Opened 9 years ago
Closed 5 years ago
#9129 closed defect (fixed)
sqrt memory leaks
Reported by:  zimmerma  Owned by:  AlexGhitza 

Priority:  major  Milestone:  sage6.2 
Component:  basic arithmetic  Keywords:  sd35.5 
Cc:  robertwb, malb, craigcitro, mderickx, was, burcin, jpflori  Merged in:  
Authors:  Volker Braun  Reviewers:  Marc Mezzarobba 
Report Upstream:  Fixed upstream, in a later stable release.  Work issues:  
Branch:  42a4f7f (Commits)  Commit:  42a4f7fa75de37cbc3ac2ebf27f846b03affcbe6 
Dependencies:  #14780  Stopgaps: 
Description (last modified by )
cf http://groups.google.com/group/sagesupport/browse_thread/thread/8c18b2b91004c35a#
m = get_memory_usage() i=0 while True: i+=1 a = 2.sqrt() if i%1000==0: print get_memory_usage(m)
Change History (51)
comment:1 Changed 9 years ago by
 Cc robertwb added
comment:2 followup: ↓ 9 Changed 9 years ago by
comment:3 Changed 9 years ago by
thanks Robert, however your file is not accessible (neither from the web nor from sage.math). Paul
comment:4 followup: ↓ 7 Changed 9 years ago by
Fixed.
comment:5 Changed 9 years ago by
with the following code in Sage 4.4.4:
for i in range(10^4): a=Mod(2^32+1,3).sqrt()
valgrind says:
==6861== 5,312,296 bytes in 9,911 blocks are possibly lost in loss record 6,212\ of 6,212 ==6861== at 0x4A0515D: malloc (vg_replace_malloc.c:195) ==6861== by 0x4D1E1B8: _PyObject_GC_Malloc (gcmodule.c:1351) ==6861== by 0x4D1E2AD: _PyObject_GC_NewVar (gcmodule.c:1383) ==6861== by 0x4C78F80: PyFrame_New (frameobject.c:642) ==6861== by 0x4CF0324: PyEval_EvalCodeEx (ceval.c:2755) ==6861== by 0x4C7997A: function_call (funcobject.c:524) ==6861== by 0x4C4EA72: PyObject_Call (abstract.c:2492) ==6861== by 0x4C5F0DE: instancemethod_call (classobject.c:2579) ==6861== by 0x4C4EA72: PyObject_Call (abstract.c:2492) ==6861== by 0x4CE9022: PyEval_CallObjectWithKeywords (ceval.c:3575) ==6861== by 0x1E356FA6: __pyx_pf_4sage_9structure_7factory_13UniqueFactory__\ _call__ (factory.c:877) ==6861== by 0x4C4EA72: PyObject_Call (abstract.c:2492) ==6861== by 0x4CAD893: slot_tp_call (typeobject.c:5378) ==6861== by 0x4C4EA72: PyObject_Call (abstract.c:2492) ==6861== by 0x1BCC2AC6: __pyx_pf_4sage_5rings_12finite_rings_11integer_mod_1\ 9IntegerMod_abstract_sqrt (integer_mod.c:6959) ==6861== by 0x4C4EA72: PyObject_Call (abstract.c:2492) ==6861== by 0x4CE9022: PyEval_CallObjectWithKeywords (ceval.c:3575) ==6861== by 0x4C6926B: methoddescr_call (descrobject.c:246) ==6861== by 0x4C4EA72: PyObject_Call (abstract.c:2492) ==6861== by 0x4CE9022: PyEval_CallObjectWithKeywords (ceval.c:3575) ==6861== by 0x1BCA21A8: __pyx_pf_4sage_5rings_12finite_rings_11integer_mod_1\ 4IntegerMod_int_sqrt (integer_mod.c:18128) ==6861== by 0x4CEEEDE: PyEval_EvalFrameEx (ceval.c:3706) ==6861== by 0x4CF0B44: PyEval_EvalCodeEx (ceval.c:2968) ==6861== by 0x4CF0C11: PyEval_EvalCode (ceval.c:522) ==6861== by 0x4CF0287: PyEval_EvalFrameEx (ceval.c:4401)
Does it say something to somebody fluent in Pyrex?
comment:6 Changed 9 years ago by
Possibly lost means that the garbage collector is being lazy and is by Python design. You need to look at the line numbers I highlighted in the above files, which are "definitely lost."
comment:7 in reply to: ↑ 4 Changed 9 years ago by
Replying to rlm:
Fixed.
not quite:
rwxxx 1 rlmill rlmill 1219074 20100708 04:58 sagesqrt.mem.log
comment:8 Changed 9 years ago by
sorry... jetlag
comment:9 in reply to: ↑ 2 ; followup: ↓ 10 Changed 9 years ago by
Replying to rlm:
Of particular importance are lines 18757, 16971, 16915, 16831, etc. (Just search through for "definitely")
those lines seem to indicate the problem lies in the Singular and/or GINAC interface. Any specialist of those interfaces out there?
comment:10 in reply to: ↑ 9 Changed 9 years ago by
 Cc malb craigcitro added
Replying to zimmerma:
those lines seem to indicate the problem lies in the Singular and/or GINAC interface.
I'm not so sure about this. Let's pick on this particular leak:
==25238== 5,320 (1,680 direct, 3,640 indirect) bytes in 35 blocks are definitely lost in loss record 17,325 of 18,340 ==25238== at 0x4C22FEB: malloc (vg_replace_malloc.c:207) ==25238== by 0x13642E4C: __pyx_f_4sage_5rings_7integer_fast_tp_new (integer.c:29882) ==25238== by 0x4EC2232: type_call (typeobject.c:731) ==25238== by 0x4E6CC77: PyObject_Call (abstract.c:2492) ==25238== by 0x218F6E2B: __pyx_f_4sage_4libs_8singular_8singular_si2sa_ZZ(snumber*, sip_sring*) (singular.cpp:3084) ==25238== by 0x21902F6D: __pyx_f_4sage_4libs_8singular_8singular_si2sa(snumber*, sip_sring*, _object*) (singular.cpp:5483) ==25238== by 0x2084D51E: __pyx_pf_4sage_5rings_10polynomial_28multi_polynomial_libsingular_23MPolynomial_libsingular_coefficients(_object*, _object*) (multi_polynomial_libsingular.cpp:27385) ==25238== by 0x4E6CC77: PyObject_Call (abstract.c:2492) ==25238== by 0x20858DB0: __pyx_pf_4sage_5rings_10polynomial_28multi_polynomial_libsingular_23MPolynomial_libsingular_gcd(_object*, _object*, _object*) (multi_polynomial_libsingular.cpp:24344) ==25238== by 0x4E6CC77: PyObject_Call (abstract.c:2492) ==25238== by 0x4F01D15: PyEval_CallObjectWithKeywords (ceval.c:3575) ==25238== by 0x4E87C25: methoddescr_call (descrobject.c:246) ==25238== by 0x4E6CC77: PyObject_Call (abstract.c:2492) ==25238== by 0x4F01D15: PyEval_CallObjectWithKeywords (ceval.c:3575) ==25238== by 0xF9E2BB9: __Pyx_PyEval_CallObjectWithKeywords (element.c:26384) ==25238== by 0xF9D7B3C: __pyx_pf_4sage_9structure_7element_16NamedBinopMethod___call__ (element.c:19673) ==25238== by 0x4E6CC77: PyObject_Call (abstract.c:2492) ==25238== by 0x15AF691C: __pyx_f_4sage_5rings_22fraction_field_element_20FractionFieldElement__add_ (fraction_field_element.c:5090) ==25238== by 0xF9BE0A1: __pyx_pf_4sage_9structure_7element_11RingElement___add__ (element.c:10804) ==25238== by 0x4E6CF6D: binary_op1 (abstract.c:917) ==25238== by 0x4E6D41F: PyNumber_Add (abstract.c:1157) ==25238== by 0x4F0611A: PyEval_EvalFrameEx (ceval.c:1189) ==25238== by 0x4F0A1C0: PyEval_EvalCodeEx (ceval.c:2968) ==25238== by 0x4F0A291: PyEval_EvalCode (ceval.c:522) ==25238== by 0x4F1E371: PyImport_ExecCodeModuleEx (import.c:675)
On line 3842 of multi_polynomial_libsingular.pyx
, we have:
if _ring.ringtype != 0: if _ring.ringtype == 4: P = self._parent.change_ring(RationalField()) res = P(self).gcd(P(right)) coef = sage.rings.integer.GCD_list(self.coefficients() + right.coefficients()) < return self._parent(coef*res)
The calls to .coefficients()
are creating the integers which are not freed. Here is the definition of that function:
cdef poly *p cdef ring *r r = (<MPolynomialRing_libsingular>self._parent)._ring if r!=currRing: rChangeCurrRing(r) base = (<MPolynomialRing_libsingular>self._parent)._base p = self._poly coeffs = list() while p: coeffs.append(si2sa(p_GetCoeff(p, r), r, base)) p = pNext(p) return coeffs
Looks innocent enough... si2sa
ends up calling:
cdef Integer si2sa_ZZ(number *n, ring *_ring): ... cdef Integer z z = Integer() z.set_from_mpz(<__mpz_struct*>n) return z
I really don't see where any of this could be going wrong. I think it has to do with the fast integer creation functions. Sage has a pool of allocated Integer objects. The integer_pool_count
seems to go up and down randomly, staying in the low range. From one loop to the next, in the original poster's first example, it goes 9, 8, 11, 10, ...
I think that the experts for this memory pool need to step up to the plate...
comment:11 Changed 9 years ago by
 Cc mderickx added
I just tried the second leak in 4.6 on OS X 10.6.4 and the results are:
for i in range(10^6): t=Mod(2^32+1,3).sqrt() if i % 10000 == 0: print i, get_memory_usage()
0 243.6796875 10000 243.6796875 20000 243.6796875 30000 243.6796875 40000 243.6796875 50000 243.6796875 60000 243.6796875 70000 243.6796875 80000 243.6796875 90000 243.6796875 100000 243.6796875 110000 243.6796875 120000 243.6796875 130000 243.6796875 140000 243.6796875 150000 243.6796875 160000 243.6796875 170000 243.6796875
After which I interupted the loop since I concluded the leak was no longer there. Can the person who reported this check it on his own machine?
comment:12 Changed 9 years ago by
The first reported memory leak is still there, but note that you don't need the extemely large random integers i the example to expose the leak. All you need is a non square integer.
Doing the example only with squares in the interval 2^400 till 2^800:
m = get_memory_usage() i=0 while True: i+=1 a = ZZ(randint(2^200,2^400)^2).sqrt() if i%1000==0: print get_memory_usage(m)
0.0 0.0 0.0 0.0
The example using 2 as my favorite non square integer:
m = get_memory_usage() i=0 while True: i+=1 a = 2.sqrt() if i%1000==0: print get_memory_usage(m)
0.76953125 1.26953125 2.01953125 2.51953125 3.01953125 3.76953125 4.26953125 5.01953125 6.51953125
comment:13 Changed 9 years ago by
I dived a bit deeper into the source code to see what is actually going on and I found that in the end the symbolic ring sqrt function is the one where things go wrong. It's not the general symbolic ring framework since other symbolic ring functions dont misbehave.
functions=['arccos', 'arccosh', 'arcsin', 'arcsinh', 'arctan', 'arctanh', 'cos', 'cosh', 'exp', 'log', 'sin', 'sinh', 'sqrt', 'tan', 'tanh'] for function in functions: print function a=SR(2) m = get_memory_usage() for i in xrange(10000): b=a.__getattribute__(function)() print get_memory_usage(m)
arccos 0.0 arccosh 0.0 arcsin 0.0 arcsinh 0.0 arctan 0.0 arctanh 0.0 cos 0.0 cosh 0.0 exp 0.0 log 0.0 sin 0.0 sinh 0.0 sqrt 7.03125 tan 0.0 tanh 0.0
I'm not able to figure out which code get's called from the symbolic ring part on, since the internal working of the symbolic ring is a bit to complex for me. Ie. the source code of SR(2).sqrt is
return new_Expression_from_GEx(self._parent, g_hold2_wrapper(g_power_construct, self._gobj, g_ex1_2, hold))
And new_Expression_from_GEx doesn't have any documentation or whatsoever.
comment:14 Changed 9 years ago by
I confirm the second leak seems to be fixed now (tried with Sage 4.6 on Fedora and 4.5.2 on Ubuntu).
The first one is still present in Sage 4.6.
Paul
comment:15 Changed 9 years ago by
the problem seems to be in the power function. With Sage 4.6:
sage: a=SR(2) sage: m = get_memory_usage() sage: for i in xrange(10000): ....: b=a.__pow__(1/3) ....: sage: print get_memory_usage(m) 11.953125
Paul
comment:16 Changed 9 years ago by
 Cc was burcin added
William, Burcin, the memory leaks seems to be in the g_pow
call at line 2458 of
symbolic/expression.pyx
(in sage 4.6).
I guess the g_pow
function is a wrapper to Ginac (from file libs/ginac/decl.pxi
)
but I could see no occurrence of pow in c_lib/include/ginac_wrap.h
.
Please can you help?
Paul
comment:17 Changed 9 years ago by
I don't have time to check now, but the pow()
method of numeric
objects should be called in this case. You can see the code here:
http://pynac.sagemath.org/hg/file/b233d9dadcfa/ginac/numeric.cpp#l297
There is nothing that immediately catches my eye there.
To experiment, you'll need a pynac development environment:
comment:18 followup: ↓ 19 Changed 9 years ago by
Sorry for the spam, but is the problem still there with the patch at #8659 applied?
comment:19 in reply to: ↑ 18 Changed 9 years ago by
Replying to burcin:
Sorry for the spam, but is the problem still there with the patch at #8659 applied?
yes, but the leak seems to be smaller:
  Sage Version 4.6, Release Date: 20101030   Type notebook() for the GUI, and license() for information.   Loading Sage library. Current Mercurial branch is: 8659 sage: a=SR(2) sage: m = get_memory_usage() sage: for i in xrange(10000): ....: b=a.__pow__(1/3) sage: print get_memory_usage(m) 4.4140625
instead of 11.703125
without the #8659 patch.
Paul
comment:20 followup: ↓ 23 Changed 8 years ago by
I added some printstatements in pynac0.2.3.p0/src/ginac/numeric.cpp
as follows:
Number_T pow(const Number_T& base, const Number_T& exp) { std::cerr << "enter pow, base=" << base << " exp=" << exp << "\n"; verbose("pow"); if (base.t != exp.t) { Number_T a, b; std::cerr << "coerce\n"; coerce(a, b, base, exp); return pow(a,b); } switch (base.t) { case DOUBLE: std::cerr << "double\n"; return std::pow(base.v._double, exp.v._double); case LONG: // TODO: change to use GMP! std::cerr << "long\n"; return std::pow((double)base.v._long, (double)exp.v._long); case PYOBJECT: std::cerr << "PYOBJECT\n"; if PyInt_Check(base.v._pyobject) { PyObject* o = Integer(PyInt_AsLong(base.v._pyobject)); PyObject* r = PyNumber_Power(o, exp.v._pyobject, Py_None); std::cerr << "PyInt_Check\n"; Py_DECREF(o); return r; } return PyNumber_Power(base.v._pyobject, exp.v._pyobject, Py_None); default: stub("invalid type: pow Number_T"); } }
Then it seems that for inexact powers that function is called twice for SR input:
sage: SR(2).__pow__(1/2) enter pow, base=2 exp=1/2 PYOBJECT enter pow, base=2 exp=1/2 PYOBJECT sqrt(2)
but for exact powers only once:
sage: SR(4).__pow__(1/2) enter pow, base=4 exp=1/2 PYOBJECT 2
For Integer input we get:
sage: Integer(2).__pow__(1/2) enter pow, base=2 exp=1/2 PYOBJECT sqrt(2) sage: Integer(4).__pow__(1/2) 2
For ZZ input we get:
sage: ZZ(2).__pow__(1/2) enter pow, base=2 exp=1/2 PYOBJECT sqrt(2) sage: ZZ(4).__pow__(1/2) 2
In all cases there is no memory leak for exact powers, but there is for inexact powers. Thus I strongly suspect some special code that detects exact powers, but where is it?
Paul
comment:21 Changed 8 years ago by
 Keywords sd35.5 added
comment:22 Changed 8 years ago by
 Cc jpflori added
comment:23 in reply to: ↑ 20 Changed 8 years ago by
Replying to zimmerma:
In all cases there is no memory leak for exact powers, but there is for inexact powers. Thus I strongly suspect some special code that detects exact powers, but where is it?
The __pow__
method of rational numbers. :) When computing 2^(1/2)
, Integer.__pow__
delegates the operation to Rational.__pow__
. If the result is not exact, Rational.__pow__
returns a symbolic expression.
This should really be tested with the patch at #8659, which eliminates the need to call Number_T::pow()
twice. Even with that patch, the leak is still there however.
comment:24 Changed 7 years ago by
 Description modified (diff)
I updated the description.
Paul
PS: it seems the "stopgaps" were deleted, but I don't know which value it was...
comment:25 Changed 6 years ago by
 Milestone changed from sage5.11 to sage5.12
comment:26 Changed 6 years ago by
update with Sage 5.11, the memory leaks is still there:
++  Sage Version 5.11, Release Date: 20130813   Type "notebook()" for the browserbased notebook interface.   Type "help()" for help.  ++ sage: m = get_memory_usage() sage: i=0 sage: while True: ....: i+=1 ....: a = 2.sqrt() ....: if i%1000==0: ....: print get_memory_usage(m) ....: 0.18359375 0.18359375 0.18359375 0.546875 0.8203125 1.5859375 1.96875 2.34765625 2.734375 3.50390625
Paul
comment:27 Changed 6 years ago by
 Milestone changed from sage6.1 to sage6.2
comment:28 Changed 6 years ago by
I'm not sure what the current status of work on this ticket is, but I have noticed that the following functions do not exhibit the demonstrated memory leak.
def memleaksr(n, x, p=1/2): m = get_memory_usage() one_half = SR(.5) for i in xrange(n): a = SR(x)^one_half if(i % 1000 == 0): print get_memory_usage(m)
def memleakonehalf(n, x, p=1/2): m = get_memory_usage() for i in xrange(n): a = SR(x) ** 1/2 if(i % 1000 == 0): print get_memory_usage(m)
From what I understand, 2.sqrt() calls the sqrt() method of the Integer class. sqrt() eventually sage.functions.other._do_sqrt() is called. If _do_sqrt is passed a precision argument, everything works fine. The memory leak seems to occur when no precision is set. Something about the variable one_half in _do_sqrt() seems to throw a kink in things.
Recap: If any precision is set, then there is no memory leak (2.sqrt(prec=52) == no memory leak)
comment:29 Changed 6 years ago by
 Description modified (diff)
the second example in the description still does a memory leak with Sage 6.0:
┌────────────────────────────────────────────────────────────────────┐ │ Sage Version 6.0, Release Date: 20131217 │ │ Type "notebook()" for the browserbased notebook interface. │ │ Type "help()" for help. │ └────────────────────────────────────────────────────────────────────┘ sage: m = get_memory_usage() sage: i=0 sage: while True: ....: i+=1 ....: a = 2.sqrt() ....: if i%1000==0: ....: print get_memory_usage(m) ....: 0.0 0.0 0.0 0.58984375 0.84375 1.234375 1.7421875 2.3828125 2.76171875 3.1484375 3.66796875 4.3203125 4.703125 5.08984375 5.4765625 6.23828125
The fact that it works with prec
does not help for this ticket, which deals with exact square root.
comment:30 Changed 6 years ago by
Yes, 2.sqrt() does still have a memory leak in 6.0
You're right, I wasn't thinking. 2.sqrt() returns a symbolic expression. My examples return a real number.
comment:31 Changed 6 years ago by
I investigated a bit, and I believe there's a Py_DECREF(restuple)
missing in Pynac's GiNaC::power::eval
, around line 590 of power.cpp
.
comment:32 followup: ↓ 33 Changed 6 years ago by
well done! Does it solve the memory leak?
Paul
comment:33 in reply to: ↑ 32 Changed 6 years ago by
Replying to zimmerma:
well done! Does it solve the memory leak?
I think it would, but I didn't actually try to fix it.
comment:34 Changed 6 years ago by
 Description modified (diff)
comment:35 Changed 5 years ago by
 Report Upstream changed from N/A to Reported upstream. No feedback yet.
comment:36 Changed 5 years ago by
Implemented at https://bitbucket.org/vbraun/pynac/commits/4c798d4cb4b50532fc525ad652d7f0db79eb08c1
With the patch it still leaks but much slower
:sage: m = get_memory_usage() :sage: i=0 :sage: while True: :....: i+=1 :....: a = 2.sqrt() :....: if i%1000==0: :....: print get_memory_usage(m) :....: : 0.0 0.0 0.0 0.0 0.0 0.0 0.25 0.25 0.25 0.25 0.25 0.5 0.5 0.5 0.5 0.5 0.5 0.875 0.875 0.875 0.875 1.00390625 1.28515625
comment:37 Changed 5 years ago by
Fixed another leak at https://bitbucket.org/vbraun/pynac/commits/598291652f2fc645f2d2f150b39040af07eb6554
Now the above script does not leak any more...
comment:38 Changed 5 years ago by
well done Volker! Just a pity it took 4 years to fix that issue...
Paul
comment:39 Changed 5 years ago by
Feel free to file the upstream bug report faster next time ;)
comment:40 followup: ↓ 42 Changed 5 years ago by
Feel free to file the upstream bug report faster next time ;)
the problem is that I had no idea in which upstream component the leak was (or in Sage), and I had no idea how to search where the leak was.
If you can give some information how we can do this in similar cases, it would be very helpful.
Paul
comment:41 Changed 5 years ago by
 Milestone changed from sage6.2 to sageduplicate/invalid/wontfix
 Status changed from new to needs_review
Will be fixed by the pynac update at #14780
comment:42 in reply to: ↑ 40 Changed 5 years ago by
Replying to zimmerma:
If you can give some information how we can do this in similar cases, it would be very helpful.
Fwiw, I used the objgraph
module to see what kind of objects were being leaked. It turned out to be triples similar to those returned by pynac.pyx
:py_rational_power_parts
—the hardest part was probably to discover that function, but the observation that the leak occurred specifically for nonsquare integers helped.
comment:43 Changed 5 years ago by
 Dependencies set to #14780
 Report Upstream changed from Reported upstream. No feedback yet. to Fixed upstream, in a later stable release.
 Status changed from needs_review to positive_review
comment:44 Changed 5 years ago by
 Reviewers set to Marc Mezzarobba
comment:45 Changed 5 years ago by
could we add a small doctest checking that the leak is gone?
Paul
comment:46 Changed 5 years ago by
 Branch set to u/vbraun/sqrt_memory_leaks
comment:47 Changed 5 years ago by
 Commit set to 2795ef570bd22ec14de45fca99ce10c89878b113
 Milestone changed from sageduplicate/invalid/wontfix to sage6.2
 Status changed from positive_review to needs_review
comment:48 Changed 5 years ago by
 Commit changed from 2795ef570bd22ec14de45fca99ce10c89878b113 to 42a4f7fa75de37cbc3ac2ebf27f846b03affcbe6
Branch pushed to git repo; I updated commit sha1. New commits:
42a4f7f  document output

comment:49 Changed 5 years ago by
 Priority changed from critical to major
 Status changed from needs_review to positive_review
lgtm. Paul, please complain if you disagree, since you were the one who asked for a regression test!
comment:50 Changed 5 years ago by
please complain if you disagree
I agree, thank you to everybody!
comment:51 Changed 5 years ago by
 Branch changed from u/vbraun/sqrt_memory_leaks to 42a4f7fa75de37cbc3ac2ebf27f846b03affcbe6
 Resolution set to fixed
 Status changed from positive_review to closed
I've run valgrind with a clean startup and quit:
http://sage.math.washington.edu/home/rlmill/sageclean.mem.log
and with an execution of the following loop:
http://sage.math.washington.edu/home/rlmill/sagesqrt.mem.log
Of particular importance are lines 18757, 16971, 16915, 16831, etc. (Just search through for "definitely")