LightSeq: A High Performance Library for Sequence Processing and Generation

[2021/06/18] :tada: :tada: :tada: LightSeq supports fast training for models in the Transformer family now, please check out here for details.

LightSeq is a high performance training and inference library for sequence processing and generation implemented in CUDA. It enables highly efficient computation of modern NLP models such as BERT, GPT, Transformer, etc. It is therefore best useful for Machine Translation, Text Generation, Dialog, Language Modelling, Sentiment Analysis, and other related tasks with sequence data.

The library is built on top of CUDA official library(cuBLAS, Thrust, CUB) and custom kernel functions which are specially fused and optimized for Transformer model family. In addition to model components, the inference library also provide easy-to deploy model management and serving backend based on TensorRT Inference Server. With LightSeq, one can easily develop modified Transformer architecture with little additional code.

Features

>>> Training

The following is a support matrix of LightSeq training library compared with DeepSpeed.

>>> Inference

The following is a support matrix of LightSeq inference library compared with TurboTransformers and FasterTransformer.

Performance

>>> Training

Here we present the experimental results on WMT14 English to German translation task based on Transformer-big models. We train Transformer models of different sizes on eight NVIDIA Tesla V100/NVIDIA Tesla A100 GPUs with data parallel and fp16 mixed precision. Fairseq with Apex is choosed as our baseline.

We compute speedup on different batch size using the WPS (real words per second) metric.

More results is available here

>>> Inference

Here we present the experimental results on neural machine translation based on Transformer-base models using beam search methods. We choose Tensorflow and FasterTransformer as a comparison. The implementation from tensor2tensor was used as the benchmark of Tensorflow.

More results is available here.

Quick Start

Complete user guide is available here.

Installation

You can install LightSeq from PyPI:

$ pip install lightseq

LightSeq installation from PyPI only supports Python 3.6 to 3.8 on Linux for now. Consider compiling from source if you have other environments:

$ PATH=/usr/local/hdf5/:$PATH ENABLE_FP32=0 ENABLE_DEBUG=0 pip install -e $PROJECT_DIR

Detailed building introduction is available here.

Fast training from Fairseq

You can experience lightning fast training by running following commands, Firstly install these requirements.

$ pip install lightseq fairseq sacremoses

Then you can train a translation task on wmt14 en2de dataset by running the following script

$ sh examples/training/fairseq/ls_fairseq_wmt14en2de.sh

To compare lightseq with fairseq, delete the arguments with ls_ prefix to using the original fairseq implementation

More usage is available here.

Fast inference from HuggingFace bart

We provide an end2end bart-base example to see how fast Lightseq is compared to HuggingFace. First you should install these requirements.

$ pip install torch tensorflow transformers lightseq
$ cd examples/inference/python

then you can check the performance by simply running following commands. hf_bart_export.py is used to transform pytorch weights to LightSeq protobuffer.

$ python export/huggingface/hf_bart_export.py
$ python test/ls_bart.py

More usage is available here.

Fast deploy inference server

We provide a docker image which contains tritonserver and lightseq's dynamic link library, and you can deploy a inference server by simply replace the model file with your own model file.

$ sudo docker pull hexisyztem/tritonserver_lightseq:22.01-1

More usage is available here.

Cite Us

If you use LightSeq in your research, please cite the following paper.

@InProceedings{wang2021lightseq,
    title = "{L}ight{S}eq: A High Performance Inference Library for Transformers",
    author = "Wang, Xiaohui and Xiong, Ying and Wei, Yang and Wang, Mingxuan and Li, Lei",
    booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Papers (NAACL-HLT)",
    month = jun,
    year = "2021",
    publisher = "Association for Computational Linguistics",
    pages = "113--120",
}

@article{wang2021lightseq2,
  title={LightSeq2: Accelerated Training for Transformer-based Models on GPUs},
  author={Wang, Xiaohui and Xiong, Ying and Qian, Xian and Wei, Yang and Li, Lei and Wang, Mingxuan},
  journal={arXiv preprint arXiv:2110.05722},
  year={2021}
}

Contact

Any questions or suggestions, please feel free to contact us at wangxiaohui.neo@bytedance.com, xiongying.taka@bytedance.com, qian.xian@bytedance.com, weiyang.god@bytedance.com, wangmingxuan.89@bytedance.com, lilei@cs.ucsb.edu

Hiring

The LightSeq team is hiring Interns/FTEs with backgrounds in deep learning system/natural language processing/computer vision/speech. We are based in Beijing and Shanghai. If you are interested, please send your resume to wangxiaohui.neo@bytedance.com.