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<h1>Source code for torch.distributed.optim.optimizer</h1><div class="highlight"><pre>
<span></span><span class="kn">import</span> <span class="nn">logging</span>
<span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">defaultdict</span>
<span class="kn">from</span> <span class="nn">threading</span> <span class="kn">import</span> <span class="n">Lock</span>
<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">List</span><span class="p">,</span> <span class="n">Optional</span>
<span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">import</span> <span class="nn">torch.distributed.autograd</span> <span class="k">as</span> <span class="nn">dist_autograd</span>
<span class="kn">import</span> <span class="nn">torch.distributed.rpc</span> <span class="k">as</span> <span class="nn">rpc</span>
<span class="kn">import</span> <span class="nn">torch.jit</span> <span class="k">as</span> <span class="nn">jit</span>
<span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span>
<span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">Tensor</span>
<span class="kn">from</span> <span class="nn">torch.distributed.rpc</span> <span class="kn">import</span> <span class="n">RRef</span>
<span class="kn">from</span> <span class="nn">.utils</span> <span class="kn">import</span> <span class="n">functional_optim_map</span>
<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"DistributedOptimizer"</span><span class="p">]</span>
<span class="n">logger</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span>
<span class="c1"># XXX: we define a _ScriptModuleOptimizer here to explicitly</span>
<span class="c1"># compile the FunctionalOptimizer class into TorchScript</span>
<span class="c1"># This is because ScriptClass instance still lives in</span>
<span class="c1"># python unless you explicitly compile it as an attribute</span>
<span class="c1"># in ScriptModule or pass it to a ScriptFunction</span>
<span class="c1"># _ScriptLocalOptimizerInterface serves as a common</span>
<span class="c1"># interface type for Optimizer ScriptModules.</span>
<span class="c1">#</span>
<span class="c1"># TODO (wanchaol): remove this once we added TorchScript</span>
<span class="c1"># class reference semantics</span>
<span class="nd">@jit</span><span class="o">.</span><span class="n">interface</span>
<span class="k">class</span> <span class="nc">_ScriptLocalOptimizerInterface</span><span class="p">:</span>
<span class="k">def</span> <span class="nf">step</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">autograd_ctx_id</span><span class="p">:</span> <span class="nb">int</span><span class="p">)</span> <span class="o">-></span> <span class="kc">None</span><span class="p">:</span>
<span class="k">pass</span>
<span class="k">class</span> <span class="nc">_ScriptLocalOptimizer</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
<span class="c1"># TorchScript does not support multithread concurrent compiling.</span>
<span class="c1"># request_callback might invoke concurrent compiling, so we</span>
<span class="c1"># serialize the compiling with a lock</span>
<span class="n">compile_lock</span> <span class="o">=</span> <span class="n">Lock</span><span class="p">()</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="n">optim_cls</span><span class="p">,</span> <span class="n">local_params_rref</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</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="bp">self</span><span class="o">.</span><span class="n">_local_params</span> <span class="o">=</span> <span class="p">[</span><span class="n">rref</span><span class="o">.</span><span class="n">local_value</span><span class="p">()</span> <span class="k">for</span> <span class="n">rref</span> <span class="ow">in</span> <span class="n">local_params_rref</span><span class="p">]</span>
<span class="bp">self</span><span class="o">.</span><span class="n">optim</span> <span class="o">=</span> <span class="n">optim_cls</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_local_params</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
<span class="nd">@jit</span><span class="o">.</span><span class="n">export</span>
<span class="k">def</span> <span class="nf">step</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">autograd_ctx_id</span><span class="p">:</span> <span class="nb">int</span><span class="p">):</span>
<span class="n">all_local_grads</span> <span class="o">=</span> <span class="n">dist_autograd</span><span class="o">.</span><span class="n">get_gradients</span><span class="p">(</span><span class="n">autograd_ctx_id</span><span class="p">)</span>
<span class="c1"># apply functional optimizer step with a list of gradients</span>
<span class="n">grads</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">Tensor</span><span class="p">]]</span> <span class="o">=</span> <span class="p">[</span>
<span class="n">all_local_grads</span><span class="p">[</span><span class="n">p</span><span class="p">]</span> <span class="k">if</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">all_local_grads</span> <span class="k">else</span> <span class="kc">None</span>
<span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_local_params</span>
<span class="p">]</span>
<span class="bp">self</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">step</span><span class="p">(</span><span class="n">grads</span><span class="p">)</span>
<span class="c1"># TODO (wanchaol): remove/merge this with ScriptLocalOptimizer once</span>
<span class="c1"># we have converted all to functional optimizer in distributed.optim</span>
<span class="k">class</span> <span class="nc">_LocalOptimizer</span><span class="p">:</span>
<span class="c1"># Ideally we would only need to share a lock for instances of</span>
<span class="c1"># _LocalOptimizer that deal with the same parameters. We are</span>
<span class="c1"># making a simplifying assumption here that if there is more</span>
<span class="c1"># than one instance of _LocalOptimizer per worker, they will</span>
<span class="c1"># be optimizing the same parameters (e.g. each data parallel</span>
<span class="c1"># trainer will create its own instance of _LocalOptimizer but</span>
<span class="c1"># they will all optimize the same parameters on each worker)</span>
<span class="n">global_lock</span> <span class="o">=</span> <span class="n">Lock</span><span class="p">()</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="n">optim_cls</span><span class="p">,</span> <span class="n">local_params_rref</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_local_params</span> <span class="o">=</span> <span class="p">[</span><span class="n">rref</span><span class="o">.</span><span class="n">local_value</span><span class="p">()</span> <span class="k">for</span> <span class="n">rref</span> <span class="ow">in</span> <span class="n">local_params_rref</span><span class="p">]</span>
<span class="bp">self</span><span class="o">.</span><span class="n">optim</span> <span class="o">=</span> <span class="n">optim_cls</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_local_params</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">step</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">autograd_ctx_id</span><span class="p">):</span>
<span class="n">all_local_grads</span> <span class="o">=</span> <span class="n">dist_autograd</span><span class="o">.</span><span class="n">get_gradients</span><span class="p">(</span><span class="n">autograd_ctx_id</span><span class="p">)</span>
<span class="k">with</span> <span class="n">_LocalOptimizer</span><span class="o">.</span><span class="n">global_lock</span><span class="p">:</span>
<span class="k">for</span> <span class="n">param</span><span class="p">,</span> <span class="n">grad</span> <span class="ow">in</span> <span class="n">all_local_grads</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
<span class="n">param</span><span class="o">.</span><span class="n">grad</span> <span class="o">=</span> <span class="n">grad</span>
<span class="bp">self</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">step</span><span class="p">()</span>
<span class="k">def</span> <span class="nf">_new_local_optimizer</span><span class="p">(</span><span class="n">optim_cls</span><span class="p">,</span> <span class="n">local_params_rref</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="k">return</span> <span class="n">rpc</span><span class="o">.</span><span class="n">RRef</span><span class="p">(</span><span class="n">_LocalOptimizer</span><span class="p">(</span><span class="n">optim_cls</span><span class="p">,</span> <span class="n">local_params_rref</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">))</span>
<span class="k">def</span> <span class="nf">_local_optimizer_step</span><span class="p">(</span><span class="n">local_optim_rref</span><span class="p">,</span> <span class="n">autograd_ctx_id</span><span class="p">):</span>
<span class="n">local_optim</span> <span class="o">=</span> <span class="n">local_optim_rref</span><span class="o">.</span><span class="n">local_value</span><span class="p">()</span>
<span class="n">local_optim</span><span class="o">.</span><span class="n">step</span><span class="p">(</span><span class="n">autograd_ctx_id</span><span class="p">)</span>
<span class="c1"># new/step functions combined with _ScriptLocalOptimizer to provide GIL-free optimizer</span>
<span class="k">def</span> <span class="nf">_new_script_local_optimizer</span><span class="p">(</span><span class="n">optim_cls</span><span class="p">,</span> <span class="n">local_params_rref</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="n">optim</span> <span class="o">=</span> <span class="n">_ScriptLocalOptimizer</span><span class="p">(</span><span class="n">optim_cls</span><span class="p">,</span> <span class="n">local_params_rref</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
<span class="k">with</span> <span class="n">_ScriptLocalOptimizer</span><span class="o">.</span><span class="n">compile_lock</span><span class="p">:</span>
<span class="n">script_optim</span> <span class="o">=</span> <span class="n">jit</span><span class="o">.</span><span class="n">script</span><span class="p">(</span><span class="n">optim</span><span class="p">)</span>
<span class="k">return</span> <span class="n">rpc</span><span class="o">.</span><span class="n">RRef</span><span class="p">(</span><span class="n">script_optim</span><span class="p">,</span> <span class="n">_ScriptLocalOptimizerInterface</span><span class="p">)</span>
<span class="nd">@jit</span><span class="o">.</span><span class="n">script</span>
<span class="k">def</span> <span class="nf">_script_local_optimizer_step</span><span class="p">(</span>
<span class="n">local_optim_rref</span><span class="p">:</span> <span class="n">RRef</span><span class="p">[</span><span class="n">_ScriptLocalOptimizerInterface</span><span class="p">],</span> <span class="n">autograd_ctx_id</span><span class="p">:</span> <span class="nb">int</span>
<span class="p">)</span> <span class="o">-></span> <span class="kc">None</span><span class="p">:</span>
<span class="n">local_optim</span> <span class="o">=</span> <span class="n">local_optim_rref</span><span class="o">.</span><span class="n">local_value</span><span class="p">()</span>
<span class="n">local_optim</span><span class="o">.</span><span class="n">step</span><span class="p">(</span><span class="n">autograd_ctx_id</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">_wait_for_all</span><span class="p">(</span><span class="n">rpc_futs</span><span class="p">):</span>
<span class="c1"># TODO: improve error propagation</span>
<span class="n">exception</span> <span class="o">=</span> <span class="kc">None</span>
<span class="n">results</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">fut</span> <span class="ow">in</span> <span class="n">rpc_futs</span><span class="p">:</span>
<span class="k">try</span><span class="p">:</span>
<span class="n">results</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">fut</span><span class="o">.</span><span class="n">wait</span><span class="p">())</span>
<span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
<span class="n">results</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">e</span><span class="p">)</span>
<span class="n">exception</span> <span class="o">=</span> <span class="n">e</span>
<span class="k">if</span> <span class="n">exception</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">raise</span> <span class="n">exception</span>
<span class="k">return</span> <span class="n">results</span>
<div class="viewcode-block" id="DistributedOptimizer"><a class="viewcode-back" href="../../../../distributed.optim.html#torch.distributed.optim.DistributedOptimizer">[docs]</a><span class="k">class</span> <span class="nc">DistributedOptimizer</span><span class="p">:</span>
<span class="w"> </span><span class="sd">"""</span>
<span class="sd"> DistributedOptimizer takes remote references to parameters scattered</span>
<span class="sd"> across workers and applies the given optimizer locally for each parameter.</span>
<span class="sd"> This class uses :meth:`~torch.distributed.autograd.get_gradients` in order</span>
<span class="sd"> to retrieve the gradients for specific parameters.</span>
<span class="sd"> Concurrent calls to</span>
<span class="sd"> :meth:`~torch.distributed.optim.DistributedOptimizer.step`,</span>
<span class="sd"> either from the same or different clients, will</span>
<span class="sd"> be serialized on each worker -- as each worker's optimizer can only work</span>
<span class="sd"> on one set of gradients at a time. However, there is no guarantee that</span>
<span class="sd"> the full forward-backward-optimizer sequence will execute for one client</span>
<span class="sd"> at a time. This means that the gradients being applied may not correspond</span>
<span class="sd"> to the latest forward pass executed on a given worker. Also, there is no</span>
<span class="sd"> guaranteed ordering across workers.</span>
<span class="sd"> `DistributedOptimizer` creates the local optimizer with TorchScript enabled</span>
<span class="sd"> by default, so that optimizer updates are not blocked by the Python Global</span>
<span class="sd"> Interpreter Lock (GIL) in the case of multithreaded training (e.g. Distributed</span>
<span class="sd"> Model Parallel). This feature is currently enabled for most optimizers. You</span>
<span class="sd"> can also follow `the recipe`__ in PyTorch tutorials to enable TorchScript support</span>
<span class="sd"> for your own custom optimizers.</span>
<span class="sd"> Args:</span>
<span class="sd"> optimizer_class (optim.Optimizer): the class of optimizer to</span>
<span class="sd"> instantiate on each worker.</span>
<span class="sd"> params_rref (list[RRef]): list of RRefs to local or remote parameters</span>
<span class="sd"> to optimize.</span>
<span class="sd"> args: arguments to pass to the optimizer constructor on each worker.</span>
<span class="sd"> kwargs: arguments to pass to the optimizer constructor on each worker.</span>
<span class="sd"> Example::</span>
<span class="sd"> >>> # xdoctest: +SKIP("distributed")</span>
<span class="sd"> >>> import torch.distributed.autograd as dist_autograd</span>
<span class="sd"> >>> import torch.distributed.rpc as rpc</span>
<span class="sd"> >>> from torch import optim</span>
<span class="sd"> >>> from torch.distributed.optim import DistributedOptimizer</span>
<span class="sd"> >>></span>
<span class="sd"> >>> with dist_autograd.context() as context_id:</span>
<span class="sd"> >>> # Forward pass.</span>
<span class="sd"> >>> rref1 = rpc.remote("worker1", torch.add, args=(torch.ones(2), 3))</span>
<span class="sd"> >>> rref2 = rpc.remote("worker1", torch.add, args=(torch.ones(2), 1))</span>
<span class="sd"> >>> loss = rref1.to_here() + rref2.to_here()</span>
<span class="sd"> >>></span>
<span class="sd"> >>> # Backward pass.</span>
<span class="sd"> >>> dist_autograd.backward(context_id, [loss.sum()])</span>
<span class="sd"> >>></span>
<span class="sd"> >>> # Optimizer.</span>
<span class="sd"> >>> dist_optim = DistributedOptimizer(</span>
<span class="sd"> >>> optim.SGD,</span>
<span class="sd"> >>> [rref1, rref2],</span>
<span class="sd"> >>> lr=0.05,</span>
<span class="sd"> >>> )</span>
<span class="sd"> >>> dist_optim.step(context_id)</span>
<span class="sd"> __ https://github.com/pytorch/tutorials/pull/1465</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="n">optimizer_class</span><span class="p">,</span> <span class="n">params_rref</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_log_api_usage_once</span><span class="p">(</span><span class="s2">"torch.distributed.optim.DistributedOptimizer"</span><span class="p">)</span>
<span class="n">per_worker_params_rref</span> <span class="o">=</span> <span class="n">defaultdict</span><span class="p">(</span><span class="nb">list</span><span class="p">)</span>
<span class="k">for</span> <span class="n">param</span> <span class="ow">in</span> <span class="n">params_rref</span><span class="p">:</span>
<span class="n">per_worker_params_rref</span><span class="p">[</span><span class="n">param</span><span class="o">.</span><span class="n">owner</span><span class="p">()]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">param</span><span class="p">)</span>
<span class="k">if</span> <span class="n">optimizer_class</span> <span class="ow">in</span> <span class="n">functional_optim_map</span> <span class="ow">and</span> <span class="n">jit</span><span class="o">.</span><span class="n">_state</span><span class="o">.</span><span class="n">_enabled</span><span class="p">:</span>
<span class="n">optim_ctor</span> <span class="o">=</span> <span class="n">functional_optim_map</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">optimizer_class</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">optim_ctor</span> <span class="o">=</span> <span class="n">optimizer_class</span>
<span class="bp">self</span><span class="o">.</span><span class="n">is_functional_optim</span> <span class="o">=</span> <span class="n">optim_ctor</span> <span class="o">!=</span> <span class="n">optimizer_class</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">is_functional_optim</span><span class="p">:</span>
<span class="n">optimizer_new_func</span> <span class="o">=</span> <span class="n">_new_script_local_optimizer</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">logger</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span>
<span class="s2">"Creating the optimizer </span><span class="si">%s</span><span class="s2"> without TorchScript support, "</span>
<span class="s2">"this might result in slow computation time in multithreading environment"</span>
<span class="s2">"(i.e. Distributed Model Parallel training on CPU) due to the Python's "</span>
<span class="s2">"Global Interpreter Lock (GIL). Please file an issue if you need this "</span>
<span class="s2">"optimizer in TorchScript. "</span><span class="p">,</span>
<span class="n">optimizer_class</span>
<span class="p">)</span>
<span class="n">optimizer_new_func</span> <span class="o">=</span> <span class="n">_new_local_optimizer</span>
<span class="n">remote_optim_futs</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">worker</span><span class="p">,</span> <span class="n">param_rrefs</span> <span class="ow">in</span> <span class="n">per_worker_params_rref</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
<span class="n">remote_optim_rref_fut</span> <span class="o">=</span> <span class="n">rpc</span><span class="o">.</span><span class="n">rpc_async</span><span class="p">(</span>
<span class="n">worker</span><span class="p">,</span>
<span class="n">optimizer_new_func</span><span class="p">,</span>
<span class="n">args</span><span class="o">=</span><span class="p">(</span><span class="n">optim_ctor</span><span class="p">,</span> <span class="n">param_rrefs</span><span class="p">)</span> <span class="o">+</span> <span class="n">args</span><span class="p">,</span>
<span class="n">kwargs</span><span class="o">=</span><span class="n">kwargs</span><span class="p">,</span>
<span class="p">)</span>
<span class="n">remote_optim_futs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">remote_optim_rref_fut</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">remote_optimizers</span> <span class="o">=</span> <span class="n">_wait_for_all</span><span class="p">(</span><span class="n">remote_optim_futs</span><span class="p">)</span>
<div class="viewcode-block" id="DistributedOptimizer.step"><a class="viewcode-back" href="../../../../distributed.optim.html#torch.distributed.optim.DistributedOptimizer.step">[docs]</a> <span class="k">def</span> <span class="nf">step</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">context_id</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""</span>
<span class="sd"> Performs a single optimization step.</span>
<span class="sd"> This will call :meth:`torch.optim.Optimizer.step` on each worker</span>
<span class="sd"> containing parameters to be optimized, and will block until all workers</span>
<span class="sd"> return. The provided ``context_id`` will be used to retrieve the</span>
<span class="sd"> corresponding :class:`~torch.distributed.autograd.context` that</span>
<span class="sd"> contains the gradients that should be applied to the parameters.</span>
<span class="sd"> Args:</span>
<span class="sd"> context_id: the autograd context id for which we should run the</span>
<span class="sd"> optimizer step.</span>
<span class="sd"> """</span>
<span class="n">dist_autograd</span><span class="o">.</span><span class="n">_is_valid_context</span><span class="p">(</span><span class="n">context_id</span><span class="p">)</span>
<span class="n">optimizer_step_func</span> <span class="o">=</span> <span class="p">(</span>
<span class="n">_script_local_optimizer_step</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">is_functional_optim</span>
<span class="k">else</span> <span class="n">_local_optimizer_step</span>
<span class="p">)</span>
<span class="n">rpc_futs</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">optimizer</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">remote_optimizers</span><span class="p">:</span>
<span class="n">rpc_futs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
<span class="n">rpc</span><span class="o">.</span><span class="n">rpc_async</span><span class="p">(</span>
<span class="n">optimizer</span><span class="o">.</span><span class="n">owner</span><span class="p">(),</span>
<span class="n">optimizer_step_func</span><span class="p">,</span>
<span class="n">args</span><span class="o">=</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">context_id</span><span class="p">),</span>
<span class="p">)</span>
<span class="p">)</span>
<span class="n">_wait_for_all</span><span class="p">(</span><span class="n">rpc_futs</span><span class="p">)</span></div></div>
</pre></div>
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