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@ -96,6 +96,7 @@ implementations.</p>
<li><a href="transformers/mlp_mixer/index.html">MLP-Mixer: An all-MLP Architecture for Vision</a></li> <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/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/vit/index.html">Vision Transformer (ViT)</a></li>
<li><a href="transformers/primer_ez/index.html">Primer EZ</a></li>
</ul> </ul>
<h4>✨ <a href="recurrent_highway_networks/index.html">Recurrent Highway Networks</a></h4> <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="lstm/index.html">LSTM</a></h4>

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@ -121,12 +121,15 @@ It does single GPU training but we implement the concept of switching as describ
<h2><a href="vit/index.html">Vision Transformer (ViT)</a></h2> <h2><a href="vit/index.html">Vision Transformer (ViT)</a></h2>
<p>This is an implementation of the paper <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> <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>
</div> </div>
<div class='code'> <div class='code'>
<div class="highlight"><pre><span class="lineno">93</span><span></span><span class="kn">from</span> <span class="nn">.configs</span> <span class="kn">import</span> <span class="n">TransformerConfigs</span> <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">94</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">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">95</span><span class="kn">from</span> <span class="nn">.mha</span> <span class="kn">import</span> <span class="n">MultiHeadAttention</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">96</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> <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>
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<h1><a href="https://nn.labml.ai/transformers/primer_ez/index.html">Primer: Searching for Efficient Transformers for Language Modeling</a></h1>
<p>This is a <a href="https://pytorch.org">PyTorch</a> implementation of the paper
<a href="https://papers.labml.ai/paper/2109.08668">Primer: Searching for Efficient Transformers for Language Modeling</a>.</p>
<p>The authors do an evolutionary search for transformer architectures.
They name the architecture found using the search Primer (PRIMitives searched transformER).
<strong>Primer EZ</strong> is the architecture with the two most robust modifications in Primer compared to
the original transformer.
Primer EZ trains a lot faster than the vanilla transformer.</p>
<h3>Squared ReLU</h3>
<p>The most effective modification found by the search is using a square ReLU instead of ReLU in
the <a href="https://nn.labml.ai/transformers/feed_forward.html">position-wise feedforward module</a>.</p>
<h3>Multi-DConv-Head Attention (MDHA)</h3>
<p>The next effective modification is a depth-wise 3 X 1 convolution after multi-head projection
for queries, keys, and values.
The convolution is along the sequence dimension and per channel (depth-wise).
To be clear, if the number of channels in each head is d_k the convolution will have 1 X 3
kernels for each of the d_k channels.</p>
<p><a href="https://nn.labml.ai/transformers/primer_ez/experiment.html">Here is the experiment code</a>, for Primer EZ.</p>
<p><a href="https://app.labml.ai/run/30adb7aa1ab211eca7310f80a114e8a4"><img alt="View Run" src="https://img.shields.io/badge/labml-experiment-brightgreen" /></a></p>
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<a href="https://papers.labml.ai">Trending Research Papers</a>
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@ -32,6 +32,7 @@ implementations.
* [MLP-Mixer: An all-MLP Architecture for Vision](transformers/mlp_mixer/index.html) * [MLP-Mixer: An all-MLP Architecture for Vision](transformers/mlp_mixer/index.html)
* [Pay Attention to MLPs (gMLP)](transformers/gmlp/index.html) * [Pay Attention to MLPs (gMLP)](transformers/gmlp/index.html)
* [Vision Transformer (ViT)](transformers/vit/index.html) * [Vision Transformer (ViT)](transformers/vit/index.html)
* [Primer EZ](transformers/primer_ez/index.html)
#### ✨ [Recurrent Highway Networks](recurrent_highway_networks/index.html) #### ✨ [Recurrent Highway Networks](recurrent_highway_networks/index.html)

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@ -88,6 +88,11 @@ This is an implementation of the paper
This is an implementation of the paper This is an implementation of the paper
[An Image Is Worth 16x16 Words: Transformers For Image Recognition At Scale](https://papers.labml.ai/paper/2010.11929). [An Image Is Worth 16x16 Words: Transformers For Image Recognition At Scale](https://papers.labml.ai/paper/2010.11929).
## [Primer EZ](primer_ez/index.html)
This is an implementation of the paper
[Primer: Searching for Efficient Transformers for Language Modeling](https://papers.labml.ai/paper/2109.08668).
""" """
from .configs import TransformerConfigs from .configs import TransformerConfigs

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# [Primer: Searching for Efficient Transformers for Language Modeling](https://nn.labml.ai/transformers/primer_ez/index.html)
This is a [PyTorch](https://pytorch.org) implementation of the paper
[Primer: Searching for Efficient Transformers for Language Modeling](https://papers.labml.ai/paper/2109.08668).
The authors do an evolutionary search for transformer architectures.
They name the architecture found using the search Primer (PRIMitives searched transformER).
**Primer EZ** is the architecture with the two most robust modifications in Primer compared to
the original transformer.
Primer EZ trains a lot faster than the vanilla transformer.
### Squared ReLU
The most effective modification found by the search is using a square ReLU instead of ReLU in
the [position-wise feedforward module](https://nn.labml.ai/transformers/feed_forward.html).
### Multi-DConv-Head Attention (MDHA)
The next effective modification is a depth-wise 3 X 1 convolution after multi-head projection
for queries, keys, and values.
The convolution is along the sequence dimension and per channel (depth-wise).
To be clear, if the number of channels in each head is d_k the convolution will have 1 X 3
kernels for each of the d_k channels.
[Here is the experiment code](https://nn.labml.ai/transformers/primer_ez/experiment.html), for Primer EZ.
[![View Run](https://img.shields.io/badge/labml-experiment-brightgreen)](https://app.labml.ai/run/30adb7aa1ab211eca7310f80a114e8a4)

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@ -37,6 +37,7 @@ implementations almost weekly.
* [MLP-Mixer: An all-MLP Architecture for Vision](https://nn.labml.ai/transformers/mlp_mixer/index.html) * [MLP-Mixer: An all-MLP Architecture for Vision](https://nn.labml.ai/transformers/mlp_mixer/index.html)
* [Pay Attention to MLPs (gMLP)](https://nn.labml.ai/transformers/gmlp/index.html) * [Pay Attention to MLPs (gMLP)](https://nn.labml.ai/transformers/gmlp/index.html)
* [Vision Transformer (ViT)](https://nn.labml.ai/transformers/vit/index.html) * [Vision Transformer (ViT)](https://nn.labml.ai/transformers/vit/index.html)
* [Primer EZ](https://nn.labml.ai/transformers/primer_ez/index.html)
#### ✨ [Recurrent Highway Networks](https://nn.labml.ai/recurrent_highway_networks/index.html) #### ✨ [Recurrent Highway Networks](https://nn.labml.ai/recurrent_highway_networks/index.html)