jjzhang166 / lightseq

LightSeq: A High Performance Library for Sequence Processing and Generation

LightSeq: A High Performance Library for Sequence Processing and Generation

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Table Of Contents

• Release Notes
• Introduction
  • Support Matrix
• Performance
  • Speedup of Transformer Training
  • Speedup of BERT Training
  • Speedup of Transformer Inference
  • Speedup of BERT Inference
• Installation
  • Install from PyPI
  • Build from Source
• Getting Started
  • LightSeq Training from Scratch
  • LightSeq Training from Fairseq
  • LightSeq Training from Hugging Face BERT
  • LightSeq Inference from Fairseq
  • LightSeq Inference from Hugging Face BERT
  • LightSeq Deployment Using Inference Server
• Cite Us
• We are Hiring!

Release Notes

[2022.10.25] Release v3.0.0 version, which supports int8 mixed-precision training and inference. [中文介绍]

[2021.06.18] Release v2.0.0 version, which supports fp16 mixed-precision training. [中文介绍]

[2019.12.06] Release v1.0.0 version, which supports fp16 mixed-precision inference. [中文介绍]

Introduction

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 and CV models such as BERT, GPT, Transformer, etc. It is therefore best useful for machine translation, text generation, image classification, and other sequence related tasks.

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.

LightSeq training and inference is very fast. Below is the overall performance:

• LightSeq fp16 training achieves a speedup of up to 3x, compared to PyTorch fp16 training.
• LightSeq int8 training achieves a speedup of up to 5x, compared to PyTorch QAT (i.e., quantization aware training).
• LightSeq fp16 and int8 inference achieve a speedup of up to 12x and 15x, compared to PyTorch fp16 inference, respectively.

Support Matrix

LightSeq supports multiple features, which is shown in the table below.

Features	Support List
Model	Transformer, BERT, BART, GPT2, ViT, T5, MT5, XGLM, VAE, Multilingual, MoE
Layer	embedding, encoder, decoder, criterion, optimizer
Precision	fp32, fp16, int8
Mode	training, inference
Compatibility	Fairseq, Hugging Face, DeepSpeed
Decoding Algorithm	beam search, diverse beam search, sampling, CRF
Others	gradient communication quantization, auto-tune GEMM algorithm

The table below shows the running modes and precision currently supported by different models.

Models	fp16 Training	fp16 Inference	int8 Training	int8 Inference
Transformer	Yes	Yes	Yes	Yes
BERT	Yes	Yes	Yes	Yes
GPT2	Yes	Yes	Yes	Yes
BART	Yes	Yes	-	-
T5	-	Yes	-	-
MT5	-	Yes	-	-
XGLM	-	Yes	-	-
ViT	Yes	Yes	Yes	Yes
VAE	-	Yes	-	-
Multilingual	-	Yes	-	Yes
MoE	-	Yes	-	-

Performance

We test the speedup of LightSeq training and inference using both fp16 and int8 mix-precision on Transformer and BERT models. The baseline is PyTorch fp16 mix-precision. Training experiments are tested on one A100 GPU and inference experiments are tested on eight A100 GPUs.

More performance results are available here.

Speedup of Transformer Training

Batch Token Size	PyTorch QAT	LightSeq fp16	LightSeq int8
512	0.36	1.99	1.86
1024	0.37	1.78	1.69
2048	0.37	1.56	1.50
4096	0.39	1.47	1.44
8192	0.41	1.44	1.44
15000	0.43	1.44	1.44

Speedup of BERT Training

Batch Token Size	PyTorch QAT	LightSeq fp16	LightSeq int8
8	0.45	2.12	1.99
16	0.44	1.92	1.80
32	0.42	1.59	1.52
64	0.46	1.62	1.58
128	0.46	1.74	1.70
256	0.46	1.68	1.73

Speedup of Transformer Inference

Batch Size	Sequence Length	LightSeq fp16	LightSeq int8
1	8	8.00	9.33
1	32	6.48	7.38
1	128	6.24	6.19
8	8	9.38	10.71
8	32	8.24	8.75
8	128	6.83	7.28
32	8	11.82	14.44
32	32	9.68	11.15
32	128	6.68	7.74

Speedup of BERT Inference

Batch Size	Sequence Length	LightSeq fp16	LightSeq int8
1	8	9.22	9.87
1	32	10.51	11.30
1	128	9.96	10.85
8	8	9.88	10.33
8	32	7.79	8.22
8	128	4.04	4.35
32	8	10.60	11.02
32	32	8.11	8.85
32	128	1.82	2.04

Installation

Install from PyPI

You can install LightSeq from PyPI, which only supports Python 3.6 to 3.8 on Linux:

pip install lightseq

Build from Source

You can also build from source:

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

Detailed building introduction is available here.

Getting Started

We provide several samples here to show the usage of LightSeq. Refer to the complete user guide and examples for more details.

LightSeq Training from Scratch

You can use the modules provided by LightSeq to build your own models. The following is an example of building a Transformer encoder layer.

First, import LightSeq Transformer encoder module:

from lightseq.training import LSTransformerEncoderLayer

Then create an encoder configuration, and create a LightSeq Transformer encoder layer initialized with the configuration:

config = LSTransformerEncoderLayer.get_config(
    max_batch_tokens=4096,
    max_seq_len=512,
    hidden_size=1024,
    intermediate_size=4096,
    nhead=16,
    attn_prob_dropout_ratio=0.1,
    activation_dropout_ratio=0.1,
    hidden_dropout_ratio=0.1,
    pre_layer_norm=True,
    activation_fn="relu",
    fp16=True,
    local_rank=0,
)
layer = LSTransformerEncoderLayer(config)

In addition to encoder layers, the other modules can be created using similar methods, and then be trained as normal PyTorch models.

More usage is available here.

LightSeq Training from Fairseq

LightSeq integrates all the fast and lightning modules into Fairseq.

First install the two following requirements:

pip install fairseq==0.10.2 sacremoses

You can train a fp16 mix-precision translation task on wmt14 en2de dataset by:

sh examples/training/fairseq/ls_fairseq_wmt14en2de.sh

(Optional) Then you can start int8 mix-precision training on the basis of fp16 pre-training models by:

sh examples/training/fairseq/ls_fairseq_quant_wmt14en2de.sh

More usage is available here.

LightSeq Training from Hugging Face BERT

LightSeq replaces the encoder layers of Hugging Face BERT with LightSeq fast layers.

First you should install these requirements:

pip install transformers seqeval datasets

Before doing next training, you need to switch to the following directory:

cd examples/training/huggingface/bert

Then you can easily fine-tune BERT for different tasks. Taking named entity recognition task as an example, you can train the BERT with fp16 mixed-precision using:

python task_ner/run_ner.sh

(Optional) You can also start int8 mix-precision training on the basis of fp16 pre-training models by:

python task_ner/run_quant_ner.sh

More usage is available here.

LightSeq Inference from Fairseq

After training using the above scripts, you can quickly infer the models using LightSeq.

You should transform the fp16 PyTorch weights to LightSeq protobuf or HDF5:

python export/fairseq/ls_fs_transformer_export.py

(Optional) You can also transform the int8 PyTorch weights to LightSeq protobuf or HDF5:

python export/fairseq/ls_fs_quant_transformer_export.py

Once obtaining the LightSeq weights, you can quickly infer them using the following code:

import lightseq.inference as lsi
model = lsi.Transformer(MODEL_PATH, MAX_BATCH_SIZE)
results = model.infer([[63, 47, 65, 1507, 88, 74, 10, 2057, 362, 9, 284, 6, 2, 1]])

Here MODEL_PATH is the path of your LightSeq weights and MAX_BATCH_SIZE is the maximal batch size of your input sentences.

You can also quickly infer the int8 LightSeq weights by replacing the lsi.Transformer with lsi.QuantTransformer.

More usage is available here.

LightSeq Inference from Hugging Face BERT

We provide an end2end bert-base example to see how fast Lightseq is compared to original Hugging Face.

First you should install the requirements and locate to the specified directory:

pip install transformers
cd examples/inference/python

Then you can check the performance by simply running the following commands. hf_bert_export.py is used to transform PyTorch weights to LightSeq protobuf or HDF5.

python export/huggingface/hf_bert_export.py
python test/ls_bert.py

More usage is available here.

LightSeq Deployment Using Inference Server

We provide a docker image which contains tritonserver and LightSeq's dynamic link library, and you can deploy an inference server by simply replacing 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 papers.

@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}
}

We are Hiring!

The LightSeq team is hiring Interns and FTEs with backgrounds in deep learning system, natural language processing, computer vision, speech, etc. We are based in Beijing and Shanghai. If you are interested, please send your resume to [e-mail].