Common data types and enumerations

A proxy to Types in C API. More...

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: \[ c_t = tanh(W_c*x_t + b_{c_x} + r_t*(U_c*h_{t-1}+b_{c_h})) \] Primitive expects 4 biases on input: \([b_{u}, b_{r}, b_{c_x}, b_{c_h}]\)
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 See also Primitive 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