text: // 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_GENERATOR_H
  #define EIGEN_CXX11_TENSOR_TENSOR_GENERATOR_H
  
  namespace Eigen {
  
  /** \class TensorGeneratorOp
    * \ingroup CXX11_Tensor_Module
    *
    * \brief Tensor generator class.
    *
    *
    */
  namespace internal {
  template<typename Generator, typename XprType>
  struct traits<TensorGeneratorOp<Generator, XprType> > : public traits<XprType>
  {
    typedef typename XprType::Scalar Scalar;
    typedef traits<XprType> XprTraits;
    typedef typename XprTraits::StorageKind StorageKind;
    typedef typename XprTraits::Index Index;
    typedef typename XprType::Nested Nested;
    typedef typename remove_reference<Nested>::type _Nested;
    static const int NumDimensions = XprTraits::NumDimensions;
    static const int Layout = XprTraits::Layout;
    typedef typename XprTraits::PointerType PointerType;
  };
  
  template<typename Generator, typename XprType>
  struct eval<TensorGeneratorOp<Generator, XprType>, Eigen::Dense>
  {
    typedef const TensorGeneratorOp<Generator, XprType>& type;
  };
  
  template<typename Generator, typename XprType>
  struct nested<TensorGeneratorOp<Generator, XprType>, 1, typename eval<TensorGeneratorOp<Generator, XprType> >::type>
  {
    typedef TensorGeneratorOp<Generator, XprType> type;
  };
  
  }  // end namespace internal
  
  
  
  template<typename Generator, typename XprType>
  class TensorGeneratorOp : public TensorBase<TensorGeneratorOp<Generator, XprType>, ReadOnlyAccessors>
  {
    public:
    typedef typename Eigen::internal::traits<TensorGeneratorOp>::Scalar Scalar;
    typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
    typedef typename XprType::CoeffReturnType CoeffReturnType;
    typedef typename Eigen::internal::nested<TensorGeneratorOp>::type Nested;
    typedef typename Eigen::internal::traits<TensorGeneratorOp>::StorageKind StorageKind;
    typedef typename Eigen::internal::traits<TensorGeneratorOp>::Index Index;
  
    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorGeneratorOp(const XprType& expr, const Generator& generator)
        : m_xpr(expr), m_generator(generator) {}
  
      EIGEN_DEVICE_FUNC
      const Generator& generator() const { return m_generator; }
  
      EIGEN_DEVICE_FUNC
      const typename internal::remove_all<typename XprType::Nested>::type&
      expression() const { return m_xpr; }
  
    protected:
      typename XprType::Nested m_xpr;
      const Generator m_generator;
  };
  
  
  // Eval as rvalue
  template<typename Generator, typename ArgType, typename Device>
  struct TensorEvaluator<const TensorGeneratorOp<Generator, ArgType>, Device>
  {
    typedef TensorGeneratorOp<Generator, ArgType> XprType;
    typedef typename XprType::Index Index;
    typedef typename TensorEvaluator<ArgType, Device>::Dimensions Dimensions;
    static const int NumDims = internal::array_size<Dimensions>::value;
    typedef typename XprType::Scalar Scalar;
    typedef typename XprType::CoeffReturnType CoeffReturnType;
    typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
    typedef StorageMemory<CoeffReturnType, Device> Storage;
    typedef typename Storage::Type EvaluatorPointerType;
    enum {
      IsAligned         = false,
      PacketAccess      = (PacketType<CoeffReturnType, Device>::size > 1),
      BlockAccess       = true,
      PreferBlockAccess = true,
      Layout            = TensorEvaluator<ArgType, Device>::Layout,
      CoordAccess       = false,  // to be implemented
      RawAccess         = false
    };
  
    typedef internal::TensorIntDivisor<Index> IndexDivisor;
  
    typedef internal::TensorBlock<CoeffReturnType, Index, NumDims, Layout>
        TensorBlock;
  
    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
        :  m_device(device), m_generator(op.generator())
    {
      TensorEvaluator<ArgType, Device> argImpl(op.expression(), device);
      m_dimensions = argImpl.dimensions();
  
      if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
        m_strides[0] = 1;
        EIGEN_UNROLL_LOOP
        for (int i = 1; i < NumDims; ++i) {
          m_strides[i] = m_strides[i - 1] * m_dimensions[i - 1];
          if (m_strides[i] != 0) m_fast_strides[i] = IndexDivisor(m_strides[i]);
        }
      } else {
        m_strides[NumDims - 1] = 1;
        EIGEN_UNROLL_LOOP
        for (int i = NumDims - 2; i >= 0; --i) {
          m_strides[i] = m_strides[i + 1] * m_dimensions[i + 1];
          if (m_strides[i] != 0) m_fast_strides[i] = IndexDivisor(m_strides[i]);
        }
      }
    }
  
    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
  
    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType /*data*/) {
      return true;
    }
    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() {
    }
  
    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
    {
      array<Index, NumDims> coords;
      extract_coordinates(index, coords);
      return m_generator(coords);
    }
  
    template<int LoadMode>
    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
    {
      const int packetSize = PacketType<CoeffReturnType, Device>::size;
      EIGEN_STATIC_ASSERT((packetSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE)
      eigen_assert(index+packetSize-1 < dimensions().TotalSize());
  
      EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[packetSize];
      for (int i = 0; i < packetSize; ++i) {
        values[i] = coeff(index+i);
      }
      PacketReturnType rslt = internal::pload<PacketReturnType>(values);
      return rslt;
    }
  
    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void getResourceRequirements(
        std::vector<internal::TensorOpResourceRequirements>* resources) const {
      Eigen::Index block_total_size_max = numext::maxi<Eigen::Index>(
          1, m_device.firstLevelCacheSize() / sizeof(Scalar));
      resources->push_back(internal::TensorOpResourceRequirements(
          internal::kSkewedInnerDims, block_total_size_max));
    }
  
    struct BlockIteratorState {
      Index stride;
      Index span;
      Index size;
      Index count;
    };
  
    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void block(
        TensorBlock* output_block) const {
      if (NumDims <= 0) return;
  
      static const bool is_col_major =
          static_cast<int>(Layout) == static_cast<int>(ColMajor);
  
      // Compute spatial coordinates for the first block element.
      array<Index, NumDims> coords;
      extract_coordinates(output_block->first_coeff_index(), coords);
      array<Index, NumDims> initial_coords = coords;
  
      CoeffReturnType* data = output_block->data();
      Index offset = 0;
  
      // Initialize output block iterator state. Dimension in this array are
      // always in inner_most -> outer_most order (col major layout).
      array<BlockIteratorState, NumDims> it;
      for (Index i = 0; i < NumDims; ++i) {
        const Index dim = is_col_major ? i : NumDims - 1 - i;
        it[i].size = output_block->block_sizes()[dim];
        it[i].stride = output_block->block_strides()[dim];
        it[i].span = it[i].stride * (it[i].size - 1);
        it[i].count = 0;
      }
      eigen_assert(it[0].stride == 1);
  
      while (it[NumDims - 1].count < it[NumDims - 1].size) {
        // Generate data for the inner-most dimension.
        for (Index i = 0; i < it[0].size; ++i) {
          *(data + offset + i) = m_generator(coords);
          coords[is_col_major ? 0 : NumDims - 1]++;
        }
        coords[is_col_major ? 0 : NumDims - 1] =
            initial_coords[is_col_major ? 0 : NumDims - 1];
  
        // For the 1d tensor we need to generate only one inner-most dimension.
        if (NumDims == 1) break;
  
        // Update offset.
        for (Index i = 1; i < NumDims; ++i) {
          if (++it[i].count < it[i].size) {
            offset += it[i].stride;
            coords[is_col_major ? i : NumDims - 1 - i]++;
            break;
          }
          if (i != NumDims - 1) it[i].count = 0;
          coords[is_col_major ? i : NumDims - 1 - i] =
              initial_coords[is_col_major ? i : NumDims - 1 - i];
          offset -= it[i].span;
        }
      }
    }
  
    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost
    costPerCoeff(bool) const {
      // TODO(rmlarsen): This is just a placeholder. Define interface to make
      // generators return their cost.
      return TensorOpCost(0, 0, TensorOpCost::AddCost<Scalar>() +
                                    TensorOpCost::MulCost<Scalar>());
    }
  
    EIGEN_DEVICE_FUNC EvaluatorPointerType  data() const { return NULL; }
  
  #ifdef EIGEN_USE_SYCL
    // binding placeholder accessors to a command group handler for SYCL
    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler&) const {}
  #endif
  
   protected:
    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
    void extract_coordinates(Index index, array<Index, NumDims>& coords) const {
      if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
        for (int i = NumDims - 1; i > 0; --i) {
          const Index idx = index / m_fast_strides[i];
          index -= idx * m_strides[i];
          coords[i] = idx;
        }
        coords[0] = index;
      } else {
        for (int i = 0; i < NumDims - 1; ++i) {
          const Index idx = index / m_fast_strides[i];
          index -= idx * m_strides[i];
          coords[i] = idx;
        }
        coords[NumDims-1] = index;
      }
    }
  
    const Device EIGEN_DEVICE_REF m_device;
    Dimensions m_dimensions;
    array<Index, NumDims> m_strides;
    array<IndexDivisor, NumDims> m_fast_strides;
    Generator m_generator;
  };
  
  } // end namespace Eigen
  
  #endif // EIGEN_CXX11_TENSOR_TENSOR_GENERATOR_H
  