pack microkernel padding improvements (#11987)

- More correct: the old code was incorrect for really large padding
(test expanded).
- Faster: padding is handled with an intermediate buffer, which allows
padded tiles to still be packed by the fast target-specific tile func,
as opposed to a super slow generic fallback. Moreover, we optimize the
common case where the padding byte pattern is a single-byte pattern so
we can use memset.

Even with that, padding is still surprisingly slow. This means that
compiler-level ways to remove padding overhead (pad fusions) are
important in a way that is not diminished by the ability to call
microkernels. @MaheshRavishankar @hanhanW

Note: in ruy and XNNPACK, padding is handled by having separate source
pointers for N strided rows being packed (so padding rows can just have
their pointer point at some zeros). This does not scale well to the
degree of generality and runtime-flexibility that we are aiming for here
(this essentially requires knowing some tile dimensions at compile time,
and being able to design the whole system around a restricted range of
values for these tile sizes). While not the fastest, the
intermediate-buffer approach taken here is not that slow in principle
(that it happens to be less fast than I hoped seems to be an accident of
how it's compiled by Clang, but I haven't been able to dig to the bottom
of that), and at least it makes for smaller code and insulates the
target-optimized kernels from any padding consideration.

At first I thought that in large-stride cases, L1 cache aliasing would
be a major issue, so I thought that the temporary buffer would be useful
to deal with that. But it almost never was a net win in benchmarks. So
the PR evolved towards its current state where the temp buffer is only
used to deal with padding. But at least it's still easy to revisit that
later.
10 files changed
tree: 08f9c733269bfae1a4df1e2f4ab225716f2a97f5
  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.