From 2d78e8e00decbd011b711126aa2f757165e941cd Mon Sep 17 00:00:00 2001 From: wehub-resource-sync Date: Mon, 13 Jul 2026 10:45:35 +0000 Subject: [PATCH] docs: make Chinese README the default --- README.md | 73 +++++++++++++++++++++++++++++-------------------------- 1 file changed, 39 insertions(+), 34 deletions(-) diff --git a/README.md b/README.md index fb9e9bc..ac5af0d 100644 --- a/README.md +++ b/README.md @@ -1,3 +1,9 @@ + +> [!NOTE] +> 本文档由 WeHub 基于上游 README 翻译整理，属于社区翻译，非官方中文文档。 +> [English](./README.en.md) · [原始项目](https://github.com/apache/tvm) · [上游 README](https://github.com/apache/tvm/blob/HEAD/README.md) +> 原作者、版权与许可证归属以原始项目及本仓库 LICENSE 文件为准。 + @@ -15,51 +21,50 @@ - Open Machine Learning Compiler Framework + 开放机器学习编译器框架 ============================================== -[Documentation](https://tvm.apache.org/docs) | -[Contributors](CONTRIBUTORS.md) | -[Community](https://tvm.apache.org/community) | -[Release Notes](NEWS.md) +[文档](https://tvm.apache.org/docs) | +[贡献者](CONTRIBUTORS.md) | +[社区](https://tvm.apache.org/community) | +[发布说明](NEWS.md) -Apache TVM is an open machine learning compilation framework, -following the following principles: +Apache TVM 是一个开放的机器学习编译框架， +遵循以下原则： -- Python-first development that enables quick customization of machine learning compiler pipelines. -- Universal deployment to bring models into minimum deployable modules. +- 以 Python 为先（Python-first）的开发方式，可快速定制机器学习编译器流水线。 +- 通用部署（Universal deployment），将模型交付为最小可部署模块。 -License +许可证 ------- -TVM is licensed under the [Apache-2.0](LICENSE) license. +TVM 根据 [Apache-2.0](LICENSE) 许可证授权。 -Getting Started +入门 --------------- -Check out the [TVM Documentation](https://tvm.apache.org/docs/) site for installation instructions, tutorials, examples, and more. -The [Getting Started with TVM](https://tvm.apache.org/docs/get_started/overview.html) tutorial is a great -place to start. +请访问 [TVM 文档](https://tvm.apache.org/docs/) 网站，获取安装说明、教程、示例等更多信息。 +[TVM 入门](https://tvm.apache.org/docs/get_started/overview.html) 教程是很好的 +起点。 -Contribute to TVM +为 TVM 做贡献 ----------------- -TVM adopts the Apache committer model. We aim to create an open-source project maintained and owned by the community. -Check out the [Contributor Guide](https://tvm.apache.org/docs/contribute/). +TVM 采用 Apache 提交者（committer）模式。我们的目标是创建一个由社区维护和拥有的开源项目。 +请参阅 [贡献者指南](https://tvm.apache.org/docs/contribute/). -History and Acknowledgement +历史与致谢 --------------------------- -TVM started as a research project for deep learning compilation. -The first version of the project benefited a lot from the following projects: +TVM 最初是作为深度学习编译的研究项目启动的。 +该项目的第一版从以下项目中受益匪浅： -- [Halide](https://github.com/halide/Halide): Part of TVM's TIR and arithmetic simplification module - originates from Halide. We also learned and adapted some parts of the lowering pipeline from Halide. -- [Loopy](https://github.com/inducer/loopy): use of integer set analysis and its loop transformation primitives. -- [Theano](https://github.com/Theano/Theano): the design inspiration of symbolic scan operator for recurrence. +- [Halide](https://github.com/halide/Halide): TVM 的 TIR 与算术化简模块的部分 + 源自 Halide。我们也借鉴并改编了 Halide 降级流水线的部分内容。 +- [Loopy](https://github.com/inducer/loopy): 对整数集分析及其循环变换原语的使用。 +- [Theano](https://github.com/Theano/Theano): 循环符号扫描算子（symbolic scan operator）的设计灵感。 -Since then, the project has gone through several rounds of redesigns. -The current design is also drastically different from the initial design, following the -development trend of the ML compiler community. +此后，该项目经历了多轮重新设计。 +当前设计与初始设计也截然不同，顺应了 ML 编译器社区的发展趋势。 -The most recent version focuses on a cross-level design with TensorIR as the tensor-level representation -and Relax as the graph-level representation and Python-first transformations. -The project's current design goal is to make the ML compiler accessible by enabling most -transformations to be customizable in Python and bringing a cross-level representation that can jointly -optimize computational graphs, tensor programs, and libraries. The project is also a foundation -infra for building Python-first vertical compilers for domains, such as LLMs. +最新版本聚焦于跨层级设计：以 TensorIR 作为张量级表示， +以 Relax 作为图级表示，并支持 Python 优先的变换。 +项目当前的设计目标是，通过让大多数变换可在 Python 中定制， +并提供可联合优化计算图、张量程序和库的跨层级表示，使 ML 编译器更易用。 +该项目也是为特定领域构建 Python 优先垂直编译器的基础 +基础设施，例如 LLM。