Matmul — torch_matmul • torch

Matmul

Usage

torch_matmul(self, other)

Arguments

self: (Tensor) the first tensor to be multiplied
other: (Tensor) the second tensor to be multiplied

Note

The 1-dimensional dot product version of this function does not support an `out` parameter.

matmul(input, other, out=NULL) -> Tensor

Matrix product of two tensors.

The behavior depends on the dimensionality of the tensors as follows:

If both tensors are 1-dimensional, the dot product (scalar) is returned.
If both arguments are 2-dimensional, the matrix-matrix product is returned.
If the first argument is 1-dimensional and the second argument is 2-dimensional, a 1 is prepended to its dimension for the purpose of the matrix multiply. After the matrix multiply, the prepended dimension is removed.
If the first argument is 2-dimensional and the second argument is 1-dimensional, the matrix-vector product is returned.
If both arguments are at least 1-dimensional and at least one argument is N-dimensional (where N > 2), then a batched matrix multiply is returned. If the first argument is 1-dimensional, a 1 is prepended to its dimension for the purpose of the batched matrix multiply and removed after. If the second argument is 1-dimensional, a 1 is appended to its dimension for the purpose of the batched matrix multiple and removed after. The non-matrix (i.e. batch) dimensions are broadcasted (and thus must be broadcastable). For example, if input is a \((j \times 1 \times n \times m)\) tensor and other is a \((k \times m \times p)\) tensor, out will be an \((j \times k \times n \times p)\) tensor.

Examples

if (torch_is_installed()) {

# vector x vector
tensor1 = torch_randn(c(3))
tensor2 = torch_randn(c(3))
torch_matmul(tensor1, tensor2)
# matrix x vector
tensor1 = torch_randn(c(3, 4))
tensor2 = torch_randn(c(4))
torch_matmul(tensor1, tensor2)
# batched matrix x broadcasted vector
tensor1 = torch_randn(c(10, 3, 4))
tensor2 = torch_randn(c(4))
torch_matmul(tensor1, tensor2)
# batched matrix x batched matrix
tensor1 = torch_randn(c(10, 3, 4))
tensor2 = torch_randn(c(10, 4, 5))
torch_matmul(tensor1, tensor2)
# batched matrix x broadcasted matrix
tensor1 = torch_randn(c(10, 3, 4))
tensor2 = torch_randn(c(4, 5))
torch_matmul(tensor1, tensor2)
}
#> torch_tensor
#> (1,.,.) = 
#>   3.0716  0.7468 -0.8310  1.0082  0.2713
#>   0.9538 -2.6430  1.0720  1.4433 -1.5622
#>  -2.3489  4.1144  3.9783  0.0552 -0.9403
#> 
#> (2,.,.) = 
#>   1.8761 -0.5561 -3.6179 -1.2761  0.2098
#>   8.8357  1.3633 -3.4486  2.8546  2.1681
#>   3.3908  1.7386 -1.9735  0.4729  1.2895
#> 
#> (3,.,.) = 
#>  -1.2626  2.4527 -1.9928 -2.6927  0.1746
#>  -2.7742 -5.0983 -4.1720 -3.1939 -0.3129
#>  -0.5180 -1.1345  1.4375  1.0112 -0.0437
#> 
#> (4,.,.) = 
#>   2.2854 -5.7677 -4.3598 -0.7493 -1.4541
#>   2.2297 -0.1045  0.3557  1.1545 -1.0148
#>   2.8247 -3.7004 -1.2771  1.4504 -0.6019
#> 
#> (5,.,.) = 
#>  -8.8498  2.2194  5.3933 -2.4148 -2.0289
#>  -4.6353  2.2389  5.8302  0.8318 -0.2607
#>  -2.2359  1.3161  3.3844  0.3085 -1.3568
#> 
#> (6,.,.) = 
#>  -3.7949 -3.7769 -4.6787 -4.3660 -0.0204
#>  -0.1510 -0.1350  0.9603  0.1589 -1.3366
#>  -0.5151 -4.7478 -4.3111 -1.6742  1.0139
#> 
#> ... [the output was truncated (use n=-1 to disable)]
#> [ CPUFloatType{10,3,5} ]