triu¶

nvtripy.triu(tensor: Tensor, diagonal: int = 0) → Tensor[source]¶

Returns the upper triangular part of each \([M, N]\) matrix in the tensor, with all other elements set to 0. If the tensor has more than two dimensions, it is treated as a batch of matrices.

Parameters:

tensor (Tensor) – [dtype=T1] The nvtripy tensor to operate on.
diagonal (int) –
The diagonal below which to zero elements. diagonal=0 indicates the main diagonal which is defined by the set of indices \({{(i, i)}}\) where \(i \in [0, min(M, N))\).

Positive values indicate the diagonal which is that many diagonals above the main one, while negative values indicate one which is below.

Returns:

[dtype=T1] A tensor of the same shape as this tensor.

Return type:

Tensor

DATA TYPE CONSTRAINTS:

T1: bool, float16, bfloat16, float32, int8, int32, int64

Example: Main Diagonal

input = tp.iota((2, 1, 3, 3), dim=2) + 1.0
output = tp.triu(input)

Local Variables¶

>>> input
tensor(
    [[[[1, 1, 1],
       [2, 2, 2],
       [3, 3, 3]]],


     [[[1, 1, 1],
       [2, 2, 2],
       [3, 3, 3]]]], 
    dtype=float32, loc=gpu:0, shape=(2, 1, 3, 3))

>>> output
tensor(
    [[[[1, 1, 1],
       [0, 2, 2],
       [0, 0, 3]]],


     [[[1, 1, 1],
       [0, 2, 2],
       [0, 0, 3]]]], 
    dtype=float32, loc=gpu:0, shape=(2, 1, 3, 3))

Example: Two Diagonals Above Main

output = tp.triu(input, diagonal=2)

Local Variables¶

>>> input
tensor(
    [[1, 1, 1, 1, 1],
     [2, 2, 2, 2, 2],
     [3, 3, 3, 3, 3],
     [4, 4, 4, 4, 4],
     [5, 5, 5, 5, 5]], 
    dtype=float32, loc=gpu:0, shape=(5, 5))

>>> output
tensor(
    [[0, 0, 1, 1, 1],
     [0, 0, 0, 2, 2],
     [0, 0, 0, 0, 3],
     [0, 0, 0, 0, 0],
     [0, 0, 0, 0, 0]], 
    dtype=float32, loc=gpu:0, shape=(5, 5))

Example: One Diagonal Below Main

output = tp.triu(input, diagonal=-1)

Local Variables¶

>>> input
tensor(
    [[1, 1, 1, 1, 1],
     [2, 2, 2, 2, 2],
     [3, 3, 3, 3, 3],
     [4, 4, 4, 4, 4],
     [5, 5, 5, 5, 5]], 
    dtype=float32, loc=gpu:0, shape=(5, 5))

>>> output
tensor(
    [[1, 1, 1, 1, 1],
     [2, 2, 2, 2, 2],
     [0, 3, 3, 3, 3],
     [0, 0, 4, 4, 4],
     [0, 0, 0, 5, 5]], 
    dtype=float32, loc=gpu:0, shape=(5, 5))