LLM GPU Optimization 🚄

Advanced LLM optimization techniques using CUDA. Features efficient attention mechanisms, custom CUDA kernels for transformers, and memory-efficient training strategies.

Features • Installation • Quick Start • Documentation • Contributing

📑 Table of Contents

• Features
• Project Structure
• Prerequisites
• Installation
• Quick Start
• Documentation
  • Optimizations
  • Memory Management
  • Benchmarks
• Contributing
• Versioning
• Authors
• Citation
• License
• Acknowledgments

✨ Features

• Flash Attention implementation
• Efficient KV-cache management
• Custom CUDA kernels for attention
• Memory-efficient transformer layers
• Multi-GPU training optimization

📁 Project Structure

graph TD
    A[llm-gpu-optimization] --> B[kernels]
    A --> C[models]
    A --> D[training]
    A --> E[benchmarks]
    B --> F[attention]
    B --> G[memory]
    C --> H[transformer]
    C --> I[tokenizer]
    D --> J[distributed]
    D --> K[optimization]
    E --> L[profiling]
    E --> M[metrics]

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Click to expand full directory structure

llm-gpu-optimization/
├── kernels/           # CUDA kernel implementations
│   ├── attention/    # Optimized attention mechanisms
│   └── memory/      # Memory management utilities
├── models/           # Model implementations
│   ├── transformer/ # Transformer architecture
│   └── tokenizer/   # Tokenization optimizations
├── training/         # Training utilities
│   ├── distributed/ # Multi-GPU training
│   └── optimization/# Training optimizations
├── benchmarks/       # Performance benchmarks
└── README.md         # Documentation

🔧 Prerequisites

• CUDA Toolkit 11.8+
• NVIDIA GPU (Compute Capability 8.0+)
• PyTorch 2.2+
• 32GB+ GPU RAM recommended
• NVLink (for multi-GPU setup)

📦 Installation

# Clone repository
git clone https://github.com/BjornMelin/llm-gpu-optimization.git
cd llm-gpu-optimization

# Create environment
python -m venv venv
source venv/bin/activate

# Install dependencies
pip install -r requirements.txt

# Build CUDA extensions
python setup.py install

🚀 Quick Start

from llm_gpu import models, optimizers

# Initialize model with optimizations
model = models.OptimizedTransformer(
    attention_type='flash',
    use_kv_cache=True
)

# Configure distributed training
trainer = optimizers.DistributedTrainer(
    model,
    memory_efficient=True,
    gradient_checkpointing=True
)

# Train with optimizations
trainer.train(dataset, batch_size=32)

📚 Documentation

Optimizations

Technique	Description	Memory Savings	Speed Improvement
Flash Attention	Efficient attention computation	80%	3x
KV Cache	Optimized key-value storage	60%	2x
Gradient Checkpointing	Memory-efficient training	70%	0.8x

Memory Management

• Dynamic memory allocation
• Gradient accumulation
• Activation checkpointing
• Memory-efficient attention patterns

Benchmarks

Performance on different model sizes:

Model Size	Batch Size	GPU	Memory Usage	Training Time
7B	32	A100-80GB	76GB	0.8s/step
13B	16	A100-80GB	71GB	1.2s/step
70B	8	8xA100	64GB/GPU	2.5s/step

🤝 Contributing

• Contributing Guidelines
• Code of Conduct
• Development Guide

📌 Versioning

We use SemVer for versioning. For available versions, see the tags on this repository.

✍️ Authors

Bjorn Melin

• GitHub: @BjornMelin
• LinkedIn: Bjorn Melin

📝 Citation

@misc{melin2024llmgpuopt,
  author = {Melin, Bjorn},
  title = {LLM GPU Optimization: Advanced CUDA Optimization for Language Models},
  year = {2024},
  publisher = {GitHub},
  url = {https://github.com/BjornMelin/llm-gpu-optimization}
}

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Flash Attention paper authors
• HuggingFace Transformers team
• NVIDIA for CUDA toolkit and documentation

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Made with 🚄 and ❤️ by Bjorn Melin