commit | 0b29f7b10cc7cd0b33666a214d8d155077ecb7f3 | [log] [tgz] |
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author | Han-Chung Wang <hanhan0912@gmail.com> | Wed Sep 25 10:40:27 2024 -0700 |
committer | GitHub <noreply@github.com> | Wed Sep 25 17:40:27 2024 +0000 |
tree | fecd89e78cc8d4cf92116402242ae48c56915a2e | |
parent | 729028390c2a1e4cad57679bf3f9d789e96f0723 [diff] |
[GPU][DT] Add support for GPU data-tiling E2E tests. (#18591) The revision introduces GPUMaterializeHostEncodingPass for early materialization. It is mainly for testing purpose while the codegen for encodings is still in progress. A experimental flag (i.e., `iree-global-opt-experimental-rocm-data-tiling`) is introduced to provide a path for GPU data-tiling e2e tests. In the tests, a `optimization_barrier` op is introduced in between of `set_encoding` op and `unset_encoding` op. The compiler could be clever to cancel data-layout transformation, so a barrier is inserted as a hint. To support the e2e path, the revision adds two additional changes: 1. Implement `MaterializeOptimizationBarrierOp` pattern, which replaces the barrier op with the same op with materialized shape. 2. Implement the fallback for unset_encoding materialization pattern. Currently only f32.f32.f32 and i8.i8.i32 mfma ops are supported. The wmma ops are not supported, so the codegen should turn encodings to nop on the targets that do not yet support the intrinsics. E.g., gfx1100. Note: this only tests set_encoding and unset_encoding. The gemm codegen is still in progress. --------- Signed-off-by: hanhanW <hanhan0912@gmail.com>
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
Package | Release status |
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GitHub release (stable) | |
GitHub release (nightly) | |
Python iree-compiler | |
Python iree-runtime |
Host platform | Build status |
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Linux | |
macOS | |
Windows |
For the full list of workflows see https://iree.dev/developers/general/github-actions/.
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.