mindspore.ops.addbmm

mindspore.ops.addbmm(input, batch1, batch2, *, beta=1, alpha=1)[source]

Applies batch matrix multiplication to batch1 and batch2, with a reduced add step and add input to the result.

The optional values alpha and beta are the matrix-matrix product between batch1 and batch2 and the scale factor for the added tensor input respectively. If beta is 0, then input will be ignored.

\[output = \beta input + \alpha (\sum_{i=0}^{b-1} {batch1_i @ batch2_i})\]
Parameters

  • input (Tensor) – Tensor to be added.

  • batch1 (Tensor) – The first batch of tensor to be multiplied.

  • batch2 (Tensor) – The second batch of tensor to be multiplied.

Keyword Arguments

  • beta (Union[int, float], optional) – Multiplier for input. Default: 1.

  • alpha (Union[int, float], optional) – Multiplier for batch1 @ batch2. Default: 1.

Returns

Tensor, has the same dtype as input.

Raises

  • TypeError – If alpha or beta is not an int or float.

  • ValueError – If batch1, batch2 cannot apply batch matrix multiplication.

Supported Platforms:

Ascend GPU CPU

Examples

>>> m = np.ones((3, 3)).astype(np.float32)
>>> arr1 = np.arange(24).astype(np.float32).reshape((2, 3, 4))
>>> arr2 = np.arange(24).astype(np.float32).reshape((2, 4, 3))
>>> a = Tensor(arr1)
>>> b = Tensor(arr2)
>>> c = Tensor(m)
>>> output = ops.addbmm(c, a, b)
>>> print(output)
[[ 949. 1009. 1069.]
 [1285. 1377. 1469.]
 [1621. 1745. 1869.]]