12/24/2023 0 Comments Attention attention 2021![]() In addition, we also have the Batch size, giving us one dimension for the number of samples. Number of Attention heads (we use 2 heads in our example).Query Size (equal to Key and Value size)- the size of the weights used by three Linear layers to produce the Query, Key, and Value matrices respectively (we use a Query size of 3 in our example).This dimension is carried forward throughout the Transformer model and hence is sometimes referred to by other names like ‘model size’ etc. Embedding Size - width of the embedding vector (we use a width of 6 in our example).There are three hyperparameters that determine the data dimensions: We’ll use one sample of our training data which consists of an input sequence (‘You are welcome’ in English) and a target sequence (‘De nada’ in Spanish). To understand exactly how the data is processed internally, let’s walk through the working of the Attention module while we are training the Transformer to solve a translation problem. This is called Multi-head attention and gives the Transformer greater power to encode multiple relationships and nuances for each word. All of these similar Attention calculations are then combined together to produce a final Attention score. The Attention module splits its Query, Key, and Value parameters N-ways and passes each split independently through a separate Head. Each of these is called an Attention Head. In the Transformer, the Attention module repeats its computations multiple times in parallel. It, therefore, produces a representation with the attention scores for each target sequence word that captures the influence of the attention scores from the input sequence as well.Īs this passes through all the Decoders in the stack, each Self-Attention and each Encoder-Decoder Attention also add their own attention scores into each word’s representation. The Encoder-Decoder Attention is therefore getting a representation of both the target sequence (from the Decoder Self-Attention) and a representation of the input sequence (from the Encoder stack). This is fed to all three parameters, Query, Key, and Value in the Self-Attention in the first Decoder which then also produces an encoded representation for each word in the target sequence, which now incorporates the attention scores for each word as well.Īfter passing through the Layer Norm, this is fed to the Query parameter in the Encoder-Decoder Attention in the first DecoderĪlong with that, the output of the final Encoder in the stack is passed to the Value and Key parameters in the Encoder-Decoder Attention. Encoder-Decoder-attention in the Decoder - the target sequence pays attention to the input sequenceĬoming to the Decoder stack, the target sequence is fed to the Output Embedding and Position Encoding, which produces an encoded representation for each word in the target sequence that captures the meaning and position of each word.Self-attention in the Decoder - the target sequence pays attention to itself.Self-attention in the Encoder - the input sequence pays attention to itself.Bleu Score ( Bleu Score and Word Error Rate are two essential metrics for NLP models)Īs we discussed in Part 2, Attention is used in the Transformer in three places:.Beam Search (Algorithm commonly used by Speech-to-Text and NLP applications to enhance predictions).How does Attention capture the relationships between words in a sentence)Īnd if you’re interested in NLP applications in general, I have some other articles you might like. ![]() Why Attention Boosts Performance (Not just what Attention does but why it works so well.Multi-head Attention - this article (Inner workings of the Attention module throughout the Transformer).How data flows and what computations are performed, including matrix representations) How it works (Internal operation end-to-end.Components of the architecture, and behavior during Training and Inference) Overview of functionality (How Transformers are used, and why they are better than RNNs.My goal throughout will be to understand not just how something works but why it works that way. Here’s a quick summary of the previous and following articles in the series. In this article, we will go a step further and dive deeper into Multi-head Attention, which is the brains of the Transformer. In the previous articles, we learned what a Transformer is, its architecture, and how it works. We are covering its functionality in a top-down manner. This is the third article in my series on Transformers.
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