[Rd] stats::fft produces inconsistent results
Andre@G||||bert @end|ng |rom chu-rouen@|r
Thu Oct 21 12:32:34 CEST 2021
> Haha, thanks : ) I guess I will probably be grouchy too if seeing so many people making the same mistakes again and again. It just happened to be me.
Fortunately, you did not get offensed. :)
This is nice to have a large community of developers for R packages, even if, sometimes, buggy packages are annoying R developers because any small change in R may "break" them even though they were actually broken from the begining.
> Indeed, I found myself often confused about when to PROTECT and when not.
A (relatively) quick explanation.
There are several �pools� of data objects that have different rules. The most common �pool� is the pool of garbage collectable R objects, that can be allocated with allocVector and is passed from R to C code and vice versa. Another pool is the malloc/free pool, that works with explicit allocation/deallocation. R does not modify the malloc/free implementation in any way, and memory leaks may happen. Operating systems may have other pools of memory (e.g. mmap'ed memory) that are not handled by R either. There is also a transient storage (R_alloc/vmaxset/vmaxget) that is automatically freed when returning from C to R, and should be used for temporary storage but not for objects returned to R code.
The PROTECT system is needed for garbage collectable objects.
The garbage collector may trigger whenever a R internal function is called. Typically, when some memory is internally allocated.
The garbage collector frees objects that are neither referenced directly nor indirectly from R code and from the PROTECT stack.
The PROTECT stack is used by C code to make sure objects that are not yet (or will never be) referenced by R code, are not destroyed when the garbage collector runs.
The functions allocating new R objects, such as allocVector(), but also coerceVector(), duplicate(),return unprotected objects, that may be destroyed the next time an internal R function is called, unless it is explicitly PROTECT'ed before. Indeed, such objects would have no reference from R code and so, would be deleted.
The PROTECT stack must be balanced on a call from R to a C function. There must be as many UNPROTECT'ions than PROTECT'ions.
The typical C code PROTECTs any object allocated as soon as it is allocated (e.g. call to allocVector or coerceVector). It UNPROTECTs temporary objects to "free" them (the actual memory release may be delayed to the next garbage collection). It UNPROTECTs the object it returns to R code. Indeed, in pure C code, there will be no garbage collection between the time the object is UNPROTECTed and the time R grabs the object. You must be very careful if you are using C++, because destructors must not call any R internal function that may trigger a garbage collection.
The arguments to the C code, do not have to be PROTECT'ed, unless they are re-allocated. For instance, it is frequent to call coerceVector or arguments and re-assign them to the C variable that represents the argument. The new object must be PROTECT'ed.
Actually, you do not need to *directly* PROTECT all objects that are allocated in the C function, but you must make sure that all objects are *indirectly* PROTECT'ed. For instance, you may allocate a VECSXP (a "list" in R) and fill the slots with newly allocated objects. You only need to PROTECT the VECSXP, since its slots are indirectly protected.
If you have any doubt, it is not a bug to over-PROTECT objects. It may slightly slow down garbage collection and use space on the PROTECTion stack, but that is rarely a big deal. You should only avoid that when that would lead to thousands or millions of protections.
As I said, the PROTECT stack must be balanced between the entry and exit of the C code. This is not a problem for 99% of functions that free all the memory they use internally except the object that is returned. Sometimes, some "background" memory, hidden to R code, may have to be allocated for more time. A call to R_PreserveObject protects the object, even after the C code returns to R, until R_ReleaseObject is called. Without an explicit call to R_ReleaseObject, memory is leaked!
There is another mechanism in R that must be known. If you call any R function from C code, or any internal R function that may fail with an error, or any internal R function that can be stopped by the user (see R_CheckUserInterrupt), then, R may call a longjmp to exit all the C code. This is very much incompatible with C++ exceptions or constructors/destructors. Rcpp can avoid, to some extent, that problem.
With C code, this means that some malloc'ed memory or allocated resources (file descriptors, sockets, etc.) may be leaked unless something is done to prevent that. All PROTECT'ed objects are automatically unprotected, so there is no problem with memory leak of garbage collectable objects. There is a R_UnwindProtect() mechanism to free temporary resources (e.g. a socket you allocated) when a longjmp is triggered. Non-memory resources (e.g. a socket) returned to R should use the R_MakeExternalPtr() mechanism to make sure that, when the memory is freed by the garbage collector, the resource is also freed.
"Writing R extensions" contains more extensive documentation, but I hope that my quick description of the system will make it easier to understand the extensive documentation.
De : Dipterix Wang <dipterix.wang using gmail.com>
Envoy� : mercredi 20 octobre 2021 22:01
� : Martin Maechler <maechler using stat.math.ethz.ch>; GILLIBERT, Andre <Andre.Gillibert using chu-rouen.fr>; bbolker using gmail.com
Cc : r-devel using r-project.org
Objet : Re: [Rd] stats::fft produces inconsistent results
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Wow, you guys are amazing!
as part of its pipeline, ravetools::mvfftw computes the mean of the
input vector **and then centers it to a mean of zero** (intentionally or
because variables are passed to compiled code by reference (someone
can feel free to correct my terminology), this means that the original
vector in R now has a mean of zero
I didn�t center the input vector in my code. The data was fed �as-is� into FFTW3. My guess is FFTW3 internally center the data. It could be that FFTW3 library behave differently on different platforms, which could explain the reproducibility issue.
"Indeed, R vectors are passed "by reference" to C code, but the semantic must be "by value", i.e. the C function must NOT change the contents of the vector, except in very specific cases.�
CRAN has already had fftw and fftwtools, the issue is the data I�m targeting at are at GB-level, copying the vectors can be memory inefficient or even use up memories. The strategy of ravetools is to import signals from local files, fft, then directly write to disk. So only one reference will be used and modifying in-place is on purpose. In fact, and the fft functions I created are not intended to be used directly by users.
However, I should've been very cautious when using these functions. This is my fault. I�ll check the whole package to make sure only one reference is used or otherwise the vectors will be copied.
This can be tested by the MAYBE_REFERENCED() macro in Rinternals.h.
Nice to learn! I�ll add it to my code.
Properly using R vectors in C code is tricky. You have to understand.
1) When you are allowed or not to modify vectors
2) When to PROTECT()vectors
3) How the garbage collector works and when it can trigger (answer : basically, when you call any internal R function)
Chapter 5 of "Writing R Extensions" documentation is quite extensive:
Indeed, I found myself often confused about when to PROTECT and when not.
... but then ravetools is not even a CRAN package, so why should you dare to use it for anything serious ?
Haha, thanks : ) I guess I will probably be grouchy too if seeing so many people making the same mistakes again and again. It just happened to be me.
But it�s good to be rigorous. Sooner or later I'll have to face these problems. It�s better to make mistakes before having made many.
- Dipterix Wang
On Oct 20, 2021, at 5:32 AM, Martin Maechler <maechler using stat.math.ethz.ch<mailto:maechler using stat.math.ethz.ch>> wrote:
on Wed, 20 Oct 2021 11:26:21 +0200 writes:
Thank you, Andr� , that's very good.
Just to state the obvious conclusion:
If Ben's suggestion is correct (and Andr� has explained *how*
that could happen) this would mean a
SEVERE BUG in package ravetools's mvfftw() function.
and it would have been (yet another) case of gaining speed by
... but then ravetools is not even a CRAN package, so why
should you dare to use it for anything serious ?
... yes, being grouchy ..
which I should rather not be.
Dipterix Wang *did* say initially that he is currently
developing ravetools so it's very reasonabl this is not yet a
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