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
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
|
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2015 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_CONVERSION_H
#define EIGEN_CXX11_TENSOR_TENSOR_CONVERSION_H
namespace Eigen {
/** \class TensorConversionOp
* \ingroup CXX11_Tensor_Module
*
* \brief Tensor conversion class. This class makes it possible to vectorize
* type casting operations when the number of scalars per packet in the source
* and the destination type differ
*/
namespace internal {
template<typename TargetType, typename XprType>
struct traits<TensorConversionOp<TargetType, XprType> >
{
// Type promotion to handle the case where the types of the lhs and the rhs are different.
typedef TargetType Scalar;
typedef typename traits<XprType>::StorageKind StorageKind;
typedef typename traits<XprType>::Index Index;
typedef typename XprType::Nested Nested;
typedef typename remove_reference<Nested>::type _Nested;
static const int NumDimensions = traits<XprType>::NumDimensions;
static const int Layout = traits<XprType>::Layout;
enum { Flags = 0 };
typedef typename ::Eigen::internal::TypeConversion<Scalar, typename traits<XprType>::PointerType>::type PointerType;
};
template<typename TargetType, typename XprType>
struct eval<TensorConversionOp<TargetType, XprType>, Eigen::Dense>
{
typedef const TensorConversionOp<TargetType, XprType>& type;
};
template<typename TargetType, typename XprType>
struct nested<TensorConversionOp<TargetType, XprType>, 1, typename eval<TensorConversionOp<TargetType, XprType> >::type>
{
typedef TensorConversionOp<TargetType, XprType> type;
};
} // end namespace internal
template <typename TensorEvaluator, typename SrcPacket, typename TgtPacket, int SrcCoeffRatio, int TgtCoeffRatio>
struct PacketConverter {
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
PacketConverter(const TensorEvaluator& impl)
: m_impl(impl) {}
template<int LoadMode, typename Index>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TgtPacket packet(Index index) const {
return internal::pcast<SrcPacket, TgtPacket>(m_impl.template packet<LoadMode>(index));
}
private:
const TensorEvaluator& m_impl;
};
template <typename TensorEvaluator, typename SrcPacket, typename TgtPacket>
struct PacketConverter<TensorEvaluator, SrcPacket, TgtPacket, 2, 1> {
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
PacketConverter(const TensorEvaluator& impl)
: m_impl(impl) {}
template<int LoadMode, typename Index>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TgtPacket packet(Index index) const {
const int SrcPacketSize = internal::unpacket_traits<SrcPacket>::size;
SrcPacket src1 = m_impl.template packet<LoadMode>(index);
SrcPacket src2 = m_impl.template packet<LoadMode>(index + SrcPacketSize);
TgtPacket result = internal::pcast<SrcPacket, TgtPacket>(src1, src2);
return result;
}
private:
const TensorEvaluator& m_impl;
};
template <typename TensorEvaluator, typename SrcPacket, typename TgtPacket>
struct PacketConverter<TensorEvaluator, SrcPacket, TgtPacket, 4, 1> {
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
PacketConverter(const TensorEvaluator& impl)
: m_impl(impl) {}
template<int LoadMode, typename Index>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TgtPacket packet(Index index) const {
const int SrcPacketSize = internal::unpacket_traits<SrcPacket>::size;
SrcPacket src1 = m_impl.template packet<LoadMode>(index);
SrcPacket src2 = m_impl.template packet<LoadMode>(index + SrcPacketSize);
SrcPacket src3 = m_impl.template packet<LoadMode>(index + 2 * SrcPacketSize);
SrcPacket src4 = m_impl.template packet<LoadMode>(index + 3 * SrcPacketSize);
TgtPacket result = internal::pcast<SrcPacket, TgtPacket>(src1, src2, src3, src4);
return result;
}
private:
const TensorEvaluator& m_impl;
};
template <typename TensorEvaluator, typename SrcPacket, typename TgtPacket>
struct PacketConverter<TensorEvaluator, SrcPacket, TgtPacket, 1, 2> {
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
PacketConverter(const TensorEvaluator& impl)
: m_impl(impl), m_maxIndex(impl.dimensions().TotalSize()) {}
template<int LoadMode, typename Index>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TgtPacket packet(Index index) const {
const int SrcPacketSize = internal::unpacket_traits<SrcPacket>::size;
// Only call m_impl.packet() when we have direct access to the underlying data. This
// ensures that we don't compute the subexpression twice. We may however load some
// coefficients twice, but in practice this doesn't negatively impact performance.
if (m_impl.data() && (index + SrcPacketSize < m_maxIndex)) {
// Force unaligned memory loads since we can't ensure alignment anymore
return internal::pcast<SrcPacket, TgtPacket>(m_impl.template packet<Unaligned>(index));
} else {
const int TgtPacketSize = internal::unpacket_traits<TgtPacket>::size;
typedef typename internal::unpacket_traits<SrcPacket>::type SrcType;
typedef typename internal::unpacket_traits<TgtPacket>::type TgtType;
internal::scalar_cast_op<SrcType, TgtType> converter;
EIGEN_ALIGN_MAX typename internal::unpacket_traits<TgtPacket>::type values[TgtPacketSize];
for (int i = 0; i < TgtPacketSize; ++i) {
values[i] = converter(m_impl.coeff(index+i));
}
TgtPacket rslt = internal::pload<TgtPacket>(values);
return rslt;
}
}
private:
const TensorEvaluator& m_impl;
const typename TensorEvaluator::Index m_maxIndex;
};
template<typename TargetType, typename XprType>
class TensorConversionOp : public TensorBase<TensorConversionOp<TargetType, XprType>, ReadOnlyAccessors>
{
public:
typedef typename internal::traits<TensorConversionOp>::Scalar Scalar;
typedef typename internal::traits<TensorConversionOp>::StorageKind StorageKind;
typedef typename internal::traits<TensorConversionOp>::Index Index;
typedef typename internal::nested<TensorConversionOp>::type Nested;
typedef Scalar CoeffReturnType;
typedef typename NumTraits<Scalar>::Real RealScalar;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorConversionOp(const XprType& xpr)
: m_xpr(xpr) {}
EIGEN_DEVICE_FUNC
const typename internal::remove_all<typename XprType::Nested>::type&
expression() const { return m_xpr; }
protected:
typename XprType::Nested m_xpr;
};
template <bool SameType, typename Eval, typename Scalar> struct ConversionSubExprEval {
static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool run(Eval& impl, Scalar*) {
impl.evalSubExprsIfNeeded(NULL);
return true;
}
};
template <typename Eval, typename Scalar> struct ConversionSubExprEval<true, Eval, Scalar> {
static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool run(Eval& impl, Scalar* data) {
return impl.evalSubExprsIfNeeded(data);
}
};
// Eval as rvalue
template<typename TargetType, typename ArgType, typename Device>
struct TensorEvaluator<const TensorConversionOp<TargetType, ArgType>, Device>
{
typedef TensorConversionOp<TargetType, ArgType> XprType;
typedef typename XprType::Index Index;
typedef typename TensorEvaluator<ArgType, Device>::Dimensions Dimensions;
typedef TargetType Scalar;
typedef TargetType CoeffReturnType;
typedef typename internal::remove_all<typename internal::traits<ArgType>::Scalar>::type SrcType;
typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
typedef typename PacketType<SrcType, Device>::type PacketSourceType;
static const int PacketSize = internal::unpacket_traits<PacketReturnType>::size;
enum {
IsAligned = false,
PacketAccess = true,
Layout = TensorEvaluator<ArgType, Device>::Layout,
RawAccess = false
};
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
: m_impl(op.expression(), device)
{
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_impl.dimensions(); }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(Scalar* data)
{
return ConversionSubExprEval<internal::is_same<TargetType, SrcType>::value, TensorEvaluator<ArgType, Device>, Scalar>::run(m_impl, data);
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup()
{
m_impl.cleanup();
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
{
internal::scalar_cast_op<SrcType, TargetType> converter;
return converter(m_impl.coeff(index));
}
template<int LoadMode>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
{
const bool Vectorizable = TensorEvaluator<ArgType, Device>::PacketAccess &
internal::type_casting_traits<SrcType, TargetType>::VectorizedCast;
return PacketConv<LoadMode, Vectorizable>::run(m_impl, index);
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost
costPerCoeff(bool vectorized) const {
const double cast_cost = TensorOpCost::CastCost<SrcType, TargetType>();
if (vectorized) {
const double SrcCoeffRatio =
internal::type_casting_traits<SrcType, TargetType>::SrcCoeffRatio;
const double TgtCoeffRatio =
internal::type_casting_traits<SrcType, TargetType>::TgtCoeffRatio;
return m_impl.costPerCoeff(vectorized) * (SrcCoeffRatio / PacketSize) +
TensorOpCost(0, 0, TgtCoeffRatio * (cast_cost / PacketSize));
} else {
return m_impl.costPerCoeff(vectorized) + TensorOpCost(0, 0, cast_cost);
}
}
EIGEN_DEVICE_FUNC typename Eigen::internal::traits<XprType>::PointerType data() const { return NULL; }
/// required by sycl in order to extract the sycl accessor
const TensorEvaluator<ArgType, Device>& impl() const { return m_impl; }
protected:
template <int LoadMode, bool ActuallyVectorize>
struct PacketConv {
static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType run(const TensorEvaluator<ArgType, Device>& impl, Index index) {
internal::scalar_cast_op<SrcType, TargetType> converter;
EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[PacketSize];
for (int i = 0; i < PacketSize; ++i) {
values[i] = converter(impl.coeff(index+i));
}
PacketReturnType rslt = internal::pload<PacketReturnType>(values);
return rslt;
}
};
template <int LoadMode>
struct PacketConv<LoadMode, true> {
static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType run(const TensorEvaluator<ArgType, Device>& impl, Index index) {
const int SrcCoeffRatio = internal::type_casting_traits<SrcType, TargetType>::SrcCoeffRatio;
const int TgtCoeffRatio = internal::type_casting_traits<SrcType, TargetType>::TgtCoeffRatio;
PacketConverter<TensorEvaluator<ArgType, Device>, PacketSourceType, PacketReturnType,
SrcCoeffRatio, TgtCoeffRatio> converter(impl);
return converter.template packet<LoadMode>(index);
}
};
TensorEvaluator<ArgType, Device> m_impl;
};
} // end namespace Eigen
#endif // EIGEN_CXX11_TENSOR_TENSOR_CONVERSION_H
|
