chore: import upstream snapshot with attribution

This commit is contained in:
wehub-resource-sync
2026-07-13 12:19:01 +08:00
commit 3b90d1192f
2172 changed files with 594509 additions and 0 deletions
@@ -0,0 +1,486 @@
<!DOCTYPE html>
<html lang="en">
<head>
<meta http-equiv="content-type" content="text/html;charset=utf-8"/>
<meta name="viewport" content="width=device-width, initial-scale=1.0"/>
<meta name="description" content="Train an auto-regressive transformer with Gated Linear Units and variants for the position-wise feedforward network (FFN)."/>
<meta name="twitter:card" content="summary"/>
<meta name="twitter:image:src" content="https://avatars1.githubusercontent.com/u/64068543?s=400&amp;v=4"/>
<meta name="twitter:title" content="Gated Linear Units and Variants"/>
<meta name="twitter:description" content="Train an auto-regressive transformer with Gated Linear Units and variants for the position-wise feedforward network (FFN)."/>
<meta name="twitter:site" content="@labmlai"/>
<meta name="twitter:creator" content="@labmlai"/>
<meta property="og:url" content="https://nn.labml.ai/transformers/glu_variants/experiment.html"/>
<meta property="og:title" content="Gated Linear Units and Variants"/>
<meta property="og:image" content="https://avatars1.githubusercontent.com/u/64068543?s=400&amp;v=4"/>
<meta property="og:site_name" content="Gated Linear Units and Variants"/>
<meta property="og:type" content="object"/>
<meta property="og:title" content="Gated Linear Units and Variants"/>
<meta property="og:description" content="Train an auto-regressive transformer with Gated Linear Units and variants for the position-wise feedforward network (FFN)."/>
<title>Gated Linear Units and Variants</title>
<link rel="shortcut icon" href="/icon.png"/>
<link rel="stylesheet" href="../../pylit.css?v=1">
<link rel="canonical" href="https://nn.labml.ai/transformers/glu_variants/experiment.html"/>
<link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/katex@0.13.18/dist/katex.min.css" integrity="sha384-zTROYFVGOfTw7JV7KUu8udsvW2fx4lWOsCEDqhBreBwlHI4ioVRtmIvEThzJHGET" crossorigin="anonymous">
<!-- Global site tag (gtag.js) - Google Analytics -->
<script async src="https://www.googletagmanager.com/gtag/js?id=G-4V3HC8HBLH"></script>
<script>
window.dataLayer = window.dataLayer || [];
function gtag() {
dataLayer.push(arguments);
}
gtag('js', new Date());
gtag('config', 'G-4V3HC8HBLH');
</script>
</head>
<body>
<div id='container'>
<div id="background"></div>
<div class='section'>
<div class='docs'>
<p>
<a class="parent" href="/">home</a>
<a class="parent" href="../index.html">transformers</a>
<a class="parent" href="index.html">glu_variants</a>
</p>
<p>
<a href="https://github.com/labmlai/annotated_deep_learning_paper_implementations" target="_blank">
<img alt="Github"
src="https://img.shields.io/github/stars/labmlai/annotated_deep_learning_paper_implementations?style=social"
style="max-width:100%;"/></a>
<a href="https://twitter.com/labmlai" rel="nofollow" target="_blank">
<img alt="Twitter"
src="https://img.shields.io/twitter/follow/labmlai?style=social"
style="max-width:100%;"/></a>
</p>
<p>
<a href="https://github.com/labmlai/annotated_deep_learning_paper_implementations/tree/master/labml_nn/transformers/glu_variants/experiment.py" target="_blank">
View code on Github</a>
</p>
</div>
</div>
<div class='section' id='section-0'>
<div class='docs doc-strings'>
<div class='section-link'>
<a href='#section-0'>#</a>
</div>
<h1>Gated Linear Units and Variants</h1>
<p>This trains a simple <a href="../../">transformer</a> model for auto-regression. We try different variants for the <a href="../feed_forward">position-wise feedforward network</a>. The reusable &amp; configurable are defined in <a href="configs.html"><code class="highlight"><span></span><span class="n">configs</span><span class="o">.</span><span class="n">py</span></code>
</a>.</p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">16</span><span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="lineno">17</span><span class="kn">from</span> <span class="nn">labml</span> <span class="kn">import</span> <span class="n">experiment</span>
<span class="lineno">18</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">19</span><span class="kn">from</span> <span class="nn">labml.utils.pytorch</span> <span class="kn">import</span> <span class="n">get_modules</span>
<span class="lineno">20</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">21</span><span class="kn">from</span> <span class="nn">labml_nn.transformers</span> <span class="kn">import</span> <span class="n">Encoder</span><span class="p">,</span> <span class="n">Generator</span><span class="p">,</span> <span class="n">TransformerConfigs</span>
<span class="lineno">22</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>
<span class="lineno">23</span><span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">nn</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>Auto regressive model</h2>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">26</span><span class="k">class</span> <span class="nc">AutoregressiveModel</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span></pre></div>
</div>
</div>
<div class='section' id='section-2'>
<div class='docs'>
<div class='section-link'>
<a href='#section-2'>#</a>
</div>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">31</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">src_embed</span><span class="p">:</span> <span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">,</span> <span class="n">encoder</span><span class="p">:</span> <span class="n">Encoder</span><span class="p">,</span> <span class="n">generator</span><span class="p">:</span> <span class="n">Generator</span><span class="p">):</span>
<span class="lineno">32</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span></pre></div>
</div>
</div>
<div class='section' id='section-3'>
<div class='docs'>
<div class='section-link'>
<a href='#section-3'>#</a>
</div>
<p>Token embedding module </p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">34</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></pre></div>
</div>
</div>
<div class='section' id='section-4'>
<div class='docs'>
<div class='section-link'>
<a href='#section-4'>#</a>
</div>
<p>Transformer based encoder </p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">36</span> <span class="bp">self</span><span class="o">.</span><span class="n">encoder</span> <span class="o">=</span> <span class="n">encoder</span></pre></div>
</div>
</div>
<div class='section' id='section-5'>
<div class='docs'>
<div class='section-link'>
<a href='#section-5'>#</a>
</div>
<p>Next token generation layer; this give logits of the the next token </p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">39</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-6'>
<div class='docs'>
<div class='section-link'>
<a href='#section-6'>#</a>
</div>
<p>This will be initialized on the first call </p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">41</span> <span class="bp">self</span><span class="o">.</span><span class="n">src_mask</span> <span class="o">=</span> <span class="kc">None</span></pre></div>
</div>
</div>
<div class='section' id='section-7'>
<div class='docs'>
<div class='section-link'>
<a href='#section-7'>#</a>
</div>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">43</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">src</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-8'>
<div class='docs'>
<div class='section-link'>
<a href='#section-8'>#</a>
</div>
<p>Create subsequent mask, so that the transformer can only pay attention to past tokens. </p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">45</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">src_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">src_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">src</span><span class="p">):</span>
<span class="lineno">46</span> <span class="bp">self</span><span class="o">.</span><span class="n">src_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">src</span><span class="p">))</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">src</span><span class="o">.</span><span class="n">device</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>Embed the tokens (<code class="highlight"><span></span><span class="n">src</span></code>
) and run it through the the transformer </p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">48</span> <span class="n">res</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="bp">self</span><span class="o">.</span><span class="n">src_embed</span><span class="p">(</span><span class="n">src</span><span class="p">),</span> <span class="bp">self</span><span class="o">.</span><span class="n">src_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>Generate logits of the next token </p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">50</span> <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">generator</span><span class="p">(</span><span class="n">res</span><span class="p">),</span> <span class="kc">None</span></pre></div>
</div>
</div>
<div class='section' id='section-11'>
<div class='docs doc-strings'>
<div class='section-link'>
<a href='#section-11'>#</a>
</div>
<h2>Configurations</h2>
<p>The default configs can and will be over-ridden when we start the experiment</p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">53</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-12'>
<div class='docs'>
<div class='section-link'>
<a href='#section-12'>#</a>
</div>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">60</span> <span class="n">transformer</span><span class="p">:</span> <span class="n">TransformerConfigs</span>
<span class="lineno">61</span> <span class="n">model</span><span class="p">:</span> <span class="n">AutoregressiveModel</span></pre></div>
</div>
</div>
<div class='section' id='section-13'>
<div class='docs doc-strings'>
<div class='section-link'>
<a href='#section-13'>#</a>
</div>
<p> Initialize the auto-regressive model</p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">64</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">65</span><span class="k">def</span> <span class="nf">autoregressive_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-14'>
<div class='docs'>
<div class='section-link'>
<a href='#section-14'>#</a>
</div>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">69</span> <span class="n">m</span> <span class="o">=</span> <span class="n">AutoregressiveModel</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">src_embed</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="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="lineno">70</span> <span class="k">return</span> <span class="n">m</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-15'>
<div class='docs doc-strings'>
<div class='section-link'>
<a href='#section-15'>#</a>
</div>
<p> Initialize the <a href="../configs.html">configurable transformer</a> encoder for our autoregressive model.</p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">73</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="lineno">74</span><span class="k">def</span> <span class="nf">transformer_c</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-16'>
<div class='docs'>
<div class='section-link'>
<a href='#section-16'>#</a>
</div>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">78</span> <span class="n">tc</span> <span class="o">=</span> <span class="n">TransformerConfigs</span><span class="p">()</span>
<span class="lineno">79</span> <span class="n">tc</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">80</span> <span class="n">tc</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>
<span class="lineno">81</span>
<span class="lineno">82</span> <span class="k">return</span> <span class="n">tc</span></pre></div>
</div>
</div>
<div class='section' id='section-17'>
<div class='docs'>
<div class='section-link'>
<a href='#section-17'>#</a>
</div>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">85</span><span class="k">def</span> <span class="nf">main</span><span class="p">():</span></pre></div>
</div>
</div>
<div class='section' id='section-18'>
<div class='docs'>
<div class='section-link'>
<a href='#section-18'>#</a>
</div>
<p>Create experiment </p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">87</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;glu_variants&quot;</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>Create configs </p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">89</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-20'>
<div class='docs'>
<div class='section-link'>
<a href='#section-20'>#</a>
</div>
<p>Load configurations </p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">91</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></pre></div>
</div>
</div>
<div class='section' id='section-21'>
<div class='docs'>
<div class='section-link'>
<a href='#section-21'>#</a>
</div>
<p>A dictionary of configurations to override </p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">93</span> <span class="p">{</span><span class="s1">&#39;tokenizer&#39;</span><span class="p">:</span> <span class="s1">&#39;character&#39;</span><span class="p">,</span>
<span class="lineno">94</span> <span class="s1">&#39;prompt_separator&#39;</span><span class="p">:</span> <span class="s1">&#39;&#39;</span><span class="p">,</span>
<span class="lineno">95</span> <span class="s1">&#39;prompt&#39;</span><span class="p">:</span> <span class="s1">&#39;It is &#39;</span><span class="p">,</span>
<span class="lineno">96</span> <span class="s1">&#39;text&#39;</span><span class="p">:</span> <span class="s1">&#39;tiny_shakespeare&#39;</span><span class="p">,</span>
<span class="lineno">97</span>
<span class="lineno">98</span> <span class="s1">&#39;optimizer.optimizer&#39;</span><span class="p">:</span> <span class="s1">&#39;Noam&#39;</span><span class="p">,</span>
<span class="lineno">99</span> <span class="s1">&#39;optimizer.learning_rate&#39;</span><span class="p">:</span> <span class="mf">1.</span><span class="p">,</span>
<span class="lineno">100</span> <span class="s1">&#39;optimizer.d_model&#39;</span><span class="p">:</span> <span class="mi">256</span><span class="p">,</span>
<span class="lineno">101</span>
<span class="lineno">102</span> <span class="s1">&#39;seq_len&#39;</span><span class="p">:</span> <span class="mi">1024</span><span class="p">,</span>
<span class="lineno">103</span> <span class="s1">&#39;epochs&#39;</span><span class="p">:</span> <span class="mi">128</span><span class="p">,</span>
<span class="lineno">104</span> <span class="s1">&#39;batch_size&#39;</span><span class="p">:</span> <span class="mi">6</span><span class="p">,</span>
<span class="lineno">105</span> <span class="s1">&#39;inner_iterations&#39;</span><span class="p">:</span> <span class="mi">10</span><span class="p">,</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>GLU Variant, one of GLU, Bilinear, ReGLU, GEGLU, SwiGLU</p>
<p>These are defined in the <a href="../configs.html#FFN">configurable FFN</a> implementation </p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">111</span> <span class="s1">&#39;transformer.ffn.glu_variant&#39;</span><span class="p">:</span> <span class="s1">&#39;Bilinear&#39;</span><span class="p">,</span></pre></div>
</div>
</div>
<div class='section' id='section-23'>
<div class='docs'>
<div class='section-link'>
<a href='#section-23'>#</a>
</div>
<p>Transformer configurations </p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">114</span> <span class="s1">&#39;transformer.d_model&#39;</span><span class="p">:</span> <span class="mi">256</span><span class="p">,</span>
<span class="lineno">115</span> <span class="s1">&#39;transformer.ffn.d_ff&#39;</span><span class="p">:</span> <span class="mi">1024</span><span class="p">,</span>
<span class="lineno">116</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">117</span> <span class="s1">&#39;transformer.n_layers&#39;</span><span class="p">:</span> <span class="mi">6</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>
<p>This is needed to initialize models </p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">120</span> <span class="n">conf</span><span class="o">.</span><span class="n">n_tokens</span> <span class="o">=</span> <span class="n">conf</span><span class="o">.</span><span class="n">text</span><span class="o">.</span><span class="n">n_tokens</span></pre></div>
</div>
</div>
<div class='section' id='section-25'>
<div class='docs'>
<div class='section-link'>
<a href='#section-25'>#</a>
</div>
<p>Set models for saving and loading </p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">123</span> <span class="n">experiment</span><span class="o">.</span><span class="n">add_pytorch_models</span><span class="p">(</span><span class="n">get_modules</span><span class="p">(</span><span class="n">conf</span><span class="p">))</span></pre></div>
</div>
</div>
<div class='section' id='section-26'>
<div class='docs'>
<div class='section-link'>
<a href='#section-26'>#</a>
</div>
<p>Start the experiment </p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">126</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>
</div>
</div>
<div class='section' id='section-27'>
<div class='docs'>
<div class='section-link'>
<a href='#section-27'>#</a>
</div>
<p><code class="highlight"><span></span><span class="n">TrainValidConfigs</span><span class="o">.</span><span class="n">run</span></code>
</p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">128</span> <span class="n">conf</span><span class="o">.</span><span class="n">run</span><span class="p">()</span>
<span class="lineno">129</span>
<span class="lineno">130</span>
<span class="lineno">131</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">132</span> <span class="n">main</span><span class="p">()</span></pre></div>
</div>
</div>
<div class='footer'>
<a href="https://labml.ai">labml.ai</a>
</div>
</div>
<script src=../../interactive.js?v=1"></script>
<script>
function handleImages() {
var images = document.querySelectorAll('p>img')
for (var i = 0; i < images.length; ++i) {
handleImage(images[i])
}
}
function handleImage(img) {
img.parentElement.style.textAlign = 'center'
var modal = document.createElement('div')
modal.id = 'modal'
var modalContent = document.createElement('div')
modal.appendChild(modalContent)
var modalImage = document.createElement('img')
modalContent.appendChild(modalImage)
var span = document.createElement('span')
span.classList.add('close')
span.textContent = 'x'
modal.appendChild(span)
img.onclick = function () {
console.log('clicked')
document.body.appendChild(modal)
modalImage.src = img.src
}
span.onclick = function () {
document.body.removeChild(modal)
}
}
handleImages()
</script>
</body>
</html>
+129
View File
@@ -0,0 +1,129 @@
<!DOCTYPE html>
<html lang="en">
<head>
<meta http-equiv="content-type" content="text/html;charset=utf-8"/>
<meta name="viewport" content="width=device-width, initial-scale=1.0"/>
<meta name="description" content="Train an auto-regressive transformer with Gated Linear Units and variants for the position-wise feedforward network (FFN)."/>
<meta name="twitter:card" content="summary"/>
<meta name="twitter:image:src" content="https://avatars1.githubusercontent.com/u/64068543?s=400&amp;v=4"/>
<meta name="twitter:title" content="Gated Linear Units and Variants"/>
<meta name="twitter:description" content="Train an auto-regressive transformer with Gated Linear Units and variants for the position-wise feedforward network (FFN)."/>
<meta name="twitter:site" content="@labmlai"/>
<meta name="twitter:creator" content="@labmlai"/>
<meta property="og:url" content="https://nn.labml.ai/transformers/glu_variants/index.html"/>
<meta property="og:title" content="Gated Linear Units and Variants"/>
<meta property="og:image" content="https://avatars1.githubusercontent.com/u/64068543?s=400&amp;v=4"/>
<meta property="og:site_name" content="Gated Linear Units and Variants"/>
<meta property="og:type" content="object"/>
<meta property="og:title" content="Gated Linear Units and Variants"/>
<meta property="og:description" content="Train an auto-regressive transformer with Gated Linear Units and variants for the position-wise feedforward network (FFN)."/>
<title>Gated Linear Units and Variants</title>
<link rel="shortcut icon" href="/icon.png"/>
<link rel="stylesheet" href="../../pylit.css?v=1">
<link rel="canonical" href="https://nn.labml.ai/transformers/glu_variants/index.html"/>
<link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/katex@0.13.18/dist/katex.min.css" integrity="sha384-zTROYFVGOfTw7JV7KUu8udsvW2fx4lWOsCEDqhBreBwlHI4ioVRtmIvEThzJHGET" crossorigin="anonymous">
<!-- Global site tag (gtag.js) - Google Analytics -->
<script async src="https://www.googletagmanager.com/gtag/js?id=G-4V3HC8HBLH"></script>
<script>
window.dataLayer = window.dataLayer || [];
function gtag() {
dataLayer.push(arguments);
}
gtag('js', new Date());
gtag('config', 'G-4V3HC8HBLH');
</script>
</head>
<body>
<div id='container'>
<div id="background"></div>
<div class='section'>
<div class='docs'>
<p>
<a class="parent" href="/">home</a>
<a class="parent" href="../index.html">transformers</a>
<a class="parent" href="index.html">glu_variants</a>
</p>
<p>
<a href="https://github.com/labmlai/annotated_deep_learning_paper_implementations" target="_blank">
<img alt="Github"
src="https://img.shields.io/github/stars/labmlai/annotated_deep_learning_paper_implementations?style=social"
style="max-width:100%;"/></a>
<a href="https://twitter.com/labmlai" rel="nofollow" target="_blank">
<img alt="Twitter"
src="https://img.shields.io/twitter/follow/labmlai?style=social"
style="max-width:100%;"/></a>
</p>
<p>
<a href="https://github.com/labmlai/annotated_deep_learning_paper_implementations/tree/master/labml_nn/transformers/glu_variants/__init__.py" target="_blank">
View code on Github</a>
</p>
</div>
</div>
<div class='section' id='section-0'>
<div class='docs doc-strings'>
<div class='section-link'>
<a href='#section-0'>#</a>
</div>
<h1>Gated Linear Units and Variants</h1>
<ul><li><a href="experiment.html">Experiment that uses <code class="highlight"><span></span><span class="n">labml</span><span class="o">.</span><span class="n">configs</span></code>
</a> </li>
<li><a href="simple.html">Simpler version from scratch</a></li></ul>
</div>
<div class='code'>
<div class="highlight"><pre></pre></div>
</div>
</div>
<div class='footer'>
<a href="https://labml.ai">labml.ai</a>
</div>
</div>
<script src=../../interactive.js?v=1"></script>
<script>
function handleImages() {
var images = document.querySelectorAll('p>img')
for (var i = 0; i < images.length; ++i) {
handleImage(images[i])
}
}
function handleImage(img) {
img.parentElement.style.textAlign = 'center'
var modal = document.createElement('div')
modal.id = 'modal'
var modalContent = document.createElement('div')
modal.appendChild(modalContent)
var modalImage = document.createElement('img')
modalContent.appendChild(modalImage)
var span = document.createElement('span')
span.classList.add('close')
span.textContent = 'x'
modal.appendChild(span)
img.onclick = function () {
console.log('clicked')
document.body.appendChild(modal)
modalImage.src = img.src
}
span.onclick = function () {
document.body.removeChild(modal)
}
}
handleImages()
</script>
</body>
</html>
File diff suppressed because one or more lines are too long