1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
|
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_CXX11_TENSOR_TENSOR_CHIPPING_H
#define EIGEN_CXX11_TENSOR_TENSOR_CHIPPING_H
namespace Eigen {
/** \class TensorKChippingReshaping
* \ingroup CXX11_Tensor_Module
*
* \brief A chip is a thin slice, corresponding to a column or a row in a 2-d tensor.
*
*
*/
namespace internal {
template<std::size_t DimId, typename XprType>
struct traits<TensorChippingOp<DimId, XprType> > : public traits<XprType>
{
typedef typename XprType::Scalar Scalar;
typedef typename internal::packet_traits<Scalar>::type Packet;
typedef typename traits<XprType>::StorageKind StorageKind;
typedef typename traits<XprType>::Index Index;
typedef typename XprType::Nested Nested;
typedef typename remove_reference<Nested>::type _Nested;
};
template<std::size_t DimId, typename XprType>
struct eval<TensorChippingOp<DimId, XprType>, Eigen::Dense>
{
typedef const TensorChippingOp<DimId, XprType>& type;
};
template<std::size_t DimId, typename XprType>
struct nested<TensorChippingOp<DimId, XprType>, 1, typename eval<TensorChippingOp<DimId, XprType> >::type>
{
typedef TensorChippingOp<DimId, XprType> type;
};
} // end namespace internal
template<std::size_t DimId, typename XprType>
class TensorChippingOp : public TensorBase<TensorChippingOp<DimId, XprType> >
{
public:
typedef typename Eigen::internal::traits<TensorChippingOp>::Scalar Scalar;
typedef typename Eigen::internal::traits<TensorChippingOp>::Packet Packet;
typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
typedef typename XprType::CoeffReturnType CoeffReturnType;
typedef typename XprType::PacketReturnType PacketReturnType;
typedef typename Eigen::internal::nested<TensorChippingOp>::type Nested;
typedef typename Eigen::internal::traits<TensorChippingOp>::StorageKind StorageKind;
typedef typename Eigen::internal::traits<TensorChippingOp>::Index Index;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorChippingOp(const XprType& expr, const Index offset)
: m_xpr(expr), m_offset(offset) {}
EIGEN_DEVICE_FUNC
const Index offset() const { return m_offset; }
EIGEN_DEVICE_FUNC
const typename internal::remove_all<typename XprType::Nested>::type&
expression() const { return m_xpr; }
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE TensorChippingOp& operator = (const OtherDerived& other)
{
typedef TensorAssignOp<TensorChippingOp, const OtherDerived> Assign;
Assign assign(*this, other);
internal::TensorExecutor<const Assign, DefaultDevice, false>::run(assign, DefaultDevice());
return *this;
}
protected:
typename XprType::Nested m_xpr;
const Index m_offset;
};
// Eval as rvalue
template<std::size_t DimId, typename ArgType, typename Device>
struct TensorEvaluator<const TensorChippingOp<DimId, ArgType>, Device>
{
typedef TensorChippingOp<DimId, ArgType> XprType;
static const int NumInputDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value;
static const int NumDims = NumInputDims-1;
typedef typename XprType::Index Index;
typedef DSizes<Index, NumDims> Dimensions;
enum {
// Alignment can't be guaranteed at compile time since it depends on the
// slice offsets.
IsAligned = false,
PacketAccess = false, // not yet implemented
};
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
: m_impl(op.expression(), device), m_device(device)
{
// We could also support the case where NumInputDims==1 if needed.
EIGEN_STATIC_ASSERT(NumInputDims >= 2, YOU_MADE_A_PROGRAMMING_MISTAKE);
EIGEN_STATIC_ASSERT(NumInputDims > DimId, YOU_MADE_A_PROGRAMMING_MISTAKE);
const typename TensorEvaluator<ArgType, Device>::Dimensions& input_dims = m_impl.dimensions();
int j = 0;
for (int i = 0; i < NumInputDims; ++i) {
if (i != DimId) {
m_dimensions[j] = input_dims[i];
++j;
}
}
m_stride = 1;
m_inputStride = 1;
for (int i = 0; i < DimId; ++i) {
m_stride *= input_dims[i];
m_inputStride *= input_dims[i];
}
m_inputStride *= input_dims[DimId];
m_inputOffset = m_stride * op.offset();
}
typedef typename XprType::Scalar Scalar;
typedef typename XprType::CoeffReturnType CoeffReturnType;
typedef typename XprType::PacketReturnType PacketReturnType;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(Scalar* /*data*/) {
m_impl.evalSubExprsIfNeeded(NULL);
return true;
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() {
m_impl.cleanup();
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
{
return m_impl.coeff(srcCoeff(index));
}
/* to be done
template<int LoadMode>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
{
}*/
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar* data() const {
Scalar* result = m_impl.data();
if (DimId == NumDims && result) {
return result + m_inputOffset;
} else {
return NULL;
}
}
protected:
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index srcCoeff(Index index) const
{
Index inputIndex;
if (DimId == 0) {
// m_stride is equal to 1, so let's avoid the integer division.
eigen_assert(m_stride == 1);
inputIndex = index * m_inputStride + m_inputOffset;
} else if (DimId == NumInputDims-1) {
// m_stride is aways greater than index, so let's avoid the integer division.
eigen_assert(m_stride > index);
inputIndex = index + m_inputOffset;
} else {
const Index idx = index / m_stride;
inputIndex = idx * m_inputStride + m_inputOffset;
index -= idx * m_stride;
inputIndex += index;
}
return inputIndex;
}
Dimensions m_dimensions;
Index m_stride;
Index m_inputOffset;
Index m_inputStride;
TensorEvaluator<ArgType, Device> m_impl;
const Device& m_device;
};
// Eval as lvalue
template<std::size_t DimId, typename ArgType, typename Device>
struct TensorEvaluator<TensorChippingOp<DimId, ArgType>, Device>
: public TensorEvaluator<const TensorChippingOp<DimId, ArgType>, Device>
{
typedef TensorEvaluator<const TensorChippingOp<DimId, ArgType>, Device> Base;
typedef TensorChippingOp<DimId, ArgType> XprType;
static const int NumInputDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value;
static const int NumDims = NumInputDims-1;
typedef typename XprType::Index Index;
typedef DSizes<Index, NumDims> Dimensions;
enum {
IsAligned = false,
PacketAccess = false,
};
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
: Base(op, device)
{ }
typedef typename XprType::Scalar Scalar;
typedef typename XprType::CoeffReturnType CoeffReturnType;
typedef typename XprType::PacketReturnType PacketReturnType;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType& coeffRef(Index index)
{
return this->m_impl.coeffRef(this->srcCoeff(index));
}
/* to be done
template <int StoreMode> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
void writePacket(Index index, const PacketReturnType& x)
{
} */
};
} // end namespace Eigen
#endif // EIGEN_CXX11_TENSOR_TENSOR_CHIPPING_H
|
