339 lines
13 KiB
C++
339 lines
13 KiB
C++
// This file is part of Eigen, a lightweight C++ template library
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// for linear algebra.
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//
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// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
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//
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// This Source Code Form is subject to the terms of the Mozilla
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// Public License v. 2.0. If a copy of the MPL was not distributed
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// with this file, You can obtain one at the mozilla.org home page
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#ifndef EIGEN_CXX11_TENSOR_TENSOR_STRIDING_H
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#define EIGEN_CXX11_TENSOR_TENSOR_STRIDING_H
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namespace Eigen {
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/** \class TensorStriding
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* \ingroup CXX11_Tensor_Module
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*
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* \brief Tensor striding class.
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*
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*
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*/
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namespace internal {
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template<typename Strides, typename XprType>
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struct traits<TensorStridingOp<Strides, XprType> > : public traits<XprType>
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{
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typedef typename XprType::Scalar Scalar;
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typedef traits<XprType> XprTraits;
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typedef typename XprTraits::StorageKind StorageKind;
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typedef typename XprTraits::Index Index;
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typedef typename XprType::Nested Nested;
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typedef typename remove_reference<Nested>::type _Nested;
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static const int NumDimensions = XprTraits::NumDimensions;
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static const int Layout = XprTraits::Layout;
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};
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template<typename Strides, typename XprType>
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struct eval<TensorStridingOp<Strides, XprType>, Eigen::Dense>
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{
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typedef const TensorStridingOp<Strides, XprType>& type;
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};
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template<typename Strides, typename XprType>
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struct nested<TensorStridingOp<Strides, XprType>, 1, typename eval<TensorStridingOp<Strides, XprType> >::type>
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{
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typedef TensorStridingOp<Strides, XprType> type;
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};
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} // end namespace internal
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template<typename Strides, typename XprType>
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class TensorStridingOp : public TensorBase<TensorStridingOp<Strides, XprType> >
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{
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public:
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typedef typename Eigen::internal::traits<TensorStridingOp>::Scalar Scalar;
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typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
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typedef typename XprType::CoeffReturnType CoeffReturnType;
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typedef typename Eigen::internal::nested<TensorStridingOp>::type Nested;
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typedef typename Eigen::internal::traits<TensorStridingOp>::StorageKind StorageKind;
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typedef typename Eigen::internal::traits<TensorStridingOp>::Index Index;
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorStridingOp(const XprType& expr, const Strides& dims)
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: m_xpr(expr), m_dims(dims) {}
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EIGEN_DEVICE_FUNC
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const Strides& strides() const { return m_dims; }
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EIGEN_DEVICE_FUNC
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const typename internal::remove_all<typename XprType::Nested>::type&
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expression() const { return m_xpr; }
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EIGEN_DEVICE_FUNC
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EIGEN_STRONG_INLINE TensorStridingOp& operator = (const TensorStridingOp& other)
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{
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typedef TensorAssignOp<TensorStridingOp, const TensorStridingOp> Assign;
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Assign assign(*this, other);
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internal::TensorExecutor<const Assign, DefaultDevice>::run(assign, DefaultDevice());
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return *this;
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}
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template<typename OtherDerived>
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EIGEN_DEVICE_FUNC
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EIGEN_STRONG_INLINE TensorStridingOp& operator = (const OtherDerived& other)
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{
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typedef TensorAssignOp<TensorStridingOp, const OtherDerived> Assign;
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Assign assign(*this, other);
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internal::TensorExecutor<const Assign, DefaultDevice>::run(assign, DefaultDevice());
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return *this;
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}
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protected:
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typename XprType::Nested m_xpr;
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const Strides m_dims;
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};
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// Eval as rvalue
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template<typename Strides, typename ArgType, typename Device>
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struct TensorEvaluator<const TensorStridingOp<Strides, ArgType>, Device>
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{
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typedef TensorStridingOp<Strides, ArgType> XprType;
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typedef typename XprType::Index Index;
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static const int NumDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value;
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typedef DSizes<Index, NumDims> Dimensions;
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typedef typename XprType::Scalar Scalar;
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typedef typename XprType::CoeffReturnType CoeffReturnType;
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typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
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static const int PacketSize = internal::unpacket_traits<PacketReturnType>::size;
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enum {
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IsAligned = /*TensorEvaluator<ArgType, Device>::IsAligned*/false,
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PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess,
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Layout = TensorEvaluator<ArgType, Device>::Layout,
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CoordAccess = false, // to be implemented
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RawAccess = false
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};
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
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: m_impl(op.expression(), device)
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{
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m_dimensions = m_impl.dimensions();
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for (int i = 0; i < NumDims; ++i) {
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m_dimensions[i] = ceilf(static_cast<float>(m_dimensions[i]) / op.strides()[i]);
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}
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const typename TensorEvaluator<ArgType, Device>::Dimensions& input_dims = m_impl.dimensions();
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if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
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m_outputStrides[0] = 1;
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m_inputStrides[0] = 1;
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for (int i = 1; i < NumDims; ++i) {
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m_outputStrides[i] = m_outputStrides[i-1] * m_dimensions[i-1];
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m_inputStrides[i] = m_inputStrides[i-1] * input_dims[i-1];
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m_inputStrides[i-1] *= op.strides()[i-1];
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}
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m_inputStrides[NumDims-1] *= op.strides()[NumDims-1];
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} else { // RowMajor
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m_outputStrides[NumDims-1] = 1;
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m_inputStrides[NumDims-1] = 1;
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for (int i = NumDims - 2; i >= 0; --i) {
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m_outputStrides[i] = m_outputStrides[i+1] * m_dimensions[i+1];
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m_inputStrides[i] = m_inputStrides[i+1] * input_dims[i+1];
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m_inputStrides[i+1] *= op.strides()[i+1];
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}
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m_inputStrides[0] *= op.strides()[0];
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}
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}
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(Scalar* /*data*/) {
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m_impl.evalSubExprsIfNeeded(NULL);
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return true;
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}
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() {
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m_impl.cleanup();
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}
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
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{
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return m_impl.coeff(srcCoeff(index));
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}
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template<int LoadMode>
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
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{
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EIGEN_STATIC_ASSERT((PacketSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE)
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eigen_assert(index+PacketSize-1 < dimensions().TotalSize());
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Index inputIndices[] = {0, 0};
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Index indices[] = {index, index + PacketSize - 1};
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if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
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for (int i = NumDims - 1; i > 0; --i) {
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const Index idx0 = indices[0] / m_outputStrides[i];
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const Index idx1 = indices[1] / m_outputStrides[i];
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inputIndices[0] += idx0 * m_inputStrides[i];
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inputIndices[1] += idx1 * m_inputStrides[i];
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indices[0] -= idx0 * m_outputStrides[i];
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indices[1] -= idx1 * m_outputStrides[i];
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}
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inputIndices[0] += indices[0] * m_inputStrides[0];
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inputIndices[1] += indices[1] * m_inputStrides[0];
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} else { // RowMajor
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for (int i = 0; i < NumDims - 1; ++i) {
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const Index idx0 = indices[0] / m_outputStrides[i];
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const Index idx1 = indices[1] / m_outputStrides[i];
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inputIndices[0] += idx0 * m_inputStrides[i];
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inputIndices[1] += idx1 * m_inputStrides[i];
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indices[0] -= idx0 * m_outputStrides[i];
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indices[1] -= idx1 * m_outputStrides[i];
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}
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inputIndices[0] += indices[0] * m_inputStrides[NumDims-1];
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inputIndices[1] += indices[1] * m_inputStrides[NumDims-1];
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}
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if (inputIndices[1] - inputIndices[0] == PacketSize - 1) {
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PacketReturnType rslt = m_impl.template packet<Unaligned>(inputIndices[0]);
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return rslt;
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}
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else {
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EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[PacketSize];
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values[0] = m_impl.coeff(inputIndices[0]);
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values[PacketSize-1] = m_impl.coeff(inputIndices[1]);
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for (int i = 1; i < PacketSize-1; ++i) {
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values[i] = coeff(index+i);
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}
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PacketReturnType rslt = internal::pload<PacketReturnType>(values);
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return rslt;
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}
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}
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const {
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double compute_cost = (NumDims - 1) * (TensorOpCost::AddCost<Index>() +
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TensorOpCost::MulCost<Index>() +
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TensorOpCost::DivCost<Index>()) +
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TensorOpCost::MulCost<Index>();
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if (vectorized) {
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compute_cost *= 2; // packet() computes two indices
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}
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const int innerDim = (static_cast<int>(Layout) == static_cast<int>(ColMajor)) ? 0 : (NumDims - 1);
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return m_impl.costPerCoeff(vectorized && m_inputStrides[innerDim] == 1) +
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// Computation is not vectorized per se, but it is done once per packet.
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TensorOpCost(0, 0, compute_cost, vectorized, PacketSize);
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}
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EIGEN_DEVICE_FUNC Scalar* data() const { return NULL; }
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protected:
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index srcCoeff(Index index) const
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{
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Index inputIndex = 0;
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if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
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for (int i = NumDims - 1; i > 0; --i) {
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const Index idx = index / m_outputStrides[i];
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inputIndex += idx * m_inputStrides[i];
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index -= idx * m_outputStrides[i];
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}
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inputIndex += index * m_inputStrides[0];
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} else { // RowMajor
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for (int i = 0; i < NumDims - 1; ++i) {
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const Index idx = index / m_outputStrides[i];
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inputIndex += idx * m_inputStrides[i];
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index -= idx * m_outputStrides[i];
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}
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inputIndex += index * m_inputStrides[NumDims-1];
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}
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return inputIndex;
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}
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Dimensions m_dimensions;
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array<Index, NumDims> m_outputStrides;
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array<Index, NumDims> m_inputStrides;
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TensorEvaluator<ArgType, Device> m_impl;
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};
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// Eval as lvalue
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template<typename Strides, typename ArgType, typename Device>
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struct TensorEvaluator<TensorStridingOp<Strides, ArgType>, Device>
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: public TensorEvaluator<const TensorStridingOp<Strides, ArgType>, Device>
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{
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typedef TensorStridingOp<Strides, ArgType> XprType;
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typedef TensorEvaluator<const XprType, Device> Base;
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// typedef typename XprType::Index Index;
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static const int NumDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value;
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// typedef DSizes<Index, NumDims> Dimensions;
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enum {
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IsAligned = /*TensorEvaluator<ArgType, Device>::IsAligned*/false,
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PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess,
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Layout = TensorEvaluator<ArgType, Device>::Layout,
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CoordAccess = false, // to be implemented
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RawAccess = false
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};
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
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: Base(op, device) { }
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typedef typename XprType::Index Index;
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typedef typename XprType::Scalar Scalar;
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typedef typename XprType::CoeffReturnType CoeffReturnType;
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typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
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static const int PacketSize = internal::unpacket_traits<PacketReturnType>::size;
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& coeffRef(Index index)
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{
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return this->m_impl.coeffRef(this->srcCoeff(index));
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}
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template <int StoreMode> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
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void writePacket(Index index, const PacketReturnType& x)
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{
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EIGEN_STATIC_ASSERT((PacketSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE)
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eigen_assert(index+PacketSize-1 < this->dimensions().TotalSize());
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Index inputIndices[] = {0, 0};
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Index indices[] = {index, index + PacketSize - 1};
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if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
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for (int i = NumDims - 1; i > 0; --i) {
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const Index idx0 = indices[0] / this->m_outputStrides[i];
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const Index idx1 = indices[1] / this->m_outputStrides[i];
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inputIndices[0] += idx0 * this->m_inputStrides[i];
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inputIndices[1] += idx1 * this->m_inputStrides[i];
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indices[0] -= idx0 * this->m_outputStrides[i];
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indices[1] -= idx1 * this->m_outputStrides[i];
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}
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inputIndices[0] += indices[0] * this->m_inputStrides[0];
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inputIndices[1] += indices[1] * this->m_inputStrides[0];
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} else { // RowMajor
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for (int i = 0; i < NumDims - 1; ++i) {
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const Index idx0 = indices[0] / this->m_outputStrides[i];
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const Index idx1 = indices[1] / this->m_outputStrides[i];
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inputIndices[0] += idx0 * this->m_inputStrides[i];
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inputIndices[1] += idx1 * this->m_inputStrides[i];
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indices[0] -= idx0 * this->m_outputStrides[i];
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indices[1] -= idx1 * this->m_outputStrides[i];
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}
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inputIndices[0] += indices[0] * this->m_inputStrides[NumDims-1];
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inputIndices[1] += indices[1] * this->m_inputStrides[NumDims-1];
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}
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if (inputIndices[1] - inputIndices[0] == PacketSize - 1) {
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this->m_impl.template writePacket<Unaligned>(inputIndices[0], x);
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}
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else {
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EIGEN_ALIGN_MAX Scalar values[PacketSize];
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internal::pstore<Scalar, PacketReturnType>(values, x);
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this->m_impl.coeffRef(inputIndices[0]) = values[0];
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this->m_impl.coeffRef(inputIndices[1]) = values[PacketSize-1];
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for (int i = 1; i < PacketSize-1; ++i) {
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this->coeffRef(index+i) = values[i];
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}
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}
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}
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};
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} // end namespace Eigen
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#endif // EIGEN_CXX11_TENSOR_TENSOR_STRIDING_H
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