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TensorInflation.h
1// This file is part of Eigen, a lightweight C++ template library
2// for linear algebra.
3//
4// Copyright (C) 2015 Ke Yang <yangke@gmail.com>
5//
6// This Source Code Form is subject to the terms of the Mozilla
7// Public License v. 2.0. If a copy of the MPL was not distributed
8// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
9
10#ifndef EIGEN_CXX11_TENSOR_TENSOR_INFLATION_H
11#define EIGEN_CXX11_TENSOR_TENSOR_INFLATION_H
12
13namespace Eigen {
14
22namespace internal {
23template<typename Strides, typename XprType>
24struct traits<TensorInflationOp<Strides, XprType> > : public traits<XprType>
25{
26 typedef typename XprType::Scalar Scalar;
27 typedef traits<XprType> XprTraits;
28 typedef typename XprTraits::StorageKind StorageKind;
29 typedef typename XprTraits::Index Index;
30 typedef typename XprType::Nested Nested;
31 typedef typename remove_reference<Nested>::type _Nested;
32 static const int NumDimensions = XprTraits::NumDimensions;
33 static const int Layout = XprTraits::Layout;
34 typedef typename XprTraits::PointerType PointerType;
35};
36
37template<typename Strides, typename XprType>
38struct eval<TensorInflationOp<Strides, XprType>, Eigen::Dense>
39{
40 typedef const TensorInflationOp<Strides, XprType>& type;
41};
42
43template<typename Strides, typename XprType>
44struct nested<TensorInflationOp<Strides, XprType>, 1, typename eval<TensorInflationOp<Strides, XprType> >::type>
45{
46 typedef TensorInflationOp<Strides, XprType> type;
47};
48
49} // end namespace internal
50
51template<typename Strides, typename XprType>
52class TensorInflationOp : public TensorBase<TensorInflationOp<Strides, XprType>, ReadOnlyAccessors>
53{
54 public:
55 typedef typename Eigen::internal::traits<TensorInflationOp>::Scalar Scalar;
56 typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
57 typedef typename XprType::CoeffReturnType CoeffReturnType;
58 typedef typename Eigen::internal::nested<TensorInflationOp>::type Nested;
59 typedef typename Eigen::internal::traits<TensorInflationOp>::StorageKind StorageKind;
60 typedef typename Eigen::internal::traits<TensorInflationOp>::Index Index;
61
62 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorInflationOp(const XprType& expr, const Strides& strides)
63 : m_xpr(expr), m_strides(strides) {}
64
65 EIGEN_DEVICE_FUNC
66 const Strides& strides() const { return m_strides; }
67
68 EIGEN_DEVICE_FUNC
69 const typename internal::remove_all<typename XprType::Nested>::type&
70 expression() const { return m_xpr; }
71
72 protected:
73 typename XprType::Nested m_xpr;
74 const Strides m_strides;
75};
76
77// Eval as rvalue
78template<typename Strides, typename ArgType, typename Device>
79struct TensorEvaluator<const TensorInflationOp<Strides, ArgType>, Device>
80{
81 typedef TensorInflationOp<Strides, ArgType> XprType;
82 typedef typename XprType::Index Index;
83 static const int NumDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value;
84 typedef DSizes<Index, NumDims> Dimensions;
85 typedef typename XprType::Scalar Scalar;
86 typedef typename XprType::CoeffReturnType CoeffReturnType;
87 typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
88 static const int PacketSize = PacketType<CoeffReturnType, Device>::size;
89 typedef StorageMemory<CoeffReturnType, Device> Storage;
90 typedef typename Storage::Type EvaluatorPointerType;
91
92 enum {
93 IsAligned = /*TensorEvaluator<ArgType, Device>::IsAligned*/ false,
94 PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess,
95 BlockAccess = false,
96 PreferBlockAccess = false,
97 Layout = TensorEvaluator<ArgType, Device>::Layout,
98 CoordAccess = false, // to be implemented
99 RawAccess = false
100 };
101
102 //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
103 typedef internal::TensorBlockNotImplemented TensorBlock;
104 //===--------------------------------------------------------------------===//
105
106 EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
107 : m_impl(op.expression(), device), m_strides(op.strides())
108 {
109 m_dimensions = m_impl.dimensions();
110 // Expand each dimension to the inflated dimension.
111 for (int i = 0; i < NumDims; ++i) {
112 m_dimensions[i] = (m_dimensions[i] - 1) * op.strides()[i] + 1;
113 }
114
115 // Remember the strides for fast division.
116 for (int i = 0; i < NumDims; ++i) {
117 m_fastStrides[i] = internal::TensorIntDivisor<Index>(m_strides[i]);
118 }
119
120 const typename TensorEvaluator<ArgType, Device>::Dimensions& input_dims = m_impl.dimensions();
121 if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
122 m_outputStrides[0] = 1;
123 m_inputStrides[0] = 1;
124 for (int i = 1; i < NumDims; ++i) {
125 m_outputStrides[i] = m_outputStrides[i-1] * m_dimensions[i-1];
126 m_inputStrides[i] = m_inputStrides[i-1] * input_dims[i-1];
127 }
128 } else { // RowMajor
129 m_outputStrides[NumDims-1] = 1;
130 m_inputStrides[NumDims-1] = 1;
131 for (int i = NumDims - 2; i >= 0; --i) {
132 m_outputStrides[i] = m_outputStrides[i+1] * m_dimensions[i+1];
133 m_inputStrides[i] = m_inputStrides[i+1] * input_dims[i+1];
134 }
135 }
136 }
137
138 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
139
140 EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType /*data*/) {
141 m_impl.evalSubExprsIfNeeded(NULL);
142 return true;
143 }
144 EIGEN_STRONG_INLINE void cleanup() {
145 m_impl.cleanup();
146 }
147
148 // Computes the input index given the output index. Returns true if the output
149 // index doesn't fall into a hole.
150 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool getInputIndex(Index index, Index* inputIndex) const
151 {
152 eigen_assert(index < dimensions().TotalSize());
153 *inputIndex = 0;
154 if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
155 EIGEN_UNROLL_LOOP
156 for (int i = NumDims - 1; i > 0; --i) {
157 const Index idx = index / m_outputStrides[i];
158 if (idx != idx / m_fastStrides[i] * m_strides[i]) {
159 return false;
160 }
161 *inputIndex += idx / m_strides[i] * m_inputStrides[i];
162 index -= idx * m_outputStrides[i];
163 }
164 if (index != index / m_fastStrides[0] * m_strides[0]) {
165 return false;
166 }
167 *inputIndex += index / m_strides[0];
168 return true;
169 } else {
170 EIGEN_UNROLL_LOOP
171 for (int i = 0; i < NumDims - 1; ++i) {
172 const Index idx = index / m_outputStrides[i];
173 if (idx != idx / m_fastStrides[i] * m_strides[i]) {
174 return false;
175 }
176 *inputIndex += idx / m_strides[i] * m_inputStrides[i];
177 index -= idx * m_outputStrides[i];
178 }
179 if (index != index / m_fastStrides[NumDims-1] * m_strides[NumDims-1]) {
180 return false;
181 }
182 *inputIndex += index / m_strides[NumDims - 1];
183 }
184 return true;
185 }
186
187 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
188 {
189 Index inputIndex = 0;
190 if (getInputIndex(index, &inputIndex)) {
191 return m_impl.coeff(inputIndex);
192 } else {
193 return Scalar(0);
194 }
195 }
196
197 // TODO(yangke): optimize this function so that we can detect and produce
198 // all-zero packets
199 template<int LoadMode>
200 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
201 {
202 EIGEN_STATIC_ASSERT((PacketSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE)
203 eigen_assert(index+PacketSize-1 < dimensions().TotalSize());
204
205 EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[PacketSize];
206 EIGEN_UNROLL_LOOP
207 for (int i = 0; i < PacketSize; ++i) {
208 values[i] = coeff(index+i);
209 }
210 PacketReturnType rslt = internal::pload<PacketReturnType>(values);
211 return rslt;
212 }
213
214 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const {
215 const double compute_cost = NumDims * (3 * TensorOpCost::DivCost<Index>() +
216 3 * TensorOpCost::MulCost<Index>() +
217 2 * TensorOpCost::AddCost<Index>());
218 const double input_size = m_impl.dimensions().TotalSize();
219 const double output_size = m_dimensions.TotalSize();
220 if (output_size == 0)
221 return TensorOpCost();
222 return m_impl.costPerCoeff(vectorized) +
223 TensorOpCost(sizeof(CoeffReturnType) * input_size / output_size, 0,
224 compute_cost, vectorized, PacketSize);
225 }
226
227 EIGEN_DEVICE_FUNC EvaluatorPointerType data() const { return NULL; }
228
229#ifdef EIGEN_USE_SYCL
230 // binding placeholder accessors to a command group handler for SYCL
231 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler &cgh) const {
232 m_impl.bind(cgh);
233 }
234#endif
235
236 protected:
237 Dimensions m_dimensions;
238 array<Index, NumDims> m_outputStrides;
239 array<Index, NumDims> m_inputStrides;
240 TensorEvaluator<ArgType, Device> m_impl;
241 const Strides m_strides;
242 array<internal::TensorIntDivisor<Index>, NumDims> m_fastStrides;
243};
244
245} // end namespace Eigen
246
247#endif // EIGEN_CXX11_TENSOR_TENSOR_INFLATION_H
Namespace containing all symbols from the Eigen library.
EIGEN_DEFAULT_DENSE_INDEX_TYPE Index