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TensorConcatenation.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_CONCATENATION_H
11#define EIGEN_CXX11_TENSOR_TENSOR_CONCATENATION_H
12
13namespace Eigen {
14
22namespace internal {
23template<typename Axis, typename LhsXprType, typename RhsXprType>
24struct traits<TensorConcatenationOp<Axis, LhsXprType, RhsXprType> >
25{
26 // Type promotion to handle the case where the types of the lhs and the rhs are different.
27 typedef typename promote_storage_type<typename LhsXprType::Scalar,
28 typename RhsXprType::Scalar>::ret Scalar;
29 typedef typename promote_storage_type<typename traits<LhsXprType>::StorageKind,
30 typename traits<RhsXprType>::StorageKind>::ret StorageKind;
31 typedef typename promote_index_type<typename traits<LhsXprType>::Index,
32 typename traits<RhsXprType>::Index>::type Index;
33 typedef typename LhsXprType::Nested LhsNested;
34 typedef typename RhsXprType::Nested RhsNested;
35 typedef typename remove_reference<LhsNested>::type _LhsNested;
36 typedef typename remove_reference<RhsNested>::type _RhsNested;
37 static const int NumDimensions = traits<LhsXprType>::NumDimensions;
38 static const int Layout = traits<LhsXprType>::Layout;
39 enum { Flags = 0 };
40};
41
42template<typename Axis, typename LhsXprType, typename RhsXprType>
43struct eval<TensorConcatenationOp<Axis, LhsXprType, RhsXprType>, Eigen::Dense>
44{
45 typedef const TensorConcatenationOp<Axis, LhsXprType, RhsXprType>& type;
46};
47
48template<typename Axis, typename LhsXprType, typename RhsXprType>
49struct nested<TensorConcatenationOp<Axis, LhsXprType, RhsXprType>, 1, typename eval<TensorConcatenationOp<Axis, LhsXprType, RhsXprType> >::type>
50{
51 typedef TensorConcatenationOp<Axis, LhsXprType, RhsXprType> type;
52};
53
54} // end namespace internal
55
56
57template<typename Axis, typename LhsXprType, typename RhsXprType>
58class TensorConcatenationOp : public TensorBase<TensorConcatenationOp<Axis, LhsXprType, RhsXprType>, WriteAccessors>
59{
60 public:
61 typedef typename internal::traits<TensorConcatenationOp>::Scalar Scalar;
62 typedef typename internal::traits<TensorConcatenationOp>::StorageKind StorageKind;
63 typedef typename internal::traits<TensorConcatenationOp>::Index Index;
64 typedef typename internal::nested<TensorConcatenationOp>::type Nested;
65 typedef typename internal::promote_storage_type<typename LhsXprType::CoeffReturnType,
66 typename RhsXprType::CoeffReturnType>::ret CoeffReturnType;
67 typedef typename NumTraits<Scalar>::Real RealScalar;
68
69 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorConcatenationOp(const LhsXprType& lhs, const RhsXprType& rhs, Axis axis)
70 : m_lhs_xpr(lhs), m_rhs_xpr(rhs), m_axis(axis) {}
71
72 EIGEN_DEVICE_FUNC
73 const typename internal::remove_all<typename LhsXprType::Nested>::type&
74 lhsExpression() const { return m_lhs_xpr; }
75
76 EIGEN_DEVICE_FUNC
77 const typename internal::remove_all<typename RhsXprType::Nested>::type&
78 rhsExpression() const { return m_rhs_xpr; }
79
80 EIGEN_DEVICE_FUNC const Axis& axis() const { return m_axis; }
81
82 EIGEN_DEVICE_FUNC
83 EIGEN_STRONG_INLINE TensorConcatenationOp& operator = (const TensorConcatenationOp& other)
84 {
85 typedef TensorAssignOp<TensorConcatenationOp, const TensorConcatenationOp> Assign;
86 Assign assign(*this, other);
87 internal::TensorExecutor<const Assign, DefaultDevice>::run(assign, DefaultDevice());
88 return *this;
89 }
90
91 template<typename OtherDerived>
92 EIGEN_DEVICE_FUNC
93 EIGEN_STRONG_INLINE TensorConcatenationOp& operator = (const OtherDerived& other)
94 {
95 typedef TensorAssignOp<TensorConcatenationOp, const OtherDerived> Assign;
96 Assign assign(*this, other);
97 internal::TensorExecutor<const Assign, DefaultDevice>::run(assign, DefaultDevice());
98 return *this;
99 }
100
101 protected:
102 typename LhsXprType::Nested m_lhs_xpr;
103 typename RhsXprType::Nested m_rhs_xpr;
104 const Axis m_axis;
105};
106
107
108// Eval as rvalue
109template<typename Axis, typename LeftArgType, typename RightArgType, typename Device>
110struct TensorEvaluator<const TensorConcatenationOp<Axis, LeftArgType, RightArgType>, Device>
111{
113 typedef typename XprType::Index Index;
114 static const int NumDims = internal::array_size<typename TensorEvaluator<LeftArgType, Device>::Dimensions>::value;
115 static const int RightNumDims = internal::array_size<typename TensorEvaluator<RightArgType, Device>::Dimensions>::value;
116 typedef DSizes<Index, NumDims> Dimensions;
117 typedef typename XprType::Scalar Scalar;
118 typedef typename XprType::CoeffReturnType CoeffReturnType;
119 typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
120 enum {
121 IsAligned = false,
124 RawAccess = false
125 };
126
127 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
128 : m_leftImpl(op.lhsExpression(), device), m_rightImpl(op.rhsExpression(), device), m_axis(op.axis())
129 {
130 EIGEN_STATIC_ASSERT((static_cast<int>(TensorEvaluator<LeftArgType, Device>::Layout) == static_cast<int>(TensorEvaluator<RightArgType, Device>::Layout) || NumDims == 1), YOU_MADE_A_PROGRAMMING_MISTAKE);
131 EIGEN_STATIC_ASSERT((NumDims == RightNumDims), YOU_MADE_A_PROGRAMMING_MISTAKE);
132 EIGEN_STATIC_ASSERT((NumDims > 0), YOU_MADE_A_PROGRAMMING_MISTAKE);
133
134 eigen_assert(0 <= m_axis && m_axis < NumDims);
135 const Dimensions& lhs_dims = m_leftImpl.dimensions();
136 const Dimensions& rhs_dims = m_rightImpl.dimensions();
137 {
138 int i = 0;
139 for (; i < m_axis; ++i) {
140 eigen_assert(lhs_dims[i] > 0);
141 eigen_assert(lhs_dims[i] == rhs_dims[i]);
142 m_dimensions[i] = lhs_dims[i];
143 }
144 eigen_assert(lhs_dims[i] > 0); // Now i == m_axis.
145 eigen_assert(rhs_dims[i] > 0);
146 m_dimensions[i] = lhs_dims[i] + rhs_dims[i];
147 for (++i; i < NumDims; ++i) {
148 eigen_assert(lhs_dims[i] > 0);
149 eigen_assert(lhs_dims[i] == rhs_dims[i]);
150 m_dimensions[i] = lhs_dims[i];
151 }
152 }
153
154 if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
155 m_leftStrides[0] = 1;
156 m_rightStrides[0] = 1;
157 m_outputStrides[0] = 1;
158
159 for (int j = 1; j < NumDims; ++j) {
160 m_leftStrides[j] = m_leftStrides[j-1] * lhs_dims[j-1];
161 m_rightStrides[j] = m_rightStrides[j-1] * rhs_dims[j-1];
162 m_outputStrides[j] = m_outputStrides[j-1] * m_dimensions[j-1];
163 }
164 } else {
165 m_leftStrides[NumDims - 1] = 1;
166 m_rightStrides[NumDims - 1] = 1;
167 m_outputStrides[NumDims - 1] = 1;
168
169 for (int j = NumDims - 2; j >= 0; --j) {
170 m_leftStrides[j] = m_leftStrides[j+1] * lhs_dims[j+1];
171 m_rightStrides[j] = m_rightStrides[j+1] * rhs_dims[j+1];
172 m_outputStrides[j] = m_outputStrides[j+1] * m_dimensions[j+1];
173 }
174 }
175 }
176
177 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
178
179 // TODO(phli): Add short-circuit memcpy evaluation if underlying data are linear?
180 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(Scalar* /*data*/)
181 {
182 m_leftImpl.evalSubExprsIfNeeded(NULL);
183 m_rightImpl.evalSubExprsIfNeeded(NULL);
184 return true;
185 }
186
187 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup()
188 {
189 m_leftImpl.cleanup();
190 m_rightImpl.cleanup();
191 }
192
193 // TODO(phli): attempt to speed this up. The integer divisions and modulo are slow.
194 // See CL/76180724 comments for more ideas.
195 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
196 {
197 // Collect dimension-wise indices (subs).
198 array<Index, NumDims> subs;
199 if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
200 for (int i = NumDims - 1; i > 0; --i) {
201 subs[i] = index / m_outputStrides[i];
202 index -= subs[i] * m_outputStrides[i];
203 }
204 subs[0] = index;
205 } else {
206 for (int i = 0; i < NumDims - 1; ++i) {
207 subs[i] = index / m_outputStrides[i];
208 index -= subs[i] * m_outputStrides[i];
209 }
210 subs[NumDims - 1] = index;
211 }
212
213 const Dimensions& left_dims = m_leftImpl.dimensions();
214 if (subs[m_axis] < left_dims[m_axis]) {
215 Index left_index;
216 if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
217 left_index = subs[0];
218 for (int i = 1; i < NumDims; ++i) {
219 left_index += (subs[i] % left_dims[i]) * m_leftStrides[i];
220 }
221 } else {
222 left_index = subs[NumDims - 1];
223 for (int i = NumDims - 2; i >= 0; --i) {
224 left_index += (subs[i] % left_dims[i]) * m_leftStrides[i];
225 }
226 }
227 return m_leftImpl.coeff(left_index);
228 } else {
229 subs[m_axis] -= left_dims[m_axis];
230 const Dimensions& right_dims = m_rightImpl.dimensions();
231 Index right_index;
232 if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
233 right_index = subs[0];
234 for (int i = 1; i < NumDims; ++i) {
235 right_index += (subs[i] % right_dims[i]) * m_rightStrides[i];
236 }
237 } else {
238 right_index = subs[NumDims - 1];
239 for (int i = NumDims - 2; i >= 0; --i) {
240 right_index += (subs[i] % right_dims[i]) * m_rightStrides[i];
241 }
242 }
243 return m_rightImpl.coeff(right_index);
244 }
245 }
246
247 // TODO(phli): Add a real vectorization.
248 template<int LoadMode>
249 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
250 {
251 const int packetSize = internal::unpacket_traits<PacketReturnType>::size;
252 EIGEN_STATIC_ASSERT((packetSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE)
253 eigen_assert(index + packetSize - 1 < dimensions().TotalSize());
254
255 EIGEN_ALIGN_MAX CoeffReturnType values[packetSize];
256 for (int i = 0; i < packetSize; ++i) {
257 values[i] = coeff(index+i);
258 }
259 PacketReturnType rslt = internal::pload<PacketReturnType>(values);
260 return rslt;
261 }
262
263 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost
264 costPerCoeff(bool vectorized) const {
265 const double compute_cost = NumDims * (2 * TensorOpCost::AddCost<Index>() +
266 2 * TensorOpCost::MulCost<Index>() +
267 TensorOpCost::DivCost<Index>() +
268 TensorOpCost::ModCost<Index>());
269 const double lhs_size = m_leftImpl.dimensions().TotalSize();
270 const double rhs_size = m_rightImpl.dimensions().TotalSize();
271 return (lhs_size / (lhs_size + rhs_size)) *
272 m_leftImpl.costPerCoeff(vectorized) +
273 (rhs_size / (lhs_size + rhs_size)) *
274 m_rightImpl.costPerCoeff(vectorized) +
275 TensorOpCost(0, 0, compute_cost);
276 }
277
278 EIGEN_DEVICE_FUNC Scalar* data() const { return NULL; }
279
280 protected:
281 Dimensions m_dimensions;
282 array<Index, NumDims> m_outputStrides;
283 array<Index, NumDims> m_leftStrides;
284 array<Index, NumDims> m_rightStrides;
285 TensorEvaluator<LeftArgType, Device> m_leftImpl;
286 TensorEvaluator<RightArgType, Device> m_rightImpl;
287 const Axis m_axis;
288};
289
290// Eval as lvalue
291template<typename Axis, typename LeftArgType, typename RightArgType, typename Device>
292 struct TensorEvaluator<TensorConcatenationOp<Axis, LeftArgType, RightArgType>, Device>
293 : public TensorEvaluator<const TensorConcatenationOp<Axis, LeftArgType, RightArgType>, Device>
294{
295 typedef TensorEvaluator<const TensorConcatenationOp<Axis, LeftArgType, RightArgType>, Device> Base;
296 typedef TensorConcatenationOp<Axis, LeftArgType, RightArgType> XprType;
297 typedef typename Base::Dimensions Dimensions;
298 enum {
299 IsAligned = false,
300 PacketAccess = TensorEvaluator<LeftArgType, Device>::PacketAccess & TensorEvaluator<RightArgType, Device>::PacketAccess,
301 Layout = TensorEvaluator<LeftArgType, Device>::Layout,
302 RawAccess = false
303 };
304
305 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(XprType& op, const Device& device)
306 : Base(op, device)
307 {
308 EIGEN_STATIC_ASSERT((static_cast<int>(Layout) == static_cast<int>(ColMajor)), YOU_MADE_A_PROGRAMMING_MISTAKE);
309 }
310
311 typedef typename XprType::Index Index;
312 typedef typename XprType::Scalar Scalar;
313 typedef typename XprType::CoeffReturnType CoeffReturnType;
314 typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
315
316 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType& coeffRef(Index index)
317 {
318 // Collect dimension-wise indices (subs).
319 array<Index, Base::NumDims> subs;
320 for (int i = Base::NumDims - 1; i > 0; --i) {
321 subs[i] = index / this->m_outputStrides[i];
322 index -= subs[i] * this->m_outputStrides[i];
323 }
324 subs[0] = index;
325
326 const Dimensions& left_dims = this->m_leftImpl.dimensions();
327 if (subs[this->m_axis] < left_dims[this->m_axis]) {
328 Index left_index = subs[0];
329 for (int i = 1; i < Base::NumDims; ++i) {
330 left_index += (subs[i] % left_dims[i]) * this->m_leftStrides[i];
331 }
332 return this->m_leftImpl.coeffRef(left_index);
333 } else {
334 subs[this->m_axis] -= left_dims[this->m_axis];
335 const Dimensions& right_dims = this->m_rightImpl.dimensions();
336 Index right_index = subs[0];
337 for (int i = 1; i < Base::NumDims; ++i) {
338 right_index += (subs[i] % right_dims[i]) * this->m_rightStrides[i];
339 }
340 return this->m_rightImpl.coeffRef(right_index);
341 }
342 }
343
344 template <int StoreMode> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
345 void writePacket(Index index, const PacketReturnType& x)
346 {
347 const int packetSize = internal::unpacket_traits<PacketReturnType>::size;
348 EIGEN_STATIC_ASSERT((packetSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE)
349 eigen_assert(index + packetSize - 1 < this->dimensions().TotalSize());
350
351 EIGEN_ALIGN_MAX CoeffReturnType values[packetSize];
352 internal::pstore<CoeffReturnType, PacketReturnType>(values, x);
353 for (int i = 0; i < packetSize; ++i) {
354 coeffRef(index+i) = values[i];
355 }
356 }
357};
358
359} // end namespace Eigen
360
361#endif // EIGEN_CXX11_TENSOR_TENSOR_CONCATENATION_H
The tensor base class.
Definition: TensorBase.h:827
Tensor concatenation class.
Definition: TensorConcatenation.h:59
Namespace containing all symbols from the Eigen library.
Definition: AdolcForward:45
A cost model used to limit the number of threads used for evaluating tensor expression.
Definition: TensorEvaluator.h:29
const Device & device() const
required by sycl in order to construct sycl buffer from raw pointer
Definition: TensorEvaluator.h:114