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 
13 namespace Eigen {
14 
22 namespace internal {
23 template<typename Axis, typename LhsXprType, typename RhsXprType>
24 struct 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 packet_traits<Scalar>::type Packet;
30  typedef typename promote_storage_type<typename traits<LhsXprType>::StorageKind,
31  typename traits<RhsXprType>::StorageKind>::ret StorageKind;
32  typedef typename promote_index_type<typename traits<LhsXprType>::Index,
33  typename traits<RhsXprType>::Index>::type Index;
34  typedef typename LhsXprType::Nested LhsNested;
35  typedef typename RhsXprType::Nested RhsNested;
36  typedef typename remove_reference<LhsNested>::type _LhsNested;
37  typedef typename remove_reference<RhsNested>::type _RhsNested;
38  static const int NumDimensions = traits<LhsXprType>::NumDimensions;
39  static const int Layout = traits<LhsXprType>::Layout;
40  enum { Flags = 0 };
41 };
42 
43 template<typename Axis, typename LhsXprType, typename RhsXprType>
44 struct eval<TensorConcatenationOp<Axis, LhsXprType, RhsXprType>, Eigen::Dense>
45 {
46  typedef const TensorConcatenationOp<Axis, LhsXprType, RhsXprType>& type;
47 };
48 
49 template<typename Axis, typename LhsXprType, typename RhsXprType>
50 struct nested<TensorConcatenationOp<Axis, LhsXprType, RhsXprType>, 1, typename eval<TensorConcatenationOp<Axis, LhsXprType, RhsXprType> >::type>
51 {
52  typedef TensorConcatenationOp<Axis, LhsXprType, RhsXprType> type;
53 };
54 
55 } // end namespace internal
56 
57 
58 template<typename Axis, typename LhsXprType, typename RhsXprType>
59 class TensorConcatenationOp : public TensorBase<TensorConcatenationOp<Axis, LhsXprType, RhsXprType>, WriteAccessors>
60 {
61  public:
62  typedef typename internal::traits<TensorConcatenationOp>::Scalar Scalar;
63  typedef typename internal::traits<TensorConcatenationOp>::Packet Packet;
64  typedef typename internal::traits<TensorConcatenationOp>::StorageKind StorageKind;
65  typedef typename internal::traits<TensorConcatenationOp>::Index Index;
66  typedef typename internal::nested<TensorConcatenationOp>::type Nested;
67  typedef typename internal::promote_storage_type<typename LhsXprType::CoeffReturnType,
68  typename RhsXprType::CoeffReturnType>::ret CoeffReturnType;
69  typedef typename internal::promote_storage_type<typename LhsXprType::PacketReturnType,
70  typename RhsXprType::PacketReturnType>::ret PacketReturnType;
71  typedef typename NumTraits<Scalar>::Real RealScalar;
72 
73  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorConcatenationOp(const LhsXprType& lhs, const RhsXprType& rhs, Axis axis)
74  : m_lhs_xpr(lhs), m_rhs_xpr(rhs), m_axis(axis) {}
75 
76  EIGEN_DEVICE_FUNC
77  const typename internal::remove_all<typename LhsXprType::Nested>::type&
78  lhsExpression() const { return m_lhs_xpr; }
79 
80  EIGEN_DEVICE_FUNC
81  const typename internal::remove_all<typename RhsXprType::Nested>::type&
82  rhsExpression() const { return m_rhs_xpr; }
83 
84  EIGEN_DEVICE_FUNC const Axis& axis() const { return m_axis; }
85 
86  EIGEN_DEVICE_FUNC
87  EIGEN_STRONG_INLINE TensorConcatenationOp& operator = (const TensorConcatenationOp& other)
88  {
89  typedef TensorAssignOp<TensorConcatenationOp, const TensorConcatenationOp> Assign;
90  Assign assign(*this, other);
91  internal::TensorExecutor<const Assign, DefaultDevice>::run(assign, DefaultDevice());
92  return *this;
93  }
94 
95  template<typename OtherDerived>
96  EIGEN_DEVICE_FUNC
97  EIGEN_STRONG_INLINE TensorConcatenationOp& operator = (const OtherDerived& other)
98  {
99  typedef TensorAssignOp<TensorConcatenationOp, const OtherDerived> Assign;
100  Assign assign(*this, other);
101  internal::TensorExecutor<const Assign, DefaultDevice>::run(assign, DefaultDevice());
102  return *this;
103  }
104 
105  protected:
106  typename LhsXprType::Nested m_lhs_xpr;
107  typename RhsXprType::Nested m_rhs_xpr;
108  const Axis m_axis;
109 };
110 
111 
112 // Eval as rvalue
113 template<typename Axis, typename LeftArgType, typename RightArgType, typename Device>
114 struct TensorEvaluator<const TensorConcatenationOp<Axis, LeftArgType, RightArgType>, Device>
115 {
117  typedef typename XprType::Index Index;
118  static const int NumDims = internal::array_size<typename TensorEvaluator<LeftArgType, Device>::Dimensions>::value;
119  static const int RightNumDims = internal::array_size<typename TensorEvaluator<RightArgType, Device>::Dimensions>::value;
120  typedef DSizes<Index, NumDims> Dimensions;
121  typedef typename XprType::Scalar Scalar;
122  typedef typename XprType::CoeffReturnType CoeffReturnType;
123  typedef typename XprType::PacketReturnType PacketReturnType;
124  enum {
125  IsAligned = false,
128  };
129 
130  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
131  : m_leftImpl(op.lhsExpression(), device), m_rightImpl(op.rhsExpression(), device), m_axis(op.axis())
132  {
133  EIGEN_STATIC_ASSERT((static_cast<int>(TensorEvaluator<LeftArgType, Device>::Layout) == static_cast<int>(TensorEvaluator<RightArgType, Device>::Layout) || NumDims == 1), YOU_MADE_A_PROGRAMMING_MISTAKE);
134  EIGEN_STATIC_ASSERT(NumDims == RightNumDims, YOU_MADE_A_PROGRAMMING_MISTAKE);
135  EIGEN_STATIC_ASSERT(NumDims > 0, YOU_MADE_A_PROGRAMMING_MISTAKE);
136 
137  eigen_assert(0 <= m_axis && m_axis < NumDims);
138  const Dimensions& lhs_dims = m_leftImpl.dimensions();
139  const Dimensions& rhs_dims = m_rightImpl.dimensions();
140  {
141  int i = 0;
142  for (; i < m_axis; ++i) {
143  eigen_assert(lhs_dims[i] > 0);
144  eigen_assert(lhs_dims[i] == rhs_dims[i]);
145  m_dimensions[i] = lhs_dims[i];
146  }
147  eigen_assert(lhs_dims[i] > 0); // Now i == m_axis.
148  eigen_assert(rhs_dims[i] > 0);
149  m_dimensions[i] = lhs_dims[i] + rhs_dims[i];
150  for (++i; i < NumDims; ++i) {
151  eigen_assert(lhs_dims[i] > 0);
152  eigen_assert(lhs_dims[i] == rhs_dims[i]);
153  m_dimensions[i] = lhs_dims[i];
154  }
155  }
156 
157  if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
158  m_leftStrides[0] = 1;
159  m_rightStrides[0] = 1;
160  m_outputStrides[0] = 1;
161 
162  for (int j = 1; j < NumDims; ++j) {
163  m_leftStrides[j] = m_leftStrides[j-1] * lhs_dims[j-1];
164  m_rightStrides[j] = m_rightStrides[j-1] * rhs_dims[j-1];
165  m_outputStrides[j] = m_outputStrides[j-1] * m_dimensions[j-1];
166  }
167  } else {
168  m_leftStrides[NumDims - 1] = 1;
169  m_rightStrides[NumDims - 1] = 1;
170  m_outputStrides[NumDims - 1] = 1;
171 
172  for (int j = NumDims - 2; j >= 0; --j) {
173  m_leftStrides[j] = m_leftStrides[j+1] * lhs_dims[j+1];
174  m_rightStrides[j] = m_rightStrides[j+1] * rhs_dims[j+1];
175  m_outputStrides[j] = m_outputStrides[j+1] * m_dimensions[j+1];
176  }
177  }
178  }
179 
180  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
181 
182  // TODO(phli): Add short-circuit memcpy evaluation if underlying data are linear?
183  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(Scalar* /*data*/)
184  {
185  m_leftImpl.evalSubExprsIfNeeded(NULL);
186  m_rightImpl.evalSubExprsIfNeeded(NULL);
187  return true;
188  }
189 
190  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup()
191  {
192  m_leftImpl.cleanup();
193  m_rightImpl.cleanup();
194  }
195 
196  // TODO(phli): attempt to speed this up. The integer divisions and modulo are slow.
197  // See CL/76180724 comments for more ideas.
198  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
199  {
200  // Collect dimension-wise indices (subs).
201  array<Index, NumDims> subs;
202  if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
203  for (int i = NumDims - 1; i > 0; --i) {
204  subs[i] = index / m_outputStrides[i];
205  index -= subs[i] * m_outputStrides[i];
206  }
207  subs[0] = index;
208  } else {
209  for (int i = 0; i < NumDims - 1; ++i) {
210  subs[i] = index / m_outputStrides[i];
211  index -= subs[i] * m_outputStrides[i];
212  }
213  subs[NumDims - 1] = index;
214  }
215 
216  const Dimensions& left_dims = m_leftImpl.dimensions();
217  if (subs[m_axis] < left_dims[m_axis]) {
218  Index left_index;
219  if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
220  left_index = subs[0];
221  for (int i = 1; i < NumDims; ++i) {
222  left_index += (subs[i] % left_dims[i]) * m_leftStrides[i];
223  }
224  } else {
225  left_index = subs[NumDims - 1];
226  for (int i = NumDims - 2; i >= 0; --i) {
227  left_index += (subs[i] % left_dims[i]) * m_leftStrides[i];
228  }
229  }
230  return m_leftImpl.coeff(left_index);
231  } else {
232  subs[m_axis] -= left_dims[m_axis];
233  const Dimensions& right_dims = m_rightImpl.dimensions();
234  Index right_index;
235  if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
236  right_index = subs[0];
237  for (int i = 1; i < NumDims; ++i) {
238  right_index += (subs[i] % right_dims[i]) * m_rightStrides[i];
239  }
240  } else {
241  right_index = subs[NumDims - 1];
242  for (int i = NumDims - 2; i >= 0; --i) {
243  right_index += (subs[i] % right_dims[i]) * m_rightStrides[i];
244  }
245  }
246  return m_rightImpl.coeff(right_index);
247  }
248  }
249 
250  // TODO(phli): Add a real vectorization.
251  template<int LoadMode>
252  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
253  {
254  static const int packetSize = internal::unpacket_traits<PacketReturnType>::size;
255  EIGEN_STATIC_ASSERT(packetSize > 1, YOU_MADE_A_PROGRAMMING_MISTAKE)
256  eigen_assert(index + packetSize - 1 < dimensions().TotalSize());
257 
258  EIGEN_ALIGN_MAX CoeffReturnType values[packetSize];
259  for (int i = 0; i < packetSize; ++i) {
260  values[i] = coeff(index+i);
261  }
262  PacketReturnType rslt = internal::pload<PacketReturnType>(values);
263  return rslt;
264  }
265 
266  EIGEN_DEVICE_FUNC Scalar* data() const { return NULL; }
267 
268  protected:
269  Dimensions m_dimensions;
270  array<Index, NumDims> m_outputStrides;
271  array<Index, NumDims> m_leftStrides;
272  array<Index, NumDims> m_rightStrides;
275  const Axis m_axis;
276 };
277 
278 // Eval as lvalue
279 template<typename Axis, typename LeftArgType, typename RightArgType, typename Device>
280  struct TensorEvaluator<TensorConcatenationOp<Axis, LeftArgType, RightArgType>, Device>
281  : public TensorEvaluator<const TensorConcatenationOp<Axis, LeftArgType, RightArgType>, Device>
282 {
285  typedef typename Base::Dimensions Dimensions;
286  enum {
287  IsAligned = false,
290  };
291 
292  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(XprType& op, const Device& device)
293  : Base(op, device)
294  {
295  EIGEN_STATIC_ASSERT((static_cast<int>(Layout) == static_cast<int>(ColMajor)), YOU_MADE_A_PROGRAMMING_MISTAKE);
296  }
297 
298  typedef typename XprType::Index Index;
299  typedef typename XprType::Scalar Scalar;
300  typedef typename XprType::CoeffReturnType CoeffReturnType;
301  typedef typename XprType::PacketReturnType PacketReturnType;
302 
303  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType& coeffRef(Index index)
304  {
305  // Collect dimension-wise indices (subs).
306  array<Index, Base::NumDims> subs;
307  for (int i = Base::NumDims - 1; i > 0; --i) {
308  subs[i] = index / this->m_outputStrides[i];
309  index -= subs[i] * this->m_outputStrides[i];
310  }
311  subs[0] = index;
312 
313  const Dimensions& left_dims = this->m_leftImpl.dimensions();
314  if (subs[this->m_axis] < left_dims[this->m_axis]) {
315  Index left_index = subs[0];
316  for (int i = 1; i < Base::NumDims; ++i) {
317  left_index += (subs[i] % left_dims[i]) * this->m_leftStrides[i];
318  }
319  return this->m_leftImpl.coeffRef(left_index);
320  } else {
321  subs[this->m_axis] -= left_dims[this->m_axis];
322  const Dimensions& right_dims = this->m_rightImpl.dimensions();
323  Index right_index = subs[0];
324  for (int i = 1; i < Base::NumDims; ++i) {
325  right_index += (subs[i] % right_dims[i]) * this->m_rightStrides[i];
326  }
327  return this->m_rightImpl.coeffRef(right_index);
328  }
329  }
330 
331  template <int StoreMode> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
332  void writePacket(Index index, const PacketReturnType& x)
333  {
334  static const int packetSize = internal::unpacket_traits<PacketReturnType>::size;
335  EIGEN_STATIC_ASSERT(packetSize > 1, YOU_MADE_A_PROGRAMMING_MISTAKE)
336  eigen_assert(index + packetSize - 1 < this->dimensions().TotalSize());
337 
338  EIGEN_ALIGN_MAX CoeffReturnType values[packetSize];
339  internal::pstore<CoeffReturnType, PacketReturnType>(values, x);
340  for (int i = 0; i < packetSize; ++i) {
341  coeffRef(index+i) = values[i];
342  }
343  }
344 };
345 
346 } // end namespace Eigen
347 
348 #endif // EIGEN_CXX11_TENSOR_TENSOR_CONCATENATION_H
Namespace containing all symbols from the Eigen library.
Definition: CXX11Meta.h:13
The tensor evaluator classes.
Definition: TensorEvaluator.h:28
The tensor base class.
Definition: TensorForwardDeclarations.h:19
Tensor concatenation class.
Definition: TensorConcatenation.h:59