paddle.fluid.layers.control_flow. reorder_lod_tensor_by_rank ( x, rank_table ) [source]

ReorderLoDTensorByRankTable operator.

Input(X) is a batch of sequences. Input(RankTable) stores new orders of the input sequence batch. The reorder_lod_tensor_by_rank operator reorders the Input(X) according to the information provided by Input(RankTable).

For example:

If the indices stored in the Input(RankTable) are [3, 0, 2, 1], the Input(X) will be reordered that the fourth sequence in Input(X) will become the first one, and then followed by the original first, third, and the second one.

This is: X = [Seq0, Seq1, Seq2, Seq3]. The indices in RankTable are [3, 0, 2, 1]. Out = [Seq3, Seq0, Seq2, Seq1] with a new LoD information.

If the LoD information of Input(X) is empty, this means Input(X) is not sequence data. This is also identical to a batch of sequences where each sequence has a fixed length 1. In this case, the reorder_lod_tensor_by_rank operator reorders each slice of Input(X) along the first axis according to Input(RankTable).

This is: X = [Slice0, Slice1, Slice2, Slice3] and its LoD information is empty. The indices in RankTable are [3, 0, 2, 1]. Out = [Slice3, Slice0, Slice2, Slice1] with no LoD information is appended.

NOTE: This operator sorts Input(X) according to a given LoDRankTable which does not need to be calculated according to Input(X). It can be calculated according to another different sequence, and then this operator sorts Input(X) according to the given LoDRankTable.

  • x (Variable) – (LoDTensor), the input lod tensor to be reordered according to Input(RankTable).

  • rank_table (Variable) – (LoDRankTable), the rank table according to which Input(X) is reordered.


LoDTensor, the reordered lod tensor.

Return type



import paddle.fluid as fluid
data_desc = (['input', [9], 0], ['ref', [5], 1])
data =[0][0], shape=data_desc[0][1])
rank_data =[1][0], shape=data_desc[1][1])
table = fluid.layers.control_flow.lod_rank_table(rank_data)
new_data = fluid.layers.reorder_lod_tensor_by_rank(
                 x=data, rank_table=table)