This vignette demonstrates how to use the new profiling functionality introduced in CmdStan 2.26.0.
Profiling identifies which parts of a Stan program are taking the longest time to run and is therefore a useful guide when working on optimizing the performance of a model.
However, be aware that the statistical assumptions that go into a model are the most important factors in overall model performance. It is often not possible to make up for model problems with just brute force computation. For ideas on how to address performance of your model from a statistical perspective, see Gelman (2020).
library(cmdstanr) check_cmdstan_toolchain(fix = TRUE, quiet = TRUE)
Consider a simple logistic regression with parameters alpha
and beta
,
covariates X
, and outcome y
.
data { int<lower=1> k; int<lower=0> n; matrix[n, k] X; array[n] int y; } parameters { vector[k] beta; real alpha; } model { beta ~ std_normal(); alpha ~ std_normal(); y ~ bernoulli_logit(X * beta + alpha); }
A simple question is how much time do the prior calculations take compared
against the likelihood? To answer this we surround the prior and likelihood
calculations with profile
statements.
profile("priors") { target += std_normal_lpdf(beta); target += std_normal_lpdf(alpha); } profile("likelihood") { target += bernoulli_logit_lpmf(y | X * beta + alpha); }
In general we recommend using a separate .stan
file, but for convenience in
this vignette we'll write the Stan program as a string and use
write_stan_file()
to write it to a temporary file.
profiling_bernoulli_logit <- write_stan_file(' data { int<lower=1> k; int<lower=0> n; matrix[n, k] X; array[n] int y; } parameters { vector[k] beta; real alpha; } model { profile("priors") { target += std_normal_lpdf(beta); target += std_normal_lpdf(alpha); } profile("likelihood") { target += bernoulli_logit_lpmf(y | X * beta + alpha); } } ')
We can then run the model as usual and Stan will collect the profiling
information for any sections with profile
statements.
# Compile the model model <- cmdstan_model(profiling_bernoulli_logit) # Generate some fake data n <- 1000 k <- 20 X <- matrix(rnorm(n * k), ncol = k) y <- 3 * X[,1] - 2 * X[,2] + 1 p <- runif(n) y <- ifelse(p < (1 / (1 + exp(-y))), 1, 0) stan_data <- list(k = ncol(X), n = nrow(X), y = y, X = X) # Run one chain of the model fit <- model$sample(data = stan_data, chains = 1)
The raw profiling information can then be accessed with the $profiles()
method, which returns a list containing one data frame per chain (profiles
across multiple chains are not automatically aggregated). Details on the column
names are available in the
CmdStan documentation.
fit$profiles()
The total_time
column is the total time spent inside a given profile
statement. It is clear that the vast majority of time is spent in the likelihood
function.
Stan's specialized glm functions can be used to make models like this faster. In this case the likelihood can be replaced with
target += bernoulli_logit_glm_lpmf(y | X, alpha, beta);
We'll keep the same profile()
statements so that the profiling information for
the new model is collected automatically just like for the previous one.
profiling_bernoulli_logit_glm <- write_stan_file(' data { int<lower=1> k; int<lower=0> n; matrix[n, k] X; array[n] int y; } parameters { vector[k] beta; real alpha; } model { profile("priors") { target += std_normal_lpdf(beta); target += std_normal_lpdf(alpha); } profile("likelihood") { target += bernoulli_logit_glm_lpmf(y | X, alpha, beta); } } ')
model_glm <- cmdstan_model(profiling_bernoulli_logit_glm) fit_glm <- model_glm$sample(data = stan_data, chains = 1)
fit_glm$profiles()
We can see from the total_time
column that this is much faster than the
previous model.
The other columns of the profiling output are documented in the CmdStan documentation.
The timing numbers are broken down by forward pass and reverse pass, and the
chain_stack
and no_chain_stack
columns contain information about how many
autodiff variables were saved in the process of performing a calculation.
These numbers are all totals -- times are the total times over the whole
calculation, and chain_stack
counts are similarly the total counts of autodiff
variables used over the whole calculation. It is often convenient to have
per-gradient calculations (which will be more stable across runs with different
seeds). To compute these, use the autodiff_calls
column.
profile_chain_1 <- fit$profiles()[[1]] per_gradient_timing <- profile_chain_1$total_time/profile_chain_1$autodiff_calls print(per_gradient_timing) # two elements for the two profile statements in the model
After sampling (or optimization or variational inference) finishes, CmdStan stores
the profiling data in CSV files in a temporary location.
The paths of the profiling CSV files can be retrieved using $profile_files()
.
fit$profile_files()
These can be saved to a more permanent location with the $save_profile_files()
method.
# see ?save_profile_files for info on optional arguments fit$save_profile_files(dir = "path/to/directory")
Gelman, Andrew, Aki Vehtari, Daniel Simpson, Charles C. Margossian, Bob Carpenter, Yuling Yao, Lauren Kennedy, Jonah Gabry, Paul-Christian Bürkner, and Martin Modrák. 2020. "Bayesian Workflow." https://arxiv.org/abs/2011.01808.
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