Building Blocks of Transformer Models

This article will explore the architecture of transformers, the underlying neural networks that power them, and explain how they have revolutionized NLP. …

This article will explore the architecture of transformers, the underlying neural networks that power them, and explain how they have revolutionized NLP.

The Transformer Architecture

Transformers are a type of neural network architecture that relies on attention mechanisms to process sequential data, such as text. They were first introduced in the paper “Attention Is All You Need” by Vaswani et al. (2017), which outlined a simple yet effective approach for modeling sequences without recurrence or convolutions. The transformer architecture consists of several building blocks:

1. Encoder-Decoder: Transformers use an encoder-decoder structure, where the input sequence is passed through the encoder to create a fixed-size representation, and the decoder generates the output sequence based on this representation. This allows for parallel processing of multiple input sequences and enables efficient translation between languages.
2. Self-Attention: Transformers use self-attention layers to compute the importance of each token in a sequence relative to all other tokens. This mechanism is crucial as it enables the model to capture long-range dependencies and contextual information, leading to improved performance on complex tasks.
3. Feedforward Networks: These are used to process the output of the self-attention layers and learn non-linear relationships between input features. They consist of two fully connected layers with a ReLU activation function in between.
4. Positional Encoding: To maintain the order of tokens in the sequence, transformers use positional encoding, which adds information about the position of each token to the input embeddings. This allows the model to make sense of the order of words in a sentence without relying on recurrence or convolutions.
5. Masking: To prevent the model from attending to future tokens during inference, transformers use masking mechanisms that ensure that only past tokens are considered for each position. This is particularly useful when generating text or translations, as it prevents the model from peeking at future words in the sequence.

The Neural Network Architecture

The neural network architecture used by transformers consists of multiple layers of these building blocks. Each layer applies a multi-head self-attention mechanism followed by a feedforward network. The outputs of each layer are then added and normalized to create the final output. Here’s a step-by-step breakdown of how the transformer model processes input sequences:

1. Input Embeddings: The input sequence is passed through an embedding layer that maps each token to a learned vector representation.
2. Positional Encoding: The positional encoding is added to the embedded tokens to capture their order in the sequence.
3. Multi-Head Self-Attention: This layer uses multiple attention heads to compute the importance of each token relative to all others. Each head learns a different representation of the input sequence, allowing the model to focus on different aspects of the data.
4. Residual Connection and Layer Normalization: The output of self-attention is added to the original embedded inputs (residual connection) and then normalized (layer normalization). This helps maintain the stability of the training process and allows for faster convergence.
5. Feedforward Networks: These are used to learn non-linear relationships between input features by processing the output of self-attention. The feedforward network consists of two fully connected layers with a ReLU activation function in between.
6. Residual Connection and Layer Normalization: The output of the feedforward network is added to the previous layer’s output and then normalized (residual connection + layer normalization).
7. Repeat: Steps 3-6 are repeated for a specified number of layers, creating a deep neural network.
8. Output Layer: The final output is passed through a linear layer to generate predictions or probabilities over the vocabulary.

Training and Inference

Training transformer models can be computationally intensive due to their depth and parallel nature, but recent advancements in hardware have made it possible to train large-scale models on a single GPU or even distributed across multiple machines. During training, the model is optimized using techniques such as stochastic gradient descent with momentum and learning rate schedules to prevent overfitting and speed up convergence.

For inference, transformers are highly parallelizable and can generate outputs for multiple input sequences in parallel. This makes them ideal for real-time applications like chatbots or machine translation systems that need to handle large volumes of data. Masking is used during inference to prevent the model from cheating by peeking at future tokens, ensuring that the output is generated one step at a time.

Conclusion

Transformers have revolutionized NLP by providing state-of-the-art performance on many tasks and paving the way for models like BERT, GPT, and T5. Understanding their architecture and neural network components is essential for building your own transformer models or leveraging pre-trained ones in your applications. By breaking down these complex systems into manageable pieces, we can appreciate their power and potential for further advancements in AI.