mindspore.ops.scatter_mul

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

Using given values to update tensor value through the mul operation, along with the input indices. This operation outputs the input_x after the update is done, which makes it convenient to use the updated value.

for each i, …, j in indices.shape:

\[\text{input_x}[\text{indices}[i, ..., j], :] \mathrel{*}= \text{updates}[i, ..., j, :]\]

Inputs of input_x and updates comply with the implicit type conversion rules to make the data types consistent. If they have different data types, the lower priority data type will be converted to the relatively highest priority data type. A RuntimeError will be reported when the data types of parameters need to be converted.

Parameters

input_x (Parameter) – The target tensor to be updated, with data type of Parameter. The shape is \((N,*)\) where \(*\) means,any number of additional dimensions.
indices (Tensor) – The index to do mul operation whose data type must be int32 or int64.
updates (Tensor) – The tensor doing the mul operation with input_x, the data type is same as input_x, the shape is indices.shape + input_x.shape[1:].

Returns

Tensor, the updated input_x, has the same shape and type as input_x.

Raises

TypeError – If use_locking is not a bool.
TypeError – If indices is not an int32 or int64.
ValueError – If the shape of updates is not equal to indices.shape + input_x.shape[1:].
RuntimeError – If the data type of input_x and updates conversion of Parameter is required when data type conversion of Parameter is not supported.

Supported Platforms:: Ascend GPU CPU

Examples

>>> input_x = Parameter(Tensor(np.array([[1.0, 1.0, 1.0], [2.0, 2.0, 2.0]]), mindspore.float32), name="x")
>>> indices = Tensor(np.array([0, 1]), mindspore.int32)
>>> updates = Tensor(np.array([[2.0, 2.0, 2.0], [2.0, 2.0, 2.0]]), mindspore.float32)
>>> output = ops.scatter_mul(input_x, indices, updates)
>>> print(output)
[[2. 2. 2.]
 [4. 4. 4.]]
>>> # for input_x will be updated after the operation is completed. input_x need to be re-initialized.
>>> input_x = Parameter(Tensor(np.array([[1.0, 1.0, 1.0], [2.0, 2.0, 2.0]]), mindspore.float32), name="x")
>>> # for indices = [[0, 1], [1, 1]]
>>> # step 1: [0, 1]
>>> # input_x[0] = [1.0, 1.0, 1.0] * [1.0, 1.0, 1.0] = [1.0, 1.0, 1.0]
>>> # input_x[1] = [2.0, 2.0, 2.0] * [3.0, 3.0, 3.0] = [6.0, 6.0, 6.0]
>>> # step 2: [1, 1]
>>> # input_x[1] = [6.0, 6.0, 6.0] * [7.0, 7.0, 7.0] = [42.0, 42.0, 42.0]
>>> # input_x[1] = [42.0, 42.0, 42.0] * [9.0, 9.0, 9.0] = [378.0, 378.0, 378.0]
>>> indices = Tensor(np.array([[0, 1], [1, 1]]), mindspore.int32)
>>> updates = Tensor(np.array([[[1.0, 1.0, 1.0], [3.0, 3.0, 3.0]],
...                            [[7.0, 7.0, 7.0], [9.0, 9.0, 9.0]]]), mindspore.float32)
>>> output = ops.scatter_mul(input_x, indices, updates)
>>> print(output)
[[  1.   1.   1.]
 [378. 378. 378.]]
>>> # for input_x will be updated after the operation is completed. input_x need to be re-initialized.
>>> input_x = Parameter(Tensor(np.array([[1.0, 1.0, 1.0], [2.0, 2.0, 2.0]]), mindspore.float32), name="x")
>>> # for indices = [[1, 0], [1, 1]]
>>> # step 1: [1, 0]
>>> # input_x[0] = [1.0, 1.0, 1.0] * [3.0, 3.0, 3.0] = [3.0, 3.0, 3.0]
>>> # input_x[1] = [2.0, 2.0, 2.0] * [1.0, 1.0, 1.0] = [2.0, 2.0, 2.0]
>>> # step 2: [1, 1]
>>> # input_x[1] = [2.0, 2.0, 2.0] * [7.0, 7.0, 7.0] = [14.0, 14.0, 14.0]
>>> # input_x[1] = [14.0, 14.0, 14.0] * [9.0, 9.0, 9.0] = [126.0, 126.0, 126.0]
>>> indices = Tensor(np.array([[1, 0], [1, 1]]), mindspore.int32)
>>> updates = Tensor(np.array([[[1.0, 1.0, 1.0], [3.0, 3.0, 3.0]],
...                            [[7.0, 7.0, 7.0], [9.0, 9.0, 9.0]]]), mindspore.float32)
>>> output = ops.scatter_mul(input_x, indices, updates)
>>> print(output)
[[  3.   3.   3.]
 [126. 126. 126.]]
>>> # for input_x will be updated after the operation is completed. input_x need to be re-initialized.
>>> input_x = Parameter(Tensor(np.array([[1.0, 1.0, 1.0], [2.0, 2.0, 2.0]]), mindspore.float32), name="x")
>>> # for indices = [[0, 1], [0, 1]]
>>> # step 1: [0, 1]
>>> # input_x[0] = [1.0, 1.0, 1.0] * [1.0, 1.0, 1.0] = [1.0, 1.0, 1.0]
>>> # input_x[1] = [2.0, 2.0, 2.0] * [3.0, 3.0, 3.0] = [6.0, 6.0, 6.0]
>>> # step 2: [0, 1]
>>> # input_x[0] = [1.0, 1.0, 1.0] * [7.0, 7.0, 7.0] = [7.0, 7.0, 7.0]
>>> # input_x[1] = [6.0, 6.0, 6.0] * [9.0, 9.0, 9.0] = [54.0, 54.0, 54.0]
>>> indices = Tensor(np.array([[0, 1], [0, 1]]), mindspore.int32)
>>> updates = Tensor(np.array([[[1.0, 1.0, 1.0], [3.0, 3.0, 3.0]],
...                            [[7.0, 7.0, 7.0], [9.0, 9.0, 9.0]]]), mindspore.float32)
>>> output = ops.scatter_mul(input_x, indices, updates)
>>> print(output)
[[ 7.  7.  7.]
 [54. 54. 54.]]