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<h1>Indexing Arrays</h1>
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<span id="indexing"></span><h1>Indexing Arrays<a class="headerlink" href="#indexing-arrays" title="Permalink to this heading">#</a></h1>
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<p>For the most part, indexing an MLX <a class="reference internal" href="../python/_autosummary/mlx.core.array.html#mlx.core.array" title="mlx.core.array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">array</span></code></a> works the same as indexing a
|
||||
NumPy <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-obj docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a>. See the <a class="reference external" href="https://numpy.org/doc/stable/user/basics.indexing.html">NumPy documentation</a> for more details on
|
||||
how that works.</p>
|
||||
<p>For example, you can use regular integers and slices (<a class="reference external" href="https://docs.python.org/3/library/functions.html#slice" title="(in Python v3.12)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">slice</span></code></a>) to index arrays:</p>
|
||||
<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>>>> <span class="nv">arr</span> <span class="o">=</span> mx.arange<span class="o">(</span><span class="m">10</span><span class="o">)</span>
|
||||
>>> arr<span class="o">[</span><span class="m">3</span><span class="o">]</span>
|
||||
array<span class="o">(</span><span class="m">3</span>, <span class="nv">dtype</span><span class="o">=</span>int32<span class="o">)</span>
|
||||
>>> arr<span class="o">[</span>-2<span class="o">]</span> <span class="c1"># negative indexing works</span>
|
||||
array<span class="o">(</span><span class="m">8</span>, <span class="nv">dtype</span><span class="o">=</span>int32<span class="o">)</span>
|
||||
>>> arr<span class="o">[</span><span class="m">2</span>:8:2<span class="o">]</span> <span class="c1"># start, stop, stride</span>
|
||||
array<span class="o">([</span><span class="m">2</span>, <span class="m">4</span>, <span class="m">6</span><span class="o">]</span>, <span class="nv">dtype</span><span class="o">=</span>int32<span class="o">)</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>For multi-dimensional arrays, the <code class="docutils literal notranslate"><span class="pre">...</span></code> or <a class="reference external" href="https://docs.python.org/3/library/constants.html#Ellipsis" title="(in Python v3.12)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Ellipsis</span></code></a> syntax works as in NumPy:</p>
|
||||
<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>>>> <span class="nv">arr</span> <span class="o">=</span> mx.arange<span class="o">(</span><span class="m">8</span><span class="o">)</span>.reshape<span class="o">(</span><span class="m">2</span>, <span class="m">2</span>, <span class="m">2</span><span class="o">)</span>
|
||||
>>> arr<span class="o">[</span>:, :, <span class="m">0</span><span class="o">]</span>
|
||||
array<span class="o">(</span><span class="m">3</span>, <span class="nv">dtype</span><span class="o">=</span>int32<span class="o">)</span>
|
||||
array<span class="o">([[</span><span class="m">0</span>, <span class="m">2</span><span class="o">]</span>,
|
||||
<span class="o">[</span><span class="m">4</span>, <span class="m">6</span><span class="o">]]</span>, <span class="nv">dtype</span><span class="o">=</span>int32
|
||||
>>> arr<span class="o">[</span>..., <span class="m">0</span><span class="o">]</span>
|
||||
array<span class="o">([[</span><span class="m">0</span>, <span class="m">2</span><span class="o">]</span>,
|
||||
<span class="o">[</span><span class="m">4</span>, <span class="m">6</span><span class="o">]]</span>, <span class="nv">dtype</span><span class="o">=</span>int32
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>You can index with <code class="docutils literal notranslate"><span class="pre">None</span></code> to create a new axis:</p>
|
||||
<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>>>> <span class="nv">arr</span> <span class="o">=</span> mx.arange<span class="o">(</span><span class="m">8</span><span class="o">)</span>
|
||||
>>> arr.shape
|
||||
<span class="o">[</span><span class="m">8</span><span class="o">]</span>
|
||||
>>> arr<span class="o">[</span>None<span class="o">]</span>.shape
|
||||
<span class="o">[</span><span class="m">1</span>, <span class="m">8</span><span class="o">]</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>You can also use an <a class="reference internal" href="../python/_autosummary/mlx.core.array.html#mlx.core.array" title="mlx.core.array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">array</span></code></a> to index another <a class="reference internal" href="../python/_autosummary/mlx.core.array.html#mlx.core.array" title="mlx.core.array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">array</span></code></a>:</p>
|
||||
<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>>>> <span class="nv">arr</span> <span class="o">=</span> mx.arange<span class="o">(</span><span class="m">10</span><span class="o">)</span>
|
||||
>>> <span class="nv">idx</span> <span class="o">=</span> mx.array<span class="o">([</span><span class="m">5</span>, <span class="m">7</span><span class="o">])</span>
|
||||
>>> arr<span class="o">[</span>idx<span class="o">]</span>
|
||||
array<span class="o">([</span><span class="m">5</span>, <span class="m">7</span><span class="o">]</span>, <span class="nv">dtype</span><span class="o">=</span>int32<span class="o">)</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>Mixing and matching integers, <a class="reference external" href="https://docs.python.org/3/library/functions.html#slice" title="(in Python v3.12)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">slice</span></code></a>, <code class="docutils literal notranslate"><span class="pre">...</span></code>, and <a class="reference internal" href="../python/_autosummary/mlx.core.array.html#mlx.core.array" title="mlx.core.array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">array</span></code></a> indices
|
||||
works just as in NumPy.</p>
|
||||
<p>Other functions which may be useful for indexing arrays are <a class="reference internal" href="../python/_autosummary/mlx.core.take.html#mlx.core.take" title="mlx.core.take"><code class="xref py py-func docutils literal notranslate"><span class="pre">take()</span></code></a> and
|
||||
<a class="reference internal" href="../python/_autosummary/mlx.core.take_along_axis.html#mlx.core.take_along_axis" title="mlx.core.take_along_axis"><code class="xref py py-func docutils literal notranslate"><span class="pre">take_along_axis()</span></code></a>.</p>
|
||||
<section id="differences-from-numpy">
|
||||
<h2>Differences from NumPy<a class="headerlink" href="#differences-from-numpy" title="Permalink to this heading">#</a></h2>
|
||||
<div class="admonition note">
|
||||
<p class="admonition-title">Note</p>
|
||||
<p>MLX indexing is different from NumPy indexing in two important ways:</p>
|
||||
<ul class="simple">
|
||||
<li><p>Indexing does not perform bounds checking. Indexing out of bounds is
|
||||
undefined behavior.</p></li>
|
||||
<li><p>Boolean mask based indexing is not yet supported.</p></li>
|
||||
</ul>
|
||||
</div>
|
||||
<p>The reason for the lack of bounds checking is that exceptions cannot propagate
|
||||
from the GPU. Performing bounds checking for array indices before launching the
|
||||
kernel would be extremely inefficient.</p>
|
||||
<p>Indexing with boolean masks is something that MLX may support in the future. In
|
||||
general, MLX has limited support for operations for which outputs
|
||||
<em>shapes</em> are dependent on input <em>data</em>. Other examples of these types of
|
||||
operations which MLX does not yet support include <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.nonzero.html#numpy.nonzero" title="(in NumPy v1.26)"><code class="xref py py-func docutils literal notranslate"><span class="pre">numpy.nonzero()</span></code></a> and the
|
||||
single input version of <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.where.html#numpy.where" title="(in NumPy v1.26)"><code class="xref py py-func docutils literal notranslate"><span class="pre">numpy.where()</span></code></a>.</p>
|
||||
</section>
|
||||
<section id="in-place-updates">
|
||||
<h2>In Place Updates<a class="headerlink" href="#in-place-updates" title="Permalink to this heading">#</a></h2>
|
||||
<p>In place updates to indexed arrays are possible in MLX. For example:</p>
|
||||
<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>>>> <span class="nv">a</span> <span class="o">=</span> mx.array<span class="o">([</span><span class="m">1</span>, <span class="m">2</span>, <span class="m">3</span><span class="o">])</span>
|
||||
>>> a<span class="o">[</span><span class="m">2</span><span class="o">]</span> <span class="o">=</span> <span class="m">0</span>
|
||||
>>> a
|
||||
array<span class="o">([</span><span class="m">1</span>, <span class="m">2</span>, <span class="m">0</span><span class="o">]</span>, <span class="nv">dtype</span><span class="o">=</span>int32<span class="o">)</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>Just as in NumPy, in place updates will be reflected in all references to the
|
||||
same array:</p>
|
||||
<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>>>> <span class="nv">a</span> <span class="o">=</span> mx.array<span class="o">([</span><span class="m">1</span>, <span class="m">2</span>, <span class="m">3</span><span class="o">])</span>
|
||||
>>> <span class="nv">b</span> <span class="o">=</span> a
|
||||
>>> b<span class="o">[</span><span class="m">2</span><span class="o">]</span> <span class="o">=</span> <span class="m">0</span>
|
||||
>>> b
|
||||
array<span class="o">([</span><span class="m">1</span>, <span class="m">2</span>, <span class="m">0</span><span class="o">]</span>, <span class="nv">dtype</span><span class="o">=</span>int32<span class="o">)</span>
|
||||
>>> a
|
||||
array<span class="o">([</span><span class="m">1</span>, <span class="m">2</span>, <span class="m">0</span><span class="o">]</span>, <span class="nv">dtype</span><span class="o">=</span>int32<span class="o">)</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>Transformations of functions which use in-place updates are allowed and work as
|
||||
expected. For 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">idx</span><span class="p">):</span>
|
||||
<span class="n">x</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span> <span class="o">=</span> <span class="mf">2.0</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">dfdx</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">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="mf">2.0</span><span class="p">,</span> <span class="mf">3.0</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="mi">1</span><span class="p">]))</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="n">dfdx</span><span class="p">)</span> <span class="c1"># Prints: array([1, 0, 1], dtype=float32)</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>In the above <code class="docutils literal notranslate"><span class="pre">dfdx</span></code> will have the correct gradient, namely zeros at <code class="docutils literal notranslate"><span class="pre">idx</span></code>
|
||||
and ones elsewhere.</p>
|
||||
</section>
|
||||
</section>
|
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|
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<div id="jb-print-docs-body" class="onlyprint">
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<h1>Lazy Evaluation</h1>
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<!-- Table of contents -->
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#why-lazy-evaluation">Why Lazy Evaluation</a><ul class="visible nav section-nav flex-column">
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<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#transforming-compute-graphs">Transforming Compute Graphs</a></li>
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<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#only-compute-what-you-use">Only Compute What You Use</a></li>
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</ul>
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#when-to-evaluate">When to Evaluate</a></li>
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<section id="lazy-evaluation">
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<span id="lazy-eval"></span><h1>Lazy Evaluation<a class="headerlink" href="#lazy-evaluation" title="Permalink to this heading">#</a></h1>
|
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<section id="why-lazy-evaluation">
|
||||
<h2>Why Lazy Evaluation<a class="headerlink" href="#why-lazy-evaluation" title="Permalink to this heading">#</a></h2>
|
||||
<p>When you perform operations in MLX, no computation actually happens. Instead a
|
||||
compute graph is recorded. The actual computation only happens if an
|
||||
<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> is performed.</p>
|
||||
<p>MLX uses lazy evaluation because it has some nice features, some of which we
|
||||
describe below.</p>
|
||||
<section id="transforming-compute-graphs">
|
||||
<h3>Transforming Compute Graphs<a class="headerlink" href="#transforming-compute-graphs" title="Permalink to this heading">#</a></h3>
|
||||
<p>Lazy evaluation let’s us record a compute graph without actually doing any
|
||||
computations. This is useful for 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> and graph optimizations like <a class="reference internal" href="../python/_autosummary/mlx.core.simplify.html#mlx.core.simplify" title="mlx.core.simplify"><code class="xref py py-func docutils literal notranslate"><span class="pre">simplify()</span></code></a>.</p>
|
||||
<p>Currently, MLX does not compile and rerun compute graphs. They are all
|
||||
generated dynamically. However, lazy evaluation makes it much easier to
|
||||
integrate compilation for future performance enhancements.</p>
|
||||
</section>
|
||||
<section id="only-compute-what-you-use">
|
||||
<h3>Only Compute What You Use<a class="headerlink" href="#only-compute-what-you-use" title="Permalink to this heading">#</a></h3>
|
||||
<p>In MLX you do not need to worry as much about computing outputs that are never
|
||||
used. For 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">a</span> <span class="o">=</span> <span class="n">fun1</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
|
||||
<span class="n">b</span> <span class="o">=</span> <span class="n">expensive_fun</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">a</span><span class="p">,</span> <span class="n">b</span>
|
||||
|
||||
<span class="n">y</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">fun</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>Here, we never actually compute the output of <code class="docutils literal notranslate"><span class="pre">expensive_fun</span></code>. Use this
|
||||
pattern with care though, as the graph of <code class="docutils literal notranslate"><span class="pre">expensive_fun</span></code> is still built, and
|
||||
that has some cost associated to it.</p>
|
||||
<p>Similarly, lazy evaluation can be beneficial for saving memory while keeping
|
||||
code simple. Say you have a very large model <code class="docutils literal notranslate"><span class="pre">Model</span></code> derived from
|
||||
<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>. You can instantiate this model with <code class="docutils literal notranslate"><span class="pre">model</span> <span class="pre">=</span> <span class="pre">Model()</span></code>.
|
||||
Typically, this will initialize all of the weights as <code class="docutils literal notranslate"><span class="pre">float32</span></code>, but the
|
||||
initialization does not actually compute anything until you perform an
|
||||
<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>. If you update the model with <code class="docutils literal notranslate"><span class="pre">float16</span></code> weights, your maximum
|
||||
consumed memory will be half that required if eager computation was used
|
||||
instead.</p>
|
||||
<p>This pattern is simple to do in MLX thanks to lazy computation:</p>
|
||||
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">model</span> <span class="o">=</span> <span class="n">Model</span><span class="p">()</span> <span class="c1"># no memory used yet</span>
|
||||
<span class="n">model</span><span class="o">.</span><span class="n">load_weights</span><span class="p">(</span><span class="s2">"weights_fp16.safetensors"</span><span class="p">)</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
</section>
|
||||
</section>
|
||||
<section id="when-to-evaluate">
|
||||
<h2>When to Evaluate<a class="headerlink" href="#when-to-evaluate" title="Permalink to this heading">#</a></h2>
|
||||
<p>A common question is when to 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>. The trade-off is between
|
||||
letting graphs get too large and not batching enough useful work.</p>
|
||||
<p>For example:</p>
|
||||
<div class="highlight-python notranslate"><div class="highlight"><pre><span></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">a</span> <span class="o">=</span> <span class="n">a</span> <span class="o">+</span> <span class="n">b</span>
|
||||
<span class="n">mx</span><span class="o">.</span><span class="n">eval</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>
|
||||
<span class="n">b</span> <span class="o">=</span> <span class="n">b</span> <span class="o">*</span> <span class="mi">2</span>
|
||||
<span class="n">mx</span><span class="o">.</span><span class="n">eval</span><span class="p">(</span><span class="n">b</span><span class="p">)</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>This is a bad idea because there is some fixed overhead with each graph
|
||||
evaluation. On the other hand, there is some slight overhead which grows with
|
||||
the compute graph size, so extremely large graphs (while computationally
|
||||
correct) can be costly.</p>
|
||||
<p>Luckily, a wide range of compute graph sizes work pretty well with MLX:
|
||||
anything from a few tens of operations to many thousands of operations per
|
||||
evaluation should be okay.</p>
|
||||
<p>Most numerical computations have an iterative outer loop (e.g. the iteration in
|
||||
stochastic gradient descent). A natural and usually efficient place to 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> is at each iteration of this outer loop.</p>
|
||||
<p>Here is a concrete example:</p>
|
||||
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> <span class="n">batch</span> <span class="ow">in</span> <span class="n">dataset</span><span class="p">:</span>
|
||||
|
||||
<span class="c1"># Nothing has been evaluated yet</span>
|
||||
<span class="n">loss</span><span class="p">,</span> <span class="n">grad</span> <span class="o">=</span> <span class="n">value_and_grad_fn</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">batch</span><span class="p">)</span>
|
||||
|
||||
<span class="c1"># Still nothing has been evaluated</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">grad</span><span class="p">)</span>
|
||||
|
||||
<span class="c1"># Evaluate the loss and the new parameters which will</span>
|
||||
<span class="c1"># run the full gradient computation and optimizer update</span>
|
||||
<span class="n">mx</span><span class="o">.</span><span class="n">eval</span><span class="p">(</span><span class="n">loss</span><span class="p">,</span> <span class="n">model</span><span class="o">.</span><span class="n">parameters</span><span class="p">())</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>An important behavior to be aware of is when the graph will be implicitly
|
||||
evaluated. Anytime you <code class="docutils literal notranslate"><span class="pre">print</span></code> an array, convert it to an
|
||||
<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-obj docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a>, or otherwise access it’s memory via <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>,
|
||||
the graph will be evaluated. Saving arrays via <a class="reference internal" href="../python/_autosummary/mlx.core.save.html#mlx.core.save" title="mlx.core.save"><code class="xref py py-func docutils literal notranslate"><span class="pre">save()</span></code></a> (or any other MLX
|
||||
saving functions) will also evaluate the array.</p>
|
||||
<p>Calling <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-func docutils literal notranslate"><span class="pre">array.item()</span></code></a> on a scalar array will also evaluate it. In the
|
||||
example above, printing the loss (<code class="docutils literal notranslate"><span class="pre">print(loss)</span></code>) or adding the loss scalar to
|
||||
a list (<code class="docutils literal notranslate"><span class="pre">losses.append(loss.item())</span></code>) would cause a graph evaluation. If
|
||||
these lines are before <code class="docutils literal notranslate"><span class="pre">mx.eval(loss,</span> <span class="pre">model.parameters())</span></code> then this
|
||||
will be a partial evaluation, computing only the forward pass.</p>
|
||||
<p>Also, calling <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> on an array or set of arrays multiple times is
|
||||
perfectly fine. This is effectively a no-op.</p>
|
||||
<div class="admonition warning">
|
||||
<p class="admonition-title">Warning</p>
|
||||
<p>Using scalar arrays for control-flow will cause an evaluation.</p>
|
||||
</div>
|
||||
<p>Here is an 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">h</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">first_layer</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="n">y</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span> <span class="c1"># An evaluation is done here!</span>
|
||||
<span class="n">z</span> <span class="o">=</span> <span class="n">second_layer_a</span><span class="p">(</span><span class="n">h</span><span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">z</span> <span class="o">=</span> <span class="n">second_layer_b</span><span class="p">(</span><span class="n">h</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">z</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>Using arrays for control flow should be done with care. The above example works
|
||||
and can even be used with gradient transformations. However, this can be very
|
||||
inefficient if evaluations are done too frequently.</p>
|
||||
</section>
|
||||
</section>
|
||||
|
||||
|
||||
</article>
|
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|
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|
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title="previous page">
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<i class="fa-solid fa-list"></i> Contents
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<nav class="bd-toc-nav page-toc">
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<ul class="visible nav section-nav flex-column">
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#why-lazy-evaluation">Why Lazy Evaluation</a><ul class="visible nav section-nav flex-column">
|
||||
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#transforming-compute-graphs">Transforming Compute Graphs</a></li>
|
||||
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#only-compute-what-you-use">Only Compute What You Use</a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#when-to-evaluate">When to Evaluate</a></li>
|
||||
</ul>
|
||||
</nav></div>
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<p class="component-author">
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By MLX Contributors
|
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</p>
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</div>
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© Copyright 2023, MLX Contributors.
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<br/>
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29
docs/build/html/usage/numpy.html
vendored
29
docs/build/html/usage/numpy.html
vendored
@@ -46,8 +46,8 @@
|
||||
<script>DOCUMENTATION_OPTIONS.pagename = 'usage/numpy';</script>
|
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<link rel="index" title="Index" href="../genindex.html" />
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<link rel="search" title="Search" href="../search.html" />
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<link rel="next" title="Linear Regression" href="../examples/linear_regression.html" />
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<link rel="prev" title="Using Streams" href="using_streams.html" />
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<link rel="next" title="Using Streams" href="using_streams.html" />
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<link rel="prev" title="Saving and Loading Arrays" href="saving_and_loading.html" />
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<meta name="viewport" content="width=device-width, initial-scale=1"/>
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<meta name="docsearch:language" content="en"/>
|
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</head>
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@@ -148,9 +148,12 @@
|
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<p aria-level="2" class="caption" role="heading"><span class="caption-text">Usage</span></p>
|
||||
<ul class="current nav bd-sidenav">
|
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<li class="toctree-l1"><a class="reference internal" href="quick_start.html">Quick Start Guide</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="lazy_evaluation.html">Lazy Evaluation</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="unified_memory.html">Unified Memory</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="using_streams.html">Using Streams</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="indexing.html">Indexing Arrays</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="saving_and_loading.html">Saving and Loading Arrays</a></li>
|
||||
<li class="toctree-l1 current active"><a class="current reference internal" href="#">Conversion to NumPy and Other Frameworks</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="using_streams.html">Using Streams</a></li>
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading"><span class="caption-text">Examples</span></p>
|
||||
<ul class="nav bd-sidenav">
|
||||
@@ -238,6 +241,7 @@
|
||||
<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.core.cosh.html">mlx.core.cosh</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.core.dequantize.html">mlx.core.dequantize</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.core.divide.html">mlx.core.divide</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.core.divmod.html">mlx.core.divmod</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.core.equal.html">mlx.core.equal</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.core.erf.html">mlx.core.erf</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.core.erfinv.html">mlx.core.erfinv</a></li>
|
||||
@@ -251,6 +255,7 @@
|
||||
<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.core.greater.html">mlx.core.greater</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.core.greater_equal.html">mlx.core.greater_equal</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.core.identity.html">mlx.core.identity</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.core.inner.html">mlx.core.inner</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.core.less.html">mlx.core.less</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.core.less_equal.html">mlx.core.less_equal</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.core.linspace.html">mlx.core.linspace</a></li>
|
||||
@@ -261,6 +266,8 @@
|
||||
<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.core.log1p.html">mlx.core.log1p</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.core.logaddexp.html">mlx.core.logaddexp</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.core.logical_not.html">mlx.core.logical_not</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.core.logical_and.html">mlx.core.logical_and</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.core.logical_or.html">mlx.core.logical_or</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.core.logsumexp.html">mlx.core.logsumexp</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.core.matmul.html">mlx.core.matmul</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.core.max.html">mlx.core.max</a></li>
|
||||
@@ -273,6 +280,7 @@
|
||||
<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.core.negative.html">mlx.core.negative</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.core.ones.html">mlx.core.ones</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.core.ones_like.html">mlx.core.ones_like</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.core.outer.html">mlx.core.outer</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.core.partition.html">mlx.core.partition</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.core.pad.html">mlx.core.pad</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.core.prod.html">mlx.core.prod</a></li>
|
||||
@@ -286,6 +294,7 @@
|
||||
<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.core.save.html">mlx.core.save</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.core.savez.html">mlx.core.savez</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.core.savez_compressed.html">mlx.core.savez_compressed</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.core.save_gguf.html">mlx.core.save_gguf</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.core.save_safetensors.html">mlx.core.save_safetensors</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.core.sigmoid.html">mlx.core.sigmoid</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.core.sign.html">mlx.core.sign</a></li>
|
||||
@@ -435,6 +444,7 @@
|
||||
<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary_functions/mlx.nn.losses.hinge_loss.html">mlx.nn.losses.hinge_loss</a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary_functions/mlx.nn.losses.huber_loss.html">mlx.nn.losses.huber_loss</a></li>
|
||||
<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>
|
||||
<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary_functions/mlx.nn.losses.cosine_similarity_loss.html">mlx.nn.losses.cosine_similarity_loss</a></li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
@@ -693,6 +703,11 @@ For instance, a function defined as <code class="docutils literal notranslate"><
|
||||
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>
|
||||
<div class="admonition warning">
|
||||
<p class="admonition-title">Warning</p>
|
||||
<p>PyTorch Support for <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#memoryview" title="(in Python v3.12)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">memoryview</span></code></a> is experimental and can break for
|
||||
multi-dimensional arrays. Casting to NumPy first is advised for now.</p>
|
||||
</div>
|
||||
<p>PyTorch supports the buffer protocol, but it requires an explicit <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#memoryview" title="(in Python v3.12)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">memoryview</span></code></a>.</p>
|
||||
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">mlx.core</span> <span class="k">as</span> <span class="nn">mx</span>
|
||||
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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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<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary_functions/mlx.nn.losses.cosine_similarity_loss.html">mlx.nn.losses.cosine_similarity_loss</a></li>
|
||||
</ul>
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||||
</li>
|
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</ul>
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||||
@@ -675,6 +685,7 @@ automatically evaluate the array.</p>
|
||||
<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>
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<p>See the page on <a class="reference internal" href="lazy_evaluation.html#lazy-eval"><span class="std std-ref">Lazy Evaluation</span></a> for more details.</p>
|
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</section>
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<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>
|
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@@ -718,11 +729,11 @@ gradient with respect to the function’s input.</p>
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808
docs/build/html/usage/saving_and_loading.html
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808
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||||
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||||
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||||
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||||
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||||
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<h1>Saving and Loading Arrays</h1>
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<span id="saving-and-loading"></span><h1>Saving and Loading Arrays<a class="headerlink" href="#saving-and-loading-arrays" title="Permalink to this heading">#</a></h1>
|
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<p>MLX supports multiple array serialization formats.</p>
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<table class="table" id="id1">
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<caption><span class="caption-text">Serialization Formats</span><a class="headerlink" href="#id1" title="Permalink to this table">#</a></caption>
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<tr class="row-odd"><th class="head"><p>Format</p></th>
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<th class="head"><p>Extension</p></th>
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<th class="head"><p>Function</p></th>
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<th class="head"><p>Notes</p></th>
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</tr>
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</thead>
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<tbody>
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<tr class="row-even"><td><p>NumPy</p></td>
|
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<td><p><code class="docutils literal notranslate"><span class="pre">.npy</span></code></p></td>
|
||||
<td><p><a class="reference internal" href="../python/_autosummary/mlx.core.save.html#mlx.core.save" title="mlx.core.save"><code class="xref py py-func docutils literal notranslate"><span class="pre">save()</span></code></a></p></td>
|
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<td><p>Single arrays only</p></td>
|
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</tr>
|
||||
<tr class="row-odd"><td><p>NumPy archive</p></td>
|
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<td><p><code class="docutils literal notranslate"><span class="pre">.npz</span></code></p></td>
|
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<td><p><a class="reference internal" href="../python/_autosummary/mlx.core.savez.html#mlx.core.savez" title="mlx.core.savez"><code class="xref py py-func docutils literal notranslate"><span class="pre">savez()</span></code></a> and <a class="reference internal" href="../python/_autosummary/mlx.core.savez_compressed.html#mlx.core.savez_compressed" title="mlx.core.savez_compressed"><code class="xref py py-func docutils literal notranslate"><span class="pre">savez_compressed()</span></code></a></p></td>
|
||||
<td><p>Multiple arrays</p></td>
|
||||
</tr>
|
||||
<tr class="row-even"><td><p>Safetensors</p></td>
|
||||
<td><p><code class="docutils literal notranslate"><span class="pre">.safetensors</span></code></p></td>
|
||||
<td><p><a class="reference internal" href="../python/_autosummary/mlx.core.save_safetensors.html#mlx.core.save_safetensors" title="mlx.core.save_safetensors"><code class="xref py py-func docutils literal notranslate"><span class="pre">save_safetensors()</span></code></a></p></td>
|
||||
<td><p>Multiple arrays</p></td>
|
||||
</tr>
|
||||
<tr class="row-odd"><td><p>GGUF</p></td>
|
||||
<td><p><code class="docutils literal notranslate"><span class="pre">.gguf</span></code></p></td>
|
||||
<td><p><a class="reference internal" href="../python/_autosummary/mlx.core.save_gguf.html#mlx.core.save_gguf" title="mlx.core.save_gguf"><code class="xref py py-func docutils literal notranslate"><span class="pre">save_gguf()</span></code></a></p></td>
|
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<td><p>Multiple arrays</p></td>
|
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</tr>
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</tbody>
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</table>
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||||
<p>The <a class="reference internal" href="../python/_autosummary/mlx.core.load.html#mlx.core.load" title="mlx.core.load"><code class="xref py py-func docutils literal notranslate"><span class="pre">load()</span></code></a> function will load any of the supported serialization
|
||||
formats. It determines the format from the extensions. The output of
|
||||
<a class="reference internal" href="../python/_autosummary/mlx.core.load.html#mlx.core.load" title="mlx.core.load"><code class="xref py py-func docutils literal notranslate"><span class="pre">load()</span></code></a> depends on the format.</p>
|
||||
<p>Here’s an example of saving a single array to a file:</p>
|
||||
<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>>>> <span class="nv">a</span> <span class="o">=</span> mx.array<span class="o">([</span><span class="m">1</span>.0<span class="o">])</span>
|
||||
>>> mx.save<span class="o">(</span><span class="s2">"array"</span>, a<span class="o">)</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>The array <code class="docutils literal notranslate"><span class="pre">a</span></code> will be saved in the file <code class="docutils literal notranslate"><span class="pre">array.npy</span></code> (notice the extension
|
||||
is automatically added). Including the extension is optional; if it is missing
|
||||
it will be added. You can load the array with:</p>
|
||||
<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>>>> mx.load<span class="o">(</span><span class="s2">"array.npy"</span>, a<span class="o">)</span>
|
||||
array<span class="o">([</span><span class="m">1</span><span class="o">]</span>, <span class="nv">dtype</span><span class="o">=</span>float32<span class="o">)</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>Here’s an example of saving several arrays to a single file:</p>
|
||||
<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>>>> <span class="nv">a</span> <span class="o">=</span> mx.array<span class="o">([</span><span class="m">1</span>.0<span class="o">])</span>
|
||||
>>> <span class="nv">b</span> <span class="o">=</span> mx.array<span class="o">([</span><span class="m">2</span>.0<span class="o">])</span>
|
||||
>>> mx.savez<span class="o">(</span><span class="s2">"arrays"</span>, a, <span class="nv">b</span><span class="o">=</span>b<span class="o">)</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>For compatibility with <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.savez.html#numpy.savez" title="(in NumPy v1.26)"><code class="xref py py-func docutils literal notranslate"><span class="pre">numpy.savez()</span></code></a> the MLX <a class="reference internal" href="../python/_autosummary/mlx.core.savez.html#mlx.core.savez" title="mlx.core.savez"><code class="xref py py-func docutils literal notranslate"><span class="pre">savez()</span></code></a> takes arrays
|
||||
as arguments. If the keywords are missing, then default names will be
|
||||
provided. This can be loaded with:</p>
|
||||
<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>>>> mx.load<span class="o">(</span><span class="s2">"arrays.npz"</span><span class="o">)</span>
|
||||
<span class="o">{</span><span class="s1">'b'</span>: array<span class="o">([</span><span class="m">2</span><span class="o">]</span>, <span class="nv">dtype</span><span class="o">=</span>float32<span class="o">)</span>, <span class="s1">'arr_0'</span>: array<span class="o">([</span><span class="m">1</span><span class="o">]</span>, <span class="nv">dtype</span><span class="o">=</span>float32<span class="o">)}</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>In this case <a class="reference internal" href="../python/_autosummary/mlx.core.load.html#mlx.core.load" title="mlx.core.load"><code class="xref py py-func docutils literal notranslate"><span class="pre">load()</span></code></a> returns a dictionary of names to arrays.</p>
|
||||
<p>The functions <a class="reference internal" href="../python/_autosummary/mlx.core.save_safetensors.html#mlx.core.save_safetensors" title="mlx.core.save_safetensors"><code class="xref py py-func docutils literal notranslate"><span class="pre">save_safetensors()</span></code></a> and <a class="reference internal" href="../python/_autosummary/mlx.core.save_gguf.html#mlx.core.save_gguf" title="mlx.core.save_gguf"><code class="xref py py-func docutils literal notranslate"><span class="pre">save_gguf()</span></code></a> are similar to
|
||||
<a class="reference internal" href="../python/_autosummary/mlx.core.savez.html#mlx.core.savez" title="mlx.core.savez"><code class="xref py py-func docutils literal notranslate"><span class="pre">savez()</span></code></a>, but they take as input a <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">dict</span></code></a> of string names to arrays:</p>
|
||||
<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>>>> <span class="nv">a</span> <span class="o">=</span> mx.array<span class="o">([</span><span class="m">1</span>.0<span class="o">])</span>
|
||||
>>> <span class="nv">b</span> <span class="o">=</span> mx.array<span class="o">([</span><span class="m">2</span>.0<span class="o">])</span>
|
||||
>>> mx.save_safetensors<span class="o">(</span><span class="s2">"arrays"</span>, <span class="o">{</span><span class="s2">"a"</span>: a, <span class="s2">"b"</span>: b<span class="o">})</span>
|
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24
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@@ -46,8 +46,8 @@
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<script>DOCUMENTATION_OPTIONS.pagename = 'usage/unified_memory';</script>
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@@ -148,9 +148,12 @@
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<p aria-level="2" class="caption" role="heading"><span class="caption-text">Usage</span></p>
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@@ -238,6 +241,7 @@
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@@ -251,6 +255,7 @@
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@@ -273,6 +280,7 @@
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<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.core.outer.html">mlx.core.outer</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.core.partition.html">mlx.core.partition</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.core.pad.html">mlx.core.pad</a></li>
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||||
<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.core.prod.html">mlx.core.prod</a></li>
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@@ -286,6 +294,7 @@
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<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.core.save_safetensors.html">mlx.core.save_safetensors</a></li>
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||||
<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.core.sigmoid.html">mlx.core.sigmoid</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.core.sign.html">mlx.core.sign</a></li>
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@@ -435,6 +444,7 @@
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|
||||
</ul>
|
||||
</li>
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||||
</ul>
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@@ -712,20 +722,20 @@ Max.</p>
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24
docs/build/html/usage/using_streams.html
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docs/build/html/usage/using_streams.html
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@@ -46,8 +46,8 @@
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<script>DOCUMENTATION_OPTIONS.pagename = 'usage/using_streams';</script>
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@@ -148,9 +148,12 @@
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<p aria-level="2" class="caption" role="heading"><span class="caption-text">Examples</span></p>
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<ul class="nav bd-sidenav">
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@@ -238,6 +241,7 @@
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<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.core.cosh.html">mlx.core.cosh</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.core.dequantize.html">mlx.core.dequantize</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.core.divmod.html">mlx.core.divmod</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.core.erfinv.html">mlx.core.erfinv</a></li>
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@@ -251,6 +255,7 @@
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<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.core.greater.html">mlx.core.greater</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.core.greater_equal.html">mlx.core.greater_equal</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.core.identity.html">mlx.core.identity</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.core.inner.html">mlx.core.inner</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.core.less.html">mlx.core.less</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.core.less_equal.html">mlx.core.less_equal</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.core.linspace.html">mlx.core.linspace</a></li>
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@@ -261,6 +266,8 @@
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<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.core.log1p.html">mlx.core.log1p</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.core.logical_or.html">mlx.core.logical_or</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.core.logsumexp.html">mlx.core.logsumexp</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.core.max.html">mlx.core.max</a></li>
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@@ -273,6 +280,7 @@
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<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.core.negative.html">mlx.core.negative</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.core.outer.html">mlx.core.outer</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.core.partition.html">mlx.core.partition</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.core.pad.html">mlx.core.pad</a></li>
|
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<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.core.prod.html">mlx.core.prod</a></li>
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@@ -286,6 +294,7 @@
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<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.core.save.html">mlx.core.save</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.core.savez_compressed.html">mlx.core.savez_compressed</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.core.save_gguf.html">mlx.core.save_gguf</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.core.save_safetensors.html">mlx.core.save_safetensors</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.core.sigmoid.html">mlx.core.sigmoid</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../python/_autosummary/mlx.core.sign.html">mlx.core.sign</a></li>
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@@ -435,6 +444,7 @@
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<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary_functions/mlx.nn.losses.hinge_loss.html">mlx.nn.losses.hinge_loss</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../python/nn/_autosummary_functions/mlx.nn.losses.huber_loss.html">mlx.nn.losses.huber_loss</a></li>
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|
||||
</ul>
|
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</li>
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@@ -668,20 +678,20 @@ run on the default stream of the provided device
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