Transformer architecture.md

#lowlevel

The Transformer architecture, introduced in the [["Attention Is All You Need"]] paper, is a [[neural network model]] designed for handling sequential data, particularly in the context of natural language processing ([[NLP]]). It primarily relies on a mechanism called [[self-attention]] to process input data. Here are some technical specifics about the Transformer architecture:

1. [[Self-Attention]] Mechanism

The self-attention mechanism is the core innovation of the Transformer architecture. It allows the model to weigh the importance of different words in a sentence relative to each other, regardless of their distance apart.

2. Multi-Head Attention

To capture different types of relationships and information, the Transformer uses multiple self-attention mechanisms in parallel, known as [[multi-head attention]]. Each head learns different aspects of the word relationships by having separate sets of Query, Key, and Value weight matrices. The outputs of all heads are concatenated and linearly transformed to form the final output.

3. Positional Encoding

Since Transformers do not inherently process data in a sequential manner, they lack a built-in sense of the order of the words. To address this, positional encoding is added to the input embeddings to provide the model with information about the position of each word in the sequence. These encodings are added element-wise to the input embeddings and are designed to give the model some awareness of the sequence's structure.

4. Layer Structure

The Transformer architecture consists of an encoder and a decoder, each made up of multiple identical layers.

• Encoder Layer: Each encoder layer consists of two main components:

  1. Multi-Head Attention: This allows the model to attend to different parts of the input sequence.
  2. Feed-Forward Network (FFN): A fully connected feed-forward network is applied to each position separately and identically. This network consists of two linear transformations with a ReLU activation in between.

  Additionally, each of these components is followed by layer normalization and residual connections, which help stabilize training and allow deeper networks.

• Decoder Layer: The decoder has a similar structure to the encoder but includes an additional [[multi-head attention]] layer between the multi-head self-attention and the feed-forward network. This extra layer performs attention over the encoder's output, allowing the decoder to focus on relevant parts of the input sequence during translation or generation tasks.

5. Output and Final Layer

For tasks like translation or text generation, the decoder produces an output at each position in the sequence, which is then passed through a softmax layer to produce a probability distribution over the vocabulary for the next word prediction.

6. Training and Optimization

The Transformer architecture is typically trained using the [[Adam optimizer]] with a [[learning rate]] schedule that includes warmup steps followed by inverse square root decay. This helps in the stabilization and efficiency of the training process.

7. Scalability and Performance

One of the key advantages of the Transformer is its ability to be scaled up to handle [[very large datasets]] and long sequences, which is crucial for training large language models like GPT and BERT.

The Transformer architecture's ability to parallelize computations and model long-range dependencies has revolutionized the field of NLP, making it a foundational component of many state-of-the-art language models today.