4 Implementing a GPT model from scratch to generate text
ver. 1.0.0, 4_implementing_a_gpt_model_from_scratch_to_generate_text
Implement, assemble, analyze, and run a GPT‑2 124M model in PyTorch from primitives to text generation.
This unit teaches how to implement every component of a GPT‑2 124M model in PyTorch and stitch them into a working autoregressive generator. You will build LayerNorm with learnable scale/shift, GELU and the feed‑forward subnetwork, residual‑connected Transformer blocks that combine causal multi‑head attention, and the full GPT model (embeddings, block stack, final norm, and output head). You also measure parameter counts and memory, understand weight tying, and implement a greedy generation loop to turn logits into text.
You learn to construct a full GPT‑2 124M architecture from first principles and run it end‑to‑end in PyTorch. Starting from a placeholder model that preserves tensor flow, you implement a LayerNorm that standardizes per‑token activations and restores expressiveness with learned scale and shift. You code the GELU activation and the position‑wise feed‑forward network that expands and contracts token representations (e.g., 768 → 3072 → 768). You observe vanishing gradients in deep stacks and fix them by adding residual shortcut connections so gradients flow through many layers.
With these building blocks, you compose causal multi‑head self‑attention, the feed‑forward sublayer, pre‑LayerNorm, dropout and residuals into a Transformer block and repeat it to form the 12‑layer stack used by the 124M model. You replace placeholders with real modules to assemble the complete GPTModel: token and positional embeddings, dropout, the transformer block stack, a final LayerNorm, and a linear output head. You run a batch through the model to verify shapes and behavior.
You quantify the resulting model: counting parameters per component, converting counts to memory footprint (float32), and explaining the effect of weight tying on the model’s apparent parameter tally. Finally, you implement a greedy autoregressive generation loop that converts logits into tokens and decodes them to text, demonstrating that an untrained model produces random output and motivating the need for pretraining.
After completing the unit you will be able to: - Specify GPT‑2 124M hyperparameters and build a placeholder that carries tensors end‑to‑end. - Implement LayerNorm with learnable affine parameters and the GELU activation. - Implement the expand‑then‑contract feed‑forward subnetwork. - Add residual shortcuts to preserve gradient flow through deep stacks. - Assemble attention, feed‑forward, pre‑LayerNorm, dropout and shortcuts into a TransformerBlock. - Build the full GPTModel from embeddings, a block stack, final normalization and head. - Compute parameter counts, estimate memory footprint, and reason about weight tying. - Write a simple greedy autoregressive loop to generate text from logits.
This prepares you to move on to giving the model meaningful weights via pretraining or loading pretrained weights for downstream use.
Materials
Source document
- Building a Large Language Model (from scratch), Sebastian Raschka, 2024, Manning Books — Link — Page 114-149