MacBook M1 users - reports of problems with Big Sur 11.3

reports of new problems with clang++ compiler with latest release of Big Sur -

also a different problem - cannot recreate: Make build stuck at Entering Directory

if anyone can help either problem, greatly appreciated.

2 Likes

Mitzi I’ve just ran rstan with the example(stan_model) stuff. I am running the latest R 4.1.0 alpha from the mac.r-project.org site for the Mac ARM architecture.

R version 4.1.0 alpha (2021-04-26 r80229)
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> library(rstan)
Loading required package: StanHeaders
Loading required package: ggplot2
rstan (Version 2.21.2, 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)
> example(stan_model, package = "rstan", run.dontrun = TRUE)

stn_md> stancode <- 'data {real y_mean;} parameters {real y;} model {y ~ normal(y_mean,1);}'

stn_md> mod <- stan_model(model_code = stancode, verbose = TRUE)

TRANSLATING MODEL '16a540c6086086816528e4524def24d9' FROM Stan CODE TO C++ CODE NOW.
successful in parsing the Stan model '16a540c6086086816528e4524def24d9'.
COMPILING THE C++ CODE FOR MODEL '16a540c6086086816528e4524def24d9' NOW.
OS: aarch64, darwin20; rstan: 2.21.2; Rcpp: 1.0.6; inline: 0.3.17 
 >> setting environment variables: 
PKG_LIBS =  '/Library/Frameworks/R.framework/Versions/4.1-arm64/Resources/library/rstan/lib//libStanServices.a' -L'/Library/Frameworks/R.framework/Versions/4.1-arm64/Resources/library/StanHeaders/lib/' -lStanHeaders -L'/Library/Frameworks/R.framework/Versions/4.1-arm64/Resources/library/RcppParallel/lib/' -ltbb -ltbbmalloc -ltbbmalloc_proxy
PKG_CPPFLAGS =   -I"/Library/Frameworks/R.framework/Versions/4.1-arm64/Resources/library/Rcpp/include/"  -I"/Library/Frameworks/R.framework/Versions/4.1-arm64/Resources/library/RcppEigen/include/"  -I"/Library/Frameworks/R.framework/Versions/4.1-arm64/Resources/library/RcppEigen/include/unsupported"  -I"/Library/Frameworks/R.framework/Versions/4.1-arm64/Resources/library/BH/include" -I"/Library/Frameworks/R.framework/Versions/4.1-arm64/Resources/library/StanHeaders/include/src/"  -I"/Library/Frameworks/R.framework/Versions/4.1-arm64/Resources/library/StanHeaders/include/"  -I"/Library/Frameworks/R.framework/Versions/4.1-arm64/Resources/library/RcppParallel/include/"  -I"/Library/Frameworks/R.framework/Versions/4.1-arm64/Resources/library/rstan/include" -DEIGEN_NO_DEBUG  -DBOOST_DISABLE_ASSERTS  -DBOOST_PENDING_INTEGER_LOG2_HPP  -DSTAN_THREADS  -DBOOST_NO_AUTO_PTR  -include '/Library/Frameworks/R.framework/Versions/4.1-arm64/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp'  -D_REENTRANT -DRCPP_PARALLEL_USE_TBB=1 
 >> Program source :

   1 : 
   2 : // includes from the plugin
   3 : // [[Rcpp::plugins(cpp14)]]
   4 : 
   5 : 
   6 : // user includes
   7 : #include <Rcpp.h>
   8 : #include <rstan/io/rlist_ref_var_context.hpp>
   9 : #include <rstan/io/r_ostream.hpp>
  10 : #include <rstan/stan_args.hpp>
  11 : #include <boost/integer/integer_log2.hpp>
  12 : // Code generated by Stan version 2.21.0
  13 : 
  14 : #include <stan/model/model_header.hpp>
  15 : 
  16 : namespace model17320678c57b_16a540c6086086816528e4524def24d9_namespace {
  17 : 
  18 : using std::istream;
  19 : using std::string;
  20 : using std::stringstream;
  21 : using std::vector;
  22 : using stan::io::dump;
  23 : using stan::math::lgamma;
  24 : using stan::model::prob_grad;
  25 : using namespace stan::math;
  26 : 
  27 : static int current_statement_begin__;
  28 : 
  29 : stan::io::program_reader prog_reader__() {
  30 :     stan::io::program_reader reader;
  31 :     reader.add_event(0, 0, "start", "model17320678c57b_16a540c6086086816528e4524def24d9");
  32 :     reader.add_event(3, 1, "end", "model17320678c57b_16a540c6086086816528e4524def24d9");
  33 :     return reader;
  34 : }
  35 : 
  36 : class model17320678c57b_16a540c6086086816528e4524def24d9
  37 :   : public stan::model::model_base_crtp<model17320678c57b_16a540c6086086816528e4524def24d9> {
  38 : private:
  39 :         double y_mean;
  40 : public:
  41 :     model17320678c57b_16a540c6086086816528e4524def24d9(rstan::io::rlist_ref_var_context& context__,
  42 :         std::ostream* pstream__ = 0)
  43 :         : model_base_crtp(0) {
  44 :         ctor_body(context__, 0, pstream__);
  45 :     }
  46 : 
  47 :     model17320678c57b_16a540c6086086816528e4524def24d9(stan::io::var_context& context__,
  48 :         unsigned int random_seed__,
  49 :         std::ostream* pstream__ = 0)
  50 :         : model_base_crtp(0) {
  51 :         ctor_body(context__, random_seed__, pstream__);
  52 :     }
  53 : 
  54 :     void ctor_body(stan::io::var_context& context__,
  55 :                    unsigned int random_seed__,
  56 :                    std::ostream* pstream__) {
  57 :         typedef double local_scalar_t__;
  58 : 
  59 :         boost::ecuyer1988 base_rng__ =
  60 :           stan::services::util::create_rng(random_seed__, 0);
  61 :         (void) base_rng__;  // suppress unused var warning
  62 : 
  63 :         current_statement_begin__ = -1;
  64 : 
  65 :         static const char* function__ = "model17320678c57b_16a540c6086086816528e4524def24d9_namespace::model17320678c57b_16a540c6086086816528e4524def24d9";
  66 :         (void) function__;  // dummy to suppress unused var warning
  67 :         size_t pos__;
  68 :         (void) pos__;  // dummy to suppress unused var warning
  69 :         std::vector<int> vals_i__;
  70 :         std::vector<double> vals_r__;
  71 :         local_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
  72 :         (void) DUMMY_VAR__;  // suppress unused var warning
  73 : 
  74 :         try {
  75 :             // initialize data block variables from context__
  76 :             current_statement_begin__ = 1;
  77 :             context__.validate_dims("data initialization", "y_mean", "double", context__.to_vec());
  78 :             y_mean = double(0);
  79 :             vals_r__ = context__.vals_r("y_mean");
  80 :             pos__ = 0;
  81 :             y_mean = vals_r__[pos__++];
  82 : 
  83 : 
  84 :             // initialize transformed data variables
  85 :             // execute transformed data statements
  86 : 
  87 :             // validate transformed data
  88 : 
  89 :             // validate, set parameter ranges
  90 :             num_params_r__ = 0U;
  91 :             param_ranges_i__.clear();
  92 :             current_statement_begin__ = 1;
  93 :             num_params_r__ += 1;
  94 :         } catch (const std::exception& e) {
  95 :             stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
  96 :             // Next line prevents compiler griping about no return
  97 :             throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
  98 :         }
  99 :     }
 100 : 
 101 :     ~model17320678c57b_16a540c6086086816528e4524def24d9() { }
 102 : 
 103 : 
 104 :     void transform_inits(const stan::io::var_context& context__,
 105 :                          std::vector<int>& params_i__,
 106 :                          std::vector<double>& params_r__,
 107 :                          std::ostream* pstream__) const {
 108 :         typedef double local_scalar_t__;
 109 :         stan::io::writer<double> writer__(params_r__, params_i__);
 110 :         size_t pos__;
 111 :         (void) pos__; // dummy call to supress warning
 112 :         std::vector<double> vals_r__;
 113 :         std::vector<int> vals_i__;
 114 : 
 115 :         current_statement_begin__ = 1;
 116 :         if (!(context__.contains_r("y")))
 117 :             stan::lang::rethrow_located(std::runtime_error(std::string("Variable y missing")), current_statement_begin__, prog_reader__());
 118 :         vals_r__ = context__.vals_r("y");
 119 :         pos__ = 0U;
 120 :         context__.validate_dims("parameter initialization", "y", "double", context__.to_vec());
 121 :         double y(0);
 122 :         y = vals_r__[pos__++];
 123 :         try {
 124 :             writer__.scalar_unconstrain(y);
 125 :         } catch (const std::exception& e) {
 126 :             stan::lang::rethrow_located(std::runtime_error(std::string("Error transforming variable y: ") + e.what()), current_statement_begin__, prog_reader__());
 127 :         }
 128 : 
 129 :         params_r__ = writer__.data_r();
 130 :         params_i__ = writer__.data_i();
 131 :     }
 132 : 
 133 :     void transform_inits(const stan::io::var_context& context,
 134 :                          Eigen::Matrix<double, Eigen::Dynamic, 1>& params_r,
 135 :                          std::ostream* pstream__) const {
 136 :       std::vector<double> params_r_vec;
 137 :       std::vector<int> params_i_vec;
 138 :       transform_inits(context, params_i_vec, params_r_vec, pstream__);
 139 :       params_r.resize(params_r_vec.size());
 140 :       for (int i = 0; i < params_r.size(); ++i)
 141 :         params_r(i) = params_r_vec[i];
 142 :     }
 143 : 
 144 : 
 145 :     template <bool propto__, bool jacobian__, typename T__>
 146 :     T__ log_prob(std::vector<T__>& params_r__,
 147 :                  std::vector<int>& params_i__,
 148 :                  std::ostream* pstream__ = 0) const {
 149 : 
 150 :         typedef T__ local_scalar_t__;
 151 : 
 152 :         local_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
 153 :         (void) DUMMY_VAR__;  // dummy to suppress unused var warning
 154 : 
 155 :         T__ lp__(0.0);
 156 :         stan::math::accumulator<T__> lp_accum__;
 157 :         try {
 158 :             stan::io::reader<local_scalar_t__> in__(params_r__, params_i__);
 159 : 
 160 :             // model parameters
 161 :             current_statement_begin__ = 1;
 162 :             local_scalar_t__ y;
 163 :             (void) y;  // dummy to suppress unused var warning
 164 :             if (jacobian__)
 165 :                 y = in__.scalar_constrain(lp__);
 166 :             else
 167 :                 y = in__.scalar_constrain();
 168 : 
 169 :             // model body
 170 : 
 171 :             current_statement_begin__ = 1;
 172 :             lp_accum__.add(normal_log<propto__>(y, y_mean, 1));
 173 : 
 174 :         } catch (const std::exception& e) {
 175 :             stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
 176 :             // Next line prevents compiler griping about no return
 177 :             throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
 178 :         }
 179 : 
 180 :         lp_accum__.add(lp__);
 181 :         return lp_accum__.sum();
 182 : 
 183 :     } // log_prob()
 184 : 
 185 :     template <bool propto, bool jacobian, typename T_>
 186 :     T_ log_prob(Eigen::Matrix<T_,Eigen::Dynamic,1>& params_r,
 187 :                std::ostream* pstream = 0) const {
 188 :       std::vector<T_> vec_params_r;
 189 :       vec_params_r.reserve(params_r.size());
 190 :       for (int i = 0; i < params_r.size(); ++i)
 191 :         vec_params_r.push_back(params_r(i));
 192 :       std::vector<int> vec_params_i;
 193 :       return log_prob<propto,jacobian,T_>(vec_params_r, vec_params_i, pstream);
 194 :     }
 195 : 
 196 : 
 197 :     void get_param_names(std::vector<std::string>& names__) const {
 198 :         names__.resize(0);
 199 :         names__.push_back("y");
 200 :     }
 201 : 
 202 : 
 203 :     void get_dims(std::vector<std::vector<size_t> >& dimss__) const {
 204 :         dimss__.resize(0);
 205 :         std::vector<size_t> dims__;
 206 :         dims__.resize(0);
 207 :         dimss__.push_back(dims__);
 208 :     }
 209 : 
 210 :     template <typename RNG>
 211 :     void write_array(RNG& base_rng__,
 212 :                      std::vector<double>& params_r__,
 213 :                      std::vector<int>& params_i__,
 214 :                      std::vector<double>& vars__,
 215 :                      bool include_tparams__ = true,
 216 :                      bool include_gqs__ = true,
 217 :                      std::ostream* pstream__ = 0) const {
 218 :         typedef double local_scalar_t__;
 219 : 
 220 :         vars__.resize(0);
 221 :         stan::io::reader<local_scalar_t__> in__(params_r__, params_i__);
 222 :         static const char* function__ = "model17320678c57b_16a540c6086086816528e4524def24d9_namespace::write_array";
 223 :         (void) function__;  // dummy to suppress unused var warning
 224 : 
 225 :         // read-transform, write parameters
 226 :         double y = in__.scalar_constrain();
 227 :         vars__.push_back(y);
 228 : 
 229 :         double lp__ = 0.0;
 230 :         (void) lp__;  // dummy to suppress unused var warning
 231 :         stan::math::accumulator<double> lp_accum__;
 232 : 
 233 :         local_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
 234 :         (void) DUMMY_VAR__;  // suppress unused var warning
 235 : 
 236 :         if (!include_tparams__ && !include_gqs__) return;
 237 : 
 238 :         try {
 239 :             if (!include_gqs__ && !include_tparams__) return;
 240 :             if (!include_gqs__) return;
 241 :         } catch (const std::exception& e) {
 242 :             stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
 243 :             // Next line prevents compiler griping about no return
 244 :             throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
 245 :         }
 246 :     }
 247 : 
 248 :     template <typename RNG>
 249 :     void write_array(RNG& base_rng,
 250 :                      Eigen::Matrix<double,Eigen::Dynamic,1>& params_r,
 251 :                      Eigen::Matrix<double,Eigen::Dynamic,1>& vars,
 252 :                      bool include_tparams = true,
 253 :                      bool include_gqs = true,
 254 :                      std::ostream* pstream = 0) const {
 255 :       std::vector<double> params_r_vec(params_r.size());
 256 :       for (int i = 0; i < params_r.size(); ++i)
 257 :         params_r_vec[i] = params_r(i);
 258 :       std::vector<double> vars_vec;
 259 :       std::vector<int> params_i_vec;
 260 :       write_array(base_rng, params_r_vec, params_i_vec, vars_vec, include_tparams, include_gqs, pstream);
 261 :       vars.resize(vars_vec.size());
 262 :       for (int i = 0; i < vars.size(); ++i)
 263 :         vars(i) = vars_vec[i];
 264 :     }
 265 : 
 266 :     std::string model_name() const {
 267 :         return "model17320678c57b_16a540c6086086816528e4524def24d9";
 268 :     }
 269 : 
 270 : 
 271 :     void constrained_param_names(std::vector<std::string>& param_names__,
 272 :                                  bool include_tparams__ = true,
 273 :                                  bool include_gqs__ = true) const {
 274 :         std::stringstream param_name_stream__;
 275 :         param_name_stream__.str(std::string());
 276 :         param_name_stream__ << "y";
 277 :         param_names__.push_back(param_name_stream__.str());
 278 : 
 279 :         if (!include_gqs__ && !include_tparams__) return;
 280 : 
 281 :         if (include_tparams__) {
 282 :         }
 283 : 
 284 :         if (!include_gqs__) return;
 285 :     }
 286 : 
 287 : 
 288 :     void unconstrained_param_names(std::vector<std::string>& param_names__,
 289 :                                    bool include_tparams__ = true,
 290 :                                    bool include_gqs__ = true) const {
 291 :         std::stringstream param_name_stream__;
 292 :         param_name_stream__.str(std::string());
 293 :         param_name_stream__ << "y";
 294 :         param_names__.push_back(param_name_stream__.str());
 295 : 
 296 :         if (!include_gqs__ && !include_tparams__) return;
 297 : 
 298 :         if (include_tparams__) {
 299 :         }
 300 : 
 301 :         if (!include_gqs__) return;
 302 :     }
 303 : 
 304 : }; // model
 305 : 
 306 : }  // namespace
 307 : 
 308 : typedef model17320678c57b_16a540c6086086816528e4524def24d9_namespace::model17320678c57b_16a540c6086086816528e4524def24d9 stan_model;
 309 : 
 310 : #ifndef USING_R
 311 : 
 312 : stan::model::model_base& new_model(
 313 :         stan::io::var_context& data_context,
 314 :         unsigned int seed,
 315 :         std::ostream* msg_stream) {
 316 :   stan_model* m = new stan_model(data_context, seed, msg_stream);
 317 :   return *m;
 318 : }
 319 : 
 320 : #endif
 321 : 
 322 : 
 323 : 
 324 : #include <rstan_next/stan_fit.hpp>
 325 : 
 326 : struct stan_model_holder {
 327 :     stan_model_holder(rstan::io::rlist_ref_var_context rcontext,
 328 :                       unsigned int random_seed)
 329 :     : rcontext_(rcontext), random_seed_(random_seed)
 330 :      {
 331 :      }
 332 : 
 333 :    //stan::math::ChainableStack ad_stack;
 334 :    rstan::io::rlist_ref_var_context rcontext_;
 335 :    unsigned int random_seed_;
 336 : };
 337 : 
 338 : Rcpp::XPtr<stan::model::model_base> model_ptr(stan_model_holder* smh) {
 339 :   Rcpp::XPtr<stan::model::model_base> model_instance(new stan_model(smh->rcontext_, smh->random_seed_), true);
 340 :   return model_instance;
 341 : }
 342 : 
 343 : Rcpp::XPtr<rstan::stan_fit_base> fit_ptr(stan_model_holder* smh) {
 344 :   return Rcpp::XPtr<rstan::stan_fit_base>(new rstan::stan_fit(model_ptr(smh), smh->random_seed_), true);
 345 : }
 346 : 
 347 : std::string model_name(stan_model_holder* smh) {
 348 :   return model_ptr(smh).get()->model_name();
 349 : }
 350 : 
 351 : RCPP_MODULE(stan_fit4model17320678c57b_16a540c6086086816528e4524def24d9_mod){
 352 :   Rcpp::class_<stan_model_holder>("stan_fit4model17320678c57b_16a540c6086086816528e4524def24d9")
 353 :   .constructor<rstan::io::rlist_ref_var_context, unsigned int>()
 354 :   .method("model_ptr", &model_ptr)
 355 :   .method("fit_ptr", &fit_ptr)
 356 :   .method("model_name", &model_name)
 357 :   ;
 358 : }
 359 : 
 360 : 
 361 : // declarations
 362 : extern "C" {
 363 : SEXP file173201a7ce1e2( ) ;
 364 : }
 365 : 
 366 : // definition
 367 : SEXP file173201a7ce1e2() {
 368 :  return Rcpp::wrap("16a540c6086086816528e4524def24d9");
 369 : }
make cmd is
  make -f '/Library/Frameworks/R.framework/Resources/etc/Makeconf' -f '/Library/Frameworks/R.framework/Resources/share/make/shlib.mk' -f '/Users/storopoli/.R/Makevars' CXX='$(CXX14) $(CXX14STD)' CXXFLAGS='$(CXX14FLAGS)' CXXPICFLAGS='$(CXX14PICFLAGS)' SHLIB_LDFLAGS='$(SHLIB_CXX14LDFLAGS)' SHLIB_LD='$(SHLIB_CXX14LD)' SHLIB='file173201a7ce1e2.so' OBJECTS='file173201a7ce1e2.o'

make would use
clang++ -arch arm64 -std=c++14 -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG   -I"/Library/Frameworks/R.framework/Versions/4.1-arm64/Resources/library/Rcpp/include/"  -I"/Library/Frameworks/R.framework/Versions/4.1-arm64/Resources/library/RcppEigen/include/"  -I"/Library/Frameworks/R.framework/Versions/4.1-arm64/Resources/library/RcppEigen/include/unsupported"  -I"/Library/Frameworks/R.framework/Versions/4.1-arm64/Resources/library/BH/include" -I"/Library/Frameworks/R.framework/Versions/4.1-arm64/Resources/library/StanHeaders/include/src/"  -I"/Library/Frameworks/R.framework/Versions/4.1-arm64/Resources/library/StanHeaders/include/"  -I"/Library/Frameworks/R.framework/Versions/4.1-arm64/Resources/library/RcppParallel/include/"  -I"/Library/Frameworks/R.framework/Versions/4.1-arm64/Resources/library/rstan/include" -DEIGEN_NO_DEBUG  -DBOOST_DISABLE_ASSERTS  -DBOOST_PENDING_INTEGER_LOG2_HPP  -DSTAN_THREADS  -DBOOST_NO_AUTO_PTR  -include '/Library/Frameworks/R.framework/Versions/4.1-arm64/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp'  -D_REENTRANT -DRCPP_PARALLEL_USE_TBB=1   -I/opt/R/arm64/include   -fPIC  -falign-functions=64 -Wall -g -O2  -c file173201a7ce1e2.cpp -o file173201a7ce1e2.o
if test  "zfile173201a7ce1e2.o" != "z"; then \
	  echo clang++ -arch arm64 -std=c++14 -dynamiclib -Wl,-headerpad_max_install_names -undefined dynamic_lookup -single_module -multiply_defined suppress -L"/Library/Frameworks/R.framework/Resources/lib" -L/opt/R/arm64/lib -o file173201a7ce1e2.so file173201a7ce1e2.o  '/Library/Frameworks/R.framework/Versions/4.1-arm64/Resources/library/rstan/lib//libStanServices.a' -L'/Library/Frameworks/R.framework/Versions/4.1-arm64/Resources/library/StanHeaders/lib/' -lStanHeaders -L'/Library/Frameworks/R.framework/Versions/4.1-arm64/Resources/library/RcppParallel/lib/' -ltbb -ltbbmalloc -ltbbmalloc_proxy  -F/Library/Frameworks/R.framework/.. -framework R -Wl,-framework -Wl,CoreFoundation; \
	  clang++ -arch arm64 -std=c++14 -dynamiclib -Wl,-headerpad_max_install_names -undefined dynamic_lookup -single_module -multiply_defined suppress -L"/Library/Frameworks/R.framework/Resources/lib" -L/opt/R/arm64/lib -o file173201a7ce1e2.so file173201a7ce1e2.o  '/Library/Frameworks/R.framework/Versions/4.1-arm64/Resources/library/rstan/lib//libStanServices.a' -L'/Library/Frameworks/R.framework/Versions/4.1-arm64/Resources/library/StanHeaders/lib/' -lStanHeaders -L'/Library/Frameworks/R.framework/Versions/4.1-arm64/Resources/library/RcppParallel/lib/' -ltbb -ltbbmalloc -ltbbmalloc_proxy  -F/Library/Frameworks/R.framework/.. -framework R -Wl,-framework -Wl,CoreFoundation; \
	fi

stn_md> fit <- sampling(mod, data = list(y_mean = 0))
starting worker pid=95285 on localhost:11912 at 12:31:40.329
starting worker pid=95303 on localhost:11912 at 12:31:40.536
starting worker pid=95321 on localhost:11912 at 12:31:40.759
starting worker pid=95339 on localhost:11912 at 12:31:40.968

SAMPLING FOR MODEL '16a540c6086086816528e4524def24d9' NOW (CHAIN 1).
Chain 1: 
Chain 1: Gradient evaluation took 6e-06 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.06 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Iteration:    1 / 2000 [  0%]  (Warmup)
Chain 1: Iteration:  200 / 2000 [ 10%]  (Warmup)
Chain 1: Iteration:  400 / 2000 [ 20%]  (Warmup)
Chain 1: Iteration:  600 / 2000 [ 30%]  (Warmup)
Chain 1: Iteration:  800 / 2000 [ 40%]  (Warmup)
Chain 1: Iteration: 1000 / 2000 [ 50%]  (Warmup)
Chain 1: Iteration: 1001 / 2000 [ 50%]  (Sampling)
Chain 1: Iteration: 1200 / 2000 [ 60%]  (Sampling)
Chain 1: Iteration: 1400 / 2000 [ 70%]  (Sampling)
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Chain 1: 
Chain 1:  Elapsed Time: 0.003566 seconds (Warm-up)
Chain 1:                0.003553 seconds (Sampling)
Chain 1:                0.007119 seconds (Total)
Chain 1: 

SAMPLING FOR MODEL '16a540c6086086816528e4524def24d9' NOW (CHAIN 2).
Chain 2: 
Chain 2: Gradient evaluation took 6e-06 seconds
Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.06 seconds.
Chain 2: Adjust your expectations accordingly!
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Chain 2: 
Chain 2:  Elapsed Time: 0.003513 seconds (Warm-up)
Chain 2:                0.003425 seconds (Sampling)
Chain 2:                0.006938 seconds (Total)
Chain 2: 

SAMPLING FOR MODEL '16a540c6086086816528e4524def24d9' NOW (CHAIN 3).
Chain 3: 
Chain 3: Gradient evaluation took 4e-06 seconds
Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.04 seconds.
Chain 3: Adjust your expectations accordingly!
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Chain 3: 
Chain 3:  Elapsed Time: 0.003681 seconds (Warm-up)
Chain 3:                0.003235 seconds (Sampling)
Chain 3:                0.006916 seconds (Total)
Chain 3: 

SAMPLING FOR MODEL '16a540c6086086816528e4524def24d9' NOW (CHAIN 4).
Chain 4: 
Chain 4: Gradient evaluation took 4e-06 seconds
Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.04 seconds.
Chain 4: Adjust your expectations accordingly!
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Chain 4: 
Chain 4:  Elapsed Time: 0.003264 seconds (Warm-up)
Chain 4:                0.003079 seconds (Sampling)
Chain 4:                0.006343 seconds (Total)
Chain 4: 

stn_md> fit2 <- sampling(mod, data = list(y_mean = 5))
starting worker pid=95364 on localhost:11912 at 12:31:42.936
starting worker pid=95383 on localhost:11912 at 12:31:43.155
starting worker pid=95401 on localhost:11912 at 12:31:43.364
starting worker pid=95419 on localhost:11912 at 12:31:43.577

SAMPLING FOR MODEL '16a540c6086086816528e4524def24d9' NOW (CHAIN 1).
Chain 1: 
Chain 1: Gradient evaluation took 5e-06 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.05 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
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Chain 1: 
Chain 1:  Elapsed Time: 0.00335 seconds (Warm-up)
Chain 1:                0.003263 seconds (Sampling)
Chain 1:                0.006613 seconds (Total)
Chain 1: 

SAMPLING FOR MODEL '16a540c6086086816528e4524def24d9' NOW (CHAIN 2).
Chain 2: 
Chain 2: Gradient evaluation took 4e-06 seconds
Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.04 seconds.
Chain 2: Adjust your expectations accordingly!
Chain 2: 
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Chain 2: 
Chain 2:  Elapsed Time: 0.003559 seconds (Warm-up)
Chain 2:                0.003296 seconds (Sampling)
Chain 2:                0.006855 seconds (Total)
Chain 2: 

SAMPLING FOR MODEL '16a540c6086086816528e4524def24d9' NOW (CHAIN 3).
Chain 3: 
Chain 3: Gradient evaluation took 5e-06 seconds
Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.05 seconds.
Chain 3: Adjust your expectations accordingly!
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Chain 3: 
Chain 3:  Elapsed Time: 0.004006 seconds (Warm-up)
Chain 3:                0.003909 seconds (Sampling)
Chain 3:                0.007915 seconds (Total)
Chain 3: 

SAMPLING FOR MODEL '16a540c6086086816528e4524def24d9' NOW (CHAIN 4).
Chain 4: 
Chain 4: Gradient evaluation took 1.3e-05 seconds
Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.13 seconds.
Chain 4: Adjust your expectations accordingly!
Chain 4: 
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Chain 4: 
Chain 4:  Elapsed Time: 0.004561 seconds (Warm-up)
Chain 4:                0.003347 seconds (Sampling)
Chain 4:                0.007908 seconds (Total)
Chain 4: 
2 Likes

Here’s the output of sessionInfo():

> sessionInfo()
R version 4.1.0 alpha (2021-04-26 r80229)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS Big Sur 11.3.1

Matrix products: default
LAPACK: /Library/Frameworks/R.framework/Versions/4.1-arm64/Resources/lib/libRlapack.dylib

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

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

other attached packages:
[1] rstan_2.21.2         ggplot2_3.3.3        StanHeaders_2.21.0-7

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.6         pillar_1.6.0       compiler_4.1.0     prettyunits_1.1.1  tools_4.1.0        pkgbuild_1.2.0    
 [7] jsonlite_1.7.2     lifecycle_1.0.0    tibble_3.1.1       gtable_0.3.0       pkgconfig_2.0.3    rlang_0.4.11      
[13] DBI_1.1.1          cli_2.5.0          curl_4.3.1         parallel_4.1.0     xfun_0.22          loo_2.4.1         
[19] gridExtra_2.3      withr_2.4.2        dplyr_1.0.6        knitr_1.33         generics_0.1.0     vctrs_0.3.8       
[25] stats4_4.1.0       grid_4.1.0         tidyselect_1.1.1   glue_1.4.2         inline_0.3.17      R6_2.5.0          
[31] processx_3.5.2     fansi_0.4.2        purrr_0.3.4        callr_3.7.0        magrittr_2.0.1     codetools_0.2-18  
[37] matrixStats_0.58.0 scales_1.1.1       ps_1.6.0           ellipsis_0.3.2     assertthat_0.2.1   colorspace_2.0-1  
[43] V8_3.4.2           utf8_1.2.1         RcppParallel_5.1.4 munsell_0.5.0      crayon_1.4.1    
1 Like