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<a href="https://github.com/labmlai/annotated_deep_learning_paper_implementations/tree/master/labml_nn/helpers/optimizer.py" target="_blank">
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<div class='code'>
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<div class="highlight"><pre><span class="lineno">1</span><span></span><span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Tuple</span>
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<span class="lineno">2</span>
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<span class="lineno">3</span><span class="kn">import</span> <span class="nn">torch</span>
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<span class="lineno">4</span><span class="kn">from</span> <span class="nn">labml</span> <span class="kn">import</span> <span class="n">tracker</span>
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<span class="lineno">5</span>
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<span class="lineno">6</span><span class="kn">from</span> <span class="nn">labml.configs</span> <span class="kn">import</span> <span class="n">BaseConfigs</span><span class="p">,</span> <span class="n">option</span><span class="p">,</span> <span class="n">meta_config</span></pre></div>
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<div class='section' id='section-1'>
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<div class='docs doc-strings'>
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<div class='section-link'>
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<a href='#section-1'>#</a>
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</div>
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<p> This creates a configurable optimizer.</p>
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<p>Arguments: learning_rate (float): Learning rate of the optimizer. Defaults to <code class="highlight"><span></span></code>
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0.01<code class="highlight"><span></span></code>
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. momentum (float): Momentum of the optimizer. Defaults to <code class="highlight"><span></span></code>
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0.5<code class="highlight"><span></span></code>
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. parameters: Model parameters to optimize. d_model (int): Embedding size of the model (for Noam optimizer). betas (Tuple<a href="float, float">float, float</a>): Betas for Adam optimizer. Defaults to <code class="highlight"><span></span></code>
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(0.9, 0.999)<code class="highlight"><span></span></code>
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. eps (float): Epsilon for Adam/RMSProp optimizers. Defaults to <code class="highlight"><span></span></code>
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1e-8<code class="highlight"><span></span></code>
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. step_factor (int): Step factor for Noam optimizer. Defaults to <code class="highlight"><span></span></code>
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1024<code class="highlight"><span></span></code>
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.</p>
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<p>Also there is a better (more options) implementation in <code class="highlight"><span></span></code>
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labml_nn<code class="highlight"><span></span></code>
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. <code class="highlight"><span></span><span class="n">We</span> <span class="n">recommend</span> <span class="n">using</span> <span class="n">that</span> <span class="o"><</span><span class="n">https</span><span class="p">:</span><span class="o">//</span><span class="n">nn</span><span class="o">.</span><span class="n">labml</span><span class="o">.</span><span class="n">ai</span><span class="o">/</span><span class="n">optimizers</span><span class="o">/</span><span class="n">configs</span><span class="o">.</span><span class="n">html</span><span class="o">></span></code>
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_.</p>
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</div>
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<div class='code'>
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<div class="highlight"><pre><span class="lineno">9</span><span class="k">class</span> <span class="nc">OptimizerConfigs</span><span class="p">(</span><span class="n">BaseConfigs</span><span class="p">):</span></pre></div>
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<div class='section' id='section-2'>
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<div class='docs'>
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<div class='section-link'>
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<a href='#section-2'>#</a>
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<div class='code'>
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<div class="highlight"><pre><span class="lineno">26</span> <span class="n">optimizer</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">Adam</span>
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<span class="lineno">27</span> <span class="n">learning_rate</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">0.01</span>
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<span class="lineno">28</span> <span class="n">momentum</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">0.5</span>
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<span class="lineno">29</span> <span class="n">parameters</span><span class="p">:</span> <span class="nb">any</span>
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<span class="lineno">30</span> <span class="n">d_model</span><span class="p">:</span> <span class="nb">int</span>
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<span class="lineno">31</span> <span class="n">betas</span><span class="p">:</span> <span class="n">Tuple</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="nb">float</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="mf">0.9</span><span class="p">,</span> <span class="mf">0.999</span><span class="p">)</span>
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<span class="lineno">32</span> <span class="n">eps</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">1e-8</span>
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<span class="lineno">33</span> <span class="n">step_factor</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">1024</span></pre></div>
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<div class='section' id='section-3'>
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<a href='#section-3'>#</a>
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<div class='code'>
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<div class="highlight"><pre><span class="lineno">35</span> <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
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<span class="lineno">36</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">_primary</span><span class="o">=</span><span class="s1">'optimizer'</span><span class="p">)</span>
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<span class="lineno">37</span>
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<span class="lineno">38</span>
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<span class="lineno">39</span><span class="n">meta_config</span><span class="p">(</span><span class="n">OptimizerConfigs</span><span class="o">.</span><span class="n">parameters</span><span class="p">)</span></pre></div>
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<div class='section' id='section-4'>
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<div class='code'>
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<div class="highlight"><pre><span class="lineno">42</span><span class="nd">@option</span><span class="p">(</span><span class="n">OptimizerConfigs</span><span class="o">.</span><span class="n">optimizer</span><span class="p">,</span> <span class="s1">'SGD'</span><span class="p">)</span>
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<span class="lineno">43</span><span class="k">def</span> <span class="nf">sgd_optimizer</span><span class="p">(</span><span class="n">c</span><span class="p">:</span> <span class="n">OptimizerConfigs</span><span class="p">):</span>
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<span class="lineno">44</span> <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">SGD</span><span class="p">(</span><span class="n">c</span><span class="o">.</span><span class="n">parameters</span><span class="p">,</span> <span class="n">c</span><span class="o">.</span><span class="n">learning_rate</span><span class="p">,</span> <span class="n">c</span><span class="o">.</span><span class="n">momentum</span><span class="p">)</span>
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<span class="lineno">45</span>
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<span class="lineno">46</span>
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<span class="lineno">47</span><span class="nd">@option</span><span class="p">(</span><span class="n">OptimizerConfigs</span><span class="o">.</span><span class="n">optimizer</span><span class="p">,</span> <span class="s1">'Adam'</span><span class="p">)</span>
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<span class="lineno">48</span><span class="k">def</span> <span class="nf">adam_optimizer</span><span class="p">(</span><span class="n">c</span><span class="p">:</span> <span class="n">OptimizerConfigs</span><span class="p">):</span>
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<span class="lineno">49</span> <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">Adam</span><span class="p">(</span><span class="n">c</span><span class="o">.</span><span class="n">parameters</span><span class="p">,</span> <span class="n">lr</span><span class="o">=</span><span class="n">c</span><span class="o">.</span><span class="n">learning_rate</span><span class="p">,</span>
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<span class="lineno">50</span> <span class="n">betas</span><span class="o">=</span><span class="n">c</span><span class="o">.</span><span class="n">betas</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="n">c</span><span class="o">.</span><span class="n">eps</span><span class="p">)</span>
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<span class="lineno">51</span>
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<span class="lineno">52</span>
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<span class="lineno">53</span><span class="k">class</span> <span class="nc">NoamOpt</span><span class="p">:</span>
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<span class="lineno">54</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">model_size</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">learning_rate</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span> <span class="n">warmup</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">step_factor</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">optimizer</span><span class="p">):</span>
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<span class="lineno">55</span> <span class="bp">self</span><span class="o">.</span><span class="n">step_factor</span> <span class="o">=</span> <span class="n">step_factor</span>
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<span class="lineno">56</span> <span class="bp">self</span><span class="o">.</span><span class="n">optimizer</span> <span class="o">=</span> <span class="n">optimizer</span>
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<span class="lineno">57</span> <span class="bp">self</span><span class="o">.</span><span class="n">warmup</span> <span class="o">=</span> <span class="n">warmup</span>
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<span class="lineno">58</span> <span class="bp">self</span><span class="o">.</span><span class="n">learning_rate</span> <span class="o">=</span> <span class="n">learning_rate</span>
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<span class="lineno">59</span> <span class="bp">self</span><span class="o">.</span><span class="n">model_size</span> <span class="o">=</span> <span class="n">model_size</span>
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<span class="lineno">60</span> <span class="bp">self</span><span class="o">.</span><span class="n">_rate</span> <span class="o">=</span> <span class="mi">0</span>
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<span class="lineno">61</span>
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<span class="lineno">62</span> <span class="k">def</span> <span class="nf">step</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
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<span class="lineno">63</span> <span class="n">rate</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">rate</span><span class="p">(</span><span class="n">tracker</span><span class="o">.</span><span class="n">get_global_step</span><span class="p">()</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">step_factor</span><span class="p">)</span>
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<span class="lineno">64</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">optimizer</span><span class="o">.</span><span class="n">param_groups</span><span class="p">:</span>
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<span class="lineno">65</span> <span class="n">p</span><span class="p">[</span><span class="s1">'lr'</span><span class="p">]</span> <span class="o">=</span> <span class="n">rate</span>
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<span class="lineno">66</span> <span class="bp">self</span><span class="o">.</span><span class="n">_rate</span> <span class="o">=</span> <span class="n">rate</span>
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<span class="lineno">67</span> <span class="bp">self</span><span class="o">.</span><span class="n">optimizer</span><span class="o">.</span><span class="n">step</span><span class="p">()</span>
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<span class="lineno">68</span>
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<span class="lineno">69</span> <span class="k">def</span> <span class="nf">rate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">step</span><span class="p">):</span>
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<span class="lineno">70</span> <span class="n">factor</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">model_size</span> <span class="o">**</span> <span class="p">(</span><span class="o">-</span><span class="mf">0.5</span><span class="p">)</span> <span class="o">*</span> <span class="nb">min</span><span class="p">(</span><span class="n">step</span> <span class="o">**</span> <span class="p">(</span><span class="o">-</span><span class="mf">0.5</span><span class="p">),</span> <span class="n">step</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">warmup</span> <span class="o">**</span> <span class="p">(</span><span class="o">-</span><span class="mf">1.5</span><span class="p">))</span>
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<span class="lineno">71</span> <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">learning_rate</span> <span class="o">*</span> <span class="n">factor</span>
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<span class="lineno">72</span>
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<span class="lineno">73</span> <span class="k">def</span> <span class="nf">zero_grad</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
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<span class="lineno">74</span> <span class="bp">self</span><span class="o">.</span><span class="n">optimizer</span><span class="o">.</span><span class="n">zero_grad</span><span class="p">()</span>
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<span class="lineno">75</span>
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<span class="lineno">76</span>
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<span class="lineno">77</span><span class="nd">@option</span><span class="p">(</span><span class="n">OptimizerConfigs</span><span class="o">.</span><span class="n">optimizer</span><span class="p">,</span> <span class="s1">'Noam'</span><span class="p">)</span>
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<span class="lineno">78</span><span class="k">def</span> <span class="nf">noam_optimizer</span><span class="p">(</span><span class="n">c</span><span class="p">:</span> <span class="n">OptimizerConfigs</span><span class="p">):</span>
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<span class="lineno">79</span> <span class="n">optimizer</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">Adam</span><span class="p">(</span><span class="n">c</span><span class="o">.</span><span class="n">parameters</span><span class="p">,</span> <span class="n">lr</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">betas</span><span class="o">=</span><span class="n">c</span><span class="o">.</span><span class="n">betas</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="n">c</span><span class="o">.</span><span class="n">eps</span><span class="p">)</span>
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<span class="lineno">80</span> <span class="k">return</span> <span class="n">NoamOpt</span><span class="p">(</span><span class="n">c</span><span class="o">.</span><span class="n">d_model</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2000</span><span class="p">,</span> <span class="n">c</span><span class="o">.</span><span class="n">step_factor</span><span class="p">,</span> <span class="n">optimizer</span><span class="p">)</span>
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<span class="lineno">81</span>
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<span class="lineno">82</span>
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<span class="lineno">83</span><span class="k">def</span> <span class="nf">_test_noam_optimizer</span><span class="p">():</span>
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<span class="lineno">84</span> <span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
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<span class="lineno">85</span> <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
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<span class="lineno">86</span>
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<span class="lineno">87</span> <span class="n">opts</span> <span class="o">=</span> <span class="p">[</span><span class="n">NoamOpt</span><span class="p">(</span><span class="mi">512</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">4000</span><span class="p">,</span> <span class="kc">None</span><span class="p">),</span>
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<span class="lineno">88</span> <span class="n">NoamOpt</span><span class="p">(</span><span class="mi">512</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">8000</span><span class="p">,</span> <span class="kc">None</span><span class="p">),</span>
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<span class="lineno">89</span> <span class="n">NoamOpt</span><span class="p">(</span><span class="mi">2048</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2000</span><span class="p">,</span> <span class="kc">None</span><span class="p">)]</span>
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<span class="lineno">90</span> <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">20000</span><span class="p">),</span> <span class="p">[[</span><span class="n">opt</span><span class="o">.</span><span class="n">rate</span><span class="p">(</span><span class="n">i</span><span class="p">)</span> <span class="k">for</span> <span class="n">opt</span> <span class="ow">in</span> <span class="n">opts</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="mi">1</span><span class="p">,</span> <span class="mi">20000</span><span class="p">)])</span>
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<span class="lineno">91</span> <span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">([</span><span class="s2">"512:4000"</span><span class="p">,</span> <span class="s2">"512:8000"</span><span class="p">,</span> <span class="s2">"256:4000"</span><span class="p">])</span>
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<span class="lineno">92</span> <span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">"Optimizer"</span><span class="p">)</span>
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<span class="lineno">93</span> <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<span class="lineno">94</span>
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<span class="lineno">95</span>
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<span class="lineno">96</span><span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s1">'__main__'</span><span class="p">:</span>
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<span class="lineno">97</span> <span class="n">_test_noam_optimizer</span><span class="p">()</span></pre></div>
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