| // 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_PADDING_H | |
| #define EIGEN_CXX11_TENSOR_TENSOR_PADDING_H | |
| // IWYU pragma: private | |
| #include "./InternalHeaderCheck.h" | |
| namespace Eigen { | |
| /** \class TensorPadding | |
| * \ingroup CXX11_Tensor_Module | |
| * | |
| * \brief Tensor padding class. | |
| * At the moment only padding with a constant value is supported. | |
| * | |
| */ | |
| namespace internal { | |
| template <typename PaddingDimensions, typename XprType> | |
| struct traits<TensorPaddingOp<PaddingDimensions, 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 PaddingDimensions, typename XprType> | |
| struct eval<TensorPaddingOp<PaddingDimensions, XprType>, Eigen::Dense> { | |
| typedef const TensorPaddingOp<PaddingDimensions, XprType>& type; | |
| }; | |
| template <typename PaddingDimensions, typename XprType> | |
| struct nested<TensorPaddingOp<PaddingDimensions, XprType>, 1, | |
| typename eval<TensorPaddingOp<PaddingDimensions, XprType> >::type> { | |
| typedef TensorPaddingOp<PaddingDimensions, XprType> type; | |
| }; | |
| } // end namespace internal | |
| template <typename PaddingDimensions, typename XprType> | |
| class TensorPaddingOp : public TensorBase<TensorPaddingOp<PaddingDimensions, XprType>, ReadOnlyAccessors> { | |
| public: | |
| typedef typename Eigen::internal::traits<TensorPaddingOp>::Scalar Scalar; | |
| typedef typename Eigen::NumTraits<Scalar>::Real RealScalar; | |
| typedef typename XprType::CoeffReturnType CoeffReturnType; | |
| typedef typename Eigen::internal::nested<TensorPaddingOp>::type Nested; | |
| typedef typename Eigen::internal::traits<TensorPaddingOp>::StorageKind StorageKind; | |
| typedef typename Eigen::internal::traits<TensorPaddingOp>::Index Index; | |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorPaddingOp(const XprType& expr, const PaddingDimensions& padding_dims, | |
| const Scalar padding_value) | |
| : m_xpr(expr), m_padding_dims(padding_dims), m_padding_value(padding_value) {} | |
| EIGEN_DEVICE_FUNC const PaddingDimensions& padding() const { return m_padding_dims; } | |
| EIGEN_DEVICE_FUNC Scalar padding_value() const { return m_padding_value; } | |
| EIGEN_DEVICE_FUNC const internal::remove_all_t<typename XprType::Nested>& expression() const { return m_xpr; } | |
| protected: | |
| typename XprType::Nested m_xpr; | |
| const PaddingDimensions m_padding_dims; | |
| const Scalar m_padding_value; | |
| }; | |
| // Eval as rvalue | |
| template <typename PaddingDimensions, typename ArgType, typename Device> | |
| struct TensorEvaluator<const TensorPaddingOp<PaddingDimensions, ArgType>, Device> { | |
| typedef TensorPaddingOp<PaddingDimensions, ArgType> XprType; | |
| typedef typename XprType::Index Index; | |
| static constexpr int NumDims = internal::array_size<PaddingDimensions>::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 = true, | |
| PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess, | |
| BlockAccess = TensorEvaluator<ArgType, Device>::RawAccess, | |
| PreferBlockAccess = true, | |
| CoordAccess = true, | |
| RawAccess = false | |
| }; | |
| typedef std::remove_const_t<Scalar> ScalarNoConst; | |
| //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===// | |
| typedef internal::TensorBlockDescriptor<NumDims, Index> TensorBlockDesc; | |
| typedef internal::TensorBlockScratchAllocator<Device> TensorBlockScratch; | |
| typedef typename internal::TensorMaterializedBlock<ScalarNoConst, NumDims, Layout, Index> TensorBlock; | |
| //===--------------------------------------------------------------------===// | |
| EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device) | |
| : m_impl(op.expression(), device), m_padding(op.padding()), m_paddingValue(op.padding_value()), m_device(device) { | |
| // The padding op doesn't change the rank of the tensor. Directly padding a scalar would lead | |
| // to a vector, which doesn't make sense. Instead one should reshape the scalar into a vector | |
| // of 1 element first and then pad. | |
| EIGEN_STATIC_ASSERT((NumDims > 0), YOU_MADE_A_PROGRAMMING_MISTAKE); | |
| // Compute dimensions | |
| m_dimensions = m_impl.dimensions(); | |
| for (int i = 0; i < NumDims; ++i) { | |
| m_dimensions[i] += m_padding[i].first + m_padding[i].second; | |
| } | |
| const typename TensorEvaluator<ArgType, Device>::Dimensions& input_dims = m_impl.dimensions(); | |
| if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { | |
| m_inputStrides[0] = 1; | |
| m_outputStrides[0] = 1; | |
| for (int i = 1; i < NumDims; ++i) { | |
| m_inputStrides[i] = m_inputStrides[i - 1] * input_dims[i - 1]; | |
| m_outputStrides[i] = m_outputStrides[i - 1] * m_dimensions[i - 1]; | |
| } | |
| m_outputStrides[NumDims] = m_outputStrides[NumDims - 1] * m_dimensions[NumDims - 1]; | |
| } else { | |
| m_inputStrides[NumDims - 1] = 1; | |
| m_outputStrides[NumDims] = 1; | |
| for (int i = NumDims - 2; i >= 0; --i) { | |
| m_inputStrides[i] = m_inputStrides[i + 1] * input_dims[i + 1]; | |
| m_outputStrides[i + 1] = m_outputStrides[i + 2] * m_dimensions[i + 1]; | |
| } | |
| m_outputStrides[0] = m_outputStrides[1] * m_dimensions[0]; | |
| } | |
| } | |
| 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 CoeffReturnType coeff(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) { | |
| const Index idx = index / m_outputStrides[i]; | |
| if (isPaddingAtIndexForDim(idx, i)) { | |
| return m_paddingValue; | |
| } | |
| inputIndex += (idx - m_padding[i].first) * m_inputStrides[i]; | |
| index -= idx * m_outputStrides[i]; | |
| } | |
| if (isPaddingAtIndexForDim(index, 0)) { | |
| return m_paddingValue; | |
| } | |
| inputIndex += (index - m_padding[0].first); | |
| } else { | |
| EIGEN_UNROLL_LOOP | |
| for (int i = 0; i < NumDims - 1; ++i) { | |
| const Index idx = index / m_outputStrides[i + 1]; | |
| if (isPaddingAtIndexForDim(idx, i)) { | |
| return m_paddingValue; | |
| } | |
| inputIndex += (idx - m_padding[i].first) * m_inputStrides[i]; | |
| index -= idx * m_outputStrides[i + 1]; | |
| } | |
| if (isPaddingAtIndexForDim(index, NumDims - 1)) { | |
| return m_paddingValue; | |
| } | |
| inputIndex += (index - m_padding[NumDims - 1].first); | |
| } | |
| return m_impl.coeff(inputIndex); | |
| } | |
| template <int LoadMode> | |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const { | |
| if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { | |
| return packetColMajor(index); | |
| } | |
| return packetRowMajor(index); | |
| } | |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const { | |
| TensorOpCost cost = m_impl.costPerCoeff(vectorized); | |
| if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { | |
| EIGEN_UNROLL_LOOP | |
| for (int i = 0; i < NumDims; ++i) updateCostPerDimension(cost, i, i == 0); | |
| } else { | |
| EIGEN_UNROLL_LOOP | |
| for (int i = NumDims - 1; i >= 0; --i) updateCostPerDimension(cost, i, i == NumDims - 1); | |
| } | |
| return cost; | |
| } | |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE internal::TensorBlockResourceRequirements getResourceRequirements() const { | |
| const size_t target_size = m_device.lastLevelCacheSize(); | |
| return internal::TensorBlockResourceRequirements::merge( | |
| internal::TensorBlockResourceRequirements::skewed<Scalar>(target_size), m_impl.getResourceRequirements()); | |
| } | |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBlock block(TensorBlockDesc& desc, TensorBlockScratch& scratch, | |
| bool /*root_of_expr_ast*/ = false) const { | |
| // If one of the dimensions is zero, return empty block view. | |
| if (desc.size() == 0) { | |
| return TensorBlock(internal::TensorBlockKind::kView, NULL, desc.dimensions()); | |
| } | |
| static const bool IsColMajor = Layout == static_cast<int>(ColMajor); | |
| const int inner_dim_idx = IsColMajor ? 0 : NumDims - 1; | |
| Index offset = desc.offset(); | |
| // Compute offsets in the output tensor corresponding to the desc.offset(). | |
| DSizes<Index, NumDims> output_offsets; | |
| for (int i = NumDims - 1; i > 0; --i) { | |
| const int dim = IsColMajor ? i : NumDims - i - 1; | |
| const int stride_dim = IsColMajor ? dim : dim + 1; | |
| output_offsets[dim] = offset / m_outputStrides[stride_dim]; | |
| offset -= output_offsets[dim] * m_outputStrides[stride_dim]; | |
| } | |
| output_offsets[inner_dim_idx] = offset; | |
| // Offsets in the input corresponding to output offsets. | |
| DSizes<Index, NumDims> input_offsets = output_offsets; | |
| for (int i = 0; i < NumDims; ++i) { | |
| const int dim = IsColMajor ? i : NumDims - i - 1; | |
| input_offsets[dim] = input_offsets[dim] - m_padding[dim].first; | |
| } | |
| // Compute offset in the input buffer (at this point it might be illegal and | |
| // point outside of the input buffer, because we don't check for negative | |
| // offsets, it will be autocorrected in the block iteration loop below). | |
| Index input_offset = 0; | |
| for (int i = 0; i < NumDims; ++i) { | |
| const int dim = IsColMajor ? i : NumDims - i - 1; | |
| input_offset += input_offsets[dim] * m_inputStrides[dim]; | |
| } | |
| // Destination buffer and scratch buffer both indexed from 0 and have the | |
| // same dimensions as the requested block (for destination buffer this | |
| // property is guaranteed by `desc.destination()`). | |
| Index output_offset = 0; | |
| const DSizes<Index, NumDims> output_strides = internal::strides<Layout>(desc.dimensions()); | |
| // NOTE(ezhulenev): We initialize bock iteration state for `NumDims - 1` | |
| // dimensions, skipping innermost dimension. In theory it should be possible | |
| // to squeeze matching innermost dimensions, however in practice that did | |
| // not show any improvements in benchmarks. Also in practice first outer | |
| // dimension usually has padding, and will prevent squeezing. | |
| // Initialize output block iterator state. Dimension in this array are | |
| // always in inner_most -> outer_most order (col major layout). | |
| array<BlockIteratorState, NumDims - 1> it; | |
| for (int i = 0; i < NumDims - 1; ++i) { | |
| const int dim = IsColMajor ? i + 1 : NumDims - i - 2; | |
| it[i].count = 0; | |
| it[i].size = desc.dimension(dim); | |
| it[i].input_stride = m_inputStrides[dim]; | |
| it[i].input_span = it[i].input_stride * (it[i].size - 1); | |
| it[i].output_stride = output_strides[dim]; | |
| it[i].output_span = it[i].output_stride * (it[i].size - 1); | |
| } | |
| const Index input_inner_dim_size = static_cast<Index>(m_impl.dimensions()[inner_dim_idx]); | |
| // Total output size. | |
| const Index output_size = desc.size(); | |
| // We will fill inner dimension of this size in the output. It might be | |
| // larger than the inner dimension in the input, so we might have to pad | |
| // before/after we copy values from the input inner dimension. | |
| const Index output_inner_dim_size = desc.dimension(inner_dim_idx); | |
| // How many values to fill with padding BEFORE reading from the input inner | |
| // dimension. | |
| const Index output_inner_pad_before_size = | |
| input_offsets[inner_dim_idx] < 0 | |
| ? numext::mini(numext::abs(input_offsets[inner_dim_idx]), output_inner_dim_size) | |
| : 0; | |
| // How many values we can actually copy from the input inner dimension. | |
| const Index output_inner_copy_size = numext::mini( | |
| // Want to copy from input. | |
| (output_inner_dim_size - output_inner_pad_before_size), | |
| // Can copy from input. | |
| numext::maxi(input_inner_dim_size - (input_offsets[inner_dim_idx] + output_inner_pad_before_size), Index(0))); | |
| eigen_assert(output_inner_copy_size >= 0); | |
| // How many values to fill with padding AFTER reading from the input inner | |
| // dimension. | |
| const Index output_inner_pad_after_size = | |
| (output_inner_dim_size - output_inner_copy_size - output_inner_pad_before_size); | |
| // Sanity check, sum of all sizes must be equal to the output size. | |
| eigen_assert(output_inner_dim_size == | |
| (output_inner_pad_before_size + output_inner_copy_size + output_inner_pad_after_size)); | |
| // Keep track of current coordinates and padding in the output. | |
| DSizes<Index, NumDims> output_coord = output_offsets; | |
| DSizes<Index, NumDims> output_padded; | |
| for (int i = 0; i < NumDims; ++i) { | |
| const int dim = IsColMajor ? i : NumDims - i - 1; | |
| output_padded[dim] = isPaddingAtIndexForDim(output_coord[dim], dim); | |
| } | |
| typedef internal::StridedLinearBufferCopy<ScalarNoConst, Index> LinCopy; | |
| // Prepare storage for the materialized padding result. | |
| const typename TensorBlock::Storage block_storage = TensorBlock::prepareStorage(desc, scratch); | |
| // TODO(ezhulenev): Squeeze multiple non-padded inner dimensions into a | |
| // single logical inner dimension. | |
| // When possible we squeeze writes for the innermost (only if non-padded) | |
| // dimension with the first padded dimension. This allows to reduce the | |
| // number of calls to LinCopy and better utilize vector instructions. | |
| const bool squeeze_writes = NumDims > 1 && | |
| // inner dimension is not padded | |
| (input_inner_dim_size == m_dimensions[inner_dim_idx]) && | |
| // and equal to the block inner dimension | |
| (input_inner_dim_size == output_inner_dim_size); | |
| const int squeeze_dim = IsColMajor ? inner_dim_idx + 1 : inner_dim_idx - 1; | |
| // Maximum coordinate on a squeeze dimension that we can write to. | |
| const Index squeeze_max_coord = | |
| squeeze_writes ? numext::mini( | |
| // max non-padded element in the input | |
| static_cast<Index>(m_dimensions[squeeze_dim] - m_padding[squeeze_dim].second), | |
| // max element in the output buffer | |
| static_cast<Index>(output_offsets[squeeze_dim] + desc.dimension(squeeze_dim))) | |
| : static_cast<Index>(0); | |
| // Iterate copying data from `m_impl.data()` to the output buffer. | |
| for (Index size = 0; size < output_size;) { | |
| // Detect if we are in the padded region (exclude innermost dimension). | |
| bool is_padded = false; | |
| for (int j = 1; j < NumDims; ++j) { | |
| const int dim = IsColMajor ? j : NumDims - j - 1; | |
| is_padded = output_padded[dim]; | |
| if (is_padded) break; | |
| } | |
| if (is_padded) { | |
| // Fill single innermost dimension with padding value. | |
| size += output_inner_dim_size; | |
| LinCopy::template Run<LinCopy::Kind::FillLinear>(typename LinCopy::Dst(output_offset, 1, block_storage.data()), | |
| typename LinCopy::Src(0, 0, &m_paddingValue), | |
| output_inner_dim_size); | |
| } else if (squeeze_writes) { | |
| // Squeeze multiple reads from innermost dimensions. | |
| const Index squeeze_num = squeeze_max_coord - output_coord[squeeze_dim]; | |
| size += output_inner_dim_size * squeeze_num; | |
| // Copy `squeeze_num` inner dimensions from input to output. | |
| LinCopy::template Run<LinCopy::Kind::Linear>(typename LinCopy::Dst(output_offset, 1, block_storage.data()), | |
| typename LinCopy::Src(input_offset, 1, m_impl.data()), | |
| output_inner_dim_size * squeeze_num); | |
| // Update iteration state for only `squeeze_num - 1` processed inner | |
| // dimensions, because we have another iteration state update at the end | |
| // of the loop that will update iteration state for the last inner | |
| // processed dimension. | |
| it[0].count += (squeeze_num - 1); | |
| input_offset += it[0].input_stride * (squeeze_num - 1); | |
| output_offset += it[0].output_stride * (squeeze_num - 1); | |
| output_coord[squeeze_dim] += (squeeze_num - 1); | |
| } else { | |
| // Single read from innermost dimension. | |
| size += output_inner_dim_size; | |
| { // Fill with padding before copying from input inner dimension. | |
| const Index out = output_offset; | |
| LinCopy::template Run<LinCopy::Kind::FillLinear>(typename LinCopy::Dst(out, 1, block_storage.data()), | |
| typename LinCopy::Src(0, 0, &m_paddingValue), | |
| output_inner_pad_before_size); | |
| } | |
| { // Copy data from input inner dimension. | |
| const Index out = output_offset + output_inner_pad_before_size; | |
| const Index in = input_offset + output_inner_pad_before_size; | |
| eigen_assert(output_inner_copy_size == 0 || m_impl.data() != NULL); | |
| LinCopy::template Run<LinCopy::Kind::Linear>(typename LinCopy::Dst(out, 1, block_storage.data()), | |
| typename LinCopy::Src(in, 1, m_impl.data()), | |
| output_inner_copy_size); | |
| } | |
| { // Fill with padding after copying from input inner dimension. | |
| const Index out = output_offset + output_inner_pad_before_size + output_inner_copy_size; | |
| LinCopy::template Run<LinCopy::Kind::FillLinear>(typename LinCopy::Dst(out, 1, block_storage.data()), | |
| typename LinCopy::Src(0, 0, &m_paddingValue), | |
| output_inner_pad_after_size); | |
| } | |
| } | |
| for (int j = 0; j < NumDims - 1; ++j) { | |
| const int dim = IsColMajor ? j + 1 : NumDims - j - 2; | |
| if (++it[j].count < it[j].size) { | |
| input_offset += it[j].input_stride; | |
| output_offset += it[j].output_stride; | |
| output_coord[dim] += 1; | |
| output_padded[dim] = isPaddingAtIndexForDim(output_coord[dim], dim); | |
| break; | |
| } | |
| it[j].count = 0; | |
| input_offset -= it[j].input_span; | |
| output_offset -= it[j].output_span; | |
| output_coord[dim] -= it[j].size - 1; | |
| output_padded[dim] = isPaddingAtIndexForDim(output_coord[dim], dim); | |
| } | |
| } | |
| return block_storage.AsTensorMaterializedBlock(); | |
| } | |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EvaluatorPointerType data() const { return NULL; } | |
| private: | |
| struct BlockIteratorState { | |
| BlockIteratorState() : count(0), size(0), input_stride(0), input_span(0), output_stride(0), output_span(0) {} | |
| Index count; | |
| Index size; | |
| Index input_stride; | |
| Index input_span; | |
| Index output_stride; | |
| Index output_span; | |
| }; | |
| EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE bool isPaddingAtIndexForDim(Index index, int dim_index) const { | |
| return (!internal::index_pair_first_statically_eq<PaddingDimensions>(dim_index, 0) && | |
| index < m_padding[dim_index].first) || | |
| (!internal::index_pair_second_statically_eq<PaddingDimensions>(dim_index, 0) && | |
| index >= m_dimensions[dim_index] - m_padding[dim_index].second); | |
| } | |
| EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE bool isLeftPaddingCompileTimeZero(int dim_index) const { | |
| return internal::index_pair_first_statically_eq<PaddingDimensions>(dim_index, 0); | |
| } | |
| EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE bool isRightPaddingCompileTimeZero(int dim_index) const { | |
| return internal::index_pair_second_statically_eq<PaddingDimensions>(dim_index, 0); | |
| } | |
| void updateCostPerDimension(TensorOpCost& cost, int i, bool first) const { | |
| const double in = static_cast<double>(m_impl.dimensions()[i]); | |
| const double out = in + m_padding[i].first + m_padding[i].second; | |
| if (out == 0) return; | |
| const double reduction = in / out; | |
| cost *= reduction; | |
| if (first) { | |
| cost += TensorOpCost(0, 0, 2 * TensorOpCost::AddCost<Index>() + reduction * (1 * TensorOpCost::AddCost<Index>())); | |
| } else { | |
| cost += TensorOpCost(0, 0, | |
| 2 * TensorOpCost::AddCost<Index>() + 2 * TensorOpCost::MulCost<Index>() + | |
| reduction * (2 * TensorOpCost::MulCost<Index>() + 1 * TensorOpCost::DivCost<Index>())); | |
| } | |
| } | |
| protected: | |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packetColMajor(Index index) const { | |
| eigen_assert(index + PacketSize - 1 < dimensions().TotalSize()); | |
| const Index initialIndex = index; | |
| Index inputIndex = 0; | |
| EIGEN_UNROLL_LOOP | |
| for (int i = NumDims - 1; i > 0; --i) { | |
| const Index firstIdx = index; | |
| const Index lastIdx = index + PacketSize - 1; | |
| const Index lastPaddedLeft = m_padding[i].first * m_outputStrides[i]; | |
| const Index firstPaddedRight = (m_dimensions[i] - m_padding[i].second) * m_outputStrides[i]; | |
| const Index lastPaddedRight = m_outputStrides[i + 1]; | |
| if (!isLeftPaddingCompileTimeZero(i) && lastIdx < lastPaddedLeft) { | |
| // all the coefficient are in the padding zone. | |
| return internal::pset1<PacketReturnType>(m_paddingValue); | |
| } else if (!isRightPaddingCompileTimeZero(i) && firstIdx >= firstPaddedRight && lastIdx < lastPaddedRight) { | |
| // all the coefficient are in the padding zone. | |
| return internal::pset1<PacketReturnType>(m_paddingValue); | |
| } else if ((isLeftPaddingCompileTimeZero(i) && isRightPaddingCompileTimeZero(i)) || | |
| (firstIdx >= lastPaddedLeft && lastIdx < firstPaddedRight)) { | |
| // all the coefficient are between the 2 padding zones. | |
| const Index idx = index / m_outputStrides[i]; | |
| inputIndex += (idx - m_padding[i].first) * m_inputStrides[i]; | |
| index -= idx * m_outputStrides[i]; | |
| } else { | |
| // Every other case | |
| return packetWithPossibleZero(initialIndex); | |
| } | |
| } | |
| const Index lastIdx = index + PacketSize - 1; | |
| const Index firstIdx = index; | |
| const Index lastPaddedLeft = m_padding[0].first; | |
| const Index firstPaddedRight = (m_dimensions[0] - m_padding[0].second); | |
| const Index lastPaddedRight = m_outputStrides[1]; | |
| if (!isLeftPaddingCompileTimeZero(0) && lastIdx < lastPaddedLeft) { | |
| // all the coefficient are in the padding zone. | |
| return internal::pset1<PacketReturnType>(m_paddingValue); | |
| } else if (!isRightPaddingCompileTimeZero(0) && firstIdx >= firstPaddedRight && lastIdx < lastPaddedRight) { | |
| // all the coefficient are in the padding zone. | |
| return internal::pset1<PacketReturnType>(m_paddingValue); | |
| } else if ((isLeftPaddingCompileTimeZero(0) && isRightPaddingCompileTimeZero(0)) || | |
| (firstIdx >= lastPaddedLeft && lastIdx < firstPaddedRight)) { | |
| // all the coefficient are between the 2 padding zones. | |
| inputIndex += (index - m_padding[0].first); | |
| return m_impl.template packet<Unaligned>(inputIndex); | |
| } | |
| // Every other case | |
| return packetWithPossibleZero(initialIndex); | |
| } | |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packetRowMajor(Index index) const { | |
| eigen_assert(index + PacketSize - 1 < dimensions().TotalSize()); | |
| const Index initialIndex = index; | |
| Index inputIndex = 0; | |
| EIGEN_UNROLL_LOOP | |
| for (int i = 0; i < NumDims - 1; ++i) { | |
| const Index firstIdx = index; | |
| const Index lastIdx = index + PacketSize - 1; | |
| const Index lastPaddedLeft = m_padding[i].first * m_outputStrides[i + 1]; | |
| const Index firstPaddedRight = (m_dimensions[i] - m_padding[i].second) * m_outputStrides[i + 1]; | |
| const Index lastPaddedRight = m_outputStrides[i]; | |
| if (!isLeftPaddingCompileTimeZero(i) && lastIdx < lastPaddedLeft) { | |
| // all the coefficient are in the padding zone. | |
| return internal::pset1<PacketReturnType>(m_paddingValue); | |
| } else if (!isRightPaddingCompileTimeZero(i) && firstIdx >= firstPaddedRight && lastIdx < lastPaddedRight) { | |
| // all the coefficient are in the padding zone. | |
| return internal::pset1<PacketReturnType>(m_paddingValue); | |
| } else if ((isLeftPaddingCompileTimeZero(i) && isRightPaddingCompileTimeZero(i)) || | |
| (firstIdx >= lastPaddedLeft && lastIdx < firstPaddedRight)) { | |
| // all the coefficient are between the 2 padding zones. | |
| const Index idx = index / m_outputStrides[i + 1]; | |
| inputIndex += (idx - m_padding[i].first) * m_inputStrides[i]; | |
| index -= idx * m_outputStrides[i + 1]; | |
| } else { | |
| // Every other case | |
| return packetWithPossibleZero(initialIndex); | |
| } | |
| } | |
| const Index lastIdx = index + PacketSize - 1; | |
| const Index firstIdx = index; | |
| const Index lastPaddedLeft = m_padding[NumDims - 1].first; | |
| const Index firstPaddedRight = (m_dimensions[NumDims - 1] - m_padding[NumDims - 1].second); | |
| const Index lastPaddedRight = m_outputStrides[NumDims - 1]; | |
| if (!isLeftPaddingCompileTimeZero(NumDims - 1) && lastIdx < lastPaddedLeft) { | |
| // all the coefficient are in the padding zone. | |
| return internal::pset1<PacketReturnType>(m_paddingValue); | |
| } else if (!isRightPaddingCompileTimeZero(NumDims - 1) && firstIdx >= firstPaddedRight && | |
| lastIdx < lastPaddedRight) { | |
| // all the coefficient are in the padding zone. | |
| return internal::pset1<PacketReturnType>(m_paddingValue); | |
| } else if ((isLeftPaddingCompileTimeZero(NumDims - 1) && isRightPaddingCompileTimeZero(NumDims - 1)) || | |
| (firstIdx >= lastPaddedLeft && lastIdx < firstPaddedRight)) { | |
| // all the coefficient are between the 2 padding zones. | |
| inputIndex += (index - m_padding[NumDims - 1].first); | |
| return m_impl.template packet<Unaligned>(inputIndex); | |
| } | |
| // Every other case | |
| return packetWithPossibleZero(initialIndex); | |
| } | |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packetWithPossibleZero(Index index) const { | |
| 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; | |
| } | |
| Dimensions m_dimensions; | |
| array<Index, NumDims + 1> m_outputStrides; | |
| array<Index, NumDims> m_inputStrides; | |
| TensorEvaluator<ArgType, Device> m_impl; | |
| PaddingDimensions m_padding; | |
| Scalar m_paddingValue; | |
| const Device EIGEN_DEVICE_REF m_device; | |
| }; | |
| } // end namespace Eigen | |
| #endif // EIGEN_CXX11_TENSOR_TENSOR_PADDING_H |