| commit | e3f2ab393fdef5a22877c982f8e2e89effdbfa91 | [log] [tgz] |
|---|---|---|
| author | Han-Chung Wang <hanhan0912@gmail.com> | Tue Nov 07 09:45:51 2023 -0800 |
| committer | GitHub <noreply@github.com> | Tue Nov 07 09:45:51 2023 -0800 |
| tree | 0e74f3d96d131f3e81654846bfdee844f42fca4b | |
| parent | 41a23ade9c5857ec56a70e3cb907ffaa9feb6c5e [diff] |
[CPU] Improve tile sizes selection for tensor.pack ops. (#15397) It disables special vector sizes for non-f32 cases because the logic is only for 16x16 transpose cases. The improvements of dispatch sizes are from vectorization. We are not able to vectorize named ops if they have dynamic shapes, which is fixed by https://github.com/llvm/llvm-project/commit/03529b99b36788ca836b7ce238ea9400ce89847b. The change allows backends to vectorize them because they become static shapes (by tiling with size=1). It is not a hard condition; we track it in https://github.com/openxla/iree/issues/15441 The revision takes the number of threads into account, so we have better performance on multi-threaded. It also reduces runtime overheads. This is a step toward to https://github.com/openxla/iree/issues/15391 and https://github.com/openxla/iree/issues/15349 It improves the performance of [tensor.pack](https://github.com/openxla/iree/issues/15349) op from 420 ms to 170 ms on 8-threaded x86 CPU.
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.