[LLVMGPU] Add VMFMA for FP8 to align layouts between chained F8 contractions. (#19020)

This PR introduces virtual intrinsics on F8 MFMA that breaks apart a
single 8xF8 read into two interleaved 4xF8 read from shared memory.

This main motivation for this virtual intrinsic is to enable faster F8
attention/chained matmuls. The reason for that is by doing interleaved
reads on K-dimension, we can match the native F8 intrisic output layout
coming from the 1st matmul to the rhs read of the 2nd matmul(with
interleaved virtual MFMA layout).

Once the layout is aligned, we just need to handle it using to_layout
lowering that does reshape on the SIMT vector.

This PR has been tested on attention of shape:
[B: 1, M: 4096, K1: 64, K2: 4096, N: 64]

as seen in this IR:
[(link)](https://gist.githubusercontent.com/raikonenfnu/4d33b5addfa9c4ec9e76918704251e39/raw/5b20c0c359e3e2df7f8db4890d3cc0590352d18a/attention_f8_perf.mlir)
 
and using this spec to specify the VMFMA on 2nd matmul and regular MFMA
on 1st matmul:
([link](https://gist.githubusercontent.com/raikonenfnu/4d33b5addfa9c4ec9e76918704251e39/raw/5b20c0c359e3e2df7f8db4890d3cc0590352d18a/attn_config.mlir))

we were able to get perf of 1.63x speed up from (reference with same
config but using MFMA_16x16x32xF16 on both contractions. With
correct/same numerics.

Signed-off-by: Stanley Winata <stanley.winata@amd.com>
4 files changed
tree: c51889cbbdbf227857f890dc7854b618de952660
  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
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  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 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.