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      1 // This file is part of Eigen, a lightweight C++ template library
      2 // for linear algebra.
      3 //
      4 // Copyright (C) 2014 Navdeep Jaitly <ndjaitly (at) google.com>
      5 //                    Benoit Steiner <benoit.steiner.goog (at) gmail.com>
      6 //
      7 // This Source Code Form is subject to the terms of the Mozilla
      8 // Public License v. 2.0. If a copy of the MPL was not distributed
      9 // with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
     10 
     11 #ifndef EIGEN_CXX11_TENSOR_TENSOR_REVERSE_H
     12 #define EIGEN_CXX11_TENSOR_TENSOR_REVERSE_H
     13 namespace Eigen {
     14 
     15 /** \class TensorReverse
     16   * \ingroup CXX11_Tensor_Module
     17   *
     18   * \brief Tensor reverse elements class.
     19   *
     20   */
     21 namespace internal {
     22 template<typename ReverseDimensions, typename XprType>
     23 struct traits<TensorReverseOp<ReverseDimensions,
     24                               XprType> > : public traits<XprType>
     25 {
     26   typedef typename XprType::Scalar Scalar;
     27   typedef traits<XprType> XprTraits;
     28   typedef typename XprTraits::StorageKind StorageKind;
     29   typedef typename XprTraits::Index Index;
     30   typedef typename XprType::Nested Nested;
     31   typedef typename remove_reference<Nested>::type _Nested;
     32   static const int NumDimensions = XprTraits::NumDimensions;
     33   static const int Layout = XprTraits::Layout;
     34 };
     35 
     36 template<typename ReverseDimensions, typename XprType>
     37 struct eval<TensorReverseOp<ReverseDimensions, XprType>, Eigen::Dense>
     38 {
     39   typedef const TensorReverseOp<ReverseDimensions, XprType>& type;
     40 };
     41 
     42 template<typename ReverseDimensions, typename XprType>
     43 struct nested<TensorReverseOp<ReverseDimensions, XprType>, 1,
     44             typename eval<TensorReverseOp<ReverseDimensions, XprType> >::type>
     45 {
     46   typedef TensorReverseOp<ReverseDimensions, XprType> type;
     47 };
     48 
     49 }  // end namespace internal
     50 
     51 template<typename ReverseDimensions, typename XprType>
     52 class TensorReverseOp : public TensorBase<TensorReverseOp<ReverseDimensions,
     53                                           XprType>, WriteAccessors>
     54 {
     55   public:
     56   typedef typename Eigen::internal::traits<TensorReverseOp>::Scalar Scalar;
     57   typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
     58   typedef typename XprType::CoeffReturnType CoeffReturnType;
     59   typedef typename Eigen::internal::nested<TensorReverseOp>::type Nested;
     60   typedef typename Eigen::internal::traits<TensorReverseOp>::StorageKind
     61                                                                     StorageKind;
     62   typedef typename Eigen::internal::traits<TensorReverseOp>::Index Index;
     63 
     64   EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorReverseOp(
     65       const XprType& expr, const ReverseDimensions& reverse_dims)
     66       : m_xpr(expr), m_reverse_dims(reverse_dims) { }
     67 
     68     EIGEN_DEVICE_FUNC
     69     const ReverseDimensions& reverse() const { return m_reverse_dims; }
     70 
     71     EIGEN_DEVICE_FUNC
     72     const typename internal::remove_all<typename XprType::Nested>::type&
     73     expression() const { return m_xpr; }
     74 
     75     EIGEN_DEVICE_FUNC
     76     EIGEN_STRONG_INLINE TensorReverseOp& operator = (const TensorReverseOp& other)
     77     {
     78       typedef TensorAssignOp<TensorReverseOp, const TensorReverseOp> Assign;
     79       Assign assign(*this, other);
     80       internal::TensorExecutor<const Assign, DefaultDevice>::run(assign, DefaultDevice());
     81       return *this;
     82     }
     83 
     84     template<typename OtherDerived>
     85     EIGEN_DEVICE_FUNC
     86     EIGEN_STRONG_INLINE TensorReverseOp& operator = (const OtherDerived& other)
     87     {
     88       typedef TensorAssignOp<TensorReverseOp, const OtherDerived> Assign;
     89       Assign assign(*this, other);
     90       internal::TensorExecutor<const Assign, DefaultDevice>::run(assign, DefaultDevice());
     91       return *this;
     92     }
     93 
     94   protected:
     95     typename XprType::Nested m_xpr;
     96     const ReverseDimensions m_reverse_dims;
     97 };
     98 
     99 // Eval as rvalue
    100 template<typename ReverseDimensions, typename ArgType, typename Device>
    101 struct TensorEvaluator<const TensorReverseOp<ReverseDimensions, ArgType>, Device>
    102 {
    103   typedef TensorReverseOp<ReverseDimensions, ArgType> XprType;
    104   typedef typename XprType::Index Index;
    105   static const int NumDims = internal::array_size<ReverseDimensions>::value;
    106   typedef DSizes<Index, NumDims> Dimensions;
    107   typedef typename XprType::Scalar Scalar;
    108   typedef typename XprType::CoeffReturnType CoeffReturnType;
    109   typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
    110   static const int PacketSize = internal::unpacket_traits<PacketReturnType>::size;
    111 
    112   enum {
    113     IsAligned = false,
    114     PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess,
    115     Layout = TensorEvaluator<ArgType, Device>::Layout,
    116     CoordAccess = false,  // to be implemented
    117     RawAccess = false
    118   };
    119 
    120   EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op,
    121                                                         const Device& device)
    122       : m_impl(op.expression(), device), m_reverse(op.reverse())
    123   {
    124     // Reversing a scalar isn't supported yet. It would be a no-op anyway.
    125     EIGEN_STATIC_ASSERT((NumDims > 0), YOU_MADE_A_PROGRAMMING_MISTAKE);
    126 
    127     // Compute strides
    128     m_dimensions = m_impl.dimensions();
    129     if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
    130       m_strides[0] = 1;
    131       for (int i = 1; i < NumDims; ++i) {
    132         m_strides[i] = m_strides[i-1] * m_dimensions[i-1];
    133       }
    134     } else {
    135       m_strides[NumDims-1] = 1;
    136       for (int i = NumDims - 2; i >= 0; --i) {
    137         m_strides[i] = m_strides[i+1] * m_dimensions[i+1];
    138       }
    139     }
    140   }
    141 
    142   EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
    143   const Dimensions& dimensions() const { return m_dimensions; }
    144 
    145   EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(Scalar*) {
    146     m_impl.evalSubExprsIfNeeded(NULL);
    147     return true;
    148   }
    149   EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() {
    150     m_impl.cleanup();
    151   }
    152 
    153   EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index reverseIndex(
    154       Index index) const {
    155     eigen_assert(index < dimensions().TotalSize());
    156     Index inputIndex = 0;
    157     if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
    158       for (int i = NumDims - 1; i > 0; --i) {
    159         Index idx = index / m_strides[i];
    160         index -= idx * m_strides[i];
    161         if (m_reverse[i]) {
    162           idx = m_dimensions[i] - idx - 1;
    163         }
    164         inputIndex += idx * m_strides[i] ;
    165       }
    166       if (m_reverse[0]) {
    167         inputIndex += (m_dimensions[0] - index - 1);
    168       } else {
    169         inputIndex += index;
    170       }
    171     } else {
    172       for (int i = 0; i < NumDims - 1; ++i) {
    173         Index idx = index / m_strides[i];
    174         index -= idx * m_strides[i];
    175         if (m_reverse[i]) {
    176           idx = m_dimensions[i] - idx - 1;
    177         }
    178         inputIndex += idx * m_strides[i] ;
    179       }
    180       if (m_reverse[NumDims-1]) {
    181         inputIndex += (m_dimensions[NumDims-1] - index - 1);
    182       } else {
    183         inputIndex += index;
    184       }
    185     }
    186     return inputIndex;
    187   }
    188 
    189   EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(
    190       Index index) const  {
    191     return m_impl.coeff(reverseIndex(index));
    192   }
    193 
    194   template<int LoadMode>
    195   EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
    196   PacketReturnType packet(Index index) const
    197   {
    198     EIGEN_STATIC_ASSERT((PacketSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE)
    199     eigen_assert(index+PacketSize-1 < dimensions().TotalSize());
    200 
    201     // TODO(ndjaitly): write a better packing routine that uses
    202     // local structure.
    203     EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type
    204                                                             values[PacketSize];
    205     for (int i = 0; i < PacketSize; ++i) {
    206       values[i] = coeff(index+i);
    207     }
    208     PacketReturnType rslt = internal::pload<PacketReturnType>(values);
    209     return rslt;
    210   }
    211 
    212   EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const {
    213     double compute_cost = NumDims * (2 * TensorOpCost::AddCost<Index>() +
    214                                      2 * TensorOpCost::MulCost<Index>() +
    215                                      TensorOpCost::DivCost<Index>());
    216     for (int i = 0; i < NumDims; ++i) {
    217       if (m_reverse[i]) {
    218         compute_cost += 2 * TensorOpCost::AddCost<Index>();
    219       }
    220     }
    221     return m_impl.costPerCoeff(vectorized) +
    222            TensorOpCost(0, 0, compute_cost, false /* vectorized */, PacketSize);
    223   }
    224 
    225   EIGEN_DEVICE_FUNC Scalar* data() const { return NULL; }
    226 
    227  protected:
    228   Dimensions m_dimensions;
    229   array<Index, NumDims> m_strides;
    230   TensorEvaluator<ArgType, Device> m_impl;
    231   ReverseDimensions m_reverse;
    232 };
    233 
    234 // Eval as lvalue
    235 
    236 template <typename ReverseDimensions, typename ArgType, typename Device>
    237 struct TensorEvaluator<TensorReverseOp<ReverseDimensions, ArgType>, Device>
    238     : public TensorEvaluator<const TensorReverseOp<ReverseDimensions, ArgType>,
    239                              Device> {
    240   typedef TensorEvaluator<const TensorReverseOp<ReverseDimensions, ArgType>,
    241                           Device> Base;
    242   typedef TensorReverseOp<ReverseDimensions, ArgType> XprType;
    243   typedef typename XprType::Index Index;
    244   static const int NumDims = internal::array_size<ReverseDimensions>::value;
    245   typedef DSizes<Index, NumDims> Dimensions;
    246 
    247   enum {
    248     IsAligned = false,
    249     PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess,
    250     Layout = TensorEvaluator<ArgType, Device>::Layout,
    251     CoordAccess = false,  // to be implemented
    252     RawAccess = false
    253   };
    254   EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op,
    255                                                         const Device& device)
    256       : Base(op, device) {}
    257 
    258   typedef typename XprType::Scalar Scalar;
    259   typedef typename XprType::CoeffReturnType CoeffReturnType;
    260   typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
    261   static const int PacketSize = internal::unpacket_traits<PacketReturnType>::size;
    262 
    263   EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
    264   const Dimensions& dimensions() const { return this->m_dimensions; }
    265 
    266   EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& coeffRef(Index index) {
    267     return this->m_impl.coeffRef(this->reverseIndex(index));
    268   }
    269 
    270   template <int StoreMode> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
    271   void writePacket(Index index, const PacketReturnType& x) {
    272     EIGEN_STATIC_ASSERT((PacketSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE)
    273     eigen_assert(index+PacketSize-1 < dimensions().TotalSize());
    274 
    275     // This code is pilfered from TensorMorphing.h
    276     EIGEN_ALIGN_MAX CoeffReturnType values[PacketSize];
    277     internal::pstore<CoeffReturnType, PacketReturnType>(values, x);
    278     for (int i = 0; i < PacketSize; ++i) {
    279       this->coeffRef(index+i) = values[i];
    280     }
    281   }
    282 
    283 };
    284 
    285 
    286 }  // end namespace Eigen
    287 
    288 #endif // EIGEN_CXX11_TENSOR_TENSOR_REVERSE_H
    289