Please, help us to better know about our user community by answering the following short survey: https://forms.gle/wpyrxWi18ox9Z5ae9
 
Loading...
Searching...
No Matches
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
13namespace Eigen {
14
22namespace internal {
23template<typename Generator, typename XprType>
24struct 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
37template<typename Generator, typename XprType>
38struct eval<TensorGeneratorOp<Generator, XprType>, Eigen::Dense>
39{
40 typedef const TensorGeneratorOp<Generator, XprType>& type;
41};
42
43template<typename Generator, typename XprType>
44struct 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
53template<typename Generator, typename XprType>
54class 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
81template<typename Generator, typename ArgType, typename Device>
82struct TensorEvaluator<const TensorGeneratorOp<Generator, ArgType>, Device>
83{
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,
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
The tensor base class.
Definition: TensorForwardDeclarations.h:56
Tensor generator class.
Definition: TensorGenerator.h:55
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