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opensecura / 3p / openxla / iree / 76ea83344340a27a6dd8264af13f50608a8e349c
commit76ea83344340a27a6dd8264af13f50608a8e349c[log] [tgz]
authorKojo Acquah <KoolJBlack@users.noreply.github.com>Wed Apr 05 16:26:58 2023 -0700
committerGitHub <noreply@github.com>Wed Apr 05 23:26:58 2023 +0000
tree7b71e8976b859d5a506e5faf11ce0ad7a28399de
parentaf4c377e392dc9f55f69b2f51c7b325fef7a1951 [diff]
Add operand type info to summarizeLinalgOp::summarizeLinalgOp() (#12570)

Appends a string of datatypes for each op operand during
`summarizeLinalgOp()`. Note that the number of datatypes listed are
based on the number of operands (input and output), not dimension. In
the case that all the types match, only one type is printed (for
brevity).

Some examples of before:
```
forward_dispatch_1_generic_1x128x768
forward_dispatch_14_matmul_128x768x768
forward_dispatch_8_batch_matmul_12x128x128x64
```

... and after: 
```
forward_dispatch_1_generic_1x128x768_i32xi32xf32
forward_dispatch_14_matmul_128x768x768_f32
forward_dispatch_8_batch_matmul_12x128x128x64_f32x
```

(see the full dump for mobilebert after tile&distribute to workgroups
[here](https://gist.github.com/KoolJBlack/90e4b7e45afcc72058391b35b49dda1b))
3 files changed
tree: 7b71e8976b859d5a506e5faf11ce0ad7a28399de
  1. .github/
  2. benchmarks/
  3. build_tools/
  4. compiler/
  5. docs/
  6. experimental/
  7. integrations/
  8. lib/
  9. llvm-external-projects/
  10. runtime/
  11. samples/
  12. tests/
  13. third_party/
  14. tools/
  15. .bazel_to_cmake.cfg.py
  16. .bazelignore
  17. .bazelrc
  18. .bazelversion
  19. .clang-format
  20. .dockerignore
  21. .gitignore
  22. .gitmodules
  23. .pylintrc
  24. .style.yapf
  25. .yamllint.yml
  26. AUTHORS
  27. BUILD.bazel
  28. CITATION.cff
  29. CMakeLists.txt
  30. configure_bazel.py
  31. CONTRIBUTING.md
  32. LICENSE
  33. README.md
  34. 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.