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<div class="section" id="module-torch.random">
<span id="torch-random"></span><h1>torch.random<a class="headerlink" href="#module-torch.random" title="Permalink to this heading">¶</a></h1>
<dl class="py function">
<dt class="sig sig-object py" id="torch.random.fork_rng">
<span class="sig-prename descclassname"><span class="pre">torch.random.</span></span><span class="sig-name descname"><span class="pre">fork_rng</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">devices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enabled</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_caller</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'fork_rng'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_devices_kw</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'devices'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'cuda'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/random.html#fork_rng"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.random.fork_rng" title="Permalink to this definition">¶</a></dt>
<dd><p>Forks the RNG, so that when you return, the RNG is reset
to the state that it was previously in.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>devices</strong> (<em>iterable</em><em> of </em><em>Device IDs</em>) – devices for which to fork
the RNG. CPU RNG state is always forked. By default, <a class="reference internal" href="#torch.random.fork_rng" title="torch.random.fork_rng"><code class="xref py py-meth docutils literal notranslate"><span class="pre">fork_rng()</span></code></a> operates
on all devices, but will emit a warning if your machine has a lot
of devices, since this function will run very slowly in that case.
If you explicitly specify devices, this warning will be suppressed</p></li>
<li><p><strong>enabled</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – if <code class="docutils literal notranslate"><span class="pre">False</span></code>, the RNG is not forked. This is a convenience
argument for easily disabling the context manager without having
to delete it and unindent your Python code under it.</p></li>
<li><p><strong>device_type</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – device type str, default is <cite>cuda</cite>. As for custom device,
see details in [Note: support the custom device with privateuse1]</p></li>
</ul>
</dd>
<dt class="field-even">Return type</dt>
<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Generator" title="(in Python v3.12)"><em>Generator</em></a></p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="torch.random.get_rng_state">
<span class="sig-prename descclassname"><span class="pre">torch.random.</span></span><span class="sig-name descname"><span class="pre">get_rng_state</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/random.html#get_rng_state"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.random.get_rng_state" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns the random number generator state as a <cite>torch.ByteTensor</cite>.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>The returned state is for the default generator on CPU only.</p>
</div>
<p>See also: <a class="reference internal" href="#torch.random.fork_rng" title="torch.random.fork_rng"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.random.fork_rng()</span></code></a>.</p>
<dl class="field-list simple">
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p><a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a></p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="torch.random.initial_seed">
<span class="sig-prename descclassname"><span class="pre">torch.random.</span></span><span class="sig-name descname"><span class="pre">initial_seed</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/random.html#initial_seed"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.random.initial_seed" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns the initial seed for generating random numbers as a
Python <cite>long</cite>.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>The returned seed is for the default generator on CPU only.</p>
</div>
<dl class="field-list simple">
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a></p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="torch.random.manual_seed">
<span class="sig-prename descclassname"><span class="pre">torch.random.</span></span><span class="sig-name descname"><span class="pre">manual_seed</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">seed</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/random.html#manual_seed"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.random.manual_seed" title="Permalink to this definition">¶</a></dt>
<dd><p>Sets the seed for generating random numbers on all devices. Returns a
<cite>torch.Generator</cite> object.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>seed</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – The desired seed. Value must be within the inclusive range
<cite>[-0x8000_0000_0000_0000, 0xffff_ffff_ffff_ffff]</cite>. Otherwise, a RuntimeError
is raised. Negative inputs are remapped to positive values with the formula
<cite>0xffff_ffff_ffff_ffff + seed</cite>.</p>
</dd>
<dt class="field-even">Return type</dt>
<dd class="field-even"><p><p id="torch.Generator"/><a class="reference internal" href="generated/torch.Generator.html#torch.Generator" title="torch._C.Generator"><em>Generator</em></a></p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="torch.random.seed">
<span class="sig-prename descclassname"><span class="pre">torch.random.</span></span><span class="sig-name descname"><span class="pre">seed</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/random.html#seed"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.random.seed" title="Permalink to this definition">¶</a></dt>
<dd><p>Sets the seed for generating random numbers to a non-deterministic
random number on all devices. Returns a 64 bit number used to seed the RNG.</p>
<dl class="field-list simple">
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a></p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="torch.random.set_rng_state">
<span class="sig-prename descclassname"><span class="pre">torch.random.</span></span><span class="sig-name descname"><span class="pre">set_rng_state</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">new_state</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/random.html#set_rng_state"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.random.set_rng_state" title="Permalink to this definition">¶</a></dt>
<dd><p>Sets the random number generator state.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>This function only works for CPU. For CUDA, please use
<a class="reference internal" href="generated/torch.manual_seed.html#torch.manual_seed" title="torch.manual_seed"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.manual_seed()</span></code></a>, which works for both CPU and CUDA.</p>
</div>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>new_state</strong> (<em>torch.ByteTensor</em>) – The desired state</p>
</dd>
</dl>
</dd></dl>
</div>
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