Issues with the experimental version of 2.26 from PR #887

rok-cesnovar commented 3 years ago

https://github.com/stan-dev/rstan/pull/887 is huge and I think the PR thread will eventually become impossible to follow. So I am opening this issue where you can post anything that is missing in the alpha 2.26 version.

Thanks @hsbadr for working on this massive pull request!

I think the checklist for making this happen is as follows:

[ ] Check that it works for the early-adopters/devs on all OS-es for R4.x (R-release)
[ ] Check that it works for the early-adopters/devs on all OS-es for R3.x (R-oldrel)

I think after this we can merge the PR and deal with smaller issues later.

[ ] Make a beta-call on Discourse for other users with a disclaimer that it might break other packages that depend on rstan.
[ ] Hash out issues after the beta call
[ ] Try to get this to CRAN - this is where my knowledge stops. I expect this is where the "fun" will start.

cdriveraus commented 3 years ago

so, the stanc() function seems? broken, in that it requires a file argument when, if you use the model_code argument, file should not be needed.

hsbadr commented 3 years ago

so, the stanc() function seems? broken, in that it requires a file argument when, if you use the model_code argument, file should not be needed.

stanc() interface and arguments didn't change. Can you provide a reproducible example?

cdriveraus commented 3 years ago

library(rstan)
st <- '
functions{
 real plus(real a){
 return a + 1;
 }
}
'

testcpp <- stanc(model_code = st)

hansvancalster commented 3 years ago

Updated:

remove.packages(c("StanHeaders", "rstan"))
install.packages("StanHeaders", repos = c("https://mc-stan.org/r-packages/", getOption("repos")))
install.packages("rstan", repos = c("https://mc-stan.org/r-packages/", getOption("repos")))

I installed using the above and then tried the following on my system (session info included below). This now seems to work :+1: Thank you @hsbadr ! Note that this is with the "broken" CRAN version of RcppParallel installed (see session info).

library(rstan)
#> Warning: package 'rstan' was built under R version 4.0.3
#> Loading required package: StanHeaders
#> Warning: package 'StanHeaders' was built under R version 4.0.3
#> rstan (Version 2.26.0.9000, GitRev: 2e1f913d3ca3)
#> For execution on a local, multicore CPU with excess RAM we recommend calling
#> options(mc.cores = parallel::detectCores()).
#> To avoid recompilation of unchanged Stan programs, we recommend calling
#> rstan_options(auto_write = TRUE)
#> Do not specify '-march=native' in 'LOCAL_CPPFLAGS' or a Makevars file
m <- stan_model(model_code = 'parameters {real y;} model {y ~ normal(0,1);}')
f <- sampling(m, iter = 100)
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 1.1e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.11 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1: 
#> Chain 1: 
#> Chain 1: WARNING: There aren't enough warmup iterations to fit the
#> Chain 1:          three stages of adaptation as currently configured.
#> Chain 1:          Reducing each adaptation stage to 15%/75%/10% of
#> Chain 1:          the given number of warmup iterations:
#> Chain 1:            init_buffer = 7
#> Chain 1:            adapt_window = 38
#> Chain 1:            term_buffer = 5
#> Chain 1: 
#> Chain 1: Iteration:  1 / 100 [  1%]  (Warmup)
#> Chain 1: Iteration: 10 / 100 [ 10%]  (Warmup)
#> Chain 1: Iteration: 20 / 100 [ 20%]  (Warmup)
#> Chain 1: Iteration: 30 / 100 [ 30%]  (Warmup)
#> Chain 1: Iteration: 40 / 100 [ 40%]  (Warmup)
#> Chain 1: Iteration: 50 / 100 [ 50%]  (Warmup)
#> Chain 1: Iteration: 51 / 100 [ 51%]  (Sampling)
#> Chain 1: Iteration: 60 / 100 [ 60%]  (Sampling)
#> Chain 1: Iteration: 70 / 100 [ 70%]  (Sampling)
#> Chain 1: Iteration: 80 / 100 [ 80%]  (Sampling)
#> Chain 1: Iteration: 90 / 100 [ 90%]  (Sampling)
#> Chain 1: Iteration: 100 / 100 [100%]  (Sampling)
#> Chain 1: 
#> Chain 1:  Elapsed Time: 0 seconds (Warm-up)
#> Chain 1:                0 seconds (Sampling)
#> Chain 1:                0 seconds (Total)
#> Chain 1: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 3e-06 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.03 seconds.
#> Chain 2: Adjust your expectations accordingly!
#> Chain 2: 
#> Chain 2: 
#> Chain 2: WARNING: There aren't enough warmup iterations to fit the
#> Chain 2:          three stages of adaptation as currently configured.
#> Chain 2:          Reducing each adaptation stage to 15%/75%/10% of
#> Chain 2:          the given number of warmup iterations:
#> Chain 2:            init_buffer = 7
#> Chain 2:            adapt_window = 38
#> Chain 2:            term_buffer = 5
#> Chain 2: 
#> Chain 2: Iteration:  1 / 100 [  1%]  (Warmup)
#> Chain 2: Iteration: 10 / 100 [ 10%]  (Warmup)
#> Chain 2: Iteration: 20 / 100 [ 20%]  (Warmup)
#> Chain 2: Iteration: 30 / 100 [ 30%]  (Warmup)
#> Chain 2: Iteration: 40 / 100 [ 40%]  (Warmup)
#> Chain 2: Iteration: 50 / 100 [ 50%]  (Warmup)
#> Chain 2: Iteration: 51 / 100 [ 51%]  (Sampling)
#> Chain 2: Iteration: 60 / 100 [ 60%]  (Sampling)
#> Chain 2: Iteration: 70 / 100 [ 70%]  (Sampling)
#> Chain 2: Iteration: 80 / 100 [ 80%]  (Sampling)
#> Chain 2: Iteration: 90 / 100 [ 90%]  (Sampling)
#> Chain 2: Iteration: 100 / 100 [100%]  (Sampling)
#> Chain 2: 
#> Chain 2:  Elapsed Time: 0 seconds (Warm-up)
#> Chain 2:                0 seconds (Sampling)
#> Chain 2:                0 seconds (Total)
#> Chain 2: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 2e-06 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.02 seconds.
#> Chain 3: Adjust your expectations accordingly!
#> Chain 3: 
#> Chain 3: 
#> Chain 3: WARNING: There aren't enough warmup iterations to fit the
#> Chain 3:          three stages of adaptation as currently configured.
#> Chain 3:          Reducing each adaptation stage to 15%/75%/10% of
#> Chain 3:          the given number of warmup iterations:
#> Chain 3:            init_buffer = 7
#> Chain 3:            adapt_window = 38
#> Chain 3:            term_buffer = 5
#> Chain 3: 
#> Chain 3: Iteration:  1 / 100 [  1%]  (Warmup)
#> Chain 3: Iteration: 10 / 100 [ 10%]  (Warmup)
#> Chain 3: Iteration: 20 / 100 [ 20%]  (Warmup)
#> Chain 3: Iteration: 30 / 100 [ 30%]  (Warmup)
#> Chain 3: Iteration: 40 / 100 [ 40%]  (Warmup)
#> Chain 3: Iteration: 50 / 100 [ 50%]  (Warmup)
#> Chain 3: Iteration: 51 / 100 [ 51%]  (Sampling)
#> Chain 3: Iteration: 60 / 100 [ 60%]  (Sampling)
#> Chain 3: Iteration: 70 / 100 [ 70%]  (Sampling)
#> Chain 3: Iteration: 80 / 100 [ 80%]  (Sampling)
#> Chain 3: Iteration: 90 / 100 [ 90%]  (Sampling)
#> Chain 3: Iteration: 100 / 100 [100%]  (Sampling)
#> Chain 3: 
#> Chain 3:  Elapsed Time: 0 seconds (Warm-up)
#> Chain 3:                0 seconds (Sampling)
#> Chain 3:                0 seconds (Total)
#> Chain 3: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 3e-06 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.03 seconds.
#> Chain 4: Adjust your expectations accordingly!
#> Chain 4: 
#> Chain 4: 
#> Chain 4: WARNING: There aren't enough warmup iterations to fit the
#> Chain 4:          three stages of adaptation as currently configured.
#> Chain 4:          Reducing each adaptation stage to 15%/75%/10% of
#> Chain 4:          the given number of warmup iterations:
#> Chain 4:            init_buffer = 7
#> Chain 4:            adapt_window = 38
#> Chain 4:            term_buffer = 5
#> Chain 4: 
#> Chain 4: Iteration:  1 / 100 [  1%]  (Warmup)
#> Chain 4: Iteration: 10 / 100 [ 10%]  (Warmup)
#> Chain 4: Iteration: 20 / 100 [ 20%]  (Warmup)
#> Chain 4: Iteration: 30 / 100 [ 30%]  (Warmup)
#> Chain 4: Iteration: 40 / 100 [ 40%]  (Warmup)
#> Chain 4: Iteration: 50 / 100 [ 50%]  (Warmup)
#> Chain 4: Iteration: 51 / 100 [ 51%]  (Sampling)
#> Chain 4: Iteration: 60 / 100 [ 60%]  (Sampling)
#> Chain 4: Iteration: 70 / 100 [ 70%]  (Sampling)
#> Chain 4: Iteration: 80 / 100 [ 80%]  (Sampling)
#> Chain 4: Iteration: 90 / 100 [ 90%]  (Sampling)
#> Chain 4: Iteration: 100 / 100 [100%]  (Sampling)
#> Chain 4: 
#> Chain 4:  Elapsed Time: 0 seconds (Warm-up)
#> Chain 4:                0 seconds (Sampling)
#> Chain 4:                0 seconds (Total)
#> Chain 4:
#> Warning: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#bulk-ess
#> Warning: Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#tail-ess

^{Created on 2021-01-20 by the reprex package (v0.3.0)}

Session info

``` r devtools::session_info() #> - Session info --------------------------------------------------------------- #> setting value #> version R version 4.0.2 (2020-06-22) #> os Windows 7 x64 SP 1 #> system x86_64, mingw32 #> ui RTerm #> language (EN) #> collate Dutch_Belgium.1252 #> ctype Dutch_Belgium.1252 #> tz Europe/Paris #> date 2021-01-20 #> #> - Packages ------------------------------------------------------------------- #> ! package * version date lib source #> assertthat 0.2.1 2019-03-21 [1] CRAN (R 4.0.0) #> callr 3.5.1 2020-10-13 [1] CRAN (R 4.0.3) #> cli 2.2.0 2020-11-20 [1] CRAN (R 4.0.3) #> codetools 0.2-16 2018-12-24 [2] CRAN (R 4.0.2) #> colorspace 2.0-0 2020-11-11 [1] CRAN (R 4.0.3) #> crayon 1.3.4 2017-09-16 [1] CRAN (R 4.0.0) #> curl 4.3 2019-12-02 [1] CRAN (R 4.0.0) #> desc 1.2.0 2018-05-01 [1] CRAN (R 4.0.0) #> devtools 2.3.1 2020-07-21 [1] CRAN (R 4.0.2) #> digest 0.6.27 2020-10-24 [1] CRAN (R 4.0.3) #> dplyr 1.0.2 2020-08-18 [1] CRAN (R 4.0.2) #> ellipsis 0.3.1 2020-05-15 [1] CRAN (R 4.0.2) #> evaluate 0.14 2019-05-28 [1] CRAN (R 4.0.0) #> fansi 0.4.1 2020-01-08 [1] CRAN (R 4.0.0) #> fs 1.5.0 2020-07-31 [1] CRAN (R 4.0.2) #> generics 0.1.0 2020-10-31 [1] CRAN (R 4.0.3) #> ggplot2 3.3.2 2020-06-19 [1] CRAN (R 4.0.2) #> glue 1.4.2 2020-08-27 [1] CRAN (R 4.0.2) #> gridExtra 2.3 2017-09-09 [1] CRAN (R 4.0.0) #> gtable 0.3.0 2019-03-25 [1] CRAN (R 4.0.0) #> highr 0.8 2019-03-20 [1] CRAN (R 4.0.0) #> htmltools 0.5.0 2020-06-16 [1] CRAN (R 4.0.2) #> inline 0.3.17 2020-12-01 [1] CRAN (R 4.0.3) #> jsonlite 1.7.2 2020-12-09 [1] CRAN (R 4.0.3) #> knitr 1.30 2020-09-22 [1] CRAN (R 4.0.3) #> lifecycle 0.2.0 2020-03-06 [1] CRAN (R 4.0.0) #> loo 2.4.1 2020-12-09 [1] CRAN (R 4.0.3) #> magrittr 2.0.1 2020-11-17 [1] CRAN (R 4.0.3) #> matrixStats 0.56.0 2020-03-13 [1] CRAN (R 4.0.0) #> memoise 1.1.0 2017-04-21 [1] CRAN (R 4.0.0) #> munsell 0.5.0 2018-06-12 [1] CRAN (R 4.0.0) #> pillar 1.4.7 2020-11-20 [1] CRAN (R 4.0.3) #> pkgbuild 1.2.0 2020-12-15 [1] CRAN (R 4.0.2) #> pkgconfig 2.0.3 2019-09-22 [1] CRAN (R 4.0.0) #> pkgload 1.1.0 2020-05-29 [1] CRAN (R 4.0.2) #> prettyunits 1.1.1 2020-01-24 [1] CRAN (R 4.0.0) #> processx 3.4.5 2020-11-30 [1] CRAN (R 4.0.3) #> ps 1.5.0 2020-12-05 [1] CRAN (R 4.0.3) #> purrr 0.3.4 2020-04-17 [1] CRAN (R 4.0.0) #> R6 2.5.0 2020-10-28 [1] CRAN (R 4.0.3) #> Rcpp 1.0.5 2020-07-06 [1] CRAN (R 4.0.2) #> D RcppParallel 5.0.2 2020-06-24 [1] CRAN (R 4.0.3) #> remotes 2.2.0 2020-07-21 [1] CRAN (R 4.0.2) #> rlang 0.4.9 2020-11-26 [1] CRAN (R 4.0.3) #> rmarkdown 2.4 2020-09-30 [1] CRAN (R 4.0.3) #> rprojroot 2.0.2 2020-11-15 [1] CRAN (R 4.0.3) #> rstan * 2.26.0.9000 2021-01-20 [1] local #> scales 1.1.1 2020-05-11 [1] CRAN (R 4.0.2) #> sessioninfo 1.1.1 2018-11-05 [1] CRAN (R 4.0.0) #> StanHeaders * 2.26.0.9000 2021-01-20 [1] local #> stringi 1.5.3 2020-09-09 [1] CRAN (R 4.0.2) #> stringr 1.4.0 2019-02-10 [1] CRAN (R 4.0.0) #> testthat 3.0.0 2020-10-31 [1] CRAN (R 4.0.3) #> tibble 3.0.4 2020-10-12 [1] CRAN (R 4.0.3) #> tidyselect 1.1.0 2020-05-11 [1] CRAN (R 4.0.2) #> usethis 1.6.1 2020-04-29 [1] CRAN (R 4.0.0) #> V8 3.4.0 2020-11-04 [1] CRAN (R 4.0.3) #> vctrs 0.3.5 2020-11-17 [1] CRAN (R 4.0.3) #> withr 2.4.0 2021-01-16 [1] CRAN (R 4.0.2) #> xfun 0.19 2020-10-30 [1] CRAN (R 4.0.3) #> yaml 2.2.1 2020-02-01 [1] CRAN (R 4.0.0) #> #> [1] C:/R/Library #> [2] C:/Program Files/R/R-4.0.2/library #> #> D -- DLL MD5 mismatch, broken installation. ```

hsbadr commented 3 years ago

library(rstan)
st <- '
functions{
 real plus(real a){
 return a + 1;
 }
}
'

testcpp <- stanc(model_code = st)

Ok. I'll look into it. It's likely b/c I added a preprocessor. Can you try: testcpp <- stanc(file = "", model_code = st) for now?

cdriveraus commented 3 years ago

That works and everything compiles and runs, and looks like normal, but the log probability / parameter values are all wrong. Will take me some time to see why.

hsbadr commented 3 years ago

That works and everything compiles and runs, and looks like normal, but the log probability / parameter values are all wrong. Will take me some time to see why.

In the current version I'm testing, it complains about plus in your code. Need to rename it.

rok-cesnovar commented 3 years ago

Yes, plus is a Stan function as well and user defined functions can not shadow Stan function names.

cdriveraus commented 3 years ago

That was just a cheap mwe, not my actual test problem ;) but sure. I've narrowed down the problem to the gradient calculation of some limited set of functions, but the functions on their own seem ok... will try some more.

Additionally, I posted a (edit) very silly issue and closed here: https://github.com/stan-dev/stanc3/issues/801 but maybe a more informative error could be considered sometime :)

jeffreypullin commented 3 years ago

One further thing that could be done for testing is to use the revdepcheck package from RStudio et. al. to check whether this new version breaks any of the existing CRAN packages with dependencies on {rstan}. Unfortunately I don't think I have the time nor computing power to do it myself at the moment though.

hsbadr commented 3 years ago

@jeffreypullin I did that (in my environment: Linux + Intel compilers + R-devel) and I'm using this version in production with almost all of its dependencies. I think we've two tasks:

Maintainers review the code to make sure they're happy with the changes for future development.
Users and/or devs test it in their use cases to catch any hidden errors (e.g., the stanc file issue that I've fixed).

The good thing is that this is now following Stan development and the package deployment has been automated. This can ensure that rstan development is coupled with Stan.

jeffreypullin commented 3 years ago

Oh wow, sorry @hsbadr I didn't realize you'd already done that! I completely agree with 1 and 2.

rok-cesnovar commented 3 years ago

The problem is the rstan tests arent really that rich. For example this case with the model code should have definitely been caught by at least one unit test. Expanding unit tests is definitely something we need to do in order to simplify the release cycle.

cdriveraus commented 3 years ago

Ok. I spent the morning diagnosing the 'incorrect gradient under certain conditions' thing, and it magically fixed itself :/ No idea how / why, maybe some remnant left in memory? Anyway, tests are passing, seems fine, speed seems nicely bumped up!

One thing: in rstan 2.21, I got this:

Variable "JAX" does not exist.
 error in 'model_ctsmgen' at line 1055, column 46
  -------------------------------------------------
  1053:   
  1054:       asymDIFFUSION[ derrind, derrind ] = 
  1055:         to_matrix( (add_diag( -sqkron_prod(JAX[ derrind, derrind ], JAX[ derrind, derrind ]),

but this parses in rstan 2.26 and then gives a compile error.

rok-cesnovar commented 3 years ago

Can you paste the entire model or link it. Thanks

cdriveraus commented 3 years ago

https://www.dropbox.com/s/1mrvpossovz3sa0/ctsm.stan?dl=0

hsbadr commented 3 years ago

@cdriveraus @rok-cesnovar I think it's more of stanc --allow-undefined option. The variable JAX seems to be undefined and in one version it's allowed in stanc and failed in compilation and in the other one stanc rejected` it. You may need to provide the headers that define it.

cdriveraus commented 3 years ago

Not sure I follow. It was just a miscapitalisation (should have been JAx) .

rok-cesnovar commented 3 years ago

Hm, I get

Semantic error in 'string', line 917, column 43 to column 46: Identifier 'JAX' not in scope.

hsbadr commented 3 years ago

Hm, I get

Semantic error in 'string', line 917, column 43 to column 46: Identifier 'JAX' not in scope.

I think this is likely a valid error and may depend on the compiler version and flags. Does it run with the old version of rsran? If not, then, it should be in a separate issue, not related to this one.

rok-cesnovar commented 3 years ago

Yes, this error I get is correct and valid.

cdriveraus commented 3 years ago

yes that is a valid error. However, when I use stanc("", model_code=x) , it doesn't give the error, which is the problem.

hsbadr commented 3 years ago

yes that is a valid error. However, when I use stanc("", model_code=x) , it doesn't give the error, which is the problem.

It just allows undefined variables, not really an issue with rstan as it's a valid error and stanc3 didn't complain. So, @rok-cesnovar, is there an option in stanc3 to check undefined variables and how to pass it to the JS?

rok-cesnovar commented 3 years ago

Hm, I am not sure why would it allow undefined variables? If I place that model here: https://rok-cesnovar.github.io/stanc3js-demo/index.html I get the error. And this link uses the exact same javascript file. So the issue might be rstan does not catch the error? Will check.

cdriveraus commented 3 years ago

if I use stanc(file=x) instead of the model_code approach, it gives the error, so I don't think this is intended (or at least, consistent!) behaviour.

hsbadr commented 3 years ago

What's the status (...$status) of the generated code?

hsbadr commented 3 years ago

@cdriveraus @rok-cesnovar It does work as expected. Have you installed the latest rstan 2.26 binaries?

> library(rstan)
Loading required package: StanHeaders
rstan (Version 2.26.0.9000, GitRev: 2e1f913d3ca3)
For execution on a local, multicore CPU with excess RAM we recommend calling
options(mc.cores = parallel::detectCores()).
To avoid recompilation of unchanged Stan programs, we recommend calling
rstan_options(auto_write = TRUE)

> cppcode <- stanc(file = "ctsm.stan")
Info: Found int division at 'string', line 57, column 10 to column 17:
  (d * d - d) / 2
Values will be rounded towards zero. If rounding is not desired you can write
the division as
  (d * d - d) / 2.0
0

Semantic error in 'string', line 867, column 43 to column 46:

Identifier 'JAX' not in scope.

> cppcode$status
[1] FALSE

cdriveraus commented 3 years ago

Maybe it's confusing because 'giving an error' is actually the desired behaviour. yes, stanc(file=x) works correctly. stanc(file="",model_code=x) does not return an error, which seems incorrect.

hsbadr commented 3 years ago

Maybe it's confusing because 'giving an error' is actually the desired behaviour. yes, stanc(file=x) works correctly. stanc(file="",model_code=x) does not return an error, which seems incorrect.

That's a hack to test the older version; don't use it 😄

And, yes, I can convert this to Error and catch the hack too.

hsbadr commented 3 years ago

@cdriveraus @rok-cesnovar I've pushed a few commits to catch those cases and stop on stanc3 errors. Give it a try after it builds the binaries and let me know if it addresses the problem.

hsbadr commented 3 years ago

@cdriveraus @rok-cesnovar Here's the new behavior:

> cppcode <- stanc(file = "ctsm.stan")
Info: Found int division at 'string', line 57, column 10 to column 17:
  (d * d - d) / 2
Values will be rounded towards zero. If rounding is not desired you can write
the division as
  (d * d - d) / 2.0
Error in stanc(file = "ctsm.stan") : 0

Semantic error in 'string', line 867, column 43 to column 46:

Identifier 'JAX' not in scope.

and for your other example:

> st <- '
+ functions{
+  real plus(real a){
+  return a + 1;
+  }
+ }
+ '
>
> testcpp <- stanc(model_code = st)
Error in stanc(model_code = st) : 0

Semantic error in 'string', line 2, column 6 to column 10:

Identifier 'plus' clashes with Stan Math library function.

> testcpp <- stanc(file = "", model_code = st)
Error in stanc(file = "", model_code = st) : Empty or invalid filename!

Reece844 commented 3 years ago

I've been testing this on my machine and some of my scripts and I've run in to some interesting errors but I can't figure out if it's just my environment. It only occurs when I modify threads_per_chain to be >1, and won't compile any stan scripts if this is set. I noticed that it is adding some includes for parallel processing, but can't figure out what's not linking. Here's the error I'm getting when trying to run the reduce_sum example script.

C:/Users/Reece/Documents/R/win-library/4.0/StanHeaders/include/stan/math/prim/functor/reduce_sum.hpp:159:41: error: no matching function for call to 'parallel_deterministic_reduce(tbb::blocked_range<long long unsigned int>, stan::math::internal::reduce_sum_impl<modelf6c5db169a0__namespace::partial_sum_rsfunctor__, void, double, const std::vector<int>&, const Eigen::Matrix<double, -1, 1, 0, -1, 1>&, Eigen::Matrix<double, -1, 1, 0, -1, 1>&>::recursive_reducer&, tbb::simple_partitioner&)'

I'm also getting these errors, "C:/Users/Reece/Documents/R/win-library/4.0/RcppParallel/include/tbb/task_scheduler_init.h:56:7: error: 'tbb::internal::no_copy::no_copy(const tbb::internal::no_copy&)' is private within this context" "filef6c35717db5.cpp:17:49: error: use of deleted function 'tbb::task_scheduler_init::task_scheduler_init(const tbb::task_scheduler_init&)'"

`- Session info -------------------------------------------------------------------------------------------------------------------------- setting value
version R version 4.0.3 (2020-10-10) os Windows 10 x64
system x86_64, mingw32
ui RStudio
language (EN)
collate English_United States.1252
ctype English_United States.1252
tz America/Chicago
date 2021-01-27

Packages ------------------------------------------------------------------------------------------------------------------------------ package version date lib source
assertthat 0.2.1 2019-03-21 [1] CRAN (R 4.0.2)
cachem 1.0.1 2021-01-21 [1] CRAN (R 4.0.3)
callr 3.5.1 2020-10-13 [1] CRAN (R 4.0.3)
cli 2.2.0 2020-11-20 [1] CRAN (R 4.0.3)
inline 0.3.17 2020-12-01 [1] CRAN (R 4.0.3)
Rcpp 1.0.6 2021-01-15 [1] CRAN (R 4.0.3)
RcppParallel 5.1.0-9000 2021-01-27 [1] Github (RcppCore/RcppParallel@3525d91)
StanHeaders 2.26.0.9000 2021-01-27 [1] git2r (@da91c52)

hsbadr commented 3 years ago

@Reece844 This looks like a TBB issue/conflict. I'm using an external oneTBB library for both RcppParallel and math, with the help of https://github.com/stan-dev/math/pull/2261 (task_scheduler_init.h has been removed from the new TBB interface).

Can you share a reprex with your model code? and maybe, test it on another machine, preferably Linux (just for env testing).

Also, does your model work with cmdstanr? If you run into similar issues, then it's most likely due to your model or env.

Reece844 commented 3 years ago

This is just the basic reduce_sum example from the Stan reference page, but it gives the same error as my own model. I can look into that tomorrow.

model_c <-"functions {
  real partial_sum(int[] y_slice,
                   int start, int end,
                   vector x,
                   vector beta) {
    return bernoulli_logit_lpmf(y_slice | beta[1] + beta[2] * x[start:end]);
  }
}
data {
  int N;
  int y[N];
  vector[N] x;
}
parameters {
  vector[2] beta;
}
model {
  int grainsize = 1;
  beta ~ std_normal();
  target += reduce_sum(partial_sum, y,
                       grainsize,
                       x, beta);
}"
library(rstan)
#> Loading required package: StanHeaders
#> rstan (Version 2.26.0.9000, GitRev: 2e1f913d3ca3)
#> For execution on a local, multicore CPU with excess RAM we recommend calling
#> options(mc.cores = parallel::detectCores()).
#> To avoid recompilation of unchanged Stan programs, we recommend calling
#> rstan_options(auto_write = TRUE)
#> For within-chain threading using `reduce_sum()` or `map_rect()` Stan functions,
#> change `threads_per_chain` option:
#> rstan_options(threads_per_chain = 1)
#> Do not specify '-march=native' in 'LOCAL_CPPFLAGS' or a Makevars file
rstan_options(threads_per_chain = 4)
stan_model(model_code  =model_c)
#> Error in compileCode(f, code, language = language, verbose = verbose): C:/Users/Reece/Documents/R/win-library/4.0/RcppEigen/include/Eigen/src/Core/ProductEvaluators.h:35:90:   required from 'Eigen::internal::evaluator<Eigen::Product<Lhs, Rhs, Option> >::evaluator(const XprType&) [with Lhs = Eigen::Product<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double, double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, 1, -1> >, const Eigen::Transpose<Eigen::Matrix<double, -1, 1> > >, Eigen::Matrix<double, -1, -1>, 0>; Rhs = Eigen::Matrix<double, -1, 1>; int Options = 0; Eigen::internal::evaluator<Eigen::Product<Lhs, Rhs, Option> >::XprType = Eigen::Product<Eigen::Product<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double, double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, 1, -1> >, const Eigen::Transpose<Eigen::Matrix<double, -1, 1> > >, Eigen::Matrix<double, -1, -1>, 0>, Eigen::Matrix<double, -1, 1>, 0>]'C:/Users/Reece/Documents/R/win-library/4.0/RcppEigen/include/Eigen/src/Core/Product.h:132:22:   required from 'Eigen::internal::dense_product_base<Lhs, Rhs, Option, 6>::operator const Scalar() const [with Lhs = Eigen::Product<Eigen::CwiseBinaryOp<Eigen::internal::scalar_product_op<double, double>, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<double>, const Eigen::Matrix<double, 1, -1> >, const Eigen::Transpose<Eigen::Matrix<double, -1, 1> > >, Eigen::Matrix<double, -1, -1>, 0>; Rhs = Eigen::Matrix<double, -1, 1>; int Option = 0; Eigen::internal::dense_product_base<Lhs, Rhs, Option, 6>::Scalar = double]'C:/Users/Reece/Documents/R/win-library/4.0/StanHeaders/include/src/stan/mcmc/hmc/hamiltonians/dense_e_metric.hpp:22:56:   required from 'double stan::mcmc::dense_e_metric<Model, BaseRNG>::T(stan::mcmc::dense_e_point&) [with Model = model1c201a45ce__namespace::model1c201a45ce_; BaseRNG = boost::random::additive_combine_engine<boost::random::linear_congruential_engine<unsigned int, 40014, 0, 2147483563>, boost::random::linear_congruential_engine<unsigned int, 40692, 0, 2147483399> >]'C:/Users/Reece/Documents/R/win-library/4.0/StanHeaders/include/src/stan/mcmc/hmc/hamiltonians/dense_e_metric.hpp:21:10:   required from hereC:/Users/Reece/Documents/R/win-library/4.0/RcppEigen/include/Eigen/src/Core/DenseCoeffsBase.h:55:30: warning: ignoring attributes on template argument 'Eigen::internal::packet_traits<double>::type' {aka '__vector(2) double'} [-Wignored-attributes]make: *** [C:/PROGRA~1/R/R-40~1.3/etc/x64/Makeconf:229: file1c20622020cc.o] Error 1
#> Error in sink(type = "output"): invalid connection

^{Created on 2021-01-27 by the reprex package (v1.0.0)}

Session info

``` r sessioninfo::session_info() #> - Session info --------------------------------------------------------------- #> setting value #> version R version 4.0.3 (2020-10-10) #> os Windows 10 x64 #> system x86_64, mingw32 #> ui RTerm #> language (EN) #> collate English_United States.1252 #> ctype English_United States.1252 #> tz America/Chicago #> date 2021-01-27 #> #> - Packages ------------------------------------------------------------------- #> package * version date lib #> assertthat 0.2.1 2019-03-21 [1] #> callr 3.5.1 2020-10-13 [1] #> cli 2.2.0 2020-11-20 [1] #> codetools 0.2-16 2018-12-24 [2] #> colorspace 2.0-0 2020-11-11 [1] #> crayon 1.3.4 2017-09-16 [1] #> curl 4.3 2019-12-02 [1] #> DBI 1.1.1 2021-01-15 [1] #> digest 0.6.27 2020-10-24 [1] #> dplyr 1.0.3 2021-01-15 [1] #> ellipsis 0.3.1 2020-05-15 [1] #> evaluate 0.14 2019-05-28 [1] #> fansi 0.4.2 2021-01-15 [1] #> fs 1.5.0 2020-07-31 [1] #> generics 0.1.0 2020-10-31 [1] #> ggplot2 3.3.3 2020-12-30 [1] #> glue 1.4.2 2020-08-27 [1] #> gridExtra 2.3 2017-09-09 [1] #> gtable 0.3.0 2019-03-25 [1] #> highr 0.8 2019-03-20 [1] #> htmltools 0.5.1.1 2021-01-22 [1] #> inline 0.3.17 2020-12-01 [1] #> jsonlite 1.7.2 2020-12-09 [1] #> knitr 1.31 2021-01-27 [1] #> lifecycle 0.2.0 2020-03-06 [1] #> loo 2.4.1 2020-12-09 [1] #> magrittr 2.0.1 2020-11-17 [1] #> matrixStats 0.57.0 2020-09-25 [1] #> munsell 0.5.0 2018-06-12 [1] #> pillar 1.4.7 2020-11-20 [1] #> pkgbuild 1.2.0 2020-12-15 [1] #> pkgconfig 2.0.3 2019-09-22 [1] #> prettyunits 1.1.1 2020-01-24 [1] #> processx 3.4.5 2020-11-30 [1] #> ps 1.5.0 2020-12-05 [1] #> purrr 0.3.4 2020-04-17 [1] #> R6 2.5.0 2020-10-28 [1] #> Rcpp 1.0.6 2021-01-15 [1] #> RcppParallel 5.1.0-9000 2021-01-27 [1] #> reprex 1.0.0 2021-01-27 [1] #> rlang 0.4.10 2020-12-30 [1] #> rmarkdown 2.6 2020-12-14 [1] #> rstan * 2.26.0.9000 2021-01-27 [1] #> rstudioapi 0.13 2020-11-12 [1] #> scales 1.1.1 2020-05-11 [1] #> sessioninfo 1.1.1 2018-11-05 [1] #> StanHeaders * 2.26.0.9000 2021-01-27 [1] #> stringi 1.5.3 2020-09-09 [1] #> stringr 1.4.0 2019-02-10 [1] #> tibble 3.0.5 2021-01-15 [1] #> tidyselect 1.1.0 2020-05-11 [1] #> V8 3.4.0 2020-11-04 [1] #> vctrs 0.3.6 2020-12-17 [1] #> withr 2.4.1 2021-01-26 [1] #> xfun 0.20 2021-01-06 [1] #> yaml 2.2.1 2020-02-01 [1] #> source #> CRAN (R 4.0.2) #> CRAN (R 4.0.3) #> CRAN (R 4.0.3) #> CRAN (R 4.0.3) #> CRAN (R 4.0.3) #> CRAN (R 4.0.2) #> CRAN (R 4.0.2) #> CRAN (R 4.0.3) #> CRAN (R 4.0.3) #> CRAN (R 4.0.3) #> CRAN (R 4.0.2) #> CRAN (R 4.0.2) #> CRAN (R 4.0.3) #> CRAN (R 4.0.2) #> CRAN (R 4.0.3) #> CRAN (R 4.0.3) #> CRAN (R 4.0.3) #> CRAN (R 4.0.3) #> CRAN (R 4.0.2) #> CRAN (R 4.0.2) #> CRAN (R 4.0.3) #> CRAN (R 4.0.3) #> CRAN (R 4.0.3) #> CRAN (R 4.0.3) #> CRAN (R 4.0.2) #> CRAN (R 4.0.3) #> CRAN (R 4.0.3) #> CRAN (R 4.0.3) #> CRAN (R 4.0.2) #> CRAN (R 4.0.3) #> CRAN (R 4.0.3) #> CRAN (R 4.0.2) #> CRAN (R 4.0.2) #> CRAN (R 4.0.3) #> CRAN (R 4.0.3) #> CRAN (R 4.0.2) #> CRAN (R 4.0.3) #> CRAN (R 4.0.3) #> Github (RcppCore/RcppParallel@3525d91) #> CRAN (R 4.0.3) #> CRAN (R 4.0.3) #> CRAN (R 4.0.3) #> git2r (@da91c52) #> CRAN (R 4.0.3) #> CRAN (R 4.0.2) #> CRAN (R 4.0.3) #> git2r (@da91c52) #> CRAN (R 4.0.3) #> CRAN (R 4.0.2) #> CRAN (R 4.0.3) #> CRAN (R 4.0.2) #> CRAN (R 4.0.3) #> CRAN (R 4.0.3) #> CRAN (R 4.0.3) #> CRAN (R 4.0.3) #> CRAN (R 4.0.2) #> #> [1] C:/Users/Reece/Documents/R/win-library/4.0 #> [2] C:/Program Files/R/R-4.0.3/library ```

hsbadr commented 3 years ago

This is just the basic reduce_sum example from the Stan reference page, but it gives the same error as my own model. I can look into that tomorrow.

This runs fine for me on Linux. I haven't tested it on Windows though, but it's clearly a TBB/env issue. It'd be great if you can continue testing just in case we can isolate it to your specific env or fix it for Windows, if needed.

andrjohns commented 3 years ago

I can also replicate the issue on Windows with RStan 2.26, so it looks like some Windows-related

andrjohns commented 3 years ago

Actually, this fails for me even without the call to rstan_options:

model_c <-"functions {
  real partial_sum(int[] y_slice,
                   int start, int end,
                   vector x,
                   vector beta) {
    return bernoulli_logit_lpmf(y_slice | beta[1] + beta[2] * x[start:end]);
  }
}
data {
  int N;
  int y[N];
  vector[N] x;
}
parameters {
  vector[2] beta;
}
model {
  int grainsize = 1;
  beta ~ std_normal();
  target += reduce_sum(partial_sum, y,
                       grainsize,
                       x, beta);
}"
library(rstan)
#> Loading required package: StanHeaders
#> rstan (Version 2.26.0.9000, GitRev: 2e1f913d3ca3)
#> For execution on a local, multicore CPU with excess RAM we recommend calling
#> options(mc.cores = parallel::detectCores()).
#> To avoid recompilation of unchanged Stan programs, we recommend calling
#> rstan_options(auto_write = TRUE)
#> Do not specify '-march=native' in 'LOCAL_CPPFLAGS' or a Makevars file

stan_model(model_code  =model_c)
#> Error in compileCode(f, code, language = language, verbose = verbose):        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^           tbb::blocked_range<std::size_t>(0, num_terms, grainsize), worker,           ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~           partitioner);           ~~~~~~~~~~~~                   make: *** [C:/Users/he184239/R/R-40~1.3/etc/x64/Makeconf:229: file4a2878a3d2b.o] Error 1
#> Error in sink(type = "output"): invalid connection

^{Created on 2021-01-28 by the reprex package (v0.3.0)}

My system info:

> readLines("~/.R/Makevars.win")
[1] "CXX14FLAGS += -mtune=native -O3 -mmmx -msse -msse2 -msse3 -mssse3 -msse4.1 -msse4.2"      
[2] "CXX14FLAGS += -Wno-deprecated-declarations -Wno-ignored-attributes -Wno-unused-function"  
[3] "CXX14FLAGS += -Wno-maybe-uninitialized -Wno-parentheses -Wno-sign-compare -Wno-attributes"
[4] "CXX14FLAGS += -Wno-unused-but-set-variable -Wno-reorder"                                  
> readLines("~/.Renviron")
[1] "R_LIBS_USER = \"C:/Users/he184239/R/win-library/4.0\""                     
[2] "HOME = \"C:/Users/he184239/Desktop/WD\""                                   
[3] "MAKEFLAGS = \"-j8\""                                                       
[4] "PATH=\"${RTOOLS40_HOME}\\usr\\bin;${RTOOLS40_HOME}\\mingw64\\bin;${PATH}\""
[5] "PATH=\"${RTOOLS40_HOME}\\usr\\bin;${RTOOLS40_HOME}\\mingw64\\bin;${PATH}\""
> devtools::session_info("rstan")
- Session info ----------------------------------------------------------------------------------------
 setting  value                       
 version  R version 4.0.3 (2020-10-10)
 os       Windows 10 x64              
 system   x86_64, mingw32             
 ui       RStudio                     
 language (EN)                        
 collate  English_Australia.1252      
 ctype    English_Australia.1252      
 tz       Australia/Perth             
 date     2021-01-28                  

- Packages --------------------------------------------------------------------------------------------
 ! package      * version     date       lib source        
   assertthat     0.2.1       2019-03-21 [1] CRAN (R 4.0.3)
   backports      1.2.1       2020-12-09 [1] CRAN (R 4.0.3)
   BH             1.75.0-0    2021-01-11 [1] CRAN (R 4.0.3)
   brio           1.1.1       2021-01-20 [1] CRAN (R 4.0.3)
   callr          3.5.1       2020-10-13 [1] CRAN (R 4.0.3)
   checkmate      2.0.0       2020-02-06 [1] CRAN (R 4.0.3)
   cli            2.2.0       2020-11-20 [1] CRAN (R 4.0.3)
   colorspace     2.0-0       2020-11-11 [1] CRAN (R 4.0.3)
   crayon         1.3.4       2017-09-16 [1] CRAN (R 4.0.3)
   curl           4.3         2019-12-02 [1] CRAN (R 4.0.3)
   desc           1.2.0       2018-05-01 [1] CRAN (R 4.0.3)
   diffobj        0.3.3       2021-01-07 [1] CRAN (R 4.0.3)
   digest         0.6.27      2020-10-24 [1] CRAN (R 4.0.3)
   ellipsis       0.3.1       2020-05-15 [1] CRAN (R 4.0.3)
   evaluate       0.14        2019-05-28 [1] CRAN (R 4.0.3)
   fansi          0.4.2       2021-01-15 [1] CRAN (R 4.0.3)
   farver         2.0.3       2020-01-16 [1] CRAN (R 4.0.3)
   ggplot2        3.3.3       2020-12-30 [1] CRAN (R 4.0.3)
   glue           1.4.2       2020-08-27 [1] CRAN (R 4.0.3)
   gridExtra      2.3         2017-09-09 [1] CRAN (R 4.0.3)
   gtable         0.3.0       2019-03-25 [1] CRAN (R 4.0.3)
   inline         0.3.17      2020-12-01 [1] CRAN (R 4.0.3)
   isoband        0.2.3       2020-12-01 [1] CRAN (R 4.0.3)
   jsonlite       1.7.2       2020-12-09 [1] CRAN (R 4.0.3)
   labeling       0.4.2       2020-10-20 [1] CRAN (R 4.0.3)
   lattice        0.20-41     2020-04-02 [2] CRAN (R 4.0.3)
   lifecycle      0.2.0       2020-03-06 [1] CRAN (R 4.0.3)
   loo            2.4.1       2020-12-09 [1] CRAN (R 4.0.3)
   magrittr       2.0.1       2020-11-17 [1] CRAN (R 4.0.3)
   MASS           7.3-53      2020-09-09 [2] CRAN (R 4.0.3)
   Matrix         1.3-2       2021-01-06 [2] CRAN (R 4.0.3)
   matrixStats    0.57.0      2020-09-25 [1] CRAN (R 4.0.3)
   mgcv           1.8-33      2020-08-27 [2] CRAN (R 4.0.3)
   munsell        0.5.0       2018-06-12 [1] CRAN (R 4.0.3)
   nlme           3.1-151     2020-12-10 [2] CRAN (R 4.0.3)
   pillar         1.4.7       2020-11-20 [1] CRAN (R 4.0.3)
   pkgbuild       1.2.0       2020-12-15 [1] CRAN (R 4.0.3)
   pkgconfig      2.0.3       2019-09-22 [1] CRAN (R 4.0.3)
   pkgload        1.1.0       2020-05-29 [1] CRAN (R 4.0.3)
   praise         1.0.0       2015-08-11 [1] CRAN (R 4.0.3)
   prettyunits    1.1.1       2020-01-24 [1] CRAN (R 4.0.3)
   processx       3.4.5       2020-11-30 [1] CRAN (R 4.0.3)
   ps             1.5.0       2020-12-05 [1] CRAN (R 4.0.3)
   R6             2.5.0       2020-10-28 [1] CRAN (R 4.0.3)
   RColorBrewer   1.1-2       2014-12-07 [1] CRAN (R 4.0.3)
   Rcpp           1.0.6       2021-01-15 [1] CRAN (R 4.0.3)
   RcppEigen      0.3.3.9.1   2020-12-17 [1] CRAN (R 4.0.3)
 D RcppParallel   5.0.2       2020-06-24 [1] CRAN (R 4.0.3)
   rematch2       2.1.2       2020-05-01 [1] CRAN (R 4.0.3)
   rlang          0.4.10      2020-12-30 [1] CRAN (R 4.0.3)
   rprojroot      2.0.2       2020-11-15 [1] CRAN (R 4.0.3)
   rstan          2.26.0.9000 2021-01-20 [1] local         
   rstudioapi     0.13        2020-11-12 [1] CRAN (R 4.0.3)
   scales         1.1.1       2020-05-11 [1] CRAN (R 4.0.3)
   StanHeaders    2.26.0.9000 2021-01-20 [1] local         
   testthat       3.0.1       2020-12-17 [1] CRAN (R 4.0.3)
   tibble         3.0.5       2021-01-15 [1] CRAN (R 4.0.3)
   utf8           1.1.4       2018-05-24 [1] CRAN (R 4.0.3)
   V8             3.4.0       2020-11-04 [1] CRAN (R 4.0.3)
   vctrs          0.3.6       2020-12-17 [1] CRAN (R 4.0.3)
   viridisLite    0.3.0       2018-02-01 [1] CRAN (R 4.0.3)
   waldo          0.2.3       2020-11-09 [1] CRAN (R 4.0.3)
   withr          2.4.1       2021-01-26 [1] CRAN (R 4.0.3)

[1] C:/Users/he184239/R/win-library/4.0
[2] C:/Users/he184239/R/R-4.0.3/library

 D -- DLL MD5 mismatch, broken installation.

hsbadr commented 3 years ago

@andrjohns Thanks for the confirmation! It looks like a TBB issue on Window, likely a compatibility with the version of TBB shipped in RcppParallel, which may or may not affect cmdstanr depending on which TBB version it links to. Can you test this with cmdstanr?

I'm also interested in testing an external oneTBB installation, which is supported by both RcppParallel and Stan math now with environment variables.

andrjohns commented 3 years ago

No problems with cmdstanr 0.2.2 using cmdstan 2.26

hsbadr commented 3 years ago

Ok. Thanks! I'll look into it.

hsbadr commented 3 years ago

@andrjohns @Reece844 Test with my development version of RcppParallel:

remove.packages("RcppParallel")
remotes::install_github("hsbadr/RcppParallel@develop")

rok-cesnovar commented 3 years ago

What version of TBB does RcppParallel ship? I believe we did not upgrade TBB in Stan Math because we wanted to stay compatible with RcppParallel.

hsbadr commented 3 years ago

What version of TBB does RcppParallel ship? I believe we did not upgrade TBB in Stan Math because we wanted to stay compatible with RcppParallel.

A 2-years older version that also have a conflicting tbb/parallel_reduce.h! See https://github.com/hsbadr/RcppParallel/commit/26cbda93dc037de90f14d4d015adba48ce396476. I'll submit this to RcppParallel after testing.

Also, a dirty workaround that works is to replace tbb/parallel_reduce.h with the one in Stan math.

hhau commented 3 years ago

This might be the best place to leave a link to this discrepancy obtained using the following:

> sessionInfo()
R version 4.0.3 (2020-10-10)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Catalina 10.15.7

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib

locale:
[1] en_GB.UTF-8/en_GB.UTF-8/en_GB.UTF-8/C/en_GB.UTF-8/en_GB.UTF-8

attached base packages:
[1] stats     graphics  grDevices datasets  utils     methods   base     

other attached packages:
[1] cmdstanr_0.3.0          rstan_2.26.0.9000       StanHeaders_2.26.0.9000

loaded via a namespace (and not attached):
 [1] tidyselect_1.1.0   xfun_0.20          purrr_0.3.4        V8_3.4.0          
 [5] colorspace_2.0-0   vctrs_0.3.6        generics_0.1.0     stats4_4.0.3      
 [9] loo_2.4.1          rlang_0.4.10       pkgbuild_1.2.0     pillar_1.4.7      
[13] glue_1.4.2         DBI_1.1.1          matrixStats_0.57.0 lifecycle_0.2.0   
[17] posterior_0.1.3    munsell_0.5.0      gtable_0.3.0       codetools_0.2-16  
[21] inline_0.3.17      knitr_1.31         callr_3.5.1        ps_1.5.0          
[25] curl_4.3           parallel_4.0.3     fansi_0.4.2        Rcpp_1.0.6        
[29] renv_0.12.0        scales_1.1.1       backports_1.2.1    checkmate_2.0.0   
[33] RcppParallel_5.0.2 jsonlite_1.7.2     abind_1.4-5        gridExtra_2.3     
[37] ggplot2_3.3.3      processx_3.4.5     dplyr_1.0.3        grid_4.0.3        
[41] cli_2.2.0          tools_4.0.3        magrittr_2.0.1     tibble_3.0.5      
[45] crayon_1.3.4       pkgconfig_2.0.3    ellipsis_0.3.1     data.table_1.13.6 
[49] prettyunits_1.1.1  assertthat_0.2.1   R6_2.5.0           compiler_4.0.3

Reece844 commented 3 years ago

A 2-years older version that also have a conflicting tbb/parallel_reduce.h! See hsbadr/RcppParallel@26cbda9. I'll submit this to RcppParallel after testing.

Sorry for the delay, I can confirm that both of these fix the compilation problems on my machine when using the reduce_sum.

hsbadr commented 3 years ago

Sorry for the delay, I can confirm that both of these fix the compilation problems on my machine when using the reduce_sum.

Great! Thanks for testing. I've submitted a PR to RcppParallel: https://github.com/RcppCore/RcppParallel/pull/151

andrjohns commented 3 years ago

It looks like GPU support will also need an additional header file included: see #715

The file already exists in the Math library, but isn't carried across when StanHeaders is built

@SteveBronder was there anything else (that you can remember) it needed?

hsbadr commented 3 years ago

@bgoodri @rok-cesnovar Now that #887 has been tested, could you merge it so that new features can be submitted in separate PRs that can be easily reviewed?

hsbadr commented 3 years ago

It looks like GPU support will also need an additional header file included: see #715

@andrjohns OpenCL headers have been added in StanHeaders by https://github.com/stan-dev/rstan/pull/887/commits/72f6aff630b2f4f26987df62c4b30e1c17a7381c. You can now #include <CL/opencl.h> or #include <CL/cl2.hpp>. You can check the packages that will be uploaded as artifacts here.

andrjohns commented 3 years ago

Thanks @hsbadr, that seems to have mostly done it. It finds the file fine, but there's a new error during linking:

Error in dyn.load(libLFile) : 
  unable to load shared object '/tmp/RtmpjVHjjp/file140e7192cfb.so':
  /tmp/RtmpjVHjjp/file140e7192cfb.so: undefined symbol: clReleaseContext

There are no issues compiling with GPU enabled via cmdstanr on the same system.

@rok-cesnovar Have you seen that before? It looks OpenCL related. For reference, my Makevars file is:

CXX14FLAGS += -O3 -std=c++1y -march=native -mtune=native -Wno-attributes
CXX14FLAGS += -Wno-ignored-attributes -Wno-deprecated-declarations
CXX14FLAGS += -lOpenCL -DSTAN_OPENCL -DOPENCL_DEVICE_ID=0 
CXX14FLAGS += -DOPENCL_PLATFORM_ID=0 -DCL_HPP_TARGET_OPENCL_VERSION=120
CXX14FLAGS += -DCL_HPP_MINIMUM_OPENCL_VERSION=120 -DCL_HPP_ENABLE_EXCEPTIONS

stan-dev / rstan

Issues with the experimental version of 2.26 from PR #887 #899