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The Transformer Architecture (V2)

Understanding LLMs
  • The Self-Attention Mechanism

  • The Multihead attention

  • The encoder

  • The decoder

  • The position embedding

  • The encoder block

  • The self-attention layer

  • The layer-normalization

  • The position-wise feed-forward network

  • The decoder block

  • The cross-attention layer

  • The predicting head

The overall architecture

The architecture is composed of an encoder and a decoder.

The self-attention layer

The self-attentions are used to replace the recurring units to capture the interactions between words within the input sequence and within the output sequence.

In the self-attention layer, we first compute the keys, queries, and values vectors.

We then compute the matrix multiplication between the keys and queries.

After a softmax transformation, this matrix of interactions is called the attention matrix. The resulting output hidden states of the attention layer are the matrix multiplication of the attention matrix and the values vectors.

Each of the resulting hidden states coming from the attention layer can be understood as a weighted average of the values, with the attentions being the weights.

The multi-head attention layer

Using multiple attention layers in parallel helps capture different interactions between the words of the input sequence. The dimension of the of the hidden states coming out of each attention head is divided by the number of heads and concatenated to the other hidden states. The resulting hidden states are combined into final hidden states using a linear layer.

To reduce the dimensionality of the hidden states, we just need to change the shape of the internal matrices:

The position embedding

The position embedding is used to add the position information to the semantic information of the words.

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