mkldnn Namespace Reference

Classes
struct	batch_normalization_backward
struct	batch_normalization_forward
struct	concat
struct	convolution_backward_data
struct	convolution_backward_weights
struct	convolution_forward
struct	deconvolution_backward_data
struct	deconvolution_backward_weights
struct	deconvolution_forward
struct	eltwise_backward
struct	eltwise_forward
struct	engine
	An execution engine. More...
struct	error
	Intel(R) MKL-DNN exception class. More...
class	handle
	A class for wrapping an Intel(R) MKL-DNN handle. It is used as the base class for primitive (mkldnn_primitive_t), engine (mkldnn_engine_t), and stream (mkldnn_stream_t) handles. An object of the mkldnn::handle class can be passed by value. This class enables wrapping: More...
class	handle_traits
	A class that provides the destructor for an Intel(R) MKL-DNN C handle. More...
struct	inner_product_backward_data
struct	inner_product_backward_weights
struct	inner_product_forward
struct	lrn_backward
struct	lrn_forward
struct	memory
	Memory primitive that describes the data. More...
struct	pooling_backward
struct	pooling_forward
struct	post_ops
class	primitive
	Base class for all computational primitives. More...
struct	primitive_attr
struct	primitive_desc
	A base class for all primitive descriptors. More...
struct	reorder
struct	rnn_backward
struct	rnn_cell
struct	rnn_forward
struct	shuffle_backward
struct	shuffle_forward
struct	softmax_backward
struct	softmax_forward
struct	stream
struct	sum
struct	view

Enumerations
enum	round_mode { round_nearest = mkldnn_round_nearest, round_down = mkldnn_round_down }
enum	padding_kind { zero = mkldnn_padding_zero }
enum	prop_kind {   forward_training = mkldnn_forward_training, forward_scoring = mkldnn_forward_scoring, forward_inference = mkldnn_forward_inference, forward = mkldnn_forward,   backward = mkldnn_backward, backward_data = mkldnn_backward_data, backward_weights = mkldnn_backward_weights, backward_bias = mkldnn_backward_bias }
enum	algorithm {   algorithm_undef = mkldnn_alg_kind_undef, convolution_auto = mkldnn_convolution_auto, convolution_direct = mkldnn_convolution_direct, convolution_winograd = mkldnn_convolution_winograd,   deconvolution_direct = mkldnn_deconvolution_direct, deconvolution_winograd = mkldnn_deconvolution_winograd, eltwise_relu = mkldnn_eltwise_relu, eltwise_tanh = mkldnn_eltwise_tanh,   eltwise_elu = mkldnn_eltwise_elu, eltwise_square = mkldnn_eltwise_square, eltwise_abs = mkldnn_eltwise_abs, eltwise_sqrt = mkldnn_eltwise_sqrt,   eltwise_linear = mkldnn_eltwise_linear, eltwise_bounded_relu = mkldnn_eltwise_bounded_relu, eltwise_soft_relu = mkldnn_eltwise_soft_relu, eltwise_logistic = mkldnn_eltwise_logistic,   eltwise_exp = mkldnn_eltwise_exp, eltwise_gelu = mkldnn_eltwise_gelu, lrn_across_channels = mkldnn_lrn_across_channels, lrn_within_channel = mkldnn_lrn_within_channel,   pooling_max = mkldnn_pooling_max, pooling_avg = mkldnn_pooling_avg, pooling_avg_include_padding = mkldnn_pooling_avg_include_padding, pooling_avg_exclude_padding = mkldnn_pooling_avg_exclude_padding,   vanilla_rnn = mkldnn_vanilla_rnn, vanilla_lstm = mkldnn_vanilla_lstm, vanilla_gru = mkldnn_vanilla_gru, gru_linear_before_reset = mkldnn_gru_linear_before_reset }
enum	batch_normalization_flag { use_global_stats = mkldnn_use_global_stats, use_scale_shift = mkldnn_use_scaleshift, fuse_bn_relu = mkldnn_fuse_bn_relu }
enum	rnn_direction {   unidirectional_left2right = mkldnn_unidirectional_left2right, unidirectional_right2left = mkldnn_unidirectional_right2left, unidirectional = mkldnn_unidirectional, bidirectional_concat = mkldnn_bidirectional_concat,   bidirectional_sum = mkldnn_bidirectional_sum }
enum	query {   undef = mkldnn_query_undef, eengine = mkldnn_query_engine, primitive_kind = mkldnn_query_primitive_kind, num_of_inputs_s32 = mkldnn_query_num_of_inputs_s32,   num_of_outputs_s32 = mkldnn_query_num_of_outputs_s32, time_estimate_f64 = mkldnn_query_time_estimate_f64, memory_consumption_s64 = mkldnn_query_memory_consumption_s64, impl_info_str = mkldnn_query_impl_info_str,   op_d = mkldnn_query_op_d, memory_d = mkldnn_query_memory_d, convolution_d = mkldnn_query_convolution_d, deconvolution_d = mkldnn_query_deconvolution_d,   shuffle_d = mkldnn_query_shuffle_d, eltwise_d = mkldnn_query_eltwise_d, softmax_d = mkldnn_query_softmax_d, pooling_d = mkldnn_query_pooling_d,   lrn_d = mkldnn_query_lrn_d, batch_normalization_d = mkldnn_query_batch_normalization_d, inner_product_d = mkldnn_query_inner_product_d, rnn_d = mkldnn_query_rnn_d,   input_pd = mkldnn_query_input_pd, output_pd = mkldnn_query_output_pd, src_pd = mkldnn_query_src_pd, diff_src_pd = mkldnn_query_diff_src_pd,   weights_pd = mkldnn_query_weights_pd, diff_weights_pd = mkldnn_query_diff_weights_pd, dst_pd = mkldnn_query_dst_pd, diff_dst_pd = mkldnn_query_diff_dst_pd,   workspace_pd = mkldnn_query_workspace_pd }

Functions
mkldnn_primitive_kind_t	convert_to_c (primitive::kind akind)
mkldnn_round_mode_t	convert_to_c (round_mode mode)
mkldnn_padding_kind_t	convert_to_c (padding_kind kind)
mkldnn_prop_kind_t	convert_to_c (prop_kind kind)
mkldnn_alg_kind_t	convert_to_c (algorithm aalgorithm)
mkldnn_batch_normalization_flag_t	convert_to_c (batch_normalization_flag aflag)
mkldnn_rnn_direction_t	convert_to_c (rnn_direction adir)
mkldnn_query_t	convert_to_c (query aquery)
memory::desc	zero_md ()
memory	null_memory (engine eng)
void	check_num_parameters (const const_mkldnn_primitive_desc_t &aprimitive_desc, int n_inputs, int n_outputs, const std::string &prim_name)
bool	is_null_memory (const const_mkldnn_primitive_t &aprimitive)
bool	operator== (mkldnn_data_type_t a, memory::data_type b)
bool	operator!= (mkldnn_data_type_t a, memory::data_type b)
bool	operator== (memory::data_type a, mkldnn_data_type_t b)
bool	operator!= (memory::data_type a, mkldnn_data_type_t b)
bool	operator== (mkldnn_memory_format_t a, memory::format b)
bool	operator!= (mkldnn_memory_format_t a, memory::format b)
bool	operator== (memory::format a, mkldnn_memory_format_t b)
bool	operator!= (memory::format a, mkldnn_memory_format_t b)