10#ifndef EIGEN_CXX11_TENSOR_TENSOR_CONCATENATION_H
11#define EIGEN_CXX11_TENSOR_TENSOR_CONCATENATION_H
23template<
typename Axis,
typename LhsXprType,
typename RhsXprType>
24struct traits<TensorConcatenationOp<Axis, LhsXprType, RhsXprType> >
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;
40 typedef typename conditional<Pointer_type_promotion<typename LhsXprType::Scalar, Scalar>::val,
41 typename traits<LhsXprType>::PointerType,
typename traits<RhsXprType>::PointerType>::type PointerType;
44template<
typename Axis,
typename LhsXprType,
typename RhsXprType>
45struct eval<TensorConcatenationOp<Axis, LhsXprType, RhsXprType>,
Eigen::Dense>
47 typedef const TensorConcatenationOp<Axis, LhsXprType, RhsXprType>& type;
50template<
typename Axis,
typename LhsXprType,
typename RhsXprType>
51struct nested<TensorConcatenationOp<Axis, LhsXprType, RhsXprType>, 1, typename eval<TensorConcatenationOp<Axis, LhsXprType, RhsXprType> >::type>
53 typedef TensorConcatenationOp<Axis, LhsXprType, RhsXprType> type;
59template<
typename Axis,
typename LhsXprType,
typename RhsXprType>
64 typedef typename internal::traits<TensorConcatenationOp>::Scalar Scalar;
65 typedef typename internal::traits<TensorConcatenationOp>::StorageKind StorageKind;
66 typedef typename internal::traits<TensorConcatenationOp>::Index Index;
67 typedef typename internal::nested<TensorConcatenationOp>::type Nested;
68 typedef typename internal::promote_storage_type<
typename LhsXprType::CoeffReturnType,
69 typename RhsXprType::CoeffReturnType>::ret CoeffReturnType;
72 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
TensorConcatenationOp(
const LhsXprType& lhs,
const RhsXprType& rhs, Axis axis)
73 : m_lhs_xpr(lhs), m_rhs_xpr(rhs), m_axis(axis) {}
76 const typename internal::remove_all<typename LhsXprType::Nested>::type&
77 lhsExpression()
const {
return m_lhs_xpr; }
80 const typename internal::remove_all<typename RhsXprType::Nested>::type&
81 rhsExpression()
const {
return m_rhs_xpr; }
83 EIGEN_DEVICE_FUNC
const Axis& axis()
const {
return m_axis; }
87 typename LhsXprType::Nested m_lhs_xpr;
88 typename RhsXprType::Nested m_rhs_xpr;
94template<
typename Axis,
typename LeftArgType,
typename RightArgType,
typename Device>
98 typedef typename XprType::Index
Index;
99 static const int NumDims = internal::array_size<typename TensorEvaluator<LeftArgType, Device>::Dimensions>::value;
100 static const int RightNumDims = internal::array_size<typename TensorEvaluator<RightArgType, Device>::Dimensions>::value;
101 typedef DSizes<Index, NumDims> Dimensions;
102 typedef typename XprType::Scalar Scalar;
103 typedef typename XprType::CoeffReturnType CoeffReturnType;
104 typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
105 typedef StorageMemory<CoeffReturnType, Device> Storage;
106 typedef typename Storage::Type EvaluatorPointerType;
119 typedef internal::TensorBlockNotImplemented TensorBlock;
122 EIGEN_STRONG_INLINE TensorEvaluator(
const XprType& op,
const Device& device)
123 : m_leftImpl(op.lhsExpression(), device), m_rightImpl(op.rhsExpression(), device), m_axis(op.axis())
125 EIGEN_STATIC_ASSERT((
static_cast<int>(TensorEvaluator<LeftArgType, Device>::Layout) ==
static_cast<int>(TensorEvaluator<RightArgType, Device>::Layout) || NumDims == 1), YOU_MADE_A_PROGRAMMING_MISTAKE);
126 EIGEN_STATIC_ASSERT((NumDims == RightNumDims), YOU_MADE_A_PROGRAMMING_MISTAKE);
127 EIGEN_STATIC_ASSERT((NumDims > 0), YOU_MADE_A_PROGRAMMING_MISTAKE);
129 eigen_assert(0 <= m_axis && m_axis < NumDims);
130 const Dimensions& lhs_dims = m_leftImpl.dimensions();
131 const Dimensions& rhs_dims = m_rightImpl.dimensions();
134 for (; i < m_axis; ++i) {
135 eigen_assert(lhs_dims[i] > 0);
136 eigen_assert(lhs_dims[i] == rhs_dims[i]);
137 m_dimensions[i] = lhs_dims[i];
139 eigen_assert(lhs_dims[i] > 0);
140 eigen_assert(rhs_dims[i] > 0);
141 m_dimensions[i] = lhs_dims[i] + rhs_dims[i];
142 for (++i; i < NumDims; ++i) {
143 eigen_assert(lhs_dims[i] > 0);
144 eigen_assert(lhs_dims[i] == rhs_dims[i]);
145 m_dimensions[i] = lhs_dims[i];
149 if (
static_cast<int>(Layout) ==
static_cast<int>(
ColMajor)) {
150 m_leftStrides[0] = 1;
151 m_rightStrides[0] = 1;
152 m_outputStrides[0] = 1;
154 for (
int j = 1; j < NumDims; ++j) {
155 m_leftStrides[j] = m_leftStrides[j-1] * lhs_dims[j-1];
156 m_rightStrides[j] = m_rightStrides[j-1] * rhs_dims[j-1];
157 m_outputStrides[j] = m_outputStrides[j-1] * m_dimensions[j-1];
160 m_leftStrides[NumDims - 1] = 1;
161 m_rightStrides[NumDims - 1] = 1;
162 m_outputStrides[NumDims - 1] = 1;
164 for (
int j = NumDims - 2; j >= 0; --j) {
165 m_leftStrides[j] = m_leftStrides[j+1] * lhs_dims[j+1];
166 m_rightStrides[j] = m_rightStrides[j+1] * rhs_dims[j+1];
167 m_outputStrides[j] = m_outputStrides[j+1] * m_dimensions[j+1];
172 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
const Dimensions& dimensions()
const {
return m_dimensions; }
175 EIGEN_STRONG_INLINE
bool evalSubExprsIfNeeded(EvaluatorPointerType)
177 m_leftImpl.evalSubExprsIfNeeded(NULL);
178 m_rightImpl.evalSubExprsIfNeeded(NULL);
182 EIGEN_STRONG_INLINE
void cleanup()
184 m_leftImpl.cleanup();
185 m_rightImpl.cleanup();
190 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index)
const
193 array<Index, NumDims> subs;
194 if (
static_cast<int>(Layout) ==
static_cast<int>(
ColMajor)) {
195 for (
int i = NumDims - 1; i > 0; --i) {
196 subs[i] = index / m_outputStrides[i];
197 index -= subs[i] * m_outputStrides[i];
201 for (
int i = 0; i < NumDims - 1; ++i) {
202 subs[i] = index / m_outputStrides[i];
203 index -= subs[i] * m_outputStrides[i];
205 subs[NumDims - 1] = index;
208 const Dimensions& left_dims = m_leftImpl.dimensions();
209 if (subs[m_axis] < left_dims[m_axis]) {
211 if (
static_cast<int>(Layout) ==
static_cast<int>(
ColMajor)) {
212 left_index = subs[0];
214 for (
int i = 1; i < NumDims; ++i) {
215 left_index += (subs[i] % left_dims[i]) * m_leftStrides[i];
218 left_index = subs[NumDims - 1];
220 for (
int i = NumDims - 2; i >= 0; --i) {
221 left_index += (subs[i] % left_dims[i]) * m_leftStrides[i];
224 return m_leftImpl.coeff(left_index);
226 subs[m_axis] -= left_dims[m_axis];
227 const Dimensions& right_dims = m_rightImpl.dimensions();
229 if (
static_cast<int>(Layout) ==
static_cast<int>(
ColMajor)) {
230 right_index = subs[0];
232 for (
int i = 1; i < NumDims; ++i) {
233 right_index += (subs[i] % right_dims[i]) * m_rightStrides[i];
236 right_index = subs[NumDims - 1];
238 for (
int i = NumDims - 2; i >= 0; --i) {
239 right_index += (subs[i] % right_dims[i]) * m_rightStrides[i];
242 return m_rightImpl.coeff(right_index);
247 template<
int LoadMode>
248 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index)
const
250 const int packetSize = PacketType<CoeffReturnType, Device>::size;
251 EIGEN_STATIC_ASSERT((packetSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE)
252 eigen_assert(index + packetSize - 1 < dimensions().TotalSize());
254 EIGEN_ALIGN_MAX CoeffReturnType values[packetSize];
256 for (
int i = 0; i < packetSize; ++i) {
257 values[i] = coeff(index+i);
259 PacketReturnType rslt = internal::pload<PacketReturnType>(values);
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);
278 EIGEN_DEVICE_FUNC EvaluatorPointerType data()
const {
return NULL; }
280 #ifdef EIGEN_USE_SYCL
282 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
void bind(cl::sycl::handler &cgh)
const {
283 m_leftImpl.bind(cgh);
284 m_rightImpl.bind(cgh);
289 Dimensions m_dimensions;
290 array<Index, NumDims> m_outputStrides;
291 array<Index, NumDims> m_leftStrides;
292 array<Index, NumDims> m_rightStrides;
293 TensorEvaluator<LeftArgType, Device> m_leftImpl;
294 TensorEvaluator<RightArgType, Device> m_rightImpl;
299template<
typename Axis,
typename LeftArgType,
typename RightArgType,
typename Device>
300 struct TensorEvaluator<TensorConcatenationOp<Axis, LeftArgType, RightArgType>, Device>
301 :
public TensorEvaluator<const TensorConcatenationOp<Axis, LeftArgType, RightArgType>, Device>
303 typedef TensorEvaluator<const TensorConcatenationOp<Axis, LeftArgType, RightArgType>, Device> Base;
304 typedef TensorConcatenationOp<Axis, LeftArgType, RightArgType> XprType;
305 typedef typename Base::Dimensions Dimensions;
308 PacketAccess = TensorEvaluator<LeftArgType, Device>::PacketAccess &&
309 TensorEvaluator<RightArgType, Device>::PacketAccess,
311 PreferBlockAccess = TensorEvaluator<LeftArgType, Device>::PreferBlockAccess ||
312 TensorEvaluator<RightArgType, Device>::PreferBlockAccess,
313 Layout = TensorEvaluator<LeftArgType, Device>::Layout,
318 typedef internal::TensorBlockNotImplemented TensorBlock;
321 EIGEN_STRONG_INLINE TensorEvaluator(XprType& op,
const Device& device)
324 EIGEN_STATIC_ASSERT((
static_cast<int>(Layout) ==
static_cast<int>(
ColMajor)), YOU_MADE_A_PROGRAMMING_MISTAKE);
327 typedef typename XprType::Index
Index;
328 typedef typename XprType::Scalar Scalar;
329 typedef typename XprType::CoeffReturnType CoeffReturnType;
330 typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
332 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType& coeffRef(Index index)
335 array<Index, Base::NumDims> subs;
336 for (
int i = Base::NumDims - 1; i > 0; --i) {
337 subs[i] = index / this->m_outputStrides[i];
338 index -= subs[i] * this->m_outputStrides[i];
342 const Dimensions& left_dims = this->m_leftImpl.dimensions();
343 if (subs[this->m_axis] < left_dims[this->m_axis]) {
344 Index left_index = subs[0];
345 for (
int i = 1; i < Base::NumDims; ++i) {
346 left_index += (subs[i] % left_dims[i]) * this->m_leftStrides[i];
348 return this->m_leftImpl.coeffRef(left_index);
350 subs[this->m_axis] -= left_dims[this->m_axis];
351 const Dimensions& right_dims = this->m_rightImpl.dimensions();
352 Index right_index = subs[0];
353 for (
int i = 1; i < Base::NumDims; ++i) {
354 right_index += (subs[i] % right_dims[i]) * this->m_rightStrides[i];
356 return this->m_rightImpl.coeffRef(right_index);
360 template <
int StoreMode> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
361 void writePacket(Index index,
const PacketReturnType& x)
363 const int packetSize = PacketType<CoeffReturnType, Device>::size;
364 EIGEN_STATIC_ASSERT((packetSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE)
365 eigen_assert(index + packetSize - 1 < this->dimensions().TotalSize());
367 EIGEN_ALIGN_MAX CoeffReturnType values[packetSize];
368 internal::pstore<CoeffReturnType, PacketReturnType>(values, x);
369 for (
int i = 0; i < packetSize; ++i) {
370 coeffRef(index+i) = values[i];
The tensor base class.
Definition: TensorForwardDeclarations.h:56
Tensor concatenation class.
Definition: TensorConcatenation.h:61
Namespace containing all symbols from the Eigen library.
EIGEN_DEFAULT_DENSE_INDEX_TYPE Index
A cost model used to limit the number of threads used for evaluating tensor expression.
Definition: TensorEvaluator.h:29