Move Codegen pass pipelines to nest on `FunctionOpInterface`. (#16665)

This PR modifies the codegen backends to have the lowering pass
pipelines nest on `FunctionOpInterface`. This allows running different
pass pipelines on functions within the dispatch. This would allow you
to have things like

```
func.func @foo_pipeline(...) {

}

func.func @bar_pipeline(...) {

}

func.func @entry_point() {
  if (<condnn for foo pipeline based lowering>) {
    foo_pipeline()
  } else {
    bar_pipeline()
  }
}
```

To connect everything the following things are done

1) The `iree_codegen.translation_info` attribute that was set on entry
   point operations are now set on the surrounding function. This
   allows implementing a lowering strategy on a function.

2) The GPU backends set the `workgroup_size` and `subgroup_size` on
   the `hal.executable.export` operation. To unwind this, the
   `translation_info` has fields for `workgroup_size` and
   `subgroup_size`. This allows GPU backends to set the expected
   `workgroup_size` and `subgroup_size` on the `translation_info`
   itself (which is now on the surrounding function).

3) A pass is added after lower strategies to
   `ReconcileTranslationInfo`. The intent of this pass is to take the
   `translation_info` on each function and set the values for
   `workgroup_size` and `subgroup_size` on the
   `hal.executable.export`. Eventually this would also be a place
   where the number of workgroups is populated on the
   `hal.executable.export` (instead of doing it on
   `TileAndDistributeToWorkgroups` as it is done today).

4) All backends `*SelectLoweringStrategy` work as Module pass. These
   need to be Module passes since transform dialect tends to inject
   the transform script within the module.

5) The `*LowerExecutableStrategy` works at `FunctionOpInterface` now.

6) The transform dialect interpreter has to run on `Module`
   granularity, so a new pass `LowerExecutableUsingTransformDialect`
   is added. This runs the transform interpreter before
   `*SelectLoweringStrategy`. After this pass is run, the
   `translation_info` is expected to have the pipeline be set to
   `None` to skip subsequent lowering pipelines.

7) Most tests are now moved to remove the boiler plates surrounding
   `hal.executable` and `hal.executable.variant`.

This does most of the heavy lifting for running lowering strategies
per function-like op. The biggest missing piece are

1) The `TileAndDistributeOnWorkgroups` ops still cannot really be run
   on a dispatch with multiple functions since it updates the
   `hal.executable.export`. To address this, the pass will have to
   move to use `scf.forall`.

2) Some optimizations expect static workgroup count. Those currently
   go upto the `hal.executable.export` op to get these values (that
   were populated by `TileAndDistributeToWorkgroups`). When moving to
   `scf.forall` this will be available withint the function.


ci-extra: build_test_all_arm64, build_test_all_windows,
build_test_all_maxos_arm64, build_test_all_macos_x86_64,
test_nvidia_a100
227 files changed
tree: d8c4316b8c547a371b217c25c15fc54a42d5854b
  1. .devcontainer/
  2. .github/
  3. build_tools/
  4. compiler/
  5. docs/
  6. experimental/
  7. integrations/
  8. lib/
  9. llvm-external-projects/
  10. runtime/
  11. samples/
  12. tests/
  13. third_party/
  14. tools/
  15. .bazel_to_cmake.cfg.py
  16. .bazelignore
  17. .bazelrc
  18. .bazelversion
  19. .clang-format
  20. .dockerignore
  21. .git-blame-ignore-revs
  22. .gitignore
  23. .gitmodules
  24. .yamllint.yml
  25. AUTHORS
  26. BUILD.bazel
  27. CITATION.cff
  28. CMakeLists.txt
  29. configure_bazel.py
  30. CONTRIBUTING.md
  31. LICENSE
  32. README.md
  33. 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 IREE Discord 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

License

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