Understanding Transformers: A Deep Dive into Modern Language Models

Introduction to Transformers

Transformers have revolutionized the field of natural language processing, powering models from small-scale applications to cutting-edge behemoths like GPT-3. In this comprehensive guide, we'll delve deep into the architecture and functioning of Transformers, with a focus on GPT-2 style models.

The Purpose of Transformers

At their core, Transformers are designed to model text. They are sequence modeling engines that take in a sequence of text and perform operations on it. The key features of Transformers include:

• Generating text by predicting the next token in a sequence
• Processing input in parallel at each sequence position
• Using attention mechanisms to move information between positions
• Conceptually handling sequences of arbitrary length (with some practical limitations)

Input and Output of a Transformer

Input: Tokens

Transformers don't directly process raw text. Instead, they work with tokens, which are essentially integers representing subwords or characters. The process of converting text to tokens is called tokenization.

Tokenization Process:

1. Start with a vocabulary of basic ASCII characters (usually 256)
2. Iteratively merge the most frequently occurring pairs of tokens
3. Continue until reaching the desired vocabulary size (e.g., 50,000 tokens)

This method, known as Byte Pair Encoding (BPE), allows the model to handle a wide range of text, including uncommon words, URLs, and punctuation.

Converting Tokens to Vectors

Once we have our sequence of token integers, we convert them to vectors using an embedding layer. This is essentially a lookup table that maps each token to a high-dimensional vector.

Output: Logits and Probabilities

The output of a Transformer is a tensor of logits - one vector for each input token position. These logits represent the model's prediction for the next token at each position in the sequence.

To convert these logits into probabilities, we use the softmax function. This gives us a probability distribution over the entire vocabulary for each position.

The Architecture of a Transformer

Let's break down the key components of a Transformer:

1. Embedding Layer

The embedding layer converts input tokens (integers) into vectors. It's a simple lookup table that maps each token to a high-dimensional vector.

2. Positional Encoding

Since Transformers process all input tokens in parallel, they need a way to understand the order of the sequence. This is achieved through positional encodings, which are added to the token embeddings.

3. Transformer Blocks

The heart of the Transformer is a stack of identical layers, each containing two main components:

a. Multi-Head Attention

Attention is the mechanism that allows the model to focus on different parts of the input when producing each output. Multi-head attention allows the model to attend to information from different representation subspaces at different positions.

Key points about attention:

• It moves information between positions in the sequence
• Each attention head operates independently and in parallel
• The attention pattern determines how much information to copy from each source token to each destination token
• The information copying process is separate from determining which tokens to attend to

b. Feed-Forward Neural Network (MLP)

After the attention layer, each position is processed by a feed-forward neural network. This typically consists of:

• A linear transformation to a higher dimension
• A non-linear activation function (usually GELU)
• Another linear transformation back to the original dimension

The MLP allows the model to perform computations on the information gathered by the attention mechanism.

4. Layer Normalization

Layer normalization is applied before each sub-layer (attention and feed-forward) in the Transformer block. It helps stabilize the learning process and reduces the training time.

5. Residual Connections

Each sub-layer in the Transformer block is wrapped in a residual connection. This means the output of each sub-layer is added to its input, allowing for better gradient flow during training.

6. Output Layer

The final layer of the Transformer converts the output of the last Transformer block into logits over the vocabulary.

The Residual Stream

A key concept in understanding Transformers is the residual stream. This is the central object that flows through the entire model, with each layer reading from and writing to it. The residual stream accumulates information as it passes through the layers, allowing for complex interactions between different parts of the input sequence.

Causal Attention

In language models like GPT-2, the attention mechanism is causal, meaning each position can only attend to previous positions in the sequence. This ensures that the model can't "cheat" by looking at future tokens when making predictions.

Generating Text with a Transformer

To generate text with a Transformer:

1. Start with an initial input sequence
2. Feed it through the model to get logits for the next token
3. Convert logits to probabilities using softmax
4. Sample a token from this probability distribution
5. Append the sampled token to the input sequence
6. Repeat steps 2-5 until the desired length is reached or a stop condition is met

Conclusion

Transformers are powerful and flexible models that have dramatically advanced the field of natural language processing. By understanding their architecture and functioning, we can better appreciate their capabilities and limitations.

While this guide provides a comprehensive overview, there's always more to learn about these fascinating models. As research progresses, we can expect to see further refinements and innovations in Transformer architecture, potentially leading to even more capable language models in the future.

Article created from: https://m.youtube.com/watch?v=bOYE6E8JrtU&list=PL7m7hLIqA0hoIUPhC26ASCVs_VrqcDpAz&index=1