| // 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_CONCATENATION_H |
| #define EIGEN_CXX11_TENSOR_TENSOR_CONCATENATION_H |
| |
| #include "./InternalHeaderCheck.h" |
| |
| namespace Eigen { |
| |
| /** \class TensorConcatenationOp |
| * \ingroup CXX11_Tensor_Module |
| * |
| * \brief Tensor concatenation class. |
| * |
| * |
| */ |
| namespace internal { |
| template<typename Axis, typename LhsXprType, typename RhsXprType> |
| struct traits<TensorConcatenationOp<Axis, LhsXprType, RhsXprType> > |
| { |
| // Type promotion to handle the case where the types of the lhs and the rhs are different. |
| typedef typename promote_storage_type<typename LhsXprType::Scalar, |
| typename RhsXprType::Scalar>::ret Scalar; |
| typedef typename promote_storage_type<typename traits<LhsXprType>::StorageKind, |
| typename traits<RhsXprType>::StorageKind>::ret 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 int NumDimensions = traits<LhsXprType>::NumDimensions; |
| static constexpr int Layout = traits<LhsXprType>::Layout; |
| enum { Flags = 0 }; |
| typedef std::conditional_t<Pointer_type_promotion<typename LhsXprType::Scalar, Scalar>::val, |
| typename traits<LhsXprType>::PointerType, typename traits<RhsXprType>::PointerType> PointerType; |
| }; |
| |
| template<typename Axis, typename LhsXprType, typename RhsXprType> |
| struct eval<TensorConcatenationOp<Axis, LhsXprType, RhsXprType>, Eigen::Dense> |
| { |
| typedef const TensorConcatenationOp<Axis, LhsXprType, RhsXprType>& type; |
| }; |
| |
| template<typename Axis, typename LhsXprType, typename RhsXprType> |
| struct nested<TensorConcatenationOp<Axis, LhsXprType, RhsXprType>, 1, typename eval<TensorConcatenationOp<Axis, LhsXprType, RhsXprType> >::type> |
| { |
| typedef TensorConcatenationOp<Axis, LhsXprType, RhsXprType> type; |
| }; |
| |
| } // end namespace internal |
| |
| |
| template<typename Axis, typename LhsXprType, typename RhsXprType> |
| class TensorConcatenationOp : public TensorBase<TensorConcatenationOp<Axis, LhsXprType, RhsXprType>, WriteAccessors> |
| { |
| public: |
| typedef TensorBase<TensorConcatenationOp<Axis, LhsXprType, RhsXprType>, WriteAccessors> Base; |
| typedef typename internal::traits<TensorConcatenationOp>::Scalar Scalar; |
| typedef typename internal::traits<TensorConcatenationOp>::StorageKind StorageKind; |
| typedef typename internal::traits<TensorConcatenationOp>::Index Index; |
| typedef typename internal::nested<TensorConcatenationOp>::type Nested; |
| typedef typename internal::promote_storage_type<typename LhsXprType::CoeffReturnType, |
| typename RhsXprType::CoeffReturnType>::ret CoeffReturnType; |
| typedef typename NumTraits<Scalar>::Real RealScalar; |
| |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorConcatenationOp(const LhsXprType& lhs, const RhsXprType& rhs, Axis axis) |
| : m_lhs_xpr(lhs), m_rhs_xpr(rhs), m_axis(axis) {} |
| |
| EIGEN_DEVICE_FUNC |
| const internal::remove_all_t<typename LhsXprType::Nested>& |
| lhsExpression() const { return m_lhs_xpr; } |
| |
| EIGEN_DEVICE_FUNC |
| const internal::remove_all_t<typename RhsXprType::Nested>& |
| rhsExpression() const { return m_rhs_xpr; } |
| |
| EIGEN_DEVICE_FUNC const Axis& axis() const { return m_axis; } |
| |
| EIGEN_TENSOR_INHERIT_ASSIGNMENT_OPERATORS(TensorConcatenationOp) |
| protected: |
| typename LhsXprType::Nested m_lhs_xpr; |
| typename RhsXprType::Nested m_rhs_xpr; |
| const Axis m_axis; |
| }; |
| |
| |
| // Eval as rvalue |
| template<typename Axis, typename LeftArgType, typename RightArgType, typename Device> |
| struct TensorEvaluator<const TensorConcatenationOp<Axis, LeftArgType, RightArgType>, Device> |
| { |
| typedef TensorConcatenationOp<Axis, LeftArgType, RightArgType> XprType; |
| typedef typename XprType::Index Index; |
| static constexpr int NumDims = internal::array_size<typename TensorEvaluator<LeftArgType, Device>::Dimensions>::value; |
| static constexpr int RightNumDims = internal::array_size<typename TensorEvaluator<RightArgType, Device>::Dimensions>::value; |
| typedef DSizes<Index, NumDims> Dimensions; |
| 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; |
| static constexpr int Layout = TensorEvaluator<LeftArgType, Device>::Layout; |
| enum { |
| IsAligned = false, |
| PacketAccess = TensorEvaluator<LeftArgType, Device>::PacketAccess && |
| TensorEvaluator<RightArgType, Device>::PacketAccess, |
| BlockAccess = false, |
| PreferBlockAccess = TensorEvaluator<LeftArgType, Device>::PreferBlockAccess || |
| TensorEvaluator<RightArgType, Device>::PreferBlockAccess, |
| RawAccess = false |
| }; |
| |
| //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===// |
| typedef internal::TensorBlockNotImplemented TensorBlock; |
| //===--------------------------------------------------------------------===// |
| |
| EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device) |
| : m_leftImpl(op.lhsExpression(), device), m_rightImpl(op.rhsExpression(), device), m_axis(op.axis()) |
| { |
| EIGEN_STATIC_ASSERT((static_cast<int>(TensorEvaluator<LeftArgType, Device>::Layout) == static_cast<int>(TensorEvaluator<RightArgType, Device>::Layout) || NumDims == 1), YOU_MADE_A_PROGRAMMING_MISTAKE); |
| EIGEN_STATIC_ASSERT((NumDims == RightNumDims), YOU_MADE_A_PROGRAMMING_MISTAKE); |
| EIGEN_STATIC_ASSERT((NumDims > 0), YOU_MADE_A_PROGRAMMING_MISTAKE); |
| |
| eigen_assert(0 <= m_axis && m_axis < NumDims); |
| const Dimensions& lhs_dims = m_leftImpl.dimensions(); |
| const Dimensions& rhs_dims = m_rightImpl.dimensions(); |
| { |
| int i = 0; |
| for (; i < m_axis; ++i) { |
| eigen_assert(lhs_dims[i] > 0); |
| eigen_assert(lhs_dims[i] == rhs_dims[i]); |
| m_dimensions[i] = lhs_dims[i]; |
| } |
| eigen_assert(lhs_dims[i] > 0); // Now i == m_axis. |
| eigen_assert(rhs_dims[i] > 0); |
| m_dimensions[i] = lhs_dims[i] + rhs_dims[i]; |
| for (++i; i < NumDims; ++i) { |
| eigen_assert(lhs_dims[i] > 0); |
| eigen_assert(lhs_dims[i] == rhs_dims[i]); |
| m_dimensions[i] = lhs_dims[i]; |
| } |
| } |
| |
| if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { |
| m_leftStrides[0] = 1; |
| m_rightStrides[0] = 1; |
| m_outputStrides[0] = 1; |
| |
| for (int j = 1; j < NumDims; ++j) { |
| m_leftStrides[j] = m_leftStrides[j-1] * lhs_dims[j-1]; |
| m_rightStrides[j] = m_rightStrides[j-1] * rhs_dims[j-1]; |
| m_outputStrides[j] = m_outputStrides[j-1] * m_dimensions[j-1]; |
| } |
| } else { |
| m_leftStrides[NumDims - 1] = 1; |
| m_rightStrides[NumDims - 1] = 1; |
| m_outputStrides[NumDims - 1] = 1; |
| |
| for (int j = NumDims - 2; j >= 0; --j) { |
| m_leftStrides[j] = m_leftStrides[j+1] * lhs_dims[j+1]; |
| m_rightStrides[j] = m_rightStrides[j+1] * rhs_dims[j+1]; |
| m_outputStrides[j] = m_outputStrides[j+1] * m_dimensions[j+1]; |
| } |
| } |
| } |
| |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; } |
| |
| // TODO(phli): Add short-circuit memcpy evaluation if underlying data are linear? |
| EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType) |
| { |
| m_leftImpl.evalSubExprsIfNeeded(NULL); |
| m_rightImpl.evalSubExprsIfNeeded(NULL); |
| return true; |
| } |
| |
| EIGEN_STRONG_INLINE void cleanup() |
| { |
| m_leftImpl.cleanup(); |
| m_rightImpl.cleanup(); |
| } |
| |
| // TODO(phli): attempt to speed this up. The integer divisions and modulo are slow. |
| // See CL/76180724 comments for more ideas. |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const |
| { |
| // Collect dimension-wise indices (subs). |
| array<Index, NumDims> subs; |
| if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { |
| for (int i = NumDims - 1; i > 0; --i) { |
| subs[i] = index / m_outputStrides[i]; |
| index -= subs[i] * m_outputStrides[i]; |
| } |
| subs[0] = index; |
| } else { |
| for (int i = 0; i < NumDims - 1; ++i) { |
| subs[i] = index / m_outputStrides[i]; |
| index -= subs[i] * m_outputStrides[i]; |
| } |
| subs[NumDims - 1] = index; |
| } |
| |
| const Dimensions& left_dims = m_leftImpl.dimensions(); |
| if (subs[m_axis] < left_dims[m_axis]) { |
| Index left_index; |
| if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { |
| left_index = subs[0]; |
| EIGEN_UNROLL_LOOP |
| for (int i = 1; i < NumDims; ++i) { |
| left_index += (subs[i] % left_dims[i]) * m_leftStrides[i]; |
| } |
| } else { |
| left_index = subs[NumDims - 1]; |
| EIGEN_UNROLL_LOOP |
| for (int i = NumDims - 2; i >= 0; --i) { |
| left_index += (subs[i] % left_dims[i]) * m_leftStrides[i]; |
| } |
| } |
| return m_leftImpl.coeff(left_index); |
| } else { |
| subs[m_axis] -= left_dims[m_axis]; |
| const Dimensions& right_dims = m_rightImpl.dimensions(); |
| Index right_index; |
| if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { |
| right_index = subs[0]; |
| EIGEN_UNROLL_LOOP |
| for (int i = 1; i < NumDims; ++i) { |
| right_index += (subs[i] % right_dims[i]) * m_rightStrides[i]; |
| } |
| } else { |
| right_index = subs[NumDims - 1]; |
| EIGEN_UNROLL_LOOP |
| for (int i = NumDims - 2; i >= 0; --i) { |
| right_index += (subs[i] % right_dims[i]) * m_rightStrides[i]; |
| } |
| } |
| return m_rightImpl.coeff(right_index); |
| } |
| } |
| |
| // TODO(phli): Add a real vectorization. |
| 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 CoeffReturnType values[packetSize]; |
| EIGEN_UNROLL_LOOP |
| 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 TensorOpCost |
| costPerCoeff(bool vectorized) const { |
| const double compute_cost = NumDims * (2 * TensorOpCost::AddCost<Index>() + |
| 2 * TensorOpCost::MulCost<Index>() + |
| TensorOpCost::DivCost<Index>() + |
| TensorOpCost::ModCost<Index>()); |
| const double lhs_size = m_leftImpl.dimensions().TotalSize(); |
| const double rhs_size = m_rightImpl.dimensions().TotalSize(); |
| return (lhs_size / (lhs_size + rhs_size)) * |
| m_leftImpl.costPerCoeff(vectorized) + |
| (rhs_size / (lhs_size + rhs_size)) * |
| m_rightImpl.costPerCoeff(vectorized) + |
| TensorOpCost(0, 0, compute_cost); |
| } |
| |
| 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 &cgh) const { |
| m_leftImpl.bind(cgh); |
| m_rightImpl.bind(cgh); |
| } |
| #endif |
| |
| protected: |
| Dimensions m_dimensions; |
| array<Index, NumDims> m_outputStrides; |
| array<Index, NumDims> m_leftStrides; |
| array<Index, NumDims> m_rightStrides; |
| TensorEvaluator<LeftArgType, Device> m_leftImpl; |
| TensorEvaluator<RightArgType, Device> m_rightImpl; |
| const Axis m_axis; |
| }; |
| |
| // Eval as lvalue |
| template<typename Axis, typename LeftArgType, typename RightArgType, typename Device> |
| struct TensorEvaluator<TensorConcatenationOp<Axis, LeftArgType, RightArgType>, Device> |
| : public TensorEvaluator<const TensorConcatenationOp<Axis, LeftArgType, RightArgType>, Device> |
| { |
| typedef TensorEvaluator<const TensorConcatenationOp<Axis, LeftArgType, RightArgType>, Device> Base; |
| typedef TensorConcatenationOp<Axis, LeftArgType, RightArgType> XprType; |
| typedef typename Base::Dimensions Dimensions; |
| static constexpr int Layout = TensorEvaluator<LeftArgType, Device>::Layout; |
| enum { |
| IsAligned = false, |
| PacketAccess = TensorEvaluator<LeftArgType, Device>::PacketAccess && |
| TensorEvaluator<RightArgType, Device>::PacketAccess, |
| BlockAccess = false, |
| PreferBlockAccess = TensorEvaluator<LeftArgType, Device>::PreferBlockAccess || |
| TensorEvaluator<RightArgType, Device>::PreferBlockAccess, |
| RawAccess = false |
| }; |
| |
| //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===// |
| typedef internal::TensorBlockNotImplemented TensorBlock; |
| //===--------------------------------------------------------------------===// |
| |
| EIGEN_STRONG_INLINE TensorEvaluator(XprType& op, const Device& device) |
| : Base(op, device) |
| { |
| EIGEN_STATIC_ASSERT((static_cast<int>(Layout) == static_cast<int>(ColMajor)), YOU_MADE_A_PROGRAMMING_MISTAKE); |
| } |
| |
| typedef typename XprType::Index Index; |
| typedef typename XprType::Scalar Scalar; |
| typedef typename XprType::CoeffReturnType CoeffReturnType; |
| typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType; |
| |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType& coeffRef(Index index) |
| { |
| // Collect dimension-wise indices (subs). |
| array<Index, Base::NumDims> subs; |
| for (int i = Base::NumDims - 1; i > 0; --i) { |
| subs[i] = index / this->m_outputStrides[i]; |
| index -= subs[i] * this->m_outputStrides[i]; |
| } |
| subs[0] = index; |
| |
| const Dimensions& left_dims = this->m_leftImpl.dimensions(); |
| if (subs[this->m_axis] < left_dims[this->m_axis]) { |
| Index left_index = subs[0]; |
| for (int i = 1; i < Base::NumDims; ++i) { |
| left_index += (subs[i] % left_dims[i]) * this->m_leftStrides[i]; |
| } |
| return this->m_leftImpl.coeffRef(left_index); |
| } else { |
| subs[this->m_axis] -= left_dims[this->m_axis]; |
| const Dimensions& right_dims = this->m_rightImpl.dimensions(); |
| Index right_index = subs[0]; |
| for (int i = 1; i < Base::NumDims; ++i) { |
| right_index += (subs[i] % right_dims[i]) * this->m_rightStrides[i]; |
| } |
| return this->m_rightImpl.coeffRef(right_index); |
| } |
| } |
| |
| template <int StoreMode> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE |
| void writePacket(Index index, const PacketReturnType& x) |
| { |
| const int packetSize = PacketType<CoeffReturnType, Device>::size; |
| EIGEN_STATIC_ASSERT((packetSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE) |
| eigen_assert(index + packetSize - 1 < this->dimensions().TotalSize()); |
| |
| EIGEN_ALIGN_MAX CoeffReturnType values[packetSize]; |
| internal::pstore<CoeffReturnType, PacketReturnType>(values, x); |
| for (int i = 0; i < packetSize; ++i) { |
| coeffRef(index+i) = values[i]; |
| } |
| } |
| }; |
| |
| } // end namespace Eigen |
| |
| #endif // EIGEN_CXX11_TENSOR_TENSOR_CONCATENATION_H |
