[GPU] Fix alignment check for scaled matmul (#22737) ## Problem The current alignment check in `GPUHeuristics.cpp` is incorrect for any intrinsic that has multiple M, N, and K dimensions. The root cause is that the product of intrinsic sizes is passed to `GPUMMASchedule` instead of passing the individual dimension sizes as a vector. ## Example https://github.com/iree-org/iree/blob/b98c1b92cb630bd696992f47df591bb2f247a8d7/compiler/src/iree/compiler/Codegen/Common/GPU/GPUHeuristics.cpp#L516-L525 Consider the scaled MFMA where `intrinsic.kSizes = [K, KB] = [4, 32]`. Instead of passing the vector `[4, 32]`, the value `128` (product: 4 × 32) is passed to `GPUMMASchedule`. https://github.com/iree-org/iree/blob/b98c1b92cb630bd696992f47df591bb2f247a8d7/compiler/src/iree/compiler/Codegen/Common/GPU/GPUHeuristics.cpp#L98-L108 Assume tile size = `[4, 1]`. The returned schedule sizes become `[4, 128]` instead of the correct `[16, 32]`. As a result, the last dimension `128` always makes the alignment check fail, since the problem size of KB is `32` and `32 % 128 != 0`. When the alignment check fails, no intrinsic is selected and the operation falls back to complete serialization. This leads to extremely slow execution for workloads like Llama 405B FP4 prefill with direct codegen. ## Solution This PR passes all intrinsic sizes as vectors to `GPUMMASchedule`. ## Performance **Llama 405B FP4 prefill direct codegen with shark-ai:** - Before: 11 minutes - After: 234 ms Closes: #22559 ci-extra: test_torch --------- Signed-off-by: Yu-Zhewen <zhewenyu@amd.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.
Releases notes are published on GitHub releases.
| Package | Release status |
|---|---|
| GitHub release (stable) | |
| GitHub release (nightly) | |
iree-base-compiler | |
iree-base-runtime |
For more details on the release process, see https://iree.dev/developers/general/release-management/.
| Operating system | Build status |
|---|---|
| Linux | |
| macOS | |
| macOS |
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
| Date | Title | Recording | Slides |
|---|---|---|---|
| 2025-06-10 | Data-Tiling in IREE: Achieving High Performance Through Compiler Design (AsiaLLVM) | recording | slides |
| 2025-05-17 | Introduction to GPU architecture and IREE's GPU CodeGen Pipeline | recording | slides |
| 2025-02-12 | The Long Tail of AI: SPIR-V in IREE and MLIR (Vulkanised) | recording | slides |
| 2024-10-01 | Unveiling the Inner Workings of IREE: An MLIR-Based Compiler for Diverse Hardware | recording | |
| 2021-06-09 | IREE Runtime Design Tech Talk | recording | slides |
| 2020-08-20 | IREE CodeGen (MLIR Open Design Meeting) | recording | slides |
| 2020-03-18 | Interactive HAL IR Walkthrough | recording | |
| 2020-01-31 | End-to-end MLIR Workflow in IREE (MLIR Open Design Meeting) | recording | slides |
IREE is licensed under the terms of the Apache 2.0 License with LLVM Exceptions. See LICENSE for more information.