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TensorArgMax.h
1// This file is part of Eigen, a lightweight C++ template library
2// for linear algebra.
3//
4// Copyright (C) 2015 Eugene Brevdo <ebrevdo@gmail.com>
5// Benoit Steiner <benoit.steiner.goog@gmail.com>
6//
7// This Source Code Form is subject to the terms of the Mozilla
8// Public License v. 2.0. If a copy of the MPL was not distributed
9// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
10
11#ifndef EIGEN_CXX11_TENSOR_TENSOR_ARG_MAX_H
12#define EIGEN_CXX11_TENSOR_TENSOR_ARG_MAX_H
13
14namespace Eigen {
15namespace internal {
16
24template<typename XprType>
25struct traits<TensorIndexTupleOp<XprType> > : public traits<XprType>
26{
27 typedef traits<XprType> XprTraits;
28 typedef typename XprTraits::StorageKind StorageKind;
29 typedef typename XprTraits::Index Index;
30 typedef Tuple<Index, typename XprTraits::Scalar> Scalar;
31 typedef typename XprType::Nested Nested;
32 typedef typename remove_reference<Nested>::type _Nested;
33 static const int NumDimensions = XprTraits::NumDimensions;
34 static const int Layout = XprTraits::Layout;
35};
36
37template<typename XprType>
38struct eval<TensorIndexTupleOp<XprType>, Eigen::Dense>
39{
40 typedef const TensorIndexTupleOp<XprType>& type;
41};
42
43template<typename XprType>
44struct nested<TensorIndexTupleOp<XprType>, 1,
45 typename eval<TensorIndexTupleOp<XprType> >::type>
46{
47 typedef TensorIndexTupleOp<XprType> type;
48};
49
50} // end namespace internal
51
52template<typename XprType>
53class TensorIndexTupleOp : public TensorBase<TensorIndexTupleOp<XprType>, ReadOnlyAccessors>
54{
55 public:
56 typedef typename Eigen::internal::traits<TensorIndexTupleOp>::Scalar Scalar;
57 typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
58 typedef typename Eigen::internal::nested<TensorIndexTupleOp>::type Nested;
59 typedef typename Eigen::internal::traits<TensorIndexTupleOp>::StorageKind StorageKind;
60 typedef typename Eigen::internal::traits<TensorIndexTupleOp>::Index Index;
61 typedef Tuple<Index, typename XprType::CoeffReturnType> CoeffReturnType;
62
63 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorIndexTupleOp(const XprType& expr)
64 : m_xpr(expr) {}
65
66 EIGEN_DEVICE_FUNC
67 const typename internal::remove_all<typename XprType::Nested>::type&
68 expression() const { return m_xpr; }
69
70 protected:
71 typename XprType::Nested m_xpr;
72};
73
74// Eval as rvalue
75template<typename ArgType, typename Device>
76struct TensorEvaluator<const TensorIndexTupleOp<ArgType>, Device>
77{
78 typedef TensorIndexTupleOp<ArgType> XprType;
79 typedef typename XprType::Index Index;
80 typedef typename XprType::Scalar Scalar;
81 typedef typename XprType::CoeffReturnType CoeffReturnType;
82
83 typedef typename TensorEvaluator<ArgType, Device>::Dimensions Dimensions;
84 static const int NumDims = internal::array_size<Dimensions>::value;
85
86 enum {
87 IsAligned = /*TensorEvaluator<ArgType, Device>::IsAligned*/ false,
88 PacketAccess = /*TensorEvaluator<ArgType, Device>::PacketAccess*/ false,
89 BlockAccess = false,
90 Layout = TensorEvaluator<ArgType, Device>::Layout,
91 CoordAccess = false, // to be implemented
92 RawAccess = false
93 };
94
95 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
96 : m_impl(op.expression(), device) { }
97
98 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const {
99 return m_impl.dimensions();
100 }
101
102 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(Scalar* /*data*/) {
103 m_impl.evalSubExprsIfNeeded(NULL);
104 return true;
105 }
106 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() {
107 m_impl.cleanup();
108 }
109
110 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
111 {
112 return CoeffReturnType(index, m_impl.coeff(index));
113 }
114
115 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost
116 costPerCoeff(bool vectorized) const {
117 return m_impl.costPerCoeff(vectorized) + TensorOpCost(0, 0, 1);
118 }
119
120 EIGEN_DEVICE_FUNC Scalar* data() const { return NULL; }
121
122 protected:
123 TensorEvaluator<ArgType, Device> m_impl;
124};
125
126namespace internal {
127
134template<typename ReduceOp, typename Dims, typename XprType>
135struct traits<TensorTupleReducerOp<ReduceOp, Dims, XprType> > : public traits<XprType>
136{
137 typedef traits<XprType> XprTraits;
138 typedef typename XprTraits::StorageKind StorageKind;
139 typedef typename XprTraits::Index Index;
140 typedef Index Scalar;
141 typedef typename XprType::Nested Nested;
142 typedef typename remove_reference<Nested>::type _Nested;
143 static const int NumDimensions = XprTraits::NumDimensions - array_size<Dims>::value;
144 static const int Layout = XprTraits::Layout;
145};
146
147template<typename ReduceOp, typename Dims, typename XprType>
148struct eval<TensorTupleReducerOp<ReduceOp, Dims, XprType>, Eigen::Dense>
149{
150 typedef const TensorTupleReducerOp<ReduceOp, Dims, XprType>& type;
151};
152
153template<typename ReduceOp, typename Dims, typename XprType>
154struct nested<TensorTupleReducerOp<ReduceOp, Dims, XprType>, 1,
155 typename eval<TensorTupleReducerOp<ReduceOp, Dims, XprType> >::type>
156{
157 typedef TensorTupleReducerOp<ReduceOp, Dims, XprType> type;
158};
159
160} // end namespace internal
161
162template<typename ReduceOp, typename Dims, typename XprType>
163class TensorTupleReducerOp : public TensorBase<TensorTupleReducerOp<ReduceOp, Dims, XprType>, ReadOnlyAccessors>
164{
165 public:
166 typedef typename Eigen::internal::traits<TensorTupleReducerOp>::Scalar Scalar;
167 typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
168 typedef typename Eigen::internal::nested<TensorTupleReducerOp>::type Nested;
169 typedef typename Eigen::internal::traits<TensorTupleReducerOp>::StorageKind StorageKind;
170 typedef typename Eigen::internal::traits<TensorTupleReducerOp>::Index Index;
171 typedef Index CoeffReturnType;
172
173 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorTupleReducerOp(const XprType& expr,
174 const ReduceOp& reduce_op,
175 const int return_dim,
176 const Dims& reduce_dims)
177 : m_xpr(expr), m_reduce_op(reduce_op), m_return_dim(return_dim), m_reduce_dims(reduce_dims) {}
178
179 EIGEN_DEVICE_FUNC
180 const typename internal::remove_all<typename XprType::Nested>::type&
181 expression() const { return m_xpr; }
182
183 EIGEN_DEVICE_FUNC
184 const ReduceOp& reduce_op() const { return m_reduce_op; }
185
186 EIGEN_DEVICE_FUNC
187 const Dims& reduce_dims() const { return m_reduce_dims; }
188
189 EIGEN_DEVICE_FUNC
190 int return_dim() const { return m_return_dim; }
191
192 protected:
193 typename XprType::Nested m_xpr;
194 const ReduceOp m_reduce_op;
195 const int m_return_dim;
196 const Dims m_reduce_dims;
197};
198
199// Eval as rvalue
200template<typename ReduceOp, typename Dims, typename ArgType, typename Device>
201struct TensorEvaluator<const TensorTupleReducerOp<ReduceOp, Dims, ArgType>, Device>
202{
203 typedef TensorTupleReducerOp<ReduceOp, Dims, ArgType> XprType;
204 typedef typename XprType::Index Index;
205 typedef typename XprType::Scalar Scalar;
206 typedef typename XprType::CoeffReturnType CoeffReturnType;
207 typedef typename TensorIndexTupleOp<ArgType>::CoeffReturnType TupleType;
208 typedef typename TensorEvaluator<const TensorReductionOp<ReduceOp, Dims, const TensorIndexTupleOp<ArgType> >, Device>::Dimensions Dimensions;
209 typedef typename TensorEvaluator<const TensorIndexTupleOp<ArgType> , Device>::Dimensions InputDimensions;
210 static const int NumDims = internal::array_size<InputDimensions>::value;
211 typedef array<Index, NumDims> StrideDims;
212
213 enum {
214 IsAligned = /*TensorEvaluator<ArgType, Device>::IsAligned*/ false,
215 PacketAccess = /*TensorEvaluator<ArgType, Device>::PacketAccess*/ false,
216 BlockAccess = false,
217 Layout = TensorEvaluator<const TensorReductionOp<ReduceOp, Dims, const TensorIndexTupleOp<ArgType> >, Device>::Layout,
218 CoordAccess = false, // to be implemented
219 RawAccess = false
220 };
221
222 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
223 : m_orig_impl(op.expression(), device),
224 m_impl(op.expression().index_tuples().reduce(op.reduce_dims(), op.reduce_op()), device),
225 m_return_dim(op.return_dim()) {
226
227 gen_strides(m_orig_impl.dimensions(), m_strides);
228 if (Layout == static_cast<int>(ColMajor)) {
229 const Index total_size = internal::array_prod(m_orig_impl.dimensions());
230 m_stride_mod = (m_return_dim < NumDims - 1) ? m_strides[m_return_dim + 1] : total_size;
231 } else {
232 const Index total_size = internal::array_prod(m_orig_impl.dimensions());
233 m_stride_mod = (m_return_dim > 0) ? m_strides[m_return_dim - 1] : total_size;
234 }
235 m_stride_div = m_strides[m_return_dim];
236 }
237
238 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const {
239 return m_impl.dimensions();
240 }
241
242 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(Scalar* /*data*/) {
243 m_impl.evalSubExprsIfNeeded(NULL);
244 return true;
245 }
246 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() {
247 m_impl.cleanup();
248 }
249
250 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const {
251 const TupleType v = m_impl.coeff(index);
252 return (m_return_dim < 0) ? v.first : (v.first % m_stride_mod) / m_stride_div;
253 }
254
255 EIGEN_DEVICE_FUNC Scalar* data() const { return NULL; }
256
257 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost
258 costPerCoeff(bool vectorized) const {
259 const double compute_cost = 1.0 +
260 (m_return_dim < 0 ? 0.0 : (TensorOpCost::ModCost<Index>() + TensorOpCost::DivCost<Index>()));
261 return m_orig_impl.costPerCoeff(vectorized) +
262 m_impl.costPerCoeff(vectorized) + TensorOpCost(0, 0, compute_cost);
263 }
264
265 private:
266 EIGEN_DEVICE_FUNC void gen_strides(const InputDimensions& dims, StrideDims& strides) {
267 if (m_return_dim < 0) {
268 return; // Won't be using the strides.
269 }
270 eigen_assert(m_return_dim < NumDims &&
271 "Asking to convert index to a dimension outside of the rank");
272
273 // Calculate m_stride_div and m_stride_mod, which are used to
274 // calculate the value of an index w.r.t. the m_return_dim.
275 if (Layout == static_cast<int>(ColMajor)) {
276 strides[0] = 1;
277 for (int i = 1; i < NumDims; ++i) {
278 strides[i] = strides[i-1] * dims[i-1];
279 }
280 } else {
281 strides[NumDims-1] = 1;
282 for (int i = NumDims - 2; i >= 0; --i) {
283 strides[i] = strides[i+1] * dims[i+1];
284 }
285 }
286 }
287
288 protected:
289 TensorEvaluator<const TensorIndexTupleOp<ArgType>, Device> m_orig_impl;
290 TensorEvaluator<const TensorReductionOp<ReduceOp, Dims, const TensorIndexTupleOp<ArgType> >, Device> m_impl;
291 const int m_return_dim;
292 StrideDims m_strides;
293 Index m_stride_mod;
294 Index m_stride_div;
295};
296
297} // end namespace Eigen
298
299#endif // EIGEN_CXX11_TENSOR_TENSOR_ARG_MAX_H
Namespace containing all symbols from the Eigen library.
Definition: AdolcForward:45
const Device & device() const
required by sycl in order to construct sycl buffer from raw pointer
Definition: TensorEvaluator.h:114