commit | f6a38acdae433749a29ebc9b7712e1f8db1b8376 | [log] [tgz] |
---|---|---|
author | Lei Zhang <antiagainst@gmail.com> | Mon May 20 23:15:06 2024 -0400 |
committer | GitHub <noreply@github.com> | Mon May 20 20:15:06 2024 -0700 |
tree | f9093a3f4c821d8c83f6ac4e3986b8a0d06ca618 | |
parent | 62a996bd9abcf8577f996306b7ff20ca2608f790 [diff] |
[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
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.
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!
See our website for more information.
Community meeting recordings: IREE YouTube channel
IREE is licensed under the terms of the Apache 2.0 License with LLVM Exceptions. See LICENSE for more information.