Introduction
Large Language Models (LLMs) like GPT-3 and BERT are at the forefront of AI advancements, powering applications from natural language understanding to generative text. These models, however, bring significant challenges in terms of memory usage and throughput. To simplify these complex concepts, we'll use a practical example with a Multi-Layer Perceptron (MLP), a foundational neural network architecture. This blog will guide you through calculating memory requirements and throughput, setting the stage for understanding these metrics in more complex models like LLMs.
The content will follow the same structure as provided previously, with the focus remaining on using the MLP example to introduce the foundational concepts of memory and throughput that apply to LLMs. This way, readers can grasp these critical aspects in a manageable context before applying them to more sophisticated models.
Understanding the MLP Model
An MLP consists of an input layer, multiple hidden layers, and an output layer, with each neuron fully connected to neurons in the subsequent layer. This foundational architecture, though simpler than LLMs, helps illustrate the core principles of managing computational resources in neural networks.
Example MLP Configuration:
Input Size: 10,000 neurons
Hidden Layers: 10 layers, each with 10,000 neurons
Output Layer: 10,000 neurons
Data Type:
float32(4 bytes per float)
Calculating Memory Requirements
1. Model Parameters Memory
Each layer's memory requirements for storing weights and biases are calculated as follows:
Weights per Layer:
Number of weights = inputs × outputs = 10⁸
Memory required = 10⁸ weights × 4 bytes/weight = 400 MB per layer
Biases per Layer:
- Memory required = 10⁴ biases × 4 bytes/bias = 40 KB per layer
Total Parameter Memory:
- For 10 layers: 10 × 400 MB = 4 GB
2. Activation Memory
Activation memory stores intermediate outputs (activations) during the forward pass, which are needed for backpropagation.
Activation Memory per Layer per Sample:
- Memory required = 10⁴ × 4 bytes = 40 KB per layer
Total Activation Memory per Sample:
11 sets of activations (including input and output layers)
Total: 11 × 40 KB = 440 KB
Batch Size Calculation:
For an 8 GB memory limit:
4 GB (parameters) + B × 440 KB (activations) ≤ 8 GB
Maximum batch size ≈ 9 samples
Calculating Throughput
Computational Cost per Layer
Forward Pass: 2nm FLOPs
Backward Pass: 4nm FLOPs
Total per layer: 6 × 10⁸ FLOPs
Total FLOPs per Sample:
- For 10 layers: 10 × 6 × 10⁸ = 6 × 10⁹ FLOPs
Throughput Calculation:
With 100 TFlops hardware capability:
- Throughput = 100 TFlops/s ÷ 6 GFlops/sample ≈ 16.67 samples/s
Connecting MLPs to LLMs
While MLPs are much simpler than LLMs, the principles of memory management and throughput calculation are directly applicable to larger models. LLMs, which can contain billions of parameters, face similar challenges but on a larger scale. Techniques such as model pruning, quantization, and the use of specialized hardware are essential for managing these complex models effectively. Understanding these basics with MLPs provides a foundation for tackling the intricacies of LLMs.
Conclusion
This guide illustrates the foundational concepts of memory and throughput in neural networks using an MLP model as a simplified example. As you move toward more complex architectures like LLMs, these core principles remain essential for optimizing performance and resource usage. Whether you are a beginner or an advanced practitioner, understanding these fundamentals will help you navigate the complexities of modern AI models and ensure efficient deployment in real-world applications.