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ByteTransformer: Optimized BERT Transformer Inference on NVIDIA GPUs

Introduction

ByteTransformer is a high-performance inference library for BERT-like transformers that offers the following features:

• Provides Python and C++ APIs, with the PyTorch plugin allowing users to enhance transformer inference with just a few lines of Python code.
• Supports both fixed-length and variable-length transformers.
• Includes end-to-end architectural-aware optimizations for the padding-free algorithm on BERT routines, including QKV encoding, softmax, feed forward network, activation, layernorm, and multi-head attention.

ByteTransformer has been widely deployed to improve in-house transformer inference serving systems at ByteDance, delivering superior performance over other transformer implementations for both fixed-length and variable-length inputs. The technical details have been published at IEEE IPDPS 2023.

Cite Us

If you use our library, please cite our research paper.

@article{zhai2022bytetransformer,
  title={ByteTransformer: A High-Performance Transformer Boosted for Variable-Length Inputs},
  author={Zhai, Yujia and Jiang, Chengquan and Wang, Leyuan and Jia, Xiaoying and Zhang, Shang and Chen, Zizhong and Liu, Xin and Zhu, Yibo},
  journal={arXiv preprint arXiv:2210.03052},
  year={2022}
}

Performance and Speedup

We compared ByteTransformer with PyTorch, TensorFlow, FasterTransformer, and DeepSpeed on an A100 GPU. The benchmark script is available in benchmark/bert_bench.sh.

1. Standard BERT batch size = 1, average sequence length = 0.6 * maximal, execution time in millisecond:

2. Standard BERT batch size = 16, average sequence length = 0.6 * maximal, execution time in millisecond:

Supported Models

Currently, only the standard BERT transformer encoder is available under this repository.

Environment requirements

• CUDA: 11.6
• CMake: >= 3.13
• PyTorch: >= 1.8
• GPU compute capability: 7.0(V100) / 7.5(T4) or 8.0(A100)
• Python: >= 3.7

Tested on: A100 + CUDA 11.6 + PyTorch 1.13.0+cu116 + Python 3.9.16

Building from Source

To build from source, run the following commands:

git submodule update --init
mkdir build && cd build
cmake -DTORCH_CUDA_ARCH_LIST="8.0" -DDataType=FP16 -DBUILD_THS=ON -DCUDAARCHS="80" ..
make

Getting Started with Unit Tests

Unit Tests in C++

To generate test data, run the following code:

cd build
# batch sz = 16, seqlen = 64, head num = 12, head sz = 64, avg seqlen = 32
python3 bert_transformer_test.py 16 64 12 64 --avg_seqlen 32 --dtype fp16 --export_data

Here, 16, 64, 12, and 64 represent batch size, sequence length, number of heads, and head size, respectively. The --avg_seqlen 32 flag is used to set the average sequence length, --dtype fp16 sets the data type, and --export_data exports the test data.

After test data is generated (*.in and *.out files are saved under the current directory), run the following command:

./bin/bert_transformer_test 16 64 12 64

Here, the arguments represent the same parameters as used in generating the test data.

Unit Tests in a PyTorch Plugin in Python

To perform the unit tests in a PyTorch plugin in Python, use the same script as for C++, but without the --export_data flag. Run the following command in the terminal:

# batch sz = 16, seqlen = 64, head num = 12, head sz = 64, avg seqlen = 32
python3 bert_transformer_test.py 16 64 12 64 --avg_seqlen 32 --dtype fp16

Again, the arguments represent the same parameters as used in generating the test data.

Benchmark

cd build
../benchmark/bert_bench.sh