Introducing the Lightning Thunder Project
Overview
Lightning Thunder is a groundbreaking initiative designed to enhance the speed of PyTorch models, making them Lightning-fast. This innovative tool acts as a source-to-source compiler tailored for PyTorch, enhancing its performance by leveraging multiple hardware executors concurrently. Thunder aims to transform the way developers and researchers approach PyTorch programming, making it more efficient and adaptable.
Key Features
Speed and Performance
Thunder delivers substantial speed improvements over standard PyTorch code by employing top-tier executors like nvFuser, torch.compile, cuDNN, and TransformerEngine FP8. By optimizing code execution, it achieves a remarkable 40% increase in training throughput for models like Llama 2 7B in single-GPU settings.
Multi-GPU Support
Recognizing the need for scalability, Thunder supports training on multiple GPUs using distributed strategies such as DDP (Distributed Data Parallel) and FSDP (Fully Sharded Data Parallel). This extends its capability to efficiently manage and execute complex models across several GPUs, although FSDP support is still undergoing development.
Getting Started with Thunder
For those eager to explore Thunder's capabilities, the project provides a straightforward entry point through the "Zero to Thunder Tutorial Studio," which requires no additional installations. For developers interested in experiencing the latest advancements, Thunder can be installed directly from the main repository branch.
Installation Guide
Users can install Thunder and optimize its performance by including additional dependencies like nvFuser and cudnn. For advanced users and contributors, Thunder's repository can be cloned and installed in an editable mode for more in-depth experimentation and development.
Example Usage
A simple "Hello World" example illustrates Thunder's efficacy in compiling and executing PyTorch code. By using Thunder's JIT (Just In Time) compiler, a Python function can be seamlessly transformed into a faster version, enhancing the overall execution speed.
Training Models
Although still in its alpha phase, Thunder already proves to be highly effective in pretraining and fine-tuning large language models such as Mistral, Llama 2, and Falcon. Integrations with projects like LitGPT showcase its promising potential for boosting performance in real-world applications.
Core Technical Features
Thunder's architecture allows it to generate optimized programs for forward and backward computations, fuse operations for efficiency, and dispatch computations to optimized kernels. A pivotal aspect of its design is the use of a multi-level intermediate representation (IR), enabling comprehensive introspection and adaptability of the computational graph.
Joining the Thunder Community
The developers of Thunder encourage community involvement, welcoming feedback, contributions, and collaboration from all interested parties. Contributions can be in the form of code, feature suggestions, or even just engaging with the project through the GitHub Issue tracker.
Conclusion
Lightning Thunder represents a significant leap forward in accelerating PyTorch model performance, supported by a commitment to innovation and collaboration. By harnessing advanced executors and optimizing workflows, Thunder sets a new standard in machine learning model execution. As it continues to evolve, the project offers exciting opportunities for users and contributors alike to push the boundaries of what's possible with PyTorch.
For detailed documentation and further instructions, users are encouraged to explore the online resources.