GPT Architecture & Components

GPT (Generative Pre-trained Transformer) is a type of deep learning model created by OpenAI. It is designed to understand and generate human-like text based on the input it receives. The architecture of GPT is based on a Neural Network called a transformer, which allows it to process and understand sequences of text. In simple terms, GPT can take a piece of text as input, understand its context, and generate a response that makes sense in that context. This is what makes GPT a powerful tool for tasks like language translation, summarization, and even creative writing.

Introduction to GPT Architecture

The GPT architecture is built on the transformer model, which consists of multiple layers of attention and neural network units. These layers help the model focus on different parts of the input text at different stages of processing. The core idea behind the architecture is to learn patterns and relationships in the input data, and then use that knowledge to generate new text. It uses a self-attention mechanism, where the model pays attention to every word in the sequence to understand its relationship to other words.

 

GPT Components

GPT consists of several key components that work together to process and generate text. Each of these components plays a specific role in the overall functioning of the model, and together they allow GPT to perform complex language tasks.

Input Embedding

The input embedding is the first component that transforms the raw input text into a numerical representation. Each word or token in the text is converted into a vector of numbers that the model can process. These vectors( embedding) capture the meaning of the words in a way that the model can understand.

 

Transformer Encoder

The transformer encoder is a key part of the architecture. It takes the embedded input text and processes it through several layers. The encoder uses a mechanism called “self-attention,” which helps it focus on different parts of the text as it moves through the layers. This enables the model to understand the relationships between words in the text, even if they are far apart from each other.

Transformer Decoder

The transformer decoder is responsible for generating the output text. After processing the input through the encoder, the decoder takes the learned information and produces a sequence of tokens (words) that form the response. The decoder also uses self-attention to ensure that each word generated is contextually relevant to the previous ones.

Output Embedding

Once the transformer decoder generates a sequence of tokens, the output embedding converts these tokens back into human-readable text. The output embedding helps map the generated tokens to actual words or phrases in the target language.

Positional Encoding

Positional encoding is a crucial part of GPT’s ability to understand the order of words in a sequence. Since the transformer model doesn’t inherently understand word order, positional encodings are added to the input embeddings to give the model information about the position of each word in the sequence. This allows GPT to maintain the structure and meaning of the input text.

Self-Attention Mechanism

The self-attention mechanism is a key feature of the transformer architecture. It enables the model to focus on different words in the input text depending on their relevance to the current task. Each word in the sequence attends to every other word, allowing the model to capture complex relationships and dependencies between words in a sentence.

Feedforward Neural Network

A feedforward neural network is applied after each attention mechanism to help further process the information. It consists of a series of layers that transform the input data and allow the model to make more complex decisions about how to process the input and generate output.

Layer Normalization

Layer normalization is used to stabilize and speed up the training of the GPT model. It helps normalize the output of each layer to ensure that the model learns more efficiently. By reducing the internal covariate shift, layer normalization ensures that each layer’s output is consistent and helps prevent training instability.

Output Layer

The output layer is the final component in the GPT model. It takes the processed information from the decoder and uses it to predict the next word or token in the sequence. The output layer generates a probability distribution over all possible words, and the word with the highest probability is selected as the output.