PLN-team
ELBO Diverged During Optimization with PLNnetwork and Torch Backend
Problem Description
When using PLNnetwork() with the torch backend on the barents dataset, I encounter an ELBO divergence error during the optimization procedure.
Error Message
Error in (function (data, params, config) :
The ELBO diverged during the optimization procedure.
Consider using:
* a different optimizer (current optimizer: RPROP)
* a smaller learning rate (current rate: 0.100)
with `control = PLN_param(backend = 'torch', config_optim = list(algorithm = ..., lr = ...))`
Minimal Reproducible Example
library(PLNmodels)
library(data.table)
library(glmnet)
set.seed(1)
data("barents", package = "PLNmodels")
task.dt <- as.data.table(t(barents$Abundance))
Y <- as.matrix(task.dt)
sample_names <- paste0("s", seq_len(nrow(Y)))
rownames(Y) <- sample_names
prepared_data <- prepare_data(counts = Y,
covariates = data.frame(row.names = sample_names))
input_cols <- colnames(Y)[1:(ncol(Y)-1)]
output_col <- colnames(Y)[ncol(Y)]
fit <- PLNnetwork(
Abundance ~ 1,
data = prepared_data,
penalties = 0.1,
control = PLNnetwork_param(
backend = "torch"
)
)
Environment Information
- R version: 4.5.0 (2025-04-11)
- Platform: aarch64-apple-darwin20
- Operating System: macOS Sequoia 15.5
- PLNmodels version: 1.2.2
- torch version: 0.14.2
This happens when I log1p transform the abundance data before the fit. Ideally, both cases (raw count and log transformed data) should work.
library(PLNmodels)
library(data.table)
library(glmnet)
set.seed(1)
data("barents", package = "PLNmodels")
task.dt <- as.data.table(t(barents$Abundance))
task.dt[, names(task.dt) := lapply(.SD, log1p)]
Y <- as.matrix(task.dt)
sample_names <- paste0("s", seq_len(nrow(Y)))
rownames(Y) <- sample_names
prepared_data <- prepare_data(counts = Y,
covariates = data.frame(row.names = sample_names))
input_cols <- colnames(Y)[1:(ncol(Y)-1)]
output_col <- colnames(Y)[ncol(Y)]
fit <- PLNnetwork(
Abundance ~ 1,
data = prepared_data,
penalties = 0.1,
control = PLNnetwork_param(
backend = "torch"
)
)
Output
Initialization...
Adjusting 1 PLN with sparse inverse covariance estimation
Joint optimization alternating gradient descent and graphical-lasso
Error in torch_tensor_cpp(data, dtype, device, requires_grad, pin_memory) :
R type not handled
Dear user
Thank you for your interest in PLNModels. I would like to point out that the PLNmodels is built for count data as it relies heavily on the Poisson distribution and takes great care to preserve the count nature of the data. You can provide real-valued matrix to the PLN*() functions as we don't check for it but that is not the intended use.
If you want to build a network after log-transforming the data, you can use a number of methods that clr or log transform abundance data (SPRING, Magma, SPIEC-EASI, etc) many of which are available in NetCoMi.
@mahendra-mariadassou That is true. But in this code, I used just count data -> https://github.com/PLN-team/PLNmodels/issues/155#issue-3249048537
I was indeed a bit hasty. Let me investigate and get back to you.
Ok, I fixed the type problem, but I still experience convergence problem so I am reopening.
- lowering the learning rate for Rprop helps
- but the overall optimization fails
So I need to further investigate.
This might be a combination of RPROP not being appropriate in this case and the learning rate being too high. It seems to work with ADAM and a small learning rate (using the dev version of the package).
library(PLNmodels)
#> This is package 'PLNmodels' version 1.2.2-9100
#> Use future::plan(multicore/multisession) to speed up PLNPCA/PLNmixture/stability_selection.
set.seed(1)
data("barents", package = "PLNmodels")
fit <- PLNnetwork(
Abundance ~ 1,
data = barents,
penalties = 0.1,
control = PLNnetwork_param(
backend = "torch",
config_optim = list(
algorithm = "ADAM",
lr = 0.01)
)
)
#>
#> Initialization...
#> Adjusting 1 PLN with sparse inverse covariance estimation
#> Joint optimization alternating gradient descent and graphical-lasso
#> sparsifying penalty = 0.1
#> Post-treatments
#> DONE!
fit$convergence
#> param nb_param objective iterations status backend convergence
#> 1 0.1 156 5068.79 20 3 torch 0.0001701754
Created on 2025-07-24 with reprex v2.1.1
Everything is working on my side.
See below for the version of CUDA, cudnn that I am using, and other session information.
> library(PLNmodels)
> packageVersion("PLNmodels")
[1] ‘1.2.2.9100’
> library(PLNmodels)
> library(data.table)
data.table 1.17.6 using 10 threads (see ?getDTthreads). Latest news: r-datatable.com
> library(glmnet)
Loading required package: Matrix
Loaded glmnet 4.1-9
> packageVersion("PLNmodels")
[1] ‘1.2.2.9100’
> set.seed(1)
> data("barents", package = "PLNmodels")
> task.dt <- as.data.table(t(barents$Abundance))
> task.dt[, names(task.dt) := lapply(.SD, log1p)]
> Y <- as.matrix(task.dt)
> sample_names <- paste0("s", seq_len(nrow(Y)))
> rownames(Y) <- sample_names
> prepared_data <- prepare_data(counts = Y,
+ covariates = data.frame(row.names = sample_names))
> input_cols <- colnames(Y)[1:(ncol(Y)-1)]
> output_col <- colnames(Y)[ncol(Y)]
>
> fit_nlopt <- PLNnetwork(
+ Abundance ~ 1,
+ data = prepared_data,
+ penalties = 0.1,
+ control = PLNnetwork_param(
+ backend = "nlopt"
+ )
+ )
Initialization...
Adjusting 1 PLN with sparse inverse covariance estimation
Joint optimization alternating gradient descent and graphical-lasso
sparsifying penalty = 0.1
Post-treatments
DONE!
> fit_torch <- PLNnetwork(
+ Abundance ~ 1,
+ data = prepared_data,
+ penalties = 0.1,
+ control = PLNnetwork_param(
+ backend = "torch",
+ config_optim = list(
+ algorithm = "ADAM",
+ lr = 0.01)
+ )
+ )
Initialization...
Adjusting 1 PLN with sparse inverse covariance estimation
Joint optimization alternating gradient descent and graphical-lasso
sparsifying penalty = 0.1
Post-treatments
DONE!
>
> fit_torch$convergence
param nb_param objective iterations status backend convergence
1 0.1 1492 2628.201 20 3 torch 3.648069e-05
> fit_nlopt$convergence
param nb_param status backend objective iterations convergence
1 0.1 1544 3 nlopt 2511.806 20 0.0001000882
> plot(fit_nlopt, "diagnostic")
> plot(fit_torch, "diagnostic")
> fit_torch_rprop <- PLNnetwork(
+ Abundance ~ 1,
+ data = prepared_data,
+ penalties = 0.1,
+ control = PLNnetwork_param(
+ backend = "torch",
+ config_optim = list(
+ algorithm = "RPROP",
+ lr = 0.01)
+ )
+ )
Initialization...
Adjusting 1 PLN with sparse inverse covariance estimation
Joint optimization alternating gradient descent and graphical-lasso
sparsifying penalty = 0.1
Post-treatments
DONE!
>
> torch::cuda_current_device()
[1] 0
> torch::cuda_is_available()
[1] TRUE
> torch::backends_cudnn_version()
[1] ‘90.1.0’
> torch::cuda_runtime_version()
[1] ‘12.4.0’
> fit_nlopt$convergence
param nb_param status backend objective iterations convergence
1 0.1 1544 3 nlopt 2511.806 20 0.0001000882
> fit_torch$convergence
param nb_param objective iterations status backend convergence
1 0.1 1492 2628.201 20 3 torch 3.648069e-05
> fit_torch_rprop$convergence
param nb_param objective iterations status backend convergence
1 0.1 1011 2627.833 20 3 torch 0.000332811
> sessionInfo()
R version 4.5.1 (2025-06-13)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 24.04.2 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so; LAPACK version 3.12.0
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=fr_FR.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=fr_FR.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=fr_FR.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=fr_FR.UTF-8 LC_IDENTIFICATION=C
time zone: Europe/Paris
tzcode source: system (glibc)
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] glmnet_4.1-9 Matrix_1.7-3 data.table_1.17.6
[4] PLNmodels_1.2.2-9100
loaded via a namespace (and not attached):
[1] future_1.58.0 generics_0.1.4 tidyr_1.3.1
[4] shape_1.4.6.1 lattice_0.22-7 listenv_0.9.1
[7] digest_0.6.37 magrittr_2.0.3 grid_4.5.1
[10] RColorBrewer_1.1-3 iterators_1.0.14 foreach_1.5.2
[13] processx_3.8.6 survival_3.8-3 torch_0.15.0
[16] ps_1.9.1 gridExtra_2.3 purrr_1.0.4
[19] glassoFast_1.0.1 scales_1.4.0 coro_1.1.0
[22] codetools_0.2-20 cli_3.6.5 rlang_1.1.6
[25] parallelly_1.45.0 future.apply_1.20.0 splines_4.5.1
[28] bit64_4.6.0-1 withr_3.0.2 corrplot_0.95
[31] tools_4.5.1 parallel_4.5.1 nloptr_2.2.1
[34] dplyr_1.1.4 ggplot2_3.5.2 globals_0.18.0
[37] vctrs_0.6.5 R6_2.6.1 lifecycle_1.0.4
[40] bit_4.6.0 MASS_7.3-65 pkgconfig_2.0.3
[43] callr_3.7.6 pillar_1.11.0 gtable_0.3.6
[46] glue_1.8.0 Rcpp_1.1.0 tibble_3.3.0
[49] tidyselect_1.2.1 rstudioapi_0.17.1 farver_2.1.2
[52] igraph_2.1.4 labeling_0.4.3 pscl_1.5.9
[55] compiler_4.5.1
>
@mahendra-mariadassou , Daniel (a Toby Hocking's PhD student I am working with on PLN) is using Mac OS: do you use mac too, by any chance?
It would explain the common error (probably the equivalent of CUDA/torch library on MacOS use different version of RPROP/ADAM).
thanks.
J.