aboutsummaryrefslogtreecommitdiffhomepage
path: root/unsupported/Eigen/CXX11/src/Tensor/TensorScan.h
blob: 0d084141d251c6b4f05bbdd2b684967219c32988 (plain)
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
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2016 Igor Babuschkin <igor@babuschk.in>
//
// 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_SCAN_H
#define EIGEN_CXX11_TENSOR_TENSOR_SCAN_H
namespace Eigen {

namespace internal {
template <typename Op, typename XprType>
struct traits<TensorScanOp<Op, XprType> >
    : public traits<XprType> {
  typedef typename XprType::Scalar Scalar;
  typedef traits<XprType> XprTraits;
  typedef typename XprTraits::StorageKind StorageKind;
  typedef typename XprType::Nested Nested;
  typedef typename remove_reference<Nested>::type _Nested;
  static const int NumDimensions = XprTraits::NumDimensions;
  static const int Layout = XprTraits::Layout;
};

template<typename Op, typename XprType>
struct eval<TensorScanOp<Op, XprType>, Eigen::Dense>
{
  typedef const TensorScanOp<Op, XprType>& type;
};

template<typename Op, typename XprType>
struct nested<TensorScanOp<Op, XprType>, 1,
            typename eval<TensorScanOp<Op, XprType> >::type>
{
  typedef TensorScanOp<Op, XprType> type;
};
} // end namespace internal

/** \class TensorScan
  * \ingroup CXX11_Tensor_Module
  *
  * \brief Tensor scan class.
  *
  */

template <typename Op, typename XprType>
class TensorScanOp
    : public TensorBase<TensorScanOp<Op, XprType>, ReadOnlyAccessors> {
public:
  typedef typename Eigen::internal::traits<TensorScanOp>::Scalar Scalar;
  typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
  typedef typename XprType::CoeffReturnType CoeffReturnType;
  typedef typename Eigen::internal::nested<TensorScanOp>::type Nested;
  typedef typename Eigen::internal::traits<TensorScanOp>::StorageKind StorageKind;
  typedef typename Eigen::internal::traits<TensorScanOp>::Index Index;

  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorScanOp(
      const XprType& expr, const Index& axis, const Op& op = Op())
      : m_expr(expr), m_axis(axis), m_accumulator(op) {}

  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
  const Index axis() const { return m_axis; }
  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
  const XprType& expression() const { return m_expr; }
  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
  const Op accumulator() const { return m_accumulator; }

protected:
  typename XprType::Nested m_expr;
  const Index m_axis;
  const Op m_accumulator;
};

// Eval as rvalue
template <typename Op, typename ArgType, typename Device>
struct TensorEvaluator<const TensorScanOp<Op, ArgType>, Device> {

  typedef TensorScanOp<Op, ArgType> XprType;
  typedef typename XprType::Index Index;
  static const int NumDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value;
  typedef DSizes<Index, NumDims> Dimensions;
  typedef typename XprType::Scalar Scalar;
  typedef typename XprType::CoeffReturnType CoeffReturnType;
  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;

  enum {
    IsAligned = false,
    PacketAccess = (internal::packet_traits<Scalar>::size > 1),
    BlockAccess = false,
    Layout = TensorEvaluator<ArgType, Device>::Layout,
    CoordAccess = false,
    RawAccess = true
  };

  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op,
                                                        const Device& device)
      : m_impl(op.expression(), device),
        m_device(device),
        m_axis(op.axis()),
        m_accumulator(op.accumulator()),
        m_dimensions(m_impl.dimensions()),
        m_size(m_dimensions[m_axis]),
        m_stride(1),
        m_output(NULL) {

    // Accumulating a scalar isn't supported.
    EIGEN_STATIC_ASSERT((NumDims > 0), YOU_MADE_A_PROGRAMMING_MISTAKE);
    eigen_assert(m_axis >= 0 && m_axis < NumDims);

    // Compute stride of scan axis
    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
      for (int i = 0; i < m_axis; ++i) {
        m_stride = m_stride * m_dimensions[i];
      }
    } else {
      for (int i = NumDims - 1; i > m_axis; --i) {
        m_stride = m_stride * m_dimensions[i];
      }
    }
  }

  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);
    if (data) {
      accumulateTo(data);
      return false;
    } else {
      m_output = static_cast<CoeffReturnType*>(m_device.allocate(dimensions().TotalSize() * sizeof(Scalar)));
      accumulateTo(m_output);
      return true;
    }
  }

  template<int LoadMode>
  EIGEN_DEVICE_FUNC PacketReturnType packet(Index index) const {
    return internal::ploadt<PacketReturnType, LoadMode>(m_output + index);
  }

  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType* data() const
  {
    return m_output;
  }

  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
  {
    return m_output[index];
  }

  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() {
    if (m_output != NULL) {
      m_device.deallocate(m_output);
      m_output = NULL;
    }
    m_impl.cleanup();
  }

protected:
  TensorEvaluator<ArgType, Device> m_impl;
  const Device& m_device;
  const Index m_axis;
  Op m_accumulator;
  const Dimensions& m_dimensions;
  const Index& m_size;
  Index m_stride;
  CoeffReturnType* m_output;

  // TODO(ibab) Parallelize this single-threaded implementation if desired
  EIGEN_DEVICE_FUNC void accumulateTo(Scalar* data) {
    // We fix the index along the scan axis to 0 and perform an
    // scan per remaining entry. The iteration is split into two nested
    // loops to avoid an integer division by keeping track of each idx1 and idx2.
    for (Index idx1 = 0; idx1 < dimensions().TotalSize() / m_size; idx1 += m_stride) {
       for (Index idx2 = 0; idx2 < m_stride; idx2++) {
          // Calculate the starting offset for the scan
          Index offset = idx1 * m_size + idx2;

          // Compute the prefix sum along the axis, starting at the calculated offset
          CoeffReturnType accum = m_accumulator.initialize();
          for (Index idx3 = 0; idx3 < m_size; idx3++) {
            Index curr = offset + idx3 * m_stride;
            m_accumulator.reduce(m_impl.coeff(curr), &accum);
            data[curr] = m_accumulator.finalize(accum);
          }
       }
    }
  }
};

}  // end namespace Eigen

#endif  // EIGEN_CXX11_TENSOR_TENSOR_SCAN_H