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Principles

Tokenization
Tokenization BasicsBPE AlgorithmGPT TokenizersBPE Training Engineering
Model Architecture
Transformer LM
From token ids to logitsEmbedding and LM Head
Attention Mechanisms
From Self-Attention to GQAAttention Sink
Position Encoding
Position Encoding BasicsRoPE Math DerivationRoPE ImplementationLength Extrapolation
GPU Programming Basics
GPU Architecture BasicsTensor LayoutTriton Basics: Vector Add
FlashAttention
Flash Attention PrinciplesFrom Naive to Auto-TuningBlock Pointers and Multi-Dim SupportCausal Masking OptimizationGrouped Query AttentionBackward Pass
Distributed Training
Data ParallelismZeRO OptimizerFully Sharded Data Parallel张量并行流水线并行多维混合并行

Hands-on Training

Overview
Pretraining
Pretraining DataTokenizer TrainingModel ArchitectureData PipelineTraining LoopMonitoring and Validation
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SystemsGPU Programming Basics

Triton Basics: Vector Add

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Learn Triton’s programming model through a simple vector add example.

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Tensor Layout

Understand physical memory layout, strides, view vs reshape, and gradient tracking.

Flash Attention

Deeply understand Flash Attention principles and Triton implementation

Table of Contents

SPMD Programming Model
Build the Kernel Step by Step
Step 1: Identify Yourself
Step 2: Compute Offsets
Step 3: Handle Boundaries
Step 4: Load, Compute, Store
Full Kernel Code
Launching the Kernel
Verify Correctness
Summary