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opensecura / 3p / openxla / iree / 17eafc9a230e31744224026d77cf46982e5c0a17
commit17eafc9a230e31744224026d77cf46982e5c0a17[log] [tgz]
authorHan-Chung Wang <hanchung@google.com>Tue Feb 28 10:28:22 2023 -0800
committerGitHub <noreply@github.com>Tue Feb 28 10:28:22 2023 -0800
treefd858c275f2fb6f33947bcf912f32554355243bc
parentc3f4a7cb64f3f804bf2ab02aad8440c601c0adc1 [diff]
[LLVMGPU] Enable tensor.pack op e2e execution on cuda. (#12370)

It takes 2.7 ms to finish the tensor.pack e2e tests. This is because the
fully dynamic tensor.pack test cases are not vectorized. There are two
fully dynamic cases in total. If we exclude the test, it takes ~0.57 ms.
The pipeline is not well tuned yet, it is mostly copied from LLVMCPU and
for correctness tests.

It also splits the pack.mlir test file into two files. One has static
inner tiling sizes, and the other has dynamic inner tiling sizes. The
dynamic cases are only used by microkernels, which aims to have unify
kernel for all the cases. Currently, the cuda side does not support it
because it introduces unbounded memory allocation.
13 files changed
tree: fd858c275f2fb6f33947bcf912f32554355243bc
  1. .github/
  2. benchmarks/
  3. build_tools/
  4. compiler/
  5. docs/
  6. experimental/
  7. integrations/
  8. llvm-external-projects/
  9. runtime/
  10. samples/
  11. tests/
  12. third_party/
  13. tools/
  14. .bazelignore
  15. .bazelrc
  16. .bazelversion
  17. .clang-format
  18. .dockerignore
  19. .gitignore
  20. .gitmodules
  21. .pylintrc
  22. .style.yapf
  23. .yamllint.yml
  24. AUTHORS
  25. BUILD.bazel
  26. CITATION.cff
  27. CMakeLists.txt
  28. configure_bazel.py
  29. CONTRIBUTING.md
  30. LICENSE
  31. README.md
  32. WORKSPACE
README.md

IREE: Intermediate Representation Execution Environment

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.

CI Status

Project Status

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!

Communication Channels

Related Project Channels

  • MLIR topic within LLVM Discourse: IREE is enabled by and heavily relies on MLIR. IREE sometimes is referred to in certain MLIR discussions. Useful if you are also interested in MLIR evolution.

Architecture Overview

IREE Architecture IREE Architecture

See our website for more information.

Presentations and Talks

  • 2021-06-09: IREE Runtime Design Tech Talk (recording and slides)
  • 2020-08-20: IREE CodeGen: MLIR Open Design Meeting Presentation (recording and slides)
  • 2020-03-18: Interactive HAL IR Walkthrough (recording)
  • 2020-01-31: End-to-end MLIR Workflow in IREE: MLIR Open Design Meeting Presentation (recording and slides)

License

IREE is licensed under the terms of the Apache 2.0 License with LLVM Exceptions. See LICENSE for more information.