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<div class="section" id="module-torch.utils.deterministic">
<span id="torch-utils-deterministic"></span><h1>torch.utils.deterministic<a class="headerlink" href="#module-torch.utils.deterministic" title="Permalink to this heading">¶</a></h1>
<dl class="py attribute">
<dt class="sig sig-object py" id="torch.utils.deterministic.fill_uninitialized_memory">
<span class="sig-prename descclassname"><span class="pre">torch.utils.deterministic.</span></span><span class="sig-name descname"><span class="pre">fill_uninitialized_memory</span></span><a class="headerlink" href="#torch.utils.deterministic.fill_uninitialized_memory" title="Permalink to this definition">¶</a></dt>
<dd><p>A <a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code></a> that, if True, causes uninitialized memory to be filled with
a known value when <a class="reference internal" href="generated/torch.use_deterministic_algorithms.html#torch.use_deterministic_algorithms" title="torch.use_deterministic_algorithms"><code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.use_deterministic_algorithms()</span></code></a> is set to
<code class="docutils literal notranslate"><span class="pre">True</span></code>. Floating point and complex values are set to NaN, and integer
values are set to the maximum value.</p>
<p>Default: <code class="docutils literal notranslate"><span class="pre">True</span></code></p>
<p>Filling uninitialized memory is detrimental to performance. So if your
program is valid and does not use uninitialized memory as the input to an
operation, then this setting can be turned off for better performance and
still be deterministic.</p>
<p>The following operations will fill uninitialized memory when this setting is
turned on:</p>
<blockquote>
<div><ul class="simple">
<li><p><a class="reference internal" href="generated/torch.Tensor.resize_.html#torch.Tensor.resize_" title="torch.Tensor.resize_"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.Tensor.resize_()</span></code></a> when called with a tensor that is not
quantized</p></li>
<li><p><a class="reference internal" href="generated/torch.empty.html#torch.empty" title="torch.empty"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.empty()</span></code></a></p></li>
<li><p><a class="reference internal" href="generated/torch.empty_strided.html#torch.empty_strided" title="torch.empty_strided"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.empty_strided()</span></code></a></p></li>
<li><p><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.empty_permuted()</span></code></p></li>
<li><p><a class="reference internal" href="generated/torch.empty_like.html#torch.empty_like" title="torch.empty_like"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.empty_like()</span></code></a></p></li>
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