How does a transformer work?
1 Answer
A transformer is a type of deep learning model architecture introduced in the paper "Attention is All You Need" by Vaswani et al. in 2017. It revolutionized various natural language processing tasks and became the foundation for many state-of-the-art models. Transformers are primarily used for tasks like machine translation, text generation, sentiment analysis, and more.
At a high level, the transformer architecture is based on a self-attention mechanism that allows the model to weigh the importance of different input elements (words or tokens) when processing a particular element. This attention mechanism enables the transformer to capture long-range dependencies and contextual relationships efficiently.
The main components of a transformer are:
Input Embeddings: The input sequence (e.g., a sentence) is first tokenized into individual elements (words or subwords) and mapped to corresponding embedding vectors. These embeddings represent the input tokens in a continuous vector space.
Positional Encoding: As the transformer architecture does not have an inherent sense of word order, positional encodings are added to the input embeddings. These positional encodings provide information about the relative positions of the tokens in the sequence.
Encoder-Decoder Structure (for sequence-to-sequence tasks): The transformer can be used for both encoder-only tasks (e.g., language modeling) and encoder-decoder tasks (e.g., machine translation). In the latter case, the model consists of both an encoder and a decoder.
Encoder Layers: The encoder contains a stack of identical layers. Each layer consists of two sub-layers:
a. Multi-Head Self-Attention Mechanism: This mechanism computes the attention scores between all input tokens in the sequence to capture their dependencies. It allows the model to focus on the most relevant tokens for each position in the sequence.
b. Feed-Forward Neural Networks: After the self-attention layer, a feed-forward neural network processes the representations from the attention layer to introduce non-linearity and additional context.
Decoder Layers: The decoder also consists of a stack of identical layers. Each layer contains three sub-layers:
a. Masked Multi-Head Self-Attention: Similar to the encoder's self-attention, but with the addition of a masking step to prevent attention to future tokens during training (ensuring auto-regressive behavior).
b. Multi-Head Encoder-Decoder Attention: This layer allows the decoder to attend to the relevant parts of the encoder's output during the decoding process.
c. Feed-Forward Neural Networks: As in the encoder, a feed-forward neural network processes the representations from the attention layers.
Output Layer: The output layer of the decoder produces the final probability distribution over the target vocabulary (e.g., words in machine translation).
During training, the model optimizes its parameters to minimize the discrepancy between its predictions and the true targets using methods like cross-entropy loss and backpropagation.
Overall, the transformer's strength lies in its ability to handle long-range dependencies, parallelize computations efficiently, and achieve state-of-the-art performance in various natural language processing tasks.
At a high level, the transformer architecture is based on a self-attention mechanism that allows the model to weigh the importance of different input elements (words or tokens) when processing a particular element. This attention mechanism enables the transformer to capture long-range dependencies and contextual relationships efficiently.
The main components of a transformer are:
Input Embeddings: The input sequence (e.g., a sentence) is first tokenized into individual elements (words or subwords) and mapped to corresponding embedding vectors. These embeddings represent the input tokens in a continuous vector space.
Positional Encoding: As the transformer architecture does not have an inherent sense of word order, positional encodings are added to the input embeddings. These positional encodings provide information about the relative positions of the tokens in the sequence.
Encoder-Decoder Structure (for sequence-to-sequence tasks): The transformer can be used for both encoder-only tasks (e.g., language modeling) and encoder-decoder tasks (e.g., machine translation). In the latter case, the model consists of both an encoder and a decoder.
Encoder Layers: The encoder contains a stack of identical layers. Each layer consists of two sub-layers:
a. Multi-Head Self-Attention Mechanism: This mechanism computes the attention scores between all input tokens in the sequence to capture their dependencies. It allows the model to focus on the most relevant tokens for each position in the sequence.
b. Feed-Forward Neural Networks: After the self-attention layer, a feed-forward neural network processes the representations from the attention layer to introduce non-linearity and additional context.
Decoder Layers: The decoder also consists of a stack of identical layers. Each layer contains three sub-layers:
a. Masked Multi-Head Self-Attention: Similar to the encoder's self-attention, but with the addition of a masking step to prevent attention to future tokens during training (ensuring auto-regressive behavior).
b. Multi-Head Encoder-Decoder Attention: This layer allows the decoder to attend to the relevant parts of the encoder's output during the decoding process.
c. Feed-Forward Neural Networks: As in the encoder, a feed-forward neural network processes the representations from the attention layers.
Output Layer: The output layer of the decoder produces the final probability distribution over the target vocabulary (e.g., words in machine translation).
During training, the model optimizes its parameters to minimize the discrepancy between its predictions and the true targets using methods like cross-entropy loss and backpropagation.
Overall, the transformer's strength lies in its ability to handle long-range dependencies, parallelize computations efficiently, and achieve state-of-the-art performance in various natural language processing tasks.