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Showing content from https://docs.pytorch.org/docs/stable/generated/torch.diag.html below:

torch.diag — PyTorch 2.7 documentation

torch.diag(input, diagonal=0, *, out=None) → Tensor¶

• If input is a vector (1-D tensor), then returns a 2-D square tensor with the elements of input as the diagonal.

• If input is a matrix (2-D tensor), then returns a 1-D tensor with the diagonal elements of input.

The argument diagonal controls which diagonal to consider:

• If diagonal = 0, it is the main diagonal.

• If diagonal > 0, it is above the main diagonal.

• If diagonal < 0, it is below the main diagonal.

Parameters

• input (Tensor) – the input tensor.

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

Keyword Arguments

out (Tensor, optional) – the output tensor.

Examples:

Get the square matrix where the input vector is the diagonal:

>>> a = torch.randn(3)
>>> a
tensor([ 0.5950,-0.0872, 2.3298])
>>> torch.diag(a)
tensor([[ 0.5950, 0.0000, 0.0000],
        [ 0.0000,-0.0872, 0.0000],
        [ 0.0000, 0.0000, 2.3298]])
>>> torch.diag(a, 1)
tensor([[ 0.0000, 0.5950, 0.0000, 0.0000],
        [ 0.0000, 0.0000,-0.0872, 0.0000],
        [ 0.0000, 0.0000, 0.0000, 2.3298],
        [ 0.0000, 0.0000, 0.0000, 0.0000]])

Get the k-th diagonal of a given matrix:

>>> a = torch.randn(3, 3)
>>> a
tensor([[-0.4264, 0.0255,-0.1064],
        [ 0.8795,-0.2429, 0.1374],
        [ 0.1029,-0.6482,-1.6300]])
>>> torch.diag(a, 0)
tensor([-0.4264,-0.2429,-1.6300])
>>> torch.diag(a, 1)
tensor([ 0.0255, 0.1374])

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