Principles

Tokenization Basics BPE Algorithm GPT Tokenizers BPE Training Engineering

Model Architecture

From token ids to logits Embedding and LM Head

Attention Mechanisms

From Self-Attention to GQA Attention Sink

Position Encoding

Position Encoding Basics RoPE Math Derivation RoPE Implementation Length Extrapolation

GPU Programming Basics

GPU Architecture Basics Tensor Layout Triton Basics: Vector Add

Flash Attention Principles From Naive Implementation to Auto-Tuning Block Pointers and Multi-Dim Support Causal Masking Optimization Grouped Query Attention Backward Pass Implementation

Distributed Training

Data Parallelism ZeRO Optimizer Fully Sharded Data Parallel Tensor Parallelism Pipeline Parallelism Multi-Dimensional Hybrid Parallelism

Hands-on Training

Pretraining Data Tokenizer Training Model Architecture Data Pipeline Training Loop Monitoring and Validation

SystemsFlashAttention

From Naive Implementation to Auto-Tuning

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Flash Attention Principles

Through interactive visualizations, gain a deep understanding of Flash Attention's core techniques: the memory bottleneck, Online Softmax, and tiled matrix multiplication.

Block Pointers and Multi-Dim Support

Scale from single sequence to Batch/Head parallelism and simplify pointer math with block pointers.

Table of Contents

Core Loop Structure

Understanding Why tl.constexpr Is Necessary

Understanding Pointer Arithmetic

Interaction Guide

Verifying Numerical Correctness

Using Auto-Tuning to Find the Best Configuration

Introducing @triton.autotune

Pipeline Parallelism

Key Parameter Analysis