commit | afe18d222e840644bf78f103fe584610c7c9d04c | [log] [tgz] |
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author | Kunwar Grover <groverkss@gmail.com> | Tue Oct 15 14:53:02 2024 +0100 |
committer | GitHub <noreply@github.com> | Tue Oct 15 14:53:02 2024 +0100 |
tree | b37fd12941ad314772d3b4ea5898c1dcf46f6a1f | |
parent | 7622770c3647cd902546fb23a831e967ee1ccf3e [diff] |
[Codegen][GPU] Add tiling cleanup pattern to fuse pad without zero gaurd (#18748) This PR adds a way to fuse tensor.pad in ApplyGPUTilingLevel when we know the pad will not ever recieve an empty slice. This is useful, when the tensor.pad is padding to the tiling size that we are tiling with, and will never generate an empty slice.
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.