Neural Transformer Block

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A Neural Transformer Block is a machine-learning model component of a Transformer architecture that processes input sequences through layers of self-attention mechanisms, normalization, and feed-forward networks to capture complex dependencies within the data.



References

2024

2023

  • chat
    • A software engineer might find the following concepts or components in traditional software development somewhat analogous to the components in a Transformer block:
      • Self-attention mechanism: This can be compared to a search algorithm that ranks the importance of items in a list based on their relationships with other items. It's like the model tries to find the most relevant words in a sentence, given a specific context.
      • Layer normalization: It can be considered a normalization technique similar to the one used in databases or data preprocessing, ensuring that values are on a consistent scale to avoid computational issues and improve efficiency.
      • Feed-forward network: This is akin to a function or subroutine in programming that takes an input, applies some operations or transformations, and produces an output. In the Transformer block, this network processes each word or token independently, helping the model learn patterns and features in the input data.
      • Cross-attention mechanism (in decoder blocks): It can be considered a reference or pointer in programming, where the decoder block looks at the output of the encoder blocks better to understand the relationship between the input and output sequences.

2023

  • chat
    • A Transformer block typically refers to a single layer within either the encoder or decoder stack of a Transformer architecture. The components within a Transformer block depend on whether it is part of the encoder or the decoder.
    • In an encoder Transformer block, you will find the following components:
      1. Multi-head self-attention mechanism: This mechanism allows the model to selectively focus on different parts of the input sequence and capture various relationships between tokens and their contexts.
      2. Layer normalization: Applied after the multi-head self-attention sub-layer to stabilize the training process and accelerate convergence.
      3. Position-wise feed-forward network: A fully connected feed-forward network applied to each position separately and identically. It consists of two linear layers with a non-linear activation function (e.g., ReLU) in between.
      4. Layer normalization: Applied after the position-wise feed-forward sub-layer.
    • In a decoder Transformer block, you will find the following components:
      1. Multi-head self-attention mechanism: Like in the encoder, this mechanism captures relationships between tokens and their contexts in the target sequence.
      2. Layer normalization: Applied after the multi-head self-attention sub-layer.
      3. Cross-attention mechanism: This additional attention mechanism attends to the output of the encoder stack, helping to align the generated output with the input sequence.
      4. Layer normalization: Applied after the cross-attention sub-layer.
      5. Position-wise feed-forward network: Like in the encoder, this network is applied to each position separately and identically.
      6. Layer normalization: Applied after the position-wise feed-forward sub-layer.

2020

2019