Overview
Transformer Engine is an NVIDIA-implemented acceleration library targeting Transformer-family models. It emphasizes low-precision (e.g., FP8) optimizations to reduce memory footprint and accelerate large-model training.
Key features
- Low-precision support: deep optimizations for FP8 and mixed-precision training.
- Framework compatibility: provides PyTorch integration and example code for easy adoption.
- Improved throughput and memory usage: suitable for large-scale and distributed training.
Use cases
- Large-scale Transformer training: improves training efficiency and reduces GPU memory usage in multi-GPU setups.
- Mixed-precision research: explore new numeric formats and trade-offs between speed and model fidelity.
Technical details
- Implemented with CUDA-backed optimized kernels, offering Python bindings, example integrations, and platform-specific acceleration for NVIDIA GPUs.