Exploring the Transformer’s Decoder Architecture: Masked Multi-Head Attention, Encoder-Decoder Attention, and Practical Implementation

This post was co-authored with Rafael Nardi.

In this text, we delve into the decoder component of the transformer architecture, specializing in its differences and similarities with the encoder. The decoder’s unique feature is its loop-like, iterative nature, which contrasts with the encoder’s linear processing. Central to the decoder are two modified types of the eye mechanism: masked multi-head attention and encoder-decoder multi-head attention.

The masked multi-head attention within the decoder ensures sequential processing of tokens, a way that forestalls each generated token from being influenced by subsequent tokens. This masking is vital for maintaining the order and coherence of the generated data. The interaction between the decoder’s output (from masked attention) and the encoder’s output is highlighted within the encoder-decoder attention. This last step gives the input context into the decoder’s process.

We can even exhibit how these concepts are implemented using Python and NumPy. We have now created a straightforward example of translating a sentence from English to Portuguese. This practical approach will help illustrate the inner workings of the decoder in a transformer model and supply a clearer understanding of its role in Large Language Models (LLMs).

As all the time, the code is on the market on our GitHub.

After describing the inner workings of the encoder in transformer architecture in our previous article, we will see the subsequent segment, the decoder part. When comparing the 2 parts of the transformer we imagine it’s instructive to emphasise the most important similarities and differences. The eye mechanism is the core of each. Specifically, it occurs in two places on the decoder. They each have necessary modifications in comparison with the only version present on the encoder: masked multi-head attention and encoder-decoder multi-head attention. Talking about differences, we indicate the…