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<li class="toctree-l2 current active"><a class="current reference internal" href="#">NEP 37 — A dispatch protocol for NumPy-like modules</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0047-array-api-standard.html">NEP 47 — Adopting the array API standard</a></li>
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<li class="toctree-l2"><a class="reference internal" href="nep-0016-abstract-array.html">NEP 16 — An abstract base class for identifying "duck arrays"</a></li>
<li class="toctree-l2"><a class="reference internal" href="nep-0017-split-out-maskedarray.html">NEP 17 — Split out masked arrays</a></li>
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<section id="nep-37-a-dispatch-protocol-for-numpy-like-modules">
<span id="nep37"></span><h1>NEP 37 — A dispatch protocol for NumPy-like modules<a class="headerlink" href="#nep-37-a-dispatch-protocol-for-numpy-like-modules" 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>Stephan Hoyer <<a class="reference external" href="mailto:shoyer%40google.com">shoyer<span>@</span>google<span>.</span>com</a>></p>
</dd>
<dt class="field-even">Author<span class="colon">:</span></dt>
<dd class="field-even"><p>Hameer Abbasi</p>
</dd>
<dt class="field-odd">Author<span class="colon">:</span></dt>
<dd class="field-odd"><p>Sebastian Berg</p>
</dd>
<dt class="field-even">Status<span class="colon">:</span></dt>
<dd class="field-even"><p>Superseded</p>
</dd>
<dt class="field-odd">Replaced-By<span class="colon">:</span></dt>
<dd class="field-odd"><p><a class="reference internal" href="nep-0056-array-api-main-namespace.html#nep56"><span class="std std-ref">NEP 56 — Array API standard support in NumPy’s main namespace</span></a></p>
</dd>
<dt class="field-even">Type<span class="colon">:</span></dt>
<dd class="field-even"><p>Standards Track</p>
</dd>
<dt class="field-odd">Created<span class="colon">:</span></dt>
<dd class="field-odd"><p>2019-12-29</p>
</dd>
<dt class="field-even">Resolution<span class="colon">:</span></dt>
<dd class="field-even"><p><a class="reference external" href="https://mail.python.org/archives/list/numpy-discussion@python.org/message/Z6AA5CL47NHBNEPTFWYOTSUVSRDGHYPN/">https://mail.python.org/archives/list/numpy-discussion@python.org/message/Z6AA5CL47NHBNEPTFWYOTSUVSRDGHYPN/</a></p>
</dd>
</dl>
<section id="abstract">
<h2>Abstract<a class="headerlink" href="#abstract" title="Link to this heading">#</a></h2>
<p>NEP-18’s <code class="docutils literal notranslate"><span class="pre">__array_function__</span></code> has been a mixed success. Some projects (e.g.,
dask, CuPy, xarray, sparse, Pint, MXNet) have enthusiastically adopted it.
Others (e.g., JAX) have been more reluctant. Here we propose a new
protocol, <code class="docutils literal notranslate"><span class="pre">__array_module__</span></code>, that we expect could eventually subsume most
use-cases for <code class="docutils literal notranslate"><span class="pre">__array_function__</span></code>. The protocol requires explicit adoption
by both users and library authors, which ensures backwards compatibility, and
is also significantly simpler than <code class="docutils literal notranslate"><span class="pre">__array_function__</span></code>, both of which we
expect will make it easier to adopt.</p>
</section>
<section id="why-array-function-hasn-t-been-enough">
<h2>Why <code class="docutils literal notranslate"><span class="pre">__array_function__</span></code> hasn’t been enough<a class="headerlink" href="#why-array-function-hasn-t-been-enough" title="Link to this heading">#</a></h2>
<p>There are two broad ways in which <a class="reference internal" href="nep-0018-array-function-protocol.html#nep18"><span class="std std-ref">NEP-18</span></a> has fallen short of its goals:</p>
<ol class="arabic">
<li><p><strong>Backwards compatibility concerns</strong>. <cite>__array_function__</cite> has significant
implications for libraries that use it:</p>
<ul>
<li><p><a class="reference external" href="https://github.com/google/jax/issues/1565">JAX</a> has been reluctant
to implement <code class="docutils literal notranslate"><span class="pre">__array_function__</span></code> in part because it is concerned about
breaking existing code: users expect NumPy functions like
<code class="docutils literal notranslate"><span class="pre">np.concatenate</span></code> to return NumPy arrays. This is a fundamental
limitation of the <code class="docutils literal notranslate"><span class="pre">__array_function__</span></code> design, which we chose to allow
overriding the existing <code class="docutils literal notranslate"><span class="pre">numpy</span></code> namespace.
Libraries like Dask and CuPy have looked at and accepted the backwards
incompatibility impact of <code class="docutils literal notranslate"><span class="pre">__array_function__</span></code>; it would still have been
better for them if that impact didn’t exist.</p>
<p>Note that projects like <a class="reference external" href="https://github.com/pytorch/pytorch/issues/22402">PyTorch</a> and <a class="reference external" href="https://github.com/scipy/scipy/issues/10362">scipy.sparse</a> have also not
adopted <code class="docutils literal notranslate"><span class="pre">__array_function__</span></code> yet, because they don’t have a
NumPy-compatible API or semantics. In the case of PyTorch, that is likely
to be added in the future. <code class="docutils literal notranslate"><span class="pre">scipy.sparse</span></code> is in the same situation as
<code class="docutils literal notranslate"><span class="pre">numpy.matrix</span></code>: its semantics are not compatible with <code class="docutils literal notranslate"><span class="pre">numpy.ndarray</span></code>
and therefore adding <code class="docutils literal notranslate"><span class="pre">__array_function__</span></code> (except to return <code class="docutils literal notranslate"><span class="pre">NotImplemented</span></code>
perhaps) is not a healthy idea.</p>
</li>
<li><p><code class="docutils literal notranslate"><span class="pre">__array_function__</span></code> currently requires an “all or nothing” approach to
implementing NumPy’s API. There is no good pathway for <strong>incremental
adoption</strong>, which is particularly problematic for established projects
for which adopting <code class="docutils literal notranslate"><span class="pre">__array_function__</span></code> would result in breaking
changes.</p></li>
</ul>
</li>
<li><p><strong>Limitations on what can be overridden.</strong> <code class="docutils literal notranslate"><span class="pre">__array_function__</span></code> has some
important gaps, most notably array creation and coercion functions:</p>
<ul class="simple">
<li><p><strong>Array creation</strong> routines (e.g., <code class="docutils literal notranslate"><span class="pre">np.arange</span></code> and those in
<code class="docutils literal notranslate"><span class="pre">np.random</span></code>) need some other mechanism for indicating what type of
arrays to create. <a class="reference internal" href="nep-0035-array-creation-dispatch-with-array-function.html#nep35"><span class="std std-ref">NEP 35</span></a>
proposed adding optional <code class="docutils literal notranslate"><span class="pre">like=</span></code> arguments to functions without
existing array arguments. However, we still lack any mechanism to
override methods on objects, such as those needed by
<code class="docutils literal notranslate"><span class="pre">np.random.RandomState</span></code>.</p></li>
<li><p><strong>Array conversion</strong> can’t reuse the existing coercion functions like
<code class="docutils literal notranslate"><span class="pre">np.asarray</span></code>, because <code class="docutils literal notranslate"><span class="pre">np.asarray</span></code> sometimes means “convert to an
exact <code class="docutils literal notranslate"><span class="pre">np.ndarray</span></code>” and other times means “convert to something _like_
a NumPy array.” This led to the <a class="reference internal" href="nep-0030-duck-array-protocol.html#nep30"><span class="std std-ref">NEP 30</span></a> proposal for
a separate <code class="docutils literal notranslate"><span class="pre">np.duckarray</span></code> function, but this still does not resolve how
to cast one duck array into a type matching another duck array.</p></li>
</ul>
</li>
</ol>
<p>Other maintainability concerns that were raised include:</p>
<ul class="simple">
<li><p>It is no longer possible to use <strong>aliases to NumPy functions</strong> within
modules that support overrides. For example, both CuPy and JAX set
<code class="docutils literal notranslate"><span class="pre">result_type</span> <span class="pre">=</span> <span class="pre">np.result_type</span></code> and now have to wrap use of
<code class="docutils literal notranslate"><span class="pre">np.result_type</span></code> in their own <code class="docutils literal notranslate"><span class="pre">result_type</span></code> function instead.</p></li>
<li><p>Implementing <strong>fall-back mechanisms</strong> for unimplemented NumPy functions
by using NumPy’s implementation is hard to get right (but see the
<a class="reference external" href="https://github.com/dask/dask/pull/5043">version from dask</a>), because
<code class="docutils literal notranslate"><span class="pre">__array_function__</span></code> does not present a consistent interface.
Converting all arguments of array type requires recursing into generic
arguments of the form <code class="docutils literal notranslate"><span class="pre">*args,</span> <span class="pre">**kwargs</span></code>.</p></li>
</ul>
</section>
<section id="get-array-module-and-the-array-module-protocol">
<h2><code class="docutils literal notranslate"><span class="pre">get_array_module</span></code> and the <code class="docutils literal notranslate"><span class="pre">__array_module__</span></code> protocol<a class="headerlink" href="#get-array-module-and-the-array-module-protocol" title="Link to this heading">#</a></h2>
<p>We propose a new user-facing mechanism for dispatching to a duck-array
implementation, <code class="docutils literal notranslate"><span class="pre">numpy.get_array_module</span></code>. <code class="docutils literal notranslate"><span class="pre">get_array_module</span></code> performs the
same type resolution as <code class="docutils literal notranslate"><span class="pre">__array_function__</span></code> and returns a module with an API
promised to match the standard interface of <code class="docutils literal notranslate"><span class="pre">numpy</span></code> that can implement
operations on all provided array types.</p>
<p>The protocol itself is both simpler and more powerful than
<code class="docutils literal notranslate"><span class="pre">__array_function__</span></code>, because it doesn’t need to worry about actually
implementing functions. We believe it resolves most of the maintainability and
functionality limitations of <code class="docutils literal notranslate"><span class="pre">__array_function__</span></code>.</p>
<p>The new protocol is opt-in, explicit and with local control; see
<a class="reference internal" href="#appendix-design-choices"><span class="std std-ref">Appendix: design choices for API overrides</span></a> for discussion on the importance of these design
features.</p>
<section id="the-array-module-contract">
<h3>The array module contract<a class="headerlink" href="#the-array-module-contract" title="Link to this heading">#</a></h3>
<p>Modules returned by <code class="docutils literal notranslate"><span class="pre">get_array_module</span></code>/<code class="docutils literal notranslate"><span class="pre">__array_module__</span></code> should make a
best effort to implement NumPy’s core functionality on new array types(s).
Unimplemented functionality should simply be omitted (e.g., accessing an
unimplemented function should raise <code class="docutils literal notranslate"><span class="pre">AttributeError</span></code>). In the future, we
anticipate codifying a protocol for requesting restricted subsets of <code class="docutils literal notranslate"><span class="pre">numpy</span></code>;
see <a class="reference internal" href="#requesting-restricted-subsets"><span class="std std-ref">Requesting restricted subsets of NumPy’s API</span></a> for more details.</p>
</section>
<section id="how-to-use-get-array-module">
<h3>How to use <code class="docutils literal notranslate"><span class="pre">get_array_module</span></code><a class="headerlink" href="#how-to-use-get-array-module" title="Link to this heading">#</a></h3>
<p>Code that wants to support generic duck arrays should explicitly call
<code class="docutils literal notranslate"><span class="pre">get_array_module</span></code> to determine an appropriate array module from which to
call functions, rather than using the <code class="docutils literal notranslate"><span class="pre">numpy</span></code> namespace directly. For
example:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># calls the appropriate version of np.something for x and y</span>
<span class="n">module</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">get_array_module</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
<span class="n">module</span><span class="o">.</span><span class="n">something</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
</pre></div>
</div>
<p>Both array creation and array conversion are supported, because dispatching is
handled by <code class="docutils literal notranslate"><span class="pre">get_array_module</span></code> rather than via the types of function
arguments. For example, to use random number generation functions or methods,
we can simply pull out the appropriate submodule:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">def</span><span class="w"> </span><span class="nf">duckarray_add_random</span><span class="p">(</span><span class="n">array</span><span class="p">):</span>
<span class="n">module</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">get_array_module</span><span class="p">(</span><span class="n">array</span><span class="p">)</span>
<span class="n">noise</span> <span class="o">=</span> <span class="n">module</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="o">*</span><span class="n">array</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="k">return</span> <span class="n">array</span> <span class="o">+</span> <span class="n">noise</span>
</pre></div>
</div>
<p>We can also write the duck-array <code class="docutils literal notranslate"><span class="pre">stack</span></code> function from
<a class="reference internal" href="nep-0030-duck-array-protocol.html#nep30"><span class="std std-ref">NEP 30</span></a>, without the need
for a new <code class="docutils literal notranslate"><span class="pre">np.duckarray</span></code> function:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">def</span><span class="w"> </span><span class="nf">duckarray_stack</span><span class="p">(</span><span class="n">arrays</span><span class="p">):</span>
<span class="n">module</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">get_array_module</span><span class="p">(</span><span class="o">*</span><span class="n">arrays</span><span class="p">)</span>
<span class="n">arrays</span> <span class="o">=</span> <span class="p">[</span><span class="n">module</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">arr</span><span class="p">)</span> <span class="k">for</span> <span class="n">arr</span> <span class="ow">in</span> <span class="n">arrays</span><span class="p">]</span>
<span class="n">shapes</span> <span class="o">=</span> <span class="p">{</span><span class="n">arr</span><span class="o">.</span><span class="n">shape</span> <span class="k">for</span> <span class="n">arr</span> <span class="ow">in</span> <span class="n">arrays</span><span class="p">}</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">shapes</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">1</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'all input arrays must have the same shape'</span><span class="p">)</span>
<span class="n">expanded_arrays</span> <span class="o">=</span> <span class="p">[</span><span class="n">arr</span><span class="p">[</span><span class="n">module</span><span class="o">.</span><span class="n">newaxis</span><span class="p">,</span> <span class="o">...</span><span class="p">]</span> <span class="k">for</span> <span class="n">arr</span> <span class="ow">in</span> <span class="n">arrays</span><span class="p">]</span>
<span class="k">return</span> <span class="n">module</span><span class="o">.</span><span class="n">concatenate</span><span class="p">(</span><span class="n">expanded_arrays</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
</pre></div>
</div>
<p>By default, <code class="docutils literal notranslate"><span class="pre">get_array_module</span></code> will return the <code class="docutils literal notranslate"><span class="pre">numpy</span></code> module if no
arguments are arrays. This fall-back can be explicitly controlled by providing
the <code class="docutils literal notranslate"><span class="pre">module</span></code> keyword-only argument. It is also possible to indicate that an
exception should be raised instead of returning a default array module by
setting <code class="docutils literal notranslate"><span class="pre">module=None</span></code>.</p>
</section>
<section id="how-to-implement-array-module">
<h3>How to implement <code class="docutils literal notranslate"><span class="pre">__array_module__</span></code><a class="headerlink" href="#how-to-implement-array-module" title="Link to this heading">#</a></h3>
<p>Libraries implementing a duck array type that want to support
<code class="docutils literal notranslate"><span class="pre">get_array_module</span></code> need to implement the corresponding protocol,
<code class="docutils literal notranslate"><span class="pre">__array_module__</span></code>. This new protocol is based on Python’s dispatch protocol
for arithmetic, and is essentially a simpler version of <code class="docutils literal notranslate"><span class="pre">__array_function__</span></code>.</p>
<p>Only one argument is passed into <code class="docutils literal notranslate"><span class="pre">__array_module__</span></code>, a Python collection of
unique array types passed into <code class="docutils literal notranslate"><span class="pre">get_array_module</span></code>, i.e., all arguments with
an <code class="docutils literal notranslate"><span class="pre">__array_module__</span></code> attribute.</p>
<p>The special method should either return a namespace with an API matching
<code class="docutils literal notranslate"><span class="pre">numpy</span></code>, or <code class="docutils literal notranslate"><span class="pre">NotImplemented</span></code>, indicating that it does not know how to
handle the operation:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">class</span><span class="w"> </span><span class="nc">MyArray</span><span class="p">:</span>
<span class="k">def</span><span class="w"> </span><span class="nf">__array_module__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">types</span><span class="p">):</span>
<span class="k">if</span> <span class="ow">not</span> <span class="nb">all</span><span class="p">(</span><span class="nb">issubclass</span><span class="p">(</span><span class="n">t</span><span class="p">,</span> <span class="n">MyArray</span><span class="p">)</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">types</span><span class="p">):</span>
<span class="k">return</span> <span class="bp">NotImplemented</span>
<span class="k">return</span> <span class="n">my_array_module</span>
</pre></div>
</div>
<section id="returning-custom-objects-from-array-module">
<h4>Returning custom objects from <code class="docutils literal notranslate"><span class="pre">__array_module__</span></code><a class="headerlink" href="#returning-custom-objects-from-array-module" title="Link to this heading">#</a></h4>
<p><code class="docutils literal notranslate"><span class="pre">my_array_module</span></code> will typically, but need not always, be a Python module.
Returning a custom objects (e.g., with functions implemented via
<code class="docutils literal notranslate"><span class="pre">__getattr__</span></code>) may be useful for some advanced use cases.</p>
<p>For example, custom objects could allow for partial implementations of duck
array modules that fall-back to NumPy (although this is not recommended in
general because such fall-back behavior can be error prone):</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">class</span><span class="w"> </span><span class="nc">MyArray</span><span class="p">:</span>
<span class="k">def</span><span class="w"> </span><span class="nf">__array_module__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">types</span><span class="p">):</span>
<span class="k">if</span> <span class="nb">all</span><span class="p">(</span><span class="nb">issubclass</span><span class="p">(</span><span class="n">t</span><span class="p">,</span> <span class="n">MyArray</span><span class="p">)</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">types</span><span class="p">):</span>
<span class="k">return</span> <span class="n">ArrayModule</span><span class="p">()</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">return</span> <span class="bp">NotImplemented</span>
<span class="k">class</span><span class="w"> </span><span class="nc">ArrayModule</span><span class="p">:</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__getattr__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="p">):</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">base_module</span>
<span class="k">return</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">base_module</span><span class="p">,</span> <span class="n">name</span><span class="p">,</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">numpy</span><span class="p">,</span> <span class="n">name</span><span class="p">))</span>
</pre></div>
</div>
</section>
<section id="subclassing-from-numpy-ndarray">
<h4>Subclassing from <code class="docutils literal notranslate"><span class="pre">numpy.ndarray</span></code><a class="headerlink" href="#subclassing-from-numpy-ndarray" title="Link to this heading">#</a></h4>
<p>All of the same guidance about well-defined type casting hierarchies from
NEP-18 still applies. <code class="docutils literal notranslate"><span class="pre">numpy.ndarray</span></code> itself contains a matching
implementation of <code class="docutils literal notranslate"><span class="pre">__array_module__</span></code>, which is convenient for subclasses:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">class</span><span class="w"> </span><span class="nc">ndarray</span><span class="p">:</span>
<span class="k">def</span><span class="w"> </span><span class="nf">__array_module__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">types</span><span class="p">):</span>
<span class="k">if</span> <span class="nb">all</span><span class="p">(</span><span class="nb">issubclass</span><span class="p">(</span><span class="n">t</span><span class="p">,</span> <span class="n">ndarray</span><span class="p">)</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">types</span><span class="p">):</span>
<span class="k">return</span> <span class="n">numpy</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">return</span> <span class="bp">NotImplemented</span>
</pre></div>
</div>
</section>
</section>
<section id="numpy-s-internal-machinery">
<h3>NumPy’s internal machinery<a class="headerlink" href="#numpy-s-internal-machinery" title="Link to this heading">#</a></h3>
<p>The type resolution rules of <code class="docutils literal notranslate"><span class="pre">get_array_module</span></code> follow the same model as
Python and NumPy’s existing dispatch protocols: subclasses are called before
super-classes, and otherwise left to right. <code class="docutils literal notranslate"><span class="pre">__array_module__</span></code> is guaranteed
to be called only a single time on each unique type.</p>
<p>The actual implementation of <cite>get_array_module</cite> will be in C, but should be
equivalent to this Python code:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">def</span><span class="w"> </span><span class="nf">get_array_module</span><span class="p">(</span><span class="o">*</span><span class="n">arrays</span><span class="p">,</span> <span class="n">default</span><span class="o">=</span><span class="n">numpy</span><span class="p">):</span>
<span class="n">implementing_arrays</span><span class="p">,</span> <span class="n">types</span> <span class="o">=</span> <span class="n">_implementing_arrays_and_types</span><span class="p">(</span><span class="n">arrays</span><span class="p">)</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">implementing_arrays</span> <span class="ow">and</span> <span class="n">default</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">return</span> <span class="n">default</span>
<span class="k">for</span> <span class="n">array</span> <span class="ow">in</span> <span class="n">implementing_arrays</span><span class="p">:</span>
<span class="n">module</span> <span class="o">=</span> <span class="n">array</span><span class="o">.</span><span class="n">__array_module__</span><span class="p">(</span><span class="n">types</span><span class="p">)</span>
<span class="k">if</span> <span class="n">module</span> <span class="ow">is</span> <span class="ow">not</span> <span class="bp">NotImplemented</span><span class="p">:</span>
<span class="k">return</span> <span class="n">module</span>
<span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">"no common array module found"</span><span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_implementing_arrays_and_types</span><span class="p">(</span><span class="n">relevant_arrays</span><span class="p">):</span>
<span class="n">types</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">implementing_arrays</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">array</span> <span class="ow">in</span> <span class="n">relevant_arrays</span><span class="p">:</span>
<span class="n">t</span> <span class="o">=</span> <span class="nb">type</span><span class="p">(</span><span class="n">array</span><span class="p">)</span>
<span class="k">if</span> <span class="n">t</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">types</span> <span class="ow">and</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">t</span><span class="p">,</span> <span class="s1">'__array_module__'</span><span class="p">):</span>
<span class="n">types</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">t</span><span class="p">)</span>
<span class="c1"># Subclasses before superclasses, otherwise left to right</span>
<span class="n">index</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">implementing_arrays</span><span class="p">)</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">old_array</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">implementing_arrays</span><span class="p">):</span>
<span class="k">if</span> <span class="nb">issubclass</span><span class="p">(</span><span class="n">t</span><span class="p">,</span> <span class="nb">type</span><span class="p">(</span><span class="n">old_array</span><span class="p">)):</span>
<span class="n">index</span> <span class="o">=</span> <span class="n">i</span>
<span class="k">break</span>
<span class="n">implementing_arrays</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="n">index</span><span class="p">,</span> <span class="n">array</span><span class="p">)</span>
<span class="k">return</span> <span class="n">implementing_arrays</span><span class="p">,</span> <span class="n">types</span>
</pre></div>
</div>
</section>
</section>
<section id="relationship-with-array-ufunc-and-array-function">
<h2>Relationship with <code class="docutils literal notranslate"><span class="pre">__array_ufunc__</span></code> and <code class="docutils literal notranslate"><span class="pre">__array_function__</span></code><a class="headerlink" href="#relationship-with-array-ufunc-and-array-function" title="Link to this heading">#</a></h2>
<section id="these-older-protocols-have-distinct-use-cases-and-should-remain">
<h3>These older protocols have distinct use-cases and should remain<a class="headerlink" href="#these-older-protocols-have-distinct-use-cases-and-should-remain" title="Link to this heading">#</a></h3>
<p><code class="docutils literal notranslate"><span class="pre">__array_module__</span></code> is intended to resolve limitations of
<code class="docutils literal notranslate"><span class="pre">__array_function__</span></code>, so it is natural to consider whether it could entirely
replace <code class="docutils literal notranslate"><span class="pre">__array_function__</span></code>. This would offer dual benefits: (1) simplifying
the user-story about how to override NumPy and (2) removing the slowdown
associated with checking for dispatch when calling every NumPy function.</p>
<p>However, <code class="docutils literal notranslate"><span class="pre">__array_module__</span></code> and <code class="docutils literal notranslate"><span class="pre">__array_function__</span></code> are pretty different
from a user perspective: it requires explicit calls to <code class="docutils literal notranslate"><span class="pre">get_array_function</span></code>,
rather than simply reusing original <code class="docutils literal notranslate"><span class="pre">numpy</span></code> functions. This is probably fine
for <em>libraries</em> that rely on duck-arrays, but may be frustratingly verbose for
interactive use.</p>
<p>Some of the dispatching use-cases for <code class="docutils literal notranslate"><span class="pre">__array_ufunc__</span></code> are also solved by
<code class="docutils literal notranslate"><span class="pre">__array_module__</span></code>, but not all of them. For example, it is still useful to
be able to define non-NumPy ufuncs (e.g., from Numba or SciPy) in a generic way
on non-NumPy arrays (e.g., with dask.array).</p>
<p>Given their existing adoption and distinct use cases, we don’t think it makes
sense to remove or deprecate <code class="docutils literal notranslate"><span class="pre">__array_function__</span></code> and <code class="docutils literal notranslate"><span class="pre">__array_ufunc__</span></code> at
this time.</p>
</section>
<section id="mixin-classes-to-implement-array-function-and-array-ufunc">
<h3>Mixin classes to implement <code class="docutils literal notranslate"><span class="pre">__array_function__</span></code> and <code class="docutils literal notranslate"><span class="pre">__array_ufunc__</span></code><a class="headerlink" href="#mixin-classes-to-implement-array-function-and-array-ufunc" title="Link to this heading">#</a></h3>
<p>Despite the user-facing differences, <code class="docutils literal notranslate"><span class="pre">__array_module__</span></code> and a module
implementing NumPy’s API still contain sufficient functionality needed to
implement dispatching with the existing duck array protocols.</p>
<p>For example, the following mixin classes would provide sensible defaults for
these special methods in terms of <code class="docutils literal notranslate"><span class="pre">get_array_module</span></code> and
<code class="docutils literal notranslate"><span class="pre">__array_module__</span></code>:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">class</span><span class="w"> </span><span class="nc">ArrayUfuncFromModuleMixin</span><span class="p">:</span>
<span class="k">def</span><span class="w"> </span><span class="nf">__array_ufunc__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ufunc</span><span class="p">,</span> <span class="n">method</span><span class="p">,</span> <span class="o">*</span><span class="n">inputs</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="n">arrays</span> <span class="o">=</span> <span class="n">inputs</span> <span class="o">+</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'out'</span><span class="p">,</span> <span class="p">())</span>
<span class="k">try</span><span class="p">:</span>
<span class="n">array_module</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">get_array_module</span><span class="p">(</span><span class="o">*</span><span class="n">arrays</span><span class="p">)</span>
<span class="k">except</span> <span class="ne">TypeError</span><span class="p">:</span>
<span class="k">return</span> <span class="bp">NotImplemented</span>
<span class="k">try</span><span class="p">:</span>
<span class="c1"># Note this may have false positive matches, if ufunc.__name__</span>
<span class="c1"># matches the name of a ufunc defined by NumPy. Unfortunately</span>
<span class="c1"># there is no way to determine in which module a ufunc was</span>
<span class="c1"># defined.</span>
<span class="n">new_ufunc</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">array_module</span><span class="p">,</span> <span class="n">ufunc</span><span class="o">.</span><span class="vm">__name__</span><span class="p">)</span>
<span class="k">except</span> <span class="ne">AttributeError</span><span class="p">:</span>
<span class="k">return</span> <span class="bp">NotImplemented</span>
<span class="k">try</span><span class="p">:</span>
<span class="nb">callable</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">new_ufunc</span><span class="p">,</span> <span class="n">method</span><span class="p">)</span>
<span class="k">except</span> <span class="ne">AttributeError</span><span class="p">:</span>
<span class="k">return</span> <span class="bp">NotImplemented</span>
<span class="k">return</span> <span class="nb">callable</span><span class="p">(</span><span class="o">*</span><span class="n">inputs</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
<span class="k">class</span><span class="w"> </span><span class="nc">ArrayFunctionFromModuleMixin</span><span class="p">:</span>
<span class="k">def</span><span class="w"> </span><span class="nf">__array_function__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">func</span><span class="p">,</span> <span class="n">types</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">):</span>
<span class="n">array_module</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">__array_module__</span><span class="p">(</span><span class="n">types</span><span class="p">)</span>
<span class="k">if</span> <span class="n">array_module</span> <span class="ow">is</span> <span class="bp">NotImplemented</span><span class="p">:</span>
<span class="k">return</span> <span class="bp">NotImplemented</span>
<span class="c1"># Traverse submodules to find the appropriate function</span>
<span class="n">modules</span> <span class="o">=</span> <span class="n">func</span><span class="o">.</span><span class="vm">__module__</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">'.'</span><span class="p">)</span>
<span class="k">assert</span> <span class="n">modules</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="s1">'numpy'</span>
<span class="k">for</span> <span class="n">submodule</span> <span class="ow">in</span> <span class="n">modules</span><span class="p">[</span><span class="mi">1</span><span class="p">:]:</span>
<span class="n">module</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">module</span><span class="p">,</span> <span class="n">submodule</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
<span class="n">new_func</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">module</span><span class="p">,</span> <span class="n">func</span><span class="o">.</span><span class="vm">__name__</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
<span class="k">if</span> <span class="n">new_func</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">return</span> <span class="bp">NotImplemented</span>
<span class="k">return</span> <span class="n">new_func</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
</pre></div>
</div>
<p>To make it easier to write duck arrays, we could also add these mixin classes
into <code class="docutils literal notranslate"><span class="pre">numpy.lib.mixins</span></code> (but the examples above may suffice).</p>
</section>
</section>
<section id="alternatives-considered">
<h2>Alternatives considered<a class="headerlink" href="#alternatives-considered" title="Link to this heading">#</a></h2>
<section id="naming">
<h3>Naming<a class="headerlink" href="#naming" title="Link to this heading">#</a></h3>
<p>We like the name <code class="docutils literal notranslate"><span class="pre">__array_module__</span></code> because it mirrors the existing
<code class="docutils literal notranslate"><span class="pre">__array_function__</span></code> and <code class="docutils literal notranslate"><span class="pre">__array_ufunc__</span></code> protocols. Another reasonable
choice could be <code class="docutils literal notranslate"><span class="pre">__array_namespace__</span></code>.</p>
<p>It is less clear what the NumPy function that calls this protocol should be
called (<code class="docutils literal notranslate"><span class="pre">get_array_module</span></code> in this proposal). Some possible alternatives:
<code class="docutils literal notranslate"><span class="pre">array_module</span></code>, <code class="docutils literal notranslate"><span class="pre">common_array_module</span></code>, <code class="docutils literal notranslate"><span class="pre">resolve_array_module</span></code>,
<code class="docutils literal notranslate"><span class="pre">get_namespace</span></code>, <code class="docutils literal notranslate"><span class="pre">get_numpy</span></code>, <code class="docutils literal notranslate"><span class="pre">get_numpylike_module</span></code>,
<code class="docutils literal notranslate"><span class="pre">get_duck_array_module</span></code>.</p>
</section>
<section id="requesting-restricted-subsets-of-numpy-s-api">
<span id="requesting-restricted-subsets"></span><h3>Requesting restricted subsets of NumPy’s API<a class="headerlink" href="#requesting-restricted-subsets-of-numpy-s-api" title="Link to this heading">#</a></h3>
<p>Over time, NumPy has accumulated a very large API surface, with over 600
attributes in the top level <code class="docutils literal notranslate"><span class="pre">numpy</span></code> module alone. It is unlikely that any
duck array library could or would want to implement all of these functions and
classes, because the frequently used subset of NumPy is much smaller.</p>
<p>We think it would be useful exercise to define “minimal” subset(s) of NumPy’s
API, omitting rarely used or non-recommended functionality. For example,
minimal NumPy might include <code class="docutils literal notranslate"><span class="pre">stack</span></code>, but not the other stacking functions
<code class="docutils literal notranslate"><span class="pre">column_stack</span></code>, <code class="docutils literal notranslate"><span class="pre">dstack</span></code>, <code class="docutils literal notranslate"><span class="pre">hstack</span></code> and <code class="docutils literal notranslate"><span class="pre">vstack</span></code>. This could clearly
indicate to duck array authors and users what functionality is core and what
functionality they can skip.</p>
<p>Support for requesting a restricted subset of NumPy’s API would be a natural
feature to include in <code class="docutils literal notranslate"><span class="pre">get_array_function</span></code> and <code class="docutils literal notranslate"><span class="pre">__array_module__</span></code>, e.g.,</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># array_module is only guaranteed to contain "minimal" NumPy</span>
<span class="n">array_module</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">get_array_module</span><span class="p">(</span><span class="o">*</span><span class="n">arrays</span><span class="p">,</span> <span class="n">request</span><span class="o">=</span><span class="s1">'minimal'</span><span class="p">)</span>
</pre></div>
</div>
<p>To facilitate testing with NumPy and use with any valid duck array library,
NumPy itself would return restricted versions of the <code class="docutils literal notranslate"><span class="pre">numpy</span></code> module when
<code class="docutils literal notranslate"><span class="pre">get_array_module</span></code> is called only on NumPy arrays. Omitted functions would
simply not exist.</p>
<p>Unfortunately, we have not yet figured out what these restricted subsets should
be, so it doesn’t make sense to do this yet. When/if we do, we could either add
new keyword arguments to <code class="docutils literal notranslate"><span class="pre">get_array_module</span></code> or add new top level functions,
e.g., <code class="docutils literal notranslate"><span class="pre">get_minimal_array_module</span></code>. We would also need to add either a new
protocol patterned off of <code class="docutils literal notranslate"><span class="pre">__array_module__</span></code> (e.g.,
<code class="docutils literal notranslate"><span class="pre">__array_module_minimal__</span></code>), or could add an optional second argument to
<code class="docutils literal notranslate"><span class="pre">__array_module__</span></code> (catching errors with <code class="docutils literal notranslate"><span class="pre">try</span></code>/<code class="docutils literal notranslate"><span class="pre">except</span></code>).</p>
</section>
<section id="a-new-namespace-for-implicit-dispatch">
<h3>A new namespace for implicit dispatch<a class="headerlink" href="#a-new-namespace-for-implicit-dispatch" title="Link to this heading">#</a></h3>
<p>Instead of supporting overrides in the main <cite>numpy</cite> namespace with
<code class="docutils literal notranslate"><span class="pre">__array_function__</span></code>, we could create a new opt-in namespace, e.g.,
<code class="docutils literal notranslate"><span class="pre">numpy.api</span></code>, with versions of NumPy functions that support dispatching. These
overrides would need new opt-in protocols, e.g., <code class="docutils literal notranslate"><span class="pre">__array_function_api__</span></code>
patterned off of <code class="docutils literal notranslate"><span class="pre">__array_function__</span></code>.</p>
<p>This would resolve the biggest limitations of <code class="docutils literal notranslate"><span class="pre">__array_function__</span></code> by being
opt-in and would also allow for unambiguously overriding functions like
<code class="docutils literal notranslate"><span class="pre">asarray</span></code>, because <code class="docutils literal notranslate"><span class="pre">np.api.asarray</span></code> would always mean “convert an
array-like object.” But it wouldn’t solve all the dispatching needs met by
<code class="docutils literal notranslate"><span class="pre">__array_module__</span></code>, and would leave us with supporting a considerably more
complex protocol both for array users and implementers.</p>
<p>We could potentially implement such a new namespace <em>via</em> the
<code class="docutils literal notranslate"><span class="pre">__array_module__</span></code> protocol. Certainly some users would find this convenient,
because it is slightly less boilerplate. But this would leave users with a
confusing choice: when should they use <cite>get_array_module</cite> vs.
<cite>np.api.something</cite>. Also, we would have to add and maintain a whole new module,
which is considerably more expensive than merely adding a function.</p>
</section>
<section id="dispatching-on-both-types-and-arrays-instead-of-only-types">
<h3>Dispatching on both types and arrays instead of only types<a class="headerlink" href="#dispatching-on-both-types-and-arrays-instead-of-only-types" title="Link to this heading">#</a></h3>
<p>Instead of supporting dispatch only via unique array types, we could also
support dispatch via array objects, e.g., by passing an <code class="docutils literal notranslate"><span class="pre">arrays</span></code> argument as
part of the <code class="docutils literal notranslate"><span class="pre">__array_module__</span></code> protocol. This could potentially be useful for
dispatch for arrays with metadata, such provided by Dask and Pint, but would
impose costs in terms of type safety and complexity.</p>
<p>For example, a library that supports arrays on both CPUs and GPUs might decide
on which device to create a new arrays from functions like <code class="docutils literal notranslate"><span class="pre">ones</span></code> based on
input arguments:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">class</span><span class="w"> </span><span class="nc">Array</span><span class="p">:</span>
<span class="k">def</span><span class="w"> </span><span class="nf">__array_module__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">types</span><span class="p">,</span> <span class="n">arrays</span><span class="p">):</span>
<span class="n">useful_arrays</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">a</span> <span class="ow">in</span> <span class="n">arrays</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">Array</span><span class="p">))</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">useful_arrays</span><span class="p">:</span>
<span class="k">return</span> <span class="bp">NotImplemented</span>
<span class="n">prefer_gpu</span> <span class="o">=</span> <span class="nb">any</span><span class="p">(</span><span class="n">a</span><span class="o">.</span><span class="n">prefer_gpu</span> <span class="k">for</span> <span class="n">a</span> <span class="ow">in</span> <span class="n">useful_arrays</span><span class="p">)</span>
<span class="k">return</span> <span class="n">ArrayModule</span><span class="p">(</span><span class="n">prefer_gpu</span><span class="p">)</span>
<span class="k">class</span><span class="w"> </span><span class="nc">ArrayModule</span><span class="p">:</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">prefer_gpu</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">prefer_gpu</span> <span class="o">=</span> <span class="n">prefer_gpu</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__getattr__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="p">):</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">base_module</span>
<span class="n">base_func</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">base_module</span><span class="p">,</span> <span class="n">name</span><span class="p">)</span>
<span class="k">return</span> <span class="n">functools</span><span class="o">.</span><span class="n">partial</span><span class="p">(</span><span class="n">base_func</span><span class="p">,</span> <span class="n">prefer_gpu</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">prefer_gpu</span><span class="p">)</span>
</pre></div>
</div>
<p>This might be useful, but it’s not clear if we really need it. Pint seems to
get along OK without any explicit array creation routines (favoring
multiplication by units, e.g., <code class="docutils literal notranslate"><span class="pre">np.ones(5)</span> <span class="pre">*</span> <span class="pre">ureg.m</span></code>), and for the most part
Dask is also OK with existing <code class="docutils literal notranslate"><span class="pre">__array_function__</span></code> style overrides (e.g.,
favoring <code class="docutils literal notranslate"><span class="pre">np.ones_like</span></code> over <code class="docutils literal notranslate"><span class="pre">np.ones</span></code>). Choosing whether to place an array
on the CPU or GPU could be solved by <a class="reference external" href="https://github.com/google/jax/pull/1668">making array creation lazy</a>.</p>
</section>
</section>
<section id="appendix-design-choices-for-api-overrides">
<span id="appendix-design-choices"></span><h2>Appendix: design choices for API overrides<a class="headerlink" href="#appendix-design-choices-for-api-overrides" title="Link to this heading">#</a></h2>
<p>There is a large range of possible design choices for overriding NumPy’s API.
Here we discuss three major axes of the design decision that guided our design
for <code class="docutils literal notranslate"><span class="pre">__array_module__</span></code>.</p>
<section id="opt-in-vs-opt-out-for-users">
<h3>Opt-in vs. opt-out for users<a class="headerlink" href="#opt-in-vs-opt-out-for-users" title="Link to this heading">#</a></h3>
<p>The <code class="docutils literal notranslate"><span class="pre">__array_ufunc__</span></code> and <code class="docutils literal notranslate"><span class="pre">__array_function__</span></code> protocols provide a
mechanism for overriding NumPy functions <em>within NumPy’s existing namespace</em>.
This means that users need to explicitly opt-out if they do not want any
overridden behavior, e.g., by casting arrays with <code class="docutils literal notranslate"><span class="pre">np.asarray()</span></code>.</p>
<p>In theory, this approach lowers the barrier for adopting these protocols in
user code and libraries, because code that uses the standard NumPy namespace is
automatically compatible. But in practice, this hasn’t worked out. For example,
most well-maintained libraries that use NumPy follow the best practice of
casting all inputs with <code class="docutils literal notranslate"><span class="pre">np.asarray()</span></code>, which they would have to explicitly
relax to use <code class="docutils literal notranslate"><span class="pre">__array_function__</span></code>. Our experience has been that making a
library compatible with a new duck array type typically requires at least a
small amount of work to accommodate differences in the data model and operations
that can be implemented efficiently.</p>
<p>These opt-out approaches also considerably complicate backwards compatibility
for libraries that adopt these protocols, because by opting in as a library
they also opt-in their users, whether they expect it or not. For winning over
libraries that have been unable to adopt <code class="docutils literal notranslate"><span class="pre">__array_function__</span></code>, an opt-in
approach seems like a must.</p>
</section>
<section id="explicit-vs-implicit-choice-of-implementation">
<h3>Explicit vs. implicit choice of implementation<a class="headerlink" href="#explicit-vs-implicit-choice-of-implementation" title="Link to this heading">#</a></h3>
<p>Both <code class="docutils literal notranslate"><span class="pre">__array_ufunc__</span></code> and <code class="docutils literal notranslate"><span class="pre">__array_function__</span></code> have implicit control over
dispatching: the dispatched functions are determined via the appropriate
protocols in every function call. This generalizes well to handling many
different types of objects, as evidenced by its use for implementing arithmetic
operators in Python, but it has an important downside for <strong>readability</strong>:
it is not longer immediately evident to readers of code what happens when a
function is called, because the function’s implementation could be overridden
by any of its arguments.</p>
<p>The <strong>speed</strong> implications are:</p>
<ul class="simple">
<li><p>When using a <em>duck-array type</em>, <code class="docutils literal notranslate"><span class="pre">get_array_module</span></code> means type checking only
needs to happen once inside each function that supports duck typing, whereas
with <code class="docutils literal notranslate"><span class="pre">__array_function__</span></code> it happens every time a NumPy function is called.
Obvious it’s going to depend on the function, but if a typical duck-array
supporting function calls into other NumPy functions 3-5 times this is a factor
of 3-5x more overhead.</p></li>
<li><p>When using <em>NumPy arrays</em>, <code class="docutils literal notranslate"><span class="pre">get_array_module</span></code> is one extra call per
function (<code class="docutils literal notranslate"><span class="pre">__array_function__</span></code> overhead remains the same), which means a
small amount of extra overhead.</p></li>
</ul>
<p>Explicit and implicit choice of implementations are not mutually exclusive
options. Indeed, most implementations of NumPy API overrides via
<code class="docutils literal notranslate"><span class="pre">__array_function__</span></code> that we are familiar with (namely, Dask, CuPy and
Sparse, but not Pint) also include an explicit way to use their version of
NumPy’s API by importing a module directly (<code class="docutils literal notranslate"><span class="pre">dask.array</span></code>, <code class="docutils literal notranslate"><span class="pre">cupy</span></code> or
<code class="docutils literal notranslate"><span class="pre">sparse</span></code>, respectively).</p>
</section>
<section id="local-vs-non-local-vs-global-control">
<h3>Local vs. non-local vs. global control<a class="headerlink" href="#local-vs-non-local-vs-global-control" title="Link to this heading">#</a></h3>
<p>The final design axis is how users control the choice of API:</p>
<ul class="simple">
<li><p><strong>Local control</strong>, as exemplified by multiple dispatch and Python protocols for
arithmetic, determines which implementation to use either by checking types
or calling methods on the direct arguments of a function.</p></li>
<li><p><strong>Non-local control</strong> such as <a class="reference external" href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.errstate.html">np.errstate</a>
overrides behavior with global-state via function decorators or
context-managers. Control is determined hierarchically, via the inner-most
context.</p></li>
<li><p><strong>Global control</strong> provides a mechanism for users to set default behavior,
either via function calls or configuration files. For example, matplotlib
allows setting a global choice of plotting backend.</p></li>
</ul>
<p>Local control is generally considered a best practice for API design, because
control flow is entirely explicit, which makes it the easiest to understand.
Non-local and global control are occasionally used, but generally either due to
ignorance or a lack of better alternatives.</p>
<p>In the case of duck typing for NumPy’s public API, we think non-local or global
control would be mistakes, mostly because they <strong>don’t compose well</strong>. If one
library sets/needs one set of overrides and then internally calls a routine
that expects another set of overrides, the resulting behavior may be very
surprising. Higher order functions are especially problematic, because the
context in which functions are evaluated may not be the context in which they
are defined.</p>
<p>One class of override use cases where we think non-local and global control are
appropriate is for choosing a backend system that is guaranteed to have an
entirely consistent interface, such as a faster alternative implementation of