commit | 37ce07359303716293933ad21fb858626065bd13 | [log] [tgz] |
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author | bjacob <benoitjacob@google.com> | Wed Feb 02 22:32:54 2022 -0500 |
committer | GitHub <noreply@github.com> | Wed Feb 02 22:32:54 2022 -0500 |
tree | d1325a7ef57e2f8cad522559f4e79c54580c43bc | |
parent | d0bb26e7a94b70384a24d2e344769d222c940fc1 [diff] |
convert-linalg-matmul-to-mmt4d improvements (#8192) - take target info, not M0/K0/N0 values. - share the target info stuff with VectorContractCustomKernels - better pass options (enum and list instead of bag of bools) - add also a enable_generic_slow bool option controlling whether to do mmt4d even in cases where we dont have a fast kernel (for tests) or not (for real users, the default). Also: trim e2e matmul tests a bit: - do not test mmt4d on vmvx for now (very slow to run, has not proven to find more bugs, and not a current focus) - for cpu-feature-specific variants, only generate tests for data types that are concerned by the variant (aarch64:+dotprod -> i8 only)
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.