PyTorch：如何通过广播两个不同形状的张量相乘

Question

I have the following two PyTorch tensors A and B.

A = torch.tensor(np.array([40, 42, 38]), dtype = torch.float64)

tensor([40., 42., 38.], dtype=torch.float64)

B = torch.tensor(np.array([[[1,2,3,4,5],[1,2,3,4,5],[1,2,3,4,5],[1,2,3,4,5],[1,2,3,4,5]], [[4,5,6,7,8],[4,5,6,7,8],[4,5,6,7,8],[4,5,6,7,8],[4,5,6,7,8]], [[7,8,9,10,11],[7,8,9,10,11],[7,8,9,10,11],[7,8,9,10,11],[7,8,9,10,11]]]), dtype = torch.float64)

tensor([[[ 1.,  2.,  3.,  4.,  5.],
         [ 1.,  2.,  3.,  4.,  5.],
         [ 1.,  2.,  3.,  4.,  5.],
         [ 1.,  2.,  3.,  4.,  5.],
         [ 1.,  2.,  3.,  4.,  5.]],

        [[ 4.,  5.,  6.,  7.,  8.],
         [ 4.,  5.,  6.,  7.,  8.],
         [ 4.,  5.,  6.,  7.,  8.],
         [ 4.,  5.,  6.,  7.,  8.],
         [ 4.,  5.,  6.,  7.,  8.]],

        [[ 7.,  8.,  9., 10., 11.],
         [ 7.,  8.,  9., 10., 11.],
         [ 7.,  8.,  9., 10., 11.],
         [ 7.,  8.,  9., 10., 11.],
         [ 7.,  8.,  9., 10., 11.]]], dtype=torch.float64)

Tensor A is of shape:

torch.Size([3])

Tensor B is of shape:

torch.Size([3, 5, 5])

How do I multiply tensor A with tensor B (using broadcasting) in such a way for eg. the first value in tensor A (ie. 40. ) is multiplied with all the values in the first 'nested' tensor in tensor B, ie.

tensor([[[ 1.,  2.,  3.,  4.,  5.],
         [ 1.,  2.,  3.,  4.,  5.],
         [ 1.,  2.,  3.,  4.,  5.],
         [ 1.,  2.,  3.,  4.,  5.],
         [ 1.,  2.,  3.,  4.,  5.]],

and so on for the other 2 values in tensor A and the other two nested tensors in tensor B, respectively.

I could do this multiplication (via broadcasting) with numpy arrays if A and B are arrays of both shape (3,) - ie. A*B - but I can't seem to figure out a counterpart of this with PyTorch tensors. Any help would really be appreciated.

Answer 1

When applying broadcasting in pytorch (as well as in numpy) you need to start at the last dimension (check out https://pytorch.org/docs/stable/notes/broadcasting.html ). If they do not match you need to reshape your tensor. In your case they can't directly be broadcasted:

      [3]  # the two values in the last dimensions are not one and do not match
[3, 5, 5]

Instead you can redefine A = A[:, None, None] before muliplying such that you get shapes

[3, 1, 1]
[3, 5, 5]

which satisfies the conditions for broadcasting.