8000 default_rng.integers(2**32) always return 0 · Issue #16066 · numpy/numpy · GitHub
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vstinner opened this issue Apr 24, 2020 · 7 comments · Fixed by #16076
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default_rng.integers(2**32) always return 0 #16066

vstinner opened this issue Apr 24, 2020 · 7 comments · Fixed by #16076
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00 - Bug 06 - Regression component: numpy.random Priority: high High priority, also add milestones for urgent issues
Milestone
1.18.4 release

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@vstinner
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Reproducing code example:

I don't understand why default_rng.integers(2**32) always return 0, whereas 2**32-1, 2**32+1, 2**40, etc. return random numbers as expected. The result is a numpy.int64, so I expected to be able to generate numbers of up 2**64-1.

$ python3.7 -c 'from numpy.random import default_rng; rng=default_rng(); print([rng.integers(2**32) for _ in range(5)])'
[0, 0, 0, 0, 0]

It's fine with 2**40:

$ python3.7 -c 'from numpy.random import default_rng; rng=default_rng(); print([rng.integers(2**40) for _ in range(5)])'
[386296210341, 896689857600, 958588149890, 364800985883, 643738305251]

Numpy/Python version information:

I'm running Fedora 31 with python3-3.7.6-2.fc31.x86_64 and python3-numpy-1.17.4-2.fc31.x86_64:

vstinner@apu$ python3.7
Python 3.7.6 (default, Jan 30 2020, 09:44:41) 
[GCC 9.2.1 20190827 (Red Hat 9.2.1-1)] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import sys, numpy; print(numpy.__version__, sys.version)
1.17.4 3.7.6 (default, Jan 30 2020, 09:44:41) 
[GCC 9.2.1 20190827 (Red Hat 9.2.1-1)]
@seberg seberg added 00 - Bug Priority: high High priority, also add milestones for urgent issues component: numpy.random labels Apr 24, 2020
@seberg
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seberg commented Apr 24, 2020

Probably it uses a 32bit generator in the background for higher speeds, but then casts 2**32 + 1 to 32bit by accident! Its definitely a pretty serious bug.

@seberg seberg added this to the 1.19.0 release milestone Apr 24, 2020
@WarrenWeckesser
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The problem is in master, too:

In [1]: import numpy as np

In [2]: np.__version__
Out[2]: '1.19.0.dev0+6d6df47'

In [3]: rng = np.random.default_rng()

In [4]: rng.integers(2**32, size=8)
Out[4]: array([0, 0, 0, 0, 0, 0, 0, 0])

This was probably introduced in #14777. I'll take a look.

@seberg
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seberg commented Apr 24, 2020

Thanks @WarrenWeckesser, the problem is also in 1.17, so it may be older than your commit. But I do think by now it goes this deep into the code and you seem to know that part. So great, was just considering to ping Kevin :).

@seberg seberg modified the milestones: 1.19.0 release, 1.18.4 release Apr 24, 2020
@seberg
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seberg commented Apr 24, 2020

If we manage, we should try to include the fix in 1.18.4 I think.

@WarrenWeckesser
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WarrenWeckesser commented Apr 24, 2020 •
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@seberg, gh-14777 was backported to 1.17.

@WarrenWeckesser
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It turns out the cause is #14501, which was backported to 1.17.3. I'm working on a fix.

@WarrenWeckesser WarrenWeckesser mentioned this issue Apr 25, 2020
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@WarrenWeckesser
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Proposed fix is in #16076

WarrenWeckesser added a commit to WarrenWeckesser/numpy that referenced this issue Apr 26, 2020
@WarrenWeckesser
BUG: random: Generator.integers(2**32) always returned 0.
e8b8401 
When the input to Generator.integers was 2**32, the value 2**32-1
was being passed as the `rng` argument to the 32-bit Lemire method,
but that method requires `rng` be strictly less then 2**32-1.

The fix was to handle 2**32-1 by calling next_uint32 directly.
This also works for the legacy code without changing the stream
of random integers from `randint`.

Closes numpygh-16066.
charris pushed a commit to charris/numpy that referenced this issue Apr 27, 2020
@WarrenWeckesser @charris
BUG: random: Generator.integers(2**32) always returned 0.
23d7021 
When the input to Generator.integers was 2**32, the value 2**32-1
was being passed as the `rng` argument to the 32-bit Lemire method,
but that method requires `rng` be strictly less then 2**32-1.

The fix was to handle 2**32-1 by calling next_uint32 directly.
This also works for the legacy code without changing the stream
of random integers from `randint`.

Closes numpygh-16066.
@charris charris mentioned this issue Apr 27, 2020
Merged
@iyanmv iyanmv mentioned this issue Nov 18, 2021
Merged
iyanmv added a commit to iyanmv/galois that referenced this issue Nov 18, 2021
@iyanmv
Bump NumPy to 1.18.4
e916418 
NumPy 1.18.4 is the first release with the new random number generator
policy (NEP 19) that is not affected by bugs gh-16066 or gh-14774.

Refs:
 - https://numpy.org/devdocs/release/1.18.4-notes.html
 - numpy/numpy#14774
 - numpy/numpy#16066
iyanmv added a commit to iyanmv/galois that referenced this issue Nov 22, 2021
@iyanmv
Bump NumPy to 1.18.4
8eaab38 
NumPy 1.18.4 is the first release with the new random number generator
policy (NEP 19) that is not affected by bugs gh-16066 or gh-14774.

Refs:
 - https://numpy.org/devdocs/release/1.18.4-notes.html
 - numpy/numpy#14774
 - numpy/numpy#16066
mhostetter pushed a commit to mhostetter/galois that referenced this issue Dec 3, 2021
@iyanmv @mhostetter
Bump NumPy to 1.18.4
7ecf8ec 
NumPy 1.18.4 is the first release with the new random number generator
policy (NEP 19) that is not affected by bugs gh-16066 or gh-14774.

Refs:
 - https://numpy.org/devdocs/release/1.18.4-notes.html
 - numpy/numpy#14774
 - numpy/numpy#16066
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BUG: random: Generator.integers(2**32) always returned 0. WarrenWeckesser/numpy
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