A transformer is a deep learning model architecture introduced in the paper "Attention is All You Need" by Vaswani et al. in 2017. It has become one of the most influential and widely used models for various natural language processing tasks due to its ability to handle long-range dependencies effectively. Transformers are particularly successful in tasks like machine translation, language modeling, text generation, and other sequence-to-sequence tasks.
At a high level, the transformer architecture relies heavily on self-attention mechanisms to process sequences of data. The core idea behind self-attention is to weigh the importance of different elements in a sequence when processing each element, allowing the model to focus on relevant parts of the input.
Let's break down the key components of a transformer:
Input Representation:
The input to the transformer is a sequence of tokens. For natural language processing tasks, these tokens can be words or subword units (like byte-pair encoding tokens). Each token is embedded into a high-dimensional vector representation (embedding) that allows the model to process the input.
Positional Encoding:
Since transformers don't have an inherent sense of word order, positional encodings are added to the input embeddings. These positional encodings are fixed vectors that provide information about the position of each token in the input sequence.
Encoder and Decoder Stacks:
Transformers are usually composed of two main parts: the encoder and the decoder. The encoder processes the input sequence, while the decoder generates the output sequence (e.g., in machine translation). Both the encoder and decoder consist of multiple layers, known as the encoder stack and decoder stack, respectively.
Self-Attention Mechanism:
The self-attention mechanism is at the heart of the transformer's ability to capture dependencies between different elements in the input sequence. It computes the importance (attention weight) that each token should give to all other tokens in the sequence. The self-attention mechanism can be understood in three steps:
a. Query, Key, and Value Vectors:
For each token in the input, three vectors are derived from the input embeddings: the query vector, the key vector, and the value vector. These vectors are used to compute the attention weights.
b. Attention Weights:
The attention weights are computed by taking the dot product between the query and key vectors and then applying a softmax function to obtain normalized weights. These weights represent the importance of each token relative to the others.
c. Weighted Sum:
Finally, the value vectors are multiplied by their respective attention weights and then summed up to produce the context vector for each token. This context vector contains information from other tokens based on their relevance to the current token.
Multi-Head Attention:
To capture different types of dependencies, the self-attention mechanism is applied multiple times in parallel, each with different learned linear projections of the input embeddings. This is called multi-head attention.
Feed-Forward Neural Networks:
After self-attention, each token's context vector goes through a feed-forward neural network. This network applies two linear transformations with a non-linear activation function (like ReLU) in between.
Normalization and Residual Connections:
After each layer (self-attention and feed-forward network), layer normalization and residual connections are applied. Layer normalization helps stabilize training, and residual connections enable information to flow directly through the layers, mitigating the vanishing gradient problem.
Decoder-Encoder Attention:
In the decoder stack, an additional attention mechanism is introduced: the decoder-encoder attention. This allows the decoder to consider the encoded input sequence's information when generating the output sequence.
Output Layer:
The output layer takes the final hidden representations from the decoder stack and generates the final prediction, which could be probabilities over a vocabulary (for language modeling) or the next token in a sequence (for sequence generation tasks).
Training a transformer involves optimization through techniques like stochastic gradient descent and backpropagation.
In summary, the transformer architecture is designed to process sequences efficiently by using self-attention mechanisms to model relationships between elements in the input sequence. This enables transformers to achieve impressive results in various natural language processing tasks and beyond.