8.3.0 Release

• Features Debugging And Performance Utilities to root cause numerical issues and performance bottlenecks.

  • MLModelValidator:
    • Helper utils to assist with finding problematic ops producing NaNs, infinities or any custom validation logic
  • MLModelComparator | TorchScriptMLModelComparator | TorchExportMLModelComparator:
    • Makes it easy to compare two CoreML models or a CoreML with a Torch model
    • Particularly useful in triaging numerical discrepancies
      • Between fp16 and fp32 variants of a CoreML model
      • Or, across different compute engines for a CoreML model
  • MLModelInspector:
    • Facilities retrieval of intermediates tensors of CoreML model
  • MLModelBenchmarker | TorchMLModelBenchmarker:
    • Utils to log model loading time, prediction latency, and the execution times of individual operations
    • Ties it down to PyTorch modules → makes it possible to identify slow PyTorch modules or nodes.
  • Remote Device:
    • Utilities to benchmark and/or debug models on connected devices
    • Works seamlessly with its siblings MLModelBenchmarker, MLModelInspector, MLModelValidator, and MLModelComparator etc.
    • Supports iOS, macOS, watchOS, and tvOS devices

• Various other bug fixes, enhancements, clean ups and optimizations

  • Uint8 support for grayscale images
  • Support for additional PyTorch operations: linalg.vecdot, pow, argmin
  • Fixes in lowering of batch_norm, ConvTranspose1d, randn
  • Improving ANE residency of top-k operation
  • Better error handling around deployment targets for PTQ APIs
  • Bug fix in weight quantization of fp16 neuralnetwork models
  • Fixing registration of compression passes
  • Correct handling of feature names during conversion of XGBoost models

• Special thanks to 3P Developers: @RGooBS24 , @M-Quadra , @smpanaro , @Zerui18 , @yushangdi , @lkb85 , @Pranaykarvi , @billmguo , @metascroy , @benoit-vinsonneau , @ccyoyou , @reneleonhardt !!