[Numpy-discussion] Change in memmap behaviour
Mon Jul 2 12:54:39 CDT 2012
> Your actual memory usage may not have increased as much as you think,
> since memmap objects don't necessarily take much memory -- it sounds
> like you're leaking virtual memory, but your resident set size
> shouldn't go up as much.
As I understand it, memmap objects retain the contents of the memmap in memory after it has been read the first time (in a lazy manner). Thus, when reading a slice of a 24GB file, only that part recides in memory. Our system reads a slice of a memmap, calculates something (say, the sum), and then deletes the memmap. It then loops through this for consequitive slices, retaining a low memory usage. Consider the following code:
import numpy as np
res = 
vecLen = 3095677412
for i in xrange(vecLen/10**8+1):
x = i * 10**8
y = min((i+1) * 10**8, vecLen)
The memory usage of this code on a 24GB file (one value for each nucleotide in the human DNA!) is 23g resident memory after the loop is finished (not 24g for some reason..).
Running the same code on 1.5.1rc1 gives a resident memory of 23m after the loop.
> That said, this is clearly a bug, and it's even worse than you mention
> -- *all* operations on memmap arrays are holding onto references to
> the original mmap object, regardless of whether they share any memory:
>>>> a = np.memmap("/etc/passwd", np.uint8, "r")
> # arithmetic
>>>> (a + 10)._mmap is a._mmap
> # fancy indexing (doesn't return a view!)
>>>> a[[1, 2, 3]]._mmap is a._mmap
>>>> a.sum()._mmap is a._mmap
> Really, only slicing should be returning a np.memmap object at all.
> Unfortunately, it is currently impossible to create an ndarray
> subclass that returns base-class ndarrays from any operations --
> __array_finalize__() has no way to do this. And this is the third
> ndarray subclass in a row that I've looked at that wanted to be able
> to do this, so I guess maybe it's something we should implement...
> In the short term, the numpy-upstream fix is to change
> numpy.core.memmap:memmap.__array_finalize__ so that it only copies
> over the ._mmap attribute of its parent if np.may_share_memory(self,
> parent) is True. Patches gratefully accepted ;-)
Great! Any idea on whether such a patch may be included in 1.7?
> In the short term, you have a few options for hacky workarounds. You
> could monkeypatch the above fix into the memmap class. You could
> manually assign None to the _mmap attribute of offending arrays (being
> careful only to do this to arrays where you know it is safe!). And for
> reduction operations like sum() in particular, what you have right now
> is not actually a scalar object -- it is a 0-dimensional array that
> holds a single scalar. You can pull this scalar out by calling .item()
> on the array, and then throw away the array itself -- the scalar won't
> have any _mmap attribute.
> def scalarify(scalar_or_0d_array):
> if isinstance(scalar_or_0d_array, np.ndarray):
> return scalar_or_0d_array.item()
> return scalar_or_0d_array
> # works on both numpy 1.5 and numpy 1.6:
> total = scalarify(a.sum())
Thank you for this! However, such a solution would have to be scattered throughout the code (probably over 100 places), and I would rather not do that. I guess the abovementioned patch would be the best solution. I do not have experience in the numpy core code, so I am also eagerly awaiting such a patch!
PhD Student, Bioinformatics, Dept. of Tumor Biology, Inst. for Cancer Research, The Norwegian Radium Hospital, Montebello, 0310 Oslo, Norway
E-mail: email@example.com, Phone: +47 93 00 94 54
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