Function Differences with torch.scatter_add

torch.scatter_add

torch.scatter_add(input, dim, index, src)

For more information, see torch.scatter_add.

mindspore.ops.tensor_scatter_elements

mindspore.ops.tensor_scatter_elements(input_x, indices, updates, axis, reduction)

For more information, see mindspore.ops.tensor_scatter_elements.

Differences

PyTorch: For all dimensions d , index.size(d) <= src.size(d) is required, i.e. index can select some or all of the data of src to be scattered into input .

MindSpore: The shape of indices must be the same as the shape of updates , i.e. all data of updates will be scattered into input_x by indices .

There is no difference in function.

Categories	Subcategories	PyTorch	MindSpore	Differences
Parameters	Parameter 1	input	input_x	Same function, different parameter names
	Parameter 2	dim	axis	Same function, different parameter names
	Parameter 3	index	indices	For MindSpore, the shape of indices must be the same as the shape of updates . For PyTorch, index.size(d) <= src.size(d) is required for all dimensions d
	Parameter 4	src	updates	Same function
	Parameter 5		reduction	reduction must be set as “add”

Code Example

# PyTorch
import torch
import numpy as np
x = torch.tensor(np.zeros((5, 5)), dtype=torch.float32)
src = torch.tensor(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]), dtype=torch.float32)
index = torch.tensor(np.array([[0, 1], [0, 1], [0, 1]]), dtype=torch.int64)
out = torch.scatter_add(x=x, dim=1, index=index, src=src)
print(out)
# tensor([[1., 2., 0., 0., 0.],
#         [4., 5., 0., 0., 0.],
#         [7., 8., 0., 0., 0.],
#         [0., 0., 0., 0., 0.],
#         [0., 0., 0., 0., 0.]])

# MindSpore
import mindspore as ms
import numpy as np
x = ms.Tensor(np.zeros((5, 5)), dtype=ms.float32)
src = ms.Tensor(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]), dtype=ms.float32)
index = ms.Tensor(np.array([[0, 1, 2], [0, 1, 2], [0, 1, 2]]), dtype=ms.int64)
out = ms.ops.tensor_scatter_elements(input_x=x, axis=1, indices=index, updates=src, reduction="add")
print(out)
# [[1. 2. 3. 0. 0.]
#  [4. 5. 6. 0. 0.]
#  [7. 8. 9. 0. 0.]
#  [0. 0. 0. 0. 0.]
#  [0. 0. 0. 0. 0.]]