TensorAssign.h

// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2014 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_ASSIGN_H
#define EIGEN_CXX11_TENSOR_TENSOR_ASSIGN_H
 
// IWYU pragma: private
#include "./InternalHeaderCheck.h"
 
namespace Eigen {
 
namespace internal {
template <typename LhsXprType, typename RhsXprType>
struct traits<TensorAssignOp<LhsXprType, RhsXprType> > {
  typedef typename LhsXprType::Scalar Scalar;
  typedef typename traits<LhsXprType>::StorageKind StorageKind;
  typedef
      typename promote_index_type<typename traits<LhsXprType>::Index, typename traits<RhsXprType>::Index>::type Index;
  typedef typename LhsXprType::Nested LhsNested;
  typedef typename RhsXprType::Nested RhsNested;
  typedef std::remove_reference_t<LhsNested> LhsNested_;
  typedef std::remove_reference_t<RhsNested> RhsNested_;
  static constexpr std::size_t NumDimensions = internal::traits<LhsXprType>::NumDimensions;
  static constexpr int Layout = internal::traits<LhsXprType>::Layout;
  typedef typename traits<LhsXprType>::PointerType PointerType;
 
  enum { Flags = 0 };
};
 
template <typename LhsXprType, typename RhsXprType>
struct eval<TensorAssignOp<LhsXprType, RhsXprType>, Eigen::Dense> {
  typedef const TensorAssignOp<LhsXprType, RhsXprType>& type;
};
 
template <typename LhsXprType, typename RhsXprType>
struct nested<TensorAssignOp<LhsXprType, RhsXprType>, 1, typename eval<TensorAssignOp<LhsXprType, RhsXprType> >::type> {
  typedef TensorAssignOp<LhsXprType, RhsXprType> type;
};
 
}  // end namespace internal

template <typename LhsXprType, typename RhsXprType>
class TensorAssignOp : public TensorBase<TensorAssignOp<LhsXprType, RhsXprType> > {
 public:
  typedef typename Eigen::internal::traits<TensorAssignOp>::Scalar Scalar;
  typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
  typedef typename LhsXprType::CoeffReturnType CoeffReturnType;
  typedef typename Eigen::internal::nested<TensorAssignOp>::type Nested;
  typedef typename Eigen::internal::traits<TensorAssignOp>::StorageKind StorageKind;
  typedef typename Eigen::internal::traits<TensorAssignOp>::Index Index;
 
  static constexpr int NumDims = Eigen::internal::traits<TensorAssignOp>::NumDimensions;
 
  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorAssignOp(LhsXprType& lhs, const RhsXprType& rhs)
      : m_lhs_xpr(lhs), m_rhs_xpr(rhs) {}

  EIGEN_DEVICE_FUNC internal::remove_all_t<typename LhsXprType::Nested>& lhsExpression() const {
    return *((internal::remove_all_t<typename LhsXprType::Nested>*)&m_lhs_xpr);
  }
 
  EIGEN_DEVICE_FUNC const internal::remove_all_t<typename RhsXprType::Nested>& rhsExpression() const {
    return m_rhs_xpr;
  }
 
 protected:
  internal::remove_all_t<typename LhsXprType::Nested>& m_lhs_xpr;
  const internal::remove_all_t<typename RhsXprType::Nested>& m_rhs_xpr;
};
 
template <typename LeftArgType, typename RightArgType, typename Device>
struct TensorEvaluator<const TensorAssignOp<LeftArgType, RightArgType>, Device> {
  typedef TensorAssignOp<LeftArgType, RightArgType> XprType;
  typedef typename XprType::Index Index;
  typedef typename XprType::Scalar Scalar;
  typedef typename XprType::CoeffReturnType CoeffReturnType;
  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
  typedef typename TensorEvaluator<RightArgType, Device>::Dimensions Dimensions;
  typedef StorageMemory<CoeffReturnType, Device> Storage;
  typedef typename Storage::Type EvaluatorPointerType;
 
  static constexpr int PacketSize = PacketType<CoeffReturnType, Device>::size;
  static constexpr int NumDims = XprType::NumDims;
  static constexpr int Layout = TensorEvaluator<LeftArgType, Device>::Layout;
 
  enum {
    IsAligned =
        int(TensorEvaluator<LeftArgType, Device>::IsAligned) & int(TensorEvaluator<RightArgType, Device>::IsAligned),
    PacketAccess = int(TensorEvaluator<LeftArgType, Device>::PacketAccess) &
                   int(TensorEvaluator<RightArgType, Device>::PacketAccess),
    BlockAccess = int(TensorEvaluator<LeftArgType, Device>::BlockAccess) &
                  int(TensorEvaluator<RightArgType, Device>::BlockAccess),
    PreferBlockAccess = int(TensorEvaluator<LeftArgType, Device>::PreferBlockAccess) |
                        int(TensorEvaluator<RightArgType, Device>::PreferBlockAccess),
    RawAccess = TensorEvaluator<LeftArgType, Device>::RawAccess
  };
 
  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
  typedef internal::TensorBlockDescriptor<NumDims, Index> TensorBlockDesc;
  typedef internal::TensorBlockScratchAllocator<Device> TensorBlockScratch;
 
  typedef typename TensorEvaluator<const RightArgType, Device>::TensorBlock RightTensorBlock;
  //===--------------------------------------------------------------------===//
 
  TensorEvaluator(const XprType& op, const Device& device)
      : m_leftImpl(op.lhsExpression(), device), m_rightImpl(op.rhsExpression(), device) {
    EIGEN_STATIC_ASSERT((static_cast<int>(TensorEvaluator<LeftArgType, Device>::Layout) ==
                         static_cast<int>(TensorEvaluator<RightArgType, Device>::Layout)),
                        YOU_MADE_A_PROGRAMMING_MISTAKE);
  }
 
  EIGEN_DEVICE_FUNC const Dimensions& dimensions() const {
    // The dimensions of the lhs and the rhs tensors should be equal to prevent
    // overflows and ensure the result is fully initialized.
    // TODO: use left impl instead if right impl dimensions are known at compile time.
    return m_rightImpl.dimensions();
  }
 
  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType) {
    eigen_assert(dimensions_match(m_leftImpl.dimensions(), m_rightImpl.dimensions()));
    m_leftImpl.evalSubExprsIfNeeded(NULL);
    // If the lhs provides raw access to its storage area (i.e. if m_leftImpl.data() returns a non
    // null value), attempt to evaluate the rhs expression in place. Returns true iff in place
    // evaluation isn't supported and the caller still needs to manually assign the values generated
    // by the rhs to the lhs.
    return m_rightImpl.evalSubExprsIfNeeded(m_leftImpl.data());
  }
 
#ifdef EIGEN_USE_THREADS
  template <typename EvalSubExprsCallback>
  EIGEN_STRONG_INLINE void evalSubExprsIfNeededAsync(EvaluatorPointerType, EvalSubExprsCallback done) {
    m_leftImpl.evalSubExprsIfNeededAsync(nullptr, [this, done](bool) {
      m_rightImpl.evalSubExprsIfNeededAsync(m_leftImpl.data(), [done](bool need_assign) { done(need_assign); });
    });
  }
#endif  // EIGEN_USE_THREADS
 
  EIGEN_STRONG_INLINE void cleanup() {
    m_leftImpl.cleanup();
    m_rightImpl.cleanup();
  }
 
  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalScalar(Index i) const {
    m_leftImpl.coeffRef(i) = m_rightImpl.coeff(i);
  }
  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalPacket(Index i) const {
    const int LhsStoreMode = TensorEvaluator<LeftArgType, Device>::IsAligned ? Aligned : Unaligned;
    const int RhsLoadMode = TensorEvaluator<RightArgType, Device>::IsAligned ? Aligned : Unaligned;
    m_leftImpl.template writePacket<LhsStoreMode>(i, m_rightImpl.template packet<RhsLoadMode>(i));
  }
  EIGEN_DEVICE_FUNC CoeffReturnType coeff(Index index) const { return m_leftImpl.coeff(index); }
  template <int LoadMode>
  EIGEN_DEVICE_FUNC PacketReturnType packet(Index index) const {
    return m_leftImpl.template packet<LoadMode>(index);
  }
 
  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const {
    // We assume that evalPacket or evalScalar is called to perform the
    // assignment and account for the cost of the write here, but reduce left
    // cost by one load because we are using m_leftImpl.coeffRef.
    TensorOpCost left = m_leftImpl.costPerCoeff(vectorized);
    return m_rightImpl.costPerCoeff(vectorized) +
           TensorOpCost(numext::maxi(0.0, left.bytes_loaded() - sizeof(CoeffReturnType)), left.bytes_stored(),
                        left.compute_cycles()) +
           TensorOpCost(0, sizeof(CoeffReturnType), 0, vectorized, PacketSize);
  }
 
  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE internal::TensorBlockResourceRequirements getResourceRequirements() const {
    return internal::TensorBlockResourceRequirements::merge(m_leftImpl.getResourceRequirements(),
                                                            m_rightImpl.getResourceRequirements());
  }
 
  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalBlock(TensorBlockDesc& desc, TensorBlockScratch& scratch) {
    if (TensorEvaluator<LeftArgType, Device>::RawAccess && m_leftImpl.data() != NULL) {
      // If destination has raw data access, we pass it as a potential
      // destination for a block descriptor evaluation.
      desc.template AddDestinationBuffer<Layout>(
          /*dst_base=*/m_leftImpl.data() + desc.offset(),
          /*dst_strides=*/internal::strides<Layout>(m_leftImpl.dimensions()));
    }
 
    RightTensorBlock block = m_rightImpl.block(desc, scratch, /*root_of_expr_ast=*/true);
    // If block was evaluated into a destination, there is no need to do assignment.
    if (block.kind() != internal::TensorBlockKind::kMaterializedInOutput) {
      m_leftImpl.writeBlock(desc, block);
    }
    block.cleanup();
  }
 
  EIGEN_DEVICE_FUNC EvaluatorPointerType data() const { return m_leftImpl.data(); }
 
 private:
  TensorEvaluator<LeftArgType, Device> m_leftImpl;
  TensorEvaluator<RightArgType, Device> m_rightImpl;
};
 
}  // namespace Eigen
 
#endif  // EIGEN_CXX11_TENSOR_TENSOR_ASSIGN_H