TensorEvaluator.h
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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_EVALUATOR_H
11 #define EIGEN_CXX11_TENSOR_TENSOR_EVALUATOR_H
12 
13 namespace Eigen {
14 
26 // Generic evaluator
27 template<typename Derived, typename Device>
29 {
30  typedef typename Derived::Index Index;
31  typedef typename Derived::Scalar Scalar;
32  typedef typename Derived::Scalar CoeffReturnType;
34  typedef typename Derived::Dimensions Dimensions;
35 
36  // NumDimensions is -1 for variable dim tensors
39 
40  enum {
41  IsAligned = Derived::IsAligned,
43  Layout = Derived::Layout,
44  CoordAccess = NumCoords > 0,
45  RawAccess = true
46  };
47 
48  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const Derived& m, const Device& device)
49  : m_data(const_cast<typename internal::traits<Derived>::template MakePointer<Scalar>::Type>(m.data())), m_dims(m.dimensions()), m_device(device), m_impl(m)
50  { }
51 
52  // Used for accessor extraction in SYCL Managed TensorMap:
53  const Derived& derived() const { return m_impl; }
54  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dims; }
55 
56  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(CoeffReturnType* dest) {
57  if (dest) {
58  m_device.memcpy((void*)dest, m_data, sizeof(Scalar) * m_dims.TotalSize());
59  return false;
60  }
61  return true;
62  }
63 
64  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() { }
65 
66  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const {
68  return m_data[index];
69  }
70 
71  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& coeffRef(Index index) {
73  return m_data[index];
74  }
75 
76  template<int LoadMode> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
77  PacketReturnType packet(Index index) const
78  {
79  return internal::ploadt<PacketReturnType, LoadMode>(m_data + index);
80  }
81 
82  template <int StoreMode> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
83  void writePacket(Index index, const PacketReturnType& x)
84  {
85  return internal::pstoret<Scalar, PacketReturnType, StoreMode>(m_data + index, x);
86  }
87 
88  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(const array<DenseIndex, NumCoords>& coords) const {
90  if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
91  return m_data[m_dims.IndexOfColMajor(coords)];
92  } else {
93  return m_data[m_dims.IndexOfRowMajor(coords)];
94  }
95  }
96 
97  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& coeffRef(const array<DenseIndex, NumCoords>& coords) {
99  if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
100  return m_data[m_dims.IndexOfColMajor(coords)];
101  } else {
102  return m_data[m_dims.IndexOfRowMajor(coords)];
103  }
104  }
105 
106  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const {
107  return TensorOpCost(sizeof(CoeffReturnType), 0, 0, vectorized,
109  }
110 
111  EIGEN_DEVICE_FUNC typename internal::traits<Derived>::template MakePointer<Scalar>::Type data() const { return m_data; }
112 
114  const Device& device() const{return m_device;}
115 
116  protected:
118  Dimensions m_dims;
119  const Device& m_device;
120  const Derived& m_impl;
121 };
122 
123 namespace {
124 template <typename T> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
125 T loadConstant(const T* address) {
126  return *address;
127 }
128 // Use the texture cache on CUDA devices whenever possible
129 #if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 350
130 template <> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
131 float loadConstant(const float* address) {
132  return __ldg(address);
133 }
134 template <> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
135 double loadConstant(const double* address) {
136  return __ldg(address);
137 }
138 template <> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
139 Eigen::half loadConstant(const Eigen::half* address) {
140  return Eigen::half(half_impl::raw_uint16_to_half(__ldg(&address->x)));
141 }
142 #endif
143 }
144 
145 
146 // Default evaluator for rvalues
147 template<typename Derived, typename Device>
148 struct TensorEvaluator<const Derived, Device>
149 {
150  typedef typename Derived::Index Index;
151  typedef typename Derived::Scalar Scalar;
154  typedef typename Derived::Dimensions Dimensions;
155 
156  // NumDimensions is -1 for variable dim tensors
159 
160  enum {
161  IsAligned = Derived::IsAligned,
163  Layout = Derived::Layout,
164  CoordAccess = NumCoords > 0,
165  RawAccess = true
166  };
167 
168  // Used for accessor extraction in SYCL Managed TensorMap:
169  const Derived& derived() const { return m_impl; }
170 
171  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const Derived& m, const Device& device)
172  : m_data(m.data()), m_dims(m.dimensions()), m_device(device), m_impl(m)
173  { }
174 
175  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dims; }
176 
177  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(CoeffReturnType* data) {
178  if (!NumTraits<typename internal::remove_const<Scalar>::type>::RequireInitialization && data) {
179  m_device.memcpy((void*)data, m_data, m_dims.TotalSize() * sizeof(Scalar));
180  return false;
181  }
182  return true;
183  }
184 
185  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() { }
186 
187  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const {
189  return loadConstant(m_data+index);
190  }
191 
192  template<int LoadMode> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
193  PacketReturnType packet(Index index) const
194  {
195  return internal::ploadt_ro<PacketReturnType, LoadMode>(m_data + index);
196  }
197 
198  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(const array<DenseIndex, NumCoords>& coords) const {
200  const Index index = (static_cast<int>(Layout) == static_cast<int>(ColMajor)) ? m_dims.IndexOfColMajor(coords)
201  : m_dims.IndexOfRowMajor(coords);
202  return loadConstant(m_data+index);
203  }
204 
205  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const {
206  return TensorOpCost(sizeof(CoeffReturnType), 0, 0, vectorized,
208  }
209 
211 
213  const Device& device() const{return m_device;}
214 
215  protected:
217  Dimensions m_dims;
218  const Device& m_device;
219  const Derived& m_impl;
220 };
221 
222 
223 
224 
225 // -------------------- CwiseNullaryOp --------------------
226 
227 template<typename NullaryOp, typename ArgType, typename Device>
228 struct TensorEvaluator<const TensorCwiseNullaryOp<NullaryOp, ArgType>, Device>
229 {
231 
232  enum {
233  IsAligned = true,
236  CoordAccess = false, // to be implemented
237  RawAccess = false
238  };
239 
240  EIGEN_DEVICE_FUNC
241  TensorEvaluator(const XprType& op, const Device& device)
242  : m_functor(op.functor()), m_argImpl(op.nestedExpression(), device), m_wrapper()
243  { }
244 
245  typedef typename XprType::Index Index;
246  typedef typename XprType::Scalar Scalar;
251 
252  EIGEN_DEVICE_FUNC const Dimensions& dimensions() const { return m_argImpl.dimensions(); }
253 
254  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(CoeffReturnType*) { return true; }
255  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() { }
256 
257  EIGEN_DEVICE_FUNC CoeffReturnType coeff(Index index) const
258  {
259  return m_wrapper(m_functor, index);
260  }
261 
262  template<int LoadMode>
263  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
264  {
265  return m_wrapper.template packetOp<PacketReturnType, Index>(m_functor, index);
266  }
267 
268  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost
269  costPerCoeff(bool vectorized) const {
270  return TensorOpCost(sizeof(CoeffReturnType), 0, 0, vectorized,
272  }
273 
274  EIGEN_DEVICE_FUNC CoeffReturnType* data() const { return NULL; }
275 
277  const TensorEvaluator<ArgType, Device>& impl() const { return m_argImpl; }
279  NullaryOp functor() const { return m_functor; }
280 
281 
282  private:
283  const NullaryOp m_functor;
286 };
287 
288 
289 
290 // -------------------- CwiseUnaryOp --------------------
291 
292 template<typename UnaryOp, typename ArgType, typename Device>
293 struct TensorEvaluator<const TensorCwiseUnaryOp<UnaryOp, ArgType>, Device>
294 {
296 
297  enum {
301  CoordAccess = false, // to be implemented
302  RawAccess = false
303  };
304 
305  EIGEN_DEVICE_FUNC TensorEvaluator(const XprType& op, const Device& device)
306  : m_functor(op.functor()),
307  m_argImpl(op.nestedExpression(), device)
308  { }
309 
310  typedef typename XprType::Index Index;
311  typedef typename XprType::Scalar Scalar;
316 
317  EIGEN_DEVICE_FUNC const Dimensions& dimensions() const { return m_argImpl.dimensions(); }
318 
319  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(Scalar*) {
320  m_argImpl.evalSubExprsIfNeeded(NULL);
321  return true;
322  }
323  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() {
324  m_argImpl.cleanup();
325  }
326 
327  EIGEN_DEVICE_FUNC CoeffReturnType coeff(Index index) const
328  {
329  return m_functor(m_argImpl.coeff(index));
330  }
331 
332  template<int LoadMode>
333  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
334  {
335  return m_functor.packetOp(m_argImpl.template packet<LoadMode>(index));
336  }
337 
338  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const {
339  const double functor_cost = internal::functor_traits<UnaryOp>::Cost;
340  return m_argImpl.costPerCoeff(vectorized) +
341  TensorOpCost(0, 0, functor_cost, vectorized, PacketSize);
342  }
343 
344  EIGEN_DEVICE_FUNC CoeffReturnType* data() const { return NULL; }
345 
347  const TensorEvaluator<ArgType, Device> & impl() const { return m_argImpl; }
349  UnaryOp functor() const { return m_functor; }
350 
351 
352  private:
353  const UnaryOp m_functor;
355 };
356 
357 
358 // -------------------- CwiseBinaryOp --------------------
359 
360 template<typename BinaryOp, typename LeftArgType, typename RightArgType, typename Device>
361 struct TensorEvaluator<const TensorCwiseBinaryOp<BinaryOp, LeftArgType, RightArgType>, Device>
362 {
364 
365  enum {
370  CoordAccess = false, // to be implemented
371  RawAccess = false
372  };
373 
374  EIGEN_DEVICE_FUNC TensorEvaluator(const XprType& op, const Device& device)
375  : m_functor(op.functor()),
376  m_leftImpl(op.lhsExpression(), device),
377  m_rightImpl(op.rhsExpression(), device)
378  {
380  eigen_assert(dimensions_match(m_leftImpl.dimensions(), m_rightImpl.dimensions()));
381  }
382 
383  typedef typename XprType::Index Index;
384  typedef typename XprType::Scalar Scalar;
389 
390  EIGEN_DEVICE_FUNC const Dimensions& dimensions() const
391  {
392  // TODO: use right impl instead if right impl dimensions are known at compile time.
393  return m_leftImpl.dimensions();
394  }
395 
396  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(CoeffReturnType*) {
397  m_leftImpl.evalSubExprsIfNeeded(NULL);
398  m_rightImpl.evalSubExprsIfNeeded(NULL);
399  return true;
400  }
401  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() {
402  m_leftImpl.cleanup();
403  m_rightImpl.cleanup();
404  }
405 
406  EIGEN_DEVICE_FUNC CoeffReturnType coeff(Index index) const
407  {
408  return m_functor(m_leftImpl.coeff(index), m_rightImpl.coeff(index));
409  }
410  template<int LoadMode>
411  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
412  {
413  return m_functor.packetOp(m_leftImpl.template packet<LoadMode>(index), m_rightImpl.template packet<LoadMode>(index));
414  }
415 
416  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost
417  costPerCoeff(bool vectorized) const {
418  const double functor_cost = internal::functor_traits<BinaryOp>::Cost;
419  return m_leftImpl.costPerCoeff(vectorized) +
420  m_rightImpl.costPerCoeff(vectorized) +
421  TensorOpCost(0, 0, functor_cost, vectorized, PacketSize);
422  }
423 
424  EIGEN_DEVICE_FUNC CoeffReturnType* data() const { return NULL; }
426  const TensorEvaluator<LeftArgType, Device>& left_impl() const { return m_leftImpl; }
428  const TensorEvaluator<RightArgType, Device>& right_impl() const { return m_rightImpl; }
430  BinaryOp functor() const { return m_functor; }
431 
432  private:
433  const BinaryOp m_functor;
436 };
437 
438 // -------------------- CwiseTernaryOp --------------------
439 
440 template<typename TernaryOp, typename Arg1Type, typename Arg2Type, typename Arg3Type, typename Device>
441 struct TensorEvaluator<const TensorCwiseTernaryOp<TernaryOp, Arg1Type, Arg2Type, Arg3Type>, Device>
442 {
444 
445  enum {
450  CoordAccess = false, // to be implemented
451  RawAccess = false
452  };
453 
454  EIGEN_DEVICE_FUNC TensorEvaluator(const XprType& op, const Device& device)
455  : m_functor(op.functor()),
456  m_arg1Impl(op.arg1Expression(), device),
457  m_arg2Impl(op.arg2Expression(), device),
458  m_arg3Impl(op.arg3Expression(), device)
459  {
461 
464  STORAGE_KIND_MUST_MATCH)
467  STORAGE_KIND_MUST_MATCH)
470  STORAGE_INDEX_MUST_MATCH)
473  STORAGE_INDEX_MUST_MATCH)
474 
475  eigen_assert(dimensions_match(m_arg1Impl.dimensions(), m_arg2Impl.dimensions()) && dimensions_match(m_arg1Impl.dimensions(), m_arg3Impl.dimensions()));
476  }
477 
478  typedef typename XprType::Index Index;
479  typedef typename XprType::Scalar Scalar;
484 
485  EIGEN_DEVICE_FUNC const Dimensions& dimensions() const
486  {
487  // TODO: use arg2 or arg3 dimensions if they are known at compile time.
488  return m_arg1Impl.dimensions();
489  }
490 
491  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(CoeffReturnType*) {
492  m_arg1Impl.evalSubExprsIfNeeded(NULL);
493  m_arg2Impl.evalSubExprsIfNeeded(NULL);
494  m_arg3Impl.evalSubExprsIfNeeded(NULL);
495  return true;
496  }
497  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() {
498  m_arg1Impl.cleanup();
499  m_arg2Impl.cleanup();
500  m_arg3Impl.cleanup();
501  }
502 
503  EIGEN_DEVICE_FUNC CoeffReturnType coeff(Index index) const
504  {
505  return m_functor(m_arg1Impl.coeff(index), m_arg2Impl.coeff(index), m_arg3Impl.coeff(index));
506  }
507  template<int LoadMode>
508  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
509  {
510  return m_functor.packetOp(m_arg1Impl.template packet<LoadMode>(index),
511  m_arg2Impl.template packet<LoadMode>(index),
512  m_arg3Impl.template packet<LoadMode>(index));
513  }
514 
515  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost
516  costPerCoeff(bool vectorized) const {
517  const double functor_cost = internal::functor_traits<TernaryOp>::Cost;
518  return m_arg1Impl.costPerCoeff(vectorized) +
519  m_arg2Impl.costPerCoeff(vectorized) +
520  m_arg3Impl.costPerCoeff(vectorized) +
521  TensorOpCost(0, 0, functor_cost, vectorized, PacketSize);
522  }
523 
524  EIGEN_DEVICE_FUNC CoeffReturnType* data() const { return NULL; }
525 
527  const TensorEvaluator<Arg1Type, Device> & arg1Impl() const { return m_arg1Impl; }
529  const TensorEvaluator<Arg2Type, Device>& arg2Impl() const { return m_arg2Impl; }
531  const TensorEvaluator<Arg3Type, Device>& arg3Impl() const { return m_arg3Impl; }
532 
533  private:
534  const TernaryOp m_functor;
538 };
539 
540 
541 // -------------------- SelectOp --------------------
542 
543 template<typename IfArgType, typename ThenArgType, typename ElseArgType, typename Device>
544 struct TensorEvaluator<const TensorSelectOp<IfArgType, ThenArgType, ElseArgType>, Device>
545 {
547  typedef typename XprType::Scalar Scalar;
548 
549  enum {
554  CoordAccess = false, // to be implemented
555  RawAccess = false
556  };
557 
558  EIGEN_DEVICE_FUNC TensorEvaluator(const XprType& op, const Device& device)
559  : m_condImpl(op.ifExpression(), device),
560  m_thenImpl(op.thenExpression(), device),
561  m_elseImpl(op.elseExpression(), device)
562  {
563  EIGEN_STATIC_ASSERT((static_cast<int>(TensorEvaluator<IfArgType, Device>::Layout) == static_cast<int>(TensorEvaluator<ThenArgType, Device>::Layout)), YOU_MADE_A_PROGRAMMING_MISTAKE);
564  EIGEN_STATIC_ASSERT((static_cast<int>(TensorEvaluator<IfArgType, Device>::Layout) == static_cast<int>(TensorEvaluator<ElseArgType, Device>::Layout)), YOU_MADE_A_PROGRAMMING_MISTAKE);
565  eigen_assert(dimensions_match(m_condImpl.dimensions(), m_thenImpl.dimensions()));
566  eigen_assert(dimensions_match(m_thenImpl.dimensions(), m_elseImpl.dimensions()));
567  }
568 
569  typedef typename XprType::Index Index;
574 
575  EIGEN_DEVICE_FUNC const Dimensions& dimensions() const
576  {
577  // TODO: use then or else impl instead if they happen to be known at compile time.
578  return m_condImpl.dimensions();
579  }
580 
581  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(CoeffReturnType*) {
582  m_condImpl.evalSubExprsIfNeeded(NULL);
583  m_thenImpl.evalSubExprsIfNeeded(NULL);
584  m_elseImpl.evalSubExprsIfNeeded(NULL);
585  return true;
586  }
587  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() {
588  m_condImpl.cleanup();
589  m_thenImpl.cleanup();
590  m_elseImpl.cleanup();
591  }
592 
593  EIGEN_DEVICE_FUNC CoeffReturnType coeff(Index index) const
594  {
595  return m_condImpl.coeff(index) ? m_thenImpl.coeff(index) : m_elseImpl.coeff(index);
596  }
597  template<int LoadMode>
598  EIGEN_DEVICE_FUNC PacketReturnType packet(Index index) const
599  {
601  for (Index i = 0; i < PacketSize; ++i) {
602  select.select[i] = m_condImpl.coeff(index+i);
603  }
604  return internal::pblend(select,
605  m_thenImpl.template packet<LoadMode>(index),
606  m_elseImpl.template packet<LoadMode>(index));
607  }
608 
609  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost
610  costPerCoeff(bool vectorized) const {
611  return m_condImpl.costPerCoeff(vectorized) +
612  m_thenImpl.costPerCoeff(vectorized)
613  .cwiseMax(m_elseImpl.costPerCoeff(vectorized));
614  }
615 
616  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType* data() const { return NULL; }
618  const TensorEvaluator<IfArgType, Device> & cond_impl() const { return m_condImpl; }
620  const TensorEvaluator<ThenArgType, Device>& then_impl() const { return m_thenImpl; }
622  const TensorEvaluator<ElseArgType, Device>& else_impl() const { return m_elseImpl; }
623 
624  private:
628 };
629 
630 
631 } // end namespace Eigen
632 
633 #endif // EIGEN_CXX11_TENSOR_TENSOR_EVALUATOR_H
Matrix3f m
#define EIGEN_ALWAYS_INLINE
Definition: Macros.h:509
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(CoeffReturnType *data)
SCALAR Scalar
Definition: bench_gemm.cpp:33
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup()
#define EIGEN_STRONG_INLINE
Definition: Macros.h:494
EIGEN_DEVICE_FUNC internal::traits< Derived >::template MakePointer< Scalar >::Type data() const
const TensorEvaluator< ElseArgType, Device > & else_impl() const
required by sycl in order to extract the accessor
Eigen::internal::traits< TensorCwiseNullaryOp >::Index Index
Definition: TensorExpr.h:60
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions & dimensions() const
Derived::Scalar CoeffReturnType
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(const array< DenseIndex, NumCoords > &coords) const
const Device & device() const
added for sycl in order to construct the buffer from the sycl device
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const Derived &m, const Device &device)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const
internal::traits< Derived >::template MakePointer< Scalar >::Type m_data
const TensorEvaluator< ArgType, Device > & impl() const
required by sycl in order to extract the accessor
Eigen::internal::traits< TensorCwiseTernaryOp >::Index Index
Definition: TensorExpr.h:272
EIGEN_DEVICE_FUNC CoeffReturnType coeff(Index index) const
const TensorEvaluator< Arg1Type, Device > & arg1Impl() const
required by sycl in order to extract the accessor
Namespace containing all symbols from the Eigen library.
Definition: jet.h:637
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void writePacket(Index index, const PacketReturnType &x)
NullaryOp functor() const
required by sycl in order to extract the accessor
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(CoeffReturnType *)
A cost model used to limit the number of threads used for evaluating tensor expression.
const TensorEvaluator< RightArgType, Device > & right_impl() const
required by sycl in order to extract the accessor
Holds information about the various numeric (i.e. scalar) types allowed by Eigen. ...
Definition: NumTraits.h:150
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const
#define EIGEN_STATIC_ASSERT(CONDITION, MSG)
Definition: StaticAssert.h:124
Eigen::internal::traits< TensorCwiseBinaryOp >::Index Index
Definition: TensorExpr.h:197
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
const TensorEvaluator< Arg3Type, Device > & arg3Impl() const
required by sycl in order to extract the accessor
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
Eigen::internal::traits< TensorCwiseUnaryOp >::Scalar Scalar
Definition: TensorExpr.h:116
const TensorEvaluator< ArgType, Device > & impl() const
required by sycl in order to extract the accessor
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(CoeffReturnType *)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup()
BinaryOp functor() const
required by sycl in order to extract the accessor
EIGEN_DEVICE_FUNC TensorEvaluator(const XprType &op, const Device &device)
EIGEN_DEVICE_FUNC TensorEvaluator(const XprType &op, const Device &device)
PacketType< CoeffReturnType, Device >::type PacketReturnType
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const
Eigen::internal::traits< TensorCwiseNullaryOp >::Scalar Scalar
Definition: TensorExpr.h:55
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar & coeffRef(const array< DenseIndex, NumCoords > &coords)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions & dimensions() const
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar & coeffRef(Index index)
EIGEN_DEFAULT_DENSE_INDEX_TYPE Index
The Index type as used for the API.
Definition: Meta.h:33
Eigen::internal::traits< TensorCwiseTernaryOp >::Scalar Scalar
Definition: TensorExpr.h:267
#define eigen_assert(x)
Definition: Macros.h:579
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(const array< DenseIndex, NumCoords > &coords) const
Eigen::internal::traits< TensorSelectOp >::Scalar Scalar
Definition: TensorExpr.h:338
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType * data() const
EIGEN_DEVICE_FUNC internal::traits< Derived >::template MakePointer< const Scalar >::Type data() const
static const int NumCoords
#define NULL
Definition: ccolamd.c:609
PacketType< CoeffReturnType, Device >::type PacketReturnType
EIGEN_DEVICE_FUNC bool dimensions_match(Dims1 &dims1, Dims2 &dims2)
Derived::Dimensions Dimensions
Eigen::internal::traits< TensorSelectOp >::Index Index
Definition: TensorExpr.h:344
const TensorEvaluator< LeftArgType, Device > & left_impl() const
required by sycl in order to extract the accessor
const TensorEvaluator< Arg2Type, Device > & arg2Impl() const
required by sycl in order to extract the accessor
Eigen::internal::traits< TensorCwiseBinaryOp >::Scalar Scalar
Definition: TensorExpr.h:192
Eigen::internal::traits< TensorCwiseUnaryOp >::Index Index
Definition: TensorExpr.h:121
const Device & device() const
required by sycl in order to construct sycl buffer from raw pointer
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(CoeffReturnType *dest)
const Derived & m_impl
UnaryOp functor() const
added for sycl in order to construct the buffer from sycl device
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(Scalar *)
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost cwiseMax(const TensorOpCost &rhs) const
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
Derived::Scalar Scalar
internal::traits< Derived >::template MakePointer< const Scalar >::Type m_data
const internal::nullary_wrapper< CoeffReturnType, NullaryOp > m_wrapper
const Derived & derived() const
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const
EIGEN_STRONG_INLINE Packet4i pblend(const Selector< 4 > &ifPacket, const Packet4i &thenPacket, const Packet4i &elsePacket)
const TensorEvaluator< ThenArgType, Device > & then_impl() const
required by sycl in order to extract the accessor
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const Derived &m, const Device &device)
unsigned short x
Definition: Half.h:57
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(CoeffReturnType *)
EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC __half_raw raw_uint16_to_half(unsigned short x)
Definition: Half.h:333
const TensorEvaluator< IfArgType, Device > & cond_impl() const
required by sycl in order to extract the accessor


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autogenerated on Sat May 8 2021 02:45:23