torch.addmm

torch.addmm(input, mat1, mat2, *, beta=1, alpha=1, out=None) → Tensor

Performs a matrix multiplication of the matrices mat1 and mat2. The matrix input is added to the final result.

If mat1 is a $(n \times m)$ tensor, mat2 is a $(m \times p)$ tensor, then input must be broadcastable with a $(n \times p)$ tensor and out will be a $(n \times p)$ tensor.

alpha and beta are scaling factors on matrix-vector product between mat1 and mat2 and the added matrix input respectively.

$\text{out} = \beta\ \text{input} + \alpha\ (\text{mat1}_i \mathbin{@} \text{mat2}_i)$

For inputs of type FloatTensor or DoubleTensor, arguments beta and alpha must be real numbers, otherwise they should be integers.

Parameters

• input (Tensor) – matrix to be added

• mat1 (Tensor) – the first matrix to be multiplied

• mat2 (Tensor) – the second matrix to be multiplied

• beta (Number, optional) – multiplier for input ($\beta$ )

• alpha (Number, optional) – multiplier for $mat1 @ mat2$ ($\alpha$ )

• out (Tensor, optional) – the output tensor.

Example:

>>> M = torch.randn(2, 3)
>>> mat1 = torch.randn(2, 3)
>>> mat2 = torch.randn(3, 3)
>>> torch.addmm(M, mat1, mat2)
tensor([[-4.8716,  1.4671, -1.3746],
        [ 0.7573, -3.9555, -2.8681]])