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csv_contents$metadata$save_warmup is "true" (character), not TRUE (logical) #957

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dholstius opened this issue Apr 25, 2024 · 5 comments
Open

csv_contents$metadata$save_warmup is "true" (character), not TRUE (logical) #957

dholstius opened this issue Apr 25, 2024 · 5 comments
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@dholstius
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dholstius commented Apr 25, 2024 •
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Summary

Update: This error does not occur with cmdstan v2.33.1.

Running cmdstan v2.34.1 (23 January 2024) and cmdstanr v0.7.1. Reprex follows.

Attempting to fit a trivial model:

library(brms)
dat <- data.frame(y = rnorm(1000))
mod <- brm(y ~ 1, data = dat, backend = "cmdstanr")

... fails with:

Error in !is.null(csv_contents$metadata$save_warmup) && csv_contents$metadata$save_warmup: invalid 'y' type in 'x && y'

Debugging with options(error = recover), it seems that csv_contents$metadata$save_warmup is "true" (character), not TRUE (logical), when we reach lines 1437-1438 of fit.R.

Reprex

library(brms)
#> Loading required package: Rcpp
#> Loading 'brms' package (version 2.21.0). Useful instructions
#> can be found by typing help('brms'). A more detailed introduction
#> to the package is available through vignette('brms_overview').
#> 
#> Attaching package: 'brms'
#> The following object is masked from 'package:stats':
#> 
#>     ar
dat <- data.frame(y = rnorm(1000))
mod <- brm(y ~ 1, data = dat, backend = "cmdstanr")
#> Start sampling
#> Running MCMC with 4 sequential chains...
#> 
#> Chain 1 Iteration:    1 / 2000 [  0%]  (Warmup) 
#> Chain 1 Iteration:  100 / 2000 [  5%]  (Warmup) 
#> Chain 1 Iteration:  200 / 2000 [ 10%]  (Warmup) 
#> Chain 1 Iteration:  300 / 2000 [ 15%]  (Warmup) 
#> Chain 1 Iteration:  400 / 2000 [ 20%]  (Warmup) 
#> Chain 1 Iteration:  500 / 2000 [ 25%]  (Warmup) 
#> Chain 1 Iteration:  600 / 2000 [ 30%]  (Warmup) 
#> Chain 1 Iteration:  700 / 2000 [ 35%]  (Warmup) 
#> Chain 1 Iteration:  800 / 2000 [ 40%]  (Warmup) 
#> Chain 1 Iteration:  900 / 2000 [ 45%]  (Warmup) 
#> Chain 1 Iteration: 1000 / 2000 [ 50%]  (Warmup) 
#> Chain 1 Iteration: 1001 / 2000 [ 50%]  (Sampling) 
#> Chain 1 Iteration: 1100 / 2000 [ 55%]  (Sampling) 
#> Chain 1 Iteration: 1200 / 2000 [ 60%]  (Sampling) 
#> Chain 1 Iteration: 1300 / 2000 [ 65%]  (Sampling) 
#> Chain 1 Iteration: 1400 / 2000 [ 70%]  (Sampling) 
#> Chain 1 Iteration: 1500 / 2000 [ 75%]  (Sampling) 
#> Chain 1 Iteration: 1600 / 2000 [ 80%]  (Sampling) 
#> Chain 1 Iteration: 1700 / 2000 [ 85%]  (Sampling) 
#> Chain 1 Iteration: 1800 / 2000 [ 90%]  (Sampling) 
#> Chain 1 Iteration: 1900 / 2000 [ 95%]  (Sampling) 
#> Chain 1 Iteration: 2000 / 2000 [100%]  (Sampling) 
#> Chain 1 finished in 0.1 seconds.
#> Chain 2 Iteration:    1 / 2000 [  0%]  (Warmup) 
#> Chain 2 Iteration:  100 / 2000 [  5%]  (Warmup) 
#> Chain 2 Iteration:  200 / 2000 [ 10%]  (Warmup) 
#> Chain 2 Iteration:  300 / 2000 [ 15%]  (Warmup) 
#> Chain 2 Iteration:  400 / 2000 [ 20%]  (Warmup) 
#> Chain 2 Iteration:  500 / 2000 [ 25%]  (Warmup) 
#> Chain 2 Iteration:  600 / 2000 [ 30%]  (Warmup) 
#> Chain 2 Iteration:  700 / 2000 [ 35%]  (Warmup) 
#> Chain 2 Iteration:  800 / 2000 [ 40%]  (Warmup) 
#> Chain 2 Iteration:  900 / 2000 [ 45%]  (Warmup) 
#> Chain 2 Iteration: 1000 / 2000 [ 50%]  (Warmup) 
#> Chain 2 Iteration: 1001 / 2000 [ 50%]  (Sampling) 
#> Chain 2 Iteration: 1100 / 2000 [ 55%]  (Sampling) 
#> Chain 2 Iteration: 1200 / 2000 [ 60%]  (Sampling) 
#> Chain 2 Iteration: 1300 / 2000 [ 65%]  (Sampling) 
#> Chain 2 Iteration: 1400 / 2000 [ 70%]  (Sampling) 
#> Chain 2 Iteration: 1500 / 2000 [ 75%]  (Sampling) 
#> Chain 2 Iteration: 1600 / 2000 [ 80%]  (Sampling) 
#> Chain 2 Iteration: 1700 / 2000 [ 85%]  (Sampling) 
#> Chain 2 Iteration: 1800 / 2000 [ 90%]  (Sampling) 
#> Chain 2 Iteration: 1900 / 2000 [ 95%]  (Sampling) 
#> Chain 2 Iteration: 2000 / 2000 [100%]  (Sampling) 
#> Chain 2 finished in 0.1 seconds.
#> Chain 3 Iteration:    1 / 2000 [  0%]  (Warmup) 
#> Chain 3 Iteration:  100 / 2000 [  5%]  (Warmup) 
#> Chain 3 Iteration:  200 / 2000 [ 10%]  (Warmup) 
#> Chain 3 Iteration:  300 / 2000 [ 15%]  (Warmup) 
#> Chain 3 Iteration:  400 / 2000 [ 20%]  (Warmup) 
#> Chain 3 Iteration:  500 / 2000 [ 25%]  (Warmup) 
#> Chain 3 Iteration:  600 / 2000 [ 30%]  (Warmup) 
#> Chain 3 Iteration:  700 / 2000 [ 35%]  (Warmup) 
#> Chain 3 Iteration:  800 / 2000 [ 40%]  (Warmup) 
#> Chain 3 Iteration:  900 / 2000 [ 45%]  (Warmup) 
#> Chain 3 Iteration: 1000 / 2000 [ 50%]  (Warmup) 
#> Chain 3 Iteration: 1001 / 2000 [ 50%]  (Sampling) 
#> Chain 3 Iteration: 1100 / 2000 [ 55%]  (Sampling) 
#> Chain 3 Iteration: 1200 / 2000 [ 60%]  (Sampling) 
#> Chain 3 Iteration: 1300 / 2000 [ 65%]  (Sampling) 
#> Chain 3 Iteration: 1400 / 2000 [ 70%]  (Sampling) 
#> Chain 3 Iteration: 1500 / 2000 [ 75%]  (Sampling) 
#> Chain 3 Iteration: 1600 / 2000 [ 80%]  (Sampling) 
#> Chain 3 Iteration: 1700 / 2000 [ 85%]  (Sampling) 
#> Chain 3 Iteration: 1800 / 2000 [ 90%]  (Sampling) 
#> Chain 3 Iteration: 1900 / 2000 [ 95%]  (Sampling) 
#> Chain 3 Iteration: 2000 / 2000 [100%]  (Sampling) 
#> Chain 3 finished in 0.1 seconds.
#> Chain 4 Iteration:    1 / 2000 [  0%]  (Warmup) 
#> Chain 4 Iteration:  100 / 2000 [  5%]  (Warmup) 
#> Chain 4 Iteration:  200 / 2000 [ 10%]  (Warmup) 
#> Chain 4 Iteration:  300 / 2000 [ 15%]  (Warmup) 
#> Chain 4 Iteration:  400 / 2000 [ 20%]  (Warmup) 
#> Chain 4 Iteration:  500 / 2000 [ 25%]  (Warmup) 
#> Chain 4 Iteration:  600 / 2000 [ 30%]  (Warmup) 
#> Chain 4 Iteration:  700 / 2000 [ 35%]  (Warmup) 
#> Chain 4 Iteration:  800 / 2000 [ 40%]  (Warmup) 
#> Chain 4 Iteration:  900 / 2000 [ 45%]  (Warmup) 
#> Chain 4 Iteration: 1000 / 2000 [ 50%]  (Warmup) 
#> Chain 4 Iteration: 1001 / 2000 [ 50%]  (Sampling) 
#> Chain 4 Iteration: 1100 / 2000 [ 55%]  (Sampling) 
#> Chain 4 Iteration: 1200 / 2000 [ 60%]  (Sampling) 
#> Chain 4 Iteration: 1300 / 2000 [ 65%]  (Sampling) 
#> Chain 4 Iteration: 1400 / 2000 [ 70%]  (Sampling) 
#> Chain 4 Iteration: 1500 / 2000 [ 75%]  (Sampling) 
#> Chain 4 Iteration: 1600 / 2000 [ 80%]  (Sampling) 
#> Chain 4 Iteration: 1700 / 2000 [ 85%]  (Sampling) 
#> Chain 4 Iteration: 1800 / 2000 [ 90%]  (Sampling) 
#> Chain 4 Iteration: 1900 / 2000 [ 95%]  (Sampling) 
#> Chain 4 Iteration: 2000 / 2000 [100%]  (Sampling) 
#> Chain 4 finished in 0.1 seconds.
#> 
#> All 4 chains finished successfully.
#> Mean chain execution time: 0.1 seconds.
#> Total execution time: 1.0 seconds.
#> Error in !is.null(csv_contents$metadata$save_warmup) && csv_contents$metadata$save_warmup: invalid 'y' type in 'x && y'

Created on 2024-04-24 with reprex v2.0.2

Session info 
sessioninfo::session_info()
#> ─ Session info ───────────────────────────────────────────────────────────────
#>  setting  value
#>  version  R version 4.2.0 (2022-04-22)
#>  os       macOS 14.4.1
#>  system   aarch64, darwin20
#>  ui       X11
#>  language (EN)
#>  collate  en_US.UTF-8
#>  ctype    en_US.UTF-8
#>  tz       America/Los_Angeles
#>  date     2024-04-24
#>  pandoc   3.1.1 @ /Applications/RStudio.app/Contents/Resources/app/quarto/bin/tools/ (via rmarkdown)
#> 
#> ─ Packages ───────────────────────────────────────────────────────────────────
#>  package        * version  date (UTC) lib source
#>  abind            1.4-5    2016-07-21 [1] CRAN (R 4.2.0)
#>  backports        1.4.1    2021-12-13 [1] CRAN (R 4.2.0)
#>  bayesplot        1.11.1   2024-02-15 [1] CRAN (R 4.2.3)
#>  bridgesampling   1.1-2    2021-04-16 [1] CRAN (R 4.2.0)
#>  brms           * 2.21.0   2024-03-20 [1] CRAN (R 4.2.0)
#>  Brobdingnag      1.2-9    2022-10-19 [1] CRAN (R 4.2.0)
#>  checkmate        2.3.1    2023-12-04 [1] CRAN (R 4.2.3)
#>  cli              3.6.2    2023-12-11 [1] CRAN (R 4.2.3)
#>  cmdstanr         0.7.1    2024-04-24 [1] Github (stan-dev/cmdstanr@2bec769)
#>  coda             0.19-4.1 2024-01-31 [1] CRAN (R 4.2.3)
#>  codetools        0.2-18   2020-11-04 [1] CRAN (R 4.2.0)
#>  colorspace       2.1-0    2023-01-23 [1] CRAN (R 4.2.0)
#>  curl             5.2.1    2024-03-01 [1] CRAN (R 4.2.3)
#>  data.table       1.15.4   2024-03-30 [1] CRAN (R 4.2.3)
#>  digest           0.6.34   2024-01-11 [1] CRAN (R 4.2.3)
#>  distributional   0.4.0    2024-02-07 [1] CRAN (R 4.2.3)
#>  dplyr            1.1.4    2023-11-17 [1] CRAN (R 4.2.3)
#>  emmeans          1.8.9    2023-10-17 [1] CRAN (R 4.2.0)
#>  estimability     1.4.1    2022-08-05 [1] CRAN (R 4.2.0)
#>  evaluate         0.23     2023-11-01 [1] CRAN (R 4.2.0)
#>  fansi            1.0.6    2023-12-08 [1] CRAN (R 4.2.3)
#>  fastmap          1.1.1    2023-02-24 [1] CRAN (R 4.2.0)
#>  fs               1.6.3    2023-07-20 [1] CRAN (R 4.2.0)
#>  generics         0.1.3    2022-07-05 [1] CRAN (R 4.2.0)
#>  ggplot2          3.5.0    2024-02-23 [1] CRAN (R 4.2.3)
#>  glue             1.7.0    2024-01-09 [1] CRAN (R 4.2.3)
#>  gridExtra        2.3      2017-09-09 [1] CRAN (R 4.2.0)
#>  gtable           0.3.4    2023-08-21 [1] CRAN (R 4.2.0)
#>  htmltools        0.5.7    2023-11-03 [1] CRAN (R 4.2.0)
#>  inline           0.3.19   2021-05-31 [1] CRAN (R 4.2.0)
#>  jsonlite         1.8.8    2023-12-04 [1] CRAN (R 4.2.3)
#>  knitr            1.45     2023-10-30 [1] CRAN (R 4.2.0)
#>  lattice          0.20-45  2021-09-22 [1] CRAN (R 4.2.0)
#>  lifecycle        1.0.4    2023-11-07 [1] CRAN (R 4.2.0)
#>  loo              2.7.0    2024-02-24 [1] CRAN (R 4.2.3)
#>  magrittr         2.0.3    2022-03-30 [1] CRAN (R 4.2.0)
#>  Matrix           1.6-5    2024-01-11 [1] CRAN (R 4.2.3)
#>  matrixStats      1.3.0    2024-04-11 [1] CRAN (R 4.2.3)
#>  munsell          0.5.0    2018-06-12 [1] CRAN (R 4.2.0)
#>  mvtnorm          1.2-3    2023-08-25 [1] CRAN (R 4.2.0)
#>  nlme             3.1-157  2022-03-25 [1] CRAN (R 4.2.0)
#>  pillar           1.9.0    2023-03-22 [1] CRAN (R 4.2.0)
#>  pkgbuild         1.4.3    2023-12-10 [1] CRAN (R 4.2.3)
#>  pkgconfig        2.0.3    2019-09-22 [1] CRAN (R 4.2.0)
#>  posterior        1.5.0    2023-10-31 [1] CRAN (R 4.2.0)
#>  processx         3.8.4    2024-03-16 [1] CRAN (R 4.2.3)
#>  ps               1.7.6    2024-01-18 [1] CRAN (R 4.2.3)
#>  purrr            1.0.2    2023-08-10 [1] CRAN (R 4.2.0)
#>  QuickJSR         1.1.3    2024-01-31 [1] CRAN (R 4.2.3)
#>  R.cache          0.16.0   2022-07-21 [1] CRAN (R 4.2.0)
#>  R.methodsS3      1.8.2    2022-06-13 [1] CRAN (R 4.2.0)
#>  R.oo             1.25.0   2022-06-12 [1] CRAN (R 4.2.0)
#>  R.utils          2.12.0   2022-06-28 [1] CRAN (R 4.2.0)
#>  R6               2.5.1    2021-08-19 [1] CRAN (R 4.2.0)
#>  Rcpp           * 1.0.12   2024-01-09 [1] CRAN (R 4.2.0)
#>  RcppParallel     5.1.7    2023-02-27 [1] CRAN (R 4.2.0)
#>  reprex           2.0.2    2022-08-17 [1] CRAN (R 4.2.0)
#>  rlang            1.1.3    2024-01-10 [1] CRAN (R 4.2.3)
#>  rmarkdown        2.25     2023-09-18 [1] CRAN (R 4.2.0)
#>  rstan            2.32.6   2024-03-05 [1] CRAN (R 4.2.3)
#>  rstantools       2.4.0    2024-01-31 [1] CRAN (R 4.2.3)
#>  rstudioapi       0.15.0   2023-07-07 [1] CRAN (R 4.2.0)
#>  scales           1.3.0    2023-11-28 [1] CRAN (R 4.2.3)
#>  sessioninfo      1.2.2    2021-12-06 [1] CRAN (R 4.2.0)
#>  StanHeaders      2.32.6   2024-03-01 [1] CRAN (R 4.2.3)
#>  stringi          1.8.3    2023-12-11 [1] CRAN (R 4.2.3)
#>  stringr          1.5.1    2023-11-14 [1] CRAN (R 4.2.3)
#>  styler           1.7.0    2022-03-13 [1] CRAN (R 4.2.0)
#>  tensorA          0.36.2.1 2023-12-13 [1] CRAN (R 4.2.3)
#>  tibble           3.2.1    2023-03-20 [1] CRAN (R 4.2.0)
#>  tidyselect       1.2.1    2024-03-11 [1] CRAN (R 4.2.3)
#>  utf8             1.2.4    2023-10-22 [1] CRAN (R 4.2.0)
#>  V8               4.4.2    2024-02-15 [1] CRAN (R 4.2.3)
#>  vctrs            0.6.5    2023-12-01 [1] CRAN (R 4.2.3)
#>  withr            3.0.0    2024-01-16 [1] CRAN (R 4.2.3)
#>  xfun             0.41     2023-11-01 [1] CRAN (R 4.2.0)
#>  xtable           1.8-4    2019-04-21 [1] CRAN (R 4.2.0)
#>  yaml             2.3.8    2023-12-11 [1] CRAN (R 4.2.3)
#> 
#>  [1] /Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library
#> 
#> ──────────────────────────────────────────────────────────────────────────────
@dholstius dholstius added the bug Something isn't working label Apr 25, 2024
@thomasgstewart
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Got the same error fitting the example in the startup guide. Like the previous commenter, I was able to verify that csv_contents$metadata$save_warmup is a character string and not a logical.

@andrjohns
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Got the same error fitting the example in the startup guide. Like the previous commenter, I was able to verify that csv_contents$metadata$save_warmup is a character string and not a logical.

Do you get the same error if you install cmdstanr from github?

remotes::install_github("stan-dev/cmdstanr")

I can't replicate the error locally

@BjarkeHautop
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BjarkeHautop commented Jul 17, 2024 •
edited
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I get the same error @andrjohns. Have attached all the code I ran from re-downloading and re-building to fitting.

install.packages("rstan") # version 2.32.6

remotes::install_github("stan-dev/cmdstanr")
library(cmdstanr)
rebuild_cmdstan()

cmdstan_version() # 2.35.0

file <- file.path(cmdstan_path(), "examples", "bernoulli", "bernoulli.stan")
mod <- cmdstan_model(file)

data_list <- list(N = 10, y = c(0,1,0,0,0,0,0,0,0,1))

fit <- mod$sample(
  data = data_list,
  seed = 123,
  chains = 4,
  parallel_chains = 4,
  refresh = 500 
)

stanfit <- rstan::read_stan_csv(fit$output_files())
# Error in if (max(save_warmup) == 0L) { : 
#  missing value where TRUE/FALSE needed
# In addition: Warning messages:
# 1: In parse_stancsv_comments(comments) : NAs introduced by coercion
# 2: In parse_stancsv_comments(comments) : NAs introduced by coercion
# 3: In parse_stancsv_comments(comments) : NAs introduced by coercion
# 4: In parse_stancsv_comments(comments) : NAs introduced by coercion

Session info

R version 4.4.1 (2024-06-14 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 22631)

Matrix products: default

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

other attached packages:
[1] cmdstanr_0.8.1.9000 rstan_2.32.6        StanHeaders_2.32.10

loaded via a namespace (and not attached):
 [1] tensorA_0.36.2.1     utf8_1.2.4           generics_0.1.3       magrittr_2.0.3      
 [5] grid_4.4.1           jsonlite_1.8.8       processx_3.8.4       pkgbuild_1.4.4      
 [9] backports_1.5.0      ps_1.7.7             gridExtra_2.3        fansi_1.0.6         
[13] QuickJSR_1.3.0       scales_1.3.0         codetools_0.2-20     abind_1.4-5         
[17] cli_3.6.3            rlang_1.1.4          munsell_0.5.1        withr_3.0.0         
[21] tools_4.4.1          inline_0.3.19        parallel_4.4.1       checkmate_2.3.1     
[25] dplyr_1.1.4          colorspace_2.1-0     ggplot2_3.5.1        curl_5.2.1          
[29] vctrs_0.6.5          posterior_1.6.0      R6_2.5.1             matrixStats_1.3.0   
[33] stats4_4.4.1         lifecycle_1.0.4      V8_4.4.2             pkgconfig_2.0.3     
[37] RcppParallel_5.1.8   pillar_1.9.0         gtable_0.3.5         loo_2.8.0           
[41] glue_1.7.0           data.table_1.15.4    Rcpp_1.0.12          xfun_0.45           
[45] tibble_3.2.1         tidyselect_1.2.1     rstudioapi_0.16.0    knitr_1.48          
[49] compiler_4.4.1       distributional_0.4.0

@andrjohns
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That's an issue with rstan's csv parsing, not cmdstanr

@jgabry
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jgabry commented Jul 18, 2024

I'm guessing it's related to this issue stan-dev/rstan#1131. There's an open PR to fix it that I'm reviewing at the moment.

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@dholstius @jgabry @thomasgstewart @andrjohns @BjarkeHautop