[Numpy-discussion] NEP: Random Number Generator Policy

Charles R Harris charlesr.harris at gmail.com
Mon Jun 4 00:22:17 EDT 2018


On Sat, Jun 2, 2018 at 1:04 PM, Robert Kern <robert.kern at gmail.com> wrote:

> As promised distressingly many months ago, I have written up a NEP about
> relaxing the stream-compatibility policy that we currently have.
>
> https://github.com/numpy/numpy/pull/11229
> https://github.com/rkern/numpy/blob/nep/rng/doc/neps/
> nep-0019-rng-policy.rst
>
> I particularly invite comment on the two lists of methods that we still
> would make strict compatibility guarantees for.
>
> ---
>
> ==============================
> Random Number Generator Policy
> ==============================
>
> :Author: Robert Kern <robert.kern at gmail.com>
> :Status: Draft
> :Type: Standards Track
> :Created: 2018-05-24
>
>
> Abstract
> --------
>
> For the past decade, NumPy has had a strict backwards compatibility policy
> for
> the number stream of all of its random number distributions.  Unlike other
> numerical components in ``numpy``, which are usually allowed to return
> different when results when they are modified if they remain correct, we
> have
> obligated the random number distributions to always produce the exact same
> numbers in every version.  The objective of our stream-compatibility
> guarantee
> was to provide exact reproducibility for simulations across numpy versions
> in
> order to promote reproducible research.  However, this policy has made it
> very
> difficult to enhance any of the distributions with faster or more accurate
> algorithms.  After a decade of experience and improvements in the
> surrounding
> ecosystem of scientific software, we believe that there are now better
> ways to
> achieve these objectives.  We propose relaxing our strict
> stream-compatibility
> policy to remove the obstacles that are in the way of accepting
> contributions
> to our random number generation capabilities.
>
>
> The Status Quo
> --------------
>
> Our current policy, in full:
>
>     A fixed seed and a fixed series of calls to ``RandomState`` methods
> using the
>     same parameters will always produce the same results up to roundoff
> error
>     except when the values were incorrect.  Incorrect values will be fixed
> and
>     the NumPy version in which the fix was made will be noted in the
> relevant
>     docstring.  Extension of existing parameter ranges and the addition of
> new
>     parameters is allowed as long the previous behavior remains unchanged.
>
> This policy was first instated in Nov 2008 (in essence; the full set of
> weasel
>

Instituted?


> words grew over time) in response to a user wanting to be sure that the
> simulations that formed the basis of their scientific publication could be
> reproduced years later, exactly, with whatever version of ``numpy`` that
> was
> current at the time.  We were keen to support reproducible research, and
> it was
> still early in the life of ``numpy.random``.  We had not seen much cause to
> change the distribution methods all that much.
>
> We also had not thought very thoroughly about the limits of what we really
> could promise (and by “we” in this section, we really mean Robert Kern,
> let’s
> be honest).  Despite all of the weasel words, our policy overpromises
> compatibility.  The same version of ``numpy`` built on different
> platforms, or
> just in a different way could cause changes in the stream, with varying
> degrees
> of rarity.  The biggest is that the ``.multivariate_normal()`` method
> relies on
> ``numpy.linalg`` functions.  Even on the same platform, if one links
> ``numpy``
> with a different LAPACK, ``.multivariate_normal()`` may well return
> completely
> different results.  More rarely, building on a different OS or CPU can
> cause
> differences in the stream.  We use C ``long`` integers internally for
> integer
> distribution (it seemed like a good idea at the time), and those can vary
> in
> size depending on the platform.  Distribution methods can overflow their
> internal C ``longs`` at different breakpoints depending on the platform and
> cause all of the random variate draws that follow to be different.
>
> And even if all of that is controlled, our policy still does not provide
> exact
> guarantees across versions.  We still do apply bug fixes when correctness
> is at
> stake.  And even if we didn’t do that, any nontrivial program does more
> than
> just draw random numbers.  They do computations on those numbers, transform
> those with numerical algorithms from the rest of ``numpy``, which is not
> subject to so strict a policy.  Trying to maintain stream-compatibility
> for our
> random number distributions does not help reproducible research for these
> reasons.
>
> The standard practice now for bit-for-bit reproducible research is to pin
> all
> of the versions of code of your software stack, possibly down to the OS
> itself.
> The landscape for accomplishing this is much easier today than it was in
> 2008.
> We now have ``pip``.  We now have virtual machines.  Those who need to
> reproduce simulations exactly now can (and ought to) do so by using the
> exact
> same version of ``numpy``.  We do not need to maintain stream-compatibility
> across ``numpy`` versions to help them.
>
> Our stream-compatibility guarantee has hindered our ability to make
> improvements to ``numpy.random``.  Several first-time contributors have
> submitted PRs to improve the distributions, usually by implementing a
> faster,
> or more accurate algorithm than the one that is currently there.
> Unfortunately, most of them would have required breaking the stream to do
> so.
> Blocked by our policy, and our inability to work around that policy, many
> of
> those contributors simply walked away.
>
>
> Implementation
> --------------
>
> We propose first freezing ``RandomState`` as it is and developing a new RNG
> subsystem alongside it.  This allows anyone who has been relying on our old
> stream-compatibility guarantee to have plenty of time to migrate.
> ``RandomState`` will be considered deprecated, but with a long deprecation
> cycle, at least a few years.  Deprecation warnings will start silent but
> become
> increasingly noisy over time.  Bugs in the current state of the code will
> *not*
> be fixed if fixing them would impact the stream.  However, if changes in
> the
> rest of ``numpy`` would break something in the ``RandomState`` code, we
> will
> fix ``RandomState`` to continue working (for example, some change in the
> C API).  No new features will be added to ``RandomState``.  Users should
> migrate to the new subsystem as they are able to.
>
> Work on a proposed `new PRNG subsystem
> <https://github.com/bashtage/randomgen>`_ is already underway.  The
> specifics
> of the new design are out of scope for this NEP and up for much
> discussion, but
> we will discuss general policies that will guide the evolution of whatever
> code
> is adopted.
>
> First, we will maintain API source compatibility just as we do with the
> rest of
> ``numpy``.  If we *must* make a breaking change, we will only do so with an
> appropriate deprecation period and warnings.
>
> Second, breaking stream-compatibility in order to introduce new features or
> improve performance will be *allowed* with *caution*.  Such changes will be
> considered features, and as such will be no faster than the standard
> release
> cadence of features (i.e. on ``X.Y`` releases, never ``X.Y.Z``).  Slowness
> is
> not a bug.  Correctness bug fixes that break stream-compatibility can
> happen on
> bugfix releases, per usual, but developers should consider if they can wait
> until the next feature release.  We encourage developers to strongly weight
> user’s pain from the break in stream-compatibility against the
> improvements.
> One example of a worthwhile improvement would be to change algorithms for
> a significant increase in performance, for example, moving from the
> `Box-Muller
> transform <https://en.wikipedia.org/wiki/Box%E2%80%93Muller_transform>`_
> method
> of Gaussian variate generation to the faster `Ziggurat algorithm
> <https://en.wikipedia.org/wiki/Ziggurat_algorithm>`_.  An example of an
> unworthy improvement would be tweaking the Ziggurat tables just a little
> bit.
>
> Any new design for the RNG subsystem will provide a choice of different
> core
> uniform PRNG algorithms.  We will be more strict about a select subset of
> methods on these core PRNG objects.  They MUST guarantee
> stream-compatibility
> for a minimal, specified set of methods which are chosen to make it easier
> to
> compose them to build other distributions.  Namely,
>
>     * ``.bytes()``
>     * ``.random_uintegers()``
>     * ``.random_sample()``
>
> Furthermore, the new design should also provide one generator class (we
> shall
> call it ``StableRandom`` for discussion purposes) that provides a slightly
> broader subset of distribution methods for which stream-compatibility is
> *guaranteed*.  The point of ``StableRandom`` is to provide something that
> can
> be used in unit tests so projects that currently have tests which rely on
> the
> precise stream can be migrated off of ``RandomState``.  For the best
> transition, ``StableRandom`` should use as its core uniform PRNG the
> current
> MT19937 algorithm.  As best as possible, the API for the distribution
> methods
> that are provided on ``StableRandom`` should match their counterparts on
> ``RandomState``.  They should provide the same stream that the current
> version
> of ``RandomState`` does.  Because their intended use is for unit tests, we
> do
> not need the performance improvements from the new algorithms that will be
> introduced by the new subsystem.
>
> The list of ``StableRandom`` methods should be chosen to support unit
> tests:
>
>     * ``.randint()``
>     * ``.uniform()``
>     * ``.normal()``
>     * ``.standard_normal()``
>     * ``.choice()``
>     * ``.shuffle()``
>     * ``.permutation()``
>
>
> Not Versioning
> --------------
>
> For a long time, we considered that the way to allow algorithmic
> improvements
> while maintaining the stream was to apply some form of versioning.  That
> is,
> every time we make a stream change in one of the distributions, we
> increment
> some version number somewhere.  ``numpy.random`` would keep all past
> versions
> of the code, and there would be a way to get the old versions.  Proposals
> of
> how to do this exactly varied widely, but we will not exhaustively list
> them
> here.  We spent years going back and forth on these designs and were not
> able
> to find one that sufficed.  Let that time lost, and more importantly, the
> contributors that we lost while we dithered, serve as evidence against the
> notion.
>
> Concretely, adding in versioning makes maintenance of ``numpy.random``
> difficult.  Necessarily, we would be keeping lots of versions of the same
> code
> around.  Adding a new algorithm safely would still be quite hard.
>
> But most importantly, versioning is fundamentally difficult to *use*
> correctly.
> We want to make it easy and straightforward to get the latest, fastest,
> best
> versions of the distribution algorithms; otherwise, what's the point?  The
> way
> to make that easy is to make the latest the default.  But the default will
> necessarily change from release to release, so the user’s code would need
> to be
> altered anyway to specify the specific version that one wants to replicate.
>
> Adding in versioning to maintain stream-compatibility would still only
> provide
> the same level of stream-compatibility that we currently do, with all of
> the
> limitations described earlier.  Given that the standard practice for such
> needs
> is to pin the release of ``numpy`` as a whole, versioning ``RandomState``
> alone
> is superfluous.
>

This section is a bit unclear. Would it be correct to say that the rng
version is the numpy version? If so, it might be best to say that up front
before justifying it.


>
>
> Discussion
> ----------
>
> - https://mail.python.org/pipermail/numpy-discussion/
> 2018-January/077608.html
> - https://github.com/numpy/numpy/pull/10124#issuecomment-350876221
>
>
> Copyright
> ---------
>
> This document has been placed in the public domain.
>
>
>
Mostly off topic, but I note that the new module proposes integers of
various lengths using the Python half open ranges. I would like to suggest
that we modify that just a hair so we can specify the whole range in the
integer interval specification. For instance, the full range of an 8 bit
unsigned integer could be given as `(0, 0)`, i.e., (0, 255 + 1). This would
be most useful for the biggest (64 bit) types, but I am more thinking of
the case where sequences of ranges can be used.

Chuck
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