How to retrieve topk values across multiple rows along with their respective indices in PyTorch?

Question

I have a tensor like this:

tensor([[[ 7.3478, -1.8058, -2.6140,  ..., -0.2719, -0.3171, -0.4737]],

    [[ 7.3606, -1.8269, -1.9825,  ..., -0.8680,  0.4894,  0.2708]]],
   grad_fn=<CatBackward>)

I want to get the topk values across both the rows. Currently what I am able to do is the following:

ipdb>  stacked.topk(2)
torch.return_types.topk(
values=tensor([[[14.3902, 14.3039]],

        [[14.8927, 12.1973]]], grad_fn=<TopkBackward>),
indices=tensor([[[60, 12]],

        [[12, 23]]]))

From the output, you can see that the top 2 values were retrieved from both rows. I want to get an output as follows:

14.8927 that maps to index 12
14.3902 that maps to index 60

Note that if the top 2 values were in the first row, it will only return the values from there and completely ignore the second row and vice versa.

Need help in this regard.

A very hacky way of doing what I am trying to say would be the following but it is very hacky and shown for a BEAM_WIDTH of 2:

BEAM_WIDTH = 2
top_k = stacked.data.topk(BEAM_WIDTH, dim=2)

v1, i1 = top_k[0][0][0], top_k[1][0][0]
v2, i2 = top_k[0][1][0], top_k[1][1][0]

i = j = 0
final = []
for _ in range(BEAM_WIDTH):
    if v1[i] >= v2[j]:
        final.append((v1[i], i1[i]))
        i += 1
    else:
        final.append((v2[j], i2[j]))
        j += 1

Answer 1

Repeated Indices

I believe this is what you want. First you would find the topk elements in the flattened list, then convert those indices back to the row-relative format.

topk_values, linear_indices = stacked.flatten().topk(2)
topk_indices = linear_indices % stacked.shape[-1]

---

Unique Indices

The previous approach doesn't enforce unique indices. If unique indices are needed then you could find the max between rows, then find the topk among that.

topk_values, topk_indices = stacked.max(dim=0)[0].flatten().topk(2)

---

Example

To demonstrate the difference between these two approaches, suppose you have

stacked = torch.tensor([[[11,8,0]],
                        [[10,9,0]]])

In the repeated indices case you would end up with

topk_values=[11, 10]
topk_indices=[0, 0]

In the unique indices case you would get

topk_values=[11, 9]
topk_indices=[0, 1]