Transformers - Part 7 - Decoder (2): masked self-attention

Summary

This video script delves into the concept of masked self-attention within the decoder of a neural network, a crucial component for parallelizing calculations during training. It explains how the decoder maintains input shape and uses embedded vectors of the same length as the output sequence. The key focus is on the masked multi-head self-attention layer, which ensures the network doesn't 'cheat' by accessing future words in the sequence, thus learning to predict unseen words effectively. The script illustrates the construction of this layer, detailing the process from computing queries and keys to applying a mask that zeroes out weights for subsequent words, culminating in the computation of new embeddings that depend solely on preceding input vectors.

Takeaways

• 🧠 Masked Self-Attention is a key component of the decoder in parallelizing calculations during training.
• 🔄 The decoder consists of 'N' decoder blocks with the same structure but different parameters, similar to the encoder.
• 📏 The input and output matrices in the decoder maintain the same shape as the embedded output sequence.
• 🔢 The number of vectors in the decoder is generally different from the number processed by the encoder.
• 🚀 The goal is to compute all next word prediction probabilities in parallel, which speeds up training.
• 🚫 The decoder should not 'cheat' by having access to the target word when predicting the next word in the sequence.
• 🔑 Queries, keys, and values are computed for each input token, which are essential for the self-attention mechanism.
• 🎭 Masking is applied to unnormalized weights to ensure that later words do not influence the current word's embedding.
• 📊 The softmax operation is used to normalize weights, but with a mask to prevent future words from influencing the current word.
• 🔍 The masked self-attention layer ensures that the embedding for a word only depends on the preceding words.
• 🔄 Multi-head self-attention combines multiple instances of masked self-attention to create a comprehensive output.

Q & A

What is the main purpose of the masked self-attention mechanism in the decoder?

The main purpose of the masked self-attention mechanism in the decoder is to enable parallelization of calculations during training and to ensure that the prediction of each word in the output sequence does not have access to future words in the sequence.

How does the decoder maintain the shape of the input?

The decoder maintains the shape of the input by ensuring that the matrices passed between the layers have the same shape as the embedded version of the output sequence, with the number of vectors being the same as the number of elements in the output sequence.

What is the significance of the number of vectors in the decoder being generally different from the encoder?

The significance is that it allows the decoder to process the output sequence in a way that is tailored to the specific requirements of generating translations, which may differ from the input sequence processed by the encoder.

Why is it important to feed the entire output sequence into the decoder at once during training?

Feeding the entire output sequence into the decoder at once during training allows for the computation of all next word prediction probabilities in parallel, which speeds up the training process.

How does the decoder prevent the network from 'cheating' during the computation of next word probabilities?

The decoder prevents cheating by designing the network such that when computing the probabilities of a word in the sequence, it does not have access to that word or any subsequent words.

What role does the start of sequence token play in the decoder's computation of word probabilities?

The start of sequence token serves as the initial input for the decoder, and it is used along with the output from the encoder to compute the probabilities for the first word in the output sequence.

Can you explain how the masked multi-head self-attention layer is constructed using an example?

The masked multi-head self-attention layer is constructed by first computing queries, keys, and values for each input token. Then, it masks the unnormalized weights to ensure that words in the sequence do not influence the computation of previous words. After normalization, the new embeddings are computed as a weighted average of the value vectors, ensuring that each word embedding only depends on the preceding input vectors.

Why is it unnecessary to compute z43 and z53 when focusing on computing y3?

It is unnecessary to compute z43 and z53 when focusing on y3 because the masked self-attention mechanism ensures that the embedding for the third word (y3) should only depend on the first three input words (x1, x2, and x3), and not on any subsequent words.

How is the masked self-attention expressed in matrix form?

In matrix form, the masked self-attention first computes queries, keys, values, and z values using weight matrices. It then applies a mask to set the weights for later words to zero when computing the weights for a specific word. After normalization, the new embeddings are computed by taking the product of the value matrix and the weight matrix for the output.

What is the final step in the computation of the masked multi-head self-attention layer?

The final step is to concatenate the different y matrices computed by the different heads and then multiply this tall matrix with a weight matrix (w_o) to obtain the output y, which has the same dimension as the input x.

How does the order of input vectors affect the masked self-attention mechanism?

The order of input vectors is important in masked self-attention because it determines the dependencies between words in the sequence. The mechanism ensures that each word embedding only depends on the preceding input vectors, reflecting the sequential nature of language.