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2024-11-05 19:54:16 +00:00
parent 3addf172d9
commit 98e590e52d
51 changed files with 2277 additions and 1802 deletions
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4
docs/build/html/usage/function_transforms.html vendored
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@@ -986,13 +986,13 @@ We will prioritize including it.</p>
<span class="n">ys</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">100</span><span class="p">,</span> <span class="mi">4096</span><span class="p">))</span>
<span class="k">def</span> <span class="nf">naive_add</span><span class="p">(</span><span class="n">xs</span><span class="p">,</span> <span class="n">ys</span><span class="p">):</span>
<span class="k">return</span> <span class="p">[</span><span class="n">xs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">+</span> <span class="n">ys</span><span class="p">[:,</span> <span class="n">i</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">xs</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">])]</span>
<span class="k">return</span> <span class="p">[</span><span class="n">xs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">+</span> <span class="n">ys</span><span class="p">[:,</span> <span class="n">i</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">xs</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])]</span>
</pre></div>
</div>
<p>Instead you can use <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> to automatically vectorize the addition:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Vectorize over the second dimension of x and the</span>
<span class="c1"># first dimension of y</span>
<span class="n">vmap_add</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">vmap</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">:</span> <span class="n">x</span> <span class="o">+</span> <span class="n">y</span><span class="p">,</span> <span class="n">in_axes</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
<span class="n">vmap_add</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">vmap</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">:</span> <span class="n">x</span> <span class="o">+</span> <span class="n">y</span><span class="p">,</span> <span class="n">in_axes</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
</pre></div>
</div>
<p>The <code class="docutils literal notranslate"><span class="pre">in_axes</span></code> parameter can be used to specify which dimensions of the

2
docs/build/html/usage/indexing.html vendored
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@@ -922,7 +922,7 @@ undefined behavior.</p></li>
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
general, MLX has limited support for operations for which output
<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 v2.1)"><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 v2.1)"><code class="xref py py-func docutils literal notranslate"><span class="pre">numpy.where()</span></code></a>.</p>

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docs/build/html/usage/lazy_evaluation.html vendored
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@@ -952,7 +952,7 @@ stochastic gradient descent). A natural and usually efficient place to use
</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 v2.1)"><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 v3.13)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">memoryview</span></code></a>,
<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v2.1)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a>, or otherwise access its memory via <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#memoryview" title="(in Python v3.13)"><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