Assume that we have two 100 by 30 matrices A and B. I want to compute ten 10 by 10 block diagonals of the matrix AB^T, and transform it as a 10 by 100 matrix by concatenating them. Is there an efficient way to do this?

Currently, I chunked the matrices A and B as ten 10 by 30 matrices and compute A_{i} * B_{i}^T for each chunk 0 <= i < 10, and concatenate it, which uses for loop. I want to do this without using any loops.

Thanks in advance.

Hi,

First step which is the extracting 10x30 matrices can be done in the same way as we extract patches out of matrices. The proper function for doing it is torch.unfold.

After this, we need to compute `ab.T`

operation over batches which torch.bmm enables us to do the dot product patch-wise.

Finally it is just a reshape to convert each 10x10 matrix to a 100 vector.

```
# initalize section - testing purposes
a = torch.ones((100, 30))
b = torch.ones((100, 30))
for i in range(10):
a[i*10:(i+1)*10, :]=i
b[i*10:(i+1)*10, :]=-i
# main code
a = a.unfold(0, 10, 10).reshape(10, -1, 30) # torch.Size([10, 10, 30])
b = b.unfold(0, 10, 10) # torch.Size([10, 30, 10])
result = torch.bmm(a, b) # torch.Size([10, 10, 10])
result = result.reshape(10, -1) # torch.Size([10, 100])
```

bests,

Nik

edit: if you have any trouble with `fold`

and `unfold`

, this discussion may help.

Thanks! I understand what unfold does.

But for `a`

, I think we need to use `transpose(-1, -2)`

instead of `reshape(10, -1, 30)`

. Am I right? It seems that the last `reshape`

for `result`

also doesn’t give a right answer.

Yes! you are right about `transpose(-1, -2)`

for `a`

. As I used same constant value for all cells of each patch, the result of transpose and reshape was identical.

Concerning the last `reshape`

, it just works like `flatten`

as we want to merge `dim=1`

and `dim=2`

into 1D tensor with size=100. In `result`

we are not transposing any dim with any other and as you mentioned in your question, it is more like concatenating them in the same way they already are. What do you think?