Implement Multi-Device Parallel Training Strategies for Efficient Large Model Training - FSDP/TP/PP/DP

What you'll learn

Distributed Training of Large Scale Machine Learning Models

How to parallelize models over workers (tensor, pipeline, data, sequence)

Collective communication in training large scale models

Using Jax and XLA to distribute models over thousands of workers

Requirements

Intermediate Python, Entry level Machine Learning knowledge, Some Jax knowledge preferred

Description

Master the art and science of distributed training for GPT-style language models in this comprehensive course using JAX and XLA. Learn how to scale up autoregressive transformer training from scratch, implementing the same techniques used by leading AI labs to train large language models efficiently across multiple devices.What You'll Learn:Distributed Training FundamentalsUnderstanding distributed computing for language modelsJAX's transformation and parallelization primitivesXLA (Accelerated Linear Algebra) compilation optimizationMastering collective communication primitives: • All-reduce for gradient aggregation • All-gather for distributed data collection • Broadcast for parameter synchronization • Scatter/Gather for efficient token distributionAdvanced Parallelization Strategies for GPTTensor Parallelism: Splitting attention heads and MLP layersPipeline Parallelism: Optimal transformer layer distributionSequence Parallelism: Handling long sequences efficientlyData Parallelism: Scaling batch processingHybrid parallelism approaches specific to GPT modelsGSPMD and Automated ShardingImplementing GSPMD for GPT model componentsAutomatic sharding strategies for attention layersOptimizing communication patterns for autoregressive modelsCustom sharding annotations for transformer blocksPractical ImplementationBuilding distributed GPT architectures from scratchEfficient parameter sharding and synchronizationHandling distributed attention computationManaging causal masks in distributed settingsImplementing efficient parameter serversPerformance Optimization and DebuggingProfiling distributed GPT trainingOptimizing attention computationImplementation of gradient checkpointingMemory optimization techniquesHandling numerical stability in distributed settingsHands-on Projects:Implement a mini-GPT from scratch with distributed trainingBuild hybrid parallelism strategies for GPT trainingCreate custom sharding strategies using GSPMDOptimize collective communication patterns for transformer blocksDeploy and manage distributed GPT training across multiple nodesWho This Course is For:Machine Learning Engineers working on language modelsDeep Learning Researchers scaling up GPT-style modelsSoftware Engineers transitioning to large-scale MLAnyone interested in training large language modelsPrerequisites:Strong Python programming skillsBasic understanding of transformer architecturesFamiliarity with language modeling conceptsBasic linear algebra and calculusExperience with any deep learning frameworkBy the end of this course, you'll understand how to implement and scale GPT model training across distributed systems. You'll master the intricacies of parallel training strategies, communication primitives, and optimization techniques specific to large language models.

Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 Introduction to Mesh

Lecture 3 Collective Communication

Lecture 4 Data Parallelism

Lecture 5 FSDP (Fully Sharded Data Parallel)

Lecture 6 FSDP+TP (Fully Sharded Data Parallel plus Tensor Parallel

Lecture 7 Pipeline Parallel

Anyone who wants to learn how to parallelize and scale machine learning models over thousands of workers using Jax and XLA.