int ldu, ldvt;
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Scalar *u, *vt, dummy;
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MatrixType localU;
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if (SVD::computeU() && !(SVD::m_computeThinU && SVD::m_computeFullV) ) {
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ldu = to_lapack(SVD::m_matrixU.outerStride());
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u = SVD::m_matrixU.data();
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} else if (SVD::computeV()) {
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localU.resize(SVD::m_rows, u_cols);
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ldu = to_lapack(localU.outerStride());
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u = localU.data();
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} else { ldu=1; u=&dummy; }
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MatrixType localV;
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if (SVD::computeU() || SVD::computeV()) {
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localV.resize(vt_rows, SVD::m_cols);
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ldvt = to_lapack(localV.outerStride());
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vt = localV.data();
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} else { ldvt=1; vt=&dummy; }
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MatrixType temp; temp = matrix;
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// actual call to ?gesdd
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lapack_int info = gesdd( matrix_order, jobz, to_lapack(SVD::m_rows), to_lapack(SVD::m_cols),
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to_lapack(temp.data()), to_lapack(temp.outerStride()), (RealScalar*)SVD::m_singularValues.data(),
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to_lapack(u), ldu, to_lapack(vt), ldvt);
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// Check the result of the LAPACK call
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if (info < 0 || !SVD::m_singularValues.allFinite()) {
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// this includes info == -4 => NaN entry in A
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SVD::m_info = InvalidInput;
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} else if (info > 0 ) {
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SVD::m_info = NoConvergence;
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} else {
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SVD::m_info = Success;
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if (SVD::m_computeThinU && SVD::m_computeFullV) {
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SVD::m_matrixU = localU.leftCols(SVD::m_matrixU.cols());
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}
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if (SVD::computeV()) {
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SVD::m_matrixV = localV.adjoint().leftCols(SVD::m_matrixV.cols());
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}
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}
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SVD::m_isInitialized = true;
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}
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};
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template<typename MatrixType_, int Options>
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BDCSVD<MatrixType_, Options>& BDCSVD_wrapper(BDCSVD<MatrixType_, Options>& svd, const MatrixType_& matrix, int computationOptions)
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{
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// we need to move to the wrapper type and back
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BDCSVD_LAPACKE<MatrixType_, Options> tmpSvd(std::move(svd));
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tmpSvd.compute_impl_lapacke(matrix, computationOptions);
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svd = std::move(tmpSvd);
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return svd;
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}
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} // end namespace lapacke_helpers
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} // end namespace internal
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#define EIGEN_LAPACKE_SDD(EIGTYPE, EIGCOLROW, OPTIONS) \
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template<> inline \
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BDCSVD<Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW, Dynamic, Dynamic>, OPTIONS>& \
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BDCSVD<Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW, Dynamic, Dynamic>, OPTIONS>::compute_impl(const Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW, Dynamic, Dynamic>& matrix, unsigned int computationOptions) {\
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return internal::lapacke_helpers::BDCSVD_wrapper(*this, matrix, computationOptions); \
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}
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#define EIGEN_LAPACK_SDD_OPTIONS(OPTIONS) \
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EIGEN_LAPACKE_SDD(double, ColMajor, OPTIONS) \
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EIGEN_LAPACKE_SDD(float, ColMajor, OPTIONS) \
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EIGEN_LAPACKE_SDD(dcomplex, ColMajor, OPTIONS) \
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EIGEN_LAPACKE_SDD(scomplex, ColMajor, OPTIONS) \
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\
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EIGEN_LAPACKE_SDD(double, RowMajor, OPTIONS) \
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EIGEN_LAPACKE_SDD(float, RowMajor, OPTIONS) \
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EIGEN_LAPACKE_SDD(dcomplex, RowMajor, OPTIONS) \
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EIGEN_LAPACKE_SDD(scomplex, RowMajor, OPTIONS)
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EIGEN_LAPACK_SDD_OPTIONS(0)
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EIGEN_LAPACK_SDD_OPTIONS(ComputeThinU)
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EIGEN_LAPACK_SDD_OPTIONS(ComputeThinV)
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EIGEN_LAPACK_SDD_OPTIONS(ComputeFullU)
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EIGEN_LAPACK_SDD_OPTIONS(ComputeFullV)
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EIGEN_LAPACK_SDD_OPTIONS(ComputeThinU | ComputeThinV)
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EIGEN_LAPACK_SDD_OPTIONS(ComputeFullU | ComputeFullV)
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EIGEN_LAPACK_SDD_OPTIONS(ComputeThinU | ComputeFullV)
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EIGEN_LAPACK_SDD_OPTIONS(ComputeFullU | ComputeThinV)
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#undef EIGEN_LAPACK_SDD_OPTIONS
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#undef EIGEN_LAPACKE_SDD
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} // end namespace Eigen
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#endif // EIGEN_BDCSVD_LAPACKE_H
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@ -72,8 +72,16 @@ JacobiSVD<Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW, Dynamic, Dynamic>, OPTION
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} else { ldvt=1; vt=&dummy; }\
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Matrix<LAPACKE_RTYPE, Dynamic, Dynamic> superb; superb.resize(m_diagSize, 1); \
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MatrixType m_temp; m_temp = matrix; \
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LAPACKE_##LAPACKE_PREFIX##gesvd( matrix_order, jobu, jobvt, internal::convert_index<lapack_int>(m_rows), internal::convert_index<lapack_int>(m_cols), (LAPACKE_TYPE*)m_temp.data(), lda, (LAPACKE_RTYPE*)m_singularValues.data(), u, ldu, vt, ldvt, superb.data()); \
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if (computeV()) m_matrixV = localV.adjoint(); \
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lapack_int info = LAPACKE_##LAPACKE_PREFIX##gesvd( matrix_order, jobu, jobvt, internal::convert_index<lapack_int>(m_rows), internal::convert_index<lapack_int>(m_cols), (LAPACKE_TYPE*)m_temp.data(), lda, (LAPACKE_RTYPE*)m_singularValues.data(), u, ldu, vt, ldvt, superb.data()); \
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/* Check the result of the LAPACK call */ \
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if (info < 0 || !m_singularValues.allFinite()) { \
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m_info = InvalidInput; \
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} else if (info > 0 ) { \
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m_info = NoConvergence; \
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} else { \
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m_info = Success; \
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if (computeV()) m_matrixV = localV.adjoint(); \
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} \
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/* for(int i=0;i<m_diagSize;i++) if (m_singularValues.coeffRef(i) < precision) { m_nonzeroSingularValues--; m_singularValues.coeffRef(i)=RealScalar(0);}*/ \
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m_isInitialized = true; \
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return *this; \
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@ -150,6 +150,7 @@ EIGEN_ALWAYS_INLINE auto FUNCTION(Args&&... args) { return call_wrapper(LAPACKE_
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EIGEN_MAKE_LAPACKE_WRAPPER(potrf)
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EIGEN_MAKE_LAPACKE_WRAPPER(getrf)
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EIGEN_MAKE_LAPACKE_WRAPPER(geqrf)
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EIGEN_MAKE_LAPACKE_WRAPPER(gesdd)
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#undef EIGEN_MAKE_LAPACKE_WRAPPER
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}
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@ -106,6 +106,12 @@ svd.compute(m1);
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\endcode</td><td>\code
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?gesvd
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\endcode</td></tr>
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<tr class="alt"><td>Singular value decomposition \n \c EIGEN_USE_LAPACKE \n \c EIGEN_USE_LAPACKE_STRICT </td><td>\code
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BDCSVD<MatrixXd> svd;
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svd.compute(m1);
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\endcode</td><td>\code
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?gesdd
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\endcode</td></tr>
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<tr><td>Eigen-value decompositions \n \c EIGEN_USE_LAPACKE \n \c EIGEN_USE_LAPACKE_STRICT </td><td>\code
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EigenSolver<MatrixXd> es(m1);
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ComplexEigenSolver<MatrixXcd> ces(m1);
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