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414 lines
17 KiB
C++
414 lines
17 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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// IWYU pragma: private
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#include "./InternalHeaderCheck.h"
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namespace Eigen {
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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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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 std::remove_reference_t<Nested> Nested_;
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static constexpr int NumDimensions = XprTraits::NumDimensions;
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static constexpr 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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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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typedef TensorShufflingOp<Shuffle, XprType> type;
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};
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} // end namespace internal
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/**
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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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template <typename Shuffle, typename XprType>
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class TensorShufflingOp : public TensorBase<TensorShufflingOp<Shuffle, XprType> > {
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public:
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typedef TensorBase<TensorShufflingOp<Shuffle, XprType> > Base;
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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 const Shuffle& shufflePermutation() const { return m_shuffle; }
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EIGEN_DEVICE_FUNC const internal::remove_all_t<typename XprType::Nested>& expression() const { return m_xpr; }
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EIGEN_TENSOR_INHERIT_ASSIGNMENT_OPERATORS(TensorShufflingOp)
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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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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 constexpr 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 constexpr 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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static constexpr int Layout = TensorEvaluator<ArgType, Device>::Layout;
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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>::RawAccess,
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PreferBlockAccess = true,
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CoordAccess = false, // to be implemented
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RawAccess = false
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};
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typedef std::remove_const_t<Scalar> ScalarNoConst;
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//===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
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typedef internal::TensorBlockDescriptor<NumDims, Index> TensorBlockDesc;
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typedef internal::TensorBlockScratchAllocator<Device> TensorBlockScratch;
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typedef typename internal::TensorMaterializedBlock<ScalarNoConst, NumDims, Layout, Index> TensorBlock;
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//===--------------------------------------------------------------------===//
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EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
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: m_device(device), m_impl(op.expression(), device) {
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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_shuffle[i] = static_cast<int>(shuffle[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] = 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] =
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internal::TensorIntDivisor<Index>(m_outputStrides[i] > 0 ? m_outputStrides[i] : Index(1));
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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] = 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] =
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internal::TensorIntDivisor<Index>(m_outputStrides[i] > 0 ? m_outputStrides[i] : Index(1));
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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_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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#ifdef EIGEN_USE_THREADS
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template <typename EvalSubExprsCallback>
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EIGEN_STRONG_INLINE void evalSubExprsIfNeededAsync(EvaluatorPointerType, EvalSubExprsCallback done) {
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m_impl.evalSubExprsIfNeededAsync(nullptr, [done](bool) { done(true); });
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}
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#endif // EIGEN_USE_THREADS
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EIGEN_STRONG_INLINE void cleanup() { m_impl.cleanup(); }
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const {
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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 static PacketReturnType Run(const Self& self, Index index) {
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EIGEN_ALIGN_MAX std::remove_const_t<CoeffReturnType> 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 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 std::remove_const_t<CoeffReturnType> 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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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 internal::TensorBlockResourceRequirements getResourceRequirements() const {
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static const int inner_dim = Layout == static_cast<int>(ColMajor) ? 0 : NumDims - 1;
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const size_t target_size = m_device.firstLevelCacheSize();
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const bool inner_dim_shuffled = m_shuffle[inner_dim] != inner_dim;
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// Shuffled inner dimensions leads to a random memory access, which is not
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// captured by default cost model bytes loaded/stored. We add this cost
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// explicitly. The number of cycles picked based on the benchmarks.
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// TODO(ezhulenev): This number was picked based on a very questionable
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// benchmarks, add benchmarks that are representative of real workloads.
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using BlockRequirements = internal::TensorBlockResourceRequirements;
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if (inner_dim_shuffled) {
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return BlockRequirements::uniform<Scalar>(target_size).addCostPerCoeff({0, 0, NumDims * 28});
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} else {
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return BlockRequirements::skewed<Scalar>(target_size);
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}
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}
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBlock block(TensorBlockDesc& desc, TensorBlockScratch& scratch,
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bool root_of_expr_ast = false) const {
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eigen_assert(m_impl.data() != NULL);
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typedef internal::TensorBlockIO<ScalarNoConst, Index, NumDims, Layout> TensorBlockIO;
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typedef typename TensorBlockIO::Dst TensorBlockIODst;
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typedef typename TensorBlockIO::Src TensorBlockIOSrc;
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const typename TensorBlock::Storage block_storage =
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TensorBlock::prepareStorage(desc, scratch, /*allow_strided_storage=*/root_of_expr_ast);
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typename TensorBlockIO::Dimensions input_strides(m_unshuffledInputStrides);
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TensorBlockIOSrc src(input_strides, m_impl.data(), srcCoeff(desc.offset()));
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TensorBlockIODst dst(block_storage.dimensions(), block_storage.strides(), block_storage.data());
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typename TensorBlockIO::DimensionsMap dst_to_src_dim_map(m_shuffle);
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TensorBlockIO::Copy(dst, src, dst_to_src_dim_map);
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return block_storage.AsTensorMaterializedBlock();
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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
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? TensorOpCost::AddCost<Index>()
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: NumDims * (2 * TensorOpCost::AddCost<Index>() +
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2 * TensorOpCost::MulCost<Index>() + 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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protected:
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index
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GetBlockOutputIndex(Index input_index, 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 * 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 * 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<int, NumDims> m_shuffle;
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array<Index, NumDims> m_inverseShuffle; // TODO(ezhulenev): Make it int type.
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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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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 constexpr 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 constexpr int PacketSize = PacketType<CoeffReturnType, Device>::size;
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static constexpr int Layout = TensorEvaluator<ArgType, Device>::Layout;
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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>::RawAccess,
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PreferBlockAccess = true,
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RawAccess = false
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};
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typedef std::remove_const_t<Scalar> ScalarNoConst;
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//===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
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typedef internal::TensorBlockDescriptor<NumDims, Index> TensorBlockDesc;
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//===--------------------------------------------------------------------===//
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EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device) : Base(op, device) {}
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType& coeffRef(Index index) const {
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return this->m_impl.coeffRef(this->srcCoeff(index));
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}
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template <int StoreMode>
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EIGEN_STRONG_INLINE void writePacket(Index index, const PacketReturnType& x) const {
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EIGEN_ALIGN_MAX std::remove_const_t<CoeffReturnType> 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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template <typename TensorBlock>
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void writeBlock(const TensorBlockDesc& desc, const TensorBlock& block) {
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eigen_assert(this->m_impl.data() != NULL);
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typedef internal::TensorBlockIO<ScalarNoConst, Index, NumDims, Layout> TensorBlockIO;
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typedef typename TensorBlockIO::Dst TensorBlockIODst;
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typedef typename TensorBlockIO::Src TensorBlockIOSrc;
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const Scalar* block_buffer = block.data();
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// TODO(ezhulenev): TensorBlockIO should be able to read from any Eigen
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// expression with coefficient and packet access as `src`.
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void* mem = NULL;
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if (block_buffer == NULL) {
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mem = this->m_device.allocate(desc.size() * sizeof(Scalar));
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ScalarNoConst* buf = static_cast<ScalarNoConst*>(mem);
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typedef internal::TensorBlockAssignment<ScalarNoConst, NumDims, typename TensorBlock::XprType, Index>
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TensorBlockAssignment;
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TensorBlockAssignment::Run(
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TensorBlockAssignment::target(desc.dimensions(), internal::strides<Layout>(desc.dimensions()), buf),
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block.expr());
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block_buffer = buf;
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}
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// Read from block.
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TensorBlockIOSrc src(internal::strides<Layout>(desc.dimensions()), block_buffer);
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// Write to the output buffer.
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typename TensorBlockIO::Dimensions output_strides(this->m_unshuffledInputStrides);
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typename TensorBlockIO::Dimensions output_dimensions;
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for (int i = 0; i < NumDims; ++i) {
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output_dimensions[this->m_shuffle[i]] = desc.dimension(i);
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}
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TensorBlockIODst dst(output_dimensions, output_strides, this->m_impl.data(), this->srcCoeff(desc.offset()));
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// Reorder dimensions according to the shuffle.
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typename TensorBlockIO::DimensionsMap dst_to_src_dim_map;
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for (int i = 0; i < NumDims; ++i) {
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dst_to_src_dim_map[i] = static_cast<int>(this->m_inverseShuffle[i]);
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}
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TensorBlockIO::Copy(dst, src, dst_to_src_dim_map);
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// Deallocate temporary buffer used for the block materialization.
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if (mem != NULL) this->m_device.deallocate(mem);
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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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