Common data types and enumerations

Enumerations
enum	dnnl::scratchpad_mode { dnnl::scratchpad_mode::library = dnnl_scratchpad_mode_library, dnnl::scratchpad_mode::user = dnnl_scratchpad_mode_user }
	Scratchpad mode. More...
enum	dnnl::prop_kind {   dnnl::prop_kind::undef = dnnl_prop_kind_undef, dnnl::prop_kind::forward_training = dnnl_forward_training, dnnl::prop_kind::forward_inference = dnnl_forward_inference, dnnl::prop_kind::forward_scoring = dnnl_forward_scoring,   dnnl::prop_kind::forward = dnnl_forward, dnnl::prop_kind::backward = dnnl_backward, dnnl::prop_kind::backward_data = dnnl_backward_data, dnnl::prop_kind::backward_weights = dnnl_backward_weights,   dnnl::prop_kind::backward_bias = dnnl_backward_bias }
	Propagation kind. More...
enum	dnnl::algorithm { ,   dnnl::algorithm::convolution_auto = dnnl_convolution_auto, dnnl::algorithm::convolution_direct = dnnl_convolution_direct, dnnl::algorithm::convolution_winograd = dnnl_convolution_winograd, dnnl::algorithm::deconvolution_direct = dnnl_deconvolution_direct,   dnnl::algorithm::deconvolution_winograd = dnnl_deconvolution_winograd, dnnl::algorithm::eltwise_relu = dnnl_eltwise_relu, dnnl::algorithm::eltwise_tanh = dnnl_eltwise_tanh, dnnl::algorithm::eltwise_elu = dnnl_eltwise_elu,   dnnl::algorithm::eltwise_square = dnnl_eltwise_square, dnnl::algorithm::eltwise_abs = dnnl_eltwise_abs, dnnl::algorithm::eltwise_sqrt = dnnl_eltwise_sqrt, dnnl::algorithm::eltwise_swish = dnnl_eltwise_swish,   dnnl::algorithm::eltwise_linear = dnnl_eltwise_linear, dnnl::algorithm::eltwise_bounded_relu = dnnl_eltwise_bounded_relu, dnnl::algorithm::eltwise_soft_relu = dnnl_eltwise_soft_relu, dnnl::algorithm::eltwise_logistic = dnnl_eltwise_logistic,   dnnl::algorithm::eltwise_exp = dnnl_eltwise_exp, dnnl::algorithm::eltwise_gelu = dnnl_eltwise_gelu, dnnl::algorithm::lrn_across_channels = dnnl_lrn_across_channels, dnnl::algorithm::lrn_within_channel = dnnl_lrn_within_channel,   dnnl::algorithm::pooling_max = dnnl_pooling_max, dnnl::algorithm::pooling_avg = dnnl_pooling_avg, dnnl::algorithm::pooling_avg_include_padding = dnnl_pooling_avg_include_padding, dnnl::algorithm::pooling_avg_exclude_padding = dnnl_pooling_avg_exclude_padding,   dnnl::algorithm::vanilla_rnn = dnnl_vanilla_rnn, dnnl::algorithm::vanilla_lstm = dnnl_vanilla_lstm, dnnl::algorithm::vanilla_gru = dnnl_vanilla_gru, dnnl::algorithm::lbr_gru = dnnl_lbr_gru,   dnnl::algorithm::binary_add = dnnl_binary_add, dnnl::algorithm::binary_mul = dnnl_binary_mul }
	Kinds of algorithms. More...
enum	dnnl::normalization_flags : unsigned { dnnl::normalization_flags::use_global_stats = dnnl_use_global_stats, dnnl::normalization_flags::use_scale_shift = dnnl_use_scaleshift, dnnl::normalization_flags::fuse_norm_relu = dnnl_fuse_norm_relu }
	Flags for batch normalization primitive. More...
enum	dnnl::query {   dnnl::query::undef = dnnl_query_undef, dnnl::query::engine = dnnl_query_engine, dnnl::query::primitive_kind = dnnl_query_primitive_kind, dnnl::query::num_of_inputs_s32 = dnnl_query_num_of_inputs_s32,   dnnl::query::num_of_outputs_s32 = dnnl_query_num_of_outputs_s32, dnnl::query::time_estimate_f64 = dnnl_query_time_estimate_f64, dnnl::query::memory_consumption_s64 = dnnl_query_memory_consumption_s64, dnnl::query::scratchpad_engine = dnnl_query_scratchpad_engine,   dnnl::query::reorder_src_engine = dnnl_query_reorder_src_engine, dnnl::query::reorder_dst_engine = dnnl_query_reorder_dst_engine, dnnl::query::impl_info_str = dnnl_query_impl_info_str, dnnl::query::op_d = dnnl_query_op_d,   dnnl::query::convolution_d = dnnl_query_convolution_d, dnnl::query::deconvolution_d = dnnl_query_deconvolution_d, dnnl::query::shuffle_d = dnnl_query_shuffle_d, dnnl::query::eltwise_d = dnnl_query_eltwise_d,   dnnl::query::softmax_d = dnnl_query_softmax_d, dnnl::query::pooling_d = dnnl_query_pooling_d, dnnl::query::lrn_d = dnnl_query_lrn_d, dnnl::query::batch_normalization_d = dnnl_query_batch_normalization_d,   dnnl::query::layer_normalization_d = dnnl_query_layer_normalization_d, dnnl::query::inner_product_d = dnnl_query_inner_product_d, dnnl::query::rnn_d = dnnl_query_rnn_d, dnnl::query::binary_d = dnnl_query_binary_d,   dnnl::query::src_md = dnnl_query_src_md, dnnl::query::diff_src_md = dnnl_query_diff_src_md, dnnl::query::weights_md = dnnl_query_weights_md, dnnl::query::diff_weights_md = dnnl_query_diff_weights_md,   dnnl::query::dst_md = dnnl_query_dst_md, dnnl::query::diff_dst_md = dnnl_query_diff_dst_md, dnnl::query::workspace_md = dnnl_query_workspace_md, dnnl::query::scratchpad_md = dnnl_query_scratchpad_md }
	Primitive descriptor query specification. More...

Detailed Description

A proxy to Types in C API.

Enumeration Type Documentation

scratchpad_mode

enum dnnl::scratchpad_mode

strong

Scratchpad mode.

Enumerator
library	The library manages scratchpad (default)
user	A user shall query and provide the scratchpad memory to primitives.

prop_kind

enum dnnl::prop_kind

strong

Propagation kind.

Enumerator
undef	Undefined propagation kind.
forward_training	Forward data propagation (training mode). In this mode primitives perform computations necessary for subsequent backward propagation.
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.
forward_scoring	Forward data propagation, alias for dnnl::prop_kind::forward_inference.
forward	Forward data propagation, alias for dnnl::prop_kind::forward_training.
backward	Backward propagation (with respect to all parameters).
backward_data	Backward data propagation.
backward_weights	Backward weights propagation.
backward_bias	Backward bias propagation.

algorithm

enum dnnl::algorithm

strong

Kinds of algorithms.

Enumerator
convolution_auto	Convolution algorithm(either direct or Winograd) is chosen just in time.
convolution_direct	Direct convolution.
convolution_winograd	Winograd convolution.
deconvolution_direct	Direct deconvolution.
deconvolution_winograd	Winograd deconvolution.
eltwise_relu	Eltwise: ReLU.
eltwise_tanh	Eltwise: hyperbolic tangent non-linearity (tanh)
eltwise_elu	Eltwise: parametric exponential linear unit (elu)
eltwise_square	Eltwise: square.
eltwise_abs	Eltwise: abs.
eltwise_sqrt	Eltwise: square root.
eltwise_swish	Eltwise: x*sigmoid(a*x)
eltwise_linear	Eltwise: linear.
eltwise_bounded_relu	Eltwise: bounded_relu.
eltwise_soft_relu	Eltwise: soft_relu.
eltwise_logistic	Eltwise: logistic.
eltwise_exp	Eltwise: exponent.
eltwise_gelu	Eltwise: gelu.
lrn_across_channels	Local response normalization (LRN) across multiple channels.
lrn_within_channel	LRN within a single channel.
pooling_max	Max pooling.
pooling_avg	Average pooling exclude padding, alias for dnnl::algorithm::pooling_avg_include_padding.
pooling_avg_include_padding	Average pooling include padding.
pooling_avg_exclude_padding	Average pooling exclude padding.
vanilla_rnn	RNN cell.
vanilla_lstm	LSTM cell.
vanilla_gru	GRU cell.
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: \f[ c_t = tanh(W_c*x_t + b_{c_x} + r_t*(U_c*h_{t-1}+b_{c_h})) \f] Primitive expects 4 biases on input: \f$[b_{u}, b_{r}, b_{c_x}, b_{c_h}]\f$
binary_add	Binary add.
binary_mul	Binary mul.

normalization_flags

enum dnnl::normalization_flags : unsigned

strong

Flags for batch normalization primitive.

Enumerator
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
use_scale_shift	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::prop_kind::backward) compute diff wrt to scale and shift (hence one extra output used) If not specified: - on backward propagation prop_kind == #dnnl::prop_kind::backward_data has the same behavior as prop_kind == #dnnl::prop_kind::backward
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)

query

enum dnnl::query

strong

Primitive descriptor query specification.

In general should be used from C++ API since required queries are directly implemented as class members (for instance, a query for source memory descriptor).

For more information see dnnl_query_t.

Enumerator
undef	no query
engine	execution engine
primitive_kind	primitive kind
num_of_inputs_s32	number of inputs expected
num_of_outputs_s32	number of outputs expected
time_estimate_f64	runtime estimation (seconds), unimplemented
memory_consumption_s64	memory consumption (bytes) extra (scratch) memory, additional to all inputs and outputs memory @sa @ref dev_guide_attributes_scratchpad
scratchpad_engine	scratchpad engine engine to be used for creating scratchpad memory
reorder_src_engine	reorder source engine
reorder_dst_engine	reorder destination engine
impl_info_str	implementation name
op_d	op descriptor
convolution_d	convolution descriptor
deconvolution_d	deconvolution descriptor
shuffle_d	shuffle descriptor
eltwise_d	eltwise descriptor
softmax_d	softmax descriptor
pooling_d	pooling descriptor
lrn_d	lrn descriptor
batch_normalization_d	batch normalization descriptor
layer_normalization_d	layer normalization descriptor
inner_product_d	inner product descriptor
rnn_d	rnn descriptor
binary_d	binary descriptor
src_md	source memory desc
diff_src_md	source gradient memory desc
weights_md	weights memory descriptor desc
diff_weights_md	weights grad. memory desc
dst_md	destination memory desc
diff_dst_md	destination grad. memory desc
workspace_md	workspace memory desc
scratchpad_md	scratchpad memory desc