AddPartial |
num inputs: 2
num outputs: 1
attributes:
- is_input0_larger: bool |
AddingConstant |
num inputs: 1
num outputs: 1
attributes:
- constant: Weight
- is_const_first: bool (default=false) |
Adding |
num inputs: 2
num outputs: 1
|
Batchnorm |
num inputs: 1
num outputs: 1
attributes:
- gamma: Weight
- beta: Weight
- moving_mean: Weight
- moving_var: Weight
- epsilon: Weight
- channel_last: bool (default=true) |
Celu |
num inputs: 1
num outputs: 1
attributes:
- alpha: float32 |
Clip |
num inputs: 1
num outputs: 1
attributes:
- clip_max_value: float32 (optional)
- clip_min_value: float32 (optional) |
Concatenate |
num inputs: variable
num outputs: 1
attributes:
- original_key: str (optional)
- axis: int32 (optional)
- original_axis: int32 (optional)
- original_inputs: int32[] (optional)
- input_index: int32[] (optional)
- input_names: str[] (optional)
description:
input_names: input activation names in original order. input_index: input index in computation order. |
Convolution |
num inputs: 1
num outputs: 1
attributes:
- in_channels: int32
- out_channels: int32
- h_kernel: int32
- w_kernel: int32
- h_stride: int32
- w_stride: int32
- h_dilation: int32
- w_dilation: int32
- west_padding: int32
- east_padding: int32
- north_padding: int32
- south_padding: int32
- padding_list: int32[] (default=[])
- group_number: int32
- weight: Weight
- bias: Weight (optional)
- channel_last: bool (default=true) |
DepthwiseConvolution |
num inputs: 1
num outputs: 1
attributes:
- in_channels: int32
- out_channels: int32
- h_kernel: int32
- w_kernel: int32
- h_stride: int32
- w_stride: int32
- h_dilation: int32
- w_dilation: int32
- west_padding: int32
- east_padding: int32
- north_padding: int32
- south_padding: int32
- padding_list: int32[] (default=[])
- group_number: int32
- weight: Weight
- bias: Weight (optional)
- channel_last: bool (default=true) |
DeviceBridge |
num inputs: 1
num outputs: 1
attributes:
- squeeze_axes: int32[]
- unsqueeze_axes: int32[]
- permute_axes: int64[]
- device: str (optional) |
DivConstant |
num inputs: 1
num outputs: 1
attributes:
- constant: Weight
- is_const_first: bool |
Div |
num inputs: 2
num outputs: 1
|
Elu |
num inputs: 1
num outputs: 1
attributes:
- leaky_alpha: float32 |
Erf |
num inputs: 1
num outputs: 1
|
Exp |
num inputs: 1
num outputs: 1
|
Gelu |
num inputs: 1
num outputs: 1
attributes:
- approximate: bool (default=false) |
GLU |
num inputs: 1
num outputs: 1
|
GroupConvolution |
num inputs: 1
num outputs: 1
attributes:
- in_channels: int32
- out_channels: int32
- h_kernel: int32
- w_kernel: int32
- h_stride: int32
- w_stride: int32
- h_dilation: int32
- w_dilation: int32
- west_padding: int32
- east_padding: int32
- north_padding: int32
- south_padding: int32
- padding_list: int32[] (default=[])
- group_number: int32
- weight: Weight
- bias: Weight (optional)
- channel_last: bool (default=true) |
GroupNormalization |
num inputs: 1
num outputs: 1
attributes:
- num_groups: int32
- scale: Weight
- bias: Weight (optional)
- epsilon: Weight
- channel_last: bool (default=true) |
HardSigmoid |
num inputs: 1
num outputs: 1
attributes:
- alpha: float32 (optional)
- beta: float32 (optional) |
Hardtanh |
num inputs: 1
num outputs: 1
attributes:
- min_val: float32 (default=-1.0)
- max_val: float32 (default=1.0) |
HardSwish |
num inputs: 1
num outputs: 1
|
HeaderView |
num inputs: 1
num outputs: 1
attributes:
- view_shape: int32[] (default=[])
- num_heads: int32 (default=-1)
- batch_order: int32 (default=0)
description:
HeaderView simplifies the following sequence of operations.
If batch_order == 0
(n,h,w,c)-[reshape]->(1, h, w, num_heads c/num_heads)
-[transpose01324]->(1, h, num_heads, w, c/num_heads)
-[reshape]->(1, hnum_heads, w, c/num_heads)
If batch_order == 1
(n,h,w,c)-[reshape]->(1, h, w, num_heads, c/num_heads)
-[transpose03124]->(1, num_heads, h, w, c/num_heads)
-[reshape]->(1, num_headsh, w, c/num_heads) |
HeaderViewRevert |
num inputs: 1
num outputs: 1
attributes:
- revert_shape: int32[] (default=[])
- num_heads: int32 (default=-1)
- batch_order: int32 (default=0)
description:
HeaderViewRevert simplifies the following sequence of operations.
If batch_order == 0
(n,h,w,c)-[reshape]->(1, h/num_heads, num_heads, w, c)
-[transpose01324]->(1, h/num_heads, w, num_heads, c)
-[reshape]->(1, h/num_heads, w, num_heads * c)
If batch_order == 1
(n,h,w,c)-[reshape]->(1, num_heads, h/num_heads, w, c)
-[transpose02314]->(1, h/num_heads, w, num_heads, c)
-[reshape]->(1, h/num_heads, w, num_heads*c) |
Identity |
num inputs: 1
num outputs: 1
|
InputConstant |
num inputs: 0
num outputs: 1
attributes:
- constant: Weight |
Input |
num inputs: 1
num outputs: 1
|
InstanceNormalization |
num inputs: 1
num outputs: 1
attributes:
- scale: Weight
- bias: Weight (optional)
- epsilon: Weight
- channel_last: bool (default=true)
- num_groups: int32 (default=-1)
- group_order: int32 (default=-1)
description:
If num_groups and order are specified, the channel dimension is grouped and rearranged before normalization.
For example, the following sequence:
(n, 1, w, c2)-[reshape]->(n, w, c, 2)
-[transpose0312]->(n, 2, w, c)
-[InstanceNorm, num_features=c]->(n, 2, w, c)
-[transpose0231]->(n, w, c, 2)
-[reshape]->(n, 1, w, c2)
is equivalent to a single operation:
(n, 1, w, c2)-[InstanceNormalization, num_features=c, num_groups=2, group_order=1]->(n, 1, w, c2)
The follosing sequence:
(n, 1, w, 2c)-[reshape]->(n,w,2,c)
-[InstanceNorm, num_features=c]->(n, w, 2, c)
-[reshape]v(n, 1, w, c2)
is equivalent to a single operation:
(n, 1, w, 2c)-[InstanceNormalization, num_features=c, num_groups=2, group_order=0]->(n, 1, w, 2c) |
L1Normalization |
num inputs: 1
num outputs: 1
attributes:
- norm_axis: int32[] |
L2Normalization |
num inputs: 1
num outputs: 1
attributes:
- norm_axis: int32[]
- epsilon: Weight (optional) |
LSTM |
num inputs: variable
num outputs: 1
attributes:
- W: Weight
- R: Weight
- B: Weight (optional)
- lstm_group: str
- input_mask: str
- output_mask: str
- P: Weight (optional)
- direction: str (default=”forward”)
- batch_first: bool (default=false)
- hidden_size: int32
description:
inputs:[input, sequence_lens, initial_h, initial_c]
outputs:[output] or [hidden_out] or [cell_out]
input_mask: “ishc” or “i”, etc,…
output_mask: “o” or “h” or “c” |
LayerNormalization |
num inputs: 1
num outputs: 1
attributes:
- normalized_shape: int64[] (default=[])
- scale: Weight
- bias: Weight (optional)
- epsilon: Weight |
Logit |
num inputs: 1
num outputs: 1
attributes:
- eps: float32 (optional)
description:
Compute y = ln(z / (1 - z)). If eps is given, we clamp input z = clamp(z, min=eps, max=1-eps) |
RNN |
num inputs: variable
num outputs: 1
attributes:
- W: Weight
- R: Weight
- B: Weight (optional)
- rnn_group: str
- input_mask: str
- output_mask: str
- direction: str (default=”forward”)
- batch_first: bool (default=false)
- hidden_size: int32
description:
inputs:[input, sequence_lens, initial_h]
outputs:[output] or [hidden_out]
input_mask: “ish” or “i”, etc,…
output_mask: “o” or “h” |
GRU |
num inputs: variable
num outputs: 1
attributes:
- W: Weight
- R: Weight
- B: Weight (optional)
- gru_group: str
- input_mask: str
- output_mask: str
- direction: str (default=”forward”)
- batch_first: bool (default=false)
- linear_before_reset: bool (default=false)
- hidden_size: int32
description:
inputs:[input, sequence_lens, initial_h]
outputs:[output] or [hidden_out]
input_mask: “ish” or “i”, etc,…
output_mask: “o” or “h” |
LeakyRelu |
num inputs: 1
num outputs: 1
attributes:
- leaky_alpha: float32 |
Log |
num inputs: 1
num outputs: 1
|
LogSigmoid |
num inputs: 1
num outputs: 1
|
MatMul |
num inputs: variable
num outputs: 1
attributes:
- constant: Weight (optional)
- is_const_first: bool (default=false)
- repeat: int32[2] (default=[-1, -1])
description:
If repeat=(0, 4), then input0 is repeated 4 times along the h-direction.
For example, you can perform a matrix multiplication with input0 of shape (1, 8, 52, 64) and
input1 of shape (1, 32, 52, 64) by concatenating input0 4 times, resulting in a input0 shape of (1, 32, 52, 64). |
Mish |
num inputs: 1
num outputs: 1
|
MultiplyConstant |
num inputs: 1
num outputs: 1
attributes:
- constant: Weight
- is_const_first: bool (default=false) |
Multiply |
num inputs: 2
num outputs: 1
|
Neg |
num inputs: 1
num outputs: 1
|
Output |
num inputs: 1
num outputs: 1
|
Pad |
num inputs: 1
num outputs: 1
attributes:
- west_padding: int32 (default=-1)
- east_padding: int32 (default=-1)
- south_padding: int32 (default=-1)
- north_padding: int32 (default=-1)
- pad_mode: str
- constant: float32 (default=0.0)
- padding_list: int32[] (optional)
description:
padding_list format should be [x1_begin, x2_begin, …, x1_end, x2_end,…],
where xi_begin is the number of pad values added at the beginning of axis |
Pooling |
num inputs: 1
num outputs: 1
attributes:
- h_kernel: int32
- w_kernel: int32
- h_stride: int32
- w_stride: int32
- h_dilation: int32 (default=1)
- w_dilation: int32 (default=1)
- west_padding: int32
- east_padding: int32
- north_padding: int32
- south_padding: int32
- padding_list: int32[] (default=[])
- pool_type: str
- pad_mode: str
- is_ceil_mode: bool (default=false)
- is_include_pad: bool (default=false)
- channel_last: bool (default=true) |
GlobalAveragePooling |
num inputs: 1
num outputs: 1
attributes:
- channel_last: bool (default=true) |
Pow |
num inputs: 1
num outputs: 1
attributes:
- exponent: float32 |
QuickGelu |
num inputs: 1
num outputs: 1
|
ReduceL2 |
num inputs: 1
num outputs: 1
attributes:
- keep_dims: bool
- reduce_axes: int32[]
- noop_with_empty_axes: int32 |
ReduceMax |
num inputs: 1
num outputs: 1
attributes:
- keep_dims: bool
- reduce_axes: int32[]
- noop_with_empty_axes: int32 |
ReduceMean |
num inputs: 1
num outputs: 1
attributes:
- keep_dims: bool
- reduce_axes: int32[]
- noop_with_empty_axes: int32 |
ReduceProd |
num inputs: 1
num outputs: 1
attributes:
- keep_dims: bool
- reduce_axes: int32[]
- noop_with_empty_axes: int32 |
ReduceSum |
num inputs: 1
num outputs: 1
attributes:
- keep_dims: bool
- reduce_axes: int32[]
- noop_with_empty_axes: int32 |
Relu |
num inputs: 1
num outputs: 1
|
Reshape |
num inputs: 1
num outputs: 1
attributes:
- new_shape: int32[] (optional)
- allowzero: int32 (default=0)
- is_const_first: bool (default=false) |
Resize |
num inputs: 1
num outputs: 1
attributes:
- resize_type: str (optional)
- resize_mode: str (optional)
- nearest_mode: str (optional)
- transform_mode: str (optional)
- roi: int32[] (optional)
- scales: float32[] (optional)
- sizes: int32[] (optional)
- cubic_coeff_a: float32 (optional)
- exclude_outside: int32 (optional)
- extrapolation_value: float32 (optional)
- antialias: int32 (optional)
- axes: int32[] (optional)
- keep_aspect_ratio_policy: str (optional) |
RmsNormalization |
num inputs: 1
num outputs: 1
attributes:
- normalized_shape: int64[] (default=[])
- epsilon: Weight
- scale: Weight |
Rsqrt |
num inputs: 1
num outputs: 1
|
Rstd |
num inputs: 1
num outputs: 1
attributes:
- reduce_axes: int32[]
- epsilon: Weight
- num_groups: int32 (default=1)
description:
computes 1/std(x). if gruops > 1, then activation is grouped along channel axis. |
Sigmoid |
num inputs: 1
num outputs: 1
attributes:
- alpha: float32 (default=1.0)
description:
alpha is added to deal with more general case like 1 / (1 + exp(-alpha * x)) |
Slice |
num inputs: 1
num outputs: 1
attributes:
- axis: int32[]
- start: int64[]
- end: int64[]
- step: int64[] (optional) |
Softmax |
num inputs: 1
num outputs: 1
attributes:
- beta: float32 (default=1.0)
- axis: int32 |
Softplus |
num inputs: 1
num outputs: 1
|
Sqrt |
num inputs: 1
num outputs: 1
|
StaggeredPadding |
num inputs: 1
num outputs: 1
attributes:
- axis: int32 (default=3)
- reverse: bool (default=false)
- window_size: int32 (default=-1)
description:
StaggeredPadding does the following:
[[[[1,1,1],
[2,2,2],
[3,3,3],
[4,4,4]]
[5,5,5]]]]
=> [[[[1,1,1,0,0,0,0],
[0,2,2,2,0,0,0],
[0,0,3,3,3,0,0],
[0,0,0,4,4,4,0],
[0,0,0,0,5,5,5]]]]
That is, each original row is shifted to the right so that its non-zero block lies on a “diagonal,” and all other elements are zero-padded.
Then slice last dim by (2window_size + 1). For example, if window_size=1 in the above, 5=21+1 elements are sliced.
=> [[[[1,1,0,0,0],
[2,2,2,0,0],
[0,3,3,3,0],
[0,0,4,4,4],
[0,0,0,5,5]]]]
If reverse
[[[[1,1,1],
[2,2,2],
[3,3,3],
[4,4,4]]
[5,5,5]]]]
=> [[[[0,0,0,0,1,1,1],
[0,0,0,2,2,2,0],
[0,0,3,3,3,0,0],
[0,4,4,4,0,0,0],
[5,5,5,0,0,0,0]]]] |
StatefulAttentionMaskWrapper |
num inputs: 1
num outputs: 1
attributes:
- past_seqlen: int32 (default=0)
- is_sliding: bool (default=false)
- sliding_window: int32 (default=1024)
- mask_type: str (default=”causal_mask”)
description:
mask_type: “causal_mask”, “padding_mask” |
StatefulKVCacheWrapper |
num inputs: 1
num outputs: 1
attributes:
- cache_name: str
- past_seqlen: int32 (default=0)
- is_sliding: bool (default=false)
- sliding_window: int32 (default=1024)
- max_cache_len: int32 (default=8192) |
StatefulCausalConvCacheWrapper |
num inputs: 1
num outputs: 1
attributes:
- cache_name: str
- cache_len: int32 (default=0) |
StatefulLSTMWrapper |
num inputs: variable
num outputs: 3
attributes:
- W: Weight
- R: Weight
- B: Weight (optional)
- input_mask: str
- output_mask: str
- P: Weight (optional)
- direction: str (default=”forward”)
- batch_first: bool (default=false)
- hidden_size: int32
- hidden_state_name: str
- cell_state_name: str
- subgraph: str
description:
When input_mask is ishc, then inputs are [input, sequence_lens, initial_h, initial_c],
and if input_mask is ihc, then inputs are [input, initial_h, initial_c].
The output_mask is h means that among [output, hidden_out, cell_out], only the hidden_out is being used. |
StatefulRNNWrapper |
num inputs: variable
num outputs: 2
attributes:
- W: Weight
- R: Weight
- B: Weight (optional)
- input_mask: str
- output_mask: str
- direction: str (default=”forward”)
- batch_first: bool (default=false)
- hidden_size: int32
- hidden_state_name: str
- subgraph: str
description:
When input_mask is ish, then inputs are [input, sequence_lens, initial_h],
and if input_mask is ih, then inputs are [input, initial_h].
The output_mask is h means that among [output, hidden_out], only the hidden_out is being used. |
StatefulGRUWrapper |
num inputs: variable
num outputs: 2
attributes:
- W: Weight
- R: Weight
- B: Weight (optional)
- input_mask: str
- output_mask: str
- direction: str (default=”forward”)
- batch_first: bool (default=false)
- linear_before_reset: bool (default=false)
- hidden_size: int32
- hidden_state_name: str
- subgraph: str
description:
When input_mask is ish, then inputs are [input, sequence_lens, initial_h],
and if input_mask is ih, then inputs are [input, initial_h].
The output_mask is h means that among [output, hidden_out], only the hidden_out is being used. |
StatefulRoPEWrapper |
num inputs: 3
num outputs: 1
attributes:
- rope_type: int32 (default=0)
- seqlen: int32 (default=0)
- rotary_emb_dim: int32 (default=2)
description:
StatefulRoPEWrapper
inputs: (q_or_k, con, sin)
outputs: (q_or_k_embed)
rope_type=> 0: llama, 1: cohere2(aya-vision) |
SubConstant |
num inputs: 1
num outputs: 1
attributes:
- constant: Weight
- is_const_first: bool |
Sub |
num inputs: 2
num outputs: 1
|
Swish |
num inputs: 1
num outputs: 1
|
Tanh |
num inputs: 1
num outputs: 1
|
TransposeConvolution |
num inputs: 1
num outputs: 1
attributes:
- in_channels: int32
- out_channels: int32
- h_kernel: int32
- w_kernel: int32
- h_stride: int32
- w_stride: int32
- h_dilation: int32
- w_dilation: int32
- west_padding: int32
- east_padding: int32
- north_padding: int32
- south_padding: int32
- padding_list: int32[] (default=[])
- og_padding_list: int32[] (default=[])
- og_out_padding_list: int32[] (default=[])
- group_number: int32
- weight: Weight
- bias: Weight (optional)
- channel_last: bool (default=true) |
Transpose |
num inputs: 1
num outputs: 1
attributes:
- transpose_axes: int64[] |
Weightedsum |
num inputs: 2
num outputs: 1
attributes:
- weight0: Weight
- weight1: Weight |
OrderedPatchify |
num inputs: 1
num outputs: 1
attributes:
- h_kernel: int32
- w_kernel: int32
- order: int32 (default=0)
description:
This operation splits a 4D input tensor into non-overlapping patches and rearranges them into a sequence
according to the specified order. Supports row-major (order=0) and column-major (order=1) layouts.
If order=0 (row-major), the equivalent is: 256 patches, each 12x12, (h_kernel=w_kernel=12)
(1,192,192,64)-[reshape]->(1,16,12,16,12,64)-[transpose 013245]->(1,16,16,12,12,64)-[reshape]->(1,256,144,64)
Note that order0 is its own inverse.
If order=1 (column-major), the equivalent is: 144 patches, each 16x16, (h_kernel=w_kernel=12)
(1,192,192,64)-[reshape]->(1,16,12,16,12,64)-[transpose 024135]->(1,12,12,16,16,64)-[reshape]->(1,144,256,64)
if order=2 (reversing order=1), h_kernel=12, w_kernel=16
(1,144,256,64)-[reshape]->(1,12,12,16,16,64)-[transpose 031425]->(1,16,12,16,12,64)-[reshape]->(1,192,192,64) |
