torch / torch
torch.baddbmm¶
-
torch.
baddbmm
(input, batch1, batch2, *, beta=1, alpha=1, out=None) → Tensor¶ Performs a batch matrix-matrix product of matrices in
batch1
andbatch2
.input
is added to the final result.batch1
andbatch2
must be 3-D tensors each containing the same number of matrices.If
batch1
is a tensor,batch2
is a tensor, theninput
must be broadcastable with a tensor andout
will be a tensor. Bothalpha
andbeta
mean the same as the scaling factors used intorch.addbmm()
.If
beta
is 0, theninput
will be ignored, and nan and inf in it will not be propagated.For inputs of type FloatTensor or DoubleTensor, arguments
beta
andalpha
must be real numbers, otherwise they should be integers.This operator supports TensorFloat32.
- Parameters
- Keyword Arguments
beta (Number, optional) – multiplier for
input
( )alpha (Number, optional) – multiplier for ( )
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])
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