| // This file is part of Eigen, a lightweight C++ template library |
| // for linear algebra. |
| // |
| // Copyright (C) 2014 Navdeep Jaitly <ndjaitly@google.com> |
| // 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_REVERSE_H |
| #define EIGEN_CXX11_TENSOR_TENSOR_REVERSE_H |
| #include "./InternalHeaderCheck.h" |
| |
| namespace Eigen { |
| |
| /** \class TensorReverse |
| * \ingroup CXX11_Tensor_Module |
| * |
| * \brief Tensor reverse elements class. |
| * |
| */ |
| namespace internal { |
| template<typename ReverseDimensions, typename XprType> |
| struct traits<TensorReverseOp<ReverseDimensions, |
| 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 std::remove_reference_t<Nested> Nested_; |
| static constexpr int NumDimensions = XprTraits::NumDimensions; |
| static constexpr int Layout = XprTraits::Layout; |
| typedef typename XprTraits::PointerType PointerType; |
| }; |
| |
| template<typename ReverseDimensions, typename XprType> |
| struct eval<TensorReverseOp<ReverseDimensions, XprType>, Eigen::Dense> |
| { |
| typedef const TensorReverseOp<ReverseDimensions, XprType>& type; |
| }; |
| |
| template<typename ReverseDimensions, typename XprType> |
| struct nested<TensorReverseOp<ReverseDimensions, XprType>, 1, |
| typename eval<TensorReverseOp<ReverseDimensions, XprType> >::type> |
| { |
| typedef TensorReverseOp<ReverseDimensions, XprType> type; |
| }; |
| |
| } // end namespace internal |
| |
| template<typename ReverseDimensions, typename XprType> |
| class TensorReverseOp : public TensorBase<TensorReverseOp<ReverseDimensions, |
| XprType>, WriteAccessors> |
| { |
| public: |
| typedef TensorBase<TensorReverseOp<ReverseDimensions, XprType>, WriteAccessors>Base; |
| typedef typename Eigen::internal::traits<TensorReverseOp>::Scalar Scalar; |
| typedef typename Eigen::NumTraits<Scalar>::Real RealScalar; |
| typedef typename XprType::CoeffReturnType CoeffReturnType; |
| typedef typename Eigen::internal::nested<TensorReverseOp>::type Nested; |
| typedef typename Eigen::internal::traits<TensorReverseOp>::StorageKind |
| StorageKind; |
| typedef typename Eigen::internal::traits<TensorReverseOp>::Index Index; |
| |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorReverseOp( |
| const XprType& expr, const ReverseDimensions& reverse_dims) |
| : m_xpr(expr), m_reverse_dims(reverse_dims) { } |
| |
| EIGEN_DEVICE_FUNC |
| const ReverseDimensions& reverse() const { return m_reverse_dims; } |
| |
| EIGEN_DEVICE_FUNC |
| const internal::remove_all_t<typename XprType::Nested>& |
| expression() const { return m_xpr; } |
| |
| EIGEN_TENSOR_INHERIT_ASSIGNMENT_OPERATORS(TensorReverseOp) |
| |
| |
| protected: |
| typename XprType::Nested m_xpr; |
| const ReverseDimensions m_reverse_dims; |
| }; |
| |
| // Eval as rvalue |
| template<typename ReverseDimensions, typename ArgType, typename Device> |
| struct TensorEvaluator<const TensorReverseOp<ReverseDimensions, ArgType>, Device> |
| { |
| typedef TensorReverseOp<ReverseDimensions, ArgType> XprType; |
| typedef typename XprType::Index Index; |
| static constexpr int NumDims = internal::array_size<ReverseDimensions>::value; |
| typedef DSizes<Index, NumDims> Dimensions; |
| typedef typename XprType::Scalar Scalar; |
| typedef typename XprType::CoeffReturnType CoeffReturnType; |
| typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType; |
| static constexpr int PacketSize = PacketType<CoeffReturnType, Device>::size; |
| typedef StorageMemory<CoeffReturnType, Device> Storage; |
| typedef typename Storage::Type EvaluatorPointerType; |
| |
| static constexpr int Layout = TensorEvaluator<ArgType, Device>::Layout; |
| enum { |
| IsAligned = false, |
| PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess, |
| BlockAccess = NumDims > 0, |
| PreferBlockAccess = true, |
| CoordAccess = false, // to be implemented |
| RawAccess = false |
| }; |
| |
| typedef internal::TensorIntDivisor<Index> IndexDivisor; |
| |
| //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===// |
| typedef internal::TensorBlockDescriptor<NumDims, Index> TensorBlockDesc; |
| typedef internal::TensorBlockScratchAllocator<Device> TensorBlockScratch; |
| |
| typedef typename TensorEvaluator<const ArgType, Device>::TensorBlock |
| ArgTensorBlock; |
| |
| typedef typename internal::TensorMaterializedBlock<CoeffReturnType, NumDims, |
| Layout, Index> |
| TensorBlock; |
| //===--------------------------------------------------------------------===// |
| |
| EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device) |
| : m_impl(op.expression(), device), |
| m_reverse(op.reverse()), |
| m_device(device) |
| { |
| // Reversing a scalar isn't supported yet. It would be a no-op anyway. |
| EIGEN_STATIC_ASSERT((NumDims > 0), YOU_MADE_A_PROGRAMMING_MISTAKE); |
| |
| // Compute strides |
| m_dimensions = m_impl.dimensions(); |
| if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { |
| m_strides[0] = 1; |
| for (int i = 1; i < NumDims; ++i) { |
| m_strides[i] = m_strides[i-1] * m_dimensions[i-1]; |
| if (m_strides[i] > 0) m_fastStrides[i] = IndexDivisor(m_strides[i]); |
| } |
| } else { |
| m_strides[NumDims-1] = 1; |
| for (int i = NumDims - 2; i >= 0; --i) { |
| m_strides[i] = m_strides[i+1] * m_dimensions[i+1]; |
| if (m_strides[i] > 0) m_fastStrides[i] = IndexDivisor(m_strides[i]); |
| } |
| } |
| } |
| |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE |
| const Dimensions& dimensions() const { return m_dimensions; } |
| |
| EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType) { |
| m_impl.evalSubExprsIfNeeded(NULL); |
| return true; |
| } |
| |
| #ifdef EIGEN_USE_THREADS |
| template <typename EvalSubExprsCallback> |
| EIGEN_STRONG_INLINE void evalSubExprsIfNeededAsync( |
| EvaluatorPointerType, EvalSubExprsCallback done) { |
| m_impl.evalSubExprsIfNeededAsync(nullptr, [done](bool) { done(true); }); |
| } |
| #endif // EIGEN_USE_THREADS |
| |
| EIGEN_STRONG_INLINE void cleanup() { |
| m_impl.cleanup(); |
| } |
| |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index reverseIndex( |
| Index index) const { |
| eigen_assert(index < dimensions().TotalSize()); |
| Index inputIndex = 0; |
| if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { |
| EIGEN_UNROLL_LOOP |
| for (int i = NumDims - 1; i > 0; --i) { |
| Index idx = index / m_fastStrides[i]; |
| index -= idx * m_strides[i]; |
| if (m_reverse[i]) { |
| idx = m_dimensions[i] - idx - 1; |
| } |
| inputIndex += idx * m_strides[i] ; |
| } |
| if (m_reverse[0]) { |
| inputIndex += (m_dimensions[0] - index - 1); |
| } else { |
| inputIndex += index; |
| } |
| } else { |
| EIGEN_UNROLL_LOOP |
| for (int i = 0; i < NumDims - 1; ++i) { |
| Index idx = index / m_fastStrides[i]; |
| index -= idx * m_strides[i]; |
| if (m_reverse[i]) { |
| idx = m_dimensions[i] - idx - 1; |
| } |
| inputIndex += idx * m_strides[i] ; |
| } |
| if (m_reverse[NumDims-1]) { |
| inputIndex += (m_dimensions[NumDims-1] - index - 1); |
| } else { |
| inputIndex += index; |
| } |
| } |
| return inputIndex; |
| } |
| |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff( |
| Index index) const { |
| return m_impl.coeff(reverseIndex(index)); |
| } |
| |
| template<int LoadMode> |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE |
| PacketReturnType packet(Index index) const |
| { |
| eigen_assert(index+PacketSize-1 < dimensions().TotalSize()); |
| |
| // TODO(ndjaitly): write a better packing routine that uses |
| // local structure. |
| EIGEN_ALIGN_MAX std::remove_const_t<CoeffReturnType> |
| values[PacketSize]; |
| EIGEN_UNROLL_LOOP |
| for (int i = 0; i < PacketSize; ++i) { |
| values[i] = coeff(index+i); |
| } |
| PacketReturnType rslt = internal::pload<PacketReturnType>(values); |
| return rslt; |
| } |
| |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE |
| internal::TensorBlockResourceRequirements getResourceRequirements() const { |
| const size_t target_size = m_device.lastLevelCacheSize(); |
| // Block evaluation reads underlying memory in reverse order, and default |
| // cost model does not properly catch this in bytes stored/loaded. |
| return internal::TensorBlockResourceRequirements::skewed<Scalar>( |
| target_size) |
| .addCostPerCoeff({0, 0, 24}); |
| } |
| |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBlock |
| block(TensorBlockDesc& desc, TensorBlockScratch& scratch, |
| bool /*root_of_expr_ast*/ = false) const { |
| // TODO(ezhulenev): If underlying tensor expression supports and prefers |
| // block evaluation we must use it. Currently we use coeff and packet |
| // access into the underlying tensor expression. |
| // static const bool useBlockAccessForArgType = |
| // TensorEvaluator<ArgType, Device>::BlockAccess && |
| // TensorEvaluator<ArgType, Device>::PreferBlockAccess; |
| |
| static const bool isColMajor = |
| static_cast<int>(Layout) == static_cast<int>(ColMajor); |
| |
| static const Index inner_dim_idx = isColMajor ? 0 : NumDims - 1; |
| const bool inner_dim_reversed = m_reverse[inner_dim_idx]; |
| |
| // Offset in the output block. |
| Index block_offset = 0; |
| |
| // Offset in the input Tensor. |
| Index input_offset = reverseIndex(desc.offset()); |
| |
| // Initialize output block iterator state. Dimension in this array are |
| // always in inner_most -> outer_most order (col major layout). |
| array<BlockIteratorState, NumDims> it; |
| for (int i = 0; i < NumDims; ++i) { |
| const int dim = isColMajor ? i : NumDims - 1 - i; |
| it[i].size = desc.dimension(dim); |
| it[i].count = 0; |
| it[i].reverse = m_reverse[dim]; |
| |
| it[i].block_stride = |
| i == 0 ? 1 : (it[i - 1].size * it[i - 1].block_stride); |
| it[i].block_span = it[i].block_stride * (it[i].size - 1); |
| |
| it[i].input_stride = m_strides[dim]; |
| it[i].input_span = it[i].input_stride * (it[i].size - 1); |
| |
| if (it[i].reverse) { |
| it[i].input_stride = -1 * it[i].input_stride; |
| it[i].input_span = -1 * it[i].input_span; |
| } |
| } |
| |
| // If multiple inner dimensions have the same reverse flag, check if we can |
| // merge them into a single virtual inner dimension. |
| int effective_inner_dim = 0; |
| for (int i = 1; i < NumDims; ++i) { |
| if (it[i].reverse != it[effective_inner_dim].reverse) break; |
| if (it[i].block_stride != it[effective_inner_dim].size) break; |
| if (it[i].block_stride != numext::abs(it[i].input_stride)) break; |
| |
| it[i].size = it[effective_inner_dim].size * it[i].size; |
| |
| it[i].block_stride = 1; |
| it[i].input_stride = (inner_dim_reversed ? -1 : 1); |
| |
| it[i].block_span = it[i].block_stride * (it[i].size - 1); |
| it[i].input_span = it[i].input_stride * (it[i].size - 1); |
| |
| effective_inner_dim = i; |
| } |
| |
| eigen_assert(it[effective_inner_dim].block_stride == 1); |
| eigen_assert(it[effective_inner_dim].input_stride == |
| (inner_dim_reversed ? -1 : 1)); |
| |
| const Index inner_dim_size = it[effective_inner_dim].size; |
| |
| // Prepare storage for the materialized reverse result. |
| const typename TensorBlock::Storage block_storage = |
| TensorBlock::prepareStorage(desc, scratch); |
| CoeffReturnType* block_buffer = block_storage.data(); |
| |
| while (it[NumDims - 1].count < it[NumDims - 1].size) { |
| // Copy inner-most dimension data from reversed location in input. |
| Index dst = block_offset; |
| Index src = input_offset; |
| |
| // NOTE(ezhulenev): Adding vectorized path with internal::preverse showed |
| // worse results in benchmarks than a simple coefficient loop. |
| if (inner_dim_reversed) { |
| for (Index i = 0; i < inner_dim_size; ++i) { |
| block_buffer[dst] = m_impl.coeff(src); |
| ++dst; |
| --src; |
| } |
| } else { |
| for (Index i = 0; i < inner_dim_size; ++i) { |
| block_buffer[dst] = m_impl.coeff(src); |
| ++dst; |
| ++src; |
| } |
| } |
| |
| // For the 1d tensor we need to generate only one inner-most dimension. |
| if ((NumDims - effective_inner_dim) == 1) break; |
| |
| // Update offset. |
| for (Index i = effective_inner_dim + 1; i < NumDims; ++i) { |
| if (++it[i].count < it[i].size) { |
| block_offset += it[i].block_stride; |
| input_offset += it[i].input_stride; |
| break; |
| } |
| if (i != NumDims - 1) it[i].count = 0; |
| block_offset -= it[i].block_span; |
| input_offset -= it[i].input_span; |
| } |
| } |
| |
| return block_storage.AsTensorMaterializedBlock(); |
| } |
| |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const { |
| double compute_cost = NumDims * (2 * TensorOpCost::AddCost<Index>() + |
| 2 * TensorOpCost::MulCost<Index>() + |
| TensorOpCost::DivCost<Index>()); |
| for (int i = 0; i < NumDims; ++i) { |
| if (m_reverse[i]) { |
| compute_cost += 2 * TensorOpCost::AddCost<Index>(); |
| } |
| } |
| return m_impl.costPerCoeff(vectorized) + |
| TensorOpCost(0, 0, compute_cost, false /* 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: |
| Dimensions m_dimensions; |
| array<Index, NumDims> m_strides; |
| array<IndexDivisor, NumDims> m_fastStrides; |
| TensorEvaluator<ArgType, Device> m_impl; |
| ReverseDimensions m_reverse; |
| const Device EIGEN_DEVICE_REF m_device; |
| |
| private: |
| struct BlockIteratorState { |
| BlockIteratorState() |
| : size(0), |
| count(0), |
| reverse(false), |
| block_stride(0), |
| block_span(0), |
| input_stride(0), |
| input_span(0) {} |
| |
| Index size; |
| Index count; |
| bool reverse; |
| Index block_stride; |
| Index block_span; |
| Index input_stride; |
| Index input_span; |
| }; |
| }; |
| |
| // Eval as lvalue |
| |
| template <typename ReverseDimensions, typename ArgType, typename Device> |
| struct TensorEvaluator<TensorReverseOp<ReverseDimensions, ArgType>, Device> |
| : public TensorEvaluator<const TensorReverseOp<ReverseDimensions, ArgType>, |
| Device> { |
| typedef TensorEvaluator<const TensorReverseOp<ReverseDimensions, ArgType>, |
| Device> Base; |
| typedef TensorReverseOp<ReverseDimensions, ArgType> XprType; |
| typedef typename XprType::Index Index; |
| static constexpr int NumDims = internal::array_size<ReverseDimensions>::value; |
| typedef DSizes<Index, NumDims> Dimensions; |
| |
| static constexpr int Layout = TensorEvaluator<ArgType, Device>::Layout; |
| enum { |
| IsAligned = false, |
| PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess, |
| BlockAccess = false, |
| PreferBlockAccess = false, |
| CoordAccess = false, // to be implemented |
| RawAccess = false |
| }; |
| EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device) |
| : Base(op, device) {} |
| |
| typedef typename XprType::Scalar Scalar; |
| typedef typename XprType::CoeffReturnType CoeffReturnType; |
| typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType; |
| static constexpr int PacketSize = PacketType<CoeffReturnType, Device>::size; |
| |
| //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===// |
| typedef internal::TensorBlockNotImplemented TensorBlock; |
| //===--------------------------------------------------------------------===// |
| |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE |
| const Dimensions& dimensions() const { return this->m_dimensions; } |
| |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& coeffRef(Index index) { |
| return this->m_impl.coeffRef(this->reverseIndex(index)); |
| } |
| |
| template <int StoreMode> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE |
| void writePacket(Index index, const PacketReturnType& x) { |
| eigen_assert(index+PacketSize-1 < dimensions().TotalSize()); |
| |
| // This code is pilfered from TensorMorphing.h |
| EIGEN_ALIGN_MAX CoeffReturnType values[PacketSize]; |
| internal::pstore<CoeffReturnType, PacketReturnType>(values, x); |
| EIGEN_UNROLL_LOOP |
| for (int i = 0; i < PacketSize; ++i) { |
| this->coeffRef(index+i) = values[i]; |
| } |
| } |
| }; |
| |
| |
| } // end namespace Eigen |
| |
| #endif // EIGEN_CXX11_TENSOR_TENSOR_REVERSE_H |
