concat
Description
Reconcatenates multiple input tensors along a specified axis.
raw_tensors indicates input tensors of the same data type.
If raw_tensors[i].shape = [D0, D1, ... Daxis(i), ... Dn], the concatenated tensor is with shape [D0, D1, ... Raxis, ... Dn].
where Raxis = sum(Daxis(i)).
See the following example.
t1 = [[1, 2, 3], [4, 5, 6]] t2 = [[7, 8, 9], [10, 11, 12]] concat([t1, t2], 0) # [[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]] concat([t1, t2], 1) # [[1, 2, 3, 7, 8, 9], [4, 5, 6, 10, 11, 12]] # Tensor t1 with shape [2, 3] # Tensor t2 with shape [2, 3] concat([t1, t2], 0).shape # [4, 3] concat([t1, t2], 1).shape # [2, 6]
The parameter axis can also be a negative integer, indicating the axis indexed axis+len(shape), which is indexed from the end.
See the following example.
t1 = [[[1, 2], [2, 3]], [[4, 4], [5, 3]]] t2 = [[[7, 4], [8, 4]], [[2, 10], [15, 11]]] concat([t1, t2], -1)
The output is as follows:
[[[ 1, 2, 7, 4], [ 2, 3, 8, 4]], [[ 4, 4, 2, 10], [ 5, 3, 15, 11]]]
Prototype
concat(raw_tensors, axis)
Parameters
- raw_tensors: a list of tensors of type tvm.tensor. The last dimension of the tensor must be 32-byte aligned.
The supported data types include: int8, uint8, int16, int32, float16, and float32.
For input tensors, the axes except axis must have the same dimensions.
- axis: axis along which concatenation is performed. The value range is [–d, d – 1]. The parameter d indicates the dimension count of raw_tensor.
Returns
res_tensor: a tvm.tensor for the result tensor
Restrictions
This API cannot be used in conjunction with other TBE DSL APIs.
Applicability
Example
from tbe import tvm from tbe import dsl shape1 = (64,128) shape2 = (64,128) input_dtype = "float16" data1 = tvm.placeholder(shape1, name="data1", dtype=input_dtype) data2 = tvm.placeholder(shape2, name="data1", dtype=input_dtype) data = [data1, data2] res = dsl.concat(data, 0) # res.shape = (128,128)