unconstrain_draws: mismatch in number dimensions declared and found in context

[bschneidr]

Describe the bug
I'm fitting a simple varying intercepts model using 'brms' and 'cmdstanr'. The model fits without an issue and I can work with the model output without issue generally, except when I try to extract unconstrained draws. In this case, I get the following message:

Exception: mismatch in number dimensions declared and found in context; processing stage=parameter initialization; variable name=sd_1; dims declared=(1); dims found=()

I can't see that there's anything wrong with the model syntax, but maybe I'm missing something. I'm guessing that somewhere in reading/writing JSON or CSV, there's a type mismatch where something should be a matrix/array but is instead a vector. But that's just a guess.

To Reproduce

library(brms)     # 2.20.3
#> Loading required package: Rcpp
#> Loading 'brms' package (version 2.20.3). 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
library(cmdstanr) # 0.6.0
#> This is cmdstanr version 0.6.0
#> - CmdStanR documentation and vignettes: mc-stan.org/cmdstanr
#> - CmdStan path: C:/Users/benja/OneDrive/Documents/.cmdstan/cmdstan-2.33.1
#> - CmdStan version: 2.33.1

# Create small example dataset
  Y <- c(1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0)
  GROUP <- rep(1:4, each = 5)

# Create Stan code and data
  stan_code <- brms::make_stancode(
    formula = Y ~ (1|GROUP),
    family  = brms::bernoulli(link = "logit"),
    data    = data.frame(Y, GROUP)
  )

  print(stan_code)
#> // generated with brms 2.20.3
#> functions {
#> }
#> data {
#>   int<lower=1> N;  // total number of observations
#>   array[N] int Y;  // response variable
#>   // data for group-level effects of ID 1
#>   int<lower=1> N_1;  // number of grouping levels
#>   int<lower=1> M_1;  // number of coefficients per level
#>   array[N] int<lower=1> J_1;  // grouping indicator per observation
#>   // group-level predictor values
#>   vector[N] Z_1_1;
#>   int prior_only;  // should the likelihood be ignored?
#> }
#> transformed data {
#> }
#> parameters {
#>   real Intercept;  // temporary intercept for centered predictors
#>   vector<lower=0>[M_1] sd_1;  // group-level standard deviations
#>   array[M_1] vector[N_1] z_1;  // standardized group-level effects
#> }
#> transformed parameters {
#>   vector[N_1] r_1_1;  // actual group-level effects
#>   real lprior = 0;  // prior contributions to the log posterior
#>   r_1_1 = (sd_1[1] * (z_1[1]));
#>   lprior += student_t_lpdf(Intercept | 3, 0, 2.5);
#>   lprior += student_t_lpdf(sd_1 | 3, 0, 2.5)
#>   - 1 * student_t_lccdf(0 | 3, 0, 2.5);
#> }
#> model {
#>   // likelihood including constants
#>   if (!prior_only) {
#>     // initialize linear predictor term
#>     vector[N] mu = rep_vector(0.0, N);
#>     mu += Intercept;
#>     for (n in 1:N) {
#>       // add more terms to the linear predictor
#>       mu[n] += r_1_1[J_1[n]] * Z_1_1[n];
#>     }
#>     target += bernoulli_logit_lpmf(Y | mu);
#>   }
#>   // priors including constants
#>   target += lprior;
#>   target += std_normal_lpdf(z_1[1]);
#> }
#> generated quantities {
#>   // actual population-level intercept
#>   real b_Intercept = Intercept;
#> }

  stan_data <- brms::make_standata(
    formula = Y ~ (1|GROUP),
    family  = brms::bernoulli(link = "logit"),
    data    = data.frame(Y, GROUP)
  )

# Write the Stan code to a file
  stan_file <- cmdstanr::write_stan_file(
    code = stan_code
  )

# Compile the model
  stan_model <- cmdstanr::cmdstan_model(
    stan_file             = stan_file,
    compile_model_methods = TRUE, # Allows access to gradient
    force_recompile       = TRUE
  )
#> In file included from stan/lib/stan_math/stan/math/prim/prob/von_mises_lccdf.hpp:5,
#>                  from stan/lib/stan_math/stan/math/prim/prob/von_mises_ccdf_log.hpp:4,
#>                  from stan/lib/stan_math/stan/math/prim/prob.hpp:356,
#>                  from stan/lib/stan_math/stan/math/prim.hpp:16,
#>                  from stan/lib/stan_math/stan/math/rev.hpp:14,
#>                  from stan/lib/stan_math/stan/math.hpp:19,
#>                  from stan/src/stan/model/model_header.hpp:4,
#>                  from C:/Users/benja/AppData/Local/Temp/RtmpQ3Mjac/model-7cc50b17e7f.hpp:2:
#> stan/lib/stan_math/stan/math/prim/prob/von_mises_cdf.hpp: In function 'stan::return_type_t<T_x, T_sigma, T_l> stan::math::von_mises_cdf(const T_x&, const T_mu&, const T_k&)':
#> stan/lib/stan_math/stan/math/prim/prob/von_mises_cdf.hpp:194: note: '-Wmisleading-indentation' is disabled from this point onwards, since column-tracking was disabled due to the size of the code/headers
#>   194 |       if (cdf_n < 0.0)
#>       |
#> stan/lib/stan_math/stan/math/prim/prob/von_mises_cdf.hpp:194: note: adding '-flarge-source-files' will allow for more column-tracking support, at the expense of compilation time and memory

# Obtain MCMC draws
  model_fit <- stan_model$sample(
    data            = stan_data,
    iter_warmup     = 10,
    iter_sampling   = 20,
    chains          = 2,
    parallel_chains = 1,
    seed            = 1999
  )
#> Running MCMC with 2 sequential chains...
#> 
#> Chain 1 WARNING: No variance estimation is 
#> Chain 1          performed for num_warmup < 20 
#> Chain 1 Iteration:  1 / 30 [  3%]  (Warmup) 
#> Chain 1 Iteration: 11 / 30 [ 36%]  (Sampling) 
#> Chain 1 Iteration: 30 / 30 [100%]  (Sampling) 
#> Chain 1 finished in 0.0 seconds.
#> Chain 2 WARNING: No variance estimation is 
#> Chain 2          performed for num_warmup < 20 
#> Chain 2 Iteration:  1 / 30 [  3%]  (Warmup) 
#> Chain 2 Iteration: 11 / 30 [ 36%]  (Sampling) 
#> Chain 2 Iteration: 30 / 30 [100%]  (Sampling) 
#> Chain 2 finished in 0.0 seconds.
#> 
#> Both chains finished successfully.
#> Mean chain execution time: 0.0 seconds.
#> Total execution time: 0.9 seconds.

# Extract unconstrained parameter draws
  model_fit$init_model_methods()
#> Compiling additional model methods...
  model_fit$unconstrain_draws()
#> Error in eval(expr, envir, enclos): Exception: mismatch in number dimensions declared and found in context; processing stage=parameter initialization; variable name=sd_1; dims declared=(1); dims found=() (in 'C:/Users/benja/AppData/Local/Temp/RtmpQ3Mjac/model-7cc50b17e7f.stan', line 19, column 2 to column 28)

^{Created on 2023-09-23 by the reprex package (v2.0.1)}

Expected behavior
I expected model_fit$unconstrain_draws() to return the unconstrained parameter draws.

Operating system

R version 4.3.1 (2023-06-16 ucrt)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 11 x64 (build 22621)

Matrix products: default

CmdStanR version number

0.6.0

Additional context
The purpose for this is I want to make some adjustments to the unconstrained parameter draws. I don't think it really matters for debugging, but in case you're interested the adjustments are to deal with complex survey data, along the lines of this discussion on Discourse: https://discourse.mc-stan.org/t/survey-weights-in-brms-stan-simulation-based-on-design-effect-feedback-sought/28625/13

[andrjohns]

Thanks for opening this, this is a tricky one!

The fact that sd_1 is a vector of length 1 is causing our pre-processing to structure it as a real rather than a vector[1] before passing to be unconstrained. This then trips the dims error, since a real has no declared dimensions.

I'll sort out a fix and add this as a test case

[bschneidr]

Thanks, @andrjohns. That explanation makes sense to me. Please let me know if there's anything else I can do to help resolve this.

[andrjohns]

A bugfix for this has now been added, can you try installing the github version of cmdstanr:

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

And then try your model again?

[jgabry]

Thanks @andrjohns for fixing this. I just tested it using the latest cmdstan release + the current cmdstanr master branch + latest brms + @bschneidr's example from above and there's no error anymore. I'm going to go ahead and close this and we can reopen if necessary (@bschneidr if you still run into an error let us know and we'll investigate further).

[bschneidr]

I tested this out and everything's playing nicely and working as expected. Thank you @jgabry and @andrjohns for resolving this so quickly!