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‎docs/master/__config__.html

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@@ -420,7 +420,8 @@ <h1>Source code for torch</h1><div class="highlight"><pre>
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<span class="s1">&#39;DoubleTensor&#39;</span><span class="p">,</span> <span class="s1">&#39;FloatTensor&#39;</span><span class="p">,</span> <span class="s1">&#39;LongTensor&#39;</span><span class="p">,</span> <span class="s1">&#39;IntTensor&#39;</span><span class="p">,</span>
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<span class="s1">&#39;ShortTensor&#39;</span><span class="p">,</span> <span class="s1">&#39;CharTensor&#39;</span><span class="p">,</span> <span class="s1">&#39;ByteTensor&#39;</span><span class="p">,</span> <span class="s1">&#39;BoolTensor&#39;</span><span class="p">,</span> <span class="s1">&#39;Tensor&#39;</span><span class="p">,</span>
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<span class="s1">&#39;lobpcg&#39;</span><span class="p">,</span> <span class="s1">&#39;use_deterministic_algorithms&#39;</span><span class="p">,</span> <span class="s1">&#39;set_deterministic&#39;</span><span class="p">,</span>
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<span class="s1">&#39;are_deterministic_algorithms_enabled&#39;</span><span class="p">,</span> <span class="s1">&#39;is_deterministic&#39;</span><span class="p">,</span>
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<span class="s1">&#39;set_warn_always&#39;</span><span class="p">,</span> <span class="s1">&#39;is_warn_always_enabled&#39;</span><span class="p">,</span>
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<span class="p">]</span>
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<span class="c1">################################################################################</span>
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<span class="n">use_deterministic_algorithms</span><span class="p">(</span><span class="n">d</span><span class="p">)</span>
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<div class="viewcode-block" id="are_deterministic_algorithms_enabled"><a class="viewcode-back" href="../generated/torch.are_deterministic_algorithms_enabled.html#torch.are_deterministic_algorithms_enabled">[docs]</a><span class="k">def</span> <span class="nf">are_deterministic_algorithms_enabled</span><span class="p">():</span>
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<span class="sa">r</span><span class="sd">&quot;&quot;&quot;Returns True if the global deterministic flag is turned on. Refer to</span>
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<span class="k">return</span> <span class="n">_C</span><span class="o">.</span><span class="n">_get_deterministic_algorithms</span><span class="p">()</span></div>
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<span class="k">def</span> <span class="nf">is_deterministic</span><span class="p">():</span>
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<span class="sa">r</span><span class="sd">&quot;&quot;&quot;When this flag is False (default) then some PyTorch warnings may only</span>
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<span class="sd"> appear once per process. This helps avoid excessive warning information.</span>
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<span class="sd"> Setting it to True causes these warnings to always appear, which may be</span>
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<span class="sd"> helpful when debugging.</span>
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<span class="sd"> Args:</span>
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<span class="sd"> b (:class:`bool`): If True, force warnings to always be emitted</span>
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<span class="sd"> &quot;&quot;&quot;</span>
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<span class="sa">r</span><span class="sd">&quot;&quot;&quot;Returns True if the global warn_always flag is turned on. Refer to</span>
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<span class="c1">################################################################################</span>
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<span class="c1"># Define Storage and Tensor classes</span>
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<span class="c1">################################################################################</span>
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<span class="k">del</span> <span class="n">_torch_docs</span><span class="p">,</span> <span class="n">_tensor_docs</span><span class="p">,</span> <span class="n">_storage_docs</span>
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<div class="viewcode-block" id="compiled_with_cxx11_abi"><a class="viewcode-back" href="../generated/torch.compiled_with_cxx11_abi.html#torch.compiled_with_cxx11_abi">[docs]</a><span class="k">def</span> <span class="nf">compiled_with_cxx11_abi</span><span class="p">():</span>
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<span class="k">def</span> <span class="nf">compiled_with_cxx11_abi</span><span class="p">():</span>
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<span class="sa">r</span><span class="sd">&quot;&quot;&quot;Returns whether PyTorch was built with _GLIBCXX_USE_CXX11_ABI=1&quot;&quot;&quot;</span>
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<span class="k">return</span> <span class="n">_C</span><span class="o">.</span><span class="n">_GLIBCXX_USE_CXX11_ABI</span></div>
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<span class="c1"># Import the ops &quot;namespace&quot;</span>

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<span class="kn">from</span> <span class="nn">textwrap</span> <span class="kn">import</span> <span class="n">dedent</span>
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<span class="c1"># This is needed. `torch._jit_internal` is imported before `torch.distributed.__init__`.</span>
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<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Tuple</span><span class="p">,</span> <span class="n">List</span><span class="p">,</span> <span class="n">Dict</span><span class="p">,</span> <span class="n">Optional</span><span class="p">,</span> <span class="n">Union</span><span class="p">,</span> <span class="n">Any</span><span class="p">,</span> <span class="n">TypeVar</span><span class="p">,</span> <span class="n">Generic</span><span class="p">,</span> <span class="n">Callable</span> <span class="c1"># noqa: F401</span>

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<span class="c1"># This function applies in case of gradient checkpointing for memory</span>
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<span class="c1"># optimization. Currently, for gradient checkpointing, we only support imperative</span>
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<span class="c1"># backwards call i.e. torch.autograd.backward() and the torch.autograd.grad() won&#39;t</span>
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<span class="c1"># work. The reason being that: torch.autograd.grad() only calculates the grads</span>
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<span class="c1"># for the inputs that are passed by user but it doesn&#39;t calculate grad for</span>
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<span class="c1"># anything else e.g. model parameters like weights, bias etc. However, for</span>
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<span class="c1"># optimization. Currently, gradient checkpointing is supported only if the</span>
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<span class="c1"># execution engine is invoked through torch.autograd.backward() and its</span>
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<span class="c1"># inputs argument is not passed. It is not supported for torch.autograd.grad().</span>
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<span class="c1"># This is because if inputs are specified, the gradient won&#39;t be calculated for</span>
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<span class="c1"># anything else e.g. model parameters like weights, bias etc.</span>
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<span class="c1">#</span>
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<span class="c1"># This function returns whether the checkpointing is valid i.e. torch.autograd.backward</span>
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<span class="c1"># or not i.e. torch.autograd.grad. The implementation works by maintaining a thread</span>

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