Package 'ast2ast'

Translate an R function into C++

Description

translate() compiles an R function into C++ code using the ast2ast expression template library. The result can be called directly from R (default) or returned as an external pointer. The C++ source code can also be retrieved without compilation.

Usage

translate(
  f,
  args_f = NULL,
  output = "R",
  derivative = NULL,
  verbose = FALSE,
  getsource = FALSE
)

Arguments

f	An R function to be translated into C++.
args_f	A helper function that defines the types of the arguments of f. Optional; defaults to NULL. See the Details section.
output	Controls what is returned: "R" (default): an R function wrapping the compiled C++. "XPtr": an external pointer to the compiled C++ function.
derivative	When derivatives are required one can set here the mode of the automatic differentiation: NULL (default): You don't require any derivatives. "forward" Determine the derivatives via forward automatic differentiation. "reverse" Determine the derivatives via reverse automatic differentiation.
verbose	Logical. If TRUE, prints output from the compilation process.
getsource	Logical. If TRUE, returns the generated C++ source code as a string instead of compiling it.

Details

Supported functions

The following R constructs are currently supported:

1. Assignment: = and <-

2. Allocation: vector, matrix, rep, logical, integer, numeric

3. Object info: length, dim

4. Arithmetic: +, -, *, /

5. Indexing: [] and [[]]

6. Math: sin, asin, sinh, cos, acos, cosh, tan, atan, tanh, sqrt, log, ^, exp

7. Concatenation: c

8. Control flow: for, while, repeat, next, break, if, else if, else

9. Comparison: ==, !=, >, <, >=, <=

10. Logical ops: &&, ||, &, |

11. Printing: print

12. Return: return

13. Catmull–Rom spline: cmr

14. Sequence operator: :

15. Helpers: is.na, is.finite, is.infinite

16. Explicit typing: type()

17. Derivative functions in forward mode: seed, unseed, and get_dot

18. Derivative functions in reverse mode: deriv

Types are static in C++ and cannot be changed within the function. Each type consists of a base data type and a data structure:

• Base types: logical, integer (or int), double

• Structures: scalar, vector (or vec), matrix (or mat)

Types are usually inferred automatically. Users may annotate explicitly, for example:

      a |> type(logical)      # scalar logical
      b |> type(vec(int))     # integer vector
      c |> type(mat(double))  # double matrix
    

Scalars in ast2ast differ from R: in R, scalars are length-1 vectors, but in C++ they are true scalars and cannot be subset.

Argument types

Arguments default to matrix(double) if args_f is NULL. To override, supply a function that annotates each argument:

      f_args <- function(a, b, c) {
        a |> type(borrow_vec(double)) |> ref()
        b |> type(borrow_mat(double)) |> ref() |> const()
        c |> type(double) |> ref()
      }
    

Supported extensions for arguments:

• borrow_vec, borrow_mat: pass inputs by reference, modifying them in place.

• const(): disallow modification of inputs.

• ref(): pass by reference (only valid when output = "XPtr").

Derivatives

ast2ast supports automatic differentiation (AD) in two modes: forward and reverse. The mode is selected via the derivative argument of translate().

• derivative = "forward" enables forward-mode AD.

• derivative = "reverse" enables reverse-mode AD.

The AD system is intentionally low-level and explicit. Rather than providing high-level wrappers (such as a built-in jacobian()), users are expected to assemble derivative computations themselves using a small set of primitive building blocks. This keeps the interface flexible, transparent, and close to the generated C++ code.

Forward mode

In forward mode, derivatives are propagated alongside values. The following functions are available:

• seed(x, i): marks the i-th component of x as the active direction (sets its derivative to 1).

• unseed(x, i): resets the derivative state of the i-th component.

• get_dot(y): extracts the derivative (dot) values of y.

A typical pattern is to loop over input dimensions, seed one component at a time, evaluate the function, extract derivatives using get_dot(), and assemble the Jacobian manually.

Reverse mode

In reverse mode, derivatives are accumulated by backpropagation from outputs to inputs. The function

• deriv(y, x)

computes the Jacobian of y with respect to x. This call must appear explicitly in the translated function. Reverse mode is particularly efficient when the number of outputs is small relative to the number of inputs.

Design philosophy

Derivative computation in ast2ast is explicit by design. The full control flow (including loops, seeding, unseeding, and accumulation) is visible in the user code and translated directly into C++. This makes the generated code easy to inspect, reason about, and modify, and avoids hidden performance costs. Important: The derivative argument only enables the AD infrastructure. It does not automatically differentiate your function. You must explicitly call seed(), unseed(), get_dot(), or deriv() inside your function, depending on the chosen mode.

Note: The generated C++ mimics R semantics closely but not exactly. Always validate compiled functions against the original R implementation before using in production. See the vignette Detailed Documentation for a full comparison, and InformationForPackageAuthors for internals.

Value

Depending on output:

• R: an R function that directly calls the compiled C++ code.

• XPtr: a function that returns an external pointer to the compiled C++ code.

Examples

## Not run: 
# Hello World
# ----------------------------------------------------------------
f <- function() {
  print("Hello World!")
}
f_cpp <- ast2ast::translate(f)
f_cpp()

# Derivatives
# ----------------------------------------------------------------
f <- function(y, x) {
  y[[1L]] <- x[[1L]] * x[[2L]]
  y[[2L]] <- x[[1L]] + x[[2L]]*x[[2L]]
  jac <- deriv(y, x)
  return(jac)
}
fcpp_reverse <- ast2ast::translate(f, derivative = "reverse")
y <- c(0, 0)
x <- c(2, 3)
fcpp_reverse(y, x)

f <- function(y, x) {
  jac <- matrix(0.0, length(y), length(x))
  for (i in 1L:length(x)) {
    seed(x, i)
    y[[1L]] <- x[[1L]] * x[[2L]]
    y[[2L]] <- x[[1L]] + x[[2L]]*x[[2L]]
    d <- get_dot(y)
    jac[TRUE, i] <- d
    unseed(x, i)
  }
  return(jac)
}
fcpp_forward <- ast2ast::translate(f, derivative = "forward")
fcpp_forward(y, x)

# Bubble sort (using [[ for scalars)
# ----------------------------------------------------------------
bubble <- function(a) {
  size <- length(a)
  for (i in 1:size) {
    for (j in 1:(size - 1)) {
      if (a[[j]] > a[[j + 1]]) {
        temp <- a[[j]]
        a[[j]] <- a[[j + 1]]
        a[[j + 1]] <- temp
      }
    }
  }
  return(a)
}
bubble_cpp <- ast2ast::translate(bubble)
bubble_cpp(runif(10))

# Fibonacci sequence
# ----------------------------------------------------------------
fib <- function(n = 10) {
  f <- integer(n)
  f[[1L]] <- 1L
  f[[2L]] <- 1L
  for (i in 3L:n) {
    f[i] <- f[i-1L] + f[i-2L]
  }
  return(f)
}
fib_cpp <- ast2ast::translate(fib)
fib_cpp(10)

# External pointer interface
# ----------------------------------------------------------------
f <- function() {
  print("Hello World from C++")
}
ptr <- ast2ast::translate(f, output = "XPtr")

# Call from C++ side
Rcpp::sourceCpp(code = "
  #include <Rcpp.h>
  typedef void (*fp)();
  // [[Rcpp::export]]
  void call_fct(Rcpp::XPtr<fp> inp) {
    fp f = *inp;
    f();
  }")
call_fct(ptr)


## End(Not run)

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