gqs() does not work with transformed parameters

Summary:

Standalone generated quantities via qgs() don't work with transformed parameters.

Description:

See this discourse topic: Using transformed parameters leads to error Wrong number of parameter values in draws from fitted model. Expecting 2 columns, found 3 columns. which, I guess, comes from stan/src/stan/services/sample/standalone_gqs.hpp.

Reproducible Steps:

library(rstan)
rstan_options(auto_write = TRUE)
options(mc.cores = parallel::detectCores())

sc <- "
transformed data{
  vector[10] y = [4.65, 6.02, 4.92, 4.77, 8.12, 6.93, 7.39, 7.6, 5.68, 2.14]';
}
parameters{
  real mu;
  real log_sigma;
}
transformed parameters{
  real<lower=0> sigma = exp(log_sigma);
}
model{
  y ~ normal(mu, sigma);
  mu ~ normal(0, 10);
  log_sigma ~ normal(0, 2.5);
}

"

sm <- stan_model(model_code = sc)

post <- sampling(sm)

sc_gq <- "
parameters{
  real mu;
  real log_sigma;
}
transformed parameters{
  real<lower=0> sigma = exp(log_sigma);
}
generated quantities{
  vector[10] y_rep;
  
  for (i in 1:10)
    y_rep[i] = normal_rng(mu, sigma);
}

"

sm_gq <- stan_model(model_code = sc_gq)

rep <- gqs(sm_gq, draws = as.matrix(post))

rep

Current Output:

Wrong number of parameter values in draws from fitted model.  Expecting 2 columns, found 3 columns.

Inference for Stan model: 9a372375694ef14366f13bf8c7378254.
1 chains, each with iter=4000; warmup=0; thin=1; 
post-warmup draws per chain=4000, total post-warmup draws=4000.

          mean se_mean sd 2.5% 25% 50% 75% 97.5% n_eff Rhat
y_rep[1]     0     NaN  0    0   0   0   0     0   NaN  NaN
y_rep[2]     0     NaN  0    0   0   0   0     0   NaN  NaN
y_rep[3]     0     NaN  0    0   0   0   0     0   NaN  NaN
y_rep[4]     0     NaN  0    0   0   0   0     0   NaN  NaN
y_rep[5]     0     NaN  0    0   0   0   0     0   NaN  NaN
y_rep[6]     0     NaN  0    0   0   0   0     0   NaN  NaN
y_rep[7]     0     NaN  0    0   0   0   0     0   NaN  NaN
y_rep[8]     0     NaN  0    0   0   0   0     0   NaN  NaN
y_rep[9]     0     NaN  0    0   0   0   0     0   NaN  NaN
y_rep[10]    0     NaN  0    0   0   0   0     0   NaN  NaN

Samples were drawn using  at Tue Nov 05 15:31:10 2019.
For each parameter, n_eff is a crude measure of effective sample size,
and Rhat is the potential scale reduction factor on split chains (at 
convergence, Rhat=1).

Expected Output:

Inference for Stan model: bdf3c0b7af993ed90b29cc81706a90ea.
1 chains, each with iter=4000; warmup=0; thin=1; 
post-warmup draws per chain=4000, total post-warmup draws=4000.

          mean se_mean   sd 2.5%  25%  50%  75% 97.5% n_eff Rhat
y_rep[1]  5.82    0.04 2.16 1.54 4.51 5.82 7.08 10.20  3487    1
y_rep[2]  5.83    0.03 2.14 1.54 4.51 5.85 7.13  9.99  3851    1
y_rep[3]  5.82    0.04 2.16 1.64 4.44 5.83 7.13 10.17  3743    1
y_rep[4]  5.75    0.04 2.19 1.36 4.48 5.75 7.10 10.09  3354    1
y_rep[5]  5.85    0.03 2.14 1.77 4.48 5.81 7.17 10.21  3879    1
y_rep[6]  5.82    0.03 2.14 1.52 4.52 5.83 7.15 10.07  3830    1
y_rep[7]  5.83    0.04 2.17 1.60 4.52 5.82 7.13 10.07  3509    1
y_rep[8]  5.86    0.03 2.08 1.64 4.52 5.85 7.17 10.09  3700    1
y_rep[9]  5.83    0.04 2.13 1.54 4.53 5.82 7.14 10.11  3362    1
y_rep[10] 5.77    0.03 2.11 1.72 4.47 5.76 7.08 10.01  3740    1

Samples were drawn using  at Tue Nov 05 15:34:33 2019.
For each parameter, n_eff is a crude measure of effective sample size,
and Rhat is the potential scale reduction factor on split chains (at 
convergence, Rhat=1).

RStan Version:

> packageVersion("rstan")
[1] ‘2.19.9’

R Version:

> R.version.string
[1] "R version 3.6.1 (2019-07-05)"

Operating System:

Win7 Pro SP1

Reproduce the supplied R example with transformed parameters and standalone gqs(), then inspect stan/src/stan/services/sample/standalone_gqs.hpp at the linked validation logic. Trace how parameter and transformed-parameter columns are handled; done means the example runs without the column-count error and produces nonzero y_rep output.