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Remove CXX11/ directory nesting for Tensor modules
libeigen/eigen!2199 Co-authored-by: Rasmus Munk Larsen <rmlarsen@gmail.com>
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Rasmus Munk Larsen
269
unsupported/Eigen/src/Tensor/TensorGenerator.h
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269
unsupported/Eigen/src/Tensor/TensorGenerator.h
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// This file is part of Eigen, a lightweight C++ template library
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// for linear algebra.
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//
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// Copyright (C) 2015 Benoit Steiner <benoit.steiner.goog@gmail.com>
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//
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// This Source Code Form is subject to the terms of the Mozilla
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// Public License v. 2.0. If a copy of the MPL was not distributed
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// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
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#ifndef EIGEN_CXX11_TENSOR_TENSOR_GENERATOR_H
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#define EIGEN_CXX11_TENSOR_TENSOR_GENERATOR_H
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// IWYU pragma: private
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#include "./InternalHeaderCheck.h"
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namespace Eigen {
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namespace internal {
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template <typename Generator, typename XprType>
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struct traits<TensorGeneratorOp<Generator, XprType> > : public traits<XprType> {
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typedef typename XprType::Scalar Scalar;
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typedef traits<XprType> XprTraits;
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typedef typename XprTraits::StorageKind StorageKind;
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typedef typename XprTraits::Index Index;
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typedef typename XprType::Nested Nested;
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typedef std::remove_reference_t<Nested> Nested_;
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static constexpr int NumDimensions = XprTraits::NumDimensions;
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static constexpr int Layout = XprTraits::Layout;
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typedef typename XprTraits::PointerType PointerType;
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};
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template <typename Generator, typename XprType>
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struct eval<TensorGeneratorOp<Generator, XprType>, Eigen::Dense> {
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typedef const TensorGeneratorOp<Generator, XprType>& type;
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};
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template <typename Generator, typename XprType>
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struct nested<TensorGeneratorOp<Generator, XprType>, 1, typename eval<TensorGeneratorOp<Generator, XprType> >::type> {
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typedef TensorGeneratorOp<Generator, XprType> type;
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};
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} // end namespace internal
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/**
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* \ingroup CXX11_Tensor_Module
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*
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* \brief Tensor generator class.
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*/
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template <typename Generator, typename XprType>
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class TensorGeneratorOp : public TensorBase<TensorGeneratorOp<Generator, XprType>, ReadOnlyAccessors> {
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public:
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typedef typename Eigen::internal::traits<TensorGeneratorOp>::Scalar Scalar;
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typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
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typedef typename XprType::CoeffReturnType CoeffReturnType;
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typedef typename Eigen::internal::nested<TensorGeneratorOp>::type Nested;
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typedef typename Eigen::internal::traits<TensorGeneratorOp>::StorageKind StorageKind;
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typedef typename Eigen::internal::traits<TensorGeneratorOp>::Index Index;
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorGeneratorOp(const XprType& expr, const Generator& generator)
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: m_xpr(expr), m_generator(generator) {}
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EIGEN_DEVICE_FUNC const Generator& generator() const { return m_generator; }
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EIGEN_DEVICE_FUNC const internal::remove_all_t<typename XprType::Nested>& expression() const { return m_xpr; }
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protected:
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typename XprType::Nested m_xpr;
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const Generator m_generator;
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};
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// Eval as rvalue
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template <typename Generator, typename ArgType, typename Device>
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struct TensorEvaluator<const TensorGeneratorOp<Generator, ArgType>, Device> {
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typedef TensorGeneratorOp<Generator, ArgType> XprType;
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typedef typename XprType::Index Index;
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typedef typename TensorEvaluator<ArgType, Device>::Dimensions Dimensions;
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static constexpr int NumDims = internal::array_size<Dimensions>::value;
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typedef typename XprType::Scalar Scalar;
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typedef typename XprType::CoeffReturnType CoeffReturnType;
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typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
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typedef StorageMemory<CoeffReturnType, Device> Storage;
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typedef typename Storage::Type EvaluatorPointerType;
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static constexpr int Layout = TensorEvaluator<ArgType, Device>::Layout;
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enum {
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IsAligned = false,
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PacketAccess = (PacketType<CoeffReturnType, Device>::size > 1),
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BlockAccess = true,
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PreferBlockAccess = true,
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CoordAccess = false, // to be implemented
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RawAccess = false
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};
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typedef internal::TensorIntDivisor<Index> IndexDivisor;
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//===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
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typedef internal::TensorBlockDescriptor<NumDims, Index> TensorBlockDesc;
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typedef internal::TensorBlockScratchAllocator<Device> TensorBlockScratch;
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typedef typename internal::TensorMaterializedBlock<CoeffReturnType, NumDims, Layout, Index> TensorBlock;
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//===--------------------------------------------------------------------===//
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EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
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: m_device(device), m_generator(op.generator()) {
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TensorEvaluator<ArgType, Device> argImpl(op.expression(), device);
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m_dimensions = argImpl.dimensions();
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if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
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m_strides[0] = 1;
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EIGEN_UNROLL_LOOP
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for (int i = 1; i < NumDims; ++i) {
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m_strides[i] = m_strides[i - 1] * m_dimensions[i - 1];
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if (m_strides[i] != 0) m_fast_strides[i] = IndexDivisor(m_strides[i]);
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}
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} else {
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m_strides[NumDims - 1] = 1;
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EIGEN_UNROLL_LOOP
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for (int i = NumDims - 2; i >= 0; --i) {
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m_strides[i] = m_strides[i + 1] * m_dimensions[i + 1];
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if (m_strides[i] != 0) m_fast_strides[i] = IndexDivisor(m_strides[i]);
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}
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}
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}
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
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EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType /*data*/) { return true; }
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EIGEN_STRONG_INLINE void cleanup() {}
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const {
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array<Index, NumDims> coords;
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extract_coordinates(index, coords);
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return m_generator(coords);
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}
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template <int LoadMode>
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const {
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const int packetSize = PacketType<CoeffReturnType, Device>::size;
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eigen_assert(index + packetSize - 1 < dimensions().TotalSize());
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EIGEN_ALIGN_MAX std::remove_const_t<CoeffReturnType> values[packetSize];
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for (int i = 0; i < packetSize; ++i) {
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values[i] = coeff(index + i);
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}
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PacketReturnType rslt = internal::pload<PacketReturnType>(values);
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return rslt;
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}
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE internal::TensorBlockResourceRequirements getResourceRequirements() const {
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const size_t target_size = m_device.firstLevelCacheSize();
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// TODO(ezhulenev): Generator should have a cost.
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return internal::TensorBlockResourceRequirements::skewed<Scalar>(target_size);
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}
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struct BlockIteratorState {
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Index stride;
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Index span;
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Index size;
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Index count;
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};
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBlock block(TensorBlockDesc& desc, TensorBlockScratch& scratch,
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bool /*root_of_expr_ast*/ = false) const {
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static const bool is_col_major = static_cast<int>(Layout) == static_cast<int>(ColMajor);
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// Compute spatial coordinates for the first block element.
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array<Index, NumDims> coords;
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extract_coordinates(desc.offset(), coords);
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array<Index, NumDims> initial_coords = coords;
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// Offset in the output block buffer.
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Index offset = 0;
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// Initialize output block iterator state. Dimension in this array are
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// always in inner_most -> outer_most order (col major layout).
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array<BlockIteratorState, NumDims> it;
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for (int i = 0; i < NumDims; ++i) {
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const int dim = is_col_major ? i : NumDims - 1 - i;
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it[i].size = desc.dimension(dim);
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it[i].stride = i == 0 ? 1 : (it[i - 1].size * it[i - 1].stride);
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it[i].span = it[i].stride * (it[i].size - 1);
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it[i].count = 0;
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}
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eigen_assert(it[0].stride == 1);
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// Prepare storage for the materialized generator result.
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const typename TensorBlock::Storage block_storage = TensorBlock::prepareStorage(desc, scratch);
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CoeffReturnType* block_buffer = block_storage.data();
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static const int packet_size = PacketType<CoeffReturnType, Device>::size;
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static const int inner_dim = is_col_major ? 0 : NumDims - 1;
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const Index inner_dim_size = it[0].size;
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const Index inner_dim_vectorized = inner_dim_size - packet_size;
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while (it[NumDims - 1].count < it[NumDims - 1].size) {
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Index i = 0;
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// Generate data for the vectorized part of the inner-most dimension.
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for (; i <= inner_dim_vectorized; i += packet_size) {
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for (Index j = 0; j < packet_size; ++j) {
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array<Index, NumDims> j_coords = coords; // Break loop dependence.
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j_coords[inner_dim] += j;
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*(block_buffer + offset + i + j) = m_generator(j_coords);
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}
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coords[inner_dim] += packet_size;
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}
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// Finalize non-vectorized part of the inner-most dimension.
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for (; i < inner_dim_size; ++i) {
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*(block_buffer + offset + i) = m_generator(coords);
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coords[inner_dim]++;
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}
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coords[inner_dim] = initial_coords[inner_dim];
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// For the 1d tensor we need to generate only one inner-most dimension.
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if (NumDims == 1) break;
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// Update offset.
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for (i = 1; i < NumDims; ++i) {
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if (++it[i].count < it[i].size) {
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offset += it[i].stride;
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coords[is_col_major ? i : NumDims - 1 - i]++;
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break;
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}
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if (i != NumDims - 1) it[i].count = 0;
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coords[is_col_major ? i : NumDims - 1 - i] = initial_coords[is_col_major ? i : NumDims - 1 - i];
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offset -= it[i].span;
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}
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}
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return block_storage.AsTensorMaterializedBlock();
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}
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool) const {
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// TODO(rmlarsen): This is just a placeholder. Define interface to make
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// generators return their cost.
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return TensorOpCost(0, 0, TensorOpCost::AddCost<Scalar>() + TensorOpCost::MulCost<Scalar>());
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}
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EIGEN_DEVICE_FUNC EvaluatorPointerType data() const { return NULL; }
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protected:
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void extract_coordinates(Index index, array<Index, NumDims>& coords) const {
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if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
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for (int i = NumDims - 1; i > 0; --i) {
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const Index idx = index / m_fast_strides[i];
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index -= idx * m_strides[i];
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coords[i] = idx;
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}
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coords[0] = index;
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} else {
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for (int i = 0; i < NumDims - 1; ++i) {
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const Index idx = index / m_fast_strides[i];
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index -= idx * m_strides[i];
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coords[i] = idx;
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}
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coords[NumDims - 1] = index;
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}
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}
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const Device EIGEN_DEVICE_REF m_device;
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Dimensions m_dimensions;
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array<Index, NumDims> m_strides;
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array<IndexDivisor, NumDims> m_fast_strides;
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Generator m_generator;
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};
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} // end namespace Eigen
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#endif // EIGEN_CXX11_TENSOR_TENSOR_GENERATOR_H
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