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TensorGenerator.h
1 // This file is part of Eigen, a lightweight C++ template library
2 // for linear algebra.
3 //
4 // Copyright (C) 2015 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_GENERATOR_H
11 #define EIGEN_CXX11_TENSOR_TENSOR_GENERATOR_H
12 
13 namespace Eigen {
14 
22 namespace internal {
23 template<typename Generator, typename XprType>
24 struct traits<TensorGeneratorOp<Generator, 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  typedef typename XprTraits::PointerType PointerType;
35 };
36 
37 template<typename Generator, typename XprType>
38 struct eval<TensorGeneratorOp<Generator, XprType>, Eigen::Dense>
39 {
40  typedef const TensorGeneratorOp<Generator, XprType>& type;
41 };
42 
43 template<typename Generator, typename XprType>
44 struct nested<TensorGeneratorOp<Generator, XprType>, 1, typename eval<TensorGeneratorOp<Generator, XprType> >::type>
45 {
46  typedef TensorGeneratorOp<Generator, XprType> type;
47 };
48 
49 } // end namespace internal
50 
51 
52 
53 template<typename Generator, typename XprType>
54 class TensorGeneratorOp : public TensorBase<TensorGeneratorOp<Generator, XprType>, ReadOnlyAccessors>
55 {
56  public:
57  typedef typename Eigen::internal::traits<TensorGeneratorOp>::Scalar Scalar;
58  typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
59  typedef typename XprType::CoeffReturnType CoeffReturnType;
60  typedef typename Eigen::internal::nested<TensorGeneratorOp>::type Nested;
61  typedef typename Eigen::internal::traits<TensorGeneratorOp>::StorageKind StorageKind;
62  typedef typename Eigen::internal::traits<TensorGeneratorOp>::Index Index;
63 
64  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorGeneratorOp(const XprType& expr, const Generator& generator)
65  : m_xpr(expr), m_generator(generator) {}
66 
67  EIGEN_DEVICE_FUNC
68  const Generator& generator() const { return m_generator; }
69 
70  EIGEN_DEVICE_FUNC
71  const typename internal::remove_all<typename XprType::Nested>::type&
72  expression() const { return m_xpr; }
73 
74  protected:
75  typename XprType::Nested m_xpr;
76  const Generator m_generator;
77 };
78 
79 
80 // Eval as rvalue
81 template<typename Generator, typename ArgType, typename Device>
82 struct TensorEvaluator<const TensorGeneratorOp<Generator, ArgType>, Device>
83 {
84  typedef TensorGeneratorOp<Generator, ArgType> XprType;
85  typedef typename XprType::Index Index;
86  typedef typename TensorEvaluator<ArgType, Device>::Dimensions Dimensions;
87  static const int NumDims = internal::array_size<Dimensions>::value;
88  typedef typename XprType::Scalar Scalar;
89  typedef typename XprType::CoeffReturnType CoeffReturnType;
90  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
91  typedef StorageMemory<CoeffReturnType, Device> Storage;
92  typedef typename Storage::Type EvaluatorPointerType;
93  enum {
94  IsAligned = false,
95  PacketAccess = (PacketType<CoeffReturnType, Device>::size > 1),
96  BlockAccess = true,
97  PreferBlockAccess = true,
98  Layout = TensorEvaluator<ArgType, Device>::Layout,
99  CoordAccess = false, // to be implemented
100  RawAccess = false
101  };
102 
103  typedef internal::TensorIntDivisor<Index> IndexDivisor;
104 
105  //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
106  typedef internal::TensorBlockDescriptor<NumDims, Index> TensorBlockDesc;
107  typedef internal::TensorBlockScratchAllocator<Device> TensorBlockScratch;
108 
109  typedef typename internal::TensorMaterializedBlock<CoeffReturnType, NumDims,
110  Layout, Index>
111  TensorBlock;
112  //===--------------------------------------------------------------------===//
113 
114  EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
115  : m_device(device), m_generator(op.generator())
116  {
117  TensorEvaluator<ArgType, Device> argImpl(op.expression(), device);
118  m_dimensions = argImpl.dimensions();
119 
120  if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
121  m_strides[0] = 1;
122  EIGEN_UNROLL_LOOP
123  for (int i = 1; i < NumDims; ++i) {
124  m_strides[i] = m_strides[i - 1] * m_dimensions[i - 1];
125  if (m_strides[i] != 0) m_fast_strides[i] = IndexDivisor(m_strides[i]);
126  }
127  } else {
128  m_strides[NumDims - 1] = 1;
129  EIGEN_UNROLL_LOOP
130  for (int i = NumDims - 2; i >= 0; --i) {
131  m_strides[i] = m_strides[i + 1] * m_dimensions[i + 1];
132  if (m_strides[i] != 0) m_fast_strides[i] = IndexDivisor(m_strides[i]);
133  }
134  }
135  }
136 
137  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
138 
139  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType /*data*/) {
140  return true;
141  }
142  EIGEN_STRONG_INLINE void cleanup() {
143  }
144 
145  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
146  {
147  array<Index, NumDims> coords;
148  extract_coordinates(index, coords);
149  return m_generator(coords);
150  }
151 
152  template<int LoadMode>
153  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
154  {
155  const int packetSize = PacketType<CoeffReturnType, Device>::size;
156  EIGEN_STATIC_ASSERT((packetSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE)
157  eigen_assert(index+packetSize-1 < dimensions().TotalSize());
158 
159  EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[packetSize];
160  for (int i = 0; i < packetSize; ++i) {
161  values[i] = coeff(index+i);
162  }
163  PacketReturnType rslt = internal::pload<PacketReturnType>(values);
164  return rslt;
165  }
166 
167  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
168  internal::TensorBlockResourceRequirements getResourceRequirements() const {
169  const size_t target_size = m_device.firstLevelCacheSize();
170  // TODO(ezhulenev): Generator should have a cost.
171  return internal::TensorBlockResourceRequirements::skewed<Scalar>(
172  target_size);
173  }
174 
175  struct BlockIteratorState {
176  Index stride;
177  Index span;
178  Index size;
179  Index count;
180  };
181 
182  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBlock
183  block(TensorBlockDesc& desc, TensorBlockScratch& scratch,
184  bool /*root_of_expr_ast*/ = false) const {
185  static const bool is_col_major =
186  static_cast<int>(Layout) == static_cast<int>(ColMajor);
187 
188  // Compute spatial coordinates for the first block element.
189  array<Index, NumDims> coords;
190  extract_coordinates(desc.offset(), coords);
191  array<Index, NumDims> initial_coords = coords;
192 
193  // Offset in the output block buffer.
194  Index offset = 0;
195 
196  // Initialize output block iterator state. Dimension in this array are
197  // always in inner_most -> outer_most order (col major layout).
198  array<BlockIteratorState, NumDims> it;
199  for (int i = 0; i < NumDims; ++i) {
200  const int dim = is_col_major ? i : NumDims - 1 - i;
201  it[i].size = desc.dimension(dim);
202  it[i].stride = i == 0 ? 1 : (it[i - 1].size * it[i - 1].stride);
203  it[i].span = it[i].stride * (it[i].size - 1);
204  it[i].count = 0;
205  }
206  eigen_assert(it[0].stride == 1);
207 
208  // Prepare storage for the materialized generator result.
209  const typename TensorBlock::Storage block_storage =
210  TensorBlock::prepareStorage(desc, scratch);
211 
212  CoeffReturnType* block_buffer = block_storage.data();
213 
214  static const int packet_size = PacketType<CoeffReturnType, Device>::size;
215 
216  static const int inner_dim = is_col_major ? 0 : NumDims - 1;
217  const Index inner_dim_size = it[0].size;
218  const Index inner_dim_vectorized = inner_dim_size - packet_size;
219 
220  while (it[NumDims - 1].count < it[NumDims - 1].size) {
221  Index i = 0;
222  // Generate data for the vectorized part of the inner-most dimension.
223  for (; i <= inner_dim_vectorized; i += packet_size) {
224  for (Index j = 0; j < packet_size; ++j) {
225  array<Index, NumDims> j_coords = coords; // Break loop dependence.
226  j_coords[inner_dim] += j;
227  *(block_buffer + offset + i + j) = m_generator(j_coords);
228  }
229  coords[inner_dim] += packet_size;
230  }
231  // Finalize non-vectorized part of the inner-most dimension.
232  for (; i < inner_dim_size; ++i) {
233  *(block_buffer + offset + i) = m_generator(coords);
234  coords[inner_dim]++;
235  }
236  coords[inner_dim] = initial_coords[inner_dim];
237 
238  // For the 1d tensor we need to generate only one inner-most dimension.
239  if (NumDims == 1) break;
240 
241  // Update offset.
242  for (i = 1; i < NumDims; ++i) {
243  if (++it[i].count < it[i].size) {
244  offset += it[i].stride;
245  coords[is_col_major ? i : NumDims - 1 - i]++;
246  break;
247  }
248  if (i != NumDims - 1) it[i].count = 0;
249  coords[is_col_major ? i : NumDims - 1 - i] =
250  initial_coords[is_col_major ? i : NumDims - 1 - i];
251  offset -= it[i].span;
252  }
253  }
254 
255  return block_storage.AsTensorMaterializedBlock();
256  }
257 
258  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost
259  costPerCoeff(bool) const {
260  // TODO(rmlarsen): This is just a placeholder. Define interface to make
261  // generators return their cost.
262  return TensorOpCost(0, 0, TensorOpCost::AddCost<Scalar>() +
263  TensorOpCost::MulCost<Scalar>());
264  }
265 
266  EIGEN_DEVICE_FUNC EvaluatorPointerType data() const { return NULL; }
267 
268 #ifdef EIGEN_USE_SYCL
269  // binding placeholder accessors to a command group handler for SYCL
270  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler&) const {}
271 #endif
272 
273  protected:
274  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
275  void extract_coordinates(Index index, array<Index, NumDims>& coords) const {
276  if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
277  for (int i = NumDims - 1; i > 0; --i) {
278  const Index idx = index / m_fast_strides[i];
279  index -= idx * m_strides[i];
280  coords[i] = idx;
281  }
282  coords[0] = index;
283  } else {
284  for (int i = 0; i < NumDims - 1; ++i) {
285  const Index idx = index / m_fast_strides[i];
286  index -= idx * m_strides[i];
287  coords[i] = idx;
288  }
289  coords[NumDims-1] = index;
290  }
291  }
292 
293  const Device EIGEN_DEVICE_REF m_device;
294  Dimensions m_dimensions;
295  array<Index, NumDims> m_strides;
296  array<IndexDivisor, NumDims> m_fast_strides;
297  Generator m_generator;
298 };
299 
300 } // end namespace Eigen
301 
302 #endif // EIGEN_CXX11_TENSOR_TENSOR_GENERATOR_H
Namespace containing all symbols from the Eigen library.
EIGEN_DEFAULT_DENSE_INDEX_TYPE Index