Function Differences with torch.Tensor.scatter_add_

torch.Tensor.scatter_add_

torch.Tensor.scatter_add_(
    dim,
    index,
    src
)

For more information, see torch.Tensor.scatter_add_.

mindspore.ops.ScatterNdAdd

class mindspore.ops.ScatterNdAdd(use_locking=False)(
    input_x,
    indices,
    update
)

For more information, see mindspore.ops.ScatterNdAdd.

Differences

PyTorch: Given an input tensor, updates the tensor and index tensor; adds the updated tensor to the input tensor based on the index tensor along the given axis.

MindSpore: Given an input tensor, updates the tensor and index tensor; adds the updated tensor to the input tensor based on the index tensor. Customizing the axes by parameters is not supported, but the axes can be specified by adjusting the shape of the index tensor.

Code Example

import mindspore as ms
import mindspore.ops as ops
import torch
import numpy as np

# In MindSpore, no parameter for specifying dimension.
input_x = ms.Parameter(ms.Tensor(np.array([1, 2, 3, 4, 5, 6, 7, 8]), ms.float32), name="x")
indices = ms.Tensor(np.array([[2], [4], [1], [7]]), ms.int32)
updates = ms.Tensor(np.array([6, 7, 8, 9]), ms.float32)
scatter_nd_add = ops.ScatterNdAdd()
output = scatter_nd_add(input_x, indices, updates)
print(output)
# Out：
# [ 1. 10.  9.  4. 12.  6.  7. 17.]

# In torch, parameter dim can be set to specify dimension.
input_x = torch.tensor(np.array([1, 2, 3, 4, 5, 6, 7, 8]).astype(np.float32))
indices = torch.tensor(np.array([2, 4, 1, 7]).astype(np.int64))
updates = torch.tensor(np.array([6, 7, 8, 9]).astype(np.float32))
output = input_x.scatter_add_(dim=0, index=indices, src=updates)
print(output)
# Out:
# tensor([ 1., 10.,  9.,  4., 12.,  6.,  7., 17.])