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Diffstat (limited to 'runtimes/nn/depend/external/eigen/Eigen/src/Core/products/SelfadjointMatrixVector.h')
| -rw-r--r-- | runtimes/nn/depend/external/eigen/Eigen/src/Core/products/SelfadjointMatrixVector.h | 260 |
1 files changed, 260 insertions, 0 deletions
diff --git a/runtimes/nn/depend/external/eigen/Eigen/src/Core/products/SelfadjointMatrixVector.h b/runtimes/nn/depend/external/eigen/Eigen/src/Core/products/SelfadjointMatrixVector.h new file mode 100644 index 000000000..3fd180e6c --- /dev/null +++ b/runtimes/nn/depend/external/eigen/Eigen/src/Core/products/SelfadjointMatrixVector.h @@ -0,0 +1,260 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr> +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_SELFADJOINT_MATRIX_VECTOR_H +#define EIGEN_SELFADJOINT_MATRIX_VECTOR_H + +namespace Eigen { + +namespace internal { + +/* Optimized selfadjoint matrix * vector product: + * This algorithm processes 2 columns at onces that allows to both reduce + * the number of load/stores of the result by a factor 2 and to reduce + * the instruction dependency. + */ + +template<typename Scalar, typename Index, int StorageOrder, int UpLo, bool ConjugateLhs, bool ConjugateRhs, int Version=Specialized> +struct selfadjoint_matrix_vector_product; + +template<typename Scalar, typename Index, int StorageOrder, int UpLo, bool ConjugateLhs, bool ConjugateRhs, int Version> +struct selfadjoint_matrix_vector_product + +{ +static EIGEN_DONT_INLINE void run( + Index size, + const Scalar* lhs, Index lhsStride, + const Scalar* rhs, + Scalar* res, + Scalar alpha); +}; + +template<typename Scalar, typename Index, int StorageOrder, int UpLo, bool ConjugateLhs, bool ConjugateRhs, int Version> +EIGEN_DONT_INLINE void selfadjoint_matrix_vector_product<Scalar,Index,StorageOrder,UpLo,ConjugateLhs,ConjugateRhs,Version>::run( + Index size, + const Scalar* lhs, Index lhsStride, + const Scalar* rhs, + Scalar* res, + Scalar alpha) +{ + typedef typename packet_traits<Scalar>::type Packet; + typedef typename NumTraits<Scalar>::Real RealScalar; + const Index PacketSize = sizeof(Packet)/sizeof(Scalar); + + enum { + IsRowMajor = StorageOrder==RowMajor ? 1 : 0, + IsLower = UpLo == Lower ? 1 : 0, + FirstTriangular = IsRowMajor == IsLower + }; + + conj_helper<Scalar,Scalar,NumTraits<Scalar>::IsComplex && EIGEN_LOGICAL_XOR(ConjugateLhs, IsRowMajor), ConjugateRhs> cj0; + conj_helper<Scalar,Scalar,NumTraits<Scalar>::IsComplex && EIGEN_LOGICAL_XOR(ConjugateLhs, !IsRowMajor), ConjugateRhs> cj1; + conj_helper<RealScalar,Scalar,false, ConjugateRhs> cjd; + + conj_helper<Packet,Packet,NumTraits<Scalar>::IsComplex && EIGEN_LOGICAL_XOR(ConjugateLhs, IsRowMajor), ConjugateRhs> pcj0; + conj_helper<Packet,Packet,NumTraits<Scalar>::IsComplex && EIGEN_LOGICAL_XOR(ConjugateLhs, !IsRowMajor), ConjugateRhs> pcj1; + + Scalar cjAlpha = ConjugateRhs ? numext::conj(alpha) : alpha; + + + Index bound = (std::max)(Index(0),size-8) & 0xfffffffe; + if (FirstTriangular) + bound = size - bound; + + for (Index j=FirstTriangular ? bound : 0; + j<(FirstTriangular ? size : bound);j+=2) + { + const Scalar* EIGEN_RESTRICT A0 = lhs + j*lhsStride; + const Scalar* EIGEN_RESTRICT A1 = lhs + (j+1)*lhsStride; + + Scalar t0 = cjAlpha * rhs[j]; + Packet ptmp0 = pset1<Packet>(t0); + Scalar t1 = cjAlpha * rhs[j+1]; + Packet ptmp1 = pset1<Packet>(t1); + + Scalar t2(0); + Packet ptmp2 = pset1<Packet>(t2); + Scalar t3(0); + Packet ptmp3 = pset1<Packet>(t3); + + Index starti = FirstTriangular ? 0 : j+2; + Index endi = FirstTriangular ? j : size; + Index alignedStart = (starti) + internal::first_default_aligned(&res[starti], endi-starti); + Index alignedEnd = alignedStart + ((endi-alignedStart)/(PacketSize))*(PacketSize); + + res[j] += cjd.pmul(numext::real(A0[j]), t0); + res[j+1] += cjd.pmul(numext::real(A1[j+1]), t1); + if(FirstTriangular) + { + res[j] += cj0.pmul(A1[j], t1); + t3 += cj1.pmul(A1[j], rhs[j]); + } + else + { + res[j+1] += cj0.pmul(A0[j+1],t0); + t2 += cj1.pmul(A0[j+1], rhs[j+1]); + } + + for (Index i=starti; i<alignedStart; ++i) + { + res[i] += cj0.pmul(A0[i], t0) + cj0.pmul(A1[i],t1); + t2 += cj1.pmul(A0[i], rhs[i]); + t3 += cj1.pmul(A1[i], rhs[i]); + } + // Yes this an optimization for gcc 4.3 and 4.4 (=> huge speed up) + // gcc 4.2 does this optimization automatically. + const Scalar* EIGEN_RESTRICT a0It = A0 + alignedStart; + const Scalar* EIGEN_RESTRICT a1It = A1 + alignedStart; + const Scalar* EIGEN_RESTRICT rhsIt = rhs + alignedStart; + Scalar* EIGEN_RESTRICT resIt = res + alignedStart; + for (Index i=alignedStart; i<alignedEnd; i+=PacketSize) + { + Packet A0i = ploadu<Packet>(a0It); a0It += PacketSize; + Packet A1i = ploadu<Packet>(a1It); a1It += PacketSize; + Packet Bi = ploadu<Packet>(rhsIt); rhsIt += PacketSize; // FIXME should be aligned in most cases + Packet Xi = pload <Packet>(resIt); + + Xi = pcj0.pmadd(A0i,ptmp0, pcj0.pmadd(A1i,ptmp1,Xi)); + ptmp2 = pcj1.pmadd(A0i, Bi, ptmp2); + ptmp3 = pcj1.pmadd(A1i, Bi, ptmp3); + pstore(resIt,Xi); resIt += PacketSize; + } + for (Index i=alignedEnd; i<endi; i++) + { + res[i] += cj0.pmul(A0[i], t0) + cj0.pmul(A1[i],t1); + t2 += cj1.pmul(A0[i], rhs[i]); + t3 += cj1.pmul(A1[i], rhs[i]); + } + + res[j] += alpha * (t2 + predux(ptmp2)); + res[j+1] += alpha * (t3 + predux(ptmp3)); + } + for (Index j=FirstTriangular ? 0 : bound;j<(FirstTriangular ? bound : size);j++) + { + const Scalar* EIGEN_RESTRICT A0 = lhs + j*lhsStride; + + Scalar t1 = cjAlpha * rhs[j]; + Scalar t2(0); + res[j] += cjd.pmul(numext::real(A0[j]), t1); + for (Index i=FirstTriangular ? 0 : j+1; i<(FirstTriangular ? j : size); i++) + { + res[i] += cj0.pmul(A0[i], t1); + t2 += cj1.pmul(A0[i], rhs[i]); + } + res[j] += alpha * t2; + } +} + +} // end namespace internal + +/*************************************************************************** +* Wrapper to product_selfadjoint_vector +***************************************************************************/ + +namespace internal { + +template<typename Lhs, int LhsMode, typename Rhs> +struct selfadjoint_product_impl<Lhs,LhsMode,false,Rhs,0,true> +{ + typedef typename Product<Lhs,Rhs>::Scalar Scalar; + + typedef internal::blas_traits<Lhs> LhsBlasTraits; + typedef typename LhsBlasTraits::DirectLinearAccessType ActualLhsType; + typedef typename internal::remove_all<ActualLhsType>::type ActualLhsTypeCleaned; + + typedef internal::blas_traits<Rhs> RhsBlasTraits; + typedef typename RhsBlasTraits::DirectLinearAccessType ActualRhsType; + typedef typename internal::remove_all<ActualRhsType>::type ActualRhsTypeCleaned; + + enum { LhsUpLo = LhsMode&(Upper|Lower) }; + + template<typename Dest> + static void run(Dest& dest, const Lhs &a_lhs, const Rhs &a_rhs, const Scalar& alpha) + { + typedef typename Dest::Scalar ResScalar; + typedef typename Rhs::Scalar RhsScalar; + typedef Map<Matrix<ResScalar,Dynamic,1>, EIGEN_PLAIN_ENUM_MIN(AlignedMax,internal::packet_traits<ResScalar>::size)> MappedDest; + + eigen_assert(dest.rows()==a_lhs.rows() && dest.cols()==a_rhs.cols()); + + typename internal::add_const_on_value_type<ActualLhsType>::type lhs = LhsBlasTraits::extract(a_lhs); + typename internal::add_const_on_value_type<ActualRhsType>::type rhs = RhsBlasTraits::extract(a_rhs); + + Scalar actualAlpha = alpha * LhsBlasTraits::extractScalarFactor(a_lhs) + * RhsBlasTraits::extractScalarFactor(a_rhs); + + enum { + EvalToDest = (Dest::InnerStrideAtCompileTime==1), + UseRhs = (ActualRhsTypeCleaned::InnerStrideAtCompileTime==1) + }; + + internal::gemv_static_vector_if<ResScalar,Dest::SizeAtCompileTime,Dest::MaxSizeAtCompileTime,!EvalToDest> static_dest; + internal::gemv_static_vector_if<RhsScalar,ActualRhsTypeCleaned::SizeAtCompileTime,ActualRhsTypeCleaned::MaxSizeAtCompileTime,!UseRhs> static_rhs; + + ei_declare_aligned_stack_constructed_variable(ResScalar,actualDestPtr,dest.size(), + EvalToDest ? dest.data() : static_dest.data()); + + ei_declare_aligned_stack_constructed_variable(RhsScalar,actualRhsPtr,rhs.size(), + UseRhs ? const_cast<RhsScalar*>(rhs.data()) : static_rhs.data()); + + if(!EvalToDest) + { + #ifdef EIGEN_DENSE_STORAGE_CTOR_PLUGIN + Index size = dest.size(); + EIGEN_DENSE_STORAGE_CTOR_PLUGIN + #endif + MappedDest(actualDestPtr, dest.size()) = dest; + } + + if(!UseRhs) + { + #ifdef EIGEN_DENSE_STORAGE_CTOR_PLUGIN + Index size = rhs.size(); + EIGEN_DENSE_STORAGE_CTOR_PLUGIN + #endif + Map<typename ActualRhsTypeCleaned::PlainObject>(actualRhsPtr, rhs.size()) = rhs; + } + + + internal::selfadjoint_matrix_vector_product<Scalar, Index, (internal::traits<ActualLhsTypeCleaned>::Flags&RowMajorBit) ? RowMajor : ColMajor, + int(LhsUpLo), bool(LhsBlasTraits::NeedToConjugate), bool(RhsBlasTraits::NeedToConjugate)>::run + ( + lhs.rows(), // size + &lhs.coeffRef(0,0), lhs.outerStride(), // lhs info + actualRhsPtr, // rhs info + actualDestPtr, // result info + actualAlpha // scale factor + ); + + if(!EvalToDest) + dest = MappedDest(actualDestPtr, dest.size()); + } +}; + +template<typename Lhs, typename Rhs, int RhsMode> +struct selfadjoint_product_impl<Lhs,0,true,Rhs,RhsMode,false> +{ + typedef typename Product<Lhs,Rhs>::Scalar Scalar; + enum { RhsUpLo = RhsMode&(Upper|Lower) }; + + template<typename Dest> + static void run(Dest& dest, const Lhs &a_lhs, const Rhs &a_rhs, const Scalar& alpha) + { + // let's simply transpose the product + Transpose<Dest> destT(dest); + selfadjoint_product_impl<Transpose<const Rhs>, int(RhsUpLo)==Upper ? Lower : Upper, false, + Transpose<const Lhs>, 0, true>::run(destT, a_rhs.transpose(), a_lhs.transpose(), alpha); + } +}; + +} // end namespace internal + +} // end namespace Eigen + +#endif // EIGEN_SELFADJOINT_MATRIX_VECTOR_H |