Dnnl_api_memory

Collaboration diagram for Dnnl_api_memory:

Classes
struct	dnnl_blocking_desc_t
struct	dnnl_wino_desc_t
	Description of tensor of weights for winograd 2x3 convolution. More...
struct	dnnl_rnn_packed_desc_t
	Description of tensor of packed weights for rnn. More...
struct	dnnl_memory_extra_desc_t
	Description of extra information stored in memory. More...
struct	dnnl_memory_desc_t
struct	dnnl_memory

Macros
#define	DNNL_MAX_NDIMS   12
#define	DNNL_RUNTIME_DIM_VAL   INT64_MIN
#define	DNNL_RUNTIME_SIZE_VAL   ((size_t)DNNL_RUNTIME_DIM_VAL)
#define	DNNL_RUNTIME_F32_VAL   (DNNL_RUNTIME_F32_VAL_REP.f)
#define	DNNL_RUNTIME_S32_VAL   DNNL_RUNTIME_S32_VAL_REP
#define	DNNL_RNN_MAX_N_PARTS   4
#define	DNNL_MEMORY_NONE   (NULL)
#define	DNNL_MEMORY_ALLOCATE   ((void *)(size_t)-1)

Typedefs
typedef int64_t	dnnl_dim_t
	A type to describe tensor dimension. More...
typedef dnnl_dim_t	dnnl_dims_t[DNNL_MAX_NDIMS]
	A type to describe tensor dimensions. More...
typedef struct dnnl_memory *	dnnl_memory_t
	A memory handle. More...
typedef const struct dnnl_memory *	const_dnnl_memory_t
	A constant memory handle. More...

Enumerations
enum	dnnl_data_type_t {   dnnl_data_type_undef = 0, dnnl_f16 = 1, dnnl_bf16 = 2, dnnl_f32 = 3,   dnnl_s32 = 4, dnnl_s8 = 5, dnnl_u8 = 6 }
	Data type specification. More...
enum	dnnl_format_kind_t {   dnnl_format_kind_undef = 0, dnnl_format_kind_any, dnnl_blocked, dnnl_format_kind_wino,   dnnl_format_kind_rnn_packed }
	Memory format kind. More...
enum	dnnl_format_tag_t {   dnnl_format_tag_undef = 0, dnnl_format_tag_any, dnnl_a, dnnl_ab,   dnnl_abc, dnnl_abcd, dnnl_acbd, dnnl_abcde,   dnnl_abcdef, dnnl_abcdefg, dnnl_abcdefgh, dnnl_abcdefghi,   dnnl_abcdefghij, dnnl_abcdefghijk, dnnl_abcdefghijkl, dnnl_abdc,   dnnl_abdec, dnnl_acb, dnnl_acbde, dnnl_acbdef,   dnnl_acdb, dnnl_acdeb, dnnl_ba, dnnl_bac,   dnnl_bacd, dnnl_bacde, dnnl_bca, dnnl_bcda,   dnnl_bcdea, dnnl_cba, dnnl_cdba, dnnl_dcab,   dnnl_cdeba, dnnl_decab, dnnl_defcab, dnnl_abced,   dnnl_abcdfe, dnnl_abcdegf, dnnl_abcdefhg, dnnl_abcdefgih,   dnnl_abcdefghji, dnnl_abcdefghikj, dnnl_abcdefghijlk, dnnl_Abc16a,   dnnl_ABc16a16b, dnnl_ABc32a32b, dnnl_ABc4a4b, dnnl_aBc16b,   dnnl_ABc16b16a, dnnl_Abc4a, dnnl_aBc32b, dnnl_aBc4b,   dnnl_ABc4b16a4b, dnnl_ABc2b8a4b, dnnl_ABc16b16a4b, dnnl_ABc16b16a2b,   dnnl_ABc4b4a, dnnl_ABc8a16b2a, dnnl_ABc8a8b, dnnl_ABc8a4b,   dnnl_aBc8b, dnnl_ABc8b16a2b, dnnl_BAc8a16b2a, dnnl_ABc8b8a,   dnnl_Abcd16a, dnnl_Abcd8a, dnnl_ABcd16a16b, dnnl_Abcd32a,   dnnl_ABcd32a32b, dnnl_aBcd16b, dnnl_ABcd16b16a, dnnl_aBCd16b16c,   dnnl_aBCd16c16b, dnnl_Abcd4a, dnnl_aBcd32b, dnnl_aBcd4b,   dnnl_ABcd4b16a4b, dnnl_ABcd16b16a4b, dnnl_ABcd16b16a2b, dnnl_ABcd4b4a,   dnnl_ABcd4a4b, dnnl_aBCd2c4b2c, dnnl_aBCd4b8c2b, dnnl_aBCd4c16b4c,   dnnl_aBCd2c8b4c, dnnl_aBCd16c16b4c, dnnl_aBCd16c16b2c, dnnl_aBCd4c4b,   dnnl_aBCd4b4c, dnnl_ABcd8a16b2a, dnnl_ABcd2b8a4b, dnnl_ABcd8a8b,   dnnl_ABcd8a4b, dnnl_aBcd8b, dnnl_aBCd4c8b2c, dnnl_ABcd8b16a2b,   dnnl_aBCd8b16c2b, dnnl_BAcd8a16b2a, dnnl_ABcd8b8a, dnnl_aBCd8b8c,   dnnl_aBCd8b4c, dnnl_aBCd8c16b2c, dnnl_ABcde8a16b2a, dnnl_aCBd8b16c2b,   dnnl_aBCd8c8b, dnnl_Abcde16a, dnnl_Abcde32a, dnnl_ABcde16a16b,   dnnl_BAcde8a16b2a, dnnl_aBCd2b4c2b, dnnl_ABcde4b16a4b, dnnl_ABcde2b8a4b,   dnnl_aBcde16b, dnnl_ABcde16b16a, dnnl_aBCde16b16c, dnnl_aBCde16c16b,   dnnl_aBCde2c8b4c, dnnl_Abcde4a, dnnl_aBcde32b, dnnl_aBcde4b,   dnnl_ABcde4b4a, dnnl_ABcde4a4b, dnnl_aBCde4b4c, dnnl_aBCde2c4b2c,   dnnl_aBCde4b8c2b, dnnl_aBCde4c16b4c, dnnl_aBCde16c16b4c, dnnl_aBCde16c16b2c,   dnnl_aBCde4c4b, dnnl_Abcde8a, dnnl_ABcde8a8b, dnnl_ABcde8a4b,   dnnl_BAcde16b16a, dnnl_aBcde8b, dnnl_ABcde8b16a2b, dnnl_aBCde8b16c2b,   dnnl_aBCde4c8b2c, dnnl_aCBde8b16c2b, dnnl_ABcde8b8a, dnnl_ABcde32a32b,   dnnl_aBCde8b8c, dnnl_aBCde8b4c, dnnl_ABc4a8b8a4b, dnnl_ABcd4a8b8a4b,   dnnl_ABcde4a8b8a4b, dnnl_BAc4b8a8b4a, dnnl_BAcd4b8a8b4a, dnnl_BAcde4b8a8b4a,   dnnl_ABcd2a8b8a2b, dnnl_aBCd4b8c8b4c, dnnl_aBCde4b8c8b4c, dnnl_aBCde2b8c8b2c,   dnnl_aBCde8c16b2c, dnnl_aBCde8c8b, dnnl_aBCde2b4c2b, dnnl_aBcdef16b,   dnnl_aBCdef16b16c, dnnl_aBCdef16c16b, dnnl_aBCdef4c16b4c, dnnl_aBCdef2c8b4c,   dnnl_aBCdef4c8b2c, dnnl_aBCdef2b4c2b, dnnl_aBcdef4b, dnnl_aBCdef4c4b,   dnnl_aBCdef4b4c, dnnl_aBCdef2c4b2c, dnnl_aBCdef4b8c2b, dnnl_aBCdef8b8c,   dnnl_aBCdef8b4c, dnnl_aBCdef8c16b2c, dnnl_aBCdef4b8c8b4c, dnnl_aBCdef8b16c2b,   dnnl_aCBdef8b16c2b, dnnl_aBCdef8c8b, dnnl_aBdc16b, dnnl_aBdC16b2c,   dnnl_aBdC16b4c, dnnl_aBdc4b, dnnl_aBdc8b, dnnl_aBdec16b,   dnnl_aBdeC16b2c, dnnl_aBdeC16b4c, dnnl_aBdec32b, dnnl_aBdec4b,   dnnl_aBdec8b, dnnl_aBdefc16b, dnnl_aBdefC16b2c, dnnl_aCBdef16c16b,   dnnl_aBdefc4b, dnnl_aBdefc8b, dnnl_Abcdef16a, dnnl_Abcdef32a,   dnnl_aBedc16b, dnnl_Acb16a, dnnl_AcB16a2b, dnnl_AcB16a4b,   dnnl_Acb4a, dnnl_Acb8a, dnnl_aCBd16b16c, dnnl_aCBd16c16b,   dnnl_aCBde16b16c, dnnl_aCBde16c16b, dnnl_Acdb16a, dnnl_AcdB16a2b,   dnnl_AcdB16a4b, dnnl_Acdb32a, dnnl_Acdb4a, dnnl_Acdb8a,   dnnl_Acdeb16a, dnnl_AcdeB16a2b, dnnl_Acdeb4a, dnnl_Acdeb8a,   dnnl_Adcb16a, dnnl_BAc16a16b, dnnl_BAc16b16a, dnnl_BAcd16a16b,   dnnl_BAcd16b16a, dnnl_aCBd4c8b8c4b, dnnl_aCBde4c8b8c4b, dnnl_aCBdef4c8b8c4b,   dnnl_BAcde16a16b, dnnl_aCBdef16b16c, dnnl_abdfce, dnnl_abdefc,   dnnl_ABc16b32a, dnnl_ABc16b64a, dnnl_ABc4b32a4b, dnnl_ABc4b64a4b,   dnnl_ABc8b32a2b, dnnl_ABc8b64a2b, dnnl_AB16b16a, dnnl_AB16b32a,   dnnl_AB16b64a, dnnl_AB8b16a2b, dnnl_AB8b32a2b, dnnl_AB8b64a2b,   dnnl_AB4b16a4b, dnnl_AB4b32a4b, dnnl_AB4b64a4b, dnnl_AB16b16a4b,   dnnl_ABcd16b32a, dnnl_ABcd16b64a, dnnl_ABcd4b32a4b, dnnl_ABcd4b64a4b,   dnnl_ABcd8b32a2b, dnnl_ABcd8b64a2b, dnnl_ABcde4b32a4b, dnnl_ABcde4b64a4b,   dnnl_ABcde16b16a4b, dnnl_ABcde16b16a2b, dnnl_ABcde16b32a, dnnl_ABcde16b64a,   dnnl_ABcde8b32a2b, dnnl_ABcde8b64a2b, dnnl_aBCdef16c16b4c, dnnl_aBCdef16c16b2c,   dnnl_AB32a32b8a4b, dnnl_AB8a4b, dnnl_AB32a32b8a2b, dnnl_AB8a2b,   dnnl_abDc32d, dnnl_abDC32d4c, dnnl_abdEc32e, dnnl_abdEC32e2c,   dnnl_abdEC32e4c, dnnl_aBdefC16b4c, dnnl_AcdeB16a4b, dnnl_ABcd16a16b2a,   dnnl_ABc16a16b2a, dnnl_aBCd16b16c2b, dnnl_aBCde16b16c2b, dnnl_Acb32a,   dnnl_AcB32a2b, dnnl_AcB32a4b, dnnl_Acb48a, dnnl_AcB48a2b,   dnnl_AcB48a4b, dnnl_Acb64a, dnnl_AcB64a2b, dnnl_AcB64a4b,   dnnl_cBa2b, dnnl_cBa4b, dnnl_aBdc32b, dnnl_aBdC32b2c,   dnnl_aBdC32b4c, dnnl_aBdc48b, dnnl_aBdC48b2c, dnnl_aBdC48b4c,   dnnl_aBdc64b, dnnl_aBdC64b2c, dnnl_aBdC64b4c, dnnl_adcb,   dnnl_adCb2c, dnnl_adCb4c, dnnl_AcdB32a2b, dnnl_AcdB32a4b,   dnnl_Acdb48a, dnnl_AcdB48a2b, dnnl_AcdB48a4b, dnnl_Acdb64a,   dnnl_AcdB64a2b, dnnl_AcdB64a4b, dnnl_cdBa2b, dnnl_cdBa4b,   dnnl_aBdeC32b2c, dnnl_aBdeC32b4c, dnnl_aBdec48b, dnnl_aBdeC48b2c,   dnnl_aBdeC48b4c, dnnl_aBdec64b, dnnl_aBdeC64b2c, dnnl_aBdeC64b4c,   dnnl_adecb, dnnl_adeCb2c, dnnl_adeCb4c, dnnl_Acdeb32a,   dnnl_AcdeB32a2b, dnnl_AcdeB32a4b, dnnl_Acdeb48a, dnnl_AcdeB48a2b,   dnnl_AcdeB48a4b, dnnl_Acdeb64a, dnnl_AcdeB64a2b, dnnl_AcdeB64a4b,   dnnl_cdeBa2b, dnnl_cdeBa4b, dnnl_aBdefc32b, dnnl_aBdefC32b2c,   dnnl_aBdefC32b4c, dnnl_aBdefc48b, dnnl_aBdefC48b2c, dnnl_aBdefC48b4c,   dnnl_aBdefc64b, dnnl_aBdefC64b2c, dnnl_aBdefC64b4c, dnnl_adefcb,   dnnl_adefCb2c, dnnl_adefCb4c, dnnl_AB16b32a4b, dnnl_AB16b48a4b,   dnnl_AB16b64a4b, dnnl_AB16b16a2b, dnnl_AB16b32a2b, dnnl_AB16b48a2b,   dnnl_AB16b64a2b, dnnl_ABc16b32a4b, dnnl_ABc16b48a4b, dnnl_ABc16b64a4b,   dnnl_ABc16b32a2b, dnnl_ABc16b48a2b, dnnl_ABc16b64a2b, dnnl_ABcd16b32a4b,   dnnl_ABcd16b48a4b, dnnl_ABcd16b64a4b, dnnl_ABcd16b32a2b, dnnl_ABcd16b48a2b,   dnnl_ABcd16b64a2b, dnnl_ABcde16b32a4b, dnnl_ABcde16b48a4b, dnnl_ABcde16b64a4b,   dnnl_ABcde16b32a2b, dnnl_ABcde16b48a2b, dnnl_ABcde16b64a2b, dnnl_ABc32a16b,   dnnl_ABcd32a16b, dnnl_ABcde32a16b, dnnl_AB48a16b, dnnl_AB48a32b,   dnnl_ABc40a16b, dnnl_ABc40a32b, dnnl_aBC48b16c, dnnl_aBC48b32c,   dnnl_ABcd40a16b, dnnl_ABcd40a32b, dnnl_abCd32c, dnnl_abdCe32c,   dnnl_abdCE32c2e, dnnl_BA16a16b2a, dnnl_BA16a32b2a, dnnl_BA16a48b2a,   dnnl_BA16a64b2a, dnnl_BA16a16b4a, dnnl_BA16a32b4a, dnnl_BA16a48b4a,   dnnl_BA16a64b4a, dnnl_ABcd8a2b, dnnl_aBdeC16c16b2c, dnnl_aBdeC16c16b4c,   dnnl_aBdefC16c16b2c, dnnl_AcB16b16a2b, dnnl_AcB16b16a4b, dnnl_AcdB16b16a2b,   dnnl_AcdB16b16a4b, dnnl_AcdeB16b16a2b, dnnl_aBdefC16c16b4c, dnnl_AcdeB16b16a4b,   dnnl_AcB16b32a2b, dnnl_AcB16b32a4b, dnnl_AcB16b48a2b, dnnl_AcB16b48a4b,   dnnl_AcB16b64a2b, dnnl_AcB16b64a4b, dnnl_aBdC16c16b2c, dnnl_aBdC16c16b4c,   dnnl_aBdC16c32b2c, dnnl_aBdC16c32b4c, dnnl_aBdC16c48b2c, dnnl_aBdC16c48b4c,   dnnl_aBdC16c64b2c, dnnl_aBdC16c64b4c, dnnl_AcdB16b32a2b, dnnl_AcdB16b32a4b,   dnnl_AcdB16b48a2b, dnnl_AcdB16b48a4b, dnnl_AcdB16b64a2b, dnnl_AcdB16b64a4b,   dnnl_aBdeC16c32b2c, dnnl_aBdeC16c32b4c, dnnl_aBdeC16c48b2c, dnnl_aBdeC16c48b4c,   dnnl_aBdeC16c64b2c, dnnl_aBdeC16c64b4c, dnnl_AcdeB16b32a2b, dnnl_AcdeB16b32a4b,   dnnl_AcdeB16b48a2b, dnnl_AcdeB16b48a4b, dnnl_AcdeB16b64a2b, dnnl_AcdeB16b64a4b,   dnnl_aBdefC16c32b2c, dnnl_aBdefC16c32b4c, dnnl_aBdefC16c48b2c, dnnl_aBdefC16c48b4c,   dnnl_aBdefC16c64b2c, dnnl_aBdefC16c64b4c, dnnl_decbA16a, dnnl_ABc4a2b,   dnnl_ABc8a2b, dnnl_aBCd8b2c, dnnl_ABcde4a2b, dnnl_ABcde8a2b,   dnnl_ABcde40a16b, dnnl_ABcde40a32b, dnnl_aBCde8b2c, dnnl_ABcde4a8b8a2b,   dnnl_ABcd4a8b8a2b, dnnl_ABc4a8b8a2b, dnnl_aBCdef4b8c8b2c, dnnl_aBCde4b8c8b2c,   dnnl_aBCd4b8c8b2c, dnnl_BAcde4b8a8b2a, dnnl_BAcd4b8a8b2a, dnnl_BAc4b8a8b2a,   dnnl_aCBdef4c8b8c2b, dnnl_aCBde4c8b8c2b, dnnl_aCBd4c8b8c2b, dnnl_aBCdef8b2c,   dnnl_AB32a16b, dnnl_AB32a32b, dnnl_BA4b8a8b2a, dnnl_BA4b8a8b4a,   dnnl_aBC32b16c, dnnl_aBC32b32c, dnnl_aCB4c8b8c2b, dnnl_aCB4c8b8c4b,   dnnl_ABcd4a2b, dnnl_ABc2b8a16b4a, dnnl_ABcd2b8a16b4a, dnnl_ABcde2b8a16b4a,   dnnl_ABc2a8b16a4b, dnnl_ABc2a8b16a2b, dnnl_ABc2b32a8b, dnnl_ABcd2a8b16a4b,   dnnl_ABcd2a8b16a2b, dnnl_aCBd2c8b16c2b, dnnl_ABcd2b32a8b, dnnl_aBCd2c8b16c2b,   dnnl_ABcde2a8b16a4b, dnnl_ABcde2a8b16a2b, dnnl_aCBde2c8b16c2b, dnnl_ABcde2b32a8b,   dnnl_aBC2b8c16b2c, dnnl_aBCd2b8c16b2c, dnnl_aBCde2b8c16b2c, dnnl_aBCdef2b8c16b2c,   dnnl_BAcde2b8a16b4a, dnnl_BAcd2b8a16b4a, dnnl_BAc2b8a16b4a, dnnl_BAcde2b8a16b2a,   dnnl_BAcd2b8a16b2a, dnnl_BAc2b8a16b2a, dnnl_aBCde2c8b16c2b, dnnl_aBCdef2c8b16c2b,   dnnl_aCBdef2c8b16c2b, dnnl_aBCd2b8c16b4c, dnnl_aBCde2b8c16b4c, dnnl_BA4b8a16b2a,   dnnl_BA4b8a16b4a, dnnl_aCB4c8b16c2b, dnnl_aCB4c8b16c4b, dnnl_BA16a16b,   dnnl_BA16a32b, dnnl_BA16a48b, dnnl_BA16a64b, dnnl_aCB16c2b,   dnnl_aCB16c4b, dnnl_BA16b2a, dnnl_BA16b4a, dnnl_aBC16b16c,   dnnl_aBC16b32c, dnnl_AB16a16b, dnnl_AB16a32b, dnnl_adbc,   dnnl_ABcde16a16b2a, dnnl_aBCdef16b16c2b, dnnl_format_tag_last, dnnl_x = dnnl_a,   dnnl_nc = dnnl_ab, dnnl_cn = dnnl_ba, dnnl_tn = dnnl_ab, dnnl_nt = dnnl_ba,   dnnl_ncw = dnnl_abc, dnnl_nwc = dnnl_acb, dnnl_nchw = dnnl_abcd, dnnl_nhwc = dnnl_acdb,   dnnl_chwn = dnnl_bcda, dnnl_ncdhw = dnnl_abcde, dnnl_ndhwc = dnnl_acdeb, dnnl_oi = dnnl_ab,   dnnl_io = dnnl_ba, dnnl_oiw = dnnl_abc, dnnl_owi = dnnl_acb, dnnl_wio = dnnl_cba,   dnnl_iwo = dnnl_bca, dnnl_oihw = dnnl_abcd, dnnl_hwio = dnnl_cdba, dnnl_ohwi = dnnl_acdb,   dnnl_ihwo = dnnl_bcda, dnnl_iohw = dnnl_bacd, dnnl_oidhw = dnnl_abcde, dnnl_iodhw = dnnl_bacde,   dnnl_dhwio = dnnl_cdeba, dnnl_odhwi = dnnl_acdeb, dnnl_idhwo = dnnl_bcdea, dnnl_goiw = dnnl_abcd,   dnnl_gowi = dnnl_abdc, dnnl_wigo = dnnl_dcab, dnnl_goihw = dnnl_abcde, dnnl_gohwi = dnnl_abdec,   dnnl_hwigo = dnnl_decab, dnnl_giohw = dnnl_acbde, dnnl_goidhw = dnnl_abcdef, dnnl_godhwi = dnnl_abdefc,   dnnl_giodhw = dnnl_acbdef, dnnl_dhwigo = dnnl_defcab, dnnl_tnc = dnnl_abc, dnnl_ntc = dnnl_bac,   dnnl_ldnc = dnnl_abcd, dnnl_ldigo = dnnl_abcde, dnnl_ldgoi = dnnl_abdec, dnnl_ldio = dnnl_abcd,   dnnl_ldoi = dnnl_abdc, dnnl_ldgo = dnnl_abcd, dnnl_ldOi32o = dnnl_abDc32d, dnnl_ldOI32o4i = dnnl_abDC32d4c,   dnnl_ldIo32i = dnnl_abCd32c, dnnl_ldgOi32o = dnnl_abdEc32e, dnnl_ldgOI32o2i = dnnl_abdEC32e2c, dnnl_ldgOI32o4i = dnnl_abdEC32e4c,   dnnl_ldgIo32i = dnnl_abdCe32c, dnnl_ldgIO32i2o = dnnl_abdCE32c2e, dnnl_nCdhw32c = dnnl_aBcde32b, dnnl_nCdhw16c = dnnl_aBcde16b,   dnnl_nCdhw4c = dnnl_aBcde4b, dnnl_nCdhw8c = dnnl_aBcde8b, dnnl_nChw32c = dnnl_aBcd32b, dnnl_nChw16c = dnnl_aBcd16b,   dnnl_nChw4c = dnnl_aBcd4b, dnnl_nChw8c = dnnl_aBcd8b, dnnl_nCw32c = dnnl_aBc32b, dnnl_nCw16c = dnnl_aBc16b,   dnnl_nCw4c = dnnl_aBc4b, dnnl_nCw8c = dnnl_aBc8b, dnnl_NCw16n16c = dnnl_ABc16a16b, dnnl_NCdhw16n16c = dnnl_ABcde16a16b,   dnnl_NChw16n16c = dnnl_ABcd16a16b, dnnl_NCw32n16c = dnnl_ABc32a16b, dnnl_NChw32n16c = dnnl_ABcd32a16b, dnnl_NCdhw32n16c = dnnl_ABcde32a16b,   dnnl_NCw32n32c = dnnl_ABc32a32b, dnnl_NChw32n32c = dnnl_ABcd32a32b, dnnl_NCdhw32n32c = dnnl_ABcde32a32b, dnnl_OI16i16o = dnnl_AB16b16a,   dnnl_OI16i32o = dnnl_AB16b32a, dnnl_OI16i64o = dnnl_AB16b64a, dnnl_OI8i16o2i = dnnl_AB8b16a2b, dnnl_OI8i32o2i = dnnl_AB8b32a2b,   dnnl_OI8i64o2i = dnnl_AB8b64a2b, dnnl_OI4i16o4i = dnnl_AB4b16a4b, dnnl_OI4i32o4i = dnnl_AB4b32a4b, dnnl_OI4i64o4i = dnnl_AB4b64a4b,   dnnl_OI16i16o4i = dnnl_AB16b16a4b, dnnl_IOw16o16i = dnnl_BAc16a16b, dnnl_IOw16i16o = dnnl_BAc16b16a, dnnl_OIw16i16o = dnnl_ABc16b16a,   dnnl_OIw16i32o = dnnl_ABc16b32a, dnnl_OIw16i64o = dnnl_ABc16b64a, dnnl_OIw16o16i = dnnl_ABc16a16b, dnnl_Oiw16o = dnnl_Abc16a,   dnnl_OIw4i16o4i = dnnl_ABc4b16a4b, dnnl_OIw4i32o4i = dnnl_ABc4b32a4b, dnnl_OIw4i64o4i = dnnl_ABc4b64a4b, dnnl_OIw2i8o4i = dnnl_ABc2b8a4b,   dnnl_OIw16i16o4i = dnnl_ABc16b16a4b, dnnl_OIw16i16o2i = dnnl_ABc16b16a2b, dnnl_OIw16o16i2o = dnnl_ABc16a16b2a, dnnl_OIw4i4o = dnnl_ABc4b4a,   dnnl_OIw4o4i = dnnl_ABc4a4b, dnnl_Oiw4o = dnnl_Abc4a, dnnl_OIw8i16o2i = dnnl_ABc8b16a2b, dnnl_OIw8i32o2i = dnnl_ABc8b32a2b,   dnnl_OIw8i64o2i = dnnl_ABc8b64a2b, dnnl_OIw8i8o = dnnl_ABc8b8a, dnnl_OIw8o16i2o = dnnl_ABc8a16b2a, dnnl_IOw8o16i2o = dnnl_BAc8a16b2a,   dnnl_OIw8o8i = dnnl_ABc8a8b, dnnl_OIw8o4i = dnnl_ABc8a4b, dnnl_Owi16o = dnnl_Acb16a, dnnl_OwI16o2i = dnnl_AcB16a2b,   dnnl_OwI16o4i = dnnl_AcB16a4b, dnnl_Owi4o = dnnl_Acb4a, dnnl_Owi8o = dnnl_Acb8a, dnnl_IOhw16i16o = dnnl_BAcd16b16a,   dnnl_IOhw16o16i = dnnl_BAcd16a16b, dnnl_Ohwi16o = dnnl_Acdb16a, dnnl_OhwI16o2i = dnnl_AcdB16a2b, dnnl_OhwI16o4i = dnnl_AcdB16a4b,   dnnl_Ohwi32o = dnnl_Acdb32a, dnnl_Ohwi4o = dnnl_Acdb4a, dnnl_Ohwi8o = dnnl_Acdb8a, dnnl_OIhw16i16o = dnnl_ABcd16b16a,   dnnl_OIhw16i32o = dnnl_ABcd16b32a, dnnl_OIhw16i64o = dnnl_ABcd16b64a, dnnl_OIhw16o16i = dnnl_ABcd16a16b, dnnl_Oihw16o = dnnl_Abcd16a,   dnnl_OIhw4i16o4i = dnnl_ABcd4b16a4b, dnnl_OIhw4i32o4i = dnnl_ABcd4b32a4b, dnnl_OIhw4i64o4i = dnnl_ABcd4b64a4b, dnnl_OIhw16i16o4i = dnnl_ABcd16b16a4b,   dnnl_OIhw16i16o2i = dnnl_ABcd16b16a2b, dnnl_OIhw16o16i2o = dnnl_ABcd16a16b2a, dnnl_OIhw4i4o = dnnl_ABcd4b4a, dnnl_OIhw4o4i = dnnl_ABcd4a4b,   dnnl_Oihw4o = dnnl_Abcd4a, dnnl_OIhw8i16o2i = dnnl_ABcd8b16a2b, dnnl_OIhw8i32o2i = dnnl_ABcd8b32a2b, dnnl_OIhw8i64o2i = dnnl_ABcd8b64a2b,   dnnl_OIhw8i8o = dnnl_ABcd8b8a, dnnl_OIhw8o16i2o = dnnl_ABcd8a16b2a, dnnl_OIhw2i8o4i = dnnl_ABcd2b8a4b, dnnl_IOhw8o16i2o = dnnl_BAcd8a16b2a,   dnnl_OIhw8o8i = dnnl_ABcd8a8b, dnnl_OIhw8o4i = dnnl_ABcd8a4b, dnnl_Owhi16o = dnnl_Adcb16a, dnnl_Odhwi16o = dnnl_Acdeb16a,   dnnl_OdhwI16o2i = dnnl_AcdeB16a2b, dnnl_OdhwI16o4i = dnnl_AcdeB16a4b, dnnl_Odhwi4o = dnnl_Acdeb4a, dnnl_Odhwi8o = dnnl_Acdeb8a,   dnnl_OIdhw16i16o = dnnl_ABcde16b16a, dnnl_OIdhw16i32o = dnnl_ABcde16b32a, dnnl_OIdhw16i64o = dnnl_ABcde16b64a, dnnl_OIdhw16o16i = dnnl_ABcde16a16b,   dnnl_Oidhw16o = dnnl_Abcde16a, dnnl_OIdhw4i4o = dnnl_ABcde4b4a, dnnl_OIdhw4o4i = dnnl_ABcde4a4b, dnnl_Oidhw4o = dnnl_Abcde4a,   dnnl_OIdhw8i16o2i = dnnl_ABcde8b16a2b, dnnl_OIdhw8i32o2i = dnnl_ABcde8b32a2b, dnnl_OIdhw8i64o2i = dnnl_ABcde8b64a2b, dnnl_OIdhw8i8o = dnnl_ABcde8b8a,   dnnl_OIdhw8o16i2o = dnnl_ABcde8a16b2a, dnnl_IOdhw8o16i2o = dnnl_BAcde8a16b2a, dnnl_OIdhw4i16o4i = dnnl_ABcde4b16a4b, dnnl_OIdhw4i32o4i = dnnl_ABcde4b32a4b,   dnnl_OIdhw4i64o4i = dnnl_ABcde4b64a4b, dnnl_OIdhw16i16o4i = dnnl_ABcde16b16a4b, dnnl_OIdhw16i16o2i = dnnl_ABcde16b16a2b, dnnl_OIdhw2i8o4i = dnnl_ABcde2b8a4b,   dnnl_OIdhw8o8i = dnnl_ABcde8a8b, dnnl_OIdhw8o4i = dnnl_ABcde8a4b, dnnl_IOdhw16i16o = dnnl_BAcde16b16a, dnnl_OIdhw4o8i8o4i = dnnl_ABcde4a8b8a4b,   dnnl_IOdhw16o16i = dnnl_BAcde16a16b, dnnl_OIdhw16o16i2o = dnnl_ABcde16a16b2a, dnnl_Goiw16g = dnnl_Abcd16a, dnnl_Goiw8g = dnnl_Abcd8a,   dnnl_Goiw4g = dnnl_Abcd4a, dnnl_gIOw16o16i = dnnl_aCBd16b16c, dnnl_gIOw16i16o = dnnl_aCBd16c16b, dnnl_gOIw16i16o = dnnl_aBCd16c16b,   dnnl_gOIw16o16i = dnnl_aBCd16b16c, dnnl_gOiw16o = dnnl_aBcd16b, dnnl_gOIw4i16o4i = dnnl_aBCd4c16b4c, dnnl_gOIw2i8o4i = dnnl_aBCd2c8b4c,   dnnl_gOIw16i16o4i = dnnl_aBCd16c16b4c, dnnl_gOIw16i16o2i = dnnl_aBCd16c16b2c, dnnl_gOIw16o16i2o = dnnl_aBCd16b16c2b, dnnl_gOIw4i4o = dnnl_aBCd4c4b,   dnnl_gOIw4o4i = dnnl_aBCd4b4c, dnnl_gOiw4o = dnnl_aBcd4b, dnnl_gOIw8i16o2i = dnnl_aBCd8c16b2c, dnnl_gOIw8i8o = dnnl_aBCd8c8b,   dnnl_gOIw8o16i2o = dnnl_aBCd8b16c2b, dnnl_gIOw8o16i2o = dnnl_aCBd8b16c2b, dnnl_gOIw8o8i = dnnl_aBCd8b8c, dnnl_gOIw8o4i = dnnl_aBCd8b4c,   dnnl_gOwi16o = dnnl_aBdc16b, dnnl_gOwI16o2i = dnnl_aBdC16b2c, dnnl_gOwI16o4i = dnnl_aBdC16b4c, dnnl_gOwi4o = dnnl_aBdc4b,   dnnl_gOwi8o = dnnl_aBdc8b, dnnl_Goiw32g = dnnl_Abcd32a, dnnl_gOIw2i4o2i = dnnl_aBCd2c4b2c, dnnl_gOIw2o4i2o = dnnl_aBCd2b4c2b,   dnnl_gOIw4i8o2i = dnnl_aBCd4c8b2c, dnnl_gOIw4o8i2o = dnnl_aBCd4b8c2b, dnnl_gIOhw16i16o = dnnl_aCBde16c16b, dnnl_gIOhw16o16i = dnnl_aCBde16b16c,   dnnl_gOhwi16o = dnnl_aBdec16b, dnnl_gOhwI16o2i = dnnl_aBdeC16b2c, dnnl_gOhwI16o4i = dnnl_aBdeC16b4c, dnnl_gOhwi32o = dnnl_aBdec32b,   dnnl_gOhwi4o = dnnl_aBdec4b, dnnl_gOhwi8o = dnnl_aBdec8b, dnnl_Goihw16g = dnnl_Abcde16a, dnnl_gOIhw16i16o = dnnl_aBCde16c16b,   dnnl_gOIhw16o16i = dnnl_aBCde16b16c, dnnl_gOihw16o = dnnl_aBcde16b, dnnl_gOIhw2i8o4i = dnnl_aBCde2c8b4c, dnnl_gOIhw4i16o4i = dnnl_aBCde4c16b4c,   dnnl_gOIhw16i16o4i = dnnl_aBCde16c16b4c, dnnl_gOIhw16i16o2i = dnnl_aBCde16c16b2c, dnnl_gOIhw16o16i2o = dnnl_aBCde16b16c2b, dnnl_gOIhw4i4o = dnnl_aBCde4c4b,   dnnl_gOIhw4o4i = dnnl_aBCde4b4c, dnnl_gOihw4o = dnnl_aBcde4b, dnnl_Goihw8g = dnnl_Abcde8a, dnnl_Goihw4g = dnnl_Abcde4a,   dnnl_gOIhw8i16o2i = dnnl_aBCde8c16b2c, dnnl_gOIhw8i8o = dnnl_aBCde8c8b, dnnl_gOIhw8o16i2o = dnnl_aBCde8b16c2b, dnnl_gIOhw8o16i2o = dnnl_aCBde8b16c2b,   dnnl_gOIhw8o8i = dnnl_aBCde8b8c, dnnl_gOIhw8o4i = dnnl_aBCde8b4c, dnnl_Goihw32g = dnnl_Abcde32a, dnnl_gOwhi16o = dnnl_aBedc16b,   dnnl_OIw4o8i8o4i = dnnl_ABc4a8b8a4b, dnnl_OIhw4o8i8o4i = dnnl_ABcd4a8b8a4b, dnnl_IOw4i8o8i4o = dnnl_BAc4b8a8b4a, dnnl_IOhw4i8o8i4o = dnnl_BAcd4b8a8b4a,   dnnl_IOdhw4i8o8i4o = dnnl_BAcde4b8a8b4a, dnnl_OIhw2o8i8o2i = dnnl_ABcd2a8b8a2b, dnnl_gOIw4o8i8o4i = dnnl_aBCd4b8c8b4c, dnnl_gOIhw4o8i8o4i = dnnl_aBCde4b8c8b4c,   dnnl_gOIdhw4o8i8o4i = dnnl_aBCdef4b8c8b4c, dnnl_gIOw4i8o8i4o = dnnl_aCBd4c8b8c4b, dnnl_gIOhw4i8o8i4o = dnnl_aCBde4c8b8c4b, dnnl_gIOdhw4i8o8i4o = dnnl_aCBdef4c8b8c4b,   dnnl_gOIhw2o8i8o2i = dnnl_aBCde2b8c8b2c, dnnl_gOIhw2i4o2i = dnnl_aBCde2c4b2c, dnnl_gOIhw2o4i2o = dnnl_aBCde2b4c2b, dnnl_gOIhw4i8o2i = dnnl_aBCde4c8b2c,   dnnl_gOIhw4o8i2o = dnnl_aBCde4b8c2b, dnnl_gIOdhw16i16o = dnnl_aCBdef16c16b, dnnl_gIOdhw16o16i = dnnl_aCBdef16b16c, dnnl_gOdhwi16o = dnnl_aBdefc16b,   dnnl_gOdhwI16o2i = dnnl_aBdefC16b2c, dnnl_gOdhwI16o4i = dnnl_aBdefC16b4c, dnnl_gOdhwi4o = dnnl_aBdefc4b, dnnl_gOdhwi8o = dnnl_aBdefc8b,   dnnl_gOIdhw16i16o = dnnl_aBCdef16c16b, dnnl_gOIdhw4i16o4i = dnnl_aBCdef4c16b4c, dnnl_gOIdhw16i16o4i = dnnl_aBCdef16c16b4c, dnnl_gOIdhw2i8o4i = dnnl_aBCdef2c8b4c,   dnnl_gOIdhw16i16o2i = dnnl_aBCdef16c16b2c, dnnl_gOIdhw16o16i = dnnl_aBCdef16b16c, dnnl_gOIdhw16o16i2o = dnnl_aBCdef16b16c2b, dnnl_gOidhw16o = dnnl_aBcdef16b,   dnnl_gOIdhw4i4o = dnnl_aBCdef4c4b, dnnl_gOIdhw4o4i = dnnl_aBCdef4b4c, dnnl_gOidhw4o = dnnl_aBcdef4b, dnnl_gOIdhw8i16o2i = dnnl_aBCdef8c16b2c,   dnnl_gOIdhw8i8o = dnnl_aBCdef8c8b, dnnl_gOIdhw8o16i2o = dnnl_aBCdef8b16c2b, dnnl_gIOdhw8o16i2o = dnnl_aCBdef8b16c2b, dnnl_gOIdhw8o8i = dnnl_aBCdef8b8c,   dnnl_gOIdhw8o4i = dnnl_aBCdef8b4c, dnnl_Goidhw16g = dnnl_Abcdef16a, dnnl_Goidhw32g = dnnl_Abcdef32a, dnnl_gOIdhw2i4o2i = dnnl_aBCdef2c4b2c,   dnnl_gOIdhw4i8o2i = dnnl_aBCdef4c8b2c, dnnl_gOIdhw2o4i2o = dnnl_aBCdef2b4c2b, dnnl_gOIdhw4o8i2o = dnnl_aBCdef4b8c2b, dnnl_Owi32o = dnnl_Acb32a,   dnnl_OwI32o2i = dnnl_AcB32a2b, dnnl_OwI32o4i = dnnl_AcB32a4b, dnnl_Owi48o = dnnl_Acb48a, dnnl_OwI48o2i = dnnl_AcB48a2b,   dnnl_OwI48o4i = dnnl_AcB48a4b, dnnl_Owi64o = dnnl_Acb64a, dnnl_OwI64o2i = dnnl_AcB64a2b, dnnl_OwI64o4i = dnnl_AcB64a4b,   dnnl_wIo2i = dnnl_cBa2b, dnnl_wIo4i = dnnl_cBa4b, dnnl_gOwi32o = dnnl_aBdc32b, dnnl_gOwI32o2i = dnnl_aBdC32b2c,   dnnl_gOwI32o4i = dnnl_aBdC32b4c, dnnl_gOwi48o = dnnl_aBdc48b, dnnl_gOwI48o2i = dnnl_aBdC48b2c, dnnl_gOwI48o4i = dnnl_aBdC48b4c,   dnnl_gOwi64o = dnnl_aBdc64b, dnnl_gOwI64o2i = dnnl_aBdC64b2c, dnnl_gOwI64o4i = dnnl_aBdC64b4c, dnnl_gwio = dnnl_adcb,   dnnl_gwIo2i = dnnl_adCb2c, dnnl_gwIo4i = dnnl_adCb4c, dnnl_OhwI32o = dnnl_Acdb32a, dnnl_OhwI32o2i = dnnl_AcdB32a2b,   dnnl_OhwI32o4i = dnnl_AcdB32a4b, dnnl_Ohwi48o = dnnl_Acdb48a, dnnl_OhwI48o2i = dnnl_AcdB48a2b, dnnl_OhwI48o4i = dnnl_AcdB48a4b,   dnnl_Ohwi64o = dnnl_Acdb64a, dnnl_OhwI64o2i = dnnl_AcdB64a2b, dnnl_OhwI64o4i = dnnl_AcdB64a4b, dnnl_hwIo2i = dnnl_cdBa2b,   dnnl_hwIo4i = dnnl_cdBa4b, dnnl_gOhwI32o = dnnl_aBdec32b, dnnl_gOhwI32o2i = dnnl_aBdeC32b2c, dnnl_gOhwI32o4i = dnnl_aBdeC32b4c,   dnnl_gOhwi48o = dnnl_aBdec48b, dnnl_gOhwI48o2i = dnnl_aBdeC48b2c, dnnl_gOhwI48o4i = dnnl_aBdeC48b4c, dnnl_gOhwi64o = dnnl_aBdec64b,   dnnl_gOhwI64o2i = dnnl_aBdeC64b2c, dnnl_gOhwI64o4i = dnnl_aBdeC64b4c, dnnl_ghwio = dnnl_adecb, dnnl_ghwIo2i = dnnl_adeCb2c,   dnnl_ghwIo4i = dnnl_adeCb4c, dnnl_Odhwi32o = dnnl_Acdeb32a, dnnl_OdhwI32o2i = dnnl_AcdeB32a2b, dnnl_OdhwI32o4i = dnnl_AcdeB32a4b,   dnnl_Odhwi48o = dnnl_Acdeb48a, dnnl_OdhwI48o2i = dnnl_AcdeB48a2b, dnnl_OdhwI48o4i = dnnl_AcdeB48a4b, dnnl_Odhwi64o = dnnl_Acdeb64a,   dnnl_OdhwI64o2i = dnnl_AcdeB64a2b, dnnl_OdhwI64o4i = dnnl_AcdeB64a4b, dnnl_dhwIo2i = dnnl_cdeBa2b, dnnl_dhwIo4i = dnnl_cdeBa4b,   dnnl_gOdhwi32o = dnnl_aBdefc32b, dnnl_gOdhwI32o2i = dnnl_aBdefC32b2c, dnnl_gOdhwI32o4i = dnnl_aBdefC32b4c, dnnl_gOdhwi48o = dnnl_aBdefc48b,   dnnl_gOdhwI48o2i = dnnl_aBdefC48b2c, dnnl_gOdhwI48o4i = dnnl_aBdefC48b4c, dnnl_gOdhwi64o = dnnl_aBdefc64b, dnnl_gOdhwI64o2i = dnnl_aBdefC64b2c,   dnnl_gOdhwI64o4i = dnnl_aBdefC64b4c, dnnl_gdhwio = dnnl_adefcb, dnnl_gdhwIo2i = dnnl_adefCb2c, dnnl_gdhwIo4i = dnnl_adefCb4c,   dnnl_OI16i32o4i = dnnl_AB16b32a4b, dnnl_OI16i48o4i = dnnl_AB16b48a4b, dnnl_OI16i64o4i = dnnl_AB16b64a4b, dnnl_OI16i16o2i = dnnl_AB16b16a2b,   dnnl_OI16i32o2i = dnnl_AB16b32a2b, dnnl_OI16i48o2i = dnnl_AB16b48a2b, dnnl_OI16i64o2i = dnnl_AB16b64a2b, dnnl_OIw16i32o4i = dnnl_ABc16b32a4b,   dnnl_OIw16i48o4i = dnnl_ABc16b48a4b, dnnl_OIw16i64o4i = dnnl_ABc16b64a4b, dnnl_OIw16i32o2i = dnnl_ABc16b32a2b, dnnl_OIw16i48o2i = dnnl_ABc16b48a2b,   dnnl_OIw16i64o2i = dnnl_ABc16b64a2b, dnnl_OIhw16i32o4i = dnnl_ABcd16b32a4b, dnnl_OIhw16i48o4i = dnnl_ABcd16b48a4b, dnnl_OIhw16i64o4i = dnnl_ABcd16b64a4b,   dnnl_OIhw16i32o2i = dnnl_ABcd16b32a2b, dnnl_OIhw16i48o2i = dnnl_ABcd16b48a2b, dnnl_OIhw16i64o2i = dnnl_ABcd16b64a2b, dnnl_OIdhw16i32o4i = dnnl_ABcde16b32a4b,   dnnl_OIdhw16i48o4i = dnnl_ABcde16b48a4b, dnnl_OIdhw16i64o4i = dnnl_ABcde16b64a4b, dnnl_OIdhw16i32o2i = dnnl_ABcde16b32a2b, dnnl_OIdhw16i48o2i = dnnl_ABcde16b48a2b,   dnnl_OIdhw16i64o2i = dnnl_ABcde16b64a2b, dnnl_OwI16i16o2i = dnnl_AcB16b16a2b, dnnl_OwI16i16o4i = dnnl_AcB16b16a4b, dnnl_OhwI16i16o2i = dnnl_AcdB16b16a2b,   dnnl_OhwI16i16o4i = dnnl_AcdB16b16a4b, dnnl_OdhwI16i16o2i = dnnl_AcdeB16b16a2b, dnnl_OdhwI16i16o4i = dnnl_AcdeB16b16a4b, dnnl_gOwI16i16o2i = dnnl_aBdC16c16b2c,   dnnl_gOwI16i16o4i = dnnl_aBdC16c16b4c, dnnl_gOhwI16i16o2i = dnnl_aBdeC16c16b2c, dnnl_gOhwI16i16o4i = dnnl_aBdeC16c16b4c, dnnl_gOdhwI16i16o2i = dnnl_aBdefC16c16b2c,   dnnl_gOdhwI16i16o4i = dnnl_aBdefC16c16b4c, dnnl_OwI16i32o2i = dnnl_AcB16b32a2b, dnnl_OwI16i32o4i = dnnl_AcB16b32a4b, dnnl_OwI16i48o2i = dnnl_AcB16b48a2b,   dnnl_OwI16i48o4i = dnnl_AcB16b48a4b, dnnl_OwI16i64o2i = dnnl_AcB16b64a2b, dnnl_OwI16i64o4i = dnnl_AcB16b64a4b, dnnl_gOwI16i32o2i = dnnl_aBdC16c32b2c,   dnnl_gOwI16i32o4i = dnnl_aBdC16c32b4c, dnnl_gOwI16i48o2i = dnnl_aBdC16c48b2c, dnnl_gOwI16i48o4i = dnnl_aBdC16c48b4c, dnnl_gOwI16i64o2i = dnnl_aBdC16c64b2c,   dnnl_gOwI16i64o4i = dnnl_aBdC16c64b4c, dnnl_OhwI16i32o2i = dnnl_AcdB16b32a2b, dnnl_OhwI16i32o4i = dnnl_AcdB16b32a4b, dnnl_OhwI16i48o2i = dnnl_AcdB16b48a2b,   dnnl_OhwI16i48o4i = dnnl_AcdB16b48a4b, dnnl_OhwI16i64o2i = dnnl_AcdB16b64a2b, dnnl_OhwI16i64o4i = dnnl_AcdB16b64a4b, dnnl_gOhwI16i32o2i = dnnl_aBdeC16c32b2c,   dnnl_gOhwI16i32o4i = dnnl_aBdeC16c32b4c, dnnl_gOhwI16i48o2i = dnnl_aBdeC16c48b2c, dnnl_gOhwI16i48o4i = dnnl_aBdeC16c48b4c, dnnl_gOhwI16i64o2i = dnnl_aBdeC16c64b2c,   dnnl_gOhwI16i64o4i = dnnl_aBdeC16c64b4c, dnnl_OdhwI16i32o2i = dnnl_AcdeB16b32a2b, dnnl_OdhwI16i32o4i = dnnl_AcdeB16b32a4b, dnnl_OdhwI16i48o2i = dnnl_AcdeB16b48a2b,   dnnl_OdhwI16i48o4i = dnnl_AcdeB16b48a4b, dnnl_OdhwI16i64o2i = dnnl_AcdeB16b64a2b, dnnl_OdhwI16i64o4i = dnnl_AcdeB16b64a4b, dnnl_gOdhwI16i32o2i = dnnl_aBdefC16c32b2c,   dnnl_gOdhwI16i32o4i = dnnl_aBdefC16c32b4c, dnnl_gOdhwI16i48o2i = dnnl_aBdefC16c48b2c, dnnl_gOdhwI16i48o4i = dnnl_aBdefC16c48b4c, dnnl_gOdhwI16i64o2i = dnnl_aBdefC16c64b2c,   dnnl_gOdhwI16i64o4i = dnnl_aBdefC16c64b4c, dnnl_hwioG16g = dnnl_decbA16a, dnnl_NCdhw40n16c = dnnl_ABcde40a16b, dnnl_NCw40n16c = dnnl_ABc40a16b,   dnnl_NChw40n16c = dnnl_ABcd40a16b, dnnl_NCw40n32c = dnnl_ABc40a32b, dnnl_NChw40n32c = dnnl_ABcd40a32b, dnnl_NCdhw40n32c = dnnl_ABcde40a32b,   dnnl_OIdhw4o8i8o2i = dnnl_ABcde4a8b8a2b, dnnl_OIhw4o8i8o2i = dnnl_ABcd4a8b8a2b, dnnl_OIw4o8i8o2i = dnnl_ABc4a8b8a2b, dnnl_gOIdhw4o8i8o2i = dnnl_aBCdef4b8c8b2c,   dnnl_gOIhw4o8i8o2i = dnnl_aBCde4b8c8b2c, dnnl_gOIw4o8i8o2i = dnnl_aBCd4b8c8b2c, dnnl_IOdhw4i8o8i2o = dnnl_BAcde4b8a8b2a, dnnl_IOhw4i8o8i2o = dnnl_BAcd4b8a8b2a,   dnnl_IOw4i8o8i2o = dnnl_BAc4b8a8b2a, dnnl_gIOdhw4i8o8i2o = dnnl_aCBdef4c8b8c2b, dnnl_gIOhw4i8o8i2o = dnnl_aCBde4c8b8c2b, dnnl_gIOw4i8o8i2o = dnnl_aCBd4c8b8c2b,   dnnl_NCw2c32n8c = dnnl_ABc2b32a8b, dnnl_NChw2c32n8c = dnnl_ABcd2b32a8b, dnnl_NCdhw2c32n8c = dnnl_ABcde2b32a8b, dnnl_OIw2i8o16i4o = dnnl_ABc2b8a16b4a,   dnnl_OIhw2i8o16i4o = dnnl_ABcd2b8a16b4a, dnnl_OIdhw2i8o16i4o = dnnl_ABcde2b8a16b4a, dnnl_OIw2o8i16o4i = dnnl_ABc2a8b16a4b, dnnl_OIw2o8i16o2i = dnnl_ABc2a8b16a2b,   dnnl_IOw2i8o16i4o = dnnl_BAc2b8a16b4a, dnnl_IOw2i8o16i2o = dnnl_BAc2b8a16b2a, dnnl_OIhw2o8i16o4i = dnnl_ABcd2a8b16a4b, dnnl_OIhw2o8i16o2i = dnnl_ABcd2a8b16a2b,   dnnl_IOhw2i8o16i4o = dnnl_BAcd2b8a16b4a, dnnl_IOhw2i8o16i2o = dnnl_BAcd2b8a16b2a, dnnl_OIdhw2o8i16o4i = dnnl_ABcde2a8b16a4b, dnnl_OIdhw2o8i16o2i = dnnl_ABcde2a8b16a2b,   dnnl_IOdhw2i8o16i4o = dnnl_BAcde2b8a16b4a, dnnl_IOdhw2i8o16i2o = dnnl_BAcde2b8a16b2a, dnnl_gOIw2o8i16o2i = dnnl_aBCd2b8c16b2c, dnnl_gIOw2i8o16i2o = dnnl_aCBd2c8b16c2b,   dnnl_gIOhw2i8o16i2o = dnnl_aBCde2c8b16c2b, dnnl_gIOdhw2i8o16i2o = dnnl_aBCdef2c8b16c2b, dnnl_gOIhw2o8i16o2i = dnnl_aBCde2b8c16b2c, dnnl_gOIdhw2o8i16o2i = dnnl_aBCdef2b8c16b2c,   dnnl_gOIw2o8i16o4i = dnnl_aBCd2b8c16b4c, dnnl_gOIhw2o8i16o4i = dnnl_aBCde2b8c16b4c }
enum	dnnl_wino_memory_format_t {   dnnl_wino_undef = 0, dnnl_wino_wei_aaOIoi, dnnl_wino_wei_aaOio, dnnl_wino_wei_aaOBiOo,   dnnl_wino_wei_OBaaIBOIio }
	Winograd-specific formats. More...
enum	dnnl_rnn_packed_memory_format_t { dnnl_packed_format_undef = 0, dnnl_ldigo_p, dnnl_ldgoi_p, dnnl_ldio_p }
enum	dnnl_memory_extra_flags_t {   dnnl_memory_extra_flag_none = 0x0U, dnnl_memory_extra_flag_compensation_conv_s8s8 = 0x1U, dnnl_memory_extra_flag_scale_adjust = 0x2U, dnnl_memory_extra_flag_rnn_u8s8_compensation = 0x4U,   dnnl_memory_extra_flag_gpu_rnn_u8s8_compensation = dnnl_memory_extra_flag_rnn_u8s8_compensation, dnnl_memory_extra_flag_compensation_conv_asymmetric_src = 0x8U, dnnl_memory_extra_flag_rnn_s8s8_compensation = 0x16U }
	Flags for memory special features. More...

Functions
dnnl_status_t DNNL_API	dnnl_memory_desc_init_by_strides (dnnl_memory_desc_t *memory_desc, int ndims, const dnnl_dims_t dims, dnnl_data_type_t data_type, const dnnl_dims_t strides)
dnnl_status_t DNNL_API	dnnl_memory_desc_init_by_tag (dnnl_memory_desc_t *memory_desc, int ndims, const dnnl_dims_t dims, dnnl_data_type_t data_type, dnnl_format_tag_t tag)
dnnl_status_t DNNL_API	dnnl_memory_desc_init_submemory (dnnl_memory_desc_t *memory_desc, const dnnl_memory_desc_t *parent_memory_desc, const dnnl_dims_t dims, const dnnl_dims_t offsets)
dnnl_status_t DNNL_API	dnnl_memory_desc_reshape (dnnl_memory_desc_t *out_memory_desc, const dnnl_memory_desc_t *in_memory_desc, int ndims, const dnnl_dims_t dims)
dnnl_status_t DNNL_API	dnnl_memory_desc_permute_axes (dnnl_memory_desc_t *out_memory_desc, const dnnl_memory_desc_t *in_memory_desc, const int *permutation)
int DNNL_API	dnnl_memory_desc_equal (const dnnl_memory_desc_t *lhs, const dnnl_memory_desc_t *rhs)
size_t DNNL_API	dnnl_memory_desc_get_size (const dnnl_memory_desc_t *memory_desc)
size_t DNNL_API	dnnl_data_type_size (dnnl_data_type_t data_type)
dnnl_status_t DNNL_API	dnnl_memory_create (dnnl_memory_t *memory, const dnnl_memory_desc_t *memory_desc, dnnl_engine_t engine, void *handle)
dnnl_status_t DNNL_API	dnnl_memory_get_memory_desc (const_dnnl_memory_t memory, const dnnl_memory_desc_t **memory_desc)
dnnl_status_t DNNL_API	dnnl_memory_get_engine (const_dnnl_memory_t memory, dnnl_engine_t *engine)
dnnl_status_t DNNL_API	dnnl_memory_map_data (const_dnnl_memory_t memory, void **mapped_ptr)
dnnl_status_t DNNL_API	dnnl_memory_unmap_data (const_dnnl_memory_t memory, void *mapped_ptr)
dnnl_status_t DNNL_API	dnnl_memory_get_data_handle (const_dnnl_memory_t memory, void **handle)
dnnl_status_t DNNL_API	dnnl_memory_set_data_handle (dnnl_memory_t memory, void *handle)
dnnl_status_t DNNL_API	dnnl_memory_set_data_handle_v2 (dnnl_memory_t memory, void *handle, dnnl_stream_t stream)
dnnl_status_t DNNL_API	dnnl_memory_destroy (dnnl_memory_t memory)

Detailed Description

Macro Definition Documentation

DNNL_MAX_NDIMS

#define DNNL_MAX_NDIMS   12

Maximum number of dimensions a tensor can have. Only restricts the amount of space used for the tensor description. Individual computational primitives may support only tensors of certain dimensions.

DNNL_MEMORY_ALLOCATE

#define DNNL_MEMORY_ALLOCATE   ((void *)(size_t)-1)

Special pointer value that indicates that the library needs to allocate an underlying buffer for a memory object.

DNNL_MEMORY_NONE

#define DNNL_MEMORY_NONE   (NULL)

Special pointer value that indicates that a memory object should not have an underlying buffer.

DNNL_RNN_MAX_N_PARTS

#define DNNL_RNN_MAX_N_PARTS   4

Maximum number of parts of RNN weights tensor that require separate computation.

DNNL_RUNTIME_DIM_VAL

#define DNNL_RUNTIME_DIM_VAL   INT64_MIN

A wildcard value for dimensions that are unknown at a primitive creation time.

DNNL_RUNTIME_F32_VAL

#define DNNL_RUNTIME_F32_VAL   (DNNL_RUNTIME_F32_VAL_REP.f)

A wildcard value for floating point values that are unknown at a primitive creation time.

DNNL_RUNTIME_S32_VAL

#define DNNL_RUNTIME_S32_VAL   DNNL_RUNTIME_S32_VAL_REP

A wildcard value for int32_t values that are unknown at a primitive creation time.

DNNL_RUNTIME_SIZE_VAL

#define DNNL_RUNTIME_SIZE_VAL   ((size_t)DNNL_RUNTIME_DIM_VAL)

A size_t counterpart of the DNNL_RUNTIME_DIM_VAL. For instance, this value is returned by dnnl_memory_desc_get_size() if either of the dimensions or strides equal to DNNL_RUNTIME_DIM_VAL.

Typedef Documentation

const_dnnl_memory_t

typedef const struct dnnl_memory* const_dnnl_memory_t

A constant memory handle.

dnnl_dim_t

typedef int64_t dnnl_dim_t

A type to describe tensor dimension.

dnnl_dims_t

typedef dnnl_dim_t dnnl_dims_t[DNNL_MAX_NDIMS]

A type to describe tensor dimensions.

dnnl_memory_t

typedef struct dnnl_memory* dnnl_memory_t

A memory handle.

Enumeration Type Documentation

dnnl_data_type_t

enum dnnl_data_type_t

Data type specification.

Enumerator
dnnl_data_type_undef	Undefined data type, used for empty memory descriptors.
dnnl_f16	16-bit/half-precision floating point.
dnnl_bf16	non-standard 16-bit (bfloat16 w/ 7 bit mantissa) floating point.
dnnl_f32	32-bit/single-precision floating point.
dnnl_s32	32-bit signed integer.
dnnl_s8	8-bit signed integer.
dnnl_u8	8-bit unsigned integer.

dnnl_format_kind_t

enum dnnl_format_kind_t

Memory format kind.

Enumerator
dnnl_format_kind_undef	Undefined memory format kind, used for empty memory descriptors.
dnnl_format_kind_any	Unspecified format kind. The primitive selects a format automatically.
dnnl_blocked	A tensor in a generic format described by the stride and blocking values in each dimension. See dnnl_blocking_desc_t for more information.
dnnl_format_kind_wino	Weights format used in 8bit Winograd convolution.
dnnl_format_kind_rnn_packed	Packed weights format used in RNN.

dnnl_format_tag_t

enum dnnl_format_tag_t

Memory format tag specification.

oneDNN formats describe physical data layout. The physical layout is described as a sequence of the dimensions as they are laid out in the memory (from the outer-most to the inner-most). Note that this order doesn't affect the logical order of the dimensions that is kept in the dims field of the dnnl_memory_desc_t structure. The logical order of the dimensions is specified by the primitive that uses the tensor.

For example, CNN 5D tensor always has its logical dimensions in the order (batch, channels, depth, height, width), while the physical layout might be NCDHW (corresponds to dnnl_ncdhw format tag) or NDHWC (corresponds to dnnl_ndhwc format tag).

int batch = 2, channels = 16, depth = 13, height = 13, width = 13;
 
int ndims = 5; // 5D tensor
dnnl_dims_t dims = {batch, channels, depth, height, width};
dnnl_memory_desc_t data_in_ncdhw;
dnnl_memory_desc_init_by_tag(
     &data_in_ncdhw, 5, dims, dnnl_f32, dnnl_ncdhw);
 
// note that in both cases dims passed are the same
dnnl_memory_desc_t data_in_ndhwc;
dnnl_memory_desc_init_by_tag(
     &data_in_ndhwc, 5, dims, dnnl_f32, dnnl_ndhwc);

Memory format tags can be further divided into two categories:

• Domain-agnostic names, i.e. names the do not depend on the tensor usage in the specific primitive. These names use letters from a to l to denote logical dimension from 1 to 12, and form the order in which the dimensions are laid in memory. For instance, dnnl_ab is used to denote 2D tensor where the second logical dimension (aka b) is the innermost, i.e. has stride = 1, and the first logical dimension (a) laid out in memory with stride equal to the size of second dimension. On the other hand, dnnl_ba is just transposed version of the same tensor: the first dimension (a) becomes the innermost one.
• Domain-specific names, i.e. names that make sense only in the context of a certain domain, such as CNN. This names are just aliases to the corresponding domain-agnostic tags and used mostly for the convenience. For example, dnnl_nc is used to denote 2D CNN activations tensor memory format, where channels are the innermost dimension and batch is an outermost one. Moreover, dnnl_nc is just an alias to dnnl_ab, since for oneDNN CNN primitives the logical dimensions of activations tensors come in order: batch, channels, spatial. In other words, batch corresponds to the first logical dimension (a), channels correspond to the second one (b).

The following domain-specific notation applies to memory format tags:

• 'n' denotes the mini-batch dimension
• 'c' denotes a channels dimension
• When there are multiple channel dimensions (for example, in convolution weights tensor), 'i' and 'o' denote dimensions of input and output channels
• 'd', 'h', and 'w' denote spatial depth, height, and width respectively

Upper-case letters indicate that the data is laid out in blocks for a particular dimension. In such cases, the format name contains both upper- and lower-case letters for that dimension with a lower-case letter preceded by the block size. For example: dnnl_nChw8c describes a format where the outermost dimension is mini-batch, followed by the channel block number, followed by the spatial height and width, and finally followed by 8-element channel blocks.

See also

dev_guide_understanding_memory_formats

Enumerator
dnnl_format_tag_undef	Undefined memory format tag.
dnnl_format_tag_any	Undefined memory format tag. The primitive selects a format automatically.
dnnl_a	plain 1D tensor
dnnl_ab	plain 2D tensor
dnnl_abc	plain 3D tensor
dnnl_abcd	plain 4D tensor
dnnl_acbd	plain 4D tensor
dnnl_abcde	plain 5D tensor
dnnl_abcdef	plain 6D tensor
dnnl_abcdefg	plain 7D tensor
dnnl_abcdefgh	plain 8D tensor
dnnl_abcdefghi	plain 9D tensor
dnnl_abcdefghij	plain 10D tensor
dnnl_abcdefghijk	plain 11D tensor
dnnl_abcdefghijkl	plain 12D tensor
dnnl_abdc	permuted 4D tensor
dnnl_abdec	permuted 5D tensor
dnnl_acb	permuted 3D tensor
dnnl_acbde	permuted 5D tensor
dnnl_acbdef	permuted 6D tensor
dnnl_acdb	permuted 4D tensor
dnnl_acdeb	permuted 5D tensor
dnnl_ba	permuted 2D tensor
dnnl_bac	permuted 3D tensor
dnnl_bacd	permuted 4D tensor
dnnl_bacde	permuted 5D tensor
dnnl_bca	permuted 3D tensor
dnnl_bcda	permuted 4D tensor
dnnl_bcdea	permuted 5D tensor
dnnl_cba	permuted 3D tensor
dnnl_cdba	permuted 4D tensor
dnnl_dcab	permuted 4D tensor
dnnl_cdeba	permuted 5D tensor
dnnl_decab	permuted 5D tensor
dnnl_defcab	permuted 6D tensor
dnnl_abced	permuted 5D tensor
dnnl_abcdfe	permuted 6D tensor
dnnl_abcdegf	permuted 7D tensor
dnnl_abcdefhg	permuted 8D tensor
dnnl_abcdefgih	permuted 9D tensor
dnnl_abcdefghji	permuted 10D tensor
dnnl_abcdefghikj	permuted 11D tensor
dnnl_abcdefghijlk	permuted 12D tensor
dnnl_Abc16a
dnnl_ABc16a16b
dnnl_ABc32a32b
dnnl_ABc4a4b
dnnl_aBc16b	3D tensor blocked by 2nd dimension with block size 16
dnnl_ABc16b16a
dnnl_Abc4a
dnnl_aBc32b	3D tensor blocked by 2nd dimension with block size 32
dnnl_aBc4b	3D tensor blocked by 2nd dimension with block size 4
dnnl_ABc4b16a4b
dnnl_ABc2b8a4b
dnnl_ABc16b16a4b
dnnl_ABc16b16a2b
dnnl_ABc4b4a
dnnl_ABc8a16b2a
dnnl_ABc8a8b
dnnl_ABc8a4b
dnnl_aBc8b	3D tensor blocked by 2nd dimension with block size 8
dnnl_ABc8b16a2b
dnnl_BAc8a16b2a
dnnl_ABc8b8a
dnnl_Abcd16a
dnnl_Abcd8a
dnnl_ABcd16a16b
dnnl_Abcd32a
dnnl_ABcd32a32b
dnnl_aBcd16b	4D tensor blocked by 2nd dimension with block size 16
dnnl_ABcd16b16a
dnnl_aBCd16b16c
dnnl_aBCd16c16b
dnnl_Abcd4a
dnnl_aBcd32b	4D tensor blocked by 2nd dimension with block size 32
dnnl_aBcd4b	4D tensor blocked by 2nd dimension with block size 4
dnnl_ABcd4b16a4b
dnnl_ABcd16b16a4b
dnnl_ABcd16b16a2b
dnnl_ABcd4b4a
dnnl_ABcd4a4b
dnnl_aBCd2c4b2c
dnnl_aBCd4b8c2b
dnnl_aBCd4c16b4c
dnnl_aBCd2c8b4c
dnnl_aBCd16c16b4c
dnnl_aBCd16c16b2c
dnnl_aBCd4c4b
dnnl_aBCd4b4c
dnnl_ABcd8a16b2a
dnnl_ABcd2b8a4b
dnnl_ABcd8a8b
dnnl_ABcd8a4b
dnnl_aBcd8b	4D tensor blocked by 2nd dimension with block size 8
dnnl_aBCd4c8b2c
dnnl_ABcd8b16a2b
dnnl_aBCd8b16c2b
dnnl_BAcd8a16b2a
dnnl_ABcd8b8a	4D tensor blocked by 1st and 2nd dimension with block size 8
dnnl_aBCd8b8c
dnnl_aBCd8b4c
dnnl_aBCd8c16b2c
dnnl_ABcde8a16b2a
dnnl_aCBd8b16c2b
dnnl_aBCd8c8b
dnnl_Abcde16a
dnnl_Abcde32a
dnnl_ABcde16a16b
dnnl_BAcde8a16b2a
dnnl_aBCd2b4c2b	4D tensor blocked by 3rd dimension with block size 4
dnnl_ABcde4b16a4b	5D tensor blocked by 1st dimension with block size 16
dnnl_ABcde2b8a4b	5D tensor blocked by 1st dimension with block size 8
dnnl_aBcde16b	5D tensor blocked by 2nd dimension with block size 16
dnnl_ABcde16b16a
dnnl_aBCde16b16c
dnnl_aBCde16c16b
dnnl_aBCde2c8b4c
dnnl_Abcde4a
dnnl_aBcde32b	5D tensor blocked by 2nd dimension with block size 32
dnnl_aBcde4b	5D tensor blocked by 2nd dimension with block size 4
dnnl_ABcde4b4a
dnnl_ABcde4a4b
dnnl_aBCde4b4c
dnnl_aBCde2c4b2c
dnnl_aBCde4b8c2b
dnnl_aBCde4c16b4c
dnnl_aBCde16c16b4c
dnnl_aBCde16c16b2c
dnnl_aBCde4c4b
dnnl_Abcde8a
dnnl_ABcde8a8b
dnnl_ABcde8a4b
dnnl_BAcde16b16a
dnnl_aBcde8b	5D tensor blocked by 2nd dimension with block size 8
dnnl_ABcde8b16a2b
dnnl_aBCde8b16c2b
dnnl_aBCde4c8b2c
dnnl_aCBde8b16c2b
dnnl_ABcde8b8a
dnnl_ABcde32a32b
dnnl_aBCde8b8c
dnnl_aBCde8b4c
dnnl_ABc4a8b8a4b
dnnl_ABcd4a8b8a4b
dnnl_ABcde4a8b8a4b
dnnl_BAc4b8a8b4a
dnnl_BAcd4b8a8b4a
dnnl_BAcde4b8a8b4a
dnnl_ABcd2a8b8a2b
dnnl_aBCd4b8c8b4c
dnnl_aBCde4b8c8b4c
dnnl_aBCde2b8c8b2c
dnnl_aBCde8c16b2c
dnnl_aBCde8c8b
dnnl_aBCde2b4c2b	5D tensor blocked by 3rd dimension with block size 4
dnnl_aBcdef16b	6D tensor blocked by 2nd dimension with block size 16
dnnl_aBCdef16b16c
dnnl_aBCdef16c16b
dnnl_aBCdef4c16b4c
dnnl_aBCdef2c8b4c	6D tensor blocked by 2nd dimension with block size 8
dnnl_aBCdef4c8b2c
dnnl_aBCdef2b4c2b	6D tensor blocked by 3rd dimension with block size 4
dnnl_aBcdef4b	6D tensor blocked by 2nd dimension with block size 4
dnnl_aBCdef4c4b
dnnl_aBCdef4b4c
dnnl_aBCdef2c4b2c
dnnl_aBCdef4b8c2b
dnnl_aBCdef8b8c
dnnl_aBCdef8b4c
dnnl_aBCdef8c16b2c
dnnl_aBCdef4b8c8b4c
dnnl_aBCdef8b16c2b
dnnl_aCBdef8b16c2b
dnnl_aBCdef8c8b
dnnl_aBdc16b
dnnl_aBdC16b2c
dnnl_aBdC16b4c
dnnl_aBdc4b
dnnl_aBdc8b
dnnl_aBdec16b
dnnl_aBdeC16b2c
dnnl_aBdeC16b4c
dnnl_aBdec32b
dnnl_aBdec4b
dnnl_aBdec8b
dnnl_aBdefc16b
dnnl_aBdefC16b2c
dnnl_aCBdef16c16b
dnnl_aBdefc4b
dnnl_aBdefc8b
dnnl_Abcdef16a
dnnl_Abcdef32a
dnnl_aBedc16b
dnnl_Acb16a
dnnl_AcB16a2b
dnnl_AcB16a4b
dnnl_Acb4a
dnnl_Acb8a
dnnl_aCBd16b16c
dnnl_aCBd16c16b
dnnl_aCBde16b16c
dnnl_aCBde16c16b
dnnl_Acdb16a
dnnl_AcdB16a2b
dnnl_AcdB16a4b
dnnl_Acdb32a
dnnl_Acdb4a
dnnl_Acdb8a
dnnl_Acdeb16a
dnnl_AcdeB16a2b
dnnl_Acdeb4a
dnnl_Acdeb8a
dnnl_Adcb16a
dnnl_BAc16a16b
dnnl_BAc16b16a
dnnl_BAcd16a16b
dnnl_BAcd16b16a
dnnl_aCBd4c8b8c4b
dnnl_aCBde4c8b8c4b
dnnl_aCBdef4c8b8c4b
dnnl_BAcde16a16b
dnnl_aCBdef16b16c
dnnl_abdfce	permuted 6D tensor
dnnl_abdefc	permuted 6D tensor
dnnl_ABc16b32a
dnnl_ABc16b64a
dnnl_ABc4b32a4b
dnnl_ABc4b64a4b
dnnl_ABc8b32a2b
dnnl_ABc8b64a2b
dnnl_AB16b16a
dnnl_AB16b32a
dnnl_AB16b64a
dnnl_AB8b16a2b
dnnl_AB8b32a2b
dnnl_AB8b64a2b
dnnl_AB4b16a4b
dnnl_AB4b32a4b
dnnl_AB4b64a4b
dnnl_AB16b16a4b
dnnl_ABcd16b32a
dnnl_ABcd16b64a
dnnl_ABcd4b32a4b
dnnl_ABcd4b64a4b
dnnl_ABcd8b32a2b
dnnl_ABcd8b64a2b
dnnl_ABcde4b32a4b
dnnl_ABcde4b64a4b
dnnl_ABcde16b16a4b
dnnl_ABcde16b16a2b
dnnl_ABcde16b32a
dnnl_ABcde16b64a
dnnl_ABcde8b32a2b
dnnl_ABcde8b64a2b
dnnl_aBCdef16c16b4c
dnnl_aBCdef16c16b2c
dnnl_AB32a32b8a4b
dnnl_AB8a4b
dnnl_AB32a32b8a2b
dnnl_AB8a2b
dnnl_abDc32d
dnnl_abDC32d4c
dnnl_abdEc32e
dnnl_abdEC32e2c
dnnl_abdEC32e4c
dnnl_aBdefC16b4c
dnnl_AcdeB16a4b
dnnl_ABcd16a16b2a
dnnl_ABc16a16b2a
dnnl_aBCd16b16c2b
dnnl_aBCde16b16c2b
dnnl_Acb32a
dnnl_AcB32a2b
dnnl_AcB32a4b
dnnl_Acb48a
dnnl_AcB48a2b
dnnl_AcB48a4b
dnnl_Acb64a
dnnl_AcB64a2b
dnnl_AcB64a4b
dnnl_cBa2b
dnnl_cBa4b
dnnl_aBdc32b
dnnl_aBdC32b2c
dnnl_aBdC32b4c
dnnl_aBdc48b
dnnl_aBdC48b2c
dnnl_aBdC48b4c
dnnl_aBdc64b
dnnl_aBdC64b2c
dnnl_aBdC64b4c
dnnl_adcb
dnnl_adCb2c
dnnl_adCb4c
dnnl_AcdB32a2b
dnnl_AcdB32a4b
dnnl_Acdb48a
dnnl_AcdB48a2b
dnnl_AcdB48a4b
dnnl_Acdb64a
dnnl_AcdB64a2b
dnnl_AcdB64a4b
dnnl_cdBa2b
dnnl_cdBa4b
dnnl_aBdeC32b2c
dnnl_aBdeC32b4c
dnnl_aBdec48b
dnnl_aBdeC48b2c
dnnl_aBdeC48b4c
dnnl_aBdec64b
dnnl_aBdeC64b2c
dnnl_aBdeC64b4c
dnnl_adecb
dnnl_adeCb2c
dnnl_adeCb4c
dnnl_Acdeb32a
dnnl_AcdeB32a2b
dnnl_AcdeB32a4b
dnnl_Acdeb48a
dnnl_AcdeB48a2b
dnnl_AcdeB48a4b
dnnl_Acdeb64a
dnnl_AcdeB64a2b
dnnl_AcdeB64a4b
dnnl_cdeBa2b
dnnl_cdeBa4b
dnnl_aBdefc32b
dnnl_aBdefC32b2c
dnnl_aBdefC32b4c
dnnl_aBdefc48b
dnnl_aBdefC48b2c
dnnl_aBdefC48b4c
dnnl_aBdefc64b
dnnl_aBdefC64b2c
dnnl_aBdefC64b4c
dnnl_adefcb
dnnl_adefCb2c
dnnl_adefCb4c
dnnl_AB16b32a4b
dnnl_AB16b48a4b
dnnl_AB16b64a4b
dnnl_AB16b16a2b
dnnl_AB16b32a2b
dnnl_AB16b48a2b
dnnl_AB16b64a2b
dnnl_ABc16b32a4b
dnnl_ABc16b48a4b
dnnl_ABc16b64a4b
dnnl_ABc16b32a2b
dnnl_ABc16b48a2b
dnnl_ABc16b64a2b
dnnl_ABcd16b32a4b
dnnl_ABcd16b48a4b
dnnl_ABcd16b64a4b
dnnl_ABcd16b32a2b
dnnl_ABcd16b48a2b
dnnl_ABcd16b64a2b
dnnl_ABcde16b32a4b
dnnl_ABcde16b48a4b
dnnl_ABcde16b64a4b
dnnl_ABcde16b32a2b
dnnl_ABcde16b48a2b
dnnl_ABcde16b64a2b
dnnl_ABc32a16b
dnnl_ABcd32a16b
dnnl_ABcde32a16b
dnnl_AB48a16b
dnnl_AB48a32b
dnnl_ABc40a16b
dnnl_ABc40a32b
dnnl_aBC48b16c
dnnl_aBC48b32c
dnnl_ABcd40a16b
dnnl_ABcd40a32b
dnnl_abCd32c
dnnl_abdCe32c
dnnl_abdCE32c2e
dnnl_BA16a16b2a
dnnl_BA16a32b2a
dnnl_BA16a48b2a
dnnl_BA16a64b2a
dnnl_BA16a16b4a
dnnl_BA16a32b4a
dnnl_BA16a48b4a
dnnl_BA16a64b4a
dnnl_ABcd8a2b
dnnl_aBdeC16c16b2c
dnnl_aBdeC16c16b4c
dnnl_aBdefC16c16b2c
dnnl_AcB16b16a2b
dnnl_AcB16b16a4b
dnnl_AcdB16b16a2b
dnnl_AcdB16b16a4b
dnnl_AcdeB16b16a2b
dnnl_aBdefC16c16b4c
dnnl_AcdeB16b16a4b
dnnl_AcB16b32a2b
dnnl_AcB16b32a4b
dnnl_AcB16b48a2b
dnnl_AcB16b48a4b
dnnl_AcB16b64a2b
dnnl_AcB16b64a4b
dnnl_aBdC16c16b2c
dnnl_aBdC16c16b4c
dnnl_aBdC16c32b2c
dnnl_aBdC16c32b4c
dnnl_aBdC16c48b2c
dnnl_aBdC16c48b4c
dnnl_aBdC16c64b2c
dnnl_aBdC16c64b4c
dnnl_AcdB16b32a2b
dnnl_AcdB16b32a4b
dnnl_AcdB16b48a2b
dnnl_AcdB16b48a4b
dnnl_AcdB16b64a2b
dnnl_AcdB16b64a4b
dnnl_aBdeC16c32b2c
dnnl_aBdeC16c32b4c
dnnl_aBdeC16c48b2c
dnnl_aBdeC16c48b4c
dnnl_aBdeC16c64b2c
dnnl_aBdeC16c64b4c
dnnl_AcdeB16b32a2b
dnnl_AcdeB16b32a4b
dnnl_AcdeB16b48a2b
dnnl_AcdeB16b48a4b
dnnl_AcdeB16b64a2b
dnnl_AcdeB16b64a4b
dnnl_aBdefC16c32b2c
dnnl_aBdefC16c32b4c
dnnl_aBdefC16c48b2c
dnnl_aBdefC16c48b4c
dnnl_aBdefC16c64b2c
dnnl_aBdefC16c64b4c
dnnl_decbA16a
dnnl_ABc4a2b
dnnl_ABc8a2b
dnnl_aBCd8b2c
dnnl_ABcde4a2b
dnnl_ABcde8a2b
dnnl_ABcde40a16b
dnnl_ABcde40a32b
dnnl_aBCde8b2c
dnnl_ABcde4a8b8a2b
dnnl_ABcd4a8b8a2b
dnnl_ABc4a8b8a2b
dnnl_aBCdef4b8c8b2c
dnnl_aBCde4b8c8b2c
dnnl_aBCd4b8c8b2c
dnnl_BAcde4b8a8b2a
dnnl_BAcd4b8a8b2a
dnnl_BAc4b8a8b2a
dnnl_aCBdef4c8b8c2b
dnnl_aCBde4c8b8c2b
dnnl_aCBd4c8b8c2b
dnnl_aBCdef8b2c
dnnl_AB32a16b
dnnl_AB32a32b
dnnl_BA4b8a8b2a
dnnl_BA4b8a8b4a
dnnl_aBC32b16c
dnnl_aBC32b32c
dnnl_aCB4c8b8c2b
dnnl_aCB4c8b8c4b
dnnl_ABcd4a2b
dnnl_ABc2b8a16b4a
dnnl_ABcd2b8a16b4a
dnnl_ABcde2b8a16b4a
dnnl_ABc2a8b16a4b
dnnl_ABc2a8b16a2b
dnnl_ABc2b32a8b
dnnl_ABcd2a8b16a4b
dnnl_ABcd2a8b16a2b
dnnl_aCBd2c8b16c2b
dnnl_ABcd2b32a8b
dnnl_aBCd2c8b16c2b
dnnl_ABcde2a8b16a4b
dnnl_ABcde2a8b16a2b
dnnl_aCBde2c8b16c2b
dnnl_ABcde2b32a8b
dnnl_aBC2b8c16b2c
dnnl_aBCd2b8c16b2c
dnnl_aBCde2b8c16b2c
dnnl_aBCdef2b8c16b2c
dnnl_BAcde2b8a16b4a
dnnl_BAcd2b8a16b4a
dnnl_BAc2b8a16b4a
dnnl_BAcde2b8a16b2a
dnnl_BAcd2b8a16b2a
dnnl_BAc2b8a16b2a
dnnl_aBCde2c8b16c2b
dnnl_aBCdef2c8b16c2b
dnnl_aCBdef2c8b16c2b
dnnl_aBCd2b8c16b4c
dnnl_aBCde2b8c16b4c
dnnl_BA4b8a16b2a
dnnl_BA4b8a16b4a
dnnl_aCB4c8b16c2b
dnnl_aCB4c8b16c4b
dnnl_BA16a16b
dnnl_BA16a32b
dnnl_BA16a48b
dnnl_BA16a64b
dnnl_aCB16c2b
dnnl_aCB16c4b
dnnl_BA16b2a
dnnl_BA16b4a
dnnl_aBC16b16c
dnnl_aBC16b32c
dnnl_AB16a16b
dnnl_AB16a32b
dnnl_adbc
dnnl_ABcde16a16b2a
dnnl_aBCdef16b16c2b
dnnl_format_tag_last	Just a sentinel, not real memory format tag. Must be changed after new format tag is added.
dnnl_x	1D tensor, an alias to dnnl_a
dnnl_nc	2D CNN activations tensor, an alias to dnnl_ab
dnnl_cn	2D CNN activations tensor, an alias to dnnl_ba
dnnl_tn	2D RNN statistics tensor, an alias to dnnl_ab
dnnl_nt	2D RNN statistics tensor, an alias to dnnl_ba
dnnl_ncw	3D CNN activations tensor, an alias to dnnl_abc
dnnl_nwc	3D CNN activations tensor, an alias to dnnl_acb
dnnl_nchw	4D CNN activations tensor, an alias to dnnl_abcd
dnnl_nhwc	4D CNN activations tensor, an alias to dnnl_acdb
dnnl_chwn	4D CNN activations tensor, an alias to dnnl_bcda
dnnl_ncdhw	5D CNN activations tensor, an alias to dnnl_abcde
dnnl_ndhwc	5D CNN activations tensor, an alias to dnnl_acdeb
dnnl_oi	2D CNN weights tensor, an alias to dnnl_ab
dnnl_io	2D CNN weights tensor, an alias to dnnl_ba
dnnl_oiw	3D CNN weights tensor, an alias to dnnl_abc
dnnl_owi	3D CNN weights tensor, an alias to dnnl_acb
dnnl_wio	3D CNN weights tensor, an alias to dnnl_cba
dnnl_iwo	3D CNN weights tensor, an alias to dnnl_bca
dnnl_oihw	4D CNN weights tensor, an alias to dnnl_abcd
dnnl_hwio	4D CNN weights tensor, an alias to dnnl_cdba
dnnl_ohwi	4D CNN weights tensor, an alias to dnnl_acdb
dnnl_ihwo	4D CNN weights tensor, an alias to dnnl_bcda
dnnl_iohw	4D CNN weights tensor, an alias to dnnl_bacd
dnnl_oidhw	5D CNN weights tensor, an alias to dnnl_abcde
dnnl_iodhw	5D CNN weights tensor, an alias to dnnl_bacde
dnnl_dhwio	5D CNN weights tensor, an alias to dnnl_cdeba
dnnl_odhwi	5D CNN weights tensor, an alias to dnnl_acdeb
dnnl_idhwo	5D CNN weights tensor, an alias to dnnl_bcdea
dnnl_goiw	4D CNN weights tensor (incl. groups), an alias to dnnl_abcd
dnnl_gowi	4D CNN weights tensor (incl. groups), an alias to dnnl_abdc
dnnl_wigo	4D CNN weights tensor (incl. groups), an alias to dnnl_dcab
dnnl_goihw	5D CNN weights tensor (incl. groups), an alias to dnnl_abcde
dnnl_gohwi	5D CNN weights tensor (incl. groups), an alias to dnnl_abdec
dnnl_hwigo	5D CNN weights tensor (incl. groups), an alias to dnnl_decab
dnnl_giohw	5D CNN weights tensor (incl. groups), an alias to dnnl_acbde
dnnl_goidhw	6D CNN weights tensor (incl. groups), an alias to dnnl_abcdef
dnnl_godhwi	6D CNN weights tensor (incl. groups), an alias to dnnl_abdefc
dnnl_giodhw	6D CNN weights tensor (incl. groups), an alias to dnnl_acbdef
dnnl_dhwigo	6D CNN weights tensor (incl. groups), an alias to dnnl_defcab
dnnl_tnc	3D RNN data tensor in the format (seq_length, batch, input channels), an alias to dnnl_abc.
dnnl_ntc	3D RNN data tensor in the format (batch, seq_length, input channels), an alias to dnnl_bac.
dnnl_ldnc	4D RNN states tensor in the format (num_layers, num_directions, batch, state channels), an alias to dnnl_abcd.
dnnl_ldigo	5D RNN weights tensor in the format (num_layers, num_directions, input_channels, num_gates, output_channels), an alias to dnnl_abcde. For LSTM cells, the gates order is input, forget, candidate and output gate. For GRU cells, the gates order is update, reset and output gate.
dnnl_ldgoi	5D RNN weights tensor in the format (num_layers, num_directions, num_gates, output_channels, input_channels), an alias to dnnl_abdec. For LSTM cells, the gates order is input, forget, candidate and output gate. For GRU cells, the gates order is update, reset and output gate.
dnnl_ldio	4D LSTM projection tensor in the format (num_layers, num_directions, num_channels_in_hidden_state, num_channels_in_recurrent_projection), an alias to dnnl_abcd.
dnnl_ldoi	4D LSTM projection tensor in the format (num_layers, num_directions, num_channels_in_recurrent_projection, num_channels_in_hidden_state), an alias to dnnl_abdc.
dnnl_ldgo	4D RNN bias tensor in the format (num_layers, num_directions, num_gates, output_channels), an alias to dnnl_abcd. For LSTM cells, the gates order is input, forget, candidate and output gate. For GRU cells, the gates order is update, reset and output gate.
dnnl_ldOi32o	5D LSTM projection tensor
dnnl_ldOI32o4i
dnnl_ldIo32i
dnnl_ldgOi32o	6D RNN weights tensor
dnnl_ldgOI32o2i
dnnl_ldgOI32o4i
dnnl_ldgIo32i
dnnl_ldgIO32i2o
dnnl_nCdhw32c	5D CNN activations tensor blocked by channels with block size 32, an alias to dnnl_aBcde32b
dnnl_nCdhw16c	5D CNN activations tensor blocked by channels with block size 16, an alias to dnnl_aBcde16b
dnnl_nCdhw4c	5D CNN activations tensor blocked by channels with block size 4, an alias to dnnl_aBcde4b
dnnl_nCdhw8c	5D CNN activations tensor blocked by channels with block size 8, an alias to dnnl_aBcde8b
dnnl_nChw32c	4D CNN activations tensor blocked by channels with block size 32, an alias to dnnl_aBcd32b
dnnl_nChw16c	4D CNN activations tensor blocked by channels with block size 16, an alias to dnnl_aBcd16b
dnnl_nChw4c	4D CNN activations tensor blocked by channels with block size 4, an alias to dnnl_aBcd4b
dnnl_nChw8c	4D CNN activations tensor blocked by channels with block size 8, an alias to dnnl_aBcd8b
dnnl_nCw32c	3D CNN activations tensor blocked by channels with block size 32, an alias to dnnl_aBc32b
dnnl_nCw16c	3D CNN activations tensor blocked by channels with block size 16, an alias to dnnl_aBc16b
dnnl_nCw4c	3D CNN activations tensor blocked by channels with block size 4, an alias to dnnl_aBc4b
dnnl_nCw8c	3D CNN activations tensor blocked by channels with block size 8, an alias to dnnl_aBc8b
dnnl_NCw16n16c
dnnl_NCdhw16n16c
dnnl_NChw16n16c
dnnl_NCw32n16c
dnnl_NChw32n16c
dnnl_NCdhw32n16c
dnnl_NCw32n32c
dnnl_NChw32n32c
dnnl_NCdhw32n32c
dnnl_OI16i16o
dnnl_OI16i32o
dnnl_OI16i64o
dnnl_OI8i16o2i
dnnl_OI8i32o2i
dnnl_OI8i64o2i
dnnl_OI4i16o4i
dnnl_OI4i32o4i
dnnl_OI4i64o4i
dnnl_OI16i16o4i
dnnl_IOw16o16i
dnnl_IOw16i16o
dnnl_OIw16i16o
dnnl_OIw16i32o
dnnl_OIw16i64o
dnnl_OIw16o16i
dnnl_Oiw16o
dnnl_OIw4i16o4i
dnnl_OIw4i32o4i
dnnl_OIw4i64o4i
dnnl_OIw2i8o4i
dnnl_OIw16i16o4i
dnnl_OIw16i16o2i
dnnl_OIw16o16i2o
dnnl_OIw4i4o
dnnl_OIw4o4i
dnnl_Oiw4o
dnnl_OIw8i16o2i
dnnl_OIw8i32o2i
dnnl_OIw8i64o2i
dnnl_OIw8i8o
dnnl_OIw8o16i2o
dnnl_IOw8o16i2o
dnnl_OIw8o8i
dnnl_OIw8o4i
dnnl_Owi16o
dnnl_OwI16o2i
dnnl_OwI16o4i
dnnl_Owi4o
dnnl_Owi8o
dnnl_IOhw16i16o
dnnl_IOhw16o16i
dnnl_Ohwi16o
dnnl_OhwI16o2i
dnnl_OhwI16o4i
dnnl_Ohwi32o
dnnl_Ohwi4o
dnnl_Ohwi8o
dnnl_OIhw16i16o
dnnl_OIhw16i32o
dnnl_OIhw16i64o
dnnl_OIhw16o16i
dnnl_Oihw16o
dnnl_OIhw4i16o4i
dnnl_OIhw4i32o4i
dnnl_OIhw4i64o4i
dnnl_OIhw16i16o4i
dnnl_OIhw16i16o2i
dnnl_OIhw16o16i2o
dnnl_OIhw4i4o
dnnl_OIhw4o4i
dnnl_Oihw4o
dnnl_OIhw8i16o2i
dnnl_OIhw8i32o2i
dnnl_OIhw8i64o2i
dnnl_OIhw8i8o
dnnl_OIhw8o16i2o
dnnl_OIhw2i8o4i
dnnl_IOhw8o16i2o
dnnl_OIhw8o8i
dnnl_OIhw8o4i
dnnl_Owhi16o
dnnl_Odhwi16o
dnnl_OdhwI16o2i
dnnl_OdhwI16o4i
dnnl_Odhwi4o
dnnl_Odhwi8o
dnnl_OIdhw16i16o
dnnl_OIdhw16i32o
dnnl_OIdhw16i64o
dnnl_OIdhw16o16i
dnnl_Oidhw16o
dnnl_OIdhw4i4o
dnnl_OIdhw4o4i
dnnl_Oidhw4o
dnnl_OIdhw8i16o2i
dnnl_OIdhw8i32o2i
dnnl_OIdhw8i64o2i
dnnl_OIdhw8i8o
dnnl_OIdhw8o16i2o
dnnl_IOdhw8o16i2o
dnnl_OIdhw4i16o4i
dnnl_OIdhw4i32o4i
dnnl_OIdhw4i64o4i
dnnl_OIdhw16i16o4i
dnnl_OIdhw16i16o2i
dnnl_OIdhw2i8o4i
dnnl_OIdhw8o8i
dnnl_OIdhw8o4i
dnnl_IOdhw16i16o
dnnl_OIdhw4o8i8o4i
dnnl_IOdhw16o16i
dnnl_OIdhw16o16i2o
dnnl_Goiw16g
dnnl_Goiw8g
dnnl_Goiw4g
dnnl_gIOw16o16i
dnnl_gIOw16i16o
dnnl_gOIw16i16o
dnnl_gOIw16o16i
dnnl_gOiw16o
dnnl_gOIw4i16o4i
dnnl_gOIw2i8o4i
dnnl_gOIw16i16o4i
dnnl_gOIw16i16o2i
dnnl_gOIw16o16i2o
dnnl_gOIw4i4o
dnnl_gOIw4o4i
dnnl_gOiw4o
dnnl_gOIw8i16o2i
dnnl_gOIw8i8o
dnnl_gOIw8o16i2o
dnnl_gIOw8o16i2o
dnnl_gOIw8o8i
dnnl_gOIw8o4i
dnnl_gOwi16o
dnnl_gOwI16o2i
dnnl_gOwI16o4i
dnnl_gOwi4o
dnnl_gOwi8o
dnnl_Goiw32g
dnnl_gOIw2i4o2i
dnnl_gOIw2o4i2o
dnnl_gOIw4i8o2i
dnnl_gOIw4o8i2o
dnnl_gIOhw16i16o
dnnl_gIOhw16o16i
dnnl_gOhwi16o
dnnl_gOhwI16o2i
dnnl_gOhwI16o4i
dnnl_gOhwi32o
dnnl_gOhwi4o
dnnl_gOhwi8o
dnnl_Goihw16g
dnnl_gOIhw16i16o
dnnl_gOIhw16o16i
dnnl_gOihw16o
dnnl_gOIhw2i8o4i
dnnl_gOIhw4i16o4i
dnnl_gOIhw16i16o4i
dnnl_gOIhw16i16o2i
dnnl_gOIhw16o16i2o
dnnl_gOIhw4i4o
dnnl_gOIhw4o4i
dnnl_gOihw4o
dnnl_Goihw8g
dnnl_Goihw4g
dnnl_gOIhw8i16o2i
dnnl_gOIhw8i8o
dnnl_gOIhw8o16i2o
dnnl_gIOhw8o16i2o
dnnl_gOIhw8o8i
dnnl_gOIhw8o4i
dnnl_Goihw32g
dnnl_gOwhi16o
dnnl_OIw4o8i8o4i
dnnl_OIhw4o8i8o4i
dnnl_IOw4i8o8i4o
dnnl_IOhw4i8o8i4o
dnnl_IOdhw4i8o8i4o
dnnl_OIhw2o8i8o2i
dnnl_gOIw4o8i8o4i
dnnl_gOIhw4o8i8o4i
dnnl_gOIdhw4o8i8o4i
dnnl_gIOw4i8o8i4o
dnnl_gIOhw4i8o8i4o
dnnl_gIOdhw4i8o8i4o
dnnl_gOIhw2o8i8o2i
dnnl_gOIhw2i4o2i
dnnl_gOIhw2o4i2o
dnnl_gOIhw4i8o2i
dnnl_gOIhw4o8i2o
dnnl_gIOdhw16i16o
dnnl_gIOdhw16o16i
dnnl_gOdhwi16o
dnnl_gOdhwI16o2i
dnnl_gOdhwI16o4i
dnnl_gOdhwi4o
dnnl_gOdhwi8o
dnnl_gOIdhw16i16o
dnnl_gOIdhw4i16o4i
dnnl_gOIdhw16i16o4i
dnnl_gOIdhw2i8o4i
dnnl_gOIdhw16i16o2i
dnnl_gOIdhw16o16i
dnnl_gOIdhw16o16i2o
dnnl_gOidhw16o
dnnl_gOIdhw4i4o
dnnl_gOIdhw4o4i
dnnl_gOidhw4o
dnnl_gOIdhw8i16o2i
dnnl_gOIdhw8i8o
dnnl_gOIdhw8o16i2o
dnnl_gIOdhw8o16i2o
dnnl_gOIdhw8o8i
dnnl_gOIdhw8o4i
dnnl_Goidhw16g
dnnl_Goidhw32g
dnnl_gOIdhw2i4o2i
dnnl_gOIdhw4i8o2i
dnnl_gOIdhw2o4i2o
dnnl_gOIdhw4o8i2o
dnnl_Owi32o
dnnl_OwI32o2i
dnnl_OwI32o4i
dnnl_Owi48o
dnnl_OwI48o2i
dnnl_OwI48o4i
dnnl_Owi64o
dnnl_OwI64o2i
dnnl_OwI64o4i
dnnl_wIo2i
dnnl_wIo4i
dnnl_gOwi32o
dnnl_gOwI32o2i
dnnl_gOwI32o4i
dnnl_gOwi48o
dnnl_gOwI48o2i
dnnl_gOwI48o4i
dnnl_gOwi64o
dnnl_gOwI64o2i
dnnl_gOwI64o4i
dnnl_gwio
dnnl_gwIo2i
dnnl_gwIo4i
dnnl_OhwI32o
dnnl_OhwI32o2i
dnnl_OhwI32o4i
dnnl_Ohwi48o
dnnl_OhwI48o2i
dnnl_OhwI48o4i
dnnl_Ohwi64o
dnnl_OhwI64o2i
dnnl_OhwI64o4i
dnnl_hwIo2i
dnnl_hwIo4i
dnnl_gOhwI32o
dnnl_gOhwI32o2i
dnnl_gOhwI32o4i
dnnl_gOhwi48o
dnnl_gOhwI48o2i
dnnl_gOhwI48o4i
dnnl_gOhwi64o
dnnl_gOhwI64o2i
dnnl_gOhwI64o4i
dnnl_ghwio
dnnl_ghwIo2i
dnnl_ghwIo4i
dnnl_Odhwi32o
dnnl_OdhwI32o2i
dnnl_OdhwI32o4i
dnnl_Odhwi48o
dnnl_OdhwI48o2i
dnnl_OdhwI48o4i
dnnl_Odhwi64o
dnnl_OdhwI64o2i
dnnl_OdhwI64o4i
dnnl_dhwIo2i
dnnl_dhwIo4i
dnnl_gOdhwi32o
dnnl_gOdhwI32o2i
dnnl_gOdhwI32o4i
dnnl_gOdhwi48o
dnnl_gOdhwI48o2i
dnnl_gOdhwI48o4i
dnnl_gOdhwi64o
dnnl_gOdhwI64o2i
dnnl_gOdhwI64o4i
dnnl_gdhwio
dnnl_gdhwIo2i
dnnl_gdhwIo4i
dnnl_OI16i32o4i
dnnl_OI16i48o4i
dnnl_OI16i64o4i
dnnl_OI16i16o2i
dnnl_OI16i32o2i
dnnl_OI16i48o2i
dnnl_OI16i64o2i
dnnl_OIw16i32o4i
dnnl_OIw16i48o4i
dnnl_OIw16i64o4i
dnnl_OIw16i32o2i
dnnl_OIw16i48o2i
dnnl_OIw16i64o2i
dnnl_OIhw16i32o4i
dnnl_OIhw16i48o4i
dnnl_OIhw16i64o4i
dnnl_OIhw16i32o2i
dnnl_OIhw16i48o2i
dnnl_OIhw16i64o2i
dnnl_OIdhw16i32o4i
dnnl_OIdhw16i48o4i
dnnl_OIdhw16i64o4i
dnnl_OIdhw16i32o2i
dnnl_OIdhw16i48o2i
dnnl_OIdhw16i64o2i
dnnl_OwI16i16o2i
dnnl_OwI16i16o4i
dnnl_OhwI16i16o2i
dnnl_OhwI16i16o4i
dnnl_OdhwI16i16o2i
dnnl_OdhwI16i16o4i
dnnl_gOwI16i16o2i
dnnl_gOwI16i16o4i
dnnl_gOhwI16i16o2i
dnnl_gOhwI16i16o4i
dnnl_gOdhwI16i16o2i
dnnl_gOdhwI16i16o4i
dnnl_OwI16i32o2i
dnnl_OwI16i32o4i
dnnl_OwI16i48o2i
dnnl_OwI16i48o4i
dnnl_OwI16i64o2i
dnnl_OwI16i64o4i
dnnl_gOwI16i32o2i
dnnl_gOwI16i32o4i
dnnl_gOwI16i48o2i
dnnl_gOwI16i48o4i
dnnl_gOwI16i64o2i
dnnl_gOwI16i64o4i
dnnl_OhwI16i32o2i
dnnl_OhwI16i32o4i
dnnl_OhwI16i48o2i
dnnl_OhwI16i48o4i
dnnl_OhwI16i64o2i
dnnl_OhwI16i64o4i
dnnl_gOhwI16i32o2i
dnnl_gOhwI16i32o4i
dnnl_gOhwI16i48o2i
dnnl_gOhwI16i48o4i
dnnl_gOhwI16i64o2i
dnnl_gOhwI16i64o4i
dnnl_OdhwI16i32o2i
dnnl_OdhwI16i32o4i
dnnl_OdhwI16i48o2i
dnnl_OdhwI16i48o4i
dnnl_OdhwI16i64o2i
dnnl_OdhwI16i64o4i
dnnl_gOdhwI16i32o2i
dnnl_gOdhwI16i32o4i
dnnl_gOdhwI16i48o2i
dnnl_gOdhwI16i48o4i
dnnl_gOdhwI16i64o2i
dnnl_gOdhwI16i64o4i
dnnl_hwioG16g
dnnl_NCdhw40n16c
dnnl_NCw40n16c
dnnl_NChw40n16c
dnnl_NCw40n32c
dnnl_NChw40n32c
dnnl_NCdhw40n32c
dnnl_OIdhw4o8i8o2i
dnnl_OIhw4o8i8o2i
dnnl_OIw4o8i8o2i
dnnl_gOIdhw4o8i8o2i
dnnl_gOIhw4o8i8o2i
dnnl_gOIw4o8i8o2i
dnnl_IOdhw4i8o8i2o
dnnl_IOhw4i8o8i2o
dnnl_IOw4i8o8i2o
dnnl_gIOdhw4i8o8i2o
dnnl_gIOhw4i8o8i2o
dnnl_gIOw4i8o8i2o
dnnl_NCw2c32n8c
dnnl_NChw2c32n8c
dnnl_NCdhw2c32n8c
dnnl_OIw2i8o16i4o
dnnl_OIhw2i8o16i4o
dnnl_OIdhw2i8o16i4o
dnnl_OIw2o8i16o4i
dnnl_OIw2o8i16o2i
dnnl_IOw2i8o16i4o
dnnl_IOw2i8o16i2o
dnnl_OIhw2o8i16o4i
dnnl_OIhw2o8i16o2i
dnnl_IOhw2i8o16i4o
dnnl_IOhw2i8o16i2o
dnnl_OIdhw2o8i16o4i
dnnl_OIdhw2o8i16o2i
dnnl_IOdhw2i8o16i4o
dnnl_IOdhw2i8o16i2o
dnnl_gOIw2o8i16o2i
dnnl_gIOw2i8o16i2o
dnnl_gIOhw2i8o16i2o
dnnl_gIOdhw2i8o16i2o
dnnl_gOIhw2o8i16o2i
dnnl_gOIdhw2o8i16o2i
dnnl_gOIw2o8i16o4i
dnnl_gOIhw2o8i16o4i

dnnl_memory_extra_flags_t

enum dnnl_memory_extra_flags_t

Flags for memory special features.

Enumerator
dnnl_memory_extra_flag_none
dnnl_memory_extra_flag_compensation_conv_s8s8	Indicates the weights have an additional buffer, that depends on the compensation_mask. For instance, in 4D case with the compensation mask equals (1 << 0) the additional buffer would consist of OC values: O[oc : 0,OC] = -128 * SUM(ic : 0,IC; kh : 0,KH; kw : 0,KW){ weights(oc, ic, kh, kw) }
dnnl_memory_extra_flag_scale_adjust
dnnl_memory_extra_flag_rnn_u8s8_compensation
dnnl_memory_extra_flag_gpu_rnn_u8s8_compensation
dnnl_memory_extra_flag_compensation_conv_asymmetric_src
dnnl_memory_extra_flag_rnn_s8s8_compensation

dnnl_rnn_packed_memory_format_t

enum dnnl_rnn_packed_memory_format_t

Enumerator
dnnl_packed_format_undef
dnnl_ldigo_p
dnnl_ldgoi_p
dnnl_ldio_p

dnnl_wino_memory_format_t

enum dnnl_wino_memory_format_t

Winograd-specific formats.

Enumerator
dnnl_wino_undef	Undefined memory format, used for empty memory descriptors.
dnnl_wino_wei_aaOIoi	Internal weights format for 2x3 Winograd.
dnnl_wino_wei_aaOio	Internal weights format for 2x3 Winograd.
dnnl_wino_wei_aaOBiOo	Internal weights format for 2x3 Winograd.
dnnl_wino_wei_OBaaIBOIio	Internal weights format for 4x3 Winograd.

Function Documentation

dnnl_data_type_size()

size_t DNNL_API dnnl_data_type_size

(

dnnl_data_type_t

data_type

)

Returns the size of data type.

Parameters

data_type

Data type.

Returns

The number of bytes occupied by data type.

dnnl_memory_create()

dnnl_status_t DNNL_API dnnl_memory_create	(	dnnl_memory_t *	memory,
		const dnnl_memory_desc_t *	memory_desc,
		dnnl_engine_t	engine,
		void *	handle
	)

Creates a memory object.

Unless handle is equal to DNNL_MEMORY_NONE, the constructed memory object will have the underlying buffer set. In this case, the buffer will be initialized as if dnnl_memory_set_data_handle() had been called.

See also

dnnl_memory_set_data_handle()

Parameters

memory	Output memory object.
memory_desc	Memory descriptor.
engine	Engine to use.
handle	Handle of the memory buffer to use as an underlying storage. A pointer to the user-allocated buffer. In this case the library doesn't own the buffer. The DNNL_MEMORY_ALLOCATE special value. Instructs the library to allocate the buffer for the memory object. In this case the library owns the buffer. DNNL_MEMORY_NONE to create dnnl_memory without an underlying buffer.

Returns

dnnl_success on success and a status describing the error otherwise.

dnnl_memory_desc_equal()

int DNNL_API dnnl_memory_desc_equal	(	const dnnl_memory_desc_t *	lhs,
		const dnnl_memory_desc_t *	rhs
	)

Compares two memory descriptors.

Use this function to identify whether a reorder is required between the two memories

Parameters

lhs	Left-hand side of the comparison.
rhs	Right-hand side of the comparison.

Returns

1 if the descriptors are the same.

0 if the descriptors are different.

dnnl_memory_desc_get_size()

size_t DNNL_API dnnl_memory_desc_get_size

(

const dnnl_memory_desc_t *

memory_desc

)

Returns the size of a memory descriptor.

Parameters

memory_desc

Memory descriptor.

Returns

The number of bytes required for memory described by a memory descriptor.

dnnl_memory_desc_init_by_strides()

dnnl_status_t DNNL_API dnnl_memory_desc_init_by_strides	(	dnnl_memory_desc_t *	memory_desc,
		int	ndims,
		const dnnl_dims_t	dims,
		dnnl_data_type_t	data_type,
		const dnnl_dims_t	strides
	)

Initializes a memory descriptor using dimensions and strides.

Note

As always, the logical order of dimensions corresponds to the abc... format tag, and the physical meaning of the dimensions depends on both the primitive that consumes the memory and the context of that consumption.

Parameters

memory_desc	Output memory descriptor.
ndims	Number of dimensions
dims	Array of dimensions.
data_type	Elements data type.
strides	Strides in each dimension.

Returns

dnnl_success on success and a status describing the error otherwise.

dnnl_memory_desc_init_by_tag()

dnnl_status_t DNNL_API dnnl_memory_desc_init_by_tag	(	dnnl_memory_desc_t *	memory_desc,
		int	ndims,
		const dnnl_dims_t	dims,
		dnnl_data_type_t	data_type,
		dnnl_format_tag_t	tag
	)

Initializes a memory descriptor using dimensions and memory format tag.

Note

As always, the logical order of dimensions corresponds to the abc... format tag, and the physical meaning of the dimensions depends on both the primitive that consumes the memory and the context of that consumption.

Parameters

memory_desc	Output memory descriptor.
ndims	Number of dimensions
dims	Array of dimensions.
data_type	Elements data type.
tag	Memory format tag. Can be dnnl_format_tag_any which would allow a primitive to chose the final memory format. In this case the format_kind field of the memory descriptor would be set to dnnl_format_kind_any.

Returns

dnnl_success on success and a status describing the error otherwise.

dnnl_memory_desc_init_submemory()

dnnl_status_t DNNL_API dnnl_memory_desc_init_submemory	(	dnnl_memory_desc_t *	memory_desc,
		const dnnl_memory_desc_t *	parent_memory_desc,
		const dnnl_dims_t	dims,
		const dnnl_dims_t	offsets
	)

Initializes a memory descriptor for a region inside an area described by an existing memory descriptor.

Warning

Some combinations of physical memory layout and/or offsets or dims may result in a failure to create a submemory.

Parameters

memory_desc	Output memory descriptor.
parent_memory_desc	An existing memory descriptor.
dims	Sizes of the region.
offsets	Offsets to the region from the encompassing memory object in each dimension

Returns

dnnl_success on success and a status describing the error otherwise.

dnnl_memory_desc_permute_axes()

dnnl_status_t DNNL_API dnnl_memory_desc_permute_axes	(	dnnl_memory_desc_t *	out_memory_desc,
		const dnnl_memory_desc_t *	in_memory_desc,
		const int *	permutation
	)

Initializes a memory descriptor by permuting axes in an existing one.

The physical memory layout representation is adjusted accordingly to maintain the consistency between the logical and physical parts of the memory descriptor.

The new memory descriptor inherits the data type. This operation is valid only for memory descriptors that have format_kind set to dnnl_blocked or dnnl_format_kind_any.

The logical axes will be permuted in the following manner:

for (i: 0 .. in_memory_desc->ndims)
    out_memory_desc->dims[permutation[i]] = in_memory_desc->dims[i];

Example:

dnnl_memory_desc_t in_md, out_md, expect_out_md;
 
const int permutation[] = {1, 0}; // swap the first and the second axes
 
dnnl_dims_t in_dims = {2, 3}, out_dims = {3, 2};
dnnl_format_tag_t in_tag = dnnl_ab, out_tag = dnnl_ba;
 
dnnl_memory_desc_init_by_tag(
        &in_md, 2, in_dims, data_type, in_tag);
dnnl_memory_desc_init_by_tag(
        &expect_out_md, 2, out_dims, data_type, out_tag);
 
dnnl_memory_desc_permute_axes(&out_md, in_md, permutation);
assert(dnnl_memory_desc_equal(&out_md, &expect_out_md));

Parameters

out_memory_desc	Output memory descriptor.
in_memory_desc	An existing memory descriptor. Must have format_kind set to dnnl_blocked or dnnl_format_kind_any.
permutation	Axes permutation (of size in_memory_desc->ndims).

Returns

dnnl_success on success and a status describing the error otherwise.

dnnl_memory_desc_reshape()

dnnl_status_t DNNL_API dnnl_memory_desc_reshape	(	dnnl_memory_desc_t *	out_memory_desc,
		const dnnl_memory_desc_t *	in_memory_desc,
		int	ndims,
		const dnnl_dims_t	dims
	)

Initializes a memory descriptor by reshaping an existing one. The new memory descriptor inherits the data type. This operation is valid only for memory descriptors that have format_kind set to dnnl_blocked or dnnl_format_kind_any.

The operation ensures the transformation of the physical memory format corresponds to the transformation of the logical dimensions. If such transformation is impossible, the function returns dnnl_invalid_arguments.

The reshape operation can be described as a combination of the following basic operations:

1. Add a dimension of size 1. This is always possible.
2. Remove a dimension of size 1. This is possible only if the dimension has no padding (i.e. padded_dims[dim] == dims[dim] && dims[dim] == 1).
3. Split a dimension into multiple ones. This is possible only if the size of the dimension is exactly equal to the product of the split ones and the dimension does not have padding (i.e. padded_dims[dim] = dims[dim]).
4. Joining multiple consecutive dimensions into a single one. As in the cases above, this requires that the dimensions do not have padding and that the memory format is such that in physical memory these dimensions are dense and have the same order as their logical counterparts. This also assumes that these dimensions are not blocked.
   • Here, dense means: stride for dim[i] == (stride for dim[i + 1]) * dim[i + 1];
   • And same order means: i < j if and only if stride for dim[j] <= stride for dim[i].

Warning

Some combinations of physical memory layout and/or offsets or dimensions may result in a failure to make a reshape.

Parameters

out_memory_desc	Output memory descriptor.
in_memory_desc	An existing memory descriptor. Must have format_kind set to dnnl_blocked or dnnl_format_kind_any.
ndims	Number of dimensions for the output memory descriptor.
dims	Dimensions for the output memory descriptor.

Returns

dnnl_success on success and a status describing the error otherwise.

dnnl_memory_destroy()

dnnl_status_t DNNL_API dnnl_memory_destroy

(

dnnl_memory_t

memory

)

Destroys a memory object.

Parameters

memory

Memory object to destroy.

Returns

dnnl_success on success and a status describing the error otherwise.

dnnl_memory_get_data_handle()

dnnl_status_t DNNL_API dnnl_memory_get_data_handle	(	const_dnnl_memory_t	memory,
		void **	handle
	)

Returns memory object's data handle.

Parameters

memory	Memory object.
handle	Output data handle. For the CPU engine, the data handle is a pointer to the actual data. For OpenCL it is a cl_mem.

Returns

dnnl_success on success and a status describing the error otherwise.

dnnl_memory_get_engine()

dnnl_status_t DNNL_API dnnl_memory_get_engine	(	const_dnnl_memory_t	memory,
		dnnl_engine_t *	engine
	)

Returns the engine of a memory object.

Parameters

memory	Memory object.
engine	Output engine on which the memory is located.

Returns

dnnl_success on success and a status describing the error otherwise.

dnnl_memory_get_memory_desc()

dnnl_status_t DNNL_API dnnl_memory_get_memory_desc	(	const_dnnl_memory_t	memory,
		const dnnl_memory_desc_t **	memory_desc
	)

Returns the memory descriptor for a memory object.

Parameters

memory	Memory object.
memory_desc	Output memory descriptor (a copy).

Returns

dnnl_success on success and a status describing the error otherwise.

dnnl_memory_map_data()

dnnl_status_t DNNL_API dnnl_memory_map_data	(	const_dnnl_memory_t	memory,
		void **	mapped_ptr
	)

Maps a memory object and returns a host-side pointer to a memory buffer with a copy of its contents.

Mapping enables explicit direct access to memory contents for the engines that do not support it implicitly.

Mapping is an exclusive operation - a memory object cannot be used in other operations until this memory object is unmapped.

Note

Any primitives working with memory should be completed before the memory is mapped. Use dnnl_stream_wait to synchronize the corresponding execution stream.

The dnnl_memory_map_data() and dnnl_memory_unmap_data() functions are mainly provided for debug and testing purposes, and their performance may be suboptimal.

Parameters

memory	Memory object.
mapped_ptr	Output pointer to the mapped buffer.

Returns

dnnl_success on success and a status describing the error otherwise.

dnnl_memory_set_data_handle()

dnnl_status_t DNNL_API dnnl_memory_set_data_handle	(	dnnl_memory_t	memory,
		void *	handle
	)

Sets the underlying memory buffer.

See the description of dnnl_memory_set_data_handle_v2() for more details.

Parameters

memory	Memory object.
handle	Data handle. For the CPU engine, the data handle is a pointer to the actual data. For OpenCL it is a cl_mem.

Returns

dnnl_success on success and a status describing the error otherwise.

dnnl_memory_set_data_handle_v2()

dnnl_status_t DNNL_API dnnl_memory_set_data_handle_v2	(	dnnl_memory_t	memory,
		void *	handle,
		dnnl_stream_t	stream
	)

Sets the underlying memory buffer.

Parameters

memory	Memory object.
handle	Data handle. For the CPU engine, the data handle is a pointer to the actual data. For OpenCL it is a cl_mem.
stream	Stream to use to execute padding in.

Returns

dnnl_success on success and a status describing the error otherwise.

dnnl_memory_unmap_data()

dnnl_status_t DNNL_API dnnl_memory_unmap_data	(	const_dnnl_memory_t	memory,
		void *	mapped_ptr
	)

Unmaps a memory object and writes back any changes made to the previously mapped memory buffer. The pointer to the mapped buffer must be obtained via the dnnl_memory_map_data() call.

Note

The dnnl_memory_map_data() and dnnl_memory_unmap_data() functions are mainly provided for debug and testing purposes, and their performance may be suboptimal.

Parameters

memory	Memory object.
mapped_ptr	Pointer to the mapped buffer that must have been obtained using the dnnl_memory_map_data() function.

Returns

dnnl_success on success and a status describing the error otherwise.