Generic

Classes
struct	mkldnn_version_t
	Version type. More...

Enumerations
enum	mkldnn_status_t {   mkldnn_success = 0, mkldnn_out_of_memory = 1, mkldnn_invalid_arguments = 2, mkldnn_unimplemented = 3,   mkldnn_iterator_ends = 4, mkldnn_runtime_error = 5, mkldnn_not_required = 6 }
	Status values returned by the library functions. More...
enum	mkldnn_data_type_t {   mkldnn_data_type_undef = 0, mkldnn_f16 = 1, mkldnn_bf16 = 2, mkldnn_f32 = 3,   mkldnn_s32 = 4, mkldnn_s8 = 5, mkldnn_u8 = 6 }
	Data type specification. More...
enum	mkldnn_format_kind_t {   mkldnn_format_kind_undef = 0, mkldnn_format_kind_any, mkldnn_blocked, mkldnn_format_kind_wino,   mkldnn_format_kind_rnn_packed }
	Memory format kind. More...
enum	mkldnn_format_tag_t {   mkldnn_format_tag_undef = 0, mkldnn_format_tag_any, mkldnn_a, mkldnn_ab,   mkldnn_abc, mkldnn_abcd, mkldnn_abcde, mkldnn_abcdef,   mkldnn_abdec, mkldnn_acb, mkldnn_acbde, mkldnn_acdb,   mkldnn_acdeb, mkldnn_ba, mkldnn_bac, mkldnn_bacd,   mkldnn_bca, mkldnn_bcda, mkldnn_bcdea, mkldnn_cba,   mkldnn_cdba, mkldnn_cdeba, mkldnn_decab , mkldnn_aBc16b ,   mkldnn_aBc4b , mkldnn_aBc8b , mkldnn_aBcd16b , mkldnn_aBcd4b ,   mkldnn_aBcd8b , mkldnn_ABcd8b8a , mkldnn_aBcde16b , mkldnn_aBcde4b ,   mkldnn_aBcde8b , mkldnn_aBcdef16b , mkldnn_aBcdef4b , mkldnn_format_tag_last,   mkldnn_x = mkldnn_a, mkldnn_nc = mkldnn_ab, mkldnn_cn = mkldnn_ba, mkldnn_ncw = mkldnn_abc,   mkldnn_nwc = mkldnn_acb, mkldnn_nchw = mkldnn_abcd, mkldnn_nhwc = mkldnn_acdb, mkldnn_chwn = mkldnn_bcda,   mkldnn_ncdhw = mkldnn_abcde, mkldnn_ndhwc = mkldnn_acdeb, mkldnn_oi = mkldnn_ab, mkldnn_io = mkldnn_ba,   mkldnn_oiw = mkldnn_abc, mkldnn_owi = mkldnn_acb, mkldnn_wio = mkldnn_cba, mkldnn_iwo = mkldnn_bca,   mkldnn_oihw = mkldnn_abcd, mkldnn_hwio = mkldnn_cdba, mkldnn_ohwi = mkldnn_acdb, mkldnn_ihwo = mkldnn_bcda,   mkldnn_iohw = mkldnn_bacd, mkldnn_oidhw = mkldnn_abcde, mkldnn_dhwio = mkldnn_cdeba, mkldnn_odhwi = mkldnn_acdeb,   mkldnn_idhwo = mkldnn_bcdea, mkldnn_goiw = mkldnn_abcd, mkldnn_goihw = mkldnn_abcde, mkldnn_hwigo = mkldnn_decab,   mkldnn_giohw = mkldnn_acbde, mkldnn_goidhw = mkldnn_abcdef, mkldnn_tnc = mkldnn_abc, mkldnn_ntc = mkldnn_bac,   mkldnn_ldnc = mkldnn_abcd, mkldnn_ldigo = mkldnn_abcde, mkldnn_ldgoi = mkldnn_abdec, mkldnn_ldgo = mkldnn_abcd,   mkldnn_nCdhw16c = mkldnn_aBcde16b, mkldnn_nCdhw4c = mkldnn_aBcde4b, mkldnn_nCdhw8c = mkldnn_aBcde8b, mkldnn_nChw16c = mkldnn_aBcd16b,   mkldnn_nChw4c = mkldnn_aBcd4b, mkldnn_nChw8c = mkldnn_aBcd8b, mkldnn_nCw16c = mkldnn_aBc16b, mkldnn_nCw4c = mkldnn_aBc4b,   mkldnn_nCw8c = mkldnn_aBc8b }
	Memory format tag specification. More...
enum	mkldnn_prop_kind_t {   mkldnn_prop_kind_undef = 0, mkldnn_forward_training = 64, mkldnn_forward_inference = 96, mkldnn_forward_scoring = mkldnn_forward_inference,   mkldnn_forward = mkldnn_forward_training, mkldnn_backward = 128, mkldnn_backward_data = 160, mkldnn_backward_weights = 192,   mkldnn_backward_bias = 193 }
	Kinds of propagation. More...
enum	mkldnn_primitive_kind_t {   mkldnn_undefined_primitive, mkldnn_reorder, mkldnn_shuffle, mkldnn_concat,   mkldnn_sum, mkldnn_convolution, mkldnn_deconvolution, mkldnn_eltwise,   mkldnn_softmax, mkldnn_pooling, mkldnn_lrn, mkldnn_batch_normalization,   mkldnn_inner_product, mkldnn_rnn, mkldnn_gemm }
	Kinds of primitives. More...
enum	mkldnn_alg_kind_t { ,   mkldnn_convolution_direct = 0x1, mkldnn_convolution_winograd = 0x2, mkldnn_convolution_auto = 0x3, mkldnn_deconvolution_direct = 0xa,   mkldnn_deconvolution_winograd = 0xb, mkldnn_eltwise_relu = 0x1f, mkldnn_eltwise_tanh = 0x2f, mkldnn_eltwise_elu = 0x3f,   mkldnn_eltwise_square = 0x4f, mkldnn_eltwise_abs = 0x5f, mkldnn_eltwise_sqrt = 0x6f, mkldnn_eltwise_linear = 0x7f,   mkldnn_eltwise_bounded_relu = 0x8f, mkldnn_eltwise_soft_relu = 0x9f, mkldnn_eltwise_logistic = 0xaf, mkldnn_eltwise_exp = 0xbf,   mkldnn_eltwise_gelu = 0xcf, mkldnn_pooling_max = 0x1ff, mkldnn_pooling_avg_include_padding = 0x2ff, mkldnn_pooling_avg_exclude_padding = 0x3ff ,   mkldnn_lrn_across_channels = 0xaff, mkldnn_lrn_within_channel = 0xbff, mkldnn_vanilla_rnn = 0x1fff, mkldnn_vanilla_lstm = 0x2fff,   mkldnn_vanilla_gru = 0x3fff, mkldnn_lbr_gru = 0x4fff }
	Kinds of algorithms. More...
enum	mkldnn_normalization_flags_t { mkldnn_use_global_stats = 0x1U, mkldnn_use_scaleshift = 0x2U, mkldnn_fuse_norm_relu = 0x4U }
	Flags for batch normalization primitive. More...

Detailed Description

Enumeration Type Documentation

mkldnn_status_t

enum mkldnn_status_t

Status values returned by the library functions.

Enumerator
mkldnn_success	The operation was successful.
mkldnn_out_of_memory	The operation failed due to an out-of-memory condition.
mkldnn_invalid_arguments	The operation failed because of incorrect function arguments.
mkldnn_unimplemented	The operation failed because requested functionality is not implemented.
mkldnn_iterator_ends	Primitive iterator passed over last primitive descriptor.
mkldnn_runtime_error	Primitive or engine failed on execution.
mkldnn_not_required	Queried element is not required for given primitive.

mkldnn_data_type_t

enum mkldnn_data_type_t

Data type specification.

Enumerator
mkldnn_data_type_undef	Undefined data type, used for empty memory descriptors.
mkldnn_f16	16-bit/half-precision floating point.
mkldnn_bf16	non-standard 16-bit (bfloat16 w/ 7 bit mantissa) floating point.
mkldnn_f32	32-bit/single-precision floating point.
mkldnn_s32	32-bit signed integer.
mkldnn_s8	8-bit signed integer.
mkldnn_u8	8-bit unsigned integer.

mkldnn_format_kind_t

enum mkldnn_format_kind_t

Memory format kind.

Enumerator
mkldnn_format_kind_undef	Undefined memory format kind, used for empty memory descriptors.
mkldnn_format_kind_any	Unspecified format kind. The primitive selects a format automatically.
mkldnn_blocked	A tensor in a generic format described by the stride and blocking values in each dimension. See mkldnn_blocking_desc_t for more information.
mkldnn_format_kind_wino	Weights format used in 8bit Winograd convolution.
mkldnn_format_kind_rnn_packed	Packed weights format used in RNN.

mkldnn_format_tag_t

enum mkldnn_format_tag_t

Memory format tag specification.

Intel MKL-DNN 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 mkldnn_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 mkldnn_ncdhw format tag) or NDHWC (corresponds to mkldnn_ndhwc format tag).

int batch = 2, channels = 16, depth = 13, height = 13, width = 13;

int ndims = 5; // 5D tensor
mkldnn_dims_t dims = {batch, channels, depth, height, width};
mkldnn_memory_desc_t data_in_ncdhw;
mkldnn_memory_desc_init_by_tag(
     &data_in_ncdhw, 5, dims, mkldnn_f32, mkldnn_ncdhw);

// note that in both cases dims passed are the same
mkldnn_memory_desc_t data_in_ndhwc;
mkldnn_memory_desc_init_by_tag(
     &data_in_ndhwc, 5, dims, mkldnn_f32, mkldnn_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, mkldnn_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, mkldnn_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, mkldnn_nc is used to denote 2D CNN activations tensor memory format, where channels are the innermost dimension and batch is an outermost one. Moreover, mkldnn_nc is just an alias to mkldnn_ab, since for Intel MKL-DNN 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: mkldnn_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

Understanding Memory Formats

Enumerator
mkldnn_format_tag_undef	Undefined memory format tag.
mkldnn_format_tag_any	Undefined memory format tag. The primitive selects a format automatically.
mkldnn_a	plain 1D tensor
mkldnn_ab	plain 2D tensor
mkldnn_abc	plain 3D tensor
mkldnn_abcd	plain 4D tensor
mkldnn_abcde	plain 5D tensor
mkldnn_abcdef	plain 6D tensor
mkldnn_abdec	permuted 5D tensor
mkldnn_acb	permuted 3D tensor
mkldnn_acbde	permuted 5D tensor
mkldnn_acdb	permuted 4D tensor
mkldnn_acdeb	permuted 5D tensor
mkldnn_ba	permuted 2D tensor
mkldnn_bac	permuted 3D tensor
mkldnn_bacd	permuted 4D tensor
mkldnn_bca	permuted 3D tensor
mkldnn_bcda	permuted 4D tensor
mkldnn_bcdea	permuted 5D tensor
mkldnn_cba	permuted 3D tensor
mkldnn_cdba	permuted 4D tensor
mkldnn_cdeba	permuted 5D tensor
mkldnn_decab	permuted 5D tensor
mkldnn_aBc16b	3D tensor blocked by 2nd dimension with block size 16
mkldnn_aBc4b	3D tensor blocked by 2nd dimension with block size 4
mkldnn_aBc8b	3D tensor blocked by 2nd dimension with block size 8
mkldnn_aBcd16b	4D tensor blocked by 2nd dimension with block size 16
mkldnn_aBcd4b	4D tensor blocked by 2nd dimension with block size 4
mkldnn_aBcd8b	4D tensor blocked by 2nd dimension with block size 8
mkldnn_ABcd8b8a	4D tensor blocked by 1st and 2nd dimension with block size 8
mkldnn_aBcde16b	5D tensor blocked by 2nd dimension with block size 16
mkldnn_aBcde4b	5D tensor blocked by 2nd dimension with block size 4
mkldnn_aBcde8b	5D tensor blocked by 2nd dimension with block size 8
mkldnn_aBcdef16b	6D tensor blocked by 2nd dimension with block size 16
mkldnn_aBcdef4b	6D tensor blocked by 2nd dimension with block size 4
mkldnn_format_tag_last	Just a sentinel, not real memory format tag. Must be changed after new format tag is added.
mkldnn_x	1D tensor, an alias to mkldnn_a
mkldnn_nc	2D CNN activations tensor, an alias to mkldnn_ab
mkldnn_cn	2D CNN activations tensor, an alias to mkldnn_ba
mkldnn_ncw	3D CNN activations tensor, an alias to mkldnn_abc
mkldnn_nwc	3D CNN activations tensor, an alias to mkldnn_acb
mkldnn_nchw	4D CNN activations tensor, an alias to mkldnn_abcd
mkldnn_nhwc	4D CNN activations tensor, an alias to mkldnn_acdb
mkldnn_chwn	4D CNN activations tensor, an alias to mkldnn_bcda
mkldnn_ncdhw	5D CNN activations tensor, an alias to mkldnn_abcde
mkldnn_ndhwc	5D CNN activations tensor, an alias to mkldnn_acdeb
mkldnn_oi	2D CNN weights tensor, an alias to mkldnn_ab
mkldnn_io	2D CNN weights tensor, an alias to mkldnn_ba
mkldnn_oiw	3D CNN weights tensor, an alias to mkldnn_abc
mkldnn_owi	3D CNN weights tensor, an alias to mkldnn_acb
mkldnn_wio	3D CNN weights tensor, an alias to mkldnn_cba
mkldnn_iwo	3D CNN weights tensor, an alias to mkldnn_bca
mkldnn_oihw	4D CNN weights tensor, an alias to mkldnn_abcd
mkldnn_hwio	4D CNN weights tensor, an alias to mkldnn_cdba
mkldnn_ohwi	4D CNN weights tensor, an alias to mkldnn_acdb
mkldnn_ihwo	4D CNN weights tensor, an alias to mkldnn_bcda
mkldnn_iohw	4D CNN weights tensor, an alias to mkldnn_bacd
mkldnn_oidhw	5D CNN weights tensor, an alias to mkldnn_abcde
mkldnn_dhwio	5D CNN weights tensor, an alias to mkldnn_cdeba
mkldnn_odhwi	5D CNN weights tensor, an alias to mkldnn_acdeb
mkldnn_idhwo	5D CNN weights tensor, an alias to mkldnn_bcdea
mkldnn_goiw	4D CNN weights tensor (incl. groups), an alias to mkldnn_abcd
mkldnn_goihw	5D CNN weights tensor (incl. groups), an alias to mkldnn_abcde
mkldnn_hwigo	5D CNN weights tensor (incl. groups), an alias to mkldnn_decab
mkldnn_giohw	5D CNN weights tensor (incl. groups), an alias to mkldnn_acbde
mkldnn_goidhw	6D CNN weights tensor (incl. groups), an alias to mkldnn_abcdef
mkldnn_tnc	3D RNN data tensor in the format (seq_length, batch, input channels).
mkldnn_ntc	3D RNN data tensor in the format (batch, seq_length, input channels).
mkldnn_ldnc	4D RNN states tensor in the format (num_layers, num_directions, batch, state channels).
mkldnn_ldigo	5D RNN weights tensor in the format (num_layers, num_directions, input_channels, num_gates, output_channels). 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.
mkldnn_ldgoi	5D RNN weights tensor in the format (num_layers, num_directions, num_gates, output_channels, input_channels). 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.
mkldnn_ldgo	4D RNN bias tensor in the format (num_layers, num_directions, num_gates, output_channels). 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.
mkldnn_nCdhw16c	5D CNN activations tensor blocked by channels with block size 16, an alias to mkldnn_aBcde16b
mkldnn_nCdhw4c	5D CNN activations tensor blocked by channels with block size 4, an alias to mkldnn_aBcde4b
mkldnn_nCdhw8c	5D CNN activations tensor blocked by channels with block size 8, an alias to mkldnn_aBcde8b
mkldnn_nChw16c	4D CNN activations tensor blocked by channels with block size 16, an alias to mkldnn_aBcd16b
mkldnn_nChw4c	4D CNN activations tensor blocked by channels with block size 4, an alias to mkldnn_aBcd4b
mkldnn_nChw8c	4D CNN activations tensor blocked by channels with block size 8, an alias to mkldnn_aBcd8b
mkldnn_nCw16c	3D CNN activations tensor blocked by channels with block size 16, an alias to mkldnn_aBc16b
mkldnn_nCw4c	3D CNN activations tensor blocked by channels with block size 4, an alias to mkldnn_aBc4b
mkldnn_nCw8c	3D CNN activations tensor blocked by channels with block size 8, an alias to mkldnn_aBc8b

mkldnn_prop_kind_t

enum mkldnn_prop_kind_t

Kinds of propagation.

Enumerator
mkldnn_prop_kind_undef	Undefined propagation type.
mkldnn_forward_training	Forward data propagation (training mode). In this mode primitives perform computations necessary for subsequent backward propagation.
mkldnn_forward_inference	Forward data propagation (inference mode). In this mode primitives perform only computations that are necessary for inference and omit computations that are necessary only for backward propagation.
mkldnn_forward_scoring	Forward data propagation (alias for mkldnn_forward_inference).
mkldnn_forward	Forward data propagation (alias for mkldnn_forward_training).
mkldnn_backward	Backward propagation (with respect to all parameters).
mkldnn_backward_data	Backward data propagation.
mkldnn_backward_weights	Backward weights propagation.
mkldnn_backward_bias	Backward bias propagation.

mkldnn_primitive_kind_t

enum mkldnn_primitive_kind_t

Kinds of primitives.

Used to implement a way to extend the library with new primitives without changing the ABI.

Enumerator
mkldnn_undefined_primitive	Undefined primitive.
mkldnn_reorder	A reorder primitive.
mkldnn_shuffle	A shuffle primitive.
mkldnn_concat	A (out-of-place) concat primitive.
mkldnn_sum	A sum primitive.
mkldnn_convolution	A convolution primitive.
mkldnn_deconvolution	A deconvolution primitive.
mkldnn_eltwise	An element-wise primitive.
mkldnn_softmax	A softmax primitive.
mkldnn_pooling	A pooling primitive.
mkldnn_lrn	An LRN primitive.
mkldnn_batch_normalization	An batch normalization primitive.
mkldnn_inner_product	An inner product primitive.
mkldnn_rnn	A rnn primitive.
mkldnn_gemm	A matrix multiplication primitive.

mkldnn_alg_kind_t

enum mkldnn_alg_kind_t

Kinds of algorithms.

Enumerator
mkldnn_convolution_direct	Direct convolution.
mkldnn_convolution_winograd	Winograd convolution.
mkldnn_convolution_auto	Convolution algorithm(either direct or Winograd) is chosen just in time.
mkldnn_deconvolution_direct	Direct deconvolution.
mkldnn_deconvolution_winograd	Winograd deconvolution.
mkldnn_eltwise_relu	Eltwise: ReLU.
mkldnn_eltwise_tanh	Eltwise: hyperbolic tangent non-linearity (tanh)
mkldnn_eltwise_elu	Eltwise: parametric exponential linear unit (elu)
mkldnn_eltwise_square	Eltwise: square.
mkldnn_eltwise_abs	Eltwise: abs.
mkldnn_eltwise_sqrt	Eltwise: square root.
mkldnn_eltwise_linear	Eltwise: linear.
mkldnn_eltwise_bounded_relu	Eltwise: bounded_relu.
mkldnn_eltwise_soft_relu	Eltwise: soft_relu.
mkldnn_eltwise_logistic	Eltwise: logistic.
mkldnn_eltwise_exp	Eltwise: exponent.
mkldnn_eltwise_gelu	Eltwise: gelu. Note Tanh approximation formula is used to approximate cumulative distribution function of a Gaussian
mkldnn_pooling_max	Max pooling.
mkldnn_pooling_avg_include_padding	Average pooling include padding.
mkldnn_pooling_avg_exclude_padding	Average pooling exclude padding.
mkldnn_lrn_across_channels	Local response normalization (LRN) across multiple channels.
mkldnn_lrn_within_channel	LRN within a single channel.
mkldnn_vanilla_rnn	RNN cell.
mkldnn_vanilla_lstm	LSTM cell.
mkldnn_vanilla_gru	GRU cell.
mkldnn_lbr_gru	GRU cell with linear before reset. Modification of original GRU cell. Differs from mkldnn_vanilla_gru in how the new memory gate is calculated: \[ c_t = tanh(W_c*x_t + b_{c_x} + r_t*(U_c*h_{t-1}+b_{c_h})) \] Primitive expects 4 biases on input: \([b_{u}, b_{r}, b_{c_x}, b_{c_h}]\)

mkldnn_normalization_flags_t

enum mkldnn_normalization_flags_t

Flags for batch normalization primitive.

Enumerator
mkldnn_use_global_stats	Use global statistics. If specified on forward propagation use mean and variance provided by user (input) on backward propagation reduces the amount of computations, since mean and variance are considered as constants If not specified: on forward propagation mean and variance are computed and stored in output on backward propagation compute full derivative wrt to data
mkldnn_use_scaleshift	Use scale and shift parameters. If specified: on forward propagation use scale and shift (aka scale and bias) for the batch normalization results on backward propagation (for prop_kind == mkldnn_backward) compute diff wrt to scale and shift (hence one extra output used) If no specified: on backward propagation prop_kind == mkldnn_backward_data has the same behavior as prop_kind == mkldnn_backward
mkldnn_fuse_norm_relu	Fuse with ReLU. If specified: on inference this option behaves the same as if the primitive were fused with ReLU via post ops API on training primitive requires workspace (required to be able to perform backward pass)