Hi everyone! I’m currently working on my project, in which I need to select entries of a tensor based on an index tensor and add values to the selected entries. However, I found out that an inplace add may not always work because of data races. For example, I have an index tensor which may have some duplicate indices, like below:

```
tensor = torch.tensor([1, 2, 3])
data = torch.tensor([4, 5, 6])
indices = torch.tensor([0, 0, 1], dtype = torch.long)
tensor[indices] += data
```

What I expect is `tensor: [10, 8, 3]`

because I want `4`

and `5`

to be added to `tensor[0]`

, but what I get is `tensor: [6, 8, 3]`

, which means only `5`

is added to `tensor[0]`

. I also encountered this problem when I tried this with 2D and 3D tensors.

I searched this issue online but found nothing useful, can you please help me with this? Thanks!

Update:

This 1D case can be resolved by `torch.scatter_add`

, but it seems that `scatter_add`

cannot deal with the below case

```
tensor = torch.tensor([[1, 2, 3],
[11, 22, 33]])
data = torch.tensor([[4, 5, 6],
[7, 8, 9],
[10, 11, 12]])
indices_x = torch.ones_like(data)
indices_y = torch.zeros_like(data)
tensor[indices_x, indices_y] += data # expect to add all values of data to tensor[1, 0]
print(tensor)
```