Developer Guide and Reference

  • 2022.1
  • 04/11/2022
  • Public Content
Contents

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 = 0x1f, dnnl_eltwise_tanh = 0x2f, dnnl_eltwise_elu = 0x3f, dnnl_eltwise_square = 0x4f, dnnl_eltwise_abs = 0x5f, dnnl_eltwise_sqrt = 0x6f, dnnl_eltwise_linear = 0x7f, dnnl_eltwise_bounded_relu = 0x8f, dnnl_eltwise_soft_relu = 0x9f, dnnl_eltwise_logistic = 0xaf, dnnl_eltwise_exp = 0xbf, dnnl_eltwise_gelu_tanh = 0xcf, dnnl_eltwise_gelu = dnnl_eltwise_gelu_tanh, dnnl_eltwise_swish = 0xdf, dnnl_eltwise_log = 0xef, dnnl_eltwise_clip = 0xff, dnnl_eltwise_clip_v2 = 0x10, dnnl_eltwise_pow = 0x20, dnnl_eltwise_gelu_erf = 0x30, dnnl_eltwise_round = 0x40, dnnl_eltwise_logsigmoid = 0x50, dnnl_eltwise_mish = 0x60, dnnl_eltwise_hardswish = 0x70, dnnl_eltwise_relu_use_dst_for_bwd = 0x100, dnnl_eltwise_tanh_use_dst_for_bwd = 0x101, dnnl_eltwise_elu_use_dst_for_bwd = 0x102, dnnl_eltwise_sqrt_use_dst_for_bwd = 0x103, dnnl_eltwise_logistic_use_dst_for_bwd = 0x104, dnnl_eltwise_exp_use_dst_for_bwd = 0x105, dnnl_eltwise_clip_v2_use_dst_for_bwd = 0x106, dnnl_pooling_max = 0x1ff, dnnl_pooling_avg_include_padding = 0x2ff, dnnl_pooling_avg_exclude_padding = 0x3ff, dnnl_pooling_avg = dnnl_pooling_avg_exclude_padding, 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_bounded_relu
Eltwise: bounded_relu.
dnnl_eltwise_soft_relu
Eltwise: soft_relu.
dnnl_eltwise_logistic
Eltwise: logistic.
dnnl_eltwise_exp
Eltwise: exponent.
dnnl_eltwise_gelu_tanh
Eltwise: gelu.
@note Tanh approximation formula is used to approximate the cumulative distribution function of a Gaussian here
dnnl_eltwise_gelu
Eltwise: tanh-based gelu (alias for dnnl_eltwise_gelu_tanh)
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_logsigmoid
Eltwise: logsigmoid.
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_pooling_avg
Average pooling (alias for dnnl_pooling_avg_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: \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$[b_{u}, b_{r}, b_{c_x}, b_{c_h}]\f$
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.

Product and Performance Information

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Performance varies by use, configuration and other factors. Learn more at www.Intel.com/PerformanceIndex.