commit | cc3b28f27c5e2c9192e7481d59d7a77a9b7e0157 | [log] [tgz] |
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author | Quinn Dawkins <quinn.dawkins@gmail.com> | Mon Oct 07 19:46:20 2024 -0400 |
committer | GitHub <noreply@github.com> | Mon Oct 07 23:46:20 2024 +0000 |
tree | 2b176aca45ab802fc7a4394362beaf17f9ccb26b | |
parent | 88cb0ab614b650a437b81a72a3a9f9eefe7b624e [diff] |
[Codegen][GPU] Improve loop fusion pattern verification (#18671) The current loop fusion patterns don't verify that the consumer loop won't be predicated after resolution. This is required because the loop fusion pattern introduces barrier semantics to the loop body which will result in invalid IR if the loop resolves to an `scf.if` (or anything that could lead to thread divergence). This also makes it so the loop fusion pattern does not require the consumer loop to have a `tensor.extract_slice`, improving the robustness of the pattern.
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.