commit | 4662c1b50bf6047274c555ef158ae0643cdc73e2 | [log] [tgz] |
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author | bjacob <benoitjacob@google.com> | Mon Feb 28 11:24:22 2022 -0500 |
committer | GitHub <noreply@github.com> | Mon Feb 28 11:24:22 2022 -0500 |
tree | 782e11ec27b8cc1d82e243470a96b12672079189 | |
parent | 5e3d0b8eee2a0635500a07ec400fd18818989d11 [diff] |
aarch64 +i8mm kernels, plus necessary infra. (#8316) * Add aarch64 +i8mm kernels, plus infra work needed. With +i8mm for the first time we have a feature that we can't assume all devices to support, not even our own testing devices. This forced implementing cpu feature detection (could have used cpuinfo but IIUC there's interest in not entrenching it in IREE and this was a good example of to do modern CPUID work on linux/arm). See the cpu_features library. There is a new 'requirements' node in the YAML traces for e2e matmul tests, and when iree-e2e-matmul-test finds that a requirement is not met, it terminates early, recording a test success but logging a message about that. This will allow tests to run on CI machines where some machines support certain features and certain don't, while being green everywhere and giving as much test coverage as possible. In particular, only the runtime-testing coverage is dependent on the CI machine, while the compilation aspect of the tests are always exercised (meaning we unconditionally compile modules with +i8mm, regardless of whether we will be able to run them). The actual asm kernel with i8mm is not good at the moment. That's because the way we are abstracting a matmul kernel as a loop around a single-shape 3-iterators vector.contract is proving a bit too limited. For now we're accumulating experimental data on this topic, not yet trying to be fast / resolve this. * clang-format * build * buildifier
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!
See our website for more information.
IREE is licensed under the terms of the Apache 2.0 License with LLVM Exceptions. See LICENSE for more information.