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<h1><a href="index.html">MLP Mixer</a> Experiment</h1>
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<p>This is an annotated PyTorch experiment to train a <a href="index.html">MLP Mixer Model</a>.</p>
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</div>
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<div class='code'>
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<div class="highlight"><pre><span class="lineno">12</span><span></span><span class="kn">from</span> <span class="nn">labml</span> <span class="kn">import</span> <span class="n">experiment</span>
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<span class="lineno">13</span><span class="kn">from</span> <span class="nn">labml.configs</span> <span class="kn">import</span> <span class="n">option</span>
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<span class="lineno">14</span><span class="kn">from</span> <span class="nn">labml_nn.transformers</span> <span class="kn">import</span> <span class="n">TransformerConfigs</span>
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<span class="lineno">15</span><span class="kn">from</span> <span class="nn">labml_nn.transformers.configs</span> <span class="kn">import</span> <span class="n">FeedForwardConfigs</span>
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<span class="lineno">16</span><span class="kn">from</span> <span class="nn">labml_nn.transformers.mlm.experiment</span> <span class="kn">import</span> <span class="n">TransformerMLM</span><span class="p">,</span> <span class="n">Configs</span> <span class="k">as</span> <span class="n">MLMConfigs</span></pre></div>
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</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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<h2>Configurations</h2>
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<p>This inherits from
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<a href="../mlm/experiment.html"><code>MLMConfigs</code></a> where we define an experiment for
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<a href="../mlm.index.html">Masked Language Models</a>.</p>
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</div>
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<div class='code'>
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<div class="highlight"><pre><span class="lineno">19</span><span class="k">class</span> <span class="nc">Configs</span><span class="p">(</span><span class="n">MLMConfigs</span><span class="p">):</span></pre></div>
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</div>
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</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>
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<p>Configurable <a href="../feed_forward.html">Feed-Forward Network</a> for the MLP</p>
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</div>
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<div class='code'>
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<div class="highlight"><pre><span class="lineno">29</span> <span class="n">mix_mlp</span><span class="p">:</span> <span class="n">FeedForwardConfigs</span></pre></div>
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</div>
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</div>
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<div class='section' id='section-3'>
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<div class='docs doc-strings'>
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<div class='section-link'>
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<a href='#section-3'>#</a>
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</div>
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<p>The mixing MLP configurations</p>
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</div>
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<div class='code'>
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<div class="highlight"><pre><span class="lineno">32</span><span class="nd">@option</span><span class="p">(</span><span class="n">Configs</span><span class="o">.</span><span class="n">mix_mlp</span><span class="p">)</span>
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<span class="lineno">33</span><span class="k">def</span> <span class="nf">_mix_mlp_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>
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</div>
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</div>
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<div class='section' id='section-4'>
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<div class='docs'>
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<div class='section-link'>
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<a href='#section-4'>#</a>
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</div>
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</div>
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<div class='code'>
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<div class="highlight"><pre><span class="lineno">38</span> <span class="n">conf</span> <span class="o">=</span> <span class="n">FeedForwardConfigs</span><span class="p">()</span></pre></div>
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</div>
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</div>
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<div class='section' id='section-5'>
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<div class='docs'>
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<div class='section-link'>
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<a href='#section-5'>#</a>
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</div>
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<p>Size of the MLP is the sequence length, because it is applied across tokens</p>
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</div>
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<div class='code'>
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<div class="highlight"><pre><span class="lineno">40</span> <span class="n">conf</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">seq_len</span></pre></div>
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</div>
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</div>
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<div class='section' id='section-6'>
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<div class='docs'>
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<div class='section-link'>
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<a href='#section-6'>#</a>
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</div>
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<p>The paper suggests $GELU$ activation</p>
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</div>
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<div class='code'>
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<div class="highlight"><pre><span class="lineno">42</span> <span class="n">conf</span><span class="o">.</span><span class="n">activation</span> <span class="o">=</span> <span class="s1">'GELU'</span></pre></div>
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</div>
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</div>
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<div class='section' id='section-7'>
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<div class='docs'>
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<div class='section-link'>
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<a href='#section-7'>#</a>
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</div>
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</div>
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<div class='code'>
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<div class="highlight"><pre><span class="lineno">45</span> <span class="k">return</span> <span class="n">conf</span></pre></div>
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</div>
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</div>
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<div class='section' id='section-8'>
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<div class='docs doc-strings'>
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<div class='section-link'>
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<a href='#section-8'>#</a>
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</div>
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<h3>Transformer configurations</h3>
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</div>
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<div class='code'>
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<div class="highlight"><pre><span class="lineno">48</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>
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<span class="lineno">49</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>
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</div>
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</div>
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<div class='section' id='section-9'>
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<div class='docs'>
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<div class='section-link'>
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<a href='#section-9'>#</a>
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</div>
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<p>We use our
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<a href="../configs.html#TransformerConfigs">configurable transformer implementation</a></p>
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</div>
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<div class='code'>
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<div class="highlight"><pre><span class="lineno">56</span> <span class="n">conf</span> <span class="o">=</span> <span class="n">TransformerConfigs</span><span class="p">()</span></pre></div>
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</div>
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</div>
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<div class='section' id='section-10'>
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<div class='docs'>
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<div class='section-link'>
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<a href='#section-10'>#</a>
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</div>
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<p>Set the vocabulary sizes for embeddings and generating logits</p>
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</div>
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<div class='code'>
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<div class="highlight"><pre><span class="lineno">58</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>
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<span class="lineno">59</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>
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</div>
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</div>
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<div class='section' id='section-11'>
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<div class='docs'>
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<div class='section-link'>
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<a href='#section-11'>#</a>
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</div>
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<p>Embedding size</p>
|
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</div>
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<div class='code'>
|
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<div class="highlight"><pre><span class="lineno">61</span> <span class="n">conf</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>
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</div>
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</div>
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<div class='section' id='section-12'>
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<div class='docs'>
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<div class='section-link'>
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<a href='#section-12'>#</a>
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</div>
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<p>Change attention module to <a href="index.html">MLPMixer</a></p>
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</div>
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<div class='code'>
|
||||
<div class="highlight"><pre><span class="lineno">63</span> <span class="kn">from</span> <span class="nn">labml_nn.transformers.mlp_mixer</span> <span class="kn">import</span> <span class="n">MLPMixer</span>
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<span class="lineno">64</span> <span class="n">conf</span><span class="o">.</span><span class="n">encoder_attn</span> <span class="o">=</span> <span class="n">MLPMixer</span><span class="p">(</span><span class="n">c</span><span class="o">.</span><span class="n">mix_mlp</span><span class="o">.</span><span class="n">ffn</span><span class="p">)</span></pre></div>
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</div>
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</div>
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<div class='section' id='section-13'>
|
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<div class='docs'>
|
||||
<div class='section-link'>
|
||||
<a href='#section-13'>#</a>
|
||||
</div>
|
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</div>
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<div class='code'>
|
||||
<div class="highlight"><pre><span class="lineno">67</span> <span class="k">return</span> <span class="n">conf</span></pre></div>
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</div>
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</div>
|
||||
<div class='section' id='section-14'>
|
||||
<div class='docs'>
|
||||
<div class='section-link'>
|
||||
<a href='#section-14'>#</a>
|
||||
</div>
|
||||
|
||||
</div>
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<div class='code'>
|
||||
<div class="highlight"><pre><span class="lineno">70</span><span class="k">def</span> <span class="nf">main</span><span class="p">():</span></pre></div>
|
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</div>
|
||||
</div>
|
||||
<div class='section' id='section-15'>
|
||||
<div class='docs'>
|
||||
<div class='section-link'>
|
||||
<a href='#section-15'>#</a>
|
||||
</div>
|
||||
<p>Create experiment</p>
|
||||
</div>
|
||||
<div class='code'>
|
||||
<div class="highlight"><pre><span class="lineno">72</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">"mlp_mixer_mlm"</span><span class="p">)</span></pre></div>
|
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</div>
|
||||
</div>
|
||||
<div class='section' id='section-16'>
|
||||
<div class='docs'>
|
||||
<div class='section-link'>
|
||||
<a href='#section-16'>#</a>
|
||||
</div>
|
||||
<p>Create configs</p>
|
||||
</div>
|
||||
<div class='code'>
|
||||
<div class="highlight"><pre><span class="lineno">74</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-17'>
|
||||
<div class='docs'>
|
||||
<div class='section-link'>
|
||||
<a href='#section-17'>#</a>
|
||||
</div>
|
||||
<p>Override configurations</p>
|
||||
</div>
|
||||
<div class='code'>
|
||||
<div class="highlight"><pre><span class="lineno">76</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-18'>
|
||||
<div class='docs'>
|
||||
<div class='section-link'>
|
||||
<a href='#section-18'>#</a>
|
||||
</div>
|
||||
<p>Batch size</p>
|
||||
</div>
|
||||
<div class='code'>
|
||||
<div class="highlight"><pre><span class="lineno">78</span> <span class="s1">'batch_size'</span><span class="p">:</span> <span class="mi">64</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>Sequence length of $32$. We use a short sequence length to train faster.
|
||||
Otherwise MLM models take forever to train.</p>
|
||||
</div>
|
||||
<div class='code'>
|
||||
<div class="highlight"><pre><span class="lineno">81</span> <span class="s1">'seq_len'</span><span class="p">:</span> <span class="mi">32</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>Train for 1024 epochs.</p>
|
||||
</div>
|
||||
<div class='code'>
|
||||
<div class="highlight"><pre><span class="lineno">84</span> <span class="s1">'epochs'</span><span class="p">:</span> <span class="mi">1024</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>Switch between training and validation for $1$ times
|
||||
per epoch</p>
|
||||
</div>
|
||||
<div class='code'>
|
||||
<div class="highlight"><pre><span class="lineno">87</span> <span class="s1">'inner_iterations'</span><span class="p">:</span> <span class="mi">1</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>Transformer configurations</p>
|
||||
</div>
|
||||
<div class='code'>
|
||||
<div class="highlight"><pre><span class="lineno">90</span> <span class="s1">'d_model'</span><span class="p">:</span> <span class="mi">128</span><span class="p">,</span>
|
||||
<span class="lineno">91</span> <span class="s1">'transformer.ffn.d_ff'</span><span class="p">:</span> <span class="mi">256</span><span class="p">,</span>
|
||||
<span class="lineno">92</span> <span class="s1">'transformer.n_heads'</span><span class="p">:</span> <span class="mi">8</span><span class="p">,</span>
|
||||
<span class="lineno">93</span> <span class="s1">'transformer.n_layers'</span><span class="p">:</span> <span class="mi">6</span><span class="p">,</span>
|
||||
<span class="lineno">94</span> <span class="s1">'transformer.ffn.activation'</span><span class="p">:</span> <span class="s1">'GELU'</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>Mixer MLP hidden layer size</p>
|
||||
</div>
|
||||
<div class='code'>
|
||||
<div class="highlight"><pre><span class="lineno">97</span> <span class="s1">'mix_mlp.d_ff'</span><span class="p">:</span> <span class="mi">128</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>Use <a href="../../optimizers/noam.html">Noam optimizer</a></p>
|
||||
</div>
|
||||
<div class='code'>
|
||||
<div class="highlight"><pre><span class="lineno">100</span> <span class="s1">'optimizer.optimizer'</span><span class="p">:</span> <span class="s1">'Noam'</span><span class="p">,</span>
|
||||
<span class="lineno">101</span> <span class="s1">'optimizer.learning_rate'</span><span class="p">:</span> <span class="mf">1.</span><span class="p">,</span>
|
||||
<span class="lineno">102</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>
|
||||
</div>
|
||||
<p>Set models for saving and loading</p>
|
||||
</div>
|
||||
<div class='code'>
|
||||
<div class="highlight"><pre><span class="lineno">105</span> <span class="n">experiment</span><span class="o">.</span><span class="n">add_pytorch_models</span><span class="p">({</span><span class="s1">'model'</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>
|
||||
</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">108</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>Run training</p>
|
||||
</div>
|
||||
<div class='code'>
|
||||
<div class="highlight"><pre><span class="lineno">110</span> <span class="n">conf</span><span class="o">.</span><span class="n">run</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>
|
||||
|
||||
</div>
|
||||
<div class='code'>
|
||||
<div class="highlight"><pre><span class="lineno">114</span><span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s1">'__main__'</span><span class="p">:</span>
|
||||
<span class="lineno">115</span> <span class="n">main</span><span class="p">()</span></pre></div>
|
||||
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|
||||
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|
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|
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<a href="https://papers.labml.ai">Trending Research Papers</a>
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<a href="https://github.com/labmlai/annotated_deep_learning_paper_implementations/tree/master/labml_nn/transformers/mlp_mixer/__init__.py">
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|
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<div class='docs doc-strings'>
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<div class='section-link'>
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<a href='#section-0'>#</a>
|
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</div>
|
||||
<h1>MLP-Mixer: An all-MLP Architecture for Vision</h1>
|
||||
<p>This is a <a href="https://pytorch.org">PyTorch</a> implementation of the paper
|
||||
<a href="https://papers.labml.ai/paper/2105.01601">MLP-Mixer: An all-MLP Architecture for Vision</a>.</p>
|
||||
<p>This paper applies the model on vision tasks.
|
||||
The model is similar to a transformer with attention layer being replaced by a MLP
|
||||
that is applied across the patches (or tokens in case of a NLP task).</p>
|
||||
<p>Our implementation of MLP Mixer is a drop in replacement for the <a href="../mha.html">self-attention layer</a>
|
||||
in <a href="../models.html">our transformer implementation</a>.
|
||||
So it’s just a couple of lines of code, transposing the tensor to apply the MLP
|
||||
across the sequence dimension.</p>
|
||||
<p>Although the paper applied MLP Mixer on vision tasks,
|
||||
we tried it on a <a href="../mlm/index.html">masked language model</a>.
|
||||
<a href="experiment.html">Here is the experiment code</a>.</p>
|
||||
<p><a href="https://app.labml.ai/run/994263d2cdb511eb961e872301f0dbab"><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">29</span><span></span><span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Optional</span>
|
||||
<span class="lineno">30</span>
|
||||
<span class="lineno">31</span><span class="kn">import</span> <span class="nn">torch</span>
|
||||
<span class="lineno">32</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>MLP Mixer</h2>
|
||||
<p>This module is a drop-in replacement for <a href="../mha.html">self-attention layer</a>.
|
||||
It transposes the input tensor before feeding it to the MLP and transposes back,
|
||||
so that the MLP is applied across the sequence dimension (across tokens or image patches) instead
|
||||
of the feature dimension.</p>
|
||||
</div>
|
||||
<div class='code'>
|
||||
<div class="highlight"><pre><span class="lineno">35</span><span class="k">class</span> <span class="nc">MLPMixer</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 doc-strings'>
|
||||
<div class='section-link'>
|
||||
<a href='#section-2'>#</a>
|
||||
</div>
|
||||
<ul>
|
||||
<li><code>ffn</code> is the MLP module.</li>
|
||||
</ul>
|
||||
</div>
|
||||
<div class='code'>
|
||||
<div class="highlight"><pre><span class="lineno">45</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">mlp</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-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">49</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">50</span> <span class="bp">self</span><span class="o">.</span><span class="n">mlp</span> <span class="o">=</span> <span class="n">mlp</span></pre></div>
|
||||
</div>
|
||||
</div>
|
||||
<div class='section' id='section-4'>
|
||||
<div class='docs doc-strings'>
|
||||
<div class='section-link'>
|
||||
<a href='#section-4'>#</a>
|
||||
</div>
|
||||
<p>The <a href="../mha.html">normal attention module</a> can be fed with different token embeddings for
|
||||
$\text{query}$,$\text{key}$, and $\text{value}$ and a mask.</p>
|
||||
<p>We follow the same function signature so that we can replace it directly.</p>
|
||||
<p>For MLP mixing, <script type="math/tex; mode=display">x = \text{query} = \text{key} = \text{value}</script> and masking is not possible.
|
||||
Shape of <code>query</code> (and <code>key</code> and <code>value</code>) is <code>[seq_len, batch_size, d_model]</code>.</p>
|
||||
</div>
|
||||
<div class='code'>
|
||||
<div class="highlight"><pre><span class="lineno">52</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">query</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">key</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">value</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">mask</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">):</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>$\text{query}$,$\text{key}$, and $\text{value}$ all should be the same</p>
|
||||
</div>
|
||||
<div class='code'>
|
||||
<div class="highlight"><pre><span class="lineno">64</span> <span class="k">assert</span> <span class="n">query</span> <span class="ow">is</span> <span class="n">key</span> <span class="ow">and</span> <span class="n">key</span> <span class="ow">is</span> <span class="n">value</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>MLP mixer doesn’t support masking. i.e. all tokens will see all other token embeddings.</p>
|
||||
</div>
|
||||
<div class='code'>
|
||||
<div class="highlight"><pre><span class="lineno">66</span> <span class="k">assert</span> <span class="n">mask</span> <span class="ow">is</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>
|
||||
<p>Assign to <code>x</code> for clarity</p>
|
||||
</div>
|
||||
<div class='code'>
|
||||
<div class="highlight"><pre><span class="lineno">69</span> <span class="n">x</span> <span class="o">=</span> <span class="n">query</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>Transpose so that the last dimension is the sequence dimension.
|
||||
New shape is <code>[d_model, batch_size, seq_len]</code></p>
|
||||
</div>
|
||||
<div class='code'>
|
||||
<div class="highlight"><pre><span class="lineno">73</span> <span class="n">x</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</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>Apply the MLP across tokens</p>
|
||||
</div>
|
||||
<div class='code'>
|
||||
<div class="highlight"><pre><span class="lineno">75</span> <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">mlp</span><span class="p">(</span><span class="n">x</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>Transpose back into original form</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="n">x</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</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>
|
||||
</div>
|
||||
|
||||
</div>
|
||||
<div class='code'>
|
||||
<div class="highlight"><pre><span class="lineno">80</span> <span class="k">return</span> <span class="n">x</span></pre></div>
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<h1><a href="https://nn.labml.ai/transformers/mlp_mixer/index.html">MLP-Mixer: An all-MLP Architecture for Vision</a></h1>
|
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<p>This is a <a href="https://pytorch.org">PyTorch</a> implementation of the paper
|
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<a href="https://papers.labml.ai/paper/2105.01601">MLP-Mixer: An all-MLP Architecture for Vision</a>.</p>
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<p>This paper applies the model on vision tasks.
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The model is similar to a transformer with attention layer being replaced by a MLP
|
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that is applied across the patches (or tokens in case of a NLP task).</p>
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<p>Our implementation of MLP Mixer is a drop in replacement for the <a href="https://nn.labml.ai/transformers/mha.html">self-attention layer</a>
|
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in <a href="https://nn.labml.ai/transformers/models.html">our transformer implementation</a>.
|
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So it’s just a couple of lines of code, transposing the tensor to apply the MLP
|
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across the sequence dimension.</p>
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<p>Although the paper applied MLP Mixer on vision tasks,
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we tried it on a <a href="https://nn.labml.ai/transformers/mlm/index.html">masked language model</a>.
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<a href="https://nn.labml.ai/transformers/mlp_mixer/experiment.html">Here is the experiment code</a>.</p>
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<p><a href="https://app.labml.ai/run/994263d2cdb511eb961e872301f0dbab"><img alt="View Run" src="https://img.shields.io/badge/labml-experiment-brightgreen" /></a></p>
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