ByteTransformer is a high-performance inference library for BERT-like transformers, targeting developers seeking to optimize inference serving on NVIDIA GPUs. It offers architectural-aware optimizations for padding-free BERT routines, delivering superior performance and reduced latency compared to standard implementations.

How It Works

This library provides both Python and C++ APIs, featuring a PyTorch plugin for easy integration. It implements end-to-end optimizations across key BERT components like QKV encoding, softmax, feed-forward networks, activation, layernorm, and multi-head attention, specifically targeting padding-free execution for efficiency.

Quick Start & Requirements

• Installation: Build from source using CMake and make. Requires git submodule update --init, mkdir build && cd build, cmake -DTORCH_CUDA_ARCH_LIST="8.0" -DDataType=FP16 -DBUILD_THS=ON -DCUDAARCHS="80" .., and make.
• Prerequisites: CUDA 11.6, CMake >= 3.13, PyTorch >= 1.8, Python >= 3.7. Requires GPU compute capability 7.0 (V100) / 7.5 (T4) or 8.0 (A100).
• Resources: Setup involves compiling from source. Benchmark script benchmark/bert_bench.sh is available.
• Docs/Demo: Technical details published at IEEE IPDPS 2023 and arXiv:2210.03052.

Highlighted Details

• Achieves superior inference performance compared to PyTorch, TensorFlow, FasterTransformer, and DeepSpeed on A100 GPUs.
• Demonstrates significant speedups, e.g., reducing latency for BERT inference with batch size 16 and sequence length 1024 from 53.21ms (PyTorch) to 24.70ms (ByteTransformer).
• Optimizes padding-free BERT routines, including QKV, softmax, FFN, activation, layernorm, and multi-head attention.
• Supports both fixed-length and variable-length transformer inputs.

Maintenance & Community

The provided README does not contain information regarding maintainers, community channels (e.g., Discord, Slack), or project roadmaps.

Licensing & Compatibility

The license type and any compatibility notes for commercial or closed-source use are not specified in the provided README.

Limitations & Caveats

Currently, only the standard BERT transformer encoder architecture is supported within this repository.