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<h1>Source code for torch.optim.asgd</h1><div class="highlight"><pre>
<span></span><span class="kn">import</span> <span class="nn">torch</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">.optimizer</span> <span class="kn">import</span> <span class="p">(</span><span class="n">Optimizer</span><span class="p">,</span> <span class="n">_use_grad_for_differentiable</span><span class="p">,</span> <span class="n">_get_value</span><span class="p">,</span> <span class="n">_default_to_fused_or_foreach</span><span class="p">,</span>
<span class="n">_differentiable_doc</span><span class="p">,</span> <span class="n">_foreach_doc</span><span class="p">,</span> <span class="n">_maximize_doc</span><span class="p">)</span>
<span class="kn">from</span> <span class="nn">torch._utils</span> <span class="kn">import</span> <span class="n">is_compiling</span>
<span class="kn">from</span> <span class="nn">torch.utils._foreach_utils</span> <span class="kn">import</span> <span class="n">_group_tensors_by_device_and_dtype</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="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"ASGD"</span><span class="p">,</span> <span class="s2">"asgd"</span><span class="p">]</span>
<span class="k">def</span> <span class="nf">_to_tensor</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
<span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="k">return</span> <span class="n">x</span>
<div class="viewcode-block" id="ASGD"><a class="viewcode-back" href="../../../generated/torch.optim.ASGD.html#torch.optim.ASGD">[docs]</a><span class="k">class</span> <span class="nc">ASGD</span><span class="p">(</span><span class="n">Optimizer</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">params</span><span class="p">,</span>
<span class="n">lr</span><span class="o">=</span><span class="mf">1e-2</span><span class="p">,</span>
<span class="n">lambd</span><span class="o">=</span><span class="mf">1e-4</span><span class="p">,</span>
<span class="n">alpha</span><span class="o">=</span><span class="mf">0.75</span><span class="p">,</span>
<span class="n">t0</span><span class="o">=</span><span class="mf">1e6</span><span class="p">,</span>
<span class="n">weight_decay</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
<span class="n">foreach</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">bool</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">maximize</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
<span class="n">differentiable</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
<span class="p">):</span>
<span class="k">if</span> <span class="ow">not</span> <span class="mf">0.0</span> <span class="o"><=</span> <span class="n">lr</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"Invalid learning rate: </span><span class="si">{}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">lr</span><span class="p">))</span>
<span class="k">if</span> <span class="ow">not</span> <span class="mf">0.0</span> <span class="o"><=</span> <span class="n">weight_decay</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"Invalid weight_decay value: </span><span class="si">{}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">weight_decay</span><span class="p">))</span>
<span class="n">defaults</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span>
<span class="n">lr</span><span class="o">=</span><span class="n">lr</span><span class="p">,</span>
<span class="n">lambd</span><span class="o">=</span><span class="n">lambd</span><span class="p">,</span>
<span class="n">alpha</span><span class="o">=</span><span class="n">alpha</span><span class="p">,</span>
<span class="n">t0</span><span class="o">=</span><span class="n">t0</span><span class="p">,</span>
<span class="n">weight_decay</span><span class="o">=</span><span class="n">weight_decay</span><span class="p">,</span>
<span class="n">foreach</span><span class="o">=</span><span class="n">foreach</span><span class="p">,</span>
<span class="n">maximize</span><span class="o">=</span><span class="n">maximize</span><span class="p">,</span>
<span class="n">differentiable</span><span class="o">=</span><span class="n">differentiable</span><span class="p">,</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">params</span><span class="p">,</span> <span class="n">defaults</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">__setstate__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">state</span><span class="p">):</span>
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">__setstate__</span><span class="p">(</span><span class="n">state</span><span class="p">)</span>
<span class="k">for</span> <span class="n">group</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">param_groups</span><span class="p">:</span>
<span class="n">group</span><span class="o">.</span><span class="n">setdefault</span><span class="p">(</span><span class="s2">"foreach"</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
<span class="n">group</span><span class="o">.</span><span class="n">setdefault</span><span class="p">(</span><span class="s2">"maximize"</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span>
<span class="n">group</span><span class="o">.</span><span class="n">setdefault</span><span class="p">(</span><span class="s2">"differentiable"</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span>
<span class="n">state_values</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">state</span><span class="o">.</span><span class="n">values</span><span class="p">())</span>
<span class="n">step_is_tensor</span> <span class="o">=</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">state_values</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">)</span> <span class="ow">and</span> <span class="n">torch</span><span class="o">.</span><span class="n">is_tensor</span><span class="p">(</span>
<span class="n">state_values</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="s2">"step"</span><span class="p">]</span>
<span class="p">)</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">step_is_tensor</span><span class="p">:</span>
<span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">state_values</span><span class="p">:</span>
<span class="n">s</span><span class="p">[</span><span class="s2">"step"</span><span class="p">]</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="nb">float</span><span class="p">(</span><span class="n">s</span><span class="p">[</span><span class="s2">"step"</span><span class="p">]))</span>
<span class="n">eta_is_tensor</span> <span class="o">=</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">state_values</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">)</span> <span class="ow">and</span> <span class="n">torch</span><span class="o">.</span><span class="n">is_tensor</span><span class="p">(</span>
<span class="n">state_values</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="s2">"eta"</span><span class="p">]</span>
<span class="p">)</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">eta_is_tensor</span><span class="p">:</span>
<span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">state_values</span><span class="p">:</span>
<span class="n">s</span><span class="p">[</span><span class="s2">"eta"</span><span class="p">]</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">s</span><span class="p">[</span><span class="s2">"eta"</span><span class="p">])</span>
<span class="n">mu_is_tensor</span> <span class="o">=</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">state_values</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">)</span> <span class="ow">and</span> <span class="n">torch</span><span class="o">.</span><span class="n">is_tensor</span><span class="p">(</span>
<span class="n">state_values</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="s2">"mu"</span><span class="p">]</span>
<span class="p">)</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">mu_is_tensor</span><span class="p">:</span>
<span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">state_values</span><span class="p">:</span>
<span class="n">s</span><span class="p">[</span><span class="s2">"mu"</span><span class="p">]</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="nb">float</span><span class="p">(</span><span class="n">s</span><span class="p">[</span><span class="s2">"mu"</span><span class="p">]))</span>
<span class="k">def</span> <span class="nf">_init_group</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">group</span><span class="p">,</span> <span class="n">params_with_grad</span><span class="p">,</span> <span class="n">grads</span><span class="p">,</span> <span class="n">mus</span><span class="p">,</span> <span class="n">axs</span><span class="p">,</span> <span class="n">etas</span><span class="p">,</span> <span class="n">state_steps</span><span class="p">):</span>
<span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">group</span><span class="p">[</span><span class="s2">"params"</span><span class="p">]:</span>
<span class="k">if</span> <span class="n">p</span><span class="o">.</span><span class="n">grad</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">params_with_grad</span><span class="o">.</span><span class="n">append</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="o">.</span><span class="n">grad</span><span class="o">.</span><span class="n">is_sparse</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">"ASGD does not support sparse gradients"</span><span class="p">)</span>
<span class="n">grads</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">p</span><span class="o">.</span><span class="n">grad</span><span class="p">)</span>
<span class="n">state</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">state</span><span class="p">[</span><span class="n">p</span><span class="p">]</span>
<span class="c1"># State initialization</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">state</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
<span class="n">state</span><span class="p">[</span><span class="s2">"step"</span><span class="p">]</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="mf">0.0</span><span class="p">)</span>
<span class="n">state</span><span class="p">[</span><span class="s2">"eta"</span><span class="p">]</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">group</span><span class="p">[</span><span class="s2">"lr"</span><span class="p">])</span>
<span class="n">state</span><span class="p">[</span><span class="s2">"mu"</span><span class="p">]</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="mf">1.0</span><span class="p">)</span>
<span class="n">state</span><span class="p">[</span><span class="s2">"ax"</span><span class="p">]</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros_like</span><span class="p">(</span>
<span class="n">p</span><span class="p">,</span> <span class="n">memory_format</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">preserve_format</span>
<span class="p">)</span>
<span class="n">mus</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">state</span><span class="p">[</span><span class="s2">"mu"</span><span class="p">])</span>
<span class="n">axs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">state</span><span class="p">[</span><span class="s2">"ax"</span><span class="p">])</span>
<span class="n">etas</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">state</span><span class="p">[</span><span class="s2">"eta"</span><span class="p">])</span>
<span class="n">state_steps</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">state</span><span class="p">[</span><span class="s2">"step"</span><span class="p">])</span>
<span class="nd">@_use_grad_for_differentiable</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">closure</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="sd">"""Performs a single optimization step.</span>
<span class="sd"> Args:</span>
<span class="sd"> closure (Callable, optional): A closure that reevaluates the model</span>
<span class="sd"> and returns the loss.</span>
<span class="sd"> """</span>
<span class="n">loss</span> <span class="o">=</span> <span class="kc">None</span>
<span class="k">if</span> <span class="n">closure</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">enable_grad</span><span class="p">():</span>
<span class="n">loss</span> <span class="o">=</span> <span class="n">closure</span><span class="p">()</span>
<span class="k">for</span> <span class="n">group</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">param_groups</span><span class="p">:</span>
<span class="n">params_with_grad</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">grads</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">mus</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">axs</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">etas</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">state_steps</span> <span class="o">=</span> <span class="p">[]</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_init_group</span><span class="p">(</span><span class="n">group</span><span class="p">,</span> <span class="n">params_with_grad</span><span class="p">,</span> <span class="n">grads</span><span class="p">,</span> <span class="n">mus</span><span class="p">,</span> <span class="n">axs</span><span class="p">,</span> <span class="n">etas</span><span class="p">,</span> <span class="n">state_steps</span><span class="p">)</span>
<span class="n">asgd</span><span class="p">(</span>
<span class="n">params_with_grad</span><span class="p">,</span>
<span class="n">grads</span><span class="p">,</span>
<span class="n">axs</span><span class="p">,</span>
<span class="n">mus</span><span class="p">,</span>
<span class="n">etas</span><span class="p">,</span>
<span class="n">state_steps</span><span class="p">,</span>
<span class="n">lambd</span><span class="o">=</span><span class="n">group</span><span class="p">[</span><span class="s2">"lambd"</span><span class="p">],</span>
<span class="n">lr</span><span class="o">=</span><span class="n">group</span><span class="p">[</span><span class="s2">"lr"</span><span class="p">],</span>
<span class="n">t0</span><span class="o">=</span><span class="n">group</span><span class="p">[</span><span class="s2">"t0"</span><span class="p">],</span>
<span class="n">alpha</span><span class="o">=</span><span class="n">group</span><span class="p">[</span><span class="s2">"alpha"</span><span class="p">],</span>
<span class="n">weight_decay</span><span class="o">=</span><span class="n">group</span><span class="p">[</span><span class="s2">"weight_decay"</span><span class="p">],</span>
<span class="n">foreach</span><span class="o">=</span><span class="n">group</span><span class="p">[</span><span class="s2">"foreach"</span><span class="p">],</span>
<span class="n">maximize</span><span class="o">=</span><span class="n">group</span><span class="p">[</span><span class="s2">"maximize"</span><span class="p">],</span>
<span class="n">differentiable</span><span class="o">=</span><span class="n">group</span><span class="p">[</span><span class="s2">"differentiable"</span><span class="p">],</span>
<span class="p">)</span>
<span class="k">return</span> <span class="n">loss</span></div>
<span class="n">ASGD</span><span class="o">.</span><span class="vm">__doc__</span> <span class="o">=</span> <span class="sa">r</span><span class="s2">"""Implements Averaged Stochastic Gradient Descent.</span>
<span class="s2"> It has been proposed in `Acceleration of stochastic approximation by</span>
<span class="s2"> averaging`_.</span>
<span class="s2"> Args:</span>
<span class="s2"> params (iterable): iterable of parameters to optimize or dicts defining</span>
<span class="s2"> parameter groups</span>
<span class="s2"> lr (float, optional): learning rate (default: 1e-2)</span>
<span class="s2"> lambd (float, optional): decay term (default: 1e-4)</span>
<span class="s2"> alpha (float, optional): power for eta update (default: 0.75)</span>
<span class="s2"> t0 (float, optional): point at which to start averaging (default: 1e6)</span>
<span class="s2"> weight_decay (float, optional): weight decay (L2 penalty) (default: 0)</span>
<span class="s2"> </span><span class="si">{foreach}</span><span class="s2"></span>
<span class="s2"> </span><span class="si">{maximize}</span><span class="s2"></span>
<span class="s2"> </span><span class="si">{differentiable}</span><span class="s2"></span>
<span class="s2"> .. _Acceleration of stochastic approximation by averaging:</span>
<span class="s2"> https://dl.acm.org/citation.cfm?id=131098</span>
<span class="s2"> """</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">foreach</span><span class="o">=</span><span class="n">_foreach_doc</span><span class="p">,</span> <span class="n">maximize</span><span class="o">=</span><span class="n">_maximize_doc</span><span class="p">,</span> <span class="n">differentiable</span><span class="o">=</span><span class="n">_differentiable_doc</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">asgd</span><span class="p">(</span>
<span class="n">params</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
<span class="n">grads</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
<span class="n">axs</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
<span class="n">mus</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
<span class="n">etas</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
<span class="n">state_steps</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
<span class="c1"># kwonly args with defaults are not supported by functions compiled with torchscript issue #70627</span>
<span class="c1"># setting this as kwarg for now as functional API is compiled by torch/distributed/optim</span>
<span class="n">foreach</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">bool</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">maximize</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
<span class="n">differentiable</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
<span class="o">*</span><span class="p">,</span>
<span class="n">lambd</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span>
<span class="n">lr</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span>
<span class="n">t0</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span>
<span class="n">alpha</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span>
<span class="n">weight_decay</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span>
<span class="p">):</span>
<span class="sa">r</span><span class="sd">"""Functional API that performs asgd algorithm computation.</span>
<span class="sd"> See :class:`~torch.optim.ASGD` for details.</span>
<span class="sd"> """</span>
<span class="k">if</span> <span class="n">foreach</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">_</span><span class="p">,</span> <span class="n">foreach</span> <span class="o">=</span> <span class="n">_default_to_fused_or_foreach</span><span class="p">(</span><span class="n">params</span><span class="p">,</span> <span class="n">differentiable</span><span class="p">,</span> <span class="n">use_fused</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="k">if</span> <span class="n">foreach</span> <span class="ow">and</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">is_scripting</span><span class="p">():</span>
<span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">"torch.jit.script not supported with foreach optimizers"</span><span class="p">)</span>
<span class="k">if</span> <span class="n">foreach</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">is_scripting</span><span class="p">():</span>
<span class="n">func</span> <span class="o">=</span> <span class="n">_multi_tensor_asgd</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">func</span> <span class="o">=</span> <span class="n">_single_tensor_asgd</span>
<span class="n">func</span><span class="p">(</span>
<span class="n">params</span><span class="p">,</span>
<span class="n">grads</span><span class="p">,</span>
<span class="n">axs</span><span class="p">,</span>
<span class="n">mus</span><span class="p">,</span>
<span class="n">etas</span><span class="p">,</span>
<span class="n">state_steps</span><span class="p">,</span>
<span class="n">lambd</span><span class="o">=</span><span class="n">lambd</span><span class="p">,</span>
<span class="n">lr</span><span class="o">=</span><span class="n">lr</span><span class="p">,</span>
<span class="n">t0</span><span class="o">=</span><span class="n">t0</span><span class="p">,</span>
<span class="n">alpha</span><span class="o">=</span><span class="n">alpha</span><span class="p">,</span>
<span class="n">weight_decay</span><span class="o">=</span><span class="n">weight_decay</span><span class="p">,</span>
<span class="n">maximize</span><span class="o">=</span><span class="n">maximize</span><span class="p">,</span>
<span class="n">differentiable</span><span class="o">=</span><span class="n">differentiable</span><span class="p">,</span>
<span class="p">)</span>
<span class="k">def</span> <span class="nf">_single_tensor_asgd</span><span class="p">(</span>
<span class="n">params</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
<span class="n">grads</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
<span class="n">axs</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
<span class="n">mus</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
<span class="n">etas</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
<span class="n">state_steps</span><span class="p">:</span> <span class="n">List</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">lambd</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span>
<span class="n">lr</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span>
<span class="n">t0</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span>
<span class="n">alpha</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span>
<span class="n">weight_decay</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span>
<span class="n">maximize</span><span class="p">:</span> <span class="nb">bool</span><span class="p">,</span>
<span class="n">differentiable</span><span class="p">:</span> <span class="nb">bool</span><span class="p">,</span>
<span class="p">):</span>
<span class="k">def</span> <span class="nf">_to_tensor</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
<span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="k">return</span> <span class="n">x</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">param</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">params</span><span class="p">):</span>
<span class="n">grad</span> <span class="o">=</span> <span class="n">grads</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
<span class="n">grad</span> <span class="o">=</span> <span class="n">grad</span> <span class="k">if</span> <span class="ow">not</span> <span class="n">maximize</span> <span class="k">else</span> <span class="o">-</span><span class="n">grad</span>
<span class="n">mu</span> <span class="o">=</span> <span class="n">mus</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
<span class="n">ax</span> <span class="o">=</span> <span class="n">axs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
<span class="n">eta</span> <span class="o">=</span> <span class="n">etas</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
<span class="n">step_t</span> <span class="o">=</span> <span class="n">state_steps</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
<span class="k">if</span> <span class="n">torch</span><span class="o">.</span><span class="n">is_complex</span><span class="p">(</span><span class="n">param</span><span class="p">):</span>
<span class="n">grad</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">view_as_real</span><span class="p">(</span><span class="n">grad</span><span class="p">)</span>
<span class="n">param</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">view_as_real</span><span class="p">(</span><span class="n">param</span><span class="p">)</span>
<span class="n">ax</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">view_as_real</span><span class="p">(</span><span class="n">ax</span><span class="p">)</span>
<span class="c1"># update step</span>
<span class="n">step_t</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="n">step</span> <span class="o">=</span> <span class="n">_get_value</span><span class="p">(</span><span class="n">step_t</span><span class="p">)</span>
<span class="k">if</span> <span class="n">weight_decay</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
<span class="n">grad</span> <span class="o">=</span> <span class="n">grad</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">param</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="n">weight_decay</span><span class="p">)</span>
<span class="n">eta_value</span> <span class="o">=</span> <span class="n">_get_value</span><span class="p">(</span><span class="n">eta</span><span class="p">)</span>
<span class="c1"># decay term</span>
<span class="n">param</span><span class="o">.</span><span class="n">mul_</span><span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">lambd</span> <span class="o">*</span> <span class="n">eta_value</span><span class="p">)</span>
<span class="c1"># update parameter</span>
<span class="n">param</span><span class="o">.</span><span class="n">add_</span><span class="p">(</span><span class="n">grad</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=-</span><span class="n">eta_value</span><span class="p">)</span>
<span class="c1"># averaging</span>
<span class="k">if</span> <span class="n">is_compiling</span><span class="p">()</span> <span class="ow">or</span> <span class="n">mu</span><span class="o">.</span><span class="n">item</span><span class="p">()</span> <span class="o">!=</span> <span class="mi">1</span><span class="p">:</span>
<span class="n">ax</span><span class="o">.</span><span class="n">add_</span><span class="p">(</span><span class="n">param</span><span class="o">.</span><span class="n">sub</span><span class="p">(</span><span class="n">ax</span><span class="p">)</span><span class="o">.</span><span class="n">mul</span><span class="p">(</span><span class="n">mu</span><span class="p">))</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">ax</span><span class="o">.</span><span class="n">copy_</span><span class="p">(</span><span class="n">param</span><span class="p">)</span>
<span class="n">new_eta</span> <span class="o">=</span> <span class="n">_to_tensor</span><span class="p">(</span><span class="n">lr</span> <span class="o">/</span> <span class="p">((</span><span class="mi">1</span> <span class="o">+</span> <span class="n">lambd</span> <span class="o">*</span> <span class="n">lr</span> <span class="o">*</span> <span class="n">step</span><span class="p">)</span> <span class="o">**</span> <span class="n">alpha</span><span class="p">))</span>
<span class="n">eta</span><span class="o">.</span><span class="n">copy_</span><span class="p">(</span><span class="n">new_eta</span><span class="p">)</span>
<span class="n">new_mu</span> <span class="o">=</span> <span class="n">_to_tensor</span><span class="p">(</span><span class="mi">1</span> <span class="o">/</span> <span class="nb">max</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">step</span> <span class="o">-</span> <span class="n">t0</span><span class="p">))</span>
<span class="n">mu</span><span class="o">.</span><span class="n">copy_</span><span class="p">(</span><span class="n">new_mu</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">_multi_tensor_asgd</span><span class="p">(</span>
<span class="n">params</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
<span class="n">grads</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
<span class="n">axs</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
<span class="n">mus</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
<span class="n">etas</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">Tensor</span><span class="p">],</span>
<span class="n">state_steps</span><span class="p">:</span> <span class="n">List</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">lambd</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span>
<span class="n">lr</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span>
<span class="n">t0</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span>
<span class="n">alpha</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span>
<span class="n">weight_decay</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span>
<span class="n">maximize</span><span class="p">:</span> <span class="nb">bool</span><span class="p">,</span>
<span class="n">differentiable</span><span class="p">:</span> <span class="nb">bool</span><span class="p">,</span>
<span class="p">):</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">params</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
<span class="k">return</span>
<span class="k">assert</span> <span class="ow">not</span> <span class="n">differentiable</span><span class="p">,</span> <span class="s2">"_foreach ops don't support autograd"</span>
<span class="n">grouped_tensors</span> <span class="o">=</span> <span class="n">_group_tensors_by_device_and_dtype</span><span class="p">([</span><span class="n">params</span><span class="p">,</span> <span class="n">grads</span><span class="p">,</span> <span class="n">axs</span><span class="p">,</span> <span class="n">mus</span><span class="p">,</span> <span class="n">etas</span><span class="p">,</span> <span class="n">state_steps</span><span class="p">])</span>
<span class="k">for</span> <span class="p">(</span><span class="n">grouped_params</span><span class="p">,</span> <span class="n">grouped_grads</span><span class="p">,</span> <span class="n">grouped_axs</span><span class="p">,</span> <span class="n">grouped_mus</span><span class="p">,</span>
<span class="n">grouped_etas</span><span class="p">,</span> <span class="n">grouped_state_steps</span><span class="p">)</span> <span class="ow">in</span> <span class="n">grouped_tensors</span><span class="o">.</span><span class="n">values</span><span class="p">():</span>
<span class="k">if</span> <span class="n">maximize</span><span class="p">:</span>
<span class="n">grouped_grads</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">_foreach_neg</span><span class="p">(</span><span class="n">grouped_grads</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">_view_complex_as_real</span><span class="p">(</span><span class="n">tensor_list</span><span class="p">):</span>
<span class="k">return</span> <span class="p">[</span>
<span class="n">torch</span><span class="o">.</span><span class="n">view_as_real</span><span class="p">(</span><span class="n">t</span><span class="p">)</span> <span class="k">if</span> <span class="n">torch</span><span class="o">.</span><span class="n">is_complex</span><span class="p">(</span><span class="n">t</span><span class="p">)</span> <span class="k">else</span> <span class="n">t</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">tensor_list</span>
<span class="p">]</span>
<span class="n">grouped_grads</span> <span class="o">=</span> <span class="n">_view_complex_as_real</span><span class="p">(</span><span class="n">grouped_grads</span><span class="p">)</span>
<span class="n">grouped_params</span> <span class="o">=</span> <span class="n">_view_complex_as_real</span><span class="p">(</span><span class="n">grouped_params</span><span class="p">)</span>
<span class="n">grouped_axs</span> <span class="o">=</span> <span class="n">_view_complex_as_real</span><span class="p">(</span><span class="n">grouped_axs</span><span class="p">)</span>
<span class="c1"># update step</span>
<span class="n">torch</span><span class="o">.</span><span class="n">_foreach_add_</span><span class="p">(</span><span class="n">grouped_state_steps</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
<span class="k">if</span> <span class="n">weight_decay</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
<span class="n">grouped_grads</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">_foreach_add</span><span class="p">(</span><span class="n">grouped_grads</span><span class="p">,</span> <span class="n">grouped_params</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="n">weight_decay</span><span class="p">)</span>
<span class="c1"># decay term</span>
<span class="n">eta</span> <span class="o">=</span> <span class="n">_get_value</span><span class="p">(</span><span class="n">grouped_etas</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
<span class="n">torch</span><span class="o">.</span><span class="n">_foreach_mul_</span><span class="p">(</span><span class="n">grouped_params</span><span class="p">,</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">lambd</span> <span class="o">*</span> <span class="n">eta</span><span class="p">)</span>
<span class="c1"># update parameter</span>
<span class="n">torch</span><span class="o">.</span><span class="n">_foreach_add_</span><span class="p">(</span><span class="n">grouped_params</span><span class="p">,</span> <span class="n">grouped_grads</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=-</span><span class="n">eta</span><span class="p">)</span>
<span class="c1"># averaging</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">grouped_axs</span><span class="p">)):</span>
<span class="k">if</span> <span class="n">is_compiling</span><span class="p">()</span> <span class="ow">or</span> <span class="n">grouped_mus</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">item</span><span class="p">()</span> <span class="o">!=</span> <span class="mi">1</span><span class="p">:</span>
<span class="n">grouped_axs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">add_</span><span class="p">(</span><span class="n">grouped_params</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">sub</span><span class="p">(</span><span class="n">grouped_axs</span><span class="p">[</span><span class="n">i</span><span class="p">])</span><span class="o">.</span><span class="n">mul</span><span class="p">(</span><span class="n">grouped_mus</span><span class="p">[</span><span class="n">i</span><span class="p">]))</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">grouped_axs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">copy_</span><span class="p">(</span><span class="n">grouped_params</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
<span class="c1"># update eta and mu</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">grouped_mus</span><span class="p">)):</span>
<span class="n">new_eta</span> <span class="o">=</span> <span class="n">_to_tensor</span><span class="p">(</span>
<span class="n">lr</span> <span class="o">/</span> <span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">lambd</span> <span class="o">*</span> <span class="n">lr</span> <span class="o">*</span> <span class="n">_get_value</span><span class="p">(</span><span class="n">grouped_state_steps</span><span class="p">[</span><span class="n">i</span><span class="p">])</span> <span class="o">**</span> <span class="n">alpha</span><span class="p">)</span>
<span class="p">)</span>
<span class="n">grouped_etas</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">copy_</span><span class="p">(</span><span class="n">new_eta</span><span class="p">)</span>
<span class="n">new_mu</span> <span class="o">=</span> <span class="n">_to_tensor</span><span class="p">(</span><span class="mi">1</span> <span class="o">/</span> <span class="nb">max</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">_get_value</span><span class="p">(</span><span class="n">grouped_state_steps</span><span class="p">[</span><span class="n">i</span><span class="p">])</span> <span class="o">-</span> <span class="n">t0</span><span class="p">))</span>
<span class="n">grouped_mus</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">copy_</span><span class="p">(</span><span class="n">new_mu</span><span class="p">)</span>
</pre></div>
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