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<h1>Source code for torch.random</h1><div class="highlight"><pre>
<span></span><span class="kn">import</span> <span class="nn">contextlib</span>
<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Generator</span>
<span class="kn">import</span> <span class="nn">warnings</span>
<span class="kn">from</span> <span class="nn">torch._C</span> <span class="kn">import</span> <span class="n">default_generator</span>
<span class="kn">import</span> <span class="nn">torch</span>
<div class="viewcode-block" id="set_rng_state"><a class="viewcode-back" href="../../generated/torch.set_rng_state.html#torch.set_rng_state">[docs]</a><span class="k">def</span> <span class="nf">set_rng_state</span><span class="p">(</span><span class="n">new_state</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">)</span> <span class="o">-></span> <span class="kc">None</span><span class="p">:</span>
<span class="sa">r</span><span class="sd">"""Sets the random number generator state.</span>
<span class="sd"> .. note: This function only works for CPU. For CUDA, please use</span>
<span class="sd"> torch.manual_seed(seed), which works for both CPU and CUDA.</span>
<span class="sd"> Args:</span>
<span class="sd"> new_state (torch.ByteTensor): The desired state</span>
<span class="sd"> """</span>
<span class="n">default_generator</span><span class="o">.</span><span class="n">set_state</span><span class="p">(</span><span class="n">new_state</span><span class="p">)</span></div>
<div class="viewcode-block" id="get_rng_state"><a class="viewcode-back" href="../../generated/torch.get_rng_state.html#torch.get_rng_state">[docs]</a><span class="k">def</span> <span class="nf">get_rng_state</span><span class="p">()</span> <span class="o">-></span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
<span class="sa">r</span><span class="sd">"""Returns the random number generator state as a `torch.ByteTensor`."""</span>
<span class="k">return</span> <span class="n">default_generator</span><span class="o">.</span><span class="n">get_state</span><span class="p">()</span></div>
<div class="viewcode-block" id="manual_seed"><a class="viewcode-back" href="../../generated/torch.manual_seed.html#torch.manual_seed">[docs]</a><span class="k">def</span> <span class="nf">manual_seed</span><span class="p">(</span><span class="n">seed</span><span class="p">)</span> <span class="o">-></span> <span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">Generator</span><span class="p">:</span>
<span class="sa">r</span><span class="sd">"""Sets the seed for generating random numbers. Returns a</span>
<span class="sd"> `torch.Generator` object.</span>
<span class="sd"> Args:</span>
<span class="sd"> seed (int): The desired seed. Value must be within the inclusive range</span>
<span class="sd"> `[-0x8000_0000_0000_0000, 0xffff_ffff_ffff_ffff]`. Otherwise, a RuntimeError</span>
<span class="sd"> is raised. Negative inputs are remapped to positive values with the formula</span>
<span class="sd"> `0xffff_ffff_ffff_ffff + seed`.</span>
<span class="sd"> """</span>
<span class="n">seed</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">seed</span><span class="p">)</span>
<span class="kn">import</span> <span class="nn">torch.cuda</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">_is_in_bad_fork</span><span class="p">():</span>
<span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">manual_seed_all</span><span class="p">(</span><span class="n">seed</span><span class="p">)</span>
<span class="kn">import</span> <span class="nn">torch.mps</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">torch</span><span class="o">.</span><span class="n">mps</span><span class="o">.</span><span class="n">_is_in_bad_fork</span><span class="p">():</span>
<span class="n">torch</span><span class="o">.</span><span class="n">mps</span><span class="o">.</span><span class="n">manual_seed</span><span class="p">(</span><span class="n">seed</span><span class="p">)</span>
<span class="k">return</span> <span class="n">default_generator</span><span class="o">.</span><span class="n">manual_seed</span><span class="p">(</span><span class="n">seed</span><span class="p">)</span></div>
<div class="viewcode-block" id="seed"><a class="viewcode-back" href="../../generated/torch.seed.html#torch.seed">[docs]</a><span class="k">def</span> <span class="nf">seed</span><span class="p">()</span> <span class="o">-></span> <span class="nb">int</span><span class="p">:</span>
<span class="sa">r</span><span class="sd">"""Sets the seed for generating random numbers to a non-deterministic</span>
<span class="sd"> random number. Returns a 64 bit number used to seed the RNG.</span>
<span class="sd"> """</span>
<span class="n">seed</span> <span class="o">=</span> <span class="n">default_generator</span><span class="o">.</span><span class="n">seed</span><span class="p">()</span>
<span class="kn">import</span> <span class="nn">torch.cuda</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">_is_in_bad_fork</span><span class="p">():</span>
<span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">manual_seed_all</span><span class="p">(</span><span class="n">seed</span><span class="p">)</span>
<span class="kn">import</span> <span class="nn">torch.mps</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">torch</span><span class="o">.</span><span class="n">mps</span><span class="o">.</span><span class="n">_is_in_bad_fork</span><span class="p">():</span>
<span class="n">torch</span><span class="o">.</span><span class="n">mps</span><span class="o">.</span><span class="n">manual_seed</span><span class="p">(</span><span class="n">seed</span><span class="p">)</span>
<span class="k">return</span> <span class="n">seed</span></div>
<div class="viewcode-block" id="initial_seed"><a class="viewcode-back" href="../../generated/torch.initial_seed.html#torch.initial_seed">[docs]</a><span class="k">def</span> <span class="nf">initial_seed</span><span class="p">()</span> <span class="o">-></span> <span class="nb">int</span><span class="p">:</span>
<span class="sa">r</span><span class="sd">"""Returns the initial seed for generating random numbers as a</span>
<span class="sd"> Python `long`.</span>
<span class="sd"> """</span>
<span class="k">return</span> <span class="n">default_generator</span><span class="o">.</span><span class="n">initial_seed</span><span class="p">()</span></div>
<span class="n">_fork_rng_warned_already</span> <span class="o">=</span> <span class="kc">False</span>
<div class="viewcode-block" id="fork_rng"><a class="viewcode-back" href="../../random.html#torch.fork_rng">[docs]</a><span class="nd">@contextlib</span><span class="o">.</span><span class="n">contextmanager</span>
<span class="k">def</span> <span class="nf">fork_rng</span><span class="p">(</span><span class="n">devices</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">enabled</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">_caller</span><span class="o">=</span><span class="s2">"fork_rng"</span><span class="p">,</span> <span class="n">_devices_kw</span><span class="o">=</span><span class="s2">"devices"</span><span class="p">)</span> <span class="o">-></span> <span class="n">Generator</span><span class="p">:</span>
<span class="sd">"""</span>
<span class="sd"> Forks the RNG, so that when you return, the RNG is reset</span>
<span class="sd"> to the state that it was previously in.</span>
<span class="sd"> Args:</span>
<span class="sd"> devices (iterable of CUDA IDs): CUDA devices for which to fork</span>
<span class="sd"> the RNG. CPU RNG state is always forked. By default, :meth:`fork_rng` operates</span>
<span class="sd"> on all devices, but will emit a warning if your machine has a lot</span>
<span class="sd"> of devices, since this function will run very slowly in that case.</span>
<span class="sd"> If you explicitly specify devices, this warning will be suppressed</span>
<span class="sd"> enabled (bool): if ``False``, the RNG is not forked. This is a convenience</span>
<span class="sd"> argument for easily disabling the context manager without having</span>
<span class="sd"> to delete it and unindent your Python code under it.</span>
<span class="sd"> """</span>
<span class="kn">import</span> <span class="nn">torch.cuda</span>
<span class="k">global</span> <span class="n">_fork_rng_warned_already</span>
<span class="c1"># Internal arguments:</span>
<span class="c1"># _caller: the function which called fork_rng, which the user used</span>
<span class="c1"># _devices_kw: the devices keyword of _caller</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">enabled</span><span class="p">:</span>
<span class="k">yield</span>
<span class="k">return</span>
<span class="k">if</span> <span class="n">devices</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">num_devices</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">device_count</span><span class="p">()</span>
<span class="k">if</span> <span class="n">num_devices</span> <span class="o">></span> <span class="mi">1</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">_fork_rng_warned_already</span><span class="p">:</span>
<span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span>
<span class="p">(</span><span class="s2">"CUDA reports that you have </span><span class="si">{num_devices}</span><span class="s2"> available devices, and you "</span>
<span class="s2">"have used </span><span class="si">{caller}</span><span class="s2"> without explicitly specifying which devices are being used. "</span>
<span class="s2">"For safety, we initialize *every* CUDA device by default, which "</span>
<span class="s2">"can be quite slow if you have a lot of GPUs. If you know that you are only "</span>
<span class="s2">"making use of a few CUDA devices, set the environment variable CUDA_VISIBLE_DEVICES "</span>
<span class="s2">"or the '</span><span class="si">{devices_kw}</span><span class="s2">' keyword argument of </span><span class="si">{caller}</span><span class="s2"> with the set of devices "</span>
<span class="s2">"you are actually using. For example, if you are using CPU only, "</span>
<span class="s2">"set CUDA_VISIBLE_DEVICES= or devices=[]; if you are using "</span>
<span class="s2">"GPU 0 only, set CUDA_VISIBLE_DEVICES=0 or devices=[0]. To initialize "</span>
<span class="s2">"all devices and suppress this warning, set the '</span><span class="si">{devices_kw}</span><span class="s2">' keyword argument "</span>
<span class="s2">"to `range(torch.cuda.device_count())`."</span>
<span class="p">)</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">num_devices</span><span class="o">=</span><span class="n">num_devices</span><span class="p">,</span> <span class="n">caller</span><span class="o">=</span><span class="n">_caller</span><span class="p">,</span> <span class="n">devices_kw</span><span class="o">=</span><span class="n">_devices_kw</span><span class="p">))</span>
<span class="n">_fork_rng_warned_already</span> <span class="o">=</span> <span class="kc">True</span>
<span class="n">devices</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">num_devices</span><span class="p">))</span>
<span class="k">else</span><span class="p">:</span>
<span class="c1"># Protect against user passing us a generator; we need to traverse this</span>
<span class="c1"># multiple times but a generator will be exhausted upon first traversal</span>
<span class="n">devices</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">devices</span><span class="p">)</span>
<span class="n">cpu_rng_state</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">get_rng_state</span><span class="p">()</span>
<span class="n">gpu_rng_states</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">device</span> <span class="ow">in</span> <span class="n">devices</span><span class="p">:</span>
<span class="n">gpu_rng_states</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">get_rng_state</span><span class="p">(</span><span class="n">device</span><span class="p">))</span>
<span class="k">try</span><span class="p">:</span>
<span class="k">yield</span>
<span class="k">finally</span><span class="p">:</span>
<span class="n">torch</span><span class="o">.</span><span class="n">set_rng_state</span><span class="p">(</span><span class="n">cpu_rng_state</span><span class="p">)</span>
<span class="k">for</span> <span class="n">device</span><span class="p">,</span> <span class="n">gpu_rng_state</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">devices</span><span class="p">,</span> <span class="n">gpu_rng_states</span><span class="p">):</span>
<span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">set_rng_state</span><span class="p">(</span><span class="n">gpu_rng_state</span><span class="p">,</span> <span class="n">device</span><span class="p">)</span></div>
</pre></div>
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