Deep Neural Network Library (DNNL)  1.1.3
Performance library for Deep Learning
dnnl_types.h
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16 
19 
20 #ifndef DNNL_TYPES_H
21 #define DNNL_TYPES_H
22 
23 #ifdef __cplusplus
24 extern "C" {
25 #endif
26 
28 #include <stddef.h>
29 #include <stdint.h>
31 
40 
42 typedef struct {
43  int major;
44  int minor;
45  int patch;
46  const char *hash;
48 
50 typedef enum {
66 
68 typedef enum {
72  dnnl_f16 = 1,
74  dnnl_bf16 = 2,
76  dnnl_f32 = 3,
78  dnnl_s32 = 4,
80  dnnl_s8 = 5,
82  dnnl_u8 = 6,
84 
86 typedef enum {
101 
170 typedef enum {
176 
177  // Semantic agnostic section
178  // The physical order of dimensions is defined by the permutation of the
179  // characters, assuming that ab..z defines the natural order.
180 
181  // Plain formats
182 
189 
190  // Permuted plain formats
191 
207 
208  // Opaque blocked formats
209 
210  dnnl_Abc16a,
211  dnnl_ABc16a16b,
214  dnnl_ABc16b16a,
215  dnnl_Abc4a,
218  dnnl_ABc4b16a4b,
219  dnnl_ABc4b4a,
220  dnnl_ABc8a16b2a,
221  dnnl_ABc8a8b,
224  dnnl_ABc8b16a2b,
225  dnnl_BAc8a16b2a,
226  dnnl_ABc8b8a,
227  dnnl_Abcd16a,
228  dnnl_ABcd16a16b,
229  dnnl_ABcd32a32b,
232  dnnl_ABcd16b16a,
233  dnnl_aBCd16b16c,
234  dnnl_aBCd16c16b,
235  dnnl_Abcd4a,
238  dnnl_ABcd4b16a4b,
239  dnnl_ABcd4b4a,
240  dnnl_aBCd4c16b4c,
241  dnnl_aBCd4c4b,
242  dnnl_ABcd8a16b2a,
243  dnnl_ABcd8a8b,
246  dnnl_ABcd8b16a2b,
247  dnnl_aBCd8b16c2b,
248  dnnl_BAcd8a16b2a,
251  dnnl_aBCd8b8c,
252  dnnl_aBCd8c16b2c,
253  dnnl_ABcde8a16b2a,
254  dnnl_aCBd8b16c2b,
255  dnnl_aBCd8c8b,
256  dnnl_Abcde16a,
257  dnnl_ABcde16a16b,
258  dnnl_BAcde8a16b2a,
261  dnnl_ABcde16b16a,
262  dnnl_aBCde16b16c,
263  dnnl_aBCde16c16b,
264  dnnl_aBCde2c8b4c,
265  dnnl_Abcde4a,
268  dnnl_ABcde4b4a,
269  dnnl_aBCde4b4c,
270  dnnl_aBCde4c16b4c,
271  dnnl_aBCde4c4b,
272  dnnl_Abcde8a,
273  dnnl_ABcde8a8b,
274  dnnl_BAcde16b16a,
277  dnnl_ABcde8b16a2b,
278  dnnl_aBCde8b16c2b,
279  dnnl_aCBde8b16c2b,
280  dnnl_ABcde8b8a,
281  dnnl_aBCde8b8c,
282  dnnl_ABcd4a8b8a4b,
283  dnnl_ABcd2a8b8a2b,
284  dnnl_aBCde4b8c8b4c,
285  dnnl_aBCde2b8c8b2c,
286  dnnl_aBCde8c16b2c,
287  dnnl_aBCde8c8b,
290  dnnl_aBCdef16b16c,
291  dnnl_aBCdef16c16b,
294  dnnl_aBCdef4c4b,
295  dnnl_aBCdef8b8c,
296  dnnl_aBCdef8c16b2c,
297  dnnl_aBCdef8b16c2b,
298  dnnl_aCBdef8b16c2b,
299  dnnl_aBCdef8c8b,
300  dnnl_aBdc16b,
301  dnnl_aBdc4b,
302  dnnl_aBdc8b,
303  dnnl_aBdec16b,
304  dnnl_aBdec32b,
305  dnnl_aBdec4b,
306  dnnl_aBdec8b,
307  dnnl_aBdefc16b,
308  dnnl_aCBdef16c16b,
309  dnnl_aBdefc4b,
310  dnnl_aBdefc8b,
311  dnnl_Abcdef16a,
312  dnnl_Acb16a,
313  dnnl_Acb4a,
314  dnnl_Acb8a,
315  dnnl_aCBd16b16c,
316  dnnl_aCBd16c16b,
317  dnnl_aCBde16b16c,
318  dnnl_aCBde16c16b,
319  dnnl_Acdb16a,
320  dnnl_Acdb32a,
321  dnnl_Acdb4a,
322  dnnl_Acdb8a,
323  dnnl_Acdeb16a,
324  dnnl_Acdeb4a,
325  dnnl_Acdeb8a,
326  dnnl_BAc16a16b,
327  dnnl_BAc16b16a,
328  dnnl_BAcd16a16b,
329  dnnl_BAcd16b16a,
330 
334 
335  // Aliases
336 
361 
392 
403 
432 
433  // Opaque data types, are not to be used explicitly
434 
435  // data
436 
464  dnnl_NCw16n16c = dnnl_ABc16a16b,
465  dnnl_NCdhw16n16c = dnnl_ABcde16a16b,
466  dnnl_NChw16n16c = dnnl_ABcd16a16b,
467  dnnl_NChw32n32c = dnnl_ABcd32a32b,
468 
469  // weights, 3D
470  dnnl_IOw16o16i = dnnl_BAc16a16b,
471  dnnl_IOw16i16o = dnnl_BAc16b16a,
472  dnnl_OIw16i16o = dnnl_ABc16b16a,
473  dnnl_OIw16o16i = dnnl_ABc16a16b,
474  dnnl_Oiw16o = dnnl_Abc16a,
475  dnnl_OIw4i16o4i = dnnl_ABc4b16a4b,
476  dnnl_OIw4i4o = dnnl_ABc4b4a,
477  dnnl_Oiw4o = dnnl_Abc4a,
478  dnnl_OIw8i16o2i = dnnl_ABc8b16a2b,
479  dnnl_OIw8i8o = dnnl_ABc8b8a,
480  dnnl_OIw8o16i2o = dnnl_ABc8a16b2a,
481  dnnl_IOw8o16i2o = dnnl_BAc8a16b2a,
482  dnnl_OIw8o8i = dnnl_ABc8a8b,
483  dnnl_Owi16o = dnnl_Acb16a,
484  dnnl_Owi4o = dnnl_Acb4a,
485  dnnl_Owi8o = dnnl_Acb8a,
486 
487  // weights, 4D
488  dnnl_IOhw16i16o = dnnl_BAcd16b16a,
489  dnnl_IOhw16o16i = dnnl_BAcd16a16b,
490  dnnl_Ohwi16o = dnnl_Acdb16a,
491  dnnl_Ohwi32o = dnnl_Acdb32a,
492  dnnl_Ohwi4o = dnnl_Acdb4a,
493  dnnl_Ohwi8o = dnnl_Acdb8a,
494  dnnl_OIhw16i16o = dnnl_ABcd16b16a,
495  dnnl_OIhw16o16i = dnnl_ABcd16a16b,
496  dnnl_Oihw16o = dnnl_Abcd16a,
497  dnnl_OIhw4i16o4i = dnnl_ABcd4b16a4b,
498  dnnl_OIhw4i4o = dnnl_ABcd4b4a,
499  dnnl_Oihw4o = dnnl_Abcd4a,
500  dnnl_OIhw8i16o2i = dnnl_ABcd8b16a2b,
501  dnnl_OIhw8i8o = dnnl_ABcd8b8a,
502  dnnl_OIhw8o16i2o = dnnl_ABcd8a16b2a,
503  dnnl_IOhw8o16i2o = dnnl_BAcd8a16b2a,
504  dnnl_OIhw8o8i = dnnl_ABcd8a8b,
505 
506  // weights, 5D
507  dnnl_Odhwi16o = dnnl_Acdeb16a,
508  dnnl_Odhwi4o = dnnl_Acdeb4a,
509  dnnl_Odhwi8o = dnnl_Acdeb8a,
510  dnnl_OIdhw16i16o = dnnl_ABcde16b16a,
511  dnnl_OIdhw16o16i = dnnl_ABcde16a16b,
512  dnnl_Oidhw16o = dnnl_Abcde16a,
513  dnnl_OIdhw4i4o = dnnl_ABcde4b4a,
514  dnnl_Oidhw4o = dnnl_Abcde4a,
515  dnnl_OIdhw8i16o2i = dnnl_ABcde8b16a2b,
516  dnnl_OIdhw8i8o = dnnl_ABcde8b8a,
517  dnnl_OIdhw8o16i2o = dnnl_ABcde8a16b2a,
518  dnnl_IOdhw8o16i2o = dnnl_BAcde8a16b2a,
519  dnnl_OIdhw8o8i = dnnl_ABcde8a8b,
520  dnnl_IOdhw16i16o = dnnl_BAcde16b16a,
521 
522  // weights w/ groups, 3D
523  dnnl_Goiw16g = dnnl_Abcd16a,
524  dnnl_gIOw16o16i = dnnl_aCBd16b16c,
525  dnnl_gIOw16i16o = dnnl_aCBd16c16b,
526  dnnl_gOIw16i16o = dnnl_aBCd16c16b,
527  dnnl_gOIw16o16i = dnnl_aBCd16b16c,
528  dnnl_gOiw16o = dnnl_aBcd16b,
529  dnnl_gOIw4i16o4i = dnnl_aBCd4c16b4c,
530  dnnl_gOIw4i4o = dnnl_aBCd4c4b,
531  dnnl_gOiw4o = dnnl_aBcd4b,
532  dnnl_gOIw8i16o2i = dnnl_aBCd8c16b2c,
533  dnnl_gOIw8i8o = dnnl_aBCd8c8b,
534  dnnl_gOIw8o16i2o = dnnl_aBCd8b16c2b,
535  dnnl_gIOw8o16i2o = dnnl_aCBd8b16c2b,
536  dnnl_gOIw8o8i = dnnl_aBCd8b8c,
537  dnnl_gOwi16o = dnnl_aBdc16b,
538  dnnl_gOwi4o = dnnl_aBdc4b,
539  dnnl_gOwi8o = dnnl_aBdc8b,
540 
541  // weights w/ groups, 4D
542  dnnl_gIOhw16i16o = dnnl_aCBde16c16b,
543  dnnl_gIOhw16o16i = dnnl_aCBde16b16c,
544  dnnl_gOhwi16o = dnnl_aBdec16b,
545  dnnl_gOhwi32o = dnnl_aBdec32b,
546  dnnl_gOhwi4o = dnnl_aBdec4b,
547  dnnl_gOhwi8o = dnnl_aBdec8b,
548  dnnl_Goihw16g = dnnl_Abcde16a,
549  dnnl_gOIhw16i16o = dnnl_aBCde16c16b,
550  dnnl_gOIhw16o16i = dnnl_aBCde16b16c,
551  dnnl_gOihw16o = dnnl_aBcde16b,
552  dnnl_gOIhw2i8o4i = dnnl_aBCde2c8b4c,
553  dnnl_gOIhw4i16o4i = dnnl_aBCde4c16b4c,
554  dnnl_gOIhw4i4o = dnnl_aBCde4c4b,
555  dnnl_gOIhw4o4i = dnnl_aBCde4b4c,
556  dnnl_gOihw4o = dnnl_aBcde4b,
557  dnnl_Goihw8g = dnnl_Abcde8a,
558  dnnl_gOIhw8i16o2i = dnnl_aBCde8c16b2c,
559  dnnl_gOIhw8i8o = dnnl_aBCde8c8b,
560  dnnl_gOIhw8o16i2o = dnnl_aBCde8b16c2b,
561  dnnl_gIOhw8o16i2o = dnnl_aCBde8b16c2b,
562  dnnl_gOIhw8o8i = dnnl_aBCde8b8c,
563 
564  dnnl_OIhw4o8i8o4i = dnnl_ABcd4a8b8a4b,
565  dnnl_OIhw2o8i8o2i = dnnl_ABcd2a8b8a2b,
566  dnnl_gOIhw4o8i8o4i = dnnl_aBCde4b8c8b4c,
567  dnnl_gOIhw2o8i8o2i = dnnl_aBCde2b8c8b2c,
568 
569  // weights w/ groups, 6D
570  dnnl_gIOdhw16i16o = dnnl_aCBdef16c16b,
571  dnnl_gOdhwi16o = dnnl_aBdefc16b,
572  dnnl_gOdhwi4o = dnnl_aBdefc4b,
573  dnnl_gOdhwi8o = dnnl_aBdefc8b,
574  dnnl_gOIdhw16i16o = dnnl_aBCdef16c16b,
575  dnnl_gOIdhw16o16i = dnnl_aBCdef16b16c,
576  dnnl_gOidhw16o = dnnl_aBcdef16b,
577  dnnl_gOIdhw4i4o = dnnl_aBCdef4c4b,
578  dnnl_gOidhw4o = dnnl_aBcdef4b,
579  dnnl_gOIdhw8i16o2i = dnnl_aBCdef8c16b2c,
580  dnnl_gOIdhw8i8o = dnnl_aBCdef8c8b,
581  dnnl_gOIdhw8o16i2o = dnnl_aBCdef8b16c2b,
582  dnnl_gIOdhw8o16i2o = dnnl_aCBdef8b16c2b,
583  dnnl_gOIdhw8o8i = dnnl_aBCdef8b8c,
584  dnnl_Goidhw16g = dnnl_Abcdef16a,
586 
588 typedef enum {
589  // TODO: suggest renames
612 
615 typedef enum {
651 
653 typedef enum {
654  dnnl_alg_kind_undef,
700  dnnl_pooling_avg = dnnl_pooling_avg_exclude_padding,
718  dnnl_lbr_gru = 0x4fff,
720  dnnl_binary_add = 0x1fff0,
722  dnnl_binary_mul = 0x1fff1,
724 
726 typedef enum {
739 
752 
766 
768 
771 
775 #define DNNL_MAX_NDIMS 12
776 
778 typedef int64_t dnnl_dim_t;
779 
782 
786 typedef struct {
790  // Innermost section
791  // ASSUMPTION: the innermost blocks are always dense
800 
802 typedef enum {
805  // Tensors of weights for 2x3 winograd convolutions.
809  // Tensor of weights for 4x3 convolution.
812 
814 typedef struct {
815  dnnl_wino_memory_format_t wino_format;
816  int r;
817  int alpha;
818  int ic;
819  int oc;
820  int ic_block;
821  int oc_block;
822  int ic2_block;
823  int oc2_block;
824  float adj_scale;
825  size_t size;
827 
828 typedef enum {
829  dnnl_packed_format_undef = 0,
830  dnnl_ldigo_p,
831  dnnl_ldgoi_p
832 } dnnl_rnn_packed_memory_format_t;
833 
836 #define DNNL_RNN_MAX_N_PARTS 4
837 
839 typedef struct {
840  dnnl_rnn_packed_memory_format_t format;
841  int n_parts;
842  int n;
843  int ldb;
844  int parts[DNNL_RNN_MAX_N_PARTS];
845  size_t part_pack_size[DNNL_RNN_MAX_N_PARTS];
846  unsigned pack_part[DNNL_RNN_MAX_N_PARTS];
847  size_t offset_compensation;
848  size_t size;
849  char reserved[200];
851 
853 typedef enum {
854  dnnl_memory_extra_flag_none = 0x0U,
863  dnnl_memory_extra_flag_scale_adjust = 0x2U,
864  dnnl_memory_extra_flag_gpu_rnn_u8s8_compensation = 0x4U,
866 
868 typedef struct {
871  uint64_t flags;
877  char reserved[64];
879 
884 typedef struct {
886  int ndims;
902 
905 
908 
912 
916 
919  union {
927  // ... other descriptions possible
928  } format_desc;
929 
932 
935 struct dnnl_memory;
936 
938 typedef struct dnnl_memory *dnnl_memory_t;
939 
941 typedef const struct dnnl_memory *const_dnnl_memory_t;
942 
943 #define DNNL_MEMORY_NONE (NULL)
944 #define DNNL_MEMORY_ALLOCATE ((void *)(size_t)-1)
945 
947 
950 
952 typedef void *dnnl_op_desc_t;
954 typedef const void *const_dnnl_op_desc_t;
955 
957 typedef struct {
991  dnnl_dims_t padding[2];
995 
998 
1000 typedef struct {
1011  int axis;
1015 
1017 typedef struct {
1049  float alpha, beta;
1051 
1053 typedef struct {
1067 
1069 typedef struct {
1096  dnnl_dims_t padding[2];
1100 
1102 typedef struct {
1120  float lrn_alpha;
1122  float lrn_beta;
1124  float lrn_k;
1125 } dnnl_lrn_desc_t;
1126 
1128 typedef struct {
1145  dnnl_memory_desc_t diff_data_scaleshift_desc;
1152  unsigned flags;
1154 
1156 typedef struct {
1175  dnnl_memory_desc_t diff_data_scaleshift_desc;
1184  unsigned flags;
1186 
1188 typedef struct {
1215 
1217 typedef enum { dnnl_rnn_flags_undef = 0x0 } dnnl_rnn_flags_t;
1218 
1220 typedef enum {
1231  dnnl_unidirectional = dnnl_unidirectional_left2right,
1233 
1235 typedef struct {
1267  dnnl_memory_desc_t placeholder2_desc;
1268 
1289  dnnl_memory_desc_t diff_placeholder2_desc;
1290 
1292  unsigned int flags;
1296  float alpha;
1297  float beta;
1298 
1299 } dnnl_rnn_desc_t;
1300 
1302 typedef struct {
1310  dnnl_memory_desc_t src_desc[2];
1314 
1316 
1319 
1321 typedef enum {
1329 
1332 struct dnnl_engine;
1334 typedef struct dnnl_engine *dnnl_engine_t;
1335 #if 0
1336 // FIXME: looks like this never happens
1338 typedef const struct dnnl_engine *const_dnnl_engine_t;
1339 #endif
1340 
1342 
1345 
1349 
1352 
1354 typedef const struct dnnl_primitive_desc_iterator
1356 
1358 
1361 
1364 struct dnnl_primitive_desc;
1365 
1368 
1371 
1373 
1376 
1378 typedef enum {
1384 
1390 struct dnnl_primitive_attr;
1391 
1395 
1398 
1417 struct dnnl_post_ops;
1418 
1421 
1423 typedef const struct dnnl_post_ops *const_dnnl_post_ops_t;
1424 
1426 
1429 
1432 struct dnnl_primitive;
1437 
1440 
1441 #define DNNL_ARG_SRC_0 1
1442 #define DNNL_ARG_SRC DNNL_ARG_SRC_0
1443 #define DNNL_ARG_SRC_LAYER DNNL_ARG_SRC_0
1444 #define DNNL_ARG_FROM DNNL_ARG_SRC_0
1445 
1446 #define DNNL_ARG_SRC_1 2
1447 #define DNNL_ARG_SRC_ITER DNNL_ARG_SRC_1
1448 
1449 #define DNNL_ARG_SRC_2 3
1450 #define DNNL_ARG_SRC_ITER_C DNNL_ARG_SRC_2
1451 
1452 #define DNNL_ARG_DST_0 17
1453 #define DNNL_ARG_DST DNNL_ARG_DST_0
1454 #define DNNL_ARG_TO DNNL_ARG_DST_0
1455 #define DNNL_ARG_DST_LAYER DNNL_ARG_DST_0
1456 
1457 #define DNNL_ARG_DST_1 18
1458 #define DNNL_ARG_DST_ITER DNNL_ARG_DST_1
1459 
1460 #define DNNL_ARG_DST_2 19
1461 #define DNNL_ARG_DST_ITER_C DNNL_ARG_DST_2
1462 
1463 #define DNNL_ARG_WEIGHTS_0 33
1464 #define DNNL_ARG_WEIGHTS DNNL_ARG_WEIGHTS_0
1465 #define DNNL_ARG_SCALE_SHIFT DNNL_ARG_WEIGHTS_0
1466 #define DNNL_ARG_WEIGHTS_LAYER DNNL_ARG_WEIGHTS_0
1467 
1468 #define DNNL_ARG_WEIGHTS_1 34
1469 #define DNNL_ARG_WEIGHTS_ITER DNNL_ARG_WEIGHTS_1
1470 
1471 #define DNNL_ARG_BIAS 41
1472 
1473 #define DNNL_ARG_MEAN 49
1474 #define DNNL_ARG_VARIANCE 50
1475 
1476 #define DNNL_ARG_WORKSPACE 64
1477 #define DNNL_ARG_SCRATCHPAD 80
1478 
1479 #define DNNL_ARG_DIFF_SRC_0 129
1480 #define DNNL_ARG_DIFF_SRC DNNL_ARG_DIFF_SRC_0
1481 #define DNNL_ARG_DIFF_SRC_LAYER DNNL_ARG_DIFF_SRC_0
1482 
1483 #define DNNL_ARG_DIFF_SRC_1 130
1484 #define DNNL_ARG_DIFF_SRC_ITER DNNL_ARG_DIFF_SRC_1
1485 
1486 #define DNNL_ARG_DIFF_SRC_2 131
1487 #define DNNL_ARG_DIFF_SRC_ITER_C DNNL_ARG_DIFF_SRC_2
1488 
1489 #define DNNL_ARG_DIFF_DST_0 145
1490 #define DNNL_ARG_DIFF_DST DNNL_ARG_DIFF_DST_0
1491 #define DNNL_ARG_DIFF_DST_LAYER DNNL_ARG_DIFF_DST_0
1492 
1493 #define DNNL_ARG_DIFF_DST_1 146
1494 #define DNNL_ARG_DIFF_DST_ITER DNNL_ARG_DIFF_DST_1
1495 
1496 #define DNNL_ARG_DIFF_DST_2 147
1497 #define DNNL_ARG_DIFF_DST_ITER_C DNNL_ARG_DIFF_DST_2
1498 
1499 #define DNNL_ARG_DIFF_WEIGHTS_0 161
1500 #define DNNL_ARG_DIFF_WEIGHTS DNNL_ARG_DIFF_WEIGHTS_0
1501 #define DNNL_ARG_DIFF_SCALE_SHIFT DNNL_ARG_DIFF_WEIGHTS_0
1502 #define DNNL_ARG_DIFF_WEIGHTS_LAYER DNNL_ARG_DIFF_WEIGHTS_0
1503 
1504 #define DNNL_ARG_DIFF_WEIGHTS_1 162
1505 #define DNNL_ARG_DIFF_WEIGHTS_ITER DNNL_ARG_DIFF_WEIGHTS_1
1506 
1507 #define DNNL_ARG_DIFF_BIAS 169
1508 
1509 #define DNNL_ARG_MULTIPLE_SRC 1024
1510 #define DNNL_ARG_MULTIPLE_DST 2048
1511 
1513 
1520 typedef struct {
1521  int arg;
1523 } dnnl_exec_arg_t;
1524 
1526 
1529 
1558 typedef enum {
1560 
1563 
1566 
1569 
1574 
1577 
1580 
1582 
1583  // memory and op descriptor section
1599 
1600  // memory descriptor section
1610 } dnnl_query_t;
1611 
1613 
1616 
1618 typedef enum {
1629 
1632 struct dnnl_stream;
1634 typedef struct dnnl_stream *dnnl_stream_t;
1636 typedef const struct dnnl_stream *const_dnnl_stream_t;
1637 
1641 
1642 #ifdef __cplusplus
1643 }
1644 #endif
1645 
1646 #endif
permuted 3D tensor
Definition: dnnl_types.h:200
dnnl_memory_desc_t diff_dst_layer_desc
Destination gradient layer memory descriptor.
Definition: dnnl_types.h:1282
const struct dnnl_primitive_attr * const_dnnl_primitive_attr_t
A constant primitive descriptor attributes handle.
Definition: dnnl_types.h:1397
4D CNN activations tensor blocked by channels with block size 8, an alias to dnnl_aBcd8b ...
Definition: dnnl_types.h:454
1D tensor, an alias to dnnl_a
Definition: dnnl_types.h:338
dnnl_alg_kind_t activation_kind
Activation function used for vanilla_rnn cell kind.
Definition: dnnl_types.h:1295
dnnl_memory_desc_t weights_layer_desc
Weights layer memory descriptor.
Definition: dnnl_types.h:1254
2D RNN statistics tensor, an alias to dnnl_ba
Definition: dnnl_types.h:346
dnnl_alg_kind_t alg_kind
The kind of pooling algorithm.
Definition: dnnl_types.h:1080
dnnl_memory_desc_t weights_desc
Weights memory descriptor.
Definition: dnnl_types.h:1201
dnnl_memory_desc_t diff_dst_iter_c_desc
Destination gradient iteration memory descriptor for cell state.
Definition: dnnl_types.h:1286
dnnl_data_type_t
Data type specification.
Definition: dnnl_types.h:68
32-bit signed integer.
Definition: dnnl_types.h:78
struct dnnl_primitive * dnnl_primitive_t
A primitive handle.
Definition: dnnl_types.h:1434
Max pooling.
Definition: dnnl_types.h:695
dnnl_primitive_kind_t primitive_kind
The kind of primitive.
Definition: dnnl_types.h:1131
3D CNN activations tensor, an alias to dnnl_abc
Definition: dnnl_types.h:348
5D CNN activations tensor blocked by channels with block size 8, an alias to dnnl_aBcde8b ...
Definition: dnnl_types.h:445
A descriptor for an RNN operation.
Definition: dnnl_types.h:1235
The operation failed because requested functionality is not implemented.
Definition: dnnl_types.h:58
non-standard 16-bit (bfloat16 w/ 7 bit mantissa) floating point.
Definition: dnnl_types.h:74
dnnl_memory_desc_t bias_desc
Bias memory descriptor.
Definition: dnnl_types.h:1205
dnnl_dim_t local_size
The number of channels to sum over (for cross-channel LRN) or the side length of the square region to...
Definition: dnnl_types.h:1118
dnnl_memory_desc_t data_scaleshift_desc
Scale and shift data and gradient memory descriptors.
Definition: dnnl_types.h:1174
2D CNN activations tensor, an alias to dnnl_ab
Definition: dnnl_types.h:340
3D tensor blocked by 2nd dimension with block size 8
Definition: dnnl_types.h:223
5D CNN activations tensor blocked by channels with block size 4, an alias to dnnl_aBcde4b ...
Definition: dnnl_types.h:442
const struct dnnl_primitive_desc * const_dnnl_primitive_desc_t
A constant primitive descriptor handle.
Definition: dnnl_types.h:1370
A reorder primitive.
Definition: dnnl_types.h:619
plain 1D tensor
Definition: dnnl_types.h:183
pooling descriptor
Definition: dnnl_types.h:1591
dnnl_alg_kind_t alg_kind
LRN algorithm.
Definition: dnnl_types.h:1111
dnnl_format_kind_t format_kind
Memory format kind.
Definition: dnnl_types.h:918
Eltwise: soft_relu.
Definition: dnnl_types.h:682
An opaque structure to describe a primitive.
dnnl_prop_kind_t prop_kind
The kind of propagation.
Definition: dnnl_types.h:1134
permuted 5D tensor
Definition: dnnl_types.h:205
dnnl_dims_t strides
Pooling kernel strides for spatial dimensions.
Definition: dnnl_types.h:1090
An inner product primitive.
Definition: dnnl_types.h:643
dnnl_dim_t group_size
number of groups in group convolution
Definition: dnnl_types.h:1013
dnnl_memory_desc_t bias_desc
Bias memory descriptor.
Definition: dnnl_types.h:977
float scale_adjust
Scale applied to the data.
Definition: dnnl_types.h:875
permuted 3D tensor
Definition: dnnl_types.h:193
dnnl_dim_t dnnl_dims_t[DNNL_MAX_NDIMS]
A type to describe tensor dimensions.
Definition: dnnl_types.h:781
Forward data propagation (training mode).
Definition: dnnl_types.h:594
dnnl_query_t
Primitive descriptor query specification.
Definition: dnnl_types.h:1558
Winograd convolution.
Definition: dnnl_types.h:658
dnnl_primitive_kind_t primitive_kind
The kind of primitive.
Definition: dnnl_types.h:1056
dnnl_alg_kind_t alg_kind
The kind of the convolution algorithm.
Definition: dnnl_types.h:967
dnnl_data_type_t accum_data_type
The accumulator data type. Initialized automatically.
Definition: dnnl_types.h:993
Primitive iterator passed over last primitive descriptor.
Definition: dnnl_types.h:60
dnnl_prop_kind_t prop_kind
The kind of propagation.
Definition: dnnl_types.h:1075
4D CNN weights tensor, an alias to dnnl_abcd
Definition: dnnl_types.h:375
dnnl_memory_t memory
Input/output memory.
Definition: dnnl_types.h:1522
Eltwise: exponent.
Definition: dnnl_types.h:686
5D tensor blocked by 2nd dimension with block size 8
Definition: dnnl_types.h:276
Eltwise: abs.
Definition: dnnl_types.h:674
An opaque structure for primitive descriptor attributes.
3D CNN weights tensor, an alias to dnnl_cba
Definition: dnnl_types.h:371
dnnl_memory_desc_t dst_desc
Destination memory descriptor.
Definition: dnnl_types.h:981
dnnl_memory_desc_t src_desc
Source memory descriptor.
Definition: dnnl_types.h:1082
float lrn_alpha
LRN alpha parameter.
Definition: dnnl_types.h:1120
dnnl_memory_desc_t diff_weights_desc
Weights gradient memory descriptor.
Definition: dnnl_types.h:1203
int64_t dnnl_dim_t
A type to describe tensor dimension.
Definition: dnnl_types.h:778
dnnl_memory_desc_t dst_iter_c_desc
Destination iter memory descriptor for cell state.
Definition: dnnl_types.h:1264
5D CNN activations tensor, an alias to dnnl_acdeb
Definition: dnnl_types.h:360
2D CNN activations tensor, an alias to dnnl_ba
Definition: dnnl_types.h:342
A sum primitive.
Definition: dnnl_types.h:625
An opaque structure to describe a memory.
dnnl_memory_desc_t dst_layer_desc
Destination layer memory descriptor.
Definition: dnnl_types.h:1260
source memory desc
Definition: dnnl_types.h:1602
Undefined primitive.
Definition: dnnl_types.h:617
softmax descriptor
Definition: dnnl_types.h:1590
const struct dnnl_stream * const_dnnl_stream_t
A constant execution stream handle.
Definition: dnnl_types.h:1636
dnnl_wino_memory_format_t
Winograd-specific formats.
Definition: dnnl_types.h:802
A tensor in a generic format described by the stride and blocking values in each dimension.
Definition: dnnl_types.h:95
permuted 5D tensor
Definition: dnnl_types.h:206
dnnl_dims_t strides
Convolution strides in each spatial dimension.
Definition: dnnl_types.h:985
Bidirectional execution of RNN primitive with summation of the results.
Definition: dnnl_types.h:1230
int softmax_axis
The axis along which to perform the softmax.
Definition: dnnl_types.h:1065
permuted 5D tensor
Definition: dnnl_types.h:192
A pooling primitive.
Definition: dnnl_types.h:635
3D CNN activations tensor blocked by channels with block size 16, an alias to dnnl_aBc16b ...
Definition: dnnl_types.h:457
weights memory descriptor desc
Definition: dnnl_types.h:1604
dnnl_dims_t dims
Dimensions in the following order:
Definition: dnnl_types.h:901
dnnl_memory_desc_t diff_weights_iter_desc
Weights gradient iter memory descriptor.
Definition: dnnl_types.h:1278
4D CNN activations tensor blocked by channels with block size 4, an alias to dnnl_aBcd4b ...
Definition: dnnl_types.h:451
plain 4D tensor
Definition: dnnl_types.h:186
The operation was successful.
Definition: dnnl_types.h:52
Description of tensor of packed weights for rnn.
Definition: dnnl_types.h:839
struct dnnl_stream * dnnl_stream_t
An execution stream handle.
Definition: dnnl_types.h:1634
plain 6D tensor
Definition: dnnl_types.h:188
A descriptor of a pooling operation.
Definition: dnnl_types.h:1069
A (out-of-place) concat primitive.
Definition: dnnl_types.h:623
Eltwise: parametric exponential linear unit (elu)
Definition: dnnl_types.h:670
destination grad. memory desc
Definition: dnnl_types.h:1607
source gradient memory desc
Definition: dnnl_types.h:1603
8-bit signed integer.
Definition: dnnl_types.h:80
Backward bias propagation.
Definition: dnnl_types.h:610
4D tensor blocked by 2nd dimension with block size 8
Definition: dnnl_types.h:245
dnnl_memory_desc_t diff_src_iter_c_desc
Source gradient iter memory descriptor for cell state.
Definition: dnnl_types.h:1274
Bidirectional execution of RNN primitive with concatenation of the results.
Definition: dnnl_types.h:1227
int inner_nblks
The number of innermost blocks, e.g. 3 in case of OIhw_4i16o4i_
Definition: dnnl_types.h:793
Out-of-order execution.
Definition: dnnl_types.h:1625
An opaque structure to describe a primitive descriptor iterator.
workspace memory desc
Definition: dnnl_types.h:1608
Backward data propagation.
Definition: dnnl_types.h:606
no query
Definition: dnnl_types.h:1559
float lrn_beta
LRN beta parameter.
Definition: dnnl_types.h:1122
dnnl_memory_desc_t src_desc
Source memory descriptor.
Definition: dnnl_types.h:1197
scratchpad memory desc
Definition: dnnl_types.h:1609
4D tensor blocked by 2nd dimension with block size 4
Definition: dnnl_types.h:237
deconvolution descriptor
Definition: dnnl_types.h:1587
dnnl_memory_desc_t src_iter_desc
Source iteration memory descriptor for hidden state.
Definition: dnnl_types.h:1250
dnnl_alg_kind_t alg_kind
The kind of the binary algorithm.
Definition: dnnl_types.h:1308
16-bit/half-precision floating point.
Definition: dnnl_types.h:72
op descriptor
Definition: dnnl_types.h:1585
dnnl_alg_kind_t cell_kind
RNN cell kind.
Definition: dnnl_types.h:1244
Undefined memory format tag.
Definition: dnnl_types.h:172
propagation kind
Definition: dnnl_types.h:1581
const struct dnnl_post_ops * const_dnnl_post_ops_t
A constant post operation chain handle.
Definition: dnnl_types.h:1423
dnnl_normalization_flags_t
Flags for batch normalization primitive.
Definition: dnnl_types.h:726
dnnl_memory_desc_t stat_desc
Statistics memory descriptor.
Definition: dnnl_types.h:1149
A descriptor of a binary operation.
Definition: dnnl_types.h:1302
Eltwise: bounded_relu.
Definition: dnnl_types.h:680
inner product descriptor
Definition: dnnl_types.h:1595
Undefined memory format, used for empty memory descriptors.
Definition: dnnl_types.h:804
4D CNN weights tensor, an alias to dnnl_bcda
Definition: dnnl_types.h:381
Version type.
Definition: dnnl_types.h:42
primitive kind
Definition: dnnl_types.h:1562
int arg
An argument index, e.g. DNNL_ARG_SRC.
Definition: dnnl_types.h:1521
dnnl_memory_desc_t diff_bias_desc
Bias gradient memory descriptor.
Definition: dnnl_types.h:1207
Packed weights format used in RNN.
Definition: dnnl_types.h:99
dnnl_memory_desc_t weights_desc
Weights memory descriptor.
Definition: dnnl_types.h:973
A descriptor of a convolution operation.
Definition: dnnl_types.h:957
dnnl_data_type_t accum_data_type
The accumulator data type. Initialized automatically.
Definition: dnnl_types.h:1213
3D CNN activations tensor, an alias to dnnl_acb
Definition: dnnl_types.h:350
dnnl_primitive_kind_t
Kinds of primitives.
Definition: dnnl_types.h:615
Default stream configuration.
Definition: dnnl_types.h:1627
permuted 5D tensor
Definition: dnnl_types.h:194
dnnl_dims_t padded_dims
Size of the data including padding in each dimension.
Definition: dnnl_types.h:907
dnnl_dim_t offset0
Offset from memory origin to the current block, non-zero only in a description of a memory sub-block...
Definition: dnnl_types.h:915
Binary add.
Definition: dnnl_types.h:720
dnnl_dims_t padded_offsets
Per-dimension offset from the padding to actual data, the top-level tensor with offsets applied must ...
Definition: dnnl_types.h:911
5D CNN weights tensor, an alias to dnnl_abcde
Definition: dnnl_types.h:385
The operation failed due to an out-of-memory condition.
Definition: dnnl_types.h:54
dnnl_memory_desc_t dst_desc
Destination memory descriptor.
Definition: dnnl_types.h:1312
dnnl_primitive_kind_t primitive_kind
The kind of primitive.
Definition: dnnl_types.h:1305
4D CNN weights tensor, an alias to dnnl_acdb
Definition: dnnl_types.h:379
shuffle descriptor
Definition: dnnl_types.h:1588
dnnl_prop_kind_t prop_kind
The kind of propagation.
Definition: dnnl_types.h:1241
binary descriptor
Definition: dnnl_types.h:1598
runtime estimation (seconds)
Definition: dnnl_types.h:1567
An opaque structure to describe an engine.
dnnl_memory_desc_t data_desc
Source and destination memory descriptor.
Definition: dnnl_types.h:1113
dnnl_memory_desc_t src_desc
Source memory descriptor.
Definition: dnnl_types.h:969
6D tensor blocked by 2nd dimension with block size 4
Definition: dnnl_types.h:293
5D CNN weights tensor (incl. groups), an alias to dnnl_acbde
Definition: dnnl_types.h:400
dnnl_memory_desc_t data_desc
Source and destination memory descriptor.
Definition: dnnl_types.h:1164
void * dnnl_op_desc_t
A pointer to any of the operation descriptors.
Definition: dnnl_types.h:952
Eltwise: linear.
Definition: dnnl_types.h:678
8-bit unsigned integer.
Definition: dnnl_types.h:82
dnnl_prop_kind_t prop_kind
The kind of propagation.
Definition: dnnl_types.h:1023
plain 2D tensor
Definition: dnnl_types.h:184
permuted 4D tensor
Definition: dnnl_types.h:204
Backward weights propagation.
Definition: dnnl_types.h:608
stub
Definition: dnnl_types.h:1601
dnnl_memory_desc_t data_desc
Source and destination memory descriptor.
Definition: dnnl_types.h:1061
dnnl_memory_desc_t diff_data_desc
Source and destination gradient memory descriptor.
Definition: dnnl_types.h:1166
A descriptor of a element-wise operation.
Definition: dnnl_types.h:1017
dnnl_dims_t inner_blks
The size of the blocks, e.g. {4, 16, 4} in case of OIhw_4i16o4i
Definition: dnnl_types.h:795
A descriptor of a Softmax operation.
Definition: dnnl_types.h:1053
permuted 5D tensor
Definition: dnnl_types.h:202
An opaque structure for a chain of post operations.
3D CNN weights tensor, an alias to dnnl_acb
Definition: dnnl_types.h:369
A descriptor of an inner product operation.
Definition: dnnl_types.h:1188
An LRN primitive.
Definition: dnnl_types.h:637
A batch normalization primitive.
Definition: dnnl_types.h:639
struct dnnl_engine * dnnl_engine_t
An engine handle.
Definition: dnnl_types.h:1334
A rnn primitive.
Definition: dnnl_types.h:645
Average pooling include padding.
Definition: dnnl_types.h:697
Local response normalization (LRN) across multiple channels.
Definition: dnnl_types.h:702
dnnl_memory_desc_t dst_desc
Destination memory descriptor.
Definition: dnnl_types.h:1086
dnnl_rnn_flags_t
Flags for RNN cell.
Definition: dnnl_types.h:1217
dnnl_memory_desc_t dst_iter_desc
Destination iter memory descriptor for hidden state.
Definition: dnnl_types.h:1262
Use scale and shift parameters.
Definition: dnnl_types.h:751
4D tensor blocked by 2nd dimension with block size 16
Definition: dnnl_types.h:231
dnnl_format_tag_t
Memory format tag specification.
Definition: dnnl_types.h:170
for creating scratchpad memory
Definition: dnnl_types.h:1576
dnnl_memory_desc_t dst_desc
Destination memory descriptor.
Definition: dnnl_types.h:1209
dnnl_memory_desc_t stat_desc
Mean and variance data memory descriptors.
Definition: dnnl_types.h:1181
Eltwise: gelu.
Definition: dnnl_types.h:691
Generic description of blocked data layout for most memory formats.
Definition: dnnl_types.h:786
weights grad. memory desc
Definition: dnnl_types.h:1605
Backward propagation (with respect to all parameters).
Definition: dnnl_types.h:604
dnnl_status_t
Status values returned by the library functions.
Definition: dnnl_types.h:50
const void * const_dnnl_op_desc_t
A pointer to any of the operation descriptors (constant variant).
Definition: dnnl_types.h:954
2D RNN statistics tensor, an alias to dnnl_ab
Definition: dnnl_types.h:344
4D CNN weights tensor, an alias to dnnl_cdba
Definition: dnnl_types.h:377
4D CNN activations tensor, an alias to dnnl_abcd
Definition: dnnl_types.h:352
number of outputs expected
Definition: dnnl_types.h:1565
dnnl_wino_desc_t wino_desc
Tensor of weights for integer 8bit winograd convolution.
Definition: dnnl_types.h:924
dnnl_alg_kind_t alg_kind
The kind of eltwise algorithm.
Definition: dnnl_types.h:1030
4D RNN states tensor in the format (num_layers, num_directions, batch, state channels).
Definition: dnnl_types.h:410
Unidirectional execution of RNN primitive from left to right.
Definition: dnnl_types.h:1222
The operation failed because of incorrect function arguments.
Definition: dnnl_types.h:56
float batch_norm_epsilon
Batch normalization epsilon parameter.
Definition: dnnl_types.h:1151
dnnl_rnn_direction_t
A direction of RNN primitive execution.
Definition: dnnl_types.h:1220
struct dnnl_memory * dnnl_memory_t
A memory handle.
Definition: dnnl_types.h:938
Use global statistics.
Definition: dnnl_types.h:738
dnnl_prop_kind_t prop_kind
The kind of propagation.
Definition: dnnl_types.h:1162
Description of tensor of weights for winograd 2x3 convolution.
Definition: dnnl_types.h:814
dnnl_prop_kind_t prop_kind
The kind of propagation.
Definition: dnnl_types.h:1059
dnnl_memory_desc_t bias_desc
Bias memory descriptor.
Definition: dnnl_types.h:1258
Direct deconvolution.
Definition: dnnl_types.h:662
Eltwise: hyperbolic tangent non-linearity (tanh)
Definition: dnnl_types.h:668
dnnl_primitive_kind_t primitive_kind
The kind of primitive.
Definition: dnnl_types.h:1105
struct dnnl_primitive_desc_iterator * dnnl_primitive_desc_iterator_t
A primitive descriptor iterator handle.
Definition: dnnl_types.h:1351
dnnl_primitive_kind_t primitive_kind
The kind of primitive.
Definition: dnnl_types.h:1238
dnnl_alg_kind_t
Kinds of algorithms.
Definition: dnnl_types.h:653
dnnl_memory_desc_t diff_src_layer_desc
Source gradient layer memory descriptor.
Definition: dnnl_types.h:1270
int axis
axis for shuffling.
Definition: dnnl_types.h:1011
dnnl_rnn_packed_desc_t rnn_packed_desc
Tensor of packed weights for RNN.
Definition: dnnl_types.h:926
dnnl_dims_t strides
The strides between the outermost blocks.
Definition: dnnl_types.h:789
dnnl_memory_desc_t placeholder_desc
Placeholders.
Definition: dnnl_types.h:1266
Queried element is not required for given primitive.
Definition: dnnl_types.h:64
Internal weights format for 2x3 Winograd.
Definition: dnnl_types.h:808
dnnl_primitive_kind_t primitive_kind
The kind of primitive.
Definition: dnnl_types.h:960
Eltwise: swish.
Definition: dnnl_types.h:693
A descriptor of a shuffle operation.
Definition: dnnl_types.h:1000
A deconvolution primitive.
Definition: dnnl_types.h:629
5D CNN activations tensor, an alias to dnnl_abcde
Definition: dnnl_types.h:358
dnnl_dims_t kernel
Pooling kernel spatial dimensions.
Definition: dnnl_types.h:1092
3D CNN activations tensor blocked by channels with block size 8, an alias to dnnl_aBc8b ...
Definition: dnnl_types.h:463
2D CNN weights tensor, an alias to dnnl_ab
Definition: dnnl_types.h:363
Weights format used in 8bit Winograd convolution.
Definition: dnnl_types.h:97
execution engine
Definition: dnnl_types.h:1561
5D RNN weights tensor in the format (num_layers, num_directions, num_gates, output_channels, input_channels).
Definition: dnnl_types.h:424
dnnl_memory_desc_t diff_src_desc
Source gradient memory descriptor.
Definition: dnnl_types.h:1199
float lrn_k
LRN k parameter.
Definition: dnnl_types.h:1124
dnnl_rnn_direction_t direction
The direction of RNN primitive execution.
Definition: dnnl_types.h:1246
In-order execution.
Definition: dnnl_types.h:1623
LRN within a single channel.
Definition: dnnl_types.h:704
5D tensor blocked by 2nd dimension with block size 4
Definition: dnnl_types.h:267
dnnl_stream_flags_t
Stream flags.
Definition: dnnl_types.h:1618
#define DNNL_MAX_NDIMS
Maximum number of dimensions a tensor can have.
Definition: dnnl_types.h:775
dnnl_memory_desc_t src_iter_c_desc
Source iteration memory descriptor for cell state.
Definition: dnnl_types.h:1252
4D CNN activations tensor, an alias to dnnl_bcda
Definition: dnnl_types.h:356
A user shall query and provide the scratchpad memory to primitives.
Definition: dnnl_types.h:1382
3D RNN data tensor in the format (batch, seq_length, input channels).
Definition: dnnl_types.h:407
4D CNN weights tensor, an alias to dnnl_bacd
Definition: dnnl_types.h:383
dnnl_memory_desc_t diff_desc
Source and Destination of gradient memory descriptor.
Definition: dnnl_types.h:1063
3D tensor blocked by 2nd dimension with block size 4
Definition: dnnl_types.h:217
Average pooling exclude padding.
Definition: dnnl_types.h:699
struct dnnl_primitive_desc * dnnl_primitive_desc_t
A primitive descriptor handle.
Definition: dnnl_types.h:1367
struct dnnl_primitive_attr * dnnl_primitive_attr_t
A primitive descriptor attributes handle that controls primitive behavior.
Definition: dnnl_types.h:1394
Internal weights format for 2x3 Winograd.
Definition: dnnl_types.h:807
CPU engine.
Definition: dnnl_types.h:1325
A descriptor of a Layer Normalization operation.
Definition: dnnl_types.h:1156
RNN cell.
Definition: dnnl_types.h:706
3D tensor blocked by 2nd dimension with block size 16
Definition: dnnl_types.h:213
dnnl_primitive_kind_t primitive_kind
The kind of primitive.
Definition: dnnl_types.h:1003
dnnl_scratchpad_mode_t
Scratchpad mode.
Definition: dnnl_types.h:1378
dnnl_convolution_desc_t dnnl_deconvolution_desc_t
A descriptor of a deconvolution operation.
Definition: dnnl_types.h:997
dnnl_dims_t dilates
Convolution dilates in each spatial dimension.
Definition: dnnl_types.h:987
5D CNN weights tensor (incl. groups), an alias to dnnl_decab
Definition: dnnl_types.h:398
dnnl_memory_desc_t data_desc
Source and destination memory descriptor.
Definition: dnnl_types.h:1136
Undefined memory format kind, used for empty memory descriptors.
Definition: dnnl_types.h:88
A softmax primitive.
Definition: dnnl_types.h:633
source engine
Definition: dnnl_types.h:1578
dnnl_memory_desc_t diff_data_desc
Source and destination gradient memory descriptor.
Definition: dnnl_types.h:1115
dnnl_prop_kind_t prop_kind
The kind of propagation.
Definition: dnnl_types.h:1108
Undefined propagation type.
Definition: dnnl_types.h:591
5D CNN weights tensor (incl. groups), an alias to dnnl_abcde
Definition: dnnl_types.h:396
4D CNN activations tensor blocked by channels with block size 16, an alias to dnnl_aBcd16b ...
Definition: dnnl_types.h:448
Memory descriptor.
Definition: dnnl_types.h:884
dnnl_primitive_kind_t primitive_kind
The kind of primitive.
Definition: dnnl_types.h:1159
const struct dnnl_primitive_desc_iterator * const_dnnl_primitive_desc_iterator_t
A constant primitive descriptor iterator handle.
Definition: dnnl_types.h:1354
Forward data propagation (alias for dnnl_forward_inference).
Definition: dnnl_types.h:600
const struct dnnl_memory * const_dnnl_memory_t
A constant memory handle.
Definition: dnnl_types.h:941
dnnl_data_type_t data_type
Data type of the tensor elements.
Definition: dnnl_types.h:904
5D CNN activations tensor blocked by channels with block size 16, an alias to dnnl_aBcde16b ...
Definition: dnnl_types.h:439
6D CNN weights tensor (incl. groups), an alias to dnnl_abcdef
Definition: dnnl_types.h:402
batch normalization descriptor
Definition: dnnl_types.h:1593
dnnl_data_type_t accum_data_type
The accumulator data type. Initialized automatically.
Definition: dnnl_types.h:1098
dnnl_blocking_desc_t blocking
Description of the data layout for memory formats that use blocking.
Definition: dnnl_types.h:922
dnnl_dims_t inner_idxs
The logical indices of the blocks, e.g.
Definition: dnnl_types.h:798
Indicates the weights have an additional buffer, that depends on the compensation_mask.
Definition: dnnl_types.h:862
Eltwise: ReLU.
Definition: dnnl_types.h:666
Binary mul.
Definition: dnnl_types.h:722
Unidirectional execution of RNN primitive from right to left.
Definition: dnnl_types.h:1224
dnnl_memory_desc_t diff_weights_desc
Weights gradient memory descriptor.
Definition: dnnl_types.h:975
dnnl_memory_desc_t data_scaleshift_desc
Scale and shift data and gradient memory descriptors.
Definition: dnnl_types.h:1144
stub
Definition: dnnl_types.h:1584
dnnl_memory_desc_t diff_dst_desc
Destination gradient memory descriptor.
Definition: dnnl_types.h:983
4D CNN weights tensor (incl. groups), an alias to dnnl_abcd
Definition: dnnl_types.h:394
dnnl_memory_desc_t diff_data_desc
Source and destination gradient memory descriptor.
Definition: dnnl_types.h:1138
5D CNN weights tensor, an alias to dnnl_bcdea
Definition: dnnl_types.h:391
An auxiliary structure to specify primitive&#39;s inputs/outputs at execution.
Definition: dnnl_types.h:1520
dnnl_memory_desc_t src_layer_desc
Source layer memory descriptor.
Definition: dnnl_types.h:1248
3D CNN weights tensor, an alias to dnnl_abc
Definition: dnnl_types.h:367
permuted 4D tensor
Definition: dnnl_types.h:201
Undefined memory format tag.
Definition: dnnl_types.h:175
5D RNN weights tensor in the format (num_layers, num_directions, input_channels, num_gates, output_channels).
Definition: dnnl_types.h:417
float layer_norm_epsilon
Layer normalization epsilon parameter.
Definition: dnnl_types.h:1183
unsigned int flags
RNN cell flags.
Definition: dnnl_types.h:1292
dnnl_prop_kind_t prop_kind
The kind of propagation.
Definition: dnnl_types.h:1006
Fuse with ReLU.
Definition: dnnl_types.h:764
3D CNN activations tensor blocked by channels with block size 4, an alias to dnnl_aBc4b ...
Definition: dnnl_types.h:460
plain 3D tensor
Definition: dnnl_types.h:185
uint64_t flags
The flags contain arbitrary extra information, such as compensation.
Definition: dnnl_types.h:871
dnnl_memory_desc_t diff_dst_iter_desc
Destination gradient iteration memory descriptor for hidden state.
Definition: dnnl_types.h:1284
Description of extra information stored in memory.
Definition: dnnl_types.h:868
4D RNN bias tensor in the format (num_layers, num_directions, num_gates, output_channels).
Definition: dnnl_types.h:431
dnnl_prop_kind_t prop_kind
The kind of propagation.
Definition: dnnl_types.h:1195
permuted 3D tensor
Definition: dnnl_types.h:203
6D tensor blocked by 2nd dimension with block size 16
Definition: dnnl_types.h:289
const struct dnnl_primitive * const_dnnl_primitive_t
A constant primitive handle.
Definition: dnnl_types.h:1436
A descriptor of a Local Response Normalization (LRN) operation.
Definition: dnnl_types.h:1102
int ndims
Number of dimensions.
Definition: dnnl_types.h:886
dnnl_memory_desc_t diff_data_desc
Source and destination gradient memory descriptor.
Definition: dnnl_types.h:1034
number of inputs expected
Definition: dnnl_types.h:1564
An element-wise primitive.
Definition: dnnl_types.h:631
Direct convolution.
Definition: dnnl_types.h:656
dnnl_memory_desc_t weights_iter_desc
Weights iteration memory descriptor.
Definition: dnnl_types.h:1256
Forward data propagation (alias for dnnl_forward_training).
Definition: dnnl_types.h:602
A matrix multiplication primitive.
Definition: dnnl_types.h:647
A shuffle primitive.
Definition: dnnl_types.h:621
5D CNN weights tensor, an alias to dnnl_cdeba
Definition: dnnl_types.h:387
Default order execution.
Definition: dnnl_types.h:1621
dnnl_primitive_kind_t primitive_kind
The kind of primitive.
Definition: dnnl_types.h:1072
The library manages scratchpad (default)
Definition: dnnl_types.h:1380
dnnl_memory_desc_t diff_bias_desc
Bias gradient memory descriptor.
Definition: dnnl_types.h:1280
A descriptor of a Batch Normalization operation.
Definition: dnnl_types.h:1128
Eltwise: square root.
Definition: dnnl_types.h:676
(scratch) memory, additional to all inputs and outputs memory (bytes)
Definition: dnnl_types.h:1573
struct dnnl_post_ops * dnnl_post_ops_t
A post operation chain handle.
Definition: dnnl_types.h:1420
permuted 5D tensor
Definition: dnnl_types.h:196
5D CNN weights tensor, an alias to dnnl_acdeb
Definition: dnnl_types.h:389
Primitive or engine failed on execution.
Definition: dnnl_types.h:62
dnnl_prop_kind_t
Kinds of propagation.
Definition: dnnl_types.h:588
lrn descriptor
Definition: dnnl_types.h:1592
permuted 4D tensor
Definition: dnnl_types.h:199
dnnl_memory_desc_t diff_placeholder_desc
Placeholders.
Definition: dnnl_types.h:1288
An opaque structure to describe a primitive descriptor.
dnnl_prop_kind_t prop_kind
The kind of propagation.
Definition: dnnl_types.h:964
dnnl_memory_desc_t data_desc
Source and destination memory descriptor.
Definition: dnnl_types.h:1032
dnnl_memory_desc_t data_desc
Source and destination memory descriptor, and source and destination gradient memory descriptor...
Definition: dnnl_types.h:1009
permuted 2D tensor
Definition: dnnl_types.h:197
A binary primitive.
Definition: dnnl_types.h:649
dnnl_memory_desc_t diff_src_desc
Source gradient memory descriptor.
Definition: dnnl_types.h:971
An opaque structure to describe an execution stream.
Forward data propagation (inference mode).
Definition: dnnl_types.h:598
Convolution algorithm(either direct or Winograd) is chosen just in time.
Definition: dnnl_types.h:660
permuted 3D tensor
Definition: dnnl_types.h:198
destination engine
Definition: dnnl_types.h:1579
GRU cell with linear before reset.
Definition: dnnl_types.h:718
dnnl_memory_desc_t diff_src_iter_desc
Source gradient iter memory descriptor for hidden state.
Definition: dnnl_types.h:1272
dnnl_memory_desc_t diff_src_desc
Source gradient memory descriptor.
Definition: dnnl_types.h:1084
GEMM descriptor.
Definition: dnnl_types.h:1597
Internal weights format for 4x3 Winograd.
Definition: dnnl_types.h:810
LSTM cell.
Definition: dnnl_types.h:708
layer normalization descriptor
Definition: dnnl_types.h:1594
convolution descriptor
Definition: dnnl_types.h:1586
dnnl_memory_desc_t diff_dst_desc
Destination gradient memory descriptor.
Definition: dnnl_types.h:1088
#define DNNL_RNN_MAX_N_PARTS
Maximum number of parts of RNN weights tensor that require separate computation.
Definition: dnnl_types.h:836
Eltwise: logistic.
Definition: dnnl_types.h:684
GRU cell.
Definition: dnnl_types.h:710
Undefined data type, used for empty memory descriptors.
Definition: dnnl_types.h:70
2D CNN weights tensor, an alias to dnnl_ba
Definition: dnnl_types.h:365
A convolution primitive.
Definition: dnnl_types.h:627
dnnl_memory_desc_t diff_dst_desc
Destination gradient memory descriptor.
Definition: dnnl_types.h:1211
4D tensor blocked by 1st and 2nd dimension with block size 8
Definition: dnnl_types.h:250
dnnl_format_kind_t
Memory format kind.
Definition: dnnl_types.h:86
dnnl_memory_desc_t diff_bias_desc
Bias gradient memory descriptor.
Definition: dnnl_types.h:979
Internal weights format for 2x3 Winograd.
Definition: dnnl_types.h:806
32-bit/single-precision floating point.
Definition: dnnl_types.h:76
destination memory desc
Definition: dnnl_types.h:1606
Unspecified format kind.
Definition: dnnl_types.h:91
dnnl_primitive_kind_t primitive_kind
The kind of primitive.
Definition: dnnl_types.h:1020
dnnl_memory_extra_flags_t
Flags for memory special features.
Definition: dnnl_types.h:853
dnnl_memory_desc_t diff_weights_layer_desc
Weights gradient layer memory descriptor.
Definition: dnnl_types.h:1276
3D CNN weights tensor, an alias to dnnl_bca
Definition: dnnl_types.h:373
Just a sentinel, not real memory format tag.
Definition: dnnl_types.h:333
rnn descriptor
Definition: dnnl_types.h:1596
eltwise descriptor
Definition: dnnl_types.h:1589
int compensation_mask
Compensation mask.
Definition: dnnl_types.h:873
Winograd deconvolution.
Definition: dnnl_types.h:664
memory consumption – extra
Definition: dnnl_types.h:1568
plain 5D tensor
Definition: dnnl_types.h:187
A layer normalization primitive.
Definition: dnnl_types.h:641
dnnl_primitive_kind_t primitive_kind
The kind of primitive.
Definition: dnnl_types.h:1191
dnnl_engine_kind_t
Kinds of engines.
Definition: dnnl_types.h:1321
An unspecified engine.
Definition: dnnl_types.h:1323
Eltwise: square.
Definition: dnnl_types.h:672
permuted 4D tensor
Definition: dnnl_types.h:195
float alpha
Algorithm specific parameter.
Definition: dnnl_types.h:1049
5D tensor blocked by 2nd dimension with block size 16
Definition: dnnl_types.h:260
GPU engine.
Definition: dnnl_types.h:1327
3D RNN data tensor in the format (seq_length, batch, input channels).
Definition: dnnl_types.h:405
4D CNN activations tensor, an alias to dnnl_acdb
Definition: dnnl_types.h:354