| // This file is part of Eigen, a lightweight C++ template library |
| // for linear algebra. |
| // |
| // Copyright (C) 2015 Benoit Steiner <benoit.steiner.goog@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_CXX11_TENSOR_TENSOR_CONVERSION_H |
| #define EIGEN_CXX11_TENSOR_TENSOR_CONVERSION_H |
| |
| namespace Eigen { |
| |
| /** \class TensorConversionOp |
| * \ingroup CXX11_Tensor_Module |
| * |
| * \brief Tensor conversion class. This class makes it possible to vectorize |
| * type casting operations when the number of scalars per packet in the source |
| * and the destination type differ |
| */ |
| namespace internal { |
| template<typename TargetType, typename XprType> |
| struct traits<TensorConversionOp<TargetType, XprType> > |
| { |
| // Type promotion to handle the case where the types of the lhs and the rhs are different. |
| typedef TargetType Scalar; |
| typedef typename traits<XprType>::StorageKind StorageKind; |
| typedef typename traits<XprType>::Index Index; |
| typedef typename XprType::Nested Nested; |
| typedef typename remove_reference<Nested>::type _Nested; |
| static const int NumDimensions = traits<XprType>::NumDimensions; |
| static const int Layout = traits<XprType>::Layout; |
| enum { Flags = 0 }; |
| }; |
| |
| template<typename TargetType, typename XprType> |
| struct eval<TensorConversionOp<TargetType, XprType>, Eigen::Dense> |
| { |
| typedef const TensorConversionOp<TargetType, XprType>& type; |
| }; |
| |
| template<typename TargetType, typename XprType> |
| struct nested<TensorConversionOp<TargetType, XprType>, 1, typename eval<TensorConversionOp<TargetType, XprType> >::type> |
| { |
| typedef TensorConversionOp<TargetType, XprType> type; |
| }; |
| |
| } // end namespace internal |
| |
| |
| template <typename TensorEvaluator, typename SrcPacket, typename TgtPacket, int SrcCoeffRatio, int TgtCoeffRatio> |
| struct PacketConverter { |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE |
| PacketConverter(const TensorEvaluator& impl) |
| : m_impl(impl) {} |
| |
| template<int LoadMode, typename Index> |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TgtPacket packet(Index index) const { |
| return internal::pcast<SrcPacket, TgtPacket>(m_impl.template packet<LoadMode>(index)); |
| } |
| |
| private: |
| const TensorEvaluator& m_impl; |
| }; |
| |
| |
| template <typename TensorEvaluator, typename SrcPacket, typename TgtPacket> |
| struct PacketConverter<TensorEvaluator, SrcPacket, TgtPacket, 2, 1> { |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE |
| PacketConverter(const TensorEvaluator& impl) |
| : m_impl(impl) {} |
| |
| template<int LoadMode, typename Index> |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TgtPacket packet(Index index) const { |
| const int SrcPacketSize = internal::unpacket_traits<SrcPacket>::size; |
| |
| SrcPacket src1 = m_impl.template packet<LoadMode>(index); |
| SrcPacket src2 = m_impl.template packet<LoadMode>(index + SrcPacketSize); |
| TgtPacket result = internal::pcast<SrcPacket, TgtPacket>(src1, src2); |
| return result; |
| } |
| |
| private: |
| const TensorEvaluator& m_impl; |
| }; |
| |
| template <typename TensorEvaluator, typename SrcPacket, typename TgtPacket> |
| struct PacketConverter<TensorEvaluator, SrcPacket, TgtPacket, 4, 1> { |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE |
| PacketConverter(const TensorEvaluator& impl) |
| : m_impl(impl) {} |
| |
| template<int LoadMode, typename Index> |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TgtPacket packet(Index index) const { |
| const int SrcPacketSize = internal::unpacket_traits<SrcPacket>::size; |
| |
| SrcPacket src1 = m_impl.template packet<LoadMode>(index); |
| SrcPacket src2 = m_impl.template packet<LoadMode>(index + SrcPacketSize); |
| SrcPacket src3 = m_impl.template packet<LoadMode>(index + 2 * SrcPacketSize); |
| SrcPacket src4 = m_impl.template packet<LoadMode>(index + 3 * SrcPacketSize); |
| TgtPacket result = internal::pcast<SrcPacket, TgtPacket>(src1, src2, src3, src4); |
| return result; |
| } |
| |
| private: |
| const TensorEvaluator& m_impl; |
| }; |
| |
| template <typename TensorEvaluator, typename SrcPacket, typename TgtPacket> |
| struct PacketConverter<TensorEvaluator, SrcPacket, TgtPacket, 1, 2> { |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE |
| PacketConverter(const TensorEvaluator& impl) |
| : m_impl(impl), m_maxIndex(impl.dimensions().TotalSize()) {} |
| |
| template<int LoadMode, typename Index> |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TgtPacket packet(Index index) const { |
| const int SrcPacketSize = internal::unpacket_traits<SrcPacket>::size; |
| // Only call m_impl.packet() when we have direct access to the underlying data. This |
| // ensures that we don't compute the subexpression twice. We may however load some |
| // coefficients twice, but in practice this doesn't negatively impact performance. |
| if (m_impl.data() && (index + SrcPacketSize < m_maxIndex)) { |
| // Force unaligned memory loads since we can't ensure alignment anymore |
| return internal::pcast<SrcPacket, TgtPacket>(m_impl.template packet<Unaligned>(index)); |
| } else { |
| const int TgtPacketSize = internal::unpacket_traits<TgtPacket>::size; |
| typedef typename internal::unpacket_traits<SrcPacket>::type SrcType; |
| typedef typename internal::unpacket_traits<TgtPacket>::type TgtType; |
| internal::scalar_cast_op<SrcType, TgtType> converter; |
| EIGEN_ALIGN_MAX typename internal::unpacket_traits<TgtPacket>::type values[TgtPacketSize]; |
| for (int i = 0; i < TgtPacketSize; ++i) { |
| values[i] = converter(m_impl.coeff(index+i)); |
| } |
| TgtPacket rslt = internal::pload<TgtPacket>(values); |
| return rslt; |
| } |
| } |
| |
| private: |
| const TensorEvaluator& m_impl; |
| const typename TensorEvaluator::Index m_maxIndex; |
| }; |
| |
| template<typename TargetType, typename XprType> |
| class TensorConversionOp : public TensorBase<TensorConversionOp<TargetType, XprType>, ReadOnlyAccessors> |
| { |
| public: |
| typedef typename internal::traits<TensorConversionOp>::Scalar Scalar; |
| typedef typename internal::traits<TensorConversionOp>::StorageKind StorageKind; |
| typedef typename internal::traits<TensorConversionOp>::Index Index; |
| typedef typename internal::nested<TensorConversionOp>::type Nested; |
| typedef Scalar CoeffReturnType; |
| typedef typename NumTraits<Scalar>::Real RealScalar; |
| |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorConversionOp(const XprType& xpr) |
| : m_xpr(xpr) {} |
| |
| EIGEN_DEVICE_FUNC |
| const typename internal::remove_all<typename XprType::Nested>::type& |
| expression() const { return m_xpr; } |
| |
| protected: |
| typename XprType::Nested m_xpr; |
| }; |
| |
| template <bool SameType, typename Eval, typename Scalar> struct ConversionSubExprEval { |
| static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool run(Eval& impl, Scalar*) { |
| impl.evalSubExprsIfNeeded(NULL); |
| return true; |
| } |
| }; |
| |
| template <typename Eval, typename Scalar> struct ConversionSubExprEval<true, Eval, Scalar> { |
| static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool run(Eval& impl, Scalar* data) { |
| return impl.evalSubExprsIfNeeded(data); |
| } |
| }; |
| |
| |
| // Eval as rvalue |
| template<typename TargetType, typename ArgType, typename Device> |
| struct TensorEvaluator<const TensorConversionOp<TargetType, ArgType>, Device> |
| { |
| typedef TensorConversionOp<TargetType, ArgType> XprType; |
| typedef typename XprType::Index Index; |
| typedef typename TensorEvaluator<ArgType, Device>::Dimensions Dimensions; |
| typedef TargetType Scalar; |
| typedef TargetType CoeffReturnType; |
| typedef typename internal::remove_all<typename internal::traits<ArgType>::Scalar>::type SrcType; |
| typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType; |
| typedef typename PacketType<SrcType, Device>::type PacketSourceType; |
| static const int PacketSize = internal::unpacket_traits<PacketReturnType>::size; |
| |
| enum { |
| IsAligned = false, |
| PacketAccess = true, |
| Layout = TensorEvaluator<ArgType, Device>::Layout, |
| RawAccess = false |
| }; |
| |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device) |
| : m_impl(op.expression(), device) |
| { |
| } |
| |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_impl.dimensions(); } |
| |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(Scalar* data) |
| { |
| return ConversionSubExprEval<internal::is_same<TargetType, SrcType>::value, TensorEvaluator<ArgType, Device>, Scalar>::run(m_impl, data); |
| } |
| |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() |
| { |
| m_impl.cleanup(); |
| } |
| |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const |
| { |
| internal::scalar_cast_op<SrcType, TargetType> converter; |
| return converter(m_impl.coeff(index)); |
| } |
| |
| template<int LoadMode> |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const |
| { |
| const bool Vectorizable = TensorEvaluator<ArgType, Device>::PacketAccess & |
| internal::type_casting_traits<SrcType, TargetType>::VectorizedCast; |
| return PacketConv<LoadMode, Vectorizable>::run(m_impl, index); |
| } |
| |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost |
| costPerCoeff(bool vectorized) const { |
| const double cast_cost = TensorOpCost::CastCost<SrcType, TargetType>(); |
| if (vectorized) { |
| const double SrcCoeffRatio = |
| internal::type_casting_traits<SrcType, TargetType>::SrcCoeffRatio; |
| const double TgtCoeffRatio = |
| internal::type_casting_traits<SrcType, TargetType>::TgtCoeffRatio; |
| return m_impl.costPerCoeff(vectorized) * (SrcCoeffRatio / PacketSize) + |
| TensorOpCost(0, 0, TgtCoeffRatio * (cast_cost / PacketSize)); |
| } else { |
| return m_impl.costPerCoeff(vectorized) + TensorOpCost(0, 0, cast_cost); |
| } |
| } |
| |
| EIGEN_DEVICE_FUNC Scalar* data() const { return NULL; } |
| |
| /// required by sycl in order to extract the sycl accessor |
| const TensorEvaluator<ArgType, Device>& impl() const { return m_impl; } |
| |
| protected: |
| template <int LoadMode, bool ActuallyVectorize> |
| struct PacketConv { |
| static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType run(const TensorEvaluator<ArgType, Device>& impl, Index index) { |
| internal::scalar_cast_op<SrcType, TargetType> converter; |
| EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[PacketSize]; |
| for (int i = 0; i < PacketSize; ++i) { |
| values[i] = converter(impl.coeff(index+i)); |
| } |
| PacketReturnType rslt = internal::pload<PacketReturnType>(values); |
| return rslt; |
| } |
| }; |
| |
| template <int LoadMode> |
| struct PacketConv<LoadMode, true> { |
| static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType run(const TensorEvaluator<ArgType, Device>& impl, Index index) { |
| const int SrcCoeffRatio = internal::type_casting_traits<SrcType, TargetType>::SrcCoeffRatio; |
| const int TgtCoeffRatio = internal::type_casting_traits<SrcType, TargetType>::TgtCoeffRatio; |
| PacketConverter<TensorEvaluator<ArgType, Device>, PacketSourceType, PacketReturnType, |
| SrcCoeffRatio, TgtCoeffRatio> converter(impl); |
| return converter.template packet<LoadMode>(index); |
| } |
| }; |
| |
| TensorEvaluator<ArgType, Device> m_impl; |
| }; |
| |
| } // end namespace Eigen |
| |
| #endif // EIGEN_CXX11_TENSOR_TENSOR_CONVERSION_H |