| <!--- Licensed to the Apache Software Foundation (ASF) under one --> |
| <!--- or more contributor license agreements. See the NOTICE file --> |
| <!--- distributed with this work for additional information --> |
| <!--- regarding copyright ownership. The ASF licenses this file --> |
| <!--- to you under the Apache License, Version 2.0 (the --> |
| <!--- "License"); you may not use this file except in compliance --> |
| <!--- with the License. You may obtain a copy of the License at --> |
| |
| <!--- http://www.apache.org/licenses/LICENSE-2.0 --> |
| |
| <!--- Unless required by applicable law or agreed to in writing, --> |
| <!--- software distributed under the License is distributed on an --> |
| <!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY --> |
| <!--- KIND, either express or implied. See the License for the --> |
| <!--- specific language governing permissions and limitations --> |
| <!--- under the License. --> |
| |
| <img src=https://raw.githubusercontent.com/apache/tvm-site/main/images/logo/tvm-logo-small.png width=128/> Open Machine Learning Compiler Framework |
| ============================================== |
| [Documentation](https://tvm.apache.org/docs) | |
| [Contributors](CONTRIBUTORS.md) | |
| [Community](https://tvm.apache.org/community) | |
| [Release Notes](NEWS.md) |
| |
| Apache TVM is an open machine learning compilation framework, |
| following the following principles: |
| |
| - Python-first development that enables quick customization of machine learning compiler pipelines. |
| - Universal deployment to bring models into minimum deployable modules. |
| |
| License |
| ------- |
| TVM is licensed under the [Apache-2.0](LICENSE) license. |
| |
| Getting Started |
| --------------- |
| 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. |
| |
| Contribute to TVM |
| ----------------- |
| 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/). |
| |
| History and Acknowledgement |
| --------------------------- |
| TVM started as a research project for deep learning compilation. |
| The first version of the project benefited a lot from the following projects: |
| |
| - [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. |
| |
| 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. |
| |
| 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. |
