Function Differences with torch.Tensor.scatter_

torch.Tensor.scatter_

torch.Tensor.scatter_(dim, index, src, reduce) -> Tensor

For more information, see torch.Tensor.scatter_.

mindspore.ops.tensor_scatter_elements

mindspore.ops.tensor_scatter_elements(
    input_x,
    indices,
    updates,
    axis=0,
    reduction='none'
) -> Tensor

For more information, see mindspore.ops.tensor_scatter_elements.

Differences

PyTorch: Replaces the element at the specified index position in the Tensor with the given value.

MindSpore: MindSpore API implements the same function as PyTorch, which is a Tensor interface with a slightly different invocation method in PyTorch.

Categories	Subcategories	PyTorch	MindSpore	Differences
Parameters	Parameter 1	dim	axis	Same function, different parameter names
	Parameter 2	index	indices	Same function, different parameter names
	Parameter 3	src	updates	Same function, different parameter names
	Parameter 4	reduce	reduction	Specification computation method. MindSpore only supports “none” and “add” modes.
	Parameter 5	-	input_x	This interface is the Tensor interface in PyTorch

Code Example

The two APIs achieve the same function.

# PyTorch
import torch

t = torch.zeros((3, 4), dtype=torch.float32)
indices = torch.tensor([[1, 2], [0, 1]])
values = torch.tensor([[3, 4], [5, 6]], dtype=torch.float32)
t.scatter_(0, indices, values)
print(t)
# tensor([[5., 0., 0., 0.],
#         [3., 6., 0., 0.],
#         [0., 4., 0., 0.]])

# MindSpore
import numpy as np
import mindspore
from mindspore import Tensor, Parameter
from mindspore import ops

input_x = Parameter(Tensor(np.zeros((3, 4)), mindspore.float32), name="x")
indices = Tensor(np.array([[1, 2], [0, 1]]), mindspore.int32)
updates = Tensor(np.array([[3, 4], [5, 6]]), mindspore.float32)
axis = 0
reduction = "none"
output = ops.tensor_scatter_elements(input_x, indices, updates, axis, reduction)
print(output)
# [[5. 0. 0. 0.]
#  [3. 6. 0. 0.]
#  [0. 4. 0. 0.]]