Skip to content

Relaxed-System-Lab/HKUST-COMP4901Y-2025spring

Repository files navigation

COMP4901Y 2025 Spring

Large-Scale Machine Learning for Foundation Models

Lecturer: Binhang Yuan.

Teaching Assistant: You Peng, Yukun Zhou, Yuxuan Li.

Overview

In recent years, foundation models have fundamentally revolutionized the state-of-the-art of artificial intelligence. Thus, the computation in the training or inference of the foundation model could be one of the most important workflows running on top of modern computer systems. This course unravels the secrets of the efficient deployment of such workflows from the system perspective. Specifically, we will i) explain how a modern machine learning system (i.e., PyTorch) works; ii) understand the performance bottleneck of machine learning computation over modern hardware (e.g., Nvidia GPUs); iii) discuss four main parallel strategies in foundation model training (data-, pipeline-, tensor model-, optimizer- parallelism); and iv) real-world deployment of foundation model including efficient inference and fine-tuning.

Syllabus

Date Topic
W1-02/04,02/06 Introduction and Logistics [Slides] & ML Preliminary [Slides] [Notebook]
W2-02/11,02/13 Stochastic Gradient Descent [Slides] [Notebook] & Automatic Differentiation [Slides] [Notebook]
W3-02/18,02/20 Language Model Architecture [Slides] & Large Scale Pretrain Overview [Slides]
W4-02/25,02/27 Nvidia GPU Performance & Collective Communication Library
W5-03/04,03/06 Data-, Pipeline- Parallel Training & Tensor Model-, Optimizer- Parallel Training
W6-03/11,03/13 Sequence-, MoE- parallelism & Mid-Term Review
W7-03/18,03/20 Mid-Term Exam & Generative Inference
W8–03/25,03/27 Inference Alogirhtm Optimizations & Inference System Optimizations
W9-04/01,04/03 Spring Break
W10-04/08,04/10 Prompt Engineering & Inference Scaling
W11-04/15,04/17 RAG & LLM Agent
W12-04/22,04/24 Parameter Efficient Fine-Tuning & RL Alignment
W13-04/29 LLM Evaluation
W14-05/06,05/08 Guest Speech & Final Review

Grading Policy

  • 4 Homework (4 $\times$ 5% $=$ 20%);
  • Mid-term exam (30%);
  • Final exam (50%).

Homework

Topic Release Due
Homework1 2025/02/13 ✔️ 2025/02/22
Homework2 2025/03/04 2025/03/12
Homework3 2025/03/20 2025/04/01
Homework4 2025/04/22 2025/04/31

About

No description, website, or topics provided.

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published