| // 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_MORPHING_H |
| #define EIGEN_CXX11_TENSOR_TENSOR_MORPHING_H |
| |
| namespace Eigen { |
| |
| /** \class TensorReshaping |
| * \ingroup CXX11_Tensor_Module |
| * |
| * \brief Tensor reshaping class. |
| * |
| * |
| */ |
| namespace internal { |
| template<typename NewDimensions, typename XprType> |
| struct traits<TensorReshapingOp<NewDimensions, 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 = array_size<NewDimensions>::value; |
| static const int Layout = XprTraits::Layout; |
| typedef typename XprTraits::PointerType PointerType; |
| }; |
| |
| template<typename NewDimensions, typename XprType> |
| struct eval<TensorReshapingOp<NewDimensions, XprType>, Eigen::Dense> |
| { |
| typedef const TensorReshapingOp<NewDimensions, XprType>EIGEN_DEVICE_REF type; |
| }; |
| |
| template<typename NewDimensions, typename XprType> |
| struct nested<TensorReshapingOp<NewDimensions, XprType>, 1, typename eval<TensorReshapingOp<NewDimensions, XprType> >::type> |
| { |
| typedef TensorReshapingOp<NewDimensions, XprType> type; |
| }; |
| |
| } // end namespace internal |
| |
| |
| |
| template<typename NewDimensions, typename XprType> |
| class TensorReshapingOp : public TensorBase<TensorReshapingOp<NewDimensions, XprType>, WriteAccessors> |
| { |
| public: |
| typedef typename Eigen::internal::traits<TensorReshapingOp>::Scalar Scalar; |
| typedef typename internal::remove_const<typename XprType::CoeffReturnType>::type CoeffReturnType; |
| typedef typename Eigen::internal::nested<TensorReshapingOp>::type Nested; |
| typedef typename Eigen::internal::traits<TensorReshapingOp>::StorageKind StorageKind; |
| typedef typename Eigen::internal::traits<TensorReshapingOp>::Index Index; |
| |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorReshapingOp(const XprType& expr, const NewDimensions& dims) |
| : m_xpr(expr), m_dims(dims) {} |
| |
| EIGEN_DEVICE_FUNC |
| const NewDimensions& dimensions() const { return m_dims; } |
| |
| EIGEN_DEVICE_FUNC |
| const typename internal::remove_all<typename XprType::Nested>::type& |
| expression() const { return m_xpr; } |
| |
| EIGEN_DEVICE_FUNC |
| EIGEN_STRONG_INLINE TensorReshapingOp& operator = (const TensorReshapingOp& other) |
| { |
| typedef TensorAssignOp<TensorReshapingOp, const TensorReshapingOp> Assign; |
| Assign assign(*this, other); |
| internal::TensorExecutor<const Assign, DefaultDevice>::run(assign, DefaultDevice()); |
| return *this; |
| } |
| |
| template<typename OtherDerived> |
| EIGEN_DEVICE_FUNC |
| EIGEN_STRONG_INLINE TensorReshapingOp& operator = (const OtherDerived& other) |
| { |
| typedef TensorAssignOp<TensorReshapingOp, const OtherDerived> Assign; |
| Assign assign(*this, other); |
| internal::TensorExecutor<const Assign, DefaultDevice>::run(assign, DefaultDevice()); |
| return *this; |
| } |
| |
| protected: |
| typename XprType::Nested m_xpr; |
| const NewDimensions m_dims; |
| }; |
| |
| |
| // Eval as rvalue |
| template<typename NewDimensions, typename ArgType, typename Device> |
| struct TensorEvaluator<const TensorReshapingOp<NewDimensions, ArgType>, Device> |
| { |
| typedef TensorReshapingOp<NewDimensions, ArgType> XprType; |
| typedef NewDimensions Dimensions; |
| |
| typedef typename XprType::Index Index; |
| typedef typename XprType::Scalar Scalar; |
| typedef typename XprType::CoeffReturnType CoeffReturnType; |
| typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType; |
| typedef StorageMemory<CoeffReturnType, Device> Storage; |
| typedef typename Storage::Type EvaluatorPointerType; |
| typedef StorageMemory<typename internal::remove_const<CoeffReturnType>::type, Device> ConstCastStorage; |
| |
| static const int NumOutputDims = internal::array_size<Dimensions>::value; |
| static const int NumInputDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value; |
| |
| enum { |
| IsAligned = TensorEvaluator<ArgType, Device>::IsAligned, |
| PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess, |
| // TODO(andydavis, wuke) Enable BlockAccess for the general case when the |
| // performance issue with block-based reshape is resolved. |
| BlockAccess = TensorEvaluator<ArgType, Device>::BlockAccess && |
| TensorEvaluator<ArgType, Device>::RawAccess && |
| NumInputDims > 0 && NumOutputDims > 0, |
| PreferBlockAccess = true, |
| Layout = TensorEvaluator<ArgType, Device>::Layout, |
| CoordAccess = false, // to be implemented |
| RawAccess = TensorEvaluator<ArgType, Device>::RawAccess |
| }; |
| |
| typedef typename internal::remove_const<Scalar>::type ScalarNoConst; |
| |
| typedef internal::TensorBlock<ScalarNoConst, Index, NumInputDims, Layout> |
| InputTensorBlock; |
| typedef internal::TensorBlock<ScalarNoConst, Index, NumOutputDims, Layout> |
| OutputTensorBlock; |
| typedef internal::TensorBlockReader<ScalarNoConst, Index, NumOutputDims, |
| Layout> |
| OutputTensorBlockReader; |
| |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device) |
| : m_impl(op.expression(), device), m_dimensions(op.dimensions()) |
| { |
| // The total size of the reshaped tensor must be equal to the total size |
| // of the input tensor. |
| eigen_assert(internal::array_prod(m_impl.dimensions()) == internal::array_prod(op.dimensions())); |
| |
| if (BlockAccess) { |
| const typename TensorEvaluator<ArgType, Device>::Dimensions& input_dims = |
| m_impl.dimensions(); |
| if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { |
| m_outputStrides[0] = 1; |
| for (int i = 1; i < NumOutputDims; ++i) { |
| m_outputStrides[i] = m_outputStrides[i - 1] * m_dimensions[i - 1]; |
| } |
| m_inputStrides[0] = 1; |
| for (int i = 1; i < NumInputDims; ++i) { |
| m_inputStrides[i] = m_inputStrides[i - 1] * input_dims[i - 1]; |
| } |
| } else { |
| m_outputStrides[NumOutputDims - 1] = 1; |
| for (int i = NumOutputDims - 2; i >= 0; --i) { |
| m_outputStrides[i] = m_outputStrides[i + 1] * m_dimensions[i + 1]; |
| } |
| m_inputStrides[NumInputDims - 1] = 1; |
| for (int i = NumInputDims - 2; i >= 0; --i) { |
| m_inputStrides[i] = m_inputStrides[i + 1] * input_dims[i + 1]; |
| } |
| } |
| } |
| } |
| |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; } |
| |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType data) { |
| return m_impl.evalSubExprsIfNeeded(data); |
| } |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() { |
| m_impl.cleanup(); |
| } |
| |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const |
| { |
| return m_impl.coeff(index); |
| } |
| |
| template<int LoadMode> |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const |
| { |
| return m_impl.template packet<LoadMode>(index); |
| } |
| |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const { |
| return m_impl.costPerCoeff(vectorized); |
| } |
| |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void getResourceRequirements( |
| std::vector<internal::TensorOpResourceRequirements>* resources) const { |
| m_impl.getResourceRequirements(resources); |
| } |
| |
| // required in block(OutputTensorBlock* output_block) const |
| // For C++03 compatibility this must be defined outside the method |
| struct BlockIteratorState { |
| Index stride; |
| Index span; |
| Index size; |
| Index count; |
| }; |
| // TODO(andydavis) Reduce the overhead of this function. |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void block( |
| OutputTensorBlock* output_block) const { |
| if (m_impl.data() != NULL) { |
| OutputTensorBlockReader::Run(output_block, m_impl.data()); |
| return; |
| } |
| |
| // Calculate output block unit-stride inner dimension length. |
| const DSizes<Index, NumOutputDims>& output_block_sizes = |
| output_block->block_sizes(); |
| Index output_inner_dim_size = 1; |
| Index output_outer_dim_start = NumOutputDims; |
| for (Index i = 0; i < NumOutputDims; ++i) { |
| const Index dim = static_cast<int>(Layout) == static_cast<int>(ColMajor) |
| ? i : NumOutputDims - i - 1; |
| output_inner_dim_size *= output_block_sizes[dim]; |
| if (output_block_sizes[dim] < m_dimensions[dim]) { |
| output_outer_dim_start = i + 1; |
| break; |
| } |
| } |
| |
| // Initialize output block iterator state. |
| array<BlockIteratorState, NumOutputDims> block_iter_state; |
| |
| for (Index i = 0; i < NumOutputDims; ++i) { |
| const Index dim = static_cast<int>(Layout) == static_cast<int>(ColMajor) |
| ? i : NumOutputDims - i - 1; |
| block_iter_state[i].size = output_block_sizes[dim]; |
| block_iter_state[i].stride = m_outputStrides[dim]; |
| block_iter_state[i].span = |
| block_iter_state[i].stride * (block_iter_state[i].size - 1); |
| block_iter_state[i].count = 0; |
| } |
| |
| const Index output_outer_dim_size = output_block_sizes.TotalSize() / |
| output_inner_dim_size; |
| const typename TensorEvaluator<ArgType, Device>::Dimensions& input_dims = |
| m_impl.dimensions(); |
| |
| Index index = output_block->first_coeff_index(); |
| for (Index outer_idx = 0; outer_idx < output_outer_dim_size; ++outer_idx) { |
| Index inner_idx = 0; |
| while (inner_idx < output_inner_dim_size) { |
| // Calculate input coords based on 'index'. |
| array<Index, NumInputDims> input_coords; |
| Index idx = index; |
| if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { |
| for (int i = NumInputDims - 1; i > 0; --i) { |
| input_coords[i] = idx / m_inputStrides[i]; |
| idx -= input_coords[i] * m_inputStrides[i]; |
| } |
| input_coords[0] = idx; |
| } else { |
| for (int i = 0; i < NumInputDims - 1; ++i) { |
| input_coords[i] = idx / m_inputStrides[i]; |
| idx -= input_coords[i] * m_inputStrides[i]; |
| } |
| input_coords[NumInputDims - 1] = idx; |
| } |
| |
| // Calculate target input block shape, using at most |
| // 'output_inner_dim_size' coefficients along the input block's inner |
| // dimensions. |
| DSizes<Index, NumInputDims> input_block_sizes; |
| Index num_to_allocate = output_inner_dim_size - inner_idx; |
| for (Index i = 0; i < NumInputDims; ++i) { |
| const Index dim = |
| static_cast<int>(Layout) == static_cast<int>(ColMajor) |
| ? i : NumInputDims - i - 1; |
| input_block_sizes[dim] = numext::mini( |
| num_to_allocate, (static_cast<Index>(input_dims[dim]) - |
| input_coords[dim])); |
| if (input_coords[dim] == 0) { |
| num_to_allocate /= input_block_sizes[dim]; |
| } else { |
| num_to_allocate = 1; |
| } |
| } |
| |
| // Calculate input block strides. |
| DSizes<Index, NumInputDims> input_block_strides; |
| if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { |
| input_block_strides[0] = 1; |
| for (int i = 1; i < NumInputDims; ++i) { |
| input_block_strides[i] = input_block_strides[i - 1] * |
| input_block_sizes[i - 1]; |
| } |
| } else { |
| input_block_strides[NumInputDims - 1] = 1; |
| for (int i = NumInputDims - 2; i >= 0; --i) { |
| input_block_strides[i] = input_block_strides[i + 1] * |
| input_block_sizes[i + 1]; |
| } |
| } |
| |
| // Instantiate and read input block from input tensor. |
| InputTensorBlock input_block(index, input_block_sizes, |
| input_block_strides, m_inputStrides, |
| output_block->data() + outer_idx * |
| output_inner_dim_size + inner_idx); |
| |
| m_impl.block(&input_block); |
| |
| const Index input_block_total_size = input_block_sizes.TotalSize(); |
| index += input_block_total_size; |
| inner_idx += input_block_total_size; |
| } |
| eigen_assert(inner_idx == output_inner_dim_size); |
| index -= output_inner_dim_size; |
| // Update index. |
| for (Index i = output_outer_dim_start; i < NumOutputDims; ++i) { |
| if (++block_iter_state[i].count < block_iter_state[i].size) { |
| index += block_iter_state[i].stride; |
| break; |
| } |
| block_iter_state[i].count = 0; |
| index -= block_iter_state[i].span; |
| } |
| } |
| } |
| |
| EIGEN_DEVICE_FUNC typename Storage::Type data() const { |
| return constCast(m_impl.data()); |
| } |
| |
| EIGEN_DEVICE_FUNC const TensorEvaluator<ArgType, Device>& impl() const { return m_impl; } |
| |
| #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: |
| TensorEvaluator<ArgType, Device> m_impl; |
| NewDimensions m_dimensions; |
| DSizes<Index, NumOutputDims> m_outputStrides; |
| DSizes<Index, NumInputDims> m_inputStrides; |
| }; |
| |
| |
| // Eval as lvalue |
| template<typename NewDimensions, typename ArgType, typename Device> |
| struct TensorEvaluator<TensorReshapingOp<NewDimensions, ArgType>, Device> |
| : public TensorEvaluator<const TensorReshapingOp<NewDimensions, ArgType>, Device> |
| |
| { |
| typedef TensorEvaluator<const TensorReshapingOp<NewDimensions, ArgType>, Device> Base; |
| typedef TensorReshapingOp<NewDimensions, ArgType> XprType; |
| typedef NewDimensions Dimensions; |
| |
| enum { |
| IsAligned = TensorEvaluator<ArgType, Device>::IsAligned, |
| PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess, |
| BlockAccess = false, |
| PreferBlockAccess = false, |
| Layout = TensorEvaluator<ArgType, Device>::Layout, |
| CoordAccess = false, // to be implemented |
| RawAccess = TensorEvaluator<ArgType, Device>::RawAccess |
| }; |
| |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device) |
| : Base(op, device) |
| { } |
| |
| typedef typename XprType::Index Index; |
| typedef typename XprType::Scalar Scalar; |
| typedef typename XprType::CoeffReturnType CoeffReturnType; |
| typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType; |
| |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType& coeffRef(Index index) |
| { |
| return this->m_impl.coeffRef(index); |
| } |
| template <int StoreMode> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE |
| void writePacket(Index index, const PacketReturnType& x) |
| { |
| this->m_impl.template writePacket<StoreMode>(index, x); |
| } |
| }; |
| |
| |
| /** \class TensorSlicing |
| * \ingroup CXX11_Tensor_Module |
| * |
| * \brief Tensor slicing class. |
| * |
| * |
| */ |
| namespace internal { |
| template<typename StartIndices, typename Sizes, typename XprType> |
| struct traits<TensorSlicingOp<StartIndices, Sizes, 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 = array_size<StartIndices>::value; |
| static const int Layout = XprTraits::Layout; |
| typedef typename XprTraits::PointerType PointerType; |
| }; |
| |
| template<typename StartIndices, typename Sizes, typename XprType> |
| struct eval<TensorSlicingOp<StartIndices, Sizes, XprType>, Eigen::Dense> |
| { |
| typedef const TensorSlicingOp<StartIndices, Sizes, XprType>EIGEN_DEVICE_REF type; |
| }; |
| |
| template<typename StartIndices, typename Sizes, typename XprType> |
| struct nested<TensorSlicingOp<StartIndices, Sizes, XprType>, 1, typename eval<TensorSlicingOp<StartIndices, Sizes, XprType> >::type> |
| { |
| typedef TensorSlicingOp<StartIndices, Sizes, XprType> type; |
| }; |
| |
| } // end namespace internal |
| |
| |
| |
| template<typename StartIndices, typename Sizes, typename XprType> |
| class TensorSlicingOp : public TensorBase<TensorSlicingOp<StartIndices, Sizes, XprType> > |
| { |
| public: |
| typedef typename Eigen::internal::traits<TensorSlicingOp>::Scalar Scalar; |
| typedef typename XprType::CoeffReturnType CoeffReturnType; |
| typedef typename Eigen::internal::nested<TensorSlicingOp>::type Nested; |
| typedef typename Eigen::internal::traits<TensorSlicingOp>::StorageKind StorageKind; |
| typedef typename Eigen::internal::traits<TensorSlicingOp>::Index Index; |
| |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorSlicingOp(const XprType& expr, const StartIndices& indices, const Sizes& sizes) |
| : m_xpr(expr), m_indices(indices), m_sizes(sizes) {} |
| |
| EIGEN_DEVICE_FUNC |
| const StartIndices& startIndices() const { return m_indices; } |
| EIGEN_DEVICE_FUNC |
| const Sizes& sizes() const { return m_sizes; } |
| |
| EIGEN_DEVICE_FUNC |
| const typename internal::remove_all<typename XprType::Nested>::type& |
| expression() const { return m_xpr; } |
| |
| template<typename OtherDerived> |
| EIGEN_DEVICE_FUNC |
| EIGEN_STRONG_INLINE TensorSlicingOp& operator = (const OtherDerived& other) |
| { |
| typedef TensorAssignOp<TensorSlicingOp, const OtherDerived> Assign; |
| Assign assign(*this, other); |
| internal::TensorExecutor<const Assign, DefaultDevice>::run(assign, DefaultDevice()); |
| return *this; |
| } |
| |
| EIGEN_DEVICE_FUNC |
| EIGEN_STRONG_INLINE TensorSlicingOp& operator = (const TensorSlicingOp& other) |
| { |
| typedef TensorAssignOp<TensorSlicingOp, const TensorSlicingOp> Assign; |
| Assign assign(*this, other); |
| internal::TensorExecutor<const Assign, DefaultDevice>::run(assign, DefaultDevice()); |
| return *this; |
| } |
| |
| |
| protected: |
| typename XprType::Nested m_xpr; |
| const StartIndices m_indices; |
| const Sizes m_sizes; |
| }; |
| |
| |
| // Fixme: figure out the exact threshold |
| namespace { |
| template <typename Index, typename Device, bool BlockAccess> struct MemcpyTriggerForSlicing { |
| EIGEN_DEVICE_FUNC MemcpyTriggerForSlicing(const Device& device) : threshold_(2 * device.numThreads()) { } |
| EIGEN_DEVICE_FUNC bool operator ()(Index total, Index contiguous) const { |
| const bool prefer_block_evaluation = BlockAccess && total > 32*1024; |
| return !prefer_block_evaluation && contiguous > threshold_; |
| } |
| |
| private: |
| Index threshold_; |
| }; |
| |
| // It is very expensive to start the memcpy kernel on GPU: we therefore only |
| // use it for large copies. |
| #ifdef EIGEN_USE_GPU |
| template <typename Index, bool BlockAccess> struct MemcpyTriggerForSlicing<Index, GpuDevice, BlockAccess> { |
| EIGEN_DEVICE_FUNC MemcpyTriggerForSlicing(const GpuDevice&) { } |
| EIGEN_DEVICE_FUNC bool operator ()(Index total, Index contiguous) const { return contiguous > 4*1024*1024; } |
| }; |
| #endif |
| |
| // It is very expensive to start the memcpy kernel on GPU: we therefore only |
| // use it for large copies. |
| #ifdef EIGEN_USE_SYCL |
| template <typename Index, bool BlockAccess> struct MemcpyTriggerForSlicing<Index, Eigen::SyclDevice, BlockAccess> { |
| EIGEN_DEVICE_FUNC MemcpyTriggerForSlicing(const SyclDevice&) { } |
| EIGEN_DEVICE_FUNC bool operator ()(Index total, Index contiguous) const { return contiguous > 4*1024*1024; } |
| }; |
| #endif |
| |
| } |
| |
| // Eval as rvalue |
| template<typename StartIndices, typename Sizes, typename ArgType, typename Device> |
| struct TensorEvaluator<const TensorSlicingOp<StartIndices, Sizes, ArgType>, Device> |
| { |
| typedef TensorSlicingOp<StartIndices, Sizes, ArgType> XprType; |
| static const int NumDims = internal::array_size<Sizes>::value; |
| |
| typedef typename XprType::Index Index; |
| typedef typename XprType::Scalar Scalar; |
| typedef typename XprType::CoeffReturnType CoeffReturnType; |
| typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType; |
| typedef Sizes Dimensions; |
| typedef StorageMemory<CoeffReturnType, Device> Storage; |
| typedef StorageMemory<typename internal::remove_const<CoeffReturnType>::type, Device> ConstCastStorage; |
| typedef typename Storage::Type EvaluatorPointerType; |
| |
| enum { |
| // Alignment can't be guaranteed at compile time since it depends on the |
| // slice offsets and sizes. |
| IsAligned = false, |
| PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess, |
| BlockAccess = TensorEvaluator<ArgType, Device>::BlockAccess, |
| PreferBlockAccess = true, |
| Layout = TensorEvaluator<ArgType, Device>::Layout, |
| CoordAccess = false, |
| RawAccess = false |
| }; |
| |
| typedef typename internal::remove_const<Scalar>::type ScalarNoConst; |
| |
| typedef internal::TensorBlock<ScalarNoConst, Index, NumDims, Layout> TensorBlock; |
| typedef typename TensorBlock::Dimensions TensorBlockDimensions; |
| |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device) |
| : m_impl(op.expression(), device), m_device(device), m_dimensions(op.sizes()), m_offsets(op.startIndices()) |
| { |
| for (Index i = 0; i < internal::array_size<Dimensions>::value; ++i) { |
| eigen_assert(m_impl.dimensions()[i] >= op.sizes()[i] + op.startIndices()[i]); |
| } |
| |
| m_is_identity = true; |
| for (int i = 0; i < internal::array_size<Dimensions>::value; ++i) { |
| eigen_assert(m_impl.dimensions()[i] >= |
| op.sizes()[i] + op.startIndices()[i]); |
| if (m_impl.dimensions()[i] != op.sizes()[i] || |
| op.startIndices()[i] != 0) { |
| m_is_identity = false; |
| } |
| } |
| |
| const typename TensorEvaluator<ArgType, Device>::Dimensions& input_dims = m_impl.dimensions(); |
| const Sizes& output_dims = op.sizes(); |
| if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { |
| m_inputStrides[0] = 1; |
| for (int i = 1; i < NumDims; ++i) { |
| m_inputStrides[i] = m_inputStrides[i-1] * input_dims[i-1]; |
| } |
| |
| // Don't initialize m_fastOutputStrides[0] since it won't ever be accessed. |
| m_outputStrides[0] = 1; |
| for (int i = 1; i < NumDims; ++i) { |
| m_outputStrides[i] = m_outputStrides[i-1] * output_dims[i-1]; |
| m_fastOutputStrides[i] = internal::TensorIntDivisor<Index>(m_outputStrides[i]); |
| } |
| } else { |
| m_inputStrides[NumDims-1] = 1; |
| for (int i = NumDims - 2; i >= 0; --i) { |
| m_inputStrides[i] = m_inputStrides[i+1] * input_dims[i+1]; |
| } |
| |
| // Don't initialize m_fastOutputStrides[NumDims-1] since it won't ever be accessed. |
| m_outputStrides[NumDims-1] = 1; |
| for (int i = NumDims - 2; i >= 0; --i) { |
| m_outputStrides[i] = m_outputStrides[i+1] * output_dims[i+1]; |
| m_fastOutputStrides[i] = internal::TensorIntDivisor<Index>(m_outputStrides[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); |
| if (!NumTraits<typename internal::remove_const<Scalar>::type>::RequireInitialization |
| && data && m_impl.data()) { |
| Index contiguous_values = 1; |
| if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { |
| for (int i = 0; i < NumDims; ++i) { |
| contiguous_values *= dimensions()[i]; |
| if (dimensions()[i] != m_impl.dimensions()[i]) { |
| break; |
| } |
| } |
| } else { |
| for (int i = NumDims-1; i >= 0; --i) { |
| contiguous_values *= dimensions()[i]; |
| if (dimensions()[i] != m_impl.dimensions()[i]) { |
| break; |
| } |
| } |
| } |
| // Use memcpy if it's going to be faster than using the regular evaluation. |
| const MemcpyTriggerForSlicing<Index, Device, BlockAccess> trigger(m_device); |
| if (trigger(internal::array_prod(dimensions()), contiguous_values)) { |
| EvaluatorPointerType src = (EvaluatorPointerType)m_impl.data(); |
| for (Index i = 0; i < internal::array_prod(dimensions()); i += contiguous_values) { |
| Index offset = srcCoeff(i); |
| m_device.memcpy((void*)(m_device.get(data + i)), m_device.get(src+offset), contiguous_values * sizeof(Scalar)); |
| } |
| return false; |
| } |
| } |
| 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> |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const |
| { |
| const int packetSize = PacketType<CoeffReturnType, Device>::size; |
| EIGEN_STATIC_ASSERT((packetSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE) |
| eigen_assert(index+packetSize-1 < internal::array_prod(dimensions())); |
| |
| if (m_is_identity) { |
| return m_impl.template packet<LoadMode>(index); |
| } |
| |
| Index inputIndices[] = {0, 0}; |
| Index indices[] = {index, index + packetSize - 1}; |
| if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { |
| EIGEN_UNROLL_LOOP |
| for (int i = NumDims - 1; i > 0; --i) { |
| const Index idx0 = indices[0] / m_fastOutputStrides[i]; |
| const Index idx1 = indices[1] / m_fastOutputStrides[i]; |
| inputIndices[0] += (idx0 + m_offsets[i]) * m_inputStrides[i]; |
| inputIndices[1] += (idx1 + m_offsets[i]) * m_inputStrides[i]; |
| indices[0] -= idx0 * m_outputStrides[i]; |
| indices[1] -= idx1 * m_outputStrides[i]; |
| } |
| inputIndices[0] += (indices[0] + m_offsets[0]); |
| inputIndices[1] += (indices[1] + m_offsets[0]); |
| } else { |
| EIGEN_UNROLL_LOOP |
| for (int i = 0; i < NumDims - 1; ++i) { |
| const Index idx0 = indices[0] / m_fastOutputStrides[i]; |
| const Index idx1 = indices[1] / m_fastOutputStrides[i]; |
| inputIndices[0] += (idx0 + m_offsets[i]) * m_inputStrides[i]; |
| inputIndices[1] += (idx1 + m_offsets[i]) * m_inputStrides[i]; |
| indices[0] -= idx0 * m_outputStrides[i]; |
| indices[1] -= idx1 * m_outputStrides[i]; |
| } |
| inputIndices[0] += (indices[0] + m_offsets[NumDims-1]); |
| inputIndices[1] += (indices[1] + m_offsets[NumDims-1]); |
| } |
| if (inputIndices[1] - inputIndices[0] == packetSize - 1) { |
| PacketReturnType rslt = m_impl.template packet<Unaligned>(inputIndices[0]); |
| return rslt; |
| } |
| else { |
| EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[packetSize]; |
| values[0] = m_impl.coeff(inputIndices[0]); |
| values[packetSize-1] = m_impl.coeff(inputIndices[1]); |
| EIGEN_UNROLL_LOOP |
| for (int i = 1; i < packetSize-1; ++i) { |
| values[i] = coeff(index+i); |
| } |
| PacketReturnType rslt = internal::pload<PacketReturnType>(values); |
| return rslt; |
| } |
| } |
| |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const { |
| return m_impl.costPerCoeff(vectorized) + TensorOpCost(0, 0, m_is_identity ? 1 : NumDims); |
| } |
| |
| 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.lastLevelCacheSize() / sizeof(Scalar)); |
| resources->push_back(internal::TensorOpResourceRequirements( |
| internal::kSkewedInnerDims, block_total_size_max)); |
| m_impl.getResourceRequirements(resources); |
| } |
| |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void block( |
| TensorBlock* output_block) const { |
| TensorBlock input_block(srcCoeff(output_block->first_coeff_index()), |
| output_block->block_sizes(), |
| output_block->block_strides(), |
| TensorBlockDimensions(m_inputStrides), |
| output_block->data()); |
| m_impl.block(&input_block); |
| } |
| |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename Storage::Type data() const { |
| typename Storage::Type result = constCast(m_impl.data()); |
| if (result) { |
| Index offset = 0; |
| if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { |
| for (int i = 0; i < NumDims; ++i) { |
| if (m_dimensions[i] != m_impl.dimensions()[i]) { |
| offset += m_offsets[i] * m_inputStrides[i]; |
| for (int j = i+1; j < NumDims; ++j) { |
| if (m_dimensions[j] > 1) { |
| return NULL; |
| } |
| offset += m_offsets[j] * m_inputStrides[j]; |
| } |
| break; |
| } |
| } |
| } else { |
| for (int i = NumDims - 1; i >= 0; --i) { |
| if (m_dimensions[i] != m_impl.dimensions()[i]) { |
| offset += m_offsets[i] * m_inputStrides[i]; |
| for (int j = i-1; j >= 0; --j) { |
| if (m_dimensions[j] > 1) { |
| return NULL; |
| } |
| offset += m_offsets[j] * m_inputStrides[j]; |
| } |
| break; |
| } |
| } |
| } |
| return result + offset; |
| } |
| return NULL; |
| } |
| #ifdef EIGEN_USE_SYCL |
| // binding placeholder accessors to a command group handler for SYCL |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler &cgh) const { |
| m_impl.bind(cgh); |
| } |
| #endif |
| |
| protected: |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index srcCoeff(Index index) const |
| { |
| Index inputIndex = 0; |
| if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { |
| EIGEN_UNROLL_LOOP |
| for (int i = NumDims - 1; i > 0; --i) { |
| const Index idx = index / m_fastOutputStrides[i]; |
| inputIndex += (idx + m_offsets[i]) * m_inputStrides[i]; |
| index -= idx * m_outputStrides[i]; |
| } |
| inputIndex += (index + m_offsets[0]); |
| } else { |
| EIGEN_UNROLL_LOOP |
| for (int i = 0; i < NumDims - 1; ++i) { |
| const Index idx = index / m_fastOutputStrides[i]; |
| inputIndex += (idx + m_offsets[i]) * m_inputStrides[i]; |
| index -= idx * m_outputStrides[i]; |
| } |
| inputIndex += (index + m_offsets[NumDims-1]); |
| } |
| return inputIndex; |
| } |
| |
| array<Index, NumDims> m_outputStrides; |
| array<internal::TensorIntDivisor<Index>, NumDims> m_fastOutputStrides; |
| array<Index, NumDims> m_inputStrides; |
| TensorEvaluator<ArgType, Device> m_impl; |
| const Device EIGEN_DEVICE_REF m_device; |
| Dimensions m_dimensions; |
| bool m_is_identity; |
| const StartIndices m_offsets; |
| }; |
| |
| |
| // Eval as lvalue |
| template<typename StartIndices, typename Sizes, typename ArgType, typename Device> |
| struct TensorEvaluator<TensorSlicingOp<StartIndices, Sizes, ArgType>, Device> |
| : public TensorEvaluator<const TensorSlicingOp<StartIndices, Sizes, ArgType>, Device> |
| { |
| typedef TensorEvaluator<const TensorSlicingOp<StartIndices, Sizes, ArgType>, Device> Base; |
| typedef TensorSlicingOp<StartIndices, Sizes, ArgType> XprType; |
| static const int NumDims = internal::array_size<Sizes>::value; |
| |
| typedef typename XprType::Index Index; |
| typedef typename XprType::Scalar Scalar; |
| typedef typename XprType::CoeffReturnType CoeffReturnType; |
| typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType; |
| typedef Sizes Dimensions; |
| |
| enum { |
| IsAligned = false, |
| PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess, |
| BlockAccess = TensorEvaluator<ArgType, Device>::BlockAccess, |
| PreferBlockAccess = true, |
| Layout = TensorEvaluator<ArgType, Device>::Layout, |
| CoordAccess = false, |
| RawAccess = (NumDims == 1) & TensorEvaluator<ArgType, Device>::RawAccess |
| }; |
| |
| typedef typename internal::remove_const<Scalar>::type ScalarNoConst; |
| |
| typedef internal::TensorBlock<ScalarNoConst, Index, NumDims, Layout> TensorBlock; |
| typedef typename TensorBlock::Dimensions TensorBlockDimensions; |
| |
| 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) |
| { |
| if (this->m_is_identity) { |
| return this->m_impl.coeffRef(index); |
| } else { |
| return this->m_impl.coeffRef(this->srcCoeff(index)); |
| } |
| } |
| |
| template <int StoreMode> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE |
| void writePacket(Index index, const PacketReturnType& x) |
| { |
| if (this->m_is_identity) { |
| this->m_impl.template writePacket<StoreMode>(index, x); |
| return; |
| } |
| |
| const int packetSize = PacketType<CoeffReturnType, Device>::size; |
| Index inputIndices[] = {0, 0}; |
| Index indices[] = {index, index + packetSize - 1}; |
| if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { |
| EIGEN_UNROLL_LOOP |
| for (int i = NumDims - 1; i > 0; --i) { |
| const Index idx0 = indices[0] / this->m_fastOutputStrides[i]; |
| const Index idx1 = indices[1] / this->m_fastOutputStrides[i]; |
| inputIndices[0] += (idx0 + this->m_offsets[i]) * this->m_inputStrides[i]; |
| inputIndices[1] += (idx1 + this->m_offsets[i]) * this->m_inputStrides[i]; |
| indices[0] -= idx0 * this->m_outputStrides[i]; |
| indices[1] -= idx1 * this->m_outputStrides[i]; |
| } |
| inputIndices[0] += (indices[0] + this->m_offsets[0]); |
| inputIndices[1] += (indices[1] + this->m_offsets[0]); |
| } else { |
| EIGEN_UNROLL_LOOP |
| for (int i = 0; i < NumDims - 1; ++i) { |
| const Index idx0 = indices[0] / this->m_fastOutputStrides[i]; |
| const Index idx1 = indices[1] / this->m_fastOutputStrides[i]; |
| inputIndices[0] += (idx0 + this->m_offsets[i]) * this->m_inputStrides[i]; |
| inputIndices[1] += (idx1 + this->m_offsets[i]) * this->m_inputStrides[i]; |
| indices[0] -= idx0 * this->m_outputStrides[i]; |
| indices[1] -= idx1 * this->m_outputStrides[i]; |
| } |
| inputIndices[0] += (indices[0] + this->m_offsets[NumDims-1]); |
| inputIndices[1] += (indices[1] + this->m_offsets[NumDims-1]); |
| } |
| if (inputIndices[1] - inputIndices[0] == packetSize - 1) { |
| this->m_impl.template writePacket<StoreMode>(inputIndices[0], x); |
| } |
| else { |
| EIGEN_ALIGN_MAX CoeffReturnType values[packetSize]; |
| internal::pstore<CoeffReturnType, PacketReturnType>(values, x); |
| this->m_impl.coeffRef(inputIndices[0]) = values[0]; |
| this->m_impl.coeffRef(inputIndices[1]) = values[packetSize-1]; |
| EIGEN_UNROLL_LOOP |
| for (int i = 1; i < packetSize-1; ++i) { |
| this->coeffRef(index+i) = values[i]; |
| } |
| } |
| } |
| |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void writeBlock( |
| const TensorBlock& block) { |
| this->m_impl.writeBlock(TensorBlock( |
| this->srcCoeff(block.first_coeff_index()), block.block_sizes(), |
| block.block_strides(), TensorBlockDimensions(this->m_inputStrides), |
| const_cast<ScalarNoConst*>(block.data()))); |
| } |
| }; |
| |
| namespace internal { |
| template<typename StartIndices, typename StopIndices, typename Strides, typename XprType> |
| struct traits<TensorStridingSlicingOp<StartIndices, StopIndices, Strides, XprType> > : public traits<XprType> |
| { |
| typedef typename XprType::Scalar Scalar; |
| typedef traits<XprType> XprTraits; |
| typedef typename XprTraits::StorageKind StorageKind; |
| typedef typename XprTraits::Index Index; |
| typedef typename XprType::Nested Nested; |
| typedef typename remove_reference<Nested>::type _Nested; |
| static const int NumDimensions = array_size<StartIndices>::value; |
| static const int Layout = XprTraits::Layout; |
| typedef typename XprTraits::PointerType PointerType; |
| }; |
| |
| template<typename StartIndices, typename StopIndices, typename Strides, typename XprType> |
| struct eval<TensorStridingSlicingOp<StartIndices, StopIndices, Strides, XprType>, Eigen::Dense> |
| { |
| typedef const TensorStridingSlicingOp<StartIndices, StopIndices, Strides, XprType>EIGEN_DEVICE_REF type; |
| }; |
| |
| template<typename StartIndices, typename StopIndices, typename Strides, typename XprType> |
| struct nested<TensorStridingSlicingOp<StartIndices, StopIndices, Strides, XprType>, 1, typename eval<TensorStridingSlicingOp<StartIndices, StopIndices, Strides, XprType> >::type> |
| { |
| typedef TensorStridingSlicingOp<StartIndices, StopIndices, Strides, XprType> type; |
| }; |
| |
| } // end namespace internal |
| |
| |
| template<typename StartIndices, typename StopIndices, typename Strides, typename XprType> |
| class TensorStridingSlicingOp : public TensorBase<TensorStridingSlicingOp<StartIndices, StopIndices, Strides, XprType> > |
| { |
| public: |
| typedef typename internal::traits<TensorStridingSlicingOp>::Scalar Scalar; |
| typedef typename XprType::CoeffReturnType CoeffReturnType; |
| typedef typename internal::nested<TensorStridingSlicingOp>::type Nested; |
| typedef typename internal::traits<TensorStridingSlicingOp>::StorageKind StorageKind; |
| typedef typename internal::traits<TensorStridingSlicingOp>::Index Index; |
| |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorStridingSlicingOp( |
| const XprType& expr, const StartIndices& startIndices, |
| const StopIndices& stopIndices, const Strides& strides) |
| : m_xpr(expr), m_startIndices(startIndices), m_stopIndices(stopIndices), |
| m_strides(strides) {} |
| |
| EIGEN_DEVICE_FUNC |
| const StartIndices& startIndices() const { return m_startIndices; } |
| EIGEN_DEVICE_FUNC |
| const StartIndices& stopIndices() const { return m_stopIndices; } |
| EIGEN_DEVICE_FUNC |
| const StartIndices& strides() const { return m_strides; } |
| |
| EIGEN_DEVICE_FUNC |
| const typename internal::remove_all<typename XprType::Nested>::type& |
| expression() const { return m_xpr; } |
| |
| EIGEN_DEVICE_FUNC |
| EIGEN_STRONG_INLINE TensorStridingSlicingOp& operator = (const TensorStridingSlicingOp& other) |
| { |
| typedef TensorAssignOp<TensorStridingSlicingOp, const TensorStridingSlicingOp> Assign; |
| Assign assign(*this, other); |
| internal::TensorExecutor<const Assign, DefaultDevice>::run( |
| assign, DefaultDevice()); |
| return *this; |
| } |
| |
| template<typename OtherDerived> |
| EIGEN_DEVICE_FUNC |
| EIGEN_STRONG_INLINE TensorStridingSlicingOp& operator = (const OtherDerived& other) |
| { |
| typedef TensorAssignOp<TensorStridingSlicingOp, const OtherDerived> Assign; |
| Assign assign(*this, other); |
| internal::TensorExecutor<const Assign, DefaultDevice>::run( |
| assign, DefaultDevice()); |
| return *this; |
| } |
| |
| protected: |
| typename XprType::Nested m_xpr; |
| const StartIndices m_startIndices; |
| const StopIndices m_stopIndices; |
| const Strides m_strides; |
| }; |
| |
| // Eval as rvalue |
| template<typename StartIndices, typename StopIndices, typename Strides, typename ArgType, typename Device> |
| struct TensorEvaluator<const TensorStridingSlicingOp<StartIndices, StopIndices, Strides, ArgType>, Device> |
| { |
| typedef TensorStridingSlicingOp<StartIndices, StopIndices, Strides, ArgType> XprType; |
| static const int NumDims = internal::array_size<Strides>::value; |
| typedef typename XprType::Index Index; |
| typedef typename XprType::Scalar Scalar; |
| typedef typename XprType::CoeffReturnType CoeffReturnType; |
| typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType; |
| typedef StorageMemory<CoeffReturnType, Device> Storage; |
| typedef typename Storage::Type EvaluatorPointerType; |
| typedef Strides Dimensions; |
| |
| enum { |
| // Alignment can't be guaranteed at compile time since it depends on the |
| // slice offsets and sizes. |
| IsAligned = false, |
| PacketAccess = false, |
| BlockAccess = false, |
| PreferBlockAccess = false, |
| Layout = TensorEvaluator<ArgType, Device>::Layout, |
| RawAccess = false |
| }; |
| |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device) |
| : m_impl(op.expression(), device), |
| m_device(device), |
| m_strides(op.strides()) |
| { |
| // Handle degenerate intervals by gracefully clamping and allowing m_dimensions to be zero |
| DSizes<Index, NumDims> startIndicesClamped, stopIndicesClamped; |
| for (ptrdiff_t i = 0; i < internal::array_size<Dimensions>::value; ++i) { |
| eigen_assert(m_strides[i] != 0 && "0 stride is invalid"); |
| if (m_strides[i] > 0) { |
| startIndicesClamped[i] = |
| clamp(op.startIndices()[i], 0, m_impl.dimensions()[i]); |
| stopIndicesClamped[i] = |
| clamp(op.stopIndices()[i], 0, m_impl.dimensions()[i]); |
| } else { |
| /* implies m_strides[i] < 0 by assert */ |
| startIndicesClamped[i] = |
| clamp(op.startIndices()[i], -1, m_impl.dimensions()[i] - 1); |
| stopIndicesClamped[i] = |
| clamp(op.stopIndices()[i], -1, m_impl.dimensions()[i] - 1); |
| } |
| m_startIndices[i] = startIndicesClamped[i]; |
| } |
| |
| typedef typename TensorEvaluator<ArgType, Device>::Dimensions InputDimensions; |
| const InputDimensions& input_dims = m_impl.dimensions(); |
| |
| // check for degenerate intervals and compute output tensor shape |
| bool degenerate = false; |
| m_is_identity = true; |
| for (int i = 0; i < NumDims; i++) { |
| Index interval = stopIndicesClamped[i] - startIndicesClamped[i]; |
| if (interval == 0 || ((interval < 0) != (m_strides[i] < 0))) { |
| m_dimensions[i] = 0; |
| degenerate = true; |
| } else { |
| m_dimensions[i] = |
| (interval / m_strides[i]) + (interval % m_strides[i] != 0 ? 1 : 0); |
| eigen_assert(m_dimensions[i] >= 0); |
| } |
| if (m_strides[i] != 1 || interval != m_impl.dimensions()[i]) { |
| m_is_identity = false; |
| } |
| } |
| |
| Strides output_dims = m_dimensions; |
| |
| if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { |
| m_inputStrides[0] = m_strides[0]; |
| m_offsets[0] = startIndicesClamped[0]; |
| Index previousDimProduct = 1; |
| for (int i = 1; i < NumDims; ++i) { |
| previousDimProduct *= input_dims[i-1]; |
| m_inputStrides[i] = previousDimProduct * m_strides[i]; |
| m_offsets[i] = startIndicesClamped[i] * previousDimProduct; |
| } |
| |
| // Don't initialize m_fastOutputStrides[0] since it won't ever be accessed. |
| m_outputStrides[0] = 1; |
| for (int i = 1; i < NumDims; ++i) { |
| m_outputStrides[i] = m_outputStrides[i-1] * output_dims[i-1]; |
| // NOTE: if tensor is degenerate, we send 1 to prevent TensorIntDivisor constructor crash |
| m_fastOutputStrides[i] = internal::TensorIntDivisor<Index>(degenerate ? 1 : m_outputStrides[i]); |
| } |
| } else { |
| m_inputStrides[NumDims-1] = m_strides[NumDims-1]; |
| m_offsets[NumDims-1] = startIndicesClamped[NumDims-1]; |
| Index previousDimProduct = 1; |
| for (int i = NumDims - 2; i >= 0; --i) { |
| previousDimProduct *= input_dims[i+1]; |
| m_inputStrides[i] = previousDimProduct * m_strides[i]; |
| m_offsets[i] = startIndicesClamped[i] * previousDimProduct; |
| } |
| |
| m_outputStrides[NumDims-1] = 1; |
| for (int i = NumDims - 2; i >= 0; --i) { |
| m_outputStrides[i] = m_outputStrides[i+1] * output_dims[i+1]; |
| // NOTE: if tensor is degenerate, we send 1 to prevent TensorIntDivisor constructor crash |
| m_fastOutputStrides[i] = internal::TensorIntDivisor<Index>(degenerate ? 1 : m_outputStrides[i]); |
| } |
| } |
| } |
| |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; } |
| |
| |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType) { |
| 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)); |
| } |
| } |
| |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const { |
| return m_impl.costPerCoeff(vectorized) + TensorOpCost(0, 0, m_is_identity ? 1 : NumDims); |
| } |
| |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename Storage::Type data() const { |
| return NULL; |
| } |
| #ifdef EIGEN_USE_SYCL |
| // binding placeholder accessors to a command group handler for SYCL |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler &cgh) const { |
| m_impl.bind(cgh); |
| } |
| #endif |
| protected: |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index srcCoeff(Index index) const |
| { |
| Index inputIndex = 0; |
| if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { |
| EIGEN_UNROLL_LOOP |
| for (int i = NumDims - 1; i >= 0; --i) { |
| const Index idx = index / m_fastOutputStrides[i]; |
| inputIndex += idx * m_inputStrides[i] + m_offsets[i]; |
| index -= idx * m_outputStrides[i]; |
| } |
| } else { |
| EIGEN_UNROLL_LOOP |
| for (int i = 0; i < NumDims; ++i) { |
| const Index idx = index / m_fastOutputStrides[i]; |
| inputIndex += idx * m_inputStrides[i] + m_offsets[i]; |
| index -= idx * m_outputStrides[i]; |
| } |
| } |
| return inputIndex; |
| } |
| |
| static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index clamp(Index value, Index min, Index max) { |
| #ifndef SYCL_DEVICE_ONLY |
| return numext::maxi(min, numext::mini(max,value)); |
| #else |
| return cl::sycl::clamp(value, min, max); |
| #endif |
| } |
| |
| array<Index, NumDims> m_outputStrides; |
| array<internal::TensorIntDivisor<Index>, NumDims> m_fastOutputStrides; |
| array<Index, NumDims> m_inputStrides; |
| bool m_is_identity; |
| TensorEvaluator<ArgType, Device> m_impl; |
| const Device EIGEN_DEVICE_REF m_device; |
| DSizes<Index, NumDims> m_startIndices; // clamped startIndices |
| DSizes<Index, NumDims> m_dimensions; |
| DSizes<Index, NumDims> m_offsets; // offset in a flattened shape |
| const Strides m_strides; |
| }; |
| |
| // Eval as lvalue |
| template<typename StartIndices, typename StopIndices, typename Strides, typename ArgType, typename Device> |
| struct TensorEvaluator<TensorStridingSlicingOp<StartIndices, StopIndices, Strides, ArgType>, Device> |
| : public TensorEvaluator<const TensorStridingSlicingOp<StartIndices, StopIndices, Strides, ArgType>, Device> |
| { |
| typedef TensorEvaluator<const TensorStridingSlicingOp<StartIndices, StopIndices, Strides, ArgType>, Device> Base; |
| typedef TensorStridingSlicingOp<StartIndices, StopIndices, Strides, ArgType> XprType; |
| static const int NumDims = internal::array_size<Strides>::value; |
| |
| enum { |
| IsAligned = false, |
| PacketAccess = false, |
| BlockAccess = false, |
| PreferBlockAccess = false, |
| Layout = TensorEvaluator<ArgType, Device>::Layout, |
| CoordAccess = TensorEvaluator<ArgType, Device>::CoordAccess, |
| RawAccess = false |
| }; |
| |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device) |
| : Base(op, device) |
| { } |
| |
| typedef typename XprType::Index Index; |
| typedef typename XprType::Scalar Scalar; |
| typedef typename XprType::CoeffReturnType CoeffReturnType; |
| typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType; |
| typedef Strides Dimensions; |
| |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType& coeffRef(Index index) |
| { |
| if (this->m_is_identity) { |
| return this->m_impl.coeffRef(index); |
| } else { |
| return this->m_impl.coeffRef(this->srcCoeff(index)); |
| } |
| } |
| }; |
| |
| |
| } // end namespace Eigen |
| |
| #endif // EIGEN_CXX11_TENSOR_TENSOR_MORPHING_H |
