LightSeq is a high-performance library for accelerating Transformer-based models (BERT, GPT, ViT, etc.) during training and inference. It targets researchers and engineers working with NLP and CV tasks like machine translation and text generation, offering significant speedups over standard PyTorch implementations.

How It Works

LightSeq leverages custom, fused CUDA kernels built on top of NVIDIA's cuBLAS, Thrust, and CUB libraries. This approach optimizes core Transformer operations for modern GPU architectures. It supports mixed-precision training and inference (fp16, int8) and integrates with popular frameworks like Fairseq and Hugging Face, enabling easy adoption and deployment.

Quick Start & Requirements

• Install from PyPI: pip install lightseq (Linux, Python 3.6-3.8 only).
• Build from Source: Requires CUDA toolkit and potentially HDF5. See detailed building introduction.
• Framework Integration: Requires fairseq, transformers, seqeval, datasets, sacremoses for specific examples.
• Deployment: Docker image available for Triton Inference Server: sudo docker pull hexisyztem/tritonserver_lightseq:22.01-1.

Highlighted Details

• Up to 3x speedup for fp16 training and 5x for int8 training compared to PyTorch.
• Up to 12x speedup for fp16 inference and 15x for int8 inference compared to PyTorch.
• Supports Transformer, BERT, BART, GPT2, ViT, T5, MT5, XGLM, VAE, Multilingual, and MoE models.
• Offers various decoding algorithms (beam search, sampling) and compatibility with DeepSpeed.

Maintenance & Community

The project has seen releases up to v3.0.0 (October 2022) with int8 support. Further community engagement details (Discord/Slack, roadmap) are not explicitly provided in the README.

Licensing & Compatibility

The README does not explicitly state a license. This requires further investigation before commercial use or integration into closed-source projects.

Limitations & Caveats

The PyPI installation is restricted to Linux and Python versions 3.6-3.8. Support for newer Python versions or other operating systems likely requires building from source. The latest release noted is from October 2022, suggesting potential maintenance gaps.