[Codegen][CPU] Route inner_tiled broadcast into m_bcst-foldable slot. (#24516)
The CPU `inner_tiled` lowering replicates whichever of LHS/RHS has fewer
lanes up to the other's lane count before calling the LLVM intrinsic.
The previous lowering emitted this as `vector.broadcast` to a
`(replicate, K)` 2-D shape followed by `vector.shape_cast` to flat, with
a comment claiming the x86 backend's instruction selector would recover
the `{1toN}` broadcast-from-memory form on its own.
Empirically that did not work for bf16 matmul codegen on Zen 4: every
`vdpbf16ps` instruction was preceded by a separate `vbroadcastss`,
doubling the per-row uop count of the hot inner loop. Two structural
reasons:
1. The IR shape mattered. LLVM's x86 ISel `m_bcst` patterns key on the
canonical `_mm512_set1_ps`-style splat: a scalar fed into `insertelement
<N x T> poison, T, 0` followed by `shufflevector <N x T>, poison, <N x
i32> zeroinitializer`, with `T` a float. Our `vector.broadcast` to a
`(replicate, K)` 2-D shape + `vector.shape_cast` lowered to a different
shufflevector pattern (or a direct `<K x elem> -> <N*K x elem>`
interleaved shuffle) that did not pattern-match.
2. The intrinsic operand position mattered. The ISA-level `m_bcst` EVEX
operand is on the *third* source of `dpbf16ps`/`vpdpwssd`/ `pmaddwd`,
and on the `b` operand (second multiplicand) of FMA's `a*b+c`. We passed
the broadcasted operand into the LHS slot, putting it where ISel cannot
fold a memory broadcast.
Rewrite the replication to bitcast the source to a 1-lane vector of
width `K * elem_bits` (with a float lane type when that width is 32 or
64 bits, matching the `_mm512_set1_ps` shape), extract the scalar,
`vector.broadcast` it to `replicate` lanes, then bitcast back. Track
whether the broadcast landed on lhs and, for the symmetric LLVM
intrinsics, route the broadcasted operand into the m_bcst-foldable slot.
For K=1 the bitcast pair is a no-op LLVM elides. vpdpbusd is asymmetric
(UI8 must stay in the second slot); its existing sign-aware routing
happens to put the broadcast in the m_bcst slot precisely in the two
orientations where the ISA allows the fold, so no change needed there.
Measured on a 4096×4096 dynamic-shape bf16×bf16 -> f32 matmul on Zen 4
(avx512_bf16, no AMX), with `--iree-opt-data-tiling
--iree-llvmcpu-enable-inner-tiled`:
- All 29 `vdpbf16ps` in the inner loop now use the `{1to16}`
memory-broadcast form (vs 0 before); all 29 separate `vbroadcastss` are
gone.
- End-to-end matmul: 80.8 ms -> 62.7 ms (1.29x faster, 16.0 it/s -> from
12.4 it/s), closing ~60% of the gap to the precompiled mmt4d ukernel
(50.5 ms).
Progress towards #24515.
Signed-off-by: Benoit Jacob <jacob.benoit.1@gmail.com>
Co-authored-by: Claude Opus 4.7 <noreply@anthropic.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.
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| Package | Release status |
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iree-base-compiler | |
iree-base-runtime |
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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.