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<h1>Source code for torch.distributed.autograd</h1><div class="highlight"><pre>
<span></span>
<span class="kn">import</span> <span class="nn">sys</span>
<span class="kn">import</span> <span class="nn">torch</span>
<span class="k">def</span> <span class="nf">is_available</span><span class="p">():</span>
<span class="k">return</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="p">,</span> <span class="s2">"_dist_autograd_init"</span><span class="p">)</span>
<span class="k">if</span> <span class="n">is_available</span><span class="p">()</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_dist_autograd_init</span><span class="p">():</span>
<span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">"Failed to initialize torch.distributed.autograd"</span><span class="p">)</span>
<span class="k">if</span> <span class="n">is_available</span><span class="p">():</span>
<span class="kn">from</span> <span class="nn">torch._C._distributed_autograd</span> <span class="kn">import</span> <span class="p">(</span>
<span class="n">get_gradients</span><span class="p">,</span>
<span class="n">backward</span><span class="p">,</span>
<span class="n">_init</span><span class="p">,</span>
<span class="n">_new_context</span><span class="p">,</span>
<span class="n">_release_context</span><span class="p">,</span>
<span class="n">_get_max_id</span><span class="p">,</span>
<span class="n">_is_valid_context</span><span class="p">,</span>
<span class="n">_retrieve_context</span><span class="p">,</span>
<span class="n">_current_context</span><span class="p">,</span>
<span class="n">_get_debug_info</span><span class="p">,</span>
<span class="n">DistAutogradContext</span><span class="p">,</span>
<span class="p">)</span>
<div class="viewcode-block" id="context"><a class="viewcode-back" href="../../../rpc.html#torch.distributed.autograd.context">[docs]</a><span class="k">class</span> <span class="nc">context</span><span class="p">:</span>
<span class="w"> </span><span class="sd">'''</span>
<span class="sd"> Context object to wrap forward and backward passes when using</span>
<span class="sd"> distributed autograd. The ``context_id`` generated in the ``with``</span>
<span class="sd"> statement is required to uniquely identify a distributed backward pass</span>
<span class="sd"> on all workers. Each worker stores metadata associated with this</span>
<span class="sd"> ``context_id``, which is required to correctly execute a distributed</span>
<span class="sd"> autograd pass.</span>
<span class="sd"> Example::</span>
<span class="sd"> >>> # xdoctest: +SKIP</span>
<span class="sd"> >>> import torch.distributed.autograd as dist_autograd</span>
<span class="sd"> >>> with dist_autograd.context() as context_id:</span>
<span class="sd"> >>> t1 = torch.rand((3, 3), requires_grad=True)</span>
<span class="sd"> >>> t2 = torch.rand((3, 3), requires_grad=True)</span>
<span class="sd"> >>> loss = rpc.rpc_sync("worker1", torch.add, args=(t1, t2)).sum()</span>
<span class="sd"> >>> dist_autograd.backward(context_id, [loss])</span>
<span class="sd"> '''</span>
<span class="k">def</span> <span class="fm">__enter__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">autograd_context</span> <span class="o">=</span> <span class="n">_new_context</span><span class="p">()</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">autograd_context</span><span class="o">.</span><span class="n">_context_id</span><span class="p">()</span>
<span class="k">def</span> <span class="fm">__exit__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">type</span><span class="p">,</span> <span class="n">value</span><span class="p">,</span> <span class="n">traceback</span><span class="p">):</span>
<span class="n">_release_context</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">autograd_context</span><span class="o">.</span><span class="n">_context_id</span><span class="p">())</span></div>
</pre></div>
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