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[CoreAI] Add a coreai_compute_unit backend option - #24375

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coreai-expose-compute-unit
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coreai-expose-compute-unit

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Adds a coreai_compute_unit backend option for the Core AI delegate, set the same way as coreai_cache_dir. It selects which compute units Core AI may use when it specializes a model on the device.

Stacked on #24374.

Option values

Each value is exactly one of the configurations the Core AI SDK provides through SpecializationOptions:

Value Core AI SpecializationOptions Allowed compute units
default (or absent) .default CPU, GPU, Neural Engine
cpu_only .cpuOnly CPU
prefer_cpu init(preferredComputeUnitKind: .cpu) CPU, GPU, Neural Engine
prefer_gpu init(preferredComputeUnitKind: .gpu) CPU, GPU, Neural Engine
prefer_neural_engine init(preferredComputeUnitKind: .neuralEngine) CPU, GPU, Neural Engine

The option mirrors the SDK and adds nothing of its own:

  • cpu_only is the only restriction. A preference maximizes use of that unit but falls back to any allowed unit for operations it can't run.
  • No other combinations. The SDK can't express ones like "CPU and Neural Engine only", because its allowed units can't be set.
  • No separate allowed/preferred settings. One string covers every configuration the SDK can build.
BackendOptions<2> options;
ET_CHECK_OK_OR_RETURN_ERROR(options.set_option("coreai_compute_unit", "cpu_only"));
LoadBackendOptionsMap map;
ET_CHECK_OK_OR_RETURN_ERROR(map.set_options("CoreAIBackend", options.view()));
ET_CHECK_OK_OR_RETURN_ERROR(module.load(map));

Implementation

  • coreai_backend.mm reads the option during init, next to coreai_cache_dir. Unknown values, non-string values and null fail with InvalidArgument before a loader is created. The error lists the valid values.
  • ETCoreAIBridge.h adds an ETCoreAIComputeUnit enum, and ETCoreAICreateModelLoader now takes it. ETCoreAIModelLoader.swift maps each value to the matching SpecializationOptions.
  • Caching: the cache key already includes the loader's specialization identity, so each value gets its own bookmark, source directory and lock stripe. default keeps its existing identity, so bookmarks from before this change still restore. clear_cache_for_pte still removes every variant of a PTE.
  • Creating a loader only to evict a bookmark uses default, because eviction doesn't depend on specialization.
  • The README documents the option.

Test plan

  • coreai_host_test passes. A new delegate test checks that each value reaches the loader factory, that a missing option means default, and that unknown and non-string values are rejected before a loader is created.
  • coreai_swift_bridge_test passes. A new Swift test checks that each value builds exactly the expected SpecializationOptions, that the five values have five different cache identities, and that default keeps the "default" identity.
  • Manual multithreaded check, using a standalone runner that loads a two-delegate fp16 PTE with the five values assigned round-robin across threads:
    • Cold, 10 threads × 50 inferences: all outputs matched the reference, with one cache key per compute unit.
    • Warm, 50 threads × 200: all 10,000 outputs matched, with no new specializations; every load restored its own compute unit's bookmark.

Summary:
Expose runtime correctness and concurrency failures through repeatable CI coverage across floating-point and quantized execution. Exercise isolated cold and warm cache workloads without changing backend behavior or masking failures.

Test Plan:
User-run native stress CTest passed in both smoke and stress runs. The shared model/operator suites are collecting failures; recorded results include operator output mismatches and model-load failures, which are intentionally not fixed here.

TODO: complete the shared-suite runs and investigate the collected failures. CoreAI-enabled Python runtime registration, native stress-target build, and scoped lint checks passed. Generic Buck validation was unavailable because its Swift owner query failed.
@metascroy
metascroy requested a review from shoumikhin as a code owner October 31, 2026 00:28
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pytorch-bot Bot commented Oct 31, 2026 •

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🔗 Helpful Links

🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/24375

Note: Links to docs will display an error until the docs builds have been completed.

❌ 3 New Failures, 2 Unclassified Failures

As of commit d05c245 with merge base 822cb60 (image):

NEW FAILURES - The following jobs have failed:

UNCLASSIFIED FAILURES - DrCI could not classify the following jobs because the workflow did not run on the merge base. The failures may be pre-existing on trunk or introduced by this PR:

This comment was automatically generated by Dr. CI and updates every 15 minutes.

@meta-cla meta-cla Bot added the CLA Signed This label is managed by the Facebook bot. Authors need to sign the CLA before a PR can be reviewed. label Oct 31, 2026
@shoumikhin

shoumikhin commented Oct 31, 2026 •

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@claude review effort=max

metascroy and others added 5 commits November 2, 2026 10:06
…8 limits

- CoreAITester: in fp16 mode, compare each output with
  atol = max(atol, 0.02 * max|reference|). FP16 rounding error follows the
  network's activation scale, so small outputs (e.g. cancelling logits) can
  exceed the shared per-element tolerance; resnet50 flaked with a 0.5 error
  on logits spanning +/-51. Op-sized outputs keep atol 0.1.
- coreai.yml: run all 8 backend-tester jobs concurrently (max-parallel 8).
- coreai.yml: allow 23 w8a8 model failures. 7 fail locally; on the xcode-27
  runners 19 w8a8 dynamic_shapes models also fail execution with Core AI
  inferenceFailed(-1).
On the xcode-27 runners, Core AI specialization of Linear(256 -> 1) fails
with AIModelError error 1 in every flow, stopping the zero-failure operator
jobs and pushing w8a8 operators past its limit. It passes on local Apple
silicon, so skip it only when GITHUB_ACTIONS is set.
On the xcode-27 runners, nearest upsample by a fractional scale (1.5) picks
different source pixels than PyTorch's floor(i / scale) mapping, failing the
zero-failure fp32 and fp16 operator jobs. Integer scales match, and the test
passes on local Apple silicon, so skip it only when GITHUB_ACTIONS is set,
alongside test_linear_feature_sizes.
Expose the Core AI SDK's SpecializationOptions configurations as a runtime
backend option: default, cpu_only, prefer_cpu, prefer_gpu and
prefer_neural_engine. The selection feeds the loader's specialization
identity, so each value is specialized and cached under its own key.

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cadence — d05c2451 Deployed Nov 3, 2026 by metascroy via hifi-op-test / hifi4 #36124
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