Coding a ChatGPT Like Transformer From Scratch in PyTorch

Overview

StatQuest with Josh Starmer provides a step-by-step guide to coding a decoder-only transformer, the foundation of models like ChatGPT, from scratch using PyTorch. The tutorial covers essential components including data preparation with tokenization and embedding, positional encoding using sine and cosine functions, masked self-attention mechanisms with query, key, and value calculations, and the overall decoder-only transformer architecture. It concludes with training the model using PyTorch Lightning and demonstrating its ability to generate responses to prompts.

Key takeaways

A decoder-only transformer, foundational for models like ChatGPT, can be built from scratch in PyTorch.
Positional encoding is crucial for transformers to understand token order, implemented using sine and cosine functions.
Masked self-attention prevents future token leakage by using a causal mask during the calculation of attention scores.
PyTorch Lightning simplifies the training process by handling boilerplate code for optimizers, training steps, and trainers.
The training data format requires shifting tokens to create input-label pairs for next-token prediction.
After training on simple prompts, the model successfully generates the desired 'Awesome EOS' response.

Chapters

0:00 Introduction and Project Setup

Goal: Code a decoder-only transformer from scratch in PyTorch.
Imports include torch, torch.nn, torch.nn.functional, Adam, TensorDataset, DataLoader, and PyTorch Lightning (L).
The code is available for free via a link in the pinned comment.

3:25 Data Preparation and Tokenization

Example prompts: 'What is StatQuest?' and 'StatQuest is what?' with the desired response 'awesome'.
Vocabulary includes: 'What', 'is', 'StatQuest', 'awesome', 'EOS'.
Tokens are mapped to integer IDs for PyTorch's nn.Embedding layer.
Input and label tensors are created by shifting tokens to predict the next token in the sequence.

10:10 Positional Encoding Implementation

Positional encoding adds information about the position of tokens in the sequence.
Uses alternating sine and cosine functions based on token position (pos) and embedding dimension index (i).
Precomputes positional encoding values into a matrix (PE) for efficiency.
The `PositionEmbedding` class calculates and adds these encodings to word embeddings.

23:34 Masked Self-Attention Mechanism

Calculates Query (Q), Key (K), and Value (V) matrices using linear transformations (nn.Linear) of token encodings.
Attention scores are computed by multiplying Q with the transpose of K, scaled by the square root of the key dimension.
A mask is applied to prevent tokens from attending to future tokens, implemented using `masked_fill` with a large negative number.
Softmax is applied to scaled similarities to get attention percentages, which are then multiplied by V to get the final attention scores.

Summary, takeaways, and chapters were generated by AI from the video's transcript and may contain errors. The video belongs to its creator, StatQuest with Josh Starmer.