torch

torch from R!

A proof of concept for calling libtorch functions from R. API will change! Use at your own risk. Most libtorch’s functionality is not implemented here too.

Installation

Before installing you should install libtorch in usr/local/lib/.

devtools::install_github("dfalbel/torch")

Linux

On Linux the fastest way to get started is to run on bash:

wget https://download.pytorch.org/libtorch/cpu/libtorch-shared-with-deps-latest.zip
sudo unzip libtorch-shared-with-deps-latest.zip -d /usr/local/lib/

You can then install the package with

devtools::install_github("dfalbel/torch")

MacOs

On MacOS the following should just work too. First install libtorch with:

wget https://download.pytorch.org/libtorch/cpu/libtorch-macos-latest.zip
sudo unzip libtorch-macos-latest.zip -d /usr/local/lib/

Finally run:

devtools::install_github("dfalbel/torch")

Example

Currently this package is only a prrof of concept and you can only create a Torch Tensor from an R object. And then convert back from a torch Tensor to an R object.

library(torch)
x <- array(runif(8), dim = c(2, 2, 2))
y <- tensor(x)
y
#> tensor 
#> (1,.,.) = 
#>   0.7624  0.9765
#>   0.8718  0.4497
#> 
#> (2,.,.) = 
#>   0.2838  0.4814
#>   0.6035  0.8246
#> [ Variable[CPUDoubleType]{2,2,2} ]
identical(x, as.array(y))
#> [1] TRUE

Simple Autograd Example

In the following snippet we let torch, using the autograd feature, calculate the derivatives:

x <- tensor(1, requires_grad = TRUE)
w <- tensor(2, requires_grad = TRUE)
b <- tensor(3, requires_grad = TRUE)

y <- w * x + b
y$backward()

x$grad
#> tensor 
#>  2
#> [ Variable[CPUDoubleType]{1} ]
w$grad
#> tensor 
#>  1
#> [ Variable[CPUDoubleType]{1} ]
b$grad
#> tensor 
#>  1
#> [ Variable[CPUDoubleType]{1} ]

Linear Regression

In the following example we are going to fit a linear regression from scratch using torch’s Autograd.

Note all methods that end with _ (eg. sub_), will modify the tensors in place.

x <- matrix(runif(100), ncol = 2)
y <- matrix(0.1 + 0.5 * x[,1] - 0.7 * x[,2], ncol = 1)

x_t <- tensor(x)
y_t <- tensor(y)

w <- tensor(matrix(rnorm(2), nrow = 2), requires_grad = TRUE)
b <- tensor(0, requires_grad = TRUE)

lr <- tensor(0.5)

for (i in 1:100) {
  y_hat <- tch_mm(x_t, w) + b
  loss <- tch_mean((y_t - y_hat)^tensor(2))
  
  loss$backward()
  
  w$data$sub_(w$grad*lr)
  b$data$sub_(b$grad*lr)
  
  w$grad$zero_()
  b$grad$zero_()
}

print(as.array(w))
#>            [,1]
#> [1,]  0.4995543
#> [2,] -0.7034766
print(as.array(b))
#> [1] 0.102176