Generic

Enumerations
enum	mkldnn_status_t {   mkldnn_success = 0, mkldnn_out_of_memory = 1, mkldnn_try_again = 2, mkldnn_invalid_arguments = 3,   mkldnn_not_ready = 4, mkldnn_unimplemented = 5, mkldnn_iterator_ends = 6, mkldnn_runtime_error = 7,   mkldnn_not_required = 8 }
	Status values returned by Intel(R) MKL-DNN functions. More...
enum	mkldnn_data_type_t {   mkldnn_data_type_undef = 0, mkldnn_f32 = 1, mkldnn_s32 = 2, mkldnn_s16 = 4,   mkldnn_s8 = 5, mkldnn_u8 = 6 }
	Data type specification. More...
enum	mkldnn_round_mode_t { mkldnn_round_nearest = 1, mkldnn_round_down = 2 }
	Rounding mode. More...
enum	mkldnn_memory_format_t {   mkldnn_format_undef = 0, mkldnn_any, mkldnn_blocked, mkldnn_x,   mkldnn_nc, mkldnn_nchw, mkldnn_nhwc, mkldnn_chwn,   mkldnn_nChw8c, mkldnn_nChw16c, mkldnn_ncdhw, mkldnn_ndhwc,   mkldnn_nCdhw16c, mkldnn_oi, mkldnn_io, mkldnn_oihw,   mkldnn_ihwo, mkldnn_hwio, mkldnn_dhwio, mkldnn_oidhw,   mkldnn_OIdhw16i16o, mkldnn_OIdhw16o16i, mkldnn_Oidhw16o, mkldnn_Odhwi16o,   mkldnn_OIhw8i8o, mkldnn_OIhw16i16o, mkldnn_OIhw4i16o4i, mkldnn_OIhw8i16o2i,   mkldnn_OIdhw8i16o2i, mkldnn_OIhw8o16i2o, mkldnn_OIhw8o8i, mkldnn_OIhw16o16i,   mkldnn_IOhw16o16i, mkldnn_Oihw8o, mkldnn_Oihw16o, mkldnn_Ohwi8o,   mkldnn_Ohwi16o, mkldnn_OhIw16o4i, mkldnn_goihw, mkldnn_hwigo,   mkldnn_gOIhw8i8o, mkldnn_gOIhw16i16o, mkldnn_gOIhw4i16o4i, mkldnn_gOIhw8i16o2i,   mkldnn_gOIdhw8i16o2i, mkldnn_gOIhw8o16i2o, mkldnn_gOIhw8o8i, mkldnn_gOIhw16o16i,   mkldnn_gIOhw16o16i, mkldnn_gOihw8o, mkldnn_gOihw16o, mkldnn_gOhwi8o,   mkldnn_gOhwi16o, mkldnn_Goihw8g, mkldnn_Goihw16g, mkldnn_gOhIw16o4i,   mkldnn_goidhw, mkldnn_gOIdhw16i16o, mkldnn_gOIdhw16o16i, mkldnn_gOidhw16o,   mkldnn_gOdhwi16o, mkldnn_ntc, mkldnn_tnc, mkldnn_ldsnc,   mkldnn_ldigo, mkldnn_ldigo_p, mkldnn_ldgoi, mkldnn_ldgoi_p,   mkldnn_ldgo, mkldnn_wino_fmt, mkldnn_format_last, mkldnn_oIhw8i = mkldnn_nChw8c,   mkldnn_oIhw16i = mkldnn_nChw16c }
	Memory format specification. More...
enum	mkldnn_padding_kind_t { mkldnn_padding_zero }
	Kinds of padding. 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_memory, mkldnn_view, mkldnn_reorder,   mkldnn_concat, mkldnn_concat_inplace, mkldnn_sum, mkldnn_convolution,   mkldnn_deconvolution, mkldnn_eltwise, mkldnn_relu = mkldnn_eltwise, mkldnn_softmax,   mkldnn_pooling, mkldnn_lrn, mkldnn_batch_normalization, mkldnn_inner_product,   mkldnn_convolution_relu, mkldnn_rnn }
	Kinds of primitives. More...
enum	mkldnn_alg_kind_t {   mkldnn_alg_kind_undef, mkldnn_convolution_direct = 1, mkldnn_convolution_winograd = 2, mkldnn_eltwise_relu = 8,   mkldnn_eltwise_tanh = 9, mkldnn_eltwise_elu = 10, mkldnn_eltwise_square = 11, mkldnn_eltwise_abs = 12,   mkldnn_eltwise_sqrt = 13, mkldnn_eltwise_linear = 14, mkldnn_eltwise_bounded_relu = 15, mkldnn_eltwise_soft_relu = 16,   mkldnn_eltwise_logistic = 17, mkldnn_pooling_max = 34, mkldnn_pooling_avg_include_padding = 40, mkldnn_pooling_avg_exclude_padding = 41,   mkldnn_pooling_avg = mkldnn_pooling_avg_exclude_padding, mkldnn_lrn_across_channels = 65, mkldnn_lrn_within_channel = 66, mkldnn_deconvolution_direct = 71,   mkldnn_deconvolution_winograd = 72, mkldnn_vanilla_rnn = 80, mkldnn_vanilla_lstm = 81, mkldnn_vanilla_gru = 82,   mkldnn_gru_linear_before_reset = 83 }
	Kinds of algorithms. More...
enum	mkldnn_batch_normalization_flag_t { mkldnn_use_global_stats = 0x1U, mkldnn_use_scaleshift = 0x2U, mkldnn_omit_stats = mkldnn_use_global_stats, mkldnn_fuse_bn_relu = 0x4U }
	Flags for batch-normalization primititve. More...

Detailed Description

Enumeration Type Documentation

mkldnn_alg_kind_t

enum mkldnn_alg_kind_t

Kinds of algorithms.

Enumerator
mkldnn_alg_kind_undef
mkldnn_convolution_direct	Direct convolution.
mkldnn_convolution_winograd	Winograd convolution.
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_pooling_max	Max pooling.
mkldnn_pooling_avg_include_padding	Average pooling include padding.
mkldnn_pooling_avg_exclude_padding	Average pooling exclude padding.
mkldnn_pooling_avg
mkldnn_lrn_across_channels	Local response normalization (LRN) across multiple channels.
mkldnn_lrn_within_channel	LRN within a single channel.
mkldnn_deconvolution_direct	Direct deconvolution.
mkldnn_deconvolution_winograd	Winograd deconvolution.
mkldnn_vanilla_rnn	RNN cell.
mkldnn_vanilla_lstm	LSTM cell.
mkldnn_vanilla_gru	GRU cell.
mkldnn_gru_linear_before_reset	GRU cell with linear before reset. Modification of original GRU cell. Differs from mkldnn_vanilla_gru in how the new memory gate is calculated: Primitive expects 4 biases on input:

mkldnn_batch_normalization_flag_t

enum mkldnn_batch_normalization_flag_t

Flags for batch-normalization primititve.

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_omit_stats	Omit statistics. Deprecated: use mkldnn_use_global_stats instead For time being had an affect on backward propagation only which allowed skipping some computations (the same semantics as mkldnn_use_global_stats)
mkldnn_fuse_bn_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)

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_f32	32-bit/single-precision floating point.
mkldnn_s32	32-bit signed integer.
mkldnn_s16	16-bit signed integer.
mkldnn_s8	8-bit signed integer.
mkldnn_u8	8-bit unsigned integer.

mkldnn_memory_format_t

enum mkldnn_memory_format_t

Memory format specification.

Intel(R) MKL-DNN uses the following notation for memory format names:

• '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
• 'h' and 'w' denote spatial width and height
• 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 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.

Note

Channel designations can be different. For example: both the 'mkldnn_nc' and 'mkldnn_io' formats can be used to describe a 2D tensor.

Enumerator
mkldnn_format_undef	Undefined memory format, used for empty memory descriptors.
mkldnn_any	Unspecified format. 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_x	1D data tensor.
mkldnn_nc	2D data tensor.
mkldnn_nchw	4D data tensor in the nchw format typically used in Caffe.
mkldnn_nhwc	4D data tensor in the nhwc format typically used in TensorFlow.
mkldnn_chwn	4D data tensor in the chwn format typically used in Neon.
mkldnn_nChw8c	4D data tensor in the nchw format with channels data laid out in memory in 8-element blocks.
mkldnn_nChw16c	4D data tensor in the nchw format with channels data laid out in memory in 16-element blocks.
mkldnn_ncdhw	5D data tensor in the ncdhw format.
mkldnn_ndhwc	5D data tensor in the ndhwc format typically used in TensorFlow.
mkldnn_nCdhw16c	5D data tensor in the ncdhw format with channels data laid out in memory in 16-element blocks.
mkldnn_oi	2D weights tensor in the format (input channels, output channels).
mkldnn_io	2D weights tensor in the format (input channels, output channels).
mkldnn_oihw	4D weights tensor in the format (input channels, output channels, width, height).
mkldnn_ihwo	4D weights tensor in the format (input channels, height, width, output channels).
mkldnn_hwio	4D weights tensor in the format (height, width, input channels, output channels).
mkldnn_dhwio	5D weights tensor in the format (depth, height, width, input channels, output channels).
mkldnn_oidhw	5D weight tensor in the oidhw format.
mkldnn_OIdhw16i16o	6D weights tensor in the oidhw format with output channels data laid out in memory in 16-element blocks and input channels data laid out in memory in 4-element blocks blocked by quadruple.
mkldnn_OIdhw16o16i	6D weights tensor in the oihw format with both input and output channels data laid out in memory in 16-element blocks.
mkldnn_Oidhw16o	5D weights tensor in the blocked version of oidhw format with output channels data laid out in memory in 16-element blocks.
mkldnn_Odhwi16o	5D weights tensor in the blocked version of oidhw format with output channels data laid out in memory in 16-element blocks.
mkldnn_OIhw8i8o	4D weights tensor in the oihw format with both input and output channels data laid out in memory in 8-element blocks.
mkldnn_OIhw16i16o	4D weights tensor in the oihw format with both input and output channels data laid out in memory in 16-element blocks.
mkldnn_OIhw4i16o4i	4D weights tensor in the oihw format with output channels data laid out in memory in 16-element blocks and input channels data laid out in memory in 4-element blocks blocked by quadruple.
mkldnn_OIhw8i16o2i	4D weights tensor in the oihw format with output channels data laid out in memory in 16-element blocks and input channels data laid out in memory in 8-element blocks blocked by pairs.
mkldnn_OIdhw8i16o2i	5D weights tensor in the oidhw format with output channels data laid out in memory in 16-element blocks and input channels data laid out in memory in 8-element blocks blocked by pairs.
mkldnn_OIhw8o16i2o	4D weights tensor in the oihw format with input channels data laid out in memory in 16-element blocks and output channels data laid out in memory in 8-element blocks blocked by pairs.
mkldnn_OIhw8o8i	4D weights tensor in the oihw format with both input and output channels data laid out in memory in 8-element blocks.
mkldnn_OIhw16o16i	4D weights tensor in the oihw format with both input and output channels data laid out in memory in 16-element blocks.
mkldnn_IOhw16o16i	4D weights tensor in the oihw format with both input and output channels data laid out in memory in 16-element blocks.
mkldnn_Oihw8o	4D weights tensor in the format (output channels, input channels, height, width) with output channels data laid out in memory in 8-element blocks.
mkldnn_Oihw16o	4D weights tensor in the format (output channels, input channels, height, width) with output channels data laid out in memory in 16-element blocks.
mkldnn_Ohwi8o	4D weights tensor in the format (output channels, width, height, input channels) with output channels data laid out in memory in 8-element blocks.
mkldnn_Ohwi16o	4D weights tensor in the format (output channels, width, height, input channels) with output channels data laid out in memory in 16-element blocks.
mkldnn_OhIw16o4i	4D weights tensor in the oihw format with both input and output channels data laid out in memory in 16-element and 4-element blocks.
mkldnn_goihw	5D weights tensor in the oihw format with extra outer dimension for groups.
mkldnn_hwigo	5D weights tensor in the hwio format with extra dimension for groups that comes after the output channels.
mkldnn_gOIhw8i8o	5D weights tensor in the blocked version of goihw format with both input and output channels data laid out in memory in 8-element blocks.
mkldnn_gOIhw16i16o	5D weights tensor in the blocked version of goihw format with both input and output channels data laid out in memory in 16-element blocks.
mkldnn_gOIhw4i16o4i	5D weights tensor in the oihw format with output channels data laid out in memory in 16-element blocks and input channels data laid out in memory in 4-element blocks blocked by quadruple.
mkldnn_gOIhw8i16o2i	5D weights tensor in the oihw format with output channels data laid out in memory in 16-element blocks and input channels data laid out in memory in 8-element blocks blocked by pairs.
mkldnn_gOIdhw8i16o2i	6D weights tensor in the oidhw format with output channels data laid out in memory in 16-element blocks and input channels data laid out in memory in 8-element blocks blocked by pairs.
mkldnn_gOIhw8o16i2o	5D weights tensor in the oihw format with input channels data laid out in memory in 16-element blocks and output channels data laid out in memory in 8-element blocks blocked by pairs.
mkldnn_gOIhw8o8i	5D weights tensor in the blocked version of goihw format with both input and output channels data laid out in memory in 8-element blocks.
mkldnn_gOIhw16o16i	5D weights tensor in the blocked version of goihw format with both input and output channels data laid out in memory in 16-element blocks.
mkldnn_gIOhw16o16i	5D weights tensor in the blocked version of goihw format with both input and output channels data laid out in memory in 16-element blocks.
mkldnn_gOihw8o	5D weights tensor in the blocked version of goihw format with output channels data laid out in memory in 8-element blocks.
mkldnn_gOihw16o	5D weights tensor in the blocked version of goihw format with output channels data laid out in memory in 16-element blocks.
mkldnn_gOhwi8o	5D weights tensor in the blocked version of goihw format with output channels data laid out in memory in 8-element blocks.
mkldnn_gOhwi16o	5D weights tensor in the blocked version of goihw format with output channels data laid out in memory in 16-element blocks.
mkldnn_Goihw8g	5D weights tensor in the blocked version of goihw format with group data laid out in memory in 8-element blocks.
mkldnn_Goihw16g	5D weights tensor in the blocked version of goihw format with group data laid out in memory in 16-element blocks.
mkldnn_gOhIw16o4i	5D weights tensor in the goihw format with both input and output channels data laid out in memory in 16-element and 4-element blocks.
mkldnn_goidhw	6D weight tensor in the goidhw format with extra dimension for groups
mkldnn_gOIdhw16i16o	6D weights tensor in the oidhw format with output channels data laid out in memory in 16-element blocks and input channels data laid out in memory in 4-element blocks blocked by quadruple.
mkldnn_gOIdhw16o16i	6D weights tensor in the blocked version of goihw format with both input and output channels data laid out in memory in 16-element blocks.
mkldnn_gOidhw16o	6D weights tensor in the blocked version of goidhw format with output channels data laid out in memory in 16-element blocks.
mkldnn_gOdhwi16o	6D weights tensor in the blocked version of goidhw format with output channels data laid out in memory in 16-element blocks.
mkldnn_ntc	3D data tensor in the format (batch, seq_length, input channels).
mkldnn_tnc	3D data tensor in the format (seq_length, batch, input channels).
mkldnn_ldsnc	5D states tensor in the format (num_layers, num_directions, num_states, batch, state channels).
mkldnn_ldigo	5D weights tensor in the format (num_layers, num_directions, input_chanels, num_gates, output_channels).
mkldnn_ldigo_p	5D weights tensor in the blocked format.
mkldnn_ldgoi	5D weights tensor in the format (num_layers, num_directions, num_gates, output_channels, input_chanels).
mkldnn_ldgoi_p	5D weights tensor in the blocked format.
mkldnn_ldgo	4D bias tensor in the format (num_layers, num_directions, num_gates, output_channels).
mkldnn_wino_fmt	General tensor format for integer 8bit winograd convolution.
mkldnn_format_last	Just a sentinel, not real memory format. Must be changed after new format is added.
mkldnn_oIhw8i	4D weights tensor in the oihw format with input channels data laid out in memory in 8-element blocks.
mkldnn_oIhw16i	4D weights tensor in the oihw format with input channels data laid out in memory in 16-element blocks.

mkldnn_padding_kind_t

enum mkldnn_padding_kind_t

Kinds of padding.

Define how to interpret the data in padding regions.

Enumerator
mkldnn_padding_zero	The data in padding regions is zero.

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 (XXX: why do we have it?).
mkldnn_memory	A memory primitive.
mkldnn_view	A view primitive.
mkldnn_reorder	A reorder primitive.
mkldnn_concat	A (out-of-place) concat primitive.
mkldnn_concat_inplace	A (in-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_relu	A ReLU primitive. Deprecated:
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_convolution_relu	A convolution primitive merged with ReLU. Deprecated:
mkldnn_rnn	A rnn primitive.

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 only perform computations that are necessary for inference and omit computations that are only necessary 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_round_mode_t

enum mkldnn_round_mode_t

Rounding mode.

Enumerator
mkldnn_round_nearest	Round nearest.
mkldnn_round_down	Round down.

mkldnn_status_t

enum mkldnn_status_t

Status values returned by Intel(R) MKL-DNN functions.

Enumerator
mkldnn_success	The operation was successful.
mkldnn_out_of_memory	The operation failed due to an out-of-memory condition.
mkldnn_try_again	The operation failed and should be retried.
mkldnn_invalid_arguments	The operation failed because of incorrect function arguments.
mkldnn_not_ready	The operation failed because a primitive was not ready for execution.
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.