torch.diagonal — PyTorch 2.7 documentation (original) (raw)

torch.diagonal(input, offset=0, dim1=0, dim2=1) → Tensor¶

Returns a partial view of input with the its diagonal elements with respect to dim1 and dim2 appended as a dimension at the end of the shape.

The argument offset controls which diagonal to consider:

• If offset = 0, it is the main diagonal.
• If offset > 0, it is above the main diagonal.
• If offset < 0, it is below the main diagonal.

Applying torch.diag_embed() to the output of this function with the same arguments yields a diagonal matrix with the diagonal entries of the input. However, torch.diag_embed() has different default dimensions, so those need to be explicitly specified.

Parameters

• input (Tensor) – the input tensor. Must be at least 2-dimensional.
• offset (int, optional) – which diagonal to consider. Default: 0 (main diagonal).
• dim1 (int, optional) – first dimension with respect to which to take diagonal. Default: 0.
• dim2 (int, optional) – second dimension with respect to which to take diagonal. Default: 1.

Note

To take a batch diagonal, pass in dim1=-2, dim2=-1.

Examples:

a = torch.randn(3, 3) a tensor([[-1.0854, 1.1431, -0.1752], [ 0.8536, -0.0905, 0.0360], [ 0.6927, -0.3735, -0.4945]])

torch.diagonal(a) tensor([-1.0854, -0.0905, -0.4945])

torch.diagonal(a, 1) tensor([ 1.1431, 0.0360])

b = torch.randn(2, 5) b tensor([[-1.7948, -1.2731, -0.3181, 2.0200, -1.6745], [ 1.8262, -1.5049, 0.4114, 1.0704, -1.2607]])

torch.diagonal(b, 1, 1, 0) tensor([1.8262])

x = torch.randn(2, 5, 4, 2) torch.diagonal(x, offset=-1, dim1=1, dim2=2) tensor([[[-1.2631, 0.3755, -1.5977, -1.8172], [-1.1065, 1.0401, -0.2235, -0.7938]],

    [[-1.7325, -0.3081,  0.6166,  0.2335],
     [ 1.0500,  0.7336, -0.3836, -1.1015]]])