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MLX
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0.0.0
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<section id="quick-start-guide">
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<h1>Quick Start Guide<a class="headerlink" href="#quick-start-guide" title="Permalink to this heading"></a></h1>
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<p>MLX is a NumPy-like array framework designed for efficient and flexible
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machine learning on Apple silicon. The Python API closely follows NumPy with
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a few exceptions. MLX also has a fully featured C++ API which closely follows
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the Python API.</p>
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<p>The main differences between MLX and NumPy are:</p>
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<blockquote>
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<div><ul class="simple">
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<li><p><strong>Composable function transformations</strong>: MLX has composable function
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transformations for automatic differentiation, automatic vectorization,
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and computation graph optimization.</p></li>
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<li><p><strong>Lazy computation</strong>: Computations in MLX are lazy. Arrays are only
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materialized when needed.</p></li>
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<li><p><strong>Multi-device</strong>: Operations can run on any of the suppoorted devices (CPU,
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GPU, …)</p></li>
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</ul>
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</div></blockquote>
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<p>The design of MLX is strongly inspired by frameworks like <a class="reference external" href="https://pytorch.org/">PyTorch</a>, <a class="reference external" href="https://github.com/google/jax">Jax</a>, and
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<a class="reference external" href="https://arrayfire.org/">ArrayFire</a>. A noteable difference from these
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frameworks and MLX is the <em>unified memory model</em>. Arrays in MLX live in shared
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memory. Operations on MLX arrays can be performed on any of the supported
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device types without performing data copies. Currently supported device types
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are the CPU and GPU.</p>
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<section id="basics">
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<h2>Basics<a class="headerlink" href="#basics" title="Permalink to this heading"></a></h2>
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<p>Import <code class="docutils literal notranslate"><span class="pre">mlx.core</span></code> and make an <a class="reference internal" href="python/_autosummary/mlx.core.array.html#mlx.core.array" title="mlx.core.array"><code class="xref py py-class docutils literal notranslate"><span class="pre">array</span></code></a>:</p>
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<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="o">>></span> <span class="kn">import</span> <span class="nn">mlx.core</span> <span class="k">as</span> <span class="nn">mx</span>
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<span class="o">>></span> <span class="n">a</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="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">])</span>
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<span class="o">>></span> <span class="n">a</span><span class="o">.</span><span class="n">shape</span>
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<span class="p">[</span><span class="mi">4</span><span class="p">]</span>
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<span class="o">>></span> <span class="n">a</span><span class="o">.</span><span class="n">dtype</span>
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<span class="n">int32</span>
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<span class="o">>></span> <span class="n">b</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">1.0</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">,</span> <span class="mf">3.0</span><span class="p">,</span> <span class="mf">4.0</span><span class="p">])</span>
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<span class="o">>></span> <span class="n">b</span><span class="o">.</span><span class="n">dtype</span>
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<span class="n">float32</span>
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</pre></div>
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</div>
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<p>Operations in MLX are lazy. The outputs of MLX operations are not computed
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until they are needed. To force an array to be evaluated use
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<a class="reference internal" href="python/_autosummary/mlx.core.eval.html#mlx.core.eval" title="mlx.core.eval"><code class="xref py py-func docutils literal notranslate"><span class="pre">eval()</span></code></a>. Arrays will automatically be evaluated in a few cases. For
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example, inspecting a scalar with <a class="reference internal" href="python/_autosummary/mlx.core.array.item.html#mlx.core.array.item" title="mlx.core.array.item"><code class="xref py py-meth docutils literal notranslate"><span class="pre">array.item()</span></code></a>, printing an array,
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or converting an array from <a class="reference internal" href="python/_autosummary/mlx.core.array.html#mlx.core.array" title="mlx.core.array"><code class="xref py py-class docutils literal notranslate"><span class="pre">array</span></code></a> to <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a> all
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automatically evaluate the array.</p>
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<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="o">>></span> <span class="n">c</span> <span class="o">=</span> <span class="n">a</span> <span class="o">+</span> <span class="n">b</span> <span class="c1"># c not yet evaluated</span>
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<span class="o">>></span> <span class="n">mx</span><span class="o">.</span><span class="n">eval</span><span class="p">(</span><span class="n">c</span><span class="p">)</span> <span class="c1"># evaluates c</span>
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<span class="o">>></span> <span class="n">c</span> <span class="o">=</span> <span class="n">a</span> <span class="o">+</span> <span class="n">b</span>
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<span class="o">>></span> <span class="nb">print</span><span class="p">(</span><span class="n">c</span><span class="p">)</span> <span class="c1"># Also evaluates c</span>
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<span class="n">array</span><span class="p">([</span><span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">8</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">float32</span><span class="p">)</span>
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<span class="o">>></span> <span class="n">c</span> <span class="o">=</span> <span class="n">a</span> <span class="o">+</span> <span class="n">b</span>
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<span class="o">>></span> <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
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<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">c</span><span class="p">)</span> <span class="c1"># Also evaluates c</span>
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<span class="n">array</span><span class="p">([</span><span class="mf">2.</span><span class="p">,</span> <span class="mf">4.</span><span class="p">,</span> <span class="mf">6.</span><span class="p">,</span> <span class="mf">8.</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">float32</span><span class="p">)</span>
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</pre></div>
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</div>
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</section>
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<section id="function-and-graph-transformations">
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<h2>Function and Graph Transformations<a class="headerlink" href="#function-and-graph-transformations" title="Permalink to this heading"></a></h2>
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<p>MLX has standard function transformations like <a class="reference internal" href="python/_autosummary/mlx.core.grad.html#mlx.core.grad" title="mlx.core.grad"><code class="xref py py-func docutils literal notranslate"><span class="pre">grad()</span></code></a> and <a class="reference internal" href="python/_autosummary/mlx.core.vmap.html#mlx.core.vmap" title="mlx.core.vmap"><code class="xref py py-func docutils literal notranslate"><span class="pre">vmap()</span></code></a>.
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Transformations can be composed arbitrarily. For example
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<code class="docutils literal notranslate"><span class="pre">grad(vmap(grad(fn)))</span></code> (or any other composition) is allowed.</p>
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<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="o">>></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">0.0</span><span class="p">)</span>
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<span class="o">>></span> <span class="n">mx</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
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<span class="n">array</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">float32</span><span class="p">)</span>
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<span class="o">>></span> <span class="n">mx</span><span class="o">.</span><span class="n">grad</span><span class="p">(</span><span class="n">mx</span><span class="o">.</span><span class="n">sin</span><span class="p">)(</span><span class="n">x</span><span class="p">)</span>
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<span class="n">array</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">float32</span><span class="p">)</span>
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<span class="o">>></span> <span class="n">mx</span><span class="o">.</span><span class="n">grad</span><span class="p">(</span><span class="n">mx</span><span class="o">.</span><span class="n">grad</span><span class="p">(</span><span class="n">mx</span><span class="o">.</span><span class="n">sin</span><span class="p">))(</span><span class="n">x</span><span class="p">)</span>
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<span class="n">array</span><span class="p">(</span><span class="o">-</span><span class="mi">0</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">float32</span><span class="p">)</span>
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</pre></div>
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</div>
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<p>Other gradient transformations include <a class="reference internal" href="python/_autosummary/mlx.core.vjp.html#mlx.core.vjp" title="mlx.core.vjp"><code class="xref py py-func docutils literal notranslate"><span class="pre">vjp()</span></code></a> for vector-Jacobian products
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and <a class="reference internal" href="python/_autosummary/mlx.core.jvp.html#mlx.core.jvp" title="mlx.core.jvp"><code class="xref py py-func docutils literal notranslate"><span class="pre">jvp()</span></code></a> for Jacobian-vector products.</p>
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<p>Use <a class="reference internal" href="python/_autosummary/mlx.core.value_and_grad.html#mlx.core.value_and_grad" title="mlx.core.value_and_grad"><code class="xref py py-func docutils literal notranslate"><span class="pre">value_and_grad()</span></code></a> to efficiently compute both a function’s output and
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gradient with respect to the function’s input.</p>
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</section>
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<section id="devices-and-streams">
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<h2>Devices and Streams<a class="headerlink" href="#devices-and-streams" title="Permalink to this heading"></a></h2>
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</section>
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</section>
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