Speed goes down by near 3 times since 1.14 #15650
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Please use text rather than images whenever possible. Possible duplicate of gh-14776. Could you check what happens if you define |
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Yes, our program calls thousands of times randint, shuffle, etc.
Here: https://github.com/juanfal/AE-4 <https://github.com/juanfal/AE-4>
Defining that constant to null experimental arrays doesn’t have any effect
```
$ time ae4.py PruebaMultiAsoc --setRandomSeed=2 --saveExcel
A B C
1: [ 100 100 100] Tot: 300
2: [ 400 400 400] Tot: 1200
3: [ 1600 1600 1600] Tot: 4800
4: [ 6400 6400 6400] Tot: 19200
5: [ 25600 25600 25600] Tot: 76800
6: [ 33140 33350 33510] Tot: 100000
7: [ 32740 33608 33652] Tot: 100000
8: [ 32936 33728 33336] Tot: 100000
9: [ 32842 33774 33384] Tot: 100000
10: [ 32794 33762 33444] Tot: 100000
real 0m6,317s
user 0m4,815s
sys 0m0,263s
mb12:38:57 AE-4 $ pip3 install --upgrade numpy
Collecting numpy
Using cached https://files.pythonhosted.org/packages/2f/5b/2cc2b9285e8b2ca8d2c1e4a2cbf1b12d70a2488ea78170de1909bca725f2/numpy-1.18.1-cp37-cp37m-macosx_10_9_x86_64.whl
Installing collected packages: numpy
Found existing installation: numpy 1.14.4
Uninstalling numpy-1.14.4:
Successfully uninstalled numpy-1.14.4
Successfully installed numpy-1.18.1
mb12:39:39 AE-4 $ ae4.py PruebaMultiAsoc --setRandomSeed=2 --saveExcel
A B C
1: [ 100 100 100] Tot: 300
2: [ 400 400 400] Tot: 1200
3: [ 1600 1600 1600] Tot: 4800
4: [ 6400 6400 6400] Tot: 19200
5: [ 25600 25600 25600] Tot: 76800
6: [ 33140 33350 33510] Tot: 100000
7: [ 32740 33608 33652] Tot: 100000
8: [ 32936 33728 33336] Tot: 100000
9: [ 32842 33774 33384] Tot: 100000
10: [ 32794 33762 33444] Tot: 100000
mb12:40:01 AE-4 $ time ae4.py PruebaMultiAsoc --setRandomSeed=2 --saveExcel
A B C
1: [ 100 100 100] Tot: 300
2: [ 400 400 400] Tot: 1200
3: [ 1600 1600 1600] Tot: 4800
4: [ 6400 6400 6400] Tot: 19200
5: [ 25600 25600 25600] Tot: 76800
6: [ 33140 33350 33510] Tot: 100000
7: [ 32740 33608 33652] Tot: 100000
8: [ 32936 33728 33336] Tot: 100000
9: [ 32842 33774 33384] Tot: 100000
10: [ 32794 33762 33444] Tot: 100000
real 0m14,391s
user 0m13,261s
sys 0m0,269s
```
… El 26 feb 2020, a las 12:37, Matti Picus ***@***.***> escribió:
Please use text rather than images whenever possible.
Possible duplicate of gh-14776 <#14776>. Could you check what happens if you define NUMPY_EXPERIMENTAL_ARRAY_FUNCTION=0, as per this note <https://numpy.org/devdocs/reference/arrays.classes.html?highlight=__array_function__#numpy.class.__array_function__> ? If indeed that is the problem, it might indicate that your code is calling numpy functions very often from python, and might benefit from a refactor to either call with larger arrays or otherwise vectorize to call the same function fewer times.
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If you are on a <4.6 Linux kernel, this could be the reason as well: gh-15545 can you check that, and check if running your scripts with |
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Is the It's difficult to hone in on what the potential problem is with so many variables and without introspecting the code. If you could generate simple examples from the parts of your code that highlight how you are using numpy-specific functionality that would be very helpful. For what it's worth, I actually see performance improvement in the numpy 1.14.6 on Python 3.6.10 >>> import numpy as np
>>> n = int(1e7)
>>> %timeit np.random.randint(0, max_int, size=n)
58.6 ms ± 1.18 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)
>>> a = np.random.random(n)
>>> %timeit np.random.shuffle(a)
501 ms ± 6.02 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)and with numpy-1.17.5 on Python 3.8.1: >>> import numpy as np
>>> n = int(1e7)
>>> %timeit np.random.randint(0, max_int, size=n)
42.3 ms ± 56.9 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)
>>> a = np.random.random(n)
>>> %timeit np.random.shuffle(a)
422 ms ± 4.08 ms per loop (mean ± std. dev. of 7 runs, 1 loop each) |
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I’m on last version of OS X. The constant declaration proposed doesn’t make any difference
Thanks for your quick answer
…--
Juan miPad
El 26 feb 2020, a las 15:46, Sebastian Berg ***@***.***> escribió:
If you are on a <4.6 Linux kernel, this could be the reason as well: gh-15545 can you check that, and check if running your scripts with NUMPY_EXPERIMENTAL_ARRAY_FUNCTION=0 makes a difference?
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@juanfal can you help us out and try to find out what part got slower? If random numbers are not the reason, and |
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Possibly related to #15511. |
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Sending you profiles of both executions. I did a change on numpy, only, kept my python version
$ python -V
Python 2.7.17
the numpy versions are in the name of the profiles, I am sending (command:
python3 -m cProfile -s time ae4.py PruebaMultiAsoc --setRandomSeed=2 > projutime.txt
- Juan F.
El 1 mar 2020, a las 23:34, Charles Harris ***@***.***> escribió:
Possibly related to #15511 <#15511>.
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A B C
1: [ 100 100 100] Tot: 300
2: [ 400 400 400] Tot: 1200
3: [ 1600 1600 1600] Tot: 4800
4: [ 6400 6400 6400] Tot: 19200
5: [ 25600 25600 25600] Tot: 76800
6: [ 33190 33308 33502] Tot: 100000
7: [ 32932 33322 33746] Tot: 100000
8: [ 32736 33256 34008] Tot: 100000
9: [ 32784 33268 33948] Tot: 100000
10: [ 32926 33102 33972] Tot: 100000
7107320 function calls (7102105 primitive calls) in 15.971 seconds
Ordered by: internal time
ncalls tottime percall cumtime percall filename:lineno(function)
702300 6.640 0.000 10.284 0.000 {method 'randint' of 'numpy.random.mtrand.RandomState' objects}
702300 1.355 0.000 3.644 0.000 _dtype.py:319(_name_get)
11 1.289 0.117 11.577 1.052 ae4.py:172(doDistribute)
10 1.238 0.124 3.762 0.376 ae4.py:328(doAssociationQueueAndConsume)
1000 0.964 0.001 0.968 0.001 {method 'shuffle' of 'numpy.random.mtrand.RandomState' objects}
702300 0.910 0.000 2.289 0.000 numerictypes.py:365(issubdtype)
1404600 0.898 0.000 1.291 0.000 numerictypes.py:293(issubclass_)
2107043 0.481 0.000 0.481 0.000 {built-in method builtins.issubclass}
10520 0.355 0.000 0.356 0.000 ae4.py:68(push)
175500 0.233 0.000 0.233 0.000 ae4.py:112(__next__)
178 0.201 0.001 0.201 0.001 {method 'read' of '_io.FileIO' objects}
206826 0.154 0.000 0.154 0.000 ae4.py:450(iFrom_id)
33/31 0.132 0.004 0.137 0.004 {built-in method _imp.create_dynamic}
175900 0.123 0.000 0.123 0.000 ae4.py:109(toIter)
123376 0.099 0.000 0.252 0.000 ae4.py:459(<listcomp>)
1000 0.093 0.000 0.637 0.001 ae4.py:271(doAssociation)
123376 0.081 0.000 0.413 0.000 ae4.py:499(iAssociatedTargetGetList)
123376 0.080 0.000 0.332 0.000 ae4.py:457(iListFrom_idList)
175516 0.075 0.000 0.309 0.000 {built-in method builtins.next}
4072 0.050 0.000 0.050 0.000 {method 'reduce' of 'numpy.ufunc' objects}
2000 0.042 0.000 0.153 0.000 {method 'permutation' of 'numpy.random.mtrand.RandomState' objects}
7182 0.037 0.000 0.057 0.000 {built-in method numpy.array}
178 0.036 0.000 0.036 0.000 {built-in method marshal.loads}
317 0.026 0.000 0.026 0.000 {built-in method builtins.compile}
2000 0.021 0.000 0.021 0.000 _internal.py:262(__array_interface__)
175596 0.020 0.000 0.020 0.000 {built-in method builtins.abs}
887 0.020 0.000 0.020 0.000 {built-in method posix.stat}
2000 0.016 0.000 0.084 0.000 _internal.py:274(_get_void_ptr)
178 0.013 0.000 0.214 0.001 <frozen importlib._bootstrap_external>:914(get_data)
2000 0.013 0.000 0.013 0.000 __init__.py:497(cast)
18 0.011 0.001 0.011 0.001 {built-in method posix.listdir}
392/389 0.009 0.000 0.014 0.000 {built-in method builtins.__build_class__}
2000 0.009 0.000 0.009 0.000 {method 'from_buffer' of '_ctypes.PyCArrayType' objects}
3018 0.009 0.000 0.010 0.000 {built-in method numpy.core._multiarray_umath.implement_array_function}
1036 0.007 0.000 0.007 0.000 {built-in method numpy.zeros}
2000 0.007 0.000 0.091 0.000 _internal.py:291(__init__)
21 0.007 0.000 0.007 0.000 {built-in method io.open}
4050 0.006 0.000 0.059 0.000 {method 'sum' of 'numpy.ndarray' objects}
412 0.005 0.000 0.042 0.000 <frozen importlib._bootstrap_external>:1356(find_spec)
664 0.005 0.000 0.005 0.000 {method 'sub' of 're.Pattern' objects}
3737 0.005 0.000 0.005 0.000 {method 'join' of 'str' objects}
10 0.005 0.000 0.021 0.002 ae4.py:703(saveConf)
71 0.005 0.000 0.005 0.000 {built-in method posix.getcwd}
2000 0.005 0.000 0.005 0.000 {built-in method _ctypes.pointer}
16843/16567 0.005 0.000 0.005 0.000 {built-in method builtins.len}
178 0.004 0.000 0.266 0.001 <frozen importlib._bootstrap_external>:793(get_code)
515/1 0.003 0.000 15.972 15.972 {built-in method builtins.exec}
1638 0.003 0.000 0.003 0.000 {method 'astype' of 'numpy.ndarray' objects}
135/44 0.003 0.000 0.008 0.000 sre_parse.py:469(_parse)
33/23 0.003 0.000 0.045 0.002 {built-in method _imp.exec_dynamic}
4050 0.003 0.000 0.052 0.000 _methods.py:36(_sum)
614 0.003 0.000 0.003 0.000 _inspect.py:67(getargs)
3669 0.003 0.000 0.003 0.000 {built-in method builtins.hasattr}
4032 0.003 0.000 0.050 0.000 _asarray.py:16(asarray)
1000 0.003 0.000 0.971 0.001 ae4.py:87(shuffle)
3000 0.003 0.000 0.003 0.000 ae4.py:281(<listcomp>)
2000 0.003 0.000 0.010 0.000 <__array_function__ internals>:2(may_share_memory)
1000 0.003 0.000 0.010 0.000 ae4.py:63(__init__)
4442 0.003 0.000 0.003 0.000 {built-in method builtins.getattr}
223 0.002 0.000 0.056 0.000 <frozen importlib._bootstrap>:882(_find_spec)
332 0.002 0.000 0.002 0.000 {method 'findall' of 're.Pattern' objects}
228/6 0.002 0.000 0.597 0.099 <frozen importlib._bootstrap>:978(_find_and_load)
1012 0.002 0.000 0.004 0.000 {built-in method builtins.iter}
9615 0.002 0.000 0.002 0.000 {built-in method builtins.isinstance}
2124 0.002 0.000 0.006 0.000 <frozen importlib._bootstrap_external>:56(_path_join)
557 0.002 0.000 0.003 0.000 <frozen importlib._bootstrap>:157(_get_module_lock)
2124 0.002 0.000 0.003 0.000 <frozen importlib._bootstrap_external>:58(<listcomp>)
557 0.002 0.000 0.002 0.000 <frozen importlib._bootstrap>:78(acquire)
317 0.002 0.000 0.052 0.000 overrides.py:154(decorator)
356 0.002 0.000 0.004 0.000 <frozen importlib._bootstrap_external>:271(cache_from_source)
994 0.002 0.000 0.004 0.000 <__array_function__ internals>:2(empty_like)
342 0.002 0.000 0.003 0.000 functools.py:37(update_wrapper)
287 0.002 0.000 0.007 0.000 overrides.py:72(verify_matching_signatures)
19 0.002 0.000 0.004 0.000 __init__.py:316(namedtuple)
216/6 0.002 0.000 0.594 0.099 <frozen importlib._bootstrap>:663(_load_unlocked)
1008/192 0.002 0.000 0.462 0.002 <frozen importlib._bootstrap>:1009(_handle_fromlist)
332 0.002 0.000 0.012 0.000 textwrap.py:414(dedent)
216 0.002 0.000 0.007 0.000 <frozen importlib._bootstrap>:504(_init_module_attrs)
557 0.002 0.000 0.002 0.000 <frozen importlib._bootstrap>:103(release)
218 0.002 0.000 0.051 0.000 <frozen importlib._bootstrap_external>:1240(_get_spec)
254/41 0.002 0.000 0.005 0.000 sre_compile.py:71(_compile)
10 0.001 0.000 0.008 0.001 ae4.py:227(doGrouping)
1520 0.001 0.000 0.001 0.000 ae4.py:90(free)
2316 0.001 0.000 0.001 0.000 <frozen importlib._bootstrap>:222(_verbose_message)
673 0.001 0.000 0.001 0.000 {built-in method __new__ of type object at 0x106521ab0}
103 0.001 0.000 0.002 0.000 sre_compile.py:276(_optimize_charset)
178 0.001 0.000 0.002 0.000 <frozen importlib._bootstrap_external>:438(_classify_pyc)
658 0.001 0.000 0.001 0.000 {method 'format' of 'str' objects}
228/6 0.001 0.000 0.596 0.099 <frozen importlib._bootstrap>:948(_find_and_load_unlocked)
505 0.001 0.000 0.018 0.000 re.py:271(_compile)
1000 0.001 0.000 0.001 0.000 ae4.py:105(__iter__)
3170/1890 0.001 0.000 0.003 0.000 encoder.py:333(_iterencode_dict)
2000 0.001 0.000 0.001 0.000 _internal.py:259(__init__)
1 0.001 0.001 0.001 0.001 {method 'read' of '_io.TextIOWrapper' objects}
1911 0.001 0.000 0.001 0.000 sre_parse.py:233(__next)
178/6 0.001 0.000 0.594 0.099 <frozen importlib._bootstrap_external>:722(exec_module)
618 0.001 0.000 0.005 0.000 _inspect.py:98(getargspec)
2000 0.001 0.000 0.001 0.000 _internal.py:340(data)
4616 0.001 0.000 0.001 0.000 {method 'rstrip' of 'str' objects}
211 0.001 0.000 0.002 0.000 <frozen importlib._bootstrap_external>:574(spec_from_file_location)
178 0.001 0.000 0.038 0.000 <frozen importlib._bootstrap_external>:523(_compile_bytecode)
216 0.001 0.000 0.002 0.000 <frozen importlib._bootstrap>:318(__exit__)
1709 0.001 0.000 0.001 0.000 sre_parse.py:164(__getitem__)
1898 0.001 0.000 0.001 0.000 {method 'rpartition' of 'str' objects}
11 0.001 0.000 0.002 0.000 function_base.py:2154(_vectorize_call)
18 0.001 0.000 0.002 0.000 <frozen importlib._bootstrap_external>:1190(_path_hooks)
10 0.001 0.000 0.005 0.001 __init__.py:120(dump)
1015 0.001 0.000 0.001 0.000 {built-in method builtins.print}
216/213 0.001 0.000 0.146 0.001 <frozen importlib._bootstrap>:576(module_from_spec)
311/101 0.001 0.000 0.001 0.000 sre_parse.py:174(getwidth)
873 0.001 0.000 0.020 0.000 <frozen importlib._bootstrap_external>:74(_path_stat)
1510/1310 0.001 0.000 0.002 0.000 encoder.py:277(_iterencode_list)
1689 0.001 0.000 0.001 0.000 {method 'startswith' of 'str' objects}
329 0.001 0.000 0.004 0.000 <frozen importlib._bootstrap>:194(_lock_unlock_module)
1587 0.001 0.000 0.002 0.000 sre_parse.py:254(get)
1875 0.001 0.000 0.001 0.000 {built-in method builtins.setattr}
1003 0.001 0.000 0.001 0.000 ae4.py:807(printv)
11 0.001 0.000 0.001 0.000 _methods.py:167(_var)
6 0.001 0.000 0.405 0.068 __init__.py:1(<module>)
211 0.001 0.000 0.003 0.000 <frozen importlib._bootstrap_external>:369(_get_cached)
211 0.001 0.000 0.002 0.000 <frozen importlib._bootstrap_external>:1351(_get_spec)
483 0.001 0.000 0.007 0.000 <frozen importlib._bootstrap_external>:1203(_path_importer_cache)
531 0.001 0.000 0.001 0.000 <frozen importlib._bootstrap>:416(parent)
1 0.001 0.001 15.972 15.972 ae4.py:8(<module>)
1443 0.001 0.000 0.001 0.000 {built-in method _imp.acquire_lock}
273 0.001 0.000 0.005 0.000 function_base.py:441(add_newdoc)
535 0.001 0.000 0.001 0.000 {built-in method from_bytes}
4269 0.001 0.000 0.001 0.000 {method 'append' of 'list' objects}
2000 0.001 0.000 0.001 0.000 ae4.py:93(__len__)
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227 0.001 0.000 0.001 0.000 <frozen importlib._bootstrap>:176(cb)
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1880 0.001 0.000 0.001 0.000 {method 'write' of '_io.TextIOWrapper' objects}
324/18 0.001 0.000 0.464 0.026 {built-in method builtins.__import__}
389 0.001 0.000 0.004 0.000 <frozen importlib._bootstrap>:403(cached)
382 0.000 0.000 0.000 0.000 {method 'update' of 'dict' objects}
122/41 0.000 0.000 0.009 0.000 sre_parse.py:411(_parse_sub)
2000 0.000 0.000 0.000 0.000 multiarray.py:1309(may_share_memory)
340 0.000 0.000 0.000 0.000 functools.py:67(wraps)
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227 0.000 0.000 0.001 0.000 <frozen importlib._bootstrap>:58(__init__)
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178 0.000 0.000 0.000 0.000 <frozen importlib._bootstrap_external>:401(_check_name_wrapper)
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666 0.000 0.000 0.000 0.000 sre_parse.py:249(match)
564 0.000 0.000 0.000 0.000 sre_parse.py:160(__len__)
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41 0.000 0.000 0.000 0.000 {built-in method builtins.sorted}
41 0.000 0.000 0.001 0.000 sre_compile.py:536(_compile_info)
614 0.000 0.000 0.000 0.000 _inspect.py:43(iscode)
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1 0.000 0.000 0.002 0.002 parse.py:28(<module>)
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Ah, sounds like you were running into this: #15460 which has been fixed recently (not released though). |
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You might be able to test against one of the pre-release wheels at https://7933911d6844c6c53a7d-47bd50c35cd79bd838daf386af554a83.ssl.cf2.rackcdn.com, try the numpy-1.19.0.dev0+20200214170841_1f9ab28 version for your architecture since the manylinux2010 ones after may not be linked with openblas. |
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I think I will close this soon, or is this a big enough issue that we need to consider backporting? |
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Ah fair enough, I guess we are not planning on another 1.17 release in any case. |
Nope. It was hard enough maintaining 1.16 and 1.17 together. 1.18 has looked pretty good so far. |
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Thanks for the info Chuck. @juanfal would be good if you can see if this seems to be the main difference. Based on your timing, it seems like 30% of the call should be gone with the fix, but you see a 3x factor, so I am not quite sure that there is not something else going on as well. |
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I did read your messages but as I am not on the beta numpy dynamic, when I saw the list of packages to install and try, I dismissed the idea of testing the last beta… My apologies. I’ll try the new public version as soon as I see it. Impressed on how actively and competently this group moves. Thanks
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… El 7 mar 2020, a las 20:59, Sebastian Berg ***@***.***> escribió:
Thanks for the info Chuck. @juanfal <https://github.com/juanfal> would be good if you can see if this seems to be the main difference. Based on your timing, it seems like 30% of the call should be gone with the fix, but you see a 3x factor, so I am not quite sure that there is not something else going on as well.
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Is this fixed in 1.19.0? There was a speed bug that was noticeable in calls to randint when only drawing 1 or a few samples. |
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@juanfal would be nice to confirm that it is indeed fixed. I am a bit unsure from the timings whether there may be more to it than the speed issue that we fixed, but for now I think it probably is the reason. |
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The difference between older versions and that was so high and disconcerting, I went back to the older. Later, after latest updates, I haven’t done precise measures, but that difference seems to be vanished.
…-Thanks
El 2 jul 2020, a las 15:17, Sebastian Berg ***@***.***> escribió:
@juanfal <https://github.com/juanfal> would be nice to confirm that it is indeed fixed. I am a bit unsure from the timings whether there may be more to it than the speed issue that we fixed, but for now I think it probably is the reason.
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Executing same code in 2 machines
and
the time it takes in 1.14 is near 3 times faster. Downgrading to 1.14 from 1.17 confirms it:
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