links

Author: vpj

docs/index.html

@@ -90,7 +90,8 @@ <h4>✨ <a href="transformers/index.html">Transformers</a></h4>
 <li><a href="transformers/mlp_mixer/index.html">MLP-Mixer: An all-MLP Architecture for Vision</a> </li>
 <li><a href="transformers/gmlp/index.html">Pay Attention to MLPs (gMLP)</a> </li>
 <li><a href="transformers/vit/index.html">Vision Transformer (ViT)</a> </li>
-<li><a href="transformers/primer_ez/index.html">Primer EZ</a></li></ul>
+<li><a href="transformers/primer_ez/index.html">Primer EZ</a> </li>
+<li><a href="transformers/hourglass/index.html">Hourglass</a></li></ul>
 <h4>✨ <a href="recurrent_highway_networks/index.html">Recurrent Highway Networks</a></h4>
 <h4>✨ <a href="lstm/index.html">LSTM</a></h4>
 <h4>✨ <a href="hypernetworks/hyper_lstm.html">HyperNetworks - HyperLSTM</a></h4>

docs/transformers/index.html

@@ -104,13 +104,15 @@ <h2><a href="vit/index.html">Vision Transformer (ViT)</a></h2>
 <p>This is an implementation of the paper <a href="https://papers.labml.ai/paper/2010.11929">An Image Is Worth 16x16 Words: Transformers For Image Recognition At Scale</a>.</p>
 <h2><a href="primer_ez/index.html">Primer EZ</a></h2>
 <p>This is an implementation of the paper <a href="https://papers.labml.ai/paper/2109.08668">Primer: Searching for Efficient Transformers for Language Modeling</a>.</p>
+<h2><a href="hour_glass/index.html">Hourglass</a></h2>
+<p>This is an implementation of the paper <a href="https://papers.labml.ai/paper/2110.13711">Hierarchical Transformers Are More Efficient Language Models</a></p>
 
         </div>
         <div class='code'>
-            <div class="highlight"><pre><span class="lineno">98</span><span></span><span class="kn">from</span> <span class="nn">.configs</span> <span class="kn">import</span> <span class="n">TransformerConfigs</span>
-<span class="lineno">99</span><span class="kn">from</span> <span class="nn">.models</span> <span class="kn">import</span> <span class="n">TransformerLayer</span><span class="p">,</span> <span class="n">Encoder</span><span class="p">,</span> <span class="n">Decoder</span><span class="p">,</span> <span class="n">Generator</span><span class="p">,</span> <span class="n">EncoderDecoder</span>
-<span class="lineno">100</span><span class="kn">from</span> <span class="nn">.mha</span> <span class="kn">import</span> <span class="n">MultiHeadAttention</span>
-<span class="lineno">101</span><span class="kn">from</span> <span class="nn">labml_nn.transformers.xl.relative_mha</span> <span class="kn">import</span> <span class="n">RelativeMultiHeadAttention</span></pre></div>
+            <div class="highlight"><pre><span class="lineno">103</span><span></span><span class="kn">from</span> <span class="nn">.configs</span> <span class="kn">import</span> <span class="n">TransformerConfigs</span>
+<span class="lineno">104</span><span class="kn">from</span> <span class="nn">.models</span> <span class="kn">import</span> <span class="n">TransformerLayer</span><span class="p">,</span> <span class="n">Encoder</span><span class="p">,</span> <span class="n">Decoder</span><span class="p">,</span> <span class="n">Generator</span><span class="p">,</span> <span class="n">EncoderDecoder</span>
+<span class="lineno">105</span><span class="kn">from</span> <span class="nn">.mha</span> <span class="kn">import</span> <span class="n">MultiHeadAttention</span>
+<span class="lineno">106</span><span class="kn">from</span> <span class="nn">labml_nn.transformers.xl.relative_mha</span> <span class="kn">import</span> <span class="n">RelativeMultiHeadAttention</span></pre></div>
         </div>
     </div>
     <div class='footer'>

labml_nn/__init__.py

@@ -36,6 +36,7 @@
 * [Pay Attention to MLPs (gMLP)](transformers/gmlp/index.html)
 * [Vision Transformer (ViT)](transformers/vit/index.html)
 * [Primer EZ](transformers/primer_ez/index.html)
+* [Hourglass](transformers/hourglass/index.html)
 
 #### ✨ [Recurrent Highway Networks](recurrent_highway_networks/index.html)
 

labml_nn/transformers/__init__.py

@@ -93,6 +93,11 @@
 
 This is an implementation of the paper
 [Primer: Searching for Efficient Transformers for Language Modeling](https://papers.labml.ai/paper/2109.08668).
+
+## [Hourglass](hour_glass/index.html)
+
+This is an implementation of the paper
+[Hierarchical Transformers Are More Efficient Language Models](https://papers.labml.ai/paper/2110.13711)
 """
 
 from .configs import TransformerConfigs

readme.md

@@ -38,6 +38,7 @@ implementations almost weekly.
 * [Pay Attention to MLPs (gMLP)](https://nn.labml.ai/transformers/gmlp/index.html)
 * [Vision Transformer (ViT)](https://nn.labml.ai/transformers/vit/index.html)
 * [Primer EZ](https://nn.labml.ai/transformers/primer_ez/index.html)
+* [Hourglass](https://nn.labml.ai/transformers/hourglass/index.html)
 
 #### ✨ [Recurrent Highway Networks](https://nn.labml.ai/recurrent_highway_networks/index.html)