186 lines
6.2 KiB
C++
186 lines
6.2 KiB
C++
// 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 the mozilla.org home page
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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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namespace Eigen {
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/** \class TensorGeneratorOp
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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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*
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*/
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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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{
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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 typename remove_reference<Nested>::type _Nested;
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static const int NumDimensions = XprTraits::NumDimensions;
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static const int Layout = XprTraits::Layout;
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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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{
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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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{
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typedef TensorGeneratorOp<Generator, XprType> type;
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};
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} // end namespace internal
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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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{
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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
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const Generator& generator() const { return m_generator; }
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EIGEN_DEVICE_FUNC
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const typename internal::remove_all<typename XprType::Nested>::type&
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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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{
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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 const 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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enum {
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IsAligned = false,
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PacketAccess = (internal::unpacket_traits<PacketReturnType>::size > 1),
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BlockAccess = false,
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Layout = TensorEvaluator<ArgType, Device>::Layout,
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CoordAccess = false, // to be implemented
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RawAccess = false
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};
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
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: m_generator(op.generator())
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{
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TensorEvaluator<ArgType, Device> impl(op.expression(), device);
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m_dimensions = impl.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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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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}
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} else {
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m_strides[NumDims - 1] = 1;
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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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}
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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_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(Scalar* /*data*/) {
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return true;
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}
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() {
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}
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
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{
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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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{
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const int packetSize = internal::unpacket_traits<PacketReturnType>::size;
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EIGEN_STATIC_ASSERT((packetSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE)
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eigen_assert(index+packetSize-1 < dimensions().TotalSize());
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EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type 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 TensorOpCost
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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>() +
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TensorOpCost::MulCost<Scalar>());
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}
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EIGEN_DEVICE_FUNC Scalar* data() const { return NULL; }
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protected:
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
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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_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_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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Dimensions m_dimensions;
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array<Index, NumDims> m_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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