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TensorBroadcasting.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_BROADCASTING_H
11#define EIGEN_CXX11_TENSOR_TENSOR_BROADCASTING_H
12
13namespace Eigen {
14
22namespace internal {
23template<typename Broadcast, typename XprType>
24struct traits<TensorBroadcastingOp<Broadcast, 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 Broadcast, typename XprType>
37struct eval<TensorBroadcastingOp<Broadcast, XprType>, Eigen::Dense>
38{
39 typedef const TensorBroadcastingOp<Broadcast, XprType>& type;
40};
41
42template<typename Broadcast, typename XprType>
43struct nested<TensorBroadcastingOp<Broadcast, XprType>, 1, typename eval<TensorBroadcastingOp<Broadcast, XprType> >::type>
44{
45 typedef TensorBroadcastingOp<Broadcast, XprType> type;
46};
47
48template <typename Dims>
49struct is_input_scalar {
50 static const bool value = false;
51};
52template <>
53struct is_input_scalar<Sizes<> > {
54 static const bool value = true;
55};
56#ifndef EIGEN_EMULATE_CXX11_META_H
57template <typename std::size_t... Indices>
58struct is_input_scalar<Sizes<Indices...> > {
59 static const bool value = (Sizes<Indices...>::total_size == 1);
60};
61#endif
62
63} // end namespace internal
64
65
66
67template<typename Broadcast, typename XprType>
68class TensorBroadcastingOp : public TensorBase<TensorBroadcastingOp<Broadcast, XprType>, ReadOnlyAccessors>
69{
70 public:
71 typedef typename Eigen::internal::traits<TensorBroadcastingOp>::Scalar Scalar;
72 typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
73 typedef typename XprType::CoeffReturnType CoeffReturnType;
74 typedef typename Eigen::internal::nested<TensorBroadcastingOp>::type Nested;
75 typedef typename Eigen::internal::traits<TensorBroadcastingOp>::StorageKind StorageKind;
76 typedef typename Eigen::internal::traits<TensorBroadcastingOp>::Index Index;
77
78 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBroadcastingOp(const XprType& expr, const Broadcast& broadcast)
79 : m_xpr(expr), m_broadcast(broadcast) {}
80
81 EIGEN_DEVICE_FUNC
82 const Broadcast& broadcast() const { return m_broadcast; }
83
84 EIGEN_DEVICE_FUNC
85 const typename internal::remove_all<typename XprType::Nested>::type&
86 expression() const { return m_xpr; }
87
88 protected:
89 typename XprType::Nested m_xpr;
90 const Broadcast m_broadcast;
91};
92
93
94// Eval as rvalue
95template<typename Broadcast, typename ArgType, typename Device>
96struct TensorEvaluator<const TensorBroadcastingOp<Broadcast, ArgType>, Device>
97{
98 typedef TensorBroadcastingOp<Broadcast, ArgType> XprType;
99 typedef typename XprType::Index Index;
100 static const int NumDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value;
101 typedef DSizes<Index, NumDims> Dimensions;
102 typedef typename XprType::Scalar Scalar;
103 typedef typename TensorEvaluator<ArgType, Device>::Dimensions InputDimensions;
104 typedef typename XprType::CoeffReturnType CoeffReturnType;
105 typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
106 static const int PacketSize = internal::unpacket_traits<PacketReturnType>::size;
107
108 enum {
109 IsAligned = true,
110 PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess,
111 Layout = TensorEvaluator<ArgType, Device>::Layout,
112 RawAccess = false
113 };
114
115 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
116 : m_broadcast(op.broadcast()),m_impl(op.expression(), device)
117 {
118 // The broadcasting op doesn't change the rank of the tensor. One can't broadcast a scalar
119 // and store the result in a scalar. Instead one should reshape the scalar into a a N-D
120 // tensor with N >= 1 of 1 element first and then broadcast.
121 EIGEN_STATIC_ASSERT((NumDims > 0), YOU_MADE_A_PROGRAMMING_MISTAKE);
122 const InputDimensions& input_dims = m_impl.dimensions();
123 const Broadcast& broadcast = op.broadcast();
124 for (int i = 0; i < NumDims; ++i) {
125 eigen_assert(input_dims[i] > 0);
126 m_dimensions[i] = input_dims[i] * broadcast[i];
127 }
128
129 if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
130 m_inputStrides[0] = 1;
131 m_outputStrides[0] = 1;
132 for (int i = 1; i < NumDims; ++i) {
133 m_inputStrides[i] = m_inputStrides[i-1] * input_dims[i-1];
134 m_outputStrides[i] = m_outputStrides[i-1] * m_dimensions[i-1];
135 }
136 } else {
137 m_inputStrides[NumDims-1] = 1;
138 m_outputStrides[NumDims-1] = 1;
139 for (int i = NumDims-2; i >= 0; --i) {
140 m_inputStrides[i] = m_inputStrides[i+1] * input_dims[i+1];
141 m_outputStrides[i] = m_outputStrides[i+1] * m_dimensions[i+1];
142 }
143 }
144 }
145
146 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
147
148 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(Scalar* /*data*/) {
149 m_impl.evalSubExprsIfNeeded(NULL);
150 return true;
151 }
152
153 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() {
154 m_impl.cleanup();
155 }
156
157 EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE CoeffReturnType coeff(Index index) const
158 {
159 if (internal::is_input_scalar<typename internal::remove_all<InputDimensions>::type>::value) {
160 return m_impl.coeff(0);
161 }
162
163 if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
164 return coeffColMajor(index);
165 } else {
166 return coeffRowMajor(index);
167 }
168 }
169
170 // TODO: attempt to speed this up. The integer divisions and modulo are slow
171 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeffColMajor(Index index) const
172 {
173 Index inputIndex = 0;
174 for (int i = NumDims - 1; i > 0; --i) {
175 const Index idx = index / m_outputStrides[i];
176 if (internal::index_statically_eq<Broadcast>(i, 1)) {
177 eigen_assert(idx < m_impl.dimensions()[i]);
178 inputIndex += idx * m_inputStrides[i];
179 } else {
180 if (internal::index_statically_eq<InputDimensions>(i, 1)) {
181 eigen_assert(idx % m_impl.dimensions()[i] == 0);
182 } else {
183 inputIndex += (idx % m_impl.dimensions()[i]) * m_inputStrides[i];
184 }
185 }
186 index -= idx * m_outputStrides[i];
187 }
188 if (internal::index_statically_eq<Broadcast>(0, 1)) {
189 eigen_assert(index < m_impl.dimensions()[0]);
190 inputIndex += index;
191 } else {
192 if (internal::index_statically_eq<InputDimensions>(0, 1)) {
193 eigen_assert(index % m_impl.dimensions()[0] == 0);
194 } else {
195 inputIndex += (index % m_impl.dimensions()[0]);
196 }
197 }
198 return m_impl.coeff(inputIndex);
199 }
200
201 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeffRowMajor(Index index) const
202 {
203 Index inputIndex = 0;
204 for (int i = 0; i < NumDims - 1; ++i) {
205 const Index idx = index / m_outputStrides[i];
206 if (internal::index_statically_eq<Broadcast>(i, 1)) {
207 eigen_assert(idx < m_impl.dimensions()[i]);
208 inputIndex += idx * m_inputStrides[i];
209 } else {
210 if (internal::index_statically_eq<InputDimensions>(i, 1)) {
211 eigen_assert(idx % m_impl.dimensions()[i] == 0);
212 } else {
213 inputIndex += (idx % m_impl.dimensions()[i]) * m_inputStrides[i];
214 }
215 }
216 index -= idx * m_outputStrides[i];
217 }
218 if (internal::index_statically_eq<Broadcast>(NumDims-1, 1)) {
219 eigen_assert(index < m_impl.dimensions()[NumDims-1]);
220 inputIndex += index;
221 } else {
222 if (internal::index_statically_eq<InputDimensions>(NumDims-1, 1)) {
223 eigen_assert(index % m_impl.dimensions()[NumDims-1] == 0);
224 } else {
225 inputIndex += (index % m_impl.dimensions()[NumDims-1]);
226 }
227 }
228 return m_impl.coeff(inputIndex);
229 }
230
231 template<int LoadMode>
232 EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE PacketReturnType packet(Index index) const
233 {
234 if (internal::is_input_scalar<typename internal::remove_all<InputDimensions>::type>::value) {
235 return internal::pset1<PacketReturnType>(m_impl.coeff(0));
236 }
237
238 if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
239 return packetColMajor<LoadMode>(index);
240 } else {
241 return packetRowMajor<LoadMode>(index);
242 }
243 }
244
245 // Ignore the LoadMode and always use unaligned loads since we can't guarantee
246 // the alignment at compile time.
247 template<int LoadMode>
248 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packetColMajor(Index index) const
249 {
250 EIGEN_STATIC_ASSERT((PacketSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE)
251 eigen_assert(index+PacketSize-1 < dimensions().TotalSize());
252
253 const Index originalIndex = index;
254
255 Index inputIndex = 0;
256 for (int i = NumDims - 1; i > 0; --i) {
257 const Index idx = index / m_outputStrides[i];
258 if (internal::index_statically_eq<Broadcast>(i, 1)) {
259 eigen_assert(idx < m_impl.dimensions()[i]);
260 inputIndex += idx * m_inputStrides[i];
261 } else {
262 if (internal::index_statically_eq<InputDimensions>(i, 1)) {
263 eigen_assert(idx % m_impl.dimensions()[i] == 0);
264 } else {
265 inputIndex += (idx % m_impl.dimensions()[i]) * m_inputStrides[i];
266 }
267 }
268 index -= idx * m_outputStrides[i];
269 }
270 Index innermostLoc;
271 if (internal::index_statically_eq<Broadcast>(0, 1)) {
272 eigen_assert(index < m_impl.dimensions()[0]);
273 innermostLoc = index;
274 } else {
275 if (internal::index_statically_eq<InputDimensions>(0, 1)) {
276 eigen_assert(index % m_impl.dimensions()[0] == 0);
277 innermostLoc = 0;
278 } else {
279 innermostLoc = index % m_impl.dimensions()[0];
280 }
281 }
282 inputIndex += innermostLoc;
283
284 // Todo: this could be extended to the second dimension if we're not
285 // broadcasting alongside the first dimension, and so on.
286 if (innermostLoc + PacketSize <= m_impl.dimensions()[0]) {
287 return m_impl.template packet<Unaligned>(inputIndex);
288 } else {
289 EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[PacketSize];
290 values[0] = m_impl.coeff(inputIndex);
291 for (int i = 1; i < PacketSize; ++i) {
292 values[i] = coeffColMajor(originalIndex+i);
293 }
294 PacketReturnType rslt = internal::pload<PacketReturnType>(values);
295 return rslt;
296 }
297 }
298
299 template<int LoadMode>
300 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packetRowMajor(Index index) const
301 {
302 EIGEN_STATIC_ASSERT((PacketSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE)
303 eigen_assert(index+PacketSize-1 < dimensions().TotalSize());
304
305 const Index originalIndex = index;
306
307 Index inputIndex = 0;
308 for (int i = 0; i < NumDims - 1; ++i) {
309 const Index idx = index / m_outputStrides[i];
310 if (internal::index_statically_eq<Broadcast>(i, 1)) {
311 eigen_assert(idx < m_impl.dimensions()[i]);
312 inputIndex += idx * m_inputStrides[i];
313 } else {
314 if (internal::index_statically_eq<InputDimensions>(i, 1)) {
315 eigen_assert(idx % m_impl.dimensions()[i] == 0);
316 } else {
317 inputIndex += (idx % m_impl.dimensions()[i]) * m_inputStrides[i];
318 }
319 }
320 index -= idx * m_outputStrides[i];
321 }
322 Index innermostLoc;
323 if (internal::index_statically_eq<Broadcast>(NumDims-1, 1)) {
324 eigen_assert(index < m_impl.dimensions()[NumDims-1]);
325 innermostLoc = index;
326 } else {
327 if (internal::index_statically_eq<InputDimensions>(NumDims-1, 1)) {
328 eigen_assert(index % m_impl.dimensions()[NumDims-1] == 0);
329 innermostLoc = 0;
330 } else {
331 innermostLoc = index % m_impl.dimensions()[NumDims-1];
332 }
333 }
334 inputIndex += innermostLoc;
335
336 // Todo: this could be extended to the second dimension if we're not
337 // broadcasting alongside the first dimension, and so on.
338 if (innermostLoc + PacketSize <= m_impl.dimensions()[NumDims-1]) {
339 return m_impl.template packet<Unaligned>(inputIndex);
340 } else {
341 EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[PacketSize];
342 values[0] = m_impl.coeff(inputIndex);
343 for (int i = 1; i < PacketSize; ++i) {
344 values[i] = coeffRowMajor(originalIndex+i);
345 }
346 PacketReturnType rslt = internal::pload<PacketReturnType>(values);
347 return rslt;
348 }
349 }
350
351 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost
352 costPerCoeff(bool vectorized) const {
353 double compute_cost = TensorOpCost::AddCost<Index>();
354 if (NumDims > 0) {
355 for (int i = NumDims - 1; i > 0; --i) {
356 compute_cost += TensorOpCost::DivCost<Index>();
357 if (internal::index_statically_eq<Broadcast>(i, 1)) {
358 compute_cost +=
359 TensorOpCost::MulCost<Index>() + TensorOpCost::AddCost<Index>();
360 } else {
361 if (!internal::index_statically_eq<InputDimensions>(i, 1)) {
362 compute_cost += TensorOpCost::MulCost<Index>() +
363 TensorOpCost::ModCost<Index>() +
364 TensorOpCost::AddCost<Index>();
365 }
366 }
367 compute_cost +=
368 TensorOpCost::MulCost<Index>() + TensorOpCost::AddCost<Index>();
369 }
370 }
371 return m_impl.costPerCoeff(vectorized) +
372 TensorOpCost(0, 0, compute_cost, vectorized, PacketSize);
373 }
374
375 EIGEN_DEVICE_FUNC Scalar* data() const { return NULL; }
376
377 const TensorEvaluator<ArgType, Device>& impl() const { return m_impl; }
378
379 Broadcast functor() const { return m_broadcast; }
380
381 protected:
382 const Broadcast m_broadcast;
383 Dimensions m_dimensions;
384 array<Index, NumDims> m_outputStrides;
385 array<Index, NumDims> m_inputStrides;
386 TensorEvaluator<ArgType, Device> m_impl;
387};
388
389
390} // end namespace Eigen
391
392#endif // EIGEN_CXX11_TENSOR_TENSOR_BROADCASTING_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