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186 lines
7.3 KiB
C++
186 lines
7.3 KiB
C++
// 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) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
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//
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// This Source Code Form is subject to the terms of the Mozilla
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// Public License v. 2.0. If a copy of the MPL was not distributed
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// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
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#ifndef EIGEN_CXX11_TENSOR_TENSOR_LAYOUT_SWAP_H
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#define EIGEN_CXX11_TENSOR_TENSOR_LAYOUT_SWAP_H
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// IWYU pragma: private
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#include "./InternalHeaderCheck.h"
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namespace Eigen {
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/** \class TensorLayoutSwap
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* \ingroup CXX11_Tensor_Module
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*
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* \brief Swap the layout from col-major to row-major, or row-major
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* to col-major, and invert the order of the dimensions.
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*
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* Beware: the dimensions are reversed by this operation. If you want to
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* preserve the ordering of the dimensions, you need to combine this
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* operation with a shuffle.
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*
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* \example:
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* Tensor<float, 2, ColMajor> input(2, 4);
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* Tensor<float, 2, RowMajor> output = input.swap_layout();
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* eigen_assert(output.dimension(0) == 4);
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* eigen_assert(output.dimension(1) == 2);
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*
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* array<int, 2> shuffle(1, 0);
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* output = input.swap_layout().shuffle(shuffle);
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* eigen_assert(output.dimension(0) == 2);
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* eigen_assert(output.dimension(1) == 4);
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*
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*/
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namespace internal {
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template <typename XprType>
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struct traits<TensorLayoutSwapOp<XprType> > : public traits<XprType> {
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typedef typename XprType::Scalar Scalar;
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typedef traits<XprType> XprTraits;
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typedef typename XprTraits::StorageKind StorageKind;
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typedef typename XprTraits::Index Index;
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typedef typename XprType::Nested Nested;
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typedef std::remove_reference_t<Nested> Nested_;
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static constexpr int NumDimensions = traits<XprType>::NumDimensions;
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static constexpr int Layout = (traits<XprType>::Layout == ColMajor) ? RowMajor : ColMajor;
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typedef typename XprTraits::PointerType PointerType;
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};
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template <typename XprType>
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struct eval<TensorLayoutSwapOp<XprType>, Eigen::Dense> {
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typedef const TensorLayoutSwapOp<XprType>& type;
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};
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template <typename XprType>
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struct nested<TensorLayoutSwapOp<XprType>, 1, typename eval<TensorLayoutSwapOp<XprType> >::type> {
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typedef TensorLayoutSwapOp<XprType> type;
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};
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} // end namespace internal
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template <typename XprType>
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class TensorLayoutSwapOp : public TensorBase<TensorLayoutSwapOp<XprType>, WriteAccessors> {
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public:
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typedef TensorBase<TensorLayoutSwapOp<XprType>, WriteAccessors> Base;
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typedef typename Eigen::internal::traits<TensorLayoutSwapOp>::Scalar Scalar;
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typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
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typedef std::remove_const_t<typename XprType::CoeffReturnType> CoeffReturnType;
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typedef typename Eigen::internal::nested<TensorLayoutSwapOp>::type Nested;
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typedef typename Eigen::internal::traits<TensorLayoutSwapOp>::StorageKind StorageKind;
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typedef typename Eigen::internal::traits<TensorLayoutSwapOp>::Index Index;
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorLayoutSwapOp(const XprType& expr) : m_xpr(expr) {}
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EIGEN_DEVICE_FUNC const internal::remove_all_t<typename XprType::Nested>& expression() const { return m_xpr; }
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EIGEN_TENSOR_INHERIT_ASSIGNMENT_OPERATORS(TensorLayoutSwapOp)
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protected:
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typename XprType::Nested m_xpr;
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};
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// Eval as rvalue
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template <typename ArgType, typename Device>
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struct TensorEvaluator<const TensorLayoutSwapOp<ArgType>, Device> {
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typedef TensorLayoutSwapOp<ArgType> XprType;
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typedef typename XprType::Index Index;
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static constexpr int NumDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value;
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typedef DSizes<Index, NumDims> Dimensions;
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static constexpr int Layout =
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(TensorEvaluator<ArgType, Device>::Layout == static_cast<int>(ColMajor)) ? RowMajor : ColMajor;
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enum {
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IsAligned = TensorEvaluator<ArgType, Device>::IsAligned,
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PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess,
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BlockAccess = false,
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PreferBlockAccess = TensorEvaluator<ArgType, Device>::PreferBlockAccess,
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CoordAccess = false, // to be implemented
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RawAccess = TensorEvaluator<ArgType, Device>::RawAccess
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};
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//===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
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typedef internal::TensorBlockNotImplemented TensorBlock;
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//===--------------------------------------------------------------------===//
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EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device) : m_impl(op.expression(), device) {
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for (int i = 0; i < NumDims; ++i) {
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m_dimensions[i] = m_impl.dimensions()[NumDims - 1 - i];
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}
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}
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typedef typename XprType::Scalar Scalar;
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typedef typename XprType::CoeffReturnType CoeffReturnType;
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typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
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typedef StorageMemory<CoeffReturnType, Device> Storage;
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typedef typename Storage::Type EvaluatorPointerType;
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
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EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType data) { return m_impl.evalSubExprsIfNeeded(data); }
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EIGEN_STRONG_INLINE void cleanup() { m_impl.cleanup(); }
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const { return m_impl.coeff(index); }
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template <int LoadMode>
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const {
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return m_impl.template packet<LoadMode>(index);
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}
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const {
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return m_impl.costPerCoeff(vectorized);
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}
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EIGEN_DEVICE_FUNC typename Storage::Type data() const { return constCast(m_impl.data()); }
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const TensorEvaluator<ArgType, Device>& impl() const { return m_impl; }
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protected:
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TensorEvaluator<ArgType, Device> m_impl;
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Dimensions m_dimensions;
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};
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// Eval as lvalue
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template <typename ArgType, typename Device>
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struct TensorEvaluator<TensorLayoutSwapOp<ArgType>, Device>
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: public TensorEvaluator<const TensorLayoutSwapOp<ArgType>, Device> {
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typedef TensorEvaluator<const TensorLayoutSwapOp<ArgType>, Device> Base;
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typedef TensorLayoutSwapOp<ArgType> XprType;
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static constexpr int Layout =
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(TensorEvaluator<ArgType, Device>::Layout == static_cast<int>(ColMajor)) ? RowMajor : ColMajor;
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enum {
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IsAligned = TensorEvaluator<ArgType, Device>::IsAligned,
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PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess,
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BlockAccess = false,
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PreferBlockAccess = TensorEvaluator<ArgType, Device>::PreferBlockAccess,
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CoordAccess = false // to be implemented
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};
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//===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
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typedef internal::TensorBlockNotImplemented TensorBlock;
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//===--------------------------------------------------------------------===//
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EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device) : Base(op, device) {}
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typedef typename XprType::Index Index;
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typedef typename XprType::Scalar Scalar;
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typedef typename XprType::CoeffReturnType CoeffReturnType;
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typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType& coeffRef(Index index) const {
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return this->m_impl.coeffRef(index);
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}
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template <int StoreMode>
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void writePacket(Index index, const PacketReturnType& x) const {
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this->m_impl.template writePacket<StoreMode>(index, x);
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}
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};
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} // end namespace Eigen
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#endif // EIGEN_CXX11_TENSOR_TENSOR_LAYOUT_SWAP_H
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