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<h1>Source code for torch.utils</h1><div class="highlight"><pre>
<span></span><span class="c1"># mypy: allow-untyped-defs</span>
<span class="kn">import</span> <span class="nn">copyreg</span>
<span class="kn">import</span> <span class="nn">os.path</span> <span class="k">as</span> <span class="nn">_osp</span>
<span class="kn">import</span> <span class="nn">weakref</span>
<span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">from</span> <span class="nn">torch.utils</span> <span class="kn">import</span> <span class="p">(</span>
<span class="n">backcompat</span> <span class="k">as</span> <span class="n">backcompat</span><span class="p">,</span>
<span class="n">collect_env</span> <span class="k">as</span> <span class="n">collect_env</span><span class="p">,</span>
<span class="n">data</span> <span class="k">as</span> <span class="n">data</span><span class="p">,</span>
<span class="n">deterministic</span> <span class="k">as</span> <span class="n">deterministic</span><span class="p">,</span>
<span class="n">hooks</span> <span class="k">as</span> <span class="n">hooks</span><span class="p">,</span>
<span class="p">)</span>
<span class="kn">from</span> <span class="nn">torch.utils.backend_registration</span> <span class="kn">import</span> <span class="p">(</span>
<span class="n">generate_methods_for_privateuse1_backend</span><span class="p">,</span>
<span class="n">rename_privateuse1_backend</span><span class="p">,</span>
<span class="p">)</span>
<span class="kn">from</span> <span class="nn">torch.utils.cpp_backtrace</span> <span class="kn">import</span> <span class="n">get_cpp_backtrace</span>
<span class="kn">from</span> <span class="nn">torch.utils.throughput_benchmark</span> <span class="kn">import</span> <span class="n">ThroughputBenchmark</span>
<div class="viewcode-block" id="set_module"><a class="viewcode-back" href="../../generated/torch.utils.set_module.html#torch.utils.set_module">[docs]</a><span class="k">def</span> <span class="nf">set_module</span><span class="p">(</span><span class="n">obj</span><span class="p">,</span> <span class="n">mod</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""</span>
<span class="sd"> Set the module attribute on a python object for a given object for nicer printing</span>
<span class="sd"> """</span>
<span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">mod</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span>
<span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">"The mod argument should be a string"</span><span class="p">)</span>
<span class="n">obj</span><span class="o">.</span><span class="vm">__module__</span> <span class="o">=</span> <span class="n">mod</span></div>
<span class="k">if</span> <span class="n">torch</span><span class="o">.</span><span class="n">_running_with_deploy</span><span class="p">():</span>
<span class="c1"># not valid inside torch_deploy interpreter, no paths exists for frozen modules</span>
<span class="n">cmake_prefix_path</span> <span class="o">=</span> <span class="kc">None</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">cmake_prefix_path</span> <span class="o">=</span> <span class="n">_osp</span><span class="o">.</span><span class="n">join</span><span class="p">(</span>
<span class="n">_osp</span><span class="o">.</span><span class="n">dirname</span><span class="p">(</span><span class="n">_osp</span><span class="o">.</span><span class="n">dirname</span><span class="p">(</span><span class="vm">__file__</span><span class="p">)),</span> <span class="s2">"share"</span><span class="p">,</span> <span class="s2">"cmake"</span>
<span class="p">)</span>
<div class="viewcode-block" id="swap_tensors"><a class="viewcode-back" href="../../generated/torch.utils.swap_tensors.html#torch.utils.swap_tensors">[docs]</a><span class="k">def</span> <span class="nf">swap_tensors</span><span class="p">(</span><span class="n">t1</span><span class="p">,</span> <span class="n">t2</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""</span>
<span class="sd"> This function swaps the content of the two Tensor objects.</span>
<span class="sd"> At a high level, this will make t1 have the content of t2 while preserving</span>
<span class="sd"> its identity.</span>
<span class="sd"> This will not work if t1 and t2 have different slots.</span>
<span class="sd"> """</span>
<span class="c1"># Ensure there are no weakrefs</span>
<span class="k">if</span> <span class="n">weakref</span><span class="o">.</span><span class="n">getweakrefs</span><span class="p">(</span><span class="n">t1</span><span class="p">):</span>
<span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">"Cannot swap t1 because it has weakref associated with it"</span><span class="p">)</span>
<span class="k">if</span> <span class="n">weakref</span><span class="o">.</span><span class="n">getweakrefs</span><span class="p">(</span><span class="n">t2</span><span class="p">):</span>
<span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">"Cannot swap t2 because it has weakref associated with it"</span><span class="p">)</span>
<span class="n">t1_slots</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">copyreg</span><span class="o">.</span><span class="n">_slotnames</span><span class="p">(</span><span class="n">t1</span><span class="o">.</span><span class="vm">__class__</span><span class="p">))</span> <span class="c1"># type: ignore[attr-defined]</span>
<span class="n">t2_slots</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">copyreg</span><span class="o">.</span><span class="n">_slotnames</span><span class="p">(</span><span class="n">t2</span><span class="o">.</span><span class="vm">__class__</span><span class="p">))</span> <span class="c1"># type: ignore[attr-defined]</span>
<span class="k">if</span> <span class="n">t1_slots</span> <span class="o">!=</span> <span class="n">t2_slots</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">"Cannot swap t1 and t2 if they have different slots"</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">swap_attr</span><span class="p">(</span><span class="n">name</span><span class="p">):</span>
<span class="n">tmp</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">t1</span><span class="p">,</span> <span class="n">name</span><span class="p">)</span>
<span class="nb">setattr</span><span class="p">(</span><span class="n">t1</span><span class="p">,</span> <span class="n">name</span><span class="p">,</span> <span class="p">(</span><span class="nb">getattr</span><span class="p">(</span><span class="n">t2</span><span class="p">,</span> <span class="n">name</span><span class="p">)))</span>
<span class="nb">setattr</span><span class="p">(</span><span class="n">t2</span><span class="p">,</span> <span class="n">name</span><span class="p">,</span> <span class="n">tmp</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">error_pre_hook</span><span class="p">(</span><span class="n">grad_outputs</span><span class="p">):</span>
<span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span>
<span class="s2">"Trying to execute AccumulateGrad node that was poisoned by swap_tensors "</span>
<span class="s2">"this can happen when you try to run backward on a tensor that was swapped. "</span>
<span class="s2">"For a module m with `torch.__future__.set_swap_module_params_on_conversion(True)` "</span>
<span class="s2">"you should not change the device or dtype of the module (e.g. `m.cpu()` or `m.half()`) "</span>
<span class="s2">"between running forward and backward. To resolve this, please only change the "</span>
<span class="s2">"device/dtype before running forward (or after both forward and backward)."</span>
<span class="p">)</span>
<span class="k">def</span> <span class="nf">check_use_count</span><span class="p">(</span><span class="n">t</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s2">"t1"</span><span class="p">):</span>
<span class="n">use_count</span> <span class="o">=</span> <span class="n">t</span><span class="o">.</span><span class="n">_use_count</span><span class="p">()</span>
<span class="n">error_str</span> <span class="o">=</span> <span class="p">(</span>
<span class="sa">f</span><span class="s2">"Expected use_count of </span><span class="si">{</span><span class="n">name</span><span class="si">}</span><span class="s2"> to be 1 or 2 with an AccumulateGrad node but got </span><span class="si">{</span><span class="n">use_count</span><span class="si">}</span><span class="s2"> "</span>
<span class="sa">f</span><span class="s2">"make sure you are not holding references to the tensor in other places."</span>
<span class="p">)</span>
<span class="k">if</span> <span class="n">use_count</span> <span class="o">></span> <span class="mi">1</span><span class="p">:</span>
<span class="k">if</span> <span class="n">use_count</span> <span class="o">==</span> <span class="mi">2</span> <span class="ow">and</span> <span class="n">t</span><span class="o">.</span><span class="n">is_leaf</span><span class="p">:</span>
<span class="n">accum_grad_node</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">autograd</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">get_gradient_edge</span><span class="p">(</span><span class="n">t</span><span class="p">)</span><span class="o">.</span><span class="n">node</span>
<span class="c1"># Make sure that the accumulate_grad node was not lazy_init-ed by get_gradient_edge</span>
<span class="k">if</span> <span class="n">t</span><span class="o">.</span><span class="n">_use_count</span><span class="p">()</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
<span class="n">accum_grad_node</span><span class="o">.</span><span class="n">register_prehook</span><span class="p">(</span><span class="n">error_pre_hook</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="n">error_str</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="n">error_str</span><span class="p">)</span>
<span class="n">check_use_count</span><span class="p">(</span><span class="n">t1</span><span class="p">,</span> <span class="s2">"t1"</span><span class="p">)</span>
<span class="n">check_use_count</span><span class="p">(</span><span class="n">t2</span><span class="p">,</span> <span class="s2">"t2"</span><span class="p">)</span>
<span class="c1"># Swap the types</span>
<span class="c1"># Note that this will fail if there are mismatched slots</span>
<span class="n">swap_attr</span><span class="p">(</span><span class="s2">"__class__"</span><span class="p">)</span>
<span class="c1"># Swap the dynamic attributes</span>
<span class="n">swap_attr</span><span class="p">(</span><span class="s2">"__dict__"</span><span class="p">)</span>
<span class="c1"># Swap the slots</span>
<span class="k">for</span> <span class="n">slot</span> <span class="ow">in</span> <span class="n">t1_slots</span><span class="p">:</span>
<span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">t1</span><span class="p">,</span> <span class="n">slot</span><span class="p">)</span> <span class="ow">and</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">t2</span><span class="p">,</span> <span class="n">slot</span><span class="p">):</span>
<span class="n">swap_attr</span><span class="p">(</span><span class="n">slot</span><span class="p">)</span>
<span class="k">elif</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">t1</span><span class="p">,</span> <span class="n">slot</span><span class="p">):</span>
<span class="nb">setattr</span><span class="p">(</span><span class="n">t2</span><span class="p">,</span> <span class="n">slot</span><span class="p">,</span> <span class="p">(</span><span class="nb">getattr</span><span class="p">(</span><span class="n">t1</span><span class="p">,</span> <span class="n">slot</span><span class="p">)))</span>
<span class="nb">delattr</span><span class="p">(</span><span class="n">t1</span><span class="p">,</span> <span class="n">slot</span><span class="p">)</span>
<span class="k">elif</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">t2</span><span class="p">,</span> <span class="n">slot</span><span class="p">):</span>
<span class="nb">setattr</span><span class="p">(</span><span class="n">t1</span><span class="p">,</span> <span class="n">slot</span><span class="p">,</span> <span class="p">(</span><span class="nb">getattr</span><span class="p">(</span><span class="n">t2</span><span class="p">,</span> <span class="n">slot</span><span class="p">)))</span>
<span class="nb">delattr</span><span class="p">(</span><span class="n">t2</span><span class="p">,</span> <span class="n">slot</span><span class="p">)</span>
<span class="c1"># Swap the at::Tensor they point to</span>
<span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_swap_tensor_impl</span><span class="p">(</span><span class="n">t1</span><span class="p">,</span> <span class="n">t2</span><span class="p">)</span></div>
</pre></div>
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