[Stream] Always map aliases to resolved resource ranges of the source op (#25026)

This ensures that all operands in the tied operand chain for a resource
will get mapped to the correct buffer range.

Previously, if the resource's buffer received a new allocation,
`stream.resource.subview` aliases (by nature of a byte subview, tied to
a different memory range) would still receive resource ranges relatively
to the old resource.
Additionally, all aliases of a given source would be processed from the
"nearest" subview in the alias chain. With multiple subviews in the same
tied operand chain, the offsets weren't computed relative to one
another.

Addressing the first problem enables the most practical use case.
SplitParameterEncoding in "consolidate" mode may need to place an
unmodified input weight into the output buffers, which means the weight
may end up in a packed allocation together with a pre-initialized output
parameter. To later fetch that input weight for dynamic initialization,
an explicit `subview` op is generated (*). Hoisting the constant out of
the execution scope establishes the new resource range, and the subview
should compute its resource range against that. Instead, the old
subrange would get used that was relative to the original SSA source
bound for deletion.
See `Stream/Transforms/test/schedule_allocation_subviews_constant.mlir`
for illustration.

(*) Whether that should be happening is a different matter. Accessing
the packed weight through `stream.async.slice` would've been preferable,
as per a [TODO in
SplitParameterEncoder](https://github.com/iree-org/iree/blob/0df50f9a8e40ab1878479124b952f45d9f8c0650/compiler/src/iree/compiler/Dialect/Stream/Transforms/SplitParameterEncoder.cpp#L1971).
Generally, we should let ScheduleAllocation operate on that abstraction
where a copy will implicitly be generated on writing to the range,
rather than have it account for explicit aliases in that of
`resource.subview`. For now I've preferred not to go into that
direction, because that switch requires larger-scale testing, and
currently explicit subview ops are expected to be supported in the input
IR to the allocator.

By implementing alias mapping as a DFS worklist and simply ignoring
values that have already been resolved to a resource range, nested
subviews, a.k.a. subview ops that reference each other, now correctly
calculate their offset by adding up their own offset to the earlier
offsets in the tied operand chain. Not sure if that does/would come up
in a real case, but at least that's a sensible side effect.

Assisted-by: Codex

---------

Signed-off-by: Artem Gindinson <gindinson@roofline.ai>
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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
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  32. LICENSE
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  35. README.md
  36. RELEASING.md
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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Operating systemBuild status
LinuxCI - Linux arm64 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.