| // 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_STRIDING_H |
| #define EIGEN_CXX11_TENSOR_TENSOR_STRIDING_H |
| |
| #include "./InternalHeaderCheck.h" |
| |
| namespace Eigen { |
| |
| /** \class TensorStriding |
| * \ingroup CXX11_Tensor_Module |
| * |
| * \brief Tensor striding class. |
| * |
| * |
| */ |
| namespace internal { |
| template<typename Strides, typename XprType> |
| struct traits<TensorStridingOp<Strides, 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 std::remove_reference_t<Nested> Nested_; |
| static constexpr int NumDimensions = XprTraits::NumDimensions; |
| static constexpr int Layout = XprTraits::Layout; |
| typedef typename XprTraits::PointerType PointerType; |
| }; |
| |
| template<typename Strides, typename XprType> |
| struct eval<TensorStridingOp<Strides, XprType>, Eigen::Dense> |
| { |
| typedef const TensorStridingOp<Strides, XprType>EIGEN_DEVICE_REF type; |
| }; |
| |
| template<typename Strides, typename XprType> |
| struct nested<TensorStridingOp<Strides, XprType>, 1, typename eval<TensorStridingOp<Strides, XprType> >::type> |
| { |
| typedef TensorStridingOp<Strides, XprType> type; |
| }; |
| |
| } // end namespace internal |
| |
| |
| |
| template<typename Strides, typename XprType> |
| class TensorStridingOp : public TensorBase<TensorStridingOp<Strides, XprType> > |
| { |
| public: |
| typedef TensorBase<TensorStridingOp<Strides, XprType> > Base; |
| typedef typename Eigen::internal::traits<TensorStridingOp>::Scalar Scalar; |
| typedef typename Eigen::NumTraits<Scalar>::Real RealScalar; |
| typedef typename XprType::CoeffReturnType CoeffReturnType; |
| typedef typename Eigen::internal::nested<TensorStridingOp>::type Nested; |
| typedef typename Eigen::internal::traits<TensorStridingOp>::StorageKind StorageKind; |
| typedef typename Eigen::internal::traits<TensorStridingOp>::Index Index; |
| |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorStridingOp(const XprType& expr, const Strides& dims) |
| : m_xpr(expr), m_dims(dims) {} |
| |
| EIGEN_DEVICE_FUNC |
| const Strides& strides() const { return m_dims; } |
| |
| EIGEN_DEVICE_FUNC |
| const internal::remove_all_t<typename XprType::Nested>& |
| expression() const { return m_xpr; } |
| |
| EIGEN_TENSOR_INHERIT_ASSIGNMENT_OPERATORS(TensorStridingOp) |
| |
| protected: |
| typename XprType::Nested m_xpr; |
| const Strides m_dims; |
| }; |
| |
| |
| // Eval as rvalue |
| template<typename Strides, typename ArgType, typename Device> |
| struct TensorEvaluator<const TensorStridingOp<Strides, ArgType>, Device> |
| { |
| typedef TensorStridingOp<Strides, ArgType> XprType; |
| typedef typename XprType::Index Index; |
| static constexpr int NumDims = internal::array_size<typename TensorEvaluator<ArgType, 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; |
| static constexpr int PacketSize = PacketType<CoeffReturnType, Device>::size; |
| typedef StorageMemory<CoeffReturnType, Device> Storage; |
| typedef typename Storage::Type EvaluatorPointerType; |
| |
| static constexpr int Layout = TensorEvaluator<ArgType, Device>::Layout; |
| enum { |
| IsAligned = /*TensorEvaluator<ArgType, Device>::IsAligned*/false, |
| PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess, |
| BlockAccess = false, |
| PreferBlockAccess = TensorEvaluator<ArgType, Device>::PreferBlockAccess, |
| CoordAccess = false, // to be implemented |
| RawAccess = false |
| }; |
| |
| //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===// |
| typedef internal::TensorBlockNotImplemented TensorBlock; |
| //===--------------------------------------------------------------------===// |
| |
| EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device) |
| : m_impl(op.expression(), device) |
| { |
| m_dimensions = m_impl.dimensions(); |
| for (int i = 0; i < NumDims; ++i) { |
| m_dimensions[i] =Eigen::numext::ceil(static_cast<float>(m_dimensions[i]) / op.strides()[i]); |
| } |
| |
| const typename TensorEvaluator<ArgType, Device>::Dimensions& input_dims = m_impl.dimensions(); |
| if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { |
| m_outputStrides[0] = 1; |
| m_inputStrides[0] = 1; |
| for (int i = 1; i < NumDims; ++i) { |
| m_outputStrides[i] = m_outputStrides[i-1] * m_dimensions[i-1]; |
| m_inputStrides[i] = m_inputStrides[i-1] * input_dims[i-1]; |
| m_inputStrides[i-1] *= op.strides()[i-1]; |
| } |
| m_inputStrides[NumDims-1] *= op.strides()[NumDims-1]; |
| } else { // RowMajor |
| m_outputStrides[NumDims-1] = 1; |
| m_inputStrides[NumDims-1] = 1; |
| for (int i = NumDims - 2; i >= 0; --i) { |
| m_outputStrides[i] = m_outputStrides[i+1] * m_dimensions[i+1]; |
| m_inputStrides[i] = m_inputStrides[i+1] * input_dims[i+1]; |
| m_inputStrides[i+1] *= op.strides()[i+1]; |
| } |
| m_inputStrides[0] *= op.strides()[0]; |
| } |
| } |
| |
| |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; } |
| |
| EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType/*data*/) { |
| m_impl.evalSubExprsIfNeeded(NULL); |
| return true; |
| } |
| EIGEN_STRONG_INLINE void cleanup() { |
| m_impl.cleanup(); |
| } |
| |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const |
| { |
| return m_impl.coeff(srcCoeff(index)); |
| } |
| |
| template<int LoadMode> |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const |
| { |
| EIGEN_STATIC_ASSERT((PacketSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE) |
| eigen_assert(index+PacketSize-1 < dimensions().TotalSize()); |
| |
| Index inputIndices[] = {0, 0}; |
| Index indices[] = {index, index + PacketSize - 1}; |
| if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { |
| EIGEN_UNROLL_LOOP |
| for (int i = NumDims - 1; i > 0; --i) { |
| const Index idx0 = indices[0] / m_outputStrides[i]; |
| const Index idx1 = indices[1] / m_outputStrides[i]; |
| inputIndices[0] += idx0 * m_inputStrides[i]; |
| inputIndices[1] += idx1 * m_inputStrides[i]; |
| indices[0] -= idx0 * m_outputStrides[i]; |
| indices[1] -= idx1 * m_outputStrides[i]; |
| } |
| inputIndices[0] += indices[0] * m_inputStrides[0]; |
| inputIndices[1] += indices[1] * m_inputStrides[0]; |
| } else { // RowMajor |
| EIGEN_UNROLL_LOOP |
| for (int i = 0; i < NumDims - 1; ++i) { |
| const Index idx0 = indices[0] / m_outputStrides[i]; |
| const Index idx1 = indices[1] / m_outputStrides[i]; |
| inputIndices[0] += idx0 * m_inputStrides[i]; |
| inputIndices[1] += idx1 * m_inputStrides[i]; |
| indices[0] -= idx0 * m_outputStrides[i]; |
| indices[1] -= idx1 * m_outputStrides[i]; |
| } |
| inputIndices[0] += indices[0] * m_inputStrides[NumDims-1]; |
| inputIndices[1] += indices[1] * m_inputStrides[NumDims-1]; |
| } |
| if (inputIndices[1] - inputIndices[0] == PacketSize - 1) { |
| PacketReturnType rslt = m_impl.template packet<Unaligned>(inputIndices[0]); |
| return rslt; |
| } |
| else { |
| EIGEN_ALIGN_MAX std::remove_const_t<CoeffReturnType> values[PacketSize]; |
| values[0] = m_impl.coeff(inputIndices[0]); |
| values[PacketSize-1] = m_impl.coeff(inputIndices[1]); |
| EIGEN_UNROLL_LOOP |
| for (int i = 1; i < PacketSize-1; ++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 { |
| double compute_cost = (NumDims - 1) * (TensorOpCost::AddCost<Index>() + |
| TensorOpCost::MulCost<Index>() + |
| TensorOpCost::DivCost<Index>()) + |
| TensorOpCost::MulCost<Index>(); |
| if (vectorized) { |
| compute_cost *= 2; // packet() computes two indices |
| } |
| const int innerDim = (static_cast<int>(Layout) == static_cast<int>(ColMajor)) ? 0 : (NumDims - 1); |
| return m_impl.costPerCoeff(vectorized && m_inputStrides[innerDim] == 1) + |
| // Computation is not vectorized per se, but it is done once per packet. |
| TensorOpCost(0, 0, compute_cost, vectorized, PacketSize); |
| } |
| |
| EIGEN_DEVICE_FUNC typename Storage::Type 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_impl.bind(cgh); |
| } |
| #endif |
| protected: |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index srcCoeff(Index index) const |
| { |
| Index inputIndex = 0; |
| if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { |
| EIGEN_UNROLL_LOOP |
| for (int i = NumDims - 1; i > 0; --i) { |
| const Index idx = index / m_outputStrides[i]; |
| inputIndex += idx * m_inputStrides[i]; |
| index -= idx * m_outputStrides[i]; |
| } |
| inputIndex += index * m_inputStrides[0]; |
| } else { // RowMajor |
| EIGEN_UNROLL_LOOP |
| for (int i = 0; i < NumDims - 1; ++i) { |
| const Index idx = index / m_outputStrides[i]; |
| inputIndex += idx * m_inputStrides[i]; |
| index -= idx * m_outputStrides[i]; |
| } |
| inputIndex += index * m_inputStrides[NumDims-1]; |
| } |
| return inputIndex; |
| } |
| |
| Dimensions m_dimensions; |
| array<Index, NumDims> m_outputStrides; |
| array<Index, NumDims> m_inputStrides; |
| TensorEvaluator<ArgType, Device> m_impl; |
| }; |
| |
| // Eval as lvalue |
| template<typename Strides, typename ArgType, typename Device> |
| struct TensorEvaluator<TensorStridingOp<Strides, ArgType>, Device> |
| : public TensorEvaluator<const TensorStridingOp<Strides, ArgType>, Device> |
| { |
| typedef TensorStridingOp<Strides, ArgType> XprType; |
| typedef TensorEvaluator<const XprType, Device> Base; |
| // typedef typename XprType::Index Index; |
| static constexpr int NumDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value; |
| // typedef DSizes<Index, NumDims> Dimensions; |
| |
| static constexpr int Layout = TensorEvaluator<ArgType, Device>::Layout; |
| enum { |
| IsAligned = /*TensorEvaluator<ArgType, Device>::IsAligned*/false, |
| PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess, |
| PreferBlockAccess = false, |
| CoordAccess = false, // to be implemented |
| RawAccess = false |
| }; |
| |
| EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device) |
| : Base(op, device) { } |
| |
| typedef typename XprType::Index Index; |
| typedef typename XprType::Scalar Scalar; |
| typedef typename XprType::CoeffReturnType CoeffReturnType; |
| typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType; |
| static constexpr int PacketSize = PacketType<CoeffReturnType, Device>::size; |
| |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& coeffRef(Index index) |
| { |
| return this->m_impl.coeffRef(this->srcCoeff(index)); |
| } |
| |
| template <int StoreMode> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE |
| void writePacket(Index index, const PacketReturnType& x) |
| { |
| EIGEN_STATIC_ASSERT((PacketSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE) |
| eigen_assert(index+PacketSize-1 < this->dimensions().TotalSize()); |
| |
| Index inputIndices[] = {0, 0}; |
| Index indices[] = {index, index + PacketSize - 1}; |
| if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { |
| EIGEN_UNROLL_LOOP |
| for (int i = NumDims - 1; i > 0; --i) { |
| const Index idx0 = indices[0] / this->m_outputStrides[i]; |
| const Index idx1 = indices[1] / this->m_outputStrides[i]; |
| inputIndices[0] += idx0 * this->m_inputStrides[i]; |
| inputIndices[1] += idx1 * this->m_inputStrides[i]; |
| indices[0] -= idx0 * this->m_outputStrides[i]; |
| indices[1] -= idx1 * this->m_outputStrides[i]; |
| } |
| inputIndices[0] += indices[0] * this->m_inputStrides[0]; |
| inputIndices[1] += indices[1] * this->m_inputStrides[0]; |
| } else { // RowMajor |
| EIGEN_UNROLL_LOOP |
| for (int i = 0; i < NumDims - 1; ++i) { |
| const Index idx0 = indices[0] / this->m_outputStrides[i]; |
| const Index idx1 = indices[1] / this->m_outputStrides[i]; |
| inputIndices[0] += idx0 * this->m_inputStrides[i]; |
| inputIndices[1] += idx1 * this->m_inputStrides[i]; |
| indices[0] -= idx0 * this->m_outputStrides[i]; |
| indices[1] -= idx1 * this->m_outputStrides[i]; |
| } |
| inputIndices[0] += indices[0] * this->m_inputStrides[NumDims-1]; |
| inputIndices[1] += indices[1] * this->m_inputStrides[NumDims-1]; |
| } |
| if (inputIndices[1] - inputIndices[0] == PacketSize - 1) { |
| this->m_impl.template writePacket<Unaligned>(inputIndices[0], x); |
| } |
| else { |
| EIGEN_ALIGN_MAX Scalar values[PacketSize]; |
| internal::pstore<Scalar, PacketReturnType>(values, x); |
| this->m_impl.coeffRef(inputIndices[0]) = values[0]; |
| this->m_impl.coeffRef(inputIndices[1]) = values[PacketSize-1]; |
| EIGEN_UNROLL_LOOP |
| for (int i = 1; i < PacketSize-1; ++i) { |
| this->coeffRef(index+i) = values[i]; |
| } |
| } |
| } |
| }; |
| |
| |
| } // end namespace Eigen |
| |
| #endif // EIGEN_CXX11_TENSOR_TENSOR_STRIDING_H |