commit | 944d180fba18660f7846eccf4ef4931284a7d38b | [log] [tgz] |
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author | Luke Hutton <luke.hutton@arm.com> | Sat May 04 14:23:52 2024 +0100 |
committer | GitHub <noreply@github.com> | Sat May 04 09:23:52 2024 -0400 |
tree | 4b45b352ab94d7f8868cd78d7d1cdaf71c5e3326 | |
parent | effa5d79930b1103c36d8cc53618a6dce1ba3760 [diff] |
[SVE] Add get_active_lane_mask builtin (#16965) Adds a `get_active_lane_mask` builtin and lowering to `llvm.get.active.lane.mask` intrinsic. This will be used in subsequent patches for expressing predicated buffer loads/stores in TIR. Further information can be found in the [RFC](https://github.com/apache/tvm-rfcs/blob/main/rfcs/0104-scalable-vectors-in-tir.md#predication). Co-authored-by: Elen Kalda <elen.kalda@arm.com> Co-authored-by: Neil Hickey <neil.hickey@arm.com> Change-Id: Id9d65f9f11503ad35dd0b3db4bfc81249a76f701
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.