#13589 closed defect (fixed)
Controlling C3 to solve once for all the Method Resolution Order issues for category classes
Reported by:  nthiery  Owned by:  nthiery 

Priority:  major  Milestone:  sage5.12 
Component:  categories  Keywords:  method resolution order, C3 
Cc:  sagecombinat, SimonKing  Merged in:  sage5.12.beta0 
Authors:  Nicolas M. Thiéry, Simon King  Reviewers:  Simon King, Florent Hivert 
Report Upstream:  N/A  Work issues:  
Branch:  Commit:  
Dependencies:  #12894, #12876, #11935, #12895, #10193  Stopgaps: 
Description (last modified by )
Teaser  Python handles multiple inheritance by computing, for each class, a linear extension of all its super classes (the Method Resolution Order, MRO). The MRO is calculated recursively from local information (the *ordered* list of the direct super classes), with the socalled ``C3`` algorithm. This algorithm can fail if the local information is not consistent; worst, there exist hiearchies of classes with provably no consistent local information. For large hierarchy of classes, like those derived from categories in Sage, maintaining consistent local information by hand does not scale and leads to unpredictable ``C3`` failures (the dreaded "could not find a consistent method resolution order"); a maintenance nightmare. This patch implements a final solution to this problem. Namely, it allows for building automatically the local information from the bare class hierarchy in such a way that guarantees that the ``C3`` algorithm will never fail. Err, but you said that this was provably impossible? Well, not if one relaxes a bit the hypotheses, but that's not something one would want to do by hand :) Details  Please see the extensive documentation at the top of the file sage/misc/c3_controlled.py in the attached patch. Content of the patch   Implement controlled C3 in sage.misc.c3_controlled.  Implement a total order in Category, and have Category use C3 controlled by this order instead of plain C3.  Tweak the current total order to minimize changes in the order of categories.  Update doctests w.r.t. remaining changes of in the order of categories.  Remove a coupld doctests displaying "all_super_categories" that did not bring useful information to the user nor intesting test, yet needed to be constantly updated; nothing but a good source of conflicts.  Rewrite doctests in sage.misc.c3 to be independent of categories since those do not use anymore this implementation of C3.  Resolve some ambiguities to make the code more independent of the order of categories. In particular, FiniteCoxeterGroups prefer __iter__ and some_elements from CoxeterGroups to that of FiniteGroups.  Update the section in the primer about order of categories.  Provide further tools in ``sage.misc.c3_controlled`` to experiment with C3 and friends.  Extract category_sample from category_graph Credits  This patch is a followup to a study of the C3 algorithm together with Florent Hivert, and to discussions with Simon King and his implementation of C3.
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Attachments (7)
Change History (94)
comment:1 Changed 6 years ago by
 Description modified (diff)
comment:2 Changed 6 years ago by
 Dependencies changed from #13501 to #13501, #12894
comment:3 Changed 6 years ago by
comment:4 Changed 6 years ago by
I currently have
trac_14159_weak_value_triple_dict.patch trac_14159_use_cdef_get.patch trac_13184_sage_5.9.beta.patch trac_14287rebased.patch trac_14217_base_functionality.patch trac_12876_category_abstract_classes_for_hom.patch trac11935_weak_pickling_by_constructionnt.patch trac_11935weak_pickling_by_constructionreviewts.patch trac_14249coercion_without_an_element.patch trac_12894classcall_setternt.patch trac_12895subcategorymethodsnt.patch trac_12895review.patch
on top of sage5.9.rc0 (these all have positive review or are even merged in sage5.10.beta), and then the patch fails to apply like this:
Füge trac_13589categoriesc3_under_controlnt.patch zur Seriendatei hinzu Wende trac_13589categoriesc3_under_controlnt.patch an Wende Patch auf Datei sage/categories/category.py an FEHLSCHLAG von Teilstück #1 in Zeile 94 Teilstück #6 wurde erfolgreich in Zeile 1105 mit Unschärfe 1 angewandt (16 Zeilen verschoben). FEHLSCHLAG von Teilstück #7 in Zeile 1289 Teilstück #9 wurde erfolgreich in Zeile 2152 mit Unschärfe 1 angewandt (58 Zeilen verschoben). 2 von 11 Teilstücken sind FEHLGESCHLAGEN  speichere Ausschuss in Datei sage/categories/category.py.rej Patch schlug fehl und Fortsetzung unmöglich (versuche v) Patch schlug fehl, Fehlerabschnitte noch im Arbeitsverzeichnis Fehler beim Anwenden. Bitte beheben und trac_13589categoriesc3_under_controlnt.patch aktualisieren
So, there is some improvement with respect to what JeanBaptiste reports. Nevertheless, it seems that dependencies should be stated, and probably the patch needs rebasing.
comment:5 Changed 6 years ago by
In particular, the patch uses some CategoryWithAxiom
, which is not defined here or in the given dependencies.
comment:6 Changed 6 years ago by
I have not been able to find CategoryWithAxiom
or category with axiom
on trac.
comment:7 Changed 6 years ago by
Yes, this patch still needs a bit of work. It should be ready tuesday or so. You can have a look at the text in the patch where I describe the purpose and principle of the patch, but don't waste time with a more detailed review at this point!
Thanks!
comment:8 Changed 6 years ago by
comment:9 Changed 6 years ago by
#12895 was next! And now I have to run behind :) Thanks for all your review work! I'll pile up some stuff for you soon and let you know :)
comment:10 Changed 6 years ago by
 Dependencies changed from #13501, #12894 to #13501, #12894, #12895
 Status changed from new to needs_review
comment:11 Changed 6 years ago by
Hi Simon,
The updated patch should be roughly close to completion. Most if not all tests should pass (they did when I was working on the patch in git; I may have screwed up my export back to mercurial, and/or some dependencies).
I still need to scan once again through the patch to check that everything is 100% doctested, and I also want to reread the explanations in sage.misc.c3_controlled. I'll do that tomorrow. But I think you can start reviewing it in particular checking whether the whole logic makes sense to you. Let me know if/when you start working on it so that we avoid conflicts.
Thanks!
Nicolas
comment:12 Changed 6 years ago by
 Dependencies changed from #13501, #12894, #12895 to #13501, #12894, #12876, #11935, #12895
comment:13 Changed 6 years ago by
For info: I am running the tests now and will report when I wake up.
comment:14 Changed 6 years ago by
All long tests passed on my machine with 5.10beta4 and the following patches applied:
trac_14612gyw_test_speedupts.patch trac_14574folded.patch trac_13735_fix_repr_lincomb.patch trac_14123binarytreesmapsrebasecs.patch trac_12876_category_abstract_classes_for_hom.patch trac11935_weak_pickling_by_constructionnt.patch trac_11935weak_pickling_by_constructionreviewts.patch trac_12895subcategorymethodsnt.patch trac_13589categoriesc3_under_controlnt.patch
comment:15 Changed 6 years ago by
Ok, patchbot is happy too except for coverage in c3_controlled, doctest continuations, startup time and startup modules. The two first ones will be easy fixes. I'll investigate the two others this morning.
comment:16 Changed 6 years ago by
 Description modified (diff)
comment:17 Changed 6 years ago by
Hi Simon,
The patch is now completely ready for review:
 I fixed the coverage and continuation issue.
 I cythoned sage.misc.c3_controlled; hopefuly this will fix the startup time regression
 I went through the whole module, improved the doc and threw away some scories.
 I don't know why the bot complains about the non existent modules sage.categories.inspect and itertools. I guess its just confused. As for sage.misc.c3_controlled, well yes, it's new :)
 I am running all long tests, and will report soon.
Thanks for your upcoming review!
Off to work on the main functorial construction patch!
Nicolas
comment:18 Changed 6 years ago by
Oops, I had forgotten a little improvement I wanted to do in the implementation of the total order. It looks a tiny bit less hacky now and could be a tiny bit faster.
All test pass. Running long tests now.
comment:19 Changed 6 years ago by
Gosh, I had fumbled my export and uploaded the wrong patch. Fixed!
comment:20 Changed 6 years ago by
 Description modified (diff)
comment:21 Changed 6 years ago by
Shoot, the Cythonisation has broken one longtest failure in sage.misc.c3_controlled. I am investigating this; the rest can be reviewed in the mean time.
The cythonization has not improved the startup time. It's not yet clear to me what can be causing the slower startup time. To be investigated ...
comment:22 followup: ↓ 23 Changed 6 years ago by
Some random remarks:
 Why is
Category._cmp_key
a cached method and not a lazy attribute?  Why is
CategoryWithParameters._cmp_key
a method and not a lazy attribute or at least a cached method?  Why has this example
sage: Groups().example().algebra(ZZ).categories() ...
been completely removed from sage/categories/groups.py? Similarlysage: Modules(Integers(9)).all_super_categories()
from sage/categories/modules.py etc.  In the documentation of primer.py:
I guess there is "If" missing after the first colon.
However this must be considered as an *implementation detail*: `C_1` and `C_2` are incomparable categories, then the order in which they appear must be mathematically irrelevant:
 In sage/misc/c3_controlled.pyx, line 123: Should be "classes that an object inherits from.", not "classes that an object inherit from."
 ... line 139, "However, this has several inconvenients:" I guess this should be "However, this has several drawbacks" or "However, this is inconvenient in several regards" or so.
Do I understand correctly: As you outline in lines 148166, the creation of classes will become slower (O(n^3)
instead of O(n^2)
for getting the MRO, etc) if one explicitly puts the desired MRO into a long list of bases. This would certainly be a reason for an increased startup time and other regressions. Therefore, in a first step, you compute short lists of bases that ensure that the C3 algorithm still reconstruct the intended MRO. However, is this additional step (namely: Computing lists of bases) takes some time, not affecting the startup time?
I still have to read the actual code (and not just the documentation). One question, though, which is in the spirit of #11935. In sage.categories.category, you have
(result, bases) = C3_sorted_merge([cat._all_super_categories for cat in self._super_categories] + [self._super_categories], key = attrcall('_cmp_key')) self._super_categories_for_classes = bases return [self] + result
I guess in many cases the result will be the same up to the base rings. Shouldn't we think of a way to avoid duplication of work? I could imagine that here is the reason for the startup time regression.
comment:23 in reply to: ↑ 22 Changed 6 years ago by
Replying to SimonKing:
Some random remarks:
 Why is
Category._cmp_key
a cached method and not a lazy attribute? Why is
CategoryWithParameters._cmp_key
a method and not a lazy attribute or at least a cached method?
To be discussed. If I remember correctly, it's slightly easier to use super in cached methods than attributes, but I guess both would work.
 Why has this example
sage: Groups().example().algebra(ZZ).categories() ...been completely removed from sage/categories/groups.py? Similarlysage: Modules(Integers(9)).all_super_categories()
from sage/categories/modules.py etc.
Because they neither bring useful test or information, and need to be updated whenever the category order changes which is a good source of conflicts.
 In the documentation of primer.py:
I guess there is "If" missing after the first colon.However this must be considered as an *implementation detail*: `C_1` and `C_2` are incomparable categories, then the order in which they appear must be mathematically irrelevant: In sage/misc/c3_controlled.pyx, line 123: Should be "classes that an object inherits from.", not "classes that an object inherit from."
 ... line 139, "However, this has several inconvenients:" I guess this should be "However, this has several drawbacks" or "However, this is inconvenient in several regards" or so.
Thanks, I'll fix that! Probably on Monday, together with the fix for the failing long test.
Do I understand correctly: As you outline in lines 148166, the creation of classes will become slower (
O(n^3)
instead ofO(n^2)
for getting the MRO, etc) if one explicitly puts the desired MRO into a long list of bases. This would certainly be a reason for an increased startup time and other regressions. Therefore, in a first step, you compute short lists of bases that ensure that the C3 algorithm still reconstruct the intended MRO. However, is this additional step (namely: Computing lists of bases) takes some time, not affecting the startup time?
Please read further :) That would be O(n^3)
if one was brute forcing
the complete mro in the list of bases. Luckily it's more clever than
that! New bases are only added when absolutely necessary; in fact, in
the current situation it turns out that no base is actually added even
for non trivial categories like Fields or GradedHopfAlgebrasWithBasis?.
I still have to read the actual code (and not just the documentation). One question, though, which is in the spirit of #11935. In sage.categories.category, you have
(result, bases) = C3_sorted_merge([cat._all_super_categories for cat in self._super_categories] + [self._super_categories], key = attrcall('_cmp_key')) self._super_categories_for_classes = bases return [self] + resultI guess in many cases the result will be the same up to the base rings. Shouldn't we think of a way to avoid duplication of work? I could imagine that here is the reason for the startup time regression.
This code is only called if all_super_categories is called. And by #11935 this should happen only once for all base ring in the same category. (unless one calls explicitly all_super_categories). I have not kept the timings under hand, but I did not see a difference in our usual elliptic curves benchmark.
Cheers,
Nicolas
comment:24 followup: ↓ 25 Changed 6 years ago by
For the record, the only failing example is this:
File "devel/sage/sage/misc/c3_controlled.pyx", line 266, in sage.misc.c3_controlled Failed example: for l in L: # long time x = HierarchyElement(10, l.to_poset()) assert x.mro == list(P) assert x.mro_controlled == list(P) assert x.all_bases_len() == 15 assert x.all_bases_controlled_len() == 19 try: x.mro_standard assert False except: pass Exception raised: Traceback (most recent call last): File "/home/simon/SAGE/prerelease/sage5.9.rc0/local/lib/python2.7/sitepackages/sage/doctest/forker.py", line 466, in _run self.execute(example, compiled, test.globs) File "/home/simon/SAGE/prerelease/sage5.9.rc0/local/lib/python2.7/sitepackages/sage/doctest/forker.py", line 825, in execute exec compiled in globs File "<doctest sage.misc.c3_controlled[35]>", line 6, in <module> assert x.all_bases_controlled_len() == Integer(19) AssertionError
Indeed, on the command line, I get
sage: for l in L: ....: print "l =",l ....: x = HierarchyElement(10, l.to_poset()) ....: print x.all_bases_controlled_len() ....: l = [10, 9, 8, 7, 6, 5, 4, 3, 2, 1] 19 l = [10, 9, 8, 7, 6, 5, 4, 3, 1, 2] 19 l = [10, 9, 8, 7, 6, 5, 4, 2, 3, 1] 19 l = [10, 9, 8, 7, 6, 5, 4, 2, 1, 3] 19 l = [10, 9, 8, 7, 6, 5, 4, 1, 3, 2] 19 l = [10, 9, 8, 7, 6, 5, 4, 1, 2, 3] 19 l = [10, 9, 8, 7, 6, 5, 3, 4, 2, 1] 18 ... l = [10, 7, 9, 8, 5, 6, 4, 1, 3, 2] 20 l = [10, 7, 9, 8, 5, 6, 4, 1, 2, 3] 20 l = [10, 7, 9, 8, 5, 6, 3, 4, 2, 1] 18 l = [10, 7, 9, 8, 5, 6, 3, 4, 1, 2] 18 ... l = [10, 7, 9, 8, 4, 5, 6, 1, 3, 2] 20 l = [10, 7, 9, 8, 4, 5, 6, 1, 2, 3] 20 l = [10, 7, 9, 8, 4, 5, 1, 6, 3, 2] 17 l = [10, 7, 9, 8, 4, 5, 1, 6, 2, 3] 17 l = [10, 7, 9, 6, 8, 5, 4, 3, 2, 1] 19 ... l = [10, 7, 9, 6, 8, 5, 4, 1, 2, 3] 19 l = [10, 7, 9, 6, 8, 5, 3, 4, 2, 1] 16 l = [10, 7, 9, 6, 8, 5, 3, 4, 1, 2] 16 l = [10, 7, 9, 6, 8, 4, 5, 3, 2, 1] 18 l = [10, 7, 9, 6, 8, 4, 5, 3, 1, 2] 18 l = [10, 7, 9, 6, 8, 4, 5, 2, 3, 1] 18 l = [10, 7, 9, 6, 8, 4, 5, 2, 1, 3] 18 l = [10, 7, 9, 6, 8, 4, 5, 1, 3, 2] 18 l = [10, 7, 9, 6, 8, 4, 5, 1, 2, 3] 18 l = [10, 7, 9, 6, 8, 4, 2, 5, 3, 1] 17 l = [10, 7, 9, 6, 8, 4, 2, 5, 1, 3] 17 l = [10, 7, 9, 6, 4, 8, 5, 3, 2, 1] 19 l = [10, 7, 9, 6, 4, 8, 5, 3, 1, 2] 19 ... l = [10, 7, 4, 8, 9, 6, 5, 1, 2, 3] 19 l = [10, 7, 4, 8, 9, 6, 2, 5, 3, 1] 16 l = [10, 7, 4, 8, 9, 6, 2, 5, 1, 3] 16 l = [10, 7, 4, 8, 9, 5, 6, 3, 2, 1] 18 l = [10, 7, 4, 8, 9, 5, 6, 3, 1, 2] 18 ... l = [10, 7, 4, 8, 5, 9, 6, 1, 2, 3] 17 l = [10, 7, 4, 8, 5, 9, 1, 6, 3, 2] 16 l = [10, 7, 4, 8, 5, 9, 1, 6, 2, 3] 16 sage:
I am not surprise that some posets are easier to control than others. Why do you expect that all_bases_controlled_len
is the same in all cases?
comment:25 in reply to: ↑ 24 Changed 6 years ago by
Replying to SimonKing:
I am not surprise that some posets are easier to control than others. Why do you expect that
all_bases_controlled_len
is the same in all cases?
The fact that the number of bases to be added does not depend on the linear extension is certainly specific to this poset. But before cythonisation this used to be the case. So I need to investigate what went wrong!
comment:26 Changed 6 years ago by
Hi Simon!
It turns out that I had just fooled myself because of a typo in the test. Even for this example, the number of bases to be added *does* depend on the linear extension.
So all is good, the python/cython implementations agree.
The updated patch:
 fixes the typos you mentionned
 reworks a bit the text to make it clearer that the code implements an optimized "add bases" trick which does not have the drawbacks of the brute force approach.
 fixes the incorrect doctest, and gather some stats on the number of bases to be added for each linear extension
 mentions the removed doctests, and the rationale for removing them, in the patch header
There remains to decide between a lazy attribute or a cached method for _cmp_key. Any idea on how to investigate the startup time welcome.
Cheers,
Nicolas
comment:27 Changed 6 years ago by
 Description modified (diff)
comment:28 followup: ↓ 29 Changed 6 years ago by
Hi Simon,
While playing with larger hierarchy of classes for the functorial construction patch, I stumbled on one execution path which was not treated correctly. I'll post an updated patch shortly.
Changed 6 years ago by
comment:29 in reply to: ↑ 28 ; followup: ↓ 31 Changed 6 years ago by
Replying to nthiery:
While playing with larger hierarchy of classes for the functorial construction patch, I stumbled on one execution path which was not treated correctly. I'll post an updated patch shortly.
Ok, the updated patch includes the (hopefuly) now correct implementation together with relevant tests. At this occasion, I declared a couple more variables for cython and added some debugging code (commented out by default).
You can look at :attachment:c3fixnt.patch if you just want to see the changes.
I guess last time I wrote such a long function was when I played around with F5! It would be a good candidate for a computer assisted proof of correctness or for automatic test generation.
comment:30 Changed 6 years ago by
I forgot to mention: all long tests pass on my machine.
comment:31 in reply to: ↑ 29 ; followup: ↓ 32 Changed 6 years ago by
Replying to nthiery:
I guess last time I wrote such a long function was when I played around with F5! It would be a good candidate for a computer assisted proof of correctness or for automatic test generation.
Do we have those things (I mean "computer assisted correctness proofs", not "F5") in Sage?
I am travelling this week. So, I will probably not be able to finish the review right now.
comment:32 in reply to: ↑ 31 Changed 6 years ago by
Do we have those things (I mean "computer assisted correctness proofs", not "F5") in Sage?
Nope. But we have experts in Orsay in the office next to ours :)
I am travelling this week. So, I will probably not be able to finish the review right now.
Ok.
comment:33 Changed 6 years ago by
 Dependencies changed from #13501, #12894, #12876, #11935, #12895 to #12894, #12876, #11935, #12895
comment:34 Changed 6 years ago by
Changed 6 years ago by
comment:35 Changed 6 years ago by
The updated patch includes a method category_sample which saves a couple lines and which I needed anyway later on. trac_13589categoriesc3_under_controlcategory_samplent.patch shows the diff.
comment:36 Changed 6 years ago by
Arr, I can't wait until we have a more semantic way to specify which patches to apply; this is way too error prone to trivial syntax errors ...
comment:37 Changed 6 years ago by
 Description modified (diff)
comment:38 Changed 5 years ago by
 Description modified (diff)
comment:39 Changed 5 years ago by
 Dependencies changed from #12894, #12876, #11935, #12895 to #12894, #12876, #11935, #12895, #10193
comment:40 Changed 5 years ago by
Apply: trac_13589categoriesc3_under_controlnt.patch
comment:41 Changed 5 years ago by
Nicolas and I just discussed: _cmp_key
should at least be a cached method, not a plain method, so that it plays nicely with super(...). However, we may try whether lazy attributes would work, because Nicolas calls super(...) only to compute the value, but the value that should eventually be used does not depend on the class.
comment:42 Changed 5 years ago by
This is a minimal example of what Nicolas wants to do:
sage: class A(object): @lazy_attribute def x(self): print "computing the attribute with A" return 1 ....: sage: class B(A): @lazy_attribute def x(self): print "this is lazy attribute with B" r= super(B,self).x self.y = r return r ....: sage: b = B() sage: b.x this is lazy attribute with B computing the attribute with A 1 sage: b.y 1
So, it seems to work with lazy attribute, and this will be much faster than calling a method (repeatedly). Trying to change it now.
comment:43 Changed 5 years ago by
 Description modified (diff)
Apply: trac_13589categoriesc3_under_controlnt.patch trac13589_cmp_key_attribute.patch
comment:44 Changed 5 years ago by
To me, the code looks fine. Patchbot does not report any errors. However, it reports a significant increase of 2.5% of startup time.
How can this be analysed?
comment:45 Changed 5 years ago by
Something findings:
 C3_sorted_merge was called 121 times with the current patch during startup.
 C3_sorted_merge is called on different Groupoids, which should not happen, because all groupoids have the same super categories. Solution: Make it
CategoryWithParameters
. Then, C3_sorted_merge is only called 93 times during startup.  There are some optimizations possible in C3_sorted_merge:
sage: L1 = Fields().all_super_categories() sage: L2 = Algebras(QQ).all_super_categories() sage: cython(""" def test1(list L): cdef list out = L[::1] def test2(list L): cdef list out = list(reversed(L)) """) ....: sage: %timeit test1(L1) 1000000 loops, best of 3: 541 ns per loop sage: %timeit test2(L1) 100000 loops, best of 3: 2.25 us per loop
and
sage: cython(""" ....: def test1(list L): ....: cdef set S = set(x for x in L) ....: def test2(list L): ....: cdef set S = set([x for x in L]) ....: """) ....: sage: %timeit test1(L1) 100000 loops, best of 3: 3.91 us per loop sage: %timeit test2(L1) 100000 loops, best of 3: 3.99 us per loop sage: %timeit test1(L1) 100000 loops, best of 3: 3.89 us per loop sage: %timeit test1(L1) 100000 loops, best of 3: 3.89 us per loop sage: %timeit test2(L1) 100000 loops, best of 3: 4.06 us per loop
In both examples above, test2 is what is currently done in C3_sorted_merge, and test1 is apparently what should be done. I am preparing a patch now.
comment:46 Changed 5 years ago by
Another observation: key(O) is called repeatedly, even though its result is already stored in O_key, so, it should be used consistently.
comment:47 Changed 5 years ago by
 Description modified (diff)
Apply trac_13589categoriesc3_under_controlnt.patch trac13589_cmp_key_attribute.patch trac13589_improve_startuptime.patch
comment:48 Changed 5 years ago by
On my computer, running startuptime once, it seemed to me that the problem was close to being solved. However, the patchbot still sees a slowdown of 2.5% with very high confidence.
So, let's try to find further tweaks.
comment:49 Changed 5 years ago by
Here is another potential improvement:
sage: cython(""" ....: cpdef inline sort_key(object x): ....: return x._cmp_key ....: def test(list L, key): ....: cdef list out = sorted(L, key=key, reverse=True) ....: """) ....: sage: class C(object): ....: def __init__(self, n): ....: self._cmp_key = n ....: sage: L = [C(ZZ.random_element(1,10^6)) for _ in range(10000)] sage: from operator import attrgetter sage: sk = attrgetter('_cmp_key') sage: %timeit test(L,sort_key) 100 loops, best of 3: 10.8 ms per loop sage: %timeit test(L,sk) 100 loops, best of 3: 12.9 ms per loop sage: %timeit test(L,sort_key) 100 loops, best of 3: 10.8 ms per loop sage: %timeit test(L,sk) 100 loops, best of 3: 12.9 ms per loop
So, in C3_controlled, we could implement this cpdef inline function as the default key for comparison.
In addition, we could decide that the key must return a tuple, because this is the format of Category._cmp_key. But this would mean we loose flexibility.
comment:50 Changed 5 years ago by
Let's do some statistics.
This is what I get in three runs of sage startuptime
, when only the dependencies of this ticket are applied.
29.550 1376.061 4 sage.all Total time (sum over exclusive time): 1455.386ms 30.054 1389.055 4 sage.all Total time (sum over exclusive time): 1469.172ms 29.410 1376.077 4 sage.all Total time (sum over exclusive time): 1455.600ms
Adding the first patch from here
28.707 1390.722 4 sage.all Total time (sum over exclusive time): 1470.469ms 28.887 1397.835 4 sage.all Total time (sum over exclusive time): 1477.698ms 28.699 1409.829 4 sage.all Total time (sum over exclusive time): 1489.124ms
Is that really a significant slowdown? I don't think so.
And when applying the other patches (including an update of the last, that I did not post yet):
29.099 1386.676 4 sage.all Total time (sum over exclusive time): 1466.655ms 29.705 1391.458 4 sage.all Total time (sum over exclusive time): 1471.162ms 35.740 1480.115 4 sage.all Total time (sum over exclusive time): 1574.091ms
What does this give us?
comment:51 Changed 5 years ago by
I've updated the patch, FWIW.
Apply trac_13589categoriesc3_under_controlnt.patch trac13589_cmp_key_attribute.patch trac13589_improve_startuptime.patch
comment:52 Changed 5 years ago by
Any idea what we shall do about the regression at startup?
comment:53 Changed 5 years ago by
Here is another possibility to speed things up: Replace itertools.count() by a cpdef method, defined in some auxiliary file.
Timing:
sage: cython(""" cdef long counter = 0 cpdef count1(): global counter counter += 1 return counter def count2(): cdef long c = 0 while True: c += 1 yield c """) sage: C = count1 sage: %timeit a = C() 10000000 loops, best of 3: 73.8 ns per loop sage: C = count2() sage: %timeit a = C.next() 1000000 loops, best of 3: 252 ns per loop sage: import itertools sage: C = itertools.count() sage: %timeit a = C.next() 1000000 loops, best of 3: 231 ns per loop
Note that this would also make the startup_module plugin happy, since it complains about the new import of itertools during startup.
comment:54 followup: ↓ 55 Changed 5 years ago by
Thanks for the timings! The startup gain should be of roughly 50*(23174)ns which will be anyway far less than a ms and negligible. I would thus tend to stick to the standard Python module itertools, and ignore the little indicator from the startup module plugin.
As for the time increase: I will post a poll on sagedevel later today.
comment:55 in reply to: ↑ 54 Changed 5 years ago by
Replying to nthiery:
Thanks for the timings! The startup gain should be of roughly 50*(23174)ns which will be anyway far less than a ms and negligible. I would thus tend to stick to the standard Python module itertools, and ignore the little indicator from the startup module plugin.
Too late... I already posted an update of my patch.
Well, I'd say let the patchbot try, at least.
As for the time increase: I will post a poll on sagedevel later today.
Thank you.
I've put the cpdef counter into sage.misc.c3_controlledI think this is a good place, given that it is needed when applying the controlled c3 in categories.
With the original patch, one had Category._cmp_key_generator.next()
, hence, two attribute lookups and a function call. With the new patch, one just has one function call, which also seems to be faster than the function call to next()
.
Let us keep our fingers crossed concerning the findings of the patchbot...
Apply trac_13589categoriesc3_under_controlnt.patch trac13589_cmp_key_attribute.patch trac13589_improve_startuptime.patch
comment:56 Changed 5 years ago by
In the computation of _cmp_key, it says:
atoms = ("FacadeSets", "Finite", "Infinite", "EnumeratedSets", "FiniteDimensionalModulesWithBasis", "GradedModules", "ModulesWithBasis", "AdditiveMagmas", "Magmas", "Semigroups", "Monoids", "FinitePermutationGroups", "Rngs", "Domains") classname = self.__class__.__name__ flag = 0 for i, axiom in enumerate(atoms): if classname.startswith(axiom): flag = flag  (1 << i)
First a Python question: Will the tuple atoms
be constructed repeatedly in this method? If this is the case, it might be better to move it to module level.
Second question: With the exception of "Finite" and "FiniteDimensionalModulesWithBases?", the atoms are pairwise incompatible, in the sense of "if classname starts with one of the atoms, then it will certainly not start with any other atom". Hence, it seems possible to rewrite it such that the for
loop is quit after the first case in which the if
clause is true.
But again, probably a speed gain in a method that is called only once will be negligible.
comment:57 Changed 5 years ago by
We have to save a total of about 70 ms in 93 function calls. This is not much, and perhaps it could be obtained by cythoning the computation of _cmp_key.
Also, you do
assert not isinstance(self, JoinCategory)
and later comment
for cat in self._super_categories: # not self.super_categories() to avoid join categories!
So, is the assertion not needed, after all?
comment:58 Changed 5 years ago by
 Description modified (diff)
Are the patch bots out of work? I've not seen any nongrey blob recently.
Anyway. I implemented a Cython version of the _cmp_key attribute. It is similar to lazy attribute, but avoids some overhead, because it is known that it is applied to instances that allow for attribute assignment.
Certainly there can not be a big speedup. We are talking here about the attempt to make 90 function calls 1 ms faster in each run. I'd really be interested to know what the startup_time plugin finds. So, let us hope that the patch bots resume work soon...
Of course, it could be that we will eventually disregard my startup time patches, should it turn out that they don't help.
Apply trac_13589categoriesc3_under_controlnt.patch trac13589_cmp_key_attribute.patch trac13589_improve_startuptime.patch trac13589_cython_cmp_key.patch
comment:59 Changed 5 years ago by
I did some timings. My benchmark is: Delete and recreated Rings()._cmp_key
in a tight loop.
With only the first two patches, I get:
sage: C = Rings() sage: C._cmp_key (6016, 12) sage: timeit("if C._cmp_key: del C._cmp_key", number=100000) 100000 loops, best of 3: 12.9 µs per loop sage: C._cmp_key (6016, 300059)
With all four patches, I get:
sage: C = Rings() sage: C._cmp_key (6016, 12) sage: timeit("if C._cmp_key: del C._cmp_key", number=100000) 100000 loops, best of 3: 901 ns per loop sage: C._cmp_key (6016, 300059)
Since the time needed to delete the attribute should be the same, it follows that the last two patches save 12 µs for each creation of the _cmp_key attribute. Better than nothing, but by far not enough to sum up to 70 ms.
comment:60 Changed 5 years ago by
I have cythoned the _cmp_key attribute of CategoryWithParameters
as well. The raw improvement is:
Without the first two patches:
sage: C = Algebras(FractionField(QQ['x'])) sage: timeit("if C._cmp_key: del C._cmp_key", number=100000) 100000 loops, best of 3: 5.38 µs per loop
With all patches:
sage: C = Algebras(FractionField(QQ['x'])) sage: timeit("if C._cmp_key: del C._cmp_key", number=100000) 100000 loops, best of 3: 1.28 µs per loop
But I think the startup time is what we are really interested in. My starting point is sage5.11.b3 with these patches applied:
trac_14471_dynamic_class_hash.patch trac_14471review.patch trac_14516crystals_speedupts.2.patch trac_14722lazy_import_at_startupnt.patch
I am giving the total time provided by sage startuptime, in 5 consecutive runs:
Total time (sum over exclusive time): 1485.052ms Total time (sum over exclusive time): 1499.287ms Total time (sum over exclusive time): 1499.921ms Total time (sum over exclusive time): 1492.086ms Total time (sum over exclusive time): 1493.375ms
After applying the first two patches, I get:
Total time (sum over exclusive time): 1495.342ms Total time (sum over exclusive time): 1504.880ms Total time (sum over exclusive time): 1502.199ms Total time (sum over exclusive time): 1497.281ms Total time (sum over exclusive time): 1508.277ms
And after applying the other two patches, I get:
Total time (sum over exclusive time): 1499.909ms Total time (sum over exclusive time): 1498.731ms Total time (sum over exclusive time): 1499.289ms Total time (sum over exclusive time): 1502.179ms Total time (sum over exclusive time): 1489.887ms
I think one sees a tendency. But we'd really need to see what the startuptime plugin has to say!
Apply trac_13589categoriesc3_under_controlnt.patch trac13589_cmp_key_attribute.patch trac13589_improve_startuptime.patch trac13589_cython_cmp_key.patch
comment:61 Changed 5 years ago by
I don't know about statistics, but here it goes:
sage: T1 = stats.TimeSeries([1485.052,1499.287,1499.921,1492.086,1493.375]) sage: T2 = stats.TimeSeries([1495.342,1504.880,1502.199,1497.281,1508.277]) sage: T3 = stats.TimeSeries([1499.909,1498.731,1499.289,1502.179,1489.887]) sage: T1.mean(), T1.variance() (1493.9442000000001, 36.77894170000064) sage: T2.mean(), T2.variance() (1501.5958, 28.378941700000148) sage: T3.mean(), T3.variance() (1497.999, 22.281242000000503)
I guess five runs are simply not enough to get anything significant, but the means seem to indicate that the last two patches reduce the regression by 50%. Admittedly, what is 4 ms, if the variance is 22 ms?
comment:62 Changed 5 years ago by
Let's try a bit more. I created time series T0,T1,T2 for 20 startuptime tests, for only the dependencies, the first two patches or all patches. I got time series
sage: sage: T0.list() [1489.553, 1480.942, 1478.854, 1486.503, 1475.071, 1475.266, 1479.731, 1476.892, 1496.149, 1489.795, 1485.942, 1492.043, 1484.346, 1482.017, 1488.826, 1473.604, 1490.681, 1488.502, 1488.512, 1485.275] sage: T1.list() [1549.77, 1533.565, 1530.685, 1532.984, 1530.423, 1528.377, 1534.03, 1522.763, 1534.116, 1531.709, 1529.705, 1561.183, 1534.724, 1524.297, 1533.473, 1540.683, 1531.19, 1528.275, 1524.075, 1527.196] sage: T2.list() [1499.464, 1493.059, 1506.909, 1486.872, 1493.657, 1496.276, 1496.01, 1493.497, 1497.991, 1492.52, 1488.484, 1515.637, 1483.794, 1496.187, 1492.245, 1496.936, 1492.311, 1490.689, 1495.659, 1490.345]
with means and standard deviations
sage: T0.mean() 1484.4252000000001 sage: T0.standard_deviation() 6.349568941854885 sage: T1.mean() 1533.16115 sage: T1.standard_deviation() 8.900689998533823 sage: T2.mean() 1494.9271000000003 sage: T2.standard_deviation() 6.912493905203355
I have no idea how to compute from these data whether there is a significant increase of x% of startup time.
comment:63 Changed 5 years ago by
 Work issues set to Add tests in c3_controlled
Hooray!!!!! Did you see what the patchbot plugins have to tell??
 Doctest coverage:
+Missing doctests misc/c3_controlled.pyx 18 / 20 = 90%
. That's my fault, I need to add tests for the functions I introduced.
 Startup modules: Of course, there is now c3_controlled. No surprise and now problem
 Startup time:
Main: 1.5331 sec (30 samples, std_dev=0.0508) Ticket: 1.5186 sec (30 samples, std_dev=0.0525) +real 0m1.482s +user 0m1.243s +sys 0m0.227s +9, 1.5900211334228516, 1.6482288837432861, 1.5097029209136963, 1.4862918853759766, 1.623434066772461, 1.4829378128051758, 1.582334041595459, 1.6098928451538086, 1.554724931716919, 1.5065560340881348, 1.495615005493164, 1.5109539031982422, 1.5232598781585693, 1.4945759773254395, 1.4797320365905762, 1.6103341579437256, 1.5885298252105713, 1.5899219512939453, 1.4962730407714844, 1.5993151664733887, 1.5715739727020264, 1.5021510124206543, 1.5274248123168945, 1.4769189357757568, 1.6029491424560547, 1.625540018081665, 1.6075868606567383, 1.5096728801727295, 1.6023800373077393] Average decrease of 0.014 secs or 0.94%. +Main: 1.5629 sec (30 samples, std_dev=0.0471) +Ticket: 1.5551 sec (30 samples, std_dev=0.0561) + +Average decrease of 0.0079 secs or 0.5%. No statistically significant difference. +With 32% confidence, startup time decreased by at least 0.25% +With 44% confidence, startup time decreased by at least 0.1%
In other words, the problem is solved!
I think I can give your patch a positive review. My patches are big enough so that I should add me as author, and someone else needs to review them. And I will soon add the missing tests in C3_controlled.
comment:64 Changed 5 years ago by
Concerning a small function I added:
sage: from operator import attrgetter sage: f = attrgetter("_cmp_key") sage: C = Rings() sage: %timeit a = f(C) 1000000 loops, best of 3: 439 ns per loop sage: from sage.misc.c3_controlled import category_sort_key sage: %timeit a = category_sort_key(C) 10000000 loops, best of 3: 154 ns per loop
That's why I added it (and: It is cpdef inline, hence, if Cython is clever, the speed difference with attrgetter will be even better).
comment:65 Changed 5 years ago by
 Work issues Add tests in c3_controlled deleted
I have added the two missing tests, updating the cython_cmp_key patch.
Apply trac_13589categoriesc3_under_controlnt.patch trac13589_cmp_key_attribute.patch trac13589_improve_startuptime.patch trac13589_cython_cmp_key.patch
comment:66 Changed 5 years ago by
 Description modified (diff)
comment:67 followup: ↓ 68 Changed 5 years ago by
Now there is a second patchbot commenting: It finds 0.5% regression (not 5%)!
comment:68 in reply to: ↑ 67 Changed 5 years ago by
Replying to SimonKing:
Now there is a second patchbot commenting: It finds 0.5% regression (not 5%)!
I misread. 0.5% is not significant. Only 0.25% is significant. So, virtually nothing.
comment:69 Changed 5 years ago by
Oh wow!
Congrats Simon :)
I was not expecting we could really do anything about that, and had just sent an email to sagedevel; counter email sent!
Thanks a lot, I'll review your patches!
comment:70 Changed 5 years ago by
 Description modified (diff)
comment:71 Changed 5 years ago by
I started to work on the things we had discussed over the phone. I attached the preliminary patch to see what the patchbot says.
comment:72 Changed 5 years ago by
One typo in the review patch: "It is sematically equivalent" should be "It is semantically equivalent". Also I think you mean
:func:`operator.attrgetter```("cmp_key")``
not
:func:`operator.attrgetter```(category)``
I wonder: You changed the default sort function from category_sort_key
to identity
(as we agreed on by phone), but you do not explicitly use the now nondefault category_sort_key
when calling c3_sorted_merge in sage.categories.category, isn't it? That should be fixed.
comment:73 Changed 5 years ago by
Worse: Compilation of sage.misc.c3_controlled fails with
Error compiling Cython file:  ... sage: from sage.misc.c3_controlled import category_sort_key sage: category_sort_key(Rings()) is Rings()._cmp_key True """ return C._cmp_key ^  sage/misc/c3_controlled.pyx:377:12: undeclared name not builtin: C
comment:74 Changed 5 years ago by
After trivially changing it, building Sage works, but it crashes at startup with
/home/simon/SAGE/prerelease/sage5.11.beta3/local/lib/python2.7/sitepackages/sage/categories/category.pyc in _all_super_categories(self=Category of category_singleton) 871 Category of rngs, 872 Category of semirings, 873 Category of monoids, 874 Category of semigroups, 875 Category of magmas, 876 Category of commutative additive groups, 877 Category of commutative additive monoids, 878 Category of commutative additive semigroups, 879 Category of additive magmas, 880 Category of sets, 881 Category of sets with partial maps, 882 Category of objects] 883 """ 884 (result, bases) = C3_sorted_merge([cat._all_super_categories 885 for cat in self._super_categories] + > 886 [self._super_categories]) self._super_categories = [Category of rngs, Category of semirings] 887 self._super_categories_for_classes = bases 888 return [self] + result 889 890 @lazy_attribute 891 def _all_super_categories_proper(self): 892 r""" 893 All the proper super categories of this category. 894 895 Since :trac:`11943`, the order of super categories is 896 determined by Python's method resolution order C3 algorithm. 897 898 .. seealso:: :meth:`all_super_categories` 899 900 .. note:: this attribute is likely to eventually become a tuple. 901 /home/simon/SAGE/prerelease/sage5.11.beta3/local/lib/python2.7/sitepackages/sage/misc/c3_controlled.so in sage.misc.c3_controlled.C3_sorted_merge (sage/misc/c3_controlled.c:4377)() KeyError: Category of sets
But this can be fixed by using key=category_sort_key
in C3_sorted_merge.
Hence, with the following diff, Sage starts:

sage/categories/category.py
diff git a/sage/categories/category.py b/sage/categories/category.py
a b 883 883 """ 884 884 (result, bases) = C3_sorted_merge([cat._all_super_categories 885 885 for cat in self._super_categories] + 886 [self._super_categories]) 886 [self._super_categories], 887 key = category_sort_key) 887 888 self._super_categories_for_classes = bases 888 889 return [self] + result 889 890 
sage/misc/c3_controlled.pyx
diff git a/sage/misc/c3_controlled.pyx b/sage/misc/c3_controlled.pyx
a b 374 374 sage: category_sort_key(Rings()) is Rings()._cmp_key 375 375 True 376 376 """ 377 return C._cmp_key377 return category._cmp_key 378 378 379 379 cdef class CmpKey: 380 380 r"""
comment:75 Changed 5 years ago by
Oops, sorry for the wasted time; I posted this in a rush and it was half baked with the last round of changes not tested ... Here is an updated patch which fixes the mentionned issues.
Changed 5 years ago by
comment:76 Changed 5 years ago by
The patch looks good. I only wonder about the findings of the startuptime plugin.
comment:77 Changed 5 years ago by
Why the heck is no patchbot giving a result? It is now 15 hours after attaching the review patch!
comment:78 Changed 5 years ago by
 Status changed from needs_review to positive_review
 Work issues set to commit message for review patch
Hooray, patchbot is back, and says:
+With 60% confidence, startup time increased by at least 0.5% +With 96% confidence, startup time increased by at least 0.25% +With 99.1% confidence, startup time increased by at least 0.1%
60% confidence is not significant, hence, we only have a significant regression of 0.25%, which I deem acceptable and a price worth paying.
So, I'm already putting it to positive review, but please add a proper commit message to the review patch!
comment:79 Changed 5 years ago by
Excellent! I'll fold the patches together and double check the commit message tomorrow.
comment:80 Changed 5 years ago by
 Description modified (diff)
 Work issues commit message for review patch deleted
Changed 5 years ago by
comment:81 Changed 5 years ago by
Patchbot getting confused again about which patch to apply.
Apply: trac_13589categoriesc3_under_controlnt.patch
Getting really tired of this ...
comment:82 Changed 5 years ago by
Without the colon perhaps?
apply trac_13589categoriesc3_under_controlnt.patch
(No idea how it works, I just want all the stuff depending on this to be checked.)
comment:83 Changed 5 years ago by
 Milestone changed from sage5.11 to sage5.12
comment:84 Changed 5 years ago by
 Merged in set to sage5.12.beta0
 Resolution set to fixed
 Status changed from positive_review to closed
comment:85 Changed 4 years ago by
While looking through the Sage sources, I noticed the following doctest, coming from this ticket:
sage: for l in L: ....: x = HierarchyElement(10, l.to_poset()) ....: try: ....: x.mro_standard ....: assert False ....: except: ....: pass ....: assert x.mro == list(P) ....: assert x.mro_controlled == list(P) ....: assert x.all_bases_len() == 15 ....: stats.append(x.all_bases_controlled_len()x.all_bases_len())
What is the purpose of the assert False
here? I am asking because
try: foo() assert False except: pass
is entirely equivalent to
try: foo() except: pass
comment:86 followup: ↓ 87 Changed 4 years ago by
bump
Hello,
Could you update your patch, there is several hunk in the last sage versions (i work without combinat to use NCSF...)
Furthermore, it could be useful to add that quickly in sage. The graded Hopf algebras seems to be the last bastion before recurrent MRO errors.
Thanks, JeanBaptiste