Allow encoding info to depend on elem type and target info. (#11290)

This makes it an implementation detail of the `MaterializeEncodingFn` to
actually use target info and element type to determine real (useful)
tile sizes. See how the `iree/compiler/` side does a
`IREE::HAL::ExecutableTargetAttr::lookup` from #11057 to get the
`target` attribute. Meanwhile, #11291 will make it easy to obtain the
details that we need based on that attribute (the existing codebase is
still wired to deal with `ExecutableVariantOp`'s, needs to be adapted
post #11057).

The element type is passed as a function argument, while the target info
is not. Accordingly, when we want to actually depend on target info, at
the place where we construct a `MaterializeEncodingTypeConverter`
passing it a `MaterializeEncodingFn`, we can pass a function that itself
depends on target info, as exemplified in this PR by the IREE side
change where we pass a lambda capturing `getOperation()`.

Motivation for this compromise:

- TLDR: minimize PR size.
- Long version: If `MaterializeEncodingFn` itself takes an `Operation*`
then we need to be able to pass that in the `getMaterializedType` call
in the lambda passed to `addConversion` in the the constructor of
`MaterializeEncodingTypeConverter`. Fine then, we could have the
`MaterializeEncodingTypeConverter` constructor take `getOperation()` as
argument, and propagate that all the way down to places where
`MaterializeEncodingFn` is called. This works, but results in a ~ 2x
bigger PR. In particular, chains of helper functions calling each other,
such as `getPackedDimsForDispatchTensor` and
`getPackedDynamicDimsForDispatchTensor`, which currently take a
`Location`, would now also need to take `Operation*`, and I was starting
to wonder whether to drop the `Location` argument, and how I would
document what these functions really do with their parameters (if we
drop the `Location` arg, then the `Operation*` arg becomes more than
just target-information, and it becomes nontrivial to describe its role
in this function's semantics).
3 files changed
tree: 3db884071670c6426e2da7abcf7b9ae4b6810210
  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.