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<h1>Source code for torch.optim.sgd</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">required</span><span class="p">,</span> <span class="n">_use_grad_for_differentiable</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">typing</span> <span class="kn">import</span> <span class="n">List</span><span class="p">,</span> <span class="n">Optional</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="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'SGD'</span><span class="p">,</span> <span class="s1">'sgd'</span><span class="p">]</span>
<div class="viewcode-block" id="SGD"><a class="viewcode-back" href="../../../generated/torch.optim.SGD.html#torch.optim.SGD">[docs]</a><span class="k">class</span> <span class="nc">SGD</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="n">required</span><span class="p">,</span> <span class="n">momentum</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">dampening</span><span class="o">=</span><span class="mi">0</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">nesterov</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">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">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">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="k">if</span> <span class="n">lr</span> <span class="ow">is</span> <span class="ow">not</span> <span class="n">required</span> <span class="ow">and</span> <span class="n">lr</span> <span class="o"><</span> <span class="mf">0.0</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="n">momentum</span> <span class="o"><</span> <span class="mf">0.0</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"Invalid momentum 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">momentum</span><span class="p">))</span>
<span class="k">if</span> <span class="n">weight_decay</span> <span class="o"><</span> <span class="mf">0.0</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">momentum</span><span class="o">=</span><span class="n">momentum</span><span class="p">,</span> <span class="n">dampening</span><span class="o">=</span><span class="n">dampening</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">nesterov</span><span class="o">=</span><span class="n">nesterov</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">foreach</span><span class="o">=</span><span class="n">foreach</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="k">if</span> <span class="n">nesterov</span> <span class="ow">and</span> <span class="p">(</span><span class="n">momentum</span> <span class="o"><=</span> <span class="mi">0</span> <span class="ow">or</span> <span class="n">dampening</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">):</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"Nesterov momentum requires a momentum and zero dampening"</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="s1">'nesterov'</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="s1">'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="s1">'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="s1">'differentiable'</span><span class="p">,</span> <span class="kc">False</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">d_p_list</span><span class="p">,</span> <span class="n">momentum_buffer_list</span><span class="p">):</span>
<span class="n">has_sparse_grad</span> <span class="o">=</span> <span class="kc">False</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="s1">'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="n">d_p_list</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="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="n">has_sparse_grad</span> <span class="o">=</span> <span class="kc">True</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="k">if</span> <span class="s1">'momentum_buffer'</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">state</span><span class="p">:</span>
<span class="n">momentum_buffer_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="kc">None</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">momentum_buffer_list</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="s1">'momentum_buffer'</span><span class="p">])</span>
<span class="k">return</span> <span class="n">has_sparse_grad</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">d_p_list</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">momentum_buffer_list</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">has_sparse_grad</span> <span class="o">=</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">d_p_list</span><span class="p">,</span> <span class="n">momentum_buffer_list</span><span class="p">)</span>
<span class="n">sgd</span><span class="p">(</span><span class="n">params_with_grad</span><span class="p">,</span>
<span class="n">d_p_list</span><span class="p">,</span>
<span class="n">momentum_buffer_list</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="s1">'weight_decay'</span><span class="p">],</span>
<span class="n">momentum</span><span class="o">=</span><span class="n">group</span><span class="p">[</span><span class="s1">'momentum'</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="s1">'lr'</span><span class="p">],</span>
<span class="n">dampening</span><span class="o">=</span><span class="n">group</span><span class="p">[</span><span class="s1">'dampening'</span><span class="p">],</span>
<span class="n">nesterov</span><span class="o">=</span><span class="n">group</span><span class="p">[</span><span class="s1">'nesterov'</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="s1">'maximize'</span><span class="p">],</span>
<span class="n">has_sparse_grad</span><span class="o">=</span><span class="n">has_sparse_grad</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="s1">'foreach'</span><span class="p">])</span>
<span class="c1"># update momentum_buffers in state</span>
<span class="k">for</span> <span class="n">p</span><span class="p">,</span> <span class="n">momentum_buffer</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">params_with_grad</span><span class="p">,</span> <span class="n">momentum_buffer_list</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="n">state</span><span class="p">[</span><span class="s1">'momentum_buffer'</span><span class="p">]</span> <span class="o">=</span> <span class="n">momentum_buffer</span>
<span class="k">return</span> <span class="n">loss</span></div>
<span class="n">SGD</span><span class="o">.</span><span class="vm">__doc__</span> <span class="o">=</span> <span class="sa">r</span><span class="s2">"""\</span>
<span class="s2"> Implements stochastic gradient descent (optionally with momentum).</span>
<span class="s2"> .. math::</span>
<span class="s2"> \begin</span><span class="si">{aligned}</span><span class="s2"></span>
<span class="s2"> &\rule</span><span class="si">{110mm}{0.4pt}</span><span class="s2"> \\</span>
<span class="s2"> &\textbf</span><span class="si">{input}</span><span class="s2"> : \gamma \text{ (lr)}, \: \theta_0 \text{ (params)}, \: f(\theta)</span>
<span class="s2"> \text{ (objective)}, \: \lambda \text{ (weight decay)}, \\</span>
<span class="s2"> &\hspace</span><span class="si">{13mm}</span><span class="s2"> \:\mu \text{ (momentum)}, \:\tau \text{ (dampening)},</span>
<span class="s2"> \:\textit{ nesterov,}\:\textit{ maximize} \\[-1.ex]</span>
<span class="s2"> &\rule</span><span class="si">{110mm}{0.4pt}</span><span class="s2"> \\</span>
<span class="s2"> &\textbf</span><span class="si">{for}</span><span class="s2"> \: t=1 \: \textbf</span><span class="si">{to}</span><span class="s2"> \: \ldots \: \textbf</span><span class="si">{do}</span><span class="s2"> \\</span>
<span class="s2"> &\hspace</span><span class="si">{5mm}</span><span class="s2">g_t \leftarrow \nabla_{\theta} f_t (\theta_{t-1}) \\</span>
<span class="s2"> &\hspace</span><span class="si">{5mm}</span><span class="s2">\textbf</span><span class="si">{if}</span><span class="s2"> \: \lambda \neq 0 \\</span>
<span class="s2"> &\hspace</span><span class="si">{10mm}</span><span class="s2"> g_t \leftarrow g_t + \lambda \theta_{t-1} \\</span>
<span class="s2"> &\hspace</span><span class="si">{5mm}</span><span class="s2">\textbf</span><span class="si">{if}</span><span class="s2"> \: \mu \neq 0 \\</span>
<span class="s2"> &\hspace</span><span class="si">{10mm}</span><span class="s2">\textbf</span><span class="si">{if}</span><span class="s2"> \: t > 1 \\</span>
<span class="s2"> &\hspace</span><span class="si">{15mm}</span><span class="s2"> \textbf</span><span class="si">{b}</span><span class="s2">_t \leftarrow \mu \textbf</span><span class="si">{b}</span><span class="s2">_{t-1} + (1-\tau) g_t \\</span>
<span class="s2"> &\hspace</span><span class="si">{10mm}</span><span class="s2">\textbf</span><span class="si">{else}</span><span class="s2"> \\</span>
<span class="s2"> &\hspace</span><span class="si">{15mm}</span><span class="s2"> \textbf</span><span class="si">{b}</span><span class="s2">_t \leftarrow g_t \\</span>
<span class="s2"> &\hspace</span><span class="si">{10mm}</span><span class="s2">\textbf</span><span class="si">{if}</span><span class="s2"> \: \textit</span><span class="si">{nesterov}</span><span class="s2"> \\</span>
<span class="s2"> &\hspace</span><span class="si">{15mm}</span><span class="s2"> g_t \leftarrow g_</span><span class="si">{t}</span><span class="s2"> + \mu \textbf</span><span class="si">{b}</span><span class="s2">_t \\</span>
<span class="s2"> &\hspace</span><span class="si">{10mm}</span><span class="s2">\textbf</span><span class="si">{else}</span><span class="s2"> \\[-1.ex]</span>
<span class="s2"> &\hspace</span><span class="si">{15mm}</span><span class="s2"> g_t \leftarrow \textbf</span><span class="si">{b}</span><span class="s2">_t \\</span>
<span class="s2"> &\hspace</span><span class="si">{5mm}</span><span class="s2">\textbf</span><span class="si">{if}</span><span class="s2"> \: \textit</span><span class="si">{maximize}</span><span class="s2"> \\</span>
<span class="s2"> &\hspace</span><span class="si">{10mm}</span><span class="s2">\theta_t \leftarrow \theta_{t-1} + \gamma g_t \\[-1.ex]</span>
<span class="s2"> &\hspace</span><span class="si">{5mm}</span><span class="s2">\textbf</span><span class="si">{else}</span><span class="s2"> \\[-1.ex]</span>
<span class="s2"> &\hspace</span><span class="si">{10mm}</span><span class="s2">\theta_t \leftarrow \theta_{t-1} - \gamma g_t \\[-1.ex]</span>
<span class="s2"> &\rule</span><span class="si">{110mm}{0.4pt}</span><span class="s2"> \\[-1.ex]</span>
<span class="s2"> &\bf</span><span class="si">{return}</span><span class="s2"> \: \theta_t \\[-1.ex]</span>
<span class="s2"> &\rule</span><span class="si">{110mm}{0.4pt}</span><span class="s2"> \\[-1.ex]</span>
<span class="s2"> \end</span><span class="si">{aligned}</span><span class="s2"></span>
<span class="s2"> Nesterov momentum is based on the formula from</span>
<span class="s2"> `On the importance of initialization and momentum in deep learning`__.</span>
<span class="s2"> """</span> <span class="o">+</span> <span class="sa">r</span><span class="s2">"""</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): learning rate</span>
<span class="s2"> momentum (float, optional): momentum factor (default: 0)</span>
<span class="s2"> weight_decay (float, optional): weight decay (L2 penalty) (default: 0)</span>
<span class="s2"> dampening (float, optional): dampening for momentum (default: 0)</span>
<span class="s2"> nesterov (bool, optional): enables Nesterov momentum (default: False)</span>
<span class="s2"> </span><span class="si">{maximize}</span><span class="s2"></span>
<span class="s2"> </span><span class="si">{foreach}</span><span class="s2"></span>
<span class="s2"> </span><span class="si">{differentiable}</span><span class="s2"></span>
<span class="s2"> """</span><span class="o">.</span><span class="n">format</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">foreach</span><span class="o">=</span><span class="n">_foreach_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="o">+</span> <span class="sa">r</span><span class="s2">"""</span>
<span class="s2"> Example:</span>
<span class="s2"> >>> # xdoctest: +SKIP</span>
<span class="s2"> >>> optimizer = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.9)</span>
<span class="s2"> >>> optimizer.zero_grad()</span>
<span class="s2"> >>> loss_fn(model(input), target).backward()</span>
<span class="s2"> >>> optimizer.step()</span>
<span class="s2"> __ http://www.cs.toronto.edu/</span><span class="si">%7E</span><span class="s2">hinton/absps/momentum.pdf</span>
<span class="s2"> .. note::</span>
<span class="s2"> The implementation of SGD with Momentum/Nesterov subtly differs from</span>
<span class="s2"> Sutskever et. al. and implementations in some other frameworks.</span>
<span class="s2"> Considering the specific case of Momentum, the update can be written as</span>
<span class="s2"> .. math::</span>
<span class="s2"> \begin</span><span class="si">{aligned}</span><span class="s2"></span>
<span class="s2"> v_{t+1} & = \mu * v_</span><span class="si">{t}</span><span class="s2"> + g_{t+1}, \\</span>
<span class="s2"> p_{t+1} & = p_</span><span class="si">{t}</span><span class="s2"> - \text</span><span class="si">{lr}</span><span class="s2"> * v_{t+1},</span>
<span class="s2"> \end</span><span class="si">{aligned}</span><span class="s2"></span>
<span class="s2"> where :math:`p`, :math:`g`, :math:`v` and :math:`\mu` denote the</span>
<span class="s2"> parameters, gradient, velocity, and momentum respectively.</span>
<span class="s2"> This is in contrast to Sutskever et. al. and</span>
<span class="s2"> other frameworks which employ an update of the form</span>
<span class="s2"> .. math::</span>
<span class="s2"> \begin</span><span class="si">{aligned}</span><span class="s2"></span>
<span class="s2"> v_{t+1} & = \mu * v_</span><span class="si">{t}</span><span class="s2"> + \text</span><span class="si">{lr}</span><span class="s2"> * g_{t+1}, \\</span>
<span class="s2"> p_{t+1} & = p_</span><span class="si">{t}</span><span class="s2"> - v_{t+1}.</span>
<span class="s2"> \end</span><span class="si">{aligned}</span><span class="s2"></span>
<span class="s2"> The Nesterov version is analogously modified.</span>
<span class="s2"> Moreover, the initial value of the momentum buffer is set to the</span>
<span class="s2"> gradient value at the first step. This is in contrast to some other</span>
<span class="s2"> frameworks that initialize it to all zeros.</span>
<span class="s2"> """</span>
<span class="k">def</span> <span class="nf">sgd</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">d_p_list</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">momentum_buffer_list</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="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">has_sparse_grad</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">None</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="o">*</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">momentum</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">dampening</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span>
<span class="n">nesterov</span><span class="p">:</span> <span class="nb">bool</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="sa">r</span><span class="sd">"""Functional API that performs SGD algorithm computation.</span>
<span class="sd"> See :class:`~torch.optim.SGD` 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="c1"># why must we be explicit about an if statement for torch.jit.is_scripting here?</span>
<span class="c1"># because JIT can't handle Optionals nor fancy conditionals when scripting</span>
<span class="k">if</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">_</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="o">=</span><span class="kc">False</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">else</span><span class="p">:</span>
<span class="n">foreach</span> <span class="o">=</span> <span class="kc">False</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="s1">'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_sgd</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">func</span> <span class="o">=</span> <span class="n">_single_tensor_sgd</span>
<span class="n">func</span><span class="p">(</span><span class="n">params</span><span class="p">,</span>
<span class="n">d_p_list</span><span class="p">,</span>
<span class="n">momentum_buffer_list</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">momentum</span><span class="o">=</span><span class="n">momentum</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">dampening</span><span class="o">=</span><span class="n">dampening</span><span class="p">,</span>
<span class="n">nesterov</span><span class="o">=</span><span class="n">nesterov</span><span class="p">,</span>
<span class="n">has_sparse_grad</span><span class="o">=</span><span class="n">has_sparse_grad</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="k">def</span> <span class="nf">_single_tensor_sgd</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">d_p_list</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">momentum_buffer_list</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">weight_decay</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span>
<span class="n">momentum</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">dampening</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span>
<span class="n">nesterov</span><span class="p">:</span> <span class="nb">bool</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">has_sparse_grad</span><span class="p">:</span> <span class="nb">bool</span><span class="p">):</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">d_p</span> <span class="o">=</span> <span class="n">d_p_list</span><span class="p">[</span><span class="n">i</span><span class="p">]</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">d_p_list</span><span class="p">[</span><span class="n">i</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">d_p</span> <span class="o">=</span> <span class="n">d_p</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="k">if</span> <span class="n">momentum</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
<span class="n">buf</span> <span class="o">=</span> <span class="n">momentum_buffer_list</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
<span class="k">if</span> <span class="n">buf</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">buf</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">clone</span><span class="p">(</span><span class="n">d_p</span><span class="p">)</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span>
<span class="n">momentum_buffer_list</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">buf</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">buf</span><span class="o">.</span><span class="n">mul_</span><span class="p">(</span><span class="n">momentum</span><span class="p">)</span><span class="o">.</span><span class="n">add_</span><span class="p">(</span><span class="n">d_p</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mi">1</span> <span class="o">-</span> <span class="n">dampening</span><span class="p">)</span>
<span class="k">if</span> <span class="n">nesterov</span><span class="p">:</span>
<span class="n">d_p</span> <span class="o">=</span> <span class="n">d_p</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">buf</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="n">momentum</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">d_p</span> <span class="o">=</span> <span class="n">buf</span>
<span class="n">param</span><span class="o">.</span><span class="n">add_</span><span class="p">(</span><span class="n">d_p</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=-</span><span class="n">lr</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">_multi_tensor_sgd</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">momentum_buffer_list</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">weight_decay</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span>
<span class="n">momentum</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">dampening</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span>
<span class="n">nesterov</span><span class="p">:</span> <span class="nb">bool</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">has_sparse_grad</span><span class="p">:</span> <span class="nb">bool</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="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">momentum_buffer_list</span><span class="p">],</span> <span class="n">with_indices</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="k">for</span> <span class="n">device_params</span><span class="p">,</span> <span class="n">device_grads</span><span class="p">,</span> <span class="n">device_momentum_buffer_list</span><span class="p">,</span> <span class="n">indices</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="n">device_has_sparse_grad</span> <span class="o">=</span> <span class="nb">any</span><span class="p">(</span><span class="n">grad</span><span class="o">.</span><span class="n">is_sparse</span> <span class="k">for</span> <span class="n">grad</span> <span class="ow">in</span> <span class="n">device_grads</span><span class="p">)</span>
<span class="k">if</span> <span class="n">maximize</span><span class="p">:</span>
<span class="n">device_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="nb">tuple</span><span class="p">(</span><span class="n">device_grads</span><span class="p">))</span> <span class="c1"># type: ignore[assignment]</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">device_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">device_grads</span><span class="p">,</span> <span class="n">device_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="k">if</span> <span class="n">momentum</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
<span class="n">bufs</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">all_states_with_momentum_buffer</span> <span class="o">=</span> <span class="kc">True</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">device_momentum_buffer_list</span><span class="p">)):</span>
<span class="k">if</span> <span class="n">device_momentum_buffer_list</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">all_states_with_momentum_buffer</span> <span class="o">=</span> <span class="kc">False</span>
<span class="k">break</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">bufs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">device_momentum_buffer_list</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
<span class="k">if</span> <span class="n">all_states_with_momentum_buffer</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">bufs</span><span class="p">,</span> <span class="n">momentum</span><span class="p">)</span>
<span class="n">torch</span><span class="o">.</span><span class="n">_foreach_add_</span><span class="p">(</span><span class="n">bufs</span><span class="p">,</span> <span class="n">device_grads</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mi">1</span> <span class="o">-</span> <span class="n">dampening</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">bufs</span> <span class="o">=</span> <span class="p">[]</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">device_momentum_buffer_list</span><span class="p">)):</span>
<span class="k">if</span> <span class="n">device_momentum_buffer_list</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">buf</span> <span class="o">=</span> <span class="n">device_momentum_buffer_list</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">momentum_buffer_list</span><span class="p">[</span><span class="n">indices</span><span class="p">[</span><span class="n">i</span><span class="p">]]</span> <span class="o">=</span> \
<span class="n">torch</span><span class="o">.</span><span class="n">clone</span><span class="p">(</span><span class="n">device_grads</span><span class="p">[</span><span class="n">i</span><span class="p">])</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">buf</span> <span class="o">=</span> <span class="n">device_momentum_buffer_list</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
<span class="n">buf</span><span class="o">.</span><span class="n">mul_</span><span class="p">(</span><span class="n">momentum</span><span class="p">)</span><span class="o">.</span><span class="n">add_</span><span class="p">(</span><span class="n">device_grads</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">alpha</span><span class="o">=</span><span class="mi">1</span> <span class="o">-</span> <span class="n">dampening</span><span class="p">)</span>
<span class="n">bufs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">buf</span><span class="p">)</span>
<span class="k">if</span> <span class="n">nesterov</span><span class="p">:</span>
<span class="n">torch</span><span class="o">.</span><span class="n">_foreach_add_</span><span class="p">(</span><span class="n">device_grads</span><span class="p">,</span> <span class="n">bufs</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="n">momentum</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">device_grads</span> <span class="o">=</span> <span class="n">bufs</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">device_has_sparse_grad</span><span class="p">:</span>
<span class="n">torch</span><span class="o">.</span><span class="n">_foreach_add_</span><span class="p">(</span><span class="n">device_params</span><span class="p">,</span> <span class="n">device_grads</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=-</span><span class="n">lr</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="c1"># foreach APIs don't support sparse</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">device_params</span><span class="p">)):</span>
<span class="n">device_params</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">device_grads</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">alpha</span><span class="o">=-</span><span class="n">lr</span><span class="p">)</span>
</pre></div>
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