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Diffstat (limited to 'runtimes/nn/depend/external/eigen/Eigen/src/Core/Redux.h')
| -rw-r--r-- | runtimes/nn/depend/external/eigen/Eigen/src/Core/Redux.h | 505 |
1 files changed, 0 insertions, 505 deletions
diff --git a/runtimes/nn/depend/external/eigen/Eigen/src/Core/Redux.h b/runtimes/nn/depend/external/eigen/Eigen/src/Core/Redux.h deleted file mode 100644 index b6e8f8887..000000000 --- a/runtimes/nn/depend/external/eigen/Eigen/src/Core/Redux.h +++ /dev/null @@ -1,505 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr> -// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com> -// -// 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_REDUX_H -#define EIGEN_REDUX_H - -namespace Eigen { - -namespace internal { - -// TODO -// * implement other kind of vectorization -// * factorize code - -/*************************************************************************** -* Part 1 : the logic deciding a strategy for vectorization and unrolling -***************************************************************************/ - -template<typename Func, typename Derived> -struct redux_traits -{ -public: - typedef typename find_best_packet<typename Derived::Scalar,Derived::SizeAtCompileTime>::type PacketType; - enum { - PacketSize = unpacket_traits<PacketType>::size, - InnerMaxSize = int(Derived::IsRowMajor) - ? Derived::MaxColsAtCompileTime - : Derived::MaxRowsAtCompileTime - }; - - enum { - MightVectorize = (int(Derived::Flags)&ActualPacketAccessBit) - && (functor_traits<Func>::PacketAccess), - MayLinearVectorize = bool(MightVectorize) && (int(Derived::Flags)&LinearAccessBit), - MaySliceVectorize = bool(MightVectorize) && int(InnerMaxSize)>=3*PacketSize - }; - -public: - enum { - Traversal = int(MayLinearVectorize) ? int(LinearVectorizedTraversal) - : int(MaySliceVectorize) ? int(SliceVectorizedTraversal) - : int(DefaultTraversal) - }; - -public: - enum { - Cost = Derived::SizeAtCompileTime == Dynamic ? HugeCost - : Derived::SizeAtCompileTime * Derived::CoeffReadCost + (Derived::SizeAtCompileTime-1) * functor_traits<Func>::Cost, - UnrollingLimit = EIGEN_UNROLLING_LIMIT * (int(Traversal) == int(DefaultTraversal) ? 1 : int(PacketSize)) - }; - -public: - enum { - Unrolling = Cost <= UnrollingLimit ? CompleteUnrolling : NoUnrolling - }; - -#ifdef EIGEN_DEBUG_ASSIGN - static void debug() - { - std::cerr << "Xpr: " << typeid(typename Derived::XprType).name() << std::endl; - std::cerr.setf(std::ios::hex, std::ios::basefield); - EIGEN_DEBUG_VAR(Derived::Flags) - std::cerr.unsetf(std::ios::hex); - EIGEN_DEBUG_VAR(InnerMaxSize) - EIGEN_DEBUG_VAR(PacketSize) - EIGEN_DEBUG_VAR(MightVectorize) - EIGEN_DEBUG_VAR(MayLinearVectorize) - EIGEN_DEBUG_VAR(MaySliceVectorize) - EIGEN_DEBUG_VAR(Traversal) - EIGEN_DEBUG_VAR(UnrollingLimit) - EIGEN_DEBUG_VAR(Unrolling) - std::cerr << std::endl; - } -#endif -}; - -/*************************************************************************** -* Part 2 : unrollers -***************************************************************************/ - -/*** no vectorization ***/ - -template<typename Func, typename Derived, int Start, int Length> -struct redux_novec_unroller -{ - enum { - HalfLength = Length/2 - }; - - typedef typename Derived::Scalar Scalar; - - EIGEN_DEVICE_FUNC - static EIGEN_STRONG_INLINE Scalar run(const Derived &mat, const Func& func) - { - return func(redux_novec_unroller<Func, Derived, Start, HalfLength>::run(mat,func), - redux_novec_unroller<Func, Derived, Start+HalfLength, Length-HalfLength>::run(mat,func)); - } -}; - -template<typename Func, typename Derived, int Start> -struct redux_novec_unroller<Func, Derived, Start, 1> -{ - enum { - outer = Start / Derived::InnerSizeAtCompileTime, - inner = Start % Derived::InnerSizeAtCompileTime - }; - - typedef typename Derived::Scalar Scalar; - - EIGEN_DEVICE_FUNC - static EIGEN_STRONG_INLINE Scalar run(const Derived &mat, const Func&) - { - return mat.coeffByOuterInner(outer, inner); - } -}; - -// This is actually dead code and will never be called. It is required -// to prevent false warnings regarding failed inlining though -// for 0 length run() will never be called at all. -template<typename Func, typename Derived, int Start> -struct redux_novec_unroller<Func, Derived, Start, 0> -{ - typedef typename Derived::Scalar Scalar; - EIGEN_DEVICE_FUNC - static EIGEN_STRONG_INLINE Scalar run(const Derived&, const Func&) { return Scalar(); } -}; - -/*** vectorization ***/ - -template<typename Func, typename Derived, int Start, int Length> -struct redux_vec_unroller -{ - enum { - PacketSize = redux_traits<Func, Derived>::PacketSize, - HalfLength = Length/2 - }; - - typedef typename Derived::Scalar Scalar; - typedef typename redux_traits<Func, Derived>::PacketType PacketScalar; - - static EIGEN_STRONG_INLINE PacketScalar run(const Derived &mat, const Func& func) - { - return func.packetOp( - redux_vec_unroller<Func, Derived, Start, HalfLength>::run(mat,func), - redux_vec_unroller<Func, Derived, Start+HalfLength, Length-HalfLength>::run(mat,func) ); - } -}; - -template<typename Func, typename Derived, int Start> -struct redux_vec_unroller<Func, Derived, Start, 1> -{ - enum { - index = Start * redux_traits<Func, Derived>::PacketSize, - outer = index / int(Derived::InnerSizeAtCompileTime), - inner = index % int(Derived::InnerSizeAtCompileTime), - alignment = Derived::Alignment - }; - - typedef typename Derived::Scalar Scalar; - typedef typename redux_traits<Func, Derived>::PacketType PacketScalar; - - static EIGEN_STRONG_INLINE PacketScalar run(const Derived &mat, const Func&) - { - return mat.template packetByOuterInner<alignment,PacketScalar>(outer, inner); - } -}; - -/*************************************************************************** -* Part 3 : implementation of all cases -***************************************************************************/ - -template<typename Func, typename Derived, - int Traversal = redux_traits<Func, Derived>::Traversal, - int Unrolling = redux_traits<Func, Derived>::Unrolling -> -struct redux_impl; - -template<typename Func, typename Derived> -struct redux_impl<Func, Derived, DefaultTraversal, NoUnrolling> -{ - typedef typename Derived::Scalar Scalar; - EIGEN_DEVICE_FUNC - static EIGEN_STRONG_INLINE Scalar run(const Derived &mat, const Func& func) - { - eigen_assert(mat.rows()>0 && mat.cols()>0 && "you are using an empty matrix"); - Scalar res; - res = mat.coeffByOuterInner(0, 0); - for(Index i = 1; i < mat.innerSize(); ++i) - res = func(res, mat.coeffByOuterInner(0, i)); - for(Index i = 1; i < mat.outerSize(); ++i) - for(Index j = 0; j < mat.innerSize(); ++j) - res = func(res, mat.coeffByOuterInner(i, j)); - return res; - } -}; - -template<typename Func, typename Derived> -struct redux_impl<Func,Derived, DefaultTraversal, CompleteUnrolling> - : public redux_novec_unroller<Func,Derived, 0, Derived::SizeAtCompileTime> -{}; - -template<typename Func, typename Derived> -struct redux_impl<Func, Derived, LinearVectorizedTraversal, NoUnrolling> -{ - typedef typename Derived::Scalar Scalar; - typedef typename redux_traits<Func, Derived>::PacketType PacketScalar; - - static Scalar run(const Derived &mat, const Func& func) - { - const Index size = mat.size(); - - const Index packetSize = redux_traits<Func, Derived>::PacketSize; - const int packetAlignment = unpacket_traits<PacketScalar>::alignment; - enum { - alignment0 = (bool(Derived::Flags & DirectAccessBit) && bool(packet_traits<Scalar>::AlignedOnScalar)) ? int(packetAlignment) : int(Unaligned), - alignment = EIGEN_PLAIN_ENUM_MAX(alignment0, Derived::Alignment) - }; - const Index alignedStart = internal::first_default_aligned(mat.nestedExpression()); - const Index alignedSize2 = ((size-alignedStart)/(2*packetSize))*(2*packetSize); - const Index alignedSize = ((size-alignedStart)/(packetSize))*(packetSize); - const Index alignedEnd2 = alignedStart + alignedSize2; - const Index alignedEnd = alignedStart + alignedSize; - Scalar res; - if(alignedSize) - { - PacketScalar packet_res0 = mat.template packet<alignment,PacketScalar>(alignedStart); - if(alignedSize>packetSize) // we have at least two packets to partly unroll the loop - { - PacketScalar packet_res1 = mat.template packet<alignment,PacketScalar>(alignedStart+packetSize); - for(Index index = alignedStart + 2*packetSize; index < alignedEnd2; index += 2*packetSize) - { - packet_res0 = func.packetOp(packet_res0, mat.template packet<alignment,PacketScalar>(index)); - packet_res1 = func.packetOp(packet_res1, mat.template packet<alignment,PacketScalar>(index+packetSize)); - } - - packet_res0 = func.packetOp(packet_res0,packet_res1); - if(alignedEnd>alignedEnd2) - packet_res0 = func.packetOp(packet_res0, mat.template packet<alignment,PacketScalar>(alignedEnd2)); - } - res = func.predux(packet_res0); - - for(Index index = 0; index < alignedStart; ++index) - res = func(res,mat.coeff(index)); - - for(Index index = alignedEnd; index < size; ++index) - res = func(res,mat.coeff(index)); - } - else // too small to vectorize anything. - // since this is dynamic-size hence inefficient anyway for such small sizes, don't try to optimize. - { - res = mat.coeff(0); - for(Index index = 1; index < size; ++index) - res = func(res,mat.coeff(index)); - } - - return res; - } -}; - -// NOTE: for SliceVectorizedTraversal we simply bypass unrolling -template<typename Func, typename Derived, int Unrolling> -struct redux_impl<Func, Derived, SliceVectorizedTraversal, Unrolling> -{ - typedef typename Derived::Scalar Scalar; - typedef typename redux_traits<Func, Derived>::PacketType PacketType; - - EIGEN_DEVICE_FUNC static Scalar run(const Derived &mat, const Func& func) - { - eigen_assert(mat.rows()>0 && mat.cols()>0 && "you are using an empty matrix"); - const Index innerSize = mat.innerSize(); - const Index outerSize = mat.outerSize(); - enum { - packetSize = redux_traits<Func, Derived>::PacketSize - }; - const Index packetedInnerSize = ((innerSize)/packetSize)*packetSize; - Scalar res; - if(packetedInnerSize) - { - PacketType packet_res = mat.template packet<Unaligned,PacketType>(0,0); - for(Index j=0; j<outerSize; ++j) - for(Index i=(j==0?packetSize:0); i<packetedInnerSize; i+=Index(packetSize)) - packet_res = func.packetOp(packet_res, mat.template packetByOuterInner<Unaligned,PacketType>(j,i)); - - res = func.predux(packet_res); - for(Index j=0; j<outerSize; ++j) - for(Index i=packetedInnerSize; i<innerSize; ++i) - res = func(res, mat.coeffByOuterInner(j,i)); - } - else // too small to vectorize anything. - // since this is dynamic-size hence inefficient anyway for such small sizes, don't try to optimize. - { - res = redux_impl<Func, Derived, DefaultTraversal, NoUnrolling>::run(mat, func); - } - - return res; - } -}; - -template<typename Func, typename Derived> -struct redux_impl<Func, Derived, LinearVectorizedTraversal, CompleteUnrolling> -{ - typedef typename Derived::Scalar Scalar; - - typedef typename redux_traits<Func, Derived>::PacketType PacketScalar; - enum { - PacketSize = redux_traits<Func, Derived>::PacketSize, - Size = Derived::SizeAtCompileTime, - VectorizedSize = (Size / PacketSize) * PacketSize - }; - EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE Scalar run(const Derived &mat, const Func& func) - { - eigen_assert(mat.rows()>0 && mat.cols()>0 && "you are using an empty matrix"); - if (VectorizedSize > 0) { - Scalar res = func.predux(redux_vec_unroller<Func, Derived, 0, Size / PacketSize>::run(mat,func)); - if (VectorizedSize != Size) - res = func(res,redux_novec_unroller<Func, Derived, VectorizedSize, Size-VectorizedSize>::run(mat,func)); - return res; - } - else { - return redux_novec_unroller<Func, Derived, 0, Size>::run(mat,func); - } - } -}; - -// evaluator adaptor -template<typename _XprType> -class redux_evaluator -{ -public: - typedef _XprType XprType; - EIGEN_DEVICE_FUNC explicit redux_evaluator(const XprType &xpr) : m_evaluator(xpr), m_xpr(xpr) {} - - typedef typename XprType::Scalar Scalar; - typedef typename XprType::CoeffReturnType CoeffReturnType; - typedef typename XprType::PacketScalar PacketScalar; - typedef typename XprType::PacketReturnType PacketReturnType; - - enum { - MaxRowsAtCompileTime = XprType::MaxRowsAtCompileTime, - MaxColsAtCompileTime = XprType::MaxColsAtCompileTime, - // TODO we should not remove DirectAccessBit and rather find an elegant way to query the alignment offset at runtime from the evaluator - Flags = evaluator<XprType>::Flags & ~DirectAccessBit, - IsRowMajor = XprType::IsRowMajor, - SizeAtCompileTime = XprType::SizeAtCompileTime, - InnerSizeAtCompileTime = XprType::InnerSizeAtCompileTime, - CoeffReadCost = evaluator<XprType>::CoeffReadCost, - Alignment = evaluator<XprType>::Alignment - }; - - EIGEN_DEVICE_FUNC Index rows() const { return m_xpr.rows(); } - EIGEN_DEVICE_FUNC Index cols() const { return m_xpr.cols(); } - EIGEN_DEVICE_FUNC Index size() const { return m_xpr.size(); } - EIGEN_DEVICE_FUNC Index innerSize() const { return m_xpr.innerSize(); } - EIGEN_DEVICE_FUNC Index outerSize() const { return m_xpr.outerSize(); } - - EIGEN_DEVICE_FUNC - CoeffReturnType coeff(Index row, Index col) const - { return m_evaluator.coeff(row, col); } - - EIGEN_DEVICE_FUNC - CoeffReturnType coeff(Index index) const - { return m_evaluator.coeff(index); } - - template<int LoadMode, typename PacketType> - PacketType packet(Index row, Index col) const - { return m_evaluator.template packet<LoadMode,PacketType>(row, col); } - - template<int LoadMode, typename PacketType> - PacketType packet(Index index) const - { return m_evaluator.template packet<LoadMode,PacketType>(index); } - - EIGEN_DEVICE_FUNC - CoeffReturnType coeffByOuterInner(Index outer, Index inner) const - { return m_evaluator.coeff(IsRowMajor ? outer : inner, IsRowMajor ? inner : outer); } - - template<int LoadMode, typename PacketType> - PacketType packetByOuterInner(Index outer, Index inner) const - { return m_evaluator.template packet<LoadMode,PacketType>(IsRowMajor ? outer : inner, IsRowMajor ? inner : outer); } - - const XprType & nestedExpression() const { return m_xpr; } - -protected: - internal::evaluator<XprType> m_evaluator; - const XprType &m_xpr; -}; - -} // end namespace internal - -/*************************************************************************** -* Part 4 : public API -***************************************************************************/ - - -/** \returns the result of a full redux operation on the whole matrix or vector using \a func - * - * The template parameter \a BinaryOp is the type of the functor \a func which must be - * an associative operator. Both current C++98 and C++11 functor styles are handled. - * - * \sa DenseBase::sum(), DenseBase::minCoeff(), DenseBase::maxCoeff(), MatrixBase::colwise(), MatrixBase::rowwise() - */ -template<typename Derived> -template<typename Func> -typename internal::traits<Derived>::Scalar -DenseBase<Derived>::redux(const Func& func) const -{ - eigen_assert(this->rows()>0 && this->cols()>0 && "you are using an empty matrix"); - - typedef typename internal::redux_evaluator<Derived> ThisEvaluator; - ThisEvaluator thisEval(derived()); - - return internal::redux_impl<Func, ThisEvaluator>::run(thisEval, func); -} - -/** \returns the minimum of all coefficients of \c *this. - * \warning the result is undefined if \c *this contains NaN. - */ -template<typename Derived> -EIGEN_STRONG_INLINE typename internal::traits<Derived>::Scalar -DenseBase<Derived>::minCoeff() const -{ - return derived().redux(Eigen::internal::scalar_min_op<Scalar,Scalar>()); -} - -/** \returns the maximum of all coefficients of \c *this. - * \warning the result is undefined if \c *this contains NaN. - */ -template<typename Derived> -EIGEN_STRONG_INLINE typename internal::traits<Derived>::Scalar -DenseBase<Derived>::maxCoeff() const -{ - return derived().redux(Eigen::internal::scalar_max_op<Scalar,Scalar>()); -} - -/** \returns the sum of all coefficients of \c *this - * - * If \c *this is empty, then the value 0 is returned. - * - * \sa trace(), prod(), mean() - */ -template<typename Derived> -EIGEN_STRONG_INLINE typename internal::traits<Derived>::Scalar -DenseBase<Derived>::sum() const -{ - if(SizeAtCompileTime==0 || (SizeAtCompileTime==Dynamic && size()==0)) - return Scalar(0); - return derived().redux(Eigen::internal::scalar_sum_op<Scalar,Scalar>()); -} - -/** \returns the mean of all coefficients of *this -* -* \sa trace(), prod(), sum() -*/ -template<typename Derived> -EIGEN_STRONG_INLINE typename internal::traits<Derived>::Scalar -DenseBase<Derived>::mean() const -{ -#ifdef __INTEL_COMPILER - #pragma warning push - #pragma warning ( disable : 2259 ) -#endif - return Scalar(derived().redux(Eigen::internal::scalar_sum_op<Scalar,Scalar>())) / Scalar(this->size()); -#ifdef __INTEL_COMPILER - #pragma warning pop -#endif -} - -/** \returns the product of all coefficients of *this - * - * Example: \include MatrixBase_prod.cpp - * Output: \verbinclude MatrixBase_prod.out - * - * \sa sum(), mean(), trace() - */ -template<typename Derived> -EIGEN_STRONG_INLINE typename internal::traits<Derived>::Scalar -DenseBase<Derived>::prod() const -{ - if(SizeAtCompileTime==0 || (SizeAtCompileTime==Dynamic && size()==0)) - return Scalar(1); - return derived().redux(Eigen::internal::scalar_product_op<Scalar>()); -} - -/** \returns the trace of \c *this, i.e. the sum of the coefficients on the main diagonal. - * - * \c *this can be any matrix, not necessarily square. - * - * \sa diagonal(), sum() - */ -template<typename Derived> -EIGEN_STRONG_INLINE typename internal::traits<Derived>::Scalar -MatrixBase<Derived>::trace() const -{ - return derived().diagonal().sum(); -} - -} // end namespace Eigen - -#endif // EIGEN_REDUX_H |