File size: 71,716 Bytes
9422383 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296 1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312 1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328 1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344 1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360 1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376 1377 1378 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392 1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 1406 1407 1408 1409 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419 1420 1421 1422 1423 1424 1425 1426 1427 1428 1429 1430 1431 1432 1433 1434 1435 1436 1437 1438 1439 1440 1441 1442 1443 1444 1445 1446 1447 1448 1449 1450 1451 1452 1453 1454 1455 1456 1457 1458 1459 1460 1461 1462 1463 1464 1465 1466 1467 1468 1469 1470 1471 1472 1473 1474 1475 1476 1477 1478 1479 1480 1481 1482 1483 1484 1485 1486 1487 1488 1489 1490 1491 | diff --git a/sources/xnnpack/include/xnnpack.h b/sources/xnnpack/include/xnnpack.h
index 1b5d0dd..0818889 100644
--- a/sources/xnnpack/include/xnnpack.h
+++ b/sources/xnnpack/include/xnnpack.h
@@ -604,19 +604,6 @@ enum xnn_status xnn_define_unary(
uint32_t output_id,
uint32_t flags);
-/// Define a Convert Node and add it to a Subgraph.
-///
-/// @param subgraph - a Subgraph object that will own the created Node.
-/// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph.
-/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its
-/// shape must match the shape of the input tensor.
-/// @param flags - binary features of the Convert Node. No supported flags are currently defined.
-XNN_DEPRECATED enum xnn_status xnn_define_convert(
- xnn_subgraph_t subgraph,
- uint32_t input_id,
- uint32_t output_id,
- uint32_t flags);
-
/// Define a 2D Convolution Node and add it to a Subgraph.
///
/// @param subgraph - a Subgraph object that will own the created Node.
@@ -812,84 +799,6 @@ enum xnn_status xnn_define_depth_to_space(
uint32_t block_size,
uint32_t flags);
-/// Define a 1D Global Average Pooling Node and add it to a Subgraph.
-///
-/// @param subgraph - a Subgraph object that will own the created Node.
-/// @param output_min - lower bound for clipping output values.
-/// @param output_max - upper bound for clipping output values.
-/// @param input_id - Value ID for the input tensor. The input tensor must be a dense tensor with 2 or more dimensions
-/// defined in the @a subgraph. Averaging is performed across the second-innermost dimension.
-/// @param output_id - Value ID for the output tensor. The output tensor must be a dense tensor with 2 or more
-/// dimensions defined in the @a subgraph.
-/// @param flags - binary features of the 1D Global Average Pooling Node. The only currently supported value is
-/// XNN_FLAG_KEEP_DIMS.
-XNN_DEPRECATED enum xnn_status xnn_define_global_average_pooling_1d(
- xnn_subgraph_t subgraph,
- float output_min,
- float output_max,
- uint32_t input_id,
- uint32_t output_id,
- uint32_t flags);
-
-/// Define a 2D Global Average Pooling Node and add it to a Subgraph.
-///
-/// @param subgraph - a Subgraph object that will own the created Node.
-/// @param output_min - lower bound for clipping output values.
-/// @param output_max - upper bound for clipping output values.
-/// @param input_id - Value ID for the input tensor. The input tensor must be a dense tensor with 3 or more dimensions
-/// defined in the @a subgraph. Averaging is performed across the second- and third-innermost
-/// dimensions.
-/// @param output_id - Value ID for the output tensor. The output tensor must be a dense tensor with 3 or more
-/// dimensions defined in the @a subgraph.
-/// @param flags - binary features of the 2D Global Average Pooling Node. The only currently supported value is
-/// XNN_FLAG_KEEP_DIMS.
-XNN_DEPRECATED enum xnn_status xnn_define_global_average_pooling_2d(
- xnn_subgraph_t subgraph,
- float output_min,
- float output_max,
- uint32_t input_id,
- uint32_t output_id,
- uint32_t flags);
-
-/// Define a 1D Global Sum Pooling Node and add it to a Subgraph.
-///
-/// @param subgraph - a Subgraph object that will own the created Node.
-/// @param output_min - lower bound for clipping output values.
-/// @param output_max - upper bound for clipping output values.
-/// @param input_id - Value ID for the input tensor. The input tensor must be a dense tensor with 2 or more dimensions
-/// defined in the @a subgraph. Averaging is performed across the second-innermost dimension.
-/// @param output_id - Value ID for the output tensor. The output tensor must be a dense tensor with 2 or more
-/// dimensions defined in the @a subgraph.
-/// @param flags - binary features of the 1D Global Sum Pooling Node. The only currently supported value is
-/// XNN_FLAG_KEEP_DIMS.
-XNN_DEPRECATED enum xnn_status xnn_define_global_sum_pooling_1d(
- xnn_subgraph_t subgraph,
- float output_min,
- float output_max,
- uint32_t input_id,
- uint32_t output_id,
- uint32_t flags);
-
-/// Define a 2D Global Sum Pooling Node and add it to a Subgraph.
-///
-/// @param subgraph - a Subgraph object that will own the created Node.
-/// @param output_min - lower bound for clipping output values.
-/// @param output_max - upper bound for clipping output values.
-/// @param input_id - Value ID for the input tensor. The input tensor must be a dense tensor with 3 or more dimensions
-/// defined in the @a subgraph. Averaging is performed across the second- and third-innermost
-/// dimensions.
-/// @param output_id - Value ID for the output tensor. The output tensor must be a dense tensor with 3 or more
-/// dimensions defined in the @a subgraph.
-/// @param flags - binary features of the 2D Global Sum Pooling Node. The only currently supported value is
-/// XNN_FLAG_KEEP_DIMS.
-XNN_DEPRECATED enum xnn_status xnn_define_global_sum_pooling_2d(
- xnn_subgraph_t subgraph,
- float output_min,
- float output_max,
- uint32_t input_id,
- uint32_t output_id,
- uint32_t flags);
-
/// Define a 2D Average Pooling Node and add it to a Subgraph.
///
/// @param subgraph - a Subgraph object that will own the created Node.
@@ -1171,60 +1080,6 @@ enum xnn_status xnn_define_binary(
uint32_t output_id,
uint32_t flags);
-/// Define a 2-Input Add Node and add it to a Subgraph.
-///
-/// The 2-Input Add Node computes elementwise addition of two tensor inputs with numpy broadcasting rules.
-///
-/// @param subgraph - a Subgraph object that will own the created Node.
-/// @param output_min - lower bound for clipping output values.
-/// @param output_max - upper bound for clipping output values.
-/// @param input1_id - Value ID for the first input tensor. The input tensor must be an N-dimensional tensor defined in
-/// the @a subgraph with each dimension either equal to the corresponding dimension of the second
-/// input, or equal to 1. In the latter case, the elements of the input tensor are broadcasted along
-/// that dimension.
-/// @param input2_id - Value ID for the second input tensor. The input tensor must be an M-dimensional tensor defined in
-/// the @a subgraph with each dimension either equal to the corresponding dimension of the first
-/// input, or equal to 1. In the latter case, the elements of the input tensor are broadcasted along
-/// that dimension.
-/// @param output_id - Value ID for the output tensor. The output tensor must be a max(N,M)-dimensional tensor defined
-/// in the @a subgraph with each dimension equal to the maximum between the corresponding dimension
-/// of the two inputs.
-/// @param flags - binary features of the Add Node. No supported flags are currently defined.
-XNN_DEPRECATED enum xnn_status xnn_define_add2(
- xnn_subgraph_t subgraph,
- float output_min,
- float output_max,
- uint32_t input1_id,
- uint32_t input2_id,
- uint32_t output_id,
- uint32_t flags);
-
-/// Define a 2-Input Multiply Node and add it to a Subgraph.
-///
-/// The 2-Input Multiply Node computes elementwise multiplication of two tensor inputs with numpy broadcasting rules.
-///
-/// @param subgraph - a Subgraph object that will own the created Node.
-/// @param input1_id - Value ID for the first input tensor. The input tensor must be an N-dimensional tensor defined in
-/// the @a subgraph with each dimension either equal to the corresponding dimension of the second
-/// input, or equal to 1. In the latter case, the elements of the input tensor are broadcasted along
-/// that dimension.
-/// @param input2_id - Value ID for the second input tensor. The input tensor must be an M-dimensional tensor defined in
-/// the @a subgraph with each dimension either equal to the corresponding dimension of the first
-/// input, or equal to 1. In the latter case, the elements of the input tensor are broadcasted along
-/// that dimension.
-/// @param output_id - Value ID for the output tensor. The output tensor must be a max(N,M)-dimensional tensor defined
-/// in the @a subgraph with each dimension equal to the maximum between the corresponding dimension
-/// of the two inputs.
-/// @param flags - binary features of the Multiply Node. No supported flags are currently defined.
-XNN_DEPRECATED enum xnn_status xnn_define_multiply2(
- xnn_subgraph_t subgraph,
- float output_min,
- float output_max,
- uint32_t input1_id,
- uint32_t input2_id,
- uint32_t output_id,
- uint32_t flags);
-
// Cap operations applied to logits (Q * K) of attention operator.
enum xnn_attention_logits_cap_type {
// No capping.
@@ -1238,135 +1093,6 @@ struct xnn_attention_logits_cap_tanh_params {
float cap;
};
-/// Define a Subtract Node and add it to a Subgraph.
-///
-/// The Subtract Node computes elementwise subtraction of two tensor inputs with numpy broadcasting rules.
-///
-/// @param subgraph - a Subgraph object that will own the created Node.
-/// @param output_min - lower bound for clipping output values.
-/// @param output_max - upper bound for clipping output values.
-/// @param input1_id - Value ID for the first input tensor. The input tensor must be an N-dimensional tensor defined in
-/// the @a subgraph with each dimension either equal to the corresponding dimension of the second
-/// input, or equal to 1. In the latter case, the elements of the input tensor are broadcasted along
-/// that dimension.
-/// @param input2_id - Value ID for the second input tensor. The input tensor must be an M-dimensional tensor defined in
-/// the @a subgraph with each dimension either equal to the corresponding dimension of the first
-/// input, or equal to 1. In the latter case, the elements of the input tensor are broadcasted along
-/// that dimension.
-/// @param output_id - Value ID for the output tensor. The output tensor must be a max(N,M)-dimensional tensor defined
-/// in the @a subgraph with each dimension equal to the maximum between the corresponding dimension
-/// of the two inputs.
-/// @param flags - binary features of the Subtract Node. No supported flags are currently defined.
-XNN_DEPRECATED enum xnn_status xnn_define_subtract(
- xnn_subgraph_t subgraph,
- float output_min,
- float output_max,
- uint32_t input1_id,
- uint32_t input2_id,
- uint32_t output_id,
- uint32_t flags);
-
-/// Define a Divide Node and add it to a Subgraph.
-///
-/// The Divide Node computes elementwise division of two tensor inputs with numpy broadcasting rules.
-///
-/// @param subgraph - a Subgraph object that will own the created Node.
-/// @param output_min - lower bound for clipping output values.
-/// @param output_max - upper bound for clipping output values.
-/// @param input1_id - Value ID for the first input tensor. The input tensor must be an N-dimensional tensor defined in
-/// the @a subgraph with each dimension either equal to the corresponding dimension of the second
-/// input, or equal to 1. In the latter case, the elements of the input tensor are broadcasted along
-/// that dimension.
-/// @param input2_id - Value ID for the second input tensor. The input tensor must be an M-dimensional tensor defined in
-/// the @a subgraph with each dimension either equal to the corresponding dimension of the first
-/// input, or equal to 1. In the latter case, the elements of the input tensor are broadcasted along
-/// that dimension.
-/// @param output_id - Value ID for the output tensor. The output tensor must be a max(N,M)-dimensional tensor defined
-/// in the @a subgraph with each dimension equal to the maximum between the corresponding dimension
-/// of the two inputs.
-/// @param flags - binary features of the Divide Node. No supported flags are currently defined.
-XNN_DEPRECATED enum xnn_status xnn_define_divide(
- xnn_subgraph_t subgraph,
- float output_min,
- float output_max,
- uint32_t input1_id,
- uint32_t input2_id,
- uint32_t output_id,
- uint32_t flags);
-
-/// Define a 2-Input Maximum Node and add it to a Subgraph.
-///
-/// The 2-Input Maximum Node computes elementwise maximum of two tensor inputs with numpy broadcasting rules.
-///
-/// @param subgraph - a Subgraph object that will own the created Node.
-/// @param input1_id - Value ID for the first input tensor. The input tensor must be an N-dimensional tensor defined in
-/// the @a subgraph with each dimension either equal to the corresponding dimension of the second
-/// input, or equal to 1. In the latter case, the elements of the input tensor are broadcasted along
-/// that dimension.
-/// @param input2_id - Value ID for the second input tensor. The input tensor must be an M-dimensional tensor defined in
-/// the @a subgraph with each dimension either equal to the corresponding dimension of the first
-/// input, or equal to 1. In the latter case, the elements of the input tensor are broadcasted along
-/// that dimension.
-/// @param output_id - Value ID for the output tensor. The output tensor must be a max(N,M)-dimensional tensor defined
-/// in the @a subgraph with each dimension equal to the maximum between the corresponding dimension
-/// of the two inputs.
-/// @param flags - binary features of the Maximum Node. No supported flags are currently defined.
-XNN_DEPRECATED enum xnn_status xnn_define_maximum2(
- xnn_subgraph_t subgraph,
- uint32_t input1_id,
- uint32_t input2_id,
- uint32_t output_id,
- uint32_t flags);
-
-/// Define a 2-Input Minimum Node and add it to a Subgraph.
-///
-/// The 2-Input Minimum Node computes elementwise minimum of two tensor inputs with numpy broadcasting rules.
-///
-/// @param subgraph - a Subgraph object that will own the created Node.
-/// @param input1_id - Value ID for the first input tensor. The input tensor must be an N-dimensional tensor defined in
-/// the @a subgraph with each dimension either equal to the corresponding dimension of the second
-/// input, or equal to 1. In the latter case, the elements of the input tensor are broadcasted along
-/// that dimension.
-/// @param input2_id - Value ID for the second input tensor. The input tensor must be an M-dimensional tensor defined in
-/// the @a subgraph with each dimension either equal to the corresponding dimension of the first
-/// input, or equal to 1. In the latter case, the elements of the input tensor are broadcasted along
-/// that dimension.
-/// @param output_id - Value ID for the output tensor. The output tensor must be a max(N,M)-dimensional tensor defined
-/// in the @a subgraph with each dimension equal to the maximum between the corresponding dimension
-/// of the two inputs.
-/// @param flags - binary features of the Minimum Node. No supported flags are currently defined.
-XNN_DEPRECATED enum xnn_status xnn_define_minimum2(
- xnn_subgraph_t subgraph,
- uint32_t input1_id,
- uint32_t input2_id,
- uint32_t output_id,
- uint32_t flags);
-
-/// Define a Squared Difference Node and add it to a Subgraph.
-///
-/// The Squared Difference Node computes elementwise squared difference of two tensor inputs with numpy broadcasting
-/// rules.
-///
-/// @param subgraph - a Subgraph object that will own the created Node.
-/// @param input1_id - Value ID for the first input tensor. The input tensor must be an N-dimensional tensor defined in
-/// the @a subgraph with each dimension either equal to the corresponding dimension of the second
-/// input, or equal to 1. In the latter case, the elements of the input tensor are broadcasted along
-/// that dimension.
-/// @param input2_id - Value ID for the second input tensor. The input tensor must be an M-dimensional tensor defined in
-/// the @a subgraph with each dimension either equal to the corresponding dimension of the first
-/// input, or equal to 1. In the latter case, the elements of the input tensor are broadcasted along
-/// that dimension.
-/// @param output_id - Value ID for the output tensor. The output tensor must be a max(N,M)-dimensional tensor defined
-/// in the @a subgraph with each dimension equal to the maximum between the corresponding dimension
-/// of the two inputs.
-/// @param flags - binary features of the Squared Difference Node. No supported flags are currently defined.
-XNN_DEPRECATED enum xnn_status xnn_define_squared_difference(
- xnn_subgraph_t subgraph,
- uint32_t input1_id,
- uint32_t input2_id,
- uint32_t output_id,
- uint32_t flags);
-
/// Define a Constant Pad Node with static padding specification and add it to a Subgraph.
///
/// @param subgraph - a Subgraph object that will own the created Node.
@@ -1466,26 +1192,6 @@ enum xnn_status xnn_define_split_dim( //
uint32_t output_id, //
uint32_t flags);
-/// Define a Mean Node and add it to a Subgraph.
-///
-/// @param subgraph - a Subgraph object that will own the created Node.
-/// @param num_reduction_axes - number of axes along which mean is computed.
-/// @param reduction_axes - axes along which mean is computed.
-/// @param input_id - Value ID for the input tensor. The input tensor must be a dense tensor with at least
-/// @a num_reduction_axes dimensions defined in the @a subgraph.
-/// @param output_id - Value ID for the output tensor. The output tensor must be a dense tensor defined in the
-/// @a subgraph with @a num_reduction_axes fewer dimensions than the input tensor (if
-/// XNN_FLAG_KEEP_DIMS is not specified), or has same dimension rank but the dimension at
-/// @a reduction_axes reduced to 1 (if XNN_FLAG_KEEP_DIMS is specified).
-/// @param flags - binary features of the Mean Node. The only currently supported value is XNN_FLAG_KEEP_DIMS
-XNN_DEPRECATED enum xnn_status xnn_define_static_mean(
- xnn_subgraph_t subgraph,
- size_t num_reduction_axes,
- const size_t* reduction_axes,
- uint32_t input_id,
- uint32_t output_id,
- uint32_t flags);
-
enum xnn_reduce_operator {
xnn_reduce_invalid = -1,
xnn_reduce_sum,
@@ -1496,27 +1202,6 @@ enum xnn_reduce_operator {
xnn_reduce_min,
};
-/// Define a Reduce Node and add it to a Subgraph.
-///
-/// @param subgraph - a Subgraph object that will own the created Node.
-/// @param num_reduction_axes - number of axes along which reduce is computed.
-/// @param reduction_axes - axes along which reduce is computed.
-/// @param input_id - Value ID for the input tensor. The input tensor must be a dense tensor with at least
-/// @a num_reduction_axes dimensions defined in the @a subgraph.
-/// @param output_id - Value ID for the output tensor. The output tensor must be a dense tensor defined in the
-/// @a subgraph with @a num_reduction_axes fewer dimensions than the input tensor (if
-/// XNN_FLAG_KEEP_DIMS is not specified), or has same dimension rank but the dimension at
-/// @a reduction_axes reduced to 1 (if XNN_FLAG_KEEP_DIMS is specified).
-/// @param flags - binary features of the Reduce Node. The only currently supported value is XNN_FLAG_KEEP_DIMS
-enum xnn_status xnn_define_static_reduce(
- xnn_subgraph_t subgraph,
- enum xnn_reduce_operator reduce_operator_type,
- size_t num_reduction_axes,
- const size_t* reduction_axes,
- uint32_t input_id,
- uint32_t output_id,
- uint32_t flags);
-
/// Define a Reduce Node and add it to a Subgraph.
///
/// @param subgraph - a Subgraph object that will own the created Node.
@@ -1690,22 +1375,6 @@ XNN_DEPRECATED enum xnn_status xnn_define_concatenate5(
uint32_t output_id,
uint32_t flags);
-/// Define a Copy Sign Node and add it to a Subgraph.
-///
-/// The Copy Sign Node copies the sign of the second input to the first input.
-///
-/// @param subgraph - a Subgraph object that will own the created Node.
-/// @param input1_id - Value ID for the first input tensor. The input tensor must be defined in the @a subgraph.
-/// @param input2_id - Value ID for the second input tensor. The input tensor must be defined in the @a subgraph.
-/// @param output_id - Value ID for the output tensor.
-/// @param flags - binary features of the Copy Sign Node. No supported flags are currently defined.
-XNN_DEPRECATED enum xnn_status xnn_define_copysign(
- xnn_subgraph_t subgraph,
- uint32_t input1_id,
- uint32_t input2_id,
- uint32_t output_id,
- uint32_t flags);
-
/// Define a Copy Node and add it to a Subgraph.
///
/// The Copy Node copies an input tensor to an output tensor.
@@ -1746,95 +1415,6 @@ enum xnn_status xnn_define_even_split(
const uint32_t* outputs,
uint32_t flags);
-/// Define a 2-Output Split Node and add it to a Subgraph.
-///
-/// The 2-Output Split Node splits an input tensor into two output tensors along a specified axis evenly.
-///
-/// @param subgraph - a Subgraph object that will own the created Node.
-/// @param split_dim - the dimension to split the input tensor along. If this is less than zero, the number of
-/// dimensions is added to it.
-/// @param input_id - Value ID for the input tensor. The input tensor must be an N-dimensional tensor defined in the @a
-/// subgraph.
-/// @param output1_id - Value ID for the first output tensor. The output tensor must be an N-dimensional tensor defined
-/// in the @a subgraph with each dimension, except the axis, equal to the corresponding dimension
-/// of the second output. The split_dim dimension is half of the input's split_dim.
-/// @param output2_id - Value ID for the second output tensor. The output tensor must be an N-dimensional tensor
-/// defined in the @a subgraph with each dimension, except the axis, equal to the corresponding
-/// dimension of the first output. The split_dim dimension is half of the input's split_dim.
-/// @param flags - binary features of the Split Node. No supported flags are currently defined.
-XNN_DEPRECATED enum xnn_status xnn_define_even_split2(
- xnn_subgraph_t subgraph,
- int32_t split_dim,
- uint32_t input_id,
- uint32_t output1_id,
- uint32_t output2_id,
- uint32_t flags);
-
-/// Define a 3-Output Split Node and add it to a Subgraph.
-///
-/// The 3-Output Split Node splits an input tensor into three output tensors along a specified axis evenly.
-///
-/// @param subgraph - a Subgraph object that will own the created Node.
-/// @param split_dim - the dimension to split the input tensor along. If this is less than zero, the number of
-/// dimensions is added to it.
-/// @param input_id - Value ID for the input tensor. The input tensor must be an N-dimensional tensor defined in the @a
-/// subgraph.
-/// @param output1_id - Value ID for the first output tensor. The output tensor must be an N-dimensional tensor defined
-/// in the @a subgraph with each dimension, except the axis, equal to the corresponding dimension
-/// of the second and third output. The split_dim dimension is one third of the input's split_dim.
-/// @param output2_id - Value ID for the second output tensor. The output tensor must be an N-dimensional tensor
-/// defined in the @a subgraph with each dimension, except the axis, equal to the corresponding
-/// dimension of the first and third output. The split_dim dimension is one third of the input's
-/// split_dim.
-/// @param output3_id - Value ID for the third output tensor. The output tensor must be an N-dimensional tensor
-/// defined in the @a subgraph with each dimension, except the axis, equal to the corresponding
-/// dimension of the second and third output. The split_dim dimension is one third of the input's
-/// split_dim.
-/// @param flags - binary features of the Split Node. No supported flags are currently defined.
-XNN_DEPRECATED enum xnn_status xnn_define_even_split3(
- xnn_subgraph_t subgraph,
- int32_t split_dim,
- uint32_t input_id,
- uint32_t output1_id,
- uint32_t output2_id,
- uint32_t output3_id,
- uint32_t flags);
-
-/// Define a 4-Output Split Node and add it to a Subgraph.
-///
-/// The 4-Output Split Node splits an input tensor into four output tensors along a specified axis evenly.
-///
-/// @param subgraph - a Subgraph object that will own the created Node.
-/// @param split_dim - the dimension to split the input tensor along. If this is less than zero, the number of
-/// dimensions is added to it.
-/// @param input_id - Value ID for the input tensor. The input tensor must be an N-dimensional tensor defined in the @a
-/// subgraph.
-/// @param output1_id - Value ID for the first output tensor. The output tensor must be an N-dimensional tensor defined
-/// in the @a subgraph with each dimension, except the axis, equal to the corresponding dimension
-/// of the other output tensors. The split_dim dimension is one fourth of the input's split_dim.
-/// @param output2_id - Value ID for the second output tensor. The output tensor must be an N-dimensional tensor
-/// defined in the @a subgraph with each dimension, except the axis, equal to the corresponding
-/// dimension of the other output tensors. The split_dim dimension is one fourth of the input's
-/// split_dim.
-/// @param output3_id - Value ID for the third output tensor. The output tensor must be an N-dimensional tensor
-/// defined in the @a subgraph with each dimension, except the axis, equal to the corresponding
-/// dimension of the other output tensors. The split_dim dimension is one fourth of the input's
-/// split_dim.
-/// @param output4_id - Value ID for the fourth output tensor. The output tensor must be an N-dimensional tensor
-/// defined in the @a subgraph with each dimension, except the axis, equal to the corresponding
-/// dimension of the other output tensors. The split_dim dimension is one fourth of the input's
-/// split_dim.
-/// @param flags - binary features of the Split Node. No supported flags are currently defined.
-XNN_DEPRECATED enum xnn_status xnn_define_even_split4(
- xnn_subgraph_t subgraph,
- int32_t split_dim,
- uint32_t input_id,
- uint32_t output1_id,
- uint32_t output2_id,
- uint32_t output3_id,
- uint32_t output4_id,
- uint32_t flags);
-
/// Define a Reshape Node with static shape specification and add it to a Subgraph.
///
/// @param subgraph - a Subgraph object that will own the created Node.
@@ -1889,23 +1469,6 @@ enum xnn_status xnn_define_static_resize_bilinear_2d(
uint32_t output_id,
uint32_t flags);
-/// Define a PReLU (Parametric ReLU) Node and add it to a Subgraph.
-///
-/// @param subgraph - a Subgraph object that will own the created Node.
-/// @param input_id - Value ID for the input tensor. The input tensor must be a 4D tensor defined in the @a subgraph
-/// with [N, H, W, channels] dimensions.
-/// @param slope_id - Value ID for the slope tensor. The slope tensor must be a 1D tensor defined in the @a subgraph with
-/// either [1] or [channels] dimensions.
-/// @param output_id - Value ID for the output tensor. The output tensor must be a 4D tensor defined in the @a subgraph
-/// with [N, H, W, channels] dimensions.
-/// @param flags - binary features of the PReLU Node. No supported flags are currently defined.
-XNN_DEPRECATED enum xnn_status xnn_define_prelu(
- xnn_subgraph_t subgraph,
- uint32_t input_id,
- uint32_t slope_id,
- uint32_t output_id,
- uint32_t flags);
-
/// Define a RoPE (Rotary Positional Embeddings) Node and add it to a Subgraph.
///
/// @param subgraph - a Subgraph object that will own the created Node.
@@ -1925,32 +1488,6 @@ enum xnn_status xnn_define_rope(
uint32_t output_id,
uint32_t flags);
-/// Define a Abs Node and add it to a Subgraph.
-///
-/// @param subgraph - a Subgraph object that will own the created Node.
-/// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph.
-/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its
-/// shape must match the shape of the input tensor.
-/// @param flags - binary features of the Abs Node. No supported flags are currently defined.
-XNN_DEPRECATED enum xnn_status xnn_define_abs(
- xnn_subgraph_t subgraph,
- uint32_t input_id,
- uint32_t output_id,
- uint32_t flags);
-
-/// Define a Bankers' Rounding Node and add it to a Subgraph.
-///
-/// @param subgraph - a Subgraph object that will own the created Node.
-/// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph.
-/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its
-/// shape must match the shape of the input tensor.
-/// @param flags - binary features of the Bankers' Rounding Node. No supported flags are currently defined.
-XNN_DEPRECATED enum xnn_status xnn_define_bankers_rounding(
- xnn_subgraph_t subgraph,
- uint32_t input_id,
- uint32_t output_id,
- uint32_t flags);
-
/// Define a Batch Matrix Multiply Node and add it to a Subgraph.
///
/// @param subgraph - a Subgraph object that will own the created Node.
@@ -1978,157 +1515,6 @@ enum xnn_status xnn_define_batch_matrix_multiply(
uint32_t output_id,
uint32_t flags);
-/// Define a Ceiling Node and add it to a Subgraph.
-///
-/// @param subgraph - a Subgraph object that will own the created Node.
-/// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph.
-/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its
-/// shape must match the shape of the input tensor.
-/// @param flags - binary features of the Ceiling Node. No supported flags are currently defined.
-XNN_DEPRECATED enum xnn_status xnn_define_ceiling(
- xnn_subgraph_t subgraph,
- uint32_t input_id,
- uint32_t output_id,
- uint32_t flags);
-
-/// Define a Clamp Node and add it to a Subgraph.
-///
-/// @param subgraph - a Subgraph object that will own the created Node.
-/// @param output_min - lower bound for clipping output values.
-/// @param output_max - upper bound for clipping output values.
-/// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph.
-/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its
-/// shape must match the shape of the input tensor.
-/// @param flags - binary features of the Clamp Node. No supported flags are currently defined.
-XNN_DEPRECATED enum xnn_status xnn_define_clamp(
- xnn_subgraph_t subgraph,
- float output_min,
- float output_max,
- uint32_t input_id,
- uint32_t output_id,
- uint32_t flags);
-
-/// Define an ELU (Exponential Linear Unit) Node and add it to a Subgraph.
-///
-/// @param subgraph - a Subgraph object that will own the created Node.
-/// @param alpha - scale factor for negative output elements.
-/// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph.
-/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its
-/// shape must match the shape of the input tensor.
-/// @param flags - binary features of the ELU Node. No supported flags are currently defined.
-XNN_DEPRECATED enum xnn_status xnn_define_elu(
- xnn_subgraph_t subgraph,
- float alpha,
- uint32_t input_id,
- uint32_t output_id,
- uint32_t flags);
-
-/// Define a Exp Node and add it to a Subgraph.
-///
-/// @param subgraph - a Subgraph object that will own the created Node.
-/// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph.
-/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its
-/// shape must match the shape of the input tensor.
-/// @param flags - binary features of the Exp Node. No supported flags are currently defined.
-XNN_DEPRECATED enum xnn_status xnn_define_exp(
- xnn_subgraph_t subgraph,
- uint32_t input_id,
- uint32_t output_id,
- uint32_t flags);
-
-/// Define a Floor Node and add it to a Subgraph.
-///
-/// @param subgraph - a Subgraph object that will own the created Node.
-/// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph.
-/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its
-/// shape must match the shape of the input tensor.
-/// @param flags - binary features of the Floor Node. No supported flags are currently defined.
-XNN_DEPRECATED enum xnn_status xnn_define_floor(
- xnn_subgraph_t subgraph,
- uint32_t input_id,
- uint32_t output_id,
- uint32_t flags);
-
-/// Define an GELU (Gaussian Error Linear Unit) Node and add it to a Subgraph.
-///
-/// @param subgraph - a Subgraph object that will own the created Node.
-/// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph.
-/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its
-/// shape must match the shape of the input tensor.
-/// @param flags - binary features of the GELU Node. No supported flags are currently defined.
-XNN_DEPRECATED enum xnn_status xnn_define_gelu(
- xnn_subgraph_t subgraph,
- uint32_t input_id,
- uint32_t output_id,
- uint32_t flags);
-
-/// Define a HardSwish Node and add it to a Subgraph.
-///
-/// @param subgraph - a Subgraph object that will own the created Node.
-/// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph.
-/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its
-/// shape must match the shape of the input tensor.
-/// @param flags - binary features of the HardSwish Node. No supported flags are currently defined.
-XNN_DEPRECATED enum xnn_status xnn_define_hardswish(
- xnn_subgraph_t subgraph,
- uint32_t input_id,
- uint32_t output_id,
- uint32_t flags);
-
-/// Define a Leaky ReLU Node and add it to a Subgraph.
-///
-/// @param subgraph - a Subgraph object that will own the created Node.
-/// @param negative_slope - scale factor for negative input elements.
-/// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph.
-/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its
-/// shape must match the shape of the input tensor.
-/// @param flags - binary features of the Leaky ReLU Node. No supported flags are currently defined.
-XNN_DEPRECATED enum xnn_status xnn_define_leaky_relu(
- xnn_subgraph_t subgraph,
- float negative_slope,
- uint32_t input_id,
- uint32_t output_id,
- uint32_t flags);
-
-/// Define a Log Node and add it to a Subgraph.
-///
-/// @param subgraph - a Subgraph object that will own the created Node.
-/// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph.
-/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its
-/// shape must match the shape of the input tensor.
-/// @param flags - binary features of the Log Node. No supported flags are currently defined.
-XNN_DEPRECATED enum xnn_status xnn_define_log(
- xnn_subgraph_t subgraph,
- uint32_t input_id,
- uint32_t output_id,
- uint32_t flags);
-
-/// Define a Negate Node and add it to a Subgraph.
-///
-/// @param subgraph - a Subgraph object that will own the created Node.
-/// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph.
-/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its
-/// shape must match the shape of the input tensor.
-/// @param flags - binary features of the Negate Node. No supported flags are currently defined.
-XNN_DEPRECATED enum xnn_status xnn_define_negate(
- xnn_subgraph_t subgraph,
- uint32_t input_id,
- uint32_t output_id,
- uint32_t flags);
-
-/// Define a Sigmoid Node and add it to a Subgraph.
-///
-/// @param subgraph - a Subgraph object that will own the created Node.
-/// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph.
-/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its
-/// shape must match the shape of the input tensor.
-/// @param flags - binary features of the Sigmoid Node. No supported flags are currently defined.
-XNN_DEPRECATED enum xnn_status xnn_define_sigmoid(
- xnn_subgraph_t subgraph,
- uint32_t input_id,
- uint32_t output_id,
- uint32_t flags);
-
/// Define a SoftMax Node and add it to a Subgraph.
///
/// @param subgraph - a Subgraph object that will own the created Node.
@@ -2164,48 +1550,6 @@ enum xnn_status xnn_define_space_to_depth_2d(
uint32_t output_id,
uint32_t flags);
-/// Define a Square Node and add it to a Subgraph.
-///
-/// @param subgraph - a Subgraph object that will own the created Node.
-/// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph.
-/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its
-/// shape must match the shape of the input tensor.
-/// @param flags - binary features of the Square Node. No supported flags are currently defined.
-XNN_DEPRECATED enum xnn_status xnn_define_square(
- xnn_subgraph_t subgraph,
- uint32_t input_id,
- uint32_t output_id,
- uint32_t flags);
-
-/// Define a Square Root Node and add it to a Subgraph.
-///
-/// @param subgraph - a Subgraph object that will own the created Node.
-/// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph.
-/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its
-/// shape must match the shape of the input tensor.
-/// @param flags - binary features of the Square Root Node. No supported flags are currently defined.
-XNN_DEPRECATED enum xnn_status xnn_define_square_root(
- xnn_subgraph_t subgraph,
- uint32_t input_id,
- uint32_t output_id,
- uint32_t flags);
-
-/// Define a Reciprocal Square Root Node and add it to a Subgraph.
-///
-/// @param subgraph - a Subgraph object that will own the created Node.
-/// @param input_id - Value ID for the input tensor. The input tensor must be
-/// defined in the @a subgraph.
-/// @param output_id - Value ID for the output tensor. The output tensor must be
-/// defined in the @a subgraph, and its
-/// shape must match the shape of the input tensor.
-/// @param flags - binary features of the Square Root Node. No supported flags
-/// are currently defined.
-XNN_DEPRECATED enum xnn_status xnn_define_reciprocal_square_root(
- xnn_subgraph_t subgraph,
- uint32_t input_id,
- uint32_t output_id,
- uint32_t flags);
-
enum xnn_status xnn_define_static_slice(
xnn_subgraph_t subgraph,
size_t num_dims,
@@ -2282,19 +1626,6 @@ enum xnn_status xnn_define_static_transpose(
uint32_t output_id,
uint32_t flags);
-/// Define a Tanh Node and add it to a Subgraph.
-///
-/// @param subgraph - a Subgraph object that will own the created Node.
-/// @param input_id - Value ID for the input tensor. The input tensor must be defined in the @a subgraph.
-/// @param output_id - Value ID for the output tensor. The output tensor must be defined in the @a subgraph, and its
-/// shape must match the shape of the input tensor.
-/// @param flags - binary features of the Tanh Node. No supported flags are currently defined.
-XNN_DEPRECATED enum xnn_status xnn_define_tanh(
- xnn_subgraph_t subgraph,
- uint32_t input_id,
- uint32_t output_id,
- uint32_t flags);
-
/// Weights cache can be finalized in these ways:
enum xnn_weights_cache_finalization_kind {
/// Weights cache is finalized, no insert operations into the weights cache is allowed, even if the "inserted"
diff --git a/sources/xnnpack/src/subgraph/deprecated.c b/sources/xnnpack/src/subgraph/deprecated.c
index 650cbf9..1feb9a3 100644
--- a/sources/xnnpack/src/subgraph/deprecated.c
+++ b/sources/xnnpack/src/subgraph/deprecated.c
@@ -5,337 +5,6 @@
#include "include/xnnpack.h"
#include "src/xnnpack/subgraph.h"
-enum xnn_status xnn_define_add2(xnn_subgraph_t subgraph, float output_min,
- float output_max, uint32_t input1_id,
- uint32_t input2_id, uint32_t output_id,
- uint32_t flags) {
- struct xnn_binary_params params;
- params.output_min = output_min;
- params.output_max = output_max;
- return xnn_define_binary(subgraph, xnn_binary_add, ¶ms, input1_id,
- input2_id, output_id, flags);
-}
-
-enum xnn_status xnn_define_subtract(xnn_subgraph_t subgraph, float output_min,
- float output_max, uint32_t input1_id,
- uint32_t input2_id, uint32_t output_id,
- uint32_t flags) {
- struct xnn_binary_params params;
- params.output_min = output_min;
- params.output_max = output_max;
- return xnn_define_binary(subgraph, xnn_binary_subtract, ¶ms, input1_id,
- input2_id, output_id, flags);
-}
-
-enum xnn_status xnn_define_multiply2(xnn_subgraph_t subgraph, float output_min,
- float output_max, uint32_t input1_id,
- uint32_t input2_id, uint32_t output_id,
- uint32_t flags) {
- struct xnn_binary_params params;
- params.output_min = output_min;
- params.output_max = output_max;
- return xnn_define_binary(subgraph, xnn_binary_multiply, ¶ms, input1_id,
- input2_id, output_id, flags);
-}
-
-enum xnn_status xnn_define_divide(xnn_subgraph_t subgraph, float output_min,
- float output_max, uint32_t input1_id,
- uint32_t input2_id, uint32_t output_id,
- uint32_t flags) {
- struct xnn_binary_params params;
- params.output_min = output_min;
- params.output_max = output_max;
- return xnn_define_binary(subgraph, xnn_binary_divide, ¶ms, input1_id,
- input2_id, output_id, flags);
-}
-
-enum xnn_status xnn_define_maximum2(xnn_subgraph_t subgraph, uint32_t input1_id,
- uint32_t input2_id, uint32_t output_id,
- uint32_t flags) {
- return xnn_define_binary(subgraph, xnn_binary_maximum, NULL, input1_id,
- input2_id, output_id, flags);
-}
-
-enum xnn_status xnn_define_minimum2(xnn_subgraph_t subgraph, uint32_t input1_id,
- uint32_t input2_id, uint32_t output_id,
- uint32_t flags) {
- return xnn_define_binary(subgraph, xnn_binary_minimum, NULL, input1_id,
- input2_id, output_id, flags);
-}
-
-enum xnn_status xnn_define_squared_difference(xnn_subgraph_t subgraph,
- uint32_t input1_id,
- uint32_t input2_id,
- uint32_t output_id,
- uint32_t flags) {
- return xnn_define_binary(subgraph, xnn_binary_squared_difference, NULL,
- input1_id, input2_id, output_id, flags);
-}
-
-enum xnn_status xnn_define_copysign(xnn_subgraph_t subgraph, uint32_t input1_id,
- uint32_t input2_id, uint32_t output_id,
- uint32_t flags) {
- return xnn_define_binary(subgraph, xnn_binary_copysign, NULL, input1_id,
- input2_id, output_id, flags);
-}
-
-enum xnn_status xnn_define_prelu(xnn_subgraph_t subgraph, uint32_t input1_id,
- uint32_t input2_id, uint32_t output_id,
- uint32_t flags) {
- return xnn_define_binary(subgraph, xnn_binary_prelu, NULL,
- input1_id, input2_id, output_id, flags);
-}
-
-enum xnn_status xnn_define_static_mean(xnn_subgraph_t subgraph,
- size_t num_reduction_axes,
- const size_t* reduction_axes,
- uint32_t input_id, uint32_t output_id,
- uint32_t flags) {
- return xnn_define_static_reduce(subgraph, xnn_reduce_mean, num_reduction_axes,
- reduction_axes, input_id, output_id, flags);
-}
-
-enum xnn_status xnn_define_global_average_pooling_1d(
- xnn_subgraph_t subgraph,
- float output_min,
- float output_max,
- uint32_t input_id,
- uint32_t output_id,
- uint32_t flags)
-{
- const struct xnn_value* input_value = &subgraph->values[input_id];
-
- size_t reduction_axes[XNN_MAX_TENSOR_DIMS];
-
- reduction_axes[0] = input_value->shape.num_dims - 2;
-
- enum xnn_status status = (xnn_define_static_reduce(
- subgraph, xnn_reduce_mean, 1, reduction_axes, input_id,
- output_id, flags));
-
- if (status != xnn_status_success) {
- return status;
- }
-
- if (output_min != -INFINITY || output_max != INFINITY) {
- return xnn_insert_clamp_node(subgraph, output_min, output_max,
- &subgraph->nodes[subgraph->num_nodes - 1]);
- }
-
- return xnn_status_success;
-}
-
-enum xnn_status xnn_define_global_average_pooling_2d(
- xnn_subgraph_t subgraph,
- float output_min,
- float output_max,
- uint32_t input_id,
- uint32_t output_id,
- uint32_t flags)
-{
- const struct xnn_value* input_value = &subgraph->values[input_id];
-
- size_t reduction_axes[XNN_MAX_TENSOR_DIMS];
-
- reduction_axes[0] = input_value->shape.num_dims - 3;
- reduction_axes[1] = input_value->shape.num_dims - 2;
-
- enum xnn_status status = xnn_define_static_reduce(
- subgraph, xnn_reduce_mean, 2, reduction_axes, input_id,
- output_id, flags);
-
- if (status != xnn_status_success) {
- return status;
- }
-
- if (output_min != -INFINITY || output_max != INFINITY) {
- return xnn_insert_clamp_node(subgraph, output_min, output_max,
- &subgraph->nodes[subgraph->num_nodes - 1]);
- }
-
- return xnn_status_success;
-}
-
-enum xnn_status xnn_define_global_sum_pooling_1d(
- xnn_subgraph_t subgraph,
- float output_min,
- float output_max,
- uint32_t input_id,
- uint32_t output_id,
- uint32_t flags)
-{
- const struct xnn_value* input_value = &subgraph->values[input_id];
- size_t reduction_axes[XNN_MAX_TENSOR_DIMS];
- reduction_axes[0] = input_value->shape.num_dims - 2;
-
- enum xnn_status status = xnn_define_static_reduce(
- subgraph, xnn_reduce_sum, 1, reduction_axes, input_id,
- output_id, flags);
-
- if (status != xnn_status_success) {
- return status;
- }
-
- if (output_min != -INFINITY || output_max != INFINITY) {
- return xnn_insert_clamp_node(subgraph, output_min, output_max,
- &subgraph->nodes[subgraph->num_nodes - 1]);
- }
-
- return xnn_status_success;
-}
-
-enum xnn_status xnn_define_global_sum_pooling_2d(
- xnn_subgraph_t subgraph,
- float output_min,
- float output_max,
- uint32_t input_id,
- uint32_t output_id,
- uint32_t flags)
-{
- const struct xnn_value* input_value = &subgraph->values[input_id];
- size_t reduction_axes[XNN_MAX_TENSOR_DIMS];
- reduction_axes[0] = input_value->shape.num_dims - 3;
- reduction_axes[1] = input_value->shape.num_dims - 2;
-
- enum xnn_status status = xnn_define_static_reduce(
- subgraph, xnn_reduce_sum, 2, reduction_axes, input_id,
- output_id, flags);
-
- if (status != xnn_status_success) {
- return status;
- }
-
- if (output_min != -INFINITY || output_max != INFINITY) {
- return xnn_insert_clamp_node(subgraph, output_min, output_max,
- &subgraph->nodes[subgraph->num_nodes - 1]);
- }
-
- return xnn_status_success;
-}
-
-enum xnn_status xnn_define_convert(xnn_subgraph_t subgraph, uint32_t input_id,
- uint32_t output_id, uint32_t flags) {
- return xnn_define_unary(subgraph, xnn_unary_convert, NULL, input_id, output_id,
- flags);
-}
-
-enum xnn_status xnn_define_abs(xnn_subgraph_t subgraph, uint32_t input_id,
- uint32_t output_id, uint32_t flags) {
- return xnn_define_unary(subgraph, xnn_unary_abs, NULL, input_id, output_id,
- flags);
-}
-
-enum xnn_status xnn_define_bankers_rounding(xnn_subgraph_t subgraph,
- uint32_t input_id,
- uint32_t output_id,
- uint32_t flags) {
- return xnn_define_unary(subgraph, xnn_unary_bankers_rounding, NULL, input_id,
- output_id, flags);
-}
-
-enum xnn_status xnn_define_ceiling(xnn_subgraph_t subgraph, uint32_t input_id,
- uint32_t output_id, uint32_t flags) {
- return xnn_define_unary(subgraph, xnn_unary_ceiling, NULL, input_id, output_id,
- flags);
-}
-
-enum xnn_status xnn_define_clamp(xnn_subgraph_t subgraph, float output_min,
- float output_max, uint32_t input_id,
- uint32_t output_id, uint32_t flags) {
- union xnn_unary_params params;
- params.clamp.min = output_min;
- params.clamp.max = output_max;
- return xnn_define_unary(subgraph, xnn_unary_clamp, ¶ms, input_id, output_id,
- flags);
-}
-
-enum xnn_status xnn_define_elu(xnn_subgraph_t subgraph, float alpha,
- uint32_t input_id, uint32_t output_id,
- uint32_t flags) {
- union xnn_unary_params params;
- params.elu.alpha = alpha;
- return xnn_define_unary(subgraph, xnn_unary_elu, ¶ms, input_id, output_id,
- flags);
-}
-
-enum xnn_status xnn_define_exp(xnn_subgraph_t subgraph, uint32_t input_id,
- uint32_t output_id, uint32_t flags) {
- return xnn_define_unary(subgraph, xnn_unary_exp, NULL, input_id, output_id,
- flags);
-}
-
-enum xnn_status xnn_define_floor(xnn_subgraph_t subgraph, uint32_t input_id,
- uint32_t output_id, uint32_t flags) {
- return xnn_define_unary(subgraph, xnn_unary_floor, NULL, input_id, output_id,
- flags);
-}
-
-enum xnn_status xnn_define_gelu(xnn_subgraph_t subgraph, uint32_t input_id,
- uint32_t output_id, uint32_t flags) {
- return xnn_define_unary(subgraph, xnn_unary_gelu, NULL, input_id, output_id,
- flags);
-}
-
-enum xnn_status xnn_define_hardswish(xnn_subgraph_t subgraph, uint32_t input_id,
- uint32_t output_id, uint32_t flags) {
- return xnn_define_unary(subgraph, xnn_unary_hardswish, NULL, input_id, output_id,
- flags);
-}
-
-enum xnn_status xnn_define_leaky_relu(xnn_subgraph_t subgraph,
- float negative_slope, uint32_t input_id,
- uint32_t output_id, uint32_t flags) {
- union xnn_unary_params params;
- params.leaky_relu.negative_slope = negative_slope;
- return xnn_define_unary(subgraph, xnn_unary_leaky_relu, ¶ms, input_id, output_id,
- flags);
-}
-
-enum xnn_status xnn_define_log(xnn_subgraph_t subgraph, uint32_t input_id,
- uint32_t output_id, uint32_t flags) {
- return xnn_define_unary(subgraph, xnn_unary_log, NULL, input_id, output_id,
- flags);
-}
-
-enum xnn_status xnn_define_negate(xnn_subgraph_t subgraph, uint32_t input_id,
- uint32_t output_id, uint32_t flags) {
- return xnn_define_unary(subgraph, xnn_unary_negate, NULL, input_id, output_id,
- flags);
-}
-
-enum xnn_status xnn_define_sigmoid(xnn_subgraph_t subgraph, uint32_t input_id,
- uint32_t output_id, uint32_t flags) {
- return xnn_define_unary(subgraph, xnn_unary_sigmoid, NULL, input_id, output_id,
- flags);
-}
-
-enum xnn_status xnn_define_square(xnn_subgraph_t subgraph, uint32_t input_id,
- uint32_t output_id, uint32_t flags) {
- return xnn_define_unary(subgraph, xnn_unary_square, NULL, input_id, output_id,
- flags);
-}
-
-enum xnn_status xnn_define_square_root(xnn_subgraph_t subgraph,
- uint32_t input_id, uint32_t output_id,
- uint32_t flags) {
- return xnn_define_unary(subgraph, xnn_unary_square_root, NULL, input_id, output_id,
- flags);
-}
-
-enum xnn_status xnn_define_reciprocal_square_root(xnn_subgraph_t subgraph,
- uint32_t input_id,
- uint32_t output_id,
- uint32_t flags) {
- return xnn_define_unary(subgraph, xnn_unary_reciprocal_square_root, NULL, input_id,
- output_id, flags);
-}
-
-enum xnn_status xnn_define_tanh(xnn_subgraph_t subgraph, uint32_t input_id,
- uint32_t output_id, uint32_t flags) {
- return xnn_define_unary(subgraph, xnn_unary_tanh, NULL, input_id, output_id,
- flags);
-}
-
enum xnn_status xnn_define_concatenate2(xnn_subgraph_t subgraph, int32_t axis, uint32_t input1_id,
uint32_t input2_id, uint32_t output_id, uint32_t flags) {
const uint32_t inputs_id[2] = {input1_id, input2_id};
@@ -366,23 +35,3 @@ enum xnn_status xnn_define_concatenate5(xnn_subgraph_t subgraph, int32_t axis, u
return xnn_define_concatenate(subgraph, axis, /*num_inputs=*/5,
inputs_id, output_id, flags);
}
-
-enum xnn_status xnn_define_even_split2(xnn_subgraph_t subgraph, int32_t split_dim, uint32_t input_id,
- uint32_t output1_id, uint32_t output2_id, uint32_t flags) {
- const uint32_t outputs_id[2] = {output1_id, output2_id};
- return xnn_define_even_split(subgraph, split_dim, input_id, /*num_outputs=*/2, outputs_id, flags);
-}
-
-enum xnn_status xnn_define_even_split3(xnn_subgraph_t subgraph, int32_t split_dim, uint32_t input_id,
- uint32_t output1_id, uint32_t output2_id,
- uint32_t output3_id, uint32_t flags) {
- const uint32_t outputs_id[3] = {output1_id, output2_id, output3_id};
- return xnn_define_even_split(subgraph, split_dim, input_id, /*num_outputs=*/3, outputs_id, flags);
-}
-
-enum xnn_status xnn_define_even_split4(xnn_subgraph_t subgraph, int32_t split_dim, uint32_t input_id,
- uint32_t output1_id, uint32_t output2_id,
- uint32_t output3_id, uint32_t output4_id, uint32_t flags) {
- const uint32_t outputs_id[4] = {output1_id, output2_id, output3_id, output4_id};
- return xnn_define_even_split(subgraph, split_dim, input_id, /*num_outputs=*/4, outputs_id, flags);
-}
diff --git a/sources/xnnpack/src/subgraph/max-pooling-2d.c b/sources/xnnpack/src/subgraph/max-pooling-2d.c
index 4a98c26..159d317 100644
--- a/sources/xnnpack/src/subgraph/max-pooling-2d.c
+++ b/sources/xnnpack/src/subgraph/max-pooling-2d.c
@@ -14,7 +14,6 @@
#include "src/xnnpack/node-type.h"
#include "src/xnnpack/operator-type.h"
#include "src/xnnpack/operator.h"
-#include "src/xnnpack/requantization.h"
#include "src/xnnpack/subgraph-validation.h"
#include "src/xnnpack/subgraph.h"
#include <pthreadpool.h>
@@ -28,101 +27,27 @@ static enum xnn_status create_max_pooling_operator(
{
assert(node->num_inputs == 1);
assert(node->num_outputs == 1);
- const uint32_t output_id = node->outputs[0];
- assert(output_id != XNN_INVALID_VALUE_ID);
- assert(output_id < num_values);
-
const uint32_t input_id = node->inputs[0];
assert(input_id < num_values);
const struct xnn_runtime_value *input_value = &values[input_id];
- const enum xnn_datatype datatype = input_value->datatype;
-
- enum xnn_status status;
- switch (datatype) {
- case xnn_datatype_fp16:
- status = xnn_create_max_pooling2d_nhwc_f16(
- node->params.pooling_2d.padding_top,
- node->params.pooling_2d.padding_right,
- node->params.pooling_2d.padding_bottom,
- node->params.pooling_2d.padding_left,
- node->params.pooling_2d.pooling_height,
- node->params.pooling_2d.pooling_width,
- node->params.pooling_2d.stride_height,
- node->params.pooling_2d.stride_width,
- node->params.pooling_2d.dilation_height,
- node->params.pooling_2d.dilation_width,
- node->activation.output_min,
- node->activation.output_max,
- node->flags,
- &opdata->operator_objects[0]);
- break;
- case xnn_datatype_fp32:
- status = xnn_create_max_pooling2d_nhwc_f32(
- node->params.pooling_2d.padding_top,
- node->params.pooling_2d.padding_right,
- node->params.pooling_2d.padding_bottom,
- node->params.pooling_2d.padding_left,
- node->params.pooling_2d.pooling_height,
- node->params.pooling_2d.pooling_width,
- node->params.pooling_2d.stride_height,
- node->params.pooling_2d.stride_width,
- node->params.pooling_2d.dilation_height,
- node->params.pooling_2d.dilation_width,
- node->activation.output_min,
- node->activation.output_max,
- node->flags,
- &opdata->operator_objects[0]);
- break;
- case xnn_datatype_qint8:
- {
- const float output_scale = values[output_id].quantization.scale;
- const int32_t output_zero_point = values[output_id].quantization.zero_point;
- const int8_t output_min = xnn_qs8_quantize(node->activation.output_min, output_scale, output_zero_point);
- const int8_t output_max = xnn_qs8_quantize(node->activation.output_max, output_scale, output_zero_point);
- status = xnn_create_max_pooling2d_nhwc_s8(
- node->params.pooling_2d.padding_top,
- node->params.pooling_2d.padding_right,
- node->params.pooling_2d.padding_bottom,
- node->params.pooling_2d.padding_left,
- node->params.pooling_2d.pooling_height,
- node->params.pooling_2d.pooling_width,
- node->params.pooling_2d.stride_height,
- node->params.pooling_2d.stride_width,
- node->params.pooling_2d.dilation_height,
- node->params.pooling_2d.dilation_width,
- output_min,
- output_max,
- node->flags,
- &opdata->operator_objects[0]);
- break;
- }
- case xnn_datatype_quint8:
- {
- const float output_scale = values[output_id].quantization.scale;
- const int32_t output_zero_point = values[output_id].quantization.zero_point;
- const uint8_t output_min = xnn_qu8_quantize(node->activation.output_min, output_scale, output_zero_point);
- const uint8_t output_max = xnn_qu8_quantize(node->activation.output_max, output_scale, output_zero_point);
- status = xnn_create_max_pooling2d_nhwc_u8(
- node->params.pooling_2d.padding_top,
- node->params.pooling_2d.padding_right,
- node->params.pooling_2d.padding_bottom,
- node->params.pooling_2d.padding_left,
- node->params.pooling_2d.pooling_height,
- node->params.pooling_2d.pooling_width,
- node->params.pooling_2d.stride_height,
- node->params.pooling_2d.stride_width,
- node->params.pooling_2d.dilation_height,
- node->params.pooling_2d.dilation_width,
- output_min,
- output_max,
- node->flags,
- &opdata->operator_objects[0]);
- break;
- }
- default:
- XNN_UNREACHABLE;
+ if (input_value->datatype != xnn_datatype_fp16) {
+ return xnn_status_invalid_parameter;
}
- return status;
+ return xnn_create_max_pooling2d_nhwc_f16(
+ node->params.pooling_2d.padding_top,
+ node->params.pooling_2d.padding_right,
+ node->params.pooling_2d.padding_bottom,
+ node->params.pooling_2d.padding_left,
+ node->params.pooling_2d.pooling_height,
+ node->params.pooling_2d.pooling_width,
+ node->params.pooling_2d.stride_height,
+ node->params.pooling_2d.stride_width,
+ node->params.pooling_2d.dilation_height,
+ node->params.pooling_2d.dilation_width,
+ node->activation.output_min,
+ node->activation.output_max,
+ node->flags,
+ &opdata->operator_objects[0]);
}
static enum xnn_status reshape_max_pooling_operator(
@@ -144,64 +69,21 @@ static enum xnn_status reshape_max_pooling_operator(
const size_t input_width = values[input_id].shape.dim[2];
const size_t channels = values[input_id].shape.dim[3];
- enum xnn_status status = xnn_status_invalid_state;
size_t output_height, output_width;
- switch (opdata->operator_objects[0]->type) {
- case xnn_operator_type_max_pooling_nhwc_f16:
- status = xnn_reshape_max_pooling2d_nhwc_f16(
- opdata->operator_objects[0],
- batch_size,
- input_height,
- input_width,
- channels,
- /*input_pixel_stride=*/channels,
- /*output_pixel_stride=*/channels,
- &output_height,
- &output_width,
- threadpool);
- break;
- case xnn_operator_type_max_pooling_nhwc_f32:
- status = xnn_reshape_max_pooling2d_nhwc_f32(
- opdata->operator_objects[0],
- batch_size,
- input_height,
- input_width,
- channels,
- /*input_pixel_stride=*/channels,
- /*output_pixel_stride=*/channels,
- &output_height,
- &output_width,
- threadpool);
- break;
- case xnn_operator_type_max_pooling_nhwc_s8:
- status = xnn_reshape_max_pooling2d_nhwc_s8(
- opdata->operator_objects[0],
- batch_size,
- input_height,
- input_width,
- channels,
- /*input_pixel_stride=*/channels,
- /*output_pixel_stride=*/channels,
- &output_height,
- &output_width,
- threadpool);
- break;
- case xnn_operator_type_max_pooling_nhwc_u8:
- status = xnn_reshape_max_pooling2d_nhwc_u8(
- opdata->operator_objects[0],
- batch_size,
- input_height,
- input_width,
- channels,
- /*input_pixel_stride=*/channels,
- /*output_pixel_stride=*/channels,
- &output_height,
- &output_width,
- threadpool);
- break;
- default:
- XNN_UNREACHABLE;
+ if (opdata->operator_objects[0]->type != xnn_operator_type_max_pooling_nhwc_f16) {
+ return xnn_status_invalid_state;
}
+ const enum xnn_status status = xnn_reshape_max_pooling2d_nhwc_f16(
+ opdata->operator_objects[0],
+ batch_size,
+ input_height,
+ input_width,
+ channels,
+ /*input_pixel_stride=*/channels,
+ /*output_pixel_stride=*/channels,
+ &output_height,
+ &output_width,
+ threadpool);
if (status != xnn_status_success) {
return status;
@@ -243,30 +125,13 @@ static enum xnn_status setup_max_pooling_operator(
void* output_data = output_value->data;
assert(output_data != NULL);
- switch (opdata->operator_objects[0]->type) {
- case xnn_operator_type_max_pooling_nhwc_f16:
- return xnn_setup_max_pooling2d_nhwc_f16(
- opdata->operator_objects[0],
- input_data,
- output_data);
- case xnn_operator_type_max_pooling_nhwc_f32:
- return xnn_setup_max_pooling2d_nhwc_f32(
- opdata->operator_objects[0],
- input_data,
- output_data);
- case xnn_operator_type_max_pooling_nhwc_s8:
- return xnn_setup_max_pooling2d_nhwc_s8(
- opdata->operator_objects[0],
- input_data,
- output_data);
- case xnn_operator_type_max_pooling_nhwc_u8:
- return xnn_setup_max_pooling2d_nhwc_u8(
- opdata->operator_objects[0],
- input_data,
- output_data);
- default:
- XNN_UNREACHABLE;
+ if (opdata->operator_objects[0]->type != xnn_operator_type_max_pooling_nhwc_f16) {
+ return xnn_status_invalid_state;
}
+ return xnn_setup_max_pooling2d_nhwc_f16(
+ opdata->operator_objects[0],
+ input_data,
+ output_data);
}
enum xnn_status xnn_define_max_pooling_2d(
@@ -345,18 +210,12 @@ enum xnn_status xnn_define_max_pooling_2d(
return status;
}
- switch (input_value->datatype) {
- case xnn_datatype_fp16:
- case xnn_datatype_fp32:
- case xnn_datatype_qint8:
- case xnn_datatype_quint8:
- break;
- default:
- xnn_log_error(
- "failed to define %s operator with input ID #%" PRIu32 ": unsupported Value datatype %s (%d)",
- xnn_node_type_to_string(xnn_node_type_max_pooling_2d), input_id,
- xnn_datatype_to_string(input_value->datatype), input_value->datatype);
- return xnn_status_invalid_parameter;
+ if (input_value->datatype != xnn_datatype_fp16) {
+ xnn_log_error(
+ "failed to define %s operator with input ID #%" PRIu32 ": unsupported Value datatype %s (%d); only FP16 is supported",
+ xnn_node_type_to_string(xnn_node_type_max_pooling_2d), input_id,
+ xnn_datatype_to_string(input_value->datatype), input_value->datatype);
+ return xnn_status_invalid_parameter;
}
status = xnn_subgraph_check_output_node_id(xnn_node_type_max_pooling_2d, output_id, subgraph->num_values);
@@ -370,21 +229,12 @@ enum xnn_status xnn_define_max_pooling_2d(
return status;
}
- switch (output_value->datatype) {
- case xnn_datatype_fp16:
- break;
- case xnn_datatype_fp32:
- break;
- case xnn_datatype_qint8:
- break;
- case xnn_datatype_quint8:
- break;
- default:
- xnn_log_error(
- "failed to define %s operator with output ID #%" PRIu32 ": unsupported Value datatype %s (%d)",
- xnn_node_type_to_string(xnn_node_type_max_pooling_2d), output_id,
- xnn_datatype_to_string(output_value->datatype), output_value->datatype);
- return xnn_status_invalid_parameter;
+ if (output_value->datatype != xnn_datatype_fp16) {
+ xnn_log_error(
+ "failed to define %s operator with output ID #%" PRIu32 ": unsupported Value datatype %s (%d); only FP16 is supported",
+ xnn_node_type_to_string(xnn_node_type_max_pooling_2d), output_id,
+ xnn_datatype_to_string(output_value->datatype), output_value->datatype);
+ return xnn_status_invalid_parameter;
}
status = xnn_subgraph_check_datatype_matches(
diff --git a/sources/xnnpack/src/subgraph/static-reduce.c b/sources/xnnpack/src/subgraph/static-reduce.c
index 766653a..304f3f9 100644
--- a/sources/xnnpack/src/subgraph/static-reduce.c
+++ b/sources/xnnpack/src/subgraph/static-reduce.c
@@ -245,24 +245,6 @@ static enum xnn_status setup_reduce_operator(
return xnn_setup_reduce_nd(opdata->operator_objects[0], workspace, input_data, output_data);
}
-enum xnn_status xnn_define_static_reduce(
- xnn_subgraph_t subgraph,
- enum xnn_reduce_operator reduce_operator,
- size_t num_reduction_axes,
- const size_t* reduction_axes,
- uint32_t input_id,
- uint32_t output_id,
- uint32_t flags)
-{
- int64_t signed_reduction_axes[XNN_MAX_TENSOR_DIMS];
- for (int i = 0; i < num_reduction_axes; i++) {
- signed_reduction_axes[i] = reduction_axes[i];
- }
- return xnn_define_static_reduce_v2(subgraph, reduce_operator,
- num_reduction_axes, signed_reduction_axes,
- input_id, output_id, flags);
-}
-
enum xnn_status xnn_define_static_reduce_v2(
xnn_subgraph_t subgraph, enum xnn_reduce_operator reduce_operator,
size_t num_reduction_axes, const int64_t* reduction_axes, uint32_t input_id,
diff --git a/sources/xnnpack/src/subgraph/unary.c b/sources/xnnpack/src/subgraph/unary.c
index ec73ba0..e1bb956 100644
--- a/sources/xnnpack/src/subgraph/unary.c
+++ b/sources/xnnpack/src/subgraph/unary.c
@@ -390,10 +390,26 @@ enum xnn_status xnn_define_unary(
return status;
}
+ if (type == xnn_unary_log) {
+ xnn_log_error("failed to define unary log node: this runtime does not support unary log");
+ return xnn_status_unsupported_parameter;
+ }
+ if (type == xnn_unary_approxgelu) {
+ xnn_log_error("failed to define approximate GELU node: this runtime does not support approximate GELU");
+ return xnn_status_unsupported_parameter;
+ }
+ if (type == xnn_unary_gelu) {
+ xnn_log_error("failed to define GELU node: this runtime does not support GELU");
+ return xnn_status_unsupported_parameter;
+ }
+ if (type == xnn_unary_elu) {
+ xnn_log_error("failed to define ELU node: this runtime does not support ELU");
+ return xnn_status_unsupported_parameter;
+ }
+
switch (type) {
case xnn_unary_clamp:
case xnn_unary_leaky_relu:
- case xnn_unary_elu:
if (!params) {
xnn_log_error("failed to define %s node with input ID #%" PRIu32
" and output ID #%" PRIu32 ": missing clamp params",
|