Train GPT-like Model with DDP: Code Walkthrough

Table of Contents

• Introduction
• Setting up the Project
• Understanding the Code Structure
• Launching the Training Job on a Single Node
• Training the Model on a Multi-GPU Node
• Launching the Training Job on a Slurm Cluster
• Exploring Fully Sharded Data Parallelism(FSDP)
• Conclusion
• Resources

Introduction

In this article, we will explore the topic of distributed training using PyTorch's Distributed Data Parallelism (DDP). We will learn how to train a real-world language model using DDP and understand the code structure required for distributed training. Additionally, we will explore launching the training job on a single node, a multi-GPU node, and a Slurm cluster. We will also briefly introduce Fully Sharded Data Parallelism (FSDP) for models that are too large to fit on a single GPU.

Setting up the Project

Before diving into the code, it is important to set up the project properly. We will clone the minGPT repository from GitHub, which provides a convenient implementation of the GPT language model. We will also organize our project files and use YAML files for easy configuration management. We'll be using Hydra, an open-source library, to Read configurations into our Python code.

Understanding the Code Structure

To understand how the distributed training works, we need to understand the code structure of the project. The project consists of several Core files, including the model implementation, the trainer class, and configuration files. We'll walk through each file and discuss its purpose and functionality.

Launching the Training Job on a Single Node

In this section, we'll learn how to launch the training job on a single node. We'll start with the main.py file, which serves as the entry point to our training job. We'll go through the code step by step, understand how the configurations are read, and initialize the model, optimizer, and data sets. Once everything is set up, we'll initialize a trainer instance and run the training job.

Training the Model on a Multi-GPU Node

Next, we'll explore training the model on a multi-GPU node. We'll SSH into an AWS P3 instance that contains four GPUs and launch the training job using Torchrun. We'll update the max epochs to Continue training from a saved snapshot and observe the training process on each GPU. We'll also discuss the concept of distributed data loaders and how each process participates in the evaluation run.

Launching the Training Job on a Slurm Cluster

In this section, we'll take our training job a step further and launch it on a Slurm cluster across two machines. We'll SSH into the cluster's head node, clone our repository, and examine the Slurm script. By updating the max epochs, we'll resume training from a previous snapshot and observe the training process on multiple machines. We'll discuss the script options and provide a brief refresher on launching multi-node jobs on Slurm.

Exploring Fully Sharded Data Parallelism (FSDP)

While DDP is suitable for most distributed training scenarios, there are cases where the model is too large to fit on a single GPU. In such cases, PyTorch offers an alternative API called Fully Sharded Data Parallelism (FSDP). We'll briefly introduce FSDP, which takes the data parallel approach a step further by shardin the model parameters, optimizer states, and gradients across distributed processes. We'll discuss the benefits of FSDP and provide resources for further exploration.

Conclusion

In conclusion, distributed training using DDP is a powerful technique for scaling up the training process. We have learned how to set up the project, understand the code structure, and launch the training job on different setups. We have also briefly explored FSDP as an alternative for larger models. With this knowledge, You can now Apply DDP to your own projects and leverage the full potential of distributed computing.

Resources

• minGPT repository on GitHub
• PyTorch website
• Hydra - easy configuration management
• Torchrun - distributed training with PyTorch

Highlights

• Learn how to train a real-world language model using PyTorch's Distributed Data Parallelism (DDP)
• Explore launching the training job on a single node, a multi-GPU node, and a Slurm cluster
• Understand the code structure and configuration management using YAML files
• Briefly introduce Fully Sharded Data Parallelism (FSDP) for training models that are too large for a single GPU

FAQ

Q: What is the benefit of using DDP for distributed training?

DDP allows us to leverage multiple GPUs and machines to speed up the training process and handle larger models. It distributes the data, model parameters, optimizer state, and gradients across multiple processes, enabling concurrent training and evaluation.

Q: Can I use DDP to train any type of model?

Yes, DDP can be used with various model architectures in PyTorch. However, it is important to consider the GPU memory limitations when scaling up to larger models. In such cases, alternative methods like Fully Sharded Data Parallelism (FSDP) can be explored.

Q: What is the role of YAML files in the project?

YAML files provide an easy way to manage configurations for the training job. Instead of modifying code directly, you can modify YAML files to change hyperparameters, data paths, and other options. This allows for easier experimentation and reproducibility.

Q: How can I resume training from a saved snapshot?

By initializing the trainer instance with a saved snapshot, you can resume training from that point. The trainer will load the model state and optimizer state from the snapshot and continue the training process. This is useful in case of interruptions or failures during training.

Q: Can DDP be used for training models with very large parameters?

DDP is suitable for most scenarios, but for models that cannot fit on a single GPU, Fully Sharded Data Parallelism (FSDP) can be a better option. FSDP shards the model parameters, optimizer states, and gradients across distributed processes, reducing the GPU memory footprint and enabling training of larger models.

Q: Where can I find more resources on FSDP and distributed training?

You can find more resources and tutorials on FSDP and distributed training on the PyTorch Website. The provided resources will help you understand and implement FSDP effectively for your specific training needs.