[Stream] Attach layouts to tensor ops in encoding specialization pass. (#19649)

The revision attaches the layouts to Stream tensor ops, e.g.,
stream.tensor.sizeof, etc. Each layout is wrapped with an attribute
where the attribute should implement the `calculateStorageSizeInBytes`
interface method. Because only the device/target knows the details. It
provides a way to materialize the serialized layouts into actual storage
size.

To teach the pass about layouts, there are few core changes happening in
the revision.

1. The revision introduces `cloneWithSimplifiedConfig` interface method
to EncodingLayoutAttrInterface. Each backend encoding attribute can
filter needed configurations when making layout resolvers in
HALAffinityAnalysisDialectInterface. This is needed because attributes
are not mutable. We always need to create a new attribute if we want to
update the parameters. The main benifit is that it makes the IR dump
much less verbose.
2. The revision also introduces `getLayout` interface method, which
returns the encoded layouts into encoding attributes. The CPU and VMVX
encoding attributes implement it in the revision, and it returns the
serialized MaterializeEncodingInfo struct as a dictionary attribute. See
below example for more details.

```
... layouts = [#iree_cpu.vmvx_encoding_layout<configuration =
  {encoding_info =
    {innerDimsPos = [0, 1],
     innerTileSizes = [8, 4],
     outerDimsPerm = [0, 1]}}>
]
```

---------

Signed-off-by: hanhanW <hanhan0912@gmail.com>
12 files changed
tree: 92230cfcbe04861815d2ef748d6aab9edfe712a9
  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.

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Project news

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Release status

Releases notes are published on GitHub releases.

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

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CI PkgCI

Nightly build status

Operating systemBuild status
LinuxCI - 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

DateTitleRecordingSlides
2021-06-09IREE Runtime Design Tech Talkrecordingslides
2020-08-20IREE CodeGen (MLIR Open Design Meeting)recordingslides
2020-03-18Interactive HAL IR Walkthroughrecording
2020-01-31End-to-end MLIR Workflow in IREE (MLIR Open Design Meeting)recordingslides

License

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