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<h1><a href="https://nn.labml.ai/transformers/switch/index.html">Switch Transformer</a></h1>
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<p>This is a miniature <a href="https://pytorch.org">PyTorch</a> implementation of the paper
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<a href="https://arxiv.org/abs/2101.03961">Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity</a>.
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Our implementation only has a few million parameters and doesn’t do model parallel distributed training.
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It does single GPU training, but we implement the concept of switching as described in the paper.</p>
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<p>The Switch Transformer uses different parameters for each token by switching among parameters,
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based on the token. So only a fraction of parameters is chosen for each token, so you
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can have more parameters but less computational cost.</p>
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<p>The switching happens at the Position-wise Feedforward network (FFN) of each transformer block.
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Position-wise feedforward network is a two sequentially fully connected layers.
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In switch transformer we have multiple FFNs (multiple experts),
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and we chose which one to use based on a router.
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The outputs a set of probabilities for picking a FFN,
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and we pick the one with the highest probability and only evaluates that.
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So essentially the computational cost is same as having a single FFN.
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In our implementation this doesn’t parallelize well when you have many or large FFNs since it’s all
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happening on a single GPU.
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In a distributed setup you would have each FFN (each very large) on a different device.</p>
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<p>The paper introduces another loss term to balance load among the experts (FFNs) and
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discusses dropping tokens when routing is not balanced.</p>
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<p>Here’s <a href="experiment.html">the training code</a> and a notebook for training a switch transformer on Tiny Shakespeare dataset.</p>
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<p><a href="https://colab.research.google.com/github/lab-ml/nn/blob/master/labml_nn/transformers/switch/experiment.ipynb"><img alt="Open In Colab" src="https://colab.research.google.com/assets/colab-badge.svg" /></a>
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