Loading...
Searching...
No Matches
TensorStriding.h
1// This file is part of Eigen, a lightweight C++ template library
2// for linear algebra.
3//
4// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@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_STRIDING_H
11#define EIGEN_CXX11_TENSOR_TENSOR_STRIDING_H
12
13namespace Eigen {
14
22namespace internal {
23template<typename Strides, typename XprType>
24struct traits<TensorStridingOp<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<TensorStridingOp<Strides, XprType>, Eigen::Dense>
38{
39 typedef const TensorStridingOp<Strides, XprType>& type;
40};
41
42template<typename Strides, typename XprType>
43struct nested<TensorStridingOp<Strides, XprType>, 1, typename eval<TensorStridingOp<Strides, XprType> >::type>
44{
45 typedef TensorStridingOp<Strides, XprType> type;
46};
47
48} // end namespace internal
49
50
51
52template<typename Strides, typename XprType>
53class TensorStridingOp : public TensorBase<TensorStridingOp<Strides, XprType> >
54{
55 public:
56 typedef typename Eigen::internal::traits<TensorStridingOp>::Scalar Scalar;
57 typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
58 typedef typename XprType::CoeffReturnType CoeffReturnType;
59 typedef typename Eigen::internal::nested<TensorStridingOp>::type Nested;
60 typedef typename Eigen::internal::traits<TensorStridingOp>::StorageKind StorageKind;
61 typedef typename Eigen::internal::traits<TensorStridingOp>::Index Index;
62
63 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorStridingOp(const XprType& expr, const Strides& dims)
64 : m_xpr(expr), m_dims(dims) {}
65
66 EIGEN_DEVICE_FUNC
67 const Strides& strides() const { return m_dims; }
68
69 EIGEN_DEVICE_FUNC
70 const typename internal::remove_all<typename XprType::Nested>::type&
71 expression() const { return m_xpr; }
72
73 EIGEN_DEVICE_FUNC
74 EIGEN_STRONG_INLINE TensorStridingOp& operator = (const TensorStridingOp& other)
75 {
76 typedef TensorAssignOp<TensorStridingOp, const TensorStridingOp> Assign;
77 Assign assign(*this, other);
78 internal::TensorExecutor<const Assign, DefaultDevice>::run(assign, DefaultDevice());
79 return *this;
80 }
81
82 template<typename OtherDerived>
83 EIGEN_DEVICE_FUNC
84 EIGEN_STRONG_INLINE TensorStridingOp& operator = (const OtherDerived& other)
85 {
86 typedef TensorAssignOp<TensorStridingOp, const OtherDerived> Assign;
87 Assign assign(*this, other);
88 internal::TensorExecutor<const Assign, DefaultDevice>::run(assign, DefaultDevice());
89 return *this;
90 }
91
92 protected:
93 typename XprType::Nested m_xpr;
94 const Strides m_dims;
95};
96
97
98// Eval as rvalue
99template<typename Strides, typename ArgType, typename Device>
100struct TensorEvaluator<const TensorStridingOp<Strides, ArgType>, Device>
101{
102 typedef TensorStridingOp<Strides, ArgType> XprType;
103 typedef typename XprType::Index Index;
104 static const int NumDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value;
105 typedef DSizes<Index, NumDims> Dimensions;
106 typedef typename XprType::Scalar Scalar;
107 typedef typename XprType::CoeffReturnType CoeffReturnType;
108 typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
109 static const int PacketSize = internal::unpacket_traits<PacketReturnType>::size;
110
111 enum {
112 IsAligned = /*TensorEvaluator<ArgType, Device>::IsAligned*/false,
113 PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess,
114 Layout = TensorEvaluator<ArgType, Device>::Layout,
115 CoordAccess = false, // to be implemented
116 RawAccess = false
117 };
118
119 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
120 : m_impl(op.expression(), device)
121 {
122 m_dimensions = m_impl.dimensions();
123 for (int i = 0; i < NumDims; ++i) {
124 m_dimensions[i] = ceilf(static_cast<float>(m_dimensions[i]) / op.strides()[i]);
125 }
126
127 const typename TensorEvaluator<ArgType, Device>::Dimensions& input_dims = m_impl.dimensions();
128 if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
129 m_outputStrides[0] = 1;
130 m_inputStrides[0] = 1;
131 for (int i = 1; i < NumDims; ++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 m_inputStrides[i-1] *= op.strides()[i-1];
135 }
136 m_inputStrides[NumDims-1] *= op.strides()[NumDims-1];
137 } else { // RowMajor
138 m_outputStrides[NumDims-1] = 1;
139 m_inputStrides[NumDims-1] = 1;
140 for (int i = NumDims - 2; i >= 0; --i) {
141 m_outputStrides[i] = m_outputStrides[i+1] * m_dimensions[i+1];
142 m_inputStrides[i] = m_inputStrides[i+1] * input_dims[i+1];
143 m_inputStrides[i+1] *= op.strides()[i+1];
144 }
145 m_inputStrides[0] *= op.strides()[0];
146 }
147 }
148
149 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
150
151 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(Scalar* /*data*/) {
152 m_impl.evalSubExprsIfNeeded(NULL);
153 return true;
154 }
155 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() {
156 m_impl.cleanup();
157 }
158
159 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
160 {
161 return m_impl.coeff(srcCoeff(index));
162 }
163
164 template<int LoadMode>
165 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
166 {
167 EIGEN_STATIC_ASSERT((PacketSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE)
168 eigen_assert(index+PacketSize-1 < dimensions().TotalSize());
169
170 Index inputIndices[] = {0, 0};
171 Index indices[] = {index, index + PacketSize - 1};
172 if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
173 for (int i = NumDims - 1; i > 0; --i) {
174 const Index idx0 = indices[0] / m_outputStrides[i];
175 const Index idx1 = indices[1] / m_outputStrides[i];
176 inputIndices[0] += idx0 * m_inputStrides[i];
177 inputIndices[1] += idx1 * m_inputStrides[i];
178 indices[0] -= idx0 * m_outputStrides[i];
179 indices[1] -= idx1 * m_outputStrides[i];
180 }
181 inputIndices[0] += indices[0] * m_inputStrides[0];
182 inputIndices[1] += indices[1] * m_inputStrides[0];
183 } else { // RowMajor
184 for (int i = 0; i < NumDims - 1; ++i) {
185 const Index idx0 = indices[0] / m_outputStrides[i];
186 const Index idx1 = indices[1] / m_outputStrides[i];
187 inputIndices[0] += idx0 * m_inputStrides[i];
188 inputIndices[1] += idx1 * m_inputStrides[i];
189 indices[0] -= idx0 * m_outputStrides[i];
190 indices[1] -= idx1 * m_outputStrides[i];
191 }
192 inputIndices[0] += indices[0] * m_inputStrides[NumDims-1];
193 inputIndices[1] += indices[1] * m_inputStrides[NumDims-1];
194 }
195 if (inputIndices[1] - inputIndices[0] == PacketSize - 1) {
196 PacketReturnType rslt = m_impl.template packet<Unaligned>(inputIndices[0]);
197 return rslt;
198 }
199 else {
200 EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[PacketSize];
201 values[0] = m_impl.coeff(inputIndices[0]);
202 values[PacketSize-1] = m_impl.coeff(inputIndices[1]);
203 for (int i = 1; i < PacketSize-1; ++i) {
204 values[i] = coeff(index+i);
205 }
206 PacketReturnType rslt = internal::pload<PacketReturnType>(values);
207 return rslt;
208 }
209 }
210
211 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const {
212 double compute_cost = (NumDims - 1) * (TensorOpCost::AddCost<Index>() +
213 TensorOpCost::MulCost<Index>() +
214 TensorOpCost::DivCost<Index>()) +
215 TensorOpCost::MulCost<Index>();
216 if (vectorized) {
217 compute_cost *= 2; // packet() computes two indices
218 }
219 const int innerDim = (static_cast<int>(Layout) == static_cast<int>(ColMajor)) ? 0 : (NumDims - 1);
220 return m_impl.costPerCoeff(vectorized && m_inputStrides[innerDim] == 1) +
221 // Computation is not vectorized per se, but it is done once per packet.
222 TensorOpCost(0, 0, compute_cost, vectorized, PacketSize);
223 }
224
225 EIGEN_DEVICE_FUNC Scalar* data() const { return NULL; }
226
227 protected:
228 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index srcCoeff(Index index) const
229 {
230 Index inputIndex = 0;
231 if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
232 for (int i = NumDims - 1; i > 0; --i) {
233 const Index idx = index / m_outputStrides[i];
234 inputIndex += idx * m_inputStrides[i];
235 index -= idx * m_outputStrides[i];
236 }
237 inputIndex += index * m_inputStrides[0];
238 } else { // RowMajor
239 for (int i = 0; i < NumDims - 1; ++i) {
240 const Index idx = index / m_outputStrides[i];
241 inputIndex += idx * m_inputStrides[i];
242 index -= idx * m_outputStrides[i];
243 }
244 inputIndex += index * m_inputStrides[NumDims-1];
245 }
246 return inputIndex;
247 }
248
249 Dimensions m_dimensions;
250 array<Index, NumDims> m_outputStrides;
251 array<Index, NumDims> m_inputStrides;
252 TensorEvaluator<ArgType, Device> m_impl;
253};
254
255
256// Eval as lvalue
257template<typename Strides, typename ArgType, typename Device>
258struct TensorEvaluator<TensorStridingOp<Strides, ArgType>, Device>
259 : public TensorEvaluator<const TensorStridingOp<Strides, ArgType>, Device>
260{
261 typedef TensorStridingOp<Strides, ArgType> XprType;
262 typedef TensorEvaluator<const XprType, Device> Base;
263 // typedef typename XprType::Index Index;
264 static const int NumDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value;
265 // typedef DSizes<Index, NumDims> Dimensions;
266
267 enum {
268 IsAligned = /*TensorEvaluator<ArgType, Device>::IsAligned*/false,
269 PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess,
270 Layout = TensorEvaluator<ArgType, Device>::Layout,
271 CoordAccess = false, // to be implemented
272 RawAccess = false
273 };
274
275 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
276 : Base(op, device) { }
277
278 typedef typename XprType::Index Index;
279 typedef typename XprType::Scalar Scalar;
280 typedef typename XprType::CoeffReturnType CoeffReturnType;
281 typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
282 static const int PacketSize = internal::unpacket_traits<PacketReturnType>::size;
283
284 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& coeffRef(Index index)
285 {
286 return this->m_impl.coeffRef(this->srcCoeff(index));
287 }
288
289 template <int StoreMode> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
290 void writePacket(Index index, const PacketReturnType& x)
291 {
292 EIGEN_STATIC_ASSERT((PacketSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE)
293 eigen_assert(index+PacketSize-1 < this->dimensions().TotalSize());
294
295 Index inputIndices[] = {0, 0};
296 Index indices[] = {index, index + PacketSize - 1};
297 if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
298 for (int i = NumDims - 1; i > 0; --i) {
299 const Index idx0 = indices[0] / this->m_outputStrides[i];
300 const Index idx1 = indices[1] / this->m_outputStrides[i];
301 inputIndices[0] += idx0 * this->m_inputStrides[i];
302 inputIndices[1] += idx1 * this->m_inputStrides[i];
303 indices[0] -= idx0 * this->m_outputStrides[i];
304 indices[1] -= idx1 * this->m_outputStrides[i];
305 }
306 inputIndices[0] += indices[0] * this->m_inputStrides[0];
307 inputIndices[1] += indices[1] * this->m_inputStrides[0];
308 } else { // RowMajor
309 for (int i = 0; i < NumDims - 1; ++i) {
310 const Index idx0 = indices[0] / this->m_outputStrides[i];
311 const Index idx1 = indices[1] / this->m_outputStrides[i];
312 inputIndices[0] += idx0 * this->m_inputStrides[i];
313 inputIndices[1] += idx1 * this->m_inputStrides[i];
314 indices[0] -= idx0 * this->m_outputStrides[i];
315 indices[1] -= idx1 * this->m_outputStrides[i];
316 }
317 inputIndices[0] += indices[0] * this->m_inputStrides[NumDims-1];
318 inputIndices[1] += indices[1] * this->m_inputStrides[NumDims-1];
319 }
320 if (inputIndices[1] - inputIndices[0] == PacketSize - 1) {
321 this->m_impl.template writePacket<Unaligned>(inputIndices[0], x);
322 }
323 else {
324 EIGEN_ALIGN_MAX Scalar values[PacketSize];
325 internal::pstore<Scalar, PacketReturnType>(values, x);
326 this->m_impl.coeffRef(inputIndices[0]) = values[0];
327 this->m_impl.coeffRef(inputIndices[1]) = values[PacketSize-1];
328 for (int i = 1; i < PacketSize-1; ++i) {
329 this->coeffRef(index+i) = values[i];
330 }
331 }
332 }
333};
334
335
336} // end namespace Eigen
337
338#endif // EIGEN_CXX11_TENSOR_TENSOR_STRIDING_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