torch.baddbmm

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

Performs a batch matrix-matrix product of matrices in batch1 and batch2. 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, then input must be broadcastable with a $(b \times n \times p)$ tensor and out will be a $(b \times n \times p)$ tensor. Both alpha and beta mean the same as the scaling factors used in torch.addbmm().

$\text{out}_i = \beta\ \text{input}_i + \alpha\ (\text{batch1}_i \mathbin{@} \text{batch2}_i)$

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

Parameters

• input (Tensor) – the tensor to be added

• 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 ($\beta$ )

• alpha (Number, optional) – multiplier for $\text{batch1} \mathbin{@} \text{batch2}$ ($\alpha$ )

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

Example:

>>> M = torch.randn(10, 3, 5)
>>> batch1 = torch.randn(10, 3, 4)
>>> batch2 = torch.randn(10, 4, 5)
>>> torch.baddbmm(M, batch1, batch2).size()
torch.Size([10, 3, 5])