Transformer Part -2 Decoder

The Transformer decoder architecture is primarily characterized by self-attention mechanisms, which allow it to efficiently process input sequences and generate output sequences. This self-attention enables the model to weigh the importance of different words in the context of the sentence, allowing it to focus on relevant parts of the input regardless of their position. Each layer of the decoder consists of multi-head self-attention followed by feedforward neural networks, with residual connections and layer normalization helping to stabilize learning and improve performance. This architecture is crucial for tasks such as language translation and text generation, as it effectively captures dependencies and contextual information across long sequences.

The decoder retrieves the embeddings corresponding to the output sequence, which represent the learned features and contextual information of the generated tokens. In addition, it also obtains embeddings for a marker referred to as the start sequence. This start sequence marker serves as a crucial reference point, signaling the beginning of the output generation process and helping the model to focus on producing coherent and contextually relevant outputs.

The word embeddings are combined with positional embeddings to create a comprehensive representation of the input sequence. This combined representation is then processed through a masked multi-head attention mechanism, which allows the model to focus on relevant parts of the input while ignoring others. The output of this attention layer is subsequently combined through a residual connection with the initial embeddings, ensuring that the original information is preserved and enhances the overall feature representation.

Layer normalization is applied to the output in order to create normalized embeddings. This process involves calculating the mean and variance of the features across the layer, allowing each feature to be adjusted based on its distribution. As a result, the embeddings produced are more robust and stable, enhancing the model's performance during training and inference by mitigating issues related to internal covariate shift.

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In the cross attention mechanism, the Key and Value vectors are derived from the output of the Encoder, while the Query vector originates from the previous output of the Decoder. Once the cross attention is computed, a residual connection is added to the resulting output. This residual connection allows the original input to influence the output, helping to retain important information. Lastly, layer normalization is applied to the combined output to stabilize and improve the training process by ensuring that the inputs to each layer maintain a consistent scale and distribution.

Following this step, the embeddings are fed into a feed forward neural network comprising two layers. This network performs a series of linear transformations, effectively processing the embeddings to extract meaningful features and relationships

In a neural network architecture, the output of the residual connection is added to the output of the feedforward network. This combined information is then processed alongside the layer normalization, which helps stabilize and improve the training of deep learning models by normalizing the activations across the layers. This integration allows for better flow of gradients during training and contributes to overall model performance.

Ultimately, a linear transformation is employed, where the number of neurons corresponds to the size of the vocabulary. Following this transformation, the softmax function is applied to produce the final output, effectively generating a probability distribution across the various vocabulary entries. This process ensures that the model can select the most relevant words or tokens based on the input it has processed.

The process is repeated five additional times within the decoder block, allowing for the generation of the final output. Each iteration refines the information further, enhancing the overall quality and coherence of the result.