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,
  types_f = NULL,
  output = "R",
  derivative = NULL,
  verbose = FALSE,
  getsource = FALSE,
  debug = TRUE
)

Arguments

Details

Supported functions

The following R constructs are currently supported:

1. Assignment: =, <-, and $ (struct field access/assignment)

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

3. Object info: length, dim, nrow, ncol

4. Arithmetic: +, -, *, /, %%, %/%, %*%, ^

5. Indexing: [], [[]], and at() (an alias for [[]])

6. Math: sin, asin, sinh, cos, acos, cosh, tan, atan, tanh, sqrt, abs, sign, log, exp, floor, ceiling, trunc, round

7. Reductions: sum, prod, mean, max, min, which.max, which.min, which, all, any

8. Vector transforms: rev, sort, cumsum

9. Matrix margins: colSums, rowSums, colMeans, rowMeans

10. Selection: ifelse

11. Linear algebra: t, chol, crossprod, tcrossprod, diag, get_diag, solve, backsolve, forwardsolve, rbind, cbind

12. Root finding / least squares: uniroot, nnls

13. Functionals (take an fn()): map, Reduce, Filter, apply

14. Optimizers / derivatives of an fn(): jacobian, lbfgsb, pso

15. Casts: as.numeric, as.integer, as.logical

16. Sequences: seq_len, seq_along

17. Concatenation: c

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

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

20. Logical ops: &&, ||, &, |, !

21. Printing: print

22. Errors: stop

23. Return: return

24. Catmull–Rom spline: cmr

25. Sequence operator: :

26. Helpers: is.na, is.nan, is.finite, is.infinite

27. Explicit typing: type()

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

29. Derivative functions in reverse mode: deriv

30. Inner functions: fn(), see below

31. Custom types: new_type(), see below

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 f's body has no argtypes(...) block. To override, make argtypes(...) the first statement of the body, one type() annotation per argument:

      f <- function(a, b, c) {
        argtypes(
          a |> type(borrow_vec(double)) |> ref(),
          b |> type(borrow_mat(double)) |> ref() |> const(),
          c |> type(double) |> ref()
        )
        # ... body ...
      }
    

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").

Inner functions

Functions can be defined inside f with fn(), which takes three positional parts: argtypes(...) (argument types, same form as the outer argtypes(...) block), return(...) (the return type), and a { } block (the function body; the braces are optional for a single statement). Inner functions may call each other, including recursively, and can be passed as values to functions expecting one, such as uniroot(). A non-const() parameter only binds to a bare variable at the call site, not to an arbitrary expression (e.g. x + 1); declare the parameter const() to accept expressions.

      factorial <- fn(
        argtypes(a |> type(int) |> const()),
        return(int),
        {
          if (a == 1L) return(a) else return(a * factorial(a - 1L))
        }
      )
    

uniroot(f, interval, tol, maxiter) takes a function of a single double returning a double (or two arguments if a fifth, extra-data argument is supplied to uniroot), and returns a struct with fields root, f_root, iter, and estim_prec. nnls(A, b) solves the non-negative least squares problem and returns the solution vector.

Functionals

These take an fn() as their first argument:

• map(f, x, ...): apply f element-wise over the given vectors (scalars broadcast). The result shape follows f's return type: scalar gives a vector, vector a matrix, matrix/array an array, new_type a collection.

• Reduce(f, x): left fold seeded with x[[1]]; f is fn(acc, elem) -> acc.

• Filter(f, x): keep the elements of vector x for which f(elem) is TRUE.

• apply(f, MARGIN, x): function first (unlike base R). x is a matrix, MARGIN is 1 (rows) or 2 (columns), f maps a vector to a scalar or vector.

Optimizers and Jacobians

• jacobian(f, x[, data]): the m-by-n Jacobian at x of f, which maps a double vector to a double vector. Requires derivative = "forward" or "reverse".

• lbfgsb(f, x, lower, upper, maxit, factr, pgtol, lmm[, data]): bound-constrained L-BFGS-B via R's own C routine. f maps a double vector to a scalar double. Under derivative = "forward"/"reverse" the gradient is exact (via jacobian); otherwise it is a central-difference approximation. Returns a struct with $par, $value, $convergence, $counts.

• pso(f, lower, upper, ngen, npop, error_threshold, global[, data]): derivative-free particle-swarm optimisation. f maps a double vector to a scalar double; returns the best parameter vector found.

For lbfgsb and pso the optional trailing data argument (any non-function, non-character value) is passed to f unchanged as a second argument.

Custom types

User-defined struct types are declared in a types_f helper function (passed to translate() via types_f =) and created with new_type() and slots():

      types_f <- function() {
        new_type(Point, slots(x |> type(double), y |> type(double)))
      }
    

On the R side, a value of a custom type is a named list with a matching class attribute, e.g. structure(list(x = 1, y = 2), class = "Point"). Fields are read and written with $, including chained access such as s$circles[[1L]]$center$x. A slot may itself be a custom type (nested structs) or collection(TypeName), a vector of a custom type; allocate one locally with vector(mode = "TypeName", n).

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: derivative computations are assembled from a small set of primitive building blocks (seed, unseed, get_dot, deriv), which keeps the interface transparent and close to the generated C++ code. A built-in jacobian(f, x) is also provided for the common case (see Optimizers and Jacobians above); it is itself implemented on top of those primitives.

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)

# Custom types
# ----------------------------------------------------------------
types_f <- function() {
  new_type(Point, slots(x |> type(double), y |> type(double)))
}
f <- function(p) {
  argtypes(p |> type(Point))
  p$x <- p$x + 1
  return(p)
}
fcpp <- ast2ast::translate(f, types_f = types_f)
p <- structure(list(x = 1, y = 2), class = "Point")
fcpp(p)

# Inner functions and uniroot
# ----------------------------------------------------------------
f <- function(interval) {
  argtypes(interval |> type(vec(double)))
  g <- fn(
    argtypes(x |> type(double)),
    return(double),
    {
      return(x^2 - 4)
    }
  )
  res <- uniroot(g, interval, 1e-10, 1000)
  return(res$root)
}
fcpp <- ast2ast::translate(f)
fcpp(c(0, 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)