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)
