lib_onnx 0.2.0-dev.1

ONNX Runtime inference for Dart and Flutter with explicit execution policies and experimental CoreML and QNN integration.

Changelog

0.2.0-dev.1 (unreleased candidate)

• Breaking: bridge ABI2 and Dart3.10+ native-assets packaging; pure Dart core no longer depends on Flutter. Old bridges/ORT mismatches are rejected.
• Checked tensor sizes, scalars/empty tensors/arbitrary ranks, complete partial-result cleanup, task-owned input copies and retained result handles.
• Real asynchronous initialization/loading/run/close, per-task termination, bounded scheduling, monotonic timeouts, removable cancellation listeners and independent errors/diagnostics.
• Enforced ORT CPU fallback constraint, explicit unsupported whitelist/hardware verification results, correct CoreML options/content caches, QNN HTP selection independent of Apple hardware.
• Removed inferred hardware observations and simulated example results; model identity includes external data. Added adapter safety/fp16 support, real small-model example and pinned genuine Qwen validation workflow.
• Added sanitizer/lifecycle/clean-consumer checks and release acceptance gates. iOS/Android accelerator and full Qwen acceptance remain open; no stable release or production acceleration claim.

0.1.0

Historical release notes below are retained. The2026-11-04 review found that async, platform readiness,16KB and hardware-observation claims were not established by the original evidence. They are superseded by the remediation report and current device matrix.

• Initial release of lib_onnx: Hardware-accelerated ONNX Runtime inference library for Dart and Flutter.
• Supported execution providers:
  • Apple CoreML Execution Provider (Apple Silicon GPU & Apple Neural Engine / ANE) on macOS (arm64) and iOS.
  • Qualcomm Snapdragon QNN HTP Execution Provider (Hexagon NPU) on Android (arm64-v8a) with 16 KB page compatibility.
  • ORT CPU Execution Provider for numeric baseline and explicit fallback.
• Pure C ABI bridge with opaque handles, non-blocking native worker thread pool for Flutter UI isolate safety.
• Three-dimensional ExecutionPolicy: Provider priority list, CPU fallback policy (strict, allowed, whitelistedOps), and hardware requirement (any, gpu, npu).
• Transparent 6-level hardware evidence system (DiagnosticReport and EvidenceLevel).
• Numerical tensors: float32, float16, int64, int32, int8, uint8, bool.
• Cooperative per-request cancellation (CancellationToken).
• EmbeddingAdapter implementing official Qwen3-Embedding Last-token pooling and L2 normalization.
• Interactive Flutter example app in example/.
• Automated test suite covering tensor operations, execution policy, embedding adapter, lifecycle, and 1,000 continuous inferences.