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<h1>Conversion to NumPy and Other Frameworks</h1>
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<section id="conversion-to-numpy-and-other-frameworks">
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<span id="numpy"></span><h1>Conversion to NumPy and Other Frameworks<a class="headerlink" href="#conversion-to-numpy-and-other-frameworks" title="Permalink to this heading">#</a></h1>
|
||||
<p>MLX array implements the <a class="reference external" href="https://docs.python.org/3/c-api/buffer.html">Python Buffer Protocol</a>.
|
||||
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>
|
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</pre></div>
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</div>
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<div class="admonition note">
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<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">"f(x) = x² ="</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">"f'(x) = 2x !="</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="Permalink to this heading">#</a></h2>
|
||||
<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="Permalink 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="Permalink 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>
|
||||
</section>
|
||||
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|
||||
|
||||
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<li class="toctree-l1"><a class="reference internal" href="using_streams.html">Using Streams</a></li>
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<p aria-level="2" class="caption" role="heading"><span class="caption-text">Examples</span></p>
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<p aria-level="2" class="caption" role="heading"><span class="caption-text">Python API Reference</span></p>
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<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary/mlx.nn.Conv2d.html">mlx.nn.Conv2d</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary/mlx.nn.BatchNorm.html">mlx.nn.BatchNorm</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary/mlx.nn.LayerNorm.html">mlx.nn.LayerNorm</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary/mlx.nn.RMSNorm.html">mlx.nn.RMSNorm</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary/mlx.nn.GroupNorm.html">mlx.nn.GroupNorm</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary/mlx.nn.InstanceNorm.html">mlx.nn.InstanceNorm</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary/mlx.nn.ALiBi.html">mlx.nn.ALiBi</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary/mlx.nn.SinusoidalPositionalEncoding.html">mlx.nn.SinusoidalPositionalEncoding</a></li>
|
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<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary_functions/mlx.nn.silu.html">mlx.nn.silu</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary_functions/mlx.nn.selu.html">mlx.nn.selu</a></li>
|
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<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary_functions/mlx.nn.mish.html">mlx.nn.mish</a></li>
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<li class="toctree-l2 has-children"><a class="reference internal" href="../python/nn/losses.html">Loss Functions</a><input class="toctree-checkbox" id="toctree-checkbox-12" name="toctree-checkbox-12" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-12"><i class="fa-solid fa-chevron-down"></i></label><ul>
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<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary_functions/mlx.nn.losses.cross_entropy.html">mlx.nn.losses.cross_entropy</a></li>
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||||
<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary_functions/mlx.nn.losses.kl_div_loss.html">mlx.nn.losses.kl_div_loss</a></li>
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||||
<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary_functions/mlx.nn.losses.l1_loss.html">mlx.nn.losses.l1_loss</a></li>
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#basics">Basics</a></li>
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#function-and-graph-transformations">Function and Graph Transformations</a></li>
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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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<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>
|
||||
<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>
|
||||
<span class="o">>></span> <span class="n">a</span><span class="o">.</span><span class="n">shape</span>
|
||||
<span class="p">[</span><span class="mi">4</span><span class="p">]</span>
|
||||
<span class="o">>></span> <span class="n">a</span><span class="o">.</span><span class="n">dtype</span>
|
||||
<span class="n">int32</span>
|
||||
<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>
|
||||
<span class="o">>></span> <span class="n">b</span><span class="o">.</span><span class="n">dtype</span>
|
||||
<span class="n">float32</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>Operations in MLX are lazy. The outputs of MLX operations are not computed
|
||||
until they are needed. To force an array to be evaluated use
|
||||
<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
|
||||
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,
|
||||
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
|
||||
automatically evaluate the array.</p>
|
||||
<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>
|
||||
<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>
|
||||
<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="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>
|
||||
<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>
|
||||
<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="o">>></span> <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</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">c</span><span class="p">)</span> <span class="c1"># Also evaluates c</span>
|
||||
<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>
|
||||
</pre></div>
|
||||
</div>
|
||||
</section>
|
||||
<section id="function-and-graph-transformations">
|
||||
<h2>Function and Graph Transformations<a class="headerlink" href="#function-and-graph-transformations" title="Permalink to this heading">#</a></h2>
|
||||
<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>.
|
||||
Transformations can be composed arbitrarily. For example
|
||||
<code class="docutils literal notranslate"><span class="pre">grad(vmap(grad(fn)))</span></code> (or any other composition) is allowed.</p>
|
||||
<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>
|
||||
<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>
|
||||
<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>
|
||||
<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>
|
||||
<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>
|
||||
<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>
|
||||
<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>
|
||||
</pre></div>
|
||||
</div>
|
||||
<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
|
||||
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>
|
||||
<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
|
||||
gradient with respect to the function’s input.</p>
|
||||
</section>
|
||||
</section>
|
||||
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|
||||
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|
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|
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|
||||
|
||||
|
||||
|
||||
<div id="jb-print-docs-body" class="onlyprint">
|
||||
<h1>Unified Memory</h1>
|
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<section id="unified-memory">
|
||||
<span id="id1"></span><h1>Unified Memory<a class="headerlink" href="#unified-memory" title="Permalink to this heading">#</a></h1>
|
||||
<p>Apple silicon has a unified memory architecture. The CPU and GPU have direct
|
||||
access to the same memory pool. MLX is designed to take advantage of that.</p>
|
||||
<p>Concretely, when you make an array in MLX you don’t have to specify its location:</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">random</span><span class="o">.</span><span class="n">normal</span><span class="p">((</span><span class="mi">100</span><span class="p">,))</span>
|
||||
<span class="n">b</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">normal</span><span class="p">((</span><span class="mi">100</span><span class="p">,))</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>Both <code class="docutils literal notranslate"><span class="pre">a</span></code> and <code class="docutils literal notranslate"><span class="pre">b</span></code> live in unified memory.</p>
|
||||
<p>In MLX, rather than moving arrays to devices, you specify the device when you
|
||||
run the operation. Any device can perform any operation on <code class="docutils literal notranslate"><span class="pre">a</span></code> and <code class="docutils literal notranslate"><span class="pre">b</span></code>
|
||||
without needing to move them from one memory location to another. For example:</p>
|
||||
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">mx</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">stream</span><span class="o">=</span><span class="n">mx</span><span class="o">.</span><span class="n">cpu</span><span class="p">)</span>
|
||||
<span class="n">mx</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">stream</span><span class="o">=</span><span class="n">mx</span><span class="o">.</span><span class="n">gpu</span><span class="p">)</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>In the above, both the CPU and the GPU will perform the same add
|
||||
operation. The operations can (and likely will) be run in parallel since
|
||||
there are no dependencies between them. See <a class="reference internal" href="using_streams.html#using-streams"><span class="std std-ref">Using Streams</span></a> for more
|
||||
information the semantics of streams in MLX.</p>
|
||||
<p>In the above <code class="docutils literal notranslate"><span class="pre">add</span></code> example, there are no dependencies between operations, so
|
||||
there is no possibility for race conditions. If there are dependencies, the
|
||||
MLX scheduler will automatically manage them. For example:</p>
|
||||
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">c</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">stream</span><span class="o">=</span><span class="n">mx</span><span class="o">.</span><span class="n">cpu</span><span class="p">)</span>
|
||||
<span class="n">d</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="n">stream</span><span class="o">=</span><span class="n">mx</span><span class="o">.</span><span class="n">gpu</span><span class="p">)</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>In the above case, the second <code class="docutils literal notranslate"><span class="pre">add</span></code> runs on the GPU but it depends on the
|
||||
output of the first <code class="docutils literal notranslate"><span class="pre">add</span></code> which is running on the CPU. MLX will
|
||||
automatically insert a dependency between the two streams so that the second
|
||||
<code class="docutils literal notranslate"><span class="pre">add</span></code> only starts executing after the first is complete and <code class="docutils literal notranslate"><span class="pre">c</span></code> is
|
||||
available.</p>
|
||||
<section id="a-simple-example">
|
||||
<h2>A Simple Example<a class="headerlink" href="#a-simple-example" title="Permalink to this heading">#</a></h2>
|
||||
<p>Here is a more interesting (albeit slightly contrived example) of how unified
|
||||
memory can be helpful. Suppose we have the following computation:</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">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">d1</span><span class="p">,</span> <span class="n">d2</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">matmul</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">stream</span><span class="o">=</span><span class="n">d1</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">500</span><span class="p">):</span>
|
||||
<span class="n">b</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">b</span><span class="p">,</span> <span class="n">stream</span><span class="o">=</span><span class="n">d2</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">x</span><span class="p">,</span> <span class="n">b</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>which we want to run with the following arguments:</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">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">4096</span><span class="p">,</span> <span class="mi">512</span><span class="p">))</span>
|
||||
<span class="n">b</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">512</span><span class="p">,</span> <span class="mi">4</span><span class="p">))</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>The first <code class="docutils literal notranslate"><span class="pre">matmul</span></code> operation is a good fit for the GPU since it’s more
|
||||
compute dense. The second sequence of operations are a better fit for the CPU,
|
||||
since they are very small and would probably be overhead bound on the GPU.</p>
|
||||
<p>If we time the computation fully on the GPU, we get 2.8 milliseconds. But if we
|
||||
run the computation with <code class="docutils literal notranslate"><span class="pre">d1=mx.gpu</span></code> and <code class="docutils literal notranslate"><span class="pre">d2=mx.cpu</span></code>, then the time is only
|
||||
about 1.4 milliseconds, about twice as fast. These times were measured on an M1
|
||||
Max.</p>
|
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||||
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||||
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||||
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||||
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<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary_functions/mlx.nn.losses.log_cosh_loss.html">mlx.nn.losses.log_cosh_loss</a></li>
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