| // 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/. |
| // SPDX-License-Identifier: MPL-2.0 |
| |
| #ifndef EIGEN_TENSOR_TENSOR_PATCH_H |
| #define EIGEN_TENSOR_TENSOR_PATCH_H |
| |
| // IWYU pragma: private |
| #include "./InternalHeaderCheck.h" |
| |
| namespace Eigen { |
| |
| namespace internal { |
| template <typename PatchDim, typename XprType> |
| struct traits<TensorPatchOp<PatchDim, XprType> > : traits<XprType> { |
| typedef typename XprType::Scalar Scalar; |
| typedef traits<XprType> XprTraits; |
| typedef typename XprTraits::StorageKind StorageKind; |
| typedef typename XprTraits::Index Index; |
| static constexpr int NumDimensions = XprTraits::NumDimensions + 1; |
| static constexpr int Layout = XprTraits::Layout; |
| typedef typename XprTraits::PointerType PointerType; |
| }; |
| |
| template <typename PatchDim, typename XprType> |
| struct eval<TensorPatchOp<PatchDim, XprType>, Eigen::Dense> { |
| typedef const TensorPatchOp<PatchDim, XprType>& type; |
| }; |
| |
| } // end namespace internal |
| |
| /** |
| * \ingroup Tensor_Module |
| * |
| * \brief Tensor patch class. |
| */ |
| template <typename PatchDim, typename XprType> |
| class TensorPatchOp : public TensorBase<TensorPatchOp<PatchDim, XprType>, ReadOnlyAccessors> { |
| public: |
| typedef typename Eigen::internal::traits<TensorPatchOp>::Scalar Scalar; |
| typedef typename Eigen::NumTraits<Scalar>::Real RealScalar; |
| typedef typename XprType::CoeffReturnType CoeffReturnType; |
| typedef typename Eigen::internal::ref_selector<TensorPatchOp>::type Nested; |
| typedef typename Eigen::internal::traits<TensorPatchOp>::StorageKind StorageKind; |
| typedef typename Eigen::internal::traits<TensorPatchOp>::Index Index; |
| |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorPatchOp(const XprType& expr, const PatchDim& patch_dims) |
| : m_xpr(expr), m_patch_dims(patch_dims) {} |
| |
| EIGEN_DEVICE_FUNC const PatchDim& patch_dims() const { return m_patch_dims; } |
| |
| EIGEN_DEVICE_FUNC const internal::remove_all_t<typename XprType::Nested>& expression() const { return m_xpr; } |
| |
| protected: |
| typename XprType::Nested m_xpr; |
| const PatchDim m_patch_dims; |
| }; |
| |
| // Eval as rvalue |
| template <typename PatchDim, typename ArgType, typename Device> |
| struct TensorEvaluator<const TensorPatchOp<PatchDim, ArgType>, Device> { |
| typedef TensorPatchOp<PatchDim, ArgType> XprType; |
| typedef typename XprType::Index Index; |
| static constexpr int NumDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value + 1; |
| 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, |
| // block() reads the argument one coefficient at a time through coeff() -- |
| // the contract the scalar executors already rely on for every evaluator -- |
| // so it requires no capability bit from the argument (same as |
| // TensorReverse). |
| BlockAccess = NumDims > 1, |
| // The coeff/packet path pays a div/mod cascade per element; the block |
| // path copies whole in-bounds boxes patch by patch. |
| PreferBlockAccess = true, |
| CoordAccess = false, |
| RawAccess = false |
| }; |
| |
| //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===// |
| typedef internal::TensorBlockDescriptor<NumDims, Index> TensorBlockDesc; |
| typedef internal::TensorBlockScratchAllocator<Device> TensorBlockScratch; |
| 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_device(device) { |
| Index num_patches = 1; |
| const typename TensorEvaluator<ArgType, Device>::Dimensions& input_dims = m_impl.dimensions(); |
| const PatchDim& patch_dims = op.patch_dims(); |
| EIGEN_IF_CONSTEXPR (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { |
| for (int i = 0; i < NumDims - 1; ++i) { |
| m_dimensions[i] = patch_dims[i]; |
| num_patches *= (input_dims[i] - patch_dims[i] + 1); |
| } |
| m_dimensions[NumDims - 1] = num_patches; |
| |
| m_inputStrides[0] = 1; |
| m_patchStrides[0] = 1; |
| for (int i = 1; i < NumDims - 1; ++i) { |
| m_inputStrides[i] = m_inputStrides[i - 1] * input_dims[i - 1]; |
| m_patchStrides[i] = m_patchStrides[i - 1] * (input_dims[i - 1] - patch_dims[i - 1] + 1); |
| } |
| m_outputStrides[0] = 1; |
| for (int i = 1; i < NumDims; ++i) { |
| m_outputStrides[i] = m_outputStrides[i - 1] * m_dimensions[i - 1]; |
| } |
| } else { |
| for (int i = 0; i < NumDims - 1; ++i) { |
| m_dimensions[i + 1] = patch_dims[i]; |
| num_patches *= (input_dims[i] - patch_dims[i] + 1); |
| } |
| m_dimensions[0] = num_patches; |
| |
| m_inputStrides[NumDims - 2] = 1; |
| m_patchStrides[NumDims - 2] = 1; |
| for (int i = NumDims - 3; i >= 0; --i) { |
| m_inputStrides[i] = m_inputStrides[i + 1] * input_dims[i + 1]; |
| m_patchStrides[i] = m_patchStrides[i + 1] * (input_dims[i + 1] - patch_dims[i + 1] + 1); |
| } |
| m_outputStrides[NumDims - 1] = 1; |
| for (int i = NumDims - 2; i >= 0; --i) { |
| m_outputStrides[i] = m_outputStrides[i + 1] * m_dimensions[i + 1]; |
| } |
| } |
| } |
| |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; } |
| |
| EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType /*data*/) { |
| m_impl.evalSubExprsIfNeeded(nullptr); |
| return true; |
| } |
| |
| EIGEN_STRONG_INLINE void cleanup() { m_impl.cleanup(); } |
| |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const { |
| Index output_stride_index = (static_cast<int>(Layout) == static_cast<int>(ColMajor)) ? NumDims - 1 : 0; |
| // Find the location of the first element of the patch. |
| Index patchIndex = index / m_outputStrides[output_stride_index]; |
| // Find the offset of the element wrt the location of the first element. |
| Index patchOffset = index - patchIndex * m_outputStrides[output_stride_index]; |
| Index inputIndex = 0; |
| EIGEN_IF_CONSTEXPR (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { |
| EIGEN_UNROLL_LOOP |
| for (int i = NumDims - 2; i > 0; --i) { |
| const Index patchIdx = patchIndex / m_patchStrides[i]; |
| patchIndex -= patchIdx * m_patchStrides[i]; |
| const Index offsetIdx = patchOffset / m_outputStrides[i]; |
| patchOffset -= offsetIdx * m_outputStrides[i]; |
| inputIndex += (patchIdx + offsetIdx) * m_inputStrides[i]; |
| } |
| } else { |
| EIGEN_UNROLL_LOOP |
| for (int i = 0; i < NumDims - 2; ++i) { |
| const Index patchIdx = patchIndex / m_patchStrides[i]; |
| patchIndex -= patchIdx * m_patchStrides[i]; |
| const Index offsetIdx = patchOffset / m_outputStrides[i + 1]; |
| patchOffset -= offsetIdx * m_outputStrides[i + 1]; |
| inputIndex += (patchIdx + offsetIdx) * m_inputStrides[i]; |
| } |
| } |
| inputIndex += (patchIndex + patchOffset); |
| return m_impl.coeff(inputIndex); |
| } |
| |
| template <int LoadMode> |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const { |
| eigen_assert(index + PacketSize - 1 < dimensions().TotalSize()); |
| |
| Index output_stride_index = (static_cast<int>(Layout) == static_cast<int>(ColMajor)) ? NumDims - 1 : 0; |
| Index indices[2] = {index, index + PacketSize - 1}; |
| Index patchIndices[2] = {indices[0] / m_outputStrides[output_stride_index], |
| indices[1] / m_outputStrides[output_stride_index]}; |
| Index patchOffsets[2] = {indices[0] - patchIndices[0] * m_outputStrides[output_stride_index], |
| indices[1] - patchIndices[1] * m_outputStrides[output_stride_index]}; |
| |
| Index inputIndices[2] = {0, 0}; |
| EIGEN_IF_CONSTEXPR (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { |
| EIGEN_UNROLL_LOOP |
| for (int i = NumDims - 2; i > 0; --i) { |
| const Index patchIdx[2] = {patchIndices[0] / m_patchStrides[i], patchIndices[1] / m_patchStrides[i]}; |
| patchIndices[0] -= patchIdx[0] * m_patchStrides[i]; |
| patchIndices[1] -= patchIdx[1] * m_patchStrides[i]; |
| |
| const Index offsetIdx[2] = {patchOffsets[0] / m_outputStrides[i], patchOffsets[1] / m_outputStrides[i]}; |
| patchOffsets[0] -= offsetIdx[0] * m_outputStrides[i]; |
| patchOffsets[1] -= offsetIdx[1] * m_outputStrides[i]; |
| |
| inputIndices[0] += (patchIdx[0] + offsetIdx[0]) * m_inputStrides[i]; |
| inputIndices[1] += (patchIdx[1] + offsetIdx[1]) * m_inputStrides[i]; |
| } |
| } else { |
| EIGEN_UNROLL_LOOP |
| for (int i = 0; i < NumDims - 2; ++i) { |
| const Index patchIdx[2] = {patchIndices[0] / m_patchStrides[i], patchIndices[1] / m_patchStrides[i]}; |
| patchIndices[0] -= patchIdx[0] * m_patchStrides[i]; |
| patchIndices[1] -= patchIdx[1] * m_patchStrides[i]; |
| |
| const Index offsetIdx[2] = {patchOffsets[0] / m_outputStrides[i + 1], patchOffsets[1] / m_outputStrides[i + 1]}; |
| patchOffsets[0] -= offsetIdx[0] * m_outputStrides[i + 1]; |
| patchOffsets[1] -= offsetIdx[1] * m_outputStrides[i + 1]; |
| |
| inputIndices[0] += (patchIdx[0] + offsetIdx[0]) * m_inputStrides[i]; |
| inputIndices[1] += (patchIdx[1] + offsetIdx[1]) * m_inputStrides[i]; |
| } |
| } |
| inputIndices[0] += (patchIndices[0] + patchOffsets[0]); |
| inputIndices[1] += (patchIndices[1] + patchOffsets[1]); |
| |
| if (inputIndices[1] - inputIndices[0] == PacketSize - 1) { |
| PacketReturnType rslt = m_impl.template packet<Unaligned>(inputIndices[0]); |
| return rslt; |
| } else { |
| EIGEN_ALIGN_TO_BOUNDARY(internal::unpacket_traits<PacketReturnType>::alignment) |
| CoeffReturnType 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 internal::TensorBlockResourceRequirements getResourceRequirements() const { |
| const size_t target_size = m_device.firstLevelCacheSize(); |
| // Every output coefficient is read once from the argument and stored |
| // once. Pass the full cost explicitly rather than adding to skewed()'s |
| // default load+store seed, which would double-count the baseline byte |
| // traffic and halve the tile size. |
| const TensorOpCost cost_per_coeff = |
| m_impl.costPerCoeff(/*vectorized=*/false) + TensorOpCost(0, sizeof(CoeffReturnType), 0); |
| return internal::TensorBlockResourceRequirements::withShapeAndSize<Scalar>( |
| internal::TensorBlockShapeType::kSkewedInnerDims, target_size, cost_per_coeff); |
| } |
| |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBlock block(TensorBlockDesc& desc, TensorBlockScratch& scratch, |
| bool /*root_of_expr_ast*/ = false) const { |
| constexpr bool is_col_major = static_cast<int>(Layout) == static_cast<int>(ColMajor); |
| |
| if (desc.size() == 0) { |
| return TensorBlock(internal::TensorBlockKind::kView, nullptr, desc.dimensions()); |
| } |
| |
| typename TensorBlock::Storage block_storage = TensorBlock::prepareStorage(desc, scratch); |
| CoeffReturnType* block_buffer = block_storage.data(); |
| |
| // Output coordinates of the block's corner. |
| array<Index, NumDims> coords; |
| Index remaining = desc.offset(); |
| EIGEN_IF_CONSTEXPR (is_col_major) { |
| for (int i = NumDims - 1; i > 0; --i) { |
| coords[i] = remaining / m_outputStrides[i]; |
| remaining -= coords[i] * m_outputStrides[i]; |
| } |
| coords[0] = remaining; |
| } else { |
| for (int i = 0; i < NumDims - 1; ++i) { |
| coords[i] = remaining / m_outputStrides[i]; |
| remaining -= coords[i] * m_outputStrides[i]; |
| } |
| coords[NumDims - 1] = remaining; |
| } |
| |
| const int patch_dim = is_col_major ? NumDims - 1 : 0; |
| const int inner_dim = is_col_major ? 0 : NumDims - 1; |
| |
| const Index num_patches_in_block = desc.dimension(patch_dim); |
| // The input's inner-most stride is 1 by construction, so the inner run is |
| // contiguous on both sides. |
| const Index inner_size = desc.dimension(inner_dim); |
| |
| // The within-patch dimensions between the inner-most one and the patch |
| // index, ordered inner-most to outer-most, plus the input offset the |
| // block's corner contributes on every within-patch dimension. |
| array<Index, NumDims> mid_sizes; |
| array<Index, NumDims> mid_src_stride; |
| array<Index, NumDims> mid_count; |
| int num_mid = 0; |
| Index src_corner = 0; |
| for (int k = 0; k < NumDims - 1; ++k) { |
| const int d = is_col_major ? k : NumDims - 1 - k; // output dimension |
| const int in_d = is_col_major ? d : d - 1; // input-strides index |
| src_corner += coords[d] * m_inputStrides[in_d]; |
| if (k > 0) { |
| mid_sizes[num_mid] = desc.dimension(d); |
| mid_src_stride[num_mid] = m_inputStrides[in_d]; |
| ++num_mid; |
| } |
| } |
| |
| // The loop nest below visits the block in exactly its memory order (the |
| // storage returned by prepareStorage() is dense with the block's own |
| // layout-order strides), so the destination is one running cursor. |
| Index dst = 0; |
| for (Index p = 0; p < num_patches_in_block; ++p) { |
| // Input offset of this patch's first element. |
| Index patch_index = coords[patch_dim] + p; |
| Index src_patch = 0; |
| EIGEN_IF_CONSTEXPR (is_col_major) { |
| for (int i = NumDims - 2; i > 0; --i) { |
| const Index idx = patch_index / m_patchStrides[i]; |
| patch_index -= idx * m_patchStrides[i]; |
| src_patch += idx * m_inputStrides[i]; |
| } |
| } else { |
| for (int i = 0; i < NumDims - 2; ++i) { |
| const Index idx = patch_index / m_patchStrides[i]; |
| patch_index -= idx * m_patchStrides[i]; |
| src_patch += idx * m_inputStrides[i]; |
| } |
| } |
| src_patch += patch_index; |
| |
| Index src = src_patch + src_corner; |
| for (int k = 0; k < num_mid; ++k) mid_count[k] = 0; |
| for (;;) { |
| for (Index j = 0; j < inner_size; ++j) { |
| block_buffer[dst + j] = m_impl.coeff(src + j); |
| } |
| dst += inner_size; |
| int k = 0; |
| for (; k < num_mid; ++k) { |
| if (++mid_count[k] < mid_sizes[k]) { |
| src += mid_src_stride[k]; |
| break; |
| } |
| mid_count[k] = 0; |
| src -= mid_src_stride[k] * (mid_sizes[k] - 1); |
| } |
| if (k == num_mid) break; |
| } |
| } |
| eigen_assert(dst == desc.size()); |
| |
| return block_storage.AsTensorMaterializedBlock(); |
| } |
| |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const { |
| const double compute_cost = NumDims * (TensorOpCost::DivCost<Index>() + TensorOpCost::MulCost<Index>() + |
| 2 * TensorOpCost::AddCost<Index>()); |
| return m_impl.costPerCoeff(vectorized) + TensorOpCost(0, 0, compute_cost, vectorized, PacketSize); |
| } |
| |
| EIGEN_DEVICE_FUNC EvaluatorPointerType data() const { return nullptr; } |
| |
| protected: |
| Dimensions m_dimensions; |
| array<Index, NumDims> m_outputStrides; |
| array<Index, NumDims - 1> m_inputStrides; |
| array<Index, NumDims - 1> m_patchStrides; |
| |
| TensorEvaluator<ArgType, Device> m_impl; |
| const Device EIGEN_DEVICE_REF m_device; |
| }; |
| |
| } // end namespace Eigen |
| |
| #endif // EIGEN_TENSOR_TENSOR_PATCH_H |
