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]))
# 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]])
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