Neural Transformer Block

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.

• Context:
  • It can range from being an Encoder Transformer Block to being a Decoder Transformer Block.
  • It can be represented in a Neural Transformer Block Software Component.
  • It can process sequence data in parallel, unlike RNNs which process data sequentially.
  • It can be utilized in a variety of domains beyond Natural Language Processing (NLP), including Computer Vision and Audio Processing.
  • It can be a part of a larger Transformer Block Stack to form complex models for tasks like sequence-to-sequence modeling.
  • It can incorporate a Multi-Head Attention Mechanism to focus on different sequence parts for a comprehensive understanding.
  • It can employ Layer Normalization to stabilize training and improve convergence speed.
  • It can include a Position-wise Feed-Forward Network for additional transformations within the block.
  • It can leverage Cross-Attention Mechanism (in decoder blocks) to align the model's output with the relevant input sequence parts.
  • ...
• Example(s):
  • keras.TransformerBlock(layers.Layer), a software component provided by keras for constructing transformer blocks.
  • The architecture described in (Duan et al., 2019) which them for predicting crowd flow.
  • ...
• Counter-Example(s):
  • Recurrent Neural Network (RNN): A neural network that processes data sequentially rather than in parallel.
  • Convolutional Neural Network (CNN): A type of neural network that is primarily used for processing grid-like data such as images.
• See: Transformer Block Stack, Sequence-to-Sequence Model, Multi-Head Attention Mechanism, Layer Normalization.

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References

2024

• https://en.wikipedia.org/wiki/Transformer_(deep_learning_architecture)
  • NOTES:
    • It can process sequence data in parallel using a Multi-Head Attention Mechanism, distinguishing it from the sequential processing characteristic of RNNs and LSTMs.
    • It can operate without recurrent units, leading to quicker training durations and enabling efficient handling of extensive datasets.
    • It can convert input text into vectors through a Word Embedding Table, contextualizing each token within a specific context window for the benefit of deeper layers.
    • It can enhance signal strength for pivotal tokens while reducing it for non-essential ones, improving the model's capacity to concentrate on significant information within the text.
    • It can be utilized in a variety of applications beyond Natural Language Processing (NLP), such as Computer Vision, Audio Processing, and Multi-Modal Tasks.
    • It can adopt an Encoder-Decoder Architecture, where the encoder prepares input sequences and the decoder crafts output sequences, supported by Scaled Dot-Product Attention and Positional Encodings.

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

• https://alvinntnu.github.io/NTNU_ENC2045_LECTURES/nlp/dl-transformers-keras.html
  • A Transformer block consists of layers of Self Attention, Normalization, and feed-forward networks (i.e., MLP or Dense)).
  • We use the TransformerBlock provided by keras (See keras official tutorial on Text Classification with Transformer.
  • 

2019

• (Duan et al., 2019) ⇒ Wenying Duan, Liu Jiang, Ning Wang, and Hong Rao. (2019). “Pre-Trained Bidirectional Temporal Representation for Crowd Flows Prediction in Regular Region.” IEEE Access 7 DOI:10.1109/ACCESS.2019.2944990
  • FIGURE 2. The structure of a Transformer Block.

    

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