| // This file is part of Eigen, a lightweight C++ template library |
| // for linear algebra. |
| // |
| // Copyright (C) 2015 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_GENERATOR_H |
| #define EIGEN_CXX11_TENSOR_TENSOR_GENERATOR_H |
| |
| namespace Eigen { |
| |
| /** \class TensorGeneratorOp |
| * \ingroup CXX11_Tensor_Module |
| * |
| * \brief Tensor generator class. |
| * |
| * |
| */ |
| namespace internal { |
| template<typename Generator, typename XprType> |
| struct traits<TensorGeneratorOp<Generator, XprType> > : public traits<XprType> |
| { |
| typedef typename XprType::Scalar Scalar; |
| typedef traits<XprType> XprTraits; |
| typedef typename XprTraits::StorageKind StorageKind; |
| typedef typename XprTraits::Index Index; |
| typedef typename XprType::Nested Nested; |
| typedef typename remove_reference<Nested>::type _Nested; |
| static const int NumDimensions = XprTraits::NumDimensions; |
| static const int Layout = XprTraits::Layout; |
| typedef typename XprTraits::PointerType PointerType; |
| }; |
| |
| template<typename Generator, typename XprType> |
| struct eval<TensorGeneratorOp<Generator, XprType>, Eigen::Dense> |
| { |
| typedef const TensorGeneratorOp<Generator, XprType>& type; |
| }; |
| |
| template<typename Generator, typename XprType> |
| struct nested<TensorGeneratorOp<Generator, XprType>, 1, typename eval<TensorGeneratorOp<Generator, XprType> >::type> |
| { |
| typedef TensorGeneratorOp<Generator, XprType> type; |
| }; |
| |
| } // end namespace internal |
| |
| |
| |
| template<typename Generator, typename XprType> |
| class TensorGeneratorOp : public TensorBase<TensorGeneratorOp<Generator, XprType>, ReadOnlyAccessors> |
| { |
| public: |
| typedef typename Eigen::internal::traits<TensorGeneratorOp>::Scalar Scalar; |
| typedef typename Eigen::NumTraits<Scalar>::Real RealScalar; |
| typedef typename XprType::CoeffReturnType CoeffReturnType; |
| typedef typename Eigen::internal::nested<TensorGeneratorOp>::type Nested; |
| typedef typename Eigen::internal::traits<TensorGeneratorOp>::StorageKind StorageKind; |
| typedef typename Eigen::internal::traits<TensorGeneratorOp>::Index Index; |
| |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorGeneratorOp(const XprType& expr, const Generator& generator) |
| : m_xpr(expr), m_generator(generator) {} |
| |
| EIGEN_DEVICE_FUNC |
| const Generator& generator() const { return m_generator; } |
| |
| EIGEN_DEVICE_FUNC |
| const typename internal::remove_all<typename XprType::Nested>::type& |
| expression() const { return m_xpr; } |
| |
| protected: |
| typename XprType::Nested m_xpr; |
| const Generator m_generator; |
| }; |
| |
| |
| // Eval as rvalue |
| template<typename Generator, typename ArgType, typename Device> |
| struct TensorEvaluator<const TensorGeneratorOp<Generator, ArgType>, Device> |
| { |
| typedef TensorGeneratorOp<Generator, ArgType> XprType; |
| typedef typename XprType::Index Index; |
| typedef typename TensorEvaluator<ArgType, Device>::Dimensions Dimensions; |
| static const int NumDims = internal::array_size<Dimensions>::value; |
| 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; |
| enum { |
| IsAligned = false, |
| PacketAccess = (PacketType<CoeffReturnType, Device>::size > 1), |
| BlockAccess = true, |
| PreferBlockAccess = true, |
| Layout = TensorEvaluator<ArgType, Device>::Layout, |
| CoordAccess = false, // to be implemented |
| RawAccess = false |
| }; |
| |
| typedef internal::TensorIntDivisor<Index> IndexDivisor; |
| |
| typedef internal::TensorBlock<CoeffReturnType, Index, NumDims, Layout> |
| TensorBlock; |
| |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device) |
| : m_device(device), m_generator(op.generator()) |
| { |
| TensorEvaluator<ArgType, Device> argImpl(op.expression(), device); |
| m_dimensions = argImpl.dimensions(); |
| |
| if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { |
| m_strides[0] = 1; |
| EIGEN_UNROLL_LOOP |
| for (int i = 1; i < NumDims; ++i) { |
| m_strides[i] = m_strides[i - 1] * m_dimensions[i - 1]; |
| if (m_strides[i] != 0) m_fast_strides[i] = IndexDivisor(m_strides[i]); |
| } |
| } else { |
| m_strides[NumDims - 1] = 1; |
| EIGEN_UNROLL_LOOP |
| 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_fast_strides[i] = IndexDivisor(m_strides[i]); |
| } |
| } |
| } |
| |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; } |
| |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType /*data*/) { |
| return true; |
| } |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() { |
| } |
| |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const |
| { |
| array<Index, NumDims> coords; |
| extract_coordinates(index, coords); |
| return m_generator(coords); |
| } |
| |
| 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 < dimensions().TotalSize()); |
| |
| EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[packetSize]; |
| 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 void getResourceRequirements( |
| std::vector<internal::TensorOpResourceRequirements>* resources) const { |
| Eigen::Index block_total_size_max = numext::maxi<Eigen::Index>( |
| 1, m_device.firstLevelCacheSize() / sizeof(Scalar)); |
| resources->push_back(internal::TensorOpResourceRequirements( |
| internal::kSkewedInnerDims, block_total_size_max)); |
| } |
| |
| struct BlockIteratorState { |
| Index stride; |
| Index span; |
| Index size; |
| Index count; |
| }; |
| |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void block( |
| TensorBlock* output_block) const { |
| if (NumDims <= 0) return; |
| |
| static const bool is_col_major = |
| static_cast<int>(Layout) == static_cast<int>(ColMajor); |
| |
| // Compute spatial coordinates for the first block element. |
| array<Index, NumDims> coords; |
| extract_coordinates(output_block->first_coeff_index(), coords); |
| array<Index, NumDims> initial_coords = coords; |
| |
| CoeffReturnType* data = output_block->data(); |
| Index offset = 0; |
| |
| // 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 (Index i = 0; i < NumDims; ++i) { |
| const Index dim = is_col_major ? i : NumDims - 1 - i; |
| it[i].size = output_block->block_sizes()[dim]; |
| it[i].stride = output_block->block_strides()[dim]; |
| it[i].span = it[i].stride * (it[i].size - 1); |
| it[i].count = 0; |
| } |
| eigen_assert(it[0].stride == 1); |
| |
| while (it[NumDims - 1].count < it[NumDims - 1].size) { |
| // Generate data for the inner-most dimension. |
| for (Index i = 0; i < it[0].size; ++i) { |
| *(data + offset + i) = m_generator(coords); |
| coords[is_col_major ? 0 : NumDims - 1]++; |
| } |
| coords[is_col_major ? 0 : NumDims - 1] = |
| initial_coords[is_col_major ? 0 : NumDims - 1]; |
| |
| // For the 1d tensor we need to generate only one inner-most dimension. |
| if (NumDims == 1) break; |
| |
| // Update offset. |
| for (Index i = 1; i < NumDims; ++i) { |
| if (++it[i].count < it[i].size) { |
| offset += it[i].stride; |
| coords[is_col_major ? i : NumDims - 1 - i]++; |
| break; |
| } |
| if (i != NumDims - 1) it[i].count = 0; |
| coords[is_col_major ? i : NumDims - 1 - i] = |
| initial_coords[is_col_major ? i : NumDims - 1 - i]; |
| offset -= it[i].span; |
| } |
| } |
| } |
| |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost |
| costPerCoeff(bool) const { |
| // TODO(rmlarsen): This is just a placeholder. Define interface to make |
| // generators return their cost. |
| return TensorOpCost(0, 0, TensorOpCost::AddCost<Scalar>() + |
| TensorOpCost::MulCost<Scalar>()); |
| } |
| |
| EIGEN_DEVICE_FUNC EvaluatorPointerType 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&) const {} |
| #endif |
| |
| protected: |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE |
| void extract_coordinates(Index index, array<Index, NumDims>& coords) const { |
| if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { |
| for (int i = NumDims - 1; i > 0; --i) { |
| const Index idx = index / m_fast_strides[i]; |
| index -= idx * m_strides[i]; |
| coords[i] = idx; |
| } |
| coords[0] = index; |
| } else { |
| for (int i = 0; i < NumDims - 1; ++i) { |
| const Index idx = index / m_fast_strides[i]; |
| index -= idx * m_strides[i]; |
| coords[i] = idx; |
| } |
| coords[NumDims-1] = index; |
| } |
| } |
| |
| const Device EIGEN_DEVICE_REF m_device; |
| Dimensions m_dimensions; |
| array<Index, NumDims> m_strides; |
| array<IndexDivisor, NumDims> m_fast_strides; |
| Generator m_generator; |
| }; |
| |
| } // end namespace Eigen |
| |
| #endif // EIGEN_CXX11_TENSOR_TENSOR_GENERATOR_H |