Transformers are a type of deep learning model architecture that has gained significant popularity due to their exceptional performance in a wide range of natural language processing tasks. The architecture was introduced in the paper "Attention Is All You Need" by Vaswani et al. in 2017, and it has since become the foundation for various state-of-the-art models like BERT, GPT, and more. The Transformer architecture is primarily designed to handle sequential data and capture long-range dependencies efficiently. Here's an overview of how the Transformer is constructed:
Input Representation:
The input sequence is first embedded into a continuous vector space representation called embeddings. Each word/token in the sequence is mapped to a high-dimensional vector, and these embeddings serve as the initial input to the Transformer.
Positional Encodings:
Since the Transformer doesn't have any inherent notion of word order, positional information needs to be incorporated explicitly. Positional encodings are added to the word embeddings to provide the model with information about the position of each token in the sequence.
Encoder and Decoder Stacks:
The Transformer architecture consists of two main components: the encoder and the decoder. Each of these components is composed of multiple layers, known as encoder layers and decoder layers respectively.
a. Encoder:
The encoder takes in the input sequence and processes it in parallel through multiple identical encoder layers. Each encoder layer consists of two main sub-layers:
Multi-Head Self-Attention:
This sub-layer computes attention weights for each word/token in the sequence, allowing the model to weigh the importance of different words with respect to each other. Multi-head attention involves computing attention in parallel using multiple different learned linear projections.
Position-wise Feed-Forward Networks:
After the attention sub-layer, a feed-forward neural network is applied independently to each position in the sequence. This helps capture complex patterns within the sequence.
b. Decoder:
The decoder also contains multiple identical decoder layers. Each decoder layer includes three sub-layers:
Masked Multi-Head Self-Attention:
Similar to the encoder's attention mechanism, but with a masking step to prevent the model from attending to future positions during training.
Multi-Head Encoder-Decoder Attention:
This sub-layer enables the decoder to focus on different parts of the input sequence while generating the output. It uses the encoder's outputs and attention mechanism.
Position-wise Feed-Forward Networks:
Same as in the encoder, this sub-layer captures complex patterns within the decoder's sequence.
Normalization and Residual Connections:
Layer normalization and residual connections are applied around each sub-layer within the encoder and decoder stacks. These techniques help stabilize the training process and enable the model to learn effectively even through deep layers.
Output Layer:
The output layer of the decoder generates the final predictions. It's typically a softmax layer that generates probabilities over the vocabulary for language generation tasks.
Overall, the Transformer architecture's key innovation is the self-attention mechanism, which allows the model to weigh the importance of different parts of the input sequence. This mechanism enables the model to capture long-range dependencies and relationships in the data, making it highly effective for a wide array of sequential data tasks.