commit | ef241f9f0dde756a729a996b165afeac3289f04e | [log] [tgz] |
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author | Stanley Winata <68087699+raikonenfnu@users.noreply.github.com> | Wed Nov 13 18:32:38 2024 -0800 |
committer | GitHub <noreply@github.com> | Wed Nov 13 21:32:38 2024 -0500 |
tree | d5f316435c979c27e8df9512dc5af976c03a866f | |
parent | a70ea8314f5f69419277ad9d90e644f7fc3c70d6 [diff] |
[LLVMGPU] Cleanup VirtualMMA functions to match refactoring on base MMAAttr (#19144) Apply similar cleanups to what is done in https://github.com/iree-org/iree/pull/19098. For most part we do: 1. Templateize getVectorType and getOpaqueMmaLayout to work on any intrinsic 2. Use common getOpaqueMmaLayout for VirtualMMA 3. Update getABCElementTypes to be similar to MMAAttr 4. Rename get.*MFMA fn to get.*MMA since MFMA is CDNA specific but in reality it does not have to be MFMA instructions Signed-off-by: Stanley Winata <stanley.winata@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-base-compiler | |
Python iree-base-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.