[VectorDistribute] Lower and distribute `async_dma` (#24299)

Pass to distribute and lower `async_dma` operations at the workgroup
level to `amdgpu.gather_to_lds` operations at the thread-level (with
threads in each subgroup collaborating).

The pass shares helpers with the existing GPU pass to distribute
operations based on layouts, but as the `async_dma` operation does not
have `vector` operands or results, lowering and distribution are
implemented as a separate pass. The changes to
`GPUNestedLayoutDistributionPatterns.cpp` are therefore mainly a code
move extracting shared helpers to the new
`GPUNestedLayoutUtils.[h|cpp]`.

The basic idea of the distribution is to construct a (nested) layout
that represents how the data-transfer is split across subgroups and
threads to perform the full transfer with direct-to-LDS compatible
operations. The layout is constructed in stages:
1. We choose the DMA size for the given target that fulfills the
requirements and determine the element tile based on the size of the
transfer per thread from the DMA size (`distributeFromInnermost`).
2. The element tile is given by the number of threads in subgroup
(`distributeFromInnermost`).
3. Outer tile is always all-ones.
4. We distribute the transfer to the configured number of subgroups
(`distributeFromOutermost`).
5. Whatever is left after these steps ends up as the batch tile of each
thread.

Once we have that layout, we can use the shared helpers for the
mechanics of distributing the operation.

The distribution fails if any of the requirements are not met. This is
mostly a defensive check, the pass inserting the `async_dma` operations
(will be added in a different PR) should only insert `async_dma`
operations if the prerequisites can be met with the available DMA sizes
for the transfer shape etc. Therefore, the pass also fails if any of the
`async_dma` operations could not be distributed and lowered.

Swizzling and gather semantics are not part of this PR and will be added
in follow-up PRs.

This is part of https://github.com/iree-org/iree/issues/23782.

Assisted-by: Claude Code and Codex

---------

Signed-off-by: Lukas Sommer <lukas.sommer@amd.com>
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  7. lib/
  8. llvm-external-projects/
  9. runtime/
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  14. .bazel_to_cmake.cfg.py
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  30. CONTRIBUTING.md
  31. LICENSE
  32. MAINTAINERS.md
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  34. README.md
  35. RELEASING.md
README.md

IREE: Intermediate Representation Execution Environment

IREE (Intermediate Representation Execution Eenvironment, 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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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

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