Move benchmark config generation to build_e2e_test_artifacts (#13291)

When working on https://github.com/openxla/iree/pull/13273, I noticed
that benchmark configs and modules are uploaded in 3 different jobs.
These artifacts can be uploaded to different GCS dirs when some of them
are re-run due to failures (because re-run jobs will create [GCS_DIR
with new run
attempts](https://github.com/openxla/iree/blob/main/.github/workflows/benchmark_compilation.yml#L44-L50)).
As a result, users might not be able to download all benchmark artifacts
from a single GCS dir URL, which can be confusing.

This PR changes the workflow to generate all benchmark modules and
configs in `build_e2e_test_artifacts` to avoid such issue. All files are
uploaded to `${GCS_URL}/e2e-test-artifacts`, a single path to download
all benchmark artifacts.

Besides the reason above, I think during the CMake build we should
generate the benchmark configs under
`${IREE_BUILD_DIR}/e2e_test_artifacts` instead of calling
`export_benchmark_config.py` separately. There are some questions about
how to pass the benchmark presets/filters through CMake configuration,
so I decided to defer that as the next step.
6 files changed
tree: 8f551383b3381b43916a9adceb12e614032f4383
  1. .devcontainer/
  2. .github/
  3. benchmarks/
  4. build_tools/
  5. compiler/
  6. docs/
  7. experimental/
  8. integrations/
  9. lib/
  10. llvm-external-projects/
  11. runtime/
  12. samples/
  13. tests/
  14. third_party/
  15. tools/
  16. .bazel_to_cmake.cfg.py
  17. .bazelignore
  18. .bazelrc
  19. .bazelversion
  20. .clang-format
  21. .dockerignore
  22. .gitignore
  23. .gitmodules
  24. .pylintrc
  25. .style.yapf
  26. .yamllint.yml
  27. AUTHORS
  28. BUILD.bazel
  29. CITATION.cff
  30. CMakeLists.txt
  31. configure_bazel.py
  32. CONTRIBUTING.md
  33. LICENSE
  34. README.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.

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.