[Metal] Fix staging buffer overflow on large update_buffer uploads (#24643)

update_buffer payloads were appended into the single device-shared,
fixed-capacity (default 128 KiB) staging buffer. Its offset only resets
when all command buffers using it are destroyed, so an oversized host
upload chunked by the generic queue_emulated_update (e.g. a ~1 MiB
upload split into 64 KiB pieces, all recorded into one command buffer)
exhausts the region on the third chunk and fails with
RESOURCE_EXHAUSTED. This also forced bulk updates to compete with
dispatch argument buffers for the budget the 128 KiB constant was sized
for.

Keep the shared staging buffer as an opportunistic fast path for
payloads that fit, and spill to a dedicated shared-storage MTLBuffer
when one does not fit the remaining capacity. Dedicated buffers are
retained for the command buffer's lifetime (released in
command_buffer_reset, which runs only after the GPU completes via the
submission resource set) and used as the blit-copy source. This handles
arbitrary update sizes and counts without touching the driver-generic
queue_emulation chunking, matching how Vulkan and CUDA capture update
payloads per-command rather than in a shared fixed region.

Fixes the aligned16_mib sub-case of
CTS/CommandBufferCopyBufferTest.CopySizeAndAlignmentClasses/metal.

Signed-off-by: Alex Vasile
<48962821+Alex-Vasile@users.noreply.github.com>

Signed-off-by: Alex Vasile <48962821+Alex-Vasile@users.noreply.github.com>
1 file changed
tree: 829c00481d5cd634a409731e1cbe133e628183c4
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  8. llvm-external-projects/
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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.

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

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

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  • 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.