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nep-0053-c-abi-evolution.html
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<section id="nep-53-evolving-the-numpy-c-api-for-numpy-2-0">
<span id="nep53"></span><h1>NEP 53 — Evolving the NumPy C-API for NumPy 2.0<a class="headerlink" href="#nep-53-evolving-the-numpy-c-api-for-numpy-2-0" title="Link to this heading">#</a></h1>
<dl class="field-list simple">
<dt class="field-odd">Author<span class="colon">:</span></dt>
<dd class="field-odd"><p>Sebastian Berg <<a class="reference external" href="mailto:sebastianb%40nvidia.com">sebastianb<span>@</span>nvidia<span>.</span>com</a>></p>
</dd>
<dt class="field-even">Status<span class="colon">:</span></dt>
<dd class="field-even"><p>Draft</p>
</dd>
<dt class="field-odd">Type<span class="colon">:</span></dt>
<dd class="field-odd"><p>Standard</p>
</dd>
<dt class="field-even">Created<span class="colon">:</span></dt>
<dd class="field-even"><p>2022-04-10</p>
</dd>
</dl>
<section id="abstract">
<h2>Abstract<a class="headerlink" href="#abstract" title="Link to this heading">#</a></h2>
<p>The NumPy C-API is used in downstream projects (often through Cython)
to extend NumPy functionality. Supporting these packages generally means
that it is slow to evolve our C-API and some changes are not possible in a
normal NumPy release because NumPy must guarantee backwards compatibility:
A downstream package compiled against an old NumPy version (e.g. 1.17)
will generally work with a new NumPy version (e.g. 1.25).</p>
<p>A NumPy 2.0 release allows to <em>partially</em> break this promise:
We can accept that a SciPy version compiled with NumPy 1.17 (e.g. SciPy 1.10)
will <em>not</em> work with NumPy 2.0.
However, it must still be easy to create a single SciPy binary that is
compatible with both NumPy 1.x and NumPy 2.0.</p>
<p>Given these constraints this NEP outlines a path forward to allow large changes
to our C-API. Similar to Python API changes proposed for NumPy 2.0 the NEP
aims to allow changes to an extend that <em>most</em> downstream packages are expected
to need no or only minor code changes.</p>
<p>The implementation of this NEP consists would consist of two steps:</p>
<ol class="arabic simple">
<li><p>As part of a general improvement, starting with NumPy 1.25 building with
NumPy will by default export an older API version to allow backwards
compatible builds with the newest available NumPy version.
(New API is not available unless opted-in.)</p></li>
<li><p>The NumPy 2.0 will:</p>
<ul class="simple">
<li><p>require recompilation of downstream packages against NumPy 2.0 to be
compatible with NumPy 2.0.</p></li>
<li><p>need a <code class="docutils literal notranslate"><span class="pre">numpy2_compat</span></code> as a dependency when running on NumPy 1.x.</p></li>
<li><p>require some downstream code changes to adapt to changed API.</p></li>
</ul>
</li>
</ol>
</section>
<section id="motivation-and-scope">
<h2>Motivation and scope<a class="headerlink" href="#motivation-and-scope" title="Link to this heading">#</a></h2>
<p>The NumPy API conists of more than 300 functions and numerous macros.
Many of these are outdated: some were only ever used within NumPy,
exist only for compatibility with NumPy’s predecessors, or have no or only
a single known downstream user (i.e. SciPy).</p>
<p>Further, many structs used by NumPy have always been public making it
impossible to change them outside of a major release.
Some changes have been planned for years and were the reason for
<code class="docutils literal notranslate"><span class="pre">NPY_NO_DEPRECATED_API</span></code> and further deprecations as explained in
<a class="reference external" href="https://numpy.org/devdocs/reference/c-api/deprecations.html#c-api-deprecations" title="(in NumPy v2.1.dev0)"><span>C API deprecations</span></a>.</p>
<p>While we probably have little reason to change the layout of the array struct
(<code class="docutils literal notranslate"><span class="pre">PyArrayObject_fields</span></code>) for example the development and improvement of
dtypes would be made easier by changing the <cite>PyArray_Descr</cite> struct.</p>
<p>This NEP proposes a few concrete changes to our C-API mainly as examples.
However, more changes will be handled on a case-by-case basis, and we do not
aim to provide a full list of changes in this NEP.</p>
<section id="adding-state-is-out-of-scope">
<h3>Adding state is out of scope<a class="headerlink" href="#adding-state-is-out-of-scope" title="Link to this heading">#</a></h3>
<p>New developments such as CPython’s support for subinterpreters and the
HPy API may require the NumPy C-API to evolve in a way that may require
(or at least prefer) state to be passed in.</p>
<p>As of now, we do not aim to include changes for this here. We cannot expect
users to do large code updates to pass in for example an <code class="docutils literal notranslate"><span class="pre">HPy</span></code> context
to many NumPy functions.</p>
<p>While we could introduce a second API for this purpose in NumPy 2.0,
we expect that this is unnecessary and that the provisions introduced here:</p>
<ul class="simple">
<li><p>the ability to compile with the newest NumPy version but be compatible with
older versions,</p></li>
<li><p>and the possibility of updating a <code class="docutils literal notranslate"><span class="pre">numpy2_compat</span></code> package.</p></li>
</ul>
<p>should allow to add such an API also in a minor release.</p>
</section>
</section>
<section id="usage-and-impact">
<h2>Usage and impact<a class="headerlink" href="#usage-and-impact" title="Link to this heading">#</a></h2>
<section id="backwards-compatible-builds">
<h3>Backwards compatible builds<a class="headerlink" href="#backwards-compatible-builds" title="Link to this heading">#</a></h3>
<p>Backwards compatible builds will be described in more details in the
documentation.
Briefly, we will allow users to use a definition like:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1">#define NPY_TARGET_VERSION NPY_1_22_API_VERSION</span>
</pre></div>
</div>
<p>to select the version they wish to compile for (lowest version to be
compatible with).
By default the backwards compatibility will be such that the resulting binary
is compatible with the oldest NumPy version which supports the same
version of Python: NumPy 1.19.x was the first to support Python 3.9 and
NumPy 1.25 supports Python 3.9 or greater, so NumPy 1.25 defaults to 1.19
compatibility.
Thus, users of <em>new</em> API may be required to add the define,
but users of who want to be compatible with older versions need not do
anything unless they wish to have exceptionally long compatibility.</p>
<p>The API additions in the past years were so limited that such a change
should be necessary at most for a hand-full of users worldwide.</p>
<p>This mechanism is much the same as the <a class="reference external" href="https://docs.python.org/3/c-api/stable.html">Python limited API</a> since NumPy’s
C-API has a similar need for ABI stability.</p>
</section>
<section id="breaking-the-c-api-and-changing-the-abi">
<h3>Breaking the C-API and changing the ABI<a class="headerlink" href="#breaking-the-c-api-and-changing-the-abi" title="Link to this heading">#</a></h3>
<p>NumPy has too many functions, many of which are aliases. The following
lists <em>examples</em> of things we plan to remove and users will have to adapt
to be compatible with NumPy 2.0:</p>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">PyArray_Mean</span></code> and <code class="docutils literal notranslate"><span class="pre">PyArray_Std</span></code> are untested implementation similar to
<code class="docutils literal notranslate"><span class="pre">arr.mean()</span></code> and <code class="docutils literal notranslate"><span class="pre">arr.std()</span></code>. We are planning on removing these as they
can be replaced with method calls relatively easily.</p></li>
<li><p>The <code class="docutils literal notranslate"><span class="pre">MapIter</span></code> set of API functions (and struct) allows to implement
advanced indexing like semantics downstream. There was a single <em>historic</em>
known user of this (theano) and the use-case would be faster and easier to
implement in a different way. The API is complicated, requires reaching
deep into NumPy to be useful and its exposure makes the implementation
more difficult. Unless new important use cases are found, we propose to
remove it.</p></li>
</ul>
<p>An example for an ABI change is to change the layout of <code class="docutils literal notranslate"><span class="pre">PyArray_Descr</span></code>
(the struct of <code class="docutils literal notranslate"><span class="pre">np.dtype</span></code> instances) to allow a larger maximum itemsize and
new flags (useful for future custom user DTypes).
For this specific change, users who access the structs fields directly
will have to change their code. A downstream search shows that this should
not be very common, the main impacts are:</p>
<ul class="simple">
<li><p>Access of the <code class="docutils literal notranslate"><span class="pre">descr->elsize</span></code> field (and others) would have to be replaced
with a macro’s like <code class="docutils literal notranslate"><span class="pre">PyDataType_ITEMSIZE(descr)</span></code> (NumPy may include a
version check when needed).</p></li>
<li><p>Implementers of user defined dtypes, will have to change a few lines of code
and luckily, there are very few of such user defined dtypes.
(The details are that we rename the struct to <code class="docutils literal notranslate"><span class="pre">PyArray_DescrProto</span></code> for the
static definition and fetch the actual instance from NumPy explicitly.)</p></li>
</ul>
<p>A last example is increasing <code class="docutils literal notranslate"><span class="pre">NPY_MAXDIMS</span></code> to <code class="docutils literal notranslate"><span class="pre">64</span></code>.
<code class="docutils literal notranslate"><span class="pre">NPY_MAXDIMS</span></code> is mainly used to statically allocate scratch space:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">func</span><span class="p">(</span><span class="n">PyArrayObject</span> <span class="o">*</span><span class="n">arr</span><span class="p">)</span> <span class="p">{</span>
<span class="n">npy_intp</span> <span class="n">shape</span><span class="p">[</span><span class="n">NPY_MAXDIMS</span><span class="p">];</span>
<span class="o">/*</span> <span class="n">Work</span> <span class="k">with</span> <span class="n">a</span> <span class="n">shape</span> <span class="ow">or</span> <span class="n">strides</span> <span class="kn">from</span> <span class="nn">the</span> <span class="n">array</span> <span class="o">*/</span>
<span class="p">}</span>
</pre></div>
</div>
<p>If NumPy changed it to 64 in a minor release, this would lead to undefined
behavior if the code was compiled with <code class="docutils literal notranslate"><span class="pre">NPY_MAXDIMS=32</span></code> but a 40 dimensional
array is passed in.
But the larger value is also a correct maximum on previous versions of NumPy
making it generally safe for NumPy 2.0 change.
(One can imagine code that wants to know the actual runtime value.
We have not seen such code in practice, but it would need to be adjusted.)</p>
</section>
<section id="impact-on-cython-users">
<h3>Impact on Cython users<a class="headerlink" href="#impact-on-cython-users" title="Link to this heading">#</a></h3>
<p>Cython users may use the NumPy C-API via <code class="docutils literal notranslate"><span class="pre">cimport</span> <span class="pre">numpy</span> <span class="pre">as</span> <span class="pre">cnp</span></code>.
Due to the uncertainty of Cython development, there are two scenarios for
impact on Cython users.</p>
<p>If Cython 3 can be relied on, Cython users would be impacted <em>less</em> than C-API
users, because Cython 3 allows us to hide struct layout changes (i.e. changes
to <code class="docutils literal notranslate"><span class="pre">PyArray_Descr</span></code>).
If this is not the case and we must support Cython 0.29.x (which is the historic branch
before Cython 3), then Cython users will also have to use a function/macro like
<code class="docutils literal notranslate"><span class="pre">PyDataType_ITEMSIZE()</span></code> (or use the Python object). This is unfortunately less
typical in Cython code, but also unlikely to be a common pattern for dtype struct
fields/attributes.</p>
<p>A further impact is that some future API additions such as new classes may
need to placed in a distinct <code class="docutils literal notranslate"><span class="pre">.pyd</span></code> file to avoid Cython generating code
that would fail on older NumPy versions.</p>
</section>
<section id="end-user-and-packaging-impact">
<h3>End-user and packaging impact<a class="headerlink" href="#end-user-and-packaging-impact" title="Link to this heading">#</a></h3>
<p>Packaging in a way that is compatible with NumPy 2.0 will require a
recompilation of downstream libraries that rely on the NumPy C-API.
This may take some time, although hopefully the process will start before
NumPy 2.0 is itself released.</p>
<p>Further, to allow bigger changes more easily in NumPy 2.0, we expect to
create a <code class="docutils literal notranslate"><span class="pre">numpy2_compat</span></code> package.
When a library is build with NumPy 2.0 but wants to support NumPy 1.x it will
have to depend on <code class="docutils literal notranslate"><span class="pre">numpy2_compat</span></code>. End-users should not need to be aware
of this dependency and an informative error can be raised when the module
is missing.</p>
</section>
<section id="some-new-api-can-be-backported">
<h3>Some new API can be backported<a class="headerlink" href="#some-new-api-can-be-backported" title="Link to this heading">#</a></h3>
<p>One large advantage of allowing users to compile with the newest version of
NumPy is that in some cases we will be able to backport new API.
Some new API functions can be written in terms of old ones or included
directly.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>It may be possible to make functions public that were present but
private in NumPy 1.x public via the compatible <code class="docutils literal notranslate"><span class="pre">numpy2_compat</span></code> package.</p>
</div>
<p>This means that at some new API additions could be made available to
downstreams users faster. They would require a new NumPy version for
<em>compilation</em> but their wheels can be backwards compatible with earlier
versions.</p>
</section>
</section>
<section id="implementation">
<h2>Implementation<a class="headerlink" href="#implementation" title="Link to this heading">#</a></h2>
<p>The first part of implementation (allowing building for an earlier API version)
is very straight forward since the NumPy C-API evolved slowly for
many years.
Some struct fields will be hidden by default and functions introduced in a
more recent version will be marked and hidden unless the
user opted in to a newer API version.
An implementation can be found in the <a class="reference external" href="https://github.com/numpy/numpy/pull/23528">PR 23528</a>.</p>
<p>The second part is mainly about identifying and implementing the desired
changes in a way that backwards compatibility will not be broken and API
breaks remain manageable for downstream libraries.
Every change we do must have a brief note on how to adapt to the
API change (i.e. alternative functions).</p>
<section id="numpy-2-compatibility-and-api-table-changes">
<h3>NumPy 2 compatibility and API table changes<a class="headerlink" href="#numpy-2-compatibility-and-api-table-changes" title="Link to this heading">#</a></h3>
<p>To allow changing the API table, NumPy 2.0 would ship a different table than
NumPy 1.x (a table is a list of functions and symbols).</p>
<p>For compatibility we would need to translate the 1.x table to the 2.0 table.
This could be done in headers only in theory, but this seems unwieldy.
We thus propose to add a <code class="docutils literal notranslate"><span class="pre">numpy2_compat</span></code> package. This package’s main
purpose would be to provide a translation of the 1.x table to the 2.x one
in a single place (filling in any necessary blanks).</p>
<p>Introducing this package solves the “transition” issue because it allows
a user to:</p>
<ul class="simple">
<li><p>Install a SciPy version that is compatible with 2.0 and 1.x</p></li>
<li><p>and keep using NumPy 1.x because of other packages they are using are not
yet compatible.</p></li>
</ul>
<p>The import of <code class="docutils literal notranslate"><span class="pre">numpy2_compat</span></code> (and an error when it is missing) will be
inserted by the NumPy eaders as part of the <code class="docutils literal notranslate"><span class="pre">import_array()</span></code> call.</p>
</section>
</section>
<section id="alternatives">
<h2>Alternatives<a class="headerlink" href="#alternatives" title="Link to this heading">#</a></h2>
<p>There are always possibilities to decide not to do certain changes (e.g. due
to downstream users noting their continued need for it). For example, the
function <code class="docutils literal notranslate"><span class="pre">PyArray_Mean</span></code> could be replaced by one to call <code class="docutils literal notranslate"><span class="pre">array.mean()</span></code>
if necessary.</p>
<p>The NEP proposes to allow larger changes to our API table by introducing a
compatibility package <code class="docutils literal notranslate"><span class="pre">numpy2_compat</span></code>.
We could do many changes without introducing such a package.</p>
<p>The default API version could be chosen to be older or as the current one.
An older version would be aimed at libraries who want a larger compatibility
than NEP 29 suggests.
Choosing the current would default to removing unnecessary compatibility shims
for users who do not distribute wheels.
The suggested default chooses to favors libraries that distribute wheels and
wish a compatibility range similar to NEP 29. This is because compatibility
shims should be light-weight and we expect few libraries require a longer
compatibility.</p>
</section>
<section id="backward-compatibility">
<h2>Backward compatibility<a class="headerlink" href="#backward-compatibility" title="Link to this heading">#</a></h2>
<p>As mentioned above backwards compatibility is achieved by:</p>
<ol class="arabic simple">
<li><p>Forcing downstream to recompile with NumPy 2.0</p></li>
<li><p>Providing a <code class="docutils literal notranslate"><span class="pre">numpy2_compat</span></code> library.</p></li>
</ol>
<p>But relies on users to adapt to changed C-API as described in the Usage and
Impact section.</p>
</section>
<section id="discussion">
<h2>Discussion<a class="headerlink" href="#discussion" title="Link to this heading">#</a></h2>
<ul class="simple">
<li><p><a class="github reference external" href="https://github.com/numpy/numpy/issues/5888">numpy/numpy#5888</a> brought up previously that it
would be helpful to allow exporting of an older API version in our headers.
This was never implemented, instead we relied on <a class="reference external" href="https://github.com/scipy/oldest-supported-numpy">oldest-support-numpy</a>.</p></li>
<li><p>A first draft of this proposal was presented at the NumPy 2.0 planning
meeting 2023-04-03.</p></li>
</ul>
</section>
<section id="references-and-footnotes">
<h2>References and footnotes<a class="headerlink" href="#references-and-footnotes" title="Link to this heading">#</a></h2>
<aside class="footnote-list brackets">
<aside class="footnote brackets" id="id1" role="doc-footnote">
<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id2">1</a><span class="fn-bracket">]</span></span>
<p>Each NEP must either be explicitly labeled as placed in the public domain (see
this NEP as an example) or licensed under the <a class="reference external" href="https://www.opencontent.org/openpub/">Open Publication License</a>.</p>
</aside>
</aside>
</section>
<section id="copyright">
<h2>Copyright<a class="headerlink" href="#copyright" title="Link to this heading">#</a></h2>
<p>This document has been placed in the public domain. <a class="footnote-reference brackets" href="#id1" id="id2" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a></p>
</section>
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#alternatives">Alternatives</a></li>
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#backward-compatibility">Backward compatibility</a></li>
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#discussion">Discussion</a></li>
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