torch.inner

torch.inner(input, other, *, out=None) → Tensor

Computes the dot product for 1D tensors. For higher dimensions, sums the product of elements from input and other along their last dimension.

Note

If either input or other is a scalar, the result is equivalent to torch.mul(input, other).

If both input and other are non-scalars, the size of their last dimension must match and the result is equivalent to torch.tensordot(input, other, dims=([-1], [-1]))

Parameters

• input (Tensor) – First input tensor

• other (Tensor) – Second input tensor

Keyword Arguments

out (Tensor, optional) – Optional output tensor to write result into. The output shape is input.shape[:-1] + other.shape[:-1].

Example:

# Dot product
>>> torch.inner(torch.tensor([1, 2, 3]), torch.tensor([0, 2, 1]))
tensor(7)

# Multidimensional input tensors
>>> a = torch.randn(2, 3)
>>> a
tensor([[0.8173, 1.0874, 1.1784],
        [0.3279, 0.1234, 2.7894]])
>>> b = torch.randn(2, 4, 3)
>>> b
tensor([[[-0.4682, -0.7159,  0.1506],
        [ 0.4034, -0.3657,  1.0387],
        [ 0.9892, -0.6684,  0.1774],
        [ 0.9482,  1.3261,  0.3917]],

        [[ 0.4537,  0.7493,  1.1724],
        [ 0.2291,  0.5749, -0.2267],
        [-0.7920,  0.3607, -0.3701],
        [ 1.3666, -0.5850, -1.7242]]])
>>> torch.inner(a, b)
tensor([[[-0.9837,  1.1560,  0.2907,  2.6785],
        [ 2.5671,  0.5452, -0.6912, -1.5509]],

        [[ 0.1782,  2.9843,  0.7366,  1.5672],
        [ 3.5115, -0.4864, -1.2476, -4.4337]]])

# Scalar input
>>> torch.inner(a, torch.tensor(2))
tensor([[1.6347, 2.1748, 2.3567],
        [0.6558, 0.2469, 5.5787]])