# Groupby aggregate product in PyTorch

I have the same problem as in the question Groupby aggregate mean in pytorch. However, I want to create the product of my tensors inside each group (or labels). Unfortunately, I couldn’t find a native PyTorch function that could solve my problem, like a hypothetical `scatter_prod_` for products (equivalent to `scatter_add_` for sums), which was the function used in @ptrblck’s answer.

Recycling the example code from @elyase’s question, consider the 2D tensor:

``````samples = torch.Tensor([
[0.1, 0.1],    #-> group / class 1
[0.2, 0.2],    #-> group / class 2
[0.4, 0.4],    #-> group / class 2
[0.0, 0.0]     #-> group / class 0
])
``````

with labels where it is true that `len(samples) == len(labels)`

``````labels = torch.LongTensor([1, 2, 2, 0])
``````

So my expected output is:

``````res == torch.Tensor([
[0.0, 0.0],
[0.1, 0.1],
[0.8, 0.8] # -> PRODUCT of [0.2, 0.2] and [0.4, 0.4]
])
``````

Here the question is, again, following @elyase’s question, how can this be done in pure PyTorch (i.e. no numpy so that I can autograd) and ideally without for loops?

`scatter_` introduced the `reduce` argument and `reduce='multiply'` should work.

1 Like

You are right. Indeed, this was posted on StackOverflow. Reposting for future searches.

``````samples = torch.Tensor([
[0.1, 0.1],    #-> group / class 1
[0.2, 0.2],    #-> group / class 2
[0.4, 0.4],    #-> group / class 2
[0.0, 0.0]     #-> group / class 0
])

labels = torch.LongTensor([1,2,2,0])

label_size = 3
sample_dim = samples.size(1)

index = labels.unsqueeze(1).repeat((1, sample_dim))

res = torch.ones(label_size, sample_dim, dtype=samples.dtype)
res.scatter_(0, index, samples, reduce='multiply')
``````