[GPU] Thread through a common target description (#17217)

This commit starts threading through a common target description
for various GPU backends. The goal is to unify how we specify
the target across CUDA, HIP, Vulkan, and others, so that we can
be consistent and reuse the some facilities.

Concretely, we introduce a `#iree_gpu.target` attribute in the
`IREEGPUDialect` to describe the GPU target. It includes a few
fields:

* The canonical target architecture for compilation, e.g.,
  sm_80 for cuda, gfx942 for hip
* A TargetWgpAttr describing the GPU features and limits in a
  single GPU workgroup processsor (that is, AMD compute unit
  or NVIDIA streaming multiprocessor), e.g., compute/storage
  capabilities, subgroup sizes, and workgroup size limits
* An optional TargetChipAttr describing GPU features for the
  final chip or product, e.g., wgp count

To help writing tests, we also introduce another command-line
option, `--iree-gpu-test-target` to specify the target. This option
will be queried if we cannot find the `#iree_gpu.target` attribute
in the wrapping IR. This avoids us to duplicate the full target
for each test to be concise and clear.

In order to support common development targets and the shorthand
attribute, we list some common AMD/NVIDIA GPU architectures
and their capabilities and limits. This is in general fine for
datacenter GPU use cases where we only ever care one architecture.

This starts with update both LLVMGPU backends (CUDA and ROCm)
to use the new target description. Vulkan side is untouched and
the next step to unify.

Progress towards https://github.com/iree-org/iree/issues/16341

ci-extra: test_nvidia_gpu,test_nvidia_a100,test_amd_mi250,test_amd_w7900
94 files changed
tree: f9093a3f4c821d8c83f6ac4e3986b8a0d06ca618
  1. .devcontainer/
  2. .github/
  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. .git-blame-ignore-revs
  22. .gitignore
  23. .gitmodules
  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.

CI Status IREE Discord 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

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.