Bump coremltools from 7.2 to 8.1

[dependabot]

Bumps coremltools from 7.2 to 8.1.

Release notes

Sourced from coremltools's releases.

coremltools 8.1

Release Notes

• Python Support
  • Compatible with Python 3.12.
• Added support for additional PyTorch operations
  • torch.clamp_max, torch.rand_like, torch.all, torch.linalg_inv, torch.nan_to_num, torch.cumprod, torch.searchsorted ops are now supported.
• Increased conversion support coverage for models produced by torch.export
  • Op translation support is at 68% parity with our mature torch.jit.traceconverter.
  • Support enumerated shape model.
  • Support ImageType input.
• Added Python bindings for the following classes:
  • MLComputePlan
  • MLComputeDevice
  • MLModelStructure
  • MLModelAsset
• Various other bug fixes, enhancements, clean ups and optimizations.
  • Favor bool mask in scaled dot product attention
  • Fix quantization crash with bool mask
• Special thanks to our external contributors for this release: @​M-Quadra @​benjaminkech @​guru-desh

coremltools 8.0

Release Notes

Compare to 7.2 (including features from 8.0b1 and 8.0b2)

• Support for Latest Dependencies
  • Compatible with the latest protobuf python package which improves serialization latency.
  • Support torch 2.4.0, numpy 2.0, scikit-learn 1.5.
• Support stateful Core ML models
  • Updates to the converter to produce Core ML models with the State Type (new type introduced in iOS18/macOS15).
  • Adds a toy stateful attention example model to show how to use in-place kv-cache.
• Increase conversion support coverage for models produced by torch.export
  • Op translation support is at 56% parity with our mature torch.jit.trace converter
  • Representative deep learning models (mobilebert, deeplab, edsr, mobilenet, vit, inception, resnet, wav2letter, emformer) have been supported
  • Representative foundation models (llama, stable diffusion) have been supported
  • The model quantized by ct.optimize.torch could be exported by torch.export and then convert.
• New Compression Features
  • coremltools.optimize
    • Support compression with more granularities: blockwise quantization, grouped channel wise palettization
    • 4 bit weight quantization and 3 bit palettization
    • Support joint compression modes (8 bit look-up-tables for palettization, pruning+quantization/palettization)
    • Vector palettization by setting cluster_dim > 1 and palettization with per channel scale by setting enable_per_channel_scale=True.
    • Experimental activation quantization (take a W16A16 Core ML model and produce a W8A8 model)
    • API updates for coremltools.optimize.coreml and coremltools.optimize.torch
  • Support some models quantized by torchao (including the ops produced by torchao such as _weight_int4pack_mm).
  • Support more ops in quantized_decomposed namespace, such as embedding_4bit, etc.
• Support new ops and fixes bugs for old ops
  • compression related ops: constexpr_blockwise_shift_scale, constexpr_lut_to_dense, constexpr_sparse_to_dense, etc
  • updates to the GRU op
  • SDPA op scaled_dot_product_attention

... (truncated)

Commits

• 29ad374 8.1 Release (#2394)
• 68b4f5a Update docs reshapeFrequncy optimization hints (#2386)
• 3d7bfc8 Enhance torch.zeros_like for GPU inference in dynamic shape (#2369)
• b809510 d - correct typo in folder name (#2366)
• 4710376 Update iOS18 GRU Docstring (#2375)
• 8d5f6bf Add torch cumsum dtype support (#2373)
• fda79b0 [Docs] Add Torch.Export Guides (#2356)
• 9022cd7 [Doc] Add example for compressing StableDiffusion model (#2368)
• 1120cdf new change_output_tensor_types API under ct.models.utils (#2364)
• 275c7e2 support for zero dimension tensors as an input for cat operation (#2354)
• Additional commits viewable in compare view

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