commit | ce108c1f53235a483eb11dffffb8770642907642 | [log] [tgz] |
---|---|---|
author | Chun-I Tsai <quic_chunit@quicinc.com> | Tue Dec 28 12:53:18 2021 +0800 |
committer | GitHub <noreply@github.com> | Tue Dec 28 12:53:18 2021 +0800 |
tree | f002ab51c13ff15532842dc8dab38a6a72735fae | |
parent | 7448eab300c0710a5649083bec53a631b0fa2ebd [diff] |
[Frontend] Add Span filling for frontends to Relay (#9723) * [Frontend] Add Span filling for frontends to Relay * Add a common span filling feature for tf1/2, tflite and pytorch. * Add test case for Span filling in each frontend. * Expose Tuple and TupleGetItem to python end * [Frontend] Add Span filling for frontends to Relay * Fix lint errors * Change default string of scope_part in Pytorch * Reorder the span position for one to many conversion * [Frontend] Add Span filling for frontends to Relay * nit fixed * Add a bool flag to control print span * refactor pytorch get span to a birefer way * [Frontend] Add Span filling for frontends to Relay * Add one more condition for spanFller * Refine the format for those pytorch node without scopeName * [Frontend] Add Span filling for frontends to Relay * Fix lint
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.