| commit | 233795f5665075f7770187ee5cea18cf7f59b85d | [log] [tgz] |
|---|---|---|
| author | bjacob <benoitjacob@google.com> | Fri Aug 26 12:00:09 2022 -0400 |
| committer | GitHub <noreply@github.com> | Fri Aug 26 16:00:09 2022 +0000 |
| tree | 05c4a22af86dba997b46a5ab3b922d704ffd9f62 | |
| parent | 094ec6d183e769893ae6e9ee98e668712a19dd24 [diff] |
Tidy the VMVX ukernels matmul interface (#10211) This makes the VMVX ukernel interface for matmul somewhat sustainable and generalizable. It's official now that the only supported case is when all operands are row-major (more general support might be wanted in the future, but would have to allow separate storage orders for each operand in order to be likely to be used). The only flag now is one bit to tell whether to accumulate into an existing accumulator, or just zero it. At the moment we always accumulate but could soon generate calls without the accumulate flag when compiling code where the accumulator operand is known to be zero-filled. In terms of optimized runtime code, it is nearly zero overhead to support that boolean degree of generality in the ukernel. The "reference" ukernel impl is changed to be a little more suggestive of how an optimized impl would look. The alpha, beta parameters are gone. There were hard to generalize to integer data types, and they were mostly gratuitous generality anyway (they didn't do the same as the namesake BLAS GEMM parameters).
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.