20 #ifndef ONEAPI_DNNL_DNNL_TYPES_H
21 #define ONEAPI_DNNL_DNNL_TYPES_H
715 dnnl_ldOI32o4i = dnnl_abDC32d4c,
716 dnnl_ldIo32i = dnnl_abCd32c,
719 dnnl_ldgOI32o2i = dnnl_abdEC32e2c,
720 dnnl_ldgOI32o4i = dnnl_abdEC32e4c,
721 dnnl_ldgIo32i = dnnl_abdCe32c,
722 dnnl_ldgIO32i2o = dnnl_abdCE32c2e,
763 dnnl_NCw16n16c = dnnl_ABc16a16b,
764 dnnl_NCdhw16n16c = dnnl_ABcde16a16b,
765 dnnl_NChw16n16c = dnnl_ABcd16a16b,
766 dnnl_NCw32n16c = dnnl_ABc32a16b,
767 dnnl_NChw32n16c = dnnl_ABcd32a16b,
768 dnnl_NCdhw32n16c = dnnl_ABcde32a16b,
769 dnnl_NCw32n32c = dnnl_ABc32a32b,
770 dnnl_NChw32n32c = dnnl_ABcd32a32b,
771 dnnl_NCdhw32n32c = dnnl_ABcde32a32b,
774 dnnl_OI16i16o = dnnl_AB16b16a,
775 dnnl_OI16i32o = dnnl_AB16b32a,
776 dnnl_OI16i64o = dnnl_AB16b64a,
777 dnnl_OI8i16o2i = dnnl_AB8b16a2b,
778 dnnl_OI8i32o2i = dnnl_AB8b32a2b,
779 dnnl_OI8i64o2i = dnnl_AB8b64a2b,
780 dnnl_OI4i16o4i = dnnl_AB4b16a4b,
781 dnnl_OI4i32o4i = dnnl_AB4b32a4b,
782 dnnl_OI4i64o4i = dnnl_AB4b64a4b,
783 dnnl_OI16i16o4i = dnnl_AB16b16a4b,
785 dnnl_IOw16o16i = dnnl_BAc16a16b,
786 dnnl_IOw16i16o = dnnl_BAc16b16a,
787 dnnl_OIw16i16o = dnnl_ABc16b16a,
788 dnnl_OIw16i32o = dnnl_ABc16b32a,
789 dnnl_OIw16i64o = dnnl_ABc16b64a,
790 dnnl_OIw16o16i = dnnl_ABc16a16b,
791 dnnl_Oiw16o = dnnl_Abc16a,
792 dnnl_OIw4i16o4i = dnnl_ABc4b16a4b,
793 dnnl_OIw4i32o4i = dnnl_ABc4b32a4b,
794 dnnl_OIw4i64o4i = dnnl_ABc4b64a4b,
795 dnnl_OIw2i8o4i = dnnl_ABc2b8a4b,
796 dnnl_OIw16i16o4i = dnnl_ABc16b16a4b,
797 dnnl_OIw16i16o2i = dnnl_ABc16b16a2b,
798 dnnl_OIw16o16i2o = dnnl_ABc16a16b2a,
799 dnnl_OIw4i4o = dnnl_ABc4b4a,
800 dnnl_OIw4o4i = dnnl_ABc4a4b,
801 dnnl_Oiw4o = dnnl_Abc4a,
802 dnnl_OIw8i16o2i = dnnl_ABc8b16a2b,
803 dnnl_OIw8i32o2i = dnnl_ABc8b32a2b,
804 dnnl_OIw8i64o2i = dnnl_ABc8b64a2b,
805 dnnl_OIw8i8o = dnnl_ABc8b8a,
806 dnnl_OIw8o16i2o = dnnl_ABc8a16b2a,
807 dnnl_IOw8o16i2o = dnnl_BAc8a16b2a,
808 dnnl_OIw8o8i = dnnl_ABc8a8b,
809 dnnl_OIw8o4i = dnnl_ABc8a4b,
810 dnnl_Owi16o = dnnl_Acb16a,
811 dnnl_OwI16o2i = dnnl_AcB16a2b,
812 dnnl_OwI16o4i = dnnl_AcB16a4b,
813 dnnl_Owi4o = dnnl_Acb4a,
814 dnnl_Owi8o = dnnl_Acb8a,
817 dnnl_IOhw16i16o = dnnl_BAcd16b16a,
818 dnnl_IOhw16o16i = dnnl_BAcd16a16b,
819 dnnl_Ohwi16o = dnnl_Acdb16a,
820 dnnl_OhwI16o2i = dnnl_AcdB16a2b,
821 dnnl_OhwI16o4i = dnnl_AcdB16a4b,
822 dnnl_Ohwi32o = dnnl_Acdb32a,
823 dnnl_Ohwi4o = dnnl_Acdb4a,
824 dnnl_Ohwi8o = dnnl_Acdb8a,
825 dnnl_OIhw16i16o = dnnl_ABcd16b16a,
826 dnnl_OIhw16i32o = dnnl_ABcd16b32a,
827 dnnl_OIhw16i64o = dnnl_ABcd16b64a,
828 dnnl_OIhw16o16i = dnnl_ABcd16a16b,
829 dnnl_Oihw16o = dnnl_Abcd16a,
830 dnnl_OIhw4i16o4i = dnnl_ABcd4b16a4b,
831 dnnl_OIhw4i32o4i = dnnl_ABcd4b32a4b,
832 dnnl_OIhw4i64o4i = dnnl_ABcd4b64a4b,
833 dnnl_OIhw16i16o4i = dnnl_ABcd16b16a4b,
834 dnnl_OIhw16i16o2i = dnnl_ABcd16b16a2b,
835 dnnl_OIhw16o16i2o = dnnl_ABcd16a16b2a,
836 dnnl_OIhw4i4o = dnnl_ABcd4b4a,
837 dnnl_OIhw4o4i = dnnl_ABcd4a4b,
838 dnnl_Oihw4o = dnnl_Abcd4a,
839 dnnl_OIhw8i16o2i = dnnl_ABcd8b16a2b,
840 dnnl_OIhw8i32o2i = dnnl_ABcd8b32a2b,
841 dnnl_OIhw8i64o2i = dnnl_ABcd8b64a2b,
843 dnnl_OIhw8o16i2o = dnnl_ABcd8a16b2a,
844 dnnl_OIhw2i8o4i = dnnl_ABcd2b8a4b,
845 dnnl_IOhw8o16i2o = dnnl_BAcd8a16b2a,
846 dnnl_OIhw8o8i = dnnl_ABcd8a8b,
847 dnnl_OIhw8o4i = dnnl_ABcd8a4b,
848 dnnl_Owhi16o = dnnl_Adcb16a,
851 dnnl_Odhwi16o = dnnl_Acdeb16a,
852 dnnl_OdhwI16o2i = dnnl_AcdeB16a2b,
853 dnnl_OdhwI16o4i = dnnl_AcdeB16a4b,
854 dnnl_Odhwi4o = dnnl_Acdeb4a,
855 dnnl_Odhwi8o = dnnl_Acdeb8a,
856 dnnl_OIdhw16i16o = dnnl_ABcde16b16a,
857 dnnl_OIdhw16i32o = dnnl_ABcde16b32a,
858 dnnl_OIdhw16i64o = dnnl_ABcde16b64a,
859 dnnl_OIdhw16o16i = dnnl_ABcde16a16b,
860 dnnl_Oidhw16o = dnnl_Abcde16a,
861 dnnl_OIdhw4i4o = dnnl_ABcde4b4a,
862 dnnl_OIdhw4o4i = dnnl_ABcde4a4b,
863 dnnl_Oidhw4o = dnnl_Abcde4a,
864 dnnl_OIdhw8i16o2i = dnnl_ABcde8b16a2b,
865 dnnl_OIdhw8i32o2i = dnnl_ABcde8b32a2b,
866 dnnl_OIdhw8i64o2i = dnnl_ABcde8b64a2b,
867 dnnl_OIdhw8i8o = dnnl_ABcde8b8a,
868 dnnl_OIdhw8o16i2o = dnnl_ABcde8a16b2a,
869 dnnl_IOdhw8o16i2o = dnnl_BAcde8a16b2a,
871 dnnl_OIdhw4i32o4i = dnnl_ABcde4b32a4b,
872 dnnl_OIdhw4i64o4i = dnnl_ABcde4b64a4b,
873 dnnl_OIdhw16i16o4i = dnnl_ABcde16b16a4b,
874 dnnl_OIdhw16i16o2i = dnnl_ABcde16b16a2b,
876 dnnl_OIdhw8o8i = dnnl_ABcde8a8b,
877 dnnl_OIdhw8o4i = dnnl_ABcde8a4b,
878 dnnl_IOdhw16i16o = dnnl_BAcde16b16a,
879 dnnl_OIdhw4o8i8o4i = dnnl_ABcde4a8b8a4b,
880 dnnl_IOdhw16o16i = dnnl_BAcde16a16b,
883 dnnl_Goiw16g = dnnl_Abcd16a,
884 dnnl_Goiw8g = dnnl_Abcd8a,
885 dnnl_Goiw4g = dnnl_Abcd4a,
886 dnnl_gIOw16o16i = dnnl_aCBd16b16c,
887 dnnl_gIOw16i16o = dnnl_aCBd16c16b,
888 dnnl_gOIw16i16o = dnnl_aBCd16c16b,
889 dnnl_gOIw16o16i = dnnl_aBCd16b16c,
891 dnnl_gOIw4i16o4i = dnnl_aBCd4c16b4c,
892 dnnl_gOIw2i8o4i = dnnl_aBCd2c8b4c,
893 dnnl_gOIw16i16o4i = dnnl_aBCd16c16b4c,
894 dnnl_gOIw16i16o2i = dnnl_aBCd16c16b2c,
895 dnnl_gOIw16o16i2o = dnnl_aBCd16b16c2b,
896 dnnl_gOIw4i4o = dnnl_aBCd4c4b,
897 dnnl_gOIw4o4i = dnnl_aBCd4b4c,
899 dnnl_gOIw8i16o2i = dnnl_aBCd8c16b2c,
900 dnnl_gOIw8i8o = dnnl_aBCd8c8b,
901 dnnl_gOIw8o16i2o = dnnl_aBCd8b16c2b,
902 dnnl_gIOw8o16i2o = dnnl_aCBd8b16c2b,
903 dnnl_gOIw8o8i = dnnl_aBCd8b8c,
904 dnnl_gOIw8o4i = dnnl_aBCd8b4c,
905 dnnl_gOwi16o = dnnl_aBdc16b,
906 dnnl_gOwI16o2i = dnnl_aBdC16b2c,
907 dnnl_gOwI16o4i = dnnl_aBdC16b4c,
908 dnnl_gOwi4o = dnnl_aBdc4b,
909 dnnl_gOwi8o = dnnl_aBdc8b,
910 dnnl_Goiw32g = dnnl_Abcd32a,
911 dnnl_gOIw2i4o2i = dnnl_aBCd2c4b2c,
913 dnnl_gOIw4i8o2i = dnnl_aBCd4c8b2c,
914 dnnl_gOIw4o8i2o = dnnl_aBCd4b8c2b,
917 dnnl_gIOhw16i16o = dnnl_aCBde16c16b,
918 dnnl_gIOhw16o16i = dnnl_aCBde16b16c,
919 dnnl_gOhwi16o = dnnl_aBdec16b,
920 dnnl_gOhwI16o2i = dnnl_aBdeC16b2c,
921 dnnl_gOhwI16o4i = dnnl_aBdeC16b4c,
922 dnnl_gOhwi32o = dnnl_aBdec32b,
923 dnnl_gOhwi4o = dnnl_aBdec4b,
924 dnnl_gOhwi8o = dnnl_aBdec8b,
925 dnnl_Goihw16g = dnnl_Abcde16a,
926 dnnl_gOIhw16i16o = dnnl_aBCde16c16b,
927 dnnl_gOIhw16o16i = dnnl_aBCde16b16c,
929 dnnl_gOIhw2i8o4i = dnnl_aBCde2c8b4c,
930 dnnl_gOIhw4i16o4i = dnnl_aBCde4c16b4c,
931 dnnl_gOIhw16i16o4i = dnnl_aBCde16c16b4c,
932 dnnl_gOIhw16i16o2i = dnnl_aBCde16c16b2c,
933 dnnl_gOIhw16o16i2o = dnnl_aBCde16b16c2b,
934 dnnl_gOIhw4i4o = dnnl_aBCde4c4b,
935 dnnl_gOIhw4o4i = dnnl_aBCde4b4c,
937 dnnl_Goihw8g = dnnl_Abcde8a,
938 dnnl_Goihw4g = dnnl_Abcde4a,
939 dnnl_gOIhw8i16o2i = dnnl_aBCde8c16b2c,
940 dnnl_gOIhw8i8o = dnnl_aBCde8c8b,
941 dnnl_gOIhw8o16i2o = dnnl_aBCde8b16c2b,
942 dnnl_gIOhw8o16i2o = dnnl_aCBde8b16c2b,
943 dnnl_gOIhw8o8i = dnnl_aBCde8b8c,
944 dnnl_gOIhw8o4i = dnnl_aBCde8b4c,
945 dnnl_Goihw32g = dnnl_Abcde32a,
946 dnnl_gOwhi16o = dnnl_aBedc16b,
948 dnnl_OIw4o8i8o4i = dnnl_ABc4a8b8a4b,
949 dnnl_OIhw4o8i8o4i = dnnl_ABcd4a8b8a4b,
950 dnnl_IOw4i8o8i4o = dnnl_BAc4b8a8b4a,
951 dnnl_IOhw4i8o8i4o = dnnl_BAcd4b8a8b4a,
952 dnnl_IOdhw4i8o8i4o = dnnl_BAcde4b8a8b4a,
954 dnnl_OIhw2o8i8o2i = dnnl_ABcd2a8b8a2b,
955 dnnl_gOIw4o8i8o4i = dnnl_aBCd4b8c8b4c,
956 dnnl_gOIhw4o8i8o4i = dnnl_aBCde4b8c8b4c,
957 dnnl_gOIdhw4o8i8o4i = dnnl_aBCdef4b8c8b4c,
958 dnnl_gIOw4i8o8i4o = dnnl_aCBd4c8b8c4b,
959 dnnl_gIOhw4i8o8i4o = dnnl_aCBde4c8b8c4b,
960 dnnl_gIOdhw4i8o8i4o = dnnl_aCBdef4c8b8c4b,
961 dnnl_gOIhw2o8i8o2i = dnnl_aBCde2b8c8b2c,
962 dnnl_gOIhw2i4o2i = dnnl_aBCde2c4b2c,
964 dnnl_gOIhw4i8o2i = dnnl_aBCde4c8b2c,
965 dnnl_gOIhw4o8i2o = dnnl_aBCde4b8c2b,
968 dnnl_gIOdhw16i16o = dnnl_aCBdef16c16b,
969 dnnl_gIOdhw16o16i = dnnl_aCBdef16b16c,
970 dnnl_gOdhwi16o = dnnl_aBdefc16b,
971 dnnl_gOdhwI16o2i = dnnl_aBdefC16b2c,
972 dnnl_gOdhwI16o4i = dnnl_aBdefC16b4c,
973 dnnl_gOdhwi4o = dnnl_aBdefc4b,
974 dnnl_gOdhwi8o = dnnl_aBdefc8b,
975 dnnl_gOIdhw16i16o = dnnl_aBCdef16c16b,
976 dnnl_gOIdhw4i16o4i = dnnl_aBCdef4c16b4c,
977 dnnl_gOIdhw16i16o4i = dnnl_aBCdef16c16b4c,
979 dnnl_gOIdhw16i16o2i = dnnl_aBCdef16c16b2c,
980 dnnl_gOIdhw16o16i = dnnl_aBCdef16b16c,
982 dnnl_gOIdhw4i4o = dnnl_aBCdef4c4b,
983 dnnl_gOIdhw4o4i = dnnl_aBCdef4b4c,
985 dnnl_gOIdhw8i16o2i = dnnl_aBCdef8c16b2c,
986 dnnl_gOIdhw8i8o = dnnl_aBCdef8c8b,
987 dnnl_gOIdhw8o16i2o = dnnl_aBCdef8b16c2b,
988 dnnl_gIOdhw8o16i2o = dnnl_aCBdef8b16c2b,
989 dnnl_gOIdhw8o8i = dnnl_aBCdef8b8c,
990 dnnl_gOIdhw8o4i = dnnl_aBCdef8b4c,
991 dnnl_Goidhw16g = dnnl_Abcdef16a,
992 dnnl_Goidhw32g = dnnl_Abcdef32a,
993 dnnl_gOIdhw2i4o2i = dnnl_aBCdef2c4b2c,
994 dnnl_gOIdhw4i8o2i = dnnl_aBCdef4c8b2c,
996 dnnl_gOIdhw4o8i2o = dnnl_aBCdef4b8c2b,
998 dnnl_Owi32o = dnnl_Acb32a,
999 dnnl_OwI32o2i = dnnl_AcB32a2b,
1000 dnnl_OwI32o4i = dnnl_AcB32a4b,
1001 dnnl_Owi48o = dnnl_Acb48a,
1002 dnnl_OwI48o2i = dnnl_AcB48a2b,
1003 dnnl_OwI48o4i = dnnl_AcB48a4b,
1004 dnnl_Owi64o = dnnl_Acb64a,
1005 dnnl_OwI64o2i = dnnl_AcB64a2b,
1006 dnnl_OwI64o4i = dnnl_AcB64a4b,
1007 dnnl_wIo2i = dnnl_cBa2b,
1008 dnnl_wIo4i = dnnl_cBa4b,
1009 dnnl_gOwi32o = dnnl_aBdc32b,
1010 dnnl_gOwI32o2i = dnnl_aBdC32b2c,
1011 dnnl_gOwI32o4i = dnnl_aBdC32b4c,
1012 dnnl_gOwi48o = dnnl_aBdc48b,
1013 dnnl_gOwI48o2i = dnnl_aBdC48b2c,
1014 dnnl_gOwI48o4i = dnnl_aBdC48b4c,
1015 dnnl_gOwi64o = dnnl_aBdc64b,
1016 dnnl_gOwI64o2i = dnnl_aBdC64b2c,
1017 dnnl_gOwI64o4i = dnnl_aBdC64b4c,
1018 dnnl_gwio = dnnl_adcb,
1019 dnnl_gwIo2i = dnnl_adCb2c,
1020 dnnl_gwIo4i = dnnl_adCb4c,
1022 dnnl_OhwI32o = dnnl_Acdb32a,
1023 dnnl_OhwI32o2i = dnnl_AcdB32a2b,
1024 dnnl_OhwI32o4i = dnnl_AcdB32a4b,
1025 dnnl_Ohwi48o = dnnl_Acdb48a,
1026 dnnl_OhwI48o2i = dnnl_AcdB48a2b,
1027 dnnl_OhwI48o4i = dnnl_AcdB48a4b,
1028 dnnl_Ohwi64o = dnnl_Acdb64a,
1029 dnnl_OhwI64o2i = dnnl_AcdB64a2b,
1030 dnnl_OhwI64o4i = dnnl_AcdB64a4b,
1031 dnnl_hwIo2i = dnnl_cdBa2b,
1032 dnnl_hwIo4i = dnnl_cdBa4b,
1033 dnnl_gOhwI32o = dnnl_aBdec32b,
1034 dnnl_gOhwI32o2i = dnnl_aBdeC32b2c,
1035 dnnl_gOhwI32o4i = dnnl_aBdeC32b4c,
1036 dnnl_gOhwi48o = dnnl_aBdec48b,
1037 dnnl_gOhwI48o2i = dnnl_aBdeC48b2c,
1038 dnnl_gOhwI48o4i = dnnl_aBdeC48b4c,
1039 dnnl_gOhwi64o = dnnl_aBdec64b,
1040 dnnl_gOhwI64o2i = dnnl_aBdeC64b2c,
1041 dnnl_gOhwI64o4i = dnnl_aBdeC64b4c,
1042 dnnl_ghwio = dnnl_adecb,
1043 dnnl_ghwIo2i = dnnl_adeCb2c,
1044 dnnl_ghwIo4i = dnnl_adeCb4c,
1046 dnnl_Odhwi32o = dnnl_Acdeb32a,
1047 dnnl_OdhwI32o2i = dnnl_AcdeB32a2b,
1048 dnnl_OdhwI32o4i = dnnl_AcdeB32a4b,
1049 dnnl_Odhwi48o = dnnl_Acdeb48a,
1050 dnnl_OdhwI48o2i = dnnl_AcdeB48a2b,
1051 dnnl_OdhwI48o4i = dnnl_AcdeB48a4b,
1052 dnnl_Odhwi64o = dnnl_Acdeb64a,
1053 dnnl_OdhwI64o2i = dnnl_AcdeB64a2b,
1054 dnnl_OdhwI64o4i = dnnl_AcdeB64a4b,
1055 dnnl_dhwIo2i = dnnl_cdeBa2b,
1056 dnnl_dhwIo4i = dnnl_cdeBa4b,
1057 dnnl_gOdhwi32o = dnnl_aBdefc32b,
1058 dnnl_gOdhwI32o2i = dnnl_aBdefC32b2c,
1059 dnnl_gOdhwI32o4i = dnnl_aBdefC32b4c,
1060 dnnl_gOdhwi48o = dnnl_aBdefc48b,
1061 dnnl_gOdhwI48o2i = dnnl_aBdefC48b2c,
1062 dnnl_gOdhwI48o4i = dnnl_aBdefC48b4c,
1063 dnnl_gOdhwi64o = dnnl_aBdefc64b,
1064 dnnl_gOdhwI64o2i = dnnl_aBdefC64b2c,
1065 dnnl_gOdhwI64o4i = dnnl_aBdefC64b4c,
1066 dnnl_gdhwio = dnnl_adefcb,
1067 dnnl_gdhwIo2i = dnnl_adefCb2c,
1068 dnnl_gdhwIo4i = dnnl_adefCb4c,
1069 dnnl_OI16i32o4i = dnnl_AB16b32a4b,
1070 dnnl_OI16i48o4i = dnnl_AB16b48a4b,
1071 dnnl_OI16i64o4i = dnnl_AB16b64a4b,
1072 dnnl_OI16i16o2i = dnnl_AB16b16a2b,
1073 dnnl_OI16i32o2i = dnnl_AB16b32a2b,
1074 dnnl_OI16i48o2i = dnnl_AB16b48a2b,
1075 dnnl_OI16i64o2i = dnnl_AB16b64a2b,
1076 dnnl_OIw16i32o4i = dnnl_ABc16b32a4b,
1077 dnnl_OIw16i48o4i = dnnl_ABc16b48a4b,
1078 dnnl_OIw16i64o4i = dnnl_ABc16b64a4b,
1079 dnnl_OIw16i32o2i = dnnl_ABc16b32a2b,
1080 dnnl_OIw16i48o2i = dnnl_ABc16b48a2b,
1081 dnnl_OIw16i64o2i = dnnl_ABc16b64a2b,
1082 dnnl_OIhw16i32o4i = dnnl_ABcd16b32a4b,
1083 dnnl_OIhw16i48o4i = dnnl_ABcd16b48a4b,
1084 dnnl_OIhw16i64o4i = dnnl_ABcd16b64a4b,
1085 dnnl_OIhw16i32o2i = dnnl_ABcd16b32a2b,
1086 dnnl_OIhw16i48o2i = dnnl_ABcd16b48a2b,
1087 dnnl_OIhw16i64o2i = dnnl_ABcd16b64a2b,
1088 dnnl_OIdhw16i32o4i = dnnl_ABcde16b32a4b,
1089 dnnl_OIdhw16i48o4i = dnnl_ABcde16b48a4b,
1090 dnnl_OIdhw16i64o4i = dnnl_ABcde16b64a4b,
1091 dnnl_OIdhw16i32o2i = dnnl_ABcde16b32a2b,
1092 dnnl_OIdhw16i48o2i = dnnl_ABcde16b48a2b,
1093 dnnl_OIdhw16i64o2i = dnnl_ABcde16b64a2b,
1186 dnnl_alg_kind_undef,
1412 #define DNNL_MAX_NDIMS 12
1416 #define DNNL_RUNTIME_DIM_VAL INT64_MIN
1421 #define DNNL_RUNTIME_SIZE_VAL ((size_t)DNNL_RUNTIME_DIM_VAL)
1425 static const union {
1428 } DNNL_RUNTIME_F32_VAL_REP = {0x7fc000d0};
1433 #define DNNL_RUNTIME_F32_VAL (DNNL_RUNTIME_F32_VAL_REP.f)
1436 static const int DNNL_RUNTIME_S32_VAL_REP = INT32_MIN;
1441 #define DNNL_RUNTIME_S32_VAL DNNL_RUNTIME_S32_VAL_REP
1495 dnnl_packed_format_undef = 0,
1499 } dnnl_rnn_packed_memory_format_t;
1503 #define DNNL_RNN_MAX_N_PARTS 4
1507 dnnl_rnn_packed_memory_format_t format;
1514 size_t offset_compensation;
1521 dnnl_memory_extra_flag_none = 0x0U,
1530 dnnl_memory_extra_flag_scale_adjust = 0x2U,
1531 dnnl_memory_extra_flag_rnn_u8s8_compensation = 0x4U,
1532 dnnl_memory_extra_flag_gpu_rnn_u8s8_compensation
1533 = dnnl_memory_extra_flag_rnn_u8s8_compensation,
1534 dnnl_memory_extra_flag_compensation_conv_asymmetric_src = 0x8U,
1535 dnnl_memory_extra_flag_rnn_s8s8_compensation = 0x16U,
1618 #define DNNL_MEMORY_NONE (NULL)
1622 #define DNNL_MEMORY_ALLOCATE ((void *)(size_t)-1)
1903 } dnnl_prelu_desc_t;
2279 typedef const struct dnnl_engine *const_dnnl_engine_t;
2395 #define DNNL_ARG_SRC_0 1
2398 #define DNNL_ARG_SRC DNNL_ARG_SRC_0
2401 #define DNNL_ARG_SRC_LAYER DNNL_ARG_SRC_0
2404 #define DNNL_ARG_FROM DNNL_ARG_SRC_0
2407 #define DNNL_ARG_SRC_1 2
2410 #define DNNL_ARG_SRC_ITER DNNL_ARG_SRC_1
2413 #define DNNL_ARG_SRC_2 3
2416 #define DNNL_ARG_SRC_ITER_C DNNL_ARG_SRC_2
2419 #define DNNL_ARG_DST_0 17
2422 #define DNNL_ARG_DST DNNL_ARG_DST_0
2425 #define DNNL_ARG_TO DNNL_ARG_DST_0
2427 #define DNNL_ARG_DST_LAYER DNNL_ARG_DST_0
2430 #define DNNL_ARG_DST_1 18
2433 #define DNNL_ARG_DST_ITER DNNL_ARG_DST_1
2436 #define DNNL_ARG_DST_2 19
2439 #define DNNL_ARG_DST_ITER_C DNNL_ARG_DST_2
2442 #define DNNL_ARG_WEIGHTS_0 33
2445 #define DNNL_ARG_WEIGHTS DNNL_ARG_WEIGHTS_0
2448 #define DNNL_ARG_SCALE_SHIFT DNNL_ARG_WEIGHTS_0
2451 #define DNNL_ARG_WEIGHTS_LAYER DNNL_ARG_WEIGHTS_0
2454 #define DNNL_ARG_WEIGHTS_1 34
2457 #define DNNL_ARG_WEIGHTS_ITER DNNL_ARG_WEIGHTS_1
2460 #define DNNL_ARG_WEIGHTS_2 35
2463 #define DNNL_ARG_WEIGHTS_PEEPHOLE DNNL_ARG_WEIGHTS_2
2466 #define DNNL_ARG_WEIGHTS_3 36
2469 #define DNNL_ARG_WEIGHTS_PROJECTION DNNL_ARG_WEIGHTS_3
2472 #define DNNL_ARG_BIAS 41
2475 #define DNNL_ARG_MEAN 49
2477 #define DNNL_ARG_VARIANCE 50
2480 #define DNNL_ARG_SCALE 51
2482 #define DNNL_ARG_SHIFT 52
2486 #define DNNL_ARG_WORKSPACE 64
2488 #define DNNL_ARG_SCRATCHPAD 80
2491 #define DNNL_ARG_DIFF_SRC_0 129
2494 #define DNNL_ARG_DIFF_SRC DNNL_ARG_DIFF_SRC_0
2497 #define DNNL_ARG_DIFF_SRC_LAYER DNNL_ARG_DIFF_SRC_0
2500 #define DNNL_ARG_DIFF_SRC_1 130
2503 #define DNNL_ARG_DIFF_SRC_ITER DNNL_ARG_DIFF_SRC_1
2506 #define DNNL_ARG_DIFF_SRC_2 131
2509 #define DNNL_ARG_DIFF_SRC_ITER_C DNNL_ARG_DIFF_SRC_2
2512 #define DNNL_ARG_DIFF_DST_0 145
2515 #define DNNL_ARG_DIFF_DST DNNL_ARG_DIFF_DST_0
2518 #define DNNL_ARG_DIFF_DST_LAYER DNNL_ARG_DIFF_DST_0
2521 #define DNNL_ARG_DIFF_DST_1 146
2524 #define DNNL_ARG_DIFF_DST_ITER DNNL_ARG_DIFF_DST_1
2527 #define DNNL_ARG_DIFF_DST_2 147
2530 #define DNNL_ARG_DIFF_DST_ITER_C DNNL_ARG_DIFF_DST_2
2533 #define DNNL_ARG_DIFF_WEIGHTS_0 161
2536 #define DNNL_ARG_DIFF_WEIGHTS DNNL_ARG_DIFF_WEIGHTS_0
2539 #define DNNL_ARG_DIFF_SCALE_SHIFT DNNL_ARG_DIFF_WEIGHTS_0
2542 #define DNNL_ARG_DIFF_WEIGHTS_LAYER DNNL_ARG_DIFF_WEIGHTS_0
2545 #define DNNL_ARG_DIFF_WEIGHTS_1 162
2548 #define DNNL_ARG_DIFF_WEIGHTS_ITER DNNL_ARG_DIFF_WEIGHTS_1
2551 #define DNNL_ARG_DIFF_WEIGHTS_2 163
2554 #define DNNL_ARG_DIFF_WEIGHTS_PEEPHOLE DNNL_ARG_DIFF_WEIGHTS_2
2557 #define DNNL_ARG_DIFF_WEIGHTS_3 164
2560 #define DNNL_ARG_DIFF_WEIGHTS_PROJECTION DNNL_ARG_DIFF_WEIGHTS_3
2563 #define DNNL_ARG_DIFF_BIAS 169
2566 #define DNNL_ARG_DIFF_SCALE 255
2568 #define DNNL_ARG_DIFF_SHIFT 256
2571 #define DNNL_ARG_ATTR_OUTPUT_SCALES 513
2575 #define DNNL_ARG_MULTIPLE_SRC 1024
2578 #define DNNL_ARG_MULTIPLE_DST 2048
2581 #define DNNL_ARG_ATTR_ZERO_POINTS 4096
2585 #define DNNL_ARG_ATTR_POST_OP_DW 8192
2588 #define DNNL_ARG_ATTR_MULTIPLE_POST_OP_BASE 16384
2592 #define DNNL_ARG_ATTR_MULTIPLE_POST_OP(idx) \
2593 (DNNL_ARG_ATTR_MULTIPLE_POST_OP_BASE * ((idx) + 1))
2596 #define DNNL_ARG_ATTR_INPUT_SCALES 1048576
2700 dnnl_query_max = 0x7fff,
2713 dnnl_stream_in_order = 0x1U,
2734 #define DNNL_RUNTIME_NONE 0u
2737 #define DNNL_RUNTIME_SEQ 1u
2740 #define DNNL_RUNTIME_OMP 2u
2743 #define DNNL_RUNTIME_TBB 4u
2746 #define DNNL_RUNTIME_THREADPOOL 8u
2749 #define DNNL_RUNTIME_OCL 256u
2752 #define DNNL_RUNTIME_SYCL 512u
2755 #define DNNL_RUNTIME_DPCPP DNNL_RUNTIME_SYCL
2769 #define DNNL_JIT_PROFILE_NONE 0u
2772 #define DNNL_JIT_PROFILE_VTUNE 1u
2775 #define DNNL_JIT_PROFILE_LINUX_PERFMAP 2u
2778 #define DNNL_JIT_PROFILE_LINUX_JITDUMP 4u
2782 #define DNNL_JIT_PROFILE_LINUX_JITDUMP_USE_TSC 8u
2785 #define DNNL_JIT_PROFILE_LINUX_PERF \
2786 (DNNL_JIT_PROFILE_LINUX_JITDUMP | DNNL_JIT_PROFILE_LINUX_PERFMAP)
struct dnnl_primitive_attr * dnnl_primitive_attr_t
A primitive descriptor attributes handle that controls primitive behavior.
Definition: dnnl_types.h:2350
struct dnnl_post_ops * dnnl_post_ops_t
A post operation chain handle.
Definition: dnnl_types.h:2376
const struct dnnl_primitive_attr * const_dnnl_primitive_attr_t
A constant primitive descriptor attributes handle.
Definition: dnnl_types.h:2353
const struct dnnl_post_ops * const_dnnl_post_ops_t
A constant post operation chain handle.
Definition: dnnl_types.h:2379
prop_kind
Propagation kind.
Definition: dnnl.hpp:435
dnnl_scratchpad_mode_t
Scratchpad mode.
Definition: dnnl_types.h:2316
@ dnnl_scratchpad_mode_user
The user manages the scratchpad allocation by querying and providing the scratchpad memory to primiti...
Definition: dnnl_types.h:2338
@ dnnl_scratchpad_mode_library
The library manages the scratchpad allocation according to the policy specified by the DNNL_ENABLE_CO...
Definition: dnnl_types.h:2333
dnnl_convolution_desc_t dnnl_deconvolution_desc_t
A descriptor of a deconvolution operation.
Definition: dnnl_types.h:1691
dnnl_engine_kind_t
Kinds of engines.
Definition: dnnl_types.h:2262
struct dnnl_engine * dnnl_engine_t
An engine handle.
Definition: dnnl_types.h:2275
@ dnnl_gpu
GPU engine.
Definition: dnnl_types.h:2268
@ dnnl_cpu
CPU engine.
Definition: dnnl_types.h:2266
@ dnnl_any_engine
An unspecified engine.
Definition: dnnl_types.h:2264
dnnl_softmax_desc_t dnnl_logsoftmax_desc_t
A descriptor of a LogSoftmax operation.
Definition: dnnl_types.h:1803
dnnl_data_type_t
Data type specification.
Definition: dnnl_types.h:62
const struct dnnl_memory * const_dnnl_memory_t
A constant memory handle.
Definition: dnnl_types.h:1614
#define DNNL_RNN_MAX_N_PARTS
Maximum number of parts of RNN weights tensor that require separate computation.
Definition: dnnl_types.h:1503
dnnl_memory_extra_flags_t
Flags for memory special features.
Definition: dnnl_types.h:1520
struct dnnl_memory * dnnl_memory_t
A memory handle.
Definition: dnnl_types.h:1611
dnnl_format_tag_t
Memory format tag specification.
Definition: dnnl_types.h:164
dnnl_dim_t dnnl_dims_t[DNNL_MAX_NDIMS]
A type to describe tensor dimensions.
Definition: dnnl_types.h:1447
int64_t dnnl_dim_t
A type to describe tensor dimension.
Definition: dnnl_types.h:1444
dnnl_format_kind_t
Memory format kind.
Definition: dnnl_types.h:80
#define DNNL_MAX_NDIMS
Maximum number of dimensions a tensor can have.
Definition: dnnl_types.h:1412
dnnl_wino_memory_format_t
Winograd-specific formats.
Definition: dnnl_types.h:1468
@ dnnl_f16
16-bit/half-precision floating point.
Definition: dnnl_types.h:66
@ dnnl_bf16
non-standard 16-bit (bfloat16 w/ 7 bit mantissa) floating point.
Definition: dnnl_types.h:68
@ dnnl_f32
32-bit/single-precision floating point.
Definition: dnnl_types.h:70
@ dnnl_data_type_undef
Undefined data type, used for empty memory descriptors.
Definition: dnnl_types.h:64
@ dnnl_s8
8-bit signed integer.
Definition: dnnl_types.h:74
@ dnnl_s32
32-bit signed integer.
Definition: dnnl_types.h:72
@ dnnl_u8
8-bit unsigned integer.
Definition: dnnl_types.h:76
@ dnnl_memory_extra_flag_compensation_conv_s8s8
Indicates the weights have an additional buffer, that depends on the compensation_mask.
Definition: dnnl_types.h:1529
@ dnnl_abcdefhg
permuted 8D tensor
Definition: dnnl_types.h:216
@ dnnl_hwigo
5D CNN weights tensor (incl. groups), an alias to dnnl_decab
Definition: dnnl_types.h:663
@ dnnl_aBCdef2b4c2b
6D tensor blocked by 3rd dimension with block size 4
Definition: dnnl_types.h:362
@ dnnl_abcdefghi
plain 9D tensor
Definition: dnnl_types.h:186
@ dnnl_acdeb
permuted 5D tensor
Definition: dnnl_types.h:199
@ dnnl_oihw
4D CNN weights tensor, an alias to dnnl_abcd
Definition: dnnl_types.h:632
@ dnnl_cn
2D CNN activations tensor, an alias to dnnl_ba
Definition: dnnl_types.h:599
@ dnnl_abcdefgh
plain 8D tensor
Definition: dnnl_types.h:185
@ dnnl_iohw
4D CNN weights tensor, an alias to dnnl_bacd
Definition: dnnl_types.h:640
@ dnnl_ldgOi32o
6D RNN weights tensor
Definition: dnnl_types.h:718
@ dnnl_nChw32c
4D CNN activations tensor blocked by channels with block size 32, an alias to dnnl_aBcd32b
Definition: dnnl_types.h:741
@ dnnl_oidhw
5D CNN weights tensor, an alias to dnnl_abcde
Definition: dnnl_types.h:642
@ dnnl_abcdefghikj
permuted 11D tensor
Definition: dnnl_types.h:219
@ dnnl_ab
plain 2D tensor
Definition: dnnl_types.h:178
@ dnnl_ABcd8b8a
4D tensor blocked by 1st and 2nd dimension with block size 8
Definition: dnnl_types.h:288
@ dnnl_cdba
permuted 4D tensor
Definition: dnnl_types.h:208
@ dnnl_abcdefghijkl
plain 12D tensor
Definition: dnnl_types.h:189
@ dnnl_owi
3D CNN weights tensor, an alias to dnnl_acb
Definition: dnnl_types.h:626
@ dnnl_aBcdef4b
6D tensor blocked by 2nd dimension with block size 4
Definition: dnnl_types.h:364
@ dnnl_wigo
4D CNN weights tensor (incl. groups), an alias to dnnl_dcab
Definition: dnnl_types.h:657
@ dnnl_gohwi
5D CNN weights tensor (incl. groups), an alias to dnnl_abdec
Definition: dnnl_types.h:661
@ dnnl_abcdegf
permuted 7D tensor
Definition: dnnl_types.h:215
@ dnnl_tnc
3D RNN data tensor in the format (seq_length, batch, input channels), an alias to dnnl_abc.
Definition: dnnl_types.h:677
@ dnnl_ldgo
4D RNN bias tensor in the format (num_layers, num_directions, num_gates, output_channels),...
Definition: dnnl_types.h:712
@ dnnl_ldOi32o
5D LSTM projection tensor
Definition: dnnl_types.h:714
@ dnnl_ldio
4D LSTM projection tensor in the format (num_layers, num_directions, num_channels_in_hidden_state,...
Definition: dnnl_types.h:701
@ dnnl_abcdfe
permuted 6D tensor
Definition: dnnl_types.h:214
@ dnnl_aBcd4b
4D tensor blocked by 2nd dimension with block size 4
Definition: dnnl_types.h:263
@ dnnl_nCdhw16c
5D CNN activations tensor blocked by channels with block size 16, an alias to dnnl_aBcde16b
Definition: dnnl_types.h:732
@ dnnl_abcde
plain 5D tensor
Definition: dnnl_types.h:182
@ dnnl_decab
permuted 5D tensor
Definition: dnnl_types.h:211
@ dnnl_bca
permuted 3D tensor
Definition: dnnl_types.h:204
@ dnnl_aBcde4b
5D tensor blocked by 2nd dimension with block size 4
Definition: dnnl_types.h:315
@ dnnl_aBc16b
3D tensor blocked by 2nd dimension with block size 16
Definition: dnnl_types.h:229
@ dnnl_godhwi
6D CNN weights tensor (incl. groups), an alias to dnnl_abdefc
Definition: dnnl_types.h:669
@ dnnl_aBcdef16b
6D tensor blocked by 2nd dimension with block size 16
Definition: dnnl_types.h:354
@ dnnl_giodhw
6D CNN weights tensor (incl. groups), an alias to dnnl_acbdef
Definition: dnnl_types.h:671
@ dnnl_aBCde2b4c2b
5D tensor blocked by 3rd dimension with block size 4
Definition: dnnl_types.h:352
@ dnnl_io
2D CNN weights tensor, an alias to dnnl_ba
Definition: dnnl_types.h:622
@ dnnl_ldoi
4D LSTM projection tensor in the format (num_layers, num_directions, num_channels_in_recurrent_projec...
Definition: dnnl_types.h:705
@ dnnl_aBc4b
3D tensor blocked by 2nd dimension with block size 4
Definition: dnnl_types.h:235
@ dnnl_hwio
4D CNN weights tensor, an alias to dnnl_cdba
Definition: dnnl_types.h:634
@ dnnl_ldnc
4D RNN states tensor in the format (num_layers, num_directions, batch, state channels),...
Definition: dnnl_types.h:683
@ dnnl_gowi
4D CNN weights tensor (incl. groups), an alias to dnnl_abdc
Definition: dnnl_types.h:655
@ dnnl_abcdefghijk
plain 11D tensor
Definition: dnnl_types.h:188
@ dnnl_bacde
permuted 5D tensor
Definition: dnnl_types.h:203
@ dnnl_aBcd16b
4D tensor blocked by 2nd dimension with block size 16
Definition: dnnl_types.h:255
@ dnnl_cba
permuted 3D tensor
Definition: dnnl_types.h:207
@ dnnl_nCw32c
3D CNN activations tensor blocked by channels with block size 32, an alias to dnnl_aBc32b
Definition: dnnl_types.h:753
@ dnnl_ntc
3D RNN data tensor in the format (batch, seq_length, input channels), an alias to dnnl_bac.
Definition: dnnl_types.h:680
@ dnnl_ldgoi
5D RNN weights tensor in the format (num_layers, num_directions, num_gates, output_channels,...
Definition: dnnl_types.h:697
@ dnnl_goidhw
6D CNN weights tensor (incl. groups), an alias to dnnl_abcdef
Definition: dnnl_types.h:667
@ dnnl_ba
permuted 2D tensor
Definition: dnnl_types.h:200
@ dnnl_acbd
plain 4D tensor
Definition: dnnl_types.h:181
@ dnnl_ABcde2b8a4b
5D tensor blocked by 1st dimension with block size 8
Definition: dnnl_types.h:304
@ dnnl_abcd
plain 4D tensor
Definition: dnnl_types.h:180
@ dnnl_format_tag_undef
Undefined memory format tag.
Definition: dnnl_types.h:166
@ dnnl_idhwo
5D CNN weights tensor, an alias to dnnl_bcdea
Definition: dnnl_types.h:650
@ dnnl_nCdhw4c
5D CNN activations tensor blocked by channels with block size 4, an alias to dnnl_aBcde4b
Definition: dnnl_types.h:735
@ dnnl_defcab
permuted 6D tensor
Definition: dnnl_types.h:212
@ dnnl_abcdef
plain 6D tensor
Definition: dnnl_types.h:183
@ dnnl_ohwi
4D CNN weights tensor, an alias to dnnl_acdb
Definition: dnnl_types.h:636
@ dnnl_nCdhw32c
5D CNN activations tensor blocked by channels with block size 32, an alias to dnnl_aBcde32b
Definition: dnnl_types.h:729
@ dnnl_nChw8c
4D CNN activations tensor blocked by channels with block size 8, an alias to dnnl_aBcd8b
Definition: dnnl_types.h:750
@ dnnl_iwo
3D CNN weights tensor, an alias to dnnl_bca
Definition: dnnl_types.h:630
@ dnnl_a
plain 1D tensor
Definition: dnnl_types.h:177
@ dnnl_goiw
4D CNN weights tensor (incl. groups), an alias to dnnl_abcd
Definition: dnnl_types.h:653
@ dnnl_nt
2D RNN statistics tensor, an alias to dnnl_ba
Definition: dnnl_types.h:603
@ dnnl_nChw4c
4D CNN activations tensor blocked by channels with block size 4, an alias to dnnl_aBcd4b
Definition: dnnl_types.h:747
@ dnnl_dhwigo
6D CNN weights tensor (incl. groups), an alias to dnnl_defcab
Definition: dnnl_types.h:673
@ dnnl_nchw
4D CNN activations tensor, an alias to dnnl_abcd
Definition: dnnl_types.h:609
@ dnnl_acbdef
permuted 6D tensor
Definition: dnnl_types.h:197
@ dnnl_acdb
permuted 4D tensor
Definition: dnnl_types.h:198
@ dnnl_wio
3D CNN weights tensor, an alias to dnnl_cba
Definition: dnnl_types.h:628
@ dnnl_aBcd8b
4D tensor blocked by 2nd dimension with block size 8
Definition: dnnl_types.h:282
@ dnnl_iodhw
5D CNN weights tensor, an alias to dnnl_bacde
Definition: dnnl_types.h:644
@ dnnl_ldigo
5D RNN weights tensor in the format (num_layers, num_directions, input_channels, num_gates,...
Definition: dnnl_types.h:690
@ dnnl_aBc8b
3D tensor blocked by 2nd dimension with block size 8
Definition: dnnl_types.h:245
@ dnnl_x
1D tensor, an alias to dnnl_a
Definition: dnnl_types.h:595
@ dnnl_nwc
3D CNN activations tensor, an alias to dnnl_acb
Definition: dnnl_types.h:607
@ dnnl_ndhwc
5D CNN activations tensor, an alias to dnnl_acdeb
Definition: dnnl_types.h:617
@ dnnl_nCw4c
3D CNN activations tensor blocked by channels with block size 4, an alias to dnnl_aBc4b
Definition: dnnl_types.h:759
@ dnnl_abcdefg
plain 7D tensor
Definition: dnnl_types.h:184
@ dnnl_aBcde8b
5D tensor blocked by 2nd dimension with block size 8
Definition: dnnl_types.h:330
@ dnnl_nChw16c
4D CNN activations tensor blocked by channels with block size 16, an alias to dnnl_aBcd16b
Definition: dnnl_types.h:744
@ dnnl_abdfce
permuted 6D tensor
Definition: dnnl_types.h:424
@ dnnl_abdec
permuted 5D tensor
Definition: dnnl_types.h:194
@ dnnl_chwn
4D CNN activations tensor, an alias to dnnl_bcda
Definition: dnnl_types.h:613
@ dnnl_bacd
permuted 4D tensor
Definition: dnnl_types.h:202
@ dnnl_ncw
3D CNN activations tensor, an alias to dnnl_abc
Definition: dnnl_types.h:605
@ dnnl_nCdhw8c
5D CNN activations tensor blocked by channels with block size 8, an alias to dnnl_aBcde8b
Definition: dnnl_types.h:738
@ dnnl_aBcde32b
5D tensor blocked by 2nd dimension with block size 32
Definition: dnnl_types.h:313
@ dnnl_nc
2D CNN activations tensor, an alias to dnnl_ab
Definition: dnnl_types.h:597
@ dnnl_tn
2D RNN statistics tensor, an alias to dnnl_ab
Definition: dnnl_types.h:601
@ dnnl_abced
permuted 5D tensor
Definition: dnnl_types.h:213
@ dnnl_bcda
permuted 4D tensor
Definition: dnnl_types.h:205
@ dnnl_acbde
permuted 5D tensor
Definition: dnnl_types.h:196
@ dnnl_aBCd2b4c2b
4D tensor blocked by 3rd dimension with block size 4
Definition: dnnl_types.h:300
@ dnnl_abcdefgih
permuted 9D tensor
Definition: dnnl_types.h:217
@ dnnl_bcdea
permuted 5D tensor
Definition: dnnl_types.h:206
@ dnnl_abdefc
permuted 6D tensor
Definition: dnnl_types.h:425
@ dnnl_aBcde16b
5D tensor blocked by 2nd dimension with block size 16
Definition: dnnl_types.h:306
@ dnnl_nCw8c
3D CNN activations tensor blocked by channels with block size 8, an alias to dnnl_aBc8b
Definition: dnnl_types.h:762
@ dnnl_abdc
permuted 4D tensor
Definition: dnnl_types.h:193
@ dnnl_ABcde4b16a4b
5D tensor blocked by 1st dimension with block size 16
Definition: dnnl_types.h:302
@ dnnl_aBcd32b
4D tensor blocked by 2nd dimension with block size 32
Definition: dnnl_types.h:261
@ dnnl_abcdefghijlk
permuted 12D tensor
Definition: dnnl_types.h:220
@ dnnl_format_tag_last
Just a sentinel, not real memory format tag.
Definition: dnnl_types.h:590
@ dnnl_odhwi
5D CNN weights tensor, an alias to dnnl_acdeb
Definition: dnnl_types.h:648
@ dnnl_abc
plain 3D tensor
Definition: dnnl_types.h:179
@ dnnl_bac
permuted 3D tensor
Definition: dnnl_types.h:201
@ dnnl_ncdhw
5D CNN activations tensor, an alias to dnnl_abcde
Definition: dnnl_types.h:615
@ dnnl_dhwio
5D CNN weights tensor, an alias to dnnl_cdeba
Definition: dnnl_types.h:646
@ dnnl_nhwc
4D CNN activations tensor, an alias to dnnl_acdb
Definition: dnnl_types.h:611
@ dnnl_oiw
3D CNN weights tensor, an alias to dnnl_abc
Definition: dnnl_types.h:624
@ dnnl_dcab
permuted 4D tensor
Definition: dnnl_types.h:209
@ dnnl_cdeba
permuted 5D tensor
Definition: dnnl_types.h:210
@ dnnl_giohw
5D CNN weights tensor (incl. groups), an alias to dnnl_acbde
Definition: dnnl_types.h:665
@ dnnl_goihw
5D CNN weights tensor (incl. groups), an alias to dnnl_abcde
Definition: dnnl_types.h:659
@ dnnl_oi
2D CNN weights tensor, an alias to dnnl_ab
Definition: dnnl_types.h:620
@ dnnl_ihwo
4D CNN weights tensor, an alias to dnnl_bcda
Definition: dnnl_types.h:638
@ dnnl_acb
permuted 3D tensor
Definition: dnnl_types.h:195
@ dnnl_aBc32b
3D tensor blocked by 2nd dimension with block size 32
Definition: dnnl_types.h:233
@ dnnl_abcdefghji
permuted 10D tensor
Definition: dnnl_types.h:218
@ dnnl_nCw16c
3D CNN activations tensor blocked by channels with block size 16, an alias to dnnl_aBc16b
Definition: dnnl_types.h:756
@ dnnl_aBCdef2c8b4c
6D tensor blocked by 2nd dimension with block size 8
Definition: dnnl_types.h:359
@ dnnl_abcdefghij
plain 10D tensor
Definition: dnnl_types.h:187
@ dnnl_format_tag_any
Undefined memory format tag.
Definition: dnnl_types.h:169
@ dnnl_blocked
A tensor in a generic format described by the stride and blocking values in each dimension.
Definition: dnnl_types.h:89
@ dnnl_format_kind_wino
Weights format used in 8bit Winograd convolution.
Definition: dnnl_types.h:91
@ dnnl_format_kind_any
Unspecified format kind.
Definition: dnnl_types.h:85
@ dnnl_format_kind_undef
Undefined memory format kind, used for empty memory descriptors.
Definition: dnnl_types.h:82
@ dnnl_format_kind_rnn_packed
Packed weights format used in RNN.
Definition: dnnl_types.h:93
@ dnnl_wino_wei_OBaaIBOIio
Internal weights format for 4x3 Winograd.
Definition: dnnl_types.h:1476
@ dnnl_wino_wei_aaOio
Internal weights format for 2x3 Winograd.
Definition: dnnl_types.h:1473
@ dnnl_wino_wei_aaOBiOo
Internal weights format for 2x3 Winograd.
Definition: dnnl_types.h:1474
@ dnnl_wino_undef
Undefined memory format, used for empty memory descriptors.
Definition: dnnl_types.h:1470
@ dnnl_wino_wei_aaOIoi
Internal weights format for 2x3 Winograd.
Definition: dnnl_types.h:1472
struct dnnl_primitive * dnnl_primitive_t
A primitive handle.
Definition: dnnl_types.h:2390
struct dnnl_primitive_desc_iterator * dnnl_primitive_desc_iterator_t
A primitive descriptor iterator handle.
Definition: dnnl_types.h:2294
dnnl_normalization_flags_t
Flags for normalization primitives.
Definition: dnnl_types.h:1335
const struct dnnl_primitive * const_dnnl_primitive_t
A constant primitive handle.
Definition: dnnl_types.h:2392
const void * const_dnnl_op_desc_t
A pointer to any of the operation descriptors (constant variant).
Definition: dnnl_types.h:1634
void * dnnl_op_desc_t
A pointer to any of the operation descriptors.
Definition: dnnl_types.h:1632
dnnl_alg_kind_t
Kinds of algorithms.
Definition: dnnl_types.h:1185
dnnl_primitive_kind_t
Kinds of primitives.
Definition: dnnl_types.h:1131
dnnl_query_t
Primitive descriptor query specification.
Definition: dnnl_types.h:2639
struct dnnl_primitive_desc * dnnl_primitive_desc_t
A primitive descriptor handle.
Definition: dnnl_types.h:2305
const struct dnnl_primitive_desc * const_dnnl_primitive_desc_t
A constant primitive descriptor handle.
Definition: dnnl_types.h:2308
dnnl_prop_kind_t
Kinds of propagation.
Definition: dnnl_types.h:1104
const struct dnnl_primitive_desc_iterator * const_dnnl_primitive_desc_iterator_t
A constant primitive descriptor iterator handle.
Definition: dnnl_types.h:2297
@ dnnl_use_scale
Use scale parameter.
Definition: dnnl_types.h:1391
@ dnnl_fuse_norm_relu
Fuse with ReLU.
Definition: dnnl_types.h:1383
@ dnnl_normalization_flags_none
Use no normalization flags.
Definition: dnnl_types.h:1344
@ dnnl_use_scaleshift
Use scale and shift parameters.
Definition: dnnl_types.h:1370
@ dnnl_use_global_stats
Use global statistics.
Definition: dnnl_types.h:1357
@ dnnl_use_shift
Use shift parameter.
Definition: dnnl_types.h:1400
@ dnnl_pooling_avg_exclude_padding
Average pooling exclude padding.
Definition: dnnl_types.h:1265
@ dnnl_eltwise_clip
Eltwise: clip.
Definition: dnnl_types.h:1231
@ dnnl_eltwise_tanh_use_dst_for_bwd
Eltwise: hyperbolic tangent non-linearity (tanh) (dst for backward)
Definition: dnnl_types.h:1249
@ dnnl_eltwise_logsigmoid
Eltwise: logsigmoid.
Definition: dnnl_types.h:1241
@ dnnl_pooling_avg
Average pooling (alias for dnnl_pooling_avg_exclude_padding)
Definition: dnnl_types.h:1267
@ dnnl_eltwise_gelu_tanh
Eltwise: gelu.
Definition: dnnl_types.h:1223
@ dnnl_resampling_linear
Linear Resampling Method.
Definition: dnnl_types.h:1313
@ dnnl_eltwise_sqrt
Eltwise: square root.
Definition: dnnl_types.h:1208
@ dnnl_binary_min
Binary min.
Definition: dnnl_types.h:1293
@ dnnl_reduction_norm_lp_sum
Reduction using lp norm.
Definition: dnnl_types.h:1327
@ dnnl_eltwise_abs
Eltwise: abs.
Definition: dnnl_types.h:1206
@ dnnl_reduction_norm_lp_power_p_max
Reduction using lp norm without final pth-root.
Definition: dnnl_types.h:1329
@ dnnl_reduction_min
Reduction using min.
Definition: dnnl_types.h:1317
@ dnnl_binary_ne
Binary not equal.
Definition: dnnl_types.h:1309
@ dnnl_eltwise_sqrt_use_dst_for_bwd
Eltwise: square root (dst for backward)
Definition: dnnl_types.h:1253
@ dnnl_eltwise_exp
Eltwise: exponent.
Definition: dnnl_types.h:1218
@ dnnl_eltwise_square
Eltwise: square.
Definition: dnnl_types.h:1204
@ dnnl_eltwise_gelu
Eltwise: tanh-based gelu (alias for dnnl_eltwise_gelu_tanh)
Definition: dnnl_types.h:1225
@ dnnl_convolution_winograd
Winograd convolution.
Definition: dnnl_types.h:1190
@ dnnl_eltwise_clip_v2_use_dst_for_bwd
Eltwise: clip version 2 (dst for backward)
Definition: dnnl_types.h:1259
@ dnnl_lrn_across_channels
Local response normalization (LRN) across multiple channels.
Definition: dnnl_types.h:1269
@ dnnl_binary_sub
Binary sub.
Definition: dnnl_types.h:1297
@ dnnl_deconvolution_direct
Direct deconvolution.
Definition: dnnl_types.h:1194
@ dnnl_binary_eq
Binary equal.
Definition: dnnl_types.h:1307
@ dnnl_eltwise_relu
Eltwise: ReLU.
Definition: dnnl_types.h:1198
@ dnnl_convolution_auto
Convolution algorithm(either direct or Winograd) is chosen just in time.
Definition: dnnl_types.h:1192
@ dnnl_eltwise_swish
Eltwise: swish.
Definition: dnnl_types.h:1227
@ dnnl_vanilla_rnn
RNN cell.
Definition: dnnl_types.h:1273
@ dnnl_eltwise_gelu_erf
Eltwise: erf-based gelu.
Definition: dnnl_types.h:1237
@ dnnl_vanilla_lstm
LSTM cell.
Definition: dnnl_types.h:1275
@ dnnl_eltwise_elu
Eltwise: exponential linear unit (elu)
Definition: dnnl_types.h:1202
@ dnnl_vanilla_gru
GRU cell.
Definition: dnnl_types.h:1277
@ dnnl_lbr_gru
GRU cell with linear before reset.
Definition: dnnl_types.h:1285
@ dnnl_eltwise_tanh
Eltwise: hyperbolic tangent non-linearity (tanh)
Definition: dnnl_types.h:1200
@ dnnl_convolution_direct
Direct convolution.
Definition: dnnl_types.h:1188
@ dnnl_eltwise_soft_relu
Eltwise: soft_relu.
Definition: dnnl_types.h:1214
@ dnnl_binary_ge
Binary greater or equal.
Definition: dnnl_types.h:1299
@ dnnl_eltwise_log
Eltwise: natural logarithm.
Definition: dnnl_types.h:1229
@ dnnl_eltwise_clip_v2
Eltwise: clip version 2.
Definition: dnnl_types.h:1233
@ dnnl_lrn_within_channel
LRN within a single channel.
Definition: dnnl_types.h:1271
@ dnnl_eltwise_elu_use_dst_for_bwd
Eltwise: exponential linear unit (elu) (dst for backward)
Definition: dnnl_types.h:1251
@ dnnl_deconvolution_winograd
Winograd deconvolution.
Definition: dnnl_types.h:1196
@ dnnl_eltwise_hardswish
Eltwise: hardswish.
Definition: dnnl_types.h:1245
@ dnnl_reduction_mul
Reduction using mul.
Definition: dnnl_types.h:1321
@ dnnl_eltwise_pow
Eltwise: pow.
Definition: dnnl_types.h:1235
@ dnnl_eltwise_relu_use_dst_for_bwd
Eltwise: ReLU (dst for backward)
Definition: dnnl_types.h:1247
@ dnnl_binary_gt
Binary greater than.
Definition: dnnl_types.h:1301
@ dnnl_reduction_max
Reduction using max.
Definition: dnnl_types.h:1315
@ dnnl_eltwise_logistic
Eltwise: logistic.
Definition: dnnl_types.h:1216
@ dnnl_binary_lt
Binary less than.
Definition: dnnl_types.h:1305
@ dnnl_pooling_avg_include_padding
Average pooling include padding.
Definition: dnnl_types.h:1263
@ dnnl_reduction_mean
Reduction using mean.
Definition: dnnl_types.h:1323
@ dnnl_binary_le
Binary less or equal.
Definition: dnnl_types.h:1303
@ dnnl_pooling_max
Max pooling.
Definition: dnnl_types.h:1261
@ dnnl_eltwise_logistic_use_dst_for_bwd
Eltwise: logistic (dst for backward)
Definition: dnnl_types.h:1255
@ dnnl_binary_add
Binary add.
Definition: dnnl_types.h:1287
@ dnnl_binary_div
Binary div.
Definition: dnnl_types.h:1295
@ dnnl_reduction_norm_lp_max
Reduction using lp norm.
Definition: dnnl_types.h:1325
@ dnnl_reduction_norm_lp_power_p_sum
Reduction using lp norm without final pth-root.
Definition: dnnl_types.h:1331
@ dnnl_eltwise_round
Eltwise: round.
Definition: dnnl_types.h:1239
@ dnnl_binary_mul
Binary mul.
Definition: dnnl_types.h:1289
@ dnnl_eltwise_mish
Eltwise: mish.
Definition: dnnl_types.h:1243
@ dnnl_reduction_sum
Reduction using sum.
Definition: dnnl_types.h:1319
@ dnnl_eltwise_exp_use_dst_for_bwd
Eltwise: exp (dst for backward)
Definition: dnnl_types.h:1257
@ dnnl_eltwise_bounded_relu
Eltwise: bounded_relu.
Definition: dnnl_types.h:1212
@ dnnl_eltwise_linear
Eltwise: linear.
Definition: dnnl_types.h:1210
@ dnnl_resampling_nearest
Nearest Neighbor Resampling Method.
Definition: dnnl_types.h:1311
@ dnnl_binary_max
Binary max.
Definition: dnnl_types.h:1291
@ dnnl_binary
A binary primitive.
Definition: dnnl_types.h:1165
@ dnnl_concat
A (out-of-place) concat primitive.
Definition: dnnl_types.h:1139
@ dnnl_reorder
A reorder primitive.
Definition: dnnl_types.h:1135
@ dnnl_primitive_kind_max
Parameter to allow internal only primitives without undefined behavior.
Definition: dnnl_types.h:1181
@ dnnl_gemm
A matrix multiplication primitive (internal).
Definition: dnnl_types.h:1163
@ dnnl_convolution
A convolution primitive.
Definition: dnnl_types.h:1143
@ dnnl_inner_product
An inner product primitive.
Definition: dnnl_types.h:1159
@ dnnl_resampling
A resampling primitive.
Definition: dnnl_types.h:1171
@ dnnl_batch_normalization
A batch normalization primitive.
Definition: dnnl_types.h:1155
@ dnnl_undefined_primitive
Undefined primitive.
Definition: dnnl_types.h:1133
@ dnnl_sum
A sum primitive.
Definition: dnnl_types.h:1141
@ dnnl_pooling_v2
A pooling version 2 primitive (pooling with dilation support).
Definition: dnnl_types.h:1173
@ dnnl_layer_normalization
A layer normalization primitive.
Definition: dnnl_types.h:1157
@ dnnl_prelu
A PReLU primitive.
Definition: dnnl_types.h:1177
@ dnnl_eltwise
An element-wise primitive.
Definition: dnnl_types.h:1147
@ dnnl_matmul
A matrix multiplication primitive.
Definition: dnnl_types.h:1169
@ dnnl_shuffle
A shuffle primitive.
Definition: dnnl_types.h:1137
@ dnnl_logsoftmax
A logsoftmax primitive.
Definition: dnnl_types.h:1167
@ dnnl_pooling
A pooling primitive.
Definition: dnnl_types.h:1151
@ dnnl_deconvolution
A deconvolution primitive.
Definition: dnnl_types.h:1145
@ dnnl_softmax
A softmax primitive.
Definition: dnnl_types.h:1149
@ dnnl_rnn
A rnn primitive.
Definition: dnnl_types.h:1161
@ dnnl_reduction
A reduction primitive.
Definition: dnnl_types.h:1175
@ dnnl_lrn
An LRN primitive.
Definition: dnnl_types.h:1153
@ dnnl_query_resampling_d
resampling descriptor
Definition: dnnl_types.h:2682
@ dnnl_query_num_of_outputs_s32
number of outputs expected
Definition: dnnl_types.h:2646
@ dnnl_query_some_md
stub
Definition: dnnl_types.h:2688
@ dnnl_query_convolution_d
convolution descriptor
Definition: dnnl_types.h:2667
@ dnnl_query_weights_md
weights memory descriptor desc
Definition: dnnl_types.h:2691
@ dnnl_query_src_md
source memory desc
Definition: dnnl_types.h:2689
@ dnnl_query_softmax_d
softmax descriptor
Definition: dnnl_types.h:2671
@ dnnl_query_binary_d
binary descriptor
Definition: dnnl_types.h:2679
@ dnnl_query_workspace_md
workspace memory desc
Definition: dnnl_types.h:2695
@ dnnl_query_matmul_d
matrix multiplication (matmul) descriptor
Definition: dnnl_types.h:2681
@ dnnl_query_num_of_inputs_s32
number of inputs expected
Definition: dnnl_types.h:2645
@ dnnl_query_op_d
op descriptor
Definition: dnnl_types.h:2666
@ dnnl_query_diff_src_md
source gradient memory desc
Definition: dnnl_types.h:2690
@ dnnl_query_scratchpad_md
scratchpad memory desc
Definition: dnnl_types.h:2696
@ dnnl_query_shuffle_d
shuffle descriptor
Definition: dnnl_types.h:2669
@ dnnl_query_memory_consumption_s64
memory consumption – extra
Definition: dnnl_types.h:2649
@ dnnl_query_inner_product_d
inner product descriptor
Definition: dnnl_types.h:2676
@ dnnl_query_deconvolution_d
deconvolution descriptor
Definition: dnnl_types.h:2668
@ dnnl_query_primitive_kind
primitive kind
Definition: dnnl_types.h:2643
@ dnnl_query_some_d
stub
Definition: dnnl_types.h:2665
@ dnnl_query_batch_normalization_d
batch normalization descriptor
Definition: dnnl_types.h:2674
@ dnnl_query_impl_info_str
for creating scratchpad memory
Definition: dnnl_types.h:2657
@ dnnl_query_time_estimate_f64
runtime estimation (seconds)
Definition: dnnl_types.h:2648
@ dnnl_query_eltwise_d
eltwise descriptor
Definition: dnnl_types.h:2670
@ dnnl_query_pooling_v2_d
pooling version 2 descriptor
Definition: dnnl_types.h:2683
@ dnnl_query_diff_weights_md
weights grad. memory desc
Definition: dnnl_types.h:2692
@ dnnl_query_reduction_d
reduction descriptor
Definition: dnnl_types.h:2684
@ dnnl_query_gemm_d
GEMM descriptor (internal)
Definition: dnnl_types.h:2678
@ dnnl_query_reorder_dst_engine
destination engine
Definition: dnnl_types.h:2660
@ dnnl_query_reorder_src_engine
source engine
Definition: dnnl_types.h:2659
@ dnnl_query_scratchpad_engine
(scratch) memory, additional to all inputs and outputs memory (bytes)
Definition: dnnl_types.h:2654
@ dnnl_query_undef
no query
Definition: dnnl_types.h:2640
@ dnnl_query_prop_kind
propagation kind
Definition: dnnl_types.h:2662
@ dnnl_query_pooling_d
pooling descriptor
Definition: dnnl_types.h:2672
@ dnnl_query_exec_arg_md
memory desc of an execute argument
Definition: dnnl_types.h:2697
@ dnnl_query_engine
execution engine
Definition: dnnl_types.h:2642
@ dnnl_query_rnn_d
rnn descriptor
Definition: dnnl_types.h:2677
@ dnnl_query_layer_normalization_d
layer normalization descriptor
Definition: dnnl_types.h:2675
@ dnnl_query_lrn_d
lrn descriptor
Definition: dnnl_types.h:2673
@ dnnl_query_dst_md
destination memory desc
Definition: dnnl_types.h:2693
@ dnnl_query_diff_dst_md
destination grad. memory desc
Definition: dnnl_types.h:2694
@ dnnl_query_prelu_d
prelu descriptor
Definition: dnnl_types.h:2685
@ dnnl_query_logsoftmax_d
logsoftmax descriptor
Definition: dnnl_types.h:2680
@ dnnl_backward_weights
Backward weights propagation.
Definition: dnnl_types.h:1124
@ dnnl_forward_inference
Forward data propagation (inference mode).
Definition: dnnl_types.h:1114
@ dnnl_backward
Backward propagation (with respect to all parameters).
Definition: dnnl_types.h:1120
@ dnnl_backward_data
Backward data propagation.
Definition: dnnl_types.h:1122
@ dnnl_prop_kind_undef
Undefined propagation type.
Definition: dnnl_types.h:1107
@ dnnl_forward
Forward data propagation (alias for dnnl_forward_training).
Definition: dnnl_types.h:1118
@ dnnl_forward_training
Forward data propagation (training mode).
Definition: dnnl_types.h:1110
@ dnnl_backward_bias
Backward bias propagation.
Definition: dnnl_types.h:1126
@ dnnl_forward_scoring
Forward data propagation (alias for dnnl_forward_inference).
Definition: dnnl_types.h:1116
dnnl_rnn_flags_t
Flags for RNN cell.
Definition: dnnl_types.h:2046
dnnl_rnn_direction_t
A direction of RNN primitive execution.
Definition: dnnl_types.h:2052
@ dnnl_rnn_flags_undef
Undefined RNN flags.
Definition: dnnl_types.h:2048
@ dnnl_unidirectional
Alias for dnnl_unidirectional_left2right.
Definition: dnnl_types.h:2064
@ dnnl_bidirectional_concat
Bidirectional execution of RNN primitive with concatenation of the results.
Definition: dnnl_types.h:2059
@ dnnl_bidirectional_sum
Bidirectional execution of RNN primitive with summation of the results.
Definition: dnnl_types.h:2062
@ dnnl_unidirectional_left2right
Unidirectional execution of RNN primitive from left to right.
Definition: dnnl_types.h:2054
@ dnnl_unidirectional_right2left
Unidirectional execution of RNN primitive from right to left.
Definition: dnnl_types.h:2056
dnnl_cpu_isa_t
CPU instruction set flags.
Definition: dnnl_types.h:2789
dnnl_cpu_isa_hints_t
CPU ISA hints flags.
Definition: dnnl_types.h:2835
@ dnnl_cpu_isa_avx512_mic
Intel Advanced Vector Extensions 512 (Intel AVX-512) subset for Intel Xeon Phi processors x200 Series...
Definition: dnnl_types.h:2804
@ dnnl_cpu_isa_avx
Intel Advanced Vector Extensions (Intel AVX)
Definition: dnnl_types.h:2797
@ dnnl_cpu_isa_avx512_core_amx
Intel AVX-512, Intel DL Boost and bfloat16 support and Intel AMX with 8-bit integer and bfloat16 supp...
Definition: dnnl_types.h:2827
@ dnnl_cpu_isa_avx512_core_vnni
Intel AVX-512 and Intel Deep Learning Boost (Intel DL Boost) support for Intel Xeon Scalable processo...
Definition: dnnl_types.h:2817
@ dnnl_cpu_isa_avx2
Intel Advanced Vector Extensions 2 (Intel AVX2)
Definition: dnnl_types.h:2800
@ dnnl_cpu_isa_all
Any ISA (excepting those listed as initial support)
Definition: dnnl_types.h:2791
@ dnnl_cpu_isa_avx512_core
Intel AVX-512 subset for Intel Xeon Scalable processor family and Intel Core processor family.
Definition: dnnl_types.h:2812
@ dnnl_cpu_isa_sse41
Intel Streaming SIMD Extensions 4.1 (Intel SSE4.1)
Definition: dnnl_types.h:2794
@ dnnl_cpu_isa_avx2_vnni
Intel AVX2 and Intel Deep Learning Boost (Intel DL Boost) support.
Definition: dnnl_types.h:2830
@ dnnl_cpu_isa_avx512_core_bf16
Intel AVX-512, Intel DL Boost and bfloat16 support for Intel Xeon Scalable processor family and Intel...
Definition: dnnl_types.h:2822
@ dnnl_cpu_isa_avx512_mic_4ops
Intel AVX-512 subset for Intel Xeon Phi processors 7235, 7285, 7295 Series.
Definition: dnnl_types.h:2808
@ dnnl_cpu_isa_no_hints
No hints (use default features)
Definition: dnnl_types.h:2837
@ dnnl_cpu_isa_prefer_ymm
Prefer to exclusively use Ymm registers for computations.
Definition: dnnl_types.h:2840
dnnl_stream_flags_t
Stream flags.
Definition: dnnl_types.h:2711
struct dnnl_stream * dnnl_stream_t
An execution stream handle.
Definition: dnnl_types.h:2724
const struct dnnl_stream * const_dnnl_stream_t
A constant execution stream handle.
Definition: dnnl_types.h:2726
@ dnnl_stream_out_of_order
Out-of-order execution.
Definition: dnnl_types.h:2715
@ dnnl_stream_default_flags
Default stream configuration.
Definition: dnnl_types.h:2717
dnnl_status_t
Status values returned by the library functions.
Definition: dnnl_types.h:39
@ dnnl_iterator_ends
Primitive iterator passed over last primitive descriptor.
Definition: dnnl_types.h:49
@ dnnl_runtime_error
Primitive or engine failed on execution.
Definition: dnnl_types.h:51
@ dnnl_unimplemented
The operation failed because requested functionality is not implemented.
Definition: dnnl_types.h:47
@ dnnl_out_of_memory
The operation failed due to an out-of-memory condition.
Definition: dnnl_types.h:43
@ dnnl_success
The operation was successful.
Definition: dnnl_types.h:41
@ dnnl_invalid_arguments
The operation failed because of incorrect function arguments.
Definition: dnnl_types.h:45
@ dnnl_not_required
Queried element is not required for given primitive.
Definition: dnnl_types.h:53
A descriptor of a Batch Normalization operation.
Definition: dnnl_types.h:1942
dnnl_memory_desc_t data_desc
Source and destination memory descriptor.
Definition: dnnl_types.h:1950
dnnl_memory_desc_t data_scaleshift_desc
Scale and shift data and gradient memory descriptors.
Definition: dnnl_types.h:1958
dnnl_memory_desc_t stat_desc
Statistics memory descriptor.
Definition: dnnl_types.h:1963
dnnl_prop_kind_t prop_kind
The kind of propagation.
Definition: dnnl_types.h:1948
dnnl_memory_desc_t diff_data_desc
Source and destination gradient memory descriptor.
Definition: dnnl_types.h:1952
dnnl_primitive_kind_t primitive_kind
The kind of primitive.
Definition: dnnl_types.h:1945
float batch_norm_epsilon
Batch normalization epsilon parameter.
Definition: dnnl_types.h:1965
A descriptor of a binary operation.
Definition: dnnl_types.h:2150
dnnl_memory_desc_t dst_desc
Destination memory descriptor.
Definition: dnnl_types.h:2161
dnnl_alg_kind_t alg_kind
The kind of the binary algorithm.
Definition: dnnl_types.h:2157
dnnl_primitive_kind_t primitive_kind
The kind of primitive.
Definition: dnnl_types.h:2153
Generic description of blocked data layout for most memory formats.
Definition: dnnl_types.h:1452
dnnl_dims_t strides
The strides between the outermost blocks.
Definition: dnnl_types.h:1455
int inner_nblks
The number of innermost blocks, e.g. 3 in case of OIhw_4i16o4i_
Definition: dnnl_types.h:1459
dnnl_dims_t inner_blks
The size of the blocks, e.g. {4, 16, 4} in case of OIhw_4i16o4i
Definition: dnnl_types.h:1461
dnnl_dims_t inner_idxs
The logical indices of the blocks, e.g.
Definition: dnnl_types.h:1464
A descriptor of a convolution operation.
Definition: dnnl_types.h:1646
dnnl_data_type_t accum_data_type
The accumulator data type. Initialized automatically.
Definition: dnnl_types.h:1682
dnnl_dims_t strides
Convolution strides in each spatial dimension.
Definition: dnnl_types.h:1674
dnnl_memory_desc_t diff_dst_desc
Destination gradient memory descriptor.
Definition: dnnl_types.h:1672
dnnl_dims_t dilates
Convolution dilates in each spatial dimension.
Definition: dnnl_types.h:1676
dnnl_memory_desc_t diff_bias_desc
Bias gradient memory descriptor.
Definition: dnnl_types.h:1668
dnnl_memory_desc_t diff_src_desc
Source gradient memory descriptor.
Definition: dnnl_types.h:1660
dnnl_prop_kind_t prop_kind
The kind of propagation.
Definition: dnnl_types.h:1653
dnnl_memory_desc_t dst_desc
Destination memory descriptor.
Definition: dnnl_types.h:1670
dnnl_alg_kind_t alg_kind
The kind of the convolution algorithm.
Definition: dnnl_types.h:1656
dnnl_primitive_kind_t primitive_kind
The kind of primitive.
Definition: dnnl_types.h:1649
dnnl_memory_desc_t diff_weights_desc
Weights gradient memory descriptor.
Definition: dnnl_types.h:1664
dnnl_memory_desc_t bias_desc
Bias memory descriptor.
Definition: dnnl_types.h:1666
dnnl_memory_desc_t src_desc
Source memory descriptor.
Definition: dnnl_types.h:1658
dnnl_memory_desc_t weights_desc
Weights memory descriptor.
Definition: dnnl_types.h:1662
A descriptor of a element-wise operation.
Definition: dnnl_types.h:1721
dnnl_memory_desc_t diff_data_desc
Source and destination gradient memory descriptor.
Definition: dnnl_types.h:1747
float alpha
Algorithm specific parameter.
Definition: dnnl_types.h:1772
dnnl_memory_desc_t data_desc
Source and destination memory descriptor.
Definition: dnnl_types.h:1745
dnnl_primitive_kind_t primitive_kind
The kind of primitive.
Definition: dnnl_types.h:1724
dnnl_prop_kind_t prop_kind
The kind of propagation.
Definition: dnnl_types.h:1727
dnnl_alg_kind_t alg_kind
The kind of eltwise algorithm.
Definition: dnnl_types.h:1743
An opaque structure to describe an engine.
A structure that contains an index and a memory object, and is used to pass arguments to dnnl_primiti...
Definition: dnnl_types.h:2600
dnnl_memory_t memory
Input/output memory.
Definition: dnnl_types.h:2602
int arg
An argument index, e.g. DNNL_ARG_SRC.
Definition: dnnl_types.h:2601
A descriptor of an inner product operation.
Definition: dnnl_types.h:2012
dnnl_prop_kind_t prop_kind
The kind of propagation.
Definition: dnnl_types.h:2019
dnnl_memory_desc_t diff_src_desc
Source gradient memory descriptor.
Definition: dnnl_types.h:2023
dnnl_memory_desc_t diff_bias_desc
Bias gradient memory descriptor.
Definition: dnnl_types.h:2031
dnnl_memory_desc_t src_desc
Source memory descriptor.
Definition: dnnl_types.h:2021
dnnl_memory_desc_t bias_desc
Bias memory descriptor.
Definition: dnnl_types.h:2029
dnnl_data_type_t accum_data_type
The accumulator data type. Initialized automatically.
Definition: dnnl_types.h:2037
dnnl_memory_desc_t diff_weights_desc
Weights gradient memory descriptor.
Definition: dnnl_types.h:2027
dnnl_memory_desc_t dst_desc
Destination memory descriptor.
Definition: dnnl_types.h:2033
dnnl_memory_desc_t diff_dst_desc
Destination gradient memory descriptor.
Definition: dnnl_types.h:2035
dnnl_primitive_kind_t primitive_kind
The kind of primitive.
Definition: dnnl_types.h:2015
dnnl_memory_desc_t weights_desc
Weights memory descriptor.
Definition: dnnl_types.h:2025
A descriptor of a Layer Normalization operation.
Definition: dnnl_types.h:1975
dnnl_memory_desc_t diff_data_desc
Source and destination gradient memory descriptor.
Definition: dnnl_types.h:1985
dnnl_prop_kind_t prop_kind
The kind of propagation.
Definition: dnnl_types.h:1981
dnnl_memory_desc_t data_scaleshift_desc
Scale and shift data and gradient memory descriptors.
Definition: dnnl_types.h:1993
float layer_norm_epsilon
Layer normalization epsilon parameter.
Definition: dnnl_types.h:2002
dnnl_primitive_kind_t primitive_kind
The kind of primitive.
Definition: dnnl_types.h:1978
dnnl_memory_desc_t stat_desc
Mean and variance data memory descriptors.
Definition: dnnl_types.h:2000
dnnl_memory_desc_t data_desc
Source and destination memory descriptor.
Definition: dnnl_types.h:1983
A descriptor of a Local Response Normalization (LRN) operation.
Definition: dnnl_types.h:1911
float lrn_k
LRN k parameter.
Definition: dnnl_types.h:1933
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:1927
float lrn_beta
LRN beta parameter.
Definition: dnnl_types.h:1931
dnnl_memory_desc_t diff_data_desc
Source and destination gradient memory descriptor.
Definition: dnnl_types.h:1924
float lrn_alpha
LRN alpha parameter.
Definition: dnnl_types.h:1929
dnnl_prop_kind_t prop_kind
The kind of propagation.
Definition: dnnl_types.h:1917
dnnl_memory_desc_t data_desc
Source and destination memory descriptor.
Definition: dnnl_types.h:1922
dnnl_alg_kind_t alg_kind
LRN algorithm.
Definition: dnnl_types.h:1920
dnnl_primitive_kind_t primitive_kind
The kind of primitive.
Definition: dnnl_types.h:1914
A descriptor of a matrix multiplication operation.
Definition: dnnl_types.h:2176
dnnl_memory_desc_t weights_desc
Weights memory descriptor.
Definition: dnnl_types.h:2183
dnnl_memory_desc_t dst_desc
Destination memory descriptor.
Definition: dnnl_types.h:2187
dnnl_primitive_kind_t primitive_kind
The kind of primitive.
Definition: dnnl_types.h:2179
dnnl_memory_desc_t bias_desc
Bias memory descriptor.
Definition: dnnl_types.h:2185
dnnl_memory_desc_t src_desc
Source memory descriptor.
Definition: dnnl_types.h:2181
dnnl_data_type_t accum_data_type
The accumulator data type. Initialized automatically.
Definition: dnnl_types.h:2189
Memory descriptor.
Definition: dnnl_types.h:1557
dnnl_wino_desc_t wino_desc
Tensor of weights for integer 8bit winograd convolution.
Definition: dnnl_types.h:1597
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:1588
dnnl_blocking_desc_t blocking
Description of the data layout for memory formats that use blocking.
Definition: dnnl_types.h:1595
dnnl_data_type_t data_type
Data type of the tensor elements.
Definition: dnnl_types.h:1577
dnnl_dims_t dims
Dimensions in the following order:
Definition: dnnl_types.h:1574
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:1584
int ndims
Number of dimensions.
Definition: dnnl_types.h:1559
dnnl_dims_t padded_dims
Size of the data including padding in each dimension.
Definition: dnnl_types.h:1580
dnnl_rnn_packed_desc_t rnn_packed_desc
Tensor of packed weights for RNN.
Definition: dnnl_types.h:1599
dnnl_format_kind_t format_kind
Memory format kind.
Definition: dnnl_types.h:1591
An opaque structure to describe a memory.
A descriptor of a pooling operation.
Definition: dnnl_types.h:1811
dnnl_primitive_kind_t primitive_kind
The kind of primitive.
Definition: dnnl_types.h:1814
dnnl_memory_desc_t dst_desc
Destination memory descriptor.
Definition: dnnl_types.h:1828
dnnl_memory_desc_t diff_src_desc
Source gradient memory descriptor.
Definition: dnnl_types.h:1826
dnnl_dims_t strides
Pooling kernel strides for spatial dimensions.
Definition: dnnl_types.h:1832
dnnl_memory_desc_t src_desc
Source memory descriptor.
Definition: dnnl_types.h:1824
dnnl_alg_kind_t alg_kind
The kind of pooling algorithm.
Definition: dnnl_types.h:1822
dnnl_prop_kind_t prop_kind
The kind of propagation.
Definition: dnnl_types.h:1817
dnnl_memory_desc_t diff_dst_desc
Destination gradient memory descriptor.
Definition: dnnl_types.h:1830
dnnl_dims_t kernel
Pooling kernel spatial dimensions.
Definition: dnnl_types.h:1834
dnnl_data_type_t accum_data_type
The accumulator data type. Initialized automatically.
Definition: dnnl_types.h:1840
A descriptor of a pooling operation.
Definition: dnnl_types.h:1849
dnnl_memory_desc_t src_desc
Source memory descriptor.
Definition: dnnl_types.h:1862
dnnl_dims_t dilation
Pooling dilations for spatial dimensions.
Definition: dnnl_types.h:1880
dnnl_prop_kind_t prop_kind
The kind of propagation.
Definition: dnnl_types.h:1855
dnnl_dims_t strides
Pooling kernel strides for spatial dimensions.
Definition: dnnl_types.h:1870
dnnl_memory_desc_t diff_dst_desc
Destination gradient memory descriptor.
Definition: dnnl_types.h:1868
dnnl_data_type_t accum_data_type
The accumulator data type. Initialized automatically.
Definition: dnnl_types.h:1878
dnnl_dims_t kernel
Pooling kernel spatial dimensions.
Definition: dnnl_types.h:1872
dnnl_primitive_kind_t primitive_kind
The kind of primitive.
Definition: dnnl_types.h:1852
dnnl_alg_kind_t alg_kind
The kind of pooling algorithm.
Definition: dnnl_types.h:1860
dnnl_memory_desc_t diff_src_desc
Source gradient memory descriptor.
Definition: dnnl_types.h:1864
dnnl_memory_desc_t dst_desc
Destination memory descriptor.
Definition: dnnl_types.h:1866
An opaque structure for a chain of post operations.
An opaque structure for primitive descriptor attributes.
An opaque structure to describe a primitive descriptor iterator.
An opaque structure to describe a primitive descriptor.
An opaque structure to describe a primitive.
A descriptor of reduction operation.
Definition: dnnl_types.h:2226
dnnl_memory_desc_t dst_desc
Destination memory descriptor.
Definition: dnnl_types.h:2239
float p
Algorithm specific parameters.
Definition: dnnl_types.h:2251
dnnl_alg_kind_t alg_kind
The kind of reduction algorithm.
Definition: dnnl_types.h:2235
dnnl_memory_desc_t src_desc
Source memory descriptor.
Definition: dnnl_types.h:2237
dnnl_primitive_kind_t primitive_kind
The kind of primitive.
Definition: dnnl_types.h:2229
A descriptor of resampling operation.
Definition: dnnl_types.h:2198
dnnl_memory_desc_t diff_dst_desc
Destination gradient memory descriptor.
Definition: dnnl_types.h:2215
dnnl_alg_kind_t alg_kind
The kind of the resampling algorithm.
Definition: dnnl_types.h:2207
dnnl_prop_kind_t prop_kind
The kind of propagation.
Definition: dnnl_types.h:2204
dnnl_memory_desc_t diff_src_desc
Source gradient memory descriptor.
Definition: dnnl_types.h:2211
dnnl_memory_desc_t dst_desc
Destination memory descriptor.
Definition: dnnl_types.h:2213
dnnl_memory_desc_t src_desc
Source memory descriptor.
Definition: dnnl_types.h:2209
dnnl_primitive_kind_t primitive_kind
The kind of primitive.
Definition: dnnl_types.h:2201
A descriptor for an RNN operation.
Definition: dnnl_types.h:2068
dnnl_rnn_direction_t direction
The direction of RNN primitive execution.
Definition: dnnl_types.h:2079
dnnl_memory_desc_t diff_src_iter_desc
Source gradient iter memory descriptor for hidden state.
Definition: dnnl_types.h:2110
dnnl_memory_desc_t weights_layer_desc
Weights layer memory descriptor.
Definition: dnnl_types.h:2087
dnnl_memory_desc_t dst_iter_c_desc
Destination iter memory descriptor for cell state.
Definition: dnnl_types.h:2097
dnnl_memory_desc_t diff_dst_iter_c_desc
Destination gradient iteration memory descriptor for cell state.
Definition: dnnl_types.h:2124
dnnl_memory_desc_t diff_weights_iter_desc
Weights gradient iter memory descriptor.
Definition: dnnl_types.h:2116
dnnl_alg_kind_t cell_kind
RNN cell kind.
Definition: dnnl_types.h:2077
dnnl_primitive_kind_t primitive_kind
The kind of primitive.
Definition: dnnl_types.h:2071
dnnl_memory_desc_t diff_dst_layer_desc
Destination gradient layer memory descriptor.
Definition: dnnl_types.h:2120
dnnl_memory_desc_t diff_src_layer_desc
Source gradient layer memory descriptor.
Definition: dnnl_types.h:2108
dnnl_memory_desc_t diff_src_iter_c_desc
Source gradient iter memory descriptor for cell state.
Definition: dnnl_types.h:2112
dnnl_memory_desc_t weights_projection_desc
Weights projection memory descriptor.
Definition: dnnl_types.h:2105
dnnl_memory_desc_t diff_weights_peephole_desc
Weights gradient peephole memory descriptor.
Definition: dnnl_types.h:2128
dnnl_memory_desc_t diff_weights_layer_desc
Weights gradient layer memory descriptor.
Definition: dnnl_types.h:2114
dnnl_prop_kind_t prop_kind
The kind of propagation.
Definition: dnnl_types.h:2074
dnnl_memory_desc_t src_iter_c_desc
Source iteration memory descriptor for cell state.
Definition: dnnl_types.h:2085
unsigned int flags
RNN cell flags.
Definition: dnnl_types.h:2135
dnnl_memory_desc_t bias_desc
Bias memory descriptor.
Definition: dnnl_types.h:2091
dnnl_memory_desc_t src_layer_desc
Source layer memory descriptor.
Definition: dnnl_types.h:2081
dnnl_memory_desc_t dst_layer_desc
Destination layer memory descriptor.
Definition: dnnl_types.h:2093
dnnl_alg_kind_t activation_kind
Activation function used for vanilla_rnn cell kind.
Definition: dnnl_types.h:2138
dnnl_memory_desc_t diff_bias_desc
Bias gradient memory descriptor.
Definition: dnnl_types.h:2118
dnnl_memory_desc_t weights_peephole_desc
Weights peephole memory descriptor.
Definition: dnnl_types.h:2101
dnnl_memory_desc_t dst_iter_desc
Destination iter memory descriptor for hidden state.
Definition: dnnl_types.h:2095
dnnl_memory_desc_t diff_weights_projection_desc
Weights gradient projection memory descriptor.
Definition: dnnl_types.h:2132
dnnl_memory_desc_t src_iter_desc
Source iteration memory descriptor for hidden state.
Definition: dnnl_types.h:2083
dnnl_memory_desc_t weights_iter_desc
Weights iteration memory descriptor.
Definition: dnnl_types.h:2089
dnnl_memory_desc_t diff_dst_iter_desc
Destination gradient iteration memory descriptor for hidden state.
Definition: dnnl_types.h:2122
Description of tensor of packed weights for rnn.
Definition: dnnl_types.h:1506
A descriptor of a shuffle operation.
Definition: dnnl_types.h:1699
int axis
Axis for shuffling.
Definition: dnnl_types.h:1710
dnnl_memory_desc_t data_desc
Source and destination memory descriptor, and source and destination gradient memory descriptor.
Definition: dnnl_types.h:1708
dnnl_primitive_kind_t primitive_kind
The kind of primitive.
Definition: dnnl_types.h:1702
dnnl_prop_kind_t prop_kind
The kind of propagation.
Definition: dnnl_types.h:1705
dnnl_dim_t group_size
Number of groups.
Definition: dnnl_types.h:1712
A descriptor of a Softmax operation.
Definition: dnnl_types.h:1781
dnnl_prop_kind_t prop_kind
The kind of propagation.
Definition: dnnl_types.h:1787
dnnl_primitive_kind_t primitive_kind
The kind of primitive.
Definition: dnnl_types.h:1784
dnnl_memory_desc_t data_desc
Source and destination memory descriptor.
Definition: dnnl_types.h:1789
int softmax_axis
The axis along which to perform the softmax.
Definition: dnnl_types.h:1793
dnnl_memory_desc_t diff_desc
Source and Destination of gradient memory descriptor.
Definition: dnnl_types.h:1791
An opaque structure to describe an execution stream.
Structure containing version information as per Semantic Versioning
Definition: dnnl_types.h:2759
const char * hash
Git hash of the sources (may be absent)
Definition: dnnl_types.h:2763
unsigned cpu_runtime
CPU runtime.
Definition: dnnl_types.h:2764
int minor
Minor version.
Definition: dnnl_types.h:2761
int patch
Patch version.
Definition: dnnl_types.h:2762
int major
Major version.
Definition: dnnl_types.h:2760
unsigned gpu_runtime
GPU runtime.
Definition: dnnl_types.h:2765
Description of tensor of weights for winograd 2x3 convolution.
Definition: dnnl_types.h:1480