mindspore.ops.tensor_scatter_mul

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mindspore.ops.tensor_scatter_mul(input_x, indices, updates)[source]

Creates a new tensor by multiplying the values from the positions in input_x indicated by indices, with values from updates. When divided values are provided for the same index, the result of the update will multiply these values respectively. Except that the updates are applied on output Tensor instead of input Parameter.

The last axis of indices is the depth of each index vectors. For each index vector, there must be a corresponding value in updates. The shape of updates should be equal to the shape of input_x[indices]. For more details, see use cases.

\[output[indices] = input\_x \times update\]

Note

  • If some values of the indices are out of bound, instead of raising an index error, the corresponding updates will not be updated to input_x.

Parameters

  • input_x (Tensor) – The input tensor. The dimension of input_x must be no less than indices.shape[-1].

  • indices (Tensor) – The index of input tensor whose data type is int32 or int64. The rank must be at least 2.

  • updates (Tensor) – The tensor to update the input tensor, has the same type as input_x, and the shape of updates should be equal to \(indices.shape[:-1] + input\_x.shape[indices.shape[-1]:]\).

Returns

Tensor, has the same shape and type as input_x.

Raises

  • TypeError – If dtype of indices is neither int32 nor int64.

  • ValueError – If length of shape of input_x is less than the last dimension of shape of indices.

  • RuntimeError – If a value of indices is not in input_x on CPU backend.

Supported Platforms:

GPU CPU

Examples

>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> input_x = Tensor(np.array([[-0.1, 0.3, 3.6], [0.4, 0.5, -3.2]]), mindspore.float32)
>>> indices = Tensor(np.array([[0, 0], [0, 0]]), mindspore.int32)
>>> updates = Tensor(np.array([1.0, 2.2]), mindspore.float32)
>>> # Next, demonstrate the approximate operation process of this operator:
>>> # 1, indices[0] = [0, 0], indices[1] = [0, 0]
>>> # 2, And input_x[0, 0] = -0.1
>>> # 3, So input_x[indices] = [-0.1, -0.1]
>>> # 4, Satisfy the above formula: input_x[indices].shape=(2) == updates.shape=(2)
>>> # 5, Perform the multiply operation for the first time:
>>> #      first_input_x = input_x[0][0] * updates[0] = [[-0.1, 0.3, 3.6], [0.4, 0.5, -3.2]]
>>> # 6, Perform the multiply operation for the second time:
>>> #      second_input_x = input_x[0][0] * updates[1] = [[-0.22, 0.3, 3.6], [0.4, 0.5, -3.2]]
>>> output = ops.tensor_scatter_mul(input_x, indices, updates)
>>> print(output)
[[-0.22  0.3   3.6  ]
 [ 0.4   0.5   -3.2 ]]