Intel® oneAPI Deep Neural Network Developer Guide and Reference
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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_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_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.