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<div id="jb-print-docs-body" class="onlyprint">
<h1>Compilation</h1>
<!-- Table of contents -->
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<ul class="visible nav section-nav flex-column">
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#basics-of-compile">Basics of Compile</a></li>
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#example-speedup">Example Speedup</a></li>
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#debugging">Debugging</a></li>
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#compiling-training-graphs">Compiling Training Graphs</a></li>
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#transformations-with-compile">Transformations with Compile</a></li>
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<article class="bd-article" role="main">
<section id="compilation">
<span id="compile"></span><h1>Compilation<a class="headerlink" href="#compilation" title="Link to this heading">#</a></h1>
<p>MLX has a <a class="reference internal" href="../python/_autosummary/mlx.core.compile.html#mlx.core.compile" title="mlx.core.compile"><code class="xref py py-func docutils literal notranslate"><span class="pre">compile()</span></code></a> function transformation which compiles computation
graphs. Function compilation results in smaller graphs by merging common work
and fusing certain operations. In many cases this can lead to big improvements
in run-time and memory use.</p>
<p>Getting started with <a class="reference internal" href="../python/_autosummary/mlx.core.compile.html#mlx.core.compile" title="mlx.core.compile"><code class="xref py py-func docutils literal notranslate"><span class="pre">compile()</span></code></a> is simple, but there are some edge cases
that are good to be aware of for more complex graphs and advanced usage.</p>
<section id="basics-of-compile">
<h2>Basics of Compile<a class="headerlink" href="#basics-of-compile" title="Link to this heading">#</a></h2>
<p>Let’s start with a simple example:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">fun</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="k">return</span> <span class="n">mx</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">x</span><span class="p">)</span> <span class="o">+</span> <span class="n">y</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">1.0</span><span class="p">)</span>
<span class="n">y</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">2.0</span><span class="p">)</span>
<span class="c1"># Regular call, no compilation</span>
<span class="c1"># Prints: array(2.36788, dtype=float32)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">fun</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="c1"># Compile the function</span>
<span class="n">compiled_fun</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">fun</span><span class="p">)</span>
<span class="c1"># Prints: array(2.36788, dtype=float32)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">compiled_fun</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>The output of both the regular function and the compiled function is the same
up to numerical precision.</p>
<p>The first time you call a compiled function, MLX will build the compute
graph, optimize it, and generate and compile code. This can be relatively
slow. However, MLX will cache compiled functions, so calling a compiled
function multiple times will not initiate a new compilation. This means you
should typically compile functions that you plan to use more than once.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">fun</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="k">return</span> <span class="n">mx</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">x</span><span class="p">)</span> <span class="o">+</span> <span class="n">y</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">1.0</span><span class="p">)</span>
<span class="n">y</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">2.0</span><span class="p">)</span>
<span class="n">compiled_fun</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">fun</span><span class="p">)</span>
<span class="c1"># Compiled here</span>
<span class="n">compiled_fun</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="c1"># Not compiled again</span>
<span class="n">compiled_fun</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="c1"># Not compiled again</span>
<span class="n">mx</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">fun</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>There are some important cases to be aware of that can cause a function to
be recompiled:</p>
<ul class="simple">
<li><p>Changing the shape or number of dimensions</p></li>
<li><p>Changing the type of any of the inputs</p></li>
<li><p>Changing the number of inputs to the function</p></li>
</ul>
<p>In certain cases only some of the compilation stack will be rerun (for
example when changing the shapes) and in other cases the full compilation
stack will be rerun (for example when changing the types). In general you
should avoid compiling functions too frequently.</p>
<p>Another idiom to watch out for is compiling functions which get created and
destroyed frequently. This can happen, for example, when compiling an anonymous
function in a loop:</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">array</span><span class="p">(</span><span class="mf">1.0</span><span class="p">)</span>
<span class="c1"># Don&#39;t do this, compiles lambda at each iteration</span>
<span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">5</span><span class="p">):</span>
<span class="n">mx</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">mx</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">mx</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">x</span><span class="p">)))(</span><span class="n">a</span><span class="p">)</span>
</pre></div>
</div>
</section>
<section id="example-speedup">
<h2>Example Speedup<a class="headerlink" href="#example-speedup" title="Link to this heading">#</a></h2>
<p>The <a class="reference internal" href="../python/nn/_autosummary_functions/mlx.nn.gelu.html#mlx.nn.gelu" title="mlx.nn.gelu"><code class="xref py py-func docutils literal notranslate"><span class="pre">mlx.nn.gelu()</span></code></a> is a nonlinear activation function commonly used with
Transformer-based models. The implementation involves several unary and binary
element-wise operations:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">gelu</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
<span class="k">return</span> <span class="n">x</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">mx</span><span class="o">.</span><span class="n">erf</span><span class="p">(</span><span class="n">x</span> <span class="o">/</span> <span class="n">math</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mi">2</span><span class="p">)))</span> <span class="o">/</span> <span class="mi">2</span>
</pre></div>
</div>
<p>If you use this function with small arrays, it will be overhead bound. If you
use it with large arrays it will be memory bandwidth bound. However, all of
the operations in the <code class="docutils literal notranslate"><span class="pre">gelu</span></code> are fusible into a single kernel with
<a class="reference internal" href="../python/_autosummary/mlx.core.compile.html#mlx.core.compile" title="mlx.core.compile"><code class="xref py py-func docutils literal notranslate"><span class="pre">compile()</span></code></a>. This can speedup both cases considerably.</p>
<p>Let’s compare the runtime of the regular function versus the compiled
function. We’ll use the following timing helper which does a warm up and
handles synchronization:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">time</span>
<span class="k">def</span> <span class="nf">timeit</span><span class="p">(</span><span class="n">fun</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
<span class="c1"># warm up</span>
<span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">10</span><span class="p">):</span>
<span class="n">mx</span><span class="o">.</span><span class="n">eval</span><span class="p">(</span><span class="n">fun</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
<span class="n">tic</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">perf_counter</span><span class="p">()</span>
<span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">100</span><span class="p">):</span>
<span class="n">mx</span><span class="o">.</span><span class="n">eval</span><span class="p">(</span><span class="n">fun</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
<span class="n">toc</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">perf_counter</span><span class="p">()</span>
<span class="n">tpi</span> <span class="o">=</span> <span class="mf">1e3</span> <span class="o">*</span> <span class="p">(</span><span class="n">toc</span> <span class="o">-</span> <span class="n">tic</span><span class="p">)</span> <span class="o">/</span> <span class="mi">100</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Time per iteration </span><span class="si">{</span><span class="n">tpi</span><span class="si">:</span><span class="s2">.3f</span><span class="si">}</span><span class="s2"> (ms)&quot;</span><span class="p">)</span>
</pre></div>
</div>
<p>Now make an array, and benchmark both functions:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">32</span><span class="p">,</span> <span class="mi">1000</span><span class="p">,</span> <span class="mi">4096</span><span class="p">))</span>
<span class="n">timeit</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">gelu</span><span class="p">,</span> <span class="n">x</span><span class="p">)</span>
<span class="n">timeit</span><span class="p">(</span><span class="n">mx</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">gelu</span><span class="p">),</span> <span class="n">x</span><span class="p">)</span>
</pre></div>
</div>
<p>On an M1 Max the times are 15.5 and 3.1 milliseconds. The compiled <code class="docutils literal notranslate"><span class="pre">gelu</span></code> is
five times faster.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>As of the latest MLX, CPU functions are not fully compiled. Compiling CPU
functions can still be helpful, but won’t typically result in as large a
speedup as compiling operations that run on the GPU.</p>
</div>
</section>
<section id="debugging">
<h2>Debugging<a class="headerlink" href="#debugging" title="Link to this heading">#</a></h2>
<p>When a compiled function is first called, it is traced with placeholder
inputs. This means you can’t evaluate arrays (for example to print their
contents) inside compiled functions.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="nd">@mx</span><span class="o">.</span><span class="n">compile</span>
<span class="k">def</span> <span class="nf">fun</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
<span class="n">z</span> <span class="o">=</span> <span class="o">-</span><span class="n">x</span>
<span class="nb">print</span><span class="p">(</span><span class="n">z</span><span class="p">)</span> <span class="c1"># Crash</span>
<span class="k">return</span> <span class="n">mx</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">z</span><span class="p">)</span>
<span class="n">fun</span><span class="p">(</span><span class="n">mx</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="mf">5.0</span><span class="p">))</span>
</pre></div>
</div>
<p>For debugging, inspecting arrays can be helpful. One way to do that is to
globally disable compilation using the <a class="reference internal" href="../python/_autosummary/mlx.core.disable_compile.html#mlx.core.disable_compile" title="mlx.core.disable_compile"><code class="xref py py-func docutils literal notranslate"><span class="pre">disable_compile()</span></code></a> function or
<code class="docutils literal notranslate"><span class="pre">MLX_DISABLE_COMPILE</span></code> flag. For example the following is okay even though
<code class="docutils literal notranslate"><span class="pre">fun</span></code> is compiled:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="nd">@mx</span><span class="o">.</span><span class="n">compile</span>
<span class="k">def</span> <span class="nf">fun</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
<span class="n">z</span> <span class="o">=</span> <span class="o">-</span><span class="n">x</span>
<span class="nb">print</span><span class="p">(</span><span class="n">z</span><span class="p">)</span> <span class="c1"># Okay</span>
<span class="k">return</span> <span class="n">mx</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">z</span><span class="p">)</span>
<span class="n">mx</span><span class="o">.</span><span class="n">disable_compile</span><span class="p">()</span>
<span class="n">fun</span><span class="p">(</span><span class="n">mx</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="mf">5.0</span><span class="p">))</span>
</pre></div>
</div>
</section>
<section id="pure-functions">
<h2>Pure Functions<a class="headerlink" href="#pure-functions" title="Link to this heading">#</a></h2>
<p>Compiled functions are intended to be <em>pure</em>; that is they should not have side
effects. For example:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">state</span> <span class="o">=</span> <span class="p">[]</span>
<span class="nd">@mx</span><span class="o">.</span><span class="n">compile</span>
<span class="k">def</span> <span class="nf">fun</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">z</span> <span class="o">=</span> <span class="n">x</span> <span class="o">+</span> <span class="n">y</span>
<span class="n">state</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">z</span><span class="p">)</span>
<span class="k">return</span> <span class="n">mx</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">z</span><span class="p">)</span>
<span class="n">fun</span><span class="p">(</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="n">mx</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="mf">2.0</span><span class="p">))</span>
<span class="c1"># Crash!</span>
<span class="nb">print</span><span class="p">(</span><span class="n">state</span><span class="p">)</span>
</pre></div>
</div>
<p>After the first call of <code class="docutils literal notranslate"><span class="pre">fun</span></code>, the <code class="docutils literal notranslate"><span class="pre">state</span></code> list will hold a placeholder
array. The placeholder does not have any data; it is only used to build the
computation graph. Printing such an array results in a crash.</p>
<p>You have two options to deal with this. The first option is to simply return
<code class="docutils literal notranslate"><span class="pre">state</span></code> as an output:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">state</span> <span class="o">=</span> <span class="p">[]</span>
<span class="nd">@mx</span><span class="o">.</span><span class="n">compile</span>
<span class="k">def</span> <span class="nf">fun</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">z</span> <span class="o">=</span> <span class="n">x</span> <span class="o">+</span> <span class="n">y</span>
<span class="n">state</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">z</span><span class="p">)</span>
<span class="k">return</span> <span class="n">mx</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">z</span><span class="p">),</span> <span class="n">state</span>
<span class="n">_</span><span class="p">,</span> <span class="n">state</span> <span class="o">=</span> <span class="n">fun</span><span class="p">(</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="n">mx</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="mf">2.0</span><span class="p">))</span>
<span class="c1"># Prints [array(3, dtype=float32)]</span>
<span class="nb">print</span><span class="p">(</span><span class="n">state</span><span class="p">)</span>
</pre></div>
</div>
<p>In some cases returning updated state can be pretty inconvenient. Hence,
<a class="reference internal" href="../python/_autosummary/mlx.core.compile.html#mlx.core.compile" title="mlx.core.compile"><code class="xref py py-func docutils literal notranslate"><span class="pre">compile()</span></code></a> has a parameter to capture implicit outputs:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">functools</span> <span class="kn">import</span> <span class="n">partial</span>
<span class="n">state</span> <span class="o">=</span> <span class="p">[]</span>
<span class="c1"># Tell compile to capture state as an output</span>
<span class="nd">@partial</span><span class="p">(</span><span class="n">mx</span><span class="o">.</span><span class="n">compile</span><span class="p">,</span> <span class="n">outputs</span><span class="o">=</span><span class="n">state</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">fun</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">z</span> <span class="o">=</span> <span class="n">x</span> <span class="o">+</span> <span class="n">y</span>
<span class="n">state</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">z</span><span class="p">)</span>
<span class="k">return</span> <span class="n">mx</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">z</span><span class="p">),</span> <span class="n">state</span>
<span class="n">fun</span><span class="p">(</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="n">mx</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="mf">2.0</span><span class="p">))</span>
<span class="c1"># Prints [array(3, dtype=float32)]</span>
<span class="nb">print</span><span class="p">(</span><span class="n">state</span><span class="p">)</span>
</pre></div>
</div>
<p>This is particularly useful for compiling a function which includes an update
to a container of arrays, as is commonly done when training the parameters of a
<a class="reference internal" href="../python/nn/module.html#mlx.nn.Module" title="mlx.nn.Module"><code class="xref py py-class docutils literal notranslate"><span class="pre">mlx.nn.Module</span></code></a>.</p>
<p>Compiled functions will also treat any inputs not in the parameter list as
constants. For example:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">state</span> <span class="o">=</span> <span class="p">[</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="nd">@mx</span><span class="o">.</span><span class="n">compile</span>
<span class="k">def</span> <span class="nf">fun</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
<span class="k">return</span> <span class="n">x</span> <span class="o">+</span> <span class="n">state</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="c1"># Prints array(2, dtype=float32)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">fun</span><span class="p">(</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="c1"># Update state</span>
<span class="n">state</span><span class="p">[</span><span class="mi">0</span><span class="p">]</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">5.0</span><span class="p">)</span>
<span class="c1"># Still prints array(2, dtype=float32)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">fun</span><span class="p">(</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>
</pre></div>
</div>
<p>In order to have the change of state reflected in the outputs of <code class="docutils literal notranslate"><span class="pre">fun</span></code> you
again have two options. The first option is to simply pass <code class="docutils literal notranslate"><span class="pre">state</span></code> as input
to the function. In some cases this can be pretty inconvenient. Hence,
<a class="reference internal" href="../python/_autosummary/mlx.core.compile.html#mlx.core.compile" title="mlx.core.compile"><code class="xref py py-func docutils literal notranslate"><span class="pre">compile()</span></code></a> also has a parameter to capture implicit inputs:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">functools</span> <span class="kn">import</span> <span class="n">partial</span>
<span class="n">state</span> <span class="o">=</span> <span class="p">[</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="c1"># Tell compile to capture state as an input</span>
<span class="nd">@partial</span><span class="p">(</span><span class="n">mx</span><span class="o">.</span><span class="n">compile</span><span class="p">,</span> <span class="n">inputs</span><span class="o">=</span><span class="n">state</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">fun</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
<span class="k">return</span> <span class="n">x</span> <span class="o">+</span> <span class="n">state</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="c1"># Prints array(2, dtype=float32)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">fun</span><span class="p">(</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="c1"># Update state</span>
<span class="n">state</span><span class="p">[</span><span class="mi">0</span><span class="p">]</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">5.0</span><span class="p">)</span>
<span class="c1"># Prints array(6, dtype=float32)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">fun</span><span class="p">(</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>
</pre></div>
</div>
</section>
<section id="compiling-training-graphs">
<h2>Compiling Training Graphs<a class="headerlink" href="#compiling-training-graphs" title="Link to this heading">#</a></h2>
<p>This section will step through how to use <a class="reference internal" href="../python/_autosummary/mlx.core.compile.html#mlx.core.compile" title="mlx.core.compile"><code class="xref py py-func docutils literal notranslate"><span class="pre">compile()</span></code></a> with a simple example
of a common setup: training a model with <a class="reference internal" href="../python/nn/module.html#mlx.nn.Module" title="mlx.nn.Module"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mlx.nn.Module</span></code></a> using an
<a class="reference internal" href="../python/optimizers/optimizer.html#mlx.optimizers.Optimizer" title="mlx.optimizers.Optimizer"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mlx.optimizers.Optimizer</span></code></a> with state. We will show how to compile the
full forward, backward, and update with <a class="reference internal" href="../python/_autosummary/mlx.core.compile.html#mlx.core.compile" title="mlx.core.compile"><code class="xref py py-func docutils literal notranslate"><span class="pre">compile()</span></code></a>.</p>
<p>To start, here is the simple example without any compilation:</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">mlx.nn</span> <span class="k">as</span> <span class="nn">nn</span>
<span class="kn">import</span> <span class="nn">mlx.optimizers</span> <span class="k">as</span> <span class="nn">optim</span>
<span class="c1"># 4 examples with 10 features each</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
<span class="c1"># 0, 1 targets</span>
<span class="n">y</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">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
<span class="c1"># Simple linear model</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
<span class="c1"># SGD with momentum</span>
<span class="n">optimizer</span> <span class="o">=</span> <span class="n">optim</span><span class="o">.</span><span class="n">SGD</span><span class="p">(</span><span class="n">learning_rate</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">momentum</span><span class="o">=</span><span class="mf">0.8</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">loss_fn</span><span class="p">(</span><span class="n">model</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">logits</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">x</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
<span class="k">return</span> <span class="n">nn</span><span class="o">.</span><span class="n">losses</span><span class="o">.</span><span class="n">binary_cross_entropy</span><span class="p">(</span><span class="n">logits</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
<span class="n">loss_and_grad_fn</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">value_and_grad</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">loss_fn</span><span class="p">)</span>
<span class="c1"># Perform 10 steps of gradient descent</span>
<span class="k">for</span> <span class="n">it</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">10</span><span class="p">):</span>
<span class="n">loss</span><span class="p">,</span> <span class="n">grads</span> <span class="o">=</span> <span class="n">loss_and_grad_fn</span><span class="p">(</span><span class="n">model</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">optimizer</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">grads</span><span class="p">)</span>
<span class="n">mx</span><span class="o">.</span><span class="n">eval</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">parameters</span><span class="p">(),</span> <span class="n">optimizer</span><span class="o">.</span><span class="n">state</span><span class="p">)</span>
</pre></div>
</div>
<p>To compile the update we can put it all in a function and compile it with the
appropriate input and output captures. Here’s the same example but compiled:</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">mlx.nn</span> <span class="k">as</span> <span class="nn">nn</span>
<span class="kn">import</span> <span class="nn">mlx.optimizers</span> <span class="k">as</span> <span class="nn">optim</span>
<span class="kn">from</span> <span class="nn">functools</span> <span class="kn">import</span> <span class="n">partial</span>
<span class="c1"># 4 examples with 10 features each</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
<span class="c1"># 0, 1 targets</span>
<span class="n">y</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">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
<span class="c1"># Simple linear model</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
<span class="c1"># SGD with momentum</span>
<span class="n">optimizer</span> <span class="o">=</span> <span class="n">optim</span><span class="o">.</span><span class="n">SGD</span><span class="p">(</span><span class="n">learning_rate</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">momentum</span><span class="o">=</span><span class="mf">0.8</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">loss_fn</span><span class="p">(</span><span class="n">model</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">logits</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">x</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
<span class="k">return</span> <span class="n">nn</span><span class="o">.</span><span class="n">losses</span><span class="o">.</span><span class="n">binary_cross_entropy</span><span class="p">(</span><span class="n">logits</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
<span class="c1"># The state that will be captured as input and output</span>
<span class="n">state</span> <span class="o">=</span> <span class="p">[</span><span class="n">model</span><span class="o">.</span><span class="n">state</span><span class="p">,</span> <span class="n">optimizer</span><span class="o">.</span><span class="n">state</span><span class="p">]</span>
<span class="nd">@partial</span><span class="p">(</span><span class="n">mx</span><span class="o">.</span><span class="n">compile</span><span class="p">,</span> <span class="n">inputs</span><span class="o">=</span><span class="n">state</span><span class="p">,</span> <span class="n">outputs</span><span class="o">=</span><span class="n">state</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">step</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">loss_and_grad_fn</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">value_and_grad</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">loss_fn</span><span class="p">)</span>
<span class="n">loss</span><span class="p">,</span> <span class="n">grads</span> <span class="o">=</span> <span class="n">loss_and_grad_fn</span><span class="p">(</span><span class="n">model</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">optimizer</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">grads</span><span class="p">)</span>
<span class="k">return</span> <span class="n">loss</span>
<span class="c1"># Perform 10 steps of gradient descent</span>
<span class="k">for</span> <span class="n">it</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">10</span><span class="p">):</span>
<span class="n">loss</span> <span class="o">=</span> <span class="n">step</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="c1"># Evaluate the model and optimizer state</span>
<span class="n">mx</span><span class="o">.</span><span class="n">eval</span><span class="p">(</span><span class="n">state</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">loss</span><span class="p">)</span>
</pre></div>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>If you are using a module which performs random sampling such as
<a class="reference internal" href="../python/nn/_autosummary/mlx.nn.Dropout.html#mlx.nn.Dropout" title="mlx.nn.Dropout"><code class="xref py py-func docutils literal notranslate"><span class="pre">mlx.nn.Dropout()</span></code></a>, make sure you also include <code class="docutils literal notranslate"><span class="pre">mx.random.state</span></code> in the
<code class="docutils literal notranslate"><span class="pre">state</span></code> captured by <a class="reference internal" href="../python/_autosummary/mlx.core.compile.html#mlx.core.compile" title="mlx.core.compile"><code class="xref py py-func docutils literal notranslate"><span class="pre">compile()</span></code></a>, i.e. <code class="docutils literal notranslate"><span class="pre">state</span> <span class="pre">=</span> <span class="pre">[model.state,</span>
<span class="pre">optimizer.state,</span> <span class="pre">mx.random.state]</span></code>.</p>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>For more examples of compiling full training graphs checkout the <a class="reference external" href="https://github.com/ml-explore/mlx-examples">MLX
Examples</a> GitHub repo.</p>
</div>
</section>
<section id="transformations-with-compile">
<h2>Transformations with Compile<a class="headerlink" href="#transformations-with-compile" title="Link to this heading">#</a></h2>
<p>In MLX function transformations are composable. You can apply any function
transformation to the output of any other function transformation. For more on
this, see the documentation on <a class="reference internal" href="function_transforms.html#function-transforms"><span class="std std-ref">function transforms</span></a>.</p>
<p>Compiling transformed functions works just as expected:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">grad_fn</span> <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">exp</span><span class="p">)</span>
<span class="n">compiled_grad_fn</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">grad_fn</span><span class="p">)</span>
<span class="c1"># Prints: array(2.71828, dtype=float32)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">grad_fn</span><span class="p">(</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="c1"># Also prints: array(2.71828, dtype=float32)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">compiled_grad_fn</span><span class="p">(</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>
</pre></div>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>In order to compile as much as possible, a transformation of a compiled
function will not by default be compiled. To compile the transformed
function simply pass it through <a class="reference internal" href="../python/_autosummary/mlx.core.compile.html#mlx.core.compile" title="mlx.core.compile"><code class="xref py py-func docutils literal notranslate"><span class="pre">compile()</span></code></a>.</p>
</div>
<p>You can also compile functions which themselves call compiled functions. A
good practice is to compile the outer most function to give <a class="reference internal" href="../python/_autosummary/mlx.core.compile.html#mlx.core.compile" title="mlx.core.compile"><code class="xref py py-func docutils literal notranslate"><span class="pre">compile()</span></code></a>
the most opportunity to optimize the computation graph:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="nd">@mx</span><span class="o">.</span><span class="n">compile</span>
<span class="k">def</span> <span class="nf">inner</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
<span class="k">return</span> <span class="n">mx</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">mx</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
<span class="k">def</span> <span class="nf">outer</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
<span class="n">inner</span><span class="p">(</span><span class="n">inner</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
<span class="c1"># Compiling the outer function is good to do as it will likely</span>
<span class="c1"># be faster even though the inner functions are compiled</span>
<span class="n">fun</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">outer</span><span class="p">)</span>
</pre></div>
</div>
</section>
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#basics-of-compile">Basics of Compile</a></li>
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#example-speedup">Example Speedup</a></li>
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#debugging">Debugging</a></li>
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pure-functions">Pure Functions</a></li>
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#compiling-training-graphs">Compiling Training Graphs</a></li>
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#transformations-with-compile">Transformations with Compile</a></li>
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