The ByteIR Project: Simplifying Model Compilation
The ByteIR Project, developed by ByteDance, aims to provide a comprehensive solution for model compilation. It integrates various components such as a compiler, runtime, and frontends to offer an end-to-end model compilation service. While these components work together seamlessly, they can also function independently.
What’s in a Name: ByteIR
Despite its name, ByteIR does not focus on defining an intermediate representation (IR) specification. Instead, the project leverages several established MLIR dialects and Google Mhlo. Most of the compiler passes in ByteIR are compatible with these chosen dialects and Mhlo, ensuring smooth performance and integration.
The Core Mission of ByteIR
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Access Cutting-Edge Models: ByteIR maintains popular frontends capable of transforming numerous state-of-the-art models into Stablehlo format. Additionally, a model zoo is in the works to support research and benchmarking.
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Seamless Operation: By aligning with upstream MLIR dialects and Google Mhlo, ByteIR provides compatible passes and utilities. This compatibility allows users to combine ByteIR passes with their own, or with existing MLIR/Mhlo passes, offering flexibility in pipeline construction.
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Architectural Flexibility: ByteIR offers extensive optimizations at graph, loop, and tensor levels within Mhlo and Linalg. This design enables developers working on deep learning ASIC compilers to focus on backend-specific optimizations.
Current Status
ByteIR is still evolving. Presently, the focus is on establishing essential building blocks and infrastructure to support model compilation across a range of deep learning accelerators, as well as conventional CPUs and GPUs. While the priority might not yet be on highly-tuned kernels for specific architectures, the team welcomes feedback and contributions that could help guide future developments.
Core Components of ByteIR
- Compiler: ByteIR houses an MLIR-based compiler optimized for CPU, GPU, and ASIC devices.
- Runtime: The ByteIR Runtime is designed to be lightweight and efficient, supporting both pre-existing and ByteIR-generated kernels.
- Frontends: ByteIR Frontends currently support TensorFlow, PyTorch, and ONNX formats.
Communication Between Components
Each ByteIR component has a clearly defined communication interface:
- Stablehlo Interface: The frontends and compiler communicate through the Stablehlo dialect, offering compatibility and flexibility during development.
- ByRE Interface: The interaction between the compiler and runtime occurs via the ByRE format. This includes the ability to emit both textual and bytecode forms.
Contributions and Recognition
ByteIR is the result of collaborative efforts by researchers and interns at ByteDance. The project has been highlighted in several public talks, offering insights into its development and capabilities. If ByteIR proves beneficial to your work, the developers encourage acknowledgment through citation.
Citing ByteIR:
@misc{byteir2023,
title = {{ByteIR}},
author = {Cao, Honghua and Chang, Li-Wen and Chen, Chongsong and Jiang, Chengquan and Jiang, Ziheng and Liu, Liyang and Liu, Yuan and Liu, Yuanqiang and Shen, Chao and Wang, Haoran and Xiao, Jianzhe and Yao, Chengji and Yuan, Hangjian and Zhang, Fucheng and Zhang, Ru and Zhang, Xuanrun and Zhang, Zhekun and Zhang, Zhiwei and Zhu, Hongyu and Liu, Xin},
url = {https://github.com//bytedance/byteir},
year = {2023}
}
Licensing
The ByteIR Project is licensed under the Apache License v2.0, ensuring openness and collaboration within the community.