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
andbatch2
, with a reduced add step (all matrix multiplications get accumulated along the first dimension).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,input
must be broadcastable with a tensor andout
will be a tensor.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.
On certain ROCm devices, when using float16 inputs this module will use different precision for backward.
- Parameters
- Keyword Arguments
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]])