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486 lines
18 KiB
C++
486 lines
18 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 http://mozilla.org/MPL/2.0/.
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#ifndef EIGEN_CXX11_TENSOR_TENSOR_SHUFFLING_H
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#define EIGEN_CXX11_TENSOR_TENSOR_SHUFFLING_H
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namespace Eigen {
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/** \class TensorShuffling
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* \ingroup CXX11_Tensor_Module
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*
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* \brief Tensor shuffling 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 Shuffle, typename XprType>
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struct traits<TensorShufflingOp<Shuffle, 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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typedef typename XprTraits::PointerType PointerType;
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};
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template<typename Shuffle, typename XprType>
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struct eval<TensorShufflingOp<Shuffle, XprType>, Eigen::Dense>
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{
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typedef const TensorShufflingOp<Shuffle, XprType>& type;
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};
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template<typename Shuffle, typename XprType>
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struct nested<TensorShufflingOp<Shuffle, XprType>, 1, typename eval<TensorShufflingOp<Shuffle, XprType> >::type>
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{
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typedef TensorShufflingOp<Shuffle, XprType> type;
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};
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} // end namespace internal
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template<typename Shuffle, typename XprType>
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class TensorShufflingOp : public TensorBase<TensorShufflingOp<Shuffle, XprType> >
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{
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public:
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typedef typename Eigen::internal::traits<TensorShufflingOp>::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<TensorShufflingOp>::type Nested;
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typedef typename Eigen::internal::traits<TensorShufflingOp>::StorageKind StorageKind;
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typedef typename Eigen::internal::traits<TensorShufflingOp>::Index Index;
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorShufflingOp(const XprType& expr, const Shuffle& shfl)
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: m_xpr(expr), m_shuffle(shfl) {}
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EIGEN_DEVICE_FUNC
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const Shuffle& shufflePermutation() const { return m_shuffle; }
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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 TensorShufflingOp& operator = (const TensorShufflingOp& other)
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{
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typedef TensorAssignOp<TensorShufflingOp, const TensorShufflingOp> 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 TensorShufflingOp& operator = (const OtherDerived& other)
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{
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typedef TensorAssignOp<TensorShufflingOp, 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 Shuffle m_shuffle;
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};
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// Eval as rvalue
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template<typename Shuffle, typename ArgType, typename Device>
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struct TensorEvaluator<const TensorShufflingOp<Shuffle, ArgType>, Device>
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{
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typedef TensorEvaluator<const TensorShufflingOp<Shuffle, ArgType>, Device> Self;
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typedef TensorShufflingOp<Shuffle, 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 = PacketType<CoeffReturnType, Device>::size;
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typedef StorageMemory<CoeffReturnType, Device> Storage;
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typedef typename Storage::Type EvaluatorPointerType;
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enum {
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IsAligned = false,
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PacketAccess = (PacketType<CoeffReturnType, Device>::size > 1),
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BlockAccess = TensorEvaluator<ArgType, Device>::BlockAccess,
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BlockAccessV2 = false,
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PreferBlockAccess = true,
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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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typedef typename internal::remove_const<Scalar>::type ScalarNoConst;
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typedef internal::TensorBlock<ScalarNoConst, Index, NumDims, Layout>
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TensorBlock;
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typedef internal::TensorBlockReader<ScalarNoConst, Index, NumDims, Layout>
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TensorBlockReader;
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//===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
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typedef internal::TensorBlockNotImplemented TensorBlockV2;
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//===--------------------------------------------------------------------===//
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op,
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const Device& device)
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: m_device(device),
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m_impl(op.expression(), device)
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{
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const typename TensorEvaluator<ArgType, Device>::Dimensions& input_dims = m_impl.dimensions();
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const Shuffle& shuffle = op.shufflePermutation();
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m_is_identity = true;
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for (int i = 0; i < NumDims; ++i) {
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m_dimensions[i] = input_dims[shuffle[i]];
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m_inverseShuffle[shuffle[i]] = i;
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if (m_is_identity && shuffle[i] != i) {
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m_is_identity = false;
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}
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}
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if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
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m_unshuffledInputStrides[0] = 1;
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m_outputStrides[0] = 1;
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for (int i = 1; i < NumDims; ++i) {
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m_unshuffledInputStrides[i] =
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m_unshuffledInputStrides[i - 1] * input_dims[i - 1];
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m_outputStrides[i] = m_outputStrides[i - 1] * m_dimensions[i - 1];
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m_fastOutputStrides[i] = internal::TensorIntDivisor<Index>(m_outputStrides[i]);
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}
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} else {
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m_unshuffledInputStrides[NumDims - 1] = 1;
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m_outputStrides[NumDims - 1] = 1;
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for (int i = NumDims - 2; i >= 0; --i) {
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m_unshuffledInputStrides[i] =
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m_unshuffledInputStrides[i + 1] * input_dims[i + 1];
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m_outputStrides[i] = m_outputStrides[i + 1] * m_dimensions[i + 1];
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m_fastOutputStrides[i] = internal::TensorIntDivisor<Index>(m_outputStrides[i]);
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}
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}
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for (int i = 0; i < NumDims; ++i) {
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m_inputStrides[i] = m_unshuffledInputStrides[shuffle[i]];
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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(EvaluatorPointerType /*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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if (m_is_identity) {
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return m_impl.coeff(index);
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} else {
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return m_impl.coeff(srcCoeff(index));
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}
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}
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template <int LoadMode, typename Self, bool ImplPacketAccess>
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struct PacketLoader {
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
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static PacketReturnType Run(const Self& self, Index index) {
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EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[PacketSize];
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EIGEN_UNROLL_LOOP
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for (int i = 0; i < PacketSize; ++i) {
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values[i] = self.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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template<int LoadMode, typename Self>
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struct PacketLoader<LoadMode, Self, true> {
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
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static PacketReturnType Run(const Self& self, Index index) {
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if (self.m_is_identity) {
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return self.m_impl.template packet<LoadMode>(index);
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} else {
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EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[PacketSize];
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EIGEN_UNROLL_LOOP
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for (int i = 0; i < PacketSize; ++i) {
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values[i] = self.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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};
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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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return PacketLoader<LoadMode, Self, TensorEvaluator<ArgType, Device>::PacketAccess>::Run(*this, index);
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}
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void getResourceRequirements(
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std::vector<internal::TensorOpResourceRequirements>* resources) const {
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Eigen::Index block_total_size_max = numext::maxi<Eigen::Index>(
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1, m_device.firstLevelCacheSize() / sizeof(Scalar));
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resources->push_back(internal::TensorOpResourceRequirements(
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internal::kUniformAllDims, block_total_size_max));
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m_impl.getResourceRequirements(resources);
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}
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void block(
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TensorBlock* output_block) const {
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if (m_impl.data() != NULL) {
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// Fast path: we have direct access to the data, so shuffle as we read.
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TensorBlockReader::Run(output_block,
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srcCoeff(output_block->first_coeff_index()),
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m_inverseShuffle,
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m_unshuffledInputStrides,
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m_impl.data());
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return;
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}
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// Slow path: read unshuffled block from the input and shuffle in-place.
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// Initialize input block sizes using input-to-output shuffle map.
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DSizes<Index, NumDims> input_block_sizes;
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for (Index i = 0; i < NumDims; ++i) {
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input_block_sizes[i] = output_block->block_sizes()[m_inverseShuffle[i]];
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}
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// Calculate input block strides.
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DSizes<Index, NumDims> input_block_strides;
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if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
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input_block_strides[0] = 1;
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for (int i = 1; i < NumDims; ++i) {
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input_block_strides[i] =
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input_block_strides[i - 1] * input_block_sizes[i - 1];
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}
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} else {
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input_block_strides[NumDims - 1] = 1;
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for (int i = NumDims - 2; i >= 0; --i) {
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input_block_strides[i] =
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input_block_strides[i + 1] * input_block_sizes[i + 1];
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}
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}
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DSizes<internal::TensorIntDivisor<Index>, NumDims> fast_input_block_strides;
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for (int i = 0; i < NumDims; ++i) {
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fast_input_block_strides[i] =
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internal::TensorIntDivisor<Index>(input_block_strides[i]);
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}
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// Read input block.
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TensorBlock input_block(srcCoeff(output_block->first_coeff_index()),
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input_block_sizes,
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input_block_strides,
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Dimensions(m_unshuffledInputStrides),
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output_block->data());
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m_impl.block(&input_block);
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// Naive In-place shuffle: random IO but block size is O(L1 cache size).
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// TODO(andydavis) Improve the performance of this in-place shuffle.
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const Index total_size = input_block_sizes.TotalSize();
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std::vector<bool> bitmap(total_size, false);
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ScalarNoConst* data = const_cast<ScalarNoConst*>(output_block->data());
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const DSizes<Index, NumDims>& output_block_strides =
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output_block->block_strides();
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for (Index input_index = 0; input_index < total_size; ++input_index) {
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if (bitmap[input_index]) {
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// Coefficient at this index has already been shuffled.
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continue;
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}
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Index output_index =
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GetBlockOutputIndex(input_index, input_block_strides,
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output_block_strides, fast_input_block_strides);
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if (output_index == input_index) {
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// Coefficient already in place.
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bitmap[output_index] = true;
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continue;
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}
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// The following loop starts at 'input_index', and shuffles
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// coefficients into their shuffled location at 'output_index'.
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// It skips through the array shuffling coefficients by following
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// the shuffle cycle starting and ending a 'start_index'.
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ScalarNoConst evicted_value;
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ScalarNoConst shuffled_value = data[input_index];
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do {
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evicted_value = data[output_index];
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data[output_index] = shuffled_value;
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shuffled_value = evicted_value;
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bitmap[output_index] = true;
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output_index =
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GetBlockOutputIndex(output_index, input_block_strides,
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output_block_strides, fast_input_block_strides);
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} while (output_index != input_index);
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data[output_index] = shuffled_value;
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bitmap[output_index] = true;
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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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const double compute_cost = m_is_identity ? TensorOpCost::AddCost<Index>() :
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NumDims * (2 * TensorOpCost::AddCost<Index>() +
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2 * TensorOpCost::MulCost<Index>() +
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TensorOpCost::DivCost<Index>());
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return m_impl.costPerCoeff(vectorized) +
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TensorOpCost(0, 0, compute_cost, m_is_identity /* vectorized */, PacketSize);
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}
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EIGEN_DEVICE_FUNC typename Storage::Type data() const { return NULL; }
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#ifdef EIGEN_USE_SYCL
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// binding placeholder accessors to a command group handler for SYCL
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler &cgh) const {
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m_impl.bind(cgh);
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}
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#endif
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protected:
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index GetBlockOutputIndex(
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Index input_index,
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const DSizes<Index, NumDims>& input_block_strides,
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const DSizes<Index, NumDims>& output_block_strides,
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const DSizes<internal::TensorIntDivisor<Index>, NumDims>& fast_input_block_strides) const {
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Index output_index = 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 = input_index / fast_input_block_strides[i];
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output_index += idx * output_block_strides[m_inverseShuffle[i]];
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input_index -= idx * input_block_strides[i];
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}
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return output_index + input_index *
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output_block_strides[m_inverseShuffle[0]];
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} else {
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for (int i = 0; i < NumDims - 1; ++i) {
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const Index idx = input_index / fast_input_block_strides[i];
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output_index += idx * output_block_strides[m_inverseShuffle[i]];
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input_index -= idx * input_block_strides[i];
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}
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return output_index + input_index *
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output_block_strides[m_inverseShuffle[NumDims - 1]];
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}
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}
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index srcCoeff(Index index) const {
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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_fastOutputStrides[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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return inputIndex + index * m_inputStrides[0];
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} else {
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for (int i = 0; i < NumDims - 1; ++i) {
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const Index idx = index / m_fastOutputStrides[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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return inputIndex + index * m_inputStrides[NumDims - 1];
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}
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}
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Dimensions m_dimensions;
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bool m_is_identity;
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array<Index, NumDims> m_inverseShuffle;
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array<Index, NumDims> m_outputStrides;
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array<internal::TensorIntDivisor<Index>, NumDims> m_fastOutputStrides;
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array<Index, NumDims> m_inputStrides;
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array<Index, NumDims> m_unshuffledInputStrides;
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const Device EIGEN_DEVICE_REF m_device;
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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 Shuffle, typename ArgType, typename Device>
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struct TensorEvaluator<TensorShufflingOp<Shuffle, ArgType>, Device>
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: public TensorEvaluator<const TensorShufflingOp<Shuffle, ArgType>, Device>
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{
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typedef TensorEvaluator<const TensorShufflingOp<Shuffle, ArgType>, Device> Base;
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typedef TensorShufflingOp<Shuffle, 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 = PacketType<CoeffReturnType, Device>::size;
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enum {
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IsAligned = false,
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PacketAccess = (PacketType<CoeffReturnType, Device>::size > 1),
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BlockAccess = TensorEvaluator<ArgType, Device>::BlockAccess,
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BlockAccessV2 = false,
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PreferBlockAccess = true,
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Layout = TensorEvaluator<ArgType, Device>::Layout,
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RawAccess = false
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};
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typedef typename internal::remove_const<Scalar>::type ScalarNoConst;
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typedef internal::TensorBlock<ScalarNoConst, Index, NumDims, Layout>
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TensorBlock;
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typedef internal::TensorBlockWriter<ScalarNoConst, Index, NumDims, Layout>
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TensorBlockWriter;
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//===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
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typedef internal::TensorBlockNotImplemented TensorBlockV2;
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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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{ }
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType& 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_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_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[PacketSize];
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internal::pstore<CoeffReturnType, PacketReturnType>(values, x);
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EIGEN_UNROLL_LOOP
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for (int i = 0; i < PacketSize; ++i) {
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this->coeffRef(index+i) = values[i];
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}
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}
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void writeBlock(
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const TensorBlock& block) {
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eigen_assert(this->m_impl.data() != NULL);
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TensorBlockWriter::Run(block, this->srcCoeff(block.first_coeff_index()),
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this->m_inverseShuffle,
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this->m_unshuffledInputStrides, this->m_impl.data());
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}
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};
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} // end namespace Eigen
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#endif // EIGEN_CXX11_TENSOR_TENSOR_SHUFFLING_H
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