[Codegen][CPU] Flatten contiguous trailing dims of transfers before unrolling. (#24517)

`VectorTransferLoweringPass` applies the MLIR transfer-lowering patterns
with `maxTransferRank=1` plus full-unroll, which fully unrolls any
rank-N>1 `vector.transfer_read`/`transfer_write` to multiple rank-1
transfers (one per index of the outer dim). For multi-dim tiles whose
trailing dims are contiguous in memory, this unrolls a single wide load
into many narrow ones, which then have to be reassembled into a wide
vector via a chain of `shufflevector`s in the hot inner loop.

Example surfacing the cost: a 4096x4096 dynamic-shape bf16xbf16->f32
matmul with `--iree-llvmcpu-enable-inner-tiled` on Zen 4 lowered to
inner_tiled with N=16, K_inner=2. The RHS for one K-step is a
`vector<16x2xbf16>` from a contiguous 64-byte slice. Unrolling to 16
separate `<2 x bfloat>` loads forced a sequence of `vpermt2d`/
`vpermt2q` per K-iteration in the inner loop to rebuild the wide RHS
register — accounting for ~3 cycles of extra work per K-step on top of
the 29 dpbf16ps doing the real work.

Apply `populateFlattenVectorTransferPatterns` *before* the
rank-reduction patterns. It rewrites a multi-dim transfer with
contiguous trailing dims into a transfer on a `memref.collapse_shape`
view + a `vector.shape_cast`, so the read ends up as a single 1-D
transfer over the collapsed view and lowers to one wide `vector.load`.
Per-fragment effect on the matmul benchmark above: 80.8 ms -> 67.1 ms
(1.20x). Combined with the m_bcst-fold broadcast routing in a sibling
commit, end-to-end gets to 53.4 ms (within 5% of the precompiled mmt4d
ukernel at 50.9 ms).

Test fallout: two pipelines now lower a per-row pack-tile load into a
single wide load over a collapsed-memref view rather than one load per
row (`aligned_unpack_generic` in pipeline_pack_unpack_tests) / write a
constant `vector<4x2xi1>` mask as a single flat `vector<8xi1>` store
(`transpose_mask` in vector_lowering). The new IR is strictly fewer ops
in both cases; updated the CHECK lines to match.

Progress towards #24515.

---------

Signed-off-by: Benoit Jacob <jacob.benoit.1@gmail.com>
Co-authored-by: Claude Opus 4.7 <noreply@anthropic.com>
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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.

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Releases notes are published on GitHub releases.

PackageRelease status
GitHub release (stable)GitHub Release
GitHub release (nightly)GitHub Release
iree-base-compilerPyPI version
iree-base-runtimePyPI version

For more details on the release process, see https://iree.dev/developers/general/release-management/.

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LinuxCI - Linux arm64 clang
macOSCI - macOS x64 clang
macOSCI - macOS arm64 clang

For the full list of workflows see https://iree.dev/developers/general/github-actions/.

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

DateTitleRecordingSlides
2025-06-10Data-Tiling in IREE: Achieving High Performance Through Compiler Design (AsiaLLVM)recordingslides
2025-05-17Introduction to GPU architecture and IREE's GPU CodeGen Pipelinerecordingslides
2025-02-12The Long Tail of AI: SPIR-V in IREE and MLIR (Vulkanised)recordingslides
2024-10-01Unveiling the Inner Workings of IREE: An MLIR-Based Compiler for Diverse Hardwarerecording
2021-06-09IREE Runtime Design Tech Talkrecordingslides
2020-08-20IREE CodeGen (MLIR Open Design Meeting)recordingslides
2020-03-18Interactive HAL IR Walkthroughrecording
2020-01-31End-to-end MLIR Workflow in IREE (MLIR Open Design Meeting)recordingslides

License

IREE is licensed under the terms of the Apache 2.0 License with LLVM Exceptions. See LICENSE for more information.