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wiki-sync-bot edited this page Oct 15, 2024 · 1 revision

AbsVal

y = abs(x)
  • one_blob_only
  • support_inplace

ArgMax

y = argmax(x, out_max_val, topk)
  • one_blob_only
param id name type default description
0 out_max_val int 0
1 topk int 1

BatchNorm

y = (x - mean) / sqrt(var + eps) * slope + bias
  • one_blob_only
  • support_inplace
param id name type default description
0 channels int 0
1 eps float 0.f
weight type shape
slope_data float [channels]
mean_data float [channels]
var_data float [channels]
bias_data float [channels]

Bias

y = x + bias
  • one_blob_only
  • support_inplace
param id name type default description
0 bias_data_size int 0
weight type shape
bias_data float [channels]

BinaryOp

This operation is used for binary computation, and the calculation rule depends on the broadcasting rule.

C = binaryop(A, B)

if with_scalar = 1:

  • one_blob_only
  • support_inplace
param id name type default description
0 op_type int 0 Operation type as follows
1 with_scalar int 0 with_scalar=0 B is a matrix, with_scalar=1 B is a scalar
2 b float 0.f When B is a scalar, B = b

Operation type:

  • 0 = ADD
  • 1 = SUB
  • 2 = MUL
  • 3 = DIV
  • 4 = MAX
  • 5 = MIN
  • 6 = POW
  • 7 = RSUB
  • 8 = RDIV
  • 9 = RPOW
  • 10 = ATAN2
  • 11 = RATAN2

BNLL

y = log(1 + e^(-x)) , x > 0
y = log(1 + e^x),     x < 0
  • one_blob_only
  • support_inplace

Cast

y = cast(x)
  • one_blob_only
  • support_packing
param id name type default description
0 type_from int 0
1 type_to int 0

Element type:

  • 0 = auto
  • 1 = float32
  • 2 = float16
  • 3 = int8
  • 4 = bfloat16

CELU

if x < 0    y = (exp(x / alpha) - 1.f) * alpha
else        y = x
  • one_blob_only
  • support_inplace
param id name type default description
0 alpha float 1.f

Clip

y = clamp(x, min, max)
  • one_blob_only
  • support_inplace
param id name type default description
0 min float -FLT_MAX
1 max float FLT_MAX

Concat

y = concat(x0, x1, x2, ...) by axis
param id name type default description
0 axis int 0

Convolution

x2 = pad(x, pads, pad_value)
x3 = conv(x2, weight, kernel, stride, dilation) + bias
y = activation(x3, act_type, act_params)
  • one_blob_only
param id name type default description
0 num_output int 0
1 kernel_w int 0
2 dilation_w int 1
3 stride_w int 1
4 pad_left int 0
5 bias_term int 0
6 weight_data_size int 0
8 int8_scale_term int 0
9 activation_type int 0
10 activation_params array [ ]
11 kernel_h int kernel_w
12 dilation_h int dilation_w
13 stride_h int stride_w
14 pad_top int pad_left
15 pad_right int pad_left
16 pad_bottom int pad_top
18 pad_value float 0.f
19 dynamic_weight int 0
weight type shape
weight_data float/fp16/int8 [kernel_w, kernel_h, num_input, num_output]
bias_data float [num_output]
weight_data_int8_scales float [num_output]
bottom_blob_int8_scales float [1]
top_blob_int8_scales float [1]

Convolution1D

x2 = pad(x, pads, pad_value)
x3 = conv1d(x2, weight, kernel, stride, dilation) + bias
y = activation(x3, act_type, act_params)
  • one_blob_only
param id name type default description
0 num_output int 0
1 kernel_w int 0
2 dilation_w int 1
3 stride_w int 1
4 pad_left int 0
5 bias_term int 0
6 weight_data_size int 0
9 activation_type int 0
10 activation_params array [ ]
15 pad_right int pad_left
18 pad_value float 0.f
19 dynamic_weight int 0
weight type shape
weight_data float/fp16/int8 [kernel_w, num_input, num_output]
bias_data float [num_output]

Convolution3D

x2 = pad(x, pads, pad_value)
x3 = conv3d(x2, weight, kernel, stride, dilation) + bias
y = activation(x3, act_type, act_params)
  • one_blob_only
param id name type default description
0 num_output int 0
1 kernel_w int 0
2 dilation_w int 1
3 stride_w int 1
4 pad_left int 0
5 bias_term int 0
6 weight_data_size int 0
9 activation_type int 0
10 activation_params array [ ]
11 kernel_h int kernel_w
12 dilation_h int dilation_w
13 stride_h int stride_w
14 pad_top int pad_left
15 pad_right int pad_left
16 pad_bottom int pad_top
17 pad_behind int pad_front
18 pad_value float 0.f
21 kernel_d int kernel_w
22 dilation_d int dilation_w
23 stride_d int stride_w
24 pad_front int pad_left
weight type shape
weight_data float/fp16/int8 [kernel_w, kernel_h, kernel_d, num_input, num_output]
bias_data float [num_output]

ConvolutionDepthWise

x2 = pad(x, pads, pad_value)
x3 = conv(x2, weight, kernel, stride, dilation, group) + bias
y = activation(x3, act_type, act_params)
  • one_blob_only
param id name type default description
0 num_output int 0
1 kernel_w int 0
2 dilation_w int 1
3 stride_w int 1
4 pad_left int 0
5 bias_term int 0
6 weight_data_size int 0
7 group int 1
8 int8_scale_term int 0
9 activation_type int 0
10 activation_params array [ ]
11 kernel_h int kernel_w
12 dilation_h int dilation_w
13 stride_h int stride_w
14 pad_top int pad_left
15 pad_right int pad_left
16 pad_bottom int pad_top
18 pad_value float 0.f
19 dynamic_weight int 0
weight type shape
weight_data float/fp16/int8 [kernel_w, kernel_h, num_input / group, num_output / group, group]
bias_data float [num_output]
weight_data_int8_scales float [group]
bottom_blob_int8_scales float [1]
top_blob_int8_scales float [1]

ConvolutionDepthWise1D

x2 = pad(x, pads, pad_value)
x3 = conv1d(x2, weight, kernel, stride, dilation, group) + bias
y = activation(x3, act_type, act_params)
  • one_blob_only
param id name type default description
0 num_output int 0
1 kernel_w int 0
2 dilation_w int 1
3 stride_w int 1
4 pad_left int 0
5 bias_term int 0
6 weight_data_size int 0
7 group int 1
9 activation_type int 0
10 activation_params array [ ]
15 pad_right int pad_left
18 pad_value float 0.f
19 dynamic_weight int 0
weight type shape
weight_data float/fp16/int8 [kernel_w, num_input / group, num_output / group, group]
bias_data float [num_output]

ConvolutionDepthWise3D

x2 = pad(x, pads, pad_value)
x3 = conv3d(x2, weight, kernel, stride, dilation, group) + bias
y = activation(x3, act_type, act_params)
  • one_blob_only
param id name type default description
0 num_output int 0
1 kernel_w int 0
2 dilation_w int 1
3 stride_w int 1
4 pad_left int 0
5 bias_term int 0
6 weight_data_size int 0
7 group int 1
9 activation_type int 0
10 activation_params array [ ]
11 kernel_h int kernel_w
12 dilation_h int dilation_w
13 stride_h int stride_w
14 pad_top int pad_left
15 pad_right int pad_left
16 pad_bottom int pad_top
17 pad_behind int pad_front
18 pad_value float 0.f
21 kernel_d int kernel_w
22 dilation_d int dilation_w
23 stride_d int stride_w
24 pad_front int pad_left
weight type shape
weight_data float/fp16/int8 [kernel_w, kernel_h, kernel_d, num_input / group, num_output / group, group]
bias_data float [num_output]

CopyTo

self[offset] = src
  • one_blob_only
param id name type default description
0 woffset int 0
1 hoffset int 0
13 doffset int 0
2 coffset int 0
9 starts array [ ]
11 axes array [ ]

Crop

y = crop(x)
  • one_blob_only
param id name type default description
0 woffset int 0
1 hoffset int 0
13 doffset int 0
2 coffset int 0
3 outw int 0
4 outh int 0
14 outd int 0
5 outc int 0
6 woffset2 int 0
7 hoffset2 int 0
15 doffset2 int 0
8 coffset2 int 0
9 starts array [ ]
10 ends array [ ]
11 axes array [ ]

CumulativeSum

If axis < 0, we use axis = x.dims + axis

It implements https://pytorch.org/docs/stable/generated/torch.cumsum.html

  • one_blob_only
  • support_inplace
param id name type default description
0 axis int 0

Deconvolution

x2 = deconv(x, weight, kernel, stride, dilation) + bias
x3 = depad(x2, pads, pad_value)
y = activation(x3, act_type, act_params)
  • one_blob_only
param id name type default description
0 num_output int 0
1 kernel_w int 0
2 dilation_w int 1
3 stride_w int 1
4 pad_left int 0
5 bias_term int 0
6 weight_data_size int 0
9 activation_type int 0
10 activation_params array [ ]
11 kernel_h int kernel_w
12 dilation_h int dilation_w
13 stride_h int stride_w
14 pad_top int pad_left
15 pad_right int pad_left
16 pad_bottom int pad_top
18 output_pad_right int 0
19 output_pad_bottom int output_pad_right
20 output_w int 0
21 output_h int output_w
28 dynamic_weight int 0
weight type shape
weight_data float/fp16 [kernel_w, kernel_h, num_input, num_output]
bias_data float [num_output]

Deconvolution1D

x2 = deconv1d(x, weight, kernel, stride, dilation) + bias
x3 = depad(x2, pads, pad_value)
y = activation(x3, act_type, act_params)
  • one_blob_only
param id name type default description
0 num_output int 0
1 kernel_w int 0
2 dilation_w int 1
3 stride_w int 1
4 pad_left int 0
5 bias_term int 0
6 weight_data_size int 0
9 activation_type int 0
10 activation_params array [ ]
15 pad_right int pad_left
18 output_pad_right int 0
20 output_w int 0
28 dynamic_weight int 0
weight type shape
weight_data float/fp16 [kernel_w, num_input, num_output]
bias_data float [num_output]

Deconvolution3D

x2 = deconv3d(x, weight, kernel, stride, dilation) + bias
x3 = depad(x2, pads, pad_value)
y = activation(x3, act_type, act_params)
  • one_blob_only
param id name type default description
0 num_output int 0
1 kernel_w int 0
2 dilation_w int 1
3 stride_w int 1
4 pad_left int 0
5 bias_term int 0
6 weight_data_size int 0
9 activation_type int 0
10 activation_params array [ ]
11 kernel_h int kernel_w
12 dilation_h int dilation_w
13 stride_h int stride_w
14 pad_top int pad_left
15 pad_right int pad_left
16 pad_bottom int pad_top
17 pad_behind int pad_front
18 output_pad_right int 0
19 output_pad_bottom int output_pad_right
20 output_pad_behind int output_pad_right
21 kernel_d int kernel_w
22 dilation_d int dilation_w
23 stride_d int stride_w
24 pad_front int pad_left
25 output_w int 0
26 output_h int output_w
27 output_d int output_w
weight type shape
weight_data float/fp16 [kernel_w, kernel_h, kernel_d, num_input, num_output]
bias_data float [num_output]

DeconvolutionDepthWise

x2 = deconv(x, weight, kernel, stride, dilation, group) + bias
x3 = depad(x2, pads, pad_value)
y = activation(x3, act_type, act_params)
  • one_blob_only
param id name type default description
0 num_output int 0
1 kernel_w int 0
2 dilation_w int 1
3 stride_w int 1
4 pad_left int 0
5 bias_term int 0
6 weight_data_size int 0
7 group int 1
9 activation_type int 0
10 activation_params array [ ]
11 kernel_h int kernel_w
12 dilation_h int dilation_w
13 stride_h int stride_w
14 pad_top int pad_left
15 pad_right int pad_left
16 pad_bottom int pad_top
18 output_pad_right int 0
19 output_pad_bottom int output_pad_right
20 output_w int 0
21 output_h int output_w
28 dynamic_weight int 0
weight type shape
weight_data float/fp16 [kernel_w, kernel_h, num_input / group, num_output / group, group]
bias_data float [num_output]

DeconvolutionDepthWise1D

x2 = deconv1d(x, weight, kernel, stride, dilation, group) + bias
x3 = depad(x2, pads, pad_value)
y = activation(x3, act_type, act_params)
  • one_blob_only
param id name type default description
0 num_output int 0
1 kernel_w int 0
2 dilation_w int 1
3 stride_w int 1
4 pad_left int 0
5 bias_term int 0
6 weight_data_size int 0
7 group int 1
9 activation_type int 0
10 activation_params array [ ]
15 pad_right int pad_left
18 output_pad_right int 0
20 output_w int 0
28 dynamic_weight int 0
weight type shape
weight_data float/fp16 [kernel_w, num_input / group, num_output / group, group]
bias_data float [num_output]

DeconvolutionDepthWise3D

x2 = deconv3d(x, weight, kernel, stride, dilation, group) + bias
x3 = depad(x2, pads, pad_value)
y = activation(x3, act_type, act_params)
  • one_blob_only
param id name type default description
0 num_output int 0
1 kernel_w int 0
2 dilation_w int 1
3 stride_w int 1
4 pad_left int 0
5 bias_term int 0
6 weight_data_size int 0
7 group int 1
9 activation_type int 0
10 activation_params array [ ]
11 kernel_h int kernel_w
12 dilation_h int dilation_w
13 stride_h int stride_w
14 pad_top int pad_left
15 pad_right int pad_left
16 pad_bottom int pad_top
17 pad_behind int pad_front
18 output_pad_right int 0
19 output_pad_bottom int output_pad_right
20 output_pad_behind int output_pad_right
21 kernel_d int kernel_w
22 dilation_d int dilation_w
23 stride_d int stride_w
24 pad_front int pad_left
25 output_w int 0
26 output_h int output_w
27 output_d int output_w
weight type shape
weight_data float/fp16 [kernel_w, kernel_h, kernel_d, num_input / group, num_output / group, group]
bias_data float [num_output]

DeformableConv2D

x2 = deformableconv2d(x, offset, mask, weight, kernel, stride, dilation) + bias
y = activation(x2, act_type, act_params)
param id name type default description
0 num_output int 0
1 kernel_w int 0
2 dilation_w int 1
3 stride_w int 1
4 pad_left int 0
5 bias_term int 0
6 weight_data_size int 0
9 activation_type int 0
10 activation_params array [ ]
11 kernel_h int kernel_w
12 dilation_h int dilation_w
13 stride_h int stride_w
14 pad_top int pad_left
15 pad_right int pad_left
16 pad_bottom int pad_top
weight type shape
weight_data float/fp16/int8 [kernel_w, kernel_h, num_input, num_output]
bias_data float [num_output]

Dequantize

y = x * scale + bias
  • one_blob_only
  • support_inplace
param id name type default description
0 scale_data_size int 1
1 bias_data_size int 0
weight type shape
scale_data float [scale_data_size]
bias_data float [bias_data_size]

Diag

y = diag(x, diagonal)
  • one_blob_only
param id name type default description
0 diagonal int 0

Dropout

y = x * scale
  • one_blob_only
param id name type default description
0 scale float 1.f

Eltwise

y = elementwise_op(x0, x1, ...)
param id name type default description
0 op_type int 0
1 coeffs array [ ]

Operation type:

  • 0 = PROD
  • 1 = SUM
  • 2 = MAX

ELU

if x < 0    y = (exp(x) - 1) * alpha
else        y = x
  • one_blob_only
  • support_inplace
param id name type default description
0 alpha float 0.1f

Embed

y = embedding(x)
param id name type default description
0 num_output int 0
1 input_dim int 0
2 bias_term int 0
3 weight_data_size int 0
18 int8_scale_term int 0
weight type shape
weight_data float [weight_data_size]
bias_term float [num_output]
weight_data_int8_scales float [1]

Exp

if base == -1   y = exp(shift + x * scale)
else            y = pow(base, (shift + x * scale))
  • one_blob_only
  • support_inplace
param id name type default description
0 base float -1.f
1 scale float 1.f
2 shift float 0.f

Flatten

Reshape blob to 1 dimension

  • one_blob_only

Fold

y = fold(x)
  • one_blob_only
param id name type default description
0 num_output int 0
1 kernel_w int 0
2 dilation_w int 1
3 stride_w int 1
4 pad_left int 0
11 kernel_h int kernel_w
12 dilation_h int dilation_w
13 stride_h int stride_w
14 pad_top int pad_left
15 pad_right int pad_left
16 pad_bottom int pad_top
20 output_w int 0
21 output_h int output_w

GELU

if fast_gelu == 1   y = 0.5 * x * (1 + tanh(0.79788452 * (x + 0.044715 * x * x * x)));
else                y = 0.5 * x * erfc(-0.70710678 * x)
  • one_blob_only
  • support_inplace
param id name type default description
0 fast_gelu int 0 use approximation

GLU

If axis < 0, we use axis = x.dims + axis

GLU(a,b)=a⊗σ(b)

where a is the first half of the input matrix and b is the second half.

axis specifies the dimension to split the input

  • one_blob_only
param id name type default description
0 axis int 0

Gemm

a = transA ? transpose(x0) : x0
b = transb ? transpose(x1) : x1
c = x2
y = (gemm(a, b) + c * beta) * alpha
param id name type default description
0 alpha float 1.f
1 beta float 1.f
2 transA int 0
3 transb int 0
4 constantA int 0
5 constantB int 0
6 constantC int 0
7 constantM int 0
8 constantN int 0
9 constantK int 0
10 constant_broadcast_type_C int 0
11 output_N1M int 0
12 output_elempack int 0
13 output_elemtype int 0
14 output_transpose int 0
18 int8_scale_term int 0
20 constant_TILE_M int 0
21 constant_TILE_N int 0
22 constant_TILE_K int 0
weight type shape
A_data float/fp16/int8 [M, K] or [K, M]
B_data float/fp16/int8 [N, K] or [K, N]
C_data float [1], [M] or [N] or [1, M] or [N,1] or [N, M]
A_data_int8_scales float [M]
B_data_int8_scales float [1]

GridSample

Given an input and a flow-field grid, computes the output using input values and pixel locations from grid.

For each output location output[:, h2, w2], the size-2 vector grid[h2, w2, 2] specifies input pixel[:, h1, w1] locations x and y, 
which are used to interpolate the output value output[:, h2, w2]

This function is often used in conjunction with affine_grid() to build Spatial Transformer Networks .
param id name type default description
0 sample_type int 1
1 padding_mode int 1
2 align_corner int 0
3 permute_fusion int 0 fuse with permute

Sample type:

  • 1 = Nearest
  • 2 = Bilinear
  • 3 = Bicubic

Padding mode:

  • 1 = zeros
  • 2 = border
  • 3 = reflection

GroupNorm

split x along channel axis into group x0, x1 ...
l2 normalize for each group x0, x1 ...
y = x * gamma + beta
  • one_blob_only
  • support_inplace
param id name type default description
0 group int 1
1 channels int 0
2 eps float 0.001f x = x / sqrt(var + eps)
3 affine int 1
weight type shape
gamma_data float [channels]
beta_data float [channels]

GRU

Apply a single-layer GRU to a feature sequence of T timesteps. The input blob shape is [w=input_size, h=T] and the output blob shape is [w=num_output, h=T].

y = gru(x)
y0, hidden y1 = gru(x0, hidden x1)
  • one_blob_only if bidirectional
param id name type default description
0 num_output int 0 hidden size of output
1 weight_data_size int 0 total size of weight matrix
2 direction int 0 0=forward, 1=reverse, 2=bidirectional
weight type shape
weight_xc_data float/fp16/int8 [input_size, num_output * 3, num_directions]
bias_c_data float/fp16/int8 [num_output, 4, num_directions]
weight_hc_data float/fp16/int8 [num_output, num_output * 3, num_directions]

Direction flag:

  • 0 = forward only
  • 1 = reverse only
  • 2 = bidirectional

HardSigmoid

y = clamp(x * alpha + beta, 0, 1)
  • one_blob_only
  • support_inplace
param id name type default description
0 alpha float 0.2f
1 beta float 0.5f

HardSwish

y = x * clamp(x * alpha + beta, 0, 1)
  • one_blob_only
  • support_inplace
param id name type default description
0 alpha float 0.2f
1 beta float 0.5f

InnerProduct

x2 = innerproduct(x, weight) + bias
y = activation(x2, act_type, act_params)
  • one_blob_only
param id name type default description
0 num_output int 0
1 bias_term int 0
2 weight_data_size int 0
8 int8_scale_term int 0
9 activation_type int 0
10 activation_params array [ ]
weight type shape
weight_data float/fp16/int8 [num_input, num_output]
bias_data float [num_output]
weight_data_int8_scales float [num_output]
bottom_blob_int8_scales float [1]

Input

y = input
  • support_inplace
param id name type default description
0 w int 0
1 h int 0
11 d int 0
2 c int 0

InstanceNorm

split x along channel axis into instance x0, x1 ...
l2 normalize for each channel instance x0, x1 ...
y = x * gamma + beta
  • one_blob_only
  • support_inplace
param id name type default description
0 channels int 0
1 eps float 0.001f x = x / sqrt(var + eps)
2 affine int 1
weight type shape
gamma_data float [channels]
beta_data float [channels]

Interp

if dynamic_target_size == 0     y = resize(x) by fixed size or scale
else                            y = resize(x0, size(x1))
  • one_blob_only if dynamic_target_size == 0
param id name type default description
0 resize_type int 0
1 height_scale float 1.f
2 width_scale float 1.f
3 output_height int 0
4 output_width int 0
5 dynamic_target_size int 0
6 align_corner int 0

Resize type:

  • 1 = Nearest
  • 2 = Bilinear
  • 3 = Bicubic

LayerNorm

split x along outmost axis into part x0, x1 ...
l2 normalize for each part x0, x1 ...
y = x * gamma + beta by elementwise
  • one_blob_only
  • support_inplace
param id name type default description
0 affine_size int 0
1 eps float 0.001f x = x / sqrt(var + eps)
2 affine int 1
weight type shape
gamma_data float [affine_size]
beta_data float [affine_size]

Log

if base == -1   y = log(shift + x * scale)
else            y = log(shift + x * scale) / log(base)
  • one_blob_only
  • support_inplace
param id name type default description
0 base float -1.f
1 scale float 1.f
2 shift float 0.f

LRN

if region_type == ACROSS_CHANNELS   square_sum = sum of channel window of local_size
if region_type == WITHIN_CHANNEL    square_sum = sum of spatial window of local_size
y = x * pow(bias + alpha * square_sum / (local_size * local_size), -beta)
  • one_blob_only
  • support_inplace
param id name type default description
0 region_type int 0
1 local_size int 5
2 alpha float 1.f
3 beta float 0.75f
4 bias float 1.f

Region type:

  • 0 = ACROSS_CHANNELS
  • 1 = WITHIN_CHANNEL

LSTM

Apply a single-layer LSTM to a feature sequence of T timesteps. The input blob shape is [w=input_size, h=T] and the output blob shape is [w=num_output, h=T].

y = lstm(x)
y0, hidden y1, cell y2 = lstm(x0, hidden x1, cell x2)
  • one_blob_only if bidirectional
param id name type default description
0 num_output int 0 output size of output
1 weight_data_size int 0 total size of IFOG weight matrix
2 direction int 0 0=forward, 1=reverse, 2=bidirectional
3 hidden_size int num_output hidden size
weight type shape
weight_xc_data float/fp16/int8 [input_size, hidden_size * 4, num_directions]
bias_c_data float/fp16/int8 [hidden_size, 4, num_directions]
weight_hc_data float/fp16/int8 [num_output, hidden_size * 4, num_directions]
weight_hr_data float/fp16/int8 [hidden_size, num_output, num_directions]

Direction flag:

  • 0 = forward only
  • 1 = reverse only
  • 2 = bidirectional

MemoryData

y = data
param id name type default description
0 w int 0
1 h int 0
11 d int 0
2 c int 0
21 load_type int 1 1=fp32
weight type shape
data float [w, h, d, c]

Mish

y = x * tanh(log(exp(x) + 1))
  • one_blob_only
  • support_inplace

MultiHeadAttention

split q k v into num_head part q0, k0, v0, q1, k1, v1 ...
for each num_head part
    xq = affine(q) / (embed_dim / num_head)
    xk = affine(k)
    xv = affine(v)
    xqk = xq * xk
    xqk = xqk + attn_mask if attn_mask exists
    softmax_inplace(xqk)
    xqkv = xqk * xv
    merge xqkv to out
y = affine(out)
param id name type default description
0 embed_dim int 0
1 num_heads int 1
2 weight_data_size int 0 qdim = weight_data_size / embed_dim
3 kdim int embed_dim
4 vdim int embed_dim
5 attn_mask int 0
6 scale float 1.f / sqrt(embed_dim / num_heads)
18 int8_scale_term int 0
weight type shape
q_weight_data float/fp16/int8 [embed_dim * qdim]
q_bias_data float [embed_dim]
k_weight_data float/fp16/int8 [embed_dim * kdim]
k_bias_data float [embed_dim]
v_weight_data float/fp16/int8 [embed_dim * vdim]
v_bias_data float [embed_dim]
out_weight_data float/fp16/int8 [qdim * embed_dim]
out_bias_data float [qdim]
q_weight_data_int8_scales float [embed_dim]
k_weight_data_int8_scales float [embed_dim]
v_weight_data_int8_scales float [embed_dim]
out_weight_data_int8_scales float [1]

MVN

if normalize_variance == 1 && across_channels == 1      y = (x - mean) / (sqrt(var) + eps) of whole blob
if normalize_variance == 1 && across_channels == 0      y = (x - mean) / (sqrt(var) + eps) of each channel
if normalize_variance == 0 && across_channels == 1      y = x - mean of whole blob
if normalize_variance == 0 && across_channels == 0      y = x - mean of each channel
  • one_blob_only
param id name type default description
0 normalize_variance int 0
1 across_channels int 0
2 eps float 0.0001f x = x / (sqrt(var) + eps)

Noop

y = x

Normalize

if across_spatial == 1 && across_channel == 1      x2 = normalize(x) of whole blob
if across_spatial == 1 && across_channel == 0      x2 = normalize(x) of each channel
if across_spatial == 0 && across_channel == 1      x2 = normalize(x) of each position
y = x2 * scale
  • one_blob_only
  • support_inplace
param id name type default description
0 across_spatial int 0
1 channel_shared int 0
2 eps float 0.0001f see eps mode
3 scale_data_size int 0
4 across_channel int 0
9 eps_mode int 0
weight type shape
scale_data float [scale_data_size]

Eps Mode:

  • 0 = caffe/mxnet x = x / sqrt(var + eps)
  • 1 = pytorch x = x / max(sqrt(var), eps)
  • 2 = tensorflow x = x / sqrt(max(var, eps))

Packing

y = wrap_packing(x)
  • one_blob_only
param id name type default description
0 out_elempack int 1
1 use_padding int 0
2 cast_type_from int 0
3 cast_type_to int 0
4 storage_type_from int 0
5 storage_type_to int 0

Padding

y = pad(x, pads)
param id name type default description
0 top int 0
1 bottom int 0
2 left int 0
3 right int 0
4 type int 0
5 value float 0
6 per_channel_pad_data_size int 0
7 front int stride_w
8 behind int pad_left
weight type shape
per_channel_pad_data float [per_channel_pad_data_size]

Padding type:

  • 0 = CONSTANT
  • 1 = REPLICATE
  • 2 = REFLECT

Permute

y = reorder(x)
param id name type default description
0 order_type int 0

Order Type:

  • 0 = WH WHC WHDC
  • 1 = HW HWC HWDC
  • 2 = WCH WDHC
  • 3 = CWH DWHC
  • 4 = HCW HDWC
  • 5 = CHW DHWC
  • 6 = WHCD
  • 7 = HWCD
  • 8 = WCHD
  • 9 = CWHD
  • 10 = HCWD
  • 11 = CHWD
  • 12 = WDCH
  • 13 = DWCH
  • 14 = WCDH
  • 15 = CWDH
  • 16 = DCWH
  • 17 = CDWH
  • 18 = HDCW
  • 19 = DHCW
  • 20 = HCDW
  • 21 = CHDW
  • 22 = DCHW
  • 23 = CDHW

PixelShuffle

if mode == 0    y = depth_to_space(x) where x channel order is sw-sh-outc
if mode == 1    y = depth_to_space(x) where x channel order is outc-sw-sh
  • one_blob_only
param id name type default description
0 upscale_factor int 1
1 mode int 0

Pooling

x2 = pad(x, pads)
x3 = pooling(x2, kernel, stride)
param id name type default description
0 pooling_type int 0
1 kernel_w int 0
2 stride_w int 1
3 pad_left int 0
4 global_pooling int 0
5 pad_mode int 0
6 avgpool_count_include_pad int 0
7 adaptive_pooling int 0
8 out_w int 0
11 kernel_h int kernel_w
12 stride_h int stride_w
13 pad_top int pad_left
14 pad_right int pad_left
15 pad_bottom int pad_top
18 out_h int out_w

Pooling type:

  • 0 = MAX
  • 1 = AVG

Pad mode:

  • 0 = full padding
  • 1 = valid padding
  • 2 = tensorflow padding=SAME or onnx padding=SAME_UPPER
  • 3 = onnx padding=SAME_LOWER

Pooling1D

x2 = pad(x, pads)
x3 = pooling1d(x2, kernel, stride)
param id name type default description
0 pooling_type int 0
1 kernel_w int 0
2 stride_w int 1
3 pad_left int 0
4 global_pooling int 0
5 pad_mode int 0
6 avgpool_count_include_pad int 0
7 adaptive_pooling int 0
8 out_w int 0
14 pad_right int pad_left

Pooling type:

  • 0 = MAX
  • 1 = AVG

Pad mode:

  • 0 = full padding
  • 1 = valid padding
  • 2 = tensorflow padding=SAME or onnx padding=SAME_UPPER
  • 3 = onnx padding=SAME_LOWER

Pooling3D

x2 = pad(x, pads)
x3 = pooling3d(x2, kernel, stride)
param id name type default description
0 pooling_type int 0
1 kernel_w int 0
2 stride_w int 1
3 pad_left int 0
4 global_pooling int 0
5 pad_mode int 0
6 avgpool_count_include_pad int 0
7 adaptive_pooling int 0
8 out_w int 0
11 kernel_h int kernel_w
12 stride_h int stride_w
13 pad_top int pad_left
14 pad_right int pad_left
15 pad_bottom int pad_top
16 pad_behind int pad_front
18 out_h int out_w
21 kernel_d int kernel_w
22 stride_d int stride_w
23 pad_front int pad_left
28 out_d int out_w

Pooling type:

  • 0 = MAX
  • 1 = AVG

Pad mode:

  • 0 = full padding
  • 1 = valid padding
  • 2 = tensorflow padding=SAME or onnx padding=SAME_UPPER
  • 3 = onnx padding=SAME_LOWER

Power

y = pow((shift + x * scale), power)
  • one_blob_only
  • support_inplace
param id name type default description
0 power float 1.f
1 scale float 1.f
2 shift float 0.f

PReLU

if x < 0    y = x * slope
else        y = x
  • one_blob_only
  • support_inplace
param id name type default description
0 num_slope int 0
weight type shape
slope_data float [num_slope]

Quantize

y = float2int8(x * scale)
  • one_blob_only
param id name type default description
0 scale_data_size int 1
weight type shape
scale_data float [scale_data_size]

Reduction

y = reduce_op(x * coeff)
  • one_blob_only
param id name type default description
0 operation int 0
1 reduce_all int 1
2 coeff float 1.f
3 axes array [ ]
4 keepdims int 0
5 fixbug0 int 0 hack for bug fix, should be 1

Operation type:

  • 0 = SUM
  • 1 = ASUM
  • 2 = SUMSQ
  • 3 = MEAN
  • 4 = MAX
  • 5 = MIN
  • 6 = PROD
  • 7 = L1
  • 8 = L2
  • 9 = LogSum
  • 10 = LogSumExp

ReLU

if x < 0    y = x * slope
else        y = x
  • one_blob_only
  • support_inplace
param id name type default description
0 slope float 0.f

Reorg

if mode == 0    y = space_to_depth(x) where x channel order is sw-sh-outc
if mode == 1    y = space_to_depth(x) where x channel order is outc-sw-sh
  • one_blob_only
param id name type default description
0 stride int 1
1 mode int 0

Requantize

x2 = x * scale_in + bias
x3 = activation(x2)
y = float2int8(x3 * scale_out)
  • one_blob_only
param id name type default description
0 scale_in_data_size int 1
1 scale_out_data_size int 1
2 bias_data_size int 0
3 activation_type int 0
4 activation_params int [ ]
weight type shape
scale_in_data float [scale_in_data_size]
scale_out_data float [scale_out_data_size]
bias_data float [bias_data_size]

Reshape

if permute == 1     y = hwc2chw(reshape(chw2hwc(x)))
else                y = reshape(x)
  • one_blob_only
param id name type default description
0 w int -233
1 h int -233
11 d int -233
2 c int -233
3 permute int 0

Reshape flag:

  • 0 = copy from bottom
  • -1 = remaining
  • -233 = drop this dim(default)

RMSNorm

split x along outmost axis into part x0, x1 ...
root mean square normalize for each part x0, x1 ...
y = x * gamma by elementwise
  • one_blob_only
  • support_inplace
param id name type default description
0 affine_size int 0
1 eps float 0.001f x = x / sqrt(var + eps)
2 affine int 1
weight type shape
gamma_data float [affine_size]

RNN

Apply a single-layer RNN to a feature sequence of T timesteps. The input blob shape is [w=input_size, h=T] and the output blob shape is [w=num_output, h=T].

y = rnn(x)
y0, hidden y1 = rnn(x0, hidden x1)
  • one_blob_only if bidirectional
param id name type default description
0 num_output int 0 hidden size of output
1 weight_data_size int 0 total size of weight matrix
2 direction int 0 0=forward, 1=reverse, 2=bidirectional
weight type shape
weight_xc_data float/fp16/int8 [input_size, num_output, num_directions]
bias_c_data float/fp16/int8 [num_output, 1, num_directions]
weight_hc_data float/fp16/int8 [num_output, num_output, num_directions]

Direction flag:

  • 0 = forward only
  • 1 = reverse only
  • 2 = bidirectional

Scale

if scale_data_size == -233  y = x0 * x1
else                        y = x * scale + bias
  • one_blob_only if scale_data_size != -233
  • support_inplace
param id name type default description
0 scale_data_size int 0
1 bias_term int 0
weight type shape
scale_data float [scale_data_size]
bias_data float [scale_data_size]

SELU

if x < 0    y = (exp(x) - 1.f) * alpha * lambda
else        y = x * lambda
  • one_blob_only
  • support_inplace
param id name type default description
0 alpha float 1.67326324f
1 lambda float 1.050700987f

Shrink

if x < -lambd y = x + bias
if x >  lambd y = x - bias
else          y = x
  • one_blob_only
  • support_inplace
param id name type default description
0 bias float 0.0f
1 lambd float 0.5f

ShuffleChannel

if reverse == 0     y = shufflechannel(x) by group
if reverse == 1     y = shufflechannel(x) by channel / group
  • one_blob_only
param id name type default description
0 group int 1
1 reverse int 0

Sigmoid

y = 1 / (1 + exp(-x))
  • one_blob_only
  • support_inplace

Slice

split x along axis into slices, each part slice size is based on slices array
param id name type default description
0 slices array [ ]
1 axis int 0
2 indices array [ ]

Softmax

softmax(x, axis)
  • one_blob_only
  • support_inplace
param id name type default description
0 axis int 0
1 fixbug0 int 0 hack for bug fix, should be 1

Softplus

y = log(exp(x) + 1)
  • one_blob_only
  • support_inplace

Split

y0, y1 ... = x

Swish

y = x / (1 + exp(-x))
  • one_blob_only
  • support_inplace

TanH

y = tanh(x)
  • one_blob_only
  • support_inplace

Threshold

if x > threshold    y = 1
else                y = 0
  • one_blob_only
  • support_inplace
param id name type default description
0 threshold float 0.f

Tile

y = repeat tiles along axis for x
  • one_blob_only
param id name type default description
0 axis int 0
1 tiles int 1
2 repeats array [ ]

UnaryOp

y = unaryop(x)
  • one_blob_only
  • support_inplace
param id name type default description
0 op_type int 0 Operation type as follows

Operation type:

  • 0 = ABS
  • 1 = NEG
  • 2 = FLOOR
  • 3 = CEIL
  • 4 = SQUARE
  • 5 = SQRT
  • 6 = RSQ
  • 7 = EXP
  • 8 = LOG
  • 9 = SIN
  • 10 = COS
  • 11 = TAN
  • 12 = ASIN
  • 13 = ACOS
  • 14 = ATAN
  • 15 = RECIPROCAL
  • 16 = TANH
  • 17 = LOG10
  • 18 = ROUND
  • 19 = TRUNC

Unfold

y = unfold(x)
  • one_blob_only
param id name type default description
0 num_output int 0
1 kernel_w int 0
2 dilation_w int 1
3 stride_w int 1
4 pad_left int 0
11 kernel_h int kernel_w
12 dilation_h int dilation_w
13 stride_h int stride_w
14 pad_top int pad_left
15 pad_right int pad_left
16 pad_bottom int pad_top
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