Tensor-aware matrix multiplication

Computes x %*% y using the accelerator if either argument is a torch tensor and an accelerator is active (vectors are reshaped so the result matches base R's %*% shape semantics); falls back to base %*% otherwise. Named explicitly (rather than overriding %*%) so base matrix multiplication semantics are preserved everywhere else. Exposed so downstream packages (e.g. spiDE) can build batched linear algebra (e.g. a shared design matrix against many per-gene/per-block weight vectors) without depending on torch internals.

Usage

matmul_gpu(x, y)

Arguments

x, y

a matrix, numeric vector, or torch tensor.

Value

x %*% y, as a matrix or torch tensor matching the input.

Examples

matmul_gpu(matrix(1:6, 2, 3), matrix(1:6, 3, 2))
#>      [,1] [,2]
#> [1,]   22   49
#> [2,]   28   64