commit | 4267fbf6a173cd742acb293fab4f77693dc4b887 | [log] [tgz] |
---|---|---|
author | Chun-I Tsai <quic_chunit@quicinc.com> | Mon May 29 05:28:33 2023 +0800 |
committer | GitHub <noreply@github.com> | Mon May 29 06:28:33 2023 +0900 |
tree | 8e45219dd5b4ab58aff698917bd7079c85dec847 | |
parent | 43f06ca42ab35c4528568720710b32f5d592eaf4 [diff] |
[IR][SIBuilder] (#14574) * [IR][SIBuilder] - Add SIBuilder to handle the span propagation between passes - Add SequentialSpan for multiple source expressions conversion between passes - Add test cases for SIBuilder and SequentialSpan * [IR][SIBuilder] - Make null implementation as base class - Add comments and change naming based on reviewing --------- Co-authored-by: Joey Tsai <chunit@qti.qualcomm.com>
Documentation | Contributors | Community | Release Notes
Apache TVM is a compiler stack for deep learning systems. It is designed to close the gap between the productivity-focused deep learning frameworks, and the performance- and efficiency-focused hardware backends. TVM works with deep learning frameworks to provide end to end compilation to different backends.
TVM is licensed under the Apache-2.0 license.
Check out the TVM Documentation site for installation instructions, tutorials, examples, and more. The Getting Started with TVM tutorial is a great place to start.
TVM adopts apache committer model, we aim to create an open source project that is maintained and owned by the community. Check out the Contributor Guide.
We learned a lot from the following projects when building TVM.