Shortcuts

# torch.triu¶

torch.triu(input, diagonal=0, *, out=None)

Returns the upper triangular part of a matrix (2-D tensor) or batch of matrices input, the other elements of the result tensor out are set to 0.

The upper triangular part of the matrix is defined as the elements on and above the diagonal.

The argument diagonal controls which diagonal to consider. If diagonal = 0, all elements on and above the main diagonal are retained. A positive value excludes just as many diagonals above the main diagonal, and similarly a negative value includes just as many diagonals below the main diagonal. The main diagonal are the set of indices $\lbrace (i, i) \rbrace$ for $i \in [0, \min\{d_{1}, d_{2}\} - 1]$ where $d_{1}, d_{2}$ are the dimensions of the matrix.

Parameters:
• input (Tensor) – the input tensor.

• diagonal (int, optional) – the diagonal to consider

Keyword Arguments:

out (Tensor, optional) – the output tensor.

Example:

>>> a = torch.randn(3, 3)
>>> a
tensor([[ 0.2309,  0.5207,  2.0049],
[ 0.2072, -1.0680,  0.6602],
[ 0.3480, -0.5211, -0.4573]])
>>> torch.triu(a)
tensor([[ 0.2309,  0.5207,  2.0049],
[ 0.0000, -1.0680,  0.6602],
[ 0.0000,  0.0000, -0.4573]])
>>> torch.triu(a, diagonal=1)
tensor([[ 0.0000,  0.5207,  2.0049],
[ 0.0000,  0.0000,  0.6602],
[ 0.0000,  0.0000,  0.0000]])
>>> torch.triu(a, diagonal=-1)
tensor([[ 0.2309,  0.5207,  2.0049],
[ 0.2072, -1.0680,  0.6602],
[ 0.0000, -0.5211, -0.4573]])

>>> b = torch.randn(4, 6)
>>> b
tensor([[ 0.5876, -0.0794, -1.8373,  0.6654,  0.2604,  1.5235],
[-0.2447,  0.9556, -1.2919,  1.3378, -0.1768, -1.0857],
[ 0.4333,  0.3146,  0.6576, -1.0432,  0.9348, -0.4410],
[-0.9888,  1.0679, -1.3337, -1.6556,  0.4798,  0.2830]])
>>> torch.triu(b, diagonal=1)
tensor([[ 0.0000, -0.0794, -1.8373,  0.6654,  0.2604,  1.5235],
[ 0.0000,  0.0000, -1.2919,  1.3378, -0.1768, -1.0857],
[ 0.0000,  0.0000,  0.0000, -1.0432,  0.9348, -0.4410],
[ 0.0000,  0.0000,  0.0000,  0.0000,  0.4798,  0.2830]])
>>> torch.triu(b, diagonal=-1)
tensor([[ 0.5876, -0.0794, -1.8373,  0.6654,  0.2604,  1.5235],
[-0.2447,  0.9556, -1.2919,  1.3378, -0.1768, -1.0857],
[ 0.0000,  0.3146,  0.6576, -1.0432,  0.9348, -0.4410],
[ 0.0000,  0.0000, -1.3337, -1.6556,  0.4798,  0.2830]])


## Docs

Access comprehensive developer documentation for PyTorch

View Docs

## Tutorials

Get in-depth tutorials for beginners and advanced developers

View Tutorials