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,.,.) = 
#>  -0.2107 -0.3879 -1.3143 -0.0430  2.2345
#>  -0.9136 -0.9068  1.8199  1.1698 -2.9135
#>   2.2450  2.6020  0.6387  1.1817 -2.4071
#> 
#> (2,.,.) = 
#>   0.2840  2.3692 -3.2117 -3.0466  6.9034
#>  -1.2769 -1.0068  1.0793  0.3924 -1.1667
#>  -0.8946  0.2128  0.1235 -0.5774  0.8398
#> 
#> (3,.,.) = 
#>   3.6449 -0.3683 -0.7667 -1.6467 -1.5919
#>  -2.3211  0.3806 -0.7047 -2.5971  4.1467
#>  -1.2207 -0.4390 -0.4703 -0.9046  1.9640
#> 
#> (4,.,.) = 
#>  -3.2062 -1.1767 -0.8870 -1.5652  4.3275
#>  -2.2964 -0.6939  0.7091 -0.4729  0.7282
#>  -0.5145  0.3625  1.0261 -1.0104 -0.8082
#> 
#> (5,.,.) = 
#>   2.2827  2.1144  0.1544 -1.0026 -1.1655
#>   0.8138  2.1802  0.6393  0.8797 -1.2138
#>   2.5046  1.4670 -1.8273 -0.5193  1.5384
#> 
#> (6,.,.) = 
#>   0.0499  4.7422 -0.1814 -2.5135  2.8208
#>   0.9313 -1.1432 -0.8069 -0.4339  0.2122
#>  -4.2823 -3.7941  2.3477  0.2831 -1.6490
#> 
#> ... [the output was truncated (use n=-1 to disable)]
#> [ CPUFloatType{10,3,5} ]