Replace mentioning of "L'Ecuyer-CMRG RNG" with generic "parallel RNG" to prepare for custom parallel RNGs

Author: HenrikBengtsson

R/001.import_future_functions.R

@@ -1,6 +1,10 @@
 ## To be imported from 'future', if available
 .debug <- NULL
 
+make_rng_seeds <- import_future("make_rng_seeds")
+next_random_seed <- import_future("next_random_seed")
+set_random_seed <- import_future("set_random_seed")
+
 ## Import private functions from 'future'
 import_future_functions <- function() {
   .debug <<- import_future(".debug", mode = "environment", default = new.env(parent = emptyenv()))

R/doFuture.R

@@ -430,7 +430,7 @@ function(obj, expr, envir, data) {   #nolint
         f <- fs[[idx]]
         label <- f$label
         if (is.null(label)) label <- "<none>"
-        message <- sprintf("UNRELIABLE VALUE: One of the foreach() iterations (%s) unexpectedly generated random numbers without declaring so. There is a risk that those random numbers are not statistically sound and the overall results might be invalid. To fix this, use '%%dorng%%' from the 'doRNG' package instead of '%%dopar%%'. This ensures that proper, parallel-safe random numbers are produced via the L'Ecuyer-CMRG method. To disable this check, set option 'doFuture.rng.onMisuse' to \"ignore\".", sQuote(label))
+        message <- sprintf("UNRELIABLE VALUE: One of the foreach() iterations (%s) unexpectedly generated random numbers without declaring so. There is a risk that those random numbers are not statistically sound and the overall results might be invalid. To fix this, use '%%dorng%%' from the 'doRNG' package instead of '%%dopar%%'. This ensures that proper, parallel-safe random numbers are produced. To disable this check, set option 'doFuture.rng.onMisuse' to \"ignore\".", sQuote(label))
         cond$message <- message
         if (inherits(cond, "warning")) {
           warning(cond)

R/doFuture2.R

@@ -172,7 +172,6 @@ doFuture2 <- function(obj, expr, envir, data) {   #nolint
   if (is.null(seed)) seed <- eval(formals(future)$seed)
   if (debug) mdebugf("seed = %s", deparse(seed))
 
-  make_rng_seeds <- import_future("make_rng_seeds")
   seeds <- make_rng_seeds(nX, seed = seed)
 
   ## Future expression (with or without setting the RNG state) and
@@ -184,8 +183,6 @@ doFuture2 <- function(obj, expr, envir, data) {   #nolint
       mdebug("RNG seeds:")
       mstr(seeds)
     }
-    next_random_seed <- import_future("next_random_seed")
-    set_random_seed <- import_future("set_random_seed")
     ## If RNG seeds are used (given or generated), make sure to reset
     ## the RNG state afterward
     oseed <- next_random_seed()    
@@ -519,7 +516,7 @@ doFuture2 <- function(obj, expr, envir, data) {   #nolint
           iterations <- seq_to_human(chunk)
           iterations <- sprintf("At least one of iterations %s", iterations)
         }
-        message <- sprintf("UNRELIABLE VALUE: %s of the foreach() %%dofuture%% { ... }, part of chunk #%d (%s), unexpectedly generated random numbers without declaring so. There is a risk that those random numbers are not statistically sound and the overall results might be invalid. To fix this, specify foreach() argument '.options.future = list(seed = TRUE)'. This ensures that proper, parallel-safe random numbers are produced via the L'Ecuyer-CMRG method. To disable this check, set option 'doFuture.rng.onMisuse' to \"ignore\".", iterations, idx, sQuote(label))
+        message <- sprintf("UNRELIABLE VALUE: %s of the foreach() %%dofuture%% { ... }, part of chunk #%d (%s), unexpectedly generated random numbers without declaring so. There is a risk that those random numbers are not statistically sound and the overall results might be invalid. To fix this, specify foreach() argument '.options.future = list(seed = TRUE)'. This ensures that proper, parallel-safe random numbers are produced. To disable this check, set option 'doFuture.rng.onMisuse' to \"ignore\".", iterations, idx, sQuote(label))
         cond$message <- message
         if (inherits(cond, "warning")) {
           warning(cond)

R/dofuture_OP.R

@@ -59,12 +59,12 @@
 #' workers, and scheduling ("chunking") strategy.
 #' 
 #' RNG reproducibility is achieved by pregenerating the random seeds for all
-#' iterations by using L'Ecuyer-CMRG RNG streams.  In each
+#' iterations by parallel RNG streams.  In each
 #' iteration, these seeds are set before evaluating the foreach expression.
 #' _Note, for large number of iterations this may introduce a large overhead._
 #'
 #' If `seed = TRUE`, then \code{\link[base:Random]{.Random.seed}}
-#' is used if it holds a L'Ecuyer-CMRG RNG seed, otherwise one is created
+#' is used if it holds a parallel RNG seed, otherwise one is created
 #' randomly.
 #'
 #' If `seed = FALSE`, it is expected that none of the foreach iterations
@@ -75,9 +75,9 @@
 #' whether random numbers were generated or not.
 #'
 #' As input, `seed` may also take a fixed initial seed (integer),
-#' either as a full L'Ecuyer-CMRG RNG seed (vector of 1+6 integers), or
-#' as a seed generating such a full L'Ecuyer-CMRG seed. This seed will
-#' be used to generated one L'Ecuyer-CMRG RNG stream for each iteration.
+#' either as a full parallel RNG seed (vector of 1+6 integers), or
+#' as a seed generating such a full seed. This seed will
+#' be used to generated one parallel RNG stream for each iteration.
 #'
 #' An alternative to specifying the `seed` option via `.options.future`,
 #' is to use the \code{\link[future:%seed%]{%seed%}} operator.  See

R/withDoRNG.R

@@ -49,8 +49,8 @@
 #' risk that those random numbers are not statistically sound and the overall
 #' results might be invalid. To fix this, use '%dorng%' from the 'doRNG'
 #' package instead of '%dopar%'. This ensures that proper, parallel-safe
-#' random numbers are produced via the L'Ecuyer-CMRG method. To disable this
-#' check, set option 'doFuture.rng.onMisuse' to "ignore".
+#' random numbers are produced. To disable this check, set option
+#' 'doFuture.rng.onMisuse' to "ignore".
 #' >
 #' ```
 #'