Intel® oneAPI Deep Neural Network Developer Guide and Reference
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Visible to Intel only — GUID: GUID-D925BF4E-54C9-4C8D-80B5-D5C16B31285D
enum dnnl_alg_kind_t
Overview
Kinds of algorithms. More…
#include <dnnl_types.h> 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 = 0x20, dnnl_eltwise_tanh, dnnl_eltwise_elu, dnnl_eltwise_square, dnnl_eltwise_abs, dnnl_eltwise_sqrt, dnnl_eltwise_linear, dnnl_eltwise_soft_relu, dnnl_eltwise_hardsigmoid, dnnl_eltwise_logistic, dnnl_eltwise_exp, dnnl_eltwise_gelu_tanh, dnnl_eltwise_swish, dnnl_eltwise_log, dnnl_eltwise_clip, dnnl_eltwise_clip_v2, dnnl_eltwise_pow, dnnl_eltwise_gelu_erf, dnnl_eltwise_round, dnnl_eltwise_mish, dnnl_eltwise_hardswish, dnnl_eltwise_relu_use_dst_for_bwd = 0x100, dnnl_eltwise_tanh_use_dst_for_bwd, dnnl_eltwise_elu_use_dst_for_bwd, dnnl_eltwise_sqrt_use_dst_for_bwd, dnnl_eltwise_logistic_use_dst_for_bwd, dnnl_eltwise_exp_use_dst_for_bwd, dnnl_eltwise_clip_v2_use_dst_for_bwd, dnnl_pooling_max = 0x1ff, dnnl_pooling_avg_include_padding = 0x2ff, dnnl_pooling_avg_exclude_padding = 0x3ff, 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_vanilla_augru = 0x5fff, dnnl_lbr_augru = 0x6fff, 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_binary_select = 0x1fffc, 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, dnnl_softmax_accurate = 0x30000, dnnl_softmax_log, };
Detailed Documentation
Kinds of algorithms.
Enum Values
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_soft_relu
Eltwise: soft_relu.
dnnl_eltwise_hardsigmoid
Eltwise: hardsigmoid.
dnnl_eltwise_logistic
Eltwise: logistic.
dnnl_eltwise_exp
Eltwise: exponent.
dnnl_eltwise_gelu_tanh
Eltwise: gelu.
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_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_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:
Primitive expects 4 biases on input: 
dnnl_vanilla_augru
AUGRU cell.
dnnl_lbr_augru
AUGRU cell with linear before reset.
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_binary_select
Binary select.
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_softmax_accurate
Softmax.
dnnl_softmax_log
Logsoftmax.