Transformers have revolutionized natural language processing and other sequential data tasks by introducing a novel architecture that relies entirely on self-attention mechanisms instead of recurrent or convolutional networks.
This innovation allows transformers to capture long-range dependencies in data efficiently and handle large-scale parallel computation, making them key to the success of models like BERT, GPT, and others.
Introduction to Transformer Architecture
Traditional sequence models like RNNs process data element-by-element, limiting parallelization and often struggling with long-range dependencies. Transformers overcome these challenges by using self-attention to weigh the relevance of every element in the sequence simultaneously, enabling the network to learn contextual relationships effectively.
Core Components of Transformers
The points below highlight the major components that constitute the Transformer framework. Their combined functionality allows deep contextual understanding and parallel computation.
1. Encoder-Decoder Structure
Encoder: The encoder takes the input sequence and generates a sequence of continuous representations capturing contextual information.
Decoder: The decoder consumes the encoder’s output along with previously generated tokens to produce the final output sequence.
Both encoders and decoders are composed of stacked layers (commonly six) featuring similar structures.
2. Multi-Head Self-Attention
Allows each token to attend to all other tokens in the input sequence, capturing dependencies regardless of distance.
Multi-head means several attention mechanisms run in parallel, allowing the model to focus on different aspects of the data simultaneously.
Computes attention scores via scaled dot-product between queries, keys, and values derived from the input embeddings.
3. Position-wise Feedforward Networks: After attention layers, the data passes through fully connected networks applied independently to each position. These networks transform representations to capture complex features.
4. Positional Encoding: Since transformers do not process data sequentially, positional encoding injects sequence order information into input embeddings. It Uses sine and cosine functions of varying frequencies to encode positional information.
5. Residual Connections and Layer Normalization: Residual connections bypass some layers to help gradient flow and prevent vanishing. Layer normalization stabilizes activations and speeds up training convergence.
How Transformers Work
Here are the essential steps that describe the internal workflow of Transformer models. These stages highlight how information is encoded, decoded, and turned into final tokens.
1. Input Preparation: Input tokens are split, embedded into vectors, and combined with positional encodings.
2. Encoding: The encoder applies multiple self-attention and feedforward layers to build context-aware representations.
3. Decoding: The decoder performs masked self-attention on previous outputs and attends to encoder outputs to predict the next token.
4. Output Generation: Output tokens are generated sequentially until completion.
