forked from pytorch/pytorch
-
Notifications
You must be signed in to change notification settings - Fork 0
/
BatchLinearAlgebra.h
321 lines (272 loc) · 9.7 KB
/
BatchLinearAlgebra.h
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
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
#pragma once
#include <c10/util/Optional.h>
#include <c10/util/string_view.h>
#include <ATen/Config.h>
#include <ATen/native/DispatchStub.h>
// Forward declare TI
namespace at {
class Tensor;
struct TensorIterator;
namespace native {
enum class TransposeType;
}
}
namespace at::native {
enum class LapackLstsqDriverType : int64_t { Gels, Gelsd, Gelsy, Gelss};
#if AT_BUILD_WITH_LAPACK()
// Define per-batch functions to be used in the implementation of batched
// linear algebra operations
template <class scalar_t>
void lapackCholesky(char uplo, int n, scalar_t *a, int lda, int *info);
template <class scalar_t>
void lapackCholeskyInverse(char uplo, int n, scalar_t *a, int lda, int *info);
template <class scalar_t, class value_t=scalar_t>
void lapackEig(char jobvl, char jobvr, int n, scalar_t *a, int lda, scalar_t *w, scalar_t* vl, int ldvl, scalar_t *vr, int ldvr, scalar_t *work, int lwork, value_t *rwork, int *info);
template <class scalar_t>
void lapackGeqrf(int m, int n, scalar_t *a, int lda, scalar_t *tau, scalar_t *work, int lwork, int *info);
template <class scalar_t>
void lapackOrgqr(int m, int n, int k, scalar_t *a, int lda, scalar_t *tau, scalar_t *work, int lwork, int *info);
template <class scalar_t>
void lapackOrmqr(char side, char trans, int m, int n, int k, scalar_t *a, int lda, scalar_t *tau, scalar_t *c, int ldc, scalar_t *work, int lwork, int *info);
template <class scalar_t, class value_t = scalar_t>
void lapackSyevd(char jobz, char uplo, int n, scalar_t* a, int lda, value_t* w, scalar_t* work, int lwork, value_t* rwork, int lrwork, int* iwork, int liwork, int* info);
template <class scalar_t>
void lapackGels(char trans, int m, int n, int nrhs,
scalar_t *a, int lda, scalar_t *b, int ldb,
scalar_t *work, int lwork, int *info);
template <class scalar_t, class value_t = scalar_t>
void lapackGelsd(int m, int n, int nrhs,
scalar_t *a, int lda, scalar_t *b, int ldb,
value_t *s, value_t rcond, int *rank,
scalar_t* work, int lwork,
value_t *rwork, int* iwork, int *info);
template <class scalar_t, class value_t = scalar_t>
void lapackGelsy(int m, int n, int nrhs,
scalar_t *a, int lda, scalar_t *b, int ldb,
int *jpvt, value_t rcond, int *rank,
scalar_t *work, int lwork, value_t* rwork, int *info);
template <class scalar_t, class value_t = scalar_t>
void lapackGelss(int m, int n, int nrhs,
scalar_t *a, int lda, scalar_t *b, int ldb,
value_t *s, value_t rcond, int *rank,
scalar_t *work, int lwork,
value_t *rwork, int *info);
template <LapackLstsqDriverType, class scalar_t, class value_t = scalar_t>
struct lapackLstsq_impl;
template <class scalar_t, class value_t>
struct lapackLstsq_impl<LapackLstsqDriverType::Gels, scalar_t, value_t> {
static void call(
char trans, int m, int n, int nrhs,
scalar_t *a, int lda, scalar_t *b, int ldb,
scalar_t *work, int lwork, int *info, // Gels flavor
int *jpvt, value_t rcond, int *rank, value_t* rwork, // Gelsy flavor
value_t *s, // Gelss flavor
int *iwork // Gelsd flavor
) {
lapackGels<scalar_t>(
trans, m, n, nrhs,
a, lda, b, ldb,
work, lwork, info);
}
};
template <class scalar_t, class value_t>
struct lapackLstsq_impl<LapackLstsqDriverType::Gelsy, scalar_t, value_t> {
static void call(
char trans, int m, int n, int nrhs,
scalar_t *a, int lda, scalar_t *b, int ldb,
scalar_t *work, int lwork, int *info, // Gels flavor
int *jpvt, value_t rcond, int *rank, value_t* rwork, // Gelsy flavor
value_t *s, // Gelss flavor
int *iwork // Gelsd flavor
) {
lapackGelsy<scalar_t, value_t>(
m, n, nrhs,
a, lda, b, ldb,
jpvt, rcond, rank,
work, lwork, rwork, info);
}
};
template <class scalar_t, class value_t>
struct lapackLstsq_impl<LapackLstsqDriverType::Gelsd, scalar_t, value_t> {
static void call(
char trans, int m, int n, int nrhs,
scalar_t *a, int lda, scalar_t *b, int ldb,
scalar_t *work, int lwork, int *info, // Gels flavor
int *jpvt, value_t rcond, int *rank, value_t* rwork, // Gelsy flavor
value_t *s, // Gelss flavor
int *iwork // Gelsd flavor
) {
lapackGelsd<scalar_t, value_t>(
m, n, nrhs,
a, lda, b, ldb,
s, rcond, rank,
work, lwork,
rwork, iwork, info);
}
};
template <class scalar_t, class value_t>
struct lapackLstsq_impl<LapackLstsqDriverType::Gelss, scalar_t, value_t> {
static void call(
char trans, int m, int n, int nrhs,
scalar_t *a, int lda, scalar_t *b, int ldb,
scalar_t *work, int lwork, int *info, // Gels flavor
int *jpvt, value_t rcond, int *rank, value_t* rwork, // Gelsy flavor
value_t *s, // Gelss flavor
int *iwork // Gelsd flavor
) {
lapackGelss<scalar_t, value_t>(
m, n, nrhs,
a, lda, b, ldb,
s, rcond, rank,
work, lwork,
rwork, info);
}
};
template <LapackLstsqDriverType driver_type, class scalar_t, class value_t = scalar_t>
void lapackLstsq(
char trans, int m, int n, int nrhs,
scalar_t *a, int lda, scalar_t *b, int ldb,
scalar_t *work, int lwork, int *info, // Gels flavor
int *jpvt, value_t rcond, int *rank, value_t* rwork, // Gelsy flavor
value_t *s, // Gelss flavor
int *iwork // Gelsd flavor
) {
lapackLstsq_impl<driver_type, scalar_t, value_t>::call(
trans, m, n, nrhs,
a, lda, b, ldb,
work, lwork, info,
jpvt, rcond, rank, rwork,
s,
iwork);
}
template <class scalar_t>
void lapackLuSolve(char trans, int n, int nrhs, scalar_t *a, int lda, int *ipiv, scalar_t *b, int ldb, int *info);
template <class scalar_t>
void lapackLu(int m, int n, scalar_t *a, int lda, int *ipiv, int *info);
template <class scalar_t>
void lapackLdlHermitian(
char uplo,
int n,
scalar_t* a,
int lda,
int* ipiv,
scalar_t* work,
int lwork,
int* info);
template <class scalar_t>
void lapackLdlSymmetric(
char uplo,
int n,
scalar_t* a,
int lda,
int* ipiv,
scalar_t* work,
int lwork,
int* info);
template <class scalar_t>
void lapackLdlSolveHermitian(
char uplo,
int n,
int nrhs,
scalar_t* a,
int lda,
int* ipiv,
scalar_t* b,
int ldb,
int* info);
template <class scalar_t>
void lapackLdlSolveSymmetric(
char uplo,
int n,
int nrhs,
scalar_t* a,
int lda,
int* ipiv,
scalar_t* b,
int ldb,
int* info);
template<class scalar_t, class value_t=scalar_t>
void lapackSvd(char jobz, int m, int n, scalar_t *a, int lda, value_t *s, scalar_t *u, int ldu, scalar_t *vt, int ldvt, scalar_t *work, int lwork, value_t *rwork, int *iwork, int *info);
#endif
#if AT_BUILD_WITH_BLAS()
template <class scalar_t>
void blasTriangularSolve(char side, char uplo, char trans, char diag, int n, int nrhs, scalar_t* a, int lda, scalar_t* b, int ldb);
#endif
using cholesky_fn = void (*)(const Tensor& /*input*/, const Tensor& /*info*/, bool /*upper*/);
DECLARE_DISPATCH(cholesky_fn, cholesky_stub);
using cholesky_inverse_fn = Tensor& (*)(Tensor& /*result*/, Tensor& /*infos*/, bool /*upper*/);
DECLARE_DISPATCH(cholesky_inverse_fn, cholesky_inverse_stub);
using linalg_eig_fn = void (*)(Tensor& /*eigenvalues*/, Tensor& /*eigenvectors*/, Tensor& /*infos*/, const Tensor& /*input*/, bool /*compute_eigenvectors*/);
DECLARE_DISPATCH(linalg_eig_fn, linalg_eig_stub);
using geqrf_fn = void (*)(const Tensor& /*input*/, const Tensor& /*tau*/);
DECLARE_DISPATCH(geqrf_fn, geqrf_stub);
using orgqr_fn = Tensor& (*)(Tensor& /*result*/, const Tensor& /*tau*/);
DECLARE_DISPATCH(orgqr_fn, orgqr_stub);
using ormqr_fn = void (*)(const Tensor& /*input*/, const Tensor& /*tau*/, const Tensor& /*other*/, bool /*left*/, bool /*transpose*/);
DECLARE_DISPATCH(ormqr_fn, ormqr_stub);
using linalg_eigh_fn = void (*)(
const Tensor& /*eigenvalues*/,
const Tensor& /*eigenvectors*/,
const Tensor& /*infos*/,
bool /*upper*/,
bool /*compute_eigenvectors*/);
DECLARE_DISPATCH(linalg_eigh_fn, linalg_eigh_stub);
using lstsq_fn = void (*)(
const Tensor& /*a*/,
Tensor& /*b*/,
Tensor& /*rank*/,
Tensor& /*singular_values*/,
Tensor& /*infos*/,
double /*rcond*/,
std::string /*driver_name*/);
DECLARE_DISPATCH(lstsq_fn, lstsq_stub);
using triangular_solve_fn = void (*)(
const Tensor& /*A*/,
const Tensor& /*B*/,
bool /*left*/,
bool /*upper*/,
TransposeType /*transpose*/,
bool /*unitriangular*/);
DECLARE_DISPATCH(triangular_solve_fn, triangular_solve_stub);
using lu_factor_fn = void (*)(
const Tensor& /*input*/,
const Tensor& /*pivots*/,
const Tensor& /*infos*/,
bool /*compute_pivots*/);
DECLARE_DISPATCH(lu_factor_fn, lu_factor_stub);
using unpack_pivots_fn = void(*)(
TensorIterator& iter,
const int64_t dim_size,
const int64_t max_pivot);
DECLARE_DISPATCH(unpack_pivots_fn, unpack_pivots_stub);
using lu_solve_fn = void (*)(
const Tensor& /*LU*/,
const Tensor& /*pivots*/,
const Tensor& /*B*/,
TransposeType /*trans*/);
DECLARE_DISPATCH(lu_solve_fn, lu_solve_stub);
using ldl_factor_fn = void (*)(
const Tensor& /*LD*/,
const Tensor& /*pivots*/,
const Tensor& /*info*/,
bool /*upper*/,
bool /*hermitian*/);
DECLARE_DISPATCH(ldl_factor_fn, ldl_factor_stub);
using svd_fn = void (*)(
const Tensor& /*A*/,
const bool /*full_matrices*/,
const bool /*compute_uv*/,
const std::optional<c10::string_view>& /*driver*/,
const Tensor& /*U*/,
const Tensor& /*S*/,
const Tensor& /*Vh*/,
const Tensor& /*info*/);
DECLARE_DISPATCH(svd_fn, svd_stub);
using ldl_solve_fn = void (*)(
const Tensor& /*LD*/,
const Tensor& /*pivots*/,
const Tensor& /*result*/,
bool /*upper*/,
bool /*hermitian*/);
DECLARE_DISPATCH(ldl_solve_fn, ldl_solve_stub);
} // namespace at::native