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mindspore.ops.scatter_nd_mul

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mindspore.ops.scatter_nd_mul(input_x, indices, updates, use_locking=False)[source]

Perform a sparse multiplication update on input_x based on the specified indices and update values.

input_x[indices[i,...,j]]=updates[i,...,j]

Note

  • Support implicit type conversion and type promotion.

  • The dimension of indices is at least 2, and its shape must be indices.shape[-1] <= len(indices.shape).

  • The shape of updates is indices.shape[:-1] + input_x.shape[indices.shape[-1]:].

Parameters
  • input_x (Union[Parameter, Tensor]) – The input parameter or tensor.

  • indices (Tensor) – The specified indices.

  • updates (Tensor) – The update values.

  • use_locking (bool) – Whether to protect the assignment by a lock. Default: False .

Returns

Tensor

Supported Platforms:

GPU CPU

Examples

>>> import mindspore
>>> input_x = mindspore.Parameter(mindspore.tensor([1, 2, 3, 4, 5, 6, 7, 8],
...                               mindspore.float32), name="x")
>>> indices = mindspore.tensor([[2], [4], [1], [7]], mindspore.int32)
>>> updates = mindspore.tensor([6, 7, 8, 9], mindspore.float32)
>>> output = mindspore.ops.scatter_nd_mul(input_x, indices, updates)
>>> print(output)
[ 1. 16. 18.  4. 35.  6.  7. 72.]
>>> input_x = mindspore.Parameter(mindspore.tensor(mindspore.ops.ones((4, 4, 4)), mindspore.int32))
>>> indices = mindspore.tensor([[0], [2]], mindspore.int32)
>>> updates = mindspore.tensor([[[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]],
...                            [[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]]], mindspore.int32)
>>> output = mindspore.ops.scatter_nd_mul(input_x, indices, updates)
>>> print(output)
[[[1 1 1 1]
  [2 2 2 2]
  [3 3 3 3]
  [4 4 4 4]]
 [[1 1 1 1]
  [1 1 1 1]
  [1 1 1 1]
  [1 1 1 1]]
 [[5 5 5 5]
  [6 6 6 6]
  [7 7 7 7]
  [8 8 8 8]]
 [[1 1 1 1]
  [1 1 1 1]
  [1 1 1 1]
  [1 1 1 1]]]