[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>
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 |
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.