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,.,.) = 
#>  -1.2249 -0.1648 -2.3938  1.7560  0.9053
#>  -1.0419  2.0118 -4.2912  7.2212 -0.7407
#>   0.1272 -1.2478  2.6422 -4.4955 -0.0153
#> 
#> (2,.,.) = 
#>   0.9233  0.7070  0.9890 -0.2460 -1.9709
#>   0.6177 -0.4424 -0.4492 -1.5433 -0.3341
#>   1.3711  0.5767  1.3911 -1.2805 -2.4189
#> 
#> (3,.,.) = 
#>   1.7171  0.4976 -0.0939 -0.3333 -1.2506
#>  -0.7838 -0.4589  0.4021  0.2197  1.8895
#>   0.3367 -0.5325  0.8675 -1.8757  0.1315
#> 
#> (4,.,.) = 
#>   1.6675  0.2451  0.0737 -1.4478 -1.8759
#>   1.4068  0.0628  0.3464 -1.7983 -1.7018
#>  -1.0183 -1.2610  1.5015 -2.8130  1.0033
#> 
#> (5,.,.) = 
#>   2.3063  0.3986 -1.8893 -0.5622 -1.8505
#>  -0.2171  0.5405 -2.6125  2.8565  0.3645
#>   0.5116  0.4476  1.1008 -0.4422 -1.4571
#> 
#> (6,.,.) = 
#>   1.2088  1.6089 -1.1047  2.7038 -2.2338
#>   0.3484  0.5903  0.7115  0.2711 -1.3215
#>   1.8408  1.9322 -3.3484  3.5272 -3.2111
#> 
#> ... [the output was truncated (use n=-1 to disable)]
#> [ CPUFloatType{10,3,5} ]