Complete LLM Transformer Engineering Notes

Complete Module: Cross-Attention, Seq2Seq, Machine Translation, Mini-T5 (64-dim)

Complete Module: Tiling, Online Softmax, IO-Aware, 3x Faster, 50% Less Memory

Full Stack 124M GPT — 100% PyTorch, No Frameworks

Complete Module: Big-O, Parallelism, FlashAttention, LoRA

Complete Module: UTF-8 Bytes, Trie, Full GPT-2 Tokenizer

Complete Module: Tries, Hash Maps, BPE from Scratch

Complete Module: Priority Queues, Heaps, Top-k, Nucleus Sampling

Master Transformer inference — KV caching, memoization, space/time optimization, and achieve 10x faster generation with Mini-GPT (64-dim).

Complete Module: Gradient Descent, Computation Graph, Train on TinyShakespeare

Complete Module: Autoregressive DP, Caching, Mini-GPT (64-dim)

Complete Module: Dynamic Programming, Memoization, LayerNorm + Residual

Complete Module: Hash Functions, Signal Processing, Sinusoidal vs Learned PE

Complete Module: Parallelism, Divide & Conquer, Multi-Head from Scratch

Complete Module: Scaled Dot-Product Attention + Positional Encoding + Visualization

Implement and understand the Scaled Dot-Product Attention mechanism from the seminal paper "Attention is All You Need" (Vaswani et al., 2017) — with visualization, intuition, and efficiency tricks (hashing for large inputs).

Master the transition from NumPy to PyTorch by understanding how core mathematical operations on arrays, matrices, and vectors map between the two libraries — with practical, runnable examples.

A full-stack, zero-to-hero journey through every core component of modern large language models — from Feedforward & Residuals (Dynamic Programming, LayerNorm, Pre-Norm) to Decoder-Only Architecture (autoregressive, KV caching, Mini-GPT), Encoder-Decoder Transformers (cross-attention, seq2seq, Mini-T5), Training Loop & Backpropagation (autograd, gradient descent, TinyShakespeare), Inference & KV Cache (10x faster generation), Beam Search & Sampling (priority queues, top-k, nucleus), Tokenization & Vocabulary (BPE, Tries, Hash Maps), Byte-level BPE (UTF-8, GPT-2 compatible), Scaling Laws & Optimization (Chinchilla, FlashAttention, LoRA), culminating in the Capstone: 124M GPT from Scratch (full model, tokenizer, training, generation — no frameworks), and finally FlashAttention from Scratch (tiling, online softmax, 3x faster, 50% less memory). Build, train, optimize, and deploy GPT-class models with 100% PyTorch, no abstractions, full control — exactly how OpenAI, Meta, and xAI do it.

FlashAttention is a groundbreaking optimization for the self-attention mechanism in Transformer models, introduced in 2022 by Tri Dao et al. It computes exact attention (no approximations) while dramatically reducing memory usage and runtime, particularly for long sequences. By leveraging GPU memory hierarchies—specifically minimizing data movement between slow High Bandwidth Memory (HBM) and fast on-chip SRAM (Shared Memory)—it addresses the quadratic O(N²) memory bottleneck of standard attention, where N is sequence length. This makes it ideal for training and inference in large language models (LLMs) like GPT, enabling longer contexts (e.g., up to 64K tokens) without quality loss. Below, I'll break down the core optimizations, algorithm, and benefits, drawing from the original paper and subsequent improvements.