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feat(core-ai): call .aimodel functions with raw tensors - #300
Implements CoreAI.runRawFunction(...), which rejected until now, so any exported .aimodel runs on device even when no task wrapper exists for it. Part of #237.
What changes
CoreAI.loadModel({ ...config, task: 'raw' }, options) loads an .aimodel asset with the Core AI runtime. It needs no coreai-models product.
CoreAI.runRawFunction(modelHandle, functionName, inputs, options) runs a function by name. Each input and output is a CoreAITensor: { dataType, shape, data }, with data holding the little-endian elements in row-major order as base64.
loadModel and inspectModel now list the functions of an .aimodel, with each input's and output's name, data type and shape (-1 marks a dynamic dimension). inspectModel returned an empty list before.
computeUnits: 'default' | 'cpu' | 'gpu' | 'neural-engine' in the loadModel options picks the compute unit Core AI specializes the model for.
The example app now type-checks @react-native-ai/core-ai from src, like the other packages, instead of its stale lib build.
Ownership and performance rules
The issue asked for these before exposing tensors:
Every call copies its inputs into new arrays and its outputs out, so JS never holds native memory and nothing outlives the call. Strided (padded) arrays are copied element by element.
Functions load their weights on their first call and stay loaded until unloadModel.
Hermes has no Float16Array, so float16 and bfloat16 inputs can be given as float32, and their outputs come back as float32 unless outputDataType: 'native' is passed.
Base64 adds a third to each tensor's size and runs on the JS thread, so the docs point per-frame work at task wrappers.
Shapes, data types and input names are checked against the function's descriptor, with errors that name what the function takes. Image (pixel buffer) inputs are not supported yet.
Model
grabbou/edsr-x2-CoreAI (2.6 MB): EDSR x2 exported with Apple's recipe as float16 with dynamic height and width (uv run models/edsr/export.py --dtype float16 --dynamic). The test uses it because it is tiny and has dynamic dimensions and half-precision tensors. Runs on iOS and macOS.
On iPhone 17 Pro (iOS 27), harness/core-ai/raw-function.device.harness.ts: 6/6 pass. It covers function descriptors from loadModel and inspectModel, a dynamic-shape run (12×20 → 24×40, gray stays gray), native float16 outputs, computeUnits: 'gpu' and rejection of wrong inputs
CoreAI.runRawFunction runs a function of a model loaded with the raw
task, which needs no coreai-models product. Tensors cross the bridge as
base64 row-major elements, copied in and out of each call, and
createTensor and readTensor convert typed arrays. Half-precision tensors
take and return float32, since Hermes has no Float16Array. inspectModel
and loadModel now list the functions of an .aimodel asset, and the
computeUnits load option picks the compute unit Core AI specializes for.
Part of #237
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
The PR adds raw .aimodel loading and named-function execution to Core AI. It adds tensor creation and reading helpers, exposes raw-function types, and updates device tests and documentation.
Changes: Raw model functions
Layer / File(s)
Summary
Tensor contracts and helpers packages/core-ai/src/types.ts, packages/core-ai/src/tensor.ts, packages/core-ai/src/index.ts, packages/core-ai/src/__tests__/tensor.test.ts
Adds tensor and raw-function types, public tensor helpers, and tests for encoding, decoding, shapes, and element counts.
Native raw model inspection and execution packages/core-ai/ios/CoreAIRawFunction.swift, packages/core-ai/ios/CoreAIImpl.swift
Adds raw model loading and execution, function descriptions, tensor input validation, and output serialization.
Raw function integration and validation harness/core-ai/helpers.ts, harness/core-ai/raw-function.device.harness.ts, packages/core-ai/README.md, packages/core-ai/docs/*
Adds the EDSR x2 model to the harness registry, tests raw function behavior on-device, and documents the raw task and tensor inputs.
sequenceDiagram
participant Client
participant CoreAIImpl
participant RawModel
participant LoadedFunction
Client->>CoreAIImpl: loadModel with raw task, source, and options
CoreAIImpl->>CoreAIImpl: validate and load raw model
CoreAIImpl-->>Client: return model handle and function descriptions
Client->>CoreAIImpl: runRawFunction with handle, name, and tensor inputs
CoreAIImpl->>RawModel: run named function with inputs
RawModel->>LoadedFunction: invoke with decoded tensors
LoadedFunction-->>RawModel: return array outputs
RawModel-->>CoreAIImpl: return serialized tensor outputs
CoreAIImpl-->>Client: resolve with outputs
Loading
Possibly related issues
callstackincubator/ai#236: This PR adds raw model function inspection and device-harness validation, which are included in the issue’s stated scope.
callstackincubator/ai#237: This PR adds raw .aimodel function calls to the Core AI surface described as unsupported by the issue.
Possibly related PRs
callstackincubator/ai#303: It covers raw model loading, execution, metadata, tensor validation, and harness tests.
callstackincubator/ai#312: It documents the same raw task, tensor inputs, function inspection, and compute-unit behavior.
callstackincubator/ai#340: It changes raw-function output validation and tensor handling, including bfloat16 conversion.
Merge Risk: Low · up to 53272
Raw-model calls have several bounded input and output edge cases, including a crash for an exceptionally large shape. The PR is mergeable with owner awareness and follow-up fixes.
Security architecture risk: Low · up to 53272
The new interface remains confined to the host application, but malformed dynamic tensor shapes can terminate the application instead of returning an error. Broader access, remote reachability, or privilege expansion is not established.
Retained concerns
Low · security · inferred: Malformed dynamic shapes can escape the raw API's rejection boundary and terminate the host process. Native decoding accepts positive values for dynamic dimensions, then multiplies dimensions and element size with trapping Int arithmetic before checking payload length. For the dynamic descriptor represented in the harness, sufficiently large dimensions reach this path with only a small base64 payload. Direct bridge callers need not use createTensor. This crash path is newly reachable because raw calls previously rejected.
Security review details
Security Blast Radius
inferred — Host JavaScript possessing a valid raw-model handle can invoke functions exported by that model. The established failure scope is the containing application process, including its other work; the evidence does not establish a remote, cross-tenant, or cross-service attack path.
Security Findings and Attack Paths
inferred — A caller can supply a tensor record directly with valid base64 and positive dynamic dimensions whose product exceeds Int capacity. Descriptor checks permit those dimensions, but the subsequent product traps before payload-length rejection. Swift error handling around the call does not recover from that process trap. This is a newly reachable availability concern, not an established privilege-escalation or disclosure path.
Trust Boundaries and Controls
observed — The native boundary checks required and unknown input names, rejects image inputs, validates rank and static dimensions, decodes base64, and checks supported types and byte counts. Copying isolates JS storage from native arrays. The optional JS helper checks element count, but the exported bridge does not require helper-produced tensors.
Resilience and Maintainability Implications
inferred — Strong references allow admitted calls to retain their model across handle removal, avoiding an evident use-after-free path. Unload is therefore an admission boundary rather than a confirmed immediate resource-reclamation boundary. The repository evidence does not resolve provider guarantees for concurrent execution or cancellation, so those uncertainties are not reported as verified security defects.
Hardening Proposals
proposed — Establish a native tensor-size policy using overflow-checked element and byte counts before conversion or allocation, rejecting invalid sizes through the existing error path. This would make failure containment independent of optional JS helpers.
Pre-merge checks: 4 · 1
Failed checks: 1 warning
Check name
Status
Explanation
Resolution
Docstring Coverage
Docstring coverage is 18.18% which is insufficient. The required threshold is 50.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 22 functions across 8 files. (3 skipped: …
Write docstrings for the functions missing them to satisfy the coverage threshold.
Passed checks: 4 passed
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Description Check
Check skipped - CodeRabbit’s high-level summary is enabled.
Title check
The title clearly and concisely describes the main change: calling functions in .aimodel assets with raw tensors through Core AI.
Linked Issues check
Check skipped because no linked issues were found for this pull request.
Out of Scope Changes check
Check skipped because no linked issues were found for this pull request.
Full details: Docstring Coverage
Explanation
Docstring coverage is 18.18% which is insufficient. The required threshold is 50.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 22 functions across 8 files. (3 skipped: 3 unsupported.)
Resolution
Write docstrings for the functions missing them to satisfy the coverage threshold.
Finishing Touches: 💡 1
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Actionable comments posted: 4
🪄 Fix comments: Fix CodeRabbit comments on this PR
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Inline comments:
Review comments at @packages/core-ai/ios/CoreAIImpl.swift:
- Around line 107-115: Update the catch branch in inspectModel so
function-loading failures preserve the collected file metadata: add the error
description to info["metadata"] under functionsError and resolve the existing
info instead of rejecting the inspection.
Review comments at @packages/core-ai/ios/CoreAIRawFunction.swift:
- Line 51: Update the output handling guard in the raw function call flow so a
missing NDArray throws CoreAIError.invalidInput with a message that identifies
the output and states image outputs are not supported yet, rather than silently
skipping it.
- Around line 159-164: Update the shape-size validation around shape.reduce so
multiplying dimensions and multiplying the resulting element count by
elementSize use overflow-checked arithmetic. Throw CoreAIError.invalidInput on
either overflow, and use the validated byte count for the data.count comparison
and error message.
Review comments at @packages/core-ai/src/tensor.ts:
- Around line 49-51: Update the tensor creation API around `typedArrays` to use
a conditional input type: require the matching typed array for `float16`,
`bfloat16`, `int64`, and `uint64`, while retaining `ArrayLike<number>` for other
data types. In the non-view conversion path, reject plain array-like inputs for
those four types before constructing the typed array; preserve typed-array
inputs and direct callers to use a `float32` tensor for half-precision values.
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Commits
Reviewing files that changed from the base of the PR and between fc129b6 and 53272b1.
Files selected for processing: 11
harness/core-ai/helpers.ts
harness/core-ai/raw-function.device.harness.ts
packages/core-ai/README.md
packages/core-ai/docs/agent-skill.md
packages/core-ai/docs/api.md
packages/core-ai/ios/CoreAIImpl.swift
packages/core-ai/ios/CoreAIRawFunction.swift
packages/core-ai/src/__tests__/tensor.test.ts
packages/core-ai/src/index.ts
packages/core-ai/src/tensor.ts
packages/core-ai/src/types.ts
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Stacked on #299. Stack for #237: #299 → #300 → #301 → #302 → #303 → #309 → #312 (docs).
Implements
CoreAI.runRawFunction(...), which rejected until now, so any exported.aimodelruns on device even when no task wrapper exists for it. Part of #237.What changes
CoreAI.loadModel({ ...config, task: 'raw' }, options)loads an.aimodelasset with the Core AI runtime. It needs nocoreai-modelsproduct.CoreAI.runRawFunction(modelHandle, functionName, inputs, options)runs a function by name. Each input and output is aCoreAITensor:{ dataType, shape, data }, withdataholding the little-endian elements in row-major order as base64.createTensor(dataType, shape, elements)andreadTensor(tensor)convert typed arrays (Float32Array,Int32Array,BigInt64Array, ...).loadModelandinspectModelnow list the functions of an.aimodel, with each input's and output's name, data type and shape (-1marks a dynamic dimension).inspectModelreturned an empty list before.computeUnits: 'default' | 'cpu' | 'gpu' | 'neural-engine'in theloadModeloptions picks the compute unit Core AI specializes the model for.@react-native-ai/core-aifromsrc, like the other packages, instead of its stalelibbuild.Ownership and performance rules
The issue asked for these before exposing tensors:
unloadModel.Float16Array, so float16 and bfloat16 inputs can be given as float32, and their outputs come back as float32 unlessoutputDataType: 'native'is passed.Model
grabbou/edsr-x2-CoreAI(2.6 MB): EDSR x2 exported with Apple's recipe as float16 with dynamic height and width (uv run models/edsr/export.py --dtype float16 --dynamic). The test uses it because it is tiny and has dynamic dimensions and half-precision tensors. Runs on iOS and macOS.Testing
bun test:createTensor/readTensorround trips, little-endian encoding, bigint elements, typed array views, size checksharness/core-ai/raw-function.device.harness.ts: 6/6 pass. It covers function descriptors fromloadModelandinspectModel, a dynamic-shape run (12×20 → 24×40, gray stays gray), native float16 outputs,computeUnits: 'gpu'and rejection of wrong inputs🤖 Generated with Claude Code
Summary by CodeRabbit
.aimodelfiles as raw models and running named functions with tensor inputs on iOS and macOS 27 or later.