[R] slow when function returning R6 object #539
Comments
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I think |
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The problem is reproducible after restarting the machine. |
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@BlindApe |
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BTW, |
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@BlindApe label must be a vector like in xgboost. |
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@guolinke , @Laurae2 , thank for your support. As you see this code takes 1 minute with a matrix of 20 columns, nearly 5 minutes with 100 columns and an eternity (I'm still waiting a crash) with 1300 columns. This is the sessionInfo(): locale: attached base packages: other attached packages: loaded via a namespace (and not attached): It's a dual xeon server with 2 x E5-2690 v2 and 256 Gb RAM. Code:
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With xgboost (dataset creation with 1300 columns takes a while, but setinfo is a blink):
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I can't reproduce it. I am getting this: > library(R.utils)
> library(lightgbm)
> data <- matrix(1, 4000000, 20)
> label <- rep(0, 4000000)
> train <- lgb.Dataset(data = data, label = label)
> Laurae::timer_func_print(setinfo(train, 'label', label))
The function ran in 2.001 milliseconds.
[1] 2.000977> library(R.utils)
> library(lightgbm)
> data <- matrix(1, 4000000, 100)
> label <- rep(0, 4000000)
> label2 <- rep(1, 4000000)
> train <- lgb.Dataset(data = data, label = label)
> Laurae::timer_func_print(setinfo(train, 'label', label2))
The function ran in 3.004 milliseconds.
[1] 3.00415From a fully fresh session: > library(R.utils)
Loading required package: R.oo
Loading required package: R.methodsS3
R.methodsS3 v1.7.1 (2016-02-15) successfully loaded. See ?R.methodsS3 for help.
R.oo v1.21.0 (2016-10-30) successfully loaded. See ?R.oo for help.
Attaching package: ‘R.oo’
The following objects are masked from ‘package:methods’:
getClasses, getMethods
The following objects are masked from ‘package:base’:
attach, detach, gc, load, save
R.utils v2.5.0 (2016-11-07) successfully loaded. See ?R.utils for help.
Attaching package: ‘R.utils’
The following object is masked from ‘package:RevoMods’:
timestamp
The following object is masked from ‘package:utils’:
timestamp
The following objects are masked from ‘package:base’:
cat, commandArgs, getOption, inherits, isOpen, parse, warnings
> library(lightgbm)
Loading required package: R6
Warning message:
package ‘R6’ was built under R version 3.3.3
> data <- matrix(1, 4000000, 100)
> label <- rep(0, 4000000)
> label2 <- rep(1, 4000000)
> train <- lgb.Dataset(data = data)
> Laurae::timer_func_print(setinfo(train, 'label', label2))
The function ran in 1.001 milliseconds.
[1] 1.000977Using a laptop here. Did I miss something from your script? |
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The problem is setinfo after lgb.Dataset. Doing data and label at same time isn't problem:
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@BlindApe My last example was done without doing data and label at the same time. It is still taking about 1 millisecond. However, when I replace all 1s by 0s, I get the issue you mentioned (but it takes only 10 seconds here, not 5 minutes). @guolinke any reason why it is slower when using 0s instead of 1s? It seems to duplicate the data also, which duplicates the memory used. |
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This is in other server, running different R version under Windows:
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Can you change |
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I was doing:
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@BlindApe |
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@BlindApe |
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Seems the problem isn't in as.numeric(). In linux server:
In Windows server:
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Time is proportional to rows and columns, and the behavior is the same both in Windows and Linux
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81.27s: require(lightgbm)
data <- matrix(1, 4000000, 20)
label <- rep(0, 4000000)
tic = proc.time()[3]
label <- as.numeric(label)
cat(proc.time()[3] - tic, 'secs', '\n')
print(typeof(label))
print(head(label))
train <- lgb.Dataset(data = data)
tic = proc.time()[3]
train$setinfo('label', label)
cat(proc.time()[3] - tic, 'secs', '\n') // 81.27 secs but running this is really strange as it does exactly the same thing but faster!:
The behavior is really strange when out of a function and running in the global environment. |
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I think the message I obtained first time I tried with 1152 columns could give a tip: Error in paste(as.character(obj), collapse = " ") : It's like internally all data were pasted in some way... |
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@Laurae2 @BlindApe And it calls |
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I ran this: library(lightgbm)
data <- matrix(1, 4000000, 20)
label <- rep(1, 4000000)
train <- lgb.Dataset(data = data)
debugonce(train$setinfo)
train$setinfo("label", label)It seems there is a problem with This post shows an issue with assignments on R6 objects and type checking leading to 30x slower assignments on small stuff: https://cran.r-project.org/web/packages/R6/vignettes/Performance.html |
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@Laurae2 I've reproduced the behaviour doing inside a naive function:
So a temporary work around is call it inside a function. |
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@Laurae2 What is the state of this issue now ? |
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@guolinke Currently can't seem to fix this, I managed to delay the issue but this is not a good workaround. |
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@BlindApe Your/Our workaround works until we print the lgb.unloader(wipe = TRUE)
require(lightgbm)
nothing <- function() {
data <- matrix(1, 4000000, 20)
label <- rep(0, 4000000)
tic = proc.time()[3]
label <- as.numeric(label)
cat(proc.time()[3] - tic, 'secs', '\n')
print(typeof(label))
print(head(label))
train <- lgb.Dataset(data = data)
tic = proc.time()[3]
train$setinfo('label', label)
cat(proc.time()[3] - tic, 'secs', '\n')
return(train)
}
train <- nothing() # FAST
train # SLOW |
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@Laurae2 |
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@guolinke The cause is in the screenshot:
I have no idea what this In RStudio, doing this reproduces exactly the issue: library(profvis)
profvis({
library(lightgbm)
data <- matrix(1, 4000000, 20)
label <- rep(1, 4000000)
train <- lgb.Dataset(data = data)
train$setinfo("label", label)
print(train)
}) |
guolinke
changed the title
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I think this https://stat.ethz.ch/R-manual/R-devel/library/base/html/invisible.html may can solve this issue. |
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@guolinke the issue is still here when returning invisibly. I start to think it is the fault of the environment / R6 object, and not of LightGBM. The print function is 100% unrelated to LightGBM, even though LightGBM seems to create the issue. |
guolinke
changed the title
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@Laurae2 after using |
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@BlindApe can you try the latest code and see what happen ? |
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@guolinke It defers the issue when printing. We may add a warning on docs about it. |
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close since it is R6`s bug. |
BlindApe commentedMay 22, 2017
I'll give a detailed enviroment sheet and a reproducible example, but meanwhile this is the problem:
level1 has 4214186 rows and 1152 columns.
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