| // This file is part of Eigen, a lightweight C++ template library |
| // for linear algebra. |
| // |
| // Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com> |
| // |
| // This Source Code Form is subject to the terms of the Mozilla |
| // Public License v. 2.0. If a copy of the MPL was not distributed |
| // with this file, You can obtain one at http://mozilla.org/MPL/2.0/. |
| |
| #ifndef EIGEN_CXX11_TENSOR_TENSOR_SHUFFLING_H |
| #define EIGEN_CXX11_TENSOR_TENSOR_SHUFFLING_H |
| |
| namespace Eigen { |
| |
| /** \class TensorShuffling |
| * \ingroup CXX11_Tensor_Module |
| * |
| * \brief Tensor shuffling class. |
| * |
| * |
| */ |
| namespace internal { |
| template<typename Shuffle, typename XprType> |
| struct traits<TensorShufflingOp<Shuffle, XprType> > : public traits<XprType> |
| { |
| typedef typename XprType::Scalar Scalar; |
| typedef traits<XprType> XprTraits; |
| typedef typename XprTraits::StorageKind StorageKind; |
| typedef typename XprTraits::Index Index; |
| typedef typename XprType::Nested Nested; |
| typedef typename remove_reference<Nested>::type _Nested; |
| static const int NumDimensions = XprTraits::NumDimensions; |
| static const int Layout = XprTraits::Layout; |
| typedef typename XprTraits::PointerType PointerType; |
| }; |
| |
| template<typename Shuffle, typename XprType> |
| struct eval<TensorShufflingOp<Shuffle, XprType>, Eigen::Dense> |
| { |
| typedef const TensorShufflingOp<Shuffle, XprType>& type; |
| }; |
| |
| template<typename Shuffle, typename XprType> |
| struct nested<TensorShufflingOp<Shuffle, XprType>, 1, typename eval<TensorShufflingOp<Shuffle, XprType> >::type> |
| { |
| typedef TensorShufflingOp<Shuffle, XprType> type; |
| }; |
| |
| } // end namespace internal |
| |
| |
| |
| template<typename Shuffle, typename XprType> |
| class TensorShufflingOp : public TensorBase<TensorShufflingOp<Shuffle, XprType> > |
| { |
| public: |
| typedef typename Eigen::internal::traits<TensorShufflingOp>::Scalar Scalar; |
| typedef typename Eigen::NumTraits<Scalar>::Real RealScalar; |
| typedef typename XprType::CoeffReturnType CoeffReturnType; |
| typedef typename Eigen::internal::nested<TensorShufflingOp>::type Nested; |
| typedef typename Eigen::internal::traits<TensorShufflingOp>::StorageKind StorageKind; |
| typedef typename Eigen::internal::traits<TensorShufflingOp>::Index Index; |
| |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorShufflingOp(const XprType& expr, const Shuffle& shfl) |
| : m_xpr(expr), m_shuffle(shfl) {} |
| |
| EIGEN_DEVICE_FUNC |
| const Shuffle& shufflePermutation() const { return m_shuffle; } |
| |
| EIGEN_DEVICE_FUNC |
| const typename internal::remove_all<typename XprType::Nested>::type& |
| expression() const { return m_xpr; } |
| |
| EIGEN_DEVICE_FUNC |
| EIGEN_STRONG_INLINE TensorShufflingOp& operator = (const TensorShufflingOp& other) |
| { |
| typedef TensorAssignOp<TensorShufflingOp, const TensorShufflingOp> Assign; |
| Assign assign(*this, other); |
| internal::TensorExecutor<const Assign, DefaultDevice>::run(assign, DefaultDevice()); |
| return *this; |
| } |
| |
| template<typename OtherDerived> |
| EIGEN_DEVICE_FUNC |
| EIGEN_STRONG_INLINE TensorShufflingOp& operator = (const OtherDerived& other) |
| { |
| typedef TensorAssignOp<TensorShufflingOp, const OtherDerived> Assign; |
| Assign assign(*this, other); |
| internal::TensorExecutor<const Assign, DefaultDevice>::run(assign, DefaultDevice()); |
| return *this; |
| } |
| |
| protected: |
| typename XprType::Nested m_xpr; |
| const Shuffle m_shuffle; |
| }; |
| |
| |
| // Eval as rvalue |
| template<typename Shuffle, typename ArgType, typename Device> |
| struct TensorEvaluator<const TensorShufflingOp<Shuffle, ArgType>, Device> |
| { |
| typedef TensorEvaluator<const TensorShufflingOp<Shuffle, ArgType>, Device> Self; |
| typedef TensorShufflingOp<Shuffle, ArgType> XprType; |
| typedef typename XprType::Index Index; |
| static const int NumDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value; |
| typedef DSizes<Index, NumDims> Dimensions; |
| typedef typename XprType::Scalar Scalar; |
| typedef typename XprType::CoeffReturnType CoeffReturnType; |
| typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType; |
| static const int PacketSize = PacketType<CoeffReturnType, Device>::size; |
| typedef StorageMemory<CoeffReturnType, Device> Storage; |
| typedef typename Storage::Type EvaluatorPointerType; |
| |
| enum { |
| IsAligned = false, |
| PacketAccess = (PacketType<CoeffReturnType, Device>::size > 1), |
| BlockAccess = TensorEvaluator<ArgType, Device>::BlockAccess, |
| PreferBlockAccess = true, |
| Layout = TensorEvaluator<ArgType, Device>::Layout, |
| CoordAccess = false, // to be implemented |
| RawAccess = false |
| }; |
| |
| typedef typename internal::remove_const<Scalar>::type ScalarNoConst; |
| |
| typedef internal::TensorBlock<ScalarNoConst, Index, NumDims, Layout> |
| TensorBlock; |
| typedef internal::TensorBlockReader<ScalarNoConst, Index, NumDims, Layout> |
| TensorBlockReader; |
| |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, |
| const Device& device) |
| : m_device(device), |
| m_impl(op.expression(), device) |
| { |
| const typename TensorEvaluator<ArgType, Device>::Dimensions& input_dims = m_impl.dimensions(); |
| const Shuffle& shuffle = op.shufflePermutation(); |
| m_is_identity = true; |
| for (int i = 0; i < NumDims; ++i) { |
| m_dimensions[i] = input_dims[shuffle[i]]; |
| m_inverseShuffle[shuffle[i]] = i; |
| if (m_is_identity && shuffle[i] != i) { |
| m_is_identity = false; |
| } |
| } |
| |
| if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { |
| m_unshuffledInputStrides[0] = 1; |
| m_outputStrides[0] = 1; |
| |
| for (int i = 1; i < NumDims; ++i) { |
| m_unshuffledInputStrides[i] = |
| m_unshuffledInputStrides[i - 1] * input_dims[i - 1]; |
| m_outputStrides[i] = m_outputStrides[i - 1] * m_dimensions[i - 1]; |
| m_fastOutputStrides[i] = internal::TensorIntDivisor<Index>(m_outputStrides[i]); |
| } |
| } else { |
| m_unshuffledInputStrides[NumDims - 1] = 1; |
| m_outputStrides[NumDims - 1] = 1; |
| for (int i = NumDims - 2; i >= 0; --i) { |
| m_unshuffledInputStrides[i] = |
| m_unshuffledInputStrides[i + 1] * input_dims[i + 1]; |
| m_outputStrides[i] = m_outputStrides[i + 1] * m_dimensions[i + 1]; |
| m_fastOutputStrides[i] = internal::TensorIntDivisor<Index>(m_outputStrides[i]); |
| } |
| } |
| |
| for (int i = 0; i < NumDims; ++i) { |
| m_inputStrides[i] = m_unshuffledInputStrides[shuffle[i]]; |
| } |
| } |
| |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; } |
| |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType /*data*/) { |
| m_impl.evalSubExprsIfNeeded(NULL); |
| return true; |
| } |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() { |
| m_impl.cleanup(); |
| } |
| |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const |
| { |
| if (m_is_identity) { |
| return m_impl.coeff(index); |
| } else { |
| return m_impl.coeff(srcCoeff(index)); |
| } |
| } |
| |
| template <int LoadMode, typename Self, bool ImplPacketAccess> |
| struct PacketLoader { |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE |
| static PacketReturnType Run(const Self& self, Index index) { |
| EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[PacketSize]; |
| EIGEN_UNROLL_LOOP |
| for (int i = 0; i < PacketSize; ++i) { |
| values[i] = self.coeff(index + i); |
| } |
| PacketReturnType rslt = internal::pload<PacketReturnType>(values); |
| return rslt; |
| } |
| }; |
| |
| template<int LoadMode, typename Self> |
| struct PacketLoader<LoadMode, Self, true> { |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE |
| static PacketReturnType Run(const Self& self, Index index) { |
| if (self.m_is_identity) { |
| return self.m_impl.template packet<LoadMode>(index); |
| } else { |
| EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[PacketSize]; |
| EIGEN_UNROLL_LOOP |
| for (int i = 0; i < PacketSize; ++i) { |
| values[i] = self.coeff(index + i); |
| } |
| PacketReturnType rslt = internal::pload<PacketReturnType>(values); |
| return rslt; |
| } |
| } |
| }; |
| |
| template<int LoadMode> |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const |
| { |
| EIGEN_STATIC_ASSERT((PacketSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE) |
| eigen_assert(index + PacketSize - 1 < dimensions().TotalSize()); |
| return PacketLoader<LoadMode, Self, TensorEvaluator<ArgType, Device>::PacketAccess>::Run(*this, index); |
| } |
| |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void getResourceRequirements( |
| std::vector<internal::TensorOpResourceRequirements>* resources) const { |
| Eigen::Index block_total_size_max = numext::maxi<Eigen::Index>( |
| 1, m_device.firstLevelCacheSize() / sizeof(Scalar)); |
| resources->push_back(internal::TensorOpResourceRequirements( |
| internal::kUniformAllDims, block_total_size_max)); |
| m_impl.getResourceRequirements(resources); |
| } |
| |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void block( |
| TensorBlock* output_block) const { |
| if (m_impl.data() != NULL) { |
| // Fast path: we have direct access to the data, so shuffle as we read. |
| TensorBlockReader::Run(output_block, |
| srcCoeff(output_block->first_coeff_index()), |
| m_inverseShuffle, |
| m_unshuffledInputStrides, |
| m_impl.data()); |
| return; |
| } |
| |
| // Slow path: read unshuffled block from the input and shuffle in-place. |
| // Initialize input block sizes using input-to-output shuffle map. |
| DSizes<Index, NumDims> input_block_sizes; |
| for (Index i = 0; i < NumDims; ++i) { |
| input_block_sizes[i] = output_block->block_sizes()[m_inverseShuffle[i]]; |
| } |
| |
| // Calculate input block strides. |
| DSizes<Index, NumDims> input_block_strides; |
| if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { |
| input_block_strides[0] = 1; |
| for (int i = 1; i < NumDims; ++i) { |
| input_block_strides[i] = |
| input_block_strides[i - 1] * input_block_sizes[i - 1]; |
| } |
| } else { |
| input_block_strides[NumDims - 1] = 1; |
| for (int i = NumDims - 2; i >= 0; --i) { |
| input_block_strides[i] = |
| input_block_strides[i + 1] * input_block_sizes[i + 1]; |
| } |
| } |
| DSizes<internal::TensorIntDivisor<Index>, NumDims> fast_input_block_strides; |
| for (int i = 0; i < NumDims; ++i) { |
| fast_input_block_strides[i] = |
| internal::TensorIntDivisor<Index>(input_block_strides[i]); |
| } |
| |
| // Read input block. |
| TensorBlock input_block(srcCoeff(output_block->first_coeff_index()), |
| input_block_sizes, |
| input_block_strides, |
| Dimensions(m_unshuffledInputStrides), |
| output_block->data()); |
| |
| m_impl.block(&input_block); |
| |
| // Naive In-place shuffle: random IO but block size is O(L1 cache size). |
| // TODO(andydavis) Improve the performance of this in-place shuffle. |
| const Index total_size = input_block_sizes.TotalSize(); |
| std::vector<bool> bitmap(total_size, false); |
| ScalarNoConst* data = const_cast<ScalarNoConst*>(output_block->data()); |
| const DSizes<Index, NumDims>& output_block_strides = |
| output_block->block_strides(); |
| for (Index input_index = 0; input_index < total_size; ++input_index) { |
| if (bitmap[input_index]) { |
| // Coefficient at this index has already been shuffled. |
| continue; |
| } |
| |
| Index output_index = |
| GetBlockOutputIndex(input_index, input_block_strides, |
| output_block_strides, fast_input_block_strides); |
| if (output_index == input_index) { |
| // Coefficient already in place. |
| bitmap[output_index] = true; |
| continue; |
| } |
| |
| // The following loop starts at 'input_index', and shuffles |
| // coefficients into their shuffled location at 'output_index'. |
| // It skips through the array shuffling coefficients by following |
| // the shuffle cycle starting and ending a 'start_index'. |
| ScalarNoConst evicted_value; |
| ScalarNoConst shuffled_value = data[input_index]; |
| do { |
| evicted_value = data[output_index]; |
| data[output_index] = shuffled_value; |
| shuffled_value = evicted_value; |
| bitmap[output_index] = true; |
| output_index = |
| GetBlockOutputIndex(output_index, input_block_strides, |
| output_block_strides, fast_input_block_strides); |
| } while (output_index != input_index); |
| |
| data[output_index] = shuffled_value; |
| bitmap[output_index] = true; |
| } |
| } |
| |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const { |
| const double compute_cost = m_is_identity ? TensorOpCost::AddCost<Index>() : |
| NumDims * (2 * TensorOpCost::AddCost<Index>() + |
| 2 * TensorOpCost::MulCost<Index>() + |
| TensorOpCost::DivCost<Index>()); |
| return m_impl.costPerCoeff(vectorized) + |
| TensorOpCost(0, 0, compute_cost, m_is_identity /* vectorized */, PacketSize); |
| } |
| |
| EIGEN_DEVICE_FUNC typename Storage::Type data() const { return NULL; } |
| |
| #ifdef EIGEN_USE_SYCL |
| // binding placeholder accessors to a command group handler for SYCL |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler &cgh) const { |
| m_impl.bind(cgh); |
| } |
| #endif |
| protected: |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index GetBlockOutputIndex( |
| Index input_index, |
| const DSizes<Index, NumDims>& input_block_strides, |
| const DSizes<Index, NumDims>& output_block_strides, |
| const DSizes<internal::TensorIntDivisor<Index>, NumDims>& fast_input_block_strides) const { |
| Index output_index = 0; |
| if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { |
| for (int i = NumDims - 1; i > 0; --i) { |
| const Index idx = input_index / fast_input_block_strides[i]; |
| output_index += idx * output_block_strides[m_inverseShuffle[i]]; |
| input_index -= idx * input_block_strides[i]; |
| } |
| return output_index + input_index * |
| output_block_strides[m_inverseShuffle[0]]; |
| } else { |
| for (int i = 0; i < NumDims - 1; ++i) { |
| const Index idx = input_index / fast_input_block_strides[i]; |
| output_index += idx * output_block_strides[m_inverseShuffle[i]]; |
| input_index -= idx * input_block_strides[i]; |
| } |
| return output_index + input_index * |
| output_block_strides[m_inverseShuffle[NumDims - 1]]; |
| } |
| } |
| |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index srcCoeff(Index index) const { |
| Index inputIndex = 0; |
| if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { |
| for (int i = NumDims - 1; i > 0; --i) { |
| const Index idx = index / m_fastOutputStrides[i]; |
| inputIndex += idx * m_inputStrides[i]; |
| index -= idx * m_outputStrides[i]; |
| } |
| return inputIndex + index * m_inputStrides[0]; |
| } else { |
| for (int i = 0; i < NumDims - 1; ++i) { |
| const Index idx = index / m_fastOutputStrides[i]; |
| inputIndex += idx * m_inputStrides[i]; |
| index -= idx * m_outputStrides[i]; |
| } |
| return inputIndex + index * m_inputStrides[NumDims - 1]; |
| } |
| } |
| |
| Dimensions m_dimensions; |
| bool m_is_identity; |
| array<Index, NumDims> m_inverseShuffle; |
| array<Index, NumDims> m_outputStrides; |
| array<internal::TensorIntDivisor<Index>, NumDims> m_fastOutputStrides; |
| array<Index, NumDims> m_inputStrides; |
| array<Index, NumDims> m_unshuffledInputStrides; |
| |
| const Device EIGEN_DEVICE_REF m_device; |
| TensorEvaluator<ArgType, Device> m_impl; |
| }; |
| |
| |
| // Eval as lvalue |
| template<typename Shuffle, typename ArgType, typename Device> |
| struct TensorEvaluator<TensorShufflingOp<Shuffle, ArgType>, Device> |
| : public TensorEvaluator<const TensorShufflingOp<Shuffle, ArgType>, Device> |
| { |
| typedef TensorEvaluator<const TensorShufflingOp<Shuffle, ArgType>, Device> Base; |
| |
| typedef TensorShufflingOp<Shuffle, ArgType> XprType; |
| typedef typename XprType::Index Index; |
| static const int NumDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value; |
| typedef DSizes<Index, NumDims> Dimensions; |
| typedef typename XprType::Scalar Scalar; |
| typedef typename XprType::CoeffReturnType CoeffReturnType; |
| typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType; |
| static const int PacketSize = PacketType<CoeffReturnType, Device>::size; |
| |
| enum { |
| IsAligned = false, |
| PacketAccess = (PacketType<CoeffReturnType, Device>::size > 1), |
| BlockAccess = TensorEvaluator<ArgType, Device>::BlockAccess, |
| PreferBlockAccess = true, |
| Layout = TensorEvaluator<ArgType, Device>::Layout, |
| RawAccess = false |
| }; |
| |
| typedef typename internal::remove_const<Scalar>::type ScalarNoConst; |
| |
| typedef internal::TensorBlock<ScalarNoConst, Index, NumDims, Layout> |
| TensorBlock; |
| typedef internal::TensorBlockWriter<ScalarNoConst, Index, NumDims, Layout> |
| TensorBlockWriter; |
| |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device) |
| : Base(op, device) |
| { } |
| |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType& coeffRef(Index index) |
| { |
| return this->m_impl.coeffRef(this->srcCoeff(index)); |
| } |
| |
| template <int StoreMode> EIGEN_STRONG_INLINE |
| void writePacket(Index index, const PacketReturnType& x) |
| { |
| EIGEN_STATIC_ASSERT((PacketSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE) |
| |
| EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[PacketSize]; |
| internal::pstore<CoeffReturnType, PacketReturnType>(values, x); |
| EIGEN_UNROLL_LOOP |
| for (int i = 0; i < PacketSize; ++i) { |
| this->coeffRef(index+i) = values[i]; |
| } |
| } |
| |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void writeBlock( |
| const TensorBlock& block) { |
| eigen_assert(this->m_impl.data() != NULL); |
| TensorBlockWriter::Run(block, this->srcCoeff(block.first_coeff_index()), |
| this->m_inverseShuffle, |
| this->m_unshuffledInputStrides, this->m_impl.data()); |
| } |
| }; |
| |
| |
| } // end namespace Eigen |
| |
| #endif // EIGEN_CXX11_TENSOR_TENSOR_SHUFFLING_H |
