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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};
35
36template<typename Strides, typename XprType>
37struct eval<TensorInflationOp<Strides, XprType>, Eigen::Dense>
38{
39 typedef const TensorInflationOp<Strides, XprType>& type;
40};
41
42template<typename Strides, typename XprType>
43struct nested<TensorInflationOp<Strides, XprType>, 1, typename eval<TensorInflationOp<Strides, XprType> >::type>
44{
45 typedef TensorInflationOp<Strides, XprType> type;
46};
47
48} // end namespace internal
49
50template<typename Strides, typename XprType>
51class TensorInflationOp : public TensorBase<TensorInflationOp<Strides, XprType>, ReadOnlyAccessors>
52{
53 public:
54 typedef typename Eigen::internal::traits<TensorInflationOp>::Scalar Scalar;
55 typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
56 typedef typename XprType::CoeffReturnType CoeffReturnType;
57 typedef typename Eigen::internal::nested<TensorInflationOp>::type Nested;
58 typedef typename Eigen::internal::traits<TensorInflationOp>::StorageKind StorageKind;
59 typedef typename Eigen::internal::traits<TensorInflationOp>::Index Index;
60
61 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorInflationOp(const XprType& expr, const Strides& strides)
62 : m_xpr(expr), m_strides(strides) {}
63
64 EIGEN_DEVICE_FUNC
65 const Strides& strides() const { return m_strides; }
66
67 EIGEN_DEVICE_FUNC
68 const typename internal::remove_all<typename XprType::Nested>::type&
69 expression() const { return m_xpr; }
70
71 protected:
72 typename XprType::Nested m_xpr;
73 const Strides m_strides;
74};
75
76// Eval as rvalue
77template<typename Strides, typename ArgType, typename Device>
78struct TensorEvaluator<const TensorInflationOp<Strides, ArgType>, Device>
79{
80 typedef TensorInflationOp<Strides, ArgType> XprType;
81 typedef typename XprType::Index Index;
82 static const int NumDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value;
83 typedef DSizes<Index, NumDims> Dimensions;
84 typedef typename XprType::Scalar Scalar;
85 typedef typename XprType::CoeffReturnType CoeffReturnType;
86 typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
87 static const int PacketSize = internal::unpacket_traits<PacketReturnType>::size;
88
89 enum {
90 IsAligned = /*TensorEvaluator<ArgType, Device>::IsAligned*/ false,
91 PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess,
92 BlockAccess = false,
93 Layout = TensorEvaluator<ArgType, Device>::Layout,
94 CoordAccess = false, // to be implemented
95 RawAccess = false
96 };
97
98 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
99 : m_impl(op.expression(), device), m_strides(op.strides())
100 {
101 m_dimensions = m_impl.dimensions();
102 // Expand each dimension to the inflated dimension.
103 for (int i = 0; i < NumDims; ++i) {
104 m_dimensions[i] = (m_dimensions[i] - 1) * op.strides()[i] + 1;
105 }
106
107 // Remember the strides for fast division.
108 for (int i = 0; i < NumDims; ++i) {
109 m_fastStrides[i] = internal::TensorIntDivisor<Index>(m_strides[i]);
110 }
111
112 const typename TensorEvaluator<ArgType, Device>::Dimensions& input_dims = m_impl.dimensions();
113 if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
114 m_outputStrides[0] = 1;
115 m_inputStrides[0] = 1;
116 for (int i = 1; i < NumDims; ++i) {
117 m_outputStrides[i] = m_outputStrides[i-1] * m_dimensions[i-1];
118 m_inputStrides[i] = m_inputStrides[i-1] * input_dims[i-1];
119 }
120 } else { // RowMajor
121 m_outputStrides[NumDims-1] = 1;
122 m_inputStrides[NumDims-1] = 1;
123 for (int i = NumDims - 2; i >= 0; --i) {
124 m_outputStrides[i] = m_outputStrides[i+1] * m_dimensions[i+1];
125 m_inputStrides[i] = m_inputStrides[i+1] * input_dims[i+1];
126 }
127 }
128 }
129
130 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
131
132 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(Scalar* /*data*/) {
133 m_impl.evalSubExprsIfNeeded(NULL);
134 return true;
135 }
136 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() {
137 m_impl.cleanup();
138 }
139
140 // Computes the input index given the output index. Returns true if the output
141 // index doesn't fall into a hole.
142 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool getInputIndex(Index index, Index* inputIndex) const
143 {
144 eigen_assert(index < dimensions().TotalSize());
145 *inputIndex = 0;
146 if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
147 for (int i = NumDims - 1; i > 0; --i) {
148 const Index idx = index / m_outputStrides[i];
149 if (idx != idx / m_fastStrides[i] * m_strides[i]) {
150 return false;
151 }
152 *inputIndex += idx / m_strides[i] * m_inputStrides[i];
153 index -= idx * m_outputStrides[i];
154 }
155 if (index != index / m_fastStrides[0] * m_strides[0]) {
156 return false;
157 }
158 *inputIndex += index / m_strides[0];
159 return true;
160 } else {
161 for (int i = 0; i < NumDims - 1; ++i) {
162 const Index idx = index / m_outputStrides[i];
163 if (idx != idx / m_fastStrides[i] * m_strides[i]) {
164 return false;
165 }
166 *inputIndex += idx / m_strides[i] * m_inputStrides[i];
167 index -= idx * m_outputStrides[i];
168 }
169 if (index != index / m_fastStrides[NumDims-1] * m_strides[NumDims-1]) {
170 return false;
171 }
172 *inputIndex += index / m_strides[NumDims - 1];
173 }
174 return true;
175 }
176
177 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
178 {
179 Index inputIndex = 0;
180 if (getInputIndex(index, &inputIndex)) {
181 return m_impl.coeff(inputIndex);
182 } else {
183 return Scalar(0);
184 }
185 }
186
187 // TODO(yangke): optimize this function so that we can detect and produce
188 // all-zero packets
189 template<int LoadMode>
190 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
191 {
192 EIGEN_STATIC_ASSERT((PacketSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE)
193 eigen_assert(index+PacketSize-1 < dimensions().TotalSize());
194
195 EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[PacketSize];
196 for (int i = 0; i < PacketSize; ++i) {
197 values[i] = coeff(index+i);
198 }
199 PacketReturnType rslt = internal::pload<PacketReturnType>(values);
200 return rslt;
201 }
202
203 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const {
204 const double compute_cost = NumDims * (3 * TensorOpCost::DivCost<Index>() +
205 3 * TensorOpCost::MulCost<Index>() +
206 2 * TensorOpCost::AddCost<Index>());
207 const double input_size = m_impl.dimensions().TotalSize();
208 const double output_size = m_dimensions.TotalSize();
209 if (output_size == 0)
210 return TensorOpCost();
211 return m_impl.costPerCoeff(vectorized) +
212 TensorOpCost(sizeof(CoeffReturnType) * input_size / output_size, 0,
213 compute_cost, vectorized, PacketSize);
214 }
215
216 EIGEN_DEVICE_FUNC Scalar* data() const { return NULL; }
217
218 protected:
219 Dimensions m_dimensions;
220 array<Index, NumDims> m_outputStrides;
221 array<Index, NumDims> m_inputStrides;
222 TensorEvaluator<ArgType, Device> m_impl;
223 const Strides m_strides;
224 array<internal::TensorIntDivisor<Index>, NumDims> m_fastStrides;
225};
226
227} // end namespace Eigen
228
229#endif // EIGEN_CXX11_TENSOR_TENSOR_INFLATION_H
Namespace containing all symbols from the Eigen library.
Definition: AdolcForward:45
const Device & device() const
required by sycl in order to construct sycl buffer from raw pointer
Definition: TensorEvaluator.h:114