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<a class="parent" href="../index.html">transformers</a>
<a class="parent" href="index.html">gpt</a>
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<h1>GPT</h1>
<p>This is a tutorial/implementation of <a href="https://openai.com/blog/better-language-models/">OpenAI GPT architecture</a> in <a href="https://pytorch.org">PyTorch</a>. We got a bunch of implementation details from <a href="https://github.com/karpathy/minGPT">minGPT</a> by <a href="https://twitter.com/karpathy">@karpathy</a>. This implementation also uses character tiny shakespeare dataset.</p>
<p>GPT model is essentially a standard transformer with a few tweaks. GPT-2 and especially GPT-3 models are quite large and won&#x27;t fit on a single GPU and will need model parallelism. This implementation doesn&#x27;t even use data parallelism and is intended to be more of a tutorial.</p>
<p>Main differences of this compared to a simple autoregressive transformer are the parameter initialization, weight decay, and learning rate schedule. For the transformer we reuse the <a href="../transformers/index.html">existing labml/nn transformer implementation</a>.</p>
<p>Here&#x27;s a notebook for training a GPT model on Tiny Shakespeare dataset.</p>
<p><a href="https://colab.research.google.com/github/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/transformers/gpt/experiment.ipynb"><img alt="Open In Colab" src="https://colab.research.google.com/assets/colab-badge.svg"></a> <a href="https://app.labml.ai/run/0324c6d0562111eba65d0242ac1c0002"><img alt="View Run" src="https://img.shields.io/badge/labml-experiment-brightgreen"></a></p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">35</span><span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="lineno">36</span><span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">nn</span>
<span class="lineno">37</span>
<span class="lineno">38</span><span class="kn">from</span> <span class="nn">labml</span> <span class="kn">import</span> <span class="n">experiment</span>
<span class="lineno">39</span><span class="kn">from</span> <span class="nn">labml.configs</span> <span class="kn">import</span> <span class="n">option</span>
<span class="lineno">40</span><span class="kn">from</span> <span class="nn">labml_helpers.module</span> <span class="kn">import</span> <span class="n">Module</span>
<span class="lineno">41</span><span class="kn">from</span> <span class="nn">labml_nn.experiments.nlp_autoregression</span> <span class="kn">import</span> <span class="n">NLPAutoRegressionConfigs</span>
<span class="lineno">42</span><span class="kn">from</span> <span class="nn">labml_nn.optimizers.configs</span> <span class="kn">import</span> <span class="n">OptimizerConfigs</span>
<span class="lineno">43</span><span class="kn">from</span> <span class="nn">labml_nn.transformers</span> <span class="kn">import</span> <span class="n">TransformerConfigs</span><span class="p">,</span> <span class="n">Encoder</span>
<span class="lineno">44</span><span class="kn">from</span> <span class="nn">labml_nn.transformers.utils</span> <span class="kn">import</span> <span class="n">subsequent_mask</span></pre></div>
</div>
</div>
<div class='section' id='section-1'>
<div class='docs doc-strings'>
<div class='section-link'>
<a href='#section-1'>#</a>
</div>
<h2>GPT model</h2>
<p>This consists of a token embedding layer, transformer encoder, and a final linear layer that gives token logits.</p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">47</span><span class="k">class</span> <span class="nc">GPT</span><span class="p">(</span><span class="n">Module</span><span class="p">):</span></pre></div>
</div>
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<div class='section' id='section-2'>
<div class='docs doc-strings'>
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<ul><li><code class="highlight"><span></span><span class="n">encoder</span></code>
is the transformer <a href="../models.html#Encoder">Encoder</a> </li>
<li><code class="highlight"><span></span><span class="n">src_embed</span></code>
is the token <a href="../models.html#EmbeddingsWithLearnedPositionalEncoding">embedding module (with positional encodings)</a> </li>
<li><code class="highlight"><span></span><span class="n">generator</span></code>
is the <a href="../models.html#Generator">final fully connected layer</a> that gives the logits.</li></ul>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">55</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">encoder</span><span class="p">:</span> <span class="n">Encoder</span><span class="p">,</span> <span class="n">src_embed</span><span class="p">:</span> <span class="n">Module</span><span class="p">,</span> <span class="n">generator</span><span class="p">:</span> <span class="n">Module</span><span class="p">):</span></pre></div>
</div>
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<div class='section' id='section-3'>
<div class='docs'>
<div class='section-link'>
<a href='#section-3'>#</a>
</div>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">62</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="lineno">63</span> <span class="bp">self</span><span class="o">.</span><span class="n">src_embed</span> <span class="o">=</span> <span class="n">src_embed</span>
<span class="lineno">64</span> <span class="bp">self</span><span class="o">.</span><span class="n">encoder</span> <span class="o">=</span> <span class="n">encoder</span>
<span class="lineno">65</span> <span class="bp">self</span><span class="o">.</span><span class="n">generator</span> <span class="o">=</span> <span class="n">generator</span></pre></div>
</div>
</div>
<div class='section' id='section-4'>
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<a href='#section-4'>#</a>
</div>
<p>The mask will be initialized on the first call </p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">68</span> <span class="bp">self</span><span class="o">.</span><span class="n">mask</span> <span class="o">=</span> <span class="kc">None</span></pre></div>
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<div class='docs'>
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<a href='#section-5'>#</a>
</div>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">70</span> <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</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></pre></div>
</div>
</div>
<div class='section' id='section-6'>
<div class='docs'>
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<a href='#section-6'>#</a>
</div>
<p>Create subsequent mask if mask is not initialized or if the size of the mask is different </p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">73</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">mask</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">mask</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> <span class="o">!=</span> <span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">):</span></pre></div>
</div>
</div>
<div class='section' id='section-7'>
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<a href='#section-7'>#</a>
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<p>Subsequent mask, will mask out tokens from seeing future tokens </p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">75</span> <span class="bp">self</span><span class="o">.</span><span class="n">mask</span> <span class="o">=</span> <span class="n">subsequent_mask</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">))</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">device</span><span class="p">)</span></pre></div>
</div>
</div>
<div class='section' id='section-8'>
<div class='docs'>
<div class='section-link'>
<a href='#section-8'>#</a>
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<p>Get the token embeddings with positional encodings </p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">77</span> <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">src_embed</span><span class="p">(</span><span class="n">x</span><span class="p">)</span></pre></div>
</div>
</div>
<div class='section' id='section-9'>
<div class='docs'>
<div class='section-link'>
<a href='#section-9'>#</a>
</div>
<p>Transformer encoder </p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">79</span> <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">encoder</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">mask</span><span class="p">)</span></pre></div>
</div>
</div>
<div class='section' id='section-10'>
<div class='docs'>
<div class='section-link'>
<a href='#section-10'>#</a>
</div>
<p>Get logits </p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">81</span> <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">generator</span><span class="p">(</span><span class="n">x</span><span class="p">)</span></pre></div>
</div>
</div>
<div class='section' id='section-11'>
<div class='docs'>
<div class='section-link'>
<a href='#section-11'>#</a>
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<p>Return results (second value is for state, since our trainer is used with RNNs also) </p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">85</span> <span class="k">return</span> <span class="n">x</span><span class="p">,</span> <span class="kc">None</span></pre></div>
</div>
</div>
<div class='section' id='section-12'>
<div class='docs doc-strings'>
<div class='section-link'>
<a href='#section-12'>#</a>
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<h2>Configurations</h2>
<p>This inherits from <a href="../../experiments/nlp_autoregression.html#NLPAutoRegressionConfigs"><code class="highlight"><span></span><span class="n">NLPAutoRegressionConfigs</span></code>
</a></p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">88</span><span class="k">class</span> <span class="nc">Configs</span><span class="p">(</span><span class="n">NLPAutoRegressionConfigs</span><span class="p">):</span></pre></div>
</div>
</div>
<div class='section' id='section-13'>
<div class='docs'>
<div class='section-link'>
<a href='#section-13'>#</a>
</div>
<p>GPT model </p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">97</span> <span class="n">model</span><span class="p">:</span> <span class="n">GPT</span></pre></div>
</div>
</div>
<div class='section' id='section-14'>
<div class='docs'>
<div class='section-link'>
<a href='#section-14'>#</a>
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<p>Transformer </p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">99</span> <span class="n">transformer</span><span class="p">:</span> <span class="n">TransformerConfigs</span></pre></div>
</div>
</div>
<div class='section' id='section-15'>
<div class='docs'>
<div class='section-link'>
<a href='#section-15'>#</a>
</div>
<p>Weight decay </p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">101</span> <span class="n">weight_decay</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">0.1</span></pre></div>
</div>
</div>
<div class='section' id='section-16'>
<div class='docs'>
<div class='section-link'>
<a href='#section-16'>#</a>
</div>
<p>Number of tokens for wamup </p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">103</span> <span class="n">warmup_steps</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">128</span> <span class="o">*</span> <span class="mi">128</span> <span class="o">*</span> <span class="mi">20</span></pre></div>
</div>
</div>
<div class='section' id='section-17'>
<div class='docs'>
<div class='section-link'>
<a href='#section-17'>#</a>
</div>
<p>Custom optimizer </p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">106</span> <span class="n">optimizer</span> <span class="o">=</span> <span class="s1">&#39;transformer_optimizer&#39;</span></pre></div>
</div>
</div>
<div class='section' id='section-18'>
<div class='docs doc-strings'>
<div class='section-link'>
<a href='#section-18'>#</a>
</div>
<h3>Transformer configurations</h3>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">109</span><span class="nd">@option</span><span class="p">(</span><span class="n">Configs</span><span class="o">.</span><span class="n">transformer</span><span class="p">,</span> <span class="s1">&#39;GPT&#39;</span><span class="p">)</span>
<span class="lineno">110</span><span class="k">def</span> <span class="nf">_transformer_configs</span><span class="p">(</span><span class="n">c</span><span class="p">:</span> <span class="n">Configs</span><span class="p">):</span></pre></div>
</div>
</div>
<div class='section' id='section-19'>
<div class='docs'>
<div class='section-link'>
<a href='#section-19'>#</a>
</div>
<p>We use our <a href="../configs.html#TransformerConfigs">configurable transformer implementation</a> </p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">117</span> <span class="n">conf</span> <span class="o">=</span> <span class="n">TransformerConfigs</span><span class="p">()</span></pre></div>
</div>
</div>
<div class='section' id='section-20'>
<div class='docs'>
<div class='section-link'>
<a href='#section-20'>#</a>
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<p>Set the vocabulary sizes for embeddings and generating logits </p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">119</span> <span class="n">conf</span><span class="o">.</span><span class="n">n_src_vocab</span> <span class="o">=</span> <span class="n">c</span><span class="o">.</span><span class="n">n_tokens</span>
<span class="lineno">120</span> <span class="n">conf</span><span class="o">.</span><span class="n">n_tgt_vocab</span> <span class="o">=</span> <span class="n">c</span><span class="o">.</span><span class="n">n_tokens</span></pre></div>
</div>
</div>
<div class='section' id='section-21'>
<div class='docs'>
<div class='section-link'>
<a href='#section-21'>#</a>
</div>
<p>GPT uses GELU activation for position wise feedforward </p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">122</span> <span class="n">conf</span><span class="o">.</span><span class="n">ffn</span><span class="o">.</span><span class="n">activation</span> <span class="o">=</span> <span class="s1">&#39;GELU&#39;</span></pre></div>
</div>
</div>
<div class='section' id='section-22'>
<div class='docs'>
<div class='section-link'>
<a href='#section-22'>#</a>
</div>
<p> </p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">125</span> <span class="k">return</span> <span class="n">conf</span></pre></div>
</div>
</div>
<div class='section' id='section-23'>
<div class='docs doc-strings'>
<div class='section-link'>
<a href='#section-23'>#</a>
</div>
<h3>Initialize weights</h3>
<p>Weights of linear layers and embedding layers are initialized to <span class="katex"><span aria-hidden="true" class="katex-html"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord mathcal" style="margin-right:0.14736em;">N</span><span class="mopen">(</span><span class="mord coloredeq eqh" style=""><span class="mord" style="">0</span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord coloredeq eqh" style=""><span class="mord" style="">0</span></span><span class="mord">.</span><span class="mord coloredeq eqh" style=""><span class="mord" style="">0</span></span><span class="mord">2</span><span class="mclose">)</span></span></span></span> instead of the default Xavier initialzation.</p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">128</span><span class="k">def</span> <span class="nf">_init_weights</span><span class="p">(</span><span class="n">module</span><span class="p">):</span></pre></div>
</div>
</div>
<div class='section' id='section-24'>
<div class='docs'>
<div class='section-link'>
<a href='#section-24'>#</a>
</div>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">137</span> <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">module</span><span class="p">,</span> <span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">Embedding</span><span class="p">)):</span>
<span class="lineno">138</span> <span class="k">return</span>
<span class="lineno">139</span>
<span class="lineno">140</span> <span class="n">module</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">normal_</span><span class="p">(</span><span class="n">mean</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">std</span><span class="o">=</span><span class="mf">0.02</span><span class="p">)</span></pre></div>
</div>
</div>
<div class='section' id='section-25'>
<div class='docs'>
<div class='section-link'>
<a href='#section-25'>#</a>
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<p>Initialize biases to <span class="katex"><span aria-hidden="true" class="katex-html"><span class="base"><span class="strut" style="height:0.64444em;vertical-align:0em;"></span><span class="mord coloredeq eqh" style=""><span class="mord" style="">0</span></span></span></span></span> </p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">143</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">module</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">)</span> <span class="ow">and</span> <span class="n">module</span><span class="o">.</span><span class="n">bias</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="lineno">144</span> <span class="n">module</span><span class="o">.</span><span class="n">bias</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">zero_</span><span class="p">()</span></pre></div>
</div>
</div>
<div class='section' id='section-26'>
<div class='docs doc-strings'>
<div class='section-link'>
<a href='#section-26'>#</a>
</div>
<p> Create GPT model and initialize weights</p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">147</span><span class="nd">@option</span><span class="p">(</span><span class="n">Configs</span><span class="o">.</span><span class="n">model</span><span class="p">)</span>
<span class="lineno">148</span><span class="k">def</span> <span class="nf">_model</span><span class="p">(</span><span class="n">c</span><span class="p">:</span> <span class="n">Configs</span><span class="p">):</span></pre></div>
</div>
</div>
<div class='section' id='section-27'>
<div class='docs'>
<div class='section-link'>
<a href='#section-27'>#</a>
</div>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">152</span> <span class="n">m</span> <span class="o">=</span> <span class="n">GPT</span><span class="p">(</span><span class="n">c</span><span class="o">.</span><span class="n">transformer</span><span class="o">.</span><span class="n">encoder</span><span class="p">,</span>
<span class="lineno">153</span> <span class="n">c</span><span class="o">.</span><span class="n">transformer</span><span class="o">.</span><span class="n">src_embed</span><span class="p">,</span>
<span class="lineno">154</span> <span class="n">c</span><span class="o">.</span><span class="n">transformer</span><span class="o">.</span><span class="n">generator</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">c</span><span class="o">.</span><span class="n">device</span><span class="p">)</span></pre></div>
</div>
</div>
<div class='section' id='section-28'>
<div class='docs'>
<div class='section-link'>
<a href='#section-28'>#</a>
</div>
<p>Apply custom weight initialization </p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">157</span> <span class="n">m</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">_init_weights</span><span class="p">)</span>
<span class="lineno">158</span>
<span class="lineno">159</span> <span class="k">return</span> <span class="n">m</span></pre></div>
</div>
</div>
<div class='section' id='section-29'>
<div class='docs doc-strings'>
<div class='section-link'>
<a href='#section-29'>#</a>
</div>
<h3>Create custom optimizer with weight decay</h3>
<p>This code is taken from <a href="https://github.com/karpathy/minGPT">minGPT</a>. This applies weight decay only to weights of linear layers.</p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">162</span><span class="nd">@option</span><span class="p">(</span><span class="n">NLPAutoRegressionConfigs</span><span class="o">.</span><span class="n">optimizer</span><span class="p">)</span>
<span class="lineno">163</span><span class="k">def</span> <span class="nf">transformer_optimizer</span><span class="p">(</span><span class="n">c</span><span class="p">:</span> <span class="n">NLPAutoRegressionConfigs</span><span class="p">):</span></pre></div>
</div>
</div>
<div class='section' id='section-30'>
<div class='docs'>
<div class='section-link'>
<a href='#section-30'>#</a>
</div>
<p>Collect names of parameters to apply weight decay </p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">171</span> <span class="n">decay</span> <span class="o">=</span> <span class="nb">set</span><span class="p">()</span>
<span class="lineno">172</span> <span class="k">for</span> <span class="n">mn</span><span class="p">,</span> <span class="n">m</span> <span class="ow">in</span> <span class="n">c</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">named_modules</span><span class="p">():</span>
<span class="lineno">173</span> <span class="k">for</span> <span class="n">pn</span><span class="p">,</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">named_parameters</span><span class="p">():</span>
<span class="lineno">174</span> <span class="n">fpn</span> <span class="o">=</span> <span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><span class="n">mn</span><span class="si">}</span><span class="s1">.</span><span class="si">{</span><span class="n">pn</span><span class="si">}</span><span class="s1">&#39;</span> <span class="k">if</span> <span class="n">mn</span> <span class="k">else</span> <span class="n">pn</span> <span class="c1"># full param name</span>
<span class="lineno">175</span>
<span class="lineno">176</span> <span class="k">if</span> <span class="n">fpn</span><span class="o">.</span><span class="n">endswith</span><span class="p">(</span><span class="s1">&#39;weight&#39;</span><span class="p">)</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">):</span>
<span class="lineno">177</span> <span class="n">decay</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">fpn</span><span class="p">)</span></pre></div>
</div>
</div>
<div class='section' id='section-31'>
<div class='docs'>
<div class='section-link'>
<a href='#section-31'>#</a>
</div>
<p>Get all the parameters </p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">180</span> <span class="n">param_dict</span> <span class="o">=</span> <span class="p">{</span><span class="n">pn</span><span class="p">:</span> <span class="n">p</span> <span class="k">for</span> <span class="n">pn</span><span class="p">,</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">c</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">named_parameters</span><span class="p">()}</span></pre></div>
</div>
</div>
<div class='section' id='section-32'>
<div class='docs'>
<div class='section-link'>
<a href='#section-32'>#</a>
</div>
<p>Parameters that are not decayed </p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">182</span> <span class="n">no_decay</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">param_dict</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span> <span class="o">-</span> <span class="n">decay</span></pre></div>
</div>
</div>
<div class='section' id='section-33'>
<div class='docs'>
<div class='section-link'>
<a href='#section-33'>#</a>
</div>
<p>create the pytorch optimizer object </p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">185</span> <span class="n">opt_groups</span> <span class="o">=</span> <span class="p">[</span>
<span class="lineno">186</span> <span class="p">{</span><span class="s2">&quot;params&quot;</span><span class="p">:</span> <span class="p">[</span><span class="n">param_dict</span><span class="p">[</span><span class="n">pn</span><span class="p">]</span> <span class="k">for</span> <span class="n">pn</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">decay</span><span class="p">))],</span> <span class="s2">&quot;weight_decay&quot;</span><span class="p">:</span> <span class="n">c</span><span class="o">.</span><span class="n">weight_decay</span><span class="p">},</span>
<span class="lineno">187</span> <span class="p">{</span><span class="s2">&quot;params&quot;</span><span class="p">:</span> <span class="p">[</span><span class="n">param_dict</span><span class="p">[</span><span class="n">pn</span><span class="p">]</span> <span class="k">for</span> <span class="n">pn</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">no_decay</span><span class="p">))],</span> <span class="s2">&quot;weight_decay&quot;</span><span class="p">:</span> <span class="mf">0.0</span><span class="p">},</span>
<span class="lineno">188</span> <span class="p">]</span></pre></div>
</div>
</div>
<div class='section' id='section-34'>
<div class='docs'>
<div class='section-link'>
<a href='#section-34'>#</a>
</div>
<p>Create a <a href="../optimizers/configs.html#OptimizerConfigs">configurable optimizer</a>, so that we can change these simply by passing a config dictionary. </p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">193</span> <span class="n">optimizer</span> <span class="o">=</span> <span class="n">OptimizerConfigs</span><span class="p">()</span></pre></div>
</div>
</div>
<div class='section' id='section-35'>
<div class='docs'>
<div class='section-link'>
<a href='#section-35'>#</a>
</div>
<p>Set parameter groups for optimization. </p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">196</span> <span class="n">optimizer</span><span class="o">.</span><span class="n">parameters</span> <span class="o">=</span> <span class="n">opt_groups</span></pre></div>
</div>
</div>
<div class='section' id='section-36'>
<div class='docs'>
<div class='section-link'>
<a href='#section-36'>#</a>
</div>
<p>Use <a href="../optimizers/adam_warmup_cosine_decay.html">cosine decay optimizer</a>. This is what GPT uses. </p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">199</span> <span class="n">optimizer</span><span class="o">.</span><span class="n">optimizer</span> <span class="o">=</span> <span class="s1">&#39;AdamWarmupCosineDecay&#39;</span></pre></div>
</div>
</div>
<div class='section' id='section-37'>
<div class='docs'>
<div class='section-link'>
<a href='#section-37'>#</a>
</div>
<p>Set model embedding size, required if we use <a href="../optimizers/noam.html">Noam optimizer</a> which has an exponential decay. </p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">202</span> <span class="n">optimizer</span><span class="o">.</span><span class="n">d_model</span> <span class="o">=</span> <span class="n">c</span><span class="o">.</span><span class="n">d_model</span></pre></div>
</div>
</div>
<div class='section' id='section-38'>
<div class='docs'>
<div class='section-link'>
<a href='#section-38'>#</a>
</div>
<p>Set default weight decay. This is not required since we set the weight decay in the parameter groups. </p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">205</span> <span class="n">optimizer</span><span class="o">.</span><span class="n">weight_decay</span> <span class="o">=</span> <span class="n">c</span><span class="o">.</span><span class="n">weight_decay</span></pre></div>
</div>
</div>
<div class='section' id='section-39'>
<div class='docs'>
<div class='section-link'>
<a href='#section-39'>#</a>
</div>
<p>GPT uses a maximum learning rate of <span class="katex"><span aria-hidden="true" class="katex-html"><span class="base"><span class="strut" style="height:0.72777em;vertical-align:-0.08333em;"></span><span class="mord">6</span><span class="mspace" style="margin-right:0.2222222222222222em;"></span><span class="mbin">×</span><span class="mspace" style="margin-right:0.2222222222222222em;"></span></span><span class="base"><span class="strut" style="height:0.848448em;vertical-align:0em;"></span><span class="mord"><span class="mord coloredeq eqf" style=""><span class="mord" style="">1</span><span class="mord coloredeq eqh" style=""><span class="mord" style="">0</span></span></span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.848448em;"><span style="top:-3.09734em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mtight">−</span><span class="mord mtight">4</span></span></span></span></span></span></span></span></span></span></span></span>. </p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">207</span> <span class="n">optimizer</span><span class="o">.</span><span class="n">learning_rate</span> <span class="o">=</span> <span class="mf">6e-4</span></pre></div>
</div>
</div>
<div class='section' id='section-40'>
<div class='docs'>
<div class='section-link'>
<a href='#section-40'>#</a>
</div>
<p><span class="katex"><span aria-hidden="true" class="katex-html"><span class="base"><span class="strut" style="height:0.8888799999999999em;vertical-align:-0.19444em;"></span><span class="mord"><span class="mord mathnormal" style="margin-right:0.05278em;">β</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.30110799999999993em;"><span style="top:-2.5500000000000003em;margin-left:-0.05278em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight">1</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mspace" style="margin-right:0.2777777777777778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2777777777777778em;"></span></span><span class="base"><span class="strut" style="height:0.8888799999999999em;vertical-align:-0.19444em;"></span><span class="mord coloredeq eqh" style=""><span class="mord" style="">0</span></span><span class="mord">.9</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord"><span class="mord mathnormal" style="margin-right:0.05278em;">β</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.30110799999999993em;"><span style="top:-2.5500000000000003em;margin-left:-0.05278em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight">2</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mspace" style="margin-right:0.2777777777777778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2777777777777778em;"></span></span><span class="base"><span class="strut" style="height:0.64444em;vertical-align:0em;"></span><span class="mord coloredeq eqh" style=""><span class="mord" style="">0</span></span><span class="mord">.95</span></span></span></span> </p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">209</span> <span class="n">optimizer</span><span class="o">.</span><span class="n">betas</span> <span class="o">=</span> <span class="p">(</span><span class="mf">0.9</span><span class="p">,</span> <span class="mf">0.95</span><span class="p">)</span></pre></div>
</div>
</div>
<div class='section' id='section-41'>
<div class='docs'>
<div class='section-link'>
<a href='#section-41'>#</a>
</div>
<p><span class="katex"><span aria-hidden="true" class="katex-html"><span class="base"><span class="strut" style="height:0.43056em;vertical-align:0em;"></span><span class="mord mathnormal">ϵ</span><span class="mspace" style="margin-right:0.2777777777777778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2777777777777778em;"></span></span><span class="base"><span class="strut" style="height:0.848448em;vertical-align:0em;"></span><span class="mord"><span class="mord coloredeq eqf" style=""><span class="mord" style="">1</span><span class="mord coloredeq eqh" style=""><span class="mord" style="">0</span></span></span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.848448em;"><span style="top:-3.09734em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mtight">−</span><span class="mord mtight">8</span></span></span></span></span></span></span></span></span></span></span></span> </p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">211</span> <span class="n">optimizer</span><span class="o">.</span><span class="n">eps</span> <span class="o">=</span> <span class="mf">1e-8</span></pre></div>
</div>
</div>
<div class='section' id='section-42'>
<div class='docs'>
<div class='section-link'>
<a href='#section-42'>#</a>
</div>
<p>Weight decay is decoupled from gradients </p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">213</span> <span class="n">optimizer</span><span class="o">.</span><span class="n">weight_decouple</span> <span class="o">=</span> <span class="kc">True</span></pre></div>
</div>
</div>
<div class='section' id='section-43'>
<div class='docs'>
<div class='section-link'>
<a href='#section-43'>#</a>
</div>
<p>Total number of optimization steps for learning rate cosine decay </p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">215</span> <span class="n">optimizer</span><span class="o">.</span><span class="n">total_steps</span> <span class="o">=</span> <span class="n">c</span><span class="o">.</span><span class="n">epochs</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">c</span><span class="o">.</span><span class="n">text</span><span class="o">.</span><span class="n">train</span><span class="p">)</span> <span class="o">//</span> <span class="p">(</span><span class="n">c</span><span class="o">.</span><span class="n">batch_size</span> <span class="o">*</span> <span class="n">c</span><span class="o">.</span><span class="n">seq_len</span><span class="p">)</span></pre></div>
</div>
</div>
<div class='section' id='section-44'>
<div class='docs'>
<div class='section-link'>
<a href='#section-44'>#</a>
</div>
<p>Number of warmup optimization steps </p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">217</span> <span class="n">optimizer</span><span class="o">.</span><span class="n">warmup</span> <span class="o">=</span> <span class="n">c</span><span class="o">.</span><span class="n">warmup_steps</span> <span class="o">//</span> <span class="p">(</span><span class="n">c</span><span class="o">.</span><span class="n">batch_size</span> <span class="o">*</span> <span class="n">c</span><span class="o">.</span><span class="n">seq_len</span><span class="p">)</span>
<span class="lineno">218</span>
<span class="lineno">219</span> <span class="k">return</span> <span class="n">optimizer</span></pre></div>
</div>
</div>
<div class='section' id='section-45'>
<div class='docs'>
<div class='section-link'>
<a href='#section-45'>#</a>
</div>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">222</span><span class="k">def</span> <span class="nf">main</span><span class="p">():</span></pre></div>
</div>
</div>
<div class='section' id='section-46'>
<div class='docs'>
<div class='section-link'>
<a href='#section-46'>#</a>
</div>
<p>Create experiment </p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">224</span> <span class="n">experiment</span><span class="o">.</span><span class="n">create</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s2">&quot;gpt&quot;</span><span class="p">)</span></pre></div>
</div>
</div>
<div class='section' id='section-47'>
<div class='docs'>
<div class='section-link'>
<a href='#section-47'>#</a>
</div>
<p>Create configs </p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">226</span> <span class="n">conf</span> <span class="o">=</span> <span class="n">Configs</span><span class="p">()</span></pre></div>
</div>
</div>
<div class='section' id='section-48'>
<div class='docs'>
<div class='section-link'>
<a href='#section-48'>#</a>
</div>
<p>Override configurations </p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">228</span> <span class="n">experiment</span><span class="o">.</span><span class="n">configs</span><span class="p">(</span><span class="n">conf</span><span class="p">,</span> <span class="p">{</span></pre></div>
</div>
</div>
<div class='section' id='section-49'>
<div class='docs'>
<div class='section-link'>
<a href='#section-49'>#</a>
</div>
<p>Use character level tokenizer </p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">230</span> <span class="s1">&#39;tokenizer&#39;</span><span class="p">:</span> <span class="s1">&#39;character&#39;</span><span class="p">,</span></pre></div>
</div>
</div>
<div class='section' id='section-50'>
<div class='docs'>
<div class='section-link'>
<a href='#section-50'>#</a>
</div>
<p>Prompt separator is blank </p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">232</span> <span class="s1">&#39;prompt_separator&#39;</span><span class="p">:</span> <span class="s1">&#39;&#39;</span><span class="p">,</span></pre></div>
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<a href='#section-51'>#</a>
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<p>Starting prompt for sampling </p>
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<div class='code'>
<div class="highlight"><pre><span class="lineno">234</span> <span class="s1">&#39;prompt&#39;</span><span class="p">:</span> <span class="s1">&#39;It is &#39;</span><span class="p">,</span></pre></div>
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<p>Use Tiny Shakespeare dataset </p>
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<div class='code'>
<div class="highlight"><pre><span class="lineno">236</span> <span class="s1">&#39;text&#39;</span><span class="p">:</span> <span class="s1">&#39;tiny_shakespeare&#39;</span><span class="p">,</span></pre></div>
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<a href='#section-53'>#</a>
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<p>Use a context size of <span class="katex"><span aria-hidden="true" class="katex-html"><span class="base"><span class="strut" style="height:0.64444em;vertical-align:0em;"></span><span class="mord">128</span></span></span></span> </p>
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<div class="highlight"><pre><span class="lineno">239</span> <span class="s1">&#39;seq_len&#39;</span><span class="p">:</span> <span class="mi">128</span><span class="p">,</span></pre></div>
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<p>Train for <span class="katex"><span aria-hidden="true" class="katex-html"><span class="base"><span class="strut" style="height:0.64444em;vertical-align:0em;"></span><span class="mord">32</span></span></span></span> epochs </p>
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<div class="highlight"><pre><span class="lineno">241</span> <span class="s1">&#39;epochs&#39;</span><span class="p">:</span> <span class="mi">32</span><span class="p">,</span></pre></div>
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<a href='#section-55'>#</a>
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<p>Batch size <span class="katex"><span aria-hidden="true" class="katex-html"><span class="base"><span class="strut" style="height:0.64444em;vertical-align:0em;"></span><span class="mord">128</span></span></span></span> </p>
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<div class="highlight"><pre><span class="lineno">243</span> <span class="s1">&#39;batch_size&#39;</span><span class="p">:</span> <span class="mi">128</span><span class="p">,</span></pre></div>
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<a href='#section-56'>#</a>
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<p>Switch between training and validation for <span class="katex"><span aria-hidden="true" class="katex-html"><span class="base"><span class="strut" style="height:0.64444em;vertical-align:0em;"></span><span class="mord coloredeq eqf" style=""><span class="mord" style="">1</span><span class="mord coloredeq eqh" style=""><span class="mord" style="">0</span></span></span></span></span></span> times per epoch </p>
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<div class="highlight"><pre><span class="lineno">246</span> <span class="s1">&#39;inner_iterations&#39;</span><span class="p">:</span> <span class="mi">10</span><span class="p">,</span></pre></div>
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<p>Transformer configurations </p>
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<div class='code'>
<div class="highlight"><pre><span class="lineno">249</span> <span class="s1">&#39;transformer.d_model&#39;</span><span class="p">:</span> <span class="mi">512</span><span class="p">,</span>
<span class="lineno">250</span> <span class="s1">&#39;transformer.ffn.d_ff&#39;</span><span class="p">:</span> <span class="mi">2048</span><span class="p">,</span>
<span class="lineno">251</span> <span class="s1">&#39;transformer.n_heads&#39;</span><span class="p">:</span> <span class="mi">8</span><span class="p">,</span>
<span class="lineno">252</span> <span class="s1">&#39;transformer.n_layers&#39;</span><span class="p">:</span> <span class="mi">6</span>
<span class="lineno">253</span> <span class="p">})</span></pre></div>
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<p>Set models for saving and loading </p>
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<div class='code'>
<div class="highlight"><pre><span class="lineno">256</span> <span class="n">experiment</span><span class="o">.</span><span class="n">add_pytorch_models</span><span class="p">({</span><span class="s1">&#39;model&#39;</span><span class="p">:</span> <span class="n">conf</span><span class="o">.</span><span class="n">model</span><span class="p">})</span></pre></div>
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<div class='section' id='section-59'>
<div class='docs'>
<div class='section-link'>
<a href='#section-59'>#</a>
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<p>Start the experiment </p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">259</span> <span class="k">with</span> <span class="n">experiment</span><span class="o">.</span><span class="n">start</span><span class="p">():</span></pre></div>
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</div>
<div class='section' id='section-60'>
<div class='docs'>
<div class='section-link'>
<a href='#section-60'>#</a>
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<p>Run training </p>
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<div class='code'>
<div class="highlight"><pre><span class="lineno">261</span> <span class="n">conf</span><span class="o">.</span><span class="n">run</span><span class="p">()</span></pre></div>
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<div class='section' id='section-61'>
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<div class='section-link'>
<a href='#section-61'>#</a>
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<p> </p>
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<div class='code'>
<div class="highlight"><pre><span class="lineno">265</span><span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s1">&#39;__main__&#39;</span><span class="p">:</span>
<span class="lineno">266</span> <span class="n">main</span><span class="p">()</span></pre></div>
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