commit | c0909a48623a66b3ed9c2854fbf4ec39cf066871 | [log] [tgz] |
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author | Andrea Faulds <andrea.faulds@amd.com> | Mon Sep 23 19:27:30 2024 +0200 |
committer | GitHub <noreply@github.com> | Mon Sep 23 13:27:30 2024 -0400 |
tree | e62c1943805618cbe3607395b43dc6e1e31de8e2 | |
parent | 0d9c5a80e2cda8c31e9d381029bb5d848de9326b [diff] |
[gpu] Use clustered gpu.subgroup_reduce for nested layout distribution (#18515) There is now support in MLIR for expressing a subgroup reduction operation that operates on several "clusters" in parallel, so it is no longer necessary to build a series of shuffles. It has been verified that, at least if the upstream patterns are used, the resulting sequence of shuffles is the same as the old code. This commit also adds a new pass, ExpandGPUOps, which uses the upstream patterns to expand these ops, and adds it to the LLVMGPU pass list. Resolves #18142. Signed-off-by: Andrea Faulds <andrea.faulds@amd.com>
IREE (Intermediate Representation Execution Environment, pronounced as “eerie”) is an MLIR-based end-to-end compiler and runtime that lowers Machine Learning (ML) models to a unified IR that scales up to meet the needs of the datacenter and down to satisfy the constraints and special considerations of mobile and edge deployments.
See our website for project details, user guides, and instructions on building from source.
IREE is still in its early phase. We have settled down on the overarching infrastructure and are actively improving various software components as well as project logistics. It is still quite far from ready for everyday use and is made available without any support at the moment. With that said, we welcome any kind of feedback on any communication channels
Package | Release status |
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GitHub release (stable) | |
GitHub release (nightly) | |
Python iree-compiler | |
Python iree-runtime |
Host platform | Build status |
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Linux | |
macOS | |
Windows |
For the full list of workflows see https://iree.dev/developers/general/github-actions/.
See our website for more information.
Community meeting recordings: IREE YouTube channel
IREE is licensed under the terms of the Apache 2.0 License with LLVM Exceptions. See LICENSE for more information.