Skip to content

GitLab

Switch branch/tag
Find file Normal viewHistoryPermalink
basis.cpp 10.4 KB
Newer Older
rusty1s's avatar
new cpu extension api  
rusty1s committed
1
2 
#include <torch/torch.h>
rusty1s's avatar
cuda basis  
rusty1s committed
3 
template <typename scalar_t> inline scalar_t linear(scalar_t v, int64_t k_mod) {
rusty1s's avatar
new cpu extension api  
rusty1s committed
4
5
6 
  return 1 - v - k_mod + 2 * v * k_mod;
}
rusty1s's avatar
cuda basis  
rusty1s committed
7
8 
template <typename scalar_t>
inline scalar_t quadratic(scalar_t v, int64_t k_mod) {
rusty1s's avatar
new cpu extension api  
rusty1s committed
9
10
11
12
13
14
15
16 
  if (k_mod == 0)
    return 0.5 * v * v - v + 0.5;
  else if (k_mod == 1)
    return -v * v + v + 0.5;
  else
    return 0.5 * v * v;
}
rusty1s's avatar
cuda basis  
rusty1s committed
17
18 
template <typename scalar_t> inline scalar_t cubic(scalar_t v, int64_t k_mod) {
  if (k_mod == 0)
rusty1s's avatar
new cpu extension api  
rusty1s committed
19 
    return (1 - v) * (1 - v) * (1 - v) / 6.0;
rusty1s's avatar
cuda basis  
rusty1s committed
20 
  else if (k_mod == 1)
rusty1s's avatar
new cpu extension api  
rusty1s committed
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 
    return (3 * v * v * v - 6 * v * v + 4) / 6;
  else if (k_mod == 2)
    return (-3 * v * v * v + 3 * v * v + 3 * v + 1) / 6;
  else
    return v * v * v / 6;
}

#define BASIS_FORWARD(M, PSEUDO, KERNEL_SIZE, IS_OPEN_SPLINE, FUNC)            \
  [&]() -> std::tuple<at::Tensor, at::Tensor> {                                \
    auto E = PSEUDO.size(0), D = PSEUDO.size(1);                               \
    auto S = (int64_t)(pow(M + 1, KERNEL_SIZE.size(0)) + 0.5);                 \
    auto basis = at::empty({E, S}, PSEUDO.type());                             \
    auto weight_index = at::empty({E, S}, KERNEL_SIZE.type());                 \
                                                                               \
    AT_DISPATCH_FLOATING_TYPES(PSEUDO.type(), "basis_forward_##M", [&] {       \
      auto pseudo_data = PSEUDO.data<scalar_t>();                              \
      auto kernel_size_data = KERNEL_SIZE.data<int64_t>();                     \
      auto is_open_spline_data = IS_OPEN_SPLINE.data<uint8_t>();               \
      auto basis_data = basis.data<scalar_t>();                                \
      auto weight_index_data = weight_index.data<int64_t>();                   \
                                                                               \
      int64_t k, wi, wi_offset;                                                \
      scalar_t b;                                                              \
                                                                               \
      for (ptrdiff_t e = 0; e < E; e++) {                                      \
        for (ptrdiff_t s = 0; s < S; s++) {                                    \
          k = s;                                                               \
          wi = 0;                                                              \
          wi_offset = 1;                                                       \
          b = 1;                                                               \
          for (ptrdiff_t d = 0; d < D; d++) {                                  \
            auto k_mod = k % (M + 1);                                          \
            k /= M + 1;                                                        \
                                                                               \
            auto v = pseudo_data[e * pseudo.stride(0) + d * pseudo.stride(1)]; \
            v *= kernel_size_data[d] - M * is_open_spline_data[d];             \
                                                                               \
            wi += (((int64_t)v + k_mod) % kernel_size_data[d]) * wi_offset;    \
            wi_offset *= kernel_size_data[d];                                  \
                                                                               \
            v -= floor(v);                                                     \
            v = FUNC<scalar_t>(v, k_mod);                                      \
            b *= v;                                                            \
          }                                                                    \
          basis_data[e * S + s] = b;                                           \
          weight_index_data[e * S + s] = wi;                                   \
        }                                                                      \
      }                                                                        \
    });                                                                        \
    return std::make_tuple(basis, weight_index);                               \
  }()
rusty1s's avatar
cuda basis  
rusty1s committed
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 
std::tuple<at::Tensor, at::Tensor> linear_fw(at::Tensor pseudo,
                                             at::Tensor kernel_size,
                                             at::Tensor is_open_spline) {
  return BASIS_FORWARD(1, pseudo, kernel_size, is_open_spline, linear);
}

std::tuple<at::Tensor, at::Tensor> quadratic_fw(at::Tensor pseudo,
                                                at::Tensor kernel_size,
                                                at::Tensor is_open_spline) {
  return BASIS_FORWARD(2, pseudo, kernel_size, is_open_spline, quadratic);
}

std::tuple<at::Tensor, at::Tensor>
cubic_fw(at::Tensor pseudo, at::Tensor kernel_size, at::Tensor is_open_spline) {
  return BASIS_FORWARD(3, pseudo, kernel_size, is_open_spline, cubic);
}

template <typename scalar_t>
inline scalar_t grad_linear(scalar_t v, int64_t k_mod) {
  return 2 * k_mod - 1;
}

template <typename scalar_t>
inline scalar_t grad_quadratic(scalar_t v, int64_t k_mod) {
  if (k_mod == 0)
    return v - 1;
  else if (k_mod == 1)
    return -2 * v + 1;
  else
    return v;
}

template <typename scalar_t>
inline scalar_t grad_cubic(scalar_t v, int64_t k_mod) {
  if (k_mod == 0)
    return (-v * v + 2 * v - 1) / 2;
  else if (k_mod == 1)
    return (3 * v * v - 4 * v) / 2;
  else if (k_mod == 2)
    return (-3 * v * v + 2 * v + 1) / 2;
  else
    return v * v / 2;
}
rusty1s's avatar
new cpu extension api  
rusty1s committed
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 
#define BASIS_BACKWARD(M, GRAD_BASIS, PSEUDO, KERNEL_SIZE, IS_OPEN_SPLINE,     \
                       FUNC, GRAD_FUNC)                                        \
  [&]() -> at::Tensor {                                                        \
    auto E = PSEUDO.size(0), D = PSEUDO.size(1);                               \
    auto S = GRAD_BASIS.size(1);                                               \
    auto grad_pseudo = at::empty({E, D}, PSEUDO.type());                       \
                                                                               \
    AT_DISPATCH_FLOATING_TYPES(PSEUDO.type(), "basis_backward_##M", [&] {      \
      auto grad_basis_data = GRAD_BASIS.data<scalar_t>();                      \
      auto pseudo_data = PSEUDO.data<scalar_t>();                              \
      auto kernel_size_data = KERNEL_SIZE.data<int64_t>();                     \
      auto is_open_spline_data = IS_OPEN_SPLINE.data<uint8_t>();               \
      auto grad_pseudo_data = grad_pseudo.data<scalar_t>();                    \
                                                                               \
      scalar_t g, tmp;                                                         \
                                                                               \
      for (ptrdiff_t e = 0; e < E; e++) {                                      \
        for (ptrdiff_t d = 0; d < D; d++) {                                    \
          g = 0;                                                               \
          for (ptrdiff_t s = 0; s < S; s++) {                                  \
            auto k_mod = (s / (int64_t)(pow(M + 1, d) + 0.5)) % (M + 1);       \
            auto v = pseudo_data[e * pseudo.stride(0) + d * pseudo.stride(1)]; \
            v *= kernel_size_data[d] - M * is_open_spline_data[d];             \
            v -= floor(v);                                                     \
            v = GRAD_FUNC<scalar_t>(v, k_mod);                                 \
            tmp = v;                                                           \
                                                                               \
            for (ptrdiff_t d_it = 1; d_it < D; d_it++) {                       \
              auto d_other = d_it - (d >= d_it);                               \
              k_mod = (s / (int64_t)(pow(M + 1, d_other) + 0.5)) % (M + 1);    \
              v = pseudo_data[e * pseudo.stride(0) +                           \
                              d_other * pseudo.stride(1)];                     \
              v *= kernel_size_data[d_other] -                                 \
                   M * is_open_spline_data[d_other];                           \
              v -= floor(v);                                                   \
              v = FUNC<scalar_t>(v, k_mod);                                    \
              tmp *= v;                                                        \
            }                                                                  \
            g += tmp * grad_basis_data[e * grad_basis.stride(0) +              \
                                       s * grad_basis.stride(1)];              \
          }                                                                    \
          g *= kernel_size_data[d] - M * is_open_spline_data[d];               \
          grad_pseudo_data[e * D + d] = g;                                     \
        }                                                                      \
      }                                                                        \
    });                                                                        \
    return grad_pseudo;                                                        \
  }()

at::Tensor linear_bw(at::Tensor grad_basis, at::Tensor pseudo,
                     at::Tensor kernel_size, at::Tensor is_open_spline) {
  return BASIS_BACKWARD(1, grad_basis, pseudo, kernel_size, is_open_spline,
                        linear, grad_linear);
}

at::Tensor quadratic_bw(at::Tensor grad_basis, at::Tensor pseudo,
                        at::Tensor kernel_size, at::Tensor is_open_spline) {
  return BASIS_BACKWARD(2, grad_basis, pseudo, kernel_size, is_open_spline,
                        quadratic, grad_quadratic);
}

at::Tensor cubic_bw(at::Tensor grad_basis, at::Tensor pseudo,
                    at::Tensor kernel_size, at::Tensor is_open_spline) {
  return BASIS_BACKWARD(3, grad_basis, pseudo, kernel_size, is_open_spline,
                        cubic, grad_cubic);
}

PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
  m.def("linear_fw", &linear_fw, "Linear Basis Forward (CPU)");
  m.def("quadratic_fw", &quadratic_fw, "Quadratic Basis Forward (CPU)");
  m.def("cubic_fw", &cubic_fw, "Cubic Basis Forward (CPU)");
  m.def("linear_bw", &linear_bw, "Linear Basis Backward (CPU)");
  m.def("quadratic_bw", &quadratic_bw, "Quadratic Basis Backward (CPU)");
  m.def("cubic_bw", &cubic_bw, "Cubic Basis Backward (CPU)");
}