torch.addbmm

torch.addbmm(input, batch1, batch2, *, beta=1, alpha=1, out=None) → Tensor

Performs a batch matrix-matrix product of matrices stored in batch1 and batch2, with a reduced add step (all matrix multiplications get accumulated along the first dimension). input is added to the final result.

batch1 and batch2 must be 3-D tensors each containing the same number of matrices.

If batch1 is a $(b \times n \times m)$ tensor, batch2 is a $(b \times m \times p)$ tensor, input must be broadcastable with a $(n \times p)$ tensor and out will be a $(n \times p)$ tensor.

$out = \beta\ \text{input} + \alpha\ (\sum_{i=0}^{b-1} \text{batch1}_i \mathbin{@} \text{batch2}_i)$

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

Parameters

• batch1 (Tensor) – the first batch of matrices to be multiplied

• batch2 (Tensor) – the second batch of matrices to be multiplied

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

• input (Tensor) – matrix to be added

• alpha (Number, optional) – multiplier for batch1 @ batch2 ($\alpha$ )

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

Example:

>>> M = torch.randn(3, 5)
>>> batch1 = torch.randn(10, 3, 4)
>>> batch2 = torch.randn(10, 4, 5)
>>> torch.addbmm(M, batch1, batch2)
tensor([[  6.6311,   0.0503,   6.9768, -12.0362,  -2.1653],
        [ -4.8185,  -1.4255,  -6.6760,   8.9453,   2.5743],
        [ -3.8202,   4.3691,   1.0943,  -1.1109,   5.4730]])