Split Your LLMs: A Comprehensive Guide to Multi-GPU Deep Learning

Large Language Models (LLMs) are revolutionizing AI, powering everything from chatbots to advanced text generation. But these massive models, packed with billions of parameters, demand serious processing power, often exceeding the capabilities of a single GPU. Splitting LLMs across multiple GPUs is crucial for efficient training and inference, unlocking the full potential of these powerful tools.

This guide provides a comprehensive overview of how to split and load LLMs across multiple GPUs, addressing memory constraints and boosting model speed.

Is Your GPU Gasping for Air? Why Split LLMs?

Modern LLMs, like PaLM and Megatron, can have parameter counts in the hundreds of billions. This sheer size creates a significant bottleneck for single-GPU systems, potentially leading to out-of-memory (OOM) errors. Splitting your LLM tackles this challenge directly, offering two key advantages:

• Memory Scalability: Distribute the model's parameters across multiple GPUs to avoid OOM errors during both training and inference. Got a massive NLP model? Multi-GPU is your friend.
• Performance Gains: Parallel processing significantly accelerates both training and inference speeds, leading to faster multi-GPU inference and training.

Understanding the Two Main Methods: Data vs. Model Parallelism

When it comes to harnessing the power of multiple GPUs, two main approaches stand out: data parallelism and model parallelism. Each offers unique benefits for different applications. Understanding their differences is key to maximizing your LLM's performance.

Data Parallelism: Strength in Numbers

Data parallelism replicates the entire model on each GPU, feeding each GPU a unique subset of the training data.

• Each GPU independently calculates gradients on its assigned data.
• Gradients are then synchronized across all GPUs.

Model Parallelism: Divide and Conquer

Model parallelism splits the model itself across multiple GPUs, with each GPU handling specific layers or parameters.

• Model parameters are distributed at varying levels of granularity, including tensor, layer, and pipeline stages.
• It's particularly crucial for situations where you need to split an LLM across multiple GPUs because a single GPU can't hold the entire model, irrespective of model parallelism vs data parallelism

Diving Deeper: Types of Model Parallelism

Model parallelism isn't a one-size-fits-all solution. Several techniques exist, each with its characteristics. Which one should you choose when splitting an LLM across multiple GPUs?

Tensor Parallelism: Slicing and Dicing Weights

Tensor parallelism splits the weight matrices within each layer across multiple GPUs.

• Each GPU processes a different portion of the matrix.
• This approach is particularly useful in large matrix multiplication operations.

Pipeline Parallelism: The Assembly Line Approach

Pipeline parallelism divides the model's layers across multiple GPUs, creating a processing pipeline.

• Each GPU processes a specific segment of the model.
• GPUs work simultaneously on different mini-batches.

Sharded Data Parallelism: Best of Both Worlds

This technique combines data parallelism with parameter sharding (splitting the model parameters).

• Each GPU stores only a portion of the model's parameters.

Managing GPU Memory: Avoiding Bottlenecks During Multi-GPU Inference with LLM Tasks

GPU memory management is a critical concern, especially with multi-GPU systems. Naively splitting models can lead to cross-GPU communication overhead and memory fragmentation. Careful resource allocation is essential.

Key Considerations for Efficiency

• Batch Size: Increasing batch sizes maximizes GPU utilization but can lead to OOM errors if not managed carefully. Leverage profiling tools to identify memory hotspots.
• Activation Checkpointing: Reduces memory usage by recomputing forward passes for selected layers during backpropagation.
• Offloading: Transfers unused GPU memory to CPU or NVMe storage. While supporting larger models, it can introduce processing delays.

Frameworks to the Rescue: Tools for Multi-GPU Deep Learning with Large Models

Fortunately, many open-source frameworks simplify multi-GPU deep learning for LLMs. Here's a look at some leading solutions:

PyTorch DistributedDataParallel (DDP): Effortless Synchronization

PyTorch's DDP is a popular choice for distributed training.

• Enables automatic gradient synchronization across multiple GPUs and nodes.
• Each process runs the same model on a data subset, averaging gradients after each training iteration.

Key Benefits:

• Ease of Use: DDP automatically wraps your model and synchronizes gradients.
• Scalability: Highly scalable for large clusters.
• Versatility: Works well with single-node multi-GPU and multi-node setups.

Hugging Face Accelerate: Simplified Multi-GPU Inference

Hugging Face's Accelerate library simplifies multi-GPU inference with minimal code changes.

• Provides automatic model sharding via device_map="auto".
• Automatically distributes models across available GPUs without manual partitioning.

Ollama: Streamlined CPU and GPU Inference

The Ollama framework offers efficient CPU and GPU inference for LLMs.

• Supports multiple GPUs by setting environment variables or editing a settings file.
• Allows specifying model partitioning among available GPUs.

Environment Variables for GPU Partitioning:

• OLLAMA_GPU_COUNT: Specifies the number of GPUs to use.
• OLLAMA_GPU_MEMORY_LIMIT: Defines the upper limit for GPU memory allocation.

vLLM: Optimized Inference for Large Models

vLLM (Versatile Large Language Model) offers high-performance inference for LLMs.

• Features an optimized transformer interpreter.
• Introduces PagedAttention to manage the memory demands of the KV cache.
• Supports distributed inference across multiple GPUs or machines.

DeepSpeed: Maximizing Scale and Efficiency for Splitting LLMs Across Multiple GPUs

Microsoft's DeepSpeed optimizes large-scale model training.

• Utilizes the ZeRO Redundancy Optimizer to partition model states across GPUs.
• Offers CPU and NVMe offloading via ZeRO-Offload and ZeRO-Infinity.

DeepSpeed Stages:

• ZeRO-1: Shards optimizer states.
• ZeRO-2: Shards optimizer + gradients.
• ZeRO-3: Shards optimizer + gradients + parameters (model weights).

Megatron-LM: NVIDIA's Powerhouse for Massive Models

NVIDIA's Megatron-LM enables training massive transformer models.

• Combines tensor parallelism with pipeline parallelism.
• Supports defining tensor parallel size (GPU distribution per layer) and pipeline parallel size (model stage segmentation).

Distributed Training Across Multiple Machines: Pushing the Boundaries

For even larger LLMs, distributed training across multiple machines is necessary. This requires setting up a distributed environment where multiple GPUs operate on each node. Let's explore the essential components and steps involved.

Master Node Setup: The Conductor of the Orchestra

• Identify the master node's IP address and port—this node manages all others.

Rank and World Size: Defining the Players

• Assign a rank to each process or node.
• The total number of processes represents the "world size".

Launch: Starting the Engines

• Use torch.distributed.launch or torchrun to initiate multiple processes across different nodes.

Network Optimization: The Super Highway for Data

• Use a high-bandwidth network like InfiniBand to minimize synchronization overhead.

Distributed LLM Training with PyTorch DDP: A Step-by-Step Guide

PyTorch's DistributedDataParallel (DDP) makes training models across multiple GPUs manageable by replicating the model on each GPU and synchronizing gradients. Here's a clear, concise guide to the key steps:

1. Initialize the Process Group: Forming the Team

Configure the distributed backend to manage communication between processes. The NCCL (NVIDIA Collective Communications Library) backend is commonly used for GPU operations:

import torch.distributed as dist
dist.init_process_group(backend="nccl")

This establishes a communication group connecting all processes, where each process corresponds to a single GPU.

2. Configure the Device and Wrap the Model with DDP: Gearing Up

Identify the GPU for the current process by checking the LOCAL_RANK environment variable, then assign the model to that GPU. Wrap the model with DistributedDataParallel:

import os
import torch

local_rank = int(os.environ["LOCAL_RANK"])
torch.cuda.set_device(local_rank)
model = MyModel().to(local_rank)
model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[local_rank])

3. Use a DistributedSampler in the DataLoader: Dividing the Spoils

Instead of using shuffle=True, use DistributedSampler to ensure each process receives a unique subset of the dataset:

from torch.utils.data import DataLoader, DistributedSampler

sampler = DistributedSampler(train_dataset)
train_loader = DataLoader(train_dataset, batch_size=BATCH_SIZE, sampler=sampler)

4. Implement the Training Loop: The Daily Grind

Within each process, run the usual training loop: retrieve data, send it to the GPU, compute loss, and execute loss.backward() followed by optimizer.step(). DDP automatically handles gradient synchronization in the backward pass. Once training is complete, clean up with dist.destroy_process_group().

5. Launch Training with torchrun: Sending it Into Motion

Launch your training script using the torchrun utility:

torchrun --nproc_per_node=4 train_ddp.py

• --nproc_per_node=4: Specifies the number of processes to launch on each node (typically the number of GPUs).
• train_ddp.py: Your training script for DDP-based distributed training.

Tackling Common Errors: A Troubleshooting Guide for LLM Distribution

Distributing LLMs across multiple GPUs can present challenges. Here's a breakdown of common errors, their causes, and solutions:

Conclusion: Splitting LLMs Across Multiple GPUs to Level up LLM Performance

Splitting LLMs across multiple GPUs is essential for tackling the computational demands of modern AI. By understanding data parallelism, model parallelism, and available frameworks, you can optimize your LLM's performance and unlock its full potential.