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,.,.) = 
#>  -1.1164  0.3940  0.3484  2.0695 -0.5228
#>  -0.9559 -0.0983 -2.1750 -2.1280  0.8184
#>   1.3234 -0.3771  2.3310  1.7787 -1.4039
#> 
#> (2,.,.) = 
#>  -0.8618 -1.0597 -0.5731  0.1435 -1.0502
#>   0.0843  0.9535 -0.4668 -0.6751  1.5336
#>  -0.8721 -0.1149 -2.3503 -2.6045  0.5173
#> 
#> (3,.,.) = 
#>   0.2155  2.2246  1.5020  2.4600  1.9676
#>  -0.9840 -0.7834 -1.9952 -1.9891 -0.4647
#>   1.6203 -0.7079  1.9915  0.8991 -0.9044
#> 
#> (4,.,.) = 
#>  -0.1419 -4.2189 -0.8079 -1.6711 -4.0958
#>   0.4448 -0.1515 -0.5459 -1.5106  0.2232
#>   2.0520  3.8454  1.1733 -0.3855  4.3862
#> 
#> (5,.,.) = 
#>  -1.8747 -0.4473 -0.7882  1.1635 -1.2258
#>   0.6930  4.2601  0.1318 -0.1396  4.7588
#>   0.7601  0.1396 -0.2970 -1.5069  0.5214
#> 
#> (6,.,.) = 
#>   2.3254  5.4225  3.7450  3.5538  4.8803
#>   2.0565  0.0569  0.6607 -1.7196  0.6934
#>   0.7582 -0.2150  1.2865  1.0094 -0.4370
#> 
#> ... [the output was truncated (use n=-1 to disable)]
#> [ CPUFloatType{10,3,5} ]