I did try assign. That was the slowest version from what my profiling could tell, as far as I recall, which really surprised me. I had expected it to be the fastest. The second slowest was using the [[ operator on environments. Or it might be the reverse for those two. They were both slower than the other versions I posted here.
Cheers On 27 Feb 2018, 17.16 +0100, Bert Gunter <bgunter.4...@gmail.com>, wrote: > No clue, but see ?assign perhaps if you have not done so already. > > -- Bert > > > > Bert Gunter > > "The trouble with having an open mind is that people keep coming along and > sticking things into it." > -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip ) > > > On Tue, Feb 27, 2018 at 6:51 AM, Thomas Mailund <thomas.mail...@gmail.com> > > wrote: > > > Interestingly, the <<- operator is also a lot faster than using a > > > namespace explicitly, and only slightly slower than using <- with local > > > variables, see below. But, surely, both must at some point insert values > > > in a given environment — either the local one, for <-, or an enclosing > > > one, for <<- — so I guess I am asking if there is a more low-level > > > assignment operation I can get my hands on without diving into C? > > > > > > > > > factorial <- function(n, acc = 1) { > > > if (n == 1) acc > > > else factorial(n - 1, n * acc) > > > } > > > > > > factorial_tr_manual <- function (n, acc = 1) > > > { > > > repeat { > > > if (n <= 1) > > > return(acc) > > > else { > > > .tailr_n <- n - 1 > > > .tailr_acc <- acc * n > > > n <- .tailr_n > > > acc <- .tailr_acc > > > next > > > } > > > } > > > } > > > > > > factorial_tr_automatic_1 <- function(n, acc = 1) { > > > .tailr_n <- n > > > .tailr_acc <- acc > > > callCC(function(escape) { > > > repeat { > > > n <- .tailr_n > > > acc <- .tailr_acc > > > if (n <= 1) { > > > escape(acc) > > > } else { > > > .tailr_n <<- n - 1 > > > .tailr_acc <<- n * acc > > > } > > > } > > > }) > > > } > > > > > > factorial_tr_automatic_2 <- function(n, acc = 1) { > > > .tailr_env <- rlang::get_env() > > > callCC(function(escape) { > > > repeat { > > > if (n <= 1) { > > > escape(acc) > > > } else { > > > .tailr_env$.tailr_n <- n - 1 > > > .tailr_env$.tailr_acc <- n * acc > > > .tailr_env$n <- .tailr_env$.tailr_n > > > .tailr_env$acc <- .tailr_env$.tailr_acc > > > } > > > } > > > }) > > > } > > > > > > microbenchmark::microbenchmark(factorial(1000), > > > factorial_tr_manual(1000), > > > factorial_tr_automatic_1(1000), > > > factorial_tr_automatic_2(1000)) > > > Unit: microseconds > > > expr min lq mean median > > > uq max neval > > > factorial(1000) 884.137 942.060 1076.3949 977.6235 > > > 1042.5035 2889.779 100 > > > factorial_tr_manual(1000) 110.215 116.919 130.2337 118.7350 > > > 122.7495 255.062 100 > > > factorial_tr_automatic_1(1000) 179.897 183.437 212.8879 187.8250 > > > 195.7670 979.352 100 > > > factorial_tr_automatic_2(1000) 508.353 534.328 601.9643 560.7830 > > > 587.8350 1424.260 100 > > > > > > Cheers > > > > > > On 26 Feb 2018, 21.12 +0100, Thomas Mailund <thomas.mail...@gmail.com>, > > > wrote: > > > > Following up on this attempt of implementing the tail-recursion > > > > optimisation — now that I’ve finally had the chance to look at it again > > > > — I find that non-local return implemented with callCC doesn’t actually > > > > incur much overhead once I do it more sensibly. I haven’t found a good > > > > way to handle parallel assignments that isn’t vastly slower than simply > > > > introducing extra variables, so I am going with that solution. However, > > > > I have now run into another problem involving those local variables — > > > > and assigning to local variables in general. > > > > > > > > Consider again the factorial function and three different ways of > > > > implementing it using the tail recursion optimisation: > > > > > > > > factorial <- function(n, acc = 1) { > > > > if (n == 1) acc > > > > else factorial(n - 1, n * acc) > > > > } > > > > > > > > factorial_tr_manual <- function (n, acc = 1) > > > > { > > > > repeat { > > > > if (n <= 1) > > > > return(acc) > > > > else { > > > > .tailr_n <- n - 1 > > > > .tailr_acc <- acc * n > > > > n <- .tailr_n > > > > acc <- .tailr_acc > > > > next > > > > } > > > > } > > > > } > > > > > > > > factorial_tr_automatic_1 <- function(n, acc = 1) { > > > > callCC(function(escape) { > > > > repeat { > > > > if (n <= 1) { > > > > escape(acc) > > > > } else { > > > > .tailr_n <- n - 1 > > > > .tailr_acc <- n * acc > > > > n <- .tailr_n > > > > acc <- .tailr_acc > > > > } > > > > } > > > > }) > > > > } > > > > > > > > factorial_tr_automatic_2 <- function(n, acc = 1) { > > > > .tailr_env <- rlang::get_env() > > > > callCC(function(escape) { > > > > repeat { > > > > if (n <= 1) { > > > > escape(acc) > > > > } else { > > > > .tailr_env$.tailr_n <- n - 1 > > > > .tailr_env$.tailr_acc <- n * acc > > > > .tailr_env$n <- .tailr_env$.tailr_n > > > > .tailr_env$acc <- .tailr_env$.tailr_acc > > > > } > > > > } > > > > }) > > > > } > > > > > > > > The factorial_tr_manual function is how I would implement the function > > > > manually while factorial_tr_automatic_1 is what my package used to come > > > > up with. It handles non-local returns, because this is something I need > > > > in general. Finally, factorial_tr_automatic_2 accesses the local > > > > variables explicitly through the environment, which is what my package > > > > currently produces. > > > > > > > > The difference between supporting non-local returns and not is tiny, > > > > but explicitly accessing variables through their environment costs me > > > > about a factor of five — something that surprised me. > > > > > > > > > microbenchmark::microbenchmark(factorial(1000), > > > > + factorial_tr_manual(1000), > > > > + factorial_tr_automatic_1(1000), > > > > + factorial_tr_automatic_2(1000)) > > > > Unit: microseconds > > > > expr min lq mean median > > > > factorial(1000) 756.357 810.4135 963.1040 856.3315 > > > > factorial_tr_manual(1000) 104.838 119.7595 198.7347 129.0870 > > > > factorial_tr_automatic_1(1000) 112.354 125.5145 211.6148 135.5255 > > > > factorial_tr_automatic_2(1000) 461.015 544.7035 688.5988 565.3240 > > > > uq max neval > > > > 945.3110 4149.099 100 > > > > 136.8200 4190.331 100 > > > > 152.9625 5944.312 100 > > > > 600.5235 7798.622 100 > > > > > > > > The simple solution, of course, is to not do that, but then I can’t > > > > handle expressions inside calls to “with”. And I would really like to, > > > > because then I can combine tail recursion with pattern matching. > > > > > > > > I can define linked lists and a length function on them like this: > > > > > > > > library(pmatch) > > > > llist := NIL | CONS(car, cdr : llist) > > > > > > > > llength <- function(llist, acc = 0) { > > > > cases(llist, > > > > NIL -> acc, > > > > CONS(car, cdr) -> llength(cdr, acc + 1)) > > > > } > > > > > > > > The tail-recursion I get out of transforming this function looks like > > > > this: > > > > > > > > llength_tr <- function (llist, acc = 0) { > > > > .tailr_env <- rlang::get_env() > > > > callCC(function(escape) { > > > > repeat { > > > > if (!rlang::is_null(..match_env <- test_pattern(llist, > > > > NIL))) > > > > with(..match_env, escape(acc)) > > > > > > > > else if (!rlang::is_null(..match_env <- > > > > test_pattern(llist, CONS(car, > > > > cdr)))) > > > > with(..match_env, { > > > > .tailr_env$.tailr_llist <- cdr > > > > .tailr_env$.tailr_acc <- acc + 1 > > > > .tailr_env$llist <- .tailr_env$.tailr_llist > > > > .tailr_env$acc <- .tailr_env$.tailr_acc > > > > }) > > > > } > > > > }) > > > > } > > > > > > > > Maybe not the prettiest code, but you are not supposed to actually see > > > > it, of course. > > > > > > > > There is not much gain in speed > > > > > > > > Unit: milliseconds > > > > expr min lq mean median uq > > > > llength(test_llist) 70.74605 76.08734 87.78418 85.81193 94.66378 > > > > llength_tr(test_llist) 45.16946 51.56856 59.09306 57.00101 63.07044 > > > > max neval > > > > 182.4894 100 > > > > 166.6990 100 > > > > > > > > but you don’t run out of stack space > > > > > > > > > llength(make_llist(1000)) > > > > Error: evaluation nested too deeply: infinite recursion / > > > > options(expressions=)? > > > > Error during wrapup: C stack usage 7990648 is too close to the limit > > > > > llength_tr(make_llist(1000)) > > > > [1] 1000 > > > > > > > > I should be able to make the function go faster if I had a faster way > > > > of handling the variable assignments, but inside “with”, I’m not sure > > > > how to do that… > > > > > > > > Any suggestions? > > > > > > > > Cheers > > > > > > > > On 11 Feb 2018, 16.48 +0100, Thomas Mailund <thomas.mail...@gmail.com>, > > > > wrote: > > > > > Hi guys, > > > > > > > > > > I am working on some code for automatically translating recursive > > > > > functions into looping functions to implemented tail-recursion > > > > > optimisations. See https://github.com/mailund/tailr > > > > > > > > > > As a toy-example, consider the factorial function > > > > > > > > > > factorial <- function(n, acc = 1) { > > > > > if (n <= 1) acc > > > > > else factorial(n - 1, acc * n) > > > > > } > > > > > > > > > > I can automatically translate this into the loop-version > > > > > > > > > > factorial_tr_1 <- function (n, acc = 1) > > > > > { > > > > > repeat { > > > > > if (n <= 1) > > > > > return(acc) > > > > > else { > > > > > .tailr_n <- n - 1 > > > > > .tailr_acc <- acc * acc > > > > > n <- .tailr_n > > > > > acc <- .tailr_acc > > > > > next > > > > > } > > > > > } > > > > > } > > > > > > > > > > which will run faster and not have problems with recursion depths. > > > > > However, I’m not entirely happy with this version for two reasons: I > > > > > am not happy with introducing the temporary variables and this > > > > > rewrite will not work if I try to over-scope an evaluation context. > > > > > > > > > > I have two related questions, one related to parallel assignments — > > > > > i.e. expressions to variables so the expression uses the old variable > > > > > values and not the new values until the assignments are all done — > > > > > and one related to restarting a loop from nested loops or from nested > > > > > expressions in `with` expressions or similar. > > > > > > > > > > I can implement parallel assignment using something like > > > > > rlang::env_bind: > > > > > > > > > > factorial_tr_2 <- function (n, acc = 1) > > > > > { > > > > > .tailr_env <- rlang::get_env() > > > > > repeat { > > > > > if (n <= 1) > > > > > return(acc) > > > > > else { > > > > > rlang::env_bind(.tailr_env, n = n - 1, acc = acc * n) > > > > > next > > > > > } > > > > > } > > > > > } > > > > > > > > > > This reduces the number of additional variables I need to one, but is > > > > > a couple of orders of magnitude slower than the first version. > > > > > > > > > > > microbenchmark::microbenchmark(factorial(100), > > > > > + factorial_tr_1(100), > > > > > + factorial_tr_2(100)) > > > > > Unit: microseconds > > > > > expr min lq mean median uq max neval > > > > > factorial(100) 53.978 60.543 77.76203 71.0635 85.947 180.251 100 > > > > > factorial_tr_1(100) 9.022 9.903 11.52563 11.0430 11.984 28.464 100 > > > > > factorial_tr_2(100) 5870.565 6109.905 6534.13607 6320.4830 6756.463 > > > > > 8177.635 100 > > > > > > > > > > > > > > > Is there another way to do parallel assignments that doesn’t cost > > > > > this much in running time? > > > > > > > > > > My other problem is the use of `next`. I would like to combine > > > > > tail-recursion optimisation with pattern matching as in > > > > > https://github.com/mailund/pmatch where I can, for example, define a > > > > > linked list like this: > > > > > > > > > > devtools::install_github("mailund/pmatch”) > > > > > library(pmatch) > > > > > llist := NIL | CONS(car, cdr : llist) > > > > > > > > > > and define a function for computing the length of a list like this: > > > > > > > > > > list_length <- function(lst, acc = 0) { > > > > > force(acc) > > > > > cases(lst, > > > > > NIL -> acc, > > > > > CONS(car, cdr) -> list_length(cdr, acc + 1)) > > > > > } > > > > > > > > > > The `cases` function creates an environment that binds variables in a > > > > > pattern-description that over-scopes the expression to the right of > > > > > `->`, so the recursive call in this example have access to the > > > > > variables `cdr` and `car`. > > > > > > > > > > I can transform a `cases` call to one that creates the environment > > > > > containing the bound variables and then evaluate this using `eval` or > > > > > `with`, but in either case, a call to `next` will not work in such a > > > > > context. The expression will be evaluated inside `bind` or `with`, > > > > > and not in the `list_lenght` function. > > > > > > > > > > A version that *will* work, is something like this > > > > > > > > > > factorial_tr_3 <- function (n, acc = 1) > > > > > { > > > > > .tailr_env <- rlang::get_env() > > > > > .tailr_frame <- rlang::current_frame() > > > > > repeat { > > > > > if (n <= 1) > > > > > rlang::return_from(.tailr_frame, acc) > > > > > else { > > > > > rlang::env_bind(.tailr_env, n = n - 1, acc = acc * n) > > > > > rlang::return_to(.tailr_frame) > > > > > } > > > > > } > > > > > } > > > > > > > > > > Here, again, for the factorial function since this is easier to > > > > > follow than the list-length function. > > > > > > > > > > This solution will also work if you return values from inside loops, > > > > > where `next` wouldn’t work either. > > > > > > > > > > Using `rlang::return_from` and `rlang::return_to` implements the > > > > > right semantics, but costs me another order of magnitude in running > > > > > time. > > > > > > > > > > microbenchmark::microbenchmark(factorial(100), > > > > > factorial_tr_1(100), > > > > > factorial_tr_2(100), > > > > > factorial_tr_3(100)) > > > > > Unit: microseconds > > > > > expr min lq mean median uq max neval > > > > > factorial(100) 52.479 60.2640 93.43069 67.5130 83.925 2062.481 100 > > > > > factorial_tr_1(100) 8.875 9.6525 49.19595 10.6945 11.217 3818.823 100 > > > > > factorial_tr_2(100) 5296.350 5525.0745 5973.77664 5737.8730 6260.128 > > > > > 8471.301 100 > > > > > factorial_tr_3(100) 77554.457 80757.0905 87307.28737 84004.0725 > > > > > 89859.169 171039.228 100 > > > > > > > > > > I can live with the “introducing extra variables” solution to > > > > > parallel assignment, and I could hack my way out of using `with` or > > > > > `bind` in rewriting `cases`, but restarting a `repeat` loop would > > > > > really make for a nicer solution. I know that `goto` is considered > > > > > harmful, but really, in this case, it is what I want. > > > > > > > > > > A `callCC` version also solves the problem > > > > > > > > > > factorial_tr_4 <- function(n, acc = 1) { > > > > > function_body <- function(continuation) { > > > > > if (n <= 1) { > > > > > continuation(acc) > > > > > } else { > > > > > continuation(list("continue", n = n - 1, acc = acc * n)) > > > > > } > > > > > } > > > > > repeat { > > > > > result <- callCC(function_body) > > > > > if (is.list(result) && result[[1]] == "continue") { > > > > > n <- result$n > > > > > acc <- result$acc > > > > > next > > > > > } else { > > > > > return(result) > > > > > } > > > > > } > > > > > } > > > > > > > > > > But this requires that I know how to distinguish between a valid > > > > > return value and a tag for “next” and is still a lot slower than the > > > > > `next` solution > > > > > > > > > > microbenchmark::microbenchmark(factorial(100), > > > > > factorial_tr_1(100), > > > > > factorial_tr_2(100), > > > > > factorial_tr_3(100), > > > > > factorial_tr_4(100)) > > > > > Unit: microseconds > > > > > expr min lq mean median uq max neval > > > > > factorial(100) 54.109 61.8095 81.33167 81.8785 89.748 243.554 100 > > > > > factorial_tr_1(100) 9.025 9.9035 11.38607 11.1990 12.008 22.375 100 > > > > > factorial_tr_2(100) 5272.524 5798.3965 6302.40467 6077.7180 6492.959 > > > > > 9967.237 100 > > > > > factorial_tr_3(100) 66186.080 72336.2810 76480.75172 73632.9665 > > > > > 75405.054 203785.673 100 > > > > > factorial_tr_4(100) 270.978 302.7890 337.48763 313.9930 334.096 > > > > > 1425.702 100 > > > > > > > > > > I don’t necessarily need the tail-recursion optimisation to be faster > > > > > than the recursive version; just getting out of the problem of too > > > > > deep recursions is a benefit, but I would rather not pay with an > > > > > order of magnitude for it. I could, of course, try to handle cases > > > > > that works with `next` in one way, and other cases using `callCC`, > > > > > but I feel it should be possible with a version that handles all > > > > > cases the same way. > > > > > > > > > > Is there any way to achieve this? > > > > > > > > > > Cheers > > > > > Thomas > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > [[alternative HTML version deleted]] > > > > > > ______________________________________________ > > > R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see > > > https://stat.ethz.ch/mailman/listinfo/r-help > > > PLEASE do read the posting guide > > > http://www.R-project.org/posting-guide.html > > > and provide commented, minimal, self-contained, reproducible code. > [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.