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Awni Hannun
2023-12-13 14:46:24 -08:00
committed by CircleCI Docs
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@@ -9,7 +9,7 @@
<meta charset="utf-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
<title>Unified Memory &#8212; MLX 0.0.4 documentation</title>
<title>Unified Memory &#8212; MLX 0.0.5 documentation</title>
@@ -134,8 +134,8 @@
<img src="_static/mlx_logo.png" class="logo__image only-light" alt="MLX 0.0.4 documentation - Home"/>
<script>document.write(`<img src="_static/mlx_logo.png" class="logo__image only-dark" alt="MLX 0.0.4 documentation - Home"/>`);</script>
<img src="_static/mlx_logo.png" class="logo__image only-light" alt="MLX 0.0.5 documentation - Home"/>
<script>document.write(`<img src="_static/mlx_logo.png" class="logo__image only-dark" alt="MLX 0.0.5 documentation - Home"/>`);</script>
</a></div>
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<li class="toctree-l2"><a class="reference internal" href="python/_autosummary/mlx.core.erfinv.html">mlx.core.erfinv</a></li>
<li class="toctree-l2"><a class="reference internal" href="python/_autosummary/mlx.core.exp.html">mlx.core.exp</a></li>
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<li class="toctree-l2"><a class="reference internal" href="python/_autosummary/mlx.core.load.html">mlx.core.load</a></li>
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<li class="toctree-l2"><a class="reference internal" href="python/_autosummary/mlx.nn.value_and_grad.html">mlx.nn.value_and_grad</a></li>
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<li class="toctree-l2"><a class="reference internal" href="python/_autosummary/mlx.nn.SiLU.html">mlx.nn.SiLU</a></li>
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<li class="toctree-l2"><a class="reference internal" href="python/_autosummary/mlx.nn.Conv2d.html">mlx.nn.Conv2d</a></li>
@@ -350,7 +356,17 @@
<li class="toctree-l2"><a class="reference internal" href="python/_autosummary_functions/mlx.nn.gelu_approx.html">mlx.nn.gelu_approx</a></li>
<li class="toctree-l2"><a class="reference internal" href="python/_autosummary_functions/mlx.nn.gelu_fast_approx.html">mlx.nn.gelu_fast_approx</a></li>
<li class="toctree-l2"><a class="reference internal" href="python/_autosummary_functions/mlx.nn.relu.html">mlx.nn.relu</a></li>
<li class="toctree-l2"><a class="reference internal" href="python/_autosummary_functions/mlx.nn.prelu.html">mlx.nn.prelu</a></li>
<li class="toctree-l2"><a class="reference internal" href="python/_autosummary_functions/mlx.nn.silu.html">mlx.nn.silu</a></li>
<li class="toctree-l2"><a class="reference internal" href="python/_autosummary_functions/mlx.nn.step.html">mlx.nn.step</a></li>
<li class="toctree-l2"><a class="reference internal" href="python/_autosummary_functions/mlx.nn.selu.html">mlx.nn.selu</a></li>
<li class="toctree-l2"><a class="reference internal" href="python/_autosummary_functions/mlx.nn.mish.html">mlx.nn.mish</a></li>
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<li class="toctree-l2"><a class="reference internal" href="python/_autosummary_functions/mlx.nn.losses.l1_loss.html">mlx.nn.losses.l1_loss</a></li>
<li class="toctree-l2"><a class="reference internal" href="python/_autosummary_functions/mlx.nn.losses.mse_loss.html">mlx.nn.losses.mse_loss</a></li>
<li class="toctree-l2"><a class="reference internal" href="python/_autosummary_functions/mlx.nn.losses.nll_loss.html">mlx.nn.losses.nll_loss</a></li>
<li class="toctree-l2"><a class="reference internal" href="python/_autosummary_functions/mlx.nn.losses.kl_div_loss.html">mlx.nn.losses.kl_div_loss</a></li>
</ul>
</li>
<li class="toctree-l1 has-children"><a class="reference internal" href="python/optimizers.html">Optimizers</a><input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-8"><i class="fa-solid fa-chevron-down"></i></label><ul>
@@ -552,7 +568,7 @@ document.write(`
<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 that.</p>
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>