Baddbmm — torch_baddbmm • torch

Baddbmm

Usage

torch_baddbmm(self, batch1, batch2, beta = 1L, alpha = 1L)

Arguments

self: (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 ( $β$ )
alpha: (Number, optional) multiplier for $batch1 @ batch2$ ( $α$ )

baddbmm(input, batch1, batch2, *, beta=1, alpha=1, out=NULL) -> 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.

${out}_{i} = β {input}_{i} + α ({batch1}_{i} @ {batch2}_{i})$ For inputs of type FloatTensor or DoubleTensor, arguments beta and alpha must be real numbers, otherwise they should be integers.

Examples

if (torch_is_installed()) {

M = torch_randn(c(10, 3, 5))
batch1 = torch_randn(c(10, 3, 4))
batch2 = torch_randn(c(10, 4, 5))
torch_baddbmm(M, batch1, batch2)
}
#> torch_tensor
#> (1,.,.) = 
#>  -5.2007  0.0807  3.0940 -1.3121  2.5306
#>   0.2644 -1.2034 -3.5998  5.4335 -0.2692
#>  -0.3976 -2.9182 -0.8564  4.3631 -2.4356
#> 
#> (2,.,.) = 
#>   0.3607  0.9992  3.1091 -1.1227  1.6882
#>   1.5847  4.5565 -4.8489 -5.2819 -6.7872
#>  -2.1788  0.9322 -0.0206 -2.1114  1.0077
#> 
#> (3,.,.) = 
#>  -0.9956  1.2538 -0.1740  0.4568 -1.5000
#>   1.1305  0.5745  0.1917 -1.0205  0.6596
#>  -2.2767  0.1652 -0.3900 -1.4924  1.5098
#> 
#> (4,.,.) = 
#>  -1.5876  1.5219 -1.7912  4.3624  3.1886
#>   4.4580  0.4052  0.2761  0.9690  2.0056
#>  -2.3469  0.2802  2.6563  0.1204 -1.4903
#> 
#> (5,.,.) = 
#>   4.2489 -1.0415  3.2728  1.4116  3.9124
#>   6.9167 -0.0519 -1.7989  0.1661  3.2426
#>  -6.8922 -1.4032 -0.7015  0.9520 -2.8125
#> 
#> (6,.,.) = 
#>  -0.6160  1.8434 -1.7277 -1.7547  1.0645
#>   4.3311  0.0461  1.9562  0.0573  0.2527
#>  -5.7161  0.5723 -9.3385 -0.7597 -0.9824
#> 
#> ... [the output was truncated (use n=-1 to disable)]
#> [ CPUFloatType{10,3,5} ]