enum dnnl::algorithm¶
Overview¶
Kinds of algorithms. More…
#include <dnnl.hpp> enum algorithm { undef = dnnl_alg_kind_undef, convolution_auto = dnnl_convolution_auto, convolution_direct = dnnl_convolution_direct, convolution_winograd = dnnl_convolution_winograd, deconvolution_direct = dnnl_deconvolution_direct, deconvolution_winograd = dnnl_deconvolution_winograd, eltwise_relu = dnnl_eltwise_relu, eltwise_tanh = dnnl_eltwise_tanh, eltwise_elu = dnnl_eltwise_elu, eltwise_square = dnnl_eltwise_square, eltwise_abs = dnnl_eltwise_abs, eltwise_sqrt = dnnl_eltwise_sqrt, eltwise_swish = dnnl_eltwise_swish, eltwise_linear = dnnl_eltwise_linear, eltwise_bounded_relu = dnnl_eltwise_bounded_relu, eltwise_soft_relu = dnnl_eltwise_soft_relu, eltwise_logsigmoid = dnnl_eltwise_logsigmoid, eltwise_mish = dnnl_eltwise_mish, eltwise_logistic = dnnl_eltwise_logistic, eltwise_exp = dnnl_eltwise_exp, eltwise_gelu = dnnl_eltwise_gelu, eltwise_gelu_tanh = dnnl_eltwise_gelu_tanh, eltwise_gelu_erf = dnnl_eltwise_gelu_erf, eltwise_log = dnnl_eltwise_log, eltwise_clip = dnnl_eltwise_clip, eltwise_clip_v2 = dnnl_eltwise_clip_v2, eltwise_pow = dnnl_eltwise_pow, eltwise_round = dnnl_eltwise_round, eltwise_hardswish = dnnl_eltwise_hardswish, eltwise_relu_use_dst_for_bwd = dnnl_eltwise_relu_use_dst_for_bwd, eltwise_tanh_use_dst_for_bwd = dnnl_eltwise_tanh_use_dst_for_bwd, eltwise_elu_use_dst_for_bwd = dnnl_eltwise_elu_use_dst_for_bwd, eltwise_sqrt_use_dst_for_bwd = dnnl_eltwise_sqrt_use_dst_for_bwd, eltwise_logistic_use_dst_for_bwd = dnnl_eltwise_logistic_use_dst_for_bwd, eltwise_exp_use_dst_for_bwd = dnnl_eltwise_exp_use_dst_for_bwd, eltwise_clip_v2_use_dst_for_bwd = dnnl_eltwise_clip_v2_use_dst_for_bwd, lrn_across_channels = dnnl_lrn_across_channels, lrn_within_channel = dnnl_lrn_within_channel, pooling_max = dnnl_pooling_max, pooling_avg = dnnl_pooling_avg, pooling_avg_include_padding = dnnl_pooling_avg_include_padding, pooling_avg_exclude_padding = dnnl_pooling_avg_exclude_padding, vanilla_rnn = dnnl_vanilla_rnn, vanilla_lstm = dnnl_vanilla_lstm, vanilla_gru = dnnl_vanilla_gru, lbr_gru = dnnl_lbr_gru, binary_add = dnnl_binary_add, binary_mul = dnnl_binary_mul, binary_max = dnnl_binary_max, binary_min = dnnl_binary_min, binary_div = dnnl_binary_div, binary_sub = dnnl_binary_sub, binary_ge = dnnl_binary_ge, binary_gt = dnnl_binary_gt, binary_le = dnnl_binary_le, binary_lt = dnnl_binary_lt, binary_eq = dnnl_binary_eq, binary_ne = dnnl_binary_ne, resampling_nearest = dnnl_resampling_nearest, resampling_linear = dnnl_resampling_linear, reduction_max = dnnl_reduction_max, reduction_min = dnnl_reduction_min, reduction_sum = dnnl_reduction_sum, reduction_mul = dnnl_reduction_mul, reduction_mean = dnnl_reduction_mean, reduction_norm_lp_max = dnnl_reduction_norm_lp_max, reduction_norm_lp_sum = dnnl_reduction_norm_lp_sum, reduction_norm_lp_power_p_max = dnnl_reduction_norm_lp_power_p_max, reduction_norm_lp_power_p_sum = dnnl_reduction_norm_lp_power_p_sum, };
Detailed Documentation¶
Kinds of algorithms.
Enum Values¶
undef
Undefined algorithm.
convolution_auto
Convolution algorithm that is chosen to be either direct or Winograd automatically.
convolution_direct
Direct convolution.
convolution_winograd
Winograd convolution.
deconvolution_direct
Direct deconvolution.
deconvolution_winograd
Winograd deconvolution.
eltwise_relu
Elementwise: rectified linear unit (ReLU)
eltwise_tanh
Elementwise: hyperbolic tangent non-linearity (tanh)
eltwise_elu
Elementwise: exponential linear unit (ELU)
eltwise_square
Elementwise: square.
eltwise_abs
Elementwise: abs.
eltwise_sqrt
Elementwise: square root.
eltwise_swish
Elementwise: swish (\(x \cdot sigmoid(a \cdot x)\))
eltwise_linear
Elementwise: linear.
eltwise_bounded_relu
Elementwise: bounded_relu.
eltwise_soft_relu
Elementwise: soft_relu.
eltwise_logsigmoid
Elementwise: logsigmoid.
eltwise_mish
Elementwise: mish.
eltwise_logistic
Elementwise: logistic.
eltwise_exp
Elementwise: exponent.
eltwise_gelu
Elementwise: gelu alias for dnnl::algorithm::eltwise_gelu_tanh.
eltwise_gelu_tanh
Elementwise: tanh-based gelu.
eltwise_gelu_erf
Elementwise: erf-based gelu.
eltwise_log
Elementwise: natural logarithm.
eltwise_clip
Elementwise: clip.
eltwise_clip_v2
Eltwise: clip version 2.
eltwise_pow
Elementwise: pow.
eltwise_round
Elementwise: round.
eltwise_hardswish
Elementwise: hardswish.
eltwise_relu_use_dst_for_bwd
Elementwise: rectified linar unit (ReLU) (dst for backward)
eltwise_tanh_use_dst_for_bwd
Elementwise: hyperbolic tangent non-linearity (tanh) (dst for backward)
eltwise_elu_use_dst_for_bwd
Elementwise: exponential linear unit (ELU) (dst for backward)
eltwise_sqrt_use_dst_for_bwd
Elementwise: square root (dst for backward)
eltwise_logistic_use_dst_for_bwd
Elementwise: logistic (dst for backward)
eltwise_exp_use_dst_for_bwd
Elementwise: exponent (dst for backward)
eltwise_clip_v2_use_dst_for_bwd
Elementwise: clip version 2 (dst for backward)
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_exclude_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.
Differs from the vanilla GRU in how the new memory gate is calculated: \(c_t = tanh(W_c*x_t + b_{c_x} + r_t*(U_c*h_{t-1}+b_{c_h}))\) LRB GRU expects 4 bias tensors on input: \([b_{u}, b_{r}, b_{c_x}, b_{c_h}]\)
binary_add
Binary add.
binary_mul
Binary mul.
binary_max
Binary max.
binary_min
Binary min.
binary_div
Binary div.
binary_sub
Binary sub.
binary_ge
Binary greater than or equal.
binary_gt
Binary greater than.
binary_le
Binary less than or equal.
binary_lt
Binary less than.
binary_eq
Binary equal.
binary_ne
Binary not equal.
resampling_nearest
Nearest Neighbor resampling method.
resampling_linear
Linear (Bilinear, Trilinear) resampling method.
reduction_max
Reduction using max operation.
reduction_min
Reduction using min operation.
reduction_sum
Reduction using sum operation.
reduction_mul
Reduction using mul operation.
reduction_mean
Reduction using mean operation.
reduction_norm_lp_max
Reduction using norm_lp_max operation.
reduction_norm_lp_sum
Reduction using norm_lp_sum operation.
reduction_norm_lp_power_p_max
Reduction using norm_lp_power_p_max operation.
reduction_norm_lp_power_p_sum
Reduction using norm_lp_power_p_sum operation.