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,.,.) = 
#>  -3.1755 -0.8197 -1.6410  1.2531  1.3869
#>   0.8883 -1.6220  0.1196 -0.5877  0.7253
#>  -2.2273 -2.4526 -2.0889 -0.2598  1.7968
#> 
#> (2,.,.) = 
#>   1.1940 -1.5529 -1.3216 -0.0645  3.4043
#>  -0.8836  0.2056 -0.4627 -0.1654 -0.4402
#>  -0.2359 -1.9771 -0.6906 -0.3362  1.3155
#> 
#> (3,.,.) = 
#>   2.7033 -0.7362  0.3108 -1.7578  0.1333
#>   2.4591  1.8003  0.8733  0.1122  0.1121
#>  -4.5826  2.3973 -2.1614 -0.2593 -2.5692
#> 
#> (4,.,.) = 
#>  -1.8950 -2.0356 -1.5786 -1.2738  0.0011
#>  -1.3114  3.1493  1.8424  1.2198 -3.6264
#>  -4.1321  1.1383 -3.4148  0.8041  1.3177
#> 
#> (5,.,.) = 
#>  -1.2444 -1.6165 -2.4460 -0.0728  2.8524
#>   0.1638  1.0144  0.1142 -0.1725 -0.7413
#>  -3.5386  0.4889 -1.8599 -0.0119 -0.8103
#> 
#> (6,.,.) = 
#>  -2.6408 -0.7031 -1.3456  0.0279 -0.0614
#>   3.9895 -2.5311  1.9181 -0.3610  1.7256
#>  -2.1188  0.0392 -0.6007  1.2825  0.5182
#> 
#> ... [the output was truncated (use n=-1 to disable)]
#> [ CPUFloatType{10,3,5} ]