[LinalgExt] Masked Attention Implementation (#18525)

Enables float/boolean mask as parameters and created linalg generic ops
to apply masking. This image (https://imgur.com/a/1MePgcy) elaborates on
the main files changed and how they enable masked attention:
- Blue boxes represent changed .cpp and .td files to
enable/pass/decompose the mask
  -  Yellow boxes represent the different op classes
- Red boxes represent test mlir files pertaining to certain .cpp/.td
implementations or ops
  
For quick reference, AggregateOpInterfaceImpl.cpp contains the bulk of
the actual mask decomposition (QK += mask)

And for clarification, TileAttention.cpp only holds the
convertToOnlineAttentionOp and getTileAttentionIndexingMaps functions;
TilingInterfaceImpl.cpp contains the main tiling capabilities in the
form of AttentionOp::getTiledImplementation and
OnlineAttentionOp::getTiledImplementation.
  

Updated version of https://github.com/iree-org/iree/pull/18461. This
version was created to include scale affine map and enable fused
attention (incorporated
https://github.com/IanWood1/iree/tree/raikonen/sdpa_mask).
- To that end, many modifications in tests are for adding the scale
affine map (without much functionality change)
- For tiling and decomposition tests, most functionality tests are
included in "tiling.mlir" and "decompose_online_attention.mlir". On the
other hand, the "tile_attention.mlir and "decompose_attention.mlir" are
old paths intended to be be retired and deprecate soon. Hence, no major
tests were added it there.

Test directory for numerical verification:
https://github.com/rohan-tan-bhowmik/iree-masked-attention-test

---------

Signed-off-by: Stanley Winata <stanley.winata@amd.com>
Co-authored-by: Stanley Winata <stanley.winata@amd.com>
Co-authored-by: Ian Wood <ianwood2024@u.northwestern.edu>
30 files changed
tree: 54232463ae3917ee389a23cb09b0d1b0573a093d
  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. .dockerignore
  20. .git-blame-ignore-revs
  21. .gitattributes
  22. .gitignore
  23. .gitmodules
  24. .pre-commit-config.yaml
  25. .yamllint.yml
  26. AUTHORS
  27. BUILD.bazel
  28. CITATION.cff
  29. CMakeLists.txt
  30. configure_bazel.py
  31. CONTRIBUTING.md
  32. LICENSE
  33. MAINTAINERS.md
  34. README.md
  35. RELEASING.md
  36. 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-compilerPyPI version
Python iree-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.