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Empty __slots__ can create untracked reference cycles #86150
Currently, we don't track instances of certain heap types based on the assumption that "no members" == "no reference cycles".
Unfortunately, it's still possible to create untracked reference cycles with one's parents. The following program leaks memory:
whileTrue:
classC:
__slots__= ()
C.i=C()
delC
The fix is simple: track all instances of user-defined classes, no exceptions. I'm not sure we were actually getting any real wins from the old behavior anyways.
The fix is simple: track all instances of user-defined classes, no exceptions
I have the fear that this may increase the time of collections substantially because even if many of them won't have references at all, they still net to be visited and they count towards the time complexity of the algorithm that detects isolated sub-cycles. I mention this because this could affect an unbounded number of instances per type. For example, some of the types that are untracked.
Maybe I am missing something but we could mark them as having GC support unconditionally but still leave them untracking and unconditionally add a tracking call on setattribute.
Maybe I am missing something but we could mark them as having GC support unconditionally but still leave them untracking and unconditionally add a tracking call on setattribute.
Hm, I’m not sure that would be enough. Consider the case of a class that registers its instances in a collection of some sort:
This is probably more common (and problematic) than my example above.
At the same time, I agree that it’s not *ideal* to track all of these objects automatically. Anyone setting __slots__ is probably planning on creating lots of “cheap” instances. If they do accidentally create cycles, though, I feel the memory hit then would be worse than any collection overhead.
I’m just not sure I see a way to fix this without tracking them all.
Performance version: 1.0.0
Report on Linux-4.4.0-190-generic-x86_64-with-glibc2.23
Number of logical CPUs: 8
Start date: 2020-10-14 07:49:46.738847
End date: 2020-10-14 08:12:58.050557
Performance version: 1.0.0
Report on Linux-4.4.0-190-generic-x86_64-with-glibc2.23
Number of logical CPUs: 8
Start date: 2020-10-14 08:29:52.009796
End date: 2020-10-14 08:52:49.230214
+-------------------------+----------------------------------------------+------------------------------------------------------------+--------------+-----------------------+
| Benchmark | 2020-10-13_20-04-master-7992579cd27f.json.gz | 2020-10-13_20-37-track-all-heap-types-63d8d25867b5.json.gz | Change | Significance |
+=========================+==============================================+============================================================+==============+=======================+
+-------------------------+----------------------------------------------+------------------------------------------------------------+--------------+-----------------------+
| chameleon | 13.7 ms | 13.1 ms | 1.04x faster | Significant (t=7.27) |
+-------------------------+----------------------------------------------+------------------------------------------------------------+--------------+-----------------------+
| chaos | 163 ms | 159 ms | 1.03x faster | Significant (t=5.39) |
+-------------------------+----------------------------------------------+------------------------------------------------------------+--------------+-----------------------+
| crypto_pyaes | 160 ms | 156 ms | 1.02x faster | Significant (t=7.29) |
+-------------------------+----------------------------------------------+------------------------------------------------------------+--------------+-----------------------+
| deltablue | 10.9 ms | 10.7 ms | 1.03x faster | Significant (t=4.36) |
+-------------------------+----------------------------------------------+------------------------------------------------------------+--------------+-----------------------+
| django_template | 73.1 ms | 71.2 ms | 1.03x faster | Significant (t=4.42) |
+-------------------------+----------------------------------------------+------------------------------------------------------------+--------------+-----------------------+
| go | 373 ms | 381 ms | 1.02x slower | Significant (t=-4.71) |
+-------------------------+----------------------------------------------+------------------------------------------------------------+--------------+-----------------------+
| nbody | 206 ms | 200 ms | 1.03x faster | Significant (t=7.52) |
+-------------------------+----------------------------------------------+------------------------------------------------------------+--------------+-----------------------+
| regex_dna | 295 ms | 288 ms | 1.02x faster | Significant (t=5.26) |
+-------------------------+----------------------------------------------+------------------------------------------------------------+--------------+-----------------------+
| richards | 103 ms | 101 ms | 1.03x faster | Significant (t=5.40) |
+-------------------------+----------------------------------------------+------------------------------------------------------------+--------------+-----------------------+
| scimark_sparse_mat_mult | 6.96 ms | 7.17 ms | 1.03x slower | Significant (t=-4.93) |
+-------------------------+----------------------------------------------+------------------------------------------------------------+--------------+-----------------------+
| spectral_norm | 218 ms | 207 ms | 1.05x faster | Significant (t=10.29) |
+-------------------------+----------------------------------------------+------------------------------------------------------------+--------------+-----------------------+
| unpack_sequence | 101 ns | 84.1 ns | 1.20x faster | Significant (t=36.84) |
+-------------------------+----------------------------------------------+------------------------------------------------------------+--------------+-----------------------+
Note that I used pyperformance==1.0.0, since 1.0.1 won't run on 3.10.
In general, it looks like the patch has no real performance impact. The result for unpack_sequence is *extremely* surprising... I'm quite skeptical of it, although it looks like it is one of the more "micro" benchmarks in the suite.
All things considered, I believe we should fix this... I personally view memory leaks as second only to crashes and incorrect behavior in terms of severity.
I think that unpack_sequence is **very** affected by code locality. I was debugging it recently with Linux perf (https://perf.wiki.kernel.org/index.php/Main_Page) and it shows that is extremely affected by L1-L2 cache performance. I still need to understand why, but yeah, I am fine ignoring it.
All things considered, I believe we should fix this... I personally view memory leaks as second only to crashes and incorrect behavior in terms of severity.
Yeah, I very much agree with you. Could you convert your patch into a PR?
No problem. I just need to rework some hacks in test_finalization where we use empty __slots__ to create non-GC types. I think I can just create a simple utility in _testcapi to untrack instances for this purpose.
Activity
brandtbucher commentedon Oct 9, 2020
Currently, we don't track instances of certain heap types based on the assumption that "no members" == "no reference cycles".
Unfortunately, it's still possible to create untracked reference cycles with one's parents. The following program leaks memory:
The fix is simple: track all instances of user-defined classes, no exceptions. I'm not sure we were actually getting any real wins from the old behavior anyways.
pablogsal commentedon Oct 9, 2020
Thanks for noticing this, Brandt!
I have the fear that this may increase the time of collections substantially because even if many of them won't have references at all, they still net to be visited and they count towards the time complexity of the algorithm that detects isolated sub-cycles. I mention this because this could affect an unbounded number of instances per type. For example, some of the types that are untracked.
Maybe I am missing something but we could mark them as having GC support unconditionally but still leave them untracking and unconditionally add a tracking call on setattribute.
brandtbucher commentedon Oct 9, 2020
Hm, I’m not sure that would be enough. Consider the case of a class that registers its instances in a collection of some sort:
This is probably more common (and problematic) than my example above.
At the same time, I agree that it’s not *ideal* to track all of these objects automatically. Anyone setting __slots__ is probably planning on creating lots of “cheap” instances. If they do accidentally create cycles, though, I feel the memory hit then would be worse than any collection overhead.
I’m just not sure I see a way to fix this without tracking them all.
1 remaining item
brandtbucher commentedon Oct 14, 2020
Using the following patch:
master...brandtbucher:track-all-heap-types
I got the following pyperformance results (with PGO/LTO and CPU isolation, insignificant rows omitted):
2020-10-13_20-04-master-7992579cd27f.json.gz
============================================
Performance version: 1.0.0
Report on Linux-4.4.0-190-generic-x86_64-with-glibc2.23
Number of logical CPUs: 8
Start date: 2020-10-14 07:49:46.738847
End date: 2020-10-14 08:12:58.050557
2020-10-13_20-37-track-all-heap-types-63d8d25867b5.json.gz
==========================================================
Performance version: 1.0.0
Report on Linux-4.4.0-190-generic-x86_64-with-glibc2.23
Number of logical CPUs: 8
Start date: 2020-10-14 08:29:52.009796
End date: 2020-10-14 08:52:49.230214
+-------------------------+----------------------------------------------+------------------------------------------------------------+--------------+-----------------------+
| Benchmark | 2020-10-13_20-04-master-7992579cd27f.json.gz | 2020-10-13_20-37-track-all-heap-types-63d8d25867b5.json.gz | Change | Significance |
+=========================+==============================================+============================================================+==============+=======================+
+-------------------------+----------------------------------------------+------------------------------------------------------------+--------------+-----------------------+
| chameleon | 13.7 ms | 13.1 ms | 1.04x faster | Significant (t=7.27) |
+-------------------------+----------------------------------------------+------------------------------------------------------------+--------------+-----------------------+
| chaos | 163 ms | 159 ms | 1.03x faster | Significant (t=5.39) |
+-------------------------+----------------------------------------------+------------------------------------------------------------+--------------+-----------------------+
| crypto_pyaes | 160 ms | 156 ms | 1.02x faster | Significant (t=7.29) |
+-------------------------+----------------------------------------------+------------------------------------------------------------+--------------+-----------------------+
| deltablue | 10.9 ms | 10.7 ms | 1.03x faster | Significant (t=4.36) |
+-------------------------+----------------------------------------------+------------------------------------------------------------+--------------+-----------------------+
| django_template | 73.1 ms | 71.2 ms | 1.03x faster | Significant (t=4.42) |
+-------------------------+----------------------------------------------+------------------------------------------------------------+--------------+-----------------------+
| go | 373 ms | 381 ms | 1.02x slower | Significant (t=-4.71) |
+-------------------------+----------------------------------------------+------------------------------------------------------------+--------------+-----------------------+
| nbody | 206 ms | 200 ms | 1.03x faster | Significant (t=7.52) |
+-------------------------+----------------------------------------------+------------------------------------------------------------+--------------+-----------------------+
| regex_dna | 295 ms | 288 ms | 1.02x faster | Significant (t=5.26) |
+-------------------------+----------------------------------------------+------------------------------------------------------------+--------------+-----------------------+
| richards | 103 ms | 101 ms | 1.03x faster | Significant (t=5.40) |
+-------------------------+----------------------------------------------+------------------------------------------------------------+--------------+-----------------------+
| scimark_sparse_mat_mult | 6.96 ms | 7.17 ms | 1.03x slower | Significant (t=-4.93) |
+-------------------------+----------------------------------------------+------------------------------------------------------------+--------------+-----------------------+
| spectral_norm | 218 ms | 207 ms | 1.05x faster | Significant (t=10.29) |
+-------------------------+----------------------------------------------+------------------------------------------------------------+--------------+-----------------------+
| unpack_sequence | 101 ns | 84.1 ns | 1.20x faster | Significant (t=36.84) |
+-------------------------+----------------------------------------------+------------------------------------------------------------+--------------+-----------------------+
Note that I used pyperformance==1.0.0, since 1.0.1 won't run on 3.10.
In general, it looks like the patch has no real performance impact. The result for unpack_sequence is *extremely* surprising... I'm quite skeptical of it, although it looks like it is one of the more "micro" benchmarks in the suite.
All things considered, I believe we should fix this... I personally view memory leaks as second only to crashes and incorrect behavior in terms of severity.
pablogsal commentedon Oct 14, 2020
Thanks a lot, Brandt for doing the benchmarking!
I think that unpack_sequence is **very** affected by code locality. I was debugging it recently with Linux perf (https://perf.wiki.kernel.org/index.php/Main_Page) and it shows that is extremely affected by L1-L2 cache performance. I still need to understand why, but yeah, I am fine ignoring it.
Yeah, I very much agree with you. Could you convert your patch into a PR?
brandtbucher commentedon Oct 14, 2020
No problem. I just need to rework some hacks in test_finalization where we use empty __slots__ to create non-GC types. I think I can just create a simple utility in _testcapi to untrack instances for this purpose.
pablogsal commentedon Oct 14, 2020
I have the feeling that I am misunderstanding what you refer to, but if is **untrack** what you need, there is gc.untrack()
brandtbucher commentedon Oct 14, 2020
I'm not sure that's true... :)
Although perhaps track()/untrack() functions *could* be useful to add to the gc module... but that's a separate conversation.
pablogsal commentedon Oct 14, 2020
Oh boy, I need to....ehem....yeah I will be back in....one second...
pablogsal commentedon Oct 14, 2020
In any case, you can expose:
https://docs.python.org/3/c-api/gcsupport.html#c.PyObject_GC_UnTrack
in the testcapi module
brandtbucher commentedon Oct 14, 2020
I'll hold off until https://bugs.python.org/issue42039 is resolved.
brandtbucher commentedon Oct 14, 2020
Actually, never mind. Because of the way finalization works, we need to create untracked *types* for these tests, not untracked *instances*.
PR up soon.
brandtbucher commentedon Oct 15, 2020
New changeset c13b847 by Brandt Bucher in branch 'master':
bpo-41984: GC track all user classes (GH-22701)
c13b847
brandtbucher commentedon Oct 15, 2020
New changeset d197b2b by Miss Skeleton (bot) in branch '3.9':
bpo-41984: GC track all user classes (GH-22701/GH-22702)
d197b2b
brandtbucher commentedon Oct 15, 2020
New changeset aeb66c1 by Miss Skeleton (bot) in branch '3.8':
bpo-41984: GC track all user classes (GH-22701/GH-22707)
aeb66c1