# sequence_expand¶

paddle.static.nn. sequence_expand ( x, y, ref_level=- 1, name=None ) [source]

Sequence Expand Layer. This layer will expand the input variable `x` according to specified level `ref_level` lod of `y`. Please note that the lod level of `x` is at most 1. If the lod level of `x` is 1, than the size of lod of `x` must be equal to the length of `ref_level` lod of `y`. If the lod level of `x` is 0, then the first dim of `x` should be equal to the size of `ref_level` of `y`. The rank of x is at least 2. When rank of `x` is greater than 2, then it would be viewed as a 2-D tensor.

Note

Please note that the input `x` should be LodTensor or Tensor, and input `y` must be LodTensor.

Following examples will explain how sequence_expand works:

```Case 1

Consider 2 sequences [a][b] and [c][d], now we want to expand them to [a][b], [a][b], [c][d] and [c][d].
Sequence [a][b] expand twice and [c][d] expands twice, so the lod which according to is [2, 2].

Input x is a 1-level LoDTensor:
x.lod  = [[2,        2]]    #lod based on length may be easier to understand
x.data = [[a], [b], [c], [d]]
x.dims = [4, 1]

input y is a LoDTensor:
y.lod = [[2,    2],    #the 0th level lod, according to this level
[3, 3, 1, 1]] #the 1st level lod, it has nothing to do with this level

ref_level: 0

then output is a 1-level LoDTensor out:
out.lod =  [[2,        2,        2,        2]]    #lod based on offset
out.data = [[a], [b], [a], [b], [c], [d], [c], [d]]
out.dims = [8, 1]

Case 2

Consider 3 sequences [a], [b], [c], now we want to expand them to [a][a], [c][c][c].
It's obvious that the lod info of expanded sequences is [2, 0, 3].

x is a Tensor:
x.data = [[a], [b], [c]]
x.dims = [3, 1]

y is a LoDTensor:
y.lod = [[2, 0, 3]]

ref_level: -1

then output is a 1-level LodTensor:
out.data = [[a], [a], [c], [c], [c]]
out.dims = [5, 1]
```
Parameters
• x (Variable) – The input variable which is a Tensor or LoDTensor, with the dims `[M, K]`. The lod level is at most 1. The data type should be float32, float64, int32 or int64.

• y (Variable) – The input variable which is a LoDTensor, the lod level is at least 1.

• ref_level (int) – Lod level of `y` to be referred by `x`. If set to -1, refer the last level of lod.

• name (str, optional) – For detailed information, please refer to Name. Usually name is no need to set and None by default.

Returns

Tensor, The expanded variable which is a LoDTensor, with dims `[N, K]`. `N` depends on the lod info of `x` and `y`. The data type is same as input.

Examples

```import paddle
import numpy as np

x = paddle.static.data(name='x', shape=[4, 1], dtype='float32')
dtype='float32', lod_level=1)

np_data = np.array([[1], [2], [3], [4]]).astype('float32')
x_lod_tensor = fluid.create_lod_tensor(np_data, [[2, 2]], place)
print(x_lod_tensor)
#lod: [[0, 2, 4]]
#    dim: 4, 1
#    layout: NCHW
#    dtype: float
#    data: [1 2 3 4]

np_data = np.array([[1], [2], [3], [4], [5], [6], [7], [8]]).astype('float32')
y_lod_tensor = fluid.create_lod_tensor(np_data, [[2, 2], [3,3,1,1]], place)
print(y_lod_tensor)
#lod: [[0, 2, 4][0, 3, 6, 7, 8]]
#    dim: 8, 1
#    layout: NCHW
#    dtype: int64_t
#    data: [0 0 1 1 1 1 1 0]

out_main = exe.run(fluid.default_main_program(),
feed={'x': x_lod_tensor, 'y': y_lod_tensor},
fetch_list=[out], return_numpy=False)
print(out_main[0])
#lod: [[0, 2, 4, 6, 8]]
#    dim: 8, 1
#    layout: NCHW
#    dtype: float
#    data: [1 2 1 2 3 4 3 4]
```