【Python】python-memory-management

http://deeplearning.net/software/theano/tutorial/python-memory-management.html#python-memory-management

 

Python Memory Management

One of the major challenges in writing (somewhat) large-scale Python programs is to keep memory usage at a minimum. However, managing memory in Python is easy—if you just don’t care. Python allocates memory transparently, manages objects using a reference count system, and frees memory when an object’s reference count falls to zero. In theory, it’s swell. In practice, you need to know a few things about Python memory management to get a memory-efficient program running. One of the things you should know, or at least get a good feel about, is the sizes of basic Python objects. Another thing is how Python manages its memory internally.

So let us begin with the size of basic objects. In Python, there’s not a lot of primitive data types: there are ints, longs (an unlimited precision version of ints), floats (which are doubles), tuples, strings, lists, dictionaries, and classes.

Basic Objects

What is the size of int? A programmer with a C or C++ background will probably guess that the size of a machine-specific int is something like 32 bits, maybe 64; and that therefore it occupies at most 8 bytes. But is that so in Python?

Let us first write a function that shows the sizes of objects (recursively if necessary):

import sys def show_sizeof(x, level=0): print "\t" * level, x.__class__, sys.getsizeof(x), x if hasattr(x, '__iter__'): if hasattr(x, 'items'): for xx in x.items(): show_sizeof(xx, level + 1) else: for xx in x: show_sizeof(xx, level + 1) 

We can now use the function to inspect the sizes of the different basic data types:

show_sizeof(None) show_sizeof(3) show_sizeof(2**63) show_sizeof(102947298469128649161972364837164) show_sizeof(918659326943756134897561304875610348756384756193485761304875613948576297485698417) 

If you have a 32-bit 2.7x Python, you’ll see:

8 None
12 3
22 9223372036854775808
28 102947298469128649161972364837164
48 918659326943756134897561304875610348756384756193485761304875613948576297485698417

and if you have a 64-bit 2.7x Python, you’ll see:

16 None
24 3
36 9223372036854775808
40 102947298469128649161972364837164
60 918659326943756134897561304875610348756384756193485761304875613948576297485698417

Let us focus on the 64-bit version (mainly because that’s what we need the most often in our case). None takes 16 bytes. int takes 24 bytes, three times as much memory as a C int64_t, despite being some kind of “machine-friendly” integer. Long integers (unbounded precision), used to represent integers larger than 2⁶³-1, have a minimum size of 36 bytes. Then it grows linearly in the logarithm of the integer represented.

Python’s floats are implementation-specific but seem to be C doubles. However, they do not eat up only 8 bytes:

show_sizeof(3.14159265358979323846264338327950288) 

Outputs

16 3.14159265359

on a 32-bit platform and

24 3.14159265359

on a 64-bit platform. That’s again, three times the size a C programmer would expect. Now, what about strings?

show_sizeof("") show_sizeof("My hovercraft is full of eels") 

outputs, on a 32 bit platform:

21
50 My hovercraft is full of eels

and

37
66 My hovercraft is full of eels

An empty string costs 37 bytes in a 64-bit environment! Memory used by string then linearly grows in the length of the (useful) string.

* * *

Other structures commonly used, tuples, lists, and dictionaries are worthwhile to examine. Lists (which are implemented as array lists, not as linked lists, with everything it entails) are arrays of references to Python objects, allowing them to be heterogeneous. Let us look at our sizes:

show_sizeof([]) show_sizeof([4, "toaster", 230.1]) 

outputs

32 []
44 [4, 'toaster', 230.1]

on a 32-bit platform and

72 []
96 [4, 'toaster', 230.1]

on a 64-bit platform. An empty list eats up 72 bytes. The size of an empty, 64-bit C++ std::list() is only 16 bytes, 4-5 times less. What about tuples? (and dictionaries?):

show_sizeof({}) show_sizeof({'a':213, 'b':2131}) 

outputs, on a 32-bit box

136 {}
 136 {'a': 213, 'b': 2131}
        32 ('a', 213)
                22 a
                12 213
        32 ('b', 2131)
                22 b
                12 2131

and

280 {}
 280 {'a': 213, 'b': 2131}
        72 ('a', 213)
                38 a
                24 213
        72 ('b', 2131)
                38 b
                24 2131

for a 64-bit box.

This last example is particularly interesting because it “doesn’t add up.” If we look at individual key/value pairs, they take 72 bytes (while their components take 38+24=62 bytes, leaving 10 bytes for the pair itself), but the dictionary takes 280 bytes (rather than a strict minimum of 144=72×2 bytes). The dictionary is supposed to be an efficient data structure for search and the two likely implementations will use more space that strictly necessary. If it’s some kind of tree, then we should pay the cost of internal nodes that contain a key and two pointers to children nodes; if it’s a hash table, then we must have some room with free entries to ensure good performance.

The (somewhat) equivalent std::map C++ structure takes 48 bytes when created (that is, empty). An empty C++ string takes 8 bytes (then allocated size grows linearly the size of the string). An integer takes 4 bytes (32 bits).

* * *

Why does all this matter? It seems that whether an empty string takes 8 bytes or 37 doesn’t change anything much. That’s true. That’s true until you need to scale. Then, you need to be really careful about how many objects you create to limit the quantity of memory your program uses. It is a problem in real-life applications. However, to devise a really good strategy about memory management, we must not only consider the sizes of objects, but how many and in which order they are created. It turns out to be very important for Python programs. One key element to understand is how Python allocates its memory internally, which we will discuss next.

Internal Memory Management

To speed-up memory allocation (and reuse) Python uses a number of lists for small objects. Each list will contain objects of similar size: there will be a list for objects 1 to 8 bytes in size, one for 9 to 16, etc. When a small object needs to be created, either we reuse a free block in the list, or we allocate a new one.

There are some internal details on how Python manages those lists into blocks, pools, and “arena”: a number of block forms a pool, pools are gathered into arena, etc., but they’re not very relevant to the point we want to make (if you really want to know, read Evan Jones’ ideas on how to improve Python’s memory allocation). The important point is that those lists never shrink.

Indeed: if an item (of size x) is deallocated (freed by lack of reference) its location is not returned to Python’s global memory pool (and even less to the system), but merely marked as free and added to the free list of items of size x. The dead object’s location will be reused if another object of compatible size is needed. If there are no dead objects available, new ones are created.

If small objects memory is never freed, then the inescapable conclusion is that, like goldfishes, these small object lists only keep growing, never shrinking, and that the memory footprint of your application is dominated by the largest number of small objects allocated at any given point.

* * *

Therefore, one should work hard to allocate only the number of small objects necessary for one task, favoring (otherwise unpythonèsque) loops where only a small number of elements are created/processed rather than (more pythonèsque) patterns where lists are created using list generation syntax then processed.

While the second pattern is more à la Python, it is rather the worst case: you end up creating lots of small objects that will come populate the small object lists, and even once the list is dead, the dead objects (now all in the free lists) will still occupy a lot of memory.

* * *

The fact that the free lists grow does not seem like much of a problem because the memory it contains is still accessible to the Python program. But from the OS’s perspective, your program’s size is the total (maximum) memory allocated to Python. Since Python returns memory to the OS on the heap (that allocates other objects than small objects) only on Windows, if you run on Linux, you can only see the total memory used by your program increase.

* * *

Let us prove my point using memory_profiler, a Python add-on module (which depends on the python-psutil package) by Fabian Pedregosa (the module’s github page). This add-on provides the decorator @profilethat allows one to monitor one specific function memory usage. It is extremely simple to use. Let us consider this small program (it makes my point entirely):

import copy import memory_profiler @profile def function(): x = range(1000000) # allocate a big list y = copy.deepcopy(x) del x return y if __name__=="__main__": function() 

invoking

python -m memory_profiler memory-profile-me.py

prints, on a 64-bit computer

Filename: memory-profile-me.py

Line #    Mem usage    Increment   Line Contents
================================================
     3                             @profile
     4      9.11 MB      0.00 MB   def function():
     5     40.05 MB     30.94 MB       x=range(1000000) # allocate a big list
     6     89.73 MB     49.68 MB       y=copy.deepcopy(x)
     7     82.10 MB     -7.63 MB       del x
     8     82.10 MB      0.00 MB       return y

This small program creates a list with 1,000,000 ints (at 24 bytes each, for ~24 million bytes) plus a list of references (at 8 bytes each, for ~8 million bytes), for about 30MB. It then deep-copies the object (which allocates ~50MB, not sure why; a simple copy would allocate only 8MB of references, plus about 24MB for the objects themselves—so there’s a large overhead here, maybe Python grew its heap preemptively). Freeing xwith del frees the reference list, kills the associated objects, but lo!, the amount of memory only goes down by the number of references, because the list itself is not in a small objects’ list, but on the heap, and the dead small objects remain in the free list, and not returned to the interpreter’s global heap.

In this example, we end up with twice the memory allocated, with 82MB, while only one list necessitating about 30MB is returned. You can see why it is easy to have memory just increase more or less surprisingly if we’re not careful.

Pickle

On a related note: is pickle wasteful?

Pickle is the standard way of (de)serializing Python objects to file. What is its memory footprint? Does it create extra copies of the data or is it rather smart about it? Consider this short example:

import memory_profiler import pickle import random def random_string(): return "".join([chr(64 + random.randint(0, 25)) for _ in xrange(20)]) @profile def create_file(): x = [(random.random(), random_string(), random.randint(0, 2 ** 64)) for _ in xrange(1000000)] pickle.dump(x, open('machin.pkl', 'w')) @profile def load_file(): y = pickle.load(open('machin.pkl', 'r')) return y if __name__=="__main__": create_file() #load_file() 

With one invocation to profile the creation of the pickled data, and one invocation to re-read it (you comment out the function not to be called). Using memory_profiler, the creation uses a lot of memory:

Filename: test-pickle.py

Line #    Mem usage    Increment   Line Contents
================================================
     8                             @profile
     9      9.18 MB      0.00 MB   def create_file():
    10      9.33 MB      0.15 MB       x=[ (random.random(),
    11                                      random_string(),
    12                                      random.randint(0,2**64))
    13    246.11 MB    236.77 MB           for _ in xrange(1000000) ]
    14
    15    481.64 MB    235.54 MB       pickle.dump(x,open('machin.pkl','w'))

and re-reading a bit less:

Filename: test-pickle.py

Line #    Mem usage    Increment   Line Contents
================================================
    18                             @profile
    19      9.18 MB      0.00 MB   def load_file():
    20    311.02 MB    301.83 MB       y=pickle.load(open('machin.pkl','r'))
    21    311.02 MB      0.00 MB       return y

So somehow, pickling is very bad for memory consumption. The initial list takes up more or less 230MB, but pickling it creates an extra 230-something MB worth of memory allocation.

Unpickling, on the other hand, seems fairly efficient. It does create more memory than the original list (300MB instead of 230-something) but it does not double the quantity of allocated memory.

Overall, then, (un)pickling should be avoided for memory-sensitive applications. What are the alternatives? Pickling preserves all the structure of a data structure, so you can recover it exactly from the pickled file at a later time. However, that might not always be needed. If the file is to contain a list as in the example above, then maybe a simple flat, text-based, file format is in order. Let us see what it gives.

A naïve implementation would give:

import memory_profiler import random import pickle def random_string(): return "".join([chr(64 + random.randint(0, 25)) for _ in xrange(20)]) @profile def create_file(): x = [(random.random(), random_string(), random.randint(0, 2 ** 64)) for _ in xrange(1000000) ] f = open('machin.flat', 'w') for xx in x: print >>f, xx f.close() @profile def load_file(): y = [] f = open('machin.flat', 'r') for line in f: y.append(eval(line)) f.close() return y if __name__== "__main__": create_file() #load_file() 

Creating the file:

Filename: test-flat.py

Line #    Mem usage    Increment   Line Contents
================================================
     8                             @profile
     9      9.19 MB      0.00 MB   def create_file():
    10      9.34 MB      0.15 MB       x=[ (random.random(),
    11                                      random_string(),
    12                                      random.randint(0, 2**64))
    13    246.09 MB    236.75 MB           for _ in xrange(1000000) ]
    14
    15    246.09 MB      0.00 MB       f=open('machin.flat', 'w')
    16    308.27 MB     62.18 MB       for xx in x:
    17                                     print >>f, xx

and reading the file back:

Filename: test-flat.py

Line #    Mem usage    Increment   Line Contents
================================================
    20                             @profile
    21      9.19 MB      0.00 MB   def load_file():
    22      9.34 MB      0.15 MB       y=[]
    23      9.34 MB      0.00 MB       f=open('machin.flat', 'r')
    24    300.99 MB    291.66 MB       for line in f:
    25    300.99 MB      0.00 MB           y.append(eval(line))
    26    301.00 MB      0.00 MB       return y

Memory consumption on writing is now much better. It still creates a lot of temporary small objects (for 60MB’s worth), but it’s not doubling memory usage. Reading is comparable (using only marginally less memory).

This particular example is trivial but it generalizes to strategies where you don’t load the whole thing first then process it but rather read a few items, process them, and reuse the allocated memory. Loading data to a Numpy array, for example, one could first create the Numpy array, then read the file line by line to fill the array: this allocates one copy of the whole data. Using pickle, you would allocate the whole data (at least) twice: once by pickle, and once through Numpy.

Or even better yet: use Numpy (or PyTables) arrays. But that’s a different topic. In the mean time, you can have a look at loading and saving another tutorial in the Theano/doc/tutorial directory.

* * *

Python design goals are radically different than, say, C design goals. While the latter is designed to give you good control on what you’re doing at the expense of more complex and explicit programming, the former is designed to let you code rapidly while hiding most (if not all) of the underlying implementation details. While this sounds nice, in a production environment ignoring the implementation inefficiencies of a language can bite you hard, and sometimes when it’s too late. I think that having a good feel of how inefficient Python is with memory management (by design!) will play an important role in whether or not your code meets production requirements, scales well, or, on the contrary, will be a burning hell of memory.