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3669498f5a
Adds copy constructors to Tensor ops, inherits assignment operators from `TensorBase`. Addresses #1863
328 lines
14 KiB
C++
328 lines
14 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) 2014 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_MAP_H
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#define EIGEN_CXX11_TENSOR_TENSOR_MAP_H
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namespace Eigen {
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// FIXME use proper doxygen documentation (e.g. \tparam MakePointer_)
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/** \class TensorMap
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* \ingroup CXX11_Tensor_Module
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*
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* \brief A tensor expression mapping an existing array of data.
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*
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*/
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/// `template <class> class MakePointer_` is added to convert the host pointer to the device pointer.
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/// It is added due to the fact that for our device compiler `T*` is not allowed.
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/// If we wanted to use the same Evaluator functions we have to convert that type to our pointer `T`.
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/// This is done through our `MakePointer_` class. By default the Type in the `MakePointer_<T>` is `T*` .
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/// Therefore, by adding the default value, we managed to convert the type and it does not break any
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/// existing code as its default value is `T*`.
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template<typename PlainObjectType, int Options_, template <class> class MakePointer_> class TensorMap : public TensorBase<TensorMap<PlainObjectType, Options_, MakePointer_> >
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{
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public:
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typedef TensorMap<PlainObjectType, Options_, MakePointer_> Self;
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typedef TensorBase<TensorMap<PlainObjectType, Options_, MakePointer_> > Base;
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#ifdef EIGEN_USE_SYCL
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typedef typename Eigen::internal::remove_reference<typename Eigen::internal::nested<Self>::type>::type Nested;
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#else
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typedef typename Eigen::internal::nested<Self>::type Nested;
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#endif
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typedef typename internal::traits<PlainObjectType>::StorageKind StorageKind;
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typedef typename internal::traits<PlainObjectType>::Index Index;
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typedef typename internal::traits<PlainObjectType>::Scalar Scalar;
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typedef typename NumTraits<Scalar>::Real RealScalar;
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typedef typename PlainObjectType::Base::CoeffReturnType CoeffReturnType;
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typedef typename MakePointer_<Scalar>::Type PointerType;
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typedef typename MakePointer_<Scalar>::ConstType PointerConstType;
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// WARN: PointerType still can be a pointer to const (const Scalar*), for
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// example in TensorMap<Tensor<const Scalar, ...>> expression. This type of
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// expression should be illegal, but adding this restriction is not possible
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// in practice (see https://bitbucket.org/eigen/eigen/pull-requests/488).
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typedef typename internal::conditional<
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bool(internal::is_lvalue<PlainObjectType>::value),
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PointerType, // use simple pointer in lvalue expressions
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PointerConstType // use const pointer in rvalue expressions
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>::type StoragePointerType;
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// If TensorMap was constructed over rvalue expression (e.g. const Tensor),
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// we should return a reference to const from operator() (and others), even
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// if TensorMap itself is not const.
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typedef typename internal::conditional<
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bool(internal::is_lvalue<PlainObjectType>::value),
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Scalar&,
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const Scalar&
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>::type StorageRefType;
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static const int Options = Options_;
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static const Index NumIndices = PlainObjectType::NumIndices;
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typedef typename PlainObjectType::Dimensions Dimensions;
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enum {
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IsAligned = ((int(Options_)&Aligned)==Aligned),
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Layout = PlainObjectType::Layout,
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CoordAccess = true,
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RawAccess = true
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};
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EIGEN_DEVICE_FUNC
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EIGEN_STRONG_INLINE TensorMap(StoragePointerType dataPtr) : m_data(dataPtr), m_dimensions() {
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// The number of dimensions used to construct a tensor must be equal to the rank of the tensor.
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EIGEN_STATIC_ASSERT((0 == NumIndices || NumIndices == Dynamic), YOU_MADE_A_PROGRAMMING_MISTAKE)
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}
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#if EIGEN_HAS_VARIADIC_TEMPLATES
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template<typename... IndexTypes> EIGEN_DEVICE_FUNC
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EIGEN_STRONG_INLINE TensorMap(StoragePointerType dataPtr, Index firstDimension, IndexTypes... otherDimensions) : m_data(dataPtr), m_dimensions(firstDimension, otherDimensions...) {
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// The number of dimensions used to construct a tensor must be equal to the rank of the tensor.
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EIGEN_STATIC_ASSERT((sizeof...(otherDimensions) + 1 == NumIndices || NumIndices == Dynamic), YOU_MADE_A_PROGRAMMING_MISTAKE)
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}
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#else
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EIGEN_DEVICE_FUNC
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EIGEN_STRONG_INLINE TensorMap(StoragePointerType dataPtr, Index firstDimension) : m_data(dataPtr), m_dimensions(firstDimension) {
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// The number of dimensions used to construct a tensor must be equal to the rank of the tensor.
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EIGEN_STATIC_ASSERT((1 == NumIndices || NumIndices == Dynamic), YOU_MADE_A_PROGRAMMING_MISTAKE)
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}
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EIGEN_DEVICE_FUNC
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EIGEN_STRONG_INLINE TensorMap(StoragePointerType dataPtr, Index dim1, Index dim2) : m_data(dataPtr), m_dimensions(dim1, dim2) {
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EIGEN_STATIC_ASSERT(2 == NumIndices || NumIndices == Dynamic, YOU_MADE_A_PROGRAMMING_MISTAKE)
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}
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EIGEN_DEVICE_FUNC
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EIGEN_STRONG_INLINE TensorMap(StoragePointerType dataPtr, Index dim1, Index dim2, Index dim3) : m_data(dataPtr), m_dimensions(dim1, dim2, dim3) {
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EIGEN_STATIC_ASSERT(3 == NumIndices || NumIndices == Dynamic, YOU_MADE_A_PROGRAMMING_MISTAKE)
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}
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EIGEN_DEVICE_FUNC
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EIGEN_STRONG_INLINE TensorMap(StoragePointerType dataPtr, Index dim1, Index dim2, Index dim3, Index dim4) : m_data(dataPtr), m_dimensions(dim1, dim2, dim3, dim4) {
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EIGEN_STATIC_ASSERT(4 == NumIndices || NumIndices == Dynamic, YOU_MADE_A_PROGRAMMING_MISTAKE)
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}
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EIGEN_DEVICE_FUNC
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EIGEN_STRONG_INLINE TensorMap(StoragePointerType dataPtr, Index dim1, Index dim2, Index dim3, Index dim4, Index dim5) : m_data(dataPtr), m_dimensions(dim1, dim2, dim3, dim4, dim5) {
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EIGEN_STATIC_ASSERT(5 == NumIndices || NumIndices == Dynamic, YOU_MADE_A_PROGRAMMING_MISTAKE)
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}
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#endif
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorMap(StoragePointerType dataPtr, const array<Index, NumIndices>& dimensions)
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: m_data(dataPtr), m_dimensions(dimensions)
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{ }
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template <typename Dimensions>
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorMap(StoragePointerType dataPtr, const Dimensions& dimensions)
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: m_data(dataPtr), m_dimensions(dimensions)
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{ }
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorMap(PlainObjectType& tensor)
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: m_data(tensor.data()), m_dimensions(tensor.dimensions())
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{ }
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EIGEN_DEVICE_FUNC
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EIGEN_STRONG_INLINE Index rank() const { return m_dimensions.rank(); }
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EIGEN_DEVICE_FUNC
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EIGEN_STRONG_INLINE Index dimension(Index n) const { return m_dimensions[n]; }
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EIGEN_DEVICE_FUNC
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EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
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EIGEN_DEVICE_FUNC
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EIGEN_STRONG_INLINE Index size() const { return m_dimensions.TotalSize(); }
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EIGEN_DEVICE_FUNC
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EIGEN_STRONG_INLINE StoragePointerType data() { return m_data; }
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EIGEN_DEVICE_FUNC
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EIGEN_STRONG_INLINE StoragePointerType data() const { return m_data; }
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EIGEN_DEVICE_FUNC
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EIGEN_STRONG_INLINE StorageRefType operator()(const array<Index, NumIndices>& indices) const
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{
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// eigen_assert(checkIndexRange(indices));
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if (PlainObjectType::Options&RowMajor) {
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const Index index = m_dimensions.IndexOfRowMajor(indices);
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return m_data[index];
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} else {
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const Index index = m_dimensions.IndexOfColMajor(indices);
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return m_data[index];
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}
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}
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EIGEN_DEVICE_FUNC
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EIGEN_STRONG_INLINE StorageRefType operator()() const
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{
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EIGEN_STATIC_ASSERT(NumIndices == 0, YOU_MADE_A_PROGRAMMING_MISTAKE)
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return m_data[0];
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}
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EIGEN_DEVICE_FUNC
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EIGEN_STRONG_INLINE StorageRefType operator()(Index index) const
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{
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eigen_internal_assert(index >= 0 && index < size());
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return m_data[index];
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}
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#if EIGEN_HAS_VARIADIC_TEMPLATES
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template<typename... IndexTypes> EIGEN_DEVICE_FUNC
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EIGEN_STRONG_INLINE StorageRefType operator()(Index firstIndex, Index secondIndex, IndexTypes... otherIndices) const
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{
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EIGEN_STATIC_ASSERT(sizeof...(otherIndices) + 2 == NumIndices, YOU_MADE_A_PROGRAMMING_MISTAKE)
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eigen_assert(internal::all((Eigen::NumTraits<Index>::highest() >= otherIndices)...));
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if (PlainObjectType::Options&RowMajor) {
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const Index index = m_dimensions.IndexOfRowMajor(array<Index, NumIndices>{{firstIndex, secondIndex, otherIndices...}});
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return m_data[index];
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} else {
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const Index index = m_dimensions.IndexOfColMajor(array<Index, NumIndices>{{firstIndex, secondIndex, otherIndices...}});
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return m_data[index];
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}
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}
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#else
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EIGEN_DEVICE_FUNC
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EIGEN_STRONG_INLINE StorageRefType operator()(Index i0, Index i1) const
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{
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if (PlainObjectType::Options&RowMajor) {
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const Index index = i1 + i0 * m_dimensions[1];
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return m_data[index];
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} else {
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const Index index = i0 + i1 * m_dimensions[0];
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return m_data[index];
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}
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}
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EIGEN_DEVICE_FUNC
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EIGEN_STRONG_INLINE StorageRefType operator()(Index i0, Index i1, Index i2) const
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{
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if (PlainObjectType::Options&RowMajor) {
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const Index index = i2 + m_dimensions[2] * (i1 + m_dimensions[1] * i0);
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return m_data[index];
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} else {
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const Index index = i0 + m_dimensions[0] * (i1 + m_dimensions[1] * i2);
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return m_data[index];
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}
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}
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EIGEN_DEVICE_FUNC
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EIGEN_STRONG_INLINE StorageRefType operator()(Index i0, Index i1, Index i2, Index i3) const
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{
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if (PlainObjectType::Options&RowMajor) {
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const Index index = i3 + m_dimensions[3] * (i2 + m_dimensions[2] * (i1 + m_dimensions[1] * i0));
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return m_data[index];
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} else {
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const Index index = i0 + m_dimensions[0] * (i1 + m_dimensions[1] * (i2 + m_dimensions[2] * i3));
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return m_data[index];
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}
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}
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EIGEN_DEVICE_FUNC
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EIGEN_STRONG_INLINE StorageRefType operator()(Index i0, Index i1, Index i2, Index i3, Index i4) const
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{
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if (PlainObjectType::Options&RowMajor) {
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const Index index = i4 + m_dimensions[4] * (i3 + m_dimensions[3] * (i2 + m_dimensions[2] * (i1 + m_dimensions[1] * i0)));
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return m_data[index];
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} else {
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const Index index = i0 + m_dimensions[0] * (i1 + m_dimensions[1] * (i2 + m_dimensions[2] * (i3 + m_dimensions[3] * i4)));
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return m_data[index];
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}
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}
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#endif
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EIGEN_DEVICE_FUNC
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EIGEN_STRONG_INLINE StorageRefType operator()(const array<Index, NumIndices>& indices)
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{
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// eigen_assert(checkIndexRange(indices));
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if (PlainObjectType::Options&RowMajor) {
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const Index index = m_dimensions.IndexOfRowMajor(indices);
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return m_data[index];
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} else {
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const Index index = m_dimensions.IndexOfColMajor(indices);
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return m_data[index];
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}
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}
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EIGEN_DEVICE_FUNC
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EIGEN_STRONG_INLINE StorageRefType operator()()
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{
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EIGEN_STATIC_ASSERT(NumIndices == 0, YOU_MADE_A_PROGRAMMING_MISTAKE)
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return m_data[0];
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}
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EIGEN_DEVICE_FUNC
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EIGEN_STRONG_INLINE StorageRefType operator()(Index index)
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{
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eigen_internal_assert(index >= 0 && index < size());
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return m_data[index];
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}
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#if EIGEN_HAS_VARIADIC_TEMPLATES
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template<typename... IndexTypes> EIGEN_DEVICE_FUNC
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EIGEN_STRONG_INLINE StorageRefType operator()(Index firstIndex, Index secondIndex, IndexTypes... otherIndices)
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{
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static_assert(sizeof...(otherIndices) + 2 == NumIndices || NumIndices == Dynamic, "Number of indices used to access a tensor coefficient must be equal to the rank of the tensor.");
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eigen_assert(internal::all((Eigen::NumTraits<Index>::highest() >= otherIndices)...));
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const std::size_t NumDims = sizeof...(otherIndices) + 2;
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if (PlainObjectType::Options&RowMajor) {
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const Index index = m_dimensions.IndexOfRowMajor(array<Index, NumDims>{{firstIndex, secondIndex, otherIndices...}});
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return m_data[index];
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} else {
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const Index index = m_dimensions.IndexOfColMajor(array<Index, NumDims>{{firstIndex, secondIndex, otherIndices...}});
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return m_data[index];
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}
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}
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#else
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EIGEN_DEVICE_FUNC
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EIGEN_STRONG_INLINE StorageRefType operator()(Index i0, Index i1)
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{
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if (PlainObjectType::Options&RowMajor) {
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const Index index = i1 + i0 * m_dimensions[1];
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return m_data[index];
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} else {
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const Index index = i0 + i1 * m_dimensions[0];
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return m_data[index];
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}
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}
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EIGEN_DEVICE_FUNC
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EIGEN_STRONG_INLINE StorageRefType operator()(Index i0, Index i1, Index i2)
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{
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if (PlainObjectType::Options&RowMajor) {
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const Index index = i2 + m_dimensions[2] * (i1 + m_dimensions[1] * i0);
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return m_data[index];
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} else {
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const Index index = i0 + m_dimensions[0] * (i1 + m_dimensions[1] * i2);
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return m_data[index];
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}
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}
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EIGEN_DEVICE_FUNC
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EIGEN_STRONG_INLINE StorageRefType operator()(Index i0, Index i1, Index i2, Index i3)
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{
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if (PlainObjectType::Options&RowMajor) {
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const Index index = i3 + m_dimensions[3] * (i2 + m_dimensions[2] * (i1 + m_dimensions[1] * i0));
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return m_data[index];
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} else {
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const Index index = i0 + m_dimensions[0] * (i1 + m_dimensions[1] * (i2 + m_dimensions[2] * i3));
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return m_data[index];
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}
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}
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EIGEN_DEVICE_FUNC
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EIGEN_STRONG_INLINE StorageRefType operator()(Index i0, Index i1, Index i2, Index i3, Index i4)
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{
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if (PlainObjectType::Options&RowMajor) {
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const Index index = i4 + m_dimensions[4] * (i3 + m_dimensions[3] * (i2 + m_dimensions[2] * (i1 + m_dimensions[1] * i0)));
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return m_data[index];
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} else {
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const Index index = i0 + m_dimensions[0] * (i1 + m_dimensions[1] * (i2 + m_dimensions[2] * (i3 + m_dimensions[3] * i4)));
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return m_data[index];
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}
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}
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#endif
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EIGEN_TENSOR_INHERIT_ASSIGNMENT_OPERATORS(TensorMap)
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private:
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StoragePointerType m_data;
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Dimensions m_dimensions;
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};
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} // end namespace Eigen
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#endif // EIGEN_CXX11_TENSOR_TENSOR_MAP_H
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