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Parallelize sampling for gen_samples and q22 with multiple repetitions #36

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1cdbf75
parallelize sampling from multivariate normal distributions with mult…
mawilson1234 Apr 12, 2023
6b1b3f4
remove unnecessary preallocation in warp3 after parallelization
mawilson1234 Apr 12, 2023
c3b72ee
fix missing args to parallel::mclapply
mawilson1234 Apr 12, 2023
ea94a97
only use parallelization if repetitions > 1 and cores > 1
mawilson1234 Apr 12, 2023
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18 changes: 10 additions & 8 deletions R/bridge_sampler.R
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Original file line number Diff line number Diff line change
Expand Up @@ -263,9 +263,10 @@ bridge_sampler.stanfit <- function(samples = NULL, stanfit_model = samples,
param_types = rep("real", ncol(samples_4_fit)),
transTypes = transTypes,
repetitions = repetitions, cores = cores,
packages = "rstan", maxiter = maxiter, silent = silent,
verbose = verbose,
r0 = 0.5, tol1 = 1e-10, tol2 = 1e-4))
packages = c("rstan", "mvtnorm"),
maxiter = maxiter, silent = silent,
verbose = verbose, r0 = 0.5, tol1 = 1e-10,
tol2 = 1e-4))
} else {
bridge_output <- do.call(what = paste0(".bridge.sampler.", method),
args = list(samples_4_fit = samples_4_fit,
Expand All @@ -278,9 +279,10 @@ bridge_sampler.stanfit <- function(samples = NULL, stanfit_model = samples,
transTypes = transTypes,
repetitions = repetitions, varlist = "stanfit",
envir = sys.frame(sys.nframe()),
cores = cores, packages = "rstan", maxiter = maxiter,
silent = silent, verbose = verbose,
r0 = 0.5, tol1 = 1e-10, tol2 = 1e-4))
cores = cores, packages = c("rstan", "mvtnorm"),
maxiter = maxiter, silent = silent,
verbose = verbose, r0 = 0.5, tol1 = 1e-10,
tol2 = 1e-4))
}

return(bridge_output)
Expand Down Expand Up @@ -490,7 +492,7 @@ bridge_sampler.stanreg <-
bridge_output <- bridge_sampler(samples = samples, log_posterior = .stan_log_posterior,
data = list(stanfit = sf), lb = lb, ub = ub,
repetitions = repetitions, method = method, cores = cores,
use_neff = use_neff, packages = "rstan",
use_neff = use_neff, packages = c("rstan", "mvtnorm"),
maxiter = maxiter, silent = silent,
verbose = verbose)
} else {
Expand All @@ -500,7 +502,7 @@ bridge_sampler.stanreg <-
repetitions = repetitions, varlist = "stanfit",
envir = sys.frame(sys.nframe()), method = method,
cores = cores, use_neff = use_neff,
packages = "rstan", maxiter = maxiter,
packages = c("rstan", "mvtnorm"), maxiter = maxiter,
silent = silent, verbose = verbose)
}
return(bridge_output)
Expand Down
45 changes: 38 additions & 7 deletions R/bridge_sampler_normal.R
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Expand Up @@ -38,12 +38,15 @@

# sample from multivariate normal distribution and evaluate for posterior samples and generated samples
q12 <- dmvnorm(samples_4_iter, mean = m, sigma = V, log = TRUE)
gen_samples <- vector(mode = "list", length = repetitions)
q22 <- vector(mode = "list", length = repetitions)
for (i in seq_len(repetitions)) {
gen_samples[[i]] <- rmvnorm(n_post, mean = m, sigma = V)
colnames(gen_samples[[i]]) <- colnames(samples_4_iter)
q22[[i]] <- dmvnorm(gen_samples[[i]], mean = m, sigma = V, log = TRUE)
if (cores == 1 || repetitions == 1) {
# if cores > 1 and repetitions > 1 we do this below using parallelization
gen_samples <- vector(mode = "list", length = repetitions)
q22 <- vector(mode = "list", length = repetitions)
for (i in seq_len(repetitions)) {
gen_samples[[i]] <- rmvnorm(n_post, mean = m, sigma = V)
colnames(gen_samples[[i]]) <- colnames(samples_4_iter)
q22[[i]] <- dmvnorm(gen_samples[[i]], mean = m, sigma = V, log = TRUE)
}
}

# evaluate log of likelihood times prior for posterior samples and generated samples
Expand All @@ -57,6 +60,19 @@
}
} else if (cores > 1) {
if ( .Platform$OS.type == "unix") {
# sample from multivariate normal distribution and evaluate for posterior samples and generated samples
if (repetitions > 1) {
gen_samples <- parallel::mclapply(seq_len(repetitions), FUN =
function(x) rmvnorm(n_post, mean = m, sigma = V),
mc.preschedule = FALSE,
mc.cores = cores)
sapply(seq_along(gen_samples), function(i) colnames(gen_samples[[i]]) <- colnames(samples_4_iter))
q22 <- parallel::mclapply(seq_along(gen_samples), FUN =
function(i) dmvnorm(gen_samples[[i]], mean = m, sigma = V, log = TRUE),
mc.preschedule = FALSE,
mc.cores = cores)
}

split1 <- .split_matrix(matrix=.invTransform2Real(samples_4_iter, lb, ub, param_types), cores=cores)
q11 <- parallel::mclapply(split1, FUN =
function(x) apply(x, 1, log_posterior, data = data, ...),
Expand All @@ -73,8 +89,23 @@
}
} else {
cl <- parallel::makeCluster(cores, useXDR = FALSE)
on.exit(parallel::stopCluster(cl))
sapply(packages, function(x) parallel::clusterCall(cl = cl, "require", package = x,
character.only = TRUE))

if (repetitions > 1) {
# sample from multivariate normal distribution and evaluate for posterior samples and generated samples
parallel::clusterExport(cl = cl, varlist = c("m", "V", "n_post"), envir = environment())
gen_samples <- parallel::clusterApplyLB(cl = cl,
x = seq_len(repetitions),
fun = function(i) rmvnorm(n_post, mean = m, sigma = V))
sapply(seq_along(gen_samples), function(i) colnames(gen_samples[[i]]) <- colnames(samples_4_iter))
parallel::clusterExport(cl = cl, varlist = "gen_samples", envir = environment())
q22 <- parallel::clusterApplyLB(cl = cl,
x = seq_along(gen_samples),
fun = function(i) dmvnorm(gen_samples[[i]], mean = m, sigma = V, log = TRUE))
}

parallel::clusterExport(cl = cl, varlist = varlist, envir = envir)

if ( ! is.null(rcppFile)) {
Expand All @@ -91,7 +122,7 @@
q21[[i]] <- parallel::parRapply(cl = cl, x = .invTransform2Real(gen_samples[[i]], lb, ub, param_types), log_posterior,
data = data, ...) + .logJacobian(gen_samples[[i]], transTypes, lb, ub)
}
parallel::stopCluster(cl)
# parallel::stopCluster(cl)
}
}
if(verbose) {
Expand Down
50 changes: 39 additions & 11 deletions R/bridge_sampler_warp3.R
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Original file line number Diff line number Diff line change
Expand Up @@ -39,21 +39,23 @@

# sample from multivariate normal distribution and evaluate for posterior samples and generated samples
q12 <- dmvnorm((samples_4_iter - matrix(m, nrow = n_post, ncol = length(m), byrow = TRUE)) %*%
t(solve(L)), sigma = diag(ncol(samples_4_fit)), log = TRUE)
q22 <- vector(mode = "list", length = repetitions)
gen_samples <- vector(mode = "list", length = repetitions)
for (i in seq_len(repetitions)) {
gen_samples[[i]] <- rmvnorm(n_post, sigma = diag(ncol(samples_4_fit)))
colnames(gen_samples[[i]]) <- colnames(samples_4_iter)
q22[[i]] <- dmvnorm(gen_samples[[i]], sigma = diag(ncol(samples_4_fit)), log = TRUE)
t(solve(L)), sigma = diag(ncol(samples_4_fit)), log = TRUE)
if (cores == 1 || repetitions == 1) {
# if cores > 1 or repetitions > 1, we do this below using parallelization
q22 <- vector(mode = "list", length = repetitions)
gen_samples <- vector(mode = "list", length = repetitions)
for (i in seq_len(repetitions)) {
gen_samples[[i]] <- rmvnorm(n_post, sigma = diag(ncol(samples_4_fit)))
colnames(gen_samples[[i]]) <- colnames(samples_4_iter)
q22[[i]] <- dmvnorm(gen_samples[[i]], sigma = diag(ncol(samples_4_fit)), log = TRUE)
}
}

e <- as.brob( exp(1) )

# evaluate log of likelihood times prior for posterior samples and generated samples
q21 <- vector(mode = "list", length = repetitions)
if (cores == 1) {

q11 <- log(e^(apply(.invTransform2Real(samples_4_iter, lb, ub, param_types), 1, log_posterior,
data = data,...) + .logJacobian(samples_4_iter, transTypes, lb, ub)) +
e^(apply(.invTransform2Real(matrix(2*m, nrow = n_post, ncol = length(m), byrow = TRUE) -
Expand All @@ -72,6 +74,18 @@
}
} else if (cores > 1) {
if ( .Platform$OS.type == "unix") {
if (repetitions > 1) {
gen_samples <- parallel::mclapply(seq_len(repetitions), FUN =
function(x) rmvnorm(n_post, sigma = diag(ncol(samples_4_fit))),
mc.preschedule = FALSE,
mc.cores = cores)
sapply(seq_along(gen_samples), function(i) colnames(gen_samples[[i]]) <- colnames(samples_4_iter))
q22 <- parallel::mclapply(seq_along(gen_samples), FUN =
function(i) dmvnorm(gen_samples[[i]], sigma = diag(ncol(samples_4_fit)), log = TRUE),
mc.preschedule = FALSE,
mc.cores = cores)
}

split1a <- .split_matrix(matrix=.invTransform2Real(samples_4_iter, lb, ub, param_types), cores=cores)
split1b <- .split_matrix(matrix=.invTransform2Real(
matrix(2*m, nrow = n_post, ncol = length(m), byrow = TRUE) - samples_4_iter, lb, ub, param_types
Expand Down Expand Up @@ -113,9 +127,23 @@
tmp_mat2, transTypes, lb, ub)))
}
} else {
# sample from multivariate normal distribution and evaluate for posterior samples and generated samples
cl <- parallel::makeCluster(cores, useXDR = FALSE)
on.exit(parallel::stopCluster(cl))
sapply(packages, function(x) parallel::clusterCall(cl = cl, "require", package = x, character.only = TRUE))


if (repetitions > 1) {
parallel::clusterExport(cl = cl, varlist = c("n_post", "samples_4_fit"), envir = environment())
gen_samples <- parallel::clusterApplyLB(cl = cl,
x = seq_len(repetitions),
fun = function(x) rmvnorm(n_post, sigma = diag(ncol(samples_4_fit))))
sapply(seq_along(gen_samples), function(i) colnames(gen_samples[[i]]) <- colnames(samples_4_iter))
parallel::clusterExport(cl = cl, varlist = "gen_samples", envir = environment())
q22 <- parallel::clusterApplyLB(cl = cl,
x = seq_along(gen_samples),
fun = function(i) dmvnorm(gen_samples[[i]], sigma = diag(ncol(samples_4_fit)), log = TRUE))
}

parallel::clusterExport(cl = cl, varlist = varlist, envir = envir)

if ( ! is.null(rcppFile)) {
Expand Down Expand Up @@ -152,7 +180,7 @@
.logJacobian(matrix(m, nrow = n_post, ncol = length(m), byrow = TRUE) +
gen_samples[[i]] %*% t(L), transTypes, lb, ub)))
}
parallel::stopCluster(cl)
# parallel::stopCluster(cl)
}

}
Expand Down