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https://github.com/bjornmelin/llm-gpu-optimization
🚄 Advanced LLM optimization techniques using CUDA. Features efficient attention mechanisms, custom CUDA kernels for transformers, and memory-efficient training strategies. ⚡
https://github.com/bjornmelin/llm-gpu-optimization
cuda deep-learning gpu-acceleration llm-optimization machine-learning memory-optimization parallel-computing transformers
Last synced: 20 days ago
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🚄 Advanced LLM optimization techniques using CUDA. Features efficient attention mechanisms, custom CUDA kernels for transformers, and memory-efficient training strategies. ⚡
- Host: GitHub
- URL: https://github.com/bjornmelin/llm-gpu-optimization
- Owner: BjornMelin
- License: mit
- Created: 2025-01-24T02:44:53.000Z (20 days ago)
- Default Branch: main
- Last Pushed: 2025-01-24T03:16:39.000Z (20 days ago)
- Last Synced: 2025-01-24T04:19:59.745Z (20 days ago)
- Topics: cuda, deep-learning, gpu-acceleration, llm-optimization, machine-learning, memory-optimization, parallel-computing, transformers
- Homepage:
- Size: 5.86 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# LLM GPU Optimization 🚄
[](https://developer.nvidia.com/cuda-toolkit)
[](https://www.python.org/downloads/)
[](https://pytorch.org/)
[](LICENSE)
[](CONTRIBUTING.md)
> Advanced LLM optimization techniques using CUDA. Features efficient attention mechanisms, custom CUDA kernels for transformers, and memory-efficient training strategies.
[Features](#features) • [Installation](#installation) • [Quick Start](#quick-start) • [Documentation](#documentation) • [Contributing](#contributing)
## 📑 Table of Contents
- [Features](#features)
- [Project Structure](#project-structure)
- [Prerequisites](#prerequisites)
- [Installation](#installation)
- [Quick Start](#quick-start)
- [Documentation](#documentation)
- [Optimizations](#optimizations)
- [Memory Management](#memory-management)
- [Benchmarks](#benchmarks)
- [Contributing](#contributing)
- [Versioning](#versioning)
- [Authors](#authors)
- [Citation](#citation)
- [License](#license)
- [Acknowledgments](#acknowledgments)
## ✨ Features
- Flash Attention implementation
- Efficient KV-cache management
- Custom CUDA kernels for attention
- Memory-efficient transformer layers
- Multi-GPU training optimization
## 📁 Project Structure
```mermaid
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]
```
Click to expand full directory structure
```plaintext
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
```bash
# 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
```python
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](CONTRIBUTING.md)
- [Code of Conduct](CODE_OF_CONDUCT.md)
- [Development Guide](DEVELOPMENT.md)
## 📌 Versioning
We use [SemVer](http://semver.org/) for versioning. For available versions, see the [tags on this repository](https://github.com/BjornMelin/llm-gpu-optimization/tags).
## ✍️ Authors
**Bjorn Melin**
- GitHub: [@BjornMelin](https://github.com/BjornMelin)
- LinkedIn: [Bjorn Melin](https://linkedin.com/in/bjorn-melin)
## 📝 Citation
```bibtex
@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](LICENSE) file for details.
## 🙏 Acknowledgments
- Flash Attention paper authors
- HuggingFace Transformers team
- NVIDIA for CUDA toolkit and documentation
---
Made with 🚄 and ❤️ by Bjorn Melin