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<div id="jb-print-docs-body" class="onlyprint">
<h1>Conversion to NumPy and Other Frameworks</h1>
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<section id="conversion-to-numpy-and-other-frameworks">
<span id="numpy"></span><h1>Conversion to NumPy and Other Frameworks<a class="headerlink" href="#conversion-to-numpy-and-other-frameworks" title="Link to this heading">#</a></h1>
<p>MLX array supports conversion between other frameworks with either:</p>
<ul class="simple">
<li><p>The <a class="reference external" href="https://docs.python.org/3/c-api/buffer.html">Python Buffer Protocol</a>.</p></li>
<li><p><a class="reference external" href="https://dmlc.github.io/dlpack/latest/">DLPack</a>.</p></li>
</ul>
<p>Let’s convert an array to NumPy and back.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">mlx.core</span> <span class="k">as</span> <span class="nn">mx</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="n">a</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
<span class="n">b</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">a</span><span class="p">)</span> <span class="c1"># copy of a</span>
<span class="n">c</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">b</span><span class="p">)</span> <span class="c1"># copy of b</span>
</pre></div>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Since NumPy does not support <code class="docutils literal notranslate"><span class="pre">bfloat16</span></code> arrays, you will need to convert to <code class="docutils literal notranslate"><span class="pre">float16</span></code> or <code class="docutils literal notranslate"><span class="pre">float32</span></code> first:
<code class="docutils literal notranslate"><span class="pre">np.array(a.astype(mx.float32))</span></code>.
Otherwise, you will receive an error like: <code class="docutils literal notranslate"><span class="pre">Item</span> <span class="pre">size</span> <span class="pre">2</span> <span class="pre">for</span> <span class="pre">PEP</span> <span class="pre">3118</span> <span class="pre">buffer</span> <span class="pre">format</span> <span class="pre">string</span> <span class="pre">does</span> <span class="pre">not</span> <span class="pre">match</span> <span class="pre">the</span> <span class="pre">dtype</span> <span class="pre">V</span> <span class="pre">item</span> <span class="pre">size</span> <span class="pre">0.</span></code></p>
</div>
<p>By default, NumPy copies data to a new array. This can be prevented by creating an array view:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">a</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
<span class="n">a_view</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">copy</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">a_view</span><span class="o">.</span><span class="n">flags</span><span class="o">.</span><span class="n">owndata</span><span class="p">)</span> <span class="c1"># False</span>
<span class="n">a_view</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span>
<span class="nb">print</span><span class="p">(</span><span class="n">a</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">item</span><span class="p">())</span> <span class="c1"># 1</span>
</pre></div>
</div>
<p>A NumPy array view is a normal NumPy array, except that it does not own its memory.
This means writing to the view is reflected in the original array.</p>
<p>While this is quite powerful to prevent copying arrays, it should be noted that external changes to the memory of arrays cannot be reflected in gradients.</p>
<p>Let’s demonstrate this in an example:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">f</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
<span class="n">x_view</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">copy</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="n">x_view</span><span class="p">[:]</span> <span class="o">*=</span> <span class="n">x_view</span> <span class="c1"># modify memory without telling mx</span>
<span class="k">return</span> <span class="n">x</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">3.0</span><span class="p">])</span>
<span class="n">y</span><span class="p">,</span> <span class="n">df</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">value_and_grad</span><span class="p">(</span><span class="n">f</span><span class="p">)(</span><span class="n">x</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;f(x) = x² =&quot;</span><span class="p">,</span> <span class="n">y</span><span class="o">.</span><span class="n">item</span><span class="p">())</span> <span class="c1"># 9.0</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;f&#39;(x) = 2x !=&quot;</span><span class="p">,</span> <span class="n">df</span><span class="o">.</span><span class="n">item</span><span class="p">())</span> <span class="c1"># 1.0</span>
</pre></div>
</div>
<p>The function <code class="docutils literal notranslate"><span class="pre">f</span></code> indirectly modifies the array <code class="docutils literal notranslate"><span class="pre">x</span></code> through a memory view.
However, this modification is not reflected in the gradient, as seen in the last line outputting <code class="docutils literal notranslate"><span class="pre">1.0</span></code>,
representing the gradient of the sum operation alone.
The squaring of <code class="docutils literal notranslate"><span class="pre">x</span></code> occurs externally to MLX, meaning that no gradient is incorporated.
It’s important to note that a similar issue arises during array conversion and copying.
For instance, a function defined as <code class="docutils literal notranslate"><span class="pre">mx.array(np.array(x)**2).sum()</span></code> would also result in an incorrect gradient,
even though no in-place operations on MLX memory are executed.</p>
<section id="pytorch">
<h2>PyTorch<a class="headerlink" href="#pytorch" title="Link to this heading">#</a></h2>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>PyTorch Support for <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#memoryview" title="(in Python v3.12)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">memoryview</span></code></a> is experimental and can break for
multi-dimensional arrays. Casting to NumPy first is advised for now.</p>
</div>
<p>PyTorch supports the buffer protocol, but it requires an explicit <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#memoryview" title="(in Python v3.12)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">memoryview</span></code></a>.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">mlx.core</span> <span class="k">as</span> <span class="nn">mx</span>
<span class="kn">import</span> <span class="nn">torch</span>
<span class="n">a</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
<span class="n">b</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="nb">memoryview</span><span class="p">(</span><span class="n">a</span><span class="p">))</span>
<span class="n">c</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">b</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
</pre></div>
</div>
<p>Conversion from PyTorch tensors back to arrays must be done via intermediate NumPy arrays with <code class="docutils literal notranslate"><span class="pre">numpy()</span></code>.</p>
</section>
<section id="jax">
<h2>JAX<a class="headerlink" href="#jax" title="Link to this heading">#</a></h2>
<p>JAX fully supports the buffer protocol.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">mlx.core</span> <span class="k">as</span> <span class="nn">mx</span>
<span class="kn">import</span> <span class="nn">jax.numpy</span> <span class="k">as</span> <span class="nn">jnp</span>
<span class="n">a</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
<span class="n">b</span> <span class="o">=</span> <span class="n">jnp</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>
<span class="n">c</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">b</span><span class="p">)</span>
</pre></div>
</div>
</section>
<section id="tensorflow">
<h2>TensorFlow<a class="headerlink" href="#tensorflow" title="Link to this heading">#</a></h2>
<p>TensorFlow supports the buffer protocol, but it requires an explicit <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#memoryview" title="(in Python v3.12)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">memoryview</span></code></a>.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">mlx.core</span> <span class="k">as</span> <span class="nn">mx</span>
<span class="kn">import</span> <span class="nn">tensorflow</span> <span class="k">as</span> <span class="nn">tf</span>
<span class="n">a</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
<span class="n">b</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">constant</span><span class="p">(</span><span class="nb">memoryview</span><span class="p">(</span><span class="n">a</span><span class="p">))</span>
<span class="n">c</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">b</span><span class="p">)</span>
</pre></div>
</div>
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