EasyDeL: Enhancing Machine Learning Training
Overview
EasyDeL is a cutting-edge open-source framework created to elevate and simplify the training processes of machine learning models, emphasizing Jax/Flax. Designed to streamline the experience of working with these models, EasyDeL facilitates efficient training and serving of Flax/Jax models on large-scale platforms such as TPU and GPU.
Key Features
- Versatile Architecture Compatibility: EasyDeL integrates various model architectures, from Transformers to newer models like RWKV and Mamba.
- Wide Model Support: It includes numerous models implemented in JAX, such as Falcon, Qwen2, Phi2, and many more.
- Advanced Training Modules: Specialized trainers, including DPOTrainer and SFTTrainer, cater to different training needs.
- Serving and API Solutions: EasyDeL offers robust solutions for serving large language models.
- Optimized Operations: It supports diverse quantization methods and performance-boosting operations like 8, 6, and 4-bit calculations.
- Conversion Tools: Seamlessly convert models between JAX-EasyDeL and PyTorch-HF formats.
Customization and Hackability
EasyDeL is designed with openness and flexibility at its core:
- Open Architecture: Each component can be inspected, modified, and customized without restrictions.
- Hackable Framework: Users are encouraged to adjust any part of the framework, from small functions to complex training loops.
- Community and Experimentation Focus: Users can share their custom codes and improvements, fostering a collective enhancement mentality for advancing ML research.
With EasyDeL, users can adapt and innovate freely, whether it is groundbreaking research or creating production-ready systems.
Advanced Optimization
- Sharding Strategies: Optimize performance on multiple devices with flexible sharding strategies.
- Algorithm Adjustments: Tailor algorithms to specific requirements or system configurations.
- Attention Mechanisms: Choose from an array of over 10 different attention mechanisms adapted for TPU, GPU, and CPU.
Future Developments
EasyDeL is on a continual growth trajectory, promising:
- Leading-Edge Updates: Regular updates to implement the latest technological advancements.
- Pre-Configured Modules: Ready-to-use blocks for quick deployment and trial.
- Scalability Enhancements: Improved tools for handling massive datasets and complex models.
- Expanded Customization Options: Increased flexibility in both architecture and training workflows.
Why EasyDeL?
- Flexibility: Its modular design enables seamless component integration and customization to meet specific needs.
- Performance: Utilizes JAX and Flax to deliver high-performance model implementations.
- Scalability: Efficiently expands from small tests to extensive model training.
- User-Friendly: With an intuitive API, it is accessible to ML beginners and seasoned professionals alike.
- Cutting-Edge Research Integration: Rapid incorporation of the latest research innovations.
Quick Start Guide
To get started with EasyDeL:
Installation
You can install EasyDeL using pip:
pip install easydel
Testing Attention Mechanisms
Example for running benchmarks:
import easydel as ed
ed.FlexibleAttentionModule.run_attention_benchmarks()
Documentation
Comprehensive guidance and examples are available at EasyDeL Documentation.
Latest Updates
Recent enhancements include:
- Default Attention: Updated to
jax_flash_attn2. - Architecture Improvements: Introduces new features and optimizations.
- Enhanced Trainers and Tools: Upgraded trainers and enhanced key components like ApiEngine.
Contributing and Support
Contributions are welcome. EasyDeL follows the Apache v2 license, ensuring open collaboration. For queries, contact [email protected].
EasyDeL is an innovative tool for anyone looking to optimize their machine learning workflows, providing both the flexibility and power required to drive forward in the field of machine learning.