mindspore.ops.addbmm

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

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

Note

batch1 and batch2 must be 3-D tensors each containing the same number of matrices.
When batch1 is a $(C, W, T)$ tensor and batch2 is a $(C, T, H)$ tensor, input must be broadcastable with $(W, H)$ tensor, and out will be a $(W, H)$ tensor.
If beta is 0, then input will be ignored.

o u t p u t = β i n p u t + α (\sum_{i = 0}^{b - 1} b a t c h 1_{i} @ b a t c h 2_{i})

Parameters

input (Tensor) – The input tensor.
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) – Scale factor for input. Default 1 .
alpha (Union[int, float], optional) – Scale factor for ( batch1 @ batch2 ). Default 1 .

Returns

Tensor

Supported Platforms:: Ascend GPU CPU

Examples

>>> import mindspore
>>> m = mindspore.ops.ones((3, 3))
>>> arr1 = mindspore.tensor([[8., 7., 6.], [5., 4., 3.], [2., 1., 0.]])
>>> arr2 = mindspore.tensor([[5., 4., 3.], [2., 1., 0.], [8., 7., 6.]])
>>> output = mindspore.ops.addbmm(m, arr1, arr2)
>>> print(output)
[[172. 136. 100.]
 [172. 136. 100.]
 [172. 136. 100.]]