ConcatOperation
Description
Combines two input tensors into an output tensor at a specified dimension.
Operator Context
Operator Function Implementation
The Concat operator is used to concatenate two input tensors into one output tensor in a specified dimension.
Definition
struct ConcatParam {
int concatDim = 0;
uint8_t rsv[12] = {0};
};
Parameters
Member |
Type |
Default Value |
Description |
|---|---|---|---|
concatDim |
int |
0 |
Dimension index for concat. When concatDim is a negative number, the access starts from the highest dimension. For example, if concatDim is -1 and the dimension count of x is dimNum, the concat dimension is dimNum - 1. |
rsv[12] |
uint8_t |
{0} |
Reserved |
Input
Parameter |
Dimension |
Data Type |
Format |
Description |
|---|---|---|---|---|
x |
[-1,…,-1] The value -1 indicates that the size of the current dimension is not restricted. |
float16/bf16 |
ND |
Tensor to be concatenated. |
y |
[-1,…,-1] The value -1 indicates that the size of the current dimension is not restricted. |
float16/bf16 |
ND |
Tensor to be concatenated. |
Output
Parameter |
Dimension |
Data Type |
Format |
Description |
|---|---|---|---|---|
output |
[-1,…,-1] The value -1 indicates that the size of the current dimension is not restricted. |
float16/bf16 |
ND |
Concatenated tensor. |
Restrictions
- Restrictions on the parameters:
- dimNum: number of dimensions of the concatenated tensor.
- -dimNum ≤ concatDim ≤ dimNum - 1.
- Input constraints
- The inputs x and y have the same dimensions.
- Dimension size of the inputs x and y must be the same except the concatDim dimension.
API Calling Example
- Input
x shape = [3, 2, 3]: [[[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]], [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]], [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]]] y shape = [3, 1, 3]: [[[2.0, 2.0, 2.0]], [[2.0, 2.0, 2.0]], [[2.0, 2.0, 2.0]]] param.concatDim = 1 (or -2)
- Output
output shape = [3, 3, 3] [[[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [2.0, 2.0, 2.0]], [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [2.0, 2.0, 2.0]], [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [2.0, 2.0, 2.0]]]