Exhaustive Concatenation between the tensors

I am trying to do the exhaustive concatenation between the tensors. So, for example, I have tensor:

a = torch.randn(3, 512)

I want to concatenate like concat(t1,t1),concat(t1,t2), concat(t1,t3), concat(t2,t1), concat(t2,t2)....

As a naive solution, I have used for loop:

ans = []
result = []
split = torch.split(a, [1, 1, 1], dim=0)

for i in range(len(split)):
    ans.append(split[i])

for t1 in ans:
    for t2 in ans:
        result.append(torch.cat((t1,t2), dim=1))

The issue is that each epoch is taking very long time and the code is slow. I tried the solution posted in question on PyTorch: How to implement attention for graph attention layer but this gives a memory error.

t1 = a.repeat(1, a.shape[0]).view(a.shape[0] * a.shape[0], -1)
t2 = a.repeat(a.shape[0], 1)
result.append(torch.cat((t1, t2), dim=1))

I am sure there is a faster way, but I was unable to figure it out.