Open Garren-H opened 4 months ago
I bet it is a problem with paths containing space char. Can you move your location and retest?
Weird thing is that there should be automatic fix for this.
I am running the same code by only changing the value of D
in the data file. For the case when D=1
the code did not result in any errors, the above was for when D=2
. Surely it would've given the same error for when D=1
if this were the case?
yeah, true it would not make any sense
Have you tried add prints to debug what can be seen and when it crashes (or at what step)?
Surely it has to be post-processing as the fit can be reconstructed using the from_csv
method from the csv files. The thing that bugs me is the final line in the .txt output from stan, which I have not previously encountered.
double free or corruption (!prev)
Can you run the model under gdb to get a backtrace from that double free?
I saw your previous issue saying it wasn’t recreatable on another machine, is this one?
I have no idea what gdb is?
I will see if I can reproduce the error on my personal device. I do however have less cores on my personal device, so it will take longer. I will report back once this has completed.
Do these issues still occur if you remove the use of profile
from the model code? We recently fixed some issues with that functionality that could be related
I am running the model with and without profiling on the cluster (changed the stan code to remove profiles) to see if the error can be reproduced on the cluster. Will take a few hours to complete
I have ran these models and it seems that this error is not reproducible either. Both the model with and without profiling completed without errors this time around
Description:
Hi all, I have ran into another issue (on a different cluster) when running my stan code. The sampling executes as expected but afterwards an error is displayed
The stan .txt output file yields:
``` method = sample (Default) sample num_samples = 1000 (Default) num_warmup = 5000 save_warmup = 0 (Default) thin = 1 (Default) adapt engaged = 1 (Default) gamma = 0.050000 (Default) delta = 0.800000 (Default) kappa = 0.750000 (Default) t0 = 10.000000 (Default) init_buffer = 75 (Default) term_buffer = 50 (Default) window = 25 (Default) save_metric = 0 (Default) algorithm = hmc (Default) hmc engine = nuts (Default) nuts max_depth = 5 metric = dense_e metric_file = (Default) stepsize = 1.000000 (Default) stepsize_jitter = 0.000000 (Default) num_chains = 8 id = 1 (Default) data file = Subsets/Alkane_Primary alcohol/Include_clusters_False/Variance_known_True/Variance_MC_known_True/rank_2/data.json init = 2 (Default) random seed = 24898 output file = /home/22796002/Pure RK PMF/Subsets/Alkane_Primary alcohol/Include_clusters_False/Variance_known_True/Variance_MC_known_True/rank_2/Step1/Pure_PMF-20240522025118.csv diagnostic_file = (Default) refresh = 100 (Default) sig_figs = 18 profile_file = /home/22796002/Pure RK PMF/Subsets/Alkane_Primary alcohol/Include_clusters_False/Variance_known_True/Variance_MC_known_True/rank_2/Step1/Pure_PMF-20240522025118-profile.csv save_cmdstan_config = 0 (Default) num_threads = 32 (Default) Gradient evaluation took 0.021501 seconds 1000 transitions using 10 leapfrog steps per transition would take 215.01 seconds. Adjust your expectations accordingly! Gradient evaluation took 0.017354 seconds 1000 transitions using 10 leapfrog steps per transition would take 173.54 seconds. Adjust your expectations accordingly! Gradient evaluation took 0.015889 seconds 1000 transitions using 10 leapfrog steps per transition would take 158.89 seconds. Adjust your expectations accordingly! Gradient evaluation took 0.016126 seconds 1000 transitions using 10 leapfrog steps per transition would take 161.26 seconds. Adjust your expectations accordingly! Gradient evaluation took 0.027377 seconds 1000 transitions using 10 leapfrog steps per transition would take 273.77 seconds. Adjust your expectations accordingly! Gradient evaluation took 0.015305 seconds 1000 transitions using 10 leapfrog steps per transition would take 153.05 seconds. Adjust your expectations accordingly! Gradient evaluation took 0.014925 seconds 1000 transitions using 10 leapfrog steps per transition would take 149.25 seconds. Adjust your expectations accordingly! Gradient evaluation took 0.013077 seconds 1000 transitions using 10 leapfrog steps per transition would take 130.77 seconds. Adjust your expectations accordingly! Chain [1] Iteration: 1 / 6000 [ 0%] (Warmup) Chain [3] Iteration: 1 / 6000 [ 0%] (Warmup) Chain [4] Iteration: 1 / 6000 [ 0%] (Warmup) Chain [2] Iteration: 1 / 6000 [ 0%] (Warmup) Chain [5] Iteration: 1 / 6000 [ 0%] (Warmup) Chain [6] Iteration: 1 / 6000 [ 0%] (Warmup) Chain [8] Iteration: 1 / 6000 [ 0%] (Warmup) Informational Message: The current Metropolis proposal is about to be rejected because of the following issue: Exception: Exception: multi_normal_lpdf: Location parameter[1] is -nan, but must be finite! (in 'Pure_PMF.stan', line 62, column 20 to column 112) (in 'Pure_PMF.stan', line 192, column 12 to line 195, column 39) If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine, but if this warning occurs often then your model may be either severely ill-conditioned or misspecified. 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(Warm-up) 1132.12 seconds (Sampling) 6590.38 seconds (Total) Chain [8] Iteration: 6000 / 6000 [100%] (Sampling) Elapsed Time: 5458.49 seconds (Warm-up) 1133.37 seconds (Sampling) 6591.87 seconds (Total) Chain [7] Iteration: 6000 / 6000 [100%] (Sampling) Elapsed Time: 5463.18 seconds (Warm-up) 1131.45 seconds (Sampling) 6594.64 seconds (Total) Chain [3] Iteration: 6000 / 6000 [100%] (Sampling) Elapsed Time: 5466.54 seconds (Warm-up) 1131.53 seconds (Sampling) 6598.07 seconds (Total) Chain [6] Iteration: 6000 / 6000 [100%] (Sampling) Elapsed Time: 5475.86 seconds (Warm-up) 1127.31 seconds (Sampling) 6603.17 seconds (Total) Chain [1] Iteration: 6000 / 6000 [100%] (Sampling) Elapsed Time: 5485.22 seconds (Warm-up) 1123.69 seconds (Sampling) 6608.91 seconds (Total) double free or corruption (!prev) ```The python error indicates (only relevant parts included)
Reconstructing the fit from .csv files does not produce any errors
Stan code for reference
``` functions { // kernel for composition matrix Kx(vector x1, vector x2, int order) { int N = rows(x1); int M = rows(x2); matrix[N, order+1] X1; matrix[M, order+1] X2; for (i in 1:order) { X1[:,i] = x1 .^(order+2-i) - x1; X2[:,i] = x2 .^(order+2-i) - x2; } X1[:,order+1] = 1e-1 * x1 .* sqrt(1-x1) .* exp(x1); X2[:,order+1] = 1e-1 * x2 .* sqrt(1-x2) .* exp(x2); return X1 * X2'; } // kernel for Temperature matrix KT(vector T1, vector T2) { int N = rows(T1); int M = rows(T2); matrix[N, 4] TT1 = append_col(append_col(append_col(rep_vector(1.0, N), T1), 1e-2* T1.^2), 1e-4* T1.^3); matrix[M, 4] TT2 = append_col(append_col(append_col(rep_vector(1.0, M), T2), 1e-2* T2.^2), 1e-4* T2.^3); return TT1 * TT2'; } // Combined kernel matrix Kernel(vector x1, vector x2, vector T1, vector T2, int order) { return Kx(x1, x2, order) .* KT(T1, T2); } // functions for the priors of the feature matrices real ps_feature_matrices(array[] int M_slice, int start, int end, array[] matrix U_raw, array[] matrix V_raw) { real all_target = 0; for (m in start:end) { all_target += std_normal_lpdf(to_vector(U_raw[m])); all_target += std_normal_lpdf(to_vector(V_raw[m])); } return all_target; } // function for the likelihood and prior on smoothed values real ps_like(array[] int N_slice, int start, int end, vector y, matrix cov_f2_f1, matrix mu_y_y_MC, matrix L_MC_inv, vector v, array[,] int Idx_all, int M, int N_T, int N_MC, int N_C, int N_known, array[] int N_points, vector v_ARD, array[,] matrix U_raw, array[,] matrix V_raw, real v_MC) { real all_target = 0; for (i in start:end) { vector[N_MC] y_MC_pred; for(t in 1:N_T) { for (m in 1:M-1) { y_MC_pred[m+N_C*(t-1)] = dot_product(U_raw[t,m,:,Idx_all[i,1]] .* v_ARD, V_raw[t,m,:,Idx_all[i,2]]); y_MC_pred[N_C-m+1+N_C*(t-1)] = dot_product(U_raw[t,m,:,Idx_all[i,2]] .* v_ARD, V_raw[t,m,:,Idx_all[i,1]]); } y_MC_pred[M+N_C*(t-1)] = dot_product(U_raw[t,M,:,Idx_all[i,1]] .* v_ARD, U_raw[t,M,:,Idx_all[i,2]]); } all_target += -0.5 * dot_self(L_MC_inv * y_MC_pred); // GP prior if (i <= N_known) { vector[N_points[i]] y_pred = mu_y_y_MC[sum(N_points[:i-1])+1:sum(N_points[:i]),:] * y_MC_pred; matrix[N_points[i], N_points[i]] cov_y = add_diag(cov_f2_f1[sum(N_points[:i-1])+1:sum(N_points[:i]), :N_points[i]], v[i]); all_target += multi_normal_lpdf(y[sum(N_points[:i-1])+1:sum(N_points[:i])] | y_pred, cov_y); } } return all_target; } } data { int N_known; // number of known mixtures int N_unknown; // number of unknown mixtures array[N_known] int N_points; // number of experimental points per known dataset int order; // order of the compositional polynomial vector[sum(N_points)] x1; // experimental composition vector[sum(N_points)] T1; // experimental temperatures vector[sum(N_points)] y1; // experimental excess enthalpy int N_T; // number of interpolated temperatures int N_C; // number of interpolated compositions vector[N_T] T2_int; // unique temperatures to interpolate vector[N_C] x2_int; // unique compositions to interpolate realI do not know if the error might be related to the previous issue raised
Versions
cmdstan 2.34.0 hff4ab46_0 conda-forge cmdstanpy 1.2.1 pyhd8ed1ab_0 conda-forge
OS: Scientific Linux 7.9 (Nitrogen)