How to multiply a tensor row-wise by a vector in PyTorch?

Question

When I have a tensor m of shape [12, 10] and a vector s of scalars with shape [12], how can I multiply each row of m with the corresponding scalar in s?

Answer 1

You need to add a corresponding singleton dimension:

m * s[:, None]

s[:, None] has size of (12, 1) when multiplying a (12, 10) tensor by a (12, 1) tensor pytorch knows to broadcast s along the second singleton dimension and perform the "element-wise" product correctly.

Answer 2

You can broadcast a vector to a higher dimensional tensor like so:

def row_mult(input, vector):
    extra_dims = (1,)*(input.dim()-1)
    return t * vector.view(-1, *extra_dims)

Answer 3

A slighty hard to understand at first, but very powerful technique is to use Einstein summation:

torch.einsum('i,ij->ij', s, m)

Answer 4

Shai's answer works if you know the number of dimensions in advance and can hardcode the correct number of None's. This can be extended to extra dimentions is required:

mask = (torch.rand(12) > 0.5).int()  
data = (torch.rand(12, 2, 3, 4))

result = data * mask[:,None,None,None]

result.shape                  # torch.Size([12, 2, 3, 4])
mask[:,None,None,None].shape  # torch.Size([12, 1, 1, 1])

If you are dealing with data of variable or unknown dimensions, then it may require manually extending mask to the correct shape

mask = (torch.rand(12) > 0.5).int()
while mask.dim() < data.dim(): mask.unsqueeze_(1)
result = data * mask

result.shape  # torch.Size([12, 2, 3, 4])
mask.shape    # torch.Size([12, 1, 1, 1])

This is a bit of an ugly solution, but it does work. There is probably a much more elegant way to correctly reshape the mask tensor inline for a variable number of dimensions