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opensecura / 3p / openxla / iree / 67a05a45aec34d779bc7ff8968bd1c93133a037c
commit67a05a45aec34d779bc7ff8968bd1c93133a037c[log] [tgz]
authorHan-Chung Wang <hanhan0912@gmail.com>Sun Dec 15 19:57:04 2024 -0800
committerGitHub <noreply@github.com>Mon Dec 16 03:57:04 2024 +0000
tree09e875a6a33b68a3ea889ad0a7c52f0d5f727b63
parentdc29ee7d1bcfcec5a58d42e29125bbda937bbbbc [diff]
[DT][NFC] Internalize transposeNarrowN logic to LayoutAttrInterface Impl  (#19453)

Whether applying transposition from narrow-N to narrow-M is backend
implementation details, and we do not need to expose it to the type
converter. The encoding itself has enough information, like indexing
maps, narrow dimensions, etc., to infer the shapes and encoding info.
Instead of updating the RankedTensorType and the attached encoding in
type converter, we can just cook the logic in `getEncodingInfo` methods.
From the encoding, we know that whether it is narrow-N case, and we can
update the MaterializeEncodingInfo correspondingly. The type converter
can infer the transposed tensor type from it. Thus, we can simplify the
logic in the type conversion.

The documentation of `transposeNarrowN` is moved to
`[CPU|GPU]EncodingExternalModels.cpp` because all the implementation
locates at the files.

Signed-off-by: hanhanW <hanhan0912@gmail.com>
8 files changed
tree: 09e875a6a33b68a3ea889ad0a7c52f0d5f727b63
  1. .github/
  2. build_tools/
  3. compiler/
  4. docs/
  5. experimental/
  6. integrations/
  7. lib/
  8. llvm-external-projects/
  9. runtime/
  10. samples/
  11. tests/
  12. third_party/
  13. tools/
  14. .bazel_to_cmake.cfg.py
  15. .bazelignore
  16. .bazelrc
  17. .bazelversion
  18. .clang-format
  19. .git-blame-ignore-revs
  20. .gitattributes
  21. .gitignore
  22. .gitmodules
  23. .pre-commit-config.yaml
  24. .yamllint.yml
  25. AUTHORS
  26. BUILD.bazel
  27. CITATION.cff
  28. CMakeLists.txt
  29. configure_bazel.py
  30. CONTRIBUTING.md
  31. LICENSE
  32. MAINTAINERS.md
  33. README.md
  34. RELEASING.md
  35. 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.

IREE Discord Status pre-commit OpenSSF Best Practices

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

Release status

PackageRelease status
GitHub release (stable)GitHub Release
GitHub release (nightly)GitHub Release
Python iree-base-compilerPyPI version
Python iree-base-runtimePyPI version

Build status

CI PkgCI

Host platformBuild status
LinuxCI - Linux x64 clang
CI - Linux arm64 clang
macOSCI - macOS x64 clang
WindowsCI - Windows x64 MSVC

For the full list of workflows see https://iree.dev/developers/general/github-actions/.

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

Community meeting recordings: IREE YouTube channel

  • 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.