8.0b2 Release

[jakesabathia2]

CI: https://gitlab.com/coremltools1/coremltools/-/pipelines/1413983249 ✅

Release Notes

• Support for Latest Dependencies
  • Compatible with the latest protobuf python package: Improves serialization latency.
  • Compatible with numpy 2.0.
  • Supports scikit-learn 1.5.
• New Core ML model utils
  • coremltools.models.utils.bisect_model can break a large Core ML model into two smaller models with similar sizes.
  • coremltools.models.utils.materialize_dynamic_shape_mlmodel can convert a flexible input shape model into a static input shape model.
• New compression features in coremltools.optimize.coreml
  • Vector palettization: By setting cluster_dim > 1 in coremltools.optimize.coreml.OpPalettizerConfig, you can do the vector palettization, where each entry in the lookup table is a vector of length cluster_dim.
  • Palettization of per channel scale: By setting enable_per_channel_scale=True in coremltools.optimize.coreml.OpPalettizerConfig, weights are normalized along the output channel using per channel scales before being palettized.
  • Joint compression: A new pattern is supported, where weights are first quantized to int8 and then palettized into n-bit look-up table with int8 entries.
  • Support conversion of palettized model with 8bits LUT produced from coremltools.optimize.torch.
• New compression features / bug fixes in coremltools.optimize.torch
  • Added conversion support for Torch models jointly compressed using the training time APIs in coremltools.optimize.torch .
  • Added vector palettization support to SKMPalettizer .
  • Fixed bug in construction of weight vectors along output channel for vector palettization with PostTrainingPalettizer and DKMPalettizer .
  • Deprecated cluter_dtype option in favor of lut_dtype in ModuleDKMPalettizerConfig .
  • Added support for quantizing ConvTranspose modules with PostTrainingQuantizer and LinearQuantizer .
  • Added static grouping for activation heuristic in GPTQ.
  • Fixed bug in how quantization scales are computed for Conv2D layer with per-block quantization in GPTQ .
  • Can now perform activation only quantization with QAT APIs.
• Experimental torch.export conversion support
  • Support conversion of stateful models with mutable buffer.
  • Support conversion of dynamic inputs shape models.
  • Support conversion of 4-bit weight compression models.
• Support new torch ops: clip .
• Various other bug fixes, enhancements, clean ups and optimizations.

[YifanShenSZ]

Let's goooooo