Collaboration diagram for Dnnl_api_primitives_common:

Classes
struct	dnnl_exec_arg_t

struct	dnnl_primitive_desc_iterator
	An opaque structure to describe a primitive descriptor iterator. More...

struct	dnnl_primitive_desc
	An opaque structure to describe a primitive descriptor. More...

struct	dnnl_primitive

Macros
#define	DNNL_ARG_SRC_0 1
	Source argument #0. More...

#define	DNNL_ARG_SRC DNNL_ARG_SRC_0

#define	DNNL_ARG_SRC_LAYER DNNL_ARG_SRC_0

#define	DNNL_ARG_FROM DNNL_ARG_SRC_0

#define	DNNL_ARG_SRC_1 2
	Source argument #1. More...

#define	DNNL_ARG_SRC_ITER DNNL_ARG_SRC_1

#define	DNNL_ARG_SRC_2 3
	Source argument #2. More...

#define	DNNL_ARG_SRC_ITER_C DNNL_ARG_SRC_2

#define	DNNL_ARG_DST_0 17
	Destination argument #0. More...

#define	DNNL_ARG_DST DNNL_ARG_DST_0

#define	DNNL_ARG_TO DNNL_ARG_DST_0

#define	DNNL_ARG_DST_LAYER DNNL_ARG_DST_0
	A special mnemonic for RNN output vector. An alias for DNNL_ARG_DST_0. More...

#define	DNNL_ARG_DST_1 18
	Destination argument #1. More...

#define	DNNL_ARG_DST_ITER DNNL_ARG_DST_1

#define	DNNL_ARG_DST_2 19
	Destination argument #2. More...

#define	DNNL_ARG_DST_ITER_C DNNL_ARG_DST_2

#define	DNNL_ARG_WEIGHTS_0 33
	Weights argument #0. More...

#define	DNNL_ARG_WEIGHTS DNNL_ARG_WEIGHTS_0

#define	DNNL_ARG_SCALE_SHIFT DNNL_ARG_WEIGHTS_0

#define	DNNL_ARG_WEIGHTS_LAYER DNNL_ARG_WEIGHTS_0

#define	DNNL_ARG_WEIGHTS_1 34
	Weights argument #1. More...

#define	DNNL_ARG_WEIGHTS_ITER DNNL_ARG_WEIGHTS_1

#define	DNNL_ARG_WEIGHTS_2 35
	Weights argument #2. More...

#define	DNNL_ARG_WEIGHTS_PEEPHOLE DNNL_ARG_WEIGHTS_2

#define	DNNL_ARG_WEIGHTS_3 36
	Weights argument #3. More...

#define	DNNL_ARG_WEIGHTS_PROJECTION DNNL_ARG_WEIGHTS_3

#define	DNNL_ARG_BIAS 41
	Bias tensor argument. More...

#define	DNNL_ARG_MEAN 49
	Mean values tensor argument. More...

#define	DNNL_ARG_VARIANCE 50
	Variance values tensor argument. More...

#define	DNNL_ARG_SCALE 51
	A special mnemonic for scale argument of normalization primitives. More...

#define	DNNL_ARG_SHIFT 52
	A special mnemonic for shift argument of normalization primitives. More...

#define	DNNL_ARG_WORKSPACE 64

#define	DNNL_ARG_SCRATCHPAD 80
	Scratchpad (temporary storage) tensor argument. More...

#define	DNNL_ARG_DIFF_SRC_0 129
	Gradient (diff) of the source argument #0. More...

#define	DNNL_ARG_DIFF_SRC DNNL_ARG_DIFF_SRC_0

#define	DNNL_ARG_DIFF_SRC_LAYER DNNL_ARG_DIFF_SRC_0

#define	DNNL_ARG_DIFF_SRC_1 130
	Gradient (diff) of the source argument #1. More...

#define	DNNL_ARG_DIFF_SRC_ITER DNNL_ARG_DIFF_SRC_1

#define	DNNL_ARG_DIFF_SRC_2 131
	Gradient (diff) of the source argument #2. More...

#define	DNNL_ARG_DIFF_SRC_ITER_C DNNL_ARG_DIFF_SRC_2

#define	DNNL_ARG_DIFF_DST_0 145
	Gradient (diff) of the destination argument #0. More...

#define	DNNL_ARG_DIFF_DST DNNL_ARG_DIFF_DST_0

#define	DNNL_ARG_DIFF_DST_LAYER DNNL_ARG_DIFF_DST_0

#define	DNNL_ARG_DIFF_DST_1 146
	Gradient (diff) of the destination argument #1. More...

#define	DNNL_ARG_DIFF_DST_ITER DNNL_ARG_DIFF_DST_1

#define	DNNL_ARG_DIFF_DST_2 147
	Gradient (diff) of the destination argument #2. More...

#define	DNNL_ARG_DIFF_DST_ITER_C DNNL_ARG_DIFF_DST_2

#define	DNNL_ARG_DIFF_WEIGHTS_0 161
	Gradient (diff) of the weights argument #0. More...

#define	DNNL_ARG_DIFF_WEIGHTS DNNL_ARG_DIFF_WEIGHTS_0

#define	DNNL_ARG_DIFF_SCALE_SHIFT DNNL_ARG_DIFF_WEIGHTS_0

#define	DNNL_ARG_DIFF_WEIGHTS_LAYER DNNL_ARG_DIFF_WEIGHTS_0

#define	DNNL_ARG_DIFF_WEIGHTS_1 162
	Gradient (diff) of the weights argument #1. More...

#define	DNNL_ARG_DIFF_WEIGHTS_ITER DNNL_ARG_DIFF_WEIGHTS_1

#define	DNNL_ARG_DIFF_WEIGHTS_2 163
	Gradient (diff) of the weights argument #2. More...

#define	DNNL_ARG_DIFF_WEIGHTS_PEEPHOLE DNNL_ARG_DIFF_WEIGHTS_2

#define	DNNL_ARG_DIFF_WEIGHTS_3 164
	Gradient (diff) of the weights argument #3. More...

#define	DNNL_ARG_DIFF_WEIGHTS_PROJECTION DNNL_ARG_DIFF_WEIGHTS_3

#define	DNNL_ARG_DIFF_BIAS 169
	Gradient (diff) of the bias tensor argument. More...

#define	DNNL_ARG_DIFF_SCALE 255
	A special mnemonic for scale argument of normalization primitives. More...

#define	DNNL_ARG_DIFF_SHIFT 256
	A special mnemonic for shift argument of normalization primitives. More...

#define	DNNL_ARG_ATTR_OUTPUT_SCALES 513
	Output scaling factors provided at execution time. More...

#define	DNNL_ARG_MULTIPLE_SRC 1024

#define	DNNL_ARG_MULTIPLE_DST 2048

#define	DNNL_ARG_ATTR_ZERO_POINTS 4096
	Zero points provided at execution time. More...

#define	DNNL_ARG_ATTR_POST_OP_DW 8192

#define	DNNL_ARG_ATTR_MULTIPLE_POST_OP_BASE 16384
	Starting point for a binary post operation. More...

#define	DNNL_ARG_ATTR_MULTIPLE_POST_OP(idx) (DNNL_ARG_ATTR_MULTIPLE_POST_OP_BASE * ((idx) + 1))

#define	DNNL_ARG_ATTR_INPUT_SCALES 1048576
	Input scaling factors provided at execution time. More...

Typedefs
typedef void *	dnnl_op_desc_t
	A pointer to any of the operation descriptors. More...

typedef const void *	const_dnnl_op_desc_t
	A pointer to any of the operation descriptors (constant variant). More...

typedef struct dnnl_primitive_desc_iterator *	dnnl_primitive_desc_iterator_t
	A primitive descriptor iterator handle. More...

typedef const struct dnnl_primitive_desc_iterator *	const_dnnl_primitive_desc_iterator_t
	A constant primitive descriptor iterator handle. More...

typedef struct dnnl_primitive_desc *	dnnl_primitive_desc_t
	A primitive descriptor handle. More...

typedef const struct dnnl_primitive_desc *	const_dnnl_primitive_desc_t
	A constant primitive descriptor handle. More...

typedef struct dnnl_primitive *	dnnl_primitive_t
	A primitive handle. More...

typedef const struct dnnl_primitive *	const_dnnl_primitive_t
	A constant primitive handle. More...

Enumerations
enum	dnnl_prop_kind_t { dnnl_prop_kind_undef = 0, dnnl_forward_training = 64, dnnl_forward_inference = 96, dnnl_forward_scoring = dnnl_forward_inference, dnnl_forward = dnnl_forward_training, dnnl_backward = 128, dnnl_backward_data = 160, dnnl_backward_weights = 192, dnnl_backward_bias = 193 }
	Kinds of propagation. More...

enum	dnnl_primitive_kind_t { dnnl_undefined_primitive, dnnl_reorder, dnnl_shuffle, dnnl_concat, dnnl_sum, dnnl_convolution, dnnl_deconvolution, dnnl_eltwise, dnnl_softmax, dnnl_pooling, dnnl_lrn, dnnl_batch_normalization, dnnl_layer_normalization, dnnl_inner_product, dnnl_rnn, dnnl_gemm, dnnl_binary, dnnl_logsoftmax, dnnl_matmul, dnnl_resampling, dnnl_pooling_v2, dnnl_reduction, dnnl_prelu, dnnl_primitive_kind_max = 0x7fff }

enum	dnnl_alg_kind_t { dnnl_alg_kind_undef, dnnl_convolution_direct = 0x1, dnnl_convolution_winograd = 0x2, dnnl_convolution_auto = 0x3, dnnl_deconvolution_direct = 0xa, dnnl_deconvolution_winograd = 0xb, dnnl_eltwise_relu = 0x1f, dnnl_eltwise_tanh = 0x2f, dnnl_eltwise_elu = 0x3f, dnnl_eltwise_square = 0x4f, dnnl_eltwise_abs = 0x5f, dnnl_eltwise_sqrt = 0x6f, dnnl_eltwise_linear = 0x7f, dnnl_eltwise_bounded_relu = 0x8f, dnnl_eltwise_soft_relu = 0x9f, dnnl_eltwise_logistic = 0xaf, dnnl_eltwise_exp = 0xbf, dnnl_eltwise_gelu_tanh = 0xcf, dnnl_eltwise_gelu = dnnl_eltwise_gelu_tanh, dnnl_eltwise_swish = 0xdf, dnnl_eltwise_log = 0xef, dnnl_eltwise_clip = 0xff, dnnl_eltwise_clip_v2 = 0x10, dnnl_eltwise_pow = 0x20, dnnl_eltwise_gelu_erf = 0x30, dnnl_eltwise_round = 0x40, dnnl_eltwise_logsigmoid = 0x50, dnnl_eltwise_mish = 0x60, dnnl_eltwise_hardswish = 0x70, dnnl_eltwise_relu_use_dst_for_bwd = 0x100, dnnl_eltwise_tanh_use_dst_for_bwd = 0x101, dnnl_eltwise_elu_use_dst_for_bwd = 0x102, dnnl_eltwise_sqrt_use_dst_for_bwd = 0x103, dnnl_eltwise_logistic_use_dst_for_bwd = 0x104, dnnl_eltwise_exp_use_dst_for_bwd = 0x105, dnnl_eltwise_clip_v2_use_dst_for_bwd = 0x106, dnnl_pooling_max = 0x1ff, dnnl_pooling_avg_include_padding = 0x2ff, dnnl_pooling_avg_exclude_padding = 0x3ff, dnnl_pooling_avg = dnnl_pooling_avg_exclude_padding, dnnl_lrn_across_channels = 0xaff, dnnl_lrn_within_channel = 0xbff, dnnl_vanilla_rnn = 0x1fff, dnnl_vanilla_lstm = 0x2fff, dnnl_vanilla_gru = 0x3fff, dnnl_lbr_gru = 0x4fff, dnnl_binary_add = 0x1fff0, dnnl_binary_mul = 0x1fff1, dnnl_binary_max = 0x1fff2, dnnl_binary_min = 0x1fff3, dnnl_binary_div = 0x1fff4, dnnl_binary_sub = 0x1fff5, dnnl_binary_ge = 0x1fff6, dnnl_binary_gt = 0x1fff7, dnnl_binary_le = 0x1fff8, dnnl_binary_lt = 0x1fff9, dnnl_binary_eq = 0x1fffa, dnnl_binary_ne = 0x1fffb, dnnl_resampling_nearest = 0x2fff0, dnnl_resampling_linear = 0x2fff1, dnnl_reduction_max, dnnl_reduction_min, dnnl_reduction_sum, dnnl_reduction_mul, dnnl_reduction_mean, dnnl_reduction_norm_lp_max, dnnl_reduction_norm_lp_sum, dnnl_reduction_norm_lp_power_p_max, dnnl_reduction_norm_lp_power_p_sum }
	Kinds of algorithms. More...

enum	dnnl_normalization_flags_t { dnnl_normalization_flags_none = 0x0U, dnnl_use_global_stats = 0x1U, dnnl_use_scaleshift = 0x2U, dnnl_fuse_norm_relu = 0x4U, dnnl_use_scale = 0x8U, dnnl_use_shift = 0x10U }
	Flags for normalization primitives. More...

enum	dnnl_query_t { dnnl_query_undef = 0, dnnl_query_engine, dnnl_query_primitive_kind, dnnl_query_num_of_inputs_s32, dnnl_query_num_of_outputs_s32, dnnl_query_time_estimate_f64, dnnl_query_memory_consumption_s64, dnnl_query_scratchpad_engine, dnnl_query_impl_info_str, dnnl_query_reorder_src_engine, dnnl_query_reorder_dst_engine, dnnl_query_prop_kind, dnnl_query_some_d = 64, dnnl_query_op_d, dnnl_query_convolution_d, dnnl_query_deconvolution_d, dnnl_query_shuffle_d, dnnl_query_eltwise_d, dnnl_query_softmax_d, dnnl_query_pooling_d, dnnl_query_lrn_d, dnnl_query_batch_normalization_d, dnnl_query_layer_normalization_d, dnnl_query_inner_product_d, dnnl_query_rnn_d, dnnl_query_gemm_d, dnnl_query_binary_d, dnnl_query_logsoftmax_d, dnnl_query_matmul_d, dnnl_query_resampling_d, dnnl_query_pooling_v2_d, dnnl_query_reduction_d, dnnl_query_prelu_d, dnnl_query_some_md = 128, dnnl_query_src_md, dnnl_query_diff_src_md, dnnl_query_weights_md, dnnl_query_diff_weights_md, dnnl_query_dst_md, dnnl_query_diff_dst_md, dnnl_query_workspace_md, dnnl_query_scratchpad_md, dnnl_query_exec_arg_md = 255, dnnl_query_max = 0x7fff }

Functions
dnnl_status_t DNNL_API	dnnl_primitive_desc_iterator_create (dnnl_primitive_desc_iterator_t *iterator, const_dnnl_op_desc_t op_desc, const_dnnl_primitive_attr_t attr, dnnl_engine_t engine, const_dnnl_primitive_desc_t hint_forward_primitive_desc)

dnnl_status_t DNNL_API	dnnl_primitive_desc_iterator_next (dnnl_primitive_desc_iterator_t iterator)

dnnl_primitive_desc_t DNNL_API	dnnl_primitive_desc_iterator_fetch (const_dnnl_primitive_desc_iterator_t iterator)

dnnl_status_t DNNL_API	dnnl_primitive_desc_iterator_destroy (dnnl_primitive_desc_iterator_t iterator)

dnnl_status_t DNNL_API	dnnl_primitive_desc_create (dnnl_primitive_desc_t *primitive_desc, const_dnnl_op_desc_t op_desc, const_dnnl_primitive_attr_t attr, dnnl_engine_t engine, const_dnnl_primitive_desc_t hint_forward_primitive_desc)

dnnl_status_t DNNL_API	dnnl_primitive_desc_clone (dnnl_primitive_desc_t *primitive_desc, const_dnnl_primitive_desc_t existing_primitive_desc)

dnnl_status_t DNNL_API	dnnl_primitive_desc_get_attr (const_dnnl_primitive_desc_t primitive_desc, const_dnnl_primitive_attr_t *attr)

dnnl_status_t DNNL_API	dnnl_primitive_desc_destroy (dnnl_primitive_desc_t primitive_desc)

dnnl_status_t DNNL_API	dnnl_primitive_desc_query (const_dnnl_primitive_desc_t primitive_desc, dnnl_query_t what, int index, void *result)

const dnnl_memory_desc_t DNNL_API *	dnnl_primitive_desc_query_md (const_dnnl_primitive_desc_t primitive_desc, dnnl_query_t what, int index)

int DNNL_API	dnnl_primitive_desc_query_s32 (const_dnnl_primitive_desc_t primitive_desc, dnnl_query_t what, int index)

dnnl_status_t DNNL_API	dnnl_primitive_create (dnnl_primitive_t *primitive, const_dnnl_primitive_desc_t primitive_desc)

dnnl_status_t DNNL_API	dnnl_primitive_execute (const_dnnl_primitive_t primitive, dnnl_stream_t stream, int nargs, const dnnl_exec_arg_t *args)

dnnl_status_t DNNL_API	dnnl_primitive_get_primitive_desc (const_dnnl_primitive_t primitive, const_dnnl_primitive_desc_t *primitive_desc)

dnnl_status_t DNNL_API	dnnl_primitive_destroy (dnnl_primitive_t primitive)

Detailed Description

Macro Definition Documentation

◆ DNNL_ARG_ATTR_INPUT_SCALES

#define DNNL_ARG_ATTR_INPUT_SCALES 1048576

Input scaling factors provided at execution time.

◆ DNNL_ARG_ATTR_MULTIPLE_POST_OP

#define DNNL_ARG_ATTR_MULTIPLE_POST_OP ( idx ) (DNNL_ARG_ATTR_MULTIPLE_POST_OP_BASE * ((idx) + 1))

Arguments for a binary post operation. Up to 32 arguments are supported. See dev_guide_attributes_post_ops_binary_fusion

◆ DNNL_ARG_ATTR_MULTIPLE_POST_OP_BASE

#define DNNL_ARG_ATTR_MULTIPLE_POST_OP_BASE 16384

Starting point for a binary post operation.

◆ DNNL_ARG_ATTR_OUTPUT_SCALES

#define DNNL_ARG_ATTR_OUTPUT_SCALES 513

Output scaling factors provided at execution time.

◆ DNNL_ARG_ATTR_POST_OP_DW

#define DNNL_ARG_ATTR_POST_OP_DW 8192

Arguments for fused depthwise convolution. See dev_guide_attributes_post_ops_depthwise_fusion

◆ DNNL_ARG_ATTR_ZERO_POINTS

#define DNNL_ARG_ATTR_ZERO_POINTS 4096

Zero points provided at execution time.

◆ DNNL_ARG_BIAS

#define DNNL_ARG_BIAS 41

Bias tensor argument.

◆ DNNL_ARG_DIFF_BIAS

#define DNNL_ARG_DIFF_BIAS 169

Gradient (diff) of the bias tensor argument.

◆ DNNL_ARG_DIFF_DST

#define DNNL_ARG_DIFF_DST DNNL_ARG_DIFF_DST_0

A special mnemonic for primitives that have a single diff destination argument. An alias for DNNL_ARG_DIFF_DST_0.

◆ DNNL_ARG_DIFF_DST_0

#define DNNL_ARG_DIFF_DST_0 145

Gradient (diff) of the destination argument #0.

◆ DNNL_ARG_DIFF_DST_1

#define DNNL_ARG_DIFF_DST_1 146

Gradient (diff) of the destination argument #1.

◆ DNNL_ARG_DIFF_DST_2

#define DNNL_ARG_DIFF_DST_2 147

Gradient (diff) of the destination argument #2.

◆ DNNL_ARG_DIFF_DST_ITER

#define DNNL_ARG_DIFF_DST_ITER DNNL_ARG_DIFF_DST_1

A special mnemonic for gradient (diff) of RNN input recurrent hidden state vector. An alias for DNNL_ARG_DIFF_DST_1.

◆ DNNL_ARG_DIFF_DST_ITER_C

#define DNNL_ARG_DIFF_DST_ITER_C DNNL_ARG_DIFF_DST_2

A special mnemonic for gradient (diff) of RNN input recurrent cell state vector. An alias for DNNL_ARG_DIFF_DST_2.

◆ DNNL_ARG_DIFF_DST_LAYER

#define DNNL_ARG_DIFF_DST_LAYER DNNL_ARG_DIFF_DST_0

A special mnemonic for gradient (diff) of RNN output vector. An alias for DNNL_ARG_DIFF_DST_0.

◆ DNNL_ARG_DIFF_SCALE

#define DNNL_ARG_DIFF_SCALE 255

A special mnemonic for scale argument of normalization primitives.

◆ DNNL_ARG_DIFF_SCALE_SHIFT

#define DNNL_ARG_DIFF_SCALE_SHIFT DNNL_ARG_DIFF_WEIGHTS_0

A special mnemonic for diff of scale and shift argument of normalization primitives. Alias for DNNL_ARG_DIFF_WEIGHTS_0.

◆ DNNL_ARG_DIFF_SHIFT

#define DNNL_ARG_DIFF_SHIFT 256

A special mnemonic for shift argument of normalization primitives.

◆ DNNL_ARG_DIFF_SRC

#define DNNL_ARG_DIFF_SRC DNNL_ARG_DIFF_SRC_0

A special mnemonic for primitives that have a single diff source argument. An alias for DNNL_ARG_DIFF_SRC_0.

◆ DNNL_ARG_DIFF_SRC_0

#define DNNL_ARG_DIFF_SRC_0 129

Gradient (diff) of the source argument #0.

◆ DNNL_ARG_DIFF_SRC_1

#define DNNL_ARG_DIFF_SRC_1 130

Gradient (diff) of the source argument #1.

◆ DNNL_ARG_DIFF_SRC_2

#define DNNL_ARG_DIFF_SRC_2 131

Gradient (diff) of the source argument #2.

◆ DNNL_ARG_DIFF_SRC_ITER

#define DNNL_ARG_DIFF_SRC_ITER DNNL_ARG_DIFF_SRC_1

A special mnemonic for gradient (diff) of RNN input recurrent hidden state vector. An alias for DNNL_ARG_DIFF_SRC_1.

◆ DNNL_ARG_DIFF_SRC_ITER_C

#define DNNL_ARG_DIFF_SRC_ITER_C DNNL_ARG_DIFF_SRC_2

A special mnemonic for gradient (diff) of RNN input recurrent cell state vector. An alias for DNNL_ARG_DIFF_SRC_1.

◆ DNNL_ARG_DIFF_SRC_LAYER

#define DNNL_ARG_DIFF_SRC_LAYER DNNL_ARG_DIFF_SRC_0

A special mnemonic for gradient (diff) of RNN input vector. An alias for DNNL_ARG_DIFF_SRC_0.

◆ DNNL_ARG_DIFF_WEIGHTS

#define DNNL_ARG_DIFF_WEIGHTS DNNL_ARG_DIFF_WEIGHTS_0

A special mnemonic for primitives that have a single diff weights argument. Alias for DNNL_ARG_DIFF_WEIGHTS_0.

◆ DNNL_ARG_DIFF_WEIGHTS_0

#define DNNL_ARG_DIFF_WEIGHTS_0 161

Gradient (diff) of the weights argument #0.

◆ DNNL_ARG_DIFF_WEIGHTS_1

#define DNNL_ARG_DIFF_WEIGHTS_1 162

Gradient (diff) of the weights argument #1.

◆ DNNL_ARG_DIFF_WEIGHTS_2

#define DNNL_ARG_DIFF_WEIGHTS_2 163

Gradient (diff) of the weights argument #2.

◆ DNNL_ARG_DIFF_WEIGHTS_3

#define DNNL_ARG_DIFF_WEIGHTS_3 164

Gradient (diff) of the weights argument #3.

◆ DNNL_ARG_DIFF_WEIGHTS_ITER

#define DNNL_ARG_DIFF_WEIGHTS_ITER DNNL_ARG_DIFF_WEIGHTS_1

A special mnemonic for diff of RNN weights applied to the recurrent input. An alias for DNNL_ARG_DIFF_WEIGHTS_1.

◆ DNNL_ARG_DIFF_WEIGHTS_LAYER

#define DNNL_ARG_DIFF_WEIGHTS_LAYER DNNL_ARG_DIFF_WEIGHTS_0

A special mnemonic for diff of RNN weights applied to the layer input. An alias for DNNL_ARG_DIFF_WEIGHTS_0.

◆ DNNL_ARG_DIFF_WEIGHTS_PEEPHOLE

#define DNNL_ARG_DIFF_WEIGHTS_PEEPHOLE DNNL_ARG_DIFF_WEIGHTS_2

A special mnemonic for diff of RNN weights applied to the peephole weights. An alias for DNNL_ARG_DIFF_WEIGHTS_2.

◆ DNNL_ARG_DIFF_WEIGHTS_PROJECTION

#define DNNL_ARG_DIFF_WEIGHTS_PROJECTION DNNL_ARG_DIFF_WEIGHTS_3

A special mnemonic for diff of RNN weights applied to the projection weights. An alias for DNNL_ARG_DIFF_WEIGHTS_3.

◆ DNNL_ARG_DST

#define DNNL_ARG_DST DNNL_ARG_DST_0

A special mnemonic for destination argument for primitives that have a single destination. An alias for DNNL_ARG_DST_0.

◆ DNNL_ARG_DST_0

#define DNNL_ARG_DST_0 17

Destination argument #0.

◆ DNNL_ARG_DST_1

#define DNNL_ARG_DST_1 18

Destination argument #1.

◆ DNNL_ARG_DST_2

#define DNNL_ARG_DST_2 19

Destination argument #2.

◆ DNNL_ARG_DST_ITER

#define DNNL_ARG_DST_ITER DNNL_ARG_DST_1

A special mnemonic for RNN input recurrent hidden state vector. An alias for DNNL_ARG_DST_1.

◆ DNNL_ARG_DST_ITER_C

#define DNNL_ARG_DST_ITER_C DNNL_ARG_DST_2

A special mnemonic for LSTM output recurrent cell state vector. An alias for DNNL_ARG_DST_2.

◆ DNNL_ARG_DST_LAYER

#define DNNL_ARG_DST_LAYER DNNL_ARG_DST_0

A special mnemonic for RNN output vector. An alias for DNNL_ARG_DST_0.

◆ DNNL_ARG_FROM

#define DNNL_ARG_FROM DNNL_ARG_SRC_0

A special mnemonic for reorder source argument. An alias for DNNL_ARG_SRC_0.

◆ DNNL_ARG_MEAN

#define DNNL_ARG_MEAN 49

Mean values tensor argument.

◆ DNNL_ARG_MULTIPLE_DST

#define DNNL_ARG_MULTIPLE_DST 2048

Starting index for destination arguments for primitives that produce a variable number of destination arguments.

◆ DNNL_ARG_MULTIPLE_SRC

#define DNNL_ARG_MULTIPLE_SRC 1024

Starting index for source arguments for primitives that take a variable number of source arguments.

◆ DNNL_ARG_SCALE

#define DNNL_ARG_SCALE 51

A special mnemonic for scale argument of normalization primitives.

◆ DNNL_ARG_SCALE_SHIFT

#define DNNL_ARG_SCALE_SHIFT DNNL_ARG_WEIGHTS_0

A special mnemonic for scale and shift argument of normalization primitives. Alias for DNNL_ARG_WEIGHTS_0.

◆ DNNL_ARG_SCRATCHPAD

#define DNNL_ARG_SCRATCHPAD 80

Scratchpad (temporary storage) tensor argument.

◆ DNNL_ARG_SHIFT

#define DNNL_ARG_SHIFT 52

A special mnemonic for shift argument of normalization primitives.

◆ DNNL_ARG_SRC

#define DNNL_ARG_SRC DNNL_ARG_SRC_0

A special mnemonic for source argument for primitives that have a single source. An alias for DNNL_ARG_SRC_0.

◆ DNNL_ARG_SRC_0

#define DNNL_ARG_SRC_0 1

Source argument #0.

◆ DNNL_ARG_SRC_1

#define DNNL_ARG_SRC_1 2

Source argument #1.

◆ DNNL_ARG_SRC_2

#define DNNL_ARG_SRC_2 3

Source argument #2.

◆ DNNL_ARG_SRC_ITER

#define DNNL_ARG_SRC_ITER DNNL_ARG_SRC_1

A special mnemonic for RNN input recurrent hidden state vector. An alias for DNNL_ARG_SRC_1.

◆ DNNL_ARG_SRC_ITER_C

#define DNNL_ARG_SRC_ITER_C DNNL_ARG_SRC_2

A special mnemonic for RNN input recurrent cell state vector. An alias for DNNL_ARG_SRC_2.

◆ DNNL_ARG_SRC_LAYER

#define DNNL_ARG_SRC_LAYER DNNL_ARG_SRC_0

A special mnemonic for RNN input vector. An alias for DNNL_ARG_SRC_0.

◆ DNNL_ARG_TO

#define DNNL_ARG_TO DNNL_ARG_DST_0

A special mnemonic for reorder destination argument. An alias for DNNL_ARG_DST_0.

◆ DNNL_ARG_VARIANCE

#define DNNL_ARG_VARIANCE 50

Variance values tensor argument.

◆ DNNL_ARG_WEIGHTS

#define DNNL_ARG_WEIGHTS DNNL_ARG_WEIGHTS_0

A special mnemonic for primitives that have a single weights argument. Alias for DNNL_ARG_WEIGHTS_0.

◆ DNNL_ARG_WEIGHTS_0

#define DNNL_ARG_WEIGHTS_0 33

Weights argument #0.

◆ DNNL_ARG_WEIGHTS_1

#define DNNL_ARG_WEIGHTS_1 34

Weights argument #1.

◆ DNNL_ARG_WEIGHTS_2

#define DNNL_ARG_WEIGHTS_2 35

Weights argument #2.

◆ DNNL_ARG_WEIGHTS_3

#define DNNL_ARG_WEIGHTS_3 36

Weights argument #3.

◆ DNNL_ARG_WEIGHTS_ITER

#define DNNL_ARG_WEIGHTS_ITER DNNL_ARG_WEIGHTS_1

A special mnemonic for RNN weights applied to the recurrent input. An alias for DNNL_ARG_WEIGHTS_1.

◆ DNNL_ARG_WEIGHTS_LAYER

#define DNNL_ARG_WEIGHTS_LAYER DNNL_ARG_WEIGHTS_0

A special mnemonic for RNN weights applied to the layer input. An alias for DNNL_ARG_WEIGHTS_0.

◆ DNNL_ARG_WEIGHTS_PEEPHOLE

#define DNNL_ARG_WEIGHTS_PEEPHOLE DNNL_ARG_WEIGHTS_2

A special mnemonic for RNN weights applied to the peephole weights. An alias for DNNL_ARG_WEIGHTS_2.

◆ DNNL_ARG_WEIGHTS_PROJECTION

#define DNNL_ARG_WEIGHTS_PROJECTION DNNL_ARG_WEIGHTS_3

A special mnemonic for RNN weights applied to the projection weights. An alias for DNNL_ARG_WEIGHTS_3.

◆ DNNL_ARG_WORKSPACE

#define DNNL_ARG_WORKSPACE 64

Workspace tensor argument. Workspace is used to pass information from forward propagation to backward propagation computations.

Typedef Documentation

◆ const_dnnl_op_desc_t

typedef const void* const_dnnl_op_desc_t

A pointer to any of the operation descriptors (constant variant).

◆ const_dnnl_primitive_desc_iterator_t

typedef const struct dnnl_primitive_desc_iterator* const_dnnl_primitive_desc_iterator_t

A constant primitive descriptor iterator handle.

◆ const_dnnl_primitive_desc_t

typedef const struct dnnl_primitive_desc* const_dnnl_primitive_desc_t

A constant primitive descriptor handle.

◆ const_dnnl_primitive_t

typedef const struct dnnl_primitive* const_dnnl_primitive_t

A constant primitive handle.

◆ dnnl_op_desc_t

typedef void* dnnl_op_desc_t

A pointer to any of the operation descriptors.

◆ dnnl_primitive_desc_iterator_t

typedef struct dnnl_primitive_desc_iterator* dnnl_primitive_desc_iterator_t

A primitive descriptor iterator handle.

◆ dnnl_primitive_desc_t

typedef struct dnnl_primitive_desc* dnnl_primitive_desc_t

A primitive descriptor handle.

◆ dnnl_primitive_t

typedef struct dnnl_primitive* dnnl_primitive_t

A primitive handle.

Enumeration Type Documentation

◆ dnnl_alg_kind_t

enum dnnl_alg_kind_t

Kinds of algorithms.

Enumerator
dnnl_alg_kind_undef
dnnl_convolution_direct	Direct convolution.
dnnl_convolution_winograd	Winograd convolution.
dnnl_convolution_auto	Convolution algorithm(either direct or Winograd) is chosen just in time.
dnnl_deconvolution_direct	Direct deconvolution.
dnnl_deconvolution_winograd	Winograd deconvolution.
dnnl_eltwise_relu	Eltwise: ReLU.
dnnl_eltwise_tanh	Eltwise: hyperbolic tangent non-linearity (tanh)
dnnl_eltwise_elu	Eltwise: exponential linear unit (elu)
dnnl_eltwise_square	Eltwise: square.
dnnl_eltwise_abs	Eltwise: abs.
dnnl_eltwise_sqrt	Eltwise: square root.
dnnl_eltwise_linear	Eltwise: linear.
dnnl_eltwise_bounded_relu	Eltwise: bounded_relu.
dnnl_eltwise_soft_relu	Eltwise: soft_relu.
dnnl_eltwise_logistic	Eltwise: logistic.
dnnl_eltwise_exp	Eltwise: exponent.
dnnl_eltwise_gelu_tanh	Eltwise: gelu @note Tanh approximation formula is used to approximate the cumulative distribution function of a Gaussian here
dnnl_eltwise_gelu	Eltwise: tanh-based gelu (alias for dnnl_eltwise_gelu_tanh)
dnnl_eltwise_swish	Eltwise: swish.
dnnl_eltwise_log	Eltwise: natural logarithm.
dnnl_eltwise_clip	Eltwise: clip.
dnnl_eltwise_clip_v2	Eltwise: clip version 2.
dnnl_eltwise_pow	Eltwise: pow.
dnnl_eltwise_gelu_erf	Eltwise: erf-based gelu.
dnnl_eltwise_round	Eltwise: round.
dnnl_eltwise_logsigmoid	Eltwise: logsigmoid.
dnnl_eltwise_mish	Eltwise: mish.
dnnl_eltwise_hardswish	Eltwise: hardswish.
dnnl_eltwise_relu_use_dst_for_bwd	Eltwise: ReLU (dst for backward)
dnnl_eltwise_tanh_use_dst_for_bwd	Eltwise: hyperbolic tangent non-linearity (tanh) (dst for backward)
dnnl_eltwise_elu_use_dst_for_bwd	Eltwise: exponential linear unit (elu) (dst for backward)
dnnl_eltwise_sqrt_use_dst_for_bwd	Eltwise: square root (dst for backward)
dnnl_eltwise_logistic_use_dst_for_bwd	Eltwise: logistic (dst for backward)
dnnl_eltwise_exp_use_dst_for_bwd	Eltwise: exp (dst for backward)
dnnl_eltwise_clip_v2_use_dst_for_bwd	Eltwise: clip version 2 (dst for backward)
dnnl_pooling_max	Max pooling.
dnnl_pooling_avg_include_padding	Average pooling include padding.
dnnl_pooling_avg_exclude_padding	Average pooling exclude padding.
dnnl_pooling_avg	Average pooling (alias for dnnl_pooling_avg_exclude_padding)
dnnl_lrn_across_channels	Local response normalization (LRN) across multiple channels.
dnnl_lrn_within_channel	LRN within a single channel.
dnnl_vanilla_rnn	RNN cell.
dnnl_vanilla_lstm	LSTM cell.
dnnl_vanilla_gru	GRU cell.
dnnl_lbr_gru	GRU cell with linear before reset Modification of original GRU cell. Differs from #dnnl_vanilla_gru in how the new memory gate is calculated: $c_t = tanh(W_cx_t + b_{c_x} + r_t(U_c*h_{t-1}+b_{c_h}))$ Primitive expects 4 biases on input: $[b_{u}, b_{r}, b_{c_x}, b_{c_h}]$
dnnl_binary_add	Binary add.
dnnl_binary_mul	Binary mul.
dnnl_binary_max	Binary max.
dnnl_binary_min	Binary min.
dnnl_binary_div	Binary div.
dnnl_binary_sub	Binary sub.
dnnl_binary_ge	Binary greater or equal.
dnnl_binary_gt	Binary greater than.
dnnl_binary_le	Binary less or equal.
dnnl_binary_lt	Binary less than.
dnnl_binary_eq	Binary equal.
dnnl_binary_ne	Binary not equal.
dnnl_resampling_nearest	Nearest Neighbor Resampling Method.
dnnl_resampling_linear	Linear Resampling Method.
dnnl_reduction_max	Reduction using max.
dnnl_reduction_min	Reduction using min.
dnnl_reduction_sum	Reduction using sum.
dnnl_reduction_mul	Reduction using mul.
dnnl_reduction_mean	Reduction using mean.
dnnl_reduction_norm_lp_max	Reduction using lp norm.
dnnl_reduction_norm_lp_sum	Reduction using lp norm.
dnnl_reduction_norm_lp_power_p_max	Reduction using lp norm without final pth-root.
dnnl_reduction_norm_lp_power_p_sum	Reduction using lp norm without final pth-root.

◆ dnnl_normalization_flags_t

enum dnnl_normalization_flags_t

Flags for normalization primitives.

Enumerator
dnnl_normalization_flags_none	Use no normalization flags If specified - on forward training propagation mean and variance are computed and stored as output - on backward propagation compute full derivative wrt data - on backward propagation prop_kind == #dnnl_backward_data has the same behavior as prop_kind == #dnnl_backward
dnnl_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 as output - on backward propagation compute full derivative wrt data
dnnl_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 == #dnnl_backward) compute diff wrt scale and shift (hence one extra output used) If no specified: - on backward propagation prop_kind == #dnnl_backward_data has the same behavior as prop_kind == #dnnl_backward
dnnl_fuse_norm_relu	Fuse with ReLU The flag implies negative slope being 0. On training this is the only configuration supported. For inference, to use non-zero negative slope consider using @ref dev_guide_attributes_post_ops. If specified: - on inference this option behaves the same as if the primitive were fused with ReLU using post ops API with zero negative slope. - on training primitive requires workspace (required to be able to perform backward pass)
dnnl_use_scale	Use scale parameter If specified: - on forward propagation use scale for the batch normalization results - on backward propagation (for prop_kind == #dnnl_backward) compute diff wrt scale (hence one extra output used)
dnnl_use_shift	Use shift parameter If specified: - on forward propagation use shift (aka bias) for the batch normalization results - on backward propagation (for prop_kind == #dnnl_backward) compute diff wrt shift (hence one extra output used)

◆ dnnl_primitive_kind_t

enum dnnl_primitive_kind_t

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

Enumerator
dnnl_undefined_primitive	Undefined primitive.
dnnl_reorder	A reorder primitive.
dnnl_shuffle	A shuffle primitive.
dnnl_concat	A (out-of-place) concat primitive.
dnnl_sum	A sum primitive.
dnnl_convolution	A convolution primitive.
dnnl_deconvolution	A deconvolution primitive.
dnnl_eltwise	An element-wise primitive.
dnnl_softmax	A softmax primitive.
dnnl_pooling	A pooling primitive.
dnnl_lrn	An LRN primitive.
dnnl_batch_normalization	A batch normalization primitive.
dnnl_layer_normalization	A layer normalization primitive.
dnnl_inner_product	An inner product primitive.
dnnl_rnn	A rnn primitive.
dnnl_gemm	A matrix multiplication primitive (internal).
dnnl_binary	A binary primitive.
dnnl_logsoftmax	A logsoftmax primitive.
dnnl_matmul	A matrix multiplication primitive.
dnnl_resampling	A resampling primitive.
dnnl_pooling_v2	A pooling version 2 primitive (pooling with dilation support).
dnnl_reduction	A reduction primitive.
dnnl_prelu	A PReLU primitive.
dnnl_primitive_kind_max	Parameter to allow internal only primitives without undefined behavior. This parameter is chosen to be valid for so long as sizeof(int) >= 2.

◆ dnnl_prop_kind_t

enum dnnl_prop_kind_t

Kinds of propagation.

Enumerator
dnnl_prop_kind_undef	Undefined propagation type.
dnnl_forward_training	Forward data propagation (training mode). In this mode primitives perform computations necessary for subsequent backward propagation.
dnnl_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.
dnnl_forward_scoring	Forward data propagation (alias for `dnnl_forward_inference`).
dnnl_forward	Forward data propagation (alias for `dnnl_forward_training`).
dnnl_backward	Backward propagation (with respect to all parameters).
dnnl_backward_data	Backward data propagation.
dnnl_backward_weights	Backward weights propagation.
dnnl_backward_bias	Backward bias propagation.

◆ dnnl_query_t

enum dnnl_query_t

Primitive descriptor query specification

For generic function dnnl_primitive_desc_query(), the type of result must agree with the queried argument. The correspondence table:

Query kind	Type of query result
dnnl_query_engine	dnnl_engine_t *
dnnl_query_scratchpad_engine	dnnl_engine_t *
dnnl_query_primitive_kind	dnnl_primitive_kind_t *
dnnl_query_*_s32	int *
dnnl_query_*_s64	dnnl_dim_t * (same as int64_t *)
dnnl_query_*_f64	double *
dnnl_query_*_str	const char **
dnnl_query_op_d	const_dnnl_op_desc_t *
dnnl_query_*_md	const dnnl_memory_desc_t **
dnnl_query_*_<op>_d	const dnnl_<op>_desc_t **
dnnl_query_*_pd	const_dnnl_primitive_desc_t *

Note: Rule of thumb: all opaque types and structures are returned by reference. All numbers are returned by value.

Warning: All returned references point to constant objects and are valid only during the lifetime of the queried primitive descriptor. Returned objects must not be destroyed by the user. If you need to keep the object longer than the lifetime of the queried primitive descriptor, use dnnl_primitive_desc_clone() to make a copy.

Enumerator
dnnl_query_undef	no query
dnnl_query_engine	execution engine
dnnl_query_primitive_kind	primitive kind
dnnl_query_num_of_inputs_s32	number of inputs expected
dnnl_query_num_of_outputs_s32	number of outputs expected
dnnl_query_time_estimate_f64	runtime estimation (seconds)
dnnl_query_memory_consumption_s64	memory consumption – extra
dnnl_query_scratchpad_engine	(scratch) memory, additional to all inputs and outputs memory (bytes) scratchpad engine – engine to be used
dnnl_query_impl_info_str	for creating scratchpad memory implementation name
dnnl_query_reorder_src_engine	source engine
dnnl_query_reorder_dst_engine	destination engine
dnnl_query_prop_kind	propagation kind
dnnl_query_some_d	stub
dnnl_query_op_d	op descriptor
dnnl_query_convolution_d	convolution descriptor
dnnl_query_deconvolution_d	deconvolution descriptor
dnnl_query_shuffle_d	shuffle descriptor
dnnl_query_eltwise_d	eltwise descriptor
dnnl_query_softmax_d	softmax descriptor
dnnl_query_pooling_d	pooling descriptor
dnnl_query_lrn_d	lrn descriptor
dnnl_query_batch_normalization_d	batch normalization descriptor
dnnl_query_layer_normalization_d	layer normalization descriptor
dnnl_query_inner_product_d	inner product descriptor
dnnl_query_rnn_d	rnn descriptor
dnnl_query_gemm_d	GEMM descriptor (internal)
dnnl_query_binary_d	binary descriptor
dnnl_query_logsoftmax_d	logsoftmax descriptor
dnnl_query_matmul_d	matrix multiplication (matmul) descriptor
dnnl_query_resampling_d	resampling descriptor
dnnl_query_pooling_v2_d	pooling version 2 descriptor
dnnl_query_reduction_d	reduction descriptor
dnnl_query_prelu_d	prelu descriptor
dnnl_query_some_md	stub
dnnl_query_src_md	source memory desc
dnnl_query_diff_src_md	source gradient memory desc
dnnl_query_weights_md	weights memory descriptor desc
dnnl_query_diff_weights_md	weights grad. memory desc
dnnl_query_dst_md	destination memory desc
dnnl_query_diff_dst_md	destination grad. memory desc
dnnl_query_workspace_md	workspace memory desc
dnnl_query_scratchpad_md	scratchpad memory desc
dnnl_query_exec_arg_md	memory desc of an execute argument
dnnl_query_max

Function Documentation

◆ dnnl_primitive_create()

dnnl_status_t DNNL_API dnnl_primitive_create	(	dnnl_primitive_t *	primitive,
		const_dnnl_primitive_desc_t	primitive_desc
	)

Creates a primitive.

Parameters

primitive	Output primitive.
primitive_desc	Primitive descriptor used to create the primitive.

Returns: dnnl_success on success and a status describing the error otherwise.

◆ dnnl_primitive_desc_clone()

dnnl_status_t DNNL_API dnnl_primitive_desc_clone	(	dnnl_primitive_desc_t *	primitive_desc,
		const_dnnl_primitive_desc_t	existing_primitive_desc
	)

Clones a primitive descriptor. The resulting primitive descriptor must be destroyed separately.

Parameters

primitive_desc	Output primitive descriptor.
existing_primitive_desc	Primitive descriptor to clone.

Returns: dnnl_success on success and a status describing the error otherwise.

◆ dnnl_primitive_desc_create()

dnnl_status_t DNNL_API dnnl_primitive_desc_create	(	dnnl_primitive_desc_t *	primitive_desc,
		const_dnnl_op_desc_t	op_desc,
		const_dnnl_primitive_attr_t	attr,
		dnnl_engine_t	engine,
		const_dnnl_primitive_desc_t	hint_forward_primitive_desc
	)

Creates a primitive descriptor. This function is equivalent to a sequence of dnnl_primitive_desc_iterator_create() and dnnl_primitive_desc_iterator_fetch(). In other words, the library will pick the first suitable implementation.

Parameters

primitive_desc	Output primitive descriptor.
op_desc	Operation descriptor.
attr	Primitive attributes (can be NULL).
engine	Engine to use.
hint_forward_primitive_desc	For backward propagation: primitive descriptor for a respective forward propagation primitive. Pass NULL for forward propagation.

Returns: dnnl_success on success and a status describing the error otherwise.

◆ dnnl_primitive_desc_destroy()

dnnl_status_t DNNL_API dnnl_primitive_desc_destroy ( dnnl_primitive_desc_t primitive_desc )

Destroys a primitive descriptor.

Parameters

primitive_desc Primitive descriptor to destroy.

Returns: dnnl_success on success and a status describing the error otherwise.

◆ dnnl_primitive_desc_get_attr()

dnnl_status_t DNNL_API dnnl_primitive_desc_get_attr	(	const_dnnl_primitive_desc_t	primitive_desc,
		const_dnnl_primitive_attr_t *	attr
	)

Returns a constant reference to the attributes of a primitive descriptor.

Warning: It is an error to destroy the resulting attr.; The lifetime of an attr is the same as that of a primitive_desc, so it is an error to use the attr once the primitive_desc has been destroyed.

Parameters

primitive_desc	Primitive descriptor.
attr	Output primitive attributes.

Returns: dnnl_success on success and a status describing the error otherwise.

◆ dnnl_primitive_desc_iterator_create()

dnnl_status_t DNNL_API dnnl_primitive_desc_iterator_create	(	dnnl_primitive_desc_iterator_t *	iterator,
		const_dnnl_op_desc_t	op_desc,
		const_dnnl_primitive_attr_t	attr,
		dnnl_engine_t	engine,
		const_dnnl_primitive_desc_t	hint_forward_primitive_desc
	)

Creates a primitive descriptor iterator.

Parameters

iterator	Output primitive descriptor iterator.
op_desc	Operation descriptor.
attr	Primitive attributes (can be NULL).
engine	Engine to use.
hint_forward_primitive_desc	For backward propagation: primitive descriptor for a respective forward propagation primitive. Pass NULL for forward propagation.

Returns: dnnl_success on success and a status describing the error otherwise.

◆ dnnl_primitive_desc_iterator_destroy()

dnnl_status_t DNNL_API dnnl_primitive_desc_iterator_destroy ( dnnl_primitive_desc_iterator_t iterator )

Destroys a primitive descriptor iterator.

Parameters

iterator Primitive descriptor iterator to destroy.

Returns: dnnl_success on success and a status describing the error otherwise.

◆ dnnl_primitive_desc_iterator_fetch()

dnnl_primitive_desc_t DNNL_API dnnl_primitive_desc_iterator_fetch ( const_dnnl_primitive_desc_iterator_t iterator )

Fetches the current primitive descriptor from a primitive descriptor iterator.

Note: The user is responsible for deleting the resulting primitive descriptor using dnnl_primitive_desc_destroy().

Parameters

iterator A primitive descriptor iterator.

Returns: A primitive descriptor.

◆ dnnl_primitive_desc_iterator_next()

dnnl_status_t DNNL_API dnnl_primitive_desc_iterator_next ( dnnl_primitive_desc_iterator_t iterator )

Advances the primitive descriptor iterator to point to the next available implementation.

Parameters

iterator A primitive descriptor iterator to advance.

Returns: dnnl_success on success and a status describing the error otherwise.; dnnl_iterator_ends if no more implementations available.

◆ dnnl_primitive_desc_query()

dnnl_status_t DNNL_API dnnl_primitive_desc_query	(	const_dnnl_primitive_desc_t	primitive_desc,
		dnnl_query_t	what,
		int	index,
		void *	result
	)

Queries a primitive descriptor for various pieces of information.

The most common use case is to query a primitive descriptor, created with source, weights, and destination memory descriptors with format tags set to dnnl_format_tag_any, for the corresponding memory descriptors (in this case the what is set to dnnl_query_src_md, dnnl_query_weights_md, and dnnl_query_dst_md respectively) so that it is possible to create memory objects and reorder primitives if necessary.

Another typical use case is to query a primitive descriptor for workspace memory descriptor (with what set to dnnl_query_workspace_md). If this query returns dnnl_not_required status, then workspace memory is not required.

Note: When querying for a memory descriptor for a scratchpad, a workspace, or an optional parameter, the query will return a pointer to a zero memory descriptor if the parameter is not needed.

A few other use cases:

query a primitive descriptor for the underlying operation descriptor (dnnl_query_convolution_d, dnnl_query_eltwise_d, dnnl_query_rnn_d, etc.)
query a primitive descriptor for the implementation information string (dnnl_query_impl_info_str)
query a primitive descriptor for the number of inputs and outputs (dnnl_query_num_of_inputs_s32 and dnnl_query_num_of_outputs_s32 respectively)

See also: dnnl_query_t for more options

Parameters

primitive_desc	Primitive descriptor.
what	Parameter to query.
index	Index of the parameter to query for.
result	Output result. The type depends on the query. For example, it must be a `dnnl_memory_desc_t*` if querying for a memory descriptor.

Returns: dnnl_success on success and a status describing the error otherwise.

◆ dnnl_primitive_desc_query_md()

const dnnl_memory_desc_t DNNL_API* dnnl_primitive_desc_query_md	(	const_dnnl_primitive_desc_t	primitive_desc,
		dnnl_query_t	what,
		int	index
	)

Queries primitive descriptor for a memory descriptor.

Note: This function is a convenience version of dnnl_primitive_desc_query().

Parameters

primitive_desc	Primitive descriptor.
what	Kind of memory descriptor parameter to query for.
index	Index of the parameter to query.

Returns: A pointer to the requested memory descriptor.; A pointer to a zero memory descriptor if the parameter is not needed.; NULL in case of any error.

◆ dnnl_primitive_desc_query_s32()

int DNNL_API dnnl_primitive_desc_query_s32	(	const_dnnl_primitive_desc_t	primitive_desc,
		dnnl_query_t	what,
		int	index
	)

Queries primitive descriptor for a signed 32bit int.

Note: This function is a convenience version of dnnl_primitive_desc_query().

Parameters

primitive_desc	Primitive descriptor.
what	Kind of the value to query for.
index	Index of the parameter to query.

Returns: The requested value.; 0 in case of any error (in particular if the queried entity is not of type int32_t). Note that 0 may also be the actual returned value.

◆ dnnl_primitive_destroy()

dnnl_status_t DNNL_API dnnl_primitive_destroy ( dnnl_primitive_t primitive )

Destroys a primitive.

Parameters

primitive The primitive to destroy.

Returns: dnnl_success on success and a status describing the error otherwise.

◆ dnnl_primitive_execute()

dnnl_status_t DNNL_API dnnl_primitive_execute	(	const_dnnl_primitive_t	primitive,
		dnnl_stream_t	stream,
		int	nargs,
		const dnnl_exec_arg_t *	args
	)

Executes a primitive.

Parameters

primitive	Primitive to execute.
stream	Stream to use.
nargs	Number of arguments.
args	Array of arguments. Each argument is an <index, dnnl_memory_t> pair. The index is one of the `DNNL_ARG_*` values such as `DNNL_ARG_SRC`. Unless runtime shapes are used (see DNNL_RUNTIME_DIM_VAL), the memory object must have the same memory descriptor as that returned by dnnl_primitive_desc_query_md(dnnl_query_exec_arg_md, index).

Returns: dnnl_success on success and a status describing the error otherwise.

Note: If any argument in

Parameters

args	is padded (padded_dims > dims), the primitive execution will assume properly zero-padded input arguments, and produce zero-padded output arguments.

◆ dnnl_primitive_get_primitive_desc()

dnnl_status_t DNNL_API dnnl_primitive_get_primitive_desc	(	const_dnnl_primitive_t	primitive,
		const_dnnl_primitive_desc_t *	primitive_desc
	)

Retrieves a constant reference to the primitive descriptor of a given primitive.

Warning: It is an error to destroy the returned object. It is owned by the primitive. The const qualifier of the returned object prevents such attempts.

Parameters

primitive	Primitive to query for the primitive descriptor.
primitive_desc	Output primitive descriptor.

Returns: dnnl_success on success and a status describing the error otherwise.

Classes

Macros

Typedefs

Enumerations

Functions

Detailed Description

Macro Definition Documentation

◆ DNNL_ARG_ATTR_INPUT_SCALES

◆ DNNL_ARG_ATTR_MULTIPLE_POST_OP

◆ DNNL_ARG_ATTR_MULTIPLE_POST_OP_BASE

◆ DNNL_ARG_ATTR_OUTPUT_SCALES

◆ DNNL_ARG_ATTR_POST_OP_DW

◆ DNNL_ARG_ATTR_ZERO_POINTS

◆ DNNL_ARG_BIAS

◆ DNNL_ARG_DIFF_BIAS

◆ DNNL_ARG_DIFF_DST

◆ DNNL_ARG_DIFF_DST_0

◆ DNNL_ARG_DIFF_DST_1

◆ DNNL_ARG_DIFF_DST_2

◆ DNNL_ARG_DIFF_DST_ITER

◆ DNNL_ARG_DIFF_DST_ITER_C

◆ DNNL_ARG_DIFF_DST_LAYER

◆ DNNL_ARG_DIFF_SCALE

◆ DNNL_ARG_DIFF_SCALE_SHIFT

◆ DNNL_ARG_DIFF_SHIFT

◆ DNNL_ARG_DIFF_SRC

◆ DNNL_ARG_DIFF_SRC_0

◆ DNNL_ARG_DIFF_SRC_1

◆ DNNL_ARG_DIFF_SRC_2

◆ DNNL_ARG_DIFF_SRC_ITER

◆ DNNL_ARG_DIFF_SRC_ITER_C

◆ DNNL_ARG_DIFF_SRC_LAYER

◆ DNNL_ARG_DIFF_WEIGHTS

◆ DNNL_ARG_DIFF_WEIGHTS_0

◆ DNNL_ARG_DIFF_WEIGHTS_1

◆ DNNL_ARG_DIFF_WEIGHTS_2

◆ DNNL_ARG_DIFF_WEIGHTS_3

◆ DNNL_ARG_DIFF_WEIGHTS_ITER

◆ DNNL_ARG_DIFF_WEIGHTS_LAYER

◆ DNNL_ARG_DIFF_WEIGHTS_PEEPHOLE

◆ DNNL_ARG_DIFF_WEIGHTS_PROJECTION

◆ DNNL_ARG_DST

◆ DNNL_ARG_DST_0

◆ DNNL_ARG_DST_1

◆ DNNL_ARG_DST_2

◆ DNNL_ARG_DST_ITER

◆ DNNL_ARG_DST_ITER_C

◆ DNNL_ARG_DST_LAYER

◆ DNNL_ARG_FROM

◆ DNNL_ARG_MEAN

◆ DNNL_ARG_MULTIPLE_DST

◆ DNNL_ARG_MULTIPLE_SRC

◆ DNNL_ARG_SCALE

◆ DNNL_ARG_SCALE_SHIFT

◆ DNNL_ARG_SCRATCHPAD

◆ DNNL_ARG_SHIFT

◆ DNNL_ARG_SRC

◆ DNNL_ARG_SRC_0

◆ DNNL_ARG_SRC_1

◆ DNNL_ARG_SRC_2

◆ DNNL_ARG_SRC_ITER

◆ DNNL_ARG_SRC_ITER_C

◆ DNNL_ARG_SRC_LAYER

◆ DNNL_ARG_TO

◆ DNNL_ARG_VARIANCE

◆ DNNL_ARG_WEIGHTS

◆ DNNL_ARG_WEIGHTS_0

◆ DNNL_ARG_WEIGHTS_1

◆ DNNL_ARG_WEIGHTS_2

◆ DNNL_ARG_WEIGHTS_3

◆ DNNL_ARG_WEIGHTS_ITER

◆ DNNL_ARG_WEIGHTS_LAYER

◆ DNNL_ARG_WEIGHTS_PEEPHOLE

◆ DNNL_ARG_WEIGHTS_PROJECTION

◆ DNNL_ARG_WORKSPACE

Typedef Documentation

◆ const_dnnl_op_desc_t

◆ const_dnnl_primitive_desc_iterator_t

◆ const_dnnl_primitive_desc_t

◆ const_dnnl_primitive_t