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<h1>Source code for torch.distributed.rpc.options</h1><div class="highlight"><pre>
<span></span><span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Dict</span><span class="p">,</span> <span class="n">List</span><span class="p">,</span> <span class="n">Optional</span><span class="p">,</span> <span class="n">Union</span>
<span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">from</span> <span class="nn">torch._C._distributed_rpc</span> <span class="kn">import</span> <span class="n">_TensorPipeRpcBackendOptionsBase</span>
<span class="kn">from</span> <span class="nn">.</span> <span class="kn">import</span> <span class="n">constants</span> <span class="k">as</span> <span class="n">rpc_contants</span>
<span class="n">DeviceType</span> <span class="o">=</span> <span class="n">Union</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">str</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">]</span>
<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"TensorPipeRpcBackendOptions"</span><span class="p">]</span>
<span class="k">def</span> <span class="nf">_to_device</span><span class="p">(</span><span class="n">device</span><span class="p">:</span> <span class="n">DeviceType</span><span class="p">)</span> <span class="o">-></span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">:</span>
<span class="n">device</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
<span class="k">if</span> <span class="n">device</span><span class="o">.</span><span class="n">type</span> <span class="o">!=</span> <span class="s2">"cuda"</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
<span class="s2">"`set_devices` expect a list of CUDA devices, but got "</span>
<span class="sa">f</span><span class="s2">"device type </span><span class="si">{</span><span class="n">device</span><span class="o">.</span><span class="n">type</span><span class="si">}</span><span class="s2">."</span>
<span class="p">)</span>
<span class="k">return</span> <span class="n">device</span>
<span class="k">def</span> <span class="nf">_to_device_map</span><span class="p">(</span>
<span class="n">device_map</span><span class="p">:</span> <span class="n">Dict</span><span class="p">[</span><span class="n">DeviceType</span><span class="p">,</span> <span class="n">DeviceType</span><span class="p">]</span>
<span class="p">)</span> <span class="o">-></span> <span class="n">Dict</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">]:</span>
<span class="n">full_device_map</span><span class="p">:</span> <span class="n">Dict</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">]</span> <span class="o">=</span> <span class="p">{}</span>
<span class="n">reverse_map</span><span class="p">:</span> <span class="n">Dict</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">]</span> <span class="o">=</span> <span class="p">{}</span>
<span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">device_map</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
<span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="n">k</span><span class="p">),</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="n">v</span><span class="p">)</span>
<span class="k">if</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">reverse_map</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
<span class="s2">"`device_map` only supports 1-to-1 mapping, "</span>
<span class="sa">f</span><span class="s2">"trying to map </span><span class="si">{</span><span class="n">k</span><span class="si">}</span><span class="s2"> and </span><span class="si">{</span><span class="n">reverse_map</span><span class="p">[</span><span class="n">v</span><span class="p">]</span><span class="si">}</span><span class="s2"> to </span><span class="si">{</span><span class="n">v</span><span class="si">}</span><span class="s2">"</span>
<span class="p">)</span>
<span class="n">full_device_map</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="n">v</span>
<span class="n">reverse_map</span><span class="p">[</span><span class="n">v</span><span class="p">]</span> <span class="o">=</span> <span class="n">k</span>
<span class="k">return</span> <span class="n">full_device_map</span>
<span class="k">def</span> <span class="nf">_to_device_list</span><span class="p">(</span><span class="n">devices</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">DeviceType</span><span class="p">])</span> <span class="o">-></span> <span class="n">List</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">]:</span>
<span class="k">return</span> <span class="nb">list</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="n">_to_device</span><span class="p">,</span> <span class="n">devices</span><span class="p">))</span>
<div class="viewcode-block" id="TensorPipeRpcBackendOptions"><a class="viewcode-back" href="../../../../rpc.html#torch.distributed.rpc.TensorPipeRpcBackendOptions">[docs]</a><span class="k">class</span> <span class="nc">TensorPipeRpcBackendOptions</span><span class="p">(</span><span class="n">_TensorPipeRpcBackendOptionsBase</span><span class="p">):</span>
<span class="w"> </span><span class="sa">r</span><span class="sd">"""</span>
<span class="sd"> The backend options for</span>
<span class="sd"> :class:`~torch.distributed.rpc.TensorPipeAgent`, derived from</span>
<span class="sd"> :class:`~torch.distributed.rpc.RpcBackendOptions`.</span>
<span class="sd"> Args:</span>
<span class="sd"> num_worker_threads (int, optional): The number of threads in the</span>
<span class="sd"> thread-pool used by</span>
<span class="sd"> :class:`~torch.distributed.rpc.TensorPipeAgent` to execute</span>
<span class="sd"> requests (default: 16).</span>
<span class="sd"> rpc_timeout (float, optional): The default timeout, in seconds,</span>
<span class="sd"> for RPC requests (default: 60 seconds). If the RPC has not</span>
<span class="sd"> completed in this timeframe, an exception indicating so will</span>
<span class="sd"> be raised. Callers can override this timeout for individual</span>
<span class="sd"> RPCs in :meth:`~torch.distributed.rpc.rpc_sync` and</span>
<span class="sd"> :meth:`~torch.distributed.rpc.rpc_async` if necessary.</span>
<span class="sd"> init_method (str, optional): The URL to initialize the distributed</span>
<span class="sd"> store used for rendezvous. It takes any value accepted for the</span>
<span class="sd"> same argument of :meth:`~torch.distributed.init_process_group`</span>
<span class="sd"> (default: ``env://``).</span>
<span class="sd"> device_maps (Dict[str, Dict], optional): Device placement mappings from</span>
<span class="sd"> this worker to the callee. Key is the callee worker name and value</span>
<span class="sd"> the dictionary (``Dict`` of ``int``, ``str``, or ``torch.device``)</span>
<span class="sd"> that maps this worker's devices to the callee worker's devices.</span>
<span class="sd"> (default: ``None``)</span>
<span class="sd"> devices (List[int, str, or ``torch.device``], optional): all local</span>
<span class="sd"> CUDA devices used by RPC agent. By Default, it will be initialized</span>
<span class="sd"> to all local devices from its own ``device_maps`` and corresponding</span>
<span class="sd"> devices from its peers' ``device_maps``. When processing CUDA RPC</span>
<span class="sd"> requests, the agent will properly synchronize CUDA streams for</span>
<span class="sd"> all devices in this ``List``.</span>
<span class="sd"> """</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
<span class="bp">self</span><span class="p">,</span>
<span class="o">*</span><span class="p">,</span>
<span class="n">num_worker_threads</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="n">rpc_contants</span><span class="o">.</span><span class="n">DEFAULT_NUM_WORKER_THREADS</span><span class="p">,</span>
<span class="n">rpc_timeout</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="n">rpc_contants</span><span class="o">.</span><span class="n">DEFAULT_RPC_TIMEOUT_SEC</span><span class="p">,</span>
<span class="n">init_method</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="n">rpc_contants</span><span class="o">.</span><span class="n">DEFAULT_INIT_METHOD</span><span class="p">,</span>
<span class="n">device_maps</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Dict</span><span class="p">[</span><span class="n">DeviceType</span><span class="p">,</span> <span class="n">DeviceType</span><span class="p">]]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">devices</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">List</span><span class="p">[</span><span class="n">DeviceType</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">_transports</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">List</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">_channels</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">List</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="p">):</span>
<span class="n">full_device_maps</span> <span class="o">=</span> <span class="p">(</span>
<span class="p">{}</span>
<span class="k">if</span> <span class="n">device_maps</span> <span class="ow">is</span> <span class="kc">None</span>
<span class="k">else</span> <span class="p">{</span><span class="n">k</span><span class="p">:</span> <span class="n">_to_device_map</span><span class="p">(</span><span class="n">v</span><span class="p">)</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">device_maps</span><span class="o">.</span><span class="n">items</span><span class="p">()}</span>
<span class="p">)</span>
<span class="n">full_device_list</span> <span class="o">=</span> <span class="p">[]</span> <span class="k">if</span> <span class="n">devices</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">_to_device_list</span><span class="p">(</span><span class="n">devices</span><span class="p">)</span>
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span>
<span class="n">num_worker_threads</span><span class="p">,</span>
<span class="n">_transports</span><span class="p">,</span>
<span class="n">_channels</span><span class="p">,</span>
<span class="n">rpc_timeout</span><span class="p">,</span>
<span class="n">init_method</span><span class="p">,</span>
<span class="n">full_device_maps</span><span class="p">,</span>
<span class="n">full_device_list</span><span class="p">,</span>
<span class="p">)</span>
<div class="viewcode-block" id="TensorPipeRpcBackendOptions.set_device_map"><a class="viewcode-back" href="../../../../rpc.html#torch.distributed.rpc.TensorPipeRpcBackendOptions.set_device_map">[docs]</a> <span class="k">def</span> <span class="nf">set_device_map</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">to</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">device_map</span><span class="p">:</span> <span class="n">Dict</span><span class="p">[</span><span class="n">DeviceType</span><span class="p">,</span> <span class="n">DeviceType</span><span class="p">]):</span>
<span class="w"> </span><span class="sa">r</span><span class="sd">"""</span>
<span class="sd"> Set device mapping between each RPC caller and callee pair. This</span>
<span class="sd"> function can be called multiple times to incrementally add</span>
<span class="sd"> device placement configurations.</span>
<span class="sd"> Args:</span>
<span class="sd"> to (str): Callee name.</span>
<span class="sd"> device_map (Dict of int, str, or torch.device): Device placement</span>
<span class="sd"> mappings from this worker to the callee. This map must be</span>
<span class="sd"> invertible.</span>
<span class="sd"> Example:</span>
<span class="sd"> >>> # xdoctest: +SKIP("distributed")</span>
<span class="sd"> >>> # both workers</span>
<span class="sd"> >>> def add(x, y):</span>
<span class="sd"> >>> print(x) # tensor([1., 1.], device='cuda:1')</span>
<span class="sd"> >>> return x + y, (x + y).to(2)</span>
<span class="sd"> >>></span>
<span class="sd"> >>> # on worker 0</span>
<span class="sd"> >>> options = TensorPipeRpcBackendOptions(</span>
<span class="sd"> >>> num_worker_threads=8,</span>
<span class="sd"> >>> device_maps={"worker1": {0: 1}}</span>
<span class="sd"> >>> # maps worker0's cuda:0 to worker1's cuda:1</span>
<span class="sd"> >>> )</span>
<span class="sd"> >>> options.set_device_map("worker1", {1: 2})</span>
<span class="sd"> >>> # maps worker0's cuda:1 to worker1's cuda:2</span>
<span class="sd"> >>></span>
<span class="sd"> >>> rpc.init_rpc(</span>
<span class="sd"> >>> "worker0",</span>
<span class="sd"> >>> rank=0,</span>
<span class="sd"> >>> world_size=2,</span>
<span class="sd"> >>> backend=rpc.BackendType.TENSORPIPE,</span>
<span class="sd"> >>> rpc_backend_options=options</span>
<span class="sd"> >>> )</span>
<span class="sd"> >>></span>
<span class="sd"> >>> x = torch.ones(2)</span>
<span class="sd"> >>> rets = rpc.rpc_sync("worker1", add, args=(x.to(0), 1))</span>
<span class="sd"> >>> # The first argument will be moved to cuda:1 on worker1. When</span>
<span class="sd"> >>> # sending the return value back, it will follow the invert of</span>
<span class="sd"> >>> # the device map, and hence will be moved back to cuda:0 and</span>
<span class="sd"> >>> # cuda:1 on worker0</span>
<span class="sd"> >>> print(rets[0]) # tensor([2., 2.], device='cuda:0')</span>
<span class="sd"> >>> print(rets[1]) # tensor([2., 2.], device='cuda:1')</span>
<span class="sd"> """</span>
<span class="n">full_device_map</span> <span class="o">=</span> <span class="n">_to_device_map</span><span class="p">(</span><span class="n">device_map</span><span class="p">)</span>
<span class="n">curr_device_maps</span> <span class="o">=</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">device_maps</span>
<span class="k">if</span> <span class="n">to</span> <span class="ow">in</span> <span class="n">curr_device_maps</span><span class="p">:</span>
<span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">full_device_map</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
<span class="k">if</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">curr_device_maps</span><span class="p">[</span><span class="n">to</span><span class="p">]</span> <span class="ow">and</span> <span class="n">v</span> <span class="o">!=</span> <span class="n">curr_device_maps</span><span class="p">[</span><span class="n">to</span><span class="p">][</span><span class="n">k</span><span class="p">]:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
<span class="s2">"`set_device_map` only supports 1-to-1 mapping, trying"</span>
<span class="sa">f</span><span class="s2">" to map </span><span class="si">{</span><span class="n">k</span><span class="si">}</span><span class="s2"> to </span><span class="si">{</span><span class="n">v</span><span class="si">}</span><span class="s2"> and </span><span class="si">{</span><span class="n">curr_device_maps</span><span class="p">[</span><span class="n">to</span><span class="p">][</span><span class="n">k</span><span class="p">]</span><span class="si">}</span><span class="s2">"</span>
<span class="p">)</span>
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">_set_device_map</span><span class="p">(</span><span class="n">to</span><span class="p">,</span> <span class="n">full_device_map</span><span class="p">)</span></div>
<div class="viewcode-block" id="TensorPipeRpcBackendOptions.set_devices"><a class="viewcode-back" href="../../../../rpc.html#torch.distributed.rpc.TensorPipeRpcBackendOptions.set_devices">[docs]</a> <span class="k">def</span> <span class="nf">set_devices</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">devices</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">DeviceType</span><span class="p">]):</span>
<span class="w"> </span><span class="sa">r</span><span class="sd">"""</span>
<span class="sd"> Set local devices used by the TensorPipe RPC agent. When processing</span>
<span class="sd"> CUDA RPC requests, the TensorPipe RPC agent will properly synchronize</span>
<span class="sd"> CUDA streams for all devices in this ``List``.</span>
<span class="sd"> Args:</span>
<span class="sd"> devices (List of int, str, or torch.device): local devices used by</span>
<span class="sd"> the TensorPipe RPC agent.</span>
<span class="sd"> """</span>
<span class="bp">self</span><span class="o">.</span><span class="n">devices</span> <span class="o">=</span> <span class="n">_to_device_list</span><span class="p">(</span><span class="n">devices</span><span class="p">)</span></div></div>
</pre></div>
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