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Varuna Jayasiri
2021-01-24 08:08:09 +05:30
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labml_nn/transformers/switch/__init__.py
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@ -12,12 +12,12 @@ This is a miniature implementation of the paper
Our implementation only has a few million parameters and doesn't do model parallel distributed training. Our implementation only has a few million parameters and doesn't do model parallel distributed training.
It does single GPU training but we implement the concept of switching as described in the paper. It does single GPU training but we implement the concept of switching as described in the paper.
The Switch Transformer is uses different parameters for each tokens by switching among parameters, The Switch Transformer uses different parameters for each token by switching among parameters,
based on the token. So only a fraction of parameters is chosen for each token, so you based on the token. So only a fraction of parameters is chosen for each token, so you
can have more parameters but a less computational cost. can have more parameters but less computational cost.
The switching happens at the Position-wise Feedforward network (FFN) of of each transformer block. The switching happens at the Position-wise Feedforward network (FFN) of of each transformer block.
Position-wise feedforward network is a two sequential fully connected layers. Position-wise feedforward network is a two sequentially fully connected layers.
In switch transformer we have multiple FFNs (multiple experts) and In switch transformer we have multiple FFNs (multiple experts) and
we chose which one to use based on a router. we chose which one to use based on a router.
The outputs a set of probabilities for picking a FFN, The outputs a set of probabilities for picking a FFN,